The Environmental Kuznets Curve and the production of waste: an explanatory analysis for the Italian industrial sector

Thesis presented by:

Alessandro Stanchi

to The Class of Social Sciences

for the degree of Doctor of Philosophy

in the subject of Management, competitiveness and development

Tutor: Prof. Marco Frey

Relatore: Prof. Marco Frey

Scuola Superiore Sant’Anna A.Y. 2013-2014

1

Nel mezzo del cammin di nostra vita mi ritrovai per una selva oscura,

ché la diritta via era smarrita.

Ahi quanto a dir qual era è cosa dura esta selva selvaggia e aspra e forte

che nel pensier rinova la paura!

Tant' è amara che poco è più morte; ma per trattar del ben ch'i' vi trovai,

dirò de l'altre cose ch'i' v'ho scorte.

Io non so ben ridir com'i' v'intrai, tant'era pien di sonno a quel punto

che la verace via abbandonai.

Dante Alighieri, Divina Commedia, Canto I, versi 1-12

O Fortuna Velut luna

Statu variabilis Sempre crescis Aut decrescis

Vita detestabilis Nunc obdurat Et tunc curat

Ludo mentis aciem Egestatem

Potestatem Dissolvit ut glaciem...

O Fortuna, Carmina Burana

"In this phial...is caught the light of Eärendil’s star... It will shine still brighter when night is about you.

May it be a light to you in dark places, when all other lights go out"

J.R.R.Tolkien, The Lord of the Rings

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Ringraziamenti La parte più difficile di un lavoro di scrittura è sempre quella dei ringraziamenti: non si sa mai se la pro-pria onestà intellettuale ha fatto sì che si sia riusciti a ricordare tutti coloro che hanno contribuito ad aiuta-re lo scrivente nella sua impresa, così come non si può essere mai certi di aver espresso con parole chiare, forti e sincere il senso di gratitudine che si prova nei confronti di coloro che vengono ricordati. Nel mio caso, il compito è davvero molto, ma molto, più facile del solito. Il debito di gratitudine che ho nei confronti delle persone che mi hanno accolto, accompagnato, guidato, incoraggiato e supportato nel completare questo percorso umano, prima che accademico e professionale, può essere descritto mediante un unico termine: immenso. E, anche così, sbaglio per difetto. Un primo ringraziamento va al mio relatore e guida, il Prof. Marco Frey, per la fiducia che mi ha sempre dimostrato, per la sua disponibilità e cortesia sempre estreme, e per i preziosi consigli non solo in merito alla ricerca, ma anche alla vita e all'ambiente accademico. Un sentito grazie va al Direttore del corso di Ph.D., il Prof. Andrea Piccaluga, per il suo spirito energico e motivante, per la sua cortesia nell'indirizzare gli studenti verso i loro obbiettivi, e per aver voluto in ogni momento dar vita ad un ambiente sereno, produttivo e di collaborazione. Per gli stessi motivi desidero ringraziare i docenti del Ph.D. che ho incontrato nel corso di questi anni: da loro ho appreso in prima persona come si possa e si debba comunicare la passione per la ricerca a chi ci a-scolta. Un affettuoso ricordo, poi, lo rivolgo a tutti i colleghi Perfezionandi: a loro auguro una carriera piena di soddisfazioni, e che li porti a mostrare all'esterno la vivacità della Scuola di cui siamo stati allievi. Un'istituzione viva e pulsante non è la semplice sommatoria delle persone che la popolano, e degli edifici che la ospitano, ma ha un qualcosa in più che la rende tale, e che la fa vivere di vita propria: ecco perché desidero esprimere tutta la felicità intellettuale di essere stato accolto all'interno della Scuola Superiore Sant'Anna di Studi Universitari e di Perfezionamento di Pisa (e, per il corso di Perfezionamento, nel suo Istituto di Management), e di esserne stato membro in un momento molto burrascoso, travagliato e anche doloroso della mia vita personale. Un ringraziamento particolare va, poi, a mia madre e a mio padre, che, poco silenziosamente ma molto stoicamente, mi supportano e mi sopportano da oramai parecchi anni, e che, da quel che vedo, non hanno intenzione di perdere questa "brutta" abitudine: spero possano essere più sereni nei tempi che verranno. In una pagina di ringraziamenti come quella che intenderei continuare a scrivere, i "grazie" sarebbero an-cora molti, ma non voglio che il senso di gratitudine che avverto come fortissimo in me possa venire scam-biato dal lettore occasionale per un vuoto esercizio di retorica, come fosse un compito necessario cui si deve adempiere per "cortesia istituzionale", per così dire. Non è così: non sarò mai abbstanza in grado di espri-mere appieno quanto io sia debitore nei confronti della Scuola Sant'Anna. Nel terminare, però, prendo immeritatamente come esempio di stile la struttura narrativa della versione di Carl Orff dei Carmina Burana (1935-1936), dove l'invocazione alla Fortuna è suonata come primo bra-no, e poi di nuovo come ultimo brano, perché la Fortuna è una ruota che gira e che torna, prima o poi, al luogo di partenza: allo stesso modo, desidero tornare a ringraziare il Prof. Marco Frey, verso il quale il mio debito di gratitudine è senza fine.

3

Table of contents RINGRAZIAMENTI 2 TABLE OF CONTENTS 3 INTRODUCTION 5

1. ECONOMIC GROWTH, ENVIRONMENT, AND THE ENVIRONMENTAL

KUZNETS CURVE FRAMEWORK 10

1.1. Introduction 101.2. Economic growth and environment: a classic dilemma 101.3. A deconstruction analysis: the IPAT framework 131.4. The Environmental Kuznets Curve (EKC) 16

1.4.1. Origins of the EKC framework 161.4.2. The basics of the Environmental Kuznets Curve 171.4.3. The main theoretical literature on the Environmental Kuznets Curve 211.4.4. A macroeconomic model for the EKC: the Green Solow Model 281.4.5. The empirical literature on the Environmental Kuznets Curve 33

1.4.5.1. Distribution of income, wealth and equity 341.4.5.2. Structural change in the economy and technological progress 361.4.5.3. International trade 371.4.5.4. Individual preferences 391.4.5.5. Energy demand, energy prices and energy intensity 40

1.4.6. A brief overview of some econometric issues related to the EKC estimation 421.5. The Waste Kuznets Curve (WKC) 451.6. Conclusions 59

2. THE PRODUCTION OF INDUSTRIAL WASTE IN THE MUD DATABASE,

DURING THE PERIOD 1998-2004 61

2.1. Introduction 612.2. Industry and its importance in the Italian economy (1998-2004) 622.3. The MUD database (1998-2004) 66

2.3.1. The production of industrial waste according to the MUD data (1998-2004) 662.4. Analysis of the coverage of the MUD database as regards the Industry in a Strict

Sense sector (1998-2004) 70

2.4.1. The overall context 702.4.2. Coverage in terms of economic activity (1998-2004) 722.4.3. Coverage in terms geographic divisions (1998-2004) 772.4.4. Methodological insights about the quality and the representativeness of the

MUD database (1998-2004) 80

2.5. The MUD database: quantitative analysis of the production of waste 822.5.1. The geographic dimension: a quantitative analysis 82

2.5.1.1. Italy and its macro-regions 822.5.1.2. Regions and provinces 94

2.5.2. Quantitative analysis from a sectorial point of view 972.5.3. Waste and economic activity 99

2.6. Conclusions 100APPENDIX A2 102

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3. ITALIAN WASTE PRODUCTION (1998-2004): A GENERAL FRAMEWORK

FOR THE ANALYSIS 124

3.1. Introduction 1243.2. A note on the theoretical framework 1243.3. The drivers of the model 124

3.3.1. Descriptive analysis 1253.3.1.1. Value-added of Industry in a Strict Sense (1998-2004) 1253.3.1.2. Energy consumption of Industry in a Strict Sense (1998-2004) 1333.3.1.3. Sorted (and non sorted) urban waste collection (1998-2004) 1383.3.1.4. Innovations and patents (1998-2004) 1423.3.1.5. Exports (1998-2004) 1433.3.1.6. Population density of the provinces, and density of the local units of In-

dustry in a Strict Sense (1998-2004) 145

3.3.1.7. Share of the value-added of Industry in a Strict Sense and of Service In-dustry on the total 146value-added (1998-2004)

146

3.3.2. Relationship between industrial waste and socio-economic drivers 1473.4. Conclusions 150

APPENDIX A3 151 4. MODEL SPECIFICATION AND ECONOMETRIC TESTING 175

4.1. Introduction 1754.2. The specification of the model 1754.3. The econometric tests 1824.4. Main results 1984.5. Conclusions 201

APPENDIX – A4 202 5. THE PRODUCTION OF WASTE OF INDUSTRY IN A STRICT SENSE: A

SIMULATION 219

5.1. Introduction 2195.2. The hypothesis of the simulation of a EKC relationship: growth rates of the Italian

local economies 219

5.3. The evolution of the production of waste: the simulation of the model 2225.3.1. The results of the simulation: the five randomly selected provinces 225

5.4. Simulation’s results: a discussion 2275.5. Conclusions 236

APPENDIX – A5 237 CONCLUSIONS 253 ACRONIMI – ACRONYMS 259 LIST OF FIGURES 260 LIST OF TABLES 263 REFERENCES 266

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INTRODUCTION

Solid waste management is strictly linked to urbanization and economic development, and is the one

service that almost every national government provides for its citizens. While service levels, costs and

environmental impacts can dramatically vary among places and nations, solid waste management is

perhaps the most important public service that nevertheless must be set up in all countries, and mu-

nicipal solid waste (MSW) management is the most relevant service a city government provides, to-

gether with the industrial solid waste (ISW) management. In low-income countries, as well as in many

developing countries, MSW is the largest single budget item for cities. The 2012 World Bank’s Urban

Development department’s report (Hoornweg and Bhada-Tata, 2012) estimates that the amount of

municipal solid waste (MSW) has risen in the past, and it will steadily rise in future: in 2002, 2,9 billion

urban residents have generated about 0,64 kg of MSW per person per day, which leads to a 0,68 billion

tonnes per year. In 2012, waste residuals in cities have increased: about 3 billion residents have gener-

ated 1,2 kg per person per day (1,3 billion tonnes per year). The estimates show that, by 2025, 4,3 bil-

lion urban residents will have produced about 1,42 kg per capita per day of municipal solid waste (that

is, 2,2 billion tonnes per year). Much of this increase will take place in rapidly growing cities of develop-

ing countries (Table I.1).

Table I.1 – Waste generation projections for 2025, region by income (from Hoornweg and Bhada-Tata, 2012)

World Bank estimates of 2005 GNI per capita: High: $ 10.726 or above; Upper middle: $ 3.466-10.725; Lower middle: $ 876-3.465; and Lower: $ 875 or less.

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Waste is mainly a by-product of the nowadays consumer-based lifestyles that drive much of the world’s

economies. As countries tend to urbanize, their economic wealth increases: their standards of living and

incomes increase, as well as consumption of goods and services, thus resulting in a corresponding in-

crease in the amount of generated waste. There are remarkable differences in waste generation rates

across countries, among cities, and even within cities, but everywhere the global nature of solid waste

contributes to the increase of GHG emissions, and to all the global issues related to products, urban

practices, and the recycling industry. Waste generation have been seen to be much lower in rural areas

since, on average, residents are usually poorer, purchase fewer items (which results in less packaging),

and have higher levels of reuse and recycling. Anyway, in any place in the world, waste production is

strictly related to economic growth and social development, and the current debate is on whether such

an increase is sustainable in environmental and in social terms, being an economic issue in both devel-

oping and developed nations: the annual global cost of solid waste management is estimated to rise

from the current 205 billion dollars to 375 billion dollars in 2025, and such costs will increase most in

low-income countries.

Table I.2 – Italian expenditure for waste management, 1997-2007 (millions of €, current-prices; from CMCC, 2010)

Source: CMCC (2010) and Istat (2008).

The focus of the present research is on the Italian case, where waste is a major source of public con-

cern and costs: according to Istat (2008), the national current-prices expenditure for waste management

in 2007 was about 21.000 millions of Euros, while in 1997 it was almost 11.000 millions of Euros,

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showing an increase of +91% (Table I.2). In 1997, the waste management expenditure was about 1,1%

of the Italian GDP, but, after ten years, its weight has been increasing up to 1,4%: the most relevant

component of such an expense are the intermediate consumptions of firms, which have a weight bigger

than 55% of the total, while the second weighing voice, the final consumptions of both households and

public sector, amounts to more than 30% of the total. Investments of the waste management sector

(which is 10% of the total expenditure) have been mainly made by the private sector, with an increase

of +100% in the period 1997-2007 (CMCC, 2010), while public sector and other social institutions have

lowered their investments by one third. As regards the employment data, in 1997 there were more than

71.156 units working in the sector, while in 2007 this number has increased to more than 106.000

(CMCC, 2010): the whole sector has doubled its employees in ten years, and all of such an increase has

to be ascribed to the private sector, while the public sector has seen an almost zero increase in that pe-

riod (+0,7%). Therefore, in line with the international experience, Italy too has seen an increase in the

expenditure of waste management costs, a growing privatizing process and a growing outsourcing one.

As waste seems to be the main social challenge that policy makers have to face every day hic et nunc,

abroad as well as in Italy, the general research question of the present work is to try to cast a light on

the relationship between waste (especially those residuals produced by firms), economic activity and the

socio-economic structure, in Italy. The vast majority of the available data and works deals with urban

waste data and its policy indications: the first idea for this research has been investigating the behaviour

of the productive sectors in producing waste, and whether such a behaviour could be defined as sus-

tainable.

The Italian national law on waste and it subsequent modifications, considers two different kinds of

waste, which can be roughly detailed (for legal descriptions, see Italian waste laws) as:

urban waste (also called municipal waste): all the residuals produced by non-productive activi-

ties;

special waste (also called industrial waste): all residuals produced by productive activities.

The Legge n. 70/1994 has first introduced the obligation, for those institutions and firms which pro-

duce or manage waste, to yearly send to the Chambers of Commerce the Modello Unico di Dichiarazi-

one (MUD) Ambientale, detailing those amounts of waste produced, collected or managed during the

previous year. The total quantity of waste produced in Italy is not equivalent to the mere summation of

urban and special waste, since there were many entities which were exempted by giving that statement:

such entities, however, had to give their waste to those subjects which could transport, recycle and dis-

pose such residuals. Those last subjects had to declare the type and the quantity of waste that they were

collecting, and all the personal data of their partner. This resulted in a complex system of reports which

has been the first in EU to be so detailed, and it has provided a rich dataset of waste production.

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The present research work deals with a provincial-level dataset of special waste, for the period 1997-

2004. During those years, the Legge n. 70/1994 was in force, and therefore the categories subjected to

present the MUD were the same for the whole period: as a consequence, the descriptive and simula-

tions’ results can be perfectly compared across those years. The same cannot be said for the subsequent

years: in 2006, the Decreto Legislativo n. 152/2006 was calling for different duties on different sub-

jects, and therefore the data are not more perfectly comparable from that point on.

The first research question of the present work deals with the description of the framework of the

production of special waste in Italy, and tries to describe the context of the production of firms’ residu-

als in Italy, indicating whether it has been experienced an increase, and the trends of the period: the use

of original and disaggregated data gives new insights on an issue which is less known and even less de-

scribed than urban waste.

The second research question investigates the possible relationships between special waste produc-

tion and the several socio-economic aspects of the Italian society, and the signs of such a link: the tar-

get has been studying whether the industrial production of waste is sustainable in economic terms, and

what are the main drivers of that production. The Environmental Kuznets Curve framework of analysis

has been used, selecting those socio-economic drivers which, according to the main literature, could be

responsible for the increase or the decrease of (the measure of) special waste. The model has been

tested using a pooled OLS technique to find out whether an Environmental Kuznets Curve can be

found for special waste production, during the period 1997-2004, and what are the variables that can

lead towards, or away from, such a behaviour.

The third research question tries to simulate the model in the framework of a growing economy,

looking whether waste could decrease in the Italian socio-economic texture: using data coming from

periods of decrease in the economic variables (as the period 2006-2013) may lead to the result that the

feasible decrease in the waste production could be led by the relative decrease in the economic activity.

Therefore, we have been interested in simulating the behaviour of the special waste production in a

growing economy set.

The work is structured as follows. Chapter 1 presents a review of the main findings of the previous

studies concerning the links among economic growth, economic development and environmental deg-

radation, choosing the quoted papers among a vast number of researches dealing with what is one of

the most studied subjects in the economic literature. The chapter introduces the classic dilemma be-

tween economic growth and environment, then it deals with the IPAT framework and its use in the

economic analysis of the environment; then the Environmental Kuznets Curve (EKC) hypothesis, with

its origins and its basic framework, the theory behind the EKC, the macroeconomic aspects of the

Green Solow Model, the main empirical studies on the EKC, and the main econometric issues related

9

to the use of the in EKC have been described. A special section deals with the application of the EKC

analysis to waste data, and the development of the so called Waste Kuznets Curve (WKC).

Chapter 2 gives the basics on the production of waste generated by firms of the sector of Industry in a

Strict Sense, based upon the information provided by the MUD database (excluding the database com-

ing from the waste collectors), for the period which goes from the year 1998 (whose statements have

been given in the year 1999) to the year 2004 (whose statements have been give in the year 2005). A

short description about Industry in a Strict Sense is given, and its importance within the Italian econ-

omy during the period 1998-2004 is highlighted. Then the description of the database which has been

used in the analysis is provided, together with the explanation on why that precise period has been cho-

sen: the importance of Industry in a Strict Sense to the framework of waste production in Italy has

been assessed, and the waste production of firms has been outlined under a geographical and a sectorial

point of view.

Chapter 3 gives an overview of the socio-economic variables which contribute to the waste production

and which have been used in the econometric test of the subsequent chapter. A brief description of

each of the drivers has been provided, in the span of time 1998-2004, at a provincial and at national

level, and how each of them might have influence on waste production is depicted.

Chapter 4 presents the specification of the model that has been tested: it has been tried to explain the

socio-economic causes behind the production of waste in the industrial sector, by the means of the

drivers described in the previous chapter. Some hypotheses about the model have been presented, as

well as its different functional forms and the econometric estimates. The statistical significance of the

variables has been tested, and the EKC behaviour of the dependent variable has been checked: the best

specification to be used in the following simulations’ section has been done by jointly using the infor-

mation provided by the Bayesian/Schwartz Information Criterion (BIC), the Akaike Information Crite-

rion (AIC) and the R-squared criterion (R2).

Chapter 5 presents the simulations of behaviour of the dependent variable of the model (waste) in a

hypothetically growing economy framework, in order to investigate whether the EKC trend can be ob-

served, for the Italian provinces, also for the future as well as for the past. After the economic crisis

that has stricken the world since 2008, the performance of the Italian economy has gone down, with

negative growth rates across the years 2010, 2011 and 2012. Therefore, two different scenarios of fic-

tional economic growth have been created, based on real and hypothetical growth estimates, for the pe-

riod 2006-2010, and based on ad hoc hypothesis, and the model has been simulated.

In the end, the Conclusions session sums up the main results of the work, and it casts a light on new

research hypotheses that might arise within this context.

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1. ECONOMIC GROWTH, ENVIRONMENT, AND THE ENVI-

RONMENTAL KUZNETS CURVE FRAMEWORK

1.1 Introduction

The present chapter presents a review of the main findings of the previous studies concerning the links

among economic growth, economic development and environmental degradation: it is not possible to

attempt to review or cite all of the rapidly growing number of studies, so it has been necessary to

choose among a vast number of researches dealing with what is one of the most studied subjects in the

economic literature.

The first section introduces the classic dilemma between economic growth and environment. Section 2

deals with the IPAT framework and its use in the economic analysis of the environment. Section 3 il-

lustrates in depth the Environmental Kuznets Curve (EKC) hypothesis, highlighting in more detail its

origins and its basic framework, then the theory behind the EKC-style curve, the macroeconomic as-

pects of the Green Solow Model, the main empirical studies on the EKC, and finally the main econo-

metric issues related to the use of the in EKC. Section 5 deals with the application of the EKC analysis

to waste data, and the development of the so called Waste Kuznets Curve (WKC). Last, Section 6

shows the concluding remarks of the review.

1.2 Economic growth and environment: a classic dilemma

The classical economic theory considers land, labour, capital and productivity (the first, so called Solow

residual, and all of its followers) as the primary factors of production. Land (and, since, environment) is

the physical capital which provides the resources that allow every economic activity to take place. How-

ever, the exhaustibility of the natural resources gave and still gives rise to concerns about its possible

effect on the productive capabilities (Tahvonen, 2000). The oil crises in the 1970s showed that the

world would have run out of oil and, consequently, of energy (Tahvonen, 2000), thus leading to a slow-

down of productivity (Stern, 2004): the scientific research always pointed out the negative effects of

economic growth on the environment, highlighting how the over-accumulation of greenhouse gases,

the upsetting of air pollution and the significant increase in the generation of waste would have been

major concerns during all the human development path.

Since the beginning of the 1950s, the environmental effects of economy have been receiving increasing

attention by researchers. The worldwide worsening of environmental quality and the always increasing

11

ecological requirements of the national economies started involving the political actions and programs

of governments and of international institutions. The economic world is intrinsically linked to the natu-

ral environment (the physical and biological world): therefore, pollution and resources use have become

a variable for micro and macro economists since the early days (Daly, 1977).

Economic growth and its consequences are among the most controversial issues of the economic his-

tory of the world. Its beginnings might be seen as a nature-friendly development story, that started with

agriculture and that soon went to excessive use of natural resources after the industrial revolution,

which stimulated the exploitation of more and more natural resources. Across the centuries, scarcity

and pollution of natural resources made societies aware of the fact that the economy depends on en-

ergy and ecological services provided by nature (Ward, 2006).

Apart from Malthus's predictions which are not suitable for economic development, neoclassical

growth models focus on capital and labour by ignoring natural resources. Modern growth theory (en-

dogenous growth theory) accepts human development, technological progress, and natural resources as

the forces behind economic growth. In this context, recent economic studies deal with optimality of the

growth process: how economic growth and environmental conservation are compatible in the long

term, sustainable development, and consequences of environmental policy for growth (Smulders, 1999).

A crucial issue for several decades has been the problem of identifying the correct relationship between

environmental emissions and economic growth (Stern, 2004; Azomahou et al., 2006; Kijima et al.,

2010), mainly because the ability to forecast emissions after an increase in economic growth may be

useful in estimating the potential magnitude of environmental problems (Riahi et al., 2011), and thus

leading to the capability to detect conditions under which economic growth leads to increased envi-

ronmental burdens (Kuosmanen et al., 2009).

In the early 1970s, the so called "Limits to Growth" elaborated by the Club of Rome (Meadows et al.,

1972) were concerning the availability of natural resources on Earth: in that book the authors were

forecasting the possibility that pollution and scarcity would stop economic growth. Moreover, for some

social and physical scientists such as Georgescu-Roegen (1971, 1977), the biophysical limits of envi-

ronment would be reached for soon, since the growing economic activities in the world would require

larger inputs of energy and material, and they would generate larger quantities of waste.

Starting from Malthus's studies, neoclassical growth models focussed on capital and labour, ignoring

natural resources, but modern growth theory (and the endogenous growth theory) has now filled the

gap, accepting human development, technological progress, and natural resources as the forces behind

economic growth. In this context, recent economic studies deal with how economic growth and envi-

ronmental conservation are compatible in the long term in the context of a sustainable development,

and assessing which consequences might arise for environmental polices for growth (Smulders, 1999).

12

The neoclassical growth theory has tried to address the problem of the nature of the link between envi-

ronment and economic growth (Solow, 1974a, b; Aghion and Howitt, 1998a, b), and a great number of

studies on that subject, both theoretical as well as empirical, has tried to investigate whether a steadily

increasing economic growth is compatible with environmental quality.

The accumulation of waste and the extraction of natural resources should overwhelm the carrying ca-

pacity of the biosphere, and result in the degradation of the environmental quality and human well-

being (Daly, 1977). On the contrary, Malenbaum (1978) wrote a study which conflicts with the Club of

Rome’s predictions, showing that the ratio of consumption of some kind of metals to income was de-

clining in developed economies: thanks to innovation, investment and technical progress, the material

input of economies is reduced directly by economic growth (Factor 10 Club, 1995; EUROSTAT,

2001).

Opposed to the biological vision stating that the bigger economy is, the bigger the pollution is, another

view suggests that economic growth may improve environmental quality through technological change,

economies of scale in pollution abatement, and increasing demand for environmental quality (Becker-

man, 1992). The Sustainable Development theory tries to study since its beginning whether environ-

mental pressures could slow down relative to economic growth at higher income levels, after an initial

increase at early stages of development. Born in 1987 by the World Commission on Environment and

Development, the Sustainable Development paradigm is based on dematerialization and de-pollution,

and considers economic growth and technological progress as the best way to improve environmental

quality, thanks to innovations, investments and technical progress, that will delink economy from its

biophysical constraint (Canas et al., 2003). In recent years, a lot of studies estimated this theoretical be-

haviour for a variety of pollutants (Cole, 2003; Copeland and Taylor, 2004; Dasgupta et al., 2002) and

other provided a quantification of the material input of an economy (Fisher-Kowalski and Amann,

2001; Cleveland and Ruth, 1999).

Recently, several authors try to assess the importance of another factor about environmental economics

issues, that is, human development. Anand and Sen (2000) attempt to explain the importance of human

development in all components of economic development, such as equity, sustainable development and

optimal growth, while Costantini and Monni (2008) state that environmental quality would not be nega-

tively affected by the economic growth, if a human development perspective is adopted. Ranis et al.

(2000) assert that a strong connection between economic growth and human development exists, and

that economic growth provides resources to allow for sustained improvements in human development.

Economic growth itself will not be sustainable unless accompanied by improvements in human devel-

opment, such as economic reforms. Gangadharan and Valenzuela (2001) state that investments in edu-

cation and health produce new human capital. Noorbakshs (1998) discussed a modified human devel-

opment index for measuring human development, considering the diminishing returns of the educa-

13

tional indicators. The papers mentioned so far do not measure the direct impacts of human develop-

ment on economic growth. Recent study highlights the impacts of human development index (HDI) by

investigating industrial pollution in selected Mediterranean countries, which have different economic

backgrounds (Raghbendra and Murthy, 2003). Since first release by the United Nations Development

Program (UNDP), the HDI includes the impacts of social variables such as health effects, political

rights, civil liberties, and education level (Sagar and Najam, 1998). Education level positively affects

many other socio-economic variables, such as political rights and civil liberties, as well as population

density: as long as education level is increasing in a country, population increase rate, and its conse-

quent pressures on the natural resources, decreases.

1.3 A deconstruction analysis: the IPAT framework

Indicators of "decoupling" or "delinking" have become increasingly popular in detecting and measuring

improvements in environmental and resource efficiency, with respect to economic activity. The general

relationship between economic activities, efficiency gains in the use of resources (or, decoupling) and

the scale of environmental impacts can be illustrated by referring to the IPAT model. Since its original

formulation by Ehrlich (1971), the model, in different versions, has been extensively used for the analy-

sis of global resource problems. As a description of the relationship between economic driving forces

and environmental impact or pressure indicators, the model is very flexible.

Extensive research on decoupling indicators for policy-evaluation purposes have been carried out by

the OECD since more than a decade (OECD, 2002), as well as various decoupling or resource-

efficiency indicators have been included in the European Environment Agency’s reports (EEA, 2003).

A few European countries started to include such indicators in official reports on environmental per-

formance (DEFRA/DTI, 2003), while some countries are experiencing the adoption of delinking-

based targets for major environmental policies.

The delinking approach can be better explained starting with the so called IPAT model. IPAT defines

total impact on the environment (I, such as atmospheric emissions, or waste production) as the (multi-

plicative) result of the impacts of population level (P), affluence (A), measured by wealth (usually,

GDP) per capita, and the impact per unit of economic activity representing the kind of technology of

the system (T):

GDP II P A T P

P GDP

14

This is an accounting identity, adopted to identify the relative role of A, P, and T for the observed

change of I over time, and possibly across countries: it is an indicator of intensity, and it measures how

many units of Impact (natural resources consumption) are required by an economic system to produce

one unit (one euro, dollar, etc.) of GDP. As a technical coefficient, it represents the resource-use effi-

ciency of the system, or, if its reciprocal GDP/I is considered, it is represents the resource productivity

in terms of GDP: it is the most aggregated way of representing in economic terms the average state of

the technology of an economy, in terms of the Impact variable. Changes in T, for a given GDP, reflect

a combination of shifts towards sectors with a different resource intensity (as an example, from manu-

facturing to services) and the diffusion of techniques with different resource requirements (from fossil

fuels to solar power). If T decreases over time, there is a gain in environmental efficiency or resource

productivity, and T can be directly examined in the delinking analysis. T is the main control variable in

the system.

As an example, with respect to China, Zhang (2000) has decomposed past CO2 emissions along the

IPAT lines, and he has found that increasing income has been the main factor increasing emissions,

while changes in aggregate population size have had a much lesser impact. His estimates show that

changes in technology (whose proxy can be energy intensity) are between those of the income and

population effects, in terms of absolute magnitude, and work in the opposite direction.

Decoupling may be absolute ore relative: it is absolute decoupling when the percentage growth of the

socio-economic indicator goes together with a percentage decrease of the environmental pressure (be it,

in absolute value, bigger or smaller than the growth of the driver). There is a relative decoupling, in-

stead, when the percentage increase of the environmental pressure is smaller than the increase of the

observed socio-economic driver, that is, when the environmental deterioration grows less than the re-

spective variable.

In other terms, the elasticity of the environmental pressure with respect to its drivers can be written as:

,

%

%I x

III

x xx

,

where I is the measure of the pressure on the environment, and x is the driver.

Therefore, two interesting options may arise:

absolute decoupling, when the driver elasticity of the environmental pressure is negative (and it is

between 0 and -1), that is: ,1 0I x , and therefore % %I x . It means that the driver

grows by 1%, and the environmental pressure decreases by the amount of the elasticity, but less

than -1%;

15

relative decoupling, when the driver elasticity of the environmental pressure is positive (and it is be-

tween 0 and +1), that is: ,0 1I x , and therefore % %I x . It means that the driver grows

by 1%, and the environmental pressure increases by the amount of the elasticity, but less than +1%.

Even if the IPAT framework is the basement for delinking analysis, it has to be noted that observation

of T on its own may produce ambiguous results. A decrease in the variable I over time is commonly de-

fined as absolute decoupling, even though it is not a delinking process, as it says nothing about the role

of economic drivers per se. Moreover, an environmental impact (I) that is slower in growing (or slowly

diminishing) than the economic drivers (P, or A, or T) is generally described as relative delinking. Thus,

a relative delinking could be strong, while absolute delinking might not occur (i.e., if I is stable or in-

creasing) if the increasing efficiency is not sufficient to compensate for the "scale effect" of other driv-

ers. A delinking process, a decreasing of T, suggests that the economy is more efficient, but offers no

explanations of what is driving this process.

In its basic accounting formulation, the IPAT framework implicitly assumes that the drivers are all in-

dependent variables: however, the evidence on the dynamics of economic systems suggests that each

driver, as well as the impact, may be reciprocally interdependent through a network of direct/indirect

causality relationships. For example, the evidence (Dinda, 2004) suggests that population dynamics (P)

can depend on GDP per capita (A), and vice versa, to some extent. Those analyses interested in exam-

ining the impact of a particular driver on the environment use a modified version of the IPAT frame-

work: so, if the target is looking at the impacts of population on the environment , it can be employed

the so called STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology)

framework, by Dietz and Rosa (1997), reviewed by Liddle (2013): they proposed a flexible, log-linear,

regression framework that allows for hypothesis testing, whose functional form is

1 2 3log log log logit i t it it it itI P A T

where i is the cross-sectional unit, and t is time period. The constants i and t are, respectively, the

country's (or cross-sectional) and time-fixed effects, and it is the error term. Affluence (A) is typically

proxied by GDP per capita, and the T term is often treated like an intensity of use variable, and some-

times modelled as a combination of log-linear factors (like urbanization or density).

Similar relationships or inverse-causation effects are also relevant for T. Theory and evidence suggest

that, in general, T can depend on GDP or GDP/P, and the contrary happens, if T refers to a key re-

source such as energy. In addition, there might be an opposite relationship between changes in the dy-

namics among P and I and T (Zoboli, 1996): for example, in a dynamic setting, I can be a driver of T, as

the emergence of natural resource and environmental scarcity stimulates invention, innovation, and dif-

16

fusion of more efficient technologies through market mechanisms (changes in relative prices) and pol-

icy actions, including price and quantity-based economic instruments. Then, a decrease in T can be re-

lated to micro and macro non-deterministic processes, for which economics can propose an open set

of interpretations, each as good as the other.

1.4 The Environmental Kuznets Curve (EKC)

1.4.1 Origins of the EKC framework

During the 1970s and the 1980s, the debate on the relationship between the environment and eco-

nomic growth had been largely influenced by the materials balance paradigm, which was asserting that

economic growth, ceteris paribus, would have led to a deterioration of the environment, and that an

economic system can only be environmentally sustainable if it is physically in a steady state, in which

the amount of resources available to the economy is constrained so that id does not overexploit its re-

sources and nature's limits (Stagl, 1999; Smulders, 2000).

A real conceptual milestone about the relationship between economic growth and the environment was

the so called Bruntland Report, which recognized the complementarities that existed between the two,

with an emphasis on the need to mainstream environmental concerns into the planning process in or-

der to ensure sustainable development (World Commission on Environment and Development, 1987).

Grossman and Krueger (1991), in their path-breaking study on the potential environmental impacts of

the North American Free Trade Agreement (NAFTA), had provided seminal evidence in support of an

inverted U-shaped relationship between economic growth (measured by increases in per capita income)

and some indicators of environmental quality. This relationship was called "Environmental Kuznets

Curve" (EKC) by Panayotou (1993).

The first influential studies of the EKC (Grossman and Krueger, 1991; Shafik and Bandyopadhyay,

1992) never referenced the IPAT framework or the Club of Rome debate, even if the question at the

heart of the EKC debate was almost identical to the one at the heart of the IPAT or the Club of Rome

debate. This is because the original concept of an EKC was first proposed by trade and development

economists in the context of an international trade agreement, rather than by environmental and re-

source economists in a pollution control context. The first main question of the research studies was

whether economic growth needs to be slowed, if not stopped, in order to avoid increasing damages to

the environment. Grossman and Krueger (1991 and 1995) provided an answer that seemed to contra-

dict the arguments against joining the North American Free Trade Agreement (NAFTA), which were

based on increasing environmental degradation, particularly in Mexico (Daly, 1993). Shafik and

17

Bandyopadhyay (1992) provided a justification for the World Bank (1992) position that increasing in-

come would likely help improve a wide array of environmental indicators.

1.4.2 The basics of the Environmental Kuznets Curve

The basic proposition of an Environmental Kuznets Curve (EKC) states that data suggest that there is

an inverted U-shaped relationship between environmental degradation and economic growth: as in-

come levels rise, pollution or some other measure of environmental degradation worsen, eventually fal-

ling once income crosses a precise threshold level. While much literature exists for many industrialized

countries on the EKC relationship for a number of pollutants (such as suspended particulate matter,

oxides of sulphur and nitrogen, carbon dioxide), the empirical literature has been relatively sparse for

developing countries (Dinda, 2004; Hilton, 2006; Ciegiset al., 2007), so that it is still questionable

whether EKC framework can be generalized, as it will be described later. If an EKC were a generalized

phenomenon, this would be anyway an indication, ceteris paribus, that environmental degradation

could fall, in the long term, as income becomes sufficiently high, and thus one solution to the problem

of environmental degradation would "simply" be to increase economic growth (Stern, 2004).

As regards the IPAT framework, the EKC is a natural IPAT extension, since its analysis addresses one

or two of the IPAT relationships, the first being the one between I and GDP, and the second between

T and GDP, or GDP/P. It may highlights empirical regularities that have some policy value, but some

argue (Galeotti, 2003) that it does not generally provide satisfactory economic explanations: the EKC

hypothesis is that the concentration or emission of a pollutant first increases with the economic driver,

then it starts to decrease, more or less proportionally, leading to a delinking from income, due to a

steady improvement in T, that is, the environmental income elasticity decreases monotonically with in-

come, and its sign changes from positive to negative at the end, leading to a turning point for an in-

verted-U shaped relationship. When relying on GDP or GDP/P as the only explanatory variable for an

analysis, the existence of an EKC could be deterministically misleading, since it would suggest that

rapid growth towards high levels of GDP automatically produces greater environmental efficiency,

bringing the economy to an absolute or relative delinking, and thus that reducing environmental impact

could be the best policy strategy.

According to the EKC hypothesis, economic growth can improve environmental degradation, after an

economy has reached an adequate level of economic growth. In the early stages of economic develop-

ment, when primary production dominates, there is an abundance of natural resource stock and a lim-

ited generation of waste, because of limited economic activity. In the course of development and

through industrialization, a significant depletion of natural resources occurs, and waste accumulates.

During this phase, there is a positive relationship between income, or economic wealth (per capita), and

18

environmental degradation (per capita). With further economic growth, services, improved technology

and information diffusion limit the material needs of an economy, and all things together result in re-

duced environmental degradation (Panayotou, 2003). Dasgupta et al. (2002) also noted that such an in-

verted U-shaped relationship can be explained in terms of weak environmental regulations, and of low

ability to pay for conservation during initial phases of economic development, followed by greater pub-

lic concern for the environment leading to more stringent regulatory standards (a policy effect), and

greater ability to pay for environmental amenities as income rises (an income effect).

Figure 1.1 – The relationship between environmental degradation and income: the Environmental Kuznets Curve (from Kaika and Zervas, 2013)

The EKC has the form shown in Figure 1.1, where the dependent variable on the vertical axis is an in-

dicator of environmental degradation, and the independent variable is income or an alternative measure

of economic growth: the relationship between environmental degradation and income is drawn as an

inverted U, and is similar to the original curve proposed by Simon Kuznets (Kuznets, 1955) concerning

the relationship between income inequality and economic growth. Indicators of environmental degrada-

tion can be anything that harms nature, from the emissions of a specific air pollutant, to the concentra-

tion of a particular pollutant in a river, or an alternative form of environmental degradation like defor-

estation. The turning point represents the level of income beyond which environmental degradation

can be delinked from the economic growth. For higher income levels, economic growth improves the

quality of environment.

19

Empirical studies test the EKC hypothesis using the following general reduced-form model (Dinda,

2004), or other modified functional forms:

2 30 1 2 3it itit it it i t itEP Y Y Y βZ

Where itEP and itY , respectively, represent a certain variable used as a proxy to the environmental pres-

sure, and some wealth measure (e.g., income) in country i, at time t; β denotes a row vector of coeffi-

cients containing other socio-economic (non-income) explanatory variables itZ ; i is an individual-

specific effect (country or other geographical unit); t is a time-specific factor; and it is the usual error

term.

Different combinations of 1 , 2 and 3 can lead to distinct shapes and interpretations of the envi-

ronmental pressure–income relationship, which can be described in the following cases:

1) 1 0 , and 2 3 0 : the environmental pressure tends to monotonically increase with economic

development. Thus, the shape of their relationship consists in a straight line with a positive slope, 1 .

2) 1 0 , and 2 3 0 : a decreasing trend of environmental pressure as income grows exists.

Thus, a straight line with a negative slope 1 can be drawn for the environmental pressure against in-

come.

3) 1 0 , 2 0 , and 3 0 , where 1 2 0 : an inverted U-shaped quadratic relationship be-

tween EP and Y exists, which represents the EKC pattern.

The peak of this quadratic curve is reached at the turning point, where 1

22TPY

.

4) 1 0 , 2 0 , and 3 0 , where 1 2 0 : these values suggest a U-shaped curve for the re-

lationship between EP and Y, with a turning point, where 1

22TPY

.

5) 1 0 , 2 0 , and 3 0 , where 1 2 3 0 : it suggests a cubic polynomial N-shaped rela-

tionship, in which the environmental pressure tends to decline after the economy reaches a certain level

of income, but climbs upwards after another higher level of income is achieved. So, environmental pol-

lution increases as a country develops, decreases once the threshold wealth is reached, but then it be-

gins increasing as national income continues to increase.

20

The two turning points are

22 2 1 3

3

3

3TPY

.

6) 1 0 , 2 0 , and 3 0 , where 1 2 3 0 : it is a cubic polynomial, but is an inverted-

N relationship between EP and Y, with two turning points, where

22 2 1 3

3

3

3TPY

.

A negative relationship between income and environmental degradation is only a transitory phenome-

non, because the U phase of the curve is followed by a phase where environmental degradation de-

creases.

7) 1 2 3 0 : it suggests an insignificant relationship between EP and Y.

Figure 1.2 – Examples of different patterns between environmental pressure and economic wealth (in-come per capita), from Wang (2007)

(1) A monotonically increasing pattern, where 1 0 , and 2 3 0 .

(2) A monotonically decreasing pattern, where 1 0 , and 2 3 0 .

(3) An inverted U-shaped EKC relationship, where 1 0 , 2 0 , and 3 0 .

(4) A U-shaped curve, where 1 0 , 2 0 , and 3 0 .

(5) An N-shaped pattern, where 1 0 , 2 0 , and 3 0 .

(6) An inverted N-shaped pattern, where 1 0 , 2 0 , and 3 0 .

21

When estimating the reduced-form regression, pooled cross-section OLS and panel estimation are usu-

ally the most preferred econometric techniques in previous studies of the literature, as it will be further

seen.

As can be easily noted, the standard model varies, depending on the study. For instance, the cubic term

of the income variable is included in studies that try to examine an N-shaped rather than an inverted U-

relationship between environmental degradation and income. Many studies work on a (natural) loga-

rithmic transformation of the standard specification, in order to avoid zero or negative indicators

(Stern, 2004). In any case, the final choice of the functional form is done on the model that best fits the

available data and has the higher explanatory power inside the data range (Lieb, 2003).

The standard equation is the solution of a structural system of (unknown) equations that form the final

relationship between environmental degradation and income. Using a reduced-form model allows to

directly measure the impact of income on environmental degradation: one of the major disadvantages

of this approach is that one does not know the underlying structural functions of the (economic) sys-

tem that led to such a relationship (Grossman and Krueger, 1995). Most empirical studies estimate the

model using time series-cross sectional data, or panel data (Shafik and Bandyopadhyay, 1992; Suri and

Chapman, 1998; Dinda et al., 2000; Richmond and Kauffmann, 2006). Only few empirical studies esti-

mate the model using time-series data, due to the lack of available data over a long period of time (De

Bruyn et al., 1998; Asafu-Adjaye, 2000; Egli, 2002).

1.4.3 The main theoretical literature on the Environmental Kuznets Curve

As masterly outlined in Carson (2010), the most important theoretical contribution concerning the pos-

sibility of an EKC relationship was made quite early when Grossman and Krueger (1991) pointed out

three possible impacts of an increase in economic activity due to a trade agreement. The first was an in-

crease in the scale of current production, which historically leads to more pollution; the second was a

change in the composition factors of the economy, which has ambiguous effects in any particular coun-

try, but could not result in a reduction in pollution everywhere, thus leading to the possibility of pollu-

tion havens and a “race to the bottom” that laid behind the debate on NAFTA creation; the third was a

shift in production techniques, the only factor that may lead to the possibility of lower pollution levels

associated with economic growth. Grossman and Krueger (1991 and 1995) argued that, during the ini-

tial stage of the developmental process, the typical economy is dominated by agriculture and similar ac-

tivities, and thus pollution intensity is generally low. But, as the economy shifts to heavy industry, pollu-

tion will tend to increase, so as, when the economy shifts into high technology and services, pollution

intensity will tend to fall. Grossman and Krueger were not the first to address the influence of growth

and trade on pollution. In the late 1980s, a related literature emerged (Sutton, 1988) that focused largely

22

on agriculture, environment, and trade, in a computable general equilibrium (CGE) framework, whose

main difficulty was their embodiment of a constant-returns-to-scale assumption to make them mathe-

matically tractable, and these works were forecasting an adverse environmental outcome. A highly in-

fluential article is Lòpez (1994), who deals with both the production and utility sides of the picture as

an explanation for the emergence of an empirical EKC, without removing any of the need for the usual

tools for the pollution analysis: the author looked at stock externalities (e.g., soil erosion), and shows

that a key issue is whether producers internalize the externality, and he comes to the result that, if they

do, then growth in income (or trade) will be reflected in improved environmental quality. Lòpez notes

that this internalization could happen via voluntary cooperative agreements, even if it might require

some corrective government action: he shows that, as the substitution elasticity between conventional

output and pollution falls, and the relative curvature of income in the utility function (the microeco-

nomic relative risk-aversion coefficient) falls, then an inverse U-shaped income–pollution relationship

can emerge.

A number of factors are commonly reported as being the proximate determinants of the EKC relation-

ship (Copeland and Taylor, 2004). The most important explanations relate to the scale, composition,

and technology effects, and all of them are connected with the production side of the economy: that is,

these explanations do not take directly into account the behaviour of consumers.

The scale effect is related to the overall dimensions of the economy. Scale is ultimately determined by

the total amount of material inputs into the process of producing goods and services, as well as the

volume of output that is consumed and fed into the environment by way of pollution and waste, and it

encapsulates two types of environmental pressure: one arising from increased use of resources (a deple-

tion effect), and the other is the increased associated waste (a pollution effect). Hence, at higher levels

of output (and, so, of income), it becomes relatively cheaper to reduce pollution, and producers are

more easily able and willing to adopt pollution-reducing measures and technologies: the scale effect

works to reduce environmental degradation or pollution at higher levels, since pollution control meas-

ures may not be affordable at small scales of production.

The composition effect deals with the proportion of each type of productive activity in the volume of

the economy’s output. As noted by Stagl (1999), the common development of the societies goes from

subsistence agriculture to more material and energy-intensive modes of agricultural and light manufac-

turing production, that are relatively more pollution-intense. Pollution intensity is highest as the econ-

omy moves into the stage of heavy industry, then it eventually declines, as society shifts towards high

technology, knowledge, and service-based industries. Within this common historical transformation of

the economies, pollution per unit of output (pollution intensity) tends to rise as the economy pro-

gresses on the development stages, but eventually falls as structural changes take place over time. Dur-

ing the earlier stages of development, the composition effect tends to strengthen the environmental

23

pressures that arise from increasing scale, while it tends to counteract such a pressure at higher levels of

development: therefore, the composition effect works to reduce environmental degradation over time,

by reducing the relative weight of those sectors of the economy that produce large waste, and by ex-

panding those sectors that produce relatively less residuals per unit of output.

The technological effect comes from the impact of improvements in the state of technology, which re-

duces pollution in two ways, indirectly by reducing the consumption of material inputs, and directly by

the fact that technological advancements make it possible to adopt better pollution control techniques.

Thus the technological effect affects both productivity and emissions: therefore, it is possible for a

naturally heavily polluting industry to record declining emissions, even as output rises, provided the in-

crease in output comes from factories using less polluting production processes. The technological ef-

fect should improve environmental quality, as economic growth progresses, by reducing the residuals

intensity of production through the invention and adoption of new technologies and standards, and

through changes in input, substituting more environmentally damaging inputs with cleaner ones.

While actual changes in environmental quality could be due to changes in one or more of the factors

outlined above, these factors could also be influenced by changes in other underlying socio-economic

factors, such as new stringent environmental regulations, increasing environmental awareness, and

higher education; wealth, then, is only one of the several factors which help to determine the connec-

tions between growth and pollution (Khanna, 2002; Panayotou, 1997; Torras and Boyce, 1998).

The most critical theoretical issue that has been (and that is being) raised is to explain how environ-

mental degradation relates to income, producing an inverted U-shape, and the theoretical literature took

a more abstract direction tied to macroeconomic work on optimal growth: within this path, Stokey

(1998), with her provocative title “Are There Limits to Growth?”, argues that the inverted U-shape of

the EKC could emerge from a situation in which pollution control efforts are not expended until a cer-

tain pollution threshold is reached, as income increases with economic growth. Beyond this threshold,

environmental degradation can begin to decline, as abatement efforts begin to increase with rising in-

come. In this model, pollution linearly increases with economic growth, until the threshold is passed

and cleaner technologies can be used: the resulting pollution-income path is therefore inverse-V-

shaped, with a sharp peak at the point where a continuum of cleaner technologies becomes available.

She shows that the key to inducing the EKC relationship is on the right capital accumulation path, with

respect to pollution control, and she also shows how a pollution tax of the right magnitude can help be-

ing this EKC path, rather than the usual command and control approach. Accordingly to this view,

Lieb (2001) argues that an EKC can only be generated when society reaches a certain point of satiation

in consumption, while Magnani (2001) asserts that, when the collective preferences of individuals for

better environmental quality are converted into public policy, the EKC path could come.

24

Other authors propose some similar explanations for the inverse-U relationship. Arrow et al. (1995) as-

sert that it could be that the pattern reflects the natural progression of economic development, from

clean agrarian economies, to polluting industrial economies, to clean service economies. Suri and

Chapman (1998) show that this mechanism may be facilitated by advanced economies, when they ex-

port their pollution-intensive production processes to less-developed countries: they note that, if the

downward-sloping path of the relationship is due to the pollution exporting, then the process of envi-

ronmental improvement will not be indefinitely replicable, since the world’s poorest countries will

never have even poorer countries which they can export their pollution to. Their pollution exporting

hypothesis implies that international trade and capital controls may be necessary. Others have suggested

that pollution stops increasing and then begins decreasing with income, because some constraint be-

comes non-binding with economic growth: Jaeger (1998) uses the assumption that, at low levels of pol-

lution, consumer's taste for clean air is satiated, and that the marginal benefit of additional environ-

mental quality is zero, and finds a reverse V-shaped pollution-income relationship, whose peak happens

when the optimum moves from a corner solution to an interior solution. John and Pecchenino (1994)

present an overlapping generations model, in which environmental quality is a stock resource that de-

grades over time, unless it is maintained by investing in the environment: in their view, an economy

which begins at the corner solution of zero environmental investment will face a decline in environ-

mental quality with time and with economic growth, until the point at which positive environmental in-

vestment is demanded, being it the moment when environmental quality begins improving with eco-

nomic growth. The pollution-income relationship of John and Pecchenino (1994) exhibits an inverse V-

shape, whose top is when the dynamic equilibrium switches from a corner solution of zero environ-

mental investment to an interior optimum, with positive environmental investments. Selden and Song

(1995) describe a variety of possible pollution-income paths in a dynamic growth model, while Chaud-

huri and Pfaff (1998) expose a particular mechanism, bundled commodities, to explain the EKC. Kelly

(1999) focuses on the irreversible nature of many pollution problems as a driving force behind the

curve.

A number of the existing studies of the EKC also make different simplifying assumptions about the

economy, in terms of how preferences, technology and other factors interact to produce an inverted U-

shaped curve, such as infinitely living agents, exogenous and (or) endogenous technological change, and

whether or not environmental degradation is a result of production activities or consumption (Selden

and Song, 1994). McConnell (1997) considers a model based on overlapping generations, in which pol-

lution is assumed to be generated by consumption, rather than by production. Jones and Manuelli

(1995) helped move the debate away from the traditional view according which pollution would have

autonomously been corrected by wealth increases: they focus on the interaction between growth, the

environment, and collective decision-making, and, in their model, economic growth is determined by

25

market interactions, and pollution regulations are set through collective decision-making by the younger

generation, who can choose to tax the pollution that will exist when they are older. Therefore, the na-

ture of collective decision-making influences the income–pollution path chosen, and, hence, societal

utility, showing that corruption does not preclude an EKC, but that the turning point would be higher

with it than without it: depending on the decision-making institution, the pollution-income relationship

can be an inverted-U, monotonically increasing, or even a "sideways-mirrored-S". The political-

economic model of Jones and Manuelli (1995) suggests that developing countries, unable to adopt effi-

cient policies, could benefit from international assistance, setting up effective environmental institu-

tions: from that, it can be seen that the various dynamic models, with multiple equilibria, imply that any

government policy which speeds the transition from one equilibrium to another (that is, encouraging

growth) would be beneficial for the environment.

The analysis of Andreoni and Levinson (2001) established itself as an instant classic due to its theoreti-

cal simplicity, the compelling intuition, and its easy-to-explain empirical evidence: their theoretical

framework is generic enough to encompass a variety of underlying forces that might give rise to in-

creasing returns, such as the intuition about better institutions (as in Jones and Manuelli, 1995), and the

one concerning better technology which emerges as the scale of production increases (as in Stokey,

1998). Their model is worth further discussion, due to its importance in the theoretical literature of the

EKC.

They first illustrate their model using a familiar Cobb–Douglas framework, in which utility depends on

consumption and pollution, with pollution in turn dependent on both consumption levels and pollution

control efforts (which reduces consumption on their side): basically, they show that just increasing re-

turns to scale in abatement are sufficient to generate the inverted U-shaped relationship between envi-

ronmental degradation and income.

In its simplest formulation, their model can be summarized into five basic equations, each being a func-

tion of time (it will not be noted in the text of the formulas):

1. a social utility function: ,U U C P U zP , where

0U

C

(consumption is a "good");

0U

P

(pollution is a "bad");

2. a pollution function: ,P P C F , where

0P

C

(consumption increases pollution);

26

0P

F

(pollution is decreased by the efforts in cleaning the environment);

3. a pollution function: P C A C C F ;

4. an abatement function: A C F ;

5. a constraint: Y C F .

In the model, U is the total social utility, C is the consumption, F is the effort expended in abating pol-

lution, A is the total abatement, Y is the income, and α and β are parameters. In this simple model, the

elasticity of pollution with respect to consumption is necessarily constrained to unity, while clean-up

efforts abate pollution with a standard concave production function. Given that society has a total in-

come (Y), which can be expended on either consumption (C), or on the effort to abate pollution (F), or

on both together; if the relative costs of C and F are normalized to unity (for simplicity of calculation),

then at any given time, C denotes the given aggregate consumption at time t, while F captures the

worth of any abatement effort in the given period t. Given these assumptions, the task faced by society

is how to allocate its monetary resources (income) between consumption and the effort to abate pollu-

tion, and, in the simplest case, where z=1, the model leads to derive the pollution–income relationship

as:

*P Y Y

,

the relative consumption-income optimal relationship as:

*C Y

,

and the relative efforts-income optimal relationship as:

*A Y

.

The derivative of equation pollution-income represents the slope of the environmental Kuznets curve:

*

1PY

Y

,

whose sign depends on the parameters α and β.

When 1 , efforts spent abating pollution have constant returns to scale, and *P

Y

,

which is a constant. If 0 and 1 , then P* rises with Y, and there is no downward sloping por-

tion of the pollution-income curve, as depicted in Figure 1.3.A.

When 1 , the second derivative of the equation is:

27

2 *

22

1P

YY

,

Thus, if 1 , the abatement exhibits diminishing returns to scale, and P*(Y) is convex, as in Fig-

ure 1.3.B.

But, if 1 , the abatement exhibits increasing returns to scale, then P*(Y) is concave, as in Figure

1.3.C. This is what has been described as an Environmental Kuznets Curve.

Therefore, for Andreoni and Levinson, an inverted U-shaped EKC relationship occurs if there are in-

creasing returns to scale in terms of the pollution control effort, while a linear relationship exists if

there are constant returns to scale, and a U-shaped relationship occurs if there are decreasing returns to

scale.

Figure 1.3 – Optimal pollution-income paths, from Andreoni and Levinson (2001)

A) Constant returns to scale B) Decreasing returns to scale C) Increasing returns to scale Note: in the text, Y=M

Increasing returns to scale in pollution control are possible and likely to happen in many cases, even if

income growth does not necessarily have to be the driving force behind them: population growth,

technological change, or shifts in consumption and trade patterns may source of increasing returns to

scale for pollution control. Although the model appears plausible and conforms to the standard specifi-

cations of the EKC models, it is doubtful if economic agents, in the aggregate of a typical developing

country's economy, abate pollution through voluntary self efforts, in the way the model describes.

Another theoretical explanation of the EKC, particularly from the point of view of the typical develop-

ing country, is the one which is based on the willingness to pay for environmental quality and services.

As pointed out by Stagl (1999), poor people have little demand for environmental quality, and, conse-

quently, they are constrained by their current income level and consumption needs, being able to do

28

nothing about improving the environment. But as society gets richer, its members have the capacity to

intensify the demand for a healthier and sustainable environment, leading to a typical EKC shape. The

pollution–income curve could, however, in theory exhibit other shapes, as in the cases of environ-

mental indicators characterized by unambiguous improvement (deterioration) as per capita income in-

creases. Alstine and Neumayer (2010) have pointed out that some environmental indicators, such as ac-

cess to clean water and adequate sanitation, belong to the first category, while global public goods (such

as carbon dioxide) belong to the second category, and they may exhibit conventional EKC shapes (if

they do at all) only at very high levels of per capita GDP. It is also theoretically possible to have a situa-

tion whereby income increases beyond a threshold, and environmental quality begins to deteriorate

thereafter (Martinez-Zarzoso and Bengochea-Morancho, 2003; Binder and Neumayer, 2005), depicting

a pollution–income curve which exhibits an N-shape.

As incomes rises, due to economic growth, individuals are more able to exercise greater political pres-

sure upon government to impose more stringent environmental control measures. Thus at higher levels

of income, the income elasticity of demand for environmental quality is higher, and economic agents

are not only willing and able to pay for a greener environment, but they are also able to successfully ex-

ert pressure on the authorities to enforce environmental regulations. Therefore, the EKC path may

show that, in some cases, institutional reforms following an income increase have forced private users

of environmental resources to internalize the social costs of their activities: but the extent to which they

can be forced to internalize such external costs of their polluting activities will depend critically on the

force of the policy authorities. Under this view, the relationship between environmental degradation

and income outlined by the EKC must be regarded as a long term relationship between environment

and income (Ciegis et al., 2007).

1.4.4 A macroeconomic model for the existence of the EKC: the Green Solow Model

As detailed above, for the last 20 years, a recent and very influential line of research has dominated the

way that economists and policymakers think about the growth and environment interactions: a broad

stream of papers have dealt with a reverse U-shaped behaviour of the measure of pollution, as wealth

increases over time, the EKC. Such a behaviour, as seen, is thought to be due to initial threshold effects

in abatement that delay the onset of policies, then to (increased) income driven policy changes that be-

come stronger with income growth, to structural changes towards a service based economy, and in-

creasing returns to abatement that drive down costs of pollution control. Theories relying on strong

compositional shifts or on increasing returns have shown some difficulties in matching with some data-

sets of aggregate data, as noted in Selden et al. (1999) and in Bruvoll and Medin (2003): their empirical

work has found that a changing composition of output plays at most a smaller part in the reduction of

29

emissions, since, while increasing returns to abatement may be important in some industries and for

some processes, a large portion of emissions come from small and sparse sources, such as vehicles,

houses and individual consumptive activities, where increasing returns to abatement seems unlikely to

happen. Moreover, increasing returns also presents strong incentives for mergers and the creation of

natural monopolies, and, unless the strength of increasing returns is carefully under control in the cho-

sen model, increasing-returns-to-scale models can even predict negative pollution emissions at large

levels of output: in the simplest version of Andreoni and Levinson (2001) model discussed above, the

theory of increasing returns to abatement has the property that pollution becomes negative for some

large, but finite, levels of output, but this feature poses problems when dealing with dynamic models

where output grows exponentially.

A seminal model dealing with the time dynamics of income and environmental degradation is the one

called "Green Solow Model" by its very authors, Brock and Taylor (2004, then refined in 2010), in

which they argue that the EKC relationship might be well explained by a simple variant of the Solow

(1956) model, where leading roles are played by technological progress in abatement and diminishing

returns to capital: the authors show that the forces of diminishing returns and technological progress,

identified as fundamental to the growth process in the Solow model, may also be fundamental to the

theoretical finding of an Environmental Kuznets Curve. The Green Solow Model also bears resem-

blance to the work of Stokey (1998), even if Stokey does not consider technological progress in abate-

ment, and to the new growth theory model of Bovenberg and Smulders (1995), because these authors

allow for a pollution augmenting technological progress, which is someway equivalent to our techno-

logical progress in abatement.

Here we present a simpler version of the Green Solow Model by Brock and Taylor, where some vari-

ables have been kept constant, for sake of simplicity. In Brock and Taylor (2005) is shown that, under

some circumstances, the abatement of polluting emissions subtracts resources to the production, and

therefore the growth rate of the economy is lower with abatement than without abatement. If the target

is abating the emissions without slowing the economy, it has to be counted on the technological pro-

gress in the environmental sector: that is, the introduction of new technologies that can reduce the

emissions coefficient per unit of output, without increasing the resources devoted to the reduction of

emissions.

All the variables of the model are function of time themselves, so it will be omitted for sake of clarity in

reading. If we denote the emissions' function as:

E e Y

where E is the quantity of emissions, e is the technological progress in the emissions sector, and Y is

the GDP, the percentage rate of variation in time can be written as:

30

E e Yt t t

E e Y

or

E e Y

E e Y

that is,

E e Yg g g

which states that the growth rate of emissions is due to the summation of the growth rate of the tech-

nological progress in the environmental sector (which reduces the polluting emissions per unit of out-

put) and the growth rate of the GDP.

The aggregate production function, without expenses for emissions, is

1Y K BL

where the productivity of labour (L), B, grows at a rate B

Bg

B

, which is the technological progress of

the sector that produces goods and services.

In this economy, therefore, there are two different types of technological progress: Bg , which continu-

ously increases the productivity of labour; and eg , which has the target of continuously reduce the

emissions per unit of product.

If the population (L) is kept constant, that is 0L

nL

, the equation of the dynamics of the per effi-

ciency unit capital is:

1k B

kg sk g

k

where K

kBL

, the rate of savings is S

sY

, and the rate of destruction of aggregate capital with time is

δ, that is, the dynamics of the aggregate capital is:

Y sY K

.

In steady state, where 0k

k

, that is the growth rate of the per efficiency unit capital is zero, it is:

1* Bs k g

which leads to the optimal stock of the per efficiency unit of capital:

31

1

1

*B

sk

g

whose transition towards the steady state is depicted in Figure 1.4.

The GDP per efficiency unit can be written as:

1K BLY Ky k

BL BL BL

.

The aggregate GDP can be written also in terms of capital per efficiency unit:

1 BL KY K BL K BL BLk

BL BL

and its growth rate over time is:

Y B L k

Y B L k

which can be also written as:

Y B kg g n g

and, if the population is constant, it is:

Y B kg g g .

From the dynamic equation of the growth of emissions with time, it can be written:

E e Y e B kg g g g g g .

In steady state, the capital grows at a zero rate, so the aggregate GDP growth rate is due only to the

technological progress in production, that is:

*Y Bg g .

In steady state, the emissions grow at a rate:

*E e Yg g g

where, if society wants that emissions continuously decrease (i.e., having a negative value of the growth

rate in steady state) it must hold:

* 0E e Yg g g

and, substituting the steady state value of the growth rate of Y, it is:

* * 0E e Y e Bg g g g g

or

*E e Bg g g

leading to the condition:

B eg g .

32

Only if the technological progress in the environmental sector ( eg ) is bigger than the technological

progress of the goods production ( Yg equal to Bg , in steady state) a permanent reduction of emissions

can take place, without negatively affecting the growth rate of goods production ( Yg ). The reason is

simple: the technological progress of goods production determines the growth rate of the economy,

and, therefore, the scale effect, which has alone a negative effect on the environmental sustainability.

The only way to compensate the negative effect on environment, so to have a reduction of emissions

with time, is having a technological progress in the environmental sector which could grow faster than

the technological progress of goods production, that is, e Y Bg g g . In other words, the "good"

technological progress, which reduces the coefficient of emissions per unit of product, must go faster

than the "bad" technological progress, which reduces the coefficient of labour per unit of product.

Using the emission relationship, it can be written:

E e B kg g g g

but

*e B Eg g g

so

*E E kg g g

and it becomes:

* 1E E Bg g sk g .

If the emissions growth rate should be negative, along the transition path it has to be necessarily

reached a level of capital per efficiency unit in correspondence to which the growth rate of the emis-

sions is zero, and after which they start decreasing. The level of the capital per efficiency unit under

which the rate of the polluting emissions is zero ( 0Eg ), is:

1

1

*B E

sk T

g g

.

Confronting the values of *k and k T , since * 0Eg , it has found that *k k T : the capital per ef-

ficiency unit by which the growth rate of the emissions goes to zero is lower than the capital per effi-

ciency unit in steady state. That is, k T is on a dynamic path towards the steady state value, *k .

Figure 1.4 shows how, as the capital per efficiency unit (and, so, the product per efficiency unit,

y k ) grows with time, the emissions first increase up to a certain maximum, and then decrease: this

is the behaviour of the EKC.

33

The evolution towards an economic growth which, at the end, becomes compatible with a continuous

decrease of the emissions is not automatic: this result is based on the hypothesis under which the rate

of growth of the technological change in the environmental sector is bigger than the growth rate of the

production of goods. But it can be like that, or it can not be like that. The dynamics of the system tell

only that, under precise conditions, the economy may exhibit an EKC behaviour. By this model, the

most important empirical regularity found in the environment literature (the EKC) and the most influ-

ential model employed in the growth literature (the Solow model) are intimately related: because of di-

minishing returns, development starts with rapid economic growth, emissions rise with output growth,

but fall with ongoing technological progress in abatement. Fast growth at first increase the emissions,

reducing the impact of the technological progress, and the emission levels rise. As countries mature and

approach their balanced growth path, the economic growth slows, and the impact of this slowed

growth on emissions is then overwhelmed by the impact of technological progress in abatement, so

that the emission levels decline. This interplay of diminishing returns and technological progress, which

are the key to the convergence properties of the Solow model, leads to a time profile of rising and then

falling emission levels, as income grows along a path of sustainable growth, bringing theoretical evi-

dence for the EKC hypothesis.

Figure 1.4 – The Green Solow Model and the EKC: the transitional dynamics towards the steady state (from Brock and Taylor, 2010)

34

1.4.5 The empirical literature on the EKC

The EKC hypothesis actually summarizes an essentially dynamic process of change across time and

space, or a across one of them only. Most papers treat the EKC like a long run phenomenon, consider-

ing such a framework a development trajectory for a single economy that grows through different

stages over time, or different economies at the same time: that is, assuming that all countries follow an

EKC path, then, at any cross-section of time, some low income countries are shaping the initial stage of

the EKC, some developing countries are approaching towards its peak (or start declining), and other

rich ones are composing the falling stage of EKC. According to the EKC upholders, several factors are

considered being responsible to shape the EKC path.

The original works of Shafik and Bandyopadhyay (1992), Grossman and Krueger (1995) and Selden

and Song (1994) estimated that income (in general, wealth) has the most substantial effect on most in-

dicators of environmental quality: their empirical findings imply that it is possible for a country to rid of

its environmental problems, although this process is not automatic. These studies started a massive

stream of works on the EKC hypothesis, both on theoretical and empirical basis, all of them focussed

to find some possible driving forces that may lead to an EKC relationship other than wealth. In the

vast EKC literature many thorough reviews exist, the most important being Stern (1998), the broad-

ranging general review of Dinda (2004), again Stern (2004), Dasgupta et al. (2004) and Carson (2010):

the next session draws its structure from the conceptual classification of the EKC causes that can be

found in those excellent papers.

1.4.5.1 Distribution of income, wealth and equity

Similarly to the original work of Kuznets (1955), some works (Torras and Boyce, 1998; Magnani, 2000;

Bimonte, 2002) have examined whether income distribution may be an underlying factor beyond an

EKC path: if the economic growth process can lead to a more equitable income distribution that im-

proves the relative position of the median agent, public awareness of environmental degradation rises

and suitable environmental regulations are imposed on the economy. The main research question is

whether economic growth increases income inequality or it could lead to a more equitable income dis-

tribution. In the model of Torras and Boyce (1998), pollution is abated, or generated, depending on the

gap of force between those agents who suffer from pollution, against those who benefit from environ-

mental degradation, and such a power is a function of the distribution of wealth. Bimonte (2002) finds

that the demand for environmental protection is determined by the increased participation of the peo-

ple who bear the burden of pollution, and such advancement comes from a more equitable income dis-

tribution, from better information access and from advanced education: therefore, if income inequality

worsens while income rises, the environment keeps deteriorating, because those who suffer from pollu-

35

tion do not have the sufficient economic power to impose environmental regulations on those who

benefit from pollution. The author observes a sample of countries which lay at the last stage of eco-

nomic development, and he argues that the participation of the agents in the growth process (depend-

ing on income inequality, information access and education) acts as a key factor responsible for trans-

lating the entire respective EKC upward or downward: this can explain why some economies that are at

the same level of economic growth may show different levels of environmental degradation (Pezzey,

1989). Indeed, as income grows, people achieve a higher standard of living, and demand for better en-

vironment induces structural changes in economy that tends to reduce environmental degradation:

when a country achieves a sufficiently high standard of living, its people attach increasing value to envi-

ronmental amenities (Selden and Song, 1994; Baldwin, 1995), and, after a particular level of wealth, the

willingness to pay for a clean environment rises more than proportionally than income (Roca, 2003):

therefore, it seems that the richer the people are, the bigger the value of a clean environment. Income

elasticity of environmental quality demand and resource goods is generally bigger than one, meaning

that clean environment and preservation are considered luxury goods. Many EKC models have empha-

sized the role of income elasticity of environmental quality demand (Beckerman, 1992; Carson et al.,

1997; Chaudhuri and Pfaff, 1998; McConnell, 1997) as the main reason to explain the reduction of

emission level: poor people have little demand for environmental quality, but, as a society becomes

richer, its members may intensify their demands for a more healthy and cleaner environment, so that

the consumers with higher incomes are willing to spend more for green products, and they also put

pressure for environmental protection and regulations (McConnell, 1997). In many cases where emis-

sions have declined with rising income, the reductions have been due to local and national institutional

reforms, such as environmental legislation and market-based incentives to reduce environmental degra-

dation (Shafik, 1994).

Empirical estimations confirm the existence of such a significant effect of income equality on pollution

abatement in certain countries or regions, even if a major limitation in examining the effect of income

distribution on environmental degradation is that there are few (if not poor) data of some quality that

measure or proxy income inequality, making it hard to examine whether the perception of environ-

mental degradation by people is significantly affected by their relative private income (Torras and

Boyce, 1998). Magnani (2000) finds that the downward slope of an EKC may emerge in high-income

countries only if economic growth does not lead to a tough increase in income inequality: he uses data

on OECD countries from 1980 to 1991, and his results indicate that income equality significantly raises

expenditures on environmental protection. Coondoo and Dinda (2008) find a similar result: they esti-

mate an EKC pattern for European countries, studying CO2 emissions and their relationship with in-

come inequality among countries. Cantore and Padillia (2010) verify a robust correlation between in-

36

come inequality and emissions distribution: they argue that the differences in GDP per capita among

rich and poor regions may be significant determinants of emission distribution among countries.

Most of authors agree that environmental policies are key determinants of the path of income–

environment relationship: public preferences are reflected through public policies related to environ-

mental quality, since some demand-side characteristics not only influence the state’s environmental pol-

icy regime, but also they also explain the mechanism through which these preferences are manifested. A

government's willingness to impose environmental regulations is a crucial factor affecting environ-

mental degradation (Bhattarai and Hammig, 2001). In a mature and growing economy, its government

is expected to properly respond to public awareness on environmental degradation, and to limit market

failures by imposing ad hoc regulations that prevent further pollution from increasing. Economic

growth is an essential condition to efficiently deal with pollution, but it is not the only condition:

Panayotou (1997) argues that, whether environmental quality improvements will take place, when, and

how, it depends on government policies, social institutions and the completeness and functioning of

markets as a whole.

1.4.5.2 Structural change in the economy and technological progress

One of the major common features of the pro EKC literature (Shafik and Bandyopadhyay, 1992; De

Bruyn et al., 1998; Dinda et al., 2000; Hettige et al., 2000) is that two fundamental driving forces of an

EKC-pattern are structural changes and technological progress. Structural changes include the transi-

tion of the production process from (the pollution-intensive) industry to (the information-based) ser-

vice sector, which is considered as less-polluting (Panayotou, 2003), and any other qualitative reforma-

tion of the economic structure in the knowledge economy.

At early stages of development, pollution is generated as a result of increasing production and extrac-

tion of natural resources, and this is the scale effect of production on environment. As can be seen in

Figure 1.1, the scale effect generates the upward trend of an EKC when production shifts from primary

production to industrial production: then, economic development gives the opportunity of investing in

information-based industry and services as well as improving production techniques or adopting

cleaner technology. These are the called respectively the composition and technological (or technique)

effect, and they can overcome the scale effect and generate the downward trend of an EKC (Dinda,

2004). As a wealthy nation can afford to spend more on R&D (Komen et al., 1997), technological pro-

gress occurs with economic growth, and the dirty technologies are replaced by upgraded new and

cleaner ones, which improve environmental quality. This is the technique effect of economic growth:

the EKC theory suggests that, historically, the negative impact on environment of the scale effect that

37

tends to prevail in initial stages of growth, but it is eventually outweighed by positive impact of the

composition and technique effects, that tend to lower the emission level (Vukina et al., 1999).

The composition effect is associated with shifts in production from the more material and energy-

intensive manufacturing sector, towards more environmentally friendly sectors, such as services or

high-technology firms (Panayotou, 2003). The development of cleaner techniques is encouraged by in-

vestments in environmental R&D for which, a particular level of economic growth must be achieved

(Neumayer, 1998). Bouvier (2004) notes that both structural changes (composition effect) and technical

progress (technique effect) focus on the economic activity of production, rather than that of consump-

tion, and that the scale effect may depend on business-cycle fluctuations, while the composition and

technique effect may operate with a slower rate.

Some authors indicate that the market mechanisms, based, as an example, upon the cost-criterion, de-

termine eventually whether a new technology, not necessarily being the cleanest one, could be adopted

(Smulders et al., 2010). Unruh and Moomaw (1998) assert that the existence of endogenous self-

regulatory market mechanisms, for those natural resources that are traded in markets, might prevent

environmental degradation from continuing to grow with income. Kadekodi and Agarwal (1999) note

that economic development may strengthen the market mechanisms, such that a developing economy

may gradually shift from non-market to market energy resources, that are considered less polluting by

free market economists.

Anyway, advances in technology over time seem to be the biggest cause of improved environmental

quality (Shafik and Bandyopadhyay, 1992). De Bruyn et al. (1998) use proper indicators, reflecting

changes in the composition and technology, and examine their effect on various indicators of emis-

sions, concluding that emissions may have declined over time probably due to technological and struc-

tural changes, and not due to economic growth, which, alone, cannot assure sufficient technological

improvements. Indeed, as noted in Dinda et al. (2000), observed changes in pollution levels, over time

or across regions, can be attributed to shifts in production techniques and to sectorial composition,

with respect to pollutants like SPM and SO2. Concerning industrial water pollution, Hettige et al.

(2000) use as explanatory variables the share of industry in total output, the share of polluting sectors in

industrial output, and the pollution intensities per unit of output in the polluting sectors: they find that

only the share of industry in total output follows an EKC pattern. It is possible, however, that struc-

tural and technological changes may have only a transient effect on pollution abatement (Pasche, 2002).

Grossman and Krueger (1995) already pointed out that improvements in indicators of urban air quality

may result from technological innovation, but this outcome reflects specific technological, political and

economic conditions of the time under examination. For Dinda (2010), an EKC may reflect, in the

short run, the cycle of life of each new technology, but, in the long run, a nonlinear EKC envelops a

series of separate EKCs, each corresponding to different and subsequent technologies.

38

1.4.5.3 International trade

Many authors assess that an EKC pattern may happen due to the effects of international trade (Suri and

Chapman, 1998; Cole, 2004; Kearsley and Riddel, 2010): trade openness might help the expansion of an

economy through increased production of (polluting) goods to support its exports. Since with higher

production higher pollution comes, when income and environmental degradation rise substantially, se-

vere environmental regulations are imposed on the economy, which results in a shift of its domestic

production of polluting goods to other (lower-income) countries, where usually a less strict environ-

mental legislation exists. This is known as the "pollution haven hypothesis" (PHH, from Dinda, 2004):

the exports of goods in a developed country generate the upward slope of its EKC, while its imports of

goods from developing countries cause the downward slope of its EKC. Free trade can therefore be

good for environment (Antweiler et al., 2001; Liddle, 2001): trade raises income levels of people in de-

veloping countries, and, by raising real income, it will create demands for environment protection, be-

cause higher income individuals want a cleaner environment. The PHH argues that low environmental

standards become a source of comparative advantage, and thus shifts in trade patterns: but lower trade

barriers could hurt environment if heavy polluters move to countries with weaker regulations. Industri-

alizing countries increase the consumption of energy required for the production of goods that are ex-

ported to industrialized countries, and industrialized countries lower their energy requirements, due to

imports of manufactured goods from the industrializing countries, pollution recipients. Suri and Chap-

man (1998) use a model that includes ratios of imports, exports and total manufacturing in GDP, as

additional independent explanatory variables other than income, in order to estimate CO2 emissions:

they show that the inclusion of trade variables raises substantially the turning point of an EKC. Under

certain circumstances, the force of the PHH hypothesis on the reduction of pollution is small: Cole

(2004) considers ten air and water pollutants, and four developed and developing trade-partners, and

they conclude that there is little evidence that pollution havens exist. Kearsley and Riddel (2010) extend

the model of Cole (2004), and they do not find sufficient evidence for the PHH as a strong explanation

of a possible EKC pattern. Panayotou et al. (2000) find that trade may help increasing emissions at a

decreasing rate, as income rises, but only for certain periods of time. Moreover, the statistical evidence

indicates significant differences among developing and developed countries: Stern (1998) notes that

heavily polluting industries are typical of the poorest States in USA, while high-income States are ori-

ented towards services and high-tech industries: therefore, the trade-specialization between these States

may explain the variations in their emissions. If relatively high environmental standards in developed

economies impose high costs on polluters, polluting activities in high-income economies face higher

regulatory costs than their counterparts in developing countries (Jaffe et al., 1995). This creates an in-

39

centive for at least some highly polluting industries to relocate, and thereby international capital reallo-

cations take place: rising capital outflows force governments in high-income countries to begin relaxing

environmental standards. This scenario has been called "race to bottom" (Mani and Wheeler, 1998).

Peters et al. (2011) report that the transfer of emissions through international trade often exceeds the

reduction of emissions at a single developed-country level: indeed, the net emissions transfers from de-

veloping to developed countries, from 1990 to 2008, have increased from the 0,4 Gt of CO2 emissions

to the 1,6 Gt of CO2 emissions. Levinson and Taylor (2004) estimate that a +1% increase in the cost

of pollution abatement in USA is associated with a +0,2% increase in net imports (or decrease in USA

net exports) from Mexico, and a +0,4% from Canada, due to imposed environmental regulations on

exports and imports. On the contrary, in Kahn (2003), the trends in international trade in USA, during

the period 1958–1994, show no evidence that pollution-intensive trade has increased, with the excep-

tion of the African nation's exports to the USA, which are mostly considered as energy-intensive rather

than pollution-intensive. Nahman and Antrobus (2005) conclude that the Southern Africa Customs

Union (SACU) may serve as a pollution haven over time for USA and UK, while, according to Eder-

ington et al. (2004), USA have not substituted domestic pollution-intensive production for imports

over the 1978–1994 period, and the value-added in the domestic manufacturing industry increases as a

result of a rise in the number of less-polluting industries, and a reduction in tariff-barriers, which result

in a compositional change in favour of dirtier industries in the USA. In the European Union case, stud-

ied by Cave and Blomquist (2008), the empirical evidence provides mixed results: imports of energy-

intensive goods from poorer countries seem to increase when more stringent environmental standards

are applied in the EU, but this is not the case with respect to toxic-intensive imports.

1.4.5.4 Individual preferences

Examining the microeconomic implications of consumer preferences on the environment is a difficult

task: suitable preferences can always lead to an EKC pattern, but there is no guarantee that such suit-

able preferences exist (Plassmann and Khanna, 2006). Some EKC studies focus on the microeconomic

implications of consumers' preferences as a partial explanation of an inverted-U pattern (McConnell,

1997; Roca, 2003). The main question is how the consumers' preferences (as regards environmental

quality) are modified when their income changes: Pearce (2003) notes that changes in income alter the

elasticity of demand for environmental quality, being the latter the change in the demanded quantity of

environmental quality with respect to a change in income. It is difficult to measure the quantity of envi-

ronmental good demanded, even if clean environment and preservation are seen more as luxury goods

(Dinda, 2004).

40

An alternative way is to measure the income elasticity of the Willingness to Pay (WTP), the change in

the willingness to pay for some environmental quality in response to a particular change in income.

Most studies report that, while the income elasticity of demand on environmental quality is marginally

over unit, the income elasticity of willingness to pay is less than unit (Pearce, 2003). Kander and Lind-

mark (2004) estimate that environment in Sweden started to be appreciated at a higher value after the

1970s, which leads to deliberate action to prevent further pollution and, as a result, pollutants de-

creased. As regards Italian household's consumption expenditures, the paper by Martini and Tiezzi

(2010) addresses the issue of whether environmental quality is a luxury good, meaning that its demand

increases more than proportionally with respect to income: they use demand analysis, combined with

household production, to estimate the marginal willingness to pay for improvements in air quality in It-

aly, and the corresponding income elasticity of willingness to pay. Studying data on Italian households’

current consumption expenditures from 1999 to 2006, they consistently find that the income elasticity

of willingness to pay for environmental quality is very close to one across income groups, and that it

decreases as a percentage of income as income increases, with interesting implications for environ-

mental policy: becoming richer does not necessarily leads to cleaner environments, but, if consumers

do not raise their environmental efforts as they get richer, even the most advanced and effective abate-

ment technology cannot prevent from pollution increasing. Furthermore, it is difficult to predict the ef-

fect of a shift in consumers' preferences, because such shift may depend on various spatial and time

conditions (Plassmann and Khanna, 2006): inhabitants of a city may not be worried by the negative

health effects of a waste treatment installation located in a sparsely populated area, or across a great dis-

tance, or, in general, people are less aware of pollution dangers when the cost of such a pollution can

be transferred to a remote future. In such cases, the inhabitants have few incentives to alter their con-

sumption patterns, unless they sincerely worry about the effects of environmental degradation to others

(Roca, 2003; Khanna and Plassmann, 2004). Different demand preferences shape different consump-

tion patterns, leading to different abatement policies, while the recognition of an environmental prob-

lem takes a long time (Kander and Lindmark, 2004; Cantore, 2010). Even when a problem has to be

faced, the actions depend on the utility of the agents, their relative economic position, and their spatial

ability to separate themselves from the source of pollution (Roca, 2003). Most of all, the income–

pollution link is a macroeconomic phenomenon, and any microeconomic foundations of this relation-

ship are hard to be analyzed in a sufficient way, as the risk is that they cannot sufficiently encompass

aggregate variables (McConnell, 1997).

41

1.4.5.5 Energy demand, energy prices and energy intensity

Most studies have found statistically significant results, confirming the EKC hypothesis for many pol-

lutants and other measures of environmental quality: when the GDP moves beyond the EKC turning

points, it is assumed that the transition to improving environmental quality takes place. The original

EKC analyses shows that the upward movement starts at the moment when low-income countries

move from agriculturally based economies to industrial economies, and that the downward shift takes

place as industrial countries move into the post-industrial phase, with services constituting the larger

part of the economy.

Energy intensity and energy conservation emerged as a crucial issues after the oil crises in the 1970s: as

a result, the structure of the oil-depending economies had to be transformed with the adoption of new

techniques that would have lowered the energy-intensity per unit of output, and the reinforcement of

lighter productive structure sectors, such as services, would have taken place (Lindmark, 2002; Kander,

2005; Tol et al., 2009). At its beginning, EKC theory and practice have not explicitly included the price

of energy as an independent variable, whose declining prices lead to an increased energy use, and to an

energy-based pollution, in both developed and developing countries: energy use, at all income levels, is

elastic to its price, especially in the long run, when declining real energy prices cause increased energy

use, even at low (but rising) levels of GDP. Furthermore, the long-run price model causes other factors

that were previously important to become insignificant: trade and energy prices are both important

variables, but trade variables become insignificant in a regression with both trade and energy prices, in

the work of Agras and Chapman (1999). The authors then argue that, for individual countries, eco-

nomic growth may be linked to a lesser increase in energy when there is a rapid growth in service indus-

tries, or when imports of more pollution intensive goods take place, or when installing domestic pollu-

tion control devices have been set up, or when energy efficiency increases. The first two options do-

mestically reduce the demand for energy, but they internationally increase the demand for energy. The

third option can increase demand for energy, while reducing specific pollutants, as many pollution con-

trol devices use more energy. The fourth, energy efficiency, at the same time, reduces demand for en-

ergy and reduces energy-based pollution, as shown in Figure 1.5, where it is shown that an increase in

energy prices is one of the few items that reduce overall global levels of energy-based pollution espe-

cially as regards the global pollutants as the CO2. As regards the oil shocks in the 1970s that led to

shifts in the energy mix, Agras and Chapman (1999) have found that energy prices played a significant

role affecting both CO2 emissions and energy consumption, even if no significant EKC-pattern arises

in their paper.

42

Figure 1.5 – Conflicting dynamics of the EKC, from Agras and Chapman (1999)

Some studies emphasize the significant effect of technology and structural changes on CO2 emissions

over time, due to the evolution of energy intensity over time, and particularly to the shifts in energy mix

and in conversion efficiency (Lindmark, 2002; Kander, 2005; Lantz and Feng, 2006; Tol et al., 2009).

Even if a reduction in energy consumption, in order to reduce CO2 emissions, can have negative ef-

fects on economic growth (Chontanawat et al., 2008), empirical studies on EKC indicate a positive rela-

tionship among energy, CO2 emissions and economic growth, since modern economic growth depends

on energy (mostly) based on fossil fuels, the primary responsible of human-related CO2 emissions

(Richmond and Kauffmann, 2006; Luzzati and Orsini, 2009; Marrero, 2010). Energy intensity changes

over time, due to changes in energy prices and energy mix (Asafu-Adjaye, 2000). Changes in fuel-mix

are associated with technological innovations (Turner and Hanley, 2011). Stern (2004) estimates that

energy intensity per unit of output has declined over time, thanks to shifts from the use of fossil fuels,

to the use of higher quality fuels and electricity, although Hamilton and Turton (2002) show that

changes in energy intensity are not common in all countries: they estimate that the large fall in the en-

ergy intensity of OECD economies over 1982-1997 has been primarily driven by the fall of energy in-

tensities in the services and industry sectors of the USA, and by the fall of energy intensities in the ser-

vices sector of the EU, but the rising energy intensity of services in Japan played an offsetting role.

1.4.6 A brief overview of some econometric issues related to the EKC estimation

The econometric foundations of EKC models have been suspected to be technically fragile: here, a

short selection of the major technical concerns of EKC studies is presented, while excellent review pa-

pers have been published on this subject, as, one for all, Stern (2004).

43

A first example of the econometric problems within the EKC landscape is illustrated in Harbaugh et al.

(2002), who examine an extended version of the dataset originally used by Grossman and Krueger

(1991): their analysis concluded that the evidence for an inverted U is much less robust than previously

thought, since the location of the turning points, as well as their very existence, are sensitive to both

slight variation in the data, and to reasonable permutations of the econometric specification.

The EKC estimation debate is also considered by some authors part of the much larger debate about

the cross-country growth studies that took a central stage in economics in the late 1980s and early

1990s (Barro, 1991; Mankiw, Romer, and Weil, 1992): Levine and Renelt (1992) noted that a vast litera-

ture uses cross-country regressions to search for empirical linkages among long-run growth rates and a

variety of economic policy, political, and institutional indicators, and they examined whether the con-

clusions from existing studies are robust or fragile to small changes in the conditioning information set,

finding that almost all results were fragile.

The major econometric issue that has dominated the early EKC discussions was the representativeness

of the samples, and the comparability of the pollution measures used. Stern (2004) pointed out that the

strongest problem was that the statistical tests usually reject random effects specifications, due to the

correlation between the random effects and the included covariates: this implies that, while the fixed-

effects model may be consistent for the sample on which the estimation ha been done, the parameter

estimates cannot be generalized to another sample. One important reason for including a homogeneous

set of geographical units in most EKC studies has been the availability of an indicator of interest meas-

ured in a somewhat comparable way: Auffhammer et al. (2009) note that, without a comprehensive

monitoring network, even measures of ambient quality across cities taken using compatible equipment

can be little comparable. As noted in Carson (2010), "Problems with data quality and non-random or incomplete

samples plague much of economics, so there is nothing unique about the EKC experience. Most good papers are upfront

about the problem". Similarly, Panayotou (1997) argues: "Data on environmental problems are notoriously patchy in

coverage and/or poor in quality. The only available data are not necessarily appropriate for testing the EKC hypothesis,

estimating its parameters, and drawing inferences about future trends".

Other critiques concern the validity of the data used, since data on environmental degradation are not

complete in coverage, and poor in quality (Stern et al., 1996), and since results usually depend strongly

on the techniques that have been used. Galeotti et al. (2006) estimate CO2 emissions using different

methods, which imply that real emissions may be well different from the estimated ones, leading to dif-

ferent outcomes in the relative studies. The existence of not sufficient data on all countries over a long

period seriously restricts any empirical comparison in attempting to find an income-pollution pattern

for all countries (De Bruyn et al., 1998), while the lack of detailed time-series data over a long period of

time forces authors to study the EKC concept with panel data (List and Gallet, 1999), with the assump-

tion of homogeneity in cross countries comparisons: with panel data, an EKC pattern is usually ex-

44

pected for the whole sample, even though some poor countries do not provide such evidence yet (and

this will be one of the results of the estimation done in the present thesis). As De Bruyn et al. (1998)

point out, finding an EKC turning point for the whole sample does not imply that each country in the

sample will follow such an EKC path: the turning point of each geographical unit of the sample may be

well different from the turning point estimated for the whole sample. De Bruyn et al. (1998), but also

other researchers (List and Gallet, 1999; Dijkgraaf and Vollebergh, 2001 and 2005), assert that empirical

EKC studies should focus only on one precise geographical unit, using time-series data.

Another econometric issue concerns the (missing) tests that have been done in the observed papers

(Lieb, 2003): most of the early empirical EKC studies do not report any tests on heteroscedasticity

(Stern et al., 1996), or do not show some omitted variable test (Stern, 2004), by the means of the

Hausman test, which studies the differences between the parameters of the random-effects and fixed-

effects models, or by the means of tests which examine the possible differences between the estimated

coefficients in different sub-samples, or by the means of the tests for serial correlation (such as the

Breusch–Godfrey serial correlation Lagrange multiplier test, or the Durbin-Watson test).

Moreover, the early EKC estimations involved potentially non-stationary variables which must satisfy

the cointegration property: the presence of non-stationary data series invalidates the use of standard

unit root tests and cointegration techniques in a time-series or a panel context, so that any result ob-

tained in such studies might be highly questionable (Wagner, 2008): as an example, Lee and Lee (2009)

estimate that the series of real GDP and CO2 emissions are composed by a mixture of stationary and

non-stationary series, so panel root tests can lead to misleading inferences.

Another important issue is the causality link, a problem which plagues most cross-country and reduced-

form models looking at growth, and thus this is not unique to the EKC framework: most EKC studies

state that pollution is generated by economic growth, but they ignore the fact that environmental deg-

radation may reversely affect the process of economic development (Arrow et al., 1995). The definition

of causality is based on Granger (1980), and its aim is to investigate whether a change in one variable

occurs before changes in another variable, and helps to predict that variable. Some papers report tests

using the Granger definition of causality (Perman and Stern, 2003), but the results are problematic, as

key variables such as income can often be shown to be integrated (nonstationary), suggesting that EKC

regressions may produce spurious results. In Dinda (2009), the high GHG-intensive economic growth

in OECD countries adversely affects their climate, which, as a consequence, constrains their further

economic growth. In other recent empirical studies, many authors find various directions of causality

among the variables, and it depends on the sample and the time under examination. For instance, in the

seminal causality-based paper by Coondoo and Dinda (2002), with respect to the link between CO2

emissions and income, in North America and Western Europe causality runs from emissions to in-

come, in Central and South America, Japan and Oceania the causality runs from income to emissions,

45

while in the country groups of Asia and Africa causality is found to be bi-directional. In Lee (2006), a

neutral relationship between energy consumption and income seems to be found in UK, Germany and

Sweden, a bi-directional causality exists in USA, an unidirectional causality from energy consumption to

income is in Canada, Belgium, the Netherlands and Switzerland, and a reverse unidirectional causality in

France, Italy and Japan. Similarly, in USA, income does not cause CO2 emissions in the long run, but

energy use does (Soytas et al., 2007). Ang (2007) shows that there is a long-run relationship among in-

come, emissions and energy consumption in France, and a unidirectional causality running from energy

consumption to income. In Central America, emissions are caused by energy consumption and income

in the short-run, but in the long-run there is a bi-directional causality between energy consumption and

emissions (Apergis and Payne, 2009). As Perrings (1987) has noted since the beginning, economy and

environment are jointly determined, and each region follows a different and peculiar pattern of pollu-

tion-income relationship.

Last, many empirical EKC studies do not explicitly allow for a geographical dimension of the pollution

issue, and many scholars implicitly assume that emissions in a region are unaffected by emissions in ad-

joining regions: using spatial econometric techniques to test SO2, NOx, CO and other pollutants from

the year 1990 to 1995, Maddison (2006) argues that studies that do not take into account the spatial ef-

fects of emissions may present an incorrect interpretation of what the changes in emissions in some

countries over time can be, since his estimates show that changes in emissions can be transmitted from

one country to its adjoining countries.

To sum up, the current EKC statistical analysis is not robust enough (Stern, 2004), and the use of struc-

tural models instead of reduced-form models in the EKC literature, and the development of complete

theoretical models, may help to correctly define the income-pollution relationship from a technical

point of view, in order to provide suitable and useful policies.

1.5 The Waste Kuznets Curve (WKC)

Most economic literature regarding waste and sustainability focuses upon optimal management of

economy-wide resource flows, whose aim is the intergenerational equity and the degree to which natu-

ral and artificial capital may be substitutable across entire economies. The economics of sustainability

stream of literature, also, tends to address sustainability as an absolute concept: either the economy is

on a sustainable path or it is not. The economic literature regarding the EKC and the decoupling of

economic activity from waste impacts is relatively macroeconomic in nature, although it does consider

both absolute and relative decoupling, while the microeconomic approach to sustainability remains rela-

tively unexplored (Wagner, 2011). Moreover, sustainable waste management actions are complicated by

46

uncertainties over many microeconomic aspects, such as waste quality, monitoring effort, the quality of

the natural endowment that private landfill managers complement with physical capital, and the liability

for harm that may arise in the management phases, but which is not automatically captured and whose

costs are not internalized by private actions: therefore, a useful step towards developing a theoretical

structure for sustainable waste management would be focussing upon how a private firm's strategic se-

lection of inputs (as regards the landfilling process, foe example) may differ from the nonstrategic selec-

tion of inputs a social planner would make.

Waste in the environment accelerates environmental degradation, as well as leading to several diseases

and death in human beings and other organisms. It also contributes to global warming, as decomposi-

tion of waste produces huge amounts of CH4, one of the major greenhouse gases (Calabrò, 2009). Ac-

cording to the IPCC, CH4 contributes with 14,3% of total greenhouse gas emissions (IPCC, 2007), and

the necessity of in depth studies on waste impact on the economic activity, and the opposite, is more

and more relevant.

Most economic literature regarding waste and sustainability focuses upon optimal management of

economy-wide resource flows, whose aim is the intergenerational equity and the degree to which natu-

ral and artificial capital may be substitutable across entire economies. The economics of sustainability

stream of literature, also, tends to address sustainability as an absolute concept: either the economy is

on a sustainable path or it is not. The economic literature regarding the EKC and the decoupling of

economic activity from waste impacts is relatively macroeconomic in nature, although it does consider

both absolute and relative decoupling, while the microeconomic approach to sustainability remains rela-

tively unexplored (Wagner, 2011). Moreover, sustainable waste management actions are complicated by

uncertainties over many microeconomic aspects, such as waste quality, monitoring effort, the quality of

the natural endowment that private landfill managers complement with physical capital, and the liability

for harm that may arise in the management phases, but which is not automatically captured and whose

costs are not internalized by private actions: therefore, a useful step towards developing a theoretical

structure for sustainable waste management would be focussing upon how a private firm's strategic se-

lection of inputs (as regards the landfilling process, for example) may differ from the nonstrategic selec-

tion of inputs a social planner would make.

As illustrated, EKC literature tries to capture both the macroeconomic and microeconomic level, since

it derives both from historical and empirical evidence, and from theoretical speculations. Among the

others, in Cole et al. (1997) and in Stern (2004), the empirical evidence coming from the first wave of

studies on the EKC testing was based on data from the 1980s and the 1990s, and they were showing

that an EKC generally existed only for those pollutants specific for air and water, and, most of all, for

those ones with a local dimension, while all those other indicators with a global nature were showing a

more or less increase with the growth of income. Nothing was clearly outlined as regards waste, be-

47

cause the weak reliability of the official sets of data. The data showed how the nature of the reversed U-

shaped curve was more compatible with all those pollutants with a local character, rather than the ones

with a much more global character, as it is the CO2 (Cole et al., 1997; Bruvoll and Medin, 2003): Volle-

bergh and Kemfert (2005), indeed, find that the decoupling between income growth and CO2 emis-

sions was not yet so strong in the industrialized countries, while Fisher-Kowalski and Amann (2001)

note that, even there where a delinking takes place, it is mostly a relative one, and not an absolute one.

This study, together with Matthews et al. (2000), shows a descriptive and a quantitative analysis about

solid materials, waste and emissions, under the point of view of the environmental accounting, based

upon input-output matrices, and it takes under examination the richest countries of the OECD area. As

regards the use of solid materials, the waste intensity with respect to the GDP shows a relative, but not

absolute, decoupling, and a growth of the use of such materials in the considered period, which goes

from 1975 to 1995. As concerns the emissions (solid and gaseous ones) too, a relative decoupling is

shown, but according to their geographical distribution: thus, local pollutants and landfilled waste ex-

hibit an absolute delinking, but it does not happen with CO2. This confirms the theory according to

which the EKC hypothesis is much more valid for those local or regional externalities, rather than for

those with a national or international nature (Bruvoll and Medin, 2003).

Even though some recent works cast some doubts on the robustness of the EKC evidence, claiming

that such and evidence can be obtained only thanks to the specific empirical model and to the particular

functional specification (Harbaugh et al., 2002; Stern, 2004 and 1998), in general many authors are still

stating that the EKC scheme could provide with results useful to understand dynamic ecological-

economic phenomena, and to evaluate public policies (Copeland and Taylor, 2004).

As regards waste production, the empirical works are quite a few, in comparison with the great deal of

studies about other pollutants, and the majority of them highlights the fact that there is not effective

evidence of an EKC, due to the fact that waste is a stock pollutant by nature, and therefore cannot be

cleared up by natural processes. Most economic literature regarding sustainability focuses upon optimal

management of economy-wide resource flows, whose aim is the intergenerational equity and the degree

to which natural and artificial capital may be substitutable across entire economies. The economics of

sustainability stream of literature also tends to address sustainability as an absolute concept: either the

economy is on a sustainable path or it is not. The economic literature regarding the EKC and the de-

coupling of economic activity from waste impacts is relatively macroeconomic in nature, although it

does consider both absolute and relative decoupling, while the microeconomic approach to sustainabil-

ity remains relatively unexplored (Wagner, 2011). Karousakis (2006) says that there is a difference be-

tween the decoupling that might arise with respect to waste production and the decoupling relative to

the disposal public policies: the first one is by far less feasible, due to the private nature of the contrast-

ing action to be implemented against solid waste, where the second one is considered more likely, con-

48

sidering the environmentalist debate which influenced the environmental policies of the last twenty

years. In contrast with what might be expected, among all the polluting materials whose possible EKC

behaviour could have been verified, solid waste (and, even less, industrial solid waste) has been studied

less than the other pollutants in the specialized literature, even less than other less common subjects, as

deforestation and biodiversity loss. Few studies try to verify the relationship between waste (or solid

materials flows) and its socio-economic drivers, and fewer are the ones aiming at extrapolating a de-

coupling linkage among the variables under examination: in Karousakis (2006) has been noted how, in

most early studies, the drivers of waste production verified by the authors have been based on micro-

economics studies on the various local communities of the USA. As an example, Table 1.1 shows some

results of early studies, in which the elasticities of waste production to income are reported.

All the data of those studies are at a micro-economic level (municipalities, families or individuals): the

income elasticity of waste production has been estimated around a range between 0,05 and 0,55, and

the characteristic common to all is given by the fact that these results have been come out from case

studies based on small datasets, and therefore they cannot be much generalized. Such an inelasticity can

be caused by a relative delinking, but it does not mean that its values will surely move to the value of 1

as the time goes on.

As regards the (so far) relatively few studies that have tried to insert solid waste (municipal or industrial)

into an EKC context, a first empirical evidence is presented in one of the seminal works of this stream

of the literature (Shafik e Bandyopadhyay, 1992), without then receiving the attention that waste pro-

duction nowadays more and more deserves: in this study too, using cross-sectional data from the 1980s

in comparisons among countries, the authors have observed how an absolute decoupling has not taken

place as regards waste, but a relative one instead, given that income elasticity of waste production has a

value around 0,31, 0,38 and 0,42, depending on the adopted specification. Gawande et al. (2000) used a

generalized negative binomial model to estimate the EKC for toxic waste in 3.141 counties and 748

metropolitan statistical areas of the USA. They found an inverted U-shape EKC, with a turning point at

$ 19.375 for county samples and $ 19.145 for metropolitan statistical areas (Table 1.1).

Table 1.1 – Estimated elasticity of urban waste production to income of some early studies

Reference Extension of the analysis Estimated elasticity Wertz (1974) Families in two suburbs in Detroit 0,27 Richardson and Havlicek (1978) Districts in Indianapolis 0,24 Hong et al. (1993) 2300 families in Portland, Oregon 0,05 Jenkins (1993) Municipalities in the USA 0,41 Reschovsky and Stone (1994) 3040 families in the State of New York 0,22 Kinnaman and Fullerton (1997) 756 municipalities in the USA 0,31 Podolsky and Spiegel (1998) 149 Municipalities in the State of New Jersey 0,55 Hong (1999) 3017 families of 20 cities in South Korea 0,10

49

Cole et al. (1997) worked with data from 1975 to 1990 for 13 OECD4 countries, and they found no

evidence for the conventional EKC for municipal waste or methane. Grossman and Krueger (1995)

used multi-country data with a quadratic-level functional model and found an inverted U-shape EKC

with a low income per capita of $ 5.047 at the turning point. That study concerned the presence of

toxic substances in water from waste. Shafik (1994) used data from 47 cities in 31 countries for the pe-

riod 1972–1988, and, using a quadratic functional model, he also found an upward straight line EKC

for waste emission. Shafik and Bandyopadhyay (1992) studied municipal waste data from 39 countries

using a quadratic fixed effects model and also found an upward straight line. Wang et al. (1998) studied

hazardous waste at the US County level for 1992; using a probit estimation model, they found an in-

verted U-shape EKC with a turning point at $ 23.000 (1990 dollars). Though the emission of toxic

waste generally decreases with increasing income per capita, the trend for municipal waste emission

does not seem to follow the conventional EKC. This raises the scenario of abatement programs being

implemented only where immediate harm exists. Slowly but consistently increasing municipal waste

emission is effectively not being controlled by producers at all levels. Whether this type of pollution,

though slow, has a long term effect or not has not generally been studied yet. Grossman and Krueger

(1995), however, looked at municipal waste and estimated the turning point at $ 5.047, supporting the

conventional EKC. Rothman (1998) pointed out that, for municipal and packaging waste, the proper

economic driver/indicator is not GDP, but rather household consumption. This is a key issue on both

conceptual and statistical grounds.

In the research report that gave birth to the EKC literature (World Bank, 1992), there were already

signs of empirical evidence for an EKC, based on regressions on 1980s data among several countries.

Some other research reports (DEFRA, 2003) show that positive income elasticities to waste generation

must be a primary policy target, since waste production is typified by a strict increasing relationship

among pollution and the several socio-economic drivers. In a further paper, Shafik (1994), using new

data for the same period, finds that the quantity of per capita urban waste linearly increases with in-

come, showing that the relative decoupling previously found (calculated again and now showing a value

of 0,38) is weak to generalize. Seppala et al. (2001), in a study about five industrialized countries such as

Japan, USA and Germany, during the period 1970-1994, has not found any trace of delinking. Cole,

Rayner and Bates (1997) analyze a dataset of 13 OECD countries, testing the hypothesis of a quadratic

relationship between income and urban waste, although without finding any evidence of an absolute

delinking: there is not any turning point, and, moreover, the found relationship forecasts a linear

growth of the indicator with the increase of income. Such a relation is considered one of the main

problems to face in the coming future, together with the reduction of the quantity of CO2 in the at-

mosphere (DEFRA, 2003): moreover, some recent studies (see below and the following tables) to-

gether try to assess that a positive turning point in the relationship between CO2 and income can be

50

reached relatively soon, while there is not any empirical evidence that leads to think that an absolute

decoupling might happen between income and waste. Data shows that waste production is destined to

be increasing with income, even if such a relationship spreads from confrontations among different

countries, and not from national or regional level analyses. The governmental agencies reports and the

EKC literature on waste both converge on one point: as of now, a very weak evidence at a macro-

economic level exists, and so there is a great need of new studies and researches dealing with the local

level point of view, be it using national, regional or local (provincial and municipal) data. This is impor-

tant not only from a descriptive point of view, but also and most of all in order to develop effective en-

vironmental policies: because of the local nature of solid waste, the different elasticity's values with re-

spect to the socio-economic drivers should be calculated at the most decentralized possible level, since

only in such a way the benefits of the macro approach, with its confrontations among countries, and

the ones of the micro approach, which takes under examination only small and limited territorial enti-

ties, can be summed up together.

Some authors have then supposed that the double nature of waste as stock or flow, as well as its typol-

ogy (hazardous and non-hazardous), could let different relationships with the socio-economic drivers

emerge: Leigh (2004) finds that non-hazardous waste and its flow in the considered time unit does not

seem to be associated with a negative income elasticity, showing an empirical evidence for EKC as re-

gards the waste-consumption link (even if the time span is limited, looking at the data on the years 2001

and 2002 only). Similarly, in Wang et al. (1998), there is an empirical evidence of negative elasticity be-

tween waste and income, but the kind of waste that is taken into account is a measure of flow and not a

stock of waste, and the analysis is a cross-section among the different States of the USA.

Some works cast a light on the importance played by other socio-economic factors, such as those driv-

ers (different from the usual measure of income) that might have influence over (and that must be de-

coupled from) the generation of waste: the most frequent of those drivers seems to be the demographic

variable. Beede and Bloom (1995) work on a cross-section of data from 36 countries, and they calculate

an income elasticity of urban solid waste of 0,34 (relative delinking), and a population elasticity of urban

solid waste of 1,04 (absence of delinking): the linkage with the population driver, anyway, does not pro-

vide significant results when, from a cross-section, the analysis shifts to a time-series study of the USA,

in the period 1970-1988, where the income elasticity of waste exhibits a value of 0,88, while the popula-

tion driver becomes not significant anymore. On the same path, Johnston and Labonne (2004) work on

a microeconomic level study, in which they use a panel of 30 OECD nations, in the period 1980-2000,

that has been related to other variables, such as consumption, urbanization degree of a territory and

demographic growth. As far as concerns the economic activity and the housing density, the obtained

results are in line with the preceding studies: the per capita consumption elasticity of waste is 0,69, the

51

housing density one is 0,85, while the elasticity of waste with respect to the ratio of the population who

live in cities is 0,15, thus all these values indicate a relative decoupling.

On the contrary, the relationship of waste with the population age is negative, as seen in Gawande et al.

(2001): in this paper, the authors test the hypothesis under which the proximity to waste disposal sites

is determinant in the individuals' relocations decisions, and they find that the income level over which

the impact of waste starts declining (the turning point) is the same of the income level that determines

the relocation from polluting sites. Such a result leads to state that even the relocating decisions of the

individuals (and, therefore, the migratory flows of people, within a nation and outside their nation) have

to be considered as drivers for an EKC of waste.

The use of other variables as determinants of waste generation is the key concept of the "ecological

footprint" definition, invented by Rees (1992): the ecological footprint, according to the authors who

support such an indicator for environmental economics analysis, is a more homogeneous measure use-

ful to calculate the environmental impact, and it is the area of land that is needed in order to produce

the resources that a nation consumes, and in order to absorb the waste that the nation generates. This

area is measured in global hectares (gha), that is, they are hectares of ecologically productive land ac-

cording to the world average land productivity (Bagliani et al., 2006a). Since it is an indicator based on

consumptions units, rather than on productive units, some authors state that it is more appropriate

than other to measure the real impact of the economic activity, especially if it includes data on waste

production it a precise area. In Bagliani et al. (2006a), such an indicator is increasing with the increase

of population growth, there where in other studies the population driver seemed to be irrelevant to the

environmental impact. In Bagliani et al. (2006b), though, the growth rate of the ecological footprint

slowly diminishes with the increase of income, without signs of any stop.

A recent study where the author uses socio-economic variables to explain not only the production of

waste, but also the management implications for it, is Karousakis (2006), where the object of the analy-

sis is, once again, urban solid waste. The observed sample consists of a panel of 30 OECD countries,

under the period 1980-2000, with 4 data per each year, and the aim is determining the drivers of the

production of waste, and the drivers that determine the value of the ratio of sorted glass waste and of

sorted paper waste with respect to the total amount of waste: as regards urban waste, it is shown how

they linearly grow with income, and that their income elasticity stays between a 0,42-0,45 range, while

the housing density variable is not significant, there where the total urban density is significant.

Some studies have investigated the policy choices at a level of single country, in order to exploit the

richness of the regional datasets, but this kind of researches does not allow their results to be general-

ized, due to the local level of the analysis. Mazzanti et al. (2008) have found some evidence of an EKC

for waste (Waste Kuznets Curve, WKC), and the signs of effectiveness of the adopted environmental

management instruments, analyzed in the paper, in reducing the waste in Italy. Their analyzed database

52

is a rich panel from APAT (the "Agenzia per la Protezione Ambientale", the environmental protection

agency), filled with data at a regional and provincial level, covering all the 103 Italian provinces in the

period 1999-2005. Among the results, it is shown how a decoupling between economic growth and

landfilling of waste is observed, and that this tendency is led by a mix of structural drivers, such as

population density and other drivers related to public management choices. However, they find that

not only structural factors are relevant. If, on one side, landfill taxes are not a significant driver of the

phenomenon, waste management tools, such as separated collection for recycling, and the tariff system

connected to waste services, have a significant effect on the amount of landfilled waste. Moreover, as

concerns the spatial interrelations across provinces, they note that the presence of incinerators in

nearby provinces increases landfill diversion, due probably to free riding behaviour or intra-provinces

agreements on waste management.

The economic literature about the (Environmental) Waste Kuznets Curve and the decoupling of eco-

nomic activity from environmental and waste impacts is relatively macroeconomic in nature. Mazzanti

and Zoboli (2009) analyze waste generation and landfilling data for the European Union, and find that

while waste generation has not yet experienced absolute decoupling at a continental level (and is there-

fore not yet consistent with the WKC hypothesis), there is some evidence of relative decoupling. They

also find that landfilling in the EU is decoupling on an absolute basis, and that therefore the EU lies on

the negative slope of the supposed WKC. The authors state that this success in diverting waste from

landfills is gained by the EU thanks to the EU Landfill Directive and its related environmental policies.

They have observed that Eastern countries appear to be performing generally quite well, thus benefiting

from EU membership and related policies, in terms of environmental performance. Absolute delinking

is far from being achieved for waste generation in the EU, but there are some first positive signs of an

increasing relative delinking for waste generation, and for robust landfill diversion, suggesting that,

while landfill diversion is currently associated to a delinking which is partly explained by EU policies,

waste prevention must be the objective of waste regulation efforts.

Some studies deal directly with the valuation of the EU Landfill Directive, and of the UK landfill tax,

adopted in 1996, a real and rare example of an environmental tax calculated on the basis of the evalua-

tion of marginal external costs. Studies taking into account the external costs are Ready and Ready

(1995), where they assume that external costs amount to $ 20 million over each landfill's lifecycle, bas-

ing their results upon the study by Nelson et al. (1992) about property value declines near landfilling

sites. Gaudet et al. (2001) conclude in their model that the constant marginal cost of disposal per land-

fill site perfectly captures any marginal external waste transportation and disposal costs. A UK specific

regional assessment on waste strategies is offered by Phillips et al. (2007), but regional based analyses

are still a rarity.

53

Morris et al. (1998) have raised some concern about the sustainable management of waste, while Morris

and Read (2001) and Burnley (2001) have then updated this analysis, highlighting some weak points.

Martin and Scott (2003) have noted that the tax, which was intended to contribute to the necessary

transition from the landfilling of waste to its recycling and reusing phase, has failed in significantly

changing the domestic behaviour of the producers of waste. They state that the available evidence

shows how this transition can be controlled and guided toward the recycling, but not towards the reuti-

lization or the minimization of waste.

Historically, landfilling is the dominant method in (municipal, hazardous and radioactive) waste man-

agement, and it remains a key but contentious aspect of modern waste management, particularly in the

context of sustainability. Significant technological change has evolved in landfilling (of all waste types)

such that the landfill is a more significant method than at any time in its history. Pearce and Turner

(1993) argue that it is not always obvious on economic grounds that increasing recycling effort is

worthwhile. Palmer et al. (1997) find that the reduction in recyclable municipal solid waste flows in the

United States is economically modest (-7,5%). Kinnaman (2006) notes that recycling costs about twice

as much per ton as disposal: the data suggest a solid waste market intervention, with the imposition of a

relatively small tax on each ton of waste disposed, since "State mandates that require municipalities to imple-

ment unit-based pricing programs, and especially curbside recycling programs, could usefully be replaced by disposal taxes

levied at the landfill". Aadland and Caplan (2006) find in their study of recycling in 40 western US cities

that several seem to operate inefficiently, while Dijkgraaf and Vollebergh (2004) find in their analysis of

waste disposal options in the Netherlands that modern landfilling performs better than incinerating (in-

cluding a waste-to-energy component) at the margin; they argue that their results can be generalized to

the wider European market, and to the US market as well.

While there is indeed an economic literature regarding solid waste management, there are only a few

papers that explore the microeconomics of optimally managing landfills as part of more general waste

management plans. Some of the key papers in the economic literature on a green design of waste man-

agement process are Dinan (1993), Palmer et al. (1997), Fullerton and Wu (1998), Choe and Fraser

(2001),Walls and Palmer (2001), and Eichner and Pethig (2001). The economic literature that focuses

on the microeconomics of waste disposal facilities has been inspired by the seminal work of Hotelling

(1931), and includes Keeler and Renkow (1994), Ready and Ready (1995), Highfill and McAsey (1997),

Gaudet et al. (2001), Ley et al. (2002), DeAngelo and Wagner (2005), and Benjamin and Wagner (2006).

Outside the EU countries, the analyses are not so many. Taseli (2007) has presented a valuation of the

EU Landfill Directive for Turkey, a potential EU country member that may be compared to some

newly entering Eastern European nation. The study shows the great difficulties that this kind of coun-

tries has to face in order to reach the long term goals, and it shows an analysis of the EU in general.

Outside the EU, the studies on landfill diversion and on waste generation are concentrated on the Far

54

East economies, there where urban land has a high price and value, since population density reaches

the highest values in the world (Lang, 2005, Ozawa, 2005; Yang and Innes, 2007). Population density

and public policies devoted to its control work as side drivers towards getting a strong decoupling both

in waste generation, and in waste landfilling.

The problem of the sustainability of the waste production with respect to the economic expansion is

not only important ex ante, but it is important ex post too, when waste has been generated, and it has

to be decided the best way to dispose of them, choosing between landfill and incineration: Dijkgraaf

and Vollebergh (2004), indeed, show that those tools, responsible for the future different stocks of

waste, are both convenient, but only when looking at different targets. Analyzing the case of the Neth-

erlands, if the objective is the decrease of the environmental costs, the best choice is landfill, while if

the target is the maximization of the social benefit, then the best choice is incineration. Such manage-

ment options are directly responsible for structural evolutions in the future production of waste, and

therefore an environmental policy aiming at waste reduction has to allow for both the drivers of waste

generation, and those factors influencing the subsequent management policies.

Notwithstanding, waste is one of the major problems that the EU tries to control (European Commis-

sion, 2003), still there is not a clear and robust empirical evidence concerning the possible decoupling

of the several different kinds of waste, but only few studies exist, as the one by Mazzanti and Zoboli

(2006), where the authors consider the EU policy on End-of-Life Vehicles (Directive 2000/53/EC on

ELVs) investigating whether interrelated sequences of single innovations in both upstream (car making)

and downstream (car recycling/recovery) should take place, and they conclude that the dynamic effi-

ciency of the incentives in ELV-like problems depends both on where, along the production-to-waste

chain, and how, in terms of net costs of allocation, the specific incentive is introduced. The study by

Martin and Scott (2003) states that the quantity of waste exhibits, in general, a positive relation with

richness, thus excluding any decoupling possibility.

As regards the EU, Mazzanti and Zoboli (2005) analyze a panel dataset for 15 and 18 European coun-

tries, and the key studied variable is urban waste (for the period 1995-2000) and packaging waste (for

the period 1997-2000): their results on packaging and municipal waste show that decoupling seems to

occur only on a relative basis, while no significant evidence on an inverted U-shape is found for both

waste indicators. In the opinion of the authors, Europe appears still close the critical turning point,

concerning the relationship between waste and consumption indicators. The estimated elasticity of

waste production with respect to the consumptions levels of families is always close to 1, even in the

richest countries.

One of the possible external causes under which a WKC is not seen is the imperfect harmonization of

the several national bureaus of statistics of the different States not only of the EU, but also of the

world: Johnston and Labonne (2004) note that, until the 1990s, many statistics are to be considered

55

weakly reliable, due to the simple lack of ad hoc offices (responsible for environmental data) in some

countries before the 1990s, and also due to the different ways of registering the data and what kind of

data, so that the very first problem to face should have been the standardization of the several systems

of environmental accounting.

Hence it is important that the researchers could work on data "bureaucratically" homogeneous, other

than geographically related (regions confronted with regions, nations with nations). The study by Chen

and Lotspiech (1998) analyzes a cross-section dataset of 479 cities of China in the year 1991, showing

that aggregate industrial output measures and sector-specific shares perform well in explaining aggre-

gate flows of industrial wastes. Cities with higher per capita GDP treat larger fractions of all residuals

classes, and show lower production of gaseous and solid residuals. The study states that population

density is positively correlated with three residual flows, and with efforts to control industrial dust, and

that treatment correlates strongly with the share of GDP arising from the industrial and construction

sectors, thus negating the existence of an EKC.

Mazzanti and Zoboli (2007) study a sample of European data of waste production from the EU25,

EU15 and EU10, taking into account both landfilled and incinerated waste. They show that, for waste

generation, there is still no absolute delinking trend, although elasticity to income drivers appears lower

than in the past (about 0,2). Landfill and other policy effects do not seem to provide backward incen-

tives for waste prevention, and, as regards landfill and incineration, the two trends are respectively de-

creasing and increasing, with policy effects providing a strong driver, which demonstrates the effective-

ness of policy even in the early stages of policy implementation. EU15 and EU10 groups of countries

show some different waste trends, and driving forces of waste generation and landfill, when analyzed

separately, leading to conclude that, although complete delinking is far from being achieved (especially

for waste generation), there are some signs of a quite significant role of the EU waste policies imple-

mented in the late 1990s and early 2000s.

Two other researches, similar to each other as regards the setting of the analysis, deal with Italian data,

and both take into account urban solid waste data (per capita and absolute level data). The first is the

one by Concu (2000), who tries to test the relationship between a proxy of private wealth (not the in-

come, but the average taxable income over which the Imposta Comunale sugli Immobili, ICI, a house

tax, was calculated) and an environmental quality indicator (the quantity of the per capita urban solid

waste), using a database at a municipal level, with 322 municipalities of Sardegna island, for the year

1997. The paper also tries to validate the common assumption on the basis of the economic planning

claim which states that environmental preservation and touristic specialization are complementary. The

results of the econometric analysis confirm that the existence of an EKC is sensible to the kind of data

that have been used, other than to the functional specification: the model shows that for that particular

environmental indicator the EKC relation does not exist, and that, moreover, the link between waste

56

and wealth is exponential, while the supposed complementary link between tourism and environment is

rejected. The study is a cross-section, and therefore it does not embed the time dimension, but it is a

matter of fact that the result is, at a local level, similar to those at a national level. With more geographi-

cally disaggregated data it is possible to better exploit the greater heterogeneity of local level datasets in

comparison with international level datasets.

According this view, again Mazzanti et al. (2006) is the first paper that runs an analysis of a provincial

and regional level dataset for an EU country: it provides empirical evidence on delinking and on the

EKC for municipal waste production in Italy, using two disaggregated panel datasets on Italian Regions

and Provinces (1996-2004 data for the 20 regions, 2000-2004 data for the 103 provinces) to estimate

the extent to which delinking among waste production and economic drivers is taking place. The em-

pirical analysis of different specifications shows mixed evidence in favour of an EKC relationship,

which significantly arises at a provincial level, there where a very high data heterogeneity exists. The

turning point, although, is at very high levels of value-added per capita (around € 23.000-26.000), which

characterise a very limited number of wealthy (Northern) Italian provinces. Their analysis, on the con-

trary, does not reveal a similar evidence for the regional dataset, and they also note a positive relation-

ship between waste production and the share of separated waste collection, which can be explained by

the sharp difference in income and waste-policy performance between Northern and Southern Italy.

Their test on some policy proxies (the diffusion of the new waste tariff regime at the local-level, and the

ability of utilities to recover waste service cost) leads to the conclusion that they are not yet having im-

pact on waste production. Therefore, an EKC is seen at a provincial level, there where data have more

variability, while at a regional level the relationship seems to be linear an positive, and the households'

consumption elasticity of waste goes from 0,17 to 0,35, according to the functional specification that is

adopted, and from 0,45 to 1,31 when the GDP elasticity of waste is taken into account. These kind of

results lead to the idea that the validity of results strongly depends on the functional form that has been

used, and on the aggregation level of the data, since the local level datasets seem to provide more ro-

bust results than national level datasets. Among the socio-economic drivers, population density is asso-

ciated with a negative effect, but it is never significant: this leads to think that the population's positive

and the negative effects on waste generation balance each other, and this is in line with the results of

the preceding studies. The authors run also an estimation using waste data whose values are expressed

in levels rather than using per capita values, and for both the geographical dimensions an EKC is not

observed: for the provinces, the income elasticity of waste is positive, and it reaches values around 0,28

and 0,34, while the housing density elasticity of waste is around 0,64 and 1,00; for the regions, the in-

come elasticity of waste lays in the interval 0,37-0,60, while the population one is estimated as 1,22.

These results lead to state that the cross-sectional analysis among nations, even if based on countries

which are someway homogeneous, can be not fully reliable, because they measure only the average ef-

57

fects that the variables have on the production of waste, while a kind of analysis that use highly disag-

gregated data at a local level, for the same nation, is not only more homogeneous in economic terms,

but also it provides more robust estimations from the statistical point of view, thus being more useful

when designing public policies.

Ultimately, the literature concerning the drivers of the generation of waste and the check of the exis-

tence of an EKC (a WKC) is unanimous to assert that the waste indicators tend to generally increase

with income or with the other socio-economic drivers, and that a reversed U-shaped curve is generally

not yet the dominant relationship. In the most industrialized countries, such a relationship might evolve

towards a declining trend (negative elasticity) only there where the waste management and disposal

policies are more developed: in an analysis which takes into account both developed and developing

Countries, those trends leading to a bell-shaped curve might be associated with those few rich Coun-

tries or with richer regions.

Many could be the reasons of this empirical evidence: Lieb (2004) notes that the pollutants that can be

"collected" in stocks generally do not exhibit bell-shaped curves with income, but they show the ten-

dency to with income. A structural reason that might explain the lack of empirical evidence for the re-

versed U-shaped curve of waste could be the fact that the change of sign that income elasticity should

exhibit (the turning point) should happen in correspondence to low levels of income, for those pollut-

ants whose production and diffusion can be easily spatially separated, that is, as an example, exporting

those pollutants or re-localizing the productive activities that cause pollution (Khanna and Plassman,

2004).

The literature studying the drivers of the generation of waste and the WKC behaviour highlights that

waste tend to increase with income or with other economic drivers, such as population, and that, in

general, a bell-shaped curve does not well describe the data. A decreasing trend (negative elasticity) can

be found in the industrialized Countries, where the waste management processes and techniques are

more advanced. Nevertheless, the WKC trends (an absolute delinking) might be connected with few

rich Countries or with few developed areas. Another structural explanation concerning the lack of em-

pirical evidence on waste might be the fact that the change in the sign of the elasticity of the relation-

ship between environment and income could take place at relatively lower levels, for those pollutants

whose production and consumption could be easily spatially separated.

Although waste policies and management techniques have been in force for some time in the EU and

across the world, empirical evidence on WKC dynamics for waste is scarce. Research on delinking for

materials and waste is far less developed than research on air pollution and greenhouse gas emissions.

58

Table 1.2 - Main studies on the Waste Kuznets Curve hypothesis

References Geographical unit

Time period Type of waste Panel / Cross-section

EKC result Turning

point Andersen et al. (2007)

Countries EU15, EU10

Years before 2000

waste and material flows

panel Inverted U-shape

-

Beede and Bloom (1995)

36 Countries several peri-ods

solid waste cross-section and time series

MSW: straight line, upward

-

Berrens et al. (1998)

US, counties (3141)

1991 hazardous waste cross-section negative elastic-ity, inverted U-shape

$ 20.253 and

$ 17.679

Cole et al. (1997) 13 OECD countries

1970–1992 urban waste panel Straight line N/A

Fischer-Kowalski and Amann (2001)

Countries O-ECD

1975-1995 landfilled waste panel absolute de-coupling

-

Gawande et al. (2000)

3,141 US coun-ties

1992 waste cross-section Inverted U-shaped

$ 19.375

Gawande et al. (2000)

748 Metropoli-tan statistical areas of USA

1992 waste cross-section Inverted U-shaped

$ 19.145

Grossman and Krueger (1995)

58 countries 1979–1990 waste panel Inverted U-shape

$ 5.047

Johnstone and Labonne (2004)

Countries O-ECD

1980-2000 urban solid waste panel positive elastic-ity, less than 1

-

Karousakis (2006) Countries O-ECD

4 years be-tween 1980-2000

urban solid waste panel

MSW: straight line, upward, elasticity 0,42-0,45

-

Mazzanti and Zo-boli (2005)

Countries EU 1995-2000 urban waste and packaging waste

panel no decoupling, elasticity close to 1

N/A

Mazzanti et al. (2006)

123 regions 1996-2004, 2000-2004

waste panel Inverted U-shaped

€ 23.000-26.000

Mazzanti Montini and Zoboli (2008)

Italy (103 prov-inces) 1999-2005 urban solid waste panel

relative decoup-ling, absolute decoupling for some provinces

€ 24.000-27.000

Raymond (2004) International data 2001-2002

waste/consumption indicators cross-section

Inverted U-shape -

Seppala et al. (2001)

five industrial-ized Countries

1975-1994 material flows panel no decoupling N/A

Shafik (1994) 47 cities in 31 countries

1972–1988 waste panel Straight line, upward

N/A

Shafik and Ban-dyopadhyay (1992)

39 countries 1985-1992 waste panel Straight line, upward

N/A

Wang et al. (1998) US, counties 1992 hazardous waste cross-section Inverted U-shape

$ 23.000 (1990 US

$)

59

1.6 Conclusions

After more than two decades of searching for EKC-style curves and patterns, it is useful to highlight

what has left among economists of the main message taken from Grossman and Krueger’s work: there

the main message was that trade and higher income levels would operate to get a better environment,

but now the supporting evidence is mostly weak, since ever better empirical estimates has not revealed

a clear and definitive causal income–pollution relationship although, as Auffhammer and Carson (2008)

show, "there may be some short and medium term gain from using income in a forecasting equation". There is still lit-

tle evidence that a stop in growth would improve pollution levels, while, instead, there is robust evi-

dence that pollution levels typically fall at high-income levels. This does not mean that an EKC path is

a sure and inevitable pattern for countries, since the research is still finding a common underlying proc-

ess which could link specific changes in income to specific changes in pollution, on the timescale of a

few years.

Dasgupta et al. (2002) have a positive, but realistic, view of what remains of the original EKC theory:

the EKC stated an inverted-U relationship between pollution and economic development, and its main

critics are that empirically estimated curves have their declining portions as a fake behaviour, either be-

cause they are cross-sectional snapshots that mask a long run race to the bottom in environmental

standards, or because industrial societies will always produce new pollutants. However, recent evidence

has raised an optimistic view by suggesting that the curve is actually flattening and shifting to the left:

the driving forces of such a change appear to be economic liberalization, clean technology diffusion,

and new approaches to pollution regulation in developing countries. But, according to Carson (2010),

"there was a lost decade or more during which environmental economists failed to focus on other potential driving forces be-

hind changes in environmental quality within a country. The debate over the income–pollution relationship allowed us as a

profession to take our eye off what really mattered. First, and perhaps foremost, it made it easy to believe that developing

countries should be able to ignore their environmental problems until they develop and become wealthier. But we now know

that developing countries can take many actions (Dasgupta et al., 2002) to improve their environmental conditions and

that those actions can have enormously positive implications for societal welfare. Second, as a group, we largely ignored the

role of population and technology, the other two factors in the IPAT equation..."

Even if it is not a robust forecasting engine, the EKC has proved itself to be a useful tool to analyze the

relationship between wealth and pollution: basing the present analysis on the EKC literature's findings

and implications that have been briefly detailed above, the present work try to study the waste produc-

tion of Italian industrial sector in the view of the issues of the EKC framework. The following chapters

deal with the descriptive and the estimation analysis of the relation between the industrial waste in Italy,

in the period 1998-2004, and the socio-economic factors that may have been responsible for its genera-

tion.

60

2. THE PRODUCTION OF INDUSTRIAL WASTE IN THE MUD DATABASE, DURING THE PERIOD 1998-2004

2.1 Introduction

The analysis developed so far and in the following chapters has its focus on those productive sectors

which form the so called Italian “Industria in Senso Stretto1” (Industry in a Strict Sense, Ind.S.S.), that

is, the sections concerning “Estrazione di minerali” (section C, Ateco 2002: mining and quarrying), “At-

tività manifatturiere” (section D, Ateco 2002: manufacturing) and “Produzione e distribuzione di ener-

gia elettrica, gas e acqua” (section E, Ateco 2002: electricity, gas and water supply). Those activities, not

only play a major role in the Italian economy, but also come to be the most pollution-intensive ones.

Last, but not least, the information provided by MUD database is more complete for those industries,

rather than the others.

The aim of the present chapter is giving a basic view on the production of waste generated by firms of

the Industry in a Strict Sense, based upon the information provided by the MUD database (the one

coming directly from the statements of the waste producers, excluding therefore the database coming

from the waste collectors), for the period which goes from the year 1998 (whose statements have been

given in the year 1999) to the year 2004 (whose statements have been give in the year 2005). The chap-

ter starts with a short description about Industry in a Strict Sense and its importance within the Italian

economy during the period 1998-2004. The following sections have been devoted to the description of

the database which has been used in the analysis: the importance of Industry in a Strict Sense to the

framework of waste production in Italy will be assessed, the coverage of the database in terms of per-

centage of the number of firms of the Registro delle Imprese (Public Register of Companies) will be

calculated, and the waste production of firms will be outlined under a geographical and a sectorial point

of view.

In all the present research paper, the section Industry in a Strict Sense will include divisions 10 to 36

(Ateco 2002), together with division 402.

1 The macro-sectors derived from the aggregation of the sections (A, B, etc.) of the Ateco 2002 – Nace 1.1 classification are: AGRICOLTURA (Agriculture): sections A and B, Ateco 2002; INDUSTRIA IN SENSO STRETTO (Industry in a Strict Sense): sections C, D and E, Ateco 2002; COSTRUZIONI (Construction): section F, Ateco 2002; COMMERCIO (Commerce): section G, Ateco 2002; SERVIZI (Services): sections H, I, J, K, L, M, N, O, P and Q, Ateco 2002.

2 In 2002, Istat released its Ateco 2002 classification, which corresponds to Nace Rev. 1.1 (January the 10th, 2002). In 2008, Istat released its new Ateco 2007, which corresponds to Nace Rev. 2 (December the 20th, 2006).

61

2.2 Industry and its importance in the Italian economy (1998-2004)

Before the huge crisis that has stricken the world economy starting from the year 2008, Italian economy

was experiencing a path of little growth, with a GDP increase of +1,9% in 2006, and of +1,5% in 2007,

which came after a period of stagnation, started in 2001 and ended in 2005, when the GDP growth has

been registered around +0% (Centro Studi Confindustria, 2006). Unlike the period 2000-2004, the

most dynamic sector in 2006 has been Industry in a Strict Sense: its value-added increased by +2,5%,

while its production, after five successive years of contraction, came to increase by +2,3% compared to

the previous year. Such an increase in productivity, even if touching all sectors, has shown its biggest

intensity as regards the manufacturing sector (section D, Ateco 2002), which has increased by +2,5%,

more than the industrial sector average (Centro Studi Confindustria, 2007).

Figure 2.1 – Value-added: Industry in a Strict Sense and Total, Italy, 1998-2004 (millions of euros of 1995)

0

200.000

400.000

600.000

800.000

1.000.000

1.200.000

Milioni di euro del 1995

1998 1999 2000 2001 2002 2003 2004

Anno

Valore aggiunto Italia: Industria in senso stretto e totale Italia

VA INDSS

VA TOTALE

In the period interested by the present study, 1998-2004, Italian Industry in a Strict Sense has faced a

declining path as concerns its value-added, going from the 220.000 millions of euros in 1998 (at con-

stant prices of 1995), to the 225.407 in 2004, and experiencing negative growth rates in the years 2001,

2002 and 2003. The loss of market shares of the Italian goods on foreign markets has been the main

reason responsible for such a negative performance of the industrial production. Such a fall has been

bigger for some sectors rather than for others: in the period 2000-2005, the activities that have experi-

62

enced the major losses in their produced quantities have been the ones with a bigger export-propensity,

such as clothing and leather industries, and electrical and transportation equipment industries (Centro

Studi Confindustria, 2005).

Against a decrease in the industrial sectors, Italy has seen a weak increase in the tertiary sector: in the

same period, the value-added of the total Italian economy grows from about 932.000 millions of euros

in 1998, to almost 1.055.00 millions of euros of 2004, with positive growth rates every year (Figure 2.1).

Figure 2.2 – Value-added: share of the Industry in a Strict Sense on the total value-added, Italy, 1998-2004

Valore aggiunto Italia: quota dell'Industria in senso stretto

0,00

5,00

10,00

15,00

20,00

25,00

30,00

1998 1999 2000 2001 2002 2003 2004

Anno

per

cen

tua

le

VA INDSS / VA TOTALE (%)

Despite a stagnation scenario for the Italian Industry in a Strict Sense during the observed span of time,

that sector has been a strong pillar in the economy of the country. As regards the percentage composi-

tion of the value-added, the Industry in a Strict Sense was still playing a leading role, giving almost one

fifth of the value-added of the global Italian economy: from a 23,88% in 1998, it went to a 21,37% in

2004, with a small decrease of little more than -2%, in favour of the third sector (Figure 2.2).

As regards the provincial level (Table A2.1, in the Appendix), the share of the value-added of the In-

dustry in a Strict Sense in the total value-added was varying a lot from region to region, and even across

provinces of the same region. In the North of Italy, Piemonte, Lombardia, Veneto and Emilia Ro-

magna were recording a much higher value than the respective national annual average, while Liguria,

Trentino Alto Adige and Valle d’Aosta were showing their characteristic economy based upon the Ser-

63

vices sector. In the Centre of Italy, only Lazio was below the national average, while in the South all the

regions were showing a value below the national average, with Calabria being at the bottom of the rank-

ing, and with the only exception of Abruzzo showing a higher value than the national average.

Concerning the provinces of Italy, looking a the value-added, those ones of Veneto and Lombardia

were the provinces which were showing a vocation for industrial activities bigger than the others in the

country, while Reggio Calabria was having the lowest share of value-added on the total of Italy, as re-

gards the Industry in a Strict Sense.

The majority of the local units of the Industry in a Strict Sense were concentrated in the divisions

“Fabbricazione e lavorazione dei prodotti in metallo, esclusi macchine e impianti” (“Manufacture of

machinery and equipment n.e.c.”, Ateco 28), and “Industrie alimentari e delle bevande” (“Manufacture

of food products and beverages”, Ateco 15), whose total sum was representing more than one third of

the local units of the whole sector (Table 2.1).

The global trend of those years has been a growing one, coming from roughly 726.000 local units in

1998, going to the almost 777.000 local units in 2004, with an increase of almost +7%. At a sectorial

level, a progressive increase of local units in the period 1998-2004 has been recorded, with some divi-

sions, such as divisions Ateco 28 and Ateco 36 (“Fabbricazione di mobili e altre industrie manifatturi-

ere”, that is “Manufacture of furniture and other manufacturing”), which were recording strong varia-

tions in their number, and some other divisions, such as division 30 (“Fabbricazione macchine per uffi-

cio, di elaboratori e sistemi informatici”, i.e. “Manufacture of computer, electronic and optical prod-

ucts”), which were showing high increases in the percentage rate (division Ateco 30, e.g., has risen al-

most +54%, in that period). On the contrary, other divisions, such as division Ateco 20 (“Industria del

legno e dei prodotti in legno e sughero, esclusi mobili; fabbricazione di articoli in materiali da intreccio

il legno”, i.e., “Manufacture of wood and of products of wood and cork, except furniture; manufacture

of articles of straw and plaiting materials”), were showing a huge fall, being forced to close many local

productive units. From the table, moreover, the small number of local units in Italy operating in the en-

ergy sector (divisions Ateco 10 to Ateco 14) can be seen, with a decrease in numbers as regards the di-

vision Ateco 10 (“Estrazione di carbon fossile e lignite-estrazione di torba”, i.e., “Mining of coal and

lignite”), and Ateco 11 (“Estrazione di petrolio greggio e gas naturale”, i.e., “Extraction of crude petro-

leum and natural gas”).

At a global level, Table 2.2 shows that the local units of Industry in a Strict Sense which were operating

in Italy during the period 1998-2004 were always around 13% of the total local units, and that they were

producing a share of the total value-added of the economy which was going from a minimum of 21,4%

(in 2004), to a maximum of 23,9% (in 1998).

64

Table 2.1 – MUD: number of local units (UL) in the Industry in a Strict Sense, Italy, 1998-2004 Description of the activity (division) Ateco 1998 1999 2000 2001 2002 2003 2004Estraz.carbon fossile e lignite-estraz.torba 10 57 58 54 50 52 47 43Estraz.petrolio greggio e gas naturale 11 189 186 194 178 175 164 167Estraz.minerali di uranio e di torio 12 4 4 4 3 3 3 3Estrazione di minerali metalliferi 13 99 93 87 80 81 77 74Altre industrie estrattive 14 7.135 7.152 7.182 7.237 7.269 7.315 7.412Industrie alimentari e delle bevande 15 91.657 93.908 98.211 103.293 107.620 111.627 115.897Industria del tabacco 16 240 279 276 260 244 237 190Industrie tessili 17 42.791 41.739 41.210 40.672 39.391 38.014 39.002Confez.articoli vestiario-prep.pellicce 18 59.298 58.354 57.667 57.252 57.098 56.485 52.694Prep.e concia cuoio-fabbr.artic.viaggio 19 30.332 29.697 29.370 29.424 29.262 28.702 27.894Ind.legno,esclusi mobili-fabbr.in paglia 20 62.097 61.534 61.011 60.557 59.899 59.358 57.157Fabbric.pasta-carta,carta e prod.di carta 21 6.435 6.500 6.515 6.512 6.564 6.622 6.638Editoria,stampa e riprod.supp.registrati 22 33.569 34.517 35.525 36.369 36.878 37.303 37.590Fabbric.coke,raffinerie,combust.nucleari 23 1.105 1.134 1.179 1.234 1.283 1.335 1.032Fabbric.prodotti chimici e fibre sintetiche 24 11.596 11.434 11.460 11.466 11.423 11.486 11.246Fabbric.artic.in gomma e mat.plastiche 25 15.731 16.009 16.536 17.030 17.328 17.466 17.598Fabbric.prodotti lavoraz.min.non metallif. 26 35.791 36.190 36.840 37.467 38.160 38.472 39.373Produzione di metalli e loro leghe 27 7.026 7.112 7.042 6.838 6.723 6.601 6.438Fabbricaz.e lav.prod.metallo,escl.macchine 28 115.229 116.746 120.050 124.283 127.216 129.213 129.569Fabbric.macchine ed appar.mecc.,instal. 29 53.731 54.537 55.653 56.767 57.436 57.961 60.279Fabbric.macchine per uff.,elaboratori 30 2.508 2.878 3.330 3.858 4.158 4.386 4.652Fabbric.di macchine ed appar.elettr.n.c.a. 31 24.059 24.254 24.823 25.316 25.428 25.474 24.082Fabbric.appar.radiotel.e app.per comunic. 32 11.039 11.082 10.864 10.427 10.123 9.874 8.273Fabbric.appar.medicali,precis.,strum.ottici 33 30.548 30.718 31.090 31.752 32.061 32.184 32.470Fabbric.autoveicoli,rimorchi e semirim. 34 3.340 3.454 3.797 4.079 4.284 4.382 4.651Fabbric.di altri mezzi di trasporto 35 6.163 6.324 6.655 7.044 7.456 7.918 8.514Fabbric.mobili-altre industrie manifatturiere 36 71.493 72.428 74.020 76.002 76.977 77.299 78.603Produz.energia elettr.,gas,acqua calda 40 3.077 3.461 3.693 4.043 4.261 4.780 5.176

Total UL 726.339 731.782 744.338 759.493 768.853 774.785 776.717

Table 2.2 – Share of the local units of the Industry in a Strict Sense on the total number of the UL, Italy, 1998-2004

Year Total UL UL

Ind.S.S.Percentage

of UL

Percentage of value-

added 1998 5.329.392 726.339 13,63 23,88 1999 5.408.784 731.782 13,53 23,36 2000 5.521.019 744.338 13,48 23,00 2001 5.622.366 759.493 13,51 22,65 2002 5.718.477 768.853 13,45 22,01 2003 5.801.947 774.785 13,35 21,52 2004 5.901.960 776.717 13,16 21,37

65

2.3 The MUD database (1998-2004)

The Legge n.70/1994, introducing rules for environmental protection practices (“Norme per la sempli-

ficazione degli adempimenti in materia ambientale”), has introduced the legal obligation to provide,

every year, the so called Unique Model of Environmental Statement (MUD: Modello Unico di Di-

chiarazione ambientale): by such a statement, those firms which, because of their economic activity,

produce and/or manage waste, municipal (urbani) and industrial (speciali), must give the statement on

how much is the weigh of their produced disposable materials. Besides giving the opportunity to get

over the existing fragmentation concerning the competences of the several bodies delegated to collect

environmental data, such a legislation has therefore made possible both an uniform data management

countrywide, and the creation of the MUD database, which was representing the most complete and

structured source of information as regards produced and managed waste in Italy. In the present re-

search, only the data coming from the production of waste will be used for the analysis (database pro-

duttori), while the data coming from the waste-management firms will not be taken into account3.

2.3.1 The production of industrial waste according to the MUD data (1998-2004)

The global production of special waste (the word “special” is used in the aforementioned law when

dealing with all those waste which are different from municipal waste, i.e., broadly speaking, waste pro-

duced in everyday life by people: for that special waste, the MUD statement has to be provided by

firms) in Italy has recorded a strong increase in the time span 1998-2004. In 1998 the total amount of

stated special waste added up to little less than 52 millions of tons, going then to 69 millions of tons in

the year 2000, to come up to 95 millions in 2004 (Figure 2.3 and 2.4). During the same period, wastes

generated by Industry in a Strict Sense sector have raised from little less than 31 millions of tons in

1998, to 40 millions in 2000, up to 48 millions in 2004. In this period of time, the share of dangerous-

labelled waste on the total amount of waste produced by the single Ind.S.S. sector has reached the value

of 10%4.

The total amount of waste was continuously growing in time (see Figure 2.3, Figure 2.4 and Figure 3.5),

with an increase of more than +80% in the years 1998-2004, while the increase of the Ind.S.S. ones has

3 In the present paper, the several quantities of waste will be always related to those quantities, stated by their respective producers, which can be found in the MUD. In order not to weigh down the reader, the indication of “produced” about waste will be omitted.

4 The mere change in legislation, e.g. the transposition into national law of the new (for that period) waste CER classifica-tion (Commission Decision of 3 May 2000, replacing Decision 94/3/EC), does not seem sufficient to explain such pro-nounced trends. Moreover, from the data of Table 2.3 it can be seen that the MUD statements in that period have increased in their number by less than +2%, as regards Ind.S.S. sector, while the total number of MUD statements (all sectors) has decreased by -4%, even if a growth of +2% can be registered in the period 2000-2004. The total increase of the stated quan-tity of waste does not seem to be related to an increase in the number of firms which start adopting the new procedure, but to a true increase in the amount of generated waste.

66

been of +56%. If one looked at two different periods, the one 1998-200, and the other 2000-2004, it

can be noticed that such an increase has been much faster in the first span of time, rather than the sec-

ond one, during which it is high anyway, with an increase of +36%, as concerns the quantity of stated

total waste, and of +18%, as regards the quantity of Ind.S.S. waste.

Therefore, while the growth of total special waste, produced by all the sectors together, was continu-

ously increasing over time, Ind.S.S. waste was exhibiting a less definite growth, with diminishing posi-

tive rates, showing a little increase in the last year only (2004): looking at Figure 2.4, it can be seen that a

waste production range was existing between all the sectors together and the Industry in a Strict Sense

only, and such a difference can suggest that the set of sectors different from Ind.S.S. was contributing

to the increase of generated waste over time more than Ind.S.S..

A detailed analysis of the production of waste for each sector (Table A2.2, in the Appendix) displays

how this growth in those sectors different from Industry in a Strict Sense was led mainly by the sector

Ateco 90 (“Smaltimento dei rifiuti solidi, delle acque di scarico e simili”, i.e., “Sewage and refuse dis-

posal, sanitation and similar activities”), whose production of waste goes from the 10,3 millions of tons

of the year 1998, to the 22,6 millions of the year 2004, highlighting that waste treatment and environ-

mental cleaning have played a growing role in the generation of waste in those years. At the second

place of the top producers, Ateco 45 sector can be found (“Costruzioni”, i.e., “Construction”), whose

more than 8 millions of tons in 1998 double its value of the year 1998. Third is Ateco 51 (“Commercio

all’ingrosso e intermediari del commercio, autoveicoli e motocicli esclusi”, i.e., “Wholesale trade and

commission trade, except of motor vehicles and motorcycles”), with more than 2,5 millions of tons in

2004.

Every analysis at a sectorial level has to take into account the several certification duties introduced by

Legge n.70/1994: in fact, the law was allowing some categories not to provide the MUD statement,

thus resulting in more or less detailed analysis, depending on the incidence of the exempted sectors on

the global amount of waste production5.

The manufacturing activities (section D, Ateco 2002, from division 15 to division 37 included) as a

whole were resulting to be the most waste-production intensive (Figure 2.5), going to the 30 millions of

tons of the year 1998, to the almost 48 ones of the year 2004, showing a continuous growth in those

seven years. The other most waste-production intense division was the Ateco 90, which concerns the

sewage and refuse disposal activities, followed by the Construction division (Ateco 45), and the whole

sector of Services (Ateco 50 to 74, and 91 to 95).

5 For a close examination of those firms which have the obligation to present a MUD statement, the reader might see the past and the current regulations.

67

Figure 2.3 – Special waste in Italy, for each type, MUD database, 1998-2004 (tons)

Figure 2.4 – Special waste production, Italy, MUD database, 1998-2004 (tons)

Rifiuti speciali e rifiuti industriali in Italia, anni 1998-2004: volumi

0

10.000.000

20.000.000

30.000.000

40.000.000

50.000.000

60.000.000

70.000.000

80.000.000

90.000.000

100.000.000

1998 1999 2000 2001 2002 2003 2004

To

nn

ella

te

Rifiuti industriali Non Pericolosi Rifiuti industriali Pericolosi Rifiuti industriali totale Totale rifiuti da MUD

0

10.000.000

20.000.000

30.000.000

40.000.000

50.000.000

60.000.000

70.000.000

80.000.000

90.000.000

100.000.000

Tonnellate

1998 1999 2000 2001 2002 2003 2004 Anno

Rifiuti speciali: totale Italia da MUD per tipo 1998-2004

Pericolosi

Non classificati

Non pericolosi

68

Figure 2.5 – Special waste production, for each macro-sector, Italy, 1998-2004 (tons)

0 5.000 10.000 15.000 20.000 25.000 30.000 35.000 40.000 45.000 50.000

Tonnellate Migliaia

Agricoltura, caccia e pesca

Estrazione di minerali

Attività manifatturiere

Energia elettrica, acqua e gas

Costruzioni

Servizi

Pubblica amministrazione

Trattamento rifiuti

Non classificate

Rifiuti speciali totali, per macro-settore, anni 1998-2004

2004

2003

2002

2001

2000

1999

1998

69

2.4 Analysis of the coverage of the MUD database as regards the Industry in a Strict Sense sector (1998-2004)

2.4.1 The overall context

As it has been briefly describe above, the overall industrial sector (and, inside this, the manufacturing

sector) was still predominant in terms of (stated) waste production in Italy. Therefore, all the following

analysis, and the related model, will be focussed on those divisions which form the Industry in a Strict

Sense sector, that is “Mining and quarrying” (Ateco 2002, division C), “Manufacturing” (Ateco 2002,

division D), and “Electricity, gas and water supply” (Ateco 2002, division E): those divisions, besides

having a predominant role in the Italian economy, were resulting to be the most waste-production in-

tense, and to benefit the most from the information included in the MUD database. In fact, the law

D.Lgs. 22/1997 (and its subsequent integrations and modifications) was providing, among the rest, for

several exemptions from the obligation to submit one’s own waste-production statement: this implies a

reduced availability of data in the MUD for those types of wastes which are peculiar to those activities

that can be under such an exemption6 (e.g., waste from sanitation, or non-dangerous waste from con-

struction and demolition, or waste from repair of electrical household goods, or maintenance and repair

of motor vehicles).

Table 2.3 – Number of MUD statements in Industry in a Strict Sense, with respect to the total number of statements of the economic activities, Italy, 1998-2004

Year Ind.S.S. Total 1998 147.390 484.497 1999 149.167 464.366 2000 148.951 450.491 2001 150.521 448.741 2002 150.467 448.706 2003 150.626 454.456 2004 150.119 463.364

Those divisions included in that definition of Industry in a Strict Sense relevant to this research are de-

tailed in Table 2.4: such divisions were going to number Ateco 10 to number 40, with the exception of

the mere division 37, “Recycling”, because such an activity is not related to a production of waste due

to an economic activity per se, but it concerns waste treatment on behalf of its real producers.

6 The reader might see the past and current regulations.

70

Table 2.4 – Ateco divisions relevant to this study

Economic activities ISTAT ATECO

code

Estrazione di carbon fossile, lignite, torba 10

Estrazione di petrolio greggio e di gas naturale e servizi connessi, esclusa la prospezione 11

Estrazione di.minerali di uranio e di torio 12 Estrazione di minerali metalliferi 13 Altre industrie estrattive 14 Industrie alimentari e delle bevande 15 Industria del tabacco 16 Industrie tessili 17

Confezione di articoli di abbigliamento; preparazione, tintura e confezione di pellicce 18

Preparazione e concia del cuoio; fabbricazione di articoli da viaggio, borse, marocchinerai, selleria e calzature

19

Industria del legno e dei prodotti in legno e sughero, esclusi mobili; fabbricazione di articoli in materiali da intreccio

20

Fabbricazione della pasta-carta, della carta e del cartone, dei prodotti di carta; stampa ed e-ditoria

21

Editoria, stampa e riproduzione di supporti registrati 22 Fabbricazione di coke, raffinerie di petrolio, trattamento dei combustibili nucleari 23 Fabbricazione di prodotti chimici e di fibre sintetiche e artificiali 24 Fabbricazione di articoli in gomma e di materie plastiche 25 Fabbricazione di prodotti della lavorazione di minerali non metalliferi 26 Metallurgia 27 Fabbricazione e lavorazione dei prodotti in metallo, esclusi macchine e impianti 28 Fabbricazione macchine ed apparecchi meccanici 29 Fabbricazione macchine per ufficio, di elaboratori e sistemi informatici 30 Fabbricazione di macchine ed apparecchi elettrici n.c.a. 31

Fabbricazione apparecchi radiotelevisivi e di apparecchiature per le comunicazioni 32

Fabbricazione apparecchi medicali, apparecchi di precisione, di strumenti ottici e di orologi 33

Fabbricazione di autoveicoli, rimorchi e semirimorchi 34 Fabbricazione di altri mezzi di trasporto 35 Fabbricazione di mobili; altre industrie manifatturiere 36 Produzione e distribuzione di energia elettrica, di gas, di calore 40

71

As regards the period 1998-2004, the above sectors have presented about 150.000 statements per year:

they were encompassing almost one third of the total number of the MUD statements, for each con-

sidered year (Table 2.3), and they were producing more than one half of the total amount of waste, de-

clared by the means of the MUD statements7 (Figure 2.4).

As concerns the temporal dimension, the descriptive analysis, carried out so far, starts from the MUD

data (and statements) of the year 1999, which are related to the quantities of waste produced in the year

1998, and ends with the MUD data (and statements) of the year 2005, which are related to waste pro-

duced in the year 2004. The analysis on the quantity of produced waste has led to think about the pos-

sible existence of a structural break around the year 20008: such an hypothesis will be statistically tested.

In order to have a complete framework of the information included in the MUD database, a descriptive

analysis of MUD data (starting from the 1999 statement, related to the 1998 waste) will follow.

2.4.2 Coverage in terms of economic activity (1998-2004)

Before using MUD data in the econometric tests, their quality and their coverage rate, related to the

studied period, have to be observed.

Valuing the quality of the data and the coverage of the MUD database is not as easily direct: this is not

only because the several changes in the legislation, occurred during the years, but also and mainly be-

cause the set of economic players that produce waste, and the other set of players that are bound to

provide the MUD statement, do not coincide. In fact, many exemptions were given, resulting in many

firms which are not bound by law to give the MUD statement, even if they do produce waste9.

For the analyzed period, the law was not providing for the rules to follow in order to create a precise

registry for those firms bound to present the MUD statement: anyway, the Registry of Enterprises of

the Chambers of Commerce has been used to value the coverage of the database, since every firm had

the obligation by law to subscribe to that registry, in the province where such a firm operates. The cov-

erage has been calculated by comparing the number of firms’ local units which give the MUD state-

ment with the total number of (actively operating) local units registered in the Enterprises Registry10.

7 The share of waste from Industry in a Strict Sense on the total of waste, declared in the MUD database, in that period, has gone from around 60% to around 50%.

8 Quite a big difference, both in the number of statements, and in the quantities of waste, during the years 1998 and 1999, seemed to be in contrast with what has happened from the year 2000: it might have been due to the change in the CER codes, and therefore the period 1998-1999 might be considered a sort of “learning and updating” period for firms.

9 Of course, another cause that might affect the coverage of the MUD database is the possible evasion, by some firms and for some kind of waste, of the obligation to present the statement.

10 A comparison based on the number of local units seemed better than one based on the number of workers of those local units.

72

The degree of coverage thus obtained is the percentage ratio between the single MUD data item and its

corresponding data item in the Registry of Enterprises: that is, the degree of coverage tells how many

local units give their MUD statement every hundred of local units operating in that given sector, or in

that given province, if the geographical dimension is considered. This coverage is reported in Table 2.5:

it shows that, on average, one fifth of those firms registered in the Registry of Enterprises11 have given

their MUD statement in the period 1998-2004. Such a coverage, then, varies across sectors and divi-

sions, depending on the size of their average firm: it goes from the value of about 5% in all the consid-

ered time span for the division “Manufacture of wearing apparel; dressing and dyeing of fur” (Ateco

18), to values under 10% for some divisions such as “Mining of metal ores “ (Ateco 13) or “Manufac-

ture of office machinery and computers” (Ateco 30), and to values over 50% for the division “Extrac-

tion of crude petroleum and natural gas; service activities incidental to oil and gas extraction, excluding

surveying” (Ateco 11). As much the variability of the database coverage among sectors is, as much vari-

able the increase or decrease trend of the local units of sectors: there were not unique trends of growth

only or decrease only, but different behaviours according to the sector. Against a high sectorial dyna-

mism, a certain stability of the global national data can be found (given by the ratio between all the local

units in the MUD and all the local units of the Register of Enterprises): as said before, that value lies

around 20%, with a maximum in 1999 (20,38%), and a minimum in 2004 (19,33%).

A possible explanation of such a variability among sectors might be represented by the different inci-

dence of handcraft companies on the total number of companies12, and, particularly in this case, by the

massive presence, in the studied sectors, of handcraft companies that might not be bound to present

the MUD statement13: in fact, the predominant size among the Italian firms is the small enterprise, be it

a handcraft company or not. As an example, focussing on manufacturing sector, the data from Eurostat

for 2003 show how Italy had got an industrial fabric in which the productive units with less than 20

employees was around less than 93% of the total (Centro Studi Confindustria, 2006). This share goes

up to 99,7% if one considers the number of the productive units which are classified as SME (Small or

Medium Enterprises, PMI), that is with a number of workers lower than 250, thus showing how little

active is the big enterprise's dimension in Italy. Such an importance of those small manufacturing firms

in the Italian productive system was confirmed also by those data concerning their weight on both the

employment and the revenues of this sector. In the year 2003, e.g., almost 41% of the workers in this

11 Local units are distinct productive or administrative bodies of the company: see the “Circolare del Ministero dell’Industria, del Commercio e dell’Artigianato n. 3202/C del 22 gennaio 1990”.

12 In the studied period, handcraft companies (in accordance with the article 2083 of the Civil Code) with less than three employees, and which do not produce dangerous-labelled waste, were not bound to present the MUD statement.

13 Handcraft companies are defined by Legge n.433/1985, which gives a broader definition of them than the one given by the Civil Code. See the current regulations for the details.

73

sector (more than 4,7 millions of workers, in total) was employed in companies with less than 19 work-

ers, and 78,8% was employed in firms with less than 250 workers.

The strong orientation of the national economy towards the small dimension’s firms (whose category

the handcraft company is a member) may explain, therefore, the very low coverage of the MUD data-

base in some sectors, as the very low coverage percentage in some regions or provinces might be sug-

gested by the high or low presence of the small handcraft company, as well as by the prevailing pres-

ence of some other kind of companies which are exempted to submit the MUD statement14.

14 Summing up, in the period 1998-2004, according to Legge n. 70/1994, these categories were exempted to submit the MUD statement (in Italian):

A) "Real" entrepreneurial activities: a) le imprese agricole (da articolo 2135 del Codice Civile) con volumi d’affari sotto i 15 milioni di Lire o 8 mila Euro, sia che producano

rifiuti pericolosi, sia che producano rifiuti non pericolosi; b) le imprese artigiane (da articolo 2083 del Codice Civile) con meno di tre dipendenti che producono rifiuti non pericolosi c) i produttori di rifiuti sanitari, che dovevano essere termo-distrutti in appositi centri; d) i produttori di rifiuti derivanti da attività di costruzione e demolizione, ma solo per i rifiuti non pericolosi; e) i produttori di rifiuti di apparecchiature elettriche ed elettroniche; f) i produttori di rifiuti derivanti da veicoli fuori uso; g) i produttori di rifiuti che li conferivano al servizio pubblico di raccolta (in questo caso la comunicazione veniva effettuata dal gestore del

servizio, limitatamente alla quantità conferita).

B) A series of specific subjects, such as: a) i rivenditori firmatari, tramite le proprie associazioni di categoria, di accordi di programma stipulati per favorire la restituzione di beni

durevoli, relativamente alle attività di ritiro, trasporto e stoccaggio di tali beni; b) i soggetti abilitati allo svolgimento delle attività di raccolta e trasporto di rifiuti in forma ambulante, limitatamente ai rifiuti che forma-

no oggetto del loro commercio; c) i soggetti che svolgevano attività di raccolta, trasporto, stoccaggio e pre-trattamento del materiale specifico a rischio, come disciplinate dal

D.M. 29/09/2000; d) i soggetti che svolgevano attività di raccolta, trasporto, stoccaggio e trasformazione dei materiali ad alto e basso rischio, disciplinati dal

D.Lgs. 508/1992; e) i soggetti che svolgevano attività di raccolta, trasporto, stoccaggio e trasformazione degli altri materiali e dei prodotti derivati, destinati

alla distruzione (ai sensi del D.L. 11 gennaio 2001, n. 1, come convertito in legge 9 marzo 2001 n. 49).

74

Table 2.5 – Coverage (in % of local units of the Registro delle Imprese, RI) of the MUD database, Industry in a Strict Sense, 1998-2004

1998 1999 2000

Ateco Economic activity UL MUD UL RI Coverage % UL MUD UL RI Coverage % UL MUD UL RI Coverage %

10 Estraz.carbon fossile e lignite-estraz.torba 14 57 24,56 9 58 15,52 6 54 11,11

11 Estraz.petrolio greggio e gas naturale 123 189 65,08 129 186 69,35 147 194 75,77

13 Estrazione di minerali metalliferi 8 99 8,08 5 93 5,38 7 87 8,05

14 + 12 Altre industrie estrattive (inclusa l'estraz. di uranio e torio) 2.209 7.139 30,94 2.278 7.156 31,83 2.280 7.186 31,73

15 Industrie alimentari e delle bevande 9.627 91.657 10,50 9.807 93.908 10,44 9.792 98.211 9,97

16 Industria del tabacco 62 240 25,83 84 279 30,11 64 276 23,19

17 Industrie tessili 5.678 42.791 13,27 5.696 41.739 13,65 5.678 41.210 13,78

18 Confez.articoli vestiario-prep.pellicce 2.990 59.298 5,04 3.213 58.354 5,51 3.069 57.667 5,32

19 Prep.e concia cuoio-fabbr.artic.viaggio 8.254 30.332 27,21 7.911 29.697 26,64 7.716 29.370 26,27

20 Ind.legno,esclusi mobili-fabbr.in paglia 10.676 62.097 17,19 10.486 61.534 17,04 10.325 61.011 16,92

21 Fabbric.pasta-carta,carta e prod.di carta 2.366 6.435 36,77 2.419 6.500 37,22 2.396 6.515 36,78

22 Editoria,stampa e riprod.supp.registrati 11.946 33.569 35,59 12.043 34.517 34,89 11.771 35.525 33,13

23 Fabbric.coke,raffinerie,combust.nucleari 359 1.105 32,49 400 1.134 35,27 376 1.179 31,89

24 Fabbric.prodotti chimici e fibre sintetiche 3.557 11.596 30,67 3.627 11.434 31,72 3.635 11.460 31,72

25 Fabbric.artic.in gomma e mat.plastiche 6.591 15.731 41,90 6.740 16.009 42,10 6.859 16.536 41,48

26 Fabbric.prodotti lavoraz.min.non metallif. 10.221 35.791 28,56 10.239 36.190 28,29 10.356 36.840 28,11

27 Produzione di metalli e loro leghe 2.746 7.026 39,08 2.853 7.112 40,12 2.877 7.042 40,85

28 Fabbricaz.e lav.prod.metallo,escl.macchine 28.627 115.229 24,84 29.824 116.746 25,55 30.473 120.050 25,38

29 Fabbric.macchine ed appar.mecc.,instal. 13.598 53.731 25,31 14.160 54.537 25,96 14.529 55.653 26,11

30 Fabbric.macchine per uff.,elaboratori 193 2.508 7,70 201 2.878 6,98 218 3.330 6,55

31 Fabbric.di macchine ed appar.elettr.n.c.a. 3.442 24.059 14,31 3.647 24.254 15,04 3.768 24.823 15,18

32 Fabbric.appar.radiotel.e app.per comunic. 930 11.039 8,42 937 11.082 8,46 931 10.864 8,57

33 Fabbric.appar.medicali,precis.,strum.ottici 5.693 30.548 18,64 4.910 30.718 15,98 4.434 31.090 14,26

34 Fabbric.autoveicoli,rimorchi e semirim. 1.289 3.340 38,59 1.372 3.454 39,72 1.424 3.797 37,50

35 Fabbric.di altri mezzi di trasporto 1.791 6.163 29,06 1.831 6.324 28,95 1.947 6.655 29,26

36 Fabbric.mobili-altre industrie manifatturiere 12.997 71.493 18,18 12.671 72.428 17,49 12.402 74.020 16,75

40 Produz.energia elettr.,gas,acqua calda 1.403 3.077 45,60 1.675 3.461 48,40 1.471 3.693 39,83

Italy 147.390 726.339 20,29 149.167 731.782 20,38 148.951 744.338 20,01

75

Table 2.5 – continued

2001 2002 2003 2004

Ateco UL MUD UL RI Coverage % UL MUD UL RI Coverage % UL MUD UL RI Coverage % UL da MUD UL RI Coverage %

10 7 50 14,00 6 52 11,54 7 47 14,89 6 43 13,95

11 148 178 83,15 91 175 52,00 128 164 78,05 92 167 55,09

13 8 80 10,00 5 81 6,17 3 77 3,90 7 74 9,46

14 + 12 2.307 7.240 31,86 2.256 7.272 31,02 2.304 7.318 31,48 2.319 7.415 31,27

15 9.859 103.293 9,54 9.752 107.620 9,06 9.960 111.627 8,92 9.947 115.897 8,58

16 64 260 24,62 58 244 23,77 70 237 29,54 48 190 25,26

17 5.587 40.672 13,74 5.408 39.391 13,73 5.173 38.014 13,61 4.931 39.002 12,64

18 3.179 57.252 5,55 3.072 57.098 5,38 2.989 56.485 5,29 2.930 52.694 5,56

19 7.706 29.424 26,19 7.391 29.262 25,26 7.282 28.702 25,37 6.873 27.894 24,64

20 10.472 60.557 17,29 10.596 59.899 17,69 10.643 59.358 17,93 10.645 57.157 18,62

21 2.403 6.512 36,90 2.418 6.564 36,84 2.441 6.622 36,86 2.462 6.638 37,09

22 11.641 36.369 32,01 11.544 36.878 31,30 11.297 37.303 30,28 11.074 37.590 29,46

23 392 1.234 31,77 392 1.283 30,55 405 1.335 30,34 427 1.032 41,38

24 3.699 11.466 32,26 3.771 11.423 33,01 3.794 11.486 33,03 3.989 11.246 35,47

25 6.922 17.030 40,65 6.846 17.328 39,51 6.838 17.466 39,15 6.828 17.598 38,80

26 10.513 37.467 28,06 10.572 38.160 27,70 10.749 38.472 27,94 10.849 39.373 27,55

27 2.933 6.838 42,89 2.901 6.723 43,15 2.915 6.601 44,16 2.874 6.438 44,64

28 31.139 124.283 25,05 31.569 127.216 24,82 31.861 129.213 24,66 32.024 129.569 24,72

29 14.779 56.767 26,03 14.925 57.436 25,99 14.986 57.961 25,86 15.236 60.279 25,28

30 237 3.858 6,14 227 4.158 5,46 239 4.386 5,45 224 4.652 4,82

31 3.815 25.316 15,07 3.864 25.428 15,20 3.859 25.474 15,15 3.869 24.082 16,07

32 967 10.427 9,27 944 10.123 9,33 934 9.874 9,46 907 8.273 10,96

33 4.257 31.752 13,41 4.302 32.061 13,42 4.264 32.184 13,25 4.237 32.470 13,05

34 1.438 4.079 35,25 1.461 4.284 34,10 1.465 4.382 33,43 1.492 4.651 32,08

35 1.934 7.044 27,46 1.983 7.456 26,60 2.039 7.918 25,75 2.125 8.514 24,96

36 12.578 76.002 16,55 12.431 76.977 16,15 12.237 77.299 15,83 11.974 78.603 15,23

40 1.537 4.043 38,02 1.682 4.261 39,47 1.744 4.780 36,49 1.730 5.176 33,42

Italy 150.521 759.493 19,82 150.467 768.853 19,57 150.626 774.785 19,44 150.119 776.717 19,33

76

2.4.3 Coverage in terms geographic divisions (1998-2004)

The MUD coverage shows a quite high variability also when taking the geographical partitions into ac-

count.

With reference to the 2004 data from MUD database (Table A2.4), it can be seen that the percentage of

enterprises which give the MUD statement is higher in the north of Italy, with the North-East showing

higher values due to its more widespread structure of firms: it goes from the 26,3% of Veneto region

(in which the province of Vicenza has the most representative value, around 33%), to the 24,6% of

Friuli Venezia Giulia (with Pordenone scoring a 32%), against the lower values of Piemonte (19,8%)

and Lombardia (21,9%). In the centre of Italy, the internal homogeneity of two regions stands out:

Marche, with its 27,4% (and all its provinces over the 26%), and Toscana (around 19%), with Pisa and

Siena at first places. In the South and in the Islands, all the percentages go down, mostly remaining un-

der the 2004 Italian average value (19,3%): this is because their economic fabric sees the medium enter-

prise being lowly represented in those areas, while the big industrial groups (belonging to the inspected

sectors) have a strong presence in some provinces, when in some others (e.g., Brindisi.) they show a

small intervention. Moreover, within the same region different values can be observed, be it in the

North, or in the South: one need to think of the spread between the 17% of Rovigo and the 33% of

Vicenza (both in Veneto), or between the 11% of Prato and the 28% of Pisa (both in Toscana). In the

South, conversely, the differences in-between provinces are not as stressed as in the Centre and in the

North, as well as the spread between regions show their highest value concerning Calabria, with its 6%,

and Abruzzo, with its 16%.

In Figure 2.6 the geographical distribution of coverage rates for the year 1998, at provincial level, in It-

aly, are shown (details in Table A2.5), while in Figure 2.7 the same distribution stands for 2004, whilst

in Figure 2.8 the difference between the two years has been reported: in the last, one can see the

marked difference between the North and the South, with the North-East being the area with the prov-

inces with the highest rates. The national average went down by half a percentage point, going from

19,39% to a 18,89%. Marche, Lombardia, Veneto, Friuli Venezia Giulia and Emilia Romagna exhibit

coverage rates over 20% during all that period, while in some regions like Calabria they were hardly

reaching 5%.

As a comparison, five provinces have been randomly selected15 (Table 2.6): among them, only Venezia

stays above the national coverage average (with a 21,85%), even if it is under the regional average of

Veneto (26,27%). Milano is a bit lower than the Italian average (19,16%), while Genova, Roma and

L’Aquila lay well below such an average (as well as their respective home regions ).

15 Such a sample will be widely used for illustration purposes in this research.

77

Table 2.6 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, 2004: 5 randomly sampled provinces

Province MUD local units 2004

RE local units 2004

MUD/RE 2004 %

Milano 11.602 60.557 19,16 Venezia 2.338 10.702 21,85 Genova 1.135 9.860 11,51 Roma 4.139 25.236 16,40 L'Aquila 450 3.290 13,68

Italy 150.119 776.717 19,33

Figure 2.6 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, Italy: 1998

Legenda

Copertura % 1998

inferiore a 10,00

10,00 - 14,99

15,00 - 19,99

20,00 - 24,99

25,00 - 29,99

30,00 e oltre

78

Figure 2.7 – Coverage (%) of the MUD local units

with respect to the RE local units, as for Industry in a Strict Sense, Italy: 2004

Legenda

Copertura % 2004

inferiore a 10,00

10,00 - 14,99

15,00 - 19,99

20,00 - 24,99

25,00 - 29,99

30,00 e oltre

Figure 2.8 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, Italy:

variations, 1998-2004

Legenda

Copertura Variazione % 1998-2004

fino a 0%

1% - 5%

6% - 10%

11% - 15%

16% - 20%

21% - 25%

oltre 25%

79

2.4.4 Methodological insights about the quality and the representativeness of the MUD data-

base (1998-2004)

The high variability of the coverage information, both at sectorial level, and at provincial level, has been

due to several legislative, institutional and structural factors.

First, the restrictions imposed by the legislation in force as of 1998, and up to 2004, have to be consid-

ered: the body of laws states who is bound to present the MUD statement, and how, and what its limi-

tations and duties are; it can even exclude some actors who can effectively produce waste during their

economic activity. Taking briefly into account the MUD database and its regulations in the selected pe-

riod, the first and most evident feature is that not all those who produce waste are legally bound to de-

clare it: the subset of those individuals bound to give the statement is extremely heterogeneous, and

such a heterogeneity was being raised by the new regulations that were modifying the different duties

and who would have been the different actors bound to present that statement, with the result of cre-

ating some confusion for those who were originally forced to have that statement.

A voluntary, or not, error in accomplishing one’s own duties join the legislative prescriptions, thus

modifying the coverage reliability of the MUD database. Concerning the first kind of error, it might be

the case in which some firms are bound to submit their MUD statement, but they do not do it simply

because they are not aware of such a requirement; or they do it, but they do it in a wrong way. As re-

gards the second kind of error, instead, some firms could deliberately limit the quantities of their waste;

some of them could decide to stop producing waste at all; some of them might decide not to submit

that statement, although being bound by law (even if such a case cannot be estimated by available data).

The analysis of the coverage here presented takes exclusively into account the data found in the MUD

database, and it does not consider some modifications that might improve the information about the

coverage: such modifications have not been implemented in the present work because, in the analyzed

database, only data of waste directly produced by the firm who declares it are present, and not data of

collectors and managers of waste on behalf of other companies (the ones that produce that waste, and

then have other specialized firms collecting it).

As regards the improvement of the coverage (and its subsequent increase in the precision of the study),

the two main categories which were exempted to submit the statement have to be considered: the

handcraft companies with less than 3 employees (and only as regards their dangerous-labelled waste),

and the local units which were directly giving away their waste to the public waste collectors: both of

them would not be found in the set of MUD statements, being thus excluded from the numerator of

the ratio but not from the denominator, but one would be able to find them using the statements of

those waste-management firms. In fact, as regards the first category, its members might have given

away their waste to waste-collecting firms (which are thus bound to declare this amount and its pro-

ducer): so, analyzing data of waste coming from waste-managing firms, it would have been possible to

80

trace the handcraft firms’ local units that, even if they had not presented that statement, have though

given away their waste to specialized companies.

Concerning the delivery of waste to the public system of waste collection, it was regarding those waste

quantities that could have been equated to the so called municipal waste. Aside from directly throwing

away waste into garbage bins (being not able to get any information about who threw it away), an

agreement between waste producers and waste-managing institutes might have been secured, by which

the seconds would have provided garbage bins: by collecting this waste, therefore, those institutes

would have had to provide all the data about the firms which they take garbage from. In such a way it is

possible to go back to the economic actors who really have produced that waste, and therefore to in-

clude them in the calculation of the coverage.

81

2.5 The MUD database: quantitative analysis of the production of waste

The following descriptive analysis wants to provide an overview of the production of waste of Industry

in a Strict Sense (here, Ateco divisions 10 to 36, plus 40), both from a geographical point of view, and

from a sectorial point of view. As regards the first, the analysis will be carried out taking into account

the macro-regions which Italy is usually divided into, and then its regions and provinces. Then the

analysis will cover the waste production of the different productive sectors.

2.5.1 The geographic dimension: a quantitative analysis

2.5.1.1 Italy and its macro-regions

The national-level data show how wastes from Industry in a Strict Sense are regularly growing in the

period 1998-2004 (Figure 2.4 and Table 2.7).

Figure 2.9 – Waste production (tons), Industry in a Strict Sense, Italy: 1998-2004

0

5.000.000

10.000.000

15.000.000

20.000.000

25.000.000

30.000.000

35.000.000

40.000.000

45.000.000

50.000.000

Tonnellate

1998 1999 2000 2001 2002 2003 2004

Non pericolosi Pericolosi Non classificati

In such a span of time, indeed, the value goes from the 30.607.336 tons of waste produced in 1998, to

the 47.662.730 tons in 2004, with an increase of +56% in that period (Table 2.7). The year 1999 is the

one which records the highest increment with respect to the previous year, with a variation of 6 mil-

lions of tons, resulting in almost the 20% of the whole waste production in 1998.

82

Table 2.7 – Waste production (tons), Industry in a Strict Sense, Italy, by label: 1998-2004

1998 1999 2000 2001 2002 2003 2004Non-dangerous 27.989.686 34.019.840 37.582.800 39.606.622 40.260.489 40.440.231 42.871.255Dangerous 2.582.688 2.621.914 2.841.607 3.078.684 3.389.207 3.628.138 4.785.396Not classified 34.962 9.733 3.577 9.374 8.021 4.516 6.079

Total 30.607.336 36.651.487 40.427.983 42.694.681 43.657.717 44.072.885 47.662.730

A first feasible explanation of such an increase might have been the fact that the quoted was period

corresponding to the beginning of the implementation of the new MUD regulations: it can be sup-

posed that waste producers of those sectors had not yet become used to the new system of rules, which

became effective during the first year of that period, and thus it can be explained the strong increase of

the first year16.

By looking at the data concerning the different waste labels, it may be observed that this increase has

been due (in absolute value) mainly to the non-dangerous waste category (Table 2.8): in fact, these had

increased by +53% in the period, with a first jump of +22% between 1998 and 1999, and a second

lower jump of about +11%, with respect to the 1998 value, in 2004. On the other hand, even if less

important for the value of the total amount of waste, it is worth noting that dangerous waste had in-

creased by roughly +85% between 1998 and 2004, with a peak in the last year, when it rose by +45%

(with respect to the 1998 value), that is, by more than a million of tons.

Table 2.8 – Waste production, Industry in a Strict Sense, Italy, by label: indices, 1998-2004

1998 1999 2000 2001 2002 2003 2004Non-dangerous 100 122 134 142 144 144 153Dangerous 100 102 110 119 131 140 185Not classified 100 28 10 27 23 13 17

Total 100 120 132 139 143 144 156

Besides peculiarities of the Italian economic and entrepreneurial development, it might be supposed

that, in some way, this increase has been caused by the transposition and the application of the new

European waste classification (Commission Decision 532, of May the 3rd 2000), becoming effective

from the 2003 statement. A change in codes, however, may explain some structural variations in the in-

ternal process of the statement declarations, but those changes that may appear as mere accounting

changes (due to the change in the regulations) are not sufficiently able to explain such a strong increas-

ing trend. Moreover, a major change in the number of individuals presenting the statement has not

16 It may be that the years 1998 and 1999 were a sort of “learning period” for the new CER codifications.

83

taken place, neither in that year, nor during the entire period: in fact, the total number of local units of

Industry in a Strict Sense, presenting the MUD statement, has changed from about 147.000 to 150.000,

that is an increase of +1% (in the time span 1998-2004). Basically, the increase in the declared quanti-

ties was not seeming to be due to an increase of the number of statements, nor it is reasonable to think

that a simple change in codes could be responsible for such a rise: the conclusion is that this growth has

been due to a “simple” increase of the average quantity of produced waste, declared in each statement.

Table 2.9 – Waste production, Industry in a Strict Sense, Italy, by label: composition (%), 1998-2004

1998 1999 2000 2001 2002 2003 2004 Non-dangerous 91,45 92,82 92,96 92,77 92,22 91,76 89,95 Dangerous 8,44 7,15 7,03 7,21 7,76 8,23 10,04 Not classified 0,11 0,03 0,01 0,02 0,02 0,01 0,01

Total 100,00 100,00 100,00 100,00 100,00 100,00 100,00

Concerning the relative percentage composition of waste (Table 2.9), it can also be noticed how the

proportion between dangerous, non-dangerous and not classified waste exhibits mostly small percent-

age variations from year to year, with the share of dangerous waste over the total waste reaching its

maximum in 2004, being almost 10% of the total. An element that may confirm a kind of “learning

process” (and its influence over the stated quantities of waste, and over its own composition) by those

who provide the MUD statement is that the not classified waste has shown a strong decrease in 1998-

2004, nearly -83%.

By taking into account the macro-regions (or macro-areas) which the Italian socio-economic fabric is

usually divided into, it can be seen how most of the production of waste in the country has been due to

firms in the North: as a whole, the North went from its almost 19 millions of tons in 1998, to more

than 29 millions of tons in 2004 (Table 2.10 and Figure 2.10), with an increase of about +53% in that

period.

Table 2.10 – Waste production (tons), Industry in a Strict Sense, Italy, by macro-regions: absolute value and percentage variation, 1998-2004

North-West North-East Centre South and Islands Italy

1998 9.043.104 9.856.496 5.387.939 6.319.798 30.607.336 1999 11.202.068 12.145.418 5.792.974 7.511.026 36.651.487 2000 12.956.504 13.050.103 6.351.980 8.069.397 40.427.983 2001 13.793.340 14.001.618 6.243.635 8.656.088 42.694.681 2002 14.818.952 14.105.717 6.448.037 8.285.011 43.657.717 2003 15.122.159 13.599.398 6.038.926 9.312.402 44.072.885 2004 15.617.303 13.462.208 6.182.547 12.400.671 47.662.730

1998 - 2004 +72,70% +36,58% +14,75% +96,22% +55,72%

84

The North-East saw a production of waste bigger than the North-West, during the years 1998-2001,

while from 2002 the area recording the highest absolute values has become the North-West, with its

more than 15 millions of tons in 2004. In percentage terms, in this period, the North-East has in-

creased its production by +37%, while the North-West exhibits a stronger increase, equal to +73%

(which is higher than the national average of that period, equal to +56%).

Within the year 2003, moreover, an increase in the production of dangerous-labelled waste can be seen

in the North-West (Figure 2.1), while in the North-East the same kind of production has grown, in

percentage terms, for all the period. Moreover, in the North-East a decrease in the production of waste

took place in the years 2003 and 2004, with the peak of its production in 2003: such a trend was not

registered for the North-West.

The Centre showed, instead, a much smaller momentum in comparison with the other macro-regions

(Figure 2.13), having a percentage increase of +15%, well below the national average: waste of the in-

dustries in the Centre went from almost 5,4 millions of tons to about 6,2 millions of tons, with a peak

in 2002.

Figure 2.10 – Waste production (tons), Industry in a Strict Sense, North of Italy, by label: 1998-2004

Produzione di rifiuti speciali dell'industria in senso stretto (tonnellate): Nord Italia

0

5.000.000

10.000.000

15.000.000

20.000.000

25.000.000

30.000.000

35.000.000

1998 1999 2000 2001 2002 2003 2004

ton

ne

llat

e Non pericolosi

Pericolosi

Non classificati

Totale

85

Figure 2.11 – Waste production (tons), Industry in a Strict Sense, North-West of Italy, by label: 1998-2004

Produzione di rifiuti speciali dell'industria in senso stretto (tonnellate): Nord-Ovest

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

14.000.000

16.000.000

1998 1999 2000 2001 2002 2003 2004

To

nn

ella

te Non pericolosi

Pericolosi

Non classificati

Totale

Figure 2.12 – Waste production (tons), Industry in a Strict Sense, North-East of Italy, by label: 1998-2004

Produzione di rifiuti speciali dell'industria in senso stretto (tonnellate): Nord-Est

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

14.000.000

16.000.000

1998 1999 2000 2001 2002 2003 2004

To

nn

ella

te Non pericolosi

Pericolosi

Non classificati

Totale

86

Figure 2.13 – Waste production (tons), Industry in a Strict Sense, Centre of Italy, by label: 1998-2004

Produzione di rifiuti speciali dell'industria in senso stretto (tonnellate): Centro

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

14.000.000

16.000.000

1998 1999 2000 2001 2002 2003 2004

To

nn

ella

te Non pericolosi

Pericolosi

Non classificati

Totale

Figure 2.14 – Waste production (tons), Industry in a Strict Sense, South of Italy and Islands, by label: 1998-2004

Produzione di rifiuti speciali dell'industria in senso stretto (tonnellate): Sud e Isole

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

14.000.000

16.000.000

1998 1999 2000 2001 2002 2003 2004

To

nn

ella

te

Non pericolosi

Pericolosi

Non classificati

Totale

Last, the South of Italy and its Islands showed a much more differentiated dynamics during time (Fig-

ure 2.14): after an increase in the years 1998-2001, a decrease in production took place in 2002, a recov-

ery in 2003, and a sudden jump in 2004, when the amount of waste has risen by about 3 millions, equal

to an increase of roughly +33%, in terms of the previous year 2003. As a whole, the South and the Is-

lands have seen their waste almost doubling in that period, with an increase of more than 6 millions of

87

tons, equal to +96%: from a label point of view, it can be seen an increase of the dangerous-labelled

waste in 2004, reaching the value of 1,9 millions of tons.

Table 2.11 – Waste production (tons), Industry in a Strict Sense, Italy: per worker and per local unit values, 1998-2004

Per worker wasteVariation %

with respect the previous yearPer local unit waste

Variation % with respect the previous year

1998 9,23 -- 217,30 -- 1999 10,88 +17,93 255,43 +17,55 2000 11,69 +7,40 279,90 +9,58 2001 12,60 +7,77 292,30 +4,43 2002 13,06 +3,68 297,65 +1,83 2003 13,30 +1,89 299,43 +0,60 2004 14,75 +10,84 324,81 +8,48

1998 - 2004 +59,81 % +49,48 %

Also from a productive unit point of view (Table 2.11), it can be recorded an increase in the production

of waste, both considering (Figure 2.15) waste per worker (and those workers are employees of those

firms’ local units which provide the MUD statement), and (Figure 2.16) waste per local unit (providing

the MUD statement). The amount of waste per worker has increased by almost +60% in the span

1998-2004, going from almost 9 tons per worker, to almost 15 per worker, with the most growing years

being 1999 and 2004 (respectively, +18% and +11%, with respect to the previous year). Similarly, the

quantities of waste produced by the local units have exhibited a strong rise: from the 217 tons per local

unit of the year 1998, to the almost 325 tons per local unit in 2004, with an increase of more than

+49%. As a whole, thus, the environmental efficiency of the productive units (be it workers or local

units) has worsened, and it has happened more as regards the per worker amount, rather than the per

local unit amount17.

Moreover, such an increase, per worker and per local unit, may indicate that: a) with a rise in the level

of waste, there has not been a similar growth in the number of firms and enterprises; b) the production

of waste has risen, on average, more than the environmental efficiency of each local unit (and each

worker).

This high growth rate in terms of productive local unit, still, does not take into account all the several

socio-economic components which have contributed to the rise of the numerator of the ratio (that is

the used indicator), but it briefly tells that a worsening in what can be considered an environmental ef-

ficiency of labour has occurred: nevertheless, the two per capita plots confirm the trend observed in the

previous graphs, being able to assert that in the considered period a generalized increase in waste pro-

17 This might have been due to the fact that the improvements in environmental protection are more easily done at a unit level (e.g., some filters installed in a local unit), rather than a single worker level.

88

duction has taken place, which has not been combined with improvements in terms of per capita envi-

ronmental efficiency, nor with better economic performances (see 3.3.1.1, about the value-added).

Also the analysis at a provincial level confirms the growth trend of the quantity of per local unit waste

(Figure 2.17, 2.18 and 2.19), and of per worker waste (Figure 2.20, 2.21 and 2.22): in the following plots

the provincial data for the indicator have been reported for 1998 and for 2004, and also its variation

during all the period has been shown.

Figure 2.15 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 1998-2004

Rifiuti dell'Industria in senso stretto per addetto (nelle UL che producono), in Italia: tonnellate per addetto

0

2

4

6

8

10

12

14

16

1998 1999 2000 2001 2002 2003 2004

ton

nel

late

pe

r ad

de

tto

Non pericolosi pro-addetto

Pericolosi pro-addetto

Totali pro-addetto

Figure 2.16 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 1998-2004

Rifiuti dell'Industria in senso stretto per UL che produce, in Italia: tonnellate per unità locale

0

50

100

150

200

250

300

350

1998 1999 2000 2001 2002 2003 2004

ton

nel

late

per

UL

Non pericolosi pro-UL

Pericolosi pro-UL

Totali pro-UL

89

Figure 2.17 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 1998

Legenda

Rif su UL, 1998

fino a 85

86 - 175

176 - 300

301 - 500

501 - 750

751 - 1.800

oltre 1.800

Figure 2.18 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 2004

Legenda

Rif su UL, 2004

fino a 85

86 - 175

176 - 300

301 - 500

501 - 750

751 - 1.800

oltre 1.800

90

Figure 2.19 – Waste production (tons), Industry in a Strict Sense, Italy:

per local unit values, variations (%), 1998-2004

Legenda

Rif su ULVariazione % 1998-2004

fino a 0%

1% - 5%

6% - 10%

11% - 15%

16% - 20%

21% - 25%

oltre 25%

Figure 2.20 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 1998

Legenda

Rif su ADD, 1998

fino a 5

6 - 10

11 - 15

16 - 20

21 - 25

26 - 30

oltre 30

91

Figure 2.21 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 2004

Legenda

Rif su ADD, 2004

fino a 5

6 - 10

11 - 15

16 - 20

21 - 25

26 - 30

oltre 30

Figure 2.22 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, variations (%), 1998-2004

Legenda

Rif su ADDVariazione % 1998-2004

fino a 0%

1% - 24%

25% - 49%

50% - 74%

75% - 99%

100% - 499%

500% e oltre

92

As regards the random sample of 5 provinces, Milano and Roma showed a per local unit production of

waste (Table 2.12) lower than the national average during all the period (both even below their own

home regional average), while Venezia, Genova and L’Aquila have always experienced values higher

than the Italian average, with Venezia and L’Aquila being even above their own home regional average.

Table 2.12 – Waste production (tons), Industry in a Strict Sense, 5 provinces: per local unit values, 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 107 132 145 147 149 157 184Venezia 303 367 422 439 409 449 482Genova 411 402 485 730 485 525 543Roma 78 106 129 91 87 82 96L'Aquila 322 387 437 355 312 283 303

Italy 217 255 280 292 298 299 325

Looking at the per worker values, Milano, Roma and L’Aquila showed values below the National aver-

age of that period, while Genova and Venezia were above it. In none of the provinces such an indicator

was about to decrease, even if Milano, Roma and Genova were showing a per worker value below their

own regional average. The industrial framework of Liguria is the one with the highest values as regards

both the indicators (per local unit and per capita).

Table 2.13 – Waste production (tons), Industry in a Strict Sense, 5 provinces: per worker values, 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004

Milano 4 5 5 5 6 6 7 Venezia 14 17 20 20 19 21 23 Genova 15 14 17 26 17 20 23 Roma 4 5 7 5 5 5 6 L'Aquila 7 10 11 9 9 9 10

Italy 9 11 12 13 13 13 15

93

2.5.1.2 Regions and provinces

Taking the regional dimension into account (Table 2.14), it can be observed that in 1998 only 7 regions

out of 20 were over the 1 million of tons of annual waste production. During the period 1998-2004,

however, this number has doubled: Friuli Venezia Giulia, Liguria, Umbria, Marche, Lazio, Campania

and Sicilia have added themselves to Piemonte, Lombardia, Veneto, Emilia Romagna, Toscana, Puglia

and Sardegna. In 2004, the biggest producer of waste is Lombardia (with more than 10 millions of

tons), followed by Veneto (more than 6,5 millions of tons) and Puglia (more than 4,5 millions of tons),

while the region with the smallest production of waste is Valle d’Aosta (125.000 tons).

In percentage terms, the regional growth has been much different (Table 2.15) across regions: Valle

d’Aosta, Toscana and Calabria have also experienced a decrease of 1998 waste levels (but, afterward,

the last two increasing that amount in the last year), and only Toscana has recorded a decrease of its in-

dustrial waste as regards the levels in 1998. Some regions with a strong industrial propensity such as

Piemonte, Lombardia, Veneto and Emilia Romagna have seen their waste production growing by less

than +100%, while other regions have showed a growth by more than +100%: Liguria and Campania

have had a growth rate of, respectively, +142% and +105%, while Sicilia has seen in 2004 an increase

of +316% with respect to 1998.

Table 2.14 – Waste production (tons), Industry in a Strict Sense, Italian regions: 1998-2004

Region 1998 1999 2000 2001 2002 2003 2004Piemonte 2.371.655 2.953.080 3.191.906 3.245.054 3.176.528 3.081.588 3.250.284Valle d'Aosta 98.400 75.722 76.087 80.313 81.434 101.913 125.299Lombardia 5.646.893 7.282.774 8.734.369 9.177.377 9.728.966 9.704.101 10.001.141Trentino Alto Adige 409.840 463.309 536.383 591.922 596.835 608.491 644.397Veneto 4.989.118 5.948.969 6.757.621 7.084.447 6.832.543 6.457.736 6.544.630Friuli Venezia Giulia 948.651 1.405.445 1.540.360 1.716.220 1.849.935 1.654.899 1.797.297Liguria 926.156 890.493 954.141 1.290.597 1.832.024 2.234.556 2.240.578Emilia Romagna 3.508.887 4.327.695 4.215.739 4.609.028 4.826.404 4.878.272 4.475.885Toscana 3.261.883 2.990.535 3.244.845 3.167.152 3.320.992 3.009.198 2.946.758Umbria 741.875 1.034.480 1.103.966 1.081.708 1.140.832 983.015 1.090.263Marche 643.733 786.957 837.290 897.563 975.783 1.017.353 1.098.854Lazio 740.448 981.002 1.165.878 1.097.212 1.010.430 1.029.360 1.046.673Abruzzo 440.840 535.304 562.630 646.150 619.603 612.092 675.099Molise 157.391 277.409 304.299 313.917 303.820 335.968 258.071Campania 566.484 837.215 943.911 1.084.911 1.125.913 1.135.279 1.160.884Puglia 2.570.599 3.037.344 2.652.416 2.980.346 2.575.804 3.498.928 4.596.643Basilicata 185.291 389.670 395.592 294.702 292.493 249.331 300.005Calabria 157.815 62.414 66.017 69.461 68.576 139.524 226.552Sicilia 586.180 713.557 883.039 862.907 880.976 1.041.341 2.438.450Sardegna 1.655.199 1.658.113 2.261.494 2.403.694 2.417.826 2.299.939 2.744.968

Italy 30.607.336 36.651.487 40.427.983 42.694.681 43.657.717 44.072.885 47.662.730

94

In the studied period, some regions have seen their share over the total Italian waste lowering (Table

2.16): this has happened to Toscana, where in 1998 its waste production was 10,66% of the total in It-

aly, and then it has gone done to 6,18%; or it can be seen what happened in Veneto, where it went

from 16,30% to 13,73%. Some others, instead, have increased their share over the total, such as Sicilia,

going from 586.180 tons in 1998 (equal to 1,92% of Italy) to 2.438.450 tons in 2004 (equal to 5,12% of

Italy).

The regional scale analysis confirms the high variability among territories, in Italy, of the production of

waste, as regards the Industry in a Strict Sense: this has been due to the different economic conditions

of the regions, since in Italy the distribution of wealth is much variable both across regions, and espe-

cially across provinces, with some of them being much richer than others in the same home region. As

regards the production of waste, the same territorial dispersion can be observed, but with some other

complications in the background: waste production and its statement are activities strictly linked with

the place in which they are produced, while the creation of wealth is not always recorded according

geographical criteria, but in terms of ownership (juridical criteria). As an example, a firm based in Mi-

lano, which does not produce goods in Milano, is increasing the economic value of Milano by selling its

goods, but, at the same time, it is increasing the production of waste in that area where effectively it is

producing its goods, making that area a waste producer, but without the relative economic power that

one could be induced to think at by looking at the waste production data of the area.

Table 2.15 – Waste production, Industry in a Strict Sense, Italian regions: indices, 1998-2004

Region 1998 1999 2000 2001 2002 2003 2004 Piemonte 100 125 135 137 134 130 137 Valle d'Aosta 100 77 77 82 83 104 127 Lombardia 100 129 155 163 172 172 177 Trentino Alto Adige 100 113 131 144 146 148 157 Veneto 100 119 135 142 137 129 131 Friuli Venezia Giulia 100 148 162 181 195 174 189 Liguria 100 96 103 139 198 241 242 Emilia Romagna 100 123 120 131 138 139 128 Toscana 100 92 99 97 102 92 90 Umbria 100 139 149 146 154 133 147 Marche 100 122 130 139 152 158 171 Lazio 100 132 157 148 136 139 141 Abruzzo 100 121 128 147 141 139 153 Molise 100 176 193 199 193 213 164 Campania 100 148 167 192 199 200 205 Puglia 100 118 103 116 100 136 179 Basilicata 100 210 213 159 158 135 162 Calabria 100 40 42 44 43 88 144 Sicilia 100 122 151 147 150 178 416 Sardegna 100 100 137 145 146 139 166

Italy 100 120 132 139 143 144 156

95

By increasing the disaggregation along the territorial dimension, it can be seen that the provincial level

variability is even higher, both between provinces of different regions, and between provinces of the

same home region (Table A2.3 and A2.5 in the Appendix). Thus, as an example, there are regions

where a single province works as the main driver for the production of waste of that region: for exam-

ple, Cagliari in the year 2004, with its 80% of the waste of Sardegna, or Taranto and Bari, which were

producing almost the 74% of the waste of Puglia. In some other cases, instead, a group of provinces

can be seen as the main drivers of waste production: in Lombardia, in the year 2004, as many as 4 prov-

inces (Brescia, Milano, Bergamo and Mantova) were producing more than 1 million of tons each, thus

making up almost the 73% of that region.

Table 2.16 – Waste production, Industry in a Strict Sense, Italian regions: percentage composition, 1998-2004

Region 1998 1999 2000 2001 2002 2003 2004Piemonte 7,75 8,06 7,90 7,60 7,28 6,99 6,82Valle d'Aosta 0,32 0,21 0,19 0,19 0,19 0,23 0,26Lombardia 18,45 19,87 21,60 21,50 22,28 22,02 20,98Trentino Alto Adige 1,34 1,26 1,33 1,39 1,37 1,38 1,35Veneto 16,30 16,23 16,72 16,59 15,65 14,65 13,73Friuli Venezia Giulia 3,10 3,83 3,81 4,02 4,24 3,75 3,77Liguria 3,03 2,43 2,36 3,02 4,20 5,07 4,70Emilia Romagna 11,46 11,81 10,43 10,80 11,06 11,07 9,39Toscana 10,66 8,16 8,03 7,42 7,61 6,83 6,18Umbria 2,42 2,82 2,73 2,53 2,61 2,23 2,29Marche 2,10 2,15 2,07 2,10 2,24 2,31 2,31Lazio 2,42 2,68 2,88 2,57 2,31 2,34 2,20Abruzzo 1,44 1,46 1,39 1,51 1,42 1,39 1,42Molise 0,51 0,76 0,75 0,74 0,70 0,76 0,54Campania 1,85 2,28 2,33 2,54 2,58 2,58 2,44Puglia 8,40 8,29 6,56 6,98 5,90 7,94 9,64Basilicata 0,61 1,06 0,98 0,69 0,67 0,57 0,63Calabria 0,52 0,17 0,16 0,16 0,16 0,32 0,48Sicilia 1,92 1,95 2,18 2,02 2,02 2,36 5,12Sardegna 5,41 4,52 5,59 5,63 5,54 5,22 5,76

Italy 100,00 100,00 100,00 100,00 100,00 100,00 100,00

Last, there are cases in which discrepancies and imbalances are inside the provinces themselves, with

some industrial sectors which are producing the most of waste of that province. As an example, in Ta-

ble A2.6, in the Appendix, it can be seen some of such circumstances: Brescia, with its “Manufacture of

basic metals” (Ateco 27) being the 72% of the total; Cagliari, with its “Manufacture of chemicals and

chemical products” (Ateco 24) representing almost 73% of the total; or Taranto, with its “Manufacture

of basic metals” (Ateco 27) reaching as much as the 96% of the total production of waste.

96

Table 2.15 – Waste production, Industry in a Strict Sense, 5 provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004 Milano 1.233.678 1.554.325 1.730.845 1.729.241 1.746.565 1.808.873 2.083.000 Venezia 721.917 841.588 996.960 1.035.438 959.833 1.046.540 1.108.657 Genova 444.694 436.962 527.381 768.377 510.323 552.625 591.445 Roma 310.342 422.598 505.368 361.308 342.398 333.778 387.446 L'Aquila 111.566 140.534 152.565 136.176 131.360 124.841 131.162

Italy 30.607.336 36.651.487 40.427.983 42.694.681 43.657.717 44.072.885 47.662.730

As regards the sample of 5 provinces, Milano, which was producing almost one fifth of the waste of

Lombardia, has produced always more than 1 million of tons of waste, during the period 1998-2004,

exceeding 2 millions in 2004. Venezia went beyond 1 million in 2001, 2003 and 2004, while Genova,

Roma and L’Aquila were at lower levels in all that time span.

2.5.2 Quantitative analysis from a sectorial point of view

By looking at the different divisions which Industry in a Strict Sense is divided into, it can be seen how

a major role in waste production was being played by “Manufacture of basic metals” (Ateco 27), which

leads the set of sectors composing Industry in a Strict Sense, during all the period: this indicates that

such an activity is, so far, the one with the highest waste-intensity. In fact, in 1998, the division Ateco

27 was representing the 22% of the total of the waste from Industry in a Strict Sense, while in 2004 this

value had gone below 20,8%, because of the increase of waste produced by other divisions of that sec-

tor (Table 2.16). As the provincial level analysis says, so the sectorial level analysis shows the presence

of a “polarization” in waste production, with some divisions weighing more than others in that (Table

A2.2, in Appendix). Not only, but there is also the case that some firms are the sole responsible for the

production of waste in a single division: see, for example, the division “Mining of uranium and thorium

ores” (Ateco 12), with less than 4 operative local units.

97

Figure 2.23 – Waste production, total of Manufacturing (Ateco 15 to 36), year 2004

Fabbric.pasta-carta,carta e prod.di carta

Fabbric.coke,raffinerie,combust.nucleari

Fabbric.prodotti chimici e fibre sintetiche

Fabbric.artic.in gomma e mat.plastiche

Produzione di metalli e loro leghe

Fabbric.autoveicoli,rimorchi e semirim.

Industrie alimentari e delle bevande

Confez.articoli vestiario-prep.pellicce

Fabbricaz.e lav.prod.metallo,escl.macchine

Fabbric.appar.medicali,precis.,strum.ottici

Fabbric.appar.radiotel.e app.per comunic.

Fabbric.mobili-altre industrie manifatturiere

Fabbric.di macchine ed appar.elettr.n.c.a.

Editoria,stampa e riprod.supp.registrati

Ind.legno,esclusi mobili-fabbr.in paglia

Prep.e concia cuoio-fabbr.artic.viaggio

Industrie tessili

Industria del tabacco

Fabbric.macchine per uff.,elaboratori

Fabbric.macchine ed appar.mecc.,instal.

Fabbric.di altri mezzi di trasporto

Fabbric.prodotti lavoraz.min.non metallif.

98

Considering the whole of Industry in a Strict Sense, the branch of mining activities (Ateco 10 to 14)

goes from 1,8% of the total, in 1998, to 3,7% in 2004, while in the same period the division “Electric-

ity, gas, steam and hot water supply” (Ateco 40) accounts for 4,3% in the beginning, to 7,4% in the last

year: the two branches altogether were producing 6,1% of total waste on Industry in a Strict Sense in

1998, shifting then to 11,1% in 2004.

As already noted above, not all the dynamics have been regular in time, so that, together with some

more or less important changes in regulations, it can be said that the peaks in waste production (or,

similarly, its downfalls) might have been due to a sort of adaptation to the new regulations, by switch-

ing waste from one division to another one simply because a change in codes have been done: under

this assumption, there might have been a certain kind of waste, belonging to a certain activity due to its

code, which has been shifted to another code, thus determining a fake dynamics. As an example, the

division “Manufacture of basic metals” (Ateco 27) was showing some peaks in production in the year

2001 and in 2004: these peaks have later been shown that were due to the production of 10.02.02 “un-

processed slag” (CER 100202, that is EWC 10.02.02) by a steel mill in Taranto. Similarly, the division

“Manufacture of food products and beverages” (Ateco 15) has experienced a marked decrease in waste

production in 2003 and 2004, mainly due to two types of waste, “soil from cleaning and washing beet”

(CER-EWC 02.04.01) and “off-specification calcium carbonate” (CER-EWC 02.04.02), both of them

produced in sugar refineries: the closure of some plants in 2003 and 2004 has caused a reduction in

waste for those two years. From these two examples it can bee seen how, in such an analysis concern-

ing “special” waste, what can be observed and positively considered at a macro level (as an example, a

decrease in waste due to an ex ante supposed advance in technology) may be then explained by a simple

reduction in the mere quantities of waste production.

During the quoted period, the weight of those predominant divisions as regards waste production in

Manufacturing activities (Ateco 15-36) has not changed (Figure 2.23): the divisions “Manufacture of

chemicals and chemical products” (Ateco 24), “Manufacture of other non-metallic mineral products”

(Ateco 26), “Manufacture of basic metals” (Ateco 27), “Manufacture of fabricated metal products, ex-

cept machinery and equipment” (Ateco 28) and “Manufacture of machinery and equipment n.e.c.” (A-

teco 29) were producing, in 1998 as in 2004, more than the half of the waste production of Manufac-

turing activities, with the same proportion of dangerous and non-dangerous waste across time.

2.5.3 Waste and economic activity

Another way to consider the production of waste and to make comparisons between different sectors

and territorial realities is to relate such a production to the respective economic activity which has gen-

erated that waste: the key point is finding an economic variable which can sum up the power of the

economy where that waste come from, and in the following chapters this variable will be the value-

99

added, which will be used to build an indictor that will be equal to the ratio between the quantity of

waste in a province and its relative value-added.

2.6 Conclusions

Industrial waste was accounting for almost the half of “special” waste produced in Italy, during the pe-

riod 1998-2004. Such a value has continuously risen in that period, against a gradual decrease of the

share of the value-added of Industry in a Strict Sense over the total value-added of Italy, together with a

stagnation in that sector’s productivity in the same period. The territorial and sectorial analysis have

shown that the percentage of individuals that present the MUD statement (over the total of the indi-

viduals) is highly diverse, taking into account both a sectorial point of view, and a geographical point of

view. The analysis of the quantities has indicated that the production of waste was continuously grow-

ing, both in absolute terms, and in relative terms (per worker and per local unit), while an increase in

economic productivity was not taking place at all. Moreover, in Italy, waste intensity was increasing,

while in Europe has decreased within the period 1998-2004. Next chapter will illustrate some of the

drivers that were causing the increase of waste, and these variables will be used in the subsequent

model.

100

Table 2.16 – Waste production (tons), Industry in a Strict Sense, Italy, by Ateco divisions: 1998-2004

Division’s description Ateco 1998 1999 2000 2001 2002 2003 2004Estraz.carbon fossile e lignite-estraz.torba 10 603 831 504 880 419 1.067 303Estraz.petrolio greggio e gas naturale 11 184.641 227.726 191.338 291.358 345.174 408.152 350.335Estraz.minerali di uranio e di torio 12 0 0 0 0 0 4 0Estrazione di minerali metalliferi 13 338 1.556 1.369 1.596 244 418 2.343Altre industrie estrattive 14 363.883 685.774 703.802 1.057.000 1.073.127 1.086.619 1.420.669Mining – Total 549.466 915.888 897.013 1.350.834 1.418.965 1.496.259 1.773.650 Industrie alimentari e delle bevande 15 4.471.574 4.964.159 4.525.414 4.704.972 4.921.436 4.393.957 3.722.273Industria del tabacco 16 26.450 24.807 28.487 30.990 30.653 29.563 23.753Industrie tessili 17 462.916 621.136 710.119 760.048 801.660 664.851 515.570Confez.articoli vestiario-prep.pellicce 18 85.007 105.892 117.134 122.181 117.584 105.729 100.842Prep.e concia cuoio-fabbr.artic.viaggio 19 608.447 754.135 906.609 887.762 890.147 716.458 653.253Ind.legno,esclusi mobili-fabbr.in paglia 20 793.446 1.045.542 1.274.653 1.587.662 2.069.354 2.079.908 2.167.434Fabbric.pasta-carta,carta e prod.di carta 21 1.342.789 1.660.520 1.853.001 1.995.263 1.939.459 1.785.876 1.827.984Editoria,stampa e riprod.supp.registrati 22 432.960 638.653 718.001 781.502 745.474 759.754 772.075Fabbric.coke,raffinerie,combust.nucleari 23 257.694 272.614 391.251 361.365 502.141 571.847 1.579.040Fabbric.prodotti chimici e fibre sintetiche 24 3.274.126 3.573.345 4.398.549 4.274.697 4.995.876 5.399.269 5.865.435Fabbric.artic.in gomma e mat.plastiche 25 574.359 681.909 759.701 822.804 870.685 828.248 906.415Fabbric.prodotti lavoraz.min.non metallif. 26 5.174.904 5.711.847 6.154.460 6.280.087 6.355.295 6.612.277 7.000.107Produzione di metalli e loro leghe 27 6.816.101 7.765.846 8.605.796 9.401.859 8.190.728 8.779.170 9.913.606Fabbricaz.e lav.prod.metallo,escl.macchine 28 1.890.581 2.650.979 3.168.771 3.371.108 3.445.274 3.472.243 3.675.475Fabbric.macchine ed appar.mecc.,instal. 29 854.138 1.074.774 1.215.769 1.217.011 1.231.760 1.220.545 1.266.741Fabbric.macchine per uff.,elaboratori 30 15.667 16.023 17.248 18.519 14.152 11.731 9.194Fabbric.di macchine ed appar.elettr.n.c.a. 31 212.683 293.164 302.664 316.507 301.466 292.089 303.407Fabbric.appar.radiotel.e app.per comunic. 32 96.721 112.718 128.270 96.615 85.309 82.064 86.506Fabbric.appar.medicali,precis.,strum.ottici 33 45.060 54.425 59.483 64.626 70.421 69.254 68.500Fabbric.autoveicoli,rimorchi e semirim. 34 603.568 1.009.261 1.085.462 1.014.165 943.361 822.528 879.682Fabbric.di altri mezzi di trasporto 35 201.316 251.137 252.571 299.908 276.778 293.271 295.534Fabbric.mobili-altre industrie manifatturiere 36 494.247 549.839 655.128 701.683 738.803 732.276 727.145Manufacturing – Total 28.734.756 33.832.727 37.328.541 39.111.334 39.537.816 39.722.908 42.359.972 Produz.energia elettr.,gas,acqua calda 40 1.323.115 1.902.870 2.202.430 2.232.512 2.700.937 2.853.728 3.529.110Energy, water and gas – Total 1.323.115 1.902.870 2.202.430 2.232.512 2.700.937 2.853.728 3.529.110

Total 30.607.337 36.651.485 40.427.984 42.694.680 43.657.718 44.072.895 47.662.731

101

APPENDIX A2

Table A2.1 – Share of the Value-added of Industry in a Strict Sense on the total Value-added of the geo-

graphical unit (%)

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 30,82 29,46 28,46 27,29 25,85 24,74 25,15 Vercelli 29,35 28,28 28,30 26,80 26,13 24,77 23,67 Novara 36,14 34,72 36,52 34,47 34,89 32,98 32,47 Cuneo 31,18 30,56 30,31 30,71 28,50 27,50 26,27 Asti 24,37 22,98 23,13 22,63 20,60 19,29 18,84 Alessandria 29,69 28,96 28,78 29,07 27,25 25,91 24,33 Biella 40,77 38,16 38,21 35,09 33,41 31,92 31,85 Verbano Cusio Ossola 26,85 26,90 26,85 24,48 22,03 20,65 20,57 Piemonte 31,14 29,93 29,52 28,50 27,05 25,83 25,62 Aosta 13,49 13,36 13,00 13,10 14,41 13,89 13,95 Valle d'Aosta 13,49 13,36 13,00 13,10 14,41 13,89 13,95 Varese 37,37 35,29 35,80 34,95 34,30 33,66 34,53 Como 34,79 33,65 33,54 34,02 34,03 32,86 34,26 Sondrio 21,80 22,20 20,39 20,26 20,54 20,01 20,42 Milano 30,27 29,30 28,09 27,29 26,97 26,58 27,60 Bergamo 38,86 37,70 36,74 37,07 34,35 33,11 32,32 Brescia 33,53 32,53 32,89 32,29 30,64 29,96 29,80 Pavia 25,14 24,43 24,55 23,53 22,57 22,96 23,44 Cremona 30,97 30,00 29,15 29,28 29,51 28,77 28,31 Mantova 33,92 32,26 32,40 33,20 31,54 29,75 30,24 Lecco 41,09 40,26 41,23 40,48 40,19 38,83 39,36 Lodi 28,78 29,00 27,90 28,05 27,45 26,44 26,34 Lombardia 32,32 31,25 30,60 30,09 29,35 28,74 29,35 Bolzano - Bozen 14,96 14,82 14,34 14,23 13,53 13,00 12,44 Trento 19,59 19,27 18,63 18,31 18,43 17,82 18,33 Trentino Alto Adige 17,13 16,93 16,34 16,15 15,78 15,20 15,12 Verona 27,04 26,54 26,87 26,18 26,24 25,73 25,77 Vicenza 39,66 38,98 38,32 38,05 36,70 35,49 35,63 Belluno 35,87 35,28 33,43 32,91 31,65 30,67 30,61 Treviso 36,89 36,93 36,00 34,63 32,50 32,07 31,77 Venezia 22,52 21,74 19,31 18,80 18,39 18,43 18,64 Padova 26,37 26,90 26,86 25,75 25,37 25,31 24,57 Rovigo 24,54 24,34 23,55 21,98 20,78 20,59 20,72 Veneto 30,44 30,17 29,36 28,55 27,65 27,21 27,10 Udine 23,94 22,57 22,25 22,39 21,93 21,92 20,71 Gorizia 22,53 21,83 20,43 19,46 18,74 18,41 17,35 Trieste 13,47 13,39 12,77 13,60 12,49 13,42 12,93 Pordenone 34,18 33,13 32,95 30,32 29,15 29,39 28,92 Friuli Venezia Giulia 24,11 23,18 22,58 21,98 21,28 21,57 20,65 Imperia 6,98 6,60 7,27 7,15 7,54 7,80 7,42 Savona 14,89 14,37 14,04 12,96 12,87 12,54 12,38 Genova 15,57 15,82 17,13 16,87 15,02 14,79 14,49 La Spezia 16,65 17,52 18,00 17,19 17,90 18,07 17,23 Liguria 14,47 14,60 15,44 14,98 14,04 13,87 13,51 Piacenza 27,05 27,34 26,01 25,17 23,90 22,66 22,27 Parma 31,38 31,18 30,03 29,65 28,05 27,30 27,03 Reggio Emilia 37,72 38,04 38,16 37,73 36,28 35,97 34,48 Modena 37,45 37,48 37,43 37,31 34,77 33,63 32,82

102

Province and region 1998 1999 2000 2001 2002 2003 2004 Bologna 27,20 26,33 25,85 24,95 26,04 25,43 24,37 Ferrara 21,40 22,08 23,14 21,92 20,13 19,53 19,16 Ravenna 21,72 21,37 20,26 21,26 22,97 22,60 21,82 Forlì - Cesena 22,61 22,20 22,02 22,78 22,82 22,60 22,27 Rimini 16,10 16,31 16,22 16,64 16,31 15,99 15,77 Emilia Romagna 28,64 28,47 28,11 27,80 27,19 26,53 25,79 Massa Carrara 19,50 19,19 19,65 19,65 17,81 17,48 16,61 Lucca 27,27 27,42 26,95 26,00 24,94 24,39 23,86 Pistoia 25,83 24,82 24,52 24,42 23,71 24,15 24,09 Firenze 25,80 24,81 24,70 24,60 23,06 21,84 21,62 Livorno 17,70 17,21 17,03 17,47 17,36 17,11 16,59 Pisa 29,26 28,04 27,98 27,19 26,82 27,04 26,80 Arezzo 31,51 31,58 29,94 29,80 29,32 28,30 27,76 Siena 19,69 18,96 19,34 18,86 18,41 17,63 17,44 Grosseto 10,40 10,29 10,03 9,95 9,38 8,73 8,73 Prato 36,20 34,32 37,68 37,47 34,75 34,90 35,19 Toscana 25,36 24,58 24,62 24,42 23,29 22,66 22,36 Perugia 21,86 21,83 21,76 21,55 21,43 20,94 19,87 Terni 24,56 24,80 25,95 25,95 28,00 26,77 25,46 Umbria 22,54 22,59 22,80 22,64 23,08 22,43 21,30 Pesaro e Urbino 25,26 25,56 25,01 25,46 25,08 24,98 25,30 Ancona 28,37 28,18 26,99 26,49 25,89 25,94 25,82 Macerata 26,77 26,46 26,09 26,21 25,74 24,43 25,54 Ascoli Piceno 28,42 28,37 26,41 27,21 27,30 26,77 27,39 Marche 27,34 27,27 26,21 26,37 26,01 25,61 26,03 Viterbo 14,05 14,48 14,28 14,60 13,26 13,02 12,80 Rieti 14,82 12,89 12,91 13,29 13,59 13,56 13,06 Roma 11,55 11,50 11,30 12,05 11,92 11,50 11,36 Latina 24,11 23,06 24,85 24,63 24,95 23,79 23,45 Frosinone 25,97 26,43 25,66 23,80 23,58 23,02 23,06 Lazio 13,75 13,61 13,51 13,99 13,88 13,44 13,29 L'Aquila 19,21 19,45 20,29 18,01 18,09 17,81 17,95 Teramo 27,71 27,81 29,43 29,67 28,29 27,16 27,30 Pescara 19,21 20,30 21,27 19,32 18,03 18,29 18,47 Chieti 27,12 28,27 28,81 28,33 28,03 28,21 27,70 Abruzzo 23,55 24,23 25,26 24,17 23,46 23,24 23,24 Campobasso 18,82 18,50 17,46 16,98 17,82 17,73 17,00 Isernia 21,71 22,36 23,48 21,89 21,50 19,63 18,55 Molise 19,67 19,59 19,24 18,45 18,90 18,32 17,49 Caserta 17,28 17,29 17,39 17,48 17,25 17,13 16,27 Benevento 10,96 11,28 11,11 10,99 11,10 11,13 10,10 Napoli 15,63 15,29 14,50 14,35 14,14 14,35 13,96 Avellino 20,04 20,20 22,04 22,17 21,59 22,36 20,92 Salerno 14,40 14,91 15,40 15,42 15,38 15,30 14,75 Campania 15,72 15,68 15,54 15,48 15,29 15,42 14,82 Foggia 11,12 11,22 11,01 11,08 10,36 10,08 9,96 Bari 16,65 16,60 16,04 15,35 16,37 15,93 15,75 Taranto 24,60 22,66 23,52 22,04 21,12 20,08 19,43 Brindisi 18,54 18,02 15,93 16,90 14,20 13,52 13,29 Lecce 14,92 14,43 14,04 13,49 13,15 12,99 12,69 Puglia 16,82 16,39 16,00 15,50 15,33 14,87 14,61 Potenza 21,50 22,29 22,45 22,67 24,17 21,80 21,36 Matera 13,56 13,84 15,54 17,70 18,03 15,13 14,56 Basilicata 18,89 19,39 20,12 21,00 22,18 19,63 19,12

103

Province and region 1998 1999 2000 2001 2002 2003 2004 Cosenza 9,90 9,80 10,25 10,95 11,32 10,50 10,71 Catanzaro 10,33 10,28 10,01 9,04 9,83 9,70 9,79 Reggio di Calabria 7,47 7,46 7,21 7,59 7,90 6,87 6,61 Crotone 13,02 12,88 14,54 15,30 14,16 13,67 13,47 Vibo Valentia 9,13 9,32 9,84 10,34 11,33 10,20 10,61 Calabria 9,45 9,42 9,66 9,93 10,31 9,52 9,52 Trapani 8,71 9,05 9,50 9,97 9,64 9,98 9,64 Palermo 12,13 11,75 10,55 10,31 10,36 10,94 10,31 Messina 8,68 8,83 8,52 7,86 8,50 8,50 7,86 Agrigento 7,57 7,72 7,67 7,62 7,63 7,39 7,08 Caltanissetta 20,90 19,09 17,75 17,25 19,51 19,10 17,53 Enna 8,33 8,55 9,09 9,10 8,88 9,07 8,69 Catania 12,06 11,83 11,62 12,06 10,60 10,44 9,96 Ragusa 8,78 8,93 9,12 8,75 8,97 8,34 7,70 Siracusa 23,37 21,45 19,14 18,04 17,95 17,72 16,27 Sicilia 12,22 11,83 11,21 11,03 10,86 10,90 10,26 Sassari 12,64 12,08 11,57 11,78 12,28 12,32 12,11 Nuoro 12,20 12,40 12,12 12,97 14,24 14,28 13,33 Cagliari 15,69 14,83 15,51 15,43 15,43 15,52 15,06 Oristano 8,76 9,11 9,42 9,27 9,75 9,84 10,25 Sardegna 13,63 13,11 13,25 13,41 13,82 13,86 13,51

Italy 23,88 23,36 23,00 22,65 22,01 21,52 21,37

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Table A2.2 – Waste production (tons), Italy, by Ateco divisions: 1998-2004

Division’s description Ateco 1998 1999 2000 2001 2002 2003 2004Agricoltura, caccia e relativi servizi 01 211.622 292.659 343.685 439.903 448.610 515.369 472.287Silvicoltura e utilizzaz.aree forestali 02 4.673 2.678 2.848 8.755 14.172 21.921 16.525Pesca,piscicoltura e servizi connessi 05 9.701 11.316 1.680 1.623 1.368 1.883 5.254Estraz.carbon fossile e lignite-estraz.torba 10 603 831 504 880 419 1.067 303Estraz.petrolio greggio e gas naturale 11 184.641 227.726 191.338 291.358 345.174 408.152 350.335Estraz.minerali di uranio e di torio 12 0 0 0 0 0 4 0Estrazione di minerali metalliferi 13 338 1.556 1.369 1.596 244 418 2.343Altre industrie estrattive 14 363.883 685.774 703.802 1.057.000 1.073.127 1.086.619 1.420.669Industrie alimentari e delle bevande 15 4.471.574 4.964.159 4.525.414 4.704.972 4.921.436 4.393.957 3.722.273Industria del tabacco 16 26.450 24.807 28.487 30.990 30.653 29.563 23.753Industrie tessili 17 462.916 621.136 710.119 760.048 801.660 664.851 515.570Confez.articoli vestiario-prep.pellicce 18 85.007 105.892 117.134 122.181 117.584 105.729 100.842Prep.e concia cuoio-fabbr.artic.viaggio 19 608.447 754.135 906.609 887.762 890.147 716.458 653.253Ind.legno,esclusi mobili-fabbr.in paglia 20 793.446 1.045.542 1.274.653 1.587.662 2.069.354 2.079.908 2.167.434Fabbric.pasta-carta,carta e prod.di carta 21 1.342.789 1.660.520 1.853.001 1.995.263 1.939.459 1.785.876 1.827.984Editoria,stampa e riprod.supp.registrati 22 432.960 638.653 718.001 781.502 745.474 759.754 772.075Fabbric.coke,raffinerie,combust.nucleari 23 257.694 272.614 391.251 361.365 502.141 571.847 1.579.040Fabbric.prodotti chimici e fibre sintetiche 24 3.274.126 3.573.345 4.398.549 4.274.697 4.995.876 5.399.269 5.865.435Fabbric.artic.in gomma e mat.plastiche 25 574.359 681.909 759.701 822.804 870.685 828.248 906.415Fabbric.prodotti lavoraz.min.non metallif. 26 5.174.904 5.711.847 6.154.460 6.280.087 6.355.295 6.612.277 7.000.107Produzione di metalli e loro leghe 27 6.816.101 7.765.846 8.605.796 9.401.859 8.190.728 8.779.170 9.913.606Fabbricaz.e lav.prod.metallo,escl.macchine 28 1.890.581 2.650.979 3.168.771 3.371.108 3.445.274 3.472.243 3.675.475Fabbric.macchine ed appar.mecc.,instal. 29 854.138 1.074.774 1.215.769 1.217.011 1.231.760 1.220.545 1.266.741Fabbric.macchine per uff.,elaboratori 30 15.667 16.023 17.248 18.519 14.152 11.731 9.194Fabbric.di macchine ed appar.elettr.n.c.a. 31 212.683 293.164 302.664 316.507 301.466 292.089 303.407Fabbric.appar.radiotel.e app.per comunic. 32 96.721 112.718 128.270 96.615 85.309 82.064 86.506Fabbric.appar.medicali,precis.,strum.ottici 33 45.060 54.425 59.483 64.626 70.421 69.254 68.500Fabbric.autoveicoli,rimorchi e semirim. 34 603.568 1.009.261 1.085.462 1.014.165 943.361 822.528 879.682Fabbric.di altri mezzi di trasporto 35 201.316 251.137 252.571 299.908 276.778 293.271 295.534Fabbric.mobili-altre industrie manifatturiere 36 494.247 549.839 655.128 701.683 738.803 732.276 727.145Recupero e preparaz. per il riciclaggio 37 1.676.421 2.263.098 2.972.821 3.600.078 4.222.210 5.233.479 5.931.670Produz.energia elettr.,gas,acqua calda 40 1.323.115 1.902.870 2.202.430 2.232.512 2.700.937 2.853.728 3.529.110Raccolta,depurazione e distribuzione acqua 41 565.705 629.914 671.239 569.181 512.675 643.618 713.579Costruzioni 45 3.621.525 4.474.145 5.575.453 5.953.951 6.594.297 8.106.150 8.825.679Comm.manut.e rip.autov. e motocicli 50 655.975 675.944 879.844 944.615 1.026.632 1.038.234 1.025.343Comm.ingr.e interm.del comm.escl.autov. 51 1.583.458 1.747.956 2.354.453 2.363.203 2.480.792 2.655.804 2.573.331Comm.dett.escl.autov-rip.beni pers. 52 270.000 233.776 246.017 261.819 279.966 261.597 295.117Alberghi e ristoranti 55 77.119 81.898 84.521 103.312 109.946 135.394 162.469Trasporti terrestri-trasp.mediante condotta 60 632.766 691.777 820.061 1.045.250 1.103.450 1.512.542 1.370.367Trasporti marittimi e per vie d'acqua 61 16.468 12.853 21.104 29.130 47.491 67.908 43.676Trasporti aerei 62 2.839 3.046 2.946 10.082 15.066 19.228 2.829Attivita' ausiliarie dei trasp.-ag.viaggi 63 174.813 223.268 286.854 333.618 402.404 402.399 432.713Poste e telecomunicazioni 64 32.837 37.185 45.025 35.385 30.674 31.514 28.660Interm.mon.e finanz.(escl.assic.e fondi p.) 65 17.369 12.729 9.655 8.885 10.294 9.248 9.018Assic.e fondi pens.(escl.ass.soc.obbl.) 66 1.970 3.187 6.511 3.116 2.801 2.109 2.445Attivita' ausil. intermediazione finanziaria 67 241 122 149 98 141 93 133Attivita' immobiliari 70 26.941 33.701 60.753 105.577 107.813 176.524 518.271Noleggio macc.e attrezz.senza operat. 71 2.237 2.125 5.629 3.688 5.024 7.002 10.138Informatica e attivita' connesse 72 3.742 4.274 6.482 4.736 8.835 5.846 8.229Ricerca e sviluppo 73 9.154 12.630 9.744 13.265 14.133 16.332 328.150Altre attivita' professionali e imprendit. 74 313.114 390.808 447.573 502.841 422.088 569.241 501.418Pubbl.amm.e difesa;assic.sociale obbligatoria 75 803.981 628.010 567.329 699.604 718.840 999.703 523.054

105

Division’s description Ateco 1998 1999 2000 2001 2002 2003 2004Istruzione 80 10.325 12.830 6.633 5.663 6.648 5.827 6.816Sanita' e altri servizi sociali 85 277.571 294.661 255.287 227.088 202.347 200.306 218.616Smaltim.rifiuti solidi, acque scarico e sim. 90 10.303.393 11.781.498 13.139.261 15.661.638 18.777.216 20.802.255 22.611.977Attivita' organizzazioni associative n.c.a. 91 7.164 8.629 4.796 5.963 6.411 10.061 15.350Attivita' ricreative, culturali sportive 92 26.377 18.745 21.835 30.205 25.394 28.417 36.320Altre attivita' dei servizi 93 90.225 110.306 116.398 124.619 126.565 312.694 207.435Serv.domestici presso famiglie e conv. 95 0 0 0 0 0 69 0Organizz. e organismi extraterritoriali 99 1.145 1.083 1.499 1.435 1.280 1.775 1.409Non classificati NC 55.599 266.876 81.535 25.856 29.987 13.413 3.500

Italy 52.093.805 61.615.211 69.477.603 75.818.862 81.413.285 87.880.849 94.564.506

106

Table A2.3 – Waste production (tons), Industry in a Strict Sense, Italy, by provinces and regions: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004

Torino 1.187.253 1.520.927 1.635.409 1.576.893 1.490.392 1.455.251 1.493.572Vercelli 287.535 343.050 347.837 347.471 321.283 319.374 374.780Novara 143.056 187.312 223.253 231.520 234.315 226.999 253.700Cuneo 304.017 400.494 437.575 448.647 493.395 444.367 579.560Asti 83.448 109.609 111.962 99.176 110.953 112.789 110.431Alessandria 146.555 156.556 181.238 224.948 238.913 242.737 241.037Biella 97.497 128.491 156.223 186.228 168.985 169.537 73.643Verbano Cusio Ossola 122.295 106.643 98.410 130.171 118.294 110.535 123.561Piemonte 2.371.655 2.953.080 3.191.906 3.245.054 3.176.528 3.081.588 3.250.284Aosta 98.400 75.722 76.087 80.313 81.434 101.913 125.299Valle d'Aosta 98.400 75.722 76.087 80.313 81.434 101.913 125.299Varese 343.944 463.973 487.407 510.078 496.571 495.279 489.999Como 176.489 309.811 297.561 287.879 281.106 325.649 329.943Sondrio 47.503 44.751 51.389 53.548 58.579 67.493 57.206Milano 1.233.678 1.554.325 1.730.845 1.729.241 1.746.565 1.808.873 2.083.000Bergamo 954.193 1.123.586 1.206.457 1.321.556 1.364.151 1.334.914 1.278.995Brescia 1.645.170 1.905.982 2.775.184 2.991.882 2.947.648 2.820.041 2.872.865Pavia 323.112 439.579 394.771 438.252 628.693 577.352 606.227Cremona 168.256 544.869 647.662 559.417 498.065 488.457 477.369Mantova 266.552 354.086 478.319 584.794 905.386 942.422 1.022.468Lecco 409.757 428.642 537.649 553.240 565.770 612.639 640.215Lodi 78.240 113.170 127.126 147.488 236.433 230.981 142.855Lombardia 5.646.893 7.282.774 8.734.369 9.177.377 9.728.966 9.704.101 10.001.141Bolzano - Bozen 127.141 184.198 202.713 195.215 175.394 154.852 184.615Trento 282.699 279.111 333.670 396.707 421.441 453.639 459.782Trentino Alto Adige 409.840 463.309 536.383 591.922 596.835 608.491 644.397Verona 1.831.187 1.996.339 2.021.516 1.937.493 1.871.885 1.789.696 1.868.049Vicenza 956.787 1.261.118 1.482.937 1.621.988 1.521.700 1.520.707 1.431.187Belluno 114.233 153.511 179.940 151.069 155.249 138.224 150.874Treviso 626.424 812.972 853.675 928.852 933.009 935.138 956.019Venezia 721.917 841.588 996.960 1.035.438 959.833 1.046.540 1.108.657Padova 501.107 584.781 899.070 809.537 861.258 765.210 849.951Rovigo 237.463 298.659 323.523 600.071 529.608 262.222 179.893Veneto 4.989.118 5.948.969 6.757.621 7.084.447 6.832.543 6.457.736 6.544.630Udine 460.142 748.972 807.238 878.682 882.862 841.664 920.531Gorizia 160.948 258.351 272.496 253.286 371.185 269.477 235.671Trieste 78.630 89.273 103.491 199.326 170.033 99.676 187.642Pordenone 248.932 308.850 357.135 384.926 425.855 444.082 453.453Friuli Venezia Giulia 948.651 1.405.445 1.540.360 1.716.220 1.849.935 1.654.899 1.797.297Imperia 3.864 4.323 4.924 5.122 4.913 5.369 5.201Savona 259.950 229.314 284.894 280.531 1.067.992 1.428.556 1.396.490Genova 444.694 436.962 527.381 768.377 510.323 552.625 591.445La Spezia 217.648 219.894 136.943 236.566 248.796 248.006 247.443Liguria 926.156 890.493 954.141 1.290.597 1.832.024 2.234.556 2.240.578Piacenza 355.436 322.905 294.311 248.380 269.259 145.163 135.626Parma 213.864 304.067 255.970 302.053 310.817 331.036 283.654Reggio Emilia 383.177 499.911 598.763 626.883 604.098 610.884 637.257Modena 883.485 1.102.519 1.081.067 1.173.040 1.121.925 1.150.535 1.166.629Bologna 482.339 619.712 646.058 740.764 701.714 734.856 704.568Ferrara 452.828 515.911 378.517 428.660 448.345 413.731 396.876Ravenna 458.443 598.336 563.896 699.027 848.721 768.131 786.784

107

Province and region 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 223.374 301.610 316.151 305.383 436.507 647.441 281.074Rimini 55.940 62.723 81.006 84.838 85.018 76.495 83.417Emilia Romagna 3.508.887 4.327.695 4.215.739 4.609.028 4.826.404 4.878.272 4.475.885Massa Carrara 587.785 580.843 597.589 623.737 541.849 470.102 479.998Lucca 659.740 676.131 692.344 723.462 696.865 687.790 575.061Pistoia 46.741 54.363 67.097 65.444 63.775 72.626 62.689Firenze 230.900 278.914 321.623 309.660 296.569 309.293 352.060Livorno 135.765 153.340 133.480 221.718 183.630 228.754 296.198Pisa 329.794 392.113 460.841 447.238 455.821 297.433 299.989Arezzo 574.046 241.565 264.754 185.190 379.602 251.778 174.854Siena 84.385 96.585 109.121 135.724 142.648 186.527 177.024Grosseto 567.423 464.081 538.679 392.590 501.309 450.323 476.779Prato 45.304 52.601 59.318 62.389 58.924 54.571 52.106Toscana 3.261.883 2.990.535 3.244.845 3.167.152 3.320.992 3.009.198 2.946.758Perugia 314.825 357.745 425.449 490.512 485.055 398.895 415.196Terni 427.050 676.735 678.517 591.196 655.778 584.120 675.068Umbria 741.875 1.034.480 1.103.966 1.081.708 1.140.832 983.015 1.090.263Pesaro e Urbino 178.620 168.195 210.567 239.082 276.764 310.586 388.922Ancona 203.522 303.266 305.465 321.096 332.330 336.292 346.880Macerata 107.744 148.481 167.743 161.890 171.945 174.485 167.333Ascoli Piceno 153.847 167.015 153.515 175.496 194.743 195.990 195.718Marche 643.733 786.957 837.290 897.563 975.783 1.017.353 1.098.854Viterbo 69.313 87.383 100.322 90.832 95.281 94.966 120.034Rieti 16.665 24.872 25.576 57.348 52.334 50.286 19.272Roma 310.342 422.598 505.368 361.308 342.398 333.778 387.446Latina 123.205 162.286 169.850 168.052 162.862 171.934 178.958Frosinone 220.923 283.863 364.761 419.672 357.555 378.397 340.962Lazio 740.448 981.002 1.165.878 1.097.212 1.010.430 1.029.360 1.046.673L'Aquila 111.566 140.534 152.565 136.176 131.360 124.841 131.162Teramo 93.459 109.584 131.728 189.351 192.973 193.981 215.126Pescara 25.969 29.856 42.667 77.651 61.387 58.914 57.784Chieti 209.846 255.331 235.671 242.972 233.883 234.355 271.027Abruzzo 440.840 535.304 562.630 646.150 619.603 612.092 675.099Campobasso 144.660 242.792 266.205 280.887 271.970 294.774 221.747Isernia 12.731 34.617 38.095 33.031 31.850 41.194 36.324Molise 157.391 277.409 304.299 313.917 303.820 335.968 258.071Caserta 108.614 183.150 212.066 248.300 232.615 249.777 278.675Benevento 9.364 12.322 12.797 18.760 23.232 24.042 28.062Napoli 202.372 293.158 319.906 400.758 398.755 368.758 398.220Avellino 83.895 114.096 154.273 184.971 196.843 167.684 159.051Salerno 162.239 234.488 244.869 232.123 274.468 325.018 296.876Campania 566.484 837.215 943.911 1.084.911 1.125.913 1.135.279 1.160.884Foggia 79.391 78.496 108.041 193.945 132.901 190.727 294.591Bari 426.933 556.740 561.883 606.205 697.835 681.597 738.789Taranto 1.899.527 1.687.972 1.146.836 1.230.728 579.166 1.379.701 2.154.615Brindisi 111.986 628.870 746.635 848.210 1.052.715 1.134.671 1.269.111Lecce 52.761 85.266 89.020 101.257 113.187 112.233 139.537Puglia 2.570.599 3.037.344 2.652.416 2.980.346 2.575.804 3.498.928 4.596.643Potenza 159.212 356.766 346.852 223.451 244.380 214.079 256.315Matera 26.079 32.904 48.739 71.252 48.113 35.251 43.690Basilicata 185.291 389.670 395.592 294.702 292.493 249.331 300.005Cosenza 8.144 11.081 20.197 30.175 22.031 65.041 46.186Catanzaro 4.384 11.433 16.308 12.729 11.934 12.822 25.401Reggio di Calabria 38.967 5.433 13.126 9.506 7.056 8.851 25.929

108

Province and region 1998 1999 2000 2001 2002 2003 2004 Crotone 102.258 30.817 12.162 13.177 23.545 46.302 123.353Vibo Valentia 4.062 3.650 4.224 3.874 4.011 6.508 5.683Calabria 157.815 62.414 66.017 69.461 68.576 139.524 226.552Trapani 56.806 69.799 76.555 96.163 134.578 132.873 195.459Palermo 302.580 278.603 331.697 345.878 256.252 213.600 337.124Messina 47.768 21.819 52.285 95.959 133.223 174.422 208.161Agrigento 5.282 19.263 20.627 3.997 4.829 10.815 9.806Caltanissetta 36.098 45.734 63.429 33.629 36.157 62.035 952.292Enna 1.006 2.068 3.713 5.010 3.222 6.948 6.990Catania 14.847 106.115 90.915 91.771 107.574 150.327 114.627Ragusa 8.815 18.915 20.984 33.028 17.327 15.201 24.575Siracusa 112.978 151.241 222.835 157.473 187.814 275.121 589.415Sicilia 586.180 713.557 883.039 862.907 880.976 1.041.341 2.438.450Sassari 176.601 232.271 271.150 245.948 273.061 256.716 453.878Nuoro 112.998 96.412 62.752 42.207 24.827 63.750 87.544Cagliari 1.361.140 1.323.007 1.922.020 2.105.706 2.108.957 1.964.608 2.186.140Oristano 4.460 6.423 5.572 9.833 10.982 14.865 17.407Sardegna 1.655.199 1.658.113 2.261.494 2.403.694 2.417.826 2.299.939 2.744.968

Italy 30.607.336 36.651.487 40.427.983 42.694.681 43.657.717 44.072.885 47.662.730

109

Table A2.4 – Local Units of MUD, Local Units of RI, and coverage (in % of local units of the Registro delle Imprese, RI) of the MUD database, Industry in a Strict Sense, by provinces and regions: 2004

Province and region Local units,MUD, 2004

Local Units,RI, 2004

MUD/RI, 2004 %

Torino 5.423 29.865 18,16 Vercelli 533 2.605 20,46 Novara 1.174 5.542 21,18 Cuneo 1.653 8.785 18,82 Asti 614 3.107 19,76 Alessandria 1.626 6.593 24,66 Biella 792 3.407 23,25 Verbano Cusio Ossola 533 2.585 20,62 Piemonte 12.348 62.489 19,76 Aosta 200 1.404 14,25 Valle d'Aosta 200 1.404 14,25 Varese 3.263 15.146 21,54 Como 2.420 10.849 22,31 Sondrio 400 2.352 17,01 Milano 11.602 60.557 19,16 Bergamo 4.433 17.018 26,05 Brescia 5.664 21.978 25,77 Pavia 1.577 7.336 21,50 Cremona 1.027 4.996 20,56 Mantova 1.396 6.598 21,16 Lecco 1.724 6.404 26,92 Lodi 530 2.604 20,35 Lombardia 34.036 155.838 21,84 Bolzano - Bozen 924 5.969 15,48 Trento 1.457 6.246 23,33 Trentino Alto Adige 2.381 12.215 19,49 Verona 3.459 13.974 24,75 Vicenza 6.029 18.199 33,13 Belluno 918 3.175 28,91 Treviso 4.673 16.302 28,67 Venezia 2.338 10.702 21,85 Padova 3.683 16.572 22,22 Rovigo 667 3.948 16,89 Veneto 21.767 82.872 26,27 Udine 1.882 7.948 23,68 Gorizia 327 1.651 19,81 Trieste 279 2.011 13,87 Pordenone 1.524 4.712 32,34 Friuli Venezia Giulia 4.012 16.322 24,58 Imperia 198 2.120 9,34 Savona 365 3.200 11,41 Genova 1.135 9.860 11,51 La Spezia 369 2.560 14,41 Liguria 2.067 17.740 11,65 Piacenza 810 4.072 19,89 Parma 1.658 7.599 21,82 Reggio Emilia 2.372 10.480 22,63 Modena 3.046 15.080 20,20 Bologna 3.679 14.993 24,54 Ferrara 762 4.285 17,78 Ravenna 1.052 4.833 21,77

110

Province and region Local units,MUD, 2004

Local Units,RI, 2004

MUD/RI, 2004 %

Forlì – Cesena 1.353 6.079 22,26 Rimini 799 4.185 19,09 Emilia Romagna 15.531 71.606 21,69 Massa Carrara 611 3.065 19,93 Lucca 1.429 6.679 21,40 Pistoia 918 6.470 14,19 Firenze 3.834 20.269 18,92 Livorno 465 3.450 13,48 Pisa 1.898 6.666 28,47 Arezzo 1.573 6.903 22,79 Siena 994 3.935 25,26 Grosseto 379 2.402 15,78 Prato 1.105 9.677 11,42 Toscana 13.206 69.516 19,00 Perugia 2.098 9.849 21,30 Terni 499 2.569 19,42 Umbria 2.597 12.418 20,91 Pesaro e Urbino 2.061 7.430 27,74 Ancona 1.847 7.009 26,35 Macerata 1.789 6.682 26,77 Ascoli Piceno 2.355 8.265 28,49 Marche 8.052 29.386 27,40 Viterbo 522 3.136 16,65 Rieti 250 1.395 17,92 Roma 4.139 25.236 16,40 Latina 1.004 5.159 19,46 Frosinone 1.228 5.509 22,29 Lazio 7.143 40.435 17,67 L'Aquila 450 3.290 13,68 Teramo 1.007 5.476 18,39 Pescara 497 3.739 13,29 Chieti 940 5.188 18,12 Abruzzo 2.894 17.693 16,36 Campobasso 393 2.603 15,10 Isernia 164 988 16,60 Molise 557 3.591 15,51 Caserta 1.259 7.043 17,88 Benevento 579 3.040 19,05 Napoli 3.919 27.130 14,45 Avellino 1.037 5.295 19,58 Salerno 1.468 11.743 12,50 Campania 8.262 54.251 15,23 Foggia 583 5.493 10,61 Bari 3.434 18.777 18,29 Taranto 605 4.236 14,28 Brindisi 447 3.446 12,97 Lecce 1.218 9.606 12,68 Puglia 6.287 41.558 15,13 Potenza 515 4.124 12,49 Matera 301 2.097 14,35 Basilicata 816 6.221 13,12 Cosenza 489 7.154 6,84 Catanzaro 259 3.456 7,49 Reggio di Calabria 243 6.669 3,64

111

Province and region Local units,MUD, 2004

Local Units,RI, 2004

MUD/RI, 2004 %

Crotone 122 1.811 6,74 Vibo Valentia 81 1.758 4,61 Calabria 1.194 20.848 5,73 Trapani 651 4.325 15,05 Palermo 1.185 8.928 13,27 Messina 522 5.724 9,12 Agrigento 527 3.347 15,75 Caltanissetta 283 2.668 10,61 Enna 190 1.417 13,41 Catania 718 9.932 7,23 Ragusa 328 2.931 11,19 Siracusa 242 3.091 7,83 Sicilia 4.646 42.363 10,97 Sassari 795 5.906 13,46 Nuoro 228 3.143 7,25 Cagliari 874 7.462 11,71 Oristano 226 1.440 15,69 Sardegna 2.123 17.951 11,83

Italy 150.119 776.717 19,33

112

Table A2.5 – Coverage (in % of local units of the Registro delle Imprese, RI) of the MUD database, In-dustry in a Strict Sense, by provinces and regions: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004

Torino 20,28 20,16 19,76 19,03 18,79 18,42 17,87 Vercelli 18,61 18,76 19,33 19,09 18,97 19,05 19,85 Novara 21,46 21,60 21,23 20,81 20,55 20,58 20,61 Cuneo 18,15 18,32 18,22 18,05 17,94 17,99 17,89 Asti 22,31 22,26 21,61 20,54 20,13 19,46 19,18 Alessandria 29,98 29,77 29,03 28,38 24,76 24,39 23,54 Biella 26,22 25,56 25,27 24,78 23,69 22,73 22,66 Verbano Cusio Ossola 19,13 19,23 18,92 18,81 19,60 20,08 20,31 Piemonte 21,45 21,39 21,03 20,45 19,85 19,56 19,23 Aosta 11,27 13,46 11,65 12,35 12,34 13,18 13,96 Valle d’Aosta 11,27 13,46 11,65 12,35 12,34 13,18 13,96 Varese 20,00 21,36 21,40 21,56 21,61 21,43 21,23 Como 20,82 21,55 22,02 22,15 22,09 21,77 21,88 Sondrio 17,94 17,82 17,18 16,75 17,27 16,56 16,84 Milano 18,32 18,81 19,03 18,91 19,11 18,99 18,66 Bergamo 27,64 27,60 27,20 26,68 26,40 26,18 25,69 Brescia 26,80 27,02 27,08 26,27 26,14 25,59 24,88 Pavia 21,19 20,73 21,24 21,01 20,83 21,09 21,01 Cremona 19,43 20,65 21,22 21,11 21,01 20,29 20,22 Mantova 22,55 21,77 21,21 21,29 20,89 20,93 20,70 Lecco 26,69 26,56 26,34 26,22 26,22 26,30 26,44 Lodi 20,19 20,24 19,80 19,14 19,53 19,99 19,93 Lombardia 21,41 21,82 21,93 21,75 21,80 21,63 21,34 Bolzano - Bozen 17,05 17,12 16,58 15,56 15,54 14,79 14,98 Trento 18,41 19,95 19,99 20,16 20,70 21,51 21,92 Trentino Alto Adige 17,72 18,50 18,27 17,89 18,15 18,20 18,53 Verona 27,98 27,67 27,20 25,68 24,79 24,67 24,45 Vicenza 33,81 33,26 33,59 33,13 32,76 32,70 32,73 Belluno 30,95 30,44 29,42 29,29 28,98 28,79 28,47 Treviso 28,80 28,65 28,53 28,61 28,54 28,33 28,29 Venezia 24,35 22,75 22,87 22,48 22,09 21,78 21,50 Padova 22,23 22,52 22,32 22,05 22,00 21,64 21,78 Rovigo 17,95 17,68 17,49 16,98 16,31 16,69 16,57 Veneto 27,46 27,08 26,95 26,48 26,14 25,95 25,89 Udine 23,14 23,72 23,43 23,42 22,55 23,18 23,04 Gorizia 18,89 20,34 20,46 19,59 19,27 19,26 19,26 Trieste 13,22 13,59 13,11 12,69 13,21 13,07 13,43 Pordenone 33,37 25,86 32,39 31,72 31,92 32,09 31,88 Friuli Venezia Giulia 24,18 22,65 24,32 24,04 23,72 24,08 24,02 Imperia 9,65 9,84 9,18 8,53 8,63 8,57 8,77 Savona 12,63 12,39 11,60 11,53 11,24 11,11 11,03 Genova 12,67 12,65 12,45 11,74 11,47 10,62 11,04 La Spezia 14,45 14,13 13,93 13,94 13,58 13,40 14,14 Liguria 12,55 12,48 12,11 11,63 11,38 10,86 11,22 Piacenza 20,70 20,38 20,53 19,25 18,64 19,46 19,60 Parma 20,65 22,02 21,73 20,97 21,30 21,64 20,88 Reggio Emilia 22,44 23,54 23,64 23,45 22,87 22,63 22,07 Modena 18,87 19,25 19,33 19,22 19,28 19,70 19,68 Bologna 24,79 25,02 24,82 24,83 24,32 24,32 24,20 Ferrara 19,86 19,17 18,44 17,31 17,43 17,55 17,50 Ravenna 22,94 22,21 22,96 22,40 22,39 21,43 21,52

113

Province 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 22,50 21,78 22,04 22,13 21,99 22,31 21,99 Rimini 18,97 18,29 19,22 18,76 19,08 19,36 18,76 Emilia Romagna 21,57 21,79 21,84 21,50 21,35 21,47 21,24 Massa Carrara 22,91 22,75 22,08 21,53 20,63 20,52 19,77 Lucca 22,86 22,30 21,93 22,39 21,88 21,24 20,77 Pistoia 14,54 14,56 14,15 14,35 13,56 13,84 13,77 Firenze 20,51 20,56 20,36 19,66 19,09 18,97 18,67 Livorno 15,30 14,55 14,96 13,89 13,95 13,43 13,16 Pisa 31,20 31,89 31,22 30,64 29,56 28,75 28,10 Arezzo 26,99 27,37 15,13 19,87 25,31 24,70 22,38 Siena 25,45 25,67 25,30 24,97 24,44 24,34 24,93 Grosseto 31,76 19,40 18,36 16,81 17,63 16,72 15,40 Prato 11,61 11,48 11,50 11,29 11,54 11,39 11,07 Toscana 20,86 20,56 19,14 19,24 19,41 19,15 18,65 Perugia 19,21 19,39 19,00 19,32 20,14 20,56 21,05 Terni 17,01 17,14 17,29 17,22 18,30 19,07 19,07 Umbria 18,77 18,94 18,66 18,91 19,77 20,26 20,64 Pesaro e Urbino 29,30 29,47 29,16 27,92 27,43 27,65 27,62 Ancona 27,37 28,04 27,73 27,11 26,54 26,11 25,97 Macerata 28,43 28,53 28,18 26,67 25,89 25,75 26,46 Ascoli Piceno 32,56 31,68 31,03 30,43 28,29 29,23 28,09 Marche 29,65 29,60 29,16 28,17 27,12 27,29 27,09 Viterbo 20,13 17,89 16,79 15,51 15,96 15,98 16,17 Rieti 18,06 26,60 20,80 23,95 20,46 17,96 17,78 Roma 17,05 16,64 15,86 15,59 15,71 16,00 16,03 Latina 18,68 18,80 18,48 18,94 18,41 19,12 19,17 Frosinone 22,66 23,38 23,16 22,15 21,97 21,44 21,75 Lazio 18,24 18,19 17,35 17,13 17,07 17,19 17,28 L'Aquila 11,99 12,36 11,57 12,52 13,31 13,78 13,16 Teramo 18,37 18,72 18,91 18,82 18,16 17,66 17,99 Pescara 13,14 13,72 13,66 13,33 13,02 12,66 13,13 Chieti 15,86 20,10 20,11 19,96 18,23 17,45 17,73 Abruzzo 15,31 16,81 16,70 16,76 16,17 15,81 15,99 Campobasso 16,38 17,10 17,66 15,80 14,99 15,50 14,52 Isernia 18,69 19,40 19,24 17,61 16,89 16,46 16,50 Molise 17,02 17,73 18,10 16,30 15,52 15,77 15,07 Caserta 14,57 16,21 15,84 17,86 17,28 17,53 17,52 Benevento 10,36 16,62 16,62 18,23 19,18 19,63 18,82 Napoli 11,31 12,18 11,93 13,67 13,82 13,86 14,10 Avellino 19,63 19,61 19,46 19,70 19,26 19,78 19,23 Salerno 9,37 9,75 10,57 10,75 11,13 11,66 12,16 Campania 11,98 13,05 13,07 14,39 14,50 14,74 14,89 Foggia 7,73 7,74 7,93 8,93 9,30 10,10 10,40 Bari 13,62 16,41 17,11 16,52 17,52 17,59 18,10 Taranto 6,92 11,66 12,21 12,75 13,31 13,90 14,07 Brindisi 8,06 9,95 10,41 10,91 11,79 12,23 12,48 Lecce 12,64 13,75 13,30 12,83 11,79 11,93 12,61 Puglia 11,48 13,65 13,96 13,83 14,20 14,50 14,93 Potenza 9,12 9,18 9,71 10,27 11,65 11,59 12,29 Matera 10,35 11,32 12,82 12,73 13,64 14,03 13,88 Basilicata 9,53 9,89 10,75 11,09 12,31 12,42 12,83 Cosenza 4,86 5,17 4,94 5,08 5,22 5,76 6,09 Catanzaro 5,68 5,80 6,58 6,23 6,19 6,33 6,31 Reggio di Calabria 4,04 3,45 3,24 3,14 3,23 3,13 3,27

114

Province 1998 1999 2000 2001 2002 2003 2004 Crotone 7,52 8,36 7,45 6,83 6,03 5,94 6,52 Vibo Valentia 2,83 2,63 2,67 2,87 3,10 3,18 3,98 Calabria 4,81 4,82 4,75 4,65 4,66 4,82 5,08 Trapani 10,71 11,55 12,75 13,27 12,32 12,76 13,25 Palermo 10,66 10,90 11,15 11,66 11,94 12,02 12,41 Messina 7,26 7,35 7,12 7,98 7,78 8,26 8,87 Agrigento 10,80 13,27 13,15 13,82 14,05 14,97 15,45 Caltanissetta 6,09 6,76 6,90 7,48 8,01 7,92 10,38 Enna 9,81 8,04 8,41 9,19 9,75 11,67 12,99 Catania 5,43 6,11 6,73 6,32 6,34 6,68 7,02 Ragusa 7,99 11,43 11,16 10,75 10,28 10,92 10,64 Siracusa 7,45 7,45 7,10 6,70 6,79 6,58 7,25 Sicilia 8,23 8,97 9,23 9,47 9,45 9,80 10,39 Sassari 16,59 15,32 14,69 14,49 13,64 13,71 13,09 Nuoro 11,00 9,33 9,39 9,75 9,38 7,52 6,87 Cagliari 13,73 13,32 12,50 12,74 12,02 11,41 11,32 Oristano 17,51 16,91 15,32 14,78 14,59 14,93 15,21 Sardegna 14,60 13,66 12,97 13,00 12,33 11,80 11,44

Italy 19,39 19,61 19,40 19,23 19,08 19,00 18,89

115

Table A2.6 – Ranking of the most pollutant provinces, as regards waste production (tons), by Ateco divisions: 2004

Waste production of Industry in a Strict Sense: provinces over 1 million of tons in 2004 Ateco

Province Province code Region 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Brescia BS Lombardia 0 8 0 0 67.393 30.119 0 12.384 2.091 3.370 19.287 40.106 15.550 1.940Cagliari CA Sardegna 267 0 0 2.331 2.725 15.654 0 0 0 40 76 1.738 1.936 58.190Taranto TA Puglia 0 0 0 0 4.485 23.114 0 900 692 0 634 101 1.109 16.062Milano MI Lombardia 0 297 0 0 38.617 44.905 0 44.833 4.195 5.603 65.851 101.206 214.666 4.807Verona VR Veneto 14 0 0 0 17.759 88.197 772 6.329 6.739 15.072 16.405 74.480 24.764 129Torino TO Piemonte 0 0 0 0 8.725 17.051 0 9.089 411 2.540 27.024 51.320 44.998 5.290Vicenza VI Veneto 0 0 0 0 10.713 34.615 58 19.157 10.044 314.775 7.683 47.743 11.541 86Savona SV Liguria 0 0 0 0 390 2.810 0 22 0 42 3.077 21.319 110 5.857Bergamo BG Lombardia 0 0 0 0 11.917 40.760 49 41.986 4.558 2.398 21.203 30.433 62.233 46Brindisi BR Puglia 0 49 0 0 1.461 5.656 0 30 104 175 585 8 31 1.033Modena MO Emilia Romagna 0 575 0 0 2.036 146.718 0 4.564 5.566 5.098 12.888 25.614 12.080 231Venezia VE Veneto 0 0 0 0 139 45.807 0 1.567 1.141 3.430 73.968 7.720 7.588 6.296Mantova MN Lombardia 0 0 0 0 2.220 39.761 0 15.726 1.824 449 515.732 34.381 23.186 12.367 Waste production of Industry in a Strict Sense: provinces under 20.000 of tons in 2004 Ateco

Province Province code Region 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Rieti RI Lazio 0 0 0 0 988 2.180 0 4.361 0 0 465 116 44 32Oristano OR Sardegna 0 0 0 0 11.859 3.468 0 0 0 0 16 100 10 224Agrigento AG Sicilia 0 0 0 0 176 3.337 0 0 0 0 97 104 18 0Enna EN Sicilia 0 380 0 0 2.280 2.932 0 0 0 0 5 141 115 2Vibo Valentia VV Calabria 0 0 0 0 12 2.950 0 0 0 0 1 0 0 25Imperia IM Liguria 0 0 0 0 109 1.842 0 1 4 0 3 11 54 3

116

Table A2.6 – continued

Waste production of Industry in a Strict Sense: provinces over 1 million of tons in 2004

Province 24 25 26 27 28 29 30 31 32 33 34 35 36 40 Total per province Brescia 28.374 27.085 138.337 2.071.799 262.072 55.359 151 10.629 90 380 62.286 1.571 5.321 17.164 2.872.865 Cagliari 1.602.629 2.926 10.275 344.837 4.631 1.222 18 3.762 47 172 121 1.080 10.443 121.020 2.186.139 Taranto 916 186 13.150 2.068.946 4.511 2.048 1 133 0 252 19 1.216 564 15.576 2.154.615 Milano 276.779 70.686 219.083 425.415 320.849 81.502 4.149 24.473 17.712 4.859 24.495 5.514 40.843 41.663 2.083.000 Verona 12.420 7.007 1.219.056 189.891 132.605 22.668 162 10.695 277 201 6.074 2.784 10.858 2.693 1.868.049 Torino 37.598 62.170 57.937 320.586 433.264 95.895 2.160 21.223 1.391 2.837 245.267 12.418 5.730 28.649 1.493.572 Vicenza 92.823 32.998 249.927 313.322 181.460 44.634 96 24.093 2.747 811 3.041 6.364 21.289 1.167 1.431.187 Savona 1.120.417 1.460 23.979 54 4.185 1.026 0 1.422 131 0 921 1.801 241 207.226 1.396.490 Bergamo 208.857 43.851 165.003 376.866 137.469 40.057 423 12.651 1.693 899 49.069 4.986 10.146 11.445 1.278.995 Brindisi 35.428 4.668 3.118 1.206 4.951 13.710 0 737 7 85 0 3.546 149 1.192.372 1.269.111 Modena 21.749 8.549 744.644 37.896 54.880 55.611 1 1.775 357 5.820 12.855 472 5.060 1.589 1.166.629 Venezia 266.868 102.216 78.289 59.563 34.632 7.043 7 1.154 187 1.237 436 20.169 15.406 373.793 1.108.657 Mantova 63.855 3.950 30.024 92.499 33.638 14.722 0 1.516 51 512 8.639 395 8.563 118.456 1.022.468 Waste production of Industry in a Strict Sense: provinces under 20.000 of tons in 2004

Province 24 25 26 27 28 29 30 31 32 33 34 35 36 40 Total per province Rieti 2.576 457 3.519 757 373 1.505 15 718 753 208 0 0 28 176 19.272 Oristano 0 389 704 37 70 85 0 100 0 0 0 51 70 222 17.407 Agrigento 13 16 3.319 64 143 96 0 35 0 0 16 1 36 2.336 9.806 Enna 90 122 742 0 46 19 0 0 0 47 8 0 23 38 6.990 Vibo Valentia 271 1 607 0 1.185 582 0 0 0 0 0 29 1 18 5.683 Imperia 306 3 335 0 1.124 1.018 0 1 0 0 0 137 3 248 5.201

117

Table A2.7 – Waste production per local unit (tons per UL, t/UL), Industry in a Strict Sense, Italy, by provinces and regions: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004

Torino 210 269 289 282 267 265 280 Vercelli 607 727 704 694 641 643 725 Novara 120 156 187 198 203 197 222 Cuneo 208 269 286 292 318 282 369 Asti 134 176 182 165 187 194 185 Alessandria 78 83 95 120 144 151 155 Biella 107 145 179 217 207 217 95 Verbano Cusio Ossola 245 211 196 261 227 210 235 Piemonte 187 232 250 257 256 252 271 Aosta 660 421 479 481 479 551 639 Valle d’Aosta 660 421 479 481 479 551 639 Varese 109 138 145 151 149 150 152 Como 73 126 119 115 114 134 139 Sondrio 121 114 135 140 145 172 144 Milano 107 132 145 147 149 157 184 Bergamo 225 262 276 301 310 302 293 Brescia 303 346 499 538 525 507 525 Pavia 210 289 253 281 405 372 393 Cremona 187 569 645 552 484 485 473 Mantova 188 258 351 426 660 682 749 Lecco 242 259 325 329 333 360 378 Lodi 164 235 262 302 471 444 275 Lombardia 170 216 256 269 285 287 301 Bolzano - Bozen 129 186 209 213 190 175 207 Trento 280 252 291 326 336 342 336 Trentino Alto Adige 206 221 253 278 274 275 285 Verona 497 545 558 545 542 517 547 Vicenza 164 218 249 271 254 255 240 Belluno 105 148 180 153 162 148 167 Treviso 134 174 183 198 199 202 207 Venezia 303 367 422 439 409 449 482 Padova 137 158 242 219 233 211 235 Rovigo 369 466 492 908 820 399 275 Veneto 227 273 308 323 314 299 305 Udine 246 394 426 468 487 455 503 Gorizia 533 800 857 809 1.178 829 741 Trieste 283 311 379 758 627 376 695 Pordenone 171 270 244 263 286 295 302 Friuli Venezia Giulia 243 385 390 438 476 419 458 Imperia 21 23 27 29 27 30 28 Savona 749 665 814 786 3.034 4.047 3.956 Genova 411 402 485 730 485 525 543 La Spezia 654 658 412 692 734 732 684 Liguria 475 455 489 669 951 1.160 1.126 Piacenza 465 423 377 329 362 185 170 Parma 145 197 165 192 192 201 179 Reggio Emilia 179 221 260 268 261 263 276 Modena 319 390 374 402 380 384 393 Bologna 134 168 176 200 192 201 194 Ferrara 567 667 506 580 598 548 529 Ravenna 446 597 537 668 793 739 757

118

Province 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 174 241 249 235 334 484 210 Rimini 72 83 103 107 107 95 106 Emilia Romagna 240 291 280 304 317 318 294 Massa Carrara 948 929 972 1.013 896 756 792 Lucca 445 460 477 493 483 489 415 Pistoia 45 53 68 66 69 78 70 Firenze 55 67 77 76 75 80 93 Livorno 288 336 280 470 380 492 652 Pisa 164 193 226 220 231 155 160 Arezzo 321 131 255 134 215 147 113 Siena 86 97 110 137 145 191 180 Grosseto 896 1.175 1.367 1.030 1.232 1.149 1.289 Prato 38 45 51 54 51 49 49 Toscana 227 211 243 234 243 224 227 Perugia 171 191 228 252 241 194 200 Terni 1.044 1.631 1.589 1.375 1.407 1.204 1.378 Umbria 330 452 481 456 460 387 425 Pesaro e Urbino 94 87 106 121 139 153 190 Ancona 126 181 176 182 188 189 191 Macerata 61 84 95 93 100 100 95 Ascoli Piceno 59 66 62 71 83 81 84 Marche 81 100 105 113 125 128 138 Viterbo 127 180 209 191 193 190 237 Rieti 75 76 96 183 190 204 78 Roma 78 106 129 91 87 82 96 Latina 150 193 199 186 178 176 181 Frosinone 204 253 320 359 303 322 285 Lazio 112 145 175 161 149 148 150 L'Aquila 322 387 437 355 312 283 303 Teramo 109 125 145 201 203 204 218 Pescara 61 66 91 166 130 126 118 Chieti 310 292 262 265 264 267 295 Abruzzo 191 208 214 238 227 223 239 Campobasso 425 663 677 739 737 752 587 Isernia 86 222 235 204 198 256 223 Molise 323 531 548 579 573 608 477 Caserta 121 183 212 218 202 209 226 Benevento 37 30 30 37 42 42 49 Napoli 72 95 104 118 112 98 104 Avellino 99 131 170 194 201 163 156 Salerno 159 223 212 193 214 241 208 Campania 97 130 144 151 150 144 144 Foggia 209 203 265 415 270 353 516 Bari 188 200 189 203 217 205 217 Taranto 7.449 3.880 2.435 2.394 1.044 2.371 3.615 Brindisi 479 2.103 2.297 2.376 2.593 2.715 2.951 Lecce 51 73 77 88 101 99 115 Puglia 615 599 498 544 444 583 741 Potenza 496 1.075 963 561 524 453 506 Matera 146 162 204 291 178 120 150 Basilicata 371 728 660 458 397 325 376 Cosenza 30 38 68 91 62 162 106 Catanzaro 26 65 80 63 58 60 117 Reggio di Calabria 203 32 80 53 36 44 119

119

Province 1998 1999 2000 2001 2002 2003 2004 Crotone 993 257 109 115 229 445 1.045 Vibo Valentia 99 94 103 84 79 121 81 Calabria 203 78 81 79 75 143 214 Trapani 141 158 149 173 257 247 341 Palermo 361 311 355 347 246 202 304 Messina 125 56 135 218 304 368 410 Agrigento 16 46 48 9 10 22 19 Caltanissetta 237 272 365 175 171 298 3.438 Enna 9 22 36 42 24 43 38 Catania 30 188 143 150 172 225 164 Ragusa 43 64 71 115 60 49 79 Siracusa 579 772 1.131 803 921 1.369 2.631 Sicilia 188 206 241 224 224 253 554 Sassari 220 302 354 316 357 324 587 Nuoro 440 430 261 162 93 285 405 Cagliari 1.815 1.757 2.583 2.568 2.553 2.393 2.587 Oristano 21 31 29 51 55 69 79 Sardegna 819 850 1.165 1.171 1.174 1.121 1.337

Italy 217 255 280 292 298 299 325

120

Table A2.8 – Waste production per worker (tons per worker, t/add), Industry in a Strict Sense, Italy, by provinces and regions: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004

Torino 5 7 7 8 8 7 8 Vercelli 17 21 20 20 19 19 21 Novara 4 5 6 6 7 6 7 Cuneo 6 8 8 9 10 9 12 Asti 6 7 7 7 8 8 8 Alessandria 4 5 5 6 7 7 7 Biella 3 5 5 7 7 7 3 Verbano Cusio Ossola 13 11 10 14 13 12 14 Piemonte 6 7 7 8 8 8 9 Aosta 20 14 15 17 18 22 28 Valle d’Aosta 20 14 15 17 18 22 28 Varese 4 5 5 6 6 6 6 Como 3 5 5 5 5 6 6 Sondrio 5 5 5 6 6 7 6 Milano 4 5 5 5 6 6 7 Bergamo 8 10 10 11 11 11 11 Brescia 13 16 22 24 24 23 24 Pavia 11 15 13 15 20 19 21 Cremona 7 21 24 21 18 18 18 Mantova 7 9 11 14 21 22 25 Lecco 9 10 12 13 13 15 16 Lodi 6 8 10 11 19 18 11 Lombardia 7 8 10 10 11 11 12 Bolzano - Bozen 5 7 8 8 7 7 8 Trento 10 10 12 14 15 16 16 Trentino Alto Adige 8 9 10 11 11 12 12 Verona 24 26 25 25 25 24 26 Vicenza 8 10 11 12 12 12 11 Belluno 4 6 6 5 6 5 6 Treviso 6 7 8 8 8 8 9 Venezia 14 17 20 20 19 21 23 Padova 7 8 12 11 11 10 11 Rovigo 16 20 22 40 37 18 14 Veneto 10 12 14 14 14 13 14 Udine 11 17 18 20 21 20 22 Gorizia 15 22 23 21 32 23 22 Trieste 8 9 10 20 16 11 20 Pordenone 6 8 9 9 10 10 11 Friuli Venezia Giulia 9 14 14 16 17 16 17 Imperia 2 2 3 3 3 3 2 Savona 26 21 28 28 110 151 152 Genova 15 14 17 26 17 20 23 La Spezia 30 29 20 30 43 36 33 Liguria 19 17 19 26 39 48 50 Piacenza 21 18 16 13 15 8 7 Parma 6 9 7 8 8 9 8 Reggio Emilia 7 9 10 10 10 10 11 Modena 11 14 13 14 14 14 14 Bologna 5 7 7 8 8 8 8 Ferrara 20 23 18 20 22 20 19 Ravenna 19 25 23 27 32 31 30

121

Province 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 8 10 10 10 14 20 9 Rimini 4 5 5 6 6 5 6 Emilia Romagna 10 12 11 12 13 13 12 Massa Carrara 71 67 75 78 69 59 64 Lucca 25 25 26 27 28 28 24 Pistoia 3 4 4 4 4 5 5 Firenze 3 4 5 5 4 5 6 Livorno 9 10 10 17 13 18 23 Pisa 10 12 13 13 15 10 10 Arezzo 21 8 12 8 13 9 7 Siena 6 7 7 9 9 12 12 Grosseto 113 105 121 85 112 106 111 Prato 2 3 3 3 3 3 3 Toscana 14 13 14 14 15 14 14 Perugia 9 10 11 13 13 10 11 Terni 39 62 63 55 59 50 60 Umbria 16 22 23 22 23 20 22 Pesaro e Urbino 5 5 6 7 8 8 10 Ancona 4 7 7 7 7 7 7 Macerata 4 5 5 5 5 6 5 Ascoli Piceno 4 5 4 5 6 6 6 Marche 4 5 6 6 6 7 7 Viterbo 8 11 13 12 12 13 15 Rieti 4 5 6 15 12 12 5 Roma 4 5 7 5 5 5 6 Latina 6 7 7 7 7 7 8 Frosinone 6 8 10 12 11 13 12 Lazio 5 6 8 8 8 8 8 L'Aquila 7 10 11 9 9 9 10 Teramo 4 5 5 8 8 8 8 Pescara 4 4 5 9 6 6 6 Chieti 9 8 8 8 7 8 9 Abruzzo 7 7 7 8 8 8 9 Campobasso 17 28 20 34 44 36 28 Isernia 3 8 9 7 8 10 8 Molise 13 21 17 24 29 28 21 Caserta 5 8 9 10 9 11 12 Benevento 2 3 3 4 4 4 5 Napoli 3 5 5 6 7 6 7 Avellino 5 6 9 10 11 9 9 Salerno 6 9 9 9 11 13 11 Campania 4 6 7 8 8 9 9 Foggia 9 9 13 23 14 20 30 Bari 12 12 12 13 15 14 15 Taranto 94 78 51 54 26 53 93 Brindisi 15 75 88 96 114 112 150 Lecce 3 4 4 6 7 7 10 Puglia 28 29 24 28 24 32 44 Potenza 10 21 20 13 13 12 15 Matera 5 6 8 11 6 5 6 Basilicata 9 17 17 12 11 10 12 Cosenza 2 3 4 6 4 12 9 Catanzaro 3 5 7 5 5 6 12 Reggio di Calabria 17 3 5 4 3 3 9

122

Province 1998 1999 2000 2001 2002 2003 2004 Crotone 67 23 9 11 16 32 88 Vibo Valentia 5 4 4 5 4 7 5 Calabria 15 6 6 6 5 11 18 Trapani 20 20 19 21 31 34 50 Palermo 23 21 21 29 22 19 31 Messina 10 4 9 16 23 31 33 Agrigento 3 9 9 2 2 4 4 Caltanissetta 9 12 17 8 9 17 259 Enna 1 3 6 5 3 7 6 Catania 2 9 7 8 8 11 8 Ragusa 4 6 7 11 5 5 7 Siracusa 16 18 28 22 25 41 83 Sicilia 13 14 16 16 17 20 46 Sassari 20 25 28 27 30 29 53 Nuoro 26 21 14 9 6 16 25 Cagliari 82 73 119 124 134 118 149 Oristano 3 4 4 6 6 8 9 Sardegna 52 50 72 74 79 73 96

Italy 9 11 12 13 13 13 15

123

3. ITALIAN WASTE PRODUCTION (1998-2004): A GENERAL FRAMEWORK FOR THE ANALYSIS

3.1 Introduction

The present chapter gives an overview of the socio-economic variables which contribute to the waste

production and which will be used in the econometric test of Chapter 4. After a brief description of

each of them in the span of time 1998-2004, at a provincial and at national level, how each driver might

have influence on waste production, and the related hypothesis, will be outlined.

3.2 A note on the theoretical framework

The model used in Chapter 4 mixes the EKC literature with the IPAT frame work, which, as illustrated

in Chapter 1, is an identity where I stands for the impact of the pollutant (usually measured in terms of

emissions’ level, such as atmospheric emissions or waste production), which must be equal to the prod-

uct of P, which is the population’s level impact, A, which is the economic assessment of a social system

wealth (such as the per capita GDP), and T, which is the impact of the technological progress18: I =

P*A*T (Impact = population * affluence * technology). The IPAT framework suggests which kind of

variables should be included as potential drivers in the equation of the tested model, in addition to the

wellbeing indicators (GDP or value-added) which the EKC literature usually concentrates on, in order

to get more reliable relations between waste production and wellbeing indicators.

In the following sections the variables selected as potential drivers will be described, as regards the se-

lected period 1998-2004.

3.3 The drivers of the model

The theoretical framework thus outlined has led to identify a set of variables to include in the econo-

metric test, with particular reference to those economic indicators which determine the production of

waste at a provincial level and have influence on it across time. According to the main indications of

the literature, a set of variables have been selected, and then a set of drivers has been created and will

be used in the econometric test. The drivers used in the model are of socio-economic nature (value-

18 Among the research project which used such a framework, see Intergovernmental Panel on Climate Change (2000). For more details on the IPAT identity, see Ehrlich and Holdren (1971), and Holdren (2000), and Chapter 1.

124

added, population density, sectorial composition of the production, and citizens’ environmental sensi-

bility), of technological nature (indicators of the innovative ability of the provinces), and of regulatory

nature.

The main driver of the waste production is the value-added of the productive activities: its use in the

econometric test has the aim of capturing not only the productive potential of the considered geo-

graphical unit, but also the quality of such a potential. The ratio of the value-added of the several divi-

sions (industry, services, etc.) on the total has been added too, in order to get the effect that the change

in the productive morphology of the territory might have on waste generation. In addition to the value-

added, a measure of the population density has been included: on one hand, the growing delocalization

of the great industrial parks and citizens’ environmental sensibility might implicate a negative relation-

ship between density and waste; on the other hand, highly populated territories can provide skilled

workers for industries, especially in those sectors known as traditional industry, whose production is

characterised by a high labour-intensity. An indicator of the ratio of the urban sorted waste collection

on the total of urban waste will lead to estimate the feasible impact that the environmental sensibility of

citizens (and, indirectly, of the local administrators elected by those citizens) might have on the produc-

tion of waste. Moreover, and indicator on the energy consumption of firms will allow to capture the ef-

fect of the energy efficiency of that given production, and the ability of the territory’s economic actors

to create economic value. As driver of technological innovation of a province, the number of patents of

the province will be used: even though patents are more a direct measure of the output of the research

and development sector, rather than a measure the investments in that field, they might explain the ef-

fect that the technological attitude of the provinces can have on waste production. Last, an index of in-

frastructures’ equipment will be used in order to evaluate the impact of the quality of the total of infra-

structures of the province on the production of waste.

3.3.1 Descriptive analysis

With reference to the main literature outlined in Chapter 1, a set of drivers for Industry in a Strict Sense

has been selected, and from those variables the drivers used in the model will be created.

3.3.1 Value-added of Industry in a Strict Sense (1998-2004)

The value-added of the several productive activities represents the main driver of waste production,

and it is the main variable that the literature on EKC emissions relates to waste production, and that is

being used in this literature as the benchmark variable to be tested when searching for an EKC behav-

iour. The value-added, indeed, includes in itself the capability to create economic value of the economic

actor: since, in its simplest meaning, it is the difference between revenues and costs, the value-added

125

conveys the capability to create value thanks to a summation of several factors, from the management

ability, to the endowment of technology, to the skills of the employees, to the ability to exploit positive

developments, etc.

In Italy, the value-added of Industry in a Strict Sense has not experienced a strong variation in the con-

sidered period (Figure 3.1), and this is because the crisis that has stricken the industrial branch starting

from the year 200019: such a crisis has brought a stagnation to the whole economic performances of

that branch, with an increase of only +1,3% as regards the entire period 1998-200420.

In order to be able to compare the different provincial situations, a measure for the waste intensity has

been created, and it is defined by the ratio between the waste production of Industry in a Strict Sense in

that province, and the value-added of Industry in a Strict Sense in that same province21. The ratio be-

tween waste production in that province and its value-added measures the economic-environmental

competitiveness of the province, its capability to create value-added against the production of a certain

amount of waste. The values of that ratio show how many tons of waste in Industry in a Strict Sense

are needed to generate on million of euros of monetary (real) value: the lower that ratio, the more vir-

tuous in environmental terms the province is, in relation with its own economy.

Figure 3.1 – Value-added (millions of euros of 1995, €), Industry in a Strict Sense, Italy: 1998-2004

Valore Aggiunto dell'Industria in senso stretto, Italia, anni 1998-2004, ai prezzi base del 1995

0

50.000

100.000

150.000

200.000

250.000

1998 1999 2000 2001 2002 2003 2004

mili

on

i di e

uro

de

l 199

5

VA IndSS

19 Studi Confindustria (2006), Note Economiche n.1 – 2006.

20 The value-added which has been considered here is the real value-added, in terms of the year 1995, and it has been de-rived by dividing the nominal values by the base price index, the reference year being the year 1995 (value 100).

21 See Table A3.1 in the Appendix for details.

126

Table A3.1 in the Appendix shows the ratio between the quantity of waste produced and the value-

added (in 1995 euros), as regards Industry in a Strict Sense, for each province, while Figure 3.2, 3.3 and

3.4 show on a map that ratio for two years, 1998 and 2004, depicting its geographical distribution on

the national territory, for each year, and also the geographical description of the difference between the

values of that ratio during the considered span of time. Such a difference, as concerns the geographical

distribution, reflects the strong heterogeneity of the productive morphology of the Italian provinces.

The highest waste-producing (in absolute terms) regions in the North (Lombardia, Veneto and

Piemonte, as an example, with something like 10, 6,5 and 3,2 millions of tons in 2004, respectively) ex-

hibit a low waste per unit of value-added ratio (respectively, 156, 255 and 144 tons per million of euros

in 2004), while their counterparts in the South, like Puglia, Sardegna or Sicilia, have a lower waste pro-

duction in absolute terms (e.g., 4,6, 2,7 and 2,4 millions of tons in 2004, respectively), but a higher

waste per unit of value-added ratio (those three regions, in 2004, were showing a value of 650, 876 and

390 tons per million of euros, respectively).

The ratio between the quantity of waste and the value-added of Industry in a Strict Sense has experi-

enced a steady growth everywhere, in the considered period: the average value for Italy, as an example,

has gone from 138 tons per million of euros in the year 1998, to 211 tons per million of euros in 2004,

with an increase of about +53% with respect to the start of the period.

127

Figure 3.2 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, Italy: 1998

Legenda

RIF/VA 1998

sotto 50

50 - 100

101 - 200

201 - 500

501 - 1.000

1.001 - 1.500

oltre 1.500

128

Figure 3.3 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, Italy: 2004

Legenda

RIF/VA 2004

sotto 50

51 - 100

101 - 200

201 - 500

501 - 1000

1.001 - 1.500

oltre 1.500

129

Figure 3.4 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry

in a Strict Sense, Italy: difference between 2004 and 1998

Legenda

RIF/VA 1998-2004

sotto -200

tra -199 e -50

tra -49 e 50

tra 51 e 200

tra 201 e 500

tra 501 e 1.000

oltre 1.000

Such an increase shows how the value-added of Industry in a Strict Sense has grown in the considered

period, but in percentage terms it has grown less than the respective waste production (Figure 3.2): in-

dustries operating in Italy either are highly waste-intensive and low value-added firms22, or, among the

developed countries, they are the less performance-increasing industries in terms of joint economic-

environmental performances. In any case, the difference between the growth rate of the production of

waste and the growth rate of the value-added (which stands for economic wellbeing) is high: against an

increase of total special waste in Italy of more than +80% in 1998-2004, and an increase of waste of

Industry in a Strict Sense of little less than +60%, in the same years a small growth of the total value-

22 Italy was late as regards innovations in comparison with other European countries, and such a gap is the result of some peculiarities of the Italian production system, which has always been oriented towards a productive structure too much un-balanced in favour of traditional productive sectors, and also as regards the average firms dimension.

130

added of Italy has taken place (around +10%), while small decrease in the value-added of Industry in a

Strict Sense has been experienced (Figure 3.2).

Figure 3.5 – Waste production and value-added, Industry in a Strict Sense and total, Italy: 1998-2004 indices

In Figure 2.5 the missing positive delinking between waste production and economic wellbeing can be

seen: the Italian production of waste has had growth rates’ values higher than the ones of the Italian

economy. In real terms, value-added has almost steadily increased by a total +10% only, in the whole

period 1998-2004.

Table 3.1 shows the high inter-temporal variability of the Industry in a Strict Sense waste-intensity indi-

cator for the Italian provinces: there, it has been reported the ranking of the Italian provinces according

such indicator, for the year 1998, the year 2004, and also their difference in the ranking of that period.

Thus, for example, Savona, which is first in 2004, was eleventh in 1998, while Brindisi, second in 2004,

was forty-first in 1998, and Caltanissetta, third in 2004, was sixty-seventh in 1998, “earning” something

like 64 positions. At the lowest positions, in a decreasing order, are Vibo Valentia, Agrigento and Im-

peria, which all “lose” 10 positions with respect to 1998. Looking at the dynamic of the ranking, it can

be seen that some provinces worsen their indicator’s value in the studied period (e.g., Siracusa, Messina

and Mantova), while some others can improve their values (e.g., Piacenza, Arezzo e Nuoro), and some

others maintain the same rank in the list (Reggio Emilia and Lodi).

80

100

120

140

160

180

200

1998 1999 2000 2001 2002 2003 2004

Rifiuti dell'Ind.S.S. da MUD Rifiuti totali da MUD Valore Aggiunto dell'Ind.S.S Valore Aggiunto totale

131

Table 3.1 – Waste production per unit of value-added (in 1995 euros, €), Industry in a Strict Sense, Italy:

ranking 2004, ranking 1998, and its difference

Province Position in 2004 Position in 1998Variation in position,

2004-1998 Savona 1 11 10 Brindisi 2 41 39 Caltanissetta 3 67 64 Grosseto 4 1 -3 Taranto 5 2 -3 Cagliari 6 4 -2 Massa Carrara 7 3 -4 Terni 8 6 -2 Siracusa 9 52 43 Sassari 10 20 10 Crotone 11 5 -6 Gorizia 12 15 3 Ravenna 13 13 0 Vercelli 14 14 0 Campobasso 15 18 3 Verona 16 7 -9 Trapani 17 30 13 Udine 18 22 4 Foggia 19 46 27 Brescia 20 21 1 Mantova 21 47 26 Venezia 22 23 1 Lucca 23 8 -15 La Spezia 24 10 -14 Aosta 25 17 -8 Ferrara 26 9 -17 Messina 27 60 33 Trieste 28 40 12 Pavia 29 31 2 Livorno 30 37 7 Pordenone 31 37 6 Pesaro e Urbino 32 39 7 Lecco 33 28 -5 Potenza 34 29 -5 Trento 34 32 -2 Cremona 35 54 19 Genova 36 25 -11 Vicenza 37 32 -5 Bari 37 34 -3 Palermo 38 24 -14 Modena 39 27 -12 Verbano Cusio Ossola 40 26 -14 Viterbo 41 43 2 Rovigo 42 19 -23 Siena 43 48 5 Perugia 44 35 -9 Padova 44 42 -2 Frosinone 45 16 -29 Treviso 45 42 -3 Reggio Emilia 45 44 -1 Nuoro 45 45 0 Cuneo 46 51 5 Bergamo 47 38 -9 Teramo 47 55 8 L'Aquila 48 35 -13 Caserta 49 59 10

132

Province Position in 2004 Position in 1998Variation in position,

2004-1998 Chieti 50 33 -17 Forlì - Cesena 51 36 -15 Ancona 52 50 -2 Pisa 53 29 -24 Asti 54 47 -7 Salerno 54 49 -5 Isernia 55 71 16 Avellino 56 53 -3 Lodi 57 57 0 Torino 58 54 -4 Lecce 59 73 14 Bologna 60 55 -5 Macerata 61 54 -7 Matera 62 55 -7 Bolzano - Bozen 63 57 -6 Piacenza 64 18 -46 Parma 65 56 -9 Belluno 66 58 -8 Ascoli Piceno 67 50 -17 Alessandria 68 65 -3 Novara 69 66 -3 Arezzo 70 12 -58 Como 71 70 -1 Rimini 72 63 -9 Catania 73 84 11 Oristano 74 68 -6 Varese 74 79 5 Sondrio 75 61 -14 Napoli 75 75 0 Latina 76 64 -12 Benevento 77 79 2 Ragusa 78 78 0 Milano 79 74 -5 Firenze 80 72 -8 Pescara 81 77 -4 Rieti 82 69 -13 Biella 83 62 -21 Reggio di Calabria 84 52 -32 Catanzaro 84 84 0 Pistoia 85 75 -10 Cosenza 86 83 -3 Enna 87 86 -1 Roma 88 76 -12 Prato 89 80 -9 Vibo Valentia 90 80 -10 Agrigento 91 81 -10 Imperia 92 82 -10

133

Table 3.2 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, 5 random provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 42 53 60 61 62 65 72Venezia 206 244 312 331 310 336 355Genova 185 173 184 265 197 214 235Roma 35 47 55 36 34 34 38L'Aquila 148 181 185 183 171 166 176

Italy 138 164 177 188 192 196 211

For every million of euros of value-added, Industry in a Strict Sense was producing 138 tons of waste in

1998, 177 in 2000, and even more in 2004, 211 tons. Table 3.2 allows comparing the Italian Industry in

a Strict Sense waste intensity with the randomly selected sample of provinces (Milano, Venezia,

Genova, Roma and L’Aquila): Milano and Rome were producing a quantity of waste per unit of value-

added below the national average, while Venezia was well above the national average23.

3.3.1.2 Energy consumption of Industry in a Strict Sense (1998-2004)

The energy input costs are a relevant component of the variable costs for industrial firms, as well as one

of the most volatile components of the total costs, since it is linked, to a great extent, to the trends of

the international quotations of the energy raw materials (petroleum, natural gas, coal). For the firm,

their prices are mainly “exogenous”, since it is determined in the international markets first, and then

on little competitive markets in the home country: firms, therefore, have not great margins to manoeu-

vre, and they can only adopt strategies which lead to less energy consumption, thanks to the implemen-

tation of more energy-efficient productive processes. Hence, the adoption of new technologies would

give not only a competitive advantage, which comes from the lesser use of energy resources, but also a

better environmental efficiency, which reduces the costs of the ex-post interventions24.

23 In 2004, the share of the value-added of Industry in a Strict Sense on the total of value-added of the province was 27,6% in Milano, 18,6% in Venezia, 14,5% in Genova, 11,4% in Roma, and 18,0% in L’Aquila, against a national value of 21,4% (Table A2.1).

24 The two main energy inputs for the whole of the Italian industrial complex are electricity and natural gas (methane), which together have absorbed 80,7% of the energy inputs’ expenses of the industrial firms in 2004, resulting in an amount of 18,2 billions of euros (Centro Studi Confindustria, 2007). Other relevant energy inputs are two petroleum derivatives, that is die-sel (which amounts at 8,9% of the total expenses in 2004) and fuel oil (2,6%), besides coal (2,5%): those three inputs to-gether absorb 14% of the total costs in 2004. The sum of all the costs of the other energy products represents 5,3% only of the total energy costs of the Italian industrial firms.

134

Figure 3.6 – Energy consumption (millions of kWh), Industry in a Strict Sense, Italy: 1998-2004

Consumo di Energia dell'Industria, Italia, anni 1998-2004, milioni di KWh

0

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

1998 1999 2000 2001 2002 2003 2004

Mili

on

i di K

Wh

Consumo di energia

Given its high importance in the energy consumptions of the country (more than 52% in 2004, Centro

Studi Confindustria, 2007), the energy demand considered relevant in waste production is the electricity

demand, measured in kWh. When comparing provinces among each other, though, it has been used a

measure widely adopted in weighted comparisons between different productive realities as regards en-

ergy consumptions (Eurostat, 2006), that is the energy intensity, which is defined as the quantity of en-

ergy (measured in kWh) consumed by each sector per monetary unit (in euros), be it value-added or

GDP. According to this indicator, if a province becomes more efficient in the use of energy resources,

value-added or GDP being equal, the value of such intensity should decrease25.

In determining such a measure, though, an important role is played by the economic morphology of

the examined economy, and, in the case of the Italian provinces, a key role is played by the industrial

composition of the provinces themselves.

From 1998 to 2004, it can be observed that the energy intensity of Italy has increased little more than

+5%, going from 0,52 kWh per euro (kWh/€) to 0,55. The aggregate value of the indicator does not

allow to outline its dynamics across regions and provinces, and, by disaggregating the analysis at a pro-

vincial level, strong positive and negative variations can be recorded, during the years: among all the

others, the high consumption with respect to the value-added of Sardegna stands out, with a value of

the indicator which is almost three times than the national average (Table A3.2). Notwithstanding a dif-

ferent growth among provinces, the energy per unit of value-added used was still lower than other de-

25 It has to be noted that the ratio of energy on value-added is a more appropriate indicator: in the ratio on GDP, indeed, in the value in the denominator stays the same energy expense.

135

veloped countries in that period (Terna, 2006): while in other countries the energy intensity was steady

or weakly decreasing, in Italy it has shown a small increase during those years.

Figure 3.7 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: 1998

Legenda

EnEl/VA 1998

sotto 35%

36% - 40%

41% - 45%

46% - 50%

oltre 50%

136

Figure 3.8 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: 2004

Legenda

EnEl/VA 2004

sotto 35%

36% - 40%

41% - 45%

46% - 50%

oltre 50%

137

Figure 3.9 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: difference 2004-1998

Legenda

EnEl/VA 1998-2004

fino a 0%

1% - 5%

6% - 10%

11% - 15%

16% - 20%

21% - 25%

oltre 25%

Table 3.3 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense,

5 random provinces: 1998-2004 Indicator Variation (%) with respect to the previous year

Province 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Milano 0,25 0,26 0,28 0,27 0,27 0,27 0,26 4,0 7,5 -1,7 -1,0 1,2 -3,4Venezia 0,83 0,87 1,01 0,97 0,99 0,99 0,94 5,1 15,0 -3,8 2,6 -0,5 -4,6Genova 0,39 0,34 0,31 0,30 0,34 0,33 0,33 -12,4 -7,8 -4,1 13,6 -2,4 -0,9Roma 0,17 0,16 0,17 0,15 0,15 0,16 0,16 -5,6 2,9 -6,4 -3,7 6,5 2,9L'Aquila 0,93 0,91 0,92 1,05 1,05 1,11 1,16 -2,5 1,4 13,1 0,2 6,3 4,0

Italy 0,52 0,52 0,54 0,55 0,54 0,55 0,55 0,7 3,4 1,7 -0,4 1,3 -0,8

The situation of the five randomly selected provinces shows a decrease of the indicator, in the period

1998-2004, as regards Roma and Genova, and an increase in Milano, Venezia and L’Aquila, against an

increase of the total Italian value. Milano, Genova and Roma stood well below the national average,

while the other two provinces were above such value, with percentage increments higher than the in-

crease of the national value.

3.3.1.3 Sorted (and non sorted) urban waste collection (1998-2004)

138

The degree of the environmental sensibility of the people and of the public institutions is given by the

quantity of waste that is sent to the recycling facilities of the municipalities, or to the consortia insti-

tuted by the municipal governments. The map of urban waste in Italy is highly variegated, with differ-

ent characteristics from province to province. At a national level, the “production” of differentiated ur-

ban waste in 2004 has more than doubled with respect to 1998, while the total amount of non sorted

waste showed a peak in 2002, and then decreased up to 23 millions of tons in 2004, which is higher

than the 1998 value of about 1,4 millions of tons more, which amounts as a growth of +6% (Table

A3.3 in the Appendix).

In terms of ratios over the total, the total national value of the share of the urban waste on the total

waste has almost doubled, from 12,1% in 1998 to 23,6% in 2004. At a provincial level, the differenti-

ated waste collection is highly different between the provinces of the same region, and between differ-

ent regions: in the first place of the ranking of the most environmentally virtuous regions in 2004 is

Treviso, with a value of its sorted waste collection of about 61%, followed by Lecco, Padova, Vicenza

and Cremona, all of them with percentages higher than 50%. Almost all the southern provinces, on the

contrary, were standing below the average, while 29 are the Italian provinces with a percentage below

10% (all of them being in the Centre and in the South), thus demonstrating the inappropriateness of

the environmental policies of some Italian municipalities about the urban waste collection, during that

period.

Figure 3.10 – Share (%) of the urban sorted waste over the total urban waste, Italy: 1998-2004

Quota dei rifiuti urbani differenziati in Italia: 1998-2004

0,0

5,0

10,0

15,0

20,0

25,0

1997 1998 1999 2000 2001 2002 2003 2004 2005

Anno

Per

cen

tual

e

139

Figure 3.11 – Share (%) of the urban sorted waste over the total urban waste, provinces of Italy: 1998

Legenda

% RDIFF 1998

sotto 5%

5% - 10%

11% - 15%

16% - 20%

21% - 25%

26% - 30%

oltre 30%

140

Figure 3.12 – Share (%) of the urban sorted waste over the total urban waste, provinces of Italy: 2004

Legenda

% RDIFF 2004

sotto 5%

5% - 10%

11% - 15%

16% - 20%

21% - 25%

26% - 30%

oltre 30%

141

Figure 3.13 – Share (%) of the urban sorted waste over the total urban waste, provinces of Italy: difference 2004-1998

Legenda

% RDIFF 1998-2004

fino a 0%

1% - 5%

6% - 10%

11% - 15%

16% - 20%

21% - 25%

oltre 25%

Table 3.4 – Share (%) of the urban sorted waste over the total urban waste of the 5 random provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004 Milano 39,88 39,92 40,73 42,64 40,49 41,58 42,71 Venezia 14,66 20,42 23,75 29,91 29,49 27,69 30,60 Genova 8,20 9,26 12,56 12,38 15,48 21,47 17,35 Roma 6,15 6,31 4,63 5,55 5,77 7,96 13,31 L'Aquila 13,22 17,10 13,90 19,06 9,46 10,44 10,06

Italy 12,15 14,83 16,57 18,69 19,46 20,92 23,63

All the 5 provinces were recording a share of the urban sorted waste over the total urban waste higher

than 10% in 2004, but a strong difference between the values of the northern provinces (Milano,

Venezia and Genova) and the values of the central ones were existing, with L’Aquila which sees its

share decreasing from a peak of 17,10% in 1999 to 10,06% in 2004.

142

3.3.1.4 Innovations and patents (1998-2004)

The main measure for the innovative capability of a country is expressed by the research and develop-

ments (R&D) expenses, borne by firms, universities and government. In the majority of the industrial-

ized countries, firms bear around 60-70% of the total R&D expenses, while in Italy such a value stays

around 48% (and it is localized more in the North and in the Centre), while the rest of it has to be as-

cribed to the public sector: such a delay in the private sector is the result of some peculiarities of the

Italian productive system, which sees its productive structure too much unbalanced towards traditional

sectors, and towards an inadequate enterprises’ dimension26.

Figure 3.14 – Total number of patents per thousand of inhabitants, Italy: 1998-2004

Numero di Brevetti totali in Italia: 1998-2004

40.000

45.000

50.000

55.000

60.000

65.000

1997 1998 1999 2000 2001 2002 2003 2004 2005

Anno

Bre

vett

i

Since the provincially disaggregated R&D statistics were not available, in the model’s specification a

proxy has been used, which is represented by the number of patents per province. The patents’ variable

can measure the technological advancement’s degree of the firms in the province, which also can de-

termine the environmental impact that such a production causes. During the 7 examined years, the

number of patents registered in Italy has experienced an alternating behaviour, with a decrease in 1999,

a rise in 2000 and 2001, a weak reduction in 2002, a steady growth in 2003, and a reprise in 2004 (Fig-

ure 3.14).

At a provincial level, Milano is the top leading province as regards patents, with a number of registered

patents which is twice the number of the second province, Rome, followed by Torino, Bologna and

Firenze. During the 1998-2004 period, in those last five provinces has been recorded almost the whole

of the patents of Italy, thus showing a deep mismatch in the techno-scientific fabric of the Italian firms,

26 Centro Studi Confindustria, 2006.

143

with its core shifted towards those provinces where the big enterprise is concentrated (Milano, Torino,

Roma). In taking the number of patents per thousand of inhabitants into account, the ranking of the

provinces does not change, and it shows the low productivity of patents (per thousand of inhabitants)

of Italy as a whole, which stays on the level of 1 patent every 1000 inhabitants for the entire period

(Table A3.4 in the Appendix).

Table 3.5 – Total number of patents per thousand of inhabitants, Italy, 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 4,66 3,65 4,94 4,70 4,53 4,07 4,24Venezia 0,46 0,42 0,45 0,47 0,41 0,48 0,45Genova 0,72 0,76 0,71 0,73 0,63 0,68 0,74Roma 2,13 2,18 2,15 2,30 2,09 2,05 2,15L'Aquila 0,11 0,12 0,19 0,14 0,18 0,18 0,21

Italy 0,94 0,88 0,92 1,04 0,99 0,98 1,04

Table 3.5 shows the values of the patents’ indicator for the selected provinces: in 2004, Milano has a

value three times bigger than the national average, Roma twice the national value, while the other cities

are well below that national datum, being always under the level of 1 patent every 1000 inhabitants.

3.3.1.5 Exports (1998-2004)

During the decade 1995-2005, Italian exports of goods and services, at constant prices, have grown

with a lower trend than the one of the world exports: while, indeed, the international commerce has

doubled during 1995-2005, the Italian exports27 have increased by only +9,2% (Centro Studi Confin-

dustria, 2006).

From 1998 to 2004, the value of exports of Industry in a Strict Sense in Italy has grown from 201 mil-

lions of euros to 229 millions (Figure 3.15): at constant 1995 prices, these exports have been stable dur-

ing 1998 and 1999, then they have increased by +1,5% during the year 2000, they have seen a weak in-

crease in 2001, and then they have fallen in 2002 and 2003, ending up with a final increase in 2004.

At a provincial level, the dynamics has been different among provinces, with some provinces being sta-

ble in their exports (e.g., Torino), while others increasing their value in the entire period (Cuneo, Lodi

and Pavia), and others decreasing in some years (Bergamo, Vicenza and Catania in 2002 and 2003).

27 During 2004, the manufacturing exports of goods have increased their value by +6,4% (corresponding to +1,7% in terms of quantities), while in 2003 those values were, respectively, -2,1% and -2,7%.

144

Figure 3.15 – Exports (millions of euros of 1995, €), Industry in a Strict Sense, Italy: 1998-2004

Esportazioni totali dell'Industria in Senso Stretto dell'Italia, 1998-2004, milioni di Euro del 1995

0

50.000

100.000

150.000

200.000

250.000

1998 1999 2000 2001 2002 2003 2004

Anno

Mili

on

i di E

uro

del

199

5

By weighing the exports of Industry in a Strict Sense of every province with its relative provincial value-

added, it can be seen how such an indicator exhibits an increase of about +13% during the considered

period, thus showing that, the national value-added having been constant in this period, for every euro

of value-added of Industry in a Strict Sense, its overall value of exports has increased (Table A3.5 in the

Appendix).

Table 3.6 – Ratio of exports over value-added, Industry in a Strict Sense, 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 0,93 0,95 1,12 1,18 1,13 1,07 1,04Venezia 0,85 0,97 1,28 1,26 1,35 1,12 1,14Genova 0,54 0,48 0,62 0,72 0,70 0,65 0,63Roma 0,56 0,57 0,58 0,47 0,52 0,41 0,44L'Aquila 0,82 0,59 1,14 1,28 1,30 1,17 1,33

Italy 0,90 0,90 1,02 1,03 1,01 0,95 1,02

Among the 5 provinces, L’Aquila is the one with the highest ratio in 2004, almost +30% above the na-

tional average, as well as the values of Milano and Venezia are, while Genova and Roma stay below the

national value during all the period.

145

3.3.1.6 Population density of the provinces, and density of the local units of Industry in a Strict

Sense (1998-2004)

The degree of “crowding” of a territory may be given by the population density, if inhabitants are taken

into account, or by the enterprise density, which is expressed by the number of local units operating in

the considered area, if firms are taken into account.

The population density is defined by the ratio between the inhabitants of a province and its extension

in Km2: such indicator, at a national level, has been constant at 189 residents per Km2 during the years

1998, 1999, 2000 and 2001, then it has increased to 190 in 2002, to 192 in 2003 and to 194 in 2004.

Such a density, then, has been much uneven across the Italian provinces (Table A3.6), with values go-

ing from 37 inhabitants per Km2 in Aosta and Nuoro in 2004, to 2.640 in Napoli, to 1.935 in Milano,

and to 1.124 in Trieste. Table 3.7 shows the details of the population density for the 5 provinces, where

it can be observed the marked difference between Milano and the other cities, with Rome having 78 in-

habitants per Km2 in 2004, Venezia 336 and Genova 476, and with L’Aquila having “only” 60 inhabi-

tants per Km2.

Table 3.7 – Population density (inhabitants per Km2), the 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 1.867 1.867 1.867 1.867 1.875 1.903 1.935Venezia 328 328 329 329 330 334 336Genova 487 484 480 477 475 474 476Roma 694 693 692 692 692 698 708L'Aquila 59 59 59 59 59 60 60

Italy 189 189 189 189 190 192 194

Table 3.8 – Local units density (units per Km2), the 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 31,7 31,6 31,6 31,5 31,0 30,6 30,6Venezia 4,0 4,1 4,2 4,3 4,3 4,3 4,3Genova 4,7 4,7 4,8 4,9 5,0 5,4 5,4Roma 4,3 4,5 4,6 4,8 4,7 4,7 4,7L'Aquila 0,6 0,6 0,6 0,6 0,6 0,6 0,7

Italy 2,4 2,4 2,5 2,5 2,6 2,6 2,6

The density of the entrepreneurial fabric of Industry in a Strict Sense has been calculated by the ratio

between the local units of the Register of Enterprises, run by the Chambers of Commerce, and the sur-

face, in Km2, of that province (Table A3.7): even when taking the production level into account, not-

withstanding an almost constant density in the considered period (the values go from 2,4 local units per

Km2 in 1998, to 2,6 in 2004), the Italian provinces exhibit a wide variety, with highly populated cities

146

having a low local units density (as an example, Torino and Palermo, respectively with 4,4 and 1,8 UL

per Km2 in 2004), against lowly populated cities having a strong concentration of local units (as Prato,

with 26,5 UL per Km2 in 2004, and Varese, with 12,6 UL per Km2).

Among the 5 provinces (Table 3.8), Milano had a strong concentration of local units during all the pe-

riod (30,6 UL per Km2 in 2004), while all the others were staying around lower levels: as an example,

Venezia had the indicator’s value of 4,3 in 2004, Genova of 5,4, Roma of 4,7, and L’Aquila of 0,7 (well

below the national average).

3.3.1.7 Share of the value-added of Industry in a Strict Sense and of Service Industry on the to-

tal value-added (1998-2004)

The share of the value-added of Industry in a Strict Sense has experienced a decrease in the period

1998-2004, going from 23,88% to 21,37% at a national level (see also 2.2). Even at a more disaggre-

gated level (provinces and regions), a decrease has been recorded too, which stands for a general de-

crease of the industrial activities in Italy. In some few cases, a light growth has taken place, as in Matera,

where the value has gone from 13,56% in 1998 to 14,56% in 2004. In general, many provinces have

seen a continuous fall of their share, in same cases with more than -7% (Biella, Brindisi and Siracusa, as

an example).

Figure 3.16 – Share (%) of the value-added of Industry in a Strict Sense and of Service Industry on the total value-added, Italy: 1998-2004

Valore aggiunto Italia, 1998-2004: quote

0

10

20

30

40

50

60

70

80

1998 1999 2000 2001 2002 2003 2004

Anno

Per

cen

tua

le

Servizi

Industria in SensoStretto

While Industry in a Strict Sense was falling in percentage terms, Service Industry has grown in the same

terms, starting with a value of 68,35% in 1998, and going to a value of 70,90% in 2004 (Figure 3.16).

Many Italian provinces were exhibiting a share of 80% and more (in 2004, this was happening in Aosta,

147

Roma, Palermo, Catania and Messina, to mention a few), while the maximum level reached by Industry

in a Strict Sense is almost 55%, in Reggio Emilia and Modena.

Table 3.9 – Share (%) of the value-added of Service Industry on the total value-added, 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 66,55 67,51 68,77 69,77 69,98 69,93 69,11Venezia 70,09 71,93 74,34 74,74 74,72 74,72 74,73Genova 81,37 81,42 79,56 79,27 80,82 81,29 80,98Roma 84,24 84,34 84,98 84,57 84,90 85,32 85,46L'Aquila 72,27 72,23 71,63 74,39 74,39 74,01 73,95

Italy 68,35 68,89 69,42 69,81 70,40 70,90 70,90

Among the selected provinces, Roma is the one which has seen the lowest variation in its sectorial

composition, while Milano and Venezia, against a decrease of about -4% in the quota of Industry in a

Strict Sense, have seen the value of their Service Industry rising by +3% (Table 3.9 and 3.10). Genova

seems to be the only one to have experienced a fall in the Service’s quota, which goes from 81,37% in

1998, to 80,98% in 2004, while L’Aquila was showing values above the national average in both sectors.

Table 3.10 – Share (%) of the value-added of Industry in a Strict Sense on the total value-added, 5 randomly selected provinces: 1998-2004

Province 1998 1999 2000 2001 2002 2003 2004Milano 30,27 29,30 28,09 27,29 26,97 26,58 27,60Venezia 22,52 21,74 19,31 18,80 18,39 18,43 18,64Genova 15,57 15,82 17,13 16,87 15,02 14,79 14,49Roma 11,55 11,50 11,30 12,05 11,92 11,50 11,36L'Aquila 19,21 19,45 20,29 18,01 18,09 17,81 17,95

Italy 23,88 23,36 23,00 22,65 22,01 21,52 21,37

3.3.2 Relationship between industrial waste and socio-economic drivers

The analysis of the main specialized literature has led to identify a series of socio-economic factors that

contribute to determine the production of pollution, and, therefore and with some modification, the

production of industrial solid waste (that is, not air pollution).

The main economic variable widely used in that literature is the measure of the economic performance,

measured by the value-added of the GDP. In using such indicator, it is taken into account not only the

production capability of a territory (usually measured by the GDP), but also the quality of this capabil-

ity, and its modifications across time: in fact, going from a low value-added to a high value-added iden-

tifies an improvement (be it qualitative or quantitative) in production, other than a possible sectorial

148

modification. The aim of the incoming analysis is verifying whether the EKC behaviour can be valid as

regards waste too: that is, the research wants to check whether the conditions exist, for the Italian prov-

inces, for a joint technological and environmental improvement of the production.

The population density (inhabitants per Km2) may have ambivalent effects, not detectable a priori. On

one hand, a negative relationship between density and industrial waste may be supposed for the indus-

trialized countries like Italy, caused by the growing delocalization of the big industrial parks, and the

even more growing concern about the implementation of right and effective environmental policies by

local governments, guided by their citizens, whose demand for environmental goods and quality in-

creases as they become richer. On the other hand, highly populated areas provide a valuable workforce

for industries, especially in those sectors of the so called “traditional industry”, whose productive activ-

ity is highly labour-intensive. Moreover, making use of the population density and of the local density,

the territorial dimension has come to be implicitly added to the variables of choice (the density is a ratio

where at denominator stays a surface measure).

The density of (polluting) local units (in UL per Km2) of Industry in a Strict Sense is an indicator for

the presence of productive units in the territory. As concerns air emissions, given their intrinsic nature

more global than the solid ones, the industrial density indicator has not been widely used in the litera-

ture: on the contrary, when dealing with emissions which are produced in a precise place, and which do

not move away unless moved by man, the industrial density might contribute to explain the dynamics

of solid waste, more than when dealing with air emissions. The density of the industrial fabric, more-

over, may have also ambivalent effects of the production of waste: on one side, indeed, a bigger con-

centration may bring to higher production of waste, but, on the other side, feasible economies of scale

may reduce such a production, by creating a virtuous cycle of disposals.

The consumptions of energy, on one side, are a clear indicator of the economic activity in a territory,

while, on the other side, they help measuring the technological advancements of the industrial fabric of

that territory, by the means of the comparison with economic performance indicators, such as the

GDP or the value-added. The ratio between the physical quantity of the consumption of energy and

the value-added is a measure of the energy intensity of the industrial production, and it captures the ef-

ficiency of such production in terms of energy: the lower the ratio, the lower the quantity of energy

which has to be used to produce that specific (monetary) unit of value-added, and the more efficient

(ceteris paribus) is that industrial activity. This indicator is highly influenced by the industrial composi-

tion of the province, by the technological level of the firms, by their dimension, and by the interactions

of those factors inside the industrial context of the province.

The shares of the several economic activities (primary, secondary and tertiary sector) on the total of the

value-added of the province help measuring the contribution that such a sector has on the economy of

the territory, in productive terms. Another feasible alternative may be measuring the shares of the

149

workforce of each sector on the total workforce, but such a framework would be not correct in this in-

stance, since the production of waste is linked to productive activities, rather than to workforce dynam-

ics, which are themselves too a consequence of productive activities. When studying the production of

industrial waste, the most important shares to be considered are the share of the value-added of Ser-

vices Industry and the one of Industry in a Strict Sense, due to the recursive empirical evidence accord-

ing which developed countries have shifted from an industrial economy to a service economy: both the

shares can be seen as two measures of the deindustrialization of a territory, explaining how the change

in the industrial morphology can contribute to less or more waste production.

The ratio of sorted urban waste collection in the province (urban sorted waste over total urban waste)

allows to get two different information, linked together: on one hand, the possible effect that the envi-

ronmental sensibility of citizens might have on the production of waste can be investigated, thus getting

a socially relevant piece of information; on the other hand, what the effects of a developed environ-

mental conscience by the side of local administrators are on waste production can be measured, and of

an increase of treating facilities for such an environmental “bad” (waste). High values for such indicator

show the political will of the province to differentiate waste, and its focus on the waste problem: all in

all, it has to be expected that the higher the sorted waste collection is, the lower the production of

waste is.

The number of patents (every 1000 inhabitants) is a proxy for the technological development of the en-

terprises in the territory. Even though patents are a technological output measure, and not a techno-

logical input measure, that is they measure only the final product of the research activity, and not also

what investments have been done to come up with those final outputs. Even if this is not the most

precise measure for technological effects, it can anyway help explaining what effects technological ad-

vancements (already included in the value-added and in the energy intensity) have on waste production.

It has to be noted that, in that measure, a key role is played by the location of the patents’ registration,

rather than by the place where such patents (and technologies) have been really developed: this “prob-

lem” can be mitigated, in the forecasting stage, by using dummy variables to correct the results.

Exports and the relative degree of international openness (measured by the ratio between exports and

GDP) play a key role in the production of industrial waste: even in presence of low levels of domestic

consumptions, great levels of waste might be produced because the economic activity of the territory is

led by foreign demand. By weighing the value of exports with the value-added of the relative territorial

entity, it can be obtained a measure that links the degree of international openness of an area with its

economic competitiveness.

150

3.4 Conclusions

From the analysis of the specialized literature, some hypothesis have been done about the socio-

economic variables which are behind the production of polluting emissions, and those drivers which

are linked to the economic cycle directly responsible for the production of waste have been selected.

Together with the value-added, which is the main indicator of the economic power of a society, other

variables have been taken into account: energy consumption, urban waste production, the degree of in-

novation represented by the number of patents, the value of exports, population density and local units

density, the shares of value-added of Industry in a Strict Sense and of Service Industry over the total

value-added. For all the variables, a brief description has been provided, in order to use them in the

econometric tests of Chapter 4, and in the econometric simulations of Chapter 5.

151

APPENDIX A3

Table A3.1 – Waste production per unit of value-added (tons per million of euros of 1995),

Industry in a Strict Sense, Italy: 1998-2004 Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Torino 89 115 125 127 124 128 131 29,7 8,2 1,6 -1,9 2,6 2,7Vercelli 312 386 365 382 370 389 457 23,6 -5,2 4,7 -3,2 5,0 17,5Novara 64 85 89 99 97 97 108 33,4 5,2 10,7 -2,4 0,1 11,6Cuneo 94 122 131 131 148 138 181 30,2 7,2 -0,2 12,9 -6,4 30,5Asti 101 135 133 117 143 149 146 33,1 -1,4 -12,1 22,7 3,9 -2,0Alessandria 66 71 78 92 105 111 111 6,8 10,6 17,5 13,8 6,1 -0,7Biella 70 96 110 145 136 140 61 37,1 13,6 32,3 -6,5 3,0 -56,4Verbano Cusio Ossola 178 153 138 199 194 190 212 -14,1 -9,9 44,0 -2,5 -2,0 11,9Piemonte 95 120 127 133 135 137 144 25,8 5,6 5,2 1,3 1,4 5,5Aosta 294 225 229 234 209 272 332 -23,5 1,7 2,1 -10,5 30,1 21,9Valle d’Aosta 294 225 229 234 209 272 332 -23,5 1,7 2,1 -10,5 30,1 21,9Varese 60 86 87 94 89 89 85 41,9 1,9 7,3 -5,5 0,3 -4,2Como 51 96 88 82 80 94 93 88,0 -7,7 -6,7 -2,7 17,7 -1,9Sondrio 71 65 81 83 89 100 82 -7,4 23,0 2,6 7,2 13,2 -17,9Milano 42 53 60 61 62 65 72 26,6 13,2 1,3 1,7 4,5 11,6Bergamo 132 158 171 183 194 192 178 20,2 7,7 7,3 6,0 -0,9 -7,2Brescia 229 270 374 391 410 388 382 18,1 38,4 4,6 4,8 -5,4 -1,3Pavia 154 218 198 219 302 278 283 41,9 -9,2 10,7 37,9 -8,0 1,7Cremona 89 300 358 310 254 249 236 236,5 19,6 -13,6 -18,0 -1,9 -5,3Mantova 101 145 191 222 338 352 370 43,0 31,5 16,6 52,1 4,1 5,0Lecco 170 186 220 228 231 249 252 9,4 17,9 4,0 1,0 8,1 1,2Lodi 80 113 130 142 224 226 135 40,7 15,5 9,1 57,7 0,7 -40,1Lombardia 89 117 139 146 156 156 156 31,7 19,4 4,9 6,6 0,3 -0,2Bolzano – Bozen 80 116 121 118 106 96 118 44,6 4,6 -2,4 -10,4 -9,3 22,9Trento 153 149 176 212 221 244 239 -2,6 18,3 20,0 4,3 10,4 -1,8Trentino Alto Adige 119 134 150 168 167 175 185 12,1 12,4 11,6 -0,3 4,6 5,6Verona 442 478 454 443 421 401 417 8,0 -4,9 -2,5 -4,8 -4,9 4,0Vicenza 153 202 229 252 247 250 234 31,6 13,6 10,2 -2,2 1,2 -6,2Belluno 78 103 122 105 114 105 114 32,8 17,6 -13,5 8,1 -7,5 8,7Treviso 116 144 150 169 177 179 182 24,3 4,5 12,4 5,0 0,8 1,8Venezia 206 244 312 331 310 336 355 18,4 27,6 6,1 -6,2 8,5 5,5Padova 122 136 195 185 198 171 189 12,0 43,4 -5,4 6,9 -13,7 10,7Rovigo 245 304 337 678 611 296 202 23,8 11,0 101,0 -9,9 -51,5 -31,7Veneto 193 226 252 271 267 252 255 17,2 11,3 7,7 -1,4 -5,6 1,0Udine 207 344 361 389 378 347 396 65,9 4,9 7,9 -2,9 -8,1 14,1Gorizia 298 475 516 505 737 519 474 59,5 8,7 -2,2 46,0 -29,5 -8,7Trieste 129 142 160 273 258 145 285 10,0 12,3 71,1 -5,5 -43,8 96,5Pordenone 138 168 191 224 248 253 269 22,3 13,8 17,0 10,8 1,7 6,6Friuli Venezia Giulia 183 271 292 330 355 307 348 48,0 7,7 13,0 7,7 -13,5 13,4Imperia 15 18 17 18 16 16 16 15,4 -1,3 4,7 -12,1 1,1 -0,7Savona 349 318 392 402 1.507 2.018 2.022 -8,8 23,3 2,5 275,0 33,9 0,2Genova 185 173 184 265 197 214 235 -6,3 6,2 44,0 -25,7 9,0 9,6La Spezia 350 324 185 343 336 335 342 -7,5 -42,9 85,8 -2,2 -0,3 2,2Liguria 230 214 207 282 421 513 527 -7,1 -3,3 36,7 49,2 21,8 2,7Piacenza 274 248 230 197 218 122 116 -9,2 -7,4 -14,4 10,4 -44,0 -4,4Parma 81 113 95 110 123 131 115 39,9 -16,1 16,1 11,9 6,3 -12,4Reggio Emilia 111 141 159 169 168 171 182 27,0 12,9 6,0 -0,4 1,4 6,7

152

Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Modena 171 208 193 207 207 219 227 21,7 -7,4 7,7 -0,2 5,6 3,7Bologna 87 111 112 134 121 128 125 28,6 0,8 19,1 -9,5 5,5 -1,9Ferrara 367 397 272 325 349 327 321 8,2 -31,4 19,4 7,3 -6,3 -1,9Ravenna 334 433 400 459 501 449 470 29,5 -7,7 14,9 9,2 -10,5 4,7Forlì – Cesena 147 197 201 191 256 377 167 33,6 2,0 -4,9 34,2 47,0 -55,6Rimini 68 75 89 87 88 81 90 10,7 18,2 -1,4 0,6 -8,0 11,5Emilia Romagna 152 185 173 190 199 204 191 21,4 -6,4 9,6 5,2 2,2 -6,2Massa Carrara 1.116 1.090 1.070 1.109 991 891 899 -2,3 -1,9 3,6 -10,6 -10,1 1,0Lucca 384 380 379 401 408 418 347 -1,0 -0,3 5,9 1,8 2,3 -17,0Pistoia 41 48 58 57 57 64 55 18,0 20,3 -2,7 0,5 12,3 -14,3Firenze 47 57 62 59 59 63 71 20,3 10,0 -5,0 0,5 5,8 13,1Livorno 138 151 130 210 176 219 281 9,5 -14,1 62,3 -16,5 24,6 28,5Pisa 165 200 234 230 233 148 149 21,1 17,3 -1,6 1,1 -36,4 0,2Arezzo 344 140 157 106 221 153 106 -59,2 11,6 -32,0 107,7 -30,8 -31,0Siena 98 111 119 152 162 214 201 13,4 7,0 28,0 6,6 31,7 -5,8Grosseto 1.768 1.388 1.642 1.202 1.543 1.484 1.557 -21,5 18,3 -26,8 28,4 -3,8 4,9Prato 28 34 33 34 36 33 32 20,0 -2,9 3,8 3,9 -6,4 -5,2Toscana 207 189 197 191 208 191 186 -8,8 4,4 -3,0 8,7 -8,2 -2,8Perugia 148 161 184 211 210 176 189 8,9 14,1 15,1 -0,5 -16,3 7,5Terni 533 786 739 642 646 586 699 47,4 -5,9 -13,2 0,6 -9,3 19,3Umbria 253 335 341 334 343 301 345 32,6 1,8 -2,2 3,0 -12,3 14,5Pesaro e Urbino 131 116 145 158 184 209 255 -11,0 24,3 9,0 17,0 13,3 22,2Ancona 95 138 137 142 150 151 152 44,1 -0,5 4,1 5,3 1,0 0,6Macerata 88 117 129 121 129 132 123 33,0 11,0 -6,1 5,8 2,4 -6,5Ascoli Piceno 95 99 96 105 114 115 112 4,2 -3,4 9,6 8,7 0,4 -2,6Marche 101 119 127 133 144 151 159 17,4 6,7 4,3 8,9 4,5 5,3Viterbo 119 149 175 158 166 166 205 24,9 17,6 -9,7 5,5 -0,2 23,5Rieti 53 90 93 205 178 168 63 71,0 2,7 121,3 -13,1 -5,5 -62,6Roma 35 47 55 36 34 34 38 33,9 16,7 -33,9 -7,8 0,3 13,2Latina 67 92 84 83 75 80 81 37,1 -8,4 -1,4 -10,3 7,0 1,6Frosinone 122 153 201 243 199 207 182 25,5 31,4 20,8 -18,2 4,4 -12,1Lazio 55 73 84 75 67 70 69 31,7 15,3 -10,3 -11,1 3,8 -0,8L'Aquila 148 181 185 183 171 166 176 22,3 2,1 -1,0 -6,5 -3,2 6,1Teramo 87 100 106 144 155 158 178 15,4 6,1 35,4 7,7 2,1 12,1Pescara 34 36 46 88 74 68 68 5,2 28,7 92,3 -15,9 -8,0 -0,9Chieti 151 172 145 154 144 144 168 13,8 -15,7 6,3 -6,5 0,0 16,8Abruzzo 111 128 122 143 139 137 153 15,4 -4,6 17,5 -3,0 -1,4 11,4Campobasso 274 452 512 554 495 550 433 64,8 13,1 8,3 -10,6 11,0 -21,1Isernia 50 135 130 118 115 151 140 167,9 -3,6 -9,0 -2,5 31,5 -7,4Molise 202 349 374 399 368 416 335 73,3 7,0 6,7 -7,8 13,0 -19,4Caserta 76 124 135 153 138 148 171 63,8 8,4 13,6 -9,9 6,9 15,8Benevento 29 36 38 56 62 63 77 25,0 3,1 48,7 11,5 1,6 21,1Napoli 41 59 66 82 80 73 82 44,6 11,7 22,9 -1,4 -9,3 12,1Avellino 91 118 138 160 168 140 137 29,2 16,6 16,1 5,3 -17,0 -1,6Salerno 96 128 127 121 133 154 146 34,0 -0,9 -4,4 9,9 15,6 -5,2Campania 61 88 96 109 110 109 115 43,9 10,0 13,1 0,7 -1,0 6,1Foggia 106 99 134 240 173 251 394 -6,2 34,6 79,7 -28,0 45,1 57,3Bari 149 184 184 204 211 211 234 23,0 0,4 10,5 3,7 -0,1 10,9Taranto 1.258 1.152 702 801 387 954 1.524 -8,5 -39,1 14,2 -51,6 146,2 59,8Brindisi 129 746 998 1.076 1.528 1.712 1.959 477,6 33,7 7,8 42,1 12,0 14,4Lecce 46 74 77 90 97 99 126 59,6 4,9 16,1 7,6 2,6 27,3Puglia 360 417 359 412 347 484 650 15,6 -13,9 14,8 -15,8 39,5 34,3Potenza 165 345 326 210 205 197 239 109,6 -5,6 -35,5 -2,3 -4,2 21,6

153

Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Matera 87 98 130 169 113 97 122 12,3 32,9 29,9 -33,0 -14,3 25,8Basilicata 146 285 275 198 181 172 210 94,4 -3,4 -27,8 -8,8 -5,1 22,2Cosenza 12 15 25 35 24 77 54 28,4 69,3 38,9 -31,3 219,9 -30,4Catanzaro 11 28 37 31 27 29 57 151,2 34,7 -17,5 -13,7 9,4 94,0Reggio di Calabria 93 12 31 21 15 20 57 -86,6 147,7 -30,7 -32,0 36,0 187,1Crotone 546 155 50 51 93 191 497 -71,5 -67,5 1,0 82,5 105,6 160,1Vibo Valentia 28 24 25 22 19 34 29 -15,6 7,1 -14,8 -12,1 77,0 -14,4Calabria 85 32 32 32 30 64 102 -62,4 -0,6 1,0 -8,0 117,1 59,0Trapani 155 179 175 206 278 254 398 14,9 -2,0 17,9 34,6 -8,5 56,7Palermo 192 183 232 237 169 136 228 -4,7 26,6 2,0 -28,8 -19,6 68,0Messina 73 32 76 150 182 240 306 -55,7 136,6 97,6 21,0 32,2 27,2Agrigento 16 58 63 12 13 29 28 254,9 9,4 -80,9 9,1 119,1 -3,5Caltanissetta 61 87 129 68 61 100 1.639 42,0 48,5 -47,2 -10,3 64,6 1534,8Enna 7 15 23 31 19 40 43 101,0 56,8 34,3 -38,1 107,7 7,0Catania 11 80 65 61 80 109 88 603,7 -18,6 -6,4 29,9 36,4 -18,8Ragusa 30 62 63 103 49 44 76 106,4 0,9 63,9 -52,3 -9,9 72,1Siracusa 93 138 220 167 198 293 673 48,8 58,6 -24,1 18,8 48,0 129,5Sicilia 91 113 141 137 135 156 390 24,6 24,4 -3,0 -1,0 15,7 149,5Sassari 239 320 371 321 332 306 550 34,0 16,1 -13,7 3,4 -7,8 79,9Nuoro 295 237 155 95 52 130 182 -19,9 -34,4 -38,6 -46,0 152,8 39,7Cagliari 944 958 1.287 1.368 1.276 1.193 1.347 1,5 34,3 6,2 -6,7 -6,6 12,9Oristano 29 37 31 54 55 72 85 30,1 -17,6 76,8 1,8 29,5 19,1Sardegna 609 617 805 820 766 722 876 1,4 30,5 1,9 -6,6 -5,7 21,3

Italy 138 164 177 188 192 196 211 19,4 7,8 5,8 2,6 2,0 7,8

154

Table A3.2 – Energy intensity (kWh per euros of 1995), Industry in a Strict Sense, Italy: 1998-2004

Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Torino 0,44 0,43 0,45 0,47 0,44 0,46 0,46 -4,1 4,4 5,3 -5,3 3,5 1,2Vercelli 0,58 0,60 0,74 0,76 0,75 0,77 0,74 3,2 24,0 3,2 -1,9 3,5 -4,6Novara 0,56 0,56 0,53 0,58 0,58 0,61 0,59 0,1 -4,2 8,0 -0,4 6,0 -3,5Cuneo 0,63 0,65 0,66 0,65 0,73 0,78 0,75 2,5 1,4 -0,8 12,4 6,3 -3,7Asti 0,44 0,45 0,41 0,42 0,43 0,43 0,45 0,4 -8,0 2,4 1,5 1,6 3,8Alessandria 0,61 0,62 0,63 0,60 0,63 0,64 0,65 1,8 1,0 -5,4 5,7 2,3 1,5Biella 0,77 0,77 0,74 0,83 0,83 0,82 0,70 0,3 -3,9 12,1 -0,1 -1,1 -15,1Verbano Cusio Ossola 0,87 0,75 0,61 0,67 0,74 0,76 0,70 -14,1 -18,1 10,1 10,6 2,7 -8,5Piemonte 0,53 0,52 0,53 0,55 0,56 0,58 0,57 -1,7 1,8 4,0 1,4 3,7 -1,4Aosta 0,82 0,81 0,80 0,89 0,83 0,95 0,97 -1,5 -0,6 11,1 -6,6 14,6 1,5Valle d’Aosta 0,82 0,81 0,80 0,89 0,83 0,95 0,97 -1,5 -0,6 11,1 -6,6 14,6 1,5Varese 0,49 0,51 0,51 0,54 0,52 0,55 0,54 4,4 0,6 6,1 -4,7 6,2 -1,3Como 0,39 0,42 0,43 0,44 0,43 0,43 0,43 8,0 4,5 0,3 -2,4 -0,2 0,5Sondrio 0,45 0,43 0,59 0,59 0,56 0,58 0,57 -4,6 37,6 1,1 -5,9 3,7 -2,1Milano 0,25 0,26 0,28 0,27 0,27 0,27 0,26 4,0 7,5 -1,7 -1,0 1,2 -3,4Bergamo 0,59 0,60 0,62 0,63 0,63 0,64 0,61 2,3 3,9 1,5 -0,8 2,0 -5,3Brescia 1,02 1,01 1,16 1,14 1,10 1,09 1,04 -0,5 14,5 -1,8 -3,2 -0,7 -5,2Pavia 0,66 0,69 0,70 0,74 0,71 0,72 0,70 5,5 1,5 5,9 -5,1 1,9 -2,9Cremona 0,68 0,73 0,78 0,78 0,74 0,75 0,72 6,2 7,3 0,4 -4,8 1,3 -4,8Mantova 0,67 0,73 0,72 0,73 0,74 0,77 0,72 9,7 -2,0 2,7 0,8 3,3 -5,6Lecco 0,50 0,53 0,52 0,53 0,51 0,54 0,54 6,2 -2,4 3,7 -5,3 5,7 0,4Lodi 0,36 0,35 0,37 0,34 0,34 0,37 0,36 -2,9 5,6 -8,4 2,0 6,7 -1,4Lombardia 0,47 0,48 0,52 0,52 0,51 0,52 0,50 2,7 8,0 1,3 -3,0 1,8 -3,5Bolzano – Bozen 0,35 0,35 0,32 0,35 0,30 0,31 0,31 2,3 -9,3 8,4 -12,4 0,7 2,0Trento 0,60 0,53 0,55 0,58 0,57 0,61 0,62 -10,9 3,3 5,8 -1,9 7,1 1,8Trentino Alto Adige 0,47 0,44 0,43 0,45 0,43 0,45 0,46 -5,7 -3,0 6,4 -6,2 5,2 1,6Verona 0,56 0,57 0,56 0,54 0,55 0,54 0,56 2,4 -1,2 -3,7 2,3 -2,3 2,7Vicenza 0,47 0,47 0,49 0,51 0,52 0,52 0,52 0,9 5,1 4,4 1,5 -0,4 -0,4Belluno 0,37 0,35 0,41 0,42 0,36 0,35 0,35 -5,1 16,1 3,9 -15,1 -3,3 0,5Treviso 0,35 0,36 0,36 0,39 0,40 0,40 0,40 0,9 0,1 7,5 4,7 0,3 -0,1Venezia 0,83 0,87 1,01 0,97 0,99 0,99 0,94 5,1 15,0 -3,8 2,6 -0,5 -4,6Padova 0,46 0,46 0,46 0,48 0,51 0,50 0,50 0,9 -0,8 6,0 5,2 -1,4 -0,1Rovigo 0,54 0,54 0,61 0,67 0,67 0,65 0,65 -0,1 13,3 9,2 -0,1 -2,0 0,2Veneto 0,50 0,51 0,53 0,54 0,56 0,55 0,55 1,2 4,5 2,0 2,3 -1,0 -1,0Udine 1,02 1,03 1,01 1,09 1,12 1,11 1,14 1,2 -2,0 8,2 2,9 -1,0 2,3Gorizia 0,62 0,62 0,67 0,74 0,70 0,69 0,68 0,1 8,9 10,6 -5,3 -2,5 -1,0Trieste 1,48 1,42 1,37 1,24 1,30 1,20 1,25 -4,0 -3,3 -10,0 5,5 -8,3 4,2Pordenone 0,55 0,54 0,55 0,61 0,61 0,58 0,61 -1,1 1,0 12,2 -0,6 -4,0 4,7Friuli Venezia Giulia 0,87 0,86 0,86 0,92 0,93 0,90 0,93 -0,9 -0,4 7,4 1,0 -2,9 3,3Imperia 0,24 0,21 0,21 0,23 0,21 0,22 0,21 -15,6 1,8 8,9 -8,4 5,8 -4,6Savona 0,62 0,61 0,59 0,56 0,53 0,52 0,52 -0,7 -3,4 -4,9 -6,4 -0,5 0,0Genova 0,39 0,34 0,31 0,30 0,34 0,33 0,33 -12,4 -7,8 -4,1 13,6 -2,4 -0,9La Spezia 0,29 0,29 0,24 0,26 0,26 0,23 0,21 -0,3 -16,2 8,3 -2,4 -10,4 -10,3Liguria 0,41 0,37 0,34 0,34 0,35 0,34 0,34 -8,7 -8,0 -1,2 2,9 -2,1 -1,9Piacenza 0,38 0,40 0,39 0,40 0,41 0,42 0,43 6,7 -2,6 0,4 4,7 1,1 3,6Parma 0,45 0,46 0,48 0,48 0,50 0,50 0,50 3,8 4,3 -0,8 4,0 1,0 -0,5Reggio Emilia 0,41 0,41 0,40 0,41 0,43 0,46 0,47 1,0 -2,4 1,9 5,1 6,7 1,6Modena 0,41 0,41 0,42 0,40 0,43 0,44 0,45 1,9 0,8 -3,3 7,3 1,9 1,2Bologna 0,28 0,29 0,31 0,32 0,31 0,32 0,33 2,2 6,9 4,4 -1,3 0,2 3,4Ferrara 0,83 0,80 0,73 0,80 0,84 0,79 0,86 -3,7 -8,2 9,4 5,1 -6,4 8,7Ravenna 0,74 0,76 0,83 0,78 0,78 0,85 0,83 3,6 8,2 -6,1 1,2 8,3 -1,9

155

Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Forlì – Cesena 0,26 0,27 0,29 0,29 0,26 0,27 0,29 2,5 6,1 2,3 -10,8 3,5 6,2Rimini 0,31 0,33 0,32 0,32 0,33 0,32 0,33 6,2 -1,1 -1,1 2,4 -1,6 1,3Emilia Romagna 0,41 0,42 0,43 0,43 0,44 0,45 0,46 2,1 1,7 0,8 3,1 1,9 2,2Massa Carrara 0,76 0,75 0,78 0,80 0,83 0,75 0,70 -1,7 4,9 1,9 4,4 -10,3 -6,0Lucca 1,01 1,04 1,03 1,03 1,04 1,12 1,08 3,0 -1,0 0,5 0,9 7,8 -3,9Pistoia 0,38 0,38 0,42 0,42 0,42 0,43 0,42 0,0 9,7 1,8 -2,1 3,9 -2,2Firenze 0,26 0,26 0,25 0,26 0,27 0,28 0,27 -0,4 -2,9 1,8 6,9 1,1 -3,4Livorno 1,51 1,62 1,67 1,76 1,80 1,83 1,86 7,3 2,7 5,4 2,5 1,8 1,5Pisa 0,33 0,34 0,36 0,36 0,35 0,37 0,36 2,7 5,6 0,5 -2,5 6,6 -3,4Arezzo 0,30 0,30 0,31 0,31 0,32 0,32 0,30 -0,1 3,4 -1,2 1,7 1,1 -5,2Siena 0,37 0,37 0,34 0,36 0,40 0,39 0,39 2,0 -7,9 5,7 10,2 -2,2 0,1Grosseto 0,40 0,40 0,44 0,44 0,45 0,46 0,41 1,0 8,7 1,4 0,6 2,9 -11,3Prato 0,50 0,52 0,50 0,52 0,51 0,49 0,46 2,8 -3,8 4,2 -2,2 -2,2 -7,4Toscana 0,50 0,51 0,51 0,52 0,54 0,55 0,54 3,0 0,1 1,9 3,6 2,3 -2,2Perugia 0,45 0,45 0,44 0,45 0,47 0,49 0,49 0,7 -1,0 0,4 5,1 3,6 0,8Terni 2,30 2,18 2,12 2,13 2,03 1,88 1,89 -5,3 -2,9 0,7 -4,7 -7,2 0,5Umbria 0,95 0,92 0,91 0,90 0,90 0,86 0,87 -3,0 -1,3 -0,8 -0,4 -3,8 1,3Pesaro e Urbino 0,28 0,27 0,30 0,30 0,33 0,34 0,34 -3,7 11,0 1,8 8,3 2,7 2,2Ancona 0,41 0,44 0,51 0,55 0,55 0,56 0,62 6,5 15,7 7,9 0,4 2,6 10,4Macerata 0,36 0,35 0,37 0,35 0,38 0,39 0,38 -3,1 5,1 -4,2 7,1 5,3 -3,6Ascoli Piceno 0,41 0,40 0,42 0,41 0,42 0,43 0,41 -1,8 3,9 -1,1 1,1 2,7 -4,8Marche 0,37 0,37 0,41 0,42 0,43 0,44 0,46 0,7 10,0 2,4 3,0 2,8 3,4Viterbo 0,21 0,24 0,27 0,27 0,24 0,25 0,25 14,5 12,4 -0,6 -8,4 1,5 -0,2Rieti 0,29 0,30 0,34 0,33 0,31 0,31 0,32 4,4 11,4 -2,2 -5,6 0,4 4,2Roma 0,17 0,16 0,17 0,15 0,15 0,16 0,16 -5,6 2,9 -6,4 -3,7 6,5 2,9Latina 0,41 0,42 0,39 0,40 0,37 0,37 0,36 2,4 -6,6 2,9 -8,9 -0,1 -0,7Frosinone 0,74 0,80 0,86 0,90 0,85 0,86 0,83 8,5 7,5 4,7 -6,1 1,1 -3,6Lazio 0,28 0,29 0,29 0,29 0,27 0,28 0,28 1,4 2,2 -2,5 -4,2 2,7 -0,6L'Aquila 0,93 0,91 0,92 1,05 1,05 1,11 1,16 -2,5 1,4 13,1 0,2 6,3 4,0Teramo 0,51 0,50 0,50 0,49 0,51 0,53 0,55 -1,1 0,2 -2,9 5,2 3,1 4,3Pescara 0,72 0,70 0,64 0,68 0,70 0,67 0,68 -2,2 -8,9 6,8 2,6 -4,4 2,4Chieti 0,60 0,60 0,61 0,66 0,63 0,64 0,67 -0,8 2,4 7,3 -3,7 1,1 4,4Abruzzo 0,66 0,65 0,64 0,68 0,68 0,70 0,73 -1,6 -1,1 5,0 1,0 2,1 4,1Campobasso 0,69 0,64 0,69 0,70 0,78 0,77 0,77 -7,6 7,7 2,1 10,8 -0,8 0,3Isernia 0,55 0,63 0,58 0,70 0,73 0,81 0,86 15,6 -9,0 21,1 4,2 11,2 6,2Molise 0,64 0,64 0,65 0,70 0,76 0,78 0,80 -0,8 2,0 7,9 8,5 3,3 2,5Caserta 0,54 0,53 0,50 0,51 0,45 0,45 0,45 -1,8 -5,5 0,5 -11,3 0,5 -1,1Benevento 0,25 0,28 0,33 0,37 0,35 0,37 0,41 9,4 19,8 13,2 -6,5 7,5 8,3Napoli 0,34 0,32 0,32 0,32 0,33 0,32 0,31 -6,0 1,4 -0,9 2,3 -1,0 -4,9Avellino 0,48 0,45 0,42 0,42 0,44 0,45 0,45 -5,8 -6,8 0,3 3,9 3,6 -0,3Salerno 0,47 0,46 0,43 0,44 0,43 0,44 0,45 -2,7 -6,2 1,6 -1,1 0,4 3,3Campania 0,41 0,40 0,39 0,39 0,39 0,39 0,39 -3,6 -1,6 0,8 -1,7 0,8 -0,5Foggia 0,47 0,44 0,42 0,46 0,50 0,55 0,55 -7,0 -3,8 9,4 8,5 9,3 0,7Bari 0,36 0,35 0,37 0,37 0,35 0,36 0,36 -4,0 6,3 1,9 -5,4 1,1 -0,2Taranto 2,64 2,62 2,44 2,55 2,45 2,50 2,57 -0,9 -6,7 4,3 -3,9 2,1 2,7Brindisi 0,94 0,98 1,05 1,12 1,04 1,09 1,01 3,6 7,3 6,6 -6,8 4,5 -7,2Lecce 0,28 0,29 0,28 0,29 0,29 0,30 0,31 1,2 -0,6 2,5 -0,8 5,4 2,6Puglia 0,87 0,85 0,85 0,87 0,84 0,86 0,88 -2,5 -0,3 2,8 -4,0 2,6 1,9Potenza 0,78 0,78 0,84 0,82 0,79 0,83 0,88 -0,1 7,6 -2,1 -3,2 5,0 6,1Matera 0,72 0,62 0,51 0,62 0,63 0,62 0,59 -14,1 -17,2 22,1 0,5 -0,9 -5,8Basilicata 0,76 0,73 0,73 0,76 0,74 0,77 0,80 -4,6 1,3 3,2 -1,8 3,7 3,2Cosenza 0,27 0,26 0,32 0,27 0,26 0,24 0,24 -4,4 25,7 -17,1 -3,6 -6,6 -0,2Catanzaro 0,25 0,23 0,24 0,27 0,27 0,27 0,28 -7,8 3,2 11,7 0,1 1,5 3,4

156

Indicator Variation (%) with respect to the previous yearProvince and region 1998 1999 2000 2001 2002 2003 2004 1999 2000 2001 2002 2003 2004Reggio di Calabria 0,28 0,26 0,28 0,30 0,30 0,32 0,31 -7,2 7,5 7,3 2,8 4,6 -2,8Crotone 2,01 0,47 0,28 0,26 0,26 0,31 0,30 -76,8 -40,7 -4,5 -2,5 18,3 -2,7Vibo Valentia 0,42 0,38 0,47 0,49 0,50 0,51 0,50 -9,8 23,5 5,2 0,9 2,1 -1,5Calabria 0,43 0,28 0,30 0,29 0,29 0,29 0,29 -34,3 7,3 -3,9 -0,8 1,6 -0,5Trapani 0,30 0,30 0,29 0,29 0,27 0,28 0,26 -2,3 -2,1 0,2 -7,2 4,4 -7,6Palermo 0,26 0,29 0,31 0,30 0,31 0,30 0,29 8,7 8,5 -4,3 3,2 -2,7 -2,0Messina 0,65 0,69 0,71 0,80 0,79 0,81 0,80 6,9 2,9 11,9 -0,7 1,9 -1,3Agrigento 0,32 0,32 0,33 0,37 0,34 0,35 0,33 0,6 4,0 11,7 -9,5 5,0 -5,5Caltanissetta 1,47 1,70 1,92 1,81 1,60 1,53 1,64 15,7 12,8 -5,7 -11,2 -4,4 6,8Enna 0,21 0,20 0,20 0,21 0,20 0,20 0,21 -3,6 -1,7 5,6 -4,4 1,4 0,8Catania 0,33 0,39 0,42 0,41 0,46 0,48 0,49 17,9 7,8 -2,3 12,0 2,8 2,9Ragusa 0,82 0,69 0,63 0,61 0,62 0,64 0,67 -15,4 -9,0 -3,4 1,2 4,3 3,5Siracusa 1,76 2,18 2,52 2,65 2,50 2,32 2,26 24,1 15,4 5,0 -5,4 -7,6 -2,3Sicilia 0,69 0,78 0,82 0,82 0,81 0,79 0,78 11,7 6,2 -0,3 -1,6 -1,9 -1,2Sassari 0,97 0,98 0,91 0,85 0,69 0,68 0,66 1,5 -7,1 -7,4 -18,8 -1,3 -2,8Nuoro 0,70 0,66 0,75 0,77 0,73 0,66 0,68 -5,5 12,7 3,0 -4,5 -10,0 2,4Cagliari 2,40 2,56 2,68 2,68 2,55 2,63 2,67 6,6 4,5 0,2 -5,1 3,4 1,4Oristano 0,20 0,18 0,17 0,19 0,18 0,22 0,21 -7,2 -9,5 14,6 -2,9 17,0 -0,3Sardegna 1,56 1,60 1,67 1,67 1,57 1,59 1,60 3,2 4,0 -0,1 -6,1 1,8 0,2

Italy 0,52 0,52 0,54 0,55 0,54 0,55 0,55 0,7 3,4 1,7 -0,4 1,3 -0,8

157

Table A3.3 – Share (%) of the urban sorted waste over the total urban waste, provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004

Torino 8,66 16,18 16,58 18,39 21,16 23,06 29,15 Vercelli 10,73 11,80 14,94 15,98 13,37 16,72 20,34 Novara 18,14 24,48 31,75 39,41 43,65 47,45 48,49 Cuneo 14,73 14,31 15,27 20,09 24,15 27,68 31,28 Asti 8,85 12,57 13,17 16,51 18,68 26,31 37,85 Alessandria 14,04 17,09 17,02 19,35 23,59 31,27 25,72 Biella 16,15 18,48 20,03 20,85 24,02 31,13 31,40 Verbano Cusio Ossola 17,69 21,24 26,08 36,27 45,89 46,03 46,19 Piemonte 11,51 16,62 17,90 20,96 24,14 27,48 31,20 Aosta 13,63 14,90 11,66 19,33 20,66 24,45 26,86 Valle d’Aosta 13,63 14,90 11,66 19,33 20,66 24,45 26,86 Varese 32,93 36,17 40,13 41,87 37,60 45,20 49,48 Como 24,86 26,90 27,91 31,16 31,88 35,29 37,82 Sondrio 26,50 29,34 31,28 32,91 32,16 33,09 34,94 Milano 39,88 39,92 40,73 42,64 40,49 41,58 42,71 Bergamo 43,60 48,71 49,57 51,26 48,46 48,39 50,00 Brescia 18,36 22,10 24,14 26,88 28,87 30,49 31,76 Pavia 18,87 21,33 20,06 24,72 21,82 22,23 22,39 Cremona 27,30 33,24 35,73 42,00 49,78 49,65 52,19 Mantova 23,97 25,93 26,28 28,23 33,41 34,88 37,92 Lecco 31,42 41,18 45,16 48,83 54,29 55,82 55,94 Lodi 35,00 38,05 43,01 42,90 42,19 45,63 49,96 Lombardia 32,84 35,29 36,57 38,91 38,19 39,89 41,55 Bolzano - Bozen 32,59 37,72 37,16 35,34 37,92 40,55 41,07 Trento 10,21 11,43 14,47 15,16 19,72 21,06 33,10 Trentino Alto Adige 18,48 21,51 23,55 22,80 27,11 29,58 36,47 Verona 14,38 18,96 23,03 30,39 34,84 35,25 36,86 Vicenza 21,32 29,95 38,54 45,57 49,39 51,07 53,60 Belluno 16,10 17,50 19,45 24,57 24,66 28,44 31,27 Treviso 30,96 38,00 41,22 46,09 52,49 58,73 60,96 Venezia 14,66 20,42 23,75 29,91 29,49 27,69 30,60 Padova 18,07 25,85 31,65 38,98 47,99 52,89 53,83 Rovigo 8,01 11,89 13,88 23,08 29,48 39,74 42,25 Veneto 18,31 24,62 29,09 35,81 40,00 42,40 44,86 Udine 14,79 20,67 26,10 26,91 28,30 29,56 29,81 Gorizia 16,55 21,15 23,69 29,85 26,53 25,17 27,33 Trieste 8,38 9,57 13,11 13,18 13,20 14,88 14,07 Pordenone 12,98 15,51 20,33 24,41 28,21 33,19 34,92 Friuli Venezia Giulia 13,21 17,28 21,95 23,98 25,14 26,84 27,63 Imperia 9,63 12,05 13,46 15,28 15,00 15,45 16,13 Savona 6,49 8,62 13,67 14,26 12,86 15,24 16,05 Genova 8,20 9,26 12,56 12,38 15,48 21,47 17,35 La Spezia 8,70 8,99 12,05 12,92 15,53 18,01 18,94 Liguria 8,13 9,47 12,84 13,23 14,90 18,95 17,12 Piacenza 21,17 23,84 24,42 23,54 26,22 27,16 30,08 Parma 14,18 17,23 18,24 21,53 22,47 25,50 31,34 Reggio Emilia 26,37 31,60 34,69 36,96 39,93 40,02 43,11 Modena 17,65 19,07 22,63 24,94 26,47 28,46 30,78 Bologna 13,74 16,93 17,69 17,58 20,53 17,46 18,31 Ferrara 13,17 18,91 21,73 20,65 29,06 30,34 33,69 Ravenna 12,90 16,78 19,11 24,95 31,03 30,19 34,04

158

Province and region 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 12,45 20,17 21,10 18,61 20,38 18,05 19,28 Rimini 15,10 16,44 15,22 27,40 18,80 16,45 21,08 Emilia Romagna 15,96 19,59 21,56 23,87 25,83 25,54 28,69 Massa Carrara 11,90 19,04 22,15 24,55 22,79 26,45 26,19 Lucca 20,68 28,43 25,99 26,06 30,02 30,76 28,37 Pistoia 8,95 13,77 16,55 18,24 21,59 27,98 30,66 Firenze 14,88 17,51 25,11 27,71 28,44 29,94 31,79 Livorno 11,47 13,32 17,55 19,58 22,83 25,74 26,41 Pisa 9,95 12,08 16,44 21,40 23,02 26,09 30,87 Arezzo 10,59 12,58 14,36 17,07 17,94 18,59 20,19 Siena 11,41 16,31 22,52 27,15 25,48 26,62 31,59 Grosseto 3,59 6,08 13,26 15,99 13,42 17,92 27,39 Prato 13,88 21,12 21,55 26,98 27,07 30,52 31,41 Toscana 12,77 16,74 20,91 23,60 24,67 27,07 29,15 Perugia 6,61 7,84 9,34 10,64 7,29 12,60 15,59 Terni 3,74 6,92 8,69 12,24 13,10 17,86 25,21 Umbria 5,86 7,61 9,18 11,05 8,77 13,92 18,08 Pesaro e Urbino 8,69 11,42 13,16 16,29 15,28 18,57 17,37 Ancona 8,01 13,02 12,41 13,99 13,53 14,67 17,15 Macerata 5,11 5,64 6,40 16,37 7,56 20,97 25,31 Ascoli Piceno 5,92 3,93 9,76 9,35 6,61 8,14 11,91 Marche 7,18 9,22 11,07 13,88 11,37 15,27 17,46 Viterbo 6,76 6,02 5,73 5,37 4,81 5,26 6,20 Rieti 2,32 3,97 4,53 7,71 6,00 4,59 3,78 Roma 6,15 6,31 4,63 5,55 5,77 7,96 13,31 Latina 1,52 1,98 1,95 2,29 4,19 5,83 6,06 Frosinone 2,00 3,24 3,19 3,11 3,31 3,42 3,51 Lazio 5,42 5,71 4,34 5,12 5,42 7,27 11,63 L'Aquila 13,22 17,10 13,90 19,06 9,46 10,44 10,06 Teramo 5,28 8,05 9,74 14,97 16,57 17,86 20,26 Pescara 2,29 3,80 4,50 6,07 5,53 7,28 10,10 Chieti 2,92 4,17 5,36 9,07 9,90 13,60 14,50 Abruzzo 6,23 8,30 8,27 12,33 10,46 12,44 13,89 Campobasso 1,68 2,30 2,05 2,81 3,49 2,77 4,72 Isernia 1,72 1,87 3,04 3,26 2,06 2,79 3,85 Molise 1,69 2,21 2,23 2,91 3,17 2,78 4,52 Caserta 2,20 1,01 2,19 3,60 7,05 10,60 14,18 Benevento 1,69 1,76 6,81 8,64 7,81 7,99 8,64 Napoli 0,57 4,31 5,26 9,19 9,04 7,35 7,66 Avellino 6,46 4,31 5,00 7,54 9,44 10,13 11,99 Salerno 1,96 2,63 3,34 11,23 11,72 15,03 18,18 Campania 1,49 3,54 4,50 8,33 9,14 9,36 10,79 Foggia 1,48 3,26 6,03 6,80 6,35 6,98 10,12 Bari 2,92 5,18 7,20 9,04 12,13 10,49 10,28 Taranto 1,34 2,35 2,39 3,35 5,87 7,80 7,89 Brindisi 1,74 2,62 3,26 4,05 4,13 4,02 5,42 Lecce 3,56 4,35 5,66 7,15 6,94 7,45 7,82 Puglia 2,39 4,04 5,67 7,01 8,52 8,26 9,06 Potenza 3,49 3,73 4,42 5,43 5,79 6,03 7,94 Matera 0,51 0,95 3,22 5,22 5,10 5,11 4,40 Basilicata 2,29 2,77 3,98 5,35 5,54 5,69 6,65 Cosenza 1,33 2,04 2,07 4,13 5,87 5,87 6,87 Catanzaro 3,17 2,13 2,90 2,90 7,02 8,75 6,63 Reggio di Calabria 4,33 0,47 1,09 0,48 2,62 4,82 9,87

159

Province and region 1998 1999 2000 2001 2002 2003 2004 Crotone 0,16 0,13 0,09 0,31 4,08 6,50 8,31 Vibo Valentia 0,23 0,30 0,54 1,96 4,09 4,47 6,52 Calabria 2,02 1,31 1,62 2,35 4,78 6,11 7,75 Trapani 6,26 6,40 9,00 4,99 5,50 7,55 16,62 Palermo 1,60 3,42 3,94 5,24 4,30 5,66 6,97 Messina 0,63 1,82 2,17 2,40 1,68 2,85 3,83 Agrigento 0,08 2,01 1,43 2,93 4,93 5,14 8,79 Caltanissetta 0,29 0,90 1,58 1,69 3,06 4,46 5,01 Enna 0,27 0,66 0,97 2,06 3,41 5,09 4,88 Catania 1,03 0,82 3,53 4,72 3,49 4,75 6,91 Ragusa 1,58 0,73 2,14 8,58 10,84 11,12 13,81 Siracusa 0,17 0,41 1,23 1,80 2,77 3,43 3,12 Sicilia 1,40 2,23 3,37 4,27 4,02 5,30 7,83 Sassari 1,40 2,45 3,61 3,97 2,37 5,62 4,14 Nuoro 1,00 0,95 1,25 1,73 2,12 3,09 6,21 Cagliari 1,65 3,27 3,98 7,20 4,58 7,07 9,97 Oristano 4,61 3,43 4,86 2,84 3,37 6,40 6,89 Sardegna 1,79 2,79 3,67 5,18 3,46 6,03 7,45

Italy 12,15 14,83 16,57 18,69 19,46 20,92 23,63

160

Table A3.4 – Total number of patents per thousand of inhabitants, provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 2,61 2,62 1,43 2,56 1,58 2,28 2,26 Vercelli 0,25 0,15 0,20 0,20 0,39 0,40 0,24 Novara 0,22 0,26 0,29 0,28 0,25 0,00 0,41 Cuneo 0,32 0,24 0,43 0,36 0,40 0,48 0,41 Asti 0,75 0,70 0,00 0,92 0,86 0,91 0,70 Alessandria 0,33 0,20 0,01 0,50 0,49 0,57 0,50 Biella 0,10 0,16 0,22 0,14 0,30 0,27 0,38 Verbano Cusio Ossola 0,08 0,14 0,14 0,12 0,19 0,23 0,22 Piemonte 1,49 1,48 0,84 1,50 1,01 1,37 1,37 Aosta 0,09 0,17 0,00 0,05 0,14 0,09 0,10 Valle d’Aosta 0,09 0,17 0,00 0,05 0,14 0,09 0,10 Varese 0,26 0,34 0,35 0,38 0,44 0,44 0,44 Como 0,23 0,21 0,26 0,32 0,31 0,31 0,40 Sondrio 0,09 0,23 0,34 0,18 0,24 0,21 0,15 Milano 4,66 3,65 4,94 4,70 4,53 4,07 4,24 Bergamo 0,37 0,42 0,53 0,43 0,41 0,37 0,49 Brescia 0,61 0,66 0,44 0,69 0,74 0,67 0,60 Pavia 0,16 0,25 0,28 0,28 0,27 0,27 0,34 Cremona 0,52 0,30 0,56 0,52 0,49 0,48 0,49 Mantova 0,40 0,09 0,01 0,48 0,40 0,41 0,41 Lecco 0,12 0,15 0,00 0,24 0,27 0,33 0,37 Lodi 0,00 0,00 0,00 0,00 0,00 0,18 0,31 Lombardia 2,13 1,71 2,23 2,18 2,11 1,91 2,00 Bolzano - Bozen 0,66 0,67 0,72 0,79 0,67 0,83 0,70 Trento 0,63 0,47 0,58 0,64 0,58 0,50 0,53 Trentino Alto Adige 0,65 0,57 0,65 0,72 0,63 0,66 0,61 Verona 1,01 1,08 1,28 1,32 1,14 1,17 1,24 Vicenza 1,13 1,21 1,35 1,34 1,21 1,09 1,34 Belluno 0,54 0,49 0,63 0,51 0,52 0,46 0,61 Treviso 1,09 1,00 1,37 1,31 1,29 1,20 1,20 Venezia 0,46 0,42 0,45 0,47 0,41 0,48 0,45 Padova 1,55 1,62 1,80 1,77 1,87 1,60 1,71 Rovigo 0,27 0,22 0,23 0,01 0,00 0,30 0,43 Veneto 0,98 1,00 1,17 1,15 1,10 1,04 1,13 Udine 1,58 1,62 1,83 1,49 1,65 1,61 1,45 Gorizia 0,24 0,20 0,11 0,32 0,32 0,44 0,43 Trieste 0,62 0,49 0,65 0,64 0,69 0,69 0,71 Pordenone 1,18 1,25 1,15 1,30 1,41 0,97 1,04 Friuli Venezia Giulia 1,13 1,13 1,22 1,13 1,24 1,13 1,08 Imperia 0,19 0,31 0,25 0,37 0,39 0,46 0,48 Savona 0,50 0,39 0,55 0,51 0,51 0,54 0,56 Genova 0,72 0,76 0,71 0,73 0,63 0,68 0,74 La Spezia 0,21 0,23 0,08 0,29 0,28 0,28 0,28 Liguria 0,54 0,56 0,54 0,58 0,53 0,57 0,61 Piacenza 0,05 0,73 0,93 0,91 0,88 0,46 0,94 Parma 0,85 0,86 1,01 0,88 0,80 1,10 0,90 Reggio Emilia 0,93 1,42 0,03 1,44 1,20 1,02 1,43 Modena 1,30 1,49 1,17 1,35 1,03 1,13 1,56 Bologna 2,25 2,46 2,04 2,57 2,70 2,32 2,36 Ferrara 1,05 1,34 1,32 1,25 1,10 1,15 1,35 Ravenna 0,35 0,05 0,00 0,56 0,69 1,35 1,32 Forlì - Cesena 0,58 0,39 0,90 0,85 1,05 0,83 0,67

161

Province and region 1998 1999 2000 2001 2002 2003 2004 Rimini 0,89 1,20 1,48 1,51 1,45 1,42 1,49 Emilia Romagna 1,15 1,33 1,12 1,45 1,41 1,36 1,50 Massa Carrara 0,21 0,16 0,20 0,21 0,30 0,31 0,34 Lucca 0,39 0,36 0,49 0,64 0,55 0,60 0,68 Pistoia 0,63 0,73 1,00 0,82 0,89 0,81 0,86 Firenze 2,08 2,14 2,35 1,90 2,06 2,14 2,12 Livorno 0,20 0,26 0,35 0,52 0,33 0,34 0,40 Pisa 0,76 1,07 1,10 1,02 0,82 0,97 1,14 Arezzo 1,02 1,01 1,16 1,23 1,07 1,16 1,04 Siena 0,30 0,33 0,03 0,57 0,58 0,56 0,67 Grosseto 0,29 0,26 0,33 0,44 0,54 0,58 0,80 Prato 0,48 0,56 0,77 0,85 0,94 0,93 1,15 Toscana 0,92 0,99 1,10 1,05 1,05 1,10 1,16 Perugia 0,50 0,62 0,82 0,76 0,99 0,85 0,90 Terni 0,41 0,25 0,38 0,35 0,32 0,39 0,59 Umbria 0,48 0,52 0,70 0,65 0,81 0,73 0,81 Pesaro e Urbino 0,49 0,42 0,59 0,52 0,43 0,45 0,51 Ancona 1,02 0,97 0,75 0,84 0,94 1,01 0,95 Macerata 1,43 1,73 1,86 1,98 1,64 1,84 2,34 Ascoli Piceno 0,24 0,27 0,05 0,38 0,42 0,39 0,60 Marche 0,78 0,82 0,76 0,88 0,83 0,89 1,05 Viterbo 0,21 0,22 0,40 0,37 0,37 0,60 0,43 Rieti 0,06 0,06 0,12 0,13 0,11 0,28 0,22 Roma 2,13 2,18 2,15 2,30 2,09 2,05 2,15 Latina 0,18 0,23 0,25 0,27 0,25 0,28 0,32 Frosinone 0,13 0,22 0,26 0,27 0,34 0,37 0,35 Lazio 1,58 1,64 1,63 1,74 1,59 1,58 1,65 L'Aquila 0,11 0,12 0,19 0,14 0,18 0,18 0,21 Teramo 0,39 0,20 0,00 0,32 0,44 0,50 0,58 Pescara 0,84 0,52 0,90 0,91 0,91 0,73 0,80 Chieti 0,14 0,16 0,22 0,23 0,35 0,39 0,52 Abruzzo 0,35 0,24 0,32 0,39 0,47 0,45 0,52 Campobasso 0,09 0,03 0,01 0,20 0,23 0,22 0,24 Isernia 0,21 0,15 0,00 0,41 0,27 0,29 0,19 Molise 0,13 0,07 0,01 0,26 0,24 0,24 0,23 Caserta 0,09 0,00 0,00 0,08 0,09 0,16 0,10 Benevento 0,17 0,00 0,00 0,13 0,15 0,22 0,26 Napoli 0,26 0,00 0,00 0,40 0,43 0,47 0,51 Avellino 0,16 0,00 0,00 0,19 0,23 0,17 0,14 Salerno 0,11 0,00 0,00 0,18 0,19 0,10 0,18 Campania 0,19 0,00 0,00 0,28 0,30 0,32 0,34 Foggia 0,09 0,08 0,10 0,15 0,20 0,22 0,16 Bari 0,31 0,36 0,01 0,47 0,53 0,50 0,49 Taranto 0,02 0,00 0,00 0,06 0,09 0,16 0,16 Brindisi 0,06 0,01 0,00 0,13 0,15 0,17 0,16 Lecce 0,11 0,01 0,17 0,26 0,31 0,17 0,38 Puglia 0,17 0,16 0,05 0,28 0,33 0,31 0,34 Potenza 0,09 0,14 0,11 0,22 0,30 0,24 0,26 Matera 0,13 0,13 0,15 0,22 0,29 0,24 0,16 Basilicata 0,10 0,14 0,12 0,22 0,29 0,24 0,23 Cosenza 0,10 0,14 0,11 0,12 0,16 0,17 0,24 Catanzaro 0,15 0,25 0,20 0,28 0,30 0,24 0,29 Reggio di Calabria 0,11 0,10 0,15 0,14 0,18 0,16 0,13 Crotone 0,00 0,05 0,10 0,05 0,07 0,12 0,19

162

Province and region 1998 1999 2000 2001 2002 2003 2004 Vibo Valentia 0,01 0,02 0,04 0,06 0,05 0,07 0,17 Calabria 0,10 0,13 0,13 0,14 0,18 0,17 0,21 Trapani 0,11 0,13 0,18 0,26 0,26 0,28 0,31 Palermo 0,16 0,15 0,23 0,25 0,24 0,25 0,27 Messina 0,10 0,09 0,10 0,15 0,14 0,19 0,24 Agrigento 0,06 0,07 0,12 0,12 0,13 0,18 0,22 Caltanissetta 0,12 0,11 0,28 0,34 0,51 0,54 0,45 Enna 0,04 0,05 0,09 0,00 0,00 0,00 0,00 Catania 0,21 0,27 0,30 0,34 0,36 0,26 0,34 Ragusa 0,16 0,16 0,20 0,17 0,23 0,19 0,18 Siracusa 0,05 0,07 0,09 0,11 0,09 0,11 0,12 Sicilia 0,13 0,15 0,20 0,23 0,24 0,23 0,26 Sassari 0,08 0,10 0,16 0,21 0,20 0,24 0,19 Nuoro 0,10 0,13 0,14 0,14 0,00 0,00 0,00 Cagliari 0,20 0,20 0,32 0,31 0,32 0,30 0,29 Oristano 0,10 0,05 0,04 0,07 0,01 0,00 0,06 Sardegna 0,14 0,15 0,22 0,23 0,21 0,20 0,19

Italy 0,94 0,88 0,92 1,04 0,99 0,98 1,04

163

Table A3.5 – Ratio of exports over value-added, Industry in a Strict Sense, provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 1,02 0,98 1,12 1,13 1,11 1,14 1,15 Vercelli 1,16 1,17 1,27 1,40 1,39 1,41 1,46 Novara 1,07 1,12 1,10 1,21 1,13 1,10 1,16 Cuneo 1,00 0,99 1,05 1,02 1,08 1,10 1,21 Asti 0,85 0,90 0,94 0,91 1,04 1,08 1,02 Alessandria 0,99 1,02 1,11 0,98 1,00 1,00 1,11 Biella 0,82 0,82 0,96 1,08 1,05 0,99 0,99 Verbano Cusio Ossola 0,60 0,56 0,66 0,72 0,69 0,66 0,73 Piemonte 1,00 0,98 1,08 1,09 1,09 1,11 1,14 Aosta 0,81 0,80 1,11 1,00 0,84 0,90 1,06 Valle d’Aosta 0,81 0,80 1,11 1,00 0,84 0,90 1,06 Varese 0,92 0,99 1,08 1,02 1,00 1,04 1,03 Como 1,15 1,18 1,27 1,19 1,13 1,06 1,06 Sondrio 0,53 0,49 0,60 0,60 0,56 0,55 0,53 Milano 0,93 0,95 1,12 1,18 1,13 1,07 1,04 Bergamo 1,01 0,98 1,09 1,10 1,05 0,95 1,12 Brescia 1,02 0,96 0,92 0,96 0,92 0,97 1,09 Pavia 0,88 0,94 1,03 1,01 1,04 1,00 1,03 Cremona 0,65 0,65 0,73 0,79 0,73 0,67 0,77 Mantova 1,05 1,12 1,27 1,26 1,24 1,21 1,18 Lecco 0,85 0,90 0,96 0,96 0,90 0,75 0,84 Lodi 0,44 0,54 0,59 0,63 0,64 0,66 0,73 Lombardia 0,94 0,95 1,07 1,10 1,05 1,01 1,03 Bolzano - Bozen 1,02 1,07 1,10 1,07 1,08 1,08 1,22 Trento 0,89 0,90 1,01 1,01 0,94 1,01 1,04 Trentino Alto Adige 0,95 0,98 1,05 1,04 1,00 1,04 1,12 Verona 1,16 1,15 1,16 1,22 1,21 1,22 1,19 Vicenza 1,33 1,42 1,56 1,63 1,65 1,29 1,63 Belluno 0,83 0,83 0,96 1,03 1,12 1,12 1,15 Treviso 1,16 1,17 1,29 1,38 1,37 1,35 1,37 Venezia 0,85 0,97 1,28 1,26 1,35 1,12 1,14 Padova 1,01 1,00 1,08 1,12 1,11 1,01 1,10 Rovigo 0,55 0,57 0,62 0,71 0,74 0,68 0,68 Veneto 1,09 1,13 1,25 1,32 1,33 1,19 1,29 Udine 1,29 1,31 1,34 1,40 1,27 1,11 1,33 Gorizia 2,56 2,03 3,03 2,39 3,01 1,94 3,29 Trieste 1,67 1,45 1,65 1,39 1,21 1,19 1,40 Pordenone 1,26 1,22 1,37 1,64 1,50 1,41 1,55 Friuli Venezia Giulia 1,46 1,37 1,56 1,57 1,51 1,30 1,60 Imperia 0,58 0,65 0,52 0,59 0,61 0,54 0,64 Savona 0,67 0,68 0,70 0,77 0,76 0,74 0,76 Genova 0,54 0,48 0,62 0,72 0,70 0,65 0,63 La Spezia 0,39 0,35 0,45 0,50 0,39 0,46 0,50 Liguria 0,54 0,50 0,60 0,69 0,65 0,62 0,63 Piacenza 0,77 0,74 0,80 0,86 0,86 0,85 0,94 Parma 0,86 0,85 0,93 0,92 1,02 1,02 1,15 Reggio Emilia 1,13 1,11 1,21 1,24 1,29 1,17 1,38 Modena 1,11 1,12 1,22 1,21 1,26 1,22 1,35 Bologna 1,02 1,03 1,13 1,18 1,13 1,12 1,25 Ferrara 1,06 0,98 0,98 1,02 0,96 1,02 1,12 Ravenna 0,93 0,98 1,08 0,93 0,84 0,80 0,89 Forlì - Cesena 0,85 0,82 0,97 1,01 0,93 0,84 0,94

164

Province and region 1998 1999 2000 2001 2002 2003 2004 Rimini 0,85 0,87 0,95 0,97 0,95 0,90 0,95 Emilia Romagna 1,00 1,00 1,10 1,11 1,11 1,07 1,20 Massa Carrara 1,69 1,53 1,58 1,49 1,53 1,54 2,18 Lucca 1,17 1,10 1,26 1,28 1,35 1,36 1,42 Pistoia 0,98 0,96 1,04 1,08 1,05 0,93 0,90 Firenze 0,96 0,99 1,10 1,07 1,08 1,00 1,08 Livorno 0,51 0,45 0,73 0,78 0,75 0,71 0,76 Pisa 0,76 0,76 0,89 0,93 0,90 0,78 0,89 Arezzo 1,37 1,39 1,93 1,67 1,58 1,32 1,31 Siena 0,79 0,84 0,90 1,12 1,09 1,08 1,13 Grosseto 0,32 0,38 0,45 0,43 0,43 0,44 0,46 Prato 1,45 1,46 1,49 1,44 1,35 1,20 1,20 Toscana 1,03 1,02 1,18 1,17 1,15 1,05 1,12 Perugia 0,50 0,50 0,55 0,55 0,54 0,50 0,54 Terni 0,83 0,77 0,88 0,80 0,83 0,79 1,00 Umbria 0,59 0,57 0,64 0,62 0,63 0,59 0,68 Pesaro e Urbino 0,94 0,85 0,98 1,01 0,97 0,89 0,91 Ancona 1,16 1,01 1,15 1,17 1,29 1,36 1,42 Macerata 0,84 0,79 0,92 0,99 0,96 0,95 0,87 Ascoli Piceno 0,92 0,83 1,03 1,08 1,01 0,96 0,87 Marche 0,99 0,89 1,04 1,08 1,08 1,07 1,06 Viterbo 0,35 0,34 0,39 0,41 0,42 0,37 0,40 Rieti 0,70 1,81 2,38 2,48 2,08 2,40 2,12 Roma 0,56 0,57 0,58 0,47 0,52 0,41 0,44 Latina 0,73 0,89 0,90 0,95 0,91 0,88 0,95 Frosinone 0,80 0,85 1,61 1,24 1,15 0,97 0,90 Lazio 0,61 0,66 0,79 0,66 0,68 0,59 0,60 L'Aquila 0,82 0,59 1,14 1,28 1,30 1,17 1,33 Teramo 0,67 0,70 0,68 0,68 0,71 0,68 0,71 Pescara 0,34 0,32 0,35 0,39 0,40 0,35 0,35 Chieti 1,67 1,41 1,55 1,60 1,54 1,53 1,78 Abruzzo 0,99 0,85 1,00 1,04 1,06 1,01 1,13 Campobasso 0,32 0,33 0,35 0,34 0,33 0,26 0,26 Isernia 1,08 1,01 0,89 1,02 1,04 1,05 1,16 Molise 0,56 0,55 0,54 0,58 0,57 0,52 0,56 Caserta 0,56 0,54 0,63 0,56 0,48 0,34 0,38 Benevento 0,12 0,12 0,15 0,19 0,18 0,12 0,15 Napoli 0,63 0,65 0,80 0,83 0,78 0,61 0,65 Avellino 0,74 0,67 0,75 0,68 0,51 0,42 0,57 Salerno 0,64 0,55 0,53 0,61 0,61 0,57 0,59 Campania 0,62 0,60 0,69 0,71 0,64 0,52 0,57 Foggia 0,50 0,58 0,61 0,42 0,34 0,29 0,30 Bari 0,62 0,67 0,79 0,84 0,74 0,68 0,74 Taranto 0,71 0,45 0,49 0,51 0,52 0,56 0,91 Brindisi 0,28 0,45 0,74 0,63 0,73 0,76 0,90 Lecce 0,61 0,60 0,62 0,67 0,57 0,50 0,49 Puglia 0,58 0,58 0,67 0,67 0,62 0,59 0,70 Potenza 0,67 0,75 0,62 0,66 0,81 0,83 0,62 Matera 0,57 0,61 0,76 0,66 0,68 0,90 0,94 Basilicata 0,64 0,72 0,65 0,66 0,78 0,85 0,70 Cosenza 0,05 0,05 0,06 0,05 0,05 0,05 0,05 Catanzaro 0,06 0,05 0,05 0,06 0,05 0,04 0,05 Reggio di Calabria 0,15 0,19 0,21 0,20 0,18 0,19 0,21 Crotone 0,07 0,05 0,04 0,07 0,09 0,13 0,15

165

Province and region 1998 1999 2000 2001 2002 2003 2004 Vibo Valentia 0,39 0,19 0,43 0,17 0,19 0,20 0,17 Calabria 0,10 0,09 0,12 0,09 0,09 0,10 0,11 Trapani 0,36 0,45 0,41 0,34 0,35 0,26 0,27 Palermo 0,48 0,30 0,30 0,27 0,26 0,23 0,23 Messina 0,38 0,36 0,61 0,46 0,42 0,43 0,71 Agrigento 0,07 0,07 0,06 0,08 0,10 0,09 0,11 Caltanissetta 0,19 0,23 0,62 0,63 0,34 0,32 0,33 Enna 0,07 0,08 0,08 0,09 0,08 0,09 0,08 Catania 0,30 0,40 0,58 0,45 0,55 0,48 0,54 Ragusa 0,12 0,15 0,18 0,17 0,20 0,17 0,21 Siracusa 1,00 1,12 2,32 2,45 2,22 2,42 2,72 Sicilia 0,45 0,45 0,73 0,67 0,62 0,61 0,70 Sassari 0,48 0,37 0,40 0,38 0,36 0,39 0,41 Nuoro 0,16 0,21 0,13 0,21 0,14 0,17 0,36 Cagliari 0,62 0,76 1,23 1,02 0,90 0,99 1,15 Oristano 0,39 0,20 0,26 0,27 0,23 0,20 0,12 Sardegna 0,50 0,53 0,80 0,68 0,60 0,66 0,77

Italy 0,90 0,90 1,02 1,03 1,01 0,95 1,02

166

Table A3.6 – Population density (inhabitants per Km2), provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 319 318 318 317 318 321 328 Vercelli 86 85 85 85 85 85 85 Novara 253 254 255 256 259 262 264 Cuneo 80 80 80 81 81 82 83 Asti 138 138 138 138 138 140 141 Alessandria 119 119 118 117 117 119 121 Biella 206 206 205 205 205 205 205 Verbano Cusio Ossola 71 71 71 70 71 71 72 Piemonte 167 166 166 166 167 168 170 Aosta 36 36 37 37 37 37 38 Valle d’Aosta 36 36 37 37 37 37 38 Varese 672 674 676 678 683 692 703 Como 413 414 416 418 422 428 435 Sondrio 55 55 55 55 55 56 56 Milano 1.867 1.867 1.867 1.867 1.875 1.903 1.935 Bergamo 349 352 355 358 362 369 376 Brescia 226 228 230 232 235 240 244 Pavia 166 166 166 167 168 170 172 Cremona 188 188 189 190 191 194 196 Mantova 160 160 161 162 163 165 167 Lecco 375 377 379 382 386 391 395 Lodi 247 249 251 253 258 263 267 Lombardia 375 376 377 379 382 387 394 Bolzano - Bozen 62 62 62 63 63 64 64 Trento 75 76 76 77 78 79 80 Trentino Alto Adige 68 68 69 69 70 71 72 Verona 259 261 263 265 269 272 276 Vicenza 285 287 290 292 296 301 305 Belluno 57 57 57 57 57 58 58 Treviso 314 316 319 321 326 333 339 Venezia 328 328 329 329 330 334 336 Padova 391 393 395 397 400 407 412 Rovigo 137 136 136 135 136 136 137 Veneto 243 244 245 246 249 252 255 Udine 105 105 106 106 106 107 108 Gorizia 292 292 293 293 297 299 302 Trieste 1.160 1.154 1.147 1.142 1.136 1.130 1.124 Pordenone 123 124 125 126 128 129 131 Friuli Venezia Giulia 150 150 150 151 152 152 153 Imperia 180 179 178 177 178 180 187 Savona 178 177 177 176 179 181 182 Genova 487 484 480 477 475 474 476 La Spezia 248 247 246 245 244 247 249 Liguria 295 293 291 290 290 291 294 Piacenza 102 102 102 102 103 105 106 Parma 113 113 114 114 115 116 120 Reggio Emilia 191 194 196 198 202 206 212 Modena 230 232 234 236 240 243 246 Bologna 244 245 246 247 250 253 255 Ferrara 132 132 131 131 131 132 133 Ravenna 186 186 187 187 189 191 197 Forlì - Cesena 148 149 150 151 152 154 156

167

Province and region 1998 1999 2000 2001 2002 2003 2004 Rimini 499 504 508 510 518 528 538 Emilia Romagna 178 178 179 180 182 184 188 Massa Carrara 171 171 171 171 171 172 174 Lucca 210 210 210 210 211 213 214 Pistoia 276 277 277 278 281 284 287 Firenze 267 266 266 266 266 273 275 Livorno 270 269 270 269 270 271 273 Pisa 156 156 157 157 158 160 161 Arezzo 99 99 100 100 101 102 103 Siena 66 66 66 66 67 68 68 Grosseto 47 47 47 47 47 48 48 Prato 610 616 622 626 633 639 654 Toscana 152 152 152 152 153 155 156 Perugia 94 95 95 96 97 98 100 Terni 104 104 104 104 104 106 107 Umbria 97 97 97 98 99 100 102 Pesaro e Urbino 119 120 121 121 123 125 126 Ancona 228 229 230 231 233 236 238 Macerata 108 108 108 109 110 112 113 Ascoli Piceno 176 176 176 177 178 180 182 Marche 150 150 151 152 153 155 157 Viterbo 80 80 80 80 81 82 83 Rieti 53 54 54 54 54 55 56 Roma 694 693 692 692 692 698 708 Latina 218 218 218 218 221 228 231 Frosinone 149 149 149 149 150 150 151 Lazio 298 297 297 297 299 302 306 L'Aquila 59 59 59 59 59 60 60 Teramo 146 147 147 148 148 150 152 Pescara 240 240 241 241 255 257 259 Chieti 148 148 148 148 148 149 151 Abruzzo 117 117 117 117 118 119 121 Campobasso 80 80 80 79 79 80 80 Isernia 59 59 59 59 59 59 59 Molise 73 73 72 72 72 72 73 Caserta 322 323 323 323 324 329 333 Benevento 140 139 139 139 138 139 140 Napoli 2.622 2.620 2.615 2.613 2.626 2.635 2.641 Avellino 155 155 154 154 155 156 157 Salerno 219 219 219 218 219 220 222 Campania 421 421 420 420 421 424 426 Foggia 97 97 96 96 96 96 96 Bari 303 303 303 304 304 306 310 Taranto 240 240 239 238 239 239 239 Brindisi 222 220 220 219 218 218 218 Lecce 289 288 286 285 287 290 292 Puglia 209 208 208 208 208 209 210 Potenza 61 61 60 60 60 60 60 Matera 60 60 59 59 59 59 59 Basilicata 60 60 60 60 60 60 60 Cosenza 112 111 111 110 110 110 110 Catanzaro 157 156 155 154 154 154 154 Reggio di Calabria 179 178 178 177 177 178 178 Crotone 102 102 101 101 101 101 101

168

Province and region 1998 1999 2000 2001 2002 2003 2004 Vibo Valentia 153 152 151 150 149 149 148 Calabria 135 134 134 133 133 133 133 Trapani 174 173 173 173 173 174 176 Palermo 249 249 248 247 248 248 248 Messina 203 203 204 204 203 203 203 Agrigento 152 150 149 147 148 150 150 Caltanissetta 130 130 129 129 128 130 129 Enna 71 70 70 69 69 68 68 Catania 297 297 297 297 298 300 302 Ragusa 183 183 183 183 184 189 190 Siracusa 190 189 188 188 188 188 189 Sicilia 195 194 194 193 193 195 195 Sassari 60 60 60 60 61 61 62 Nuoro 38 38 38 38 38 37 37 Cagliari 111 111 110 110 111 111 111 Oristano 59 59 58 58 58 58 59 Sardegna 68 68 68 68 68 68 68

Italy 189 189 189 189 190 192 194

169

Table A3.7 – Local units density (units per Km2), provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 4,1 4,1 4,2 4,3 4,4 4,4 4,4 Vercelli 1,2 1,2 1,2 1,3 1,3 1,2 1,2 Novara 4,2 4,2 4,2 4,2 4,2 4,2 4,1 Cuneo 1,2 1,2 1,2 1,2 1,3 1,3 1,3 Asti 1,8 1,9 1,9 1,9 2,0 2,0 2,1 Alessandria 1,8 1,8 1,8 1,9 1,9 1,9 1,9 Biella 3,8 3,8 3,8 3,8 3,8 3,8 3,7 Verbano Cusio Ossola 1,2 1,2 1,2 1,2 1,2 1,2 1,1 Piemonte 2,3 2,3 2,4 2,4 2,5 2,5 2,5 Aosta 0,4 0,4 0,4 0,4 0,4 0,4 0,4 Valle d’Aosta 0,4 0,4 0,4 0,4 0,4 0,4 0,4 Varese 13,2 13,1 13,1 13,0 12,9 12,8 12,6 Como 9,0 8,8 8,8 8,8 8,7 8,6 8,4 Sondrio 0,7 0,7 0,7 0,7 0,7 0,7 0,7 Milano 31,7 31,6 31,6 31,5 31,0 30,6 30,6 Bergamo 5,6 5,7 5,9 6,0 6,1 6,2 6,3 Brescia 4,2 4,3 4,3 4,4 4,5 4,5 4,6 Pavia 2,5 2,5 2,5 2,5 2,5 2,5 2,5 Cremona 2,6 2,6 2,7 2,7 2,8 2,8 2,8 Mantova 2,7 2,7 2,7 2,8 2,8 2,8 2,8 Lecco 7,8 7,6 7,7 7,9 7,9 7,9 7,8 Lodi 3,0 3,0 3,1 3,3 3,3 3,3 3,3 Lombardia 6,5 6,5 6,5 6,6 6,6 6,5 6,5 Bolzano - Bozen 0,8 0,8 0,8 0,8 0,8 0,8 0,8 Trento 0,9 0,9 0,9 1,0 1,0 1,0 1,0 Trentino Alto Adige 0,8 0,8 0,9 0,9 0,9 0,9 0,9 Verona 4,2 4,2 4,3 4,4 4,5 4,5 4,5 Vicenza 6,3 6,4 6,5 6,6 6,7 6,7 6,7 Belluno 1,0 0,9 0,9 0,9 0,9 0,9 0,9 Treviso 6,5 6,6 6,6 6,6 6,6 6,6 6,6 Venezia 4,0 4,1 4,2 4,3 4,3 4,3 4,3 Padova 7,7 7,7 7,8 7,8 7,8 7,8 7,7 Rovigo 2,0 2,0 2,1 2,2 2,2 2,2 2,2 Veneto 4,3 4,4 4,4 4,5 4,5 4,5 4,5 Udine 1,6 1,6 1,6 1,6 1,6 1,6 1,6 Gorizia 3,4 3,4 3,3 3,4 3,5 3,6 3,5 Trieste 9,9 10,0 9,8 9,8 9,7 9,6 9,5 Pordenone 1,9 1,9 2,0 2,0 2,1 2,1 2,1 Friuli Venezia Giulia 2,1 2,1 2,1 2,1 2,1 2,1 2,1 Imperia 1,7 1,7 1,7 1,8 1,8 1,8 1,8 Savona 1,8 1,8 2,0 2,0 2,0 2,1 2,1 Genova 4,7 4,7 4,8 4,9 5,0 5,4 5,4 La Spezia 2,6 2,7 2,7 2,8 2,8 2,9 2,9 Liguria 2,9 2,9 3,0 3,1 3,1 3,3 3,3 Piacenza 1,4 1,4 1,5 1,5 1,5 1,6 1,6 Parma 2,1 2,0 2,1 2,2 2,2 2,2 2,2 Reggio Emilia 4,1 4,2 4,2 4,3 4,4 4,5 4,6 Modena 5,5 5,5 5,6 5,6 5,7 5,7 5,6 Bologna 3,9 4,0 4,0 4,0 4,1 4,1 4,0 Ferrara 1,5 1,5 1,5 1,6 1,6 1,6 1,6 Ravenna 2,4 2,4 2,5 2,5 2,6 2,6 2,6 Forlì - Cesena 2,4 2,4 2,4 2,5 2,5 2,5 2,6

170

Province and region 1998 1999 2000 2001 2002 2003 2004 Rimini 7,7 7,7 7,7 7,9 7,8 7,8 7,8 Emilia Romagna 3,1 3,1 3,1 3,2 3,2 3,2 3,2 Massa Carrara 2,3 2,4 2,4 2,5 2,5 2,6 2,6 Lucca 3,7 3,7 3,7 3,7 3,7 3,7 3,8 Pistoia 7,4 7,2 7,2 7,1 7,0 6,9 6,7 Firenze 5,8 5,8 5,8 5,9 5,9 5,8 5,8 Livorno 2,5 2,6 2,6 2,8 2,8 2,8 2,8 Pisa 2,6 2,6 2,7 2,7 2,7 2,7 2,7 Arezzo 2,0 2,1 2,1 2,2 2,2 2,1 2,1 Siena 1,0 1,0 1,0 1,0 1,1 1,0 1,0 Grosseto 0,4 0,5 0,5 0,5 0,5 0,5 0,5 Prato 28,3 27,9 27,8 28,1 27,6 27,0 26,5 Toscana 3,0 3,0 3,0 3,1 3,1 3,0 3,0 Perugia 1,5 1,5 1,6 1,6 1,6 1,6 1,6 Terni 1,1 1,1 1,2 1,2 1,2 1,2 1,2 Umbria 1,4 1,4 1,5 1,5 1,5 1,5 1,5 Pesaro e Urbino 2,3 2,3 2,4 2,5 2,5 2,5 2,6 Ancona 3,0 3,1 3,2 3,4 3,4 3,5 3,6 Macerata 2,2 2,2 2,3 2,4 2,4 2,4 2,4 Ascoli Piceno 3,8 3,8 3,8 3,9 4,0 4,0 4,0 Marche 2,7 2,8 2,8 2,9 3,0 3,0 3,0 Viterbo 0,7 0,8 0,8 0,8 0,9 0,9 0,9 Rieti 0,4 0,4 0,5 0,5 0,5 0,5 0,5 Roma 4,3 4,5 4,6 4,8 4,7 4,7 4,7 Latina 1,9 2,0 2,1 2,1 2,2 2,3 2,3 Frosinone 1,5 1,5 1,5 1,6 1,7 1,7 1,7 Lazio 2,1 2,2 2,2 2,3 2,3 2,4 2,3 L'Aquila 0,6 0,6 0,6 0,6 0,6 0,6 0,7 Teramo 2,4 2,4 2,5 2,6 2,7 2,8 2,8 Pescara 2,7 2,7 2,8 2,9 3,0 3,0 3,1 Chieti 1,6 1,7 1,7 1,8 1,9 1,9 2,0 Abruzzo 1,4 1,4 1,5 1,5 1,6 1,6 1,6 Campobasso 0,7 0,7 0,8 0,8 0,8 0,9 0,9 Isernia 0,5 0,5 0,6 0,6 0,6 0,6 0,6 Molise 0,6 0,7 0,7 0,7 0,8 0,8 0,8 Caserta 2,3 2,3 2,4 2,4 2,5 2,6 2,7 Benevento 1,2 1,2 1,3 1,3 1,4 1,4 1,5 Napoli 21,1 21,6 21,9 21,2 21,9 23,1 23,2 Avellino 1,6 1,6 1,7 1,7 1,8 1,9 1,9 Salerno 2,2 2,2 2,2 2,3 2,3 2,4 2,4 Campania 3,6 3,6 3,7 3,7 3,8 3,9 4,0 Foggia 0,7 0,7 0,7 0,7 0,7 0,7 0,8 Bari 3,2 3,3 3,4 3,5 3,6 3,7 3,7 Taranto 1,5 1,5 1,6 1,7 1,7 1,7 1,7 Brindisi 1,6 1,6 1,7 1,8 1,9 1,9 1,9 Lecce 3,0 3,1 3,1 3,3 3,5 3,5 3,5 Puglia 1,9 1,9 2,0 2,0 2,1 2,1 2,1 Potenza 0,5 0,6 0,6 0,6 0,6 0,6 0,6 Matera 0,5 0,5 0,5 0,6 0,6 0,6 0,6 Basilicata 0,5 0,5 0,6 0,6 0,6 0,6 0,6 Cosenza 0,8 0,9 0,9 1,0 1,0 1,0 1,1 Catanzaro 1,3 1,3 1,3 1,3 1,4 1,4 1,4 Reggio di Calabria 1,5 1,6 1,6 1,8 1,9 2,0 2,1 Crotone 0,8 0,8 0,9 1,0 1,0 1,0 1,1

171

Province and region 1998 1999 2000 2001 2002 2003 2004 Vibo Valentia 1,3 1,3 1,3 1,4 1,4 1,5 1,5 Calabria 1,1 1,1 1,1 1,2 1,3 1,3 1,4 Trapani 1,5 1,6 1,6 1,7 1,7 1,7 1,8 Palermo 1,6 1,6 1,7 1,7 1,8 1,8 1,8 Messina 1,6 1,6 1,7 1,7 1,7 1,8 1,8 Agrigento 1,0 1,0 1,1 1,1 1,1 1,1 1,1 Caltanissetta 1,2 1,2 1,2 1,2 1,2 1,2 1,3 Enna 0,5 0,5 0,5 0,5 0,5 0,5 0,6 Catania 2,6 2,6 2,7 2,7 2,8 2,8 2,8 Ragusa 1,6 1,6 1,6 1,6 1,7 1,8 1,8 Siracusa 1,2 1,2 1,3 1,4 1,4 1,4 1,5 Sicilia 1,5 1,5 1,5 1,6 1,6 1,6 1,6 Sassari 0,6 0,7 0,7 0,7 0,7 0,8 0,8 Nuoro 0,3 0,3 0,4 0,4 0,4 0,4 0,4 Cagliari 0,8 0,8 0,9 0,9 1,0 1,0 1,1 Oristano 0,5 0,5 0,5 0,5 0,5 0,5 0,5 Sardegna 0,6 0,6 0,6 0,7 0,7 0,7 0,7

Italy 2,4 2,4 2,5 2,5 2,6 2,6 2,6

172

Table A3.8 – Share (%) of the value-added of Service Industry on the total value-added, provinces of Italy: 1998-2004

Province and region 1998 1999 2000 2001 2002 2003 2004 Torino 65,00 66,15 67,07 68,11 70,00 70,82 70,83 Vercelli 59,55 61,76 62,97 63,45 64,37 65,55 65,77 Novara 55,35 56,09 55,72 57,05 56,98 59,09 58,53 Cuneo 55,66 57,30 58,26 56,94 58,16 59,13 58,85 Asti 64,91 65,56 65,76 66,29 65,66 67,67 69,41 Alessandria 61,80 62,08 63,22 62,83 64,50 65,50 65,31 Biella 55,36 56,57 57,19 60,49 59,63 62,21 61,71 Verbano Cusio Ossola 67,41 67,17 67,15 69,74 69,04 71,22 70,55 Piemonte 62,18 63,28 64,00 64,69 65,93 67,04 66,86 Aosta 78,76 78,35 79,94 80,32 79,83 81,22 81,15 Valle d’Aosta 78,76 78,35 79,94 80,32 79,83 81,22 81,15 Varese 58,41 60,39 59,64 60,23 61,91 62,91 62,87 Como 60,13 61,48 61,72 61,06 61,90 62,40 62,03 Sondrio 69,45 69,48 70,92 71,43 73,04 74,70 73,85 Milano 66,55 67,51 68,77 69,77 69,98 69,93 69,11 Bergamo 53,49 54,99 55,17 55,09 56,74 58,51 58,52 Brescia 57,05 58,29 58,37 58,87 60,91 61,29 61,86 Pavia 65,42 66,62 66,15 68,05 68,58 68,92 68,30 Cremona 56,16 56,32 56,66 56,23 58,38 60,64 60,76 Mantova 52,42 54,43 53,95 52,56 55,33 56,40 55,38 Lecco 53,92 54,87 53,80 54,57 55,58 58,22 58,09 Lodi 60,14 59,01 59,50 58,20 60,44 58,99 59,71 Lombardia 61,96 63,15 63,74 64,32 65,19 65,62 65,12 Bolzano - Bozen 72,36 72,50 71,94 71,12 70,35 71,57 71,91 Trento 71,90 72,33 72,60 72,69 70,80 70,91 71,25 Trentino Alto Adige 72,14 72,42 72,25 71,85 70,56 71,27 71,61 Verona 63,01 63,33 63,25 63,62 64,57 65,74 65,63 Vicenza 52,35 52,85 54,12 54,87 55,74 55,53 56,02 Belluno 57,25 57,64 60,12 60,94 63,58 63,67 64,09 Treviso 53,61 54,41 55,85 57,03 57,62 57,19 57,08 Venezia 70,09 71,93 74,34 74,74 74,72 74,72 74,73 Padova 65,32 64,98 65,52 66,62 66,20 67,26 67,21 Rovigo 61,75 61,98 62,85 65,05 65,05 66,23 64,45 Veneto 60,80 61,38 62,56 63,39 63,89 64,24 64,20 Udine 67,73 69,25 69,27 68,86 69,20 70,55 70,65 Gorizia 70,28 71,00 72,23 73,17 73,33 75,20 76,15 Trieste 83,57 83,85 84,51 83,63 84,38 83,70 84,27 Pordenone 56,97 57,56 57,74 60,10 59,47 59,58 59,61 Friuli Venezia Giulia 68,69 69,60 70,10 70,56 70,55 71,15 71,49 Imperia 79,53 78,31 78,25 79,28 80,05 80,95 81,83 Savona 76,14 76,01 75,80 74,98 77,76 78,02 77,61 Genova 81,37 81,42 79,56 79,27 80,82 81,29 80,98 La Spezia 75,34 74,62 73,57 74,80 75,40 75,76 75,74 Liguria 79,38 79,15 77,94 77,95 79,48 79,97 79,84 Piacenza 62,00 62,95 63,40 63,74 64,07 64,28 64,64 Parma 59,73 60,64 61,43 61,70 61,10 60,96 61,07 Reggio Emilia 52,95 53,28 52,57 52,48 53,06 54,79 55,45 Modena 54,55 55,28 55,09 55,05 56,83 56,41 56,36 Bologna 66,75 67,23 67,78 68,37 67,82 68,16 68,89 Ferrara 67,35 66,85 64,84 65,46 67,13 67,47 66,99 Ravenna 67,84 68,16 69,09 67,65 68,57 70,26 70,18

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Province and region 1998 1999 2000 2001 2002 2003 2004 Forlì - Cesena 67,85 67,23 67,65 66,44 68,23 69,54 69,69 Rimini 75,97 75,72 76,85 75,91 77,59 77,34 77,58 Emilia Romagna 62,88 63,24 63,45 63,42 64,08 64,55 64,82 Massa Carrara 75,59 75,82 75,93 75,73 77,80 77,34 78,05 Lucca 67,35 67,82 68,69 69,17 69,18 69,77 69,59 Pistoia 65,55 66,17 65,87 65,53 67,35 68,48 68,00 Firenze 69,93 70,56 70,68 70,68 72,45 73,74 73,31 Livorno 76,93 77,20 77,60 77,63 76,41 76,48 76,13 Pisa 63,86 65,80 65,65 65,85 65,10 67,78 67,01 Arezzo 61,35 61,31 62,33 62,52 63,11 62,40 63,34 Siena 71,05 71,48 71,05 70,61 72,26 72,08 72,50 Grosseto 75,94 77,28 77,85 78,97 79,43 80,04 79,04 Prato 57,34 59,52 57,21 58,62 60,88 60,93 61,48 Toscana 68,31 69,14 69,18 69,33 70,33 71,15 71,00 Perugia 69,70 69,39 69,74 69,72 69,55 69,90 70,23 Terni 67,32 68,01 66,61 67,21 66,89 68,81 68,75 Umbria 69,10 69,04 68,96 69,10 68,89 69,62 69,85 Pesaro e Urbino 65,33 65,51 66,76 66,72 66,86 66,18 66,36 Ancona 63,76 63,97 65,50 66,36 66,62 67,34 67,35 Macerata 65,53 64,87 65,59 66,46 66,89 67,54 67,51 Ascoli Piceno 63,86 64,26 66,09 64,94 66,07 67,96 68,62 Marche 64,50 64,58 65,95 66,12 66,60 67,27 67,46 Viterbo 67,90 69,66 70,76 70,39 70,33 71,74 70,64 Rieti 71,37 71,63 72,66 71,95 72,98 73,39 73,44 Roma 84,24 84,34 84,98 84,57 84,90 85,32 85,46 Latina 62,84 63,40 63,41 63,57 62,96 63,26 63,29 Frosinone 63,74 64,06 66,36 68,56 67,90 70,20 70,34 Lazio 80,11 80,40 81,18 81,01 81,12 81,61 81,70 L'Aquila 72,27 72,23 71,63 74,39 74,39 74,01 73,95 Teramo 61,28 60,87 59,99 59,74 60,25 61,88 63,15 Pescara 73,01 72,23 71,16 72,90 72,41 74,60 74,87 Chieti 61,92 61,22 61,78 62,62 64,69 64,49 65,41 Abruzzo 66,79 66,32 65,85 67,09 67,72 68,49 69,09 Campobasso 71,22 70,45 71,58 71,58 73,08 73,09 73,77 Isernia 64,79 66,73 66,48 67,65 66,82 69,77 70,46 Molise 69,33 69,39 70,07 70,40 71,24 72,05 72,72 Caserta 67,12 67,17 67,06 66,87 65,48 66,19 66,12 Benevento 71,56 72,77 74,51 75,61 75,18 75,55 77,04 Napoli 80,02 80,04 81,02 81,27 81,23 81,68 82,27 Avellino 65,96 65,87 65,34 65,76 67,27 66,96 67,51 Salerno 75,02 74,50 74,21 73,42 74,34 75,05 75,06 Campania 75,71 75,65 76,08 76,14 76,13 76,59 76,94 Foggia 70,22 71,92 72,15 75,93 75,48 76,15 75,63 Bari 73,53 72,94 74,44 74,90 75,05 74,70 75,28 Taranto 65,27 63,97 64,37 66,00 66,71 67,58 66,83 Brindisi 70,05 71,19 73,41 72,92 75,27 75,52 75,49 Lecce 75,89 74,90 75,33 75,69 78,13 78,55 78,45 Puglia 71,85 71,64 72,62 73,67 74,48 74,64 74,65 Potenza 67,01 66,77 68,09 67,30 66,85 67,47 67,82 Matera 66,43 63,40 66,29 66,69 67,99 68,39 68,51 Basilicata 66,82 65,61 67,49 67,09 67,22 67,77 68,05 Cosenza 77,75 77,16 78,27 76,51 77,25 77,68 78,75 Catanzaro 77,60 75,37 77,67 79,33 78,53 77,55 78,64 Reggio di Calabria 82,62 80,10 81,20 80,61 80,89 82,85 79,81

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Province and region 1998 1999 2000 2001 2002 2003 2004 Crotone 74,22 72,21 70,43 68,34 67,85 70,09 69,79 Vibo Valentia 79,90 76,58 77,47 77,72 78,75 78,40 79,11 Calabria 79,04 77,23 78,28 77,68 77,89 78,61 78,36 Trapani 75,93 76,37 75,35 75,97 76,67 77,95 77,75 Palermo 81,75 82,93 83,49 84,16 84,60 85,04 85,37 Messina 81,49 81,17 82,61 84,35 84,21 83,43 84,18 Agrigento 78,91 78,88 78,55 79,82 80,32 79,81 80,11 Caltanissetta 66,61 70,26 71,52 70,48 71,61 71,32 72,83 Enna 73,99 74,12 73,93 75,36 76,21 74,77 74,95 Catania 79,19 80,20 80,79 79,75 81,22 80,63 80,66 Ragusa 68,37 70,39 68,66 70,05 72,81 69,25 70,69 Siracusa 64,37 66,30 69,75 71,49 69,61 67,47 69,68 Sicilia 76,89 77,94 78,59 79,23 79,83 79,21 79,75 Sassari 76,90 77,84 78,25 77,00 77,44 78,16 78,24 Nuoro 71,16 72,76 74,78 76,09 76,59 75,16 76,12 Cagliari 74,82 75,95 75,95 75,77 75,20 75,39 75,98 Oristano 70,92 71,11 70,71 71,62 70,49 70,98 69,45 Sardegna 74,51 75,55 75,98 75,81 75,64 75,76 76,10

Italy 68,35 68,89 69,42 69,81 70,40 70,90 70,90

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4. MODEL SPECIFICATION AND ECONOMETRIC TESTING

4.1 Introduction

This chapter presents the specification of the model that will be tested, and its use in the econometric

tests. The aim of the chapter is trying to explain the socio-economic causes behind the production of

waste in the industrial sector, by the means of the drivers described in the past chapter. Some hypothe-

ses about the model are presented, as well as its different functional forms and the econometric esti-

mates. The statistical significance of the variables will be tested, and the EKC behaviour of the depend-

ent variable will be checked.

4.2 The specification of the model

Starting from the main specialized literature, in order to select the most suitable model, a wide array of

specifications has been analyzed, and, following Auffhammer and Carson (2006), the best specification

has been found after some econometric tests. Initially, a general model has been considered, following

the mainstream indications of the EKC literature, while the following selection of the best specification

has been done by jointly using the information provided by the Bayesian/Schwartz Information Crite-

rion (BIC), the Akaike Information Criterion (AIC) and the R-squared criterion (R2). More specifically,

when fitting models, it is possible to increase the likelihood by adding parameters, but doing so the re-

sult might be an overfitting: the BIC resolves this problem by introducing a penalty term for the num-

ber of parameters in the model. The penalty term is larger in BIC than in AIC, and that is why the BIC

indicator has been used as the tool to choose the best specification.

The general model, in its shorter form, can be written as:

(1) , ,it itit iW f Driver Fixed territorial effects

where i (i=1, 2,…, 103) is the specific province, t (t=1998, 1999,…, 2004) is the year of the observa-

tion, Wit is a measure of the production of waste of Industry in a Strict Sense, in the i-th province, dur-

ing the t-th year, (Driver)it is a vector containing a set of socio-economic variables, which should explain

the waste production differences among time and space, (Fixed territorial effects)i is a vector of indicators

which express the peculiarities of a province, εit is the error term with the standard characteristics, and

f(…) is the functional form adopted according to the specification of the model.

As regards the dependent variable (Wit ), two different indicators have been used and tested:

176

1. the total quantity of waste produced by Industry in a Strict Sense, per number of workers in the

local units operating and giving the MUD statement: that is, the (Wit / workerit ) ratio;

2. the total quantity of waste produced by Industry in a Strict Sense, per unit of value-added of

Industry in a Strict Sense in the province: that is, the (Wit / value-addedit ) ratio28.

In Chapter 3 it has been discussed how both of those indicators are performance measures. While the

first measures the quantity of waste in terms of the number of workers that “produce” that waste, the

second measures the productive efficiency in the creation of industrial waste, that is it takes also into

account the technological relationship between waste production and value production, which is the

very aim of the production of waste.

As regards the set of independent variables, (Driver)it , the principle driver is the value-added of the pro-

ductive activities. Some different transformations of the value-added have been considered:

1. the value-added of Industry in a Strict Sense in the province, per se;

2. the average value-added of Industry in a Strict Sense, measured as the value-added per local unit

of Industry in a Strict Sense;

3. the value-added of Industry in a Strict Sense per worker29 of Industry in a Strict Sense.

The average value-added of a province measures the average efficiency of the entrepreneurial fabric of

that area: it indicates the contribution to the economic performance of every local unit, considering

workers, technologies and management skills as a whole, but it does not take into account the real di-

mension of the local units operating in the territory, while the value-added per worker does.

Besides the value-added and the socio-economic drivers, other explanatory variables have been used in

the tests, in order to take into account also the territorial characteristics of the province, such as the

population density and the local units' density.

As regards the population density, on one hand, a negative relationship between density and industrial

waste may be supposed for the industrialized countries like Italy, caused by the growing delocalization

of the big industrial parks, and by the more growing demand for environmental goods and quality. On

the other hand, highly populated areas provide a valuable workforce for industries, especially in those

sectors of the so called “traditional industry”, whose productive activity is highly labour-intensive.

The density of the industrial fabric, expressed by the local units' density, may have also ambivalent ef-

fects of the production of waste: on one side, indeed, a bigger concentration may bring to higher pro-

duction of waste, but, on the other side, feasible economies of scale may reduce such a production, by

creating a virtuous cycle of disposals.

28 All the monetary values have been expressed in real terms, on the basis of the prices of 1995, thanks to the tables of the Istituto Tagliacarne.

29 Worker does not mean single employee, but refers to the units of labour: see www.istat.it for the definitions of worker.

177

Table 4.1 – Variables and their descriptive statistics (1998-2004)

Description of the variable Acronym Unit of

measurement average minimum maximum

Waste of Industry in a Strict Sense per unit of value-added of Industry in a Strict Sense

RIF/VAI tons per million of eu-

ros of 1995 232,757 7,360 2.022,306

Waste of Industry in a Strict Sense per unit of labour of Industry in a Strict Sense

RIF/ADDtons per unit of labour

(worker: addetto) 1,562 0,002 188,520

Value-added of Industry in a Strict Sense

VAI millions of euros of

1995 2.188,753 136,754 29.441,587

Value-added of Industry in a Strict Sense per unit of labour of Industry in a Strict Sense

VAI/ADDmillions of euros of 1995 per unit of la-

bour (worker: addetto)0,041 0,026 0,066

Value-added per local unit of Industry in a Strict Sense

VAI/UL millions of euros of 1995 per local unit

0,266 0,069 0,479

Population density POPDENSinhabitants per squa-red kilometre (Km2)

243,006 36,350 2.640,920

Density of local units of Industry in a Strict Sense of the Register of Enter-prises

ULDENSlocal units per squared

kilometre (Km2) 3,411 0,332 31,708

Electrical energy intensity of Industry in a Strict Sense

EN_CONSkilowatt-hour per euro

of 1995 0,623 0,149 2,683

Share (%) of the value-added of In-dustry in a Strict Sense on the total value-added

INDSS percentage 21,247 5,743 41,233

Share (%) of the value-added of Ser-vice Industry on the total value-added

SERV percentage 0,691 0,524 0,855

Number of registered patents per thousand of inhabitants

B_AB patents per 1000 in-

habitants 0,568 0,000 4,944

Share (%) of the urban sorted waste over the total urban waste

RDIFF percentage 16,833 0,082 60,965

Ratio of the exports of Industry in a Strict Sense over the value-added of Industry in a Strict Sense

EXP/VA Number without unit of measurement (€/€)

0,847 0,040 3,285

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The two density indicators are highly correlated among themselves, and they can be considered as two

alternative measures: both of them will be included in some preliminary specifications, but it has to be

thought that the local units' density might be more suitable to the present research30.

Among the technological drivers, energy consumptions has been included in the model specification, in

terms of energy intensity: the ratio between the quantity of the consumption of energy and the value-

added is a measure of the energy intensity of the industrial production, and it captures the efficiency of

such production in terms of energy: This indicator is highly influenced by the industrial composition of

the province, by the technological level of the firms, by their dimension, and by the interactions of

those factors inside the industrial context of the province.

Two measures of the sectorial composition of the industrial fabric of the province have been consid-

ered: the share of the value-added of Services Industry, and the share of the value-added of Industry in

a Strict Sense. The share of the value-added of Industry in a Strict Sense on the total value-added of the

province measures the direct effect that the industrial composition of the province might have on waste

production. On the other side, the share of the value-added of Services Industry measures the effect

that an increase in the province of the number of firms operating in the Services might have on the

production of waste of Industry in a Strict Sense. Both the shares can be seen as two measures of the

deindustrialization of a territory, explaining how the change in the industrial morphology can contribute

to less or more waste production. By definition, the two measures are correlated between themselves:

therefore, they have both been included in the first tests only, while only one of them has been kept in

the subsequent tests: the significance tests are in favour of the use of the share of the Services in the

model.

An indicator of the sorted urban waste collection in the province (urban sorted waste over total urban

waste) allows getting the possible effect that the environmental sensibility of citizens might have on the

production of waste, and that the local governments’ policies indirectly might have too.

Without a precise variable describing the technological innovation power, the number of patents (every

1000 inhabitants) is a proxy for the technological development of the enterprises in the territory. Pat-

ents are a technological output measure, and not a technological input measure, that is they measure

only the final product of the research activity, and not also what investments have been done to come

up with those final outputs: even if this is not the most precise measure for technological effects, it can

help anyway explaining what effects technological advancements (already included in the value-added

and in the energy intensity) have on waste production. As already note above, patents might be regis-

tered in a location where such patents (and technologies) have not been really developed: in order to

mitigate such a problem, a preliminary analysis has been done on patents data, and it has come out that

30 This hypothesis has been confirmed by the tests.

179

Milano is the city which polarizes all the patents in Italy, and therefore a dummy variable taking into ac-

count this polarization has been added to the model.

Another driver of the production of waste of Industry in a Strict Sense is represented by exports,

measured by the relative degree of international openness, that is, the ratio between exports of Industry

in a Strict Sense and GDP of the same Industry in a Strict Sense. By weighing the value of exports with

the value-added of the relative territorial entity, a measure that links the degree of international open-

ness of an area with its economic competitiveness can be obtained.

Last, in order to capture the territorial specificities of the Italian industrial context, and of some prov-

inces particularly, fixed territorial effects have been used in terms of indicators for macro-areas (North-

West, North-East, Centre, South and Islands) and for specific provinces, because, in terms of waste

production, the preliminary descriptive analysis has confirmed the peculiarities of some provinces as

Milano, Savona, Brindisi, Caltanissetta and Cagliari31.

Once identified the feasible drivers, the most appropriate formal specification and functional form has

been tested, working on two separate samples of data, having selected two different time spans: one

sample contains data from 1998 to 2004, with a total of 721 observations (103 provinces and 7 years),

the other sample containing data from 2000 to 2004, with a total of 515 observations (103 provinces

and 5 years).

As in Dinda et al. (2000), some of the variables have been expressed in logarithmic form, as regards

both the dependent variable and the variables related to the value-added transformations. The loga-

rithmic specification has the property to be able to let interpret the logarithms’ coefficients in terms of

elasticity of the dependent variable with respect the considered driver, that is, the coefficient of the

driver measures the percentage change in the dependent variable resulting in the considered percentage

change of the independent variable. Moreover, as in Galeotti et al. (2006), the use of logarithmic speci-

fication helps to minimize the problem of heteroscedasticity, and it mitigates the impact of nonlinear

relationships between waste measure and the explanatory variables.

In the set-up of the model, the square of the value of the variables (in logarithmic terms) concerning

the value-added has been used, in order to verify the existence of non linear relationships among the

dependent variable and the independent drivers, and to test the feasible presence of an EKC behaviour.

Following Aufhammer and Carson (2006), the following general specification has been tested:

31 Milano has the largest number of patents, and a polarizing force as regards the presence of huge economic activities. Savona, in 2002 and 2003, has seen a strong increase of waste production, due to the remediation of polluted sites. Such a remediation activity has caused in 2004 a peak of waste of Industry in a Strict Sense in Caltanissetta too. Brindisi has shown an increase of production of waste in 1998 and 1999, due to industrial ashes produced in thermo-electrical power plants. And Taranto has had peaks of waste in 2001 and 2004, caused by the activity of its steel mills.

180

(2) log (RIFit/VAIit) = constant + β1 log(VAIit) + β2 [log(VAIit)]2 +

β3 log(VAIit/ADDit) + β4 [log(VAIit/ADDit)]2 +

β5 log(VAIit/ULit) + β6 [log(VAIit/ULit)]2 +

β7 POPDENSit + β8 ULDENSit + β9 EN_CONSit + β8 INDSSit +

β9SERVit + β10 B_ABit + β11 RDIFFit + β12 (EXP it/VA it) +

β13 NORD_ESTi + β14 CENTROi + β15 SUDi + β16 ISOLEi +

β17 MILANOi + β18 SAVONAi + β19 BRINDISIi +

β20 TARANTOi + β21 CALTANISSETTAi + β22 CAGLIARIi +

it

where i and t respectively indicate the specific province and the year of the observation, log(…) is the

natural logarithmic function, RIFit is the quantity of waste of Industry in a Strict Sense, VAIit is the va-

lue-added of Industry in a Strict Sense, ADDit is the number of workers (units of labour) of Industry in

a Strict Sense, ULit is the number of local units of Industry in a Strict Sense, POPDENSit is the popula-

tion density, ULDENSit is the local units density of Industry in a Strict Sense, EN_CONSit is the energy

intensity of Industry in a Strict Sense, INDSSit and SERVit are respectively the share of the value-added

of Industry in a Strict Sense and of Services on the total value-added, B_ABit is the number of patents

every thousand of inhabitants, RDIFFit is the share of urban sorted waste over total urban waste, EXPit

are the exports of Industry in a Strict Sense, NORD_ESTi, CENTROi, SUDi and ISOLEi are dummy

variables indicating that the i-th province may be either in the North-East, or in the Centre, or in the

South, or in the Islands, while MILANOi, SAVONAi, BRINDISIi, TARANTOi, CALTANISSETTAi

e CAGLIARIi are dummy variables related to the specific province.

Therefore, RIFit/VAIit denotes the quantity of waste of Industry in a Strict Sense on the value-added of

Industry in a Strict Sense, VAIit/ADDit is the value-added per worker of Industry in a Strict Sense,

VAIit/ULit is the value-added per local unit of Industry in a Strict Sense, EXPit/VAit is the ratio of ex-

ports of Industry in a Strict Sense over the value-added of Industry. Last, it is the usual error term.

Besides the specification (2), the following one has been also tested: it has the same explanatory vari-

ables, but with a different dependent variable, that is (the logarithm of) the quantity of waste per

worker, instead of (the logarithm of) the quantity of waste per value-added:

181

(3) log (RIFit/ADDit) = constant + β1 log(VAIit) + β2 [log(VAIit)]2 +

β3 log(VAIit/ADDit) + β4 [log(VAIit/ADDit)]2 +

β5 log(VAIit/ULit) + β6 [log(VAIit/ULit)]2 +

β7 POPDENSit + β8 ULDENSit + β9 EN_CONSit + β8 INDSSit +

β9SERVit + β10 B_ABit + β11 RDIFFit + β12 (EXP it/VA it) +

β13 NORD_ESTi + β14 CENTROi + β15 SUDi + β16 ISOLEi +

β17 MILANOi + β18 SAVONAi + β19 BRINDISIi +

β20 TARANTOi + β21 CALTANISSETTAi + β22 CAGLIARIi +

it

The careful examination of the data has confirmed that, during the 1998-2004 period, the considered

variables have shown a great variability among provinces, but a sort of stability inside every province.

These information have led to use the Pooled OLS estimation, since it is more suitable when dealing

with such data. Johnston and Di Nardo (1997) recall that the POLS estimators ignore the panel struc-

ture of the data, treat observations as being serially uncorrelated for a given individual, with homosce-

dastic errors across individuals, and time periods.

The specifications (2) and (3) are still sufficiently general to include a set of other possible specifica-

tions. Separately, for the set 1998-2004 and the set 2000-2004, the POLS method has been applied and

the functional forms have been tested: using the BIC, AIC and R-squared criterions, discarding those

particular specifications which were not statistically significant, a preferred final specification has been

found (see below). As a first step, two regressions have been run, omitting the population density in the

first, and the local units’ density in the second. By confronting the BIC scores, the one with the lower

value has been kept, which is the one with the local units’ density. The subsequent step has led to an-

other double regression, one with the share of the value-added of Industry in a Strict Sense on the total

value-added as explanatory variable, and the other with the share of the value-added of Service Industry

on the total value-added as another (different) explanatory variable: the BIC confrontation has been is

in favour of the specification with the share of the value-added of Service Industry as explanatory vari-

able32. By keeping on acting in such a way, from the more general to the particular, as in Aufhammer

and Carson (2006), both in model (2) and (3), a final set of more statistically significant specifications

have been found.

32 R-squared and AIC both lead to the same result.

182

4.3 The econometric tests

The most interesting results of the econometric tests are shown in the following Table 4.2, 4.3, 4.4 and

4.5, while more results can be found in Table A4.1, A4.2, A4.3, A4.4, A4.5, A4.6, A4.7 and A4.8 in the

Appendix. Table 4.2 and 4.3 show the results of the most statistically significant specification, where

the main economic performance driver is the value-added per local unit (UL), and the tested periods

are 1998-2004 and 2000-2004. Table 4.4 and 4.5 give the results of the other valuable specification,

where the performance indicator is the value-added per “worker” (unit of labour), and again the two

separately tested sets are 1998-2004 and 2000-2004.

In Table 4.2, the specification under the column (1) has all the considered potential drivers, but its re-

sults are not all statistically significant: the value-added (in absolute terms) is not significant, neither the

structural composition of the province, nor some geographical variables. The subsequent specifications

are obtained by dropping along the way those less significant variables, such as the population density

or the share of the value-added of Industry in a Strict Sense on the total value-added of the province, or

some territorial variables such as the dummy for the South and the Islands. The specification under the

column (9) is the most significant in statistical terms, with only few territorial effects, the North-East,

and some provincial effects, such as Milano, Savona, Brindisi and Caltanissetta. Table 4.3 shows the es-

timates for the set 2000-2004, but substantial differences cannot be found with respect to the other set,

1998-2004, which explains that the year 2000 has not experienced that structural break that the new leg-

islation, adopted in that period, might had induced, due to the time concerning its implementation, or,

moreover, that the supposed structural break has not produced relevant consequences. For the sake of

the good exposition of the analysis, the last column of Table 4.2 and Table 4.3, relating to the “better”

specification, has been reported in Table 4.6, while their counterparts in Table 4.4 and 4.5 have been

reported in Table 4.7.

Therefore, using the above mentioned performance indicators for econometric models, and, first of all,

according to the BIC value, the econometric specification that better explains the relationship between

waste of Industry in a Strict Sense and its drivers is the one given in Table 4.633, while Table 4.7 shows

the results of another, less significant, specification34.

Both the tables are referring to a model where the dependent variable is the same, that is (the logarithm

of) the quantity of waste of Industry in a Strict Sense per value-added, in the province35: in both tables,

the first column provides the results of the estimates of the 1998-2004 set, while the second gives the

results of the 2000-2004 set. The econometric specifications of Table 4.6, labelled S-1, and 4.7, labelled 33 Table A4.9 in the Appendix shows only the sign and statistical significance of the coefficients of Table 4.6.

34 From now onwards, the specification in Table 4.6 is labelled as S-1, while the one in Table 4.7 is labelled as S-2.

35 The results of the specifications using the quantity of waste per worker as the dependent variable have not been shown here in the main text, but have been included in the Appendix, because the results in Table 4.6 and 4.7 are the most statisti-cally significant of all.

183

S-2, are quite similar as regards many drivers: the difference among them is that in S-1, among the ex-

planatory variables, the value-added per local unit (in logarithms) and its square have been used, while

those drivers have been substituted by the value-added per worker (in logarithms) and its square: both

of them are more significant with respect to specifications including (the logarithm of) the average

value-added and its square.

The specification shown in Table 4.6 is the one with the lowest BIC score, and, therefore, it is the

“best” among all the different specifications tested, as regards both the 1998-2004 set, and the 2000-

2004 set.

Focusing the analysis on the results obtained for the set 2000-2004 in the specification S-1, in Table 4.6,

it can be seen that the majority of coefficients are statistically significant at the conventional levels of

confidence, and that they can provide useful insights for the economic analysis of the framework.

The estimates of the coefficient of the logarithms of the value-added and of the value-added per local

unit are significant, both in economic and in statistical terms. They imply a positive elasticity of the

measure of waste production (weighed by the value-added) on the value-added, but a negative elasticity

of that measure on the square of the value-added, and on both the terms representing the value-added

per local unit36. Values and signs are consistent with the hypothesis of a reversed-U path of the produc-

tion of waste (per value-added) on Industry in a Strict Sense with respect to the value-added: initially

the production of waste rises up to a point of maximum, and then it decreases as the value-added in-

creases. The turning point's value and its trend depend on the behaviour of the value-added per local

unit: these results are quite new for non-urban waste data, at such a disaggregation level, and they pro-

vide empirical evidence in favour of an Environmental Kuznets Curve (EKC) for non-urban waste,

sometimes called Waste Kuznets Curve37 (WKC).

The estimates of the coefficient of the density of local units has a negative sign in both the sets: this in-

dicates that if the number of local units increases, a decrease in the production of waste might have

place, thus confirming the valuable effect of economies of scale.

The higher the energy intensity, the higher the value of what we can call as “waste intensity”: high levels

of waste intensity might be reached by firms that are not the most technological ones, and this is the

reason why the decrease in energy efficiency (a higher energy intensity) might lead to a decrease in

waste efficiency (a higher waste intensity).

The share of the value-added of Services has a positive coefficient, ma it is very little and nearly the

same in both the sets. This is the only coefficient not statistically significant in the specification S-1, and

so its precision is not reliable as the other’s one: it has to be noted, however, that it is significant in

36 The coefficient of the square of the logarithm of the value-added is the only coefficient statistically significant at 10% only.

37 See Chapter 1 for the correspondent literature review.

184

other specification, S-2, in Table 4.7. The positive sign states that, as the Services of a province is grow-

ing, the quantity of waste (per value-added) in Industry in a Strict Sense grows as well: this result, even

if statistically weak, seems to suggest that the deindustrialization which has taken place in that period is

so that the companies of Industry in a Strict Sense surviving in the market are more waste-intensive.

All things being equal, the number of patents per thousand of inhabitants, that is the technological

proxy, is negatively correlated with the waste production per value-added: against an increase in regis-

tered patents in the province, the quantity of waste produced by Industry in a Strict Sense decreases,

thus indicating that the innovative fabric of the province can lead to better results in terms of less waste

production.

The share of sorted urban waste on the total urban waste has a positive relationship with the produc-

tion of industrial waste: this result is a bit unexpected, since one would think that a stronger aptitude

towards sorted waste collection, and a more effective sorted waste collection of a province, would have

led to a lower amount of non-urban waste, and so that the relationship among the variables would have

been negative. Such a positive correlation, however, could be explained by the fact that a greater pres-

ence of industrial complexes may be possible there where the higher concentration of workers is. It has

to be noted, anyway, that the estimated coefficient is very little.

The estimates of the coefficient relating to the exports implies that the bigger the share of exports of

Industry in a Strict Sense on the value-added of a province is, the higher is the waste intensity of Indus-

try in a Strict Sense in that area: if in the province the exports component takes a more prominent role

with respect to the other activities, then the waste produced by the industrial fabric tends to increase,

due to the major external demand of goods produced in that province.

The specification S-2 includes also some indicators related to territorial peculiarities: North-East, Cen-

tre, South and Islands. The specification S-1 in Table 4.6 shows only one of those macro-areas vari-

ables, that is the North-East dummy variable, since this is the only driver to be statistically significant in

the several specifications under examination. This variable captures the distinctive characteristics in the

productive structure of the North-East in comparison with the rest of the country: in the other areas of

Italy the industrial context is characterized by a stronger presence of big firms, and also by a polariza-

tion of the industrial activities in few provinces, a fact that has been captured by province specific indi-

cators (Milano, Savona, etc.). On the contrary, in the North-East, the productive structure has a more

widespread distribution, and there are not provinces with a by far stronger presence of big industries

than the other provinces of that area. However, as the specification underlines, the North-East pro-

duces a significantly greater quantity of waste (intensity) per value-added with respect to the rest of It-

aly: all the other conditions being equal, a province situated the North-East produces +27% more

waste (intensity) than a province in another macro-area of Italy.

(the text continues after Tables 4.2 to Table 4.5)

185

Table 4.2 – Regressions results. Dependent variable: waste per value-added. Specification S-1, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,464 0,420 0,908 0,552 0,857 0,535 0,738 0,803 0,804 -0,376 -0,376 (0,391)** -0,370 (0,387)** -0,365 (0,384)* (0,358)** (0,359)** [Log(VA Industry in a S.S.)]^2 -0,020 -0,016 -0,058 -0,024 -0,056 -0,023 -0,049 -0,043 -0,043 -0,026 -0,026 (0,027)** -0,026 (0,027)** -0,026 (0,027)* (0,025)* (0,025)* Log(VA Industry in a S.S. per UL) -4,000 -3,849 -3,024 -3,895 -2,970 -3,789 -3,056 -3,864 -3,867 (0,536)*** (0,531)*** (0,550)*** (0,531)*** (0,546)*** (0,529)*** (0,551)*** (0,512)*** (0,507)*** [Log(VA Industry in a S.S. per UL)]^2 -1,251 -1,237 -1,103 -1,253 -1,090 -1,230 -1,103 -1,276 -1,273 (0,164)*** (0,164)*** (0,172)*** (0,164)*** (0,171)*** (0,164)*** (0,172)*** (0,161)*** (0,161)*** Population density 0,034 (0,018)* UL density of Industry in a S.S. -0,095 -0,073 -0,068 -0,069 -0,068 -0,067 (0,014)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,777 0,763 0,778 0,797 0,761 0,774 0,758 0,744 0,842 (0,087)*** (0,087)*** (0,092)*** (0,085)*** (0,090)*** (0,087)*** (0,091)*** (0,083)*** (0,064)*** Share of the VA of Industry in a S.S. on total VA 0,018 0,014 -0,023 -0,005 -0,015 -0,012 -0,011 (0,011)** -0,005 (0,006)*** Share of the VA of Services on total VA 0,015 0,018 -0,009 0,008 0,009 0,008 0,007 -0,010 (0,010)* -0,010 (0,005)* (0,005)* (0,005)* -0,005 Number of patents per thousand of inhabitants -0,276 -0,303 -0,211 -0,277 -0,221 -0,291 -0,225 -0,188 -0,183 (0,074)*** (0,073)*** (0,076)*** (0,072)*** (0,075)*** (0,073)*** (0,076)*** (0,067)*** (0,067)*** Share of the urban sorted waste on the total urban waste 0,014 0,014 0,012 0,013 0,012 0,014 0,011 0,014 0,014 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,188 0,187 0,114 0,195 0,107 0,199 0,083 0,265 0,234 (0,074)** (0,074)** -0,077 (0,074)*** -0,077 (0,073)*** -0,076 (0,071)*** (0,070)*** North-East 0,379 0,390 0,333 0,352 0,351 0,363 0,379 0,248 0,252 (0,089)*** (0,089)*** (0,094)*** (0,087)*** (0,091)*** (0,086)*** (0,091)*** (0,073)*** (0,073)*** Centre 0,212 0,208 0,184 0,193 0,191 0,203 0,189 (0,094)** (0,095)** (0,100)* (0,094)** (0,099)* (0,095)** (0,100)* South -0,126 -0,089 -0,108 -0,127 -0,089 -0,105 -0,079 -0,116 -0,114 -0,120 -0,113 -0,118 -0,114 -0,120 Islands -0,095 -0,078 -0,059 -0,130 -0,031 -0,107 -0,002 -0,140 -0,140 -0,147 -0,138 -0,144 -0,138 -0,145

186

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Milano 2,380 2,303 0,621 2,186 0,620 2,202 0,642 1,928 1,926 (0,413)*** (0,411)*** -0,382 (0,407)*** -0,382 (0,403)*** (0,383)* (0,398)*** (0,399)*** Savona 1,637 1,619 1,515 1,586 1,528 1,584 1,571 1,529 1,532 (0,262)*** (0,262)*** (0,275)*** (0,262)*** (0,275)*** (0,261)*** (0,275)*** (0,254)*** (0,254)*** Brindisi 1,625 1,603 1,583 1,603 1,582 1,600 1,586 1,511 1,459 (0,257)*** (0,257)*** (0,270)*** (0,257)*** (0,270)*** (0,257)*** (0,271)*** (0,255)*** (0,254)*** Taranto 0,708 0,697 0,536 0,621 0,570 0,658 0,596 0,617 (0,300)** (0,301)** (0,316)* (0,298)** (0,313)* (0,299)** (0,315)* (0,297)** Caltanisetta -0,758 -0,737 -0,806 -0,755 -0,799 -0,726 -0,833 -0,799 -0,924 (0,277)*** (0,277)*** (0,292)*** (0,278)*** (0,291)*** (0,277)*** (0,292)*** (0,272)*** (0,264)*** Cagliari 0,389 0,370 0,460 0,342 0,478 0,359 0,490 0,288 -0,301 -0,301 -0,317 -0,301 -0,316 -0,301 -0,317 -0,299 Constant coefficient -2,243 -2,099 -0,035 -1,028 -0,523 -1,501 -0,973 -2,447 -2,439 -1,389 -1,389 -1,440 -1,273 -1,335 -1,303 -1,372 (1,278)* (1,278)* Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,530 0,520 0,470 0,520 0,470 0,520 0,470 0,510 0,510 AIC 1.451,549 1.453,239 1.525,656 1.454,973 1.524,496 1.452,817 1.528,158 1.459,420 1.460,025 BIC 1.556,903 1.554,013 1.621,849 1.551,166 1.616,108 1.549,010 1.619,771 1.541,872 1.533,315 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

187

Table 4.3 – Regressions results. Dependent variable: waste per value-added. Specification S-1, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,705 0,667 1,070 0,762 1,013 0,754 0,899 1,023 1,008 -0,437 -0,438 (0,456)** (0,433)* (0,453)** (0,426)* (0,448)** (0,417)** (0,417)** [Log(VA Industry in a S.S.)]^2 -0,036 -0,032 -0,068 -0,038 -0,065 -0,037 -0,059 -0,058 -0,056 -0,031 -0,031 (0,032)** -0,030 (0,032)** -0,030 (0,031)* (0,029)** (0,029)* Log(VA Industry in a S.S. per UL) -3,707 -3,501 -2,782 -3,545 -2,707 -3,458 -2,809 -3,513 -3,561 (0,616)*** (0,610)*** (0,631)*** (0,610)*** (0,627)*** (0,608)*** (0,632)*** (0,585)*** (0,578)*** [Log(VA Industry in a S.S. per UL)]^2 -1,152 -1,130 -1,014 -1,145 -0,996 -1,127 -1,010 -1,160 -1,170 (0,186)*** (0,186)*** (0,194)*** (0,186)*** (0,193)*** (0,186)*** (0,195)*** (0,183)*** (0,182)*** Population density 0,044 (0,021)** UL density of Industry in a S.S. -0,097 -0,068 -0,064 -0,065 -0,064 -0,064 (0,017)*** (0,010)*** (0,009)*** (0,009)*** (0,009)*** (0,009)*** Energy consumption of Industry in a S.S. per unit of VA 0,732 0,712 0,742 0,742 0,718 0,721 0,723 0,692 0,787 (0,103)*** (0,103)*** (0,108)*** (0,101)*** (0,105)*** (0,102)*** (0,108)*** (0,096)*** (0,074)*** Share of the VA of Industry in a S.S. on total VA 0,017 0,011 -0,024 -0,005 -0,013 -0,014 -0,013 (0,013)* -0,007 (0,007)* Share of the VA of Services on total VA 0,011 0,015 -0,011 0,007 0,006 0,008 0,007 -0,011 -0,011 -0,011 -0,006 -0,006 -0,005 -0,005 Number of patents per thousand of inhabitants -0,302 -0,336 -0,267 -0,315 -0,280 -0,326 -0,285 -0,219 -0,215 (0,086)*** (0,085)*** (0,089)*** (0,084)*** (0,088)*** (0,084)*** (0,088)*** (0,076)*** (0,076)*** Share of the urban sorted waste on the total urban waste 0,012 0,012 0,009 0,011 0,010 0,012 0,008 0,013 0,012 (0,004)*** (0,004)*** (0,004)** (0,004)*** (0,004)** (0,004)*** (0,004)** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,130 0,131 0,072 0,136 0,064 0,141 0,040 0,207 0,180 -0,082 -0,082 -0,086 (0,082)* -0,086 (0,082)* -0,085 (0,078)*** (0,076)** North-East 0,403 0,415 0,364 0,382 0,389 0,392 0,418 0,266 0,272 (0,104)*** (0,105)*** (0,109)*** (0,102)*** (0,107)*** (0,101)*** (0,106)*** (0,085)*** (0,085)*** Centre 0,214 0,213 0,189 0,199 0,199 0,211 0,191 (0,111)* (0,111)* -0,116 (0,111)* (0,116)* (0,111)* -0,117 South -0,129 -0,075 -0,106 -0,108 -0,080 -0,083 -0,089 -0,139 -0,137 -0,144 -0,135 -0,142 -0,137 -0,144 Islands -0,092 -0,064 -0,064 -0,111 -0,025 -0,084 -0,015 -0,167 -0,167 -0,175 -0,164 -0,171 -0,166 -0,174

188

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Milano 2,532 2,440 0,916 2,339 0,920 2,358 0,952 2,066 2,064 (0,479)*** (0,479)*** (0,449)** (0,473)*** (0,449)** (0,469)*** (0,450)** (0,461)*** (0,461)*** Savona 1,918 1,900 1,800 1,864 1,824 1,869 1,866 1,804 1,809 (0,301)*** (0,302)*** (0,316)*** (0,301)*** (0,315)*** (0,300)*** (0,315)*** (0,291)*** (0,291)*** Brindisi 1,951 1,930 1,933 1,930 1,934 1,929 1,938 1,852 1,800 (0,294)*** (0,295)*** (0,309)*** (0,295)*** (0,309)*** (0,295)*** (0,310)*** (0,292)*** (0,291)*** Taranto 0,542 0,532 0,364 0,464 0,411 0,500 0,423 0,478 -0,345 -0,346 -0,362 -0,342 -0,359 -0,344 -0,361 -0,341 Caltanisetta -0,580 -0,550 -0,633 -0,572 -0,619 -0,545 -0,654 -0,613 -0,736 (0,318)* (0,319)* (0,335)* (0,319)* (0,334)* (0,319)* (0,335)* (0,314)* (0,303)** Cagliari 0,462 0,445 0,521 0,419 0,546 0,437 0,549 0,373 -0,347 -0,348 -0,365 -0,348 -0,364 -0,348 -0,365 -0,345 Constant coefficient -2,511 -2,355 -0,182 -1,398 -0,859 -1,863 -1,170 -2,838 -2,810 -1,652 -1,656 -1,707 -1,501 -1,571 -1,550 -1,627 (1,518)* (1,516)* Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,540 0,530 0,490 0,530 0,490 0,530 0,480 0,530 0,520 AIC 1.010,067 1.012,682 1.060,940 1.012,610 1.060,009 1.011,425 1.062,561 1.014,304 1.012,883 BIC 1.107,683 1.106,053 1.150,068 1.101,737 1.144,892 1.100,552 1.147,444 1.090,699 1.080,790 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

189

Table 4.4 – Regressions results. Dependent variable: waste per value-added. Specification S-2, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 1,210 1,163 1,658 1,368 1,613 1,242 1,493 1,746 1,736 (0,375)*** (0,374)*** (0,389)*** (0,366)*** (0,382)*** (0,362)*** (0,380)*** (0,352)*** (0,352)*** [Log(VA Industry in a S.S.)]^2 -0,073 -0,068 -0,108 -0,081 -0,106 -0,072 -0,100 -0,110 -0,108 (0,026)*** (0,026)*** (0,027)*** (0,026)*** (0,027)*** (0,026)*** (0,027)*** (0,025)*** (0,025)*** Log(VA Industry in a S.S. per worker) -18,263 -17,732 -18,031 -17,875 -17,999 -18,016 -17,322 -14,873 -15,087 (5,191)*** (5,190)*** (5,453)*** (5,208)*** (5,451)*** (5,178)*** (5,452)*** (5,209)*** (5,212)*** [Log(VA Industry in a S.S. per worker)]^2 -2,779 -2,698 -2,796 -2,731 -2,790 -2,743 -2,688 -2,267 -2,294 (0,810)*** (0,810)*** (0,851)*** (0,813)*** (0,851)*** (0,808)*** (0,851)*** (0,813)*** (0,814)*** Population density 0,030 (0,017)* UL density of Industry in a S.S. -0,088 -0,071 -0,063 -0,068 -0,066 -0,066 (0,013)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,752 0,761 0,850 0,799 0,842 0,775 0,820 0,734 0,832 (0,086)*** (0,086)*** (0,090)*** (0,085)*** (0,088)*** (0,084)*** (0,088)*** (0,084)*** (0,065)*** Share of the VA of Industry in a S.S. on total VA 0,011 0,010 -0,022 -0,015 -0,016 -0,011 -0,011 (0,011)* (0,005)*** (0,005)*** Share of the VA of Services on total VA 0,021 0,024 -0,006 0,017 0,011 0,015 0,015 (0,010)** (0,010)** -0,010 (0,004)*** (0,005)** (0,004)*** (0,004)*** Number of patents per thousand of inhabitants -0,214 -0,242 -0,154 -0,205 -0,162 -0,233 -0,168 -0,091 -0,087 (0,077)*** (0,075)*** (0,079)** (0,074)*** (0,077)** (0,075)*** (0,078)** -0,069 -0,069 Share of the urban sorted waste on the total urban waste 0,015 0,014 0,010 0,014 0,010 0,014 0,009 0,015 0,014 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,234 0,240 0,189 0,254 0,183 0,250 0,159 0,361 0,333 (0,075)*** (0,075)*** (0,079)** (0,075)*** (0,078)** (0,074)*** (0,077)** (0,070)*** (0,068)*** North-East 0,332 0,342 0,290 0,296 0,302 0,322 0,338 0,166 0,166 (0,093)*** (0,093)*** (0,097)*** (0,091)*** (0,096)*** (0,090)*** (0,095)*** (0,077)** (0,077)** Centre 0,328 0,305 0,205 0,291 0,206 0,297 0,219 (0,094)*** (0,094)*** (0,098)** (0,094)*** (0,098)** (0,093)*** (0,098)** South -0,052 -0,051 -0,193 -0,092 -0,185 -0,073 -0,150 -0,109 -0,109 (0,113)* -0,108 -0,113 -0,106 -0,111 Islands -0,155 -0,181 -0,315 -0,241 -0,302 -0,215 -0,240 -0,133 -0,132 (0,138)** (0,130)* (0,136)** (0,126)* (0,133)*

190

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Milano 2,262 2,252 0,706 2,084 0,708 2,199 0,715 1,829 1,831 (0,414)*** (0,414)*** (0,392)* (0,410)*** (0,392)* (0,410)*** (0,393)* (0,407)*** (0,408)*** Savona 1,919 1,896 1,720 1,849 1,729 1,870 1,772 1,780 1,787 (0,270)*** (0,270)*** (0,283)*** (0,270)*** (0,282)*** (0,268)*** (0,282)*** (0,265)*** (0,265)*** Brindisi 1,677 1,669 1,681 1,672 1,680 1,668 1,683 1,631 1,577 (0,263)*** (0,264)*** (0,277)*** (0,265)*** (0,277)*** (0,264)*** (0,278)*** (0,264)*** (0,262)*** Taranto 0,452 0,451 0,362 0,360 0,386 0,425 0,424 0,454 -0,310 -0,311 -0,326 -0,309 -0,324 -0,309 -0,325 -0,309 Caltanisetta -0,131 -0,123 -0,301 -0,156 -0,296 -0,114 -0,337 -0,329 -0,437 -0,290 -0,290 -0,304 -0,291 -0,304 -0,290 -0,304 -0,286 -0,280 Cagliari 0,680 0,641 0,621 0,610 0,629 0,626 0,657 0,448 (0,306)** (0,306)** (0,321)* (0,307)** (0,321)* (0,305)** (0,321)** -0,304 Constant coefficient -32,218 -31,413 -30,178 -30,165 -30,502 -31,495 -29,865 -27,968 -28,388 (8,444)*** (8,444)*** (8,872)*** (8,459)*** (8,853)*** (8,442)*** (8,888)*** (8,530)*** (8,535)*** Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,500 0,500 0,440 0,490 0,440 0,500 0,440 0,480 0,480 AIC 1.492,170 1.493,287 1.563,783 1.497,488 1.562,192 1.492,012 1.565,673 1.510,044 1.509,501 BIC 1.597,525 1.594,061 1.659,976 1.593,682 1.653,805 1.588,205 1.657,286 1.592,496 1.582,791 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

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Table 4.5 – Regressions results. Dependent variable: waste per value-added. Specification S-2, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 1,320 1,266 1,666 1,429 1,615 1,345 1,520 1,812 1,791 (0,441)*** (0,441)*** (0,459)*** (0,434)*** (0,453)*** (0,429)*** (0,450)*** (0,415)*** (0,415)*** [Log(VA Industry in a S.S.)]^2 -0,081 -0,075 -0,108 -0,085 -0,105 -0,079 -0,100 -0,114 -0,112 (0,031)*** (0,031)** (0,032)*** (0,030)*** (0,032)*** (0,030)*** (0,032)*** (0,029)*** (0,029)*** Log(VA Industry in a S.S. per worker) -15,538 -14,620 -13,627 -14,375 -13,688 -14,785 -13,175 -11,468 -11,402 (5,939)*** (5,939)** (6,225)** (5,952)** (6,221)** (5,932)** (6,227)** (5,932)* (5,937)* [Log(VA Industry in a S.S. per worker)]^2 -2,379 -2,238 -2,122 -2,207 -2,130 -2,264 -2,053 -1,760 -1,744 (0,931)** (0,931)** (0,976)** (0,933)** (0,975)** (0,930)** (0,976)** (0,931)* (0,931)* Population density 0,040 (0,020)** UL density of Industry in a S.S. -0,090 -0,067 -0,060 -0,065 -0,063 -0,063 (0,015)*** (0,009)*** (0,009)*** (0,009)*** (0,009)*** (0,009)*** Energy consumption of Industry in a S.S. per unit of VA 0,686 0,698 0,801 0,737 0,789 0,714 0,774 0,673 0,775 (0,101)*** (0,101)*** (0,105)*** (0,099)*** (0,103)*** (0,099)*** (0,103)*** (0,097)*** (0,075)*** Share of the VA of Industry in a S.S. on total VA 0,013 0,010 -0,021 -0,012 -0,012 -0,013 -0,013 -0,013 (0,006)** (0,006)* Share of the VA of Services on total VA 0,018 0,022 -0,008 0,014 0,007 0,013 0,013 -0,012 (0,012)* -0,011 (0,005)*** -0,005 (0,005)** (0,005)** Number of patents per thousand of inhabitants -0,235 -0,271 -0,209 -0,240 -0,219 -0,262 -0,224 -0,122 -0,117 (0,089)*** (0,088)*** (0,092)** (0,086)*** (0,090)** (0,087)*** (0,091)** -0,079 -0,079 Share of the urban sorted waste on the total urban waste 0,013 0,012 0,008 0,012 0,008 0,012 0,007 0,013 0,012 (0,004)*** (0,004)*** (0,004)** (0,004)*** (0,004)** (0,004)*** (0,004)* (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,175 0,184 0,142 0,195 0,136 0,194 0,114 0,298 0,269 (0,084)** (0,084)** -0,088 (0,084)** -0,087 (0,083)** -0,086 (0,077)*** (0,074)*** North-East 0,360 0,374 0,329 0,329 0,346 0,351 0,378 0,192 0,194 (0,109)*** (0,109)*** (0,114)*** (0,107)*** (0,111)*** (0,104)*** (0,110)*** (0,090)** (0,090)** Centre 0,327 0,297 0,194 0,281 0,197 0,288 0,205 (0,109)*** (0,108)*** (0,113)* (0,108)*** (0,112)* (0,108)*** (0,112)* South -0,038 -0,038 -0,192 -0,076 -0,183 -0,057 -0,160 -0,128 -0,129 -0,133 -0,127 -0,132 -0,126 -0,131 Islands -0,109 -0,144 -0,293 -0,202 -0,275 -0,178 -0,227 -0,157 -0,156 (0,162)* -0,153 (0,160)* -0,149 -0,157

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(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Milano 2,329 2,316 0,887 2,158 0,895 2,257 0,908 1,864 1,853 (0,482)*** (0,483)*** (0,460)* (0,477)*** (0,460)* (0,477)*** (0,461)** (0,470)*** (0,470)*** Savona 2,184 2,154 1,975 2,100 1,990 2,123 2,032 2,021 2,028 (0,310)*** (0,310)*** (0,324)*** (0,310)*** (0,323)*** (0,307)*** (0,323)*** (0,304)*** (0,304)*** Brindisi 2,034 2,020 2,033 2,024 2,032 2,018 2,037 1,987 1,930 (0,302)*** (0,303)*** (0,318)*** (0,304)*** (0,318)*** (0,303)*** (0,318)*** (0,302)*** (0,300)*** Taranto 0,378 0,377 0,260 0,288 0,292 0,349 0,315 0,394 -0,356 -0,357 -0,374 -0,355 -0,371 -0,355 -0,373 -0,355 Caltanisetta -0,062 -0,050 -0,235 -0,091 -0,226 -0,046 -0,260 -0,241 -0,364 -0,334 -0,335 -0,350 -0,335 -0,349 -0,334 -0,350 -0,329 -0,320 Cagliari 0,759 0,708 0,670 0,675 0,682 0,692 0,702 0,541 (0,353)** (0,353)** (0,370)* (0,353)* (0,369)* (0,352)** (0,370)* -0,351 Constant coefficient -27,643 -26,255 -22,818 -24,470 -23,417 -26,110 -22,879 -22,220 -22,132 (9,629)*** (9,633)*** (10,088)** (9,611)** (10,045)** (9,627)*** (10,101)** (9,678)** (9,687)** Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,510 0,510 0,460 0,500 0,460 0,510 0,450 0,490 0,490 AIC 1.039,565 1.041,712 1.089,539 1.043,422 1.088,038 1.040,308 1.090,012 1.049,676 1.048,640 BIC 1.137,181 1.135,083 1.178,667 1.132,550 1.172,921 1.129,435 1.174,895 1.126,071 1.116,547 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

193

As regards the other provincial dummy variables, all things being equal, the industries in Milano,

Savona and Brindisi produce much more industrial waste (intensity) than the average of the Italian

provinces, with Milano that, alone, generates a quantity of waste (intensity) greater by a factor equal to

2,064 times (the coefficient in S-1). On the contrary, Caltanissetta produces -74% of the average quan-

tity of waste of the Italian provinces38.

After a comparison between the estimates in the two columns of Table 4.6, it is clear the results ob-

tained with the two sets, 1998-2004 and 2000-2004, are statistically robust. In some cases the respective

estimates’ values are different from each other, but from a statistical point of view they are not different

by taking the standard errors into account. The statistical significance diminishes in a couple of coeffi-

cients (exports and Caltanissetta) by going from the set 1998-2004 to the set 2000-2004, but this is due

perhaps to the minor number of observations used in the smaller set, even if the statistical significance

remains at the conventional levels anyway.

These overview of the results has led to conclude that the relationship between the waste production

(intensity) of Industry in a Strict Sense and its drivers of the tested model has been stable in the two

analyzed sets, and it can be affirmed that there is no statistical evidence of that “feared” structural break

in the year 2000, induced by the new environmental legislation.

The results in the specification S-2 (Table 4.7) are very similar to those in the S-1, as regards the signs

of the coefficients, but some of the coefficients are less statistically significant. In particular, the num-

ber of patents per thousand inhabitants and the Caltanissetta indicator are not significant in neither of

the sets, and the driver representing the value-added per worker in the smaller set (2000-2004) is sig-

nificant at the 10% level. By taking the standard errors into account, the estimates of the respective

common drivers are similar to each other, and they are not different in statistical terms.

As regards the coefficients of the logarithm of the value-added per worker and per local unit, and of its

square, both their estimates are negative, and, together with the estimates of the coefficients of the

value-added alone, imply a positive elasticity of the production of waste (intensity) to the value-added,

as was happening before. The coefficient of the squared terms of the value-added, however, is negative,

as well as the logarithms of the value-added per worker39 and per local unit, and their squared terms, are

negative. In this case too, signs and values are consistent with the existence of an Environmental

Kuznets Curve, whose turning point and curvature depend on the tendency of the value-added per

worker.

38 The dummy variable of Taranto has been omitted since it is not statistically significant.

39 The coefficient of the square of the logarithm of the value-added is the only one being statistically significant at 10% level only.

194

Even in Table 4.7 the estimates of the coefficients of the two columns are not statistically different

from each others, thus confirming the stability of the model’s relationship between the waste produc-

tion of Industry in a Strict Sense and its drivers.

As regards the 5 selected provinces, it is evident that Milano only shows a bundle of structural charac-

teristics much different from the national average (its tendency about patents has already been noted

above, but it has also to be noted his peculiarity about high levels of value-added), while the others

(Genova, Venezia, L’Aquila and Roma) do not behave in a different way with respect to the other Ital-

ian provinces.

As final technical note, it has to be said that the usual EKC literature assumes that errors in the regres-

sions are normally distributed when the studied sample comes from a large panel dataset (Dinda, 2004):

spherical errors occur when errors have both uniform variance (homoscedasticity) and are uncorrelated

with each other. As regards EKC studies (Carson, 2010), this assumption might be violated more with

small samples, rather than with large samples, as the one that has been used: moreover, the p-value of

the regressions outputs are always higher than the 0,05 value of significance, and so the residuals are

normally distributed (Gujarati and Porter, 2009).

As regards the homoscedasticity assumption, Gujarati and Porter (2009) explain that heteroscedasticity

does not cause ordinary least squares coefficient estimates to be biased, but that although it can cause

ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be bi-

ased, possibly above or below the true population variance. Thus, regression analysis using heterosce-

dastic data will still provide an unbiased estimate for the relationship between the predictor variables

and the outcome, but standard errors (and therefore inferences) obtained from data analysis are sus-

pect: the OLS estimator is still consistent (unbiased), but is not more BLUE (best linear unbiased esti-

mator). As regards EKC tests (Stern 2004), the homoscedasticity assumption holds for those data com-

ing from local and solid pollutants (as waste), while it is much more probable that such an assumption

might be violated with those data concerning aerial and global pollutants (like GHG or CO2).

Last, the problem of multicollinearity among variables could constitute a major issue: multicollinearity

is a statistical phenomenon in which two or more predictor variables in a multiple regression model are

highly correlated, meaning that one can be linearly predicted from the others: in such a situation, the

coefficient estimates of the multiple regression may change erratically, in response to small changes in

the model or the data. It has to be noted that multicollinearity does not reduce the predictive power, or

reliability, of the model as a whole: it only affects calculations regarding individual predictors. That is, a

multiple regression model with correlated predictors can indicate how well the entire bundle of predic-

tors can predict the outcome variable, but it may not give valid results about any individual predictor,

or about which predictors are redundant with respect to others. Therefore, so long as the underlying

specification is correct, multicollinearity does not actually bias the results, but it just produces large

195

standard errors in the related independent variables. In order to check the presence of multicollinearity,

a variance inflation factor (VIF) test has been done, and the values are all below the threshold value of

5, below which a multicollinearity problem does not exist (Aufhammer and Carson, 2008; Gujarati and

Porter, 2009)

To sum up quickly, the estimates obtained thanks to the model testing are quite stable, both as regards

sets from different periods of time (with the same specification), and as concerns the two different final

specifications. The present analysis has shown the existence of economically and statistically significant

correlations between the waste intensity of the Industry in a Strict Sense and the drivers of the model.

196

Table 4.6 – Specification S-1: value-added per UL of Industry in a Strict Sense as performance driver

Explanatory variables 1998-2004 2000-2004

Log(VA Industry in a S.S.) 0,804 1,008 (0,359)** (0,417)** [Log(VA Industry in a S.S.)]^2 -0,043 -0,056 (0,025)* (0,029)* Log(VA Industry in a S.S. per UL) -3,867 -3,561 (0,507)*** (0,578)*** [Log(VA Industry in a S.S. per UL)]^2 -1,273 -1,17 (0,161)*** (0,182)*** UL density of Industry in a S.S. -0,067 -0,064 (0,008)*** (0,009)*** Energy consumption of Industry in a S.S. per unit of VA 0,842 0,787 (0,064)*** (0,074)*** Share of the VA of Services on total VA 0,007 0,007 (0,005) (0,005) Number of patents per thousand of inhabitants -0,183 -0,215 (0,067)*** (0,076)*** Share of the urban sorted waste on the total urban waste 0,014 0,012 (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,234 0,18 (0,070)*** (0,076)** Geographical and provincial indicators

North-East 0,252 0,272 (0,073)*** (0,085)***

Milano 1,926 2,064 (0,399)*** (0,461)***

Savona 1,532 1,809 (0,254)*** (0,291)***

Brindisi 1,459 1,8 (0,254)*** (0,291)***

Caltanisetta -0,924 -0,736

(0,264)*** (0,303)**

Constant coefficient -2,439 -2,81

(1,278)* (1,516)*

Number of observations (province * year) 721 515

R-squared 0,51 0,52 AIC 1460,025 1012,883 BIC 1533,315 1080,79 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

197

Table 4.6 – Specification S-2: value-added per worker of Industry in a Strict Sense as performance driver

Explanatory variables 1998-2004 2000-2004

Log(VA Industry in a S.S.) 1,736 1,791 (0,352)*** (0,415)*** [Log(VA Industry in a S.S.)]^2 -0,108 -0,112 (0,025)*** (0,029)*** Log(VA Industry in a S.S. per worker) -15,087 -11,402 (5,212)*** (5,937)* [Log(VA Industry in a S.S. per worker)]^2 -2,294 -1,744 (0,814)*** (0,931)* UL density of Industry in a S.S. -0,066 -0,063 (0,008)*** (0,009)*** Energy consumption of Industry in a S.S. per unit of VA 0,832 0,775 (0,065)*** (0,075)*** Share of the VA of Services on total VA 0,015 0,013 (0,004)*** (0,005)** Number of patents per thousand of inhabitants -0,087 -0,117 (0,069) (0,079) Share of the urban sorted waste on the total urban waste 0,014 0,012 (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,333 0,269 (0,068)*** (0,074)*** Geographical and provincial indicators

North-East 0,166 0,194 (0,077)** (0,090)**

Milano 1,831 1,853 (0,408)*** (0,470)***

Savona 1,787 2,028 (0,265)*** (0,304)***

Brindisi 1,577 1,93 (0,262)*** (0,300)***

Caltanisetta -0,437 -0,364

(0,28) (0,32)

Constant coefficient -28,388 -22,132

(8,535)*** (9,687)**

Number of observations (province * year) 721 515

R-squared 0,48 0,49 AIC 1509,501 1048,64 BIC 1582,791 1116,547 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

198

4.4. Main results

The goodness of fit of the model can be seen not only by the indicators in the tables, but also graphi-

cally as reported in Figure 4.1 and 4.3, where the historical (that is, real) values, for the years 1998-2004,

of the logarithm of the value-added (in millions of euros of 1995) have been reported on the x-axis,

while on the y-axis lie the historical (that is, real) data and the theoretical (that is, estimated by the

model) values of the logarithm of the quantity of waste (in tons) per unit of value-added (in millions of

euros of 1995). The same fit can be seen from Figure 4.2 and 4.4, where the same variables are not

more expressed in logarithms, but in absolute terms.

Figure 4.1 shows the plot of such relationship on the basis of the specification S-1 (whose values have

been calculated according to Table 4.6), while Figure 4.3 sows the same relationship on the basis of the

other specification, S-2 (whose values have been calculated according to Table 4.7): the only difference

of S-2 from S-1 is that, among the independent variables, the value-added per worker of S-1 has been

substituted by the value-added per unit of labour in S-2. The plots help showing the goodness of fit of

the model and they suggest an interesting new empirical evidence for the relationship between non-

urban waste and economic development.

In the two figures, the two scatter plots for the period 1998-2004 has been reported: besides the scatter

plot of the historical data of the set (the blue points), the scatter plot of the estimated values for the

same years has been depicted. Both the shapes of the plots lead to suppose the existence of a reverse

U-shaped relationship among the variables, be it more or less pronounced: such relationship confirms

the hypothesis of a bell-shaped trend of the waste variable (here, waste intensity) with respect to its ref-

erence driver, the value-added. When trying to derive forecasts from the model, in the following chap-

ter, this binary graphical relationship will be graphically tested again. The results obtained so far seem to

be interestingly new, as regards non-urban waste: the little bunch of research papers which have dealt

with testing the EKC behaviour for waste has mainly found that the relationship between waste and

economic performance is increasing. Mazzanti, Montini and Zoboli (2006) have found a bell-shaped

curve for urban waste, but with high levels of the turning points of the value-added (therefore, when an

area is very rich), and with a dataset relating to four years only (2000-2004).

The results here presented highlight for the first time the confirmation of the hypothesis according to

which the waste of Industry in a Strict Sense can have a reversed U-shaped behaviour with respect to

the value-added of the same sector, in the relative provinces, and, if such hypothesis will be confirmed

in the following simulations, it can be said that the production of waste of Industry in a Strict Sense in

Italy can be environmentally sustainable, since, after an initial growth due to the increase of economic

wealth, it can decrease thanks to the increase in environmental goods that such wealth tends to cause.

199

Figure 4.1 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-1, 1998-2004, logarithms.

Dependent variable: (logarithm of) waste per value-added (tons per millions of euros of 1995) Independent variable: (logarithm of) value-added (millions of euros of 1995)

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 5,00 6,00 7,00 8,00 9,00 10,00 11,00

log (milioni di Euro del 1995)

log

(t

/ m

ilio

ni

di

Eu

ro d

el 1

995)

Dati storici Dati dalla Specificazione 1

Figure 4.2 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-1, 1998-2004, absolute levels.

Dependent variable: waste per value-added (tons per millions of euros of 1995) Independent variable: value-added (millions of euros of 1995)

0

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milioni di euro del 1995

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Dati storici Dati dalla Specificazione 1

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Figure 4.3 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-2, 1998-2004, logarithms.

Dependent variable: (logarithm of) waste per value-added (tons per millions of euros of 1995) Independent variable: (logarithm of) value-added (millions of euros of 1995)

0,00

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Dati storici Dati dalla Specificazione 2

Figure 4.2 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-2, 1998-2004, absolute levels.

Dependent variable: waste per value-added (tons per millions of euros of 1995) Independent variable: value-added (millions of euros of 1995)

0

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201

4.5 Conclusions

The stages of specification, estimation and analysis of the results of the model have been formulated

into three phases.

In the first stage, after having identified the variables to be included into the basic model, with particu-

lar reference to those economic indicators that contribute to determine the production of waste and

that can influence its trend in the course of time, and after having dealt with the correspondent data,

the econometric model to be estimated has been specified, according to the mainstream EKC literature.

Moreover, the more appropriate methodological tools have been chosen, in order to better develop the

informative content of the MUD database, and the choice has been driven to the use of the pooled

OLS estimator. In the second stage, the best formal specification for the model has been found by re-

peated tests and by discarding the less statistically significant variables, in order to come up with the

“best” quantitative relationship among the variables. In the third stage, the presence of a reverse U-

shaped trend has been studied, both numerically and graphically.

The results here obtained are quite innovative in the relevant academic and non-academic literature on

industrial waste, and as regards their economic determinants too, both in terms of abundance of the

proposed model, and in terms of the contribution of the present research to the Environmental

Kuznets Curve literature: they show that the waste of Industry in a Strict Sense in the Italian provinces,

after an initial growth due to the increase of economic wealth, can decrease thanks to the increase in

environmental goods that such wealth tends to cause, and therefore waste of that sector can have a re-

versed U-shaped behaviour with respect to the value-added of the same sector, in the relative prov-

inces. This hypothesis, for the non-urban waste, has not been studied yet in previous research papers in

the literature, nor has it been studied for any other country, leaving space for more research on these

topics.

202

APPENDIX – A4

Table A4.1 – Regressions results. Dependent variable: waste per worker. Specification S-1, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) -0,059 -0,066 0,314 0,096 0,323 -0,066 0,102 0,121 0,113 -0,359 -0,358 -0,367 -0,353 -0,362 -0,347 -0,360 -0,341 -0,341 [Log(VA Industry in a S.S.)]^2 0,014 0,015 -0,018 0,006 -0,018 0,015 -0,007 0,001 0,002 -0,025 -0,025 -0,026 -0,025 -0,025 -0,025 -0,025 -0,024 -0,024 Log(VA Industry in a S.S. per UL) -2,025 -2,003 -1,362 -2,058 -1,371 -2,003 -1,401 -2,048 -2,126 (0,512)*** (0,506)*** (0,515)*** (0,507)*** (0,512)*** (0,503)*** (0,517)*** (0,487)*** (0,482)*** [Log(VA Industry in a S.S. per UL)]^2 -0,644 -0,642 -0,538 -0,662 -0,540 -0,642 -0,538 -0,649 -0,666 (0,157)*** (0,156)*** (0,161)*** (0,157)*** (0,160)*** (0,156)*** (0,162)*** (0,154)*** (0,153)*** Population density 0,005 -0,017 UL density of Industry in a S.S. -0,060 -0,057 -0,051 -0,057 -0,057 -0,057 (0,014)*** (0,008)*** (0,008)*** (0,007)*** (0,007)*** (0,007)*** Energy consumption of Industry in a S.S. per unit of VA 0,814 0,812 0,824 0,853 0,827 0,812 0,798 0,826 0,915 (0,083)*** (0,083)*** (0,086)*** (0,082)*** (0,084)*** (0,082)*** (0,086)*** (0,079)*** (0,061)*** Share of the VA of Industry in a S.S. on total VA 0,001 0,000 -0,029 -0,023 -0,030 -0,011 -0,011 (0,010)*** (0,005)*** (0,005)*** Share of the VA of Services on total VA 0,022 0,022 0,001 0,022 0,023 0,022 0,022 (0,009)** (0,009)** -0,009 (0,004)*** (0,005)*** (0,004)*** (0,004)*** Number of patents per thousand of inhabitants -0,290 -0,294 -0,222 -0,262 -0,221 -0,294 -0,240 -0,207 -0,205 (0,071)*** (0,070)*** (0,071)*** (0,069)*** (0,071)*** (0,069)*** (0,071)*** (0,063)*** (0,063)*** Share of the urban sorted waste on the total urban waste 0,014 0,014 0,013 0,014 0,013 0,014 0,011 0,014 0,013 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,002)*** (0,002)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. -0,007 -0,007 -0,064 0,003 -0,063 -0,007 -0,102 0,048 0,028 -0,070 -0,070 -0,072 -0,071 -0,072 -0,070 -0,071 -0,068 -0,066 North-East 0,313 0,315 0,271 0,269 0,268 0,315 0,328 0,206 0,208 (0,085)*** (0,085)*** (0,088)*** (0,083)*** (0,086)*** (0,082)*** (0,086)*** (0,070)*** (0,070)*** Centre 0,212 0,212 0,193 0,194 0,192 0,212 0,200 (0,090)** (0,090)** (0,093)** (0,090)** (0,093)** (0,090)** (0,094)**

203

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 South -0,073 -0,068 -0,083 -0,114 -0,086 -0,068 -0,047 -0,110 -0,109 -0,113 -0,108 -0,111 -0,108 -0,112 Islands 0,112 0,114 0,129 0,051 0,124 0,114 0,200 -0,134 -0,133 -0,138 -0,131 -0,135 -0,132 -0,136 Milano 1,825 1,814 0,506 1,670 0,506 1,814 0,533 1,606 1,618 (0,394)*** (0,392)*** -0,358 (0,389)*** -0,358 (0,384)*** -0,360 (0,379)*** (0,379)*** Savona 1,570 1,567 1,486 1,527 1,484 1,567 1,557 1,484 1,485 (0,250)*** (0,250)*** (0,258)*** (0,250)*** (0,257)*** (0,248)*** (0,258)*** (0,242)*** (0,242)*** Brindisi 1,728 1,725 1,709 1,725 1,709 1,725 1,713 1,605 1,558 (0,245)*** (0,245)*** (0,253)*** (0,245)*** (0,253)*** (0,244)*** (0,254)*** (0,243)*** (0,241)*** Taranto 0,463 0,461 0,336 0,368 0,330 0,461 0,410 0,350 -0,287 -0,286 -0,296 -0,285 -0,293 -0,284 -0,296 -0,282 Caltanisetta -0,326 -0,323 -0,377 -0,346 -0,378 -0,323 -0,411 -0,276 -0,387 -0,264 -0,264 -0,273 -0,265 -0,273 -0,264 -0,274 -0,259 -0,252 Cagliari 0,441 0,438 0,508 0,404 0,504 0,438 0,545 0,456 -0,287 -0,287 (0,297)* -0,287 (0,296)* -0,286 (0,298)* -0,285 Constant coefficient -1,473 -1,452 0,152 -0,142 0,233 -1,452 -1,018 -2,136 -2,204 -1,326 -1,323 -1,349 -1,214 -1,250 -1,239 -1,288 (1,217)* (1,216)* Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,470 0,470 0,440 0,470 0,440 0,470 0,430 0,460 0,460 AIC 1.384,887 1.382,972 1.431,347 1.387,121 1.429,374 1.380,972 1.437,316 1.388,651 1.388,003 BIC 1.490,242 1.483,746 1.527,540 1.483,314 1.520,986 1.477,166 1.528,929 1.471,103 1.461,294 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

204

Table A4.2 – Regressions results. Dependent variable: waste per worker. Specification S-1, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,044 0,033 0,327 0,13 0,308 -0,018 0,097 0,161 0,138 -0,416 -0,416 -0,424 -0,411 -0,421 -0,404 -0,418 -0,396 -0,396 [Log(VA Industry in a S.S.)]^2 0,007 0,009 -0,017 0,003 -0,016 0,012 -0,005 -0,002 0,001 -0,029 -0,029 -0,03 -0,029 -0,029 -0,028 -0,029 -0,028 -0,028 Log(VA Industry in a S.S. per UL) -1,878 -1,818 -1,293 -1,863 -1,268 -1,843 -1,33 -1,902 -2,038 (0,586)*** (0,579)*** (0,587)** (0,578)*** (0,582)** (0,576)*** (0,591)** (0,556)*** (0,550)*** [Log(VA Industry in a S.S. per UL)]^2 -0,606 -0,6 -0,516 -0,616 -0,509 -0,602 -0,509 -0,61 -0,641 (0,177)*** (0,176)*** (0,181)*** (0,176)*** (0,180)*** (0,176)*** (0,182)*** (0,174)*** (0,173)*** Population density 0,013 -0,02 UL density of Industry in a S.S. -0,058 -0,05 -0,045 -0,051 -0,052 -0,053 (0,016)*** (0,009)*** (0,009)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,781 0,776 0,798 0,807 0,79 0,77 0,772 0,787 0,872 (0,098)*** (0,098)*** (0,100)*** (0,095)*** (0,098)*** (0,097)*** (0,101)*** (0,092)*** (0,071)*** Share of the VA of Industry in a S.S. on total VA -0,005 -0,007 -0,033 -0,023 -0,029 -0,013 -0,013 (0,012)*** (0,006)*** (0,006)*** Share of the VA of Services on total VA 0,014 0,016 -0,004 0,02 0,02 0,021 0,02 -0,011 -0,011 -0,01 (0,005)*** (0,005)*** (0,005)*** (0,005)*** Number of patents per thousand of inhabitants -0,313 -0,323 -0,273 -0,302 -0,278 -0,329 -0,297 -0,235 -0,232 (0,082)*** (0,081)*** (0,083)*** (0,080)*** (0,081)*** (0,080)*** (0,083)*** (0,072)*** (0,073)*** Share of the urban sorted waste on the total urban waste 0,012 0,012 0,01 0,011 0,01 0,011 0,008 0,011 0,011 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. -0,062 -0,062 -0,106 -0,057 -0,108 -0,068 -0,147 -0,008 -0,021 -0,078 -0,078 -0,08 -0,078 -0,08 -0,077 (0,079)* -0,074 -0,072 North-East 0,319 0,322 0,285 0,288 0,293 0,336 0,357 0,217 0,219 (0,099)*** (0,099)*** (0,102)*** (0,096)*** (0,099)*** (0,095)*** (0,099)*** (0,081)*** (0,081)*** Centre 0,222 0,222 0,205 0,208 0,208 0,223 0,208 (0,105)** (0,105)** (0,108)* (0,105)** (0,108)* (0,105)** (0,109)* South -0,086 -0,07 -0,092 -0,103 -0,084 -0,065 -0,07 -0,132 -0,13 -0,134 -0,128 -0,132 -0,13 -0,134 Islands 0,106 0,115 0,115 0,067 0,128 0,126 0,18 -0,159 -0,159 -0,163 -0,155 -0,159 -0,157 -0,162 Milano 1,885 1,858 0,747 1,755 0,748 1,907 0,794 1,662 1,678

205

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 (0,456)*** (0,454)*** (0,418)* (0,449)*** (0,418)* (0,445)*** (0,420)* (0,438)*** (0,438)*** Savona 1,83 1,825 1,752 1,789 1,76 1,844 1,841 1,754 1,754 (0,287)*** (0,286)*** (0,294)*** (0,286)*** (0,293)*** (0,284)*** (0,294)*** (0,276)*** (0,277)*** Brindisi 1,956 1,95 1,952 1,949 1,953 1,951 1,959 1,823 1,776 (0,280)*** (0,280)*** (0,288)*** (0,280)*** (0,288)*** (0,280)*** (0,290)*** (0,278)*** (0,276)*** Taranto 0,242 0,239 0,116 0,17 0,132 0,257 0,196 0,151 -0,328 -0,328 -0,337 -0,325 -0,333 -0,326 -0,337 -0,324 Caltanisetta -0,201 -0,193 -0,253 -0,215 -0,249 -0,195 -0,281 -0,151 -0,257 -0,303 -0,303 -0,311 -0,303 -0,311 -0,302 -0,313 -0,298 -0,289 Cagliari 0,565 0,56 0,615 0,534 0,624 0,565 0,653 0,579 (0,330)* (0,330)* (0,339)* -0,33 (0,338)* (0,329)* (0,341)* (0,328)* Constant coefficient -0,932 -0,887 0,698 0,087 0,47 -1,176 -0,628 -1,873 -1,964 -1,573 -1,571 -1,588 -1,424 -1,46 -1,469 -1,52 -1,443 -1,442 Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,49 0,49 0,46 0,49 0,46 0,49 0,45 0,48 0,48 AIC 959,752 958,183 986,503 958,407 984,643 956,470 992,011 961,638 960,858 BIC 1.057,367 1.051,555 1.075,630 1.047,534 1.069,526 1.045,598 1.076,894 1.038,033 1.028,765 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

206

Table A4.3 – Regressions results. Dependent variable: waste per worker. Specification S-2, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,497 0,492 0,847 0,671 0,843 0,455 0,644 0,713 0,704 -0,348 -0,347 (0,353)** (0,339)** (0,347)** -0,335 (0,346)* (0,324)** (0,325)** [Log(VA Industry in a S.S.)]^2 -0,026 -0,026 -0,054 -0,037 -0,054 -0,023 -0,044 -0,043 -0,042 -0,024 -0,024 (0,025)** -0,024 (0,024)** -0,024 (0,024)* (0,023)* (0,023)* Log(VA Industry in a S.S. per worker) -17,149 -17,091 -17,306 -17,216 -17,303 -16,958 -16,432 -15,294 -15,467 (4,819)*** (4,807)*** (4,954)*** (4,822)*** (4,951)*** (4,794)*** (4,964)*** (4,804)*** (4,813)*** [Log(VA Industry in a S.S. per worker)]^2 -2,737 -2,728 -2,799 -2,757 -2,798 -2,707 -2,665 -2,437 -2,458 (0,752)*** (0,750)*** (0,773)*** (0,753)*** (0,773)*** (0,748)*** (0,775)*** (0,750)*** (0,752)*** Population density 0,003 -0,016 UL density of Industry in a S.S. -0,053 -0,051 -0,044 -0,052 -0,050 -0,050 (0,012)*** (0,008)*** (0,007)*** (0,007)*** (0,007)*** (0,007)*** Energy consumption of Industry in a S.S. per unit of VA 0,739 0,740 0,804 0,773 0,803 0,733 0,767 0,746 0,850 (0,080)*** (0,079)*** (0,081)*** (0,078)*** (0,080)*** (0,078)*** (0,080)*** (0,077)*** (0,060)*** Share of the VA of Industry in a S.S. on total VA -0,004 -0,004 -0,027 -0,026 -0,027 -0,011 -0,011 (0,010)*** (0,005)*** (0,005)*** Share of the VA of Services on total VA 0,021 0,021 -0,001 0,025 0,020 0,026 0,026 (0,009)** (0,009)** -0,009 (0,004)*** (0,004)*** (0,004)*** (0,004)*** Number of patents per thousand of inhabitants -0,232 -0,236 -0,173 -0,204 -0,173 -0,239 -0,190 -0,133 -0,128 (0,071)*** (0,070)*** (0,071)** (0,069)*** (0,070)** (0,069)*** (0,071)*** (0,064)** (0,064)** Share of the urban sorted waste on the total urban waste 0,014 0,014 0,011 0,014 0,011 0,014 0,010 0,012 0,012 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,002)*** (0,002)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,027 0,028 -0,009 0,040 -0,009 0,023 -0,045 0,073 0,048 -0,070 -0,070 -0,071 -0,070 -0,071 -0,069 -0,070 -0,065 -0,063 North-East 0,301 0,302 0,265 0,262 0,267 0,312 0,324 0,144 0,143 (0,086)*** (0,086)*** (0,089)*** (0,085)*** (0,087)*** (0,083)*** (0,086)*** (0,071)** (0,071)** Centre 0,325 0,323 0,251 0,310 0,251 0,327 0,267 (0,088)*** (0,087)*** (0,089)*** (0,087)*** (0,089)*** (0,086)*** (0,089)*** South 0,045 0,045 -0,057 0,010 -0,056 0,055 -0,003 -0,101 -0,101 -0,103 -0,100 -0,102 -0,098 -0,101 Islands 0,161 0,158 0,061 0,105 0,063 0,174 0,155 -0,123 -0,122 -0,125 -0,121 -0,124 -0,117 -0,121 Milano 1,688 1,687 0,576 1,540 0,576 1,711 0,587 1,416 1,416

207

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 (0,384)*** (0,384)*** -0,356 (0,380)*** -0,356 (0,379)*** -0,358 (0,375)*** (0,376)*** Savona 1,659 1,657 1,530 1,615 1,531 1,669 1,594 1,504 1,508 (0,251)*** (0,250)*** (0,257)*** (0,250)*** (0,256)*** (0,248)*** (0,257)*** (0,245)*** (0,245)*** Brindisi 1,739 1,738 1,747 1,741 1,747 1,738 1,749 1,653 1,595 (0,244)*** (0,244)*** (0,252)*** (0,245)*** (0,251)*** (0,244)*** (0,253)*** (0,243)*** (0,242)*** Taranto 0,417 0,417 0,353 0,337 0,355 0,429 0,429 0,352 -0,288 -0,288 -0,296 -0,287 -0,294 -0,286 -0,296 -0,285 Caltanisetta -0,079 -0,079 -0,206 -0,107 -0,206 -0,083 -0,251 -0,069 -0,185 -0,269 -0,269 -0,276 -0,269 -0,276 -0,268 -0,277 -0,263 -0,258 Cagliari 0,613 0,609 0,594 0,582 0,595 0,616 0,640 0,619 (0,284)** (0,283)** (0,292)** (0,284)** (0,291)** (0,283)** (0,293)** (0,281)** Constant coefficient -28,587 -28,498 -27,610 -27,406 -27,642 -28,460 -27,225 -26,710 -27,099 (7,838)*** (7,821)*** (8,060)*** (7,832)*** (8,041)*** (7,816)*** (8,093)*** (7,866)*** (7,881)*** Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,470 0,470 0,440 0,470 0,440 0,470 0,430 0,460 0,460 AIC 1.384,803 1.382,847 1.425,401 1.386,392 1.423,405 1.381,030 1.430,543 1.393,263 1.394,646 BIC 1.490,158 1.483,621 1.521,594 1.482,585 1.515,018 1.477,224 1.522,155 1.475,714 1.467,937 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

208

Table A4.4 – Regressions results. Dependent variable: waste per worker. Specification S-2, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,590 0,571 0,845 0,688 0,814 0,512 0,641 0,733 0,716 -0,408 -0,407 (0,414)** (0,399)* (0,408)** -0,395 -0,407 (0,382)* (0,383)* [Log(VA Industry in a S.S.)]^2 -0,033 -0,031 -0,053 -0,038 -0,052 -0,028 -0,043 -0,045 -0,043 -0,029 -0,028 (0,029)* -0,028 (0,029)* -0,028 -0,029 (0,027)* -0,027 Log(VA Industry in a S.S. per worker) -15,186 -14,868 -14,187 -14,693 -14,224 -14,742 -13,555 -13,353 -13,251 (5,489)*** (5,470)*** (5,614)** (5,475)*** (5,609)** (5,463)*** (5,635)** (5,455)** (5,473)** [Log(VA Industry in a S.S. per worker)]^2 -2,458 -2,409 -2,330 -2,387 -2,334 -2,389 -2,233 -2,161 -2,142 (0,860)*** (0,857)*** (0,880)*** (0,858)*** (0,879)*** (0,856)*** (0,883)** (0,856)** (0,859)** Population density 0,014 -0,018 UL density of Industry in a S.S. -0,054 -0,046 -0,041 -0,048 -0,046 -0,046 (0,014)*** (0,009)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,697 0,701 0,771 0,729 0,764 0,689 0,733 0,705 0,807 (0,093)*** (0,093)*** (0,094)*** (0,091)*** (0,093)*** (0,091)*** (0,093)*** (0,090)*** (0,069)*** Share of the VA of Industry in a S.S. on total VA -0,007 -0,008 -0,029 -0,024 -0,024 -0,012 -0,012 (0,012)** (0,006)*** (0,006)*** Share of the VA of Services on total VA 0,014 0,016 -0,005 0,022 0,017 0,023 0,023 -0,011 -0,011 -0,010 (0,005)*** (0,005)*** (0,005)*** (0,005)*** Number of patents per thousand of inhabitants -0,245 -0,258 -0,215 -0,235 -0,221 -0,264 -0,236 -0,155 -0,149 (0,083)*** (0,081)*** (0,083)*** (0,079)*** (0,081)*** (0,080)*** (0,082)*** (0,073)** (0,073)** Share of the urban sorted waste on the total urban waste 0,012 0,012 0,009 0,011 0,009 0,012 0,008 0,010 0,009 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. -0,022 -0,019 -0,048 -0,012 -0,052 -0,028 -0,086 0,020 -0,002 -0,077 -0,077 -0,079 -0,077 -0,079 -0,076 -0,078 -0,071 -0,068 North-East 0,306 0,311 0,281 0,279 0,291 0,328 0,348 0,160 0,159 (0,101)*** (0,100)*** (0,103)*** (0,098)*** (0,100)*** (0,096)*** (0,099)*** (0,082)* (0,083)* Centre 0,327 0,316 0,246 0,305 0,248 0,323 0,262 (0,101)*** (0,100)*** (0,102)** (0,100)*** (0,101)** (0,099)*** (0,102)** South 0,039 0,039 -0,067 0,012 -0,061 0,054 -0,022 -0,118 -0,118 -0,120 -0,117 -0,119 -0,116 -0,119 Islands 0,183 0,171 0,069 0,130 0,080 0,197 0,161 -0,145 -0,144 -0,146 -0,141 -0,144 -0,138 -0,142 Milano 1,716 1,712 0,732 1,599 0,737 1,756 0,762 1,426 1,412

209

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 (0,445)*** (0,445)*** (0,415)* (0,439)*** (0,415)* (0,439)*** (0,417)* (0,432)*** (0,434)*** Savona 1,897 1,887 1,764 1,848 1,773 1,911 1,844 1,747 1,748 (0,286)*** (0,286)*** (0,293)*** (0,285)*** (0,292)*** (0,283)*** (0,292)*** (0,279)*** (0,280)*** Brindisi 2,017 2,012 2,021 2,015 2,020 2,013 2,027 1,913 1,856 (0,279)*** (0,279)*** (0,287)*** (0,279)*** (0,286)*** (0,279)*** (0,288)*** (0,278)*** (0,277)*** Taranto 0,267 0,267 0,187 0,203 0,206 0,288 0,263 0,203 -0,329 -0,329 -0,338 -0,327 -0,335 -0,327 -0,338 -0,326 Caltanisetta -0,033 -0,029 -0,155 -0,057 -0,150 -0,032 -0,191 -0,012 -0,135 -0,308 -0,308 -0,315 -0,308 -0,315 -0,308 -0,317 -0,302 -0,295 Cagliari 0,731 0,714 0,687 0,690 0,695 0,725 0,732 0,741 (0,326)** (0,325)** (0,334)** (0,325)** (0,333)** (0,324)** (0,335)** (0,323)** Constant coefficient -24,830 -24,349 -21,992 -23,072 -22,354 -24,460 -22,077 -23,025 -22,927 (8,900)*** (8,873)*** (9,098)** (8,840)*** (9,057)** (8,865)*** (9,141)** (8,901)*** (8,930)** Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,490 0,490 0,460 0,490 0,460 0,490 0,460 0,480 0,470 AIC 958,428 957,013 983,210 957,252 981,433 955,420 987,145 963,445 964,885 BIC 1.056,044 1.050,385 1.072,337 1.046,379 1.066,316 1.044,548 1.072,029 1.039,840 1.032,791 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

210

Table A4.5 – Regressions results. Dependent variable: waste per worker. Specification S-3, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,162 0,127 0,504 0,262 0,489 0,145 0,315 0,356 0,345 -0,359 -0,357 -0,366 -0,351 -0,361 -0,347 -0,361 -0,342 -0,343 [Log(VA Industry in a S.S.)]^2 -0,001 0,002 -0,030 -0,006 -0,030 0,001 -0,021 -0,014 -0,013 -0,025 -0,025 -0,026 -0,025 -0,025 -0,025 -0,025 -0,024 -0,024 Log(VA Industry in a S.S. per UL) -2,459 -2,311 -1,512 -2,419 -1,487 -2,292 -1,658 -2,437 -2,462 (0,571)*** (0,555)*** (0,563)*** (0,553)*** (0,554)*** (0,547)*** (0,563)*** (0,526)*** (0,516)*** [Log(VA Industry in a S.S. per UL)]^2 -0,675 -0,656 -0,520 -0,682 -0,515 -0,653 -0,540 -0,687 -0,693 (0,162)*** (0,161)*** (0,166)*** (0,161)*** (0,165)*** (0,161)*** (0,167)*** (0,157)*** (0,156)*** Log(VA Industry in a S.S. per worker) -11,519 -11,748 -14,713 -11,553 -14,776 -11,880 -13,307 -10,236 -10,390 (4,960)** (4,957)** (5,114)*** (4,965)** (5,104)*** (4,913)** (5,109)*** (4,875)** (4,869)** [Log(VA Industry in a S.S. per worker)]^2 -1,925 -1,949 -2,393 -1,931 -2,401 -1,969 -2,190 -1,720 -1,738 (0,769)** (0,768)** (0,793)*** (0,770)** (0,792)*** (0,762)** (0,793)*** (0,756)** (0,756)** Population density 0,019 -0,018 UL density of Industry in a S.S. -0,069 -0,057 -0,052 -0,056 -0,056 -0,056 (0,014)*** (0,008)*** (0,008)*** (0,007)*** (0,007)*** (0,007)*** Energy consumption of Industry in a S.S. per unit of VA 0,798 0,790 0,798 0,822 0,793 0,792 0,776 0,803 0,899 (0,083)*** (0,082)*** (0,085)*** (0,081)*** (0,083)*** (0,082)*** (0,085)*** (0,078)*** (0,061)*** Share of the VA of Industry in a S.S. on total VA 0,005 0,002 -0,028 -0,015 -0,026 -0,011 -0,011 (0,010)*** (0,006)** (0,006)*** Share of the VA of Services on total VA 0,015 0,018 -0,002 0,016 0,017 0,015 0,015 -0,009 (0,009)* -0,009 (0,005)*** (0,005)*** (0,005)*** (0,005)*** Number of patents per thousand of inhabitants -0,252 -0,268 -0,192 -0,244 -0,195 -0,266 -0,211 -0,175 -0,172 (0,071)*** (0,070)*** (0,071)*** (0,068)*** (0,070)*** (0,069)*** (0,071)*** (0,064)*** (0,064)*** Share of the urban sorted waste on the total urban waste 0,013 0,013 0,011 0,012 0,011 0,013 0,010 0,012 0,012 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,002)*** (0,002)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,020 0,016 -0,049 0,027 -0,051 0,017 -0,079 0,079 0,052 -0,071 -0,071 -0,073 -0,071 -0,072 -0,070 -0,072 -0,068 -0,067 North-East 0,319 0,321 0,266 0,290 0,270 0,316 0,327 0,203 0,205 (0,085)*** (0,085)*** (0,088)*** (0,084)*** (0,087)*** (0,082)*** (0,086)*** (0,072)*** (0,072)*** Centre 0,233 0,232 0,224 0,216 0,226 0,232 0,225 (0,090)** (0,090)** (0,094)** (0,090)** (0,093)** (0,090)** (0,094)** South -0,077 -0,055 -0,063 -0,091 -0,057 -0,057 -0,033

211

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 -0,110 -0,108 -0,112 -0,107 -0,110 -0,108 -0,112 Islands 0,086 0,098 0,116 0,047 0,124 0,094 0,180 -0,133 -0,132 -0,137 -0,130 -0,134 -0,131 -0,135 Milano 1,936 1,887 0,588 1,782 0,587 1,870 0,611 1,643 1,649 (0,390)*** (0,388)*** (0,355)* (0,385)*** (0,355)* (0,380)*** (0,356)* (0,375)*** (0,375)*** Savona 1,502 1,502 1,451 1,460 1,456 1,499 1,496 1,395 1,402 (0,250)*** (0,250)*** (0,259)*** (0,250)*** (0,258)*** (0,250)*** (0,260)*** (0,242)*** (0,243)*** Brindisi 1,735 1,720 1,691 1,723 1,690 1,718 1,703 1,611 1,559 (0,243)*** (0,243)*** (0,251)*** (0,243)*** (0,251)*** (0,242)*** (0,252)*** (0,241)*** (0,240)*** Taranto 0,587 0,566 0,410 0,510 0,416 0,558 0,502 0,472 (0,288)** (0,288)** -0,297 (0,287)* -0,296 (0,285)* (0,296)* (0,283)* Caltanisetta -0,421 -0,387 -0,375 -0,427 -0,369 -0,382 -0,450 -0,388 -0,498 -0,278 -0,277 -0,287 -0,277 -0,285 -0,275 -0,286 -0,270 (0,264)* Cagliari 0,417 0,412 0,498 0,379 0,504 0,411 0,522 0,403 -0,285 -0,285 (0,295)* -0,285 (0,293)* -0,285 (0,296)* -0,283 Constant coefficient -19,574 -19,889 -23,089 -18,537 -23,322 -20,010 -21,881 -18,098 -18,411 (8,044)** (8,040)** (8,311)*** (8,025)** (8,255)*** (8,014)** (8,335)*** (7,990)** (7,983)** Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,490 0,490 0,450 0,480 0,450 0,490 0,440 0,480 0,470 AIC 1.369,783 1.369,050 1.418,092 1.370,885 1.416,160 1.367,095 1.423,418 1.375,406 1.375,370 BIC 1.484,299 1.478,985 1.523,447 1.476,240 1.516,934 1.472,450 1.524,192 1.467,019 1.457,821 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

212

Table A4.6 – Regressions results. Dependent variable: waste per worker. Specification S-3, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,295 0,247 0,532 0,325 0,491 0,231 0,346 0,428 0,410 -0,416 -0,415 -0,424 -0,409 -0,420 -0,405 -0,420 -0,398 -0,398 [Log(VA Industry in a S.S.)]^2 -0,010 -0,006 -0,031 -0,010 -0,029 -0,005 -0,021 -0,019 -0,017 -0,029 -0,029 -0,030 -0,029 -0,029 -0,028 -0,029 -0,028 -0,028 Log(VA Industry in a S.S. per UL) -2,494 -2,247 -1,592 -2,324 -1,512 -2,265 -1,739 -2,405 -2,523 (0,655)*** (0,635)*** (0,644)** (0,632)*** (0,633)** (0,627)*** (0,644)*** (0,598)*** (0,586)*** [Log(VA Industry in a S.S. per UL)]^2 -0,674 -0,642 -0,534 -0,661 -0,516 -0,645 -0,549 -0,672 -0,698 (0,183)*** (0,182)*** (0,186)*** (0,181)*** (0,185)*** (0,181)*** (0,187)*** (0,178)*** (0,176)*** Log(VA Industry in a S.S. per worker) -9,623 -9,904 -11,581 -9,570 -11,880 -9,795 -10,296 -8,203 -7,795 (5,646)* (5,650)* (5,815)** (5,643)* (5,796)** (5,615)* (5,821)* -5,556 -5,545 [Log(VA Industry in a S.S. per worker)]^2 -1,653 -1,679 -1,925 -1,633 -1,967 -1,663 -1,741 -1,423 -1,362 (0,878)* (0,879)* (0,905)** (0,879)* (0,903)** (0,874)* (0,907)* -0,866 -0,865 Population density 0,031 -0,020 UL density of Industry in a S.S. -0,071 -0,051 -0,048 -0,051 -0,052 -0,052 (0,016)*** (0,009)*** (0,009)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,771 0,756 0,774 0,779 0,758 0,754 0,754 0,769 0,861 (0,098)*** (0,097)*** (0,100)*** (0,095)*** (0,098)*** (0,097)*** (0,100)*** (0,091)*** (0,071)*** Share of the VA of Industry in a S.S. on total VA 0,002 -0,002 -0,030 -0,014 -0,023 -0,013 -0,013 (0,012)** (0,007)* (0,007)*** Share of the VA of Services on total VA 0,008 0,012 -0,007 0,013 0,013 0,013 0,012 -0,011 -0,011 -0,010 (0,006)** (0,006)** (0,006)** (0,006)** Number of patents per thousand of inhabitants -0,269 -0,294 -0,241 -0,278 -0,249 -0,296 -0,263 -0,202 -0,200 (0,082)*** (0,081)*** (0,082)*** (0,079)*** (0,082)*** (0,080)*** (0,082)*** (0,073)*** (0,073)*** Share of the urban sorted waste on the total urban waste 0,010 0,010 0,009 0,010 0,009 0,010 0,007 0,010 0,010 (0,003)*** (0,003)*** (0,003)** (0,003)*** (0,003)** (0,003)*** (0,003)** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. -0,031 -0,036 -0,087 -0,030 -0,093 -0,037 -0,118 0,028 0,010 -0,079 -0,079 -0,081 -0,079 -0,080 -0,078 -0,080 -0,075 -0,073 North-East 0,326 0,330 0,284 0,308 0,298 0,336 0,355 0,221 0,227 (0,100)*** (0,100)*** (0,102)*** (0,098)*** (0,100)*** (0,095)*** (0,099)*** (0,083)*** (0,083)*** Centre 0,221 0,225 0,216 0,212 0,224 0,225 0,211 (0,106)** (0,106)** (0,109)** (0,105)** (0,108)** (0,106)** (0,110)* South -0,108 -0,067 -0,085 -0,093 -0,067 -0,066 -0,070

213

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 -0,132 -0,129 -0,133 -0,127 -0,131 -0,129 -0,134 Islands 0,075 0,098 0,101 0,062 0,126 0,102 0,156 -0,157 -0,157 -0,162 -0,153 -0,157 -0,155 -0,161 Milano 1,998 1,924 0,790 1,851 0,792 1,941 0,834 1,692 1,701 (0,451)*** (0,449)*** (0,414)* (0,445)*** (0,414)* (0,439)*** (0,416)** (0,432)*** (0,432)*** Savona 1,716 1,723 1,671 1,688 1,693 1,727 1,728 1,625 1,624 (0,287)*** (0,288)*** (0,296)*** (0,286)*** (0,294)*** (0,286)*** (0,297)*** (0,277)*** (0,277)*** Brindisi 1,986 1,966 1,961 1,968 1,959 1,967 1,973 1,850 1,801 (0,277)*** (0,277)*** (0,285)*** (0,277)*** (0,285)*** (0,277)*** (0,287)*** (0,275)*** (0,273)*** Taranto 0,406 0,377 0,231 0,335 0,253 0,385 0,323 0,301 -0,328 -0,328 -0,337 -0,326 -0,336 -0,325 -0,337 -0,323 Caltanisetta -0,360 -0,305 -0,320 -0,336 -0,299 -0,309 -0,388 -0,311 -0,435 -0,318 -0,316 -0,326 -0,315 -0,324 -0,315 -0,326 -0,309 -0,299 Cagliari 0,506 0,509 0,583 0,483 0,604 0,509 0,599 0,501 -0,328 -0,329 (0,338)* -0,328 (0,337)* -0,328 (0,340)* -0,326 Constant coefficient -16,134 -16,490 -17,567 -15,235 -18,471 -16,415 -16,671 -14,563 -13,938 (9,111)* (9,120)* (9,398)* (9,051)* (9,302)** (9,102)* (9,438)* -9,056 -9,042 Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,510 0,500 0,470 0,500 0,470 0,500 0,470 0,490 0,490 AIC 947,334 947,774 977,901 947,044 976,403 945,811 982,117 950,789 949,558 BIC 1.053,438 1.049,634 1.075,516 1.044,659 1.069,775 1.043,426 1.075,489 1.035,672 1.025,953 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per worker of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

214

Table A4.7 – Regressions results. Dependent variable: waste per value-added. Specification S-3, 1998-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,481 0,432 0,901 0,591 0,875 0,495 0,702 0,780 0,770 -0,379 -0,378 (0,392)** -0,372 (0,387)** -0,367 (0,386)* (0,362)** (0,362)** [Log(VA Industry in a S.S.)]^2 -0,022 -0,017 -0,058 -0,027 -0,057 -0,021 -0,048 -0,043 -0,042 -0,027 -0,027 (0,027)** -0,026 (0,027)** -0,026 (0,027)* (0,025)* -0,025 Log(VA Industry in a S.S. per UL) -3,414 -3,203 -2,206 -3,331 -2,163 -3,133 -2,360 -3,336 -3,328 (0,603)*** (0,587)*** (0,603)*** (0,586)*** (0,594)*** (0,579)*** (0,603)*** (0,557)*** (0,546)*** [Log(VA Industry in a S.S. per UL)]^2 -1,130 -1,103 -0,934 -1,134 -0,925 -1,093 -0,955 -1,168 -1,166 (0,171)*** (0,171)*** (0,178)*** (0,171)*** (0,176)*** (0,170)*** (0,178)*** (0,167)*** (0,165)*** Log(VA Industry in a S.S. per worker) -11,922 -12,250 -15,947 -12,019 -16,056 -12,726 -14,466 -10,336 -10,593 (5,241)** (5,242)** (5,479)*** (5,254)** (5,470)*** (5,197)** (5,473)*** (5,158)** (5,151)** [Log(VA Industry in a S.S. per worker)]^2 -1,805 -1,841 -2,394 -1,819 -2,407 -1,910 -2,180 -1,541 -1,574 (0,812)** (0,812)** (0,850)*** (0,815)** (0,848)*** (0,806)** (0,849)** (0,800)* (0,800)** Population density 0,028 -0,019 UL density of Industry in a S.S. -0,089 -0,071 -0,065 -0,069 -0,067 -0,067 (0,015)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** (0,008)*** Energy consumption of Industry in a S.S. per unit of VA 0,758 0,747 0,757 0,784 0,749 0,753 0,733 0,732 0,825 (0,087)*** (0,087)*** (0,091)*** (0,086)*** (0,089)*** (0,087)*** (0,091)*** (0,083)*** (0,065)*** Share of the VA of Industry in a S.S. on total VA 0,012 0,008 -0,029 -0,012 -0,026 -0,012 -0,012 (0,011)*** (0,007)* (0,007)*** Share of the VA of Services on total VA 0,018 0,021 -0,004 0,015 0,016 0,015 0,014 (0,010)* (0,010)** -0,010 (0,005)*** (0,006)*** (0,005)*** (0,005)*** Number of patents per thousand of inhabitants -0,261 -0,284 -0,189 -0,254 -0,194 -0,276 -0,208 -0,168 -0,164 (0,075)*** (0,074)*** (0,076)** (0,072)*** (0,075)** (0,073)*** (0,076)*** (0,067)** (0,068)** Share of the urban sorted waste on the total urban waste 0,015 0,014 0,013 0,014 0,013 0,015 0,011 0,015 0,014 (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S. 0,161 0,155 0,074 0,168 0,070 0,160 0,042 0,232 0,203 (0,075)** (0,075)** -0,078 (0,075)** -0,077 (0,075)** -0,077 (0,072)*** (0,071)*** North-East 0,340 0,343 0,275 0,306 0,281 0,326 0,338 0,198 0,199 (0,090)*** (0,090)*** (0,094)*** (0,089)*** (0,093)*** (0,087)*** (0,092)*** (0,076)*** (0,076)*** Centre 0,250 0,249 0,239 0,230 0,243 0,249 0,240

215

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 (0,095)*** (0,096)*** (0,100)** (0,095)** (0,100)** (0,096)*** (0,101)** South -0,094 -0,062 -0,072 -0,106 -0,063 -0,070 -0,041 -0,116 -0,114 -0,120 -0,113 -0,118 -0,114 -0,120 Islands -0,079 -0,063 -0,039 -0,123 -0,026 -0,078 0,028 -0,140 -0,140 -0,147 -0,137 -0,143 -0,138 -0,145 Milano 2,353 2,282 0,662 2,158 0,662 2,224 0,686 1,928 1,930 (0,413)*** (0,410)*** (0,380)* (0,407)*** (0,380)* (0,402)*** (0,382)* (0,397)*** (0,397)*** Savona 1,728 1,729 1,665 1,679 1,674 1,716 1,712 1,621 1,631 (0,265)*** (0,265)*** (0,278)*** (0,265)*** (0,277)*** (0,264)*** (0,278)*** (0,257)*** (0,257)*** Brindisi 1,573 1,550 1,515 1,555 1,513 1,546 1,527 1,466 1,414 (0,257)*** (0,257)*** (0,269)*** (0,257)*** (0,269)*** (0,256)*** (0,270)*** (0,255)*** (0,253)*** Taranto 0,602 0,572 0,378 0,506 0,389 0,543 0,475 0,518 (0,305)** (0,304)* -0,318 (0,304)* -0,317 (0,301)* -0,317 (0,300)* Caltanisetta -0,528 -0,479 -0,464 -0,526 -0,454 -0,460 -0,543 -0,575 -0,677 (0,294)* -0,293 -0,307 (0,293)* -0,306 -0,291 (0,307)* (0,286)** (0,279)** Cagliari 0,444 0,437 0,543 0,398 0,553 0,434 0,569 0,343 -0,301 -0,301 (0,316)* -0,301 (0,314)* -0,301 (0,317)* -0,299 Constant coefficient -21,408 -21,859 -25,850 -20,256 -26,250 -22,294 -24,577 -19,474 -19,944 (8,500)** (8,502)** (8,905)*** (8,492)** (8,846)*** (8,477)*** (8,929)*** (8,455)** (8,445)** Number of observations (province * year) 721 721 721 721 721 721 721 721 721 R-squared 0,530 0,530 0,480 0,530 0,480 0,530 0,480 0,520 0,520 AIC 1.449,275 1.449,599 1.517,639 1.452,417 1.515,813 1.448,127 1.522,722 1.456,927 1.456,549 BIC 1.563,791 1.559,535 1.622,994 1.557,771 1.616,587 1.553,482 1.623,496 1.548,540 1.539,000 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

216

Table A4.8 – Regressions results. Dependent variable: waste per value-added. Specification S-3, 2000-2004

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 Log(VA Industry in a S.S.) 0,710 0,648 1,023 0,766 0,982 0,695 0,840 0,994 0,972 -0,442 -0,442 (0,459)** (0,436)* (0,454)** -0,432 (0,454)* (0,423)** (0,423)** [Log(VA Industry in a S.S.)]^2 -0,037 -0,031 -0,065 -0,038 -0,063 -0,034 -0,056 -0,057 -0,055 -0,031 -0,031 (0,032)** -0,031 (0,032)** -0,030 (0,032)* (0,030)* (0,030)* Log(VA Industry in a S.S. per UL) -3,292 -2,972 -2,109 -3,089 -2,030 -2,919 -2,254 -3,130 -3,196 (0,696)*** (0,677)*** (0,696)*** (0,673)*** (0,685)*** (0,668)*** (0,696)*** (0,637)*** (0,624)*** [Log(VA Industry in a S.S. per UL)]^2 -1,065 -1,024 -0,880 -1,052 -0,863 -1,017 -0,895 -1,084 -1,100 (0,195)*** (0,194)*** (0,202)*** (0,193)*** (0,200)*** (0,193)*** (0,202)*** (0,189)*** (0,187)*** Log(VA Industry in a S.S. per worker) -9,717 -10,082 -12,290 -9,574 -12,586 -10,396 -11,030 -7,711 -7,475 -6,007 (6,018)* (6,292)* -6,019 (6,271)** (5,982)* (6,292)* -5,917 -5,904 [Log(VA Industry in a S.S. per worker)]^2 -1,490 -1,523 -1,848 -1,454 -1,889 -1,569 -1,667 -1,158 -1,119 -0,934 -0,937 (0,979)* -0,937 (0,977)* (0,932)* (0,980)* -0,922 -0,921 Population density 0,040 (0,022)* UL density of Industry in a S.S. -0,093 -0,067 -0,062 -0,065 -0,064 -0,064 (0,017)*** (0,010)*** (0,009)*** (0,009)*** (0,009)*** (0,009)*** Energy consumption of Industry in a S.S. per unit of VA 0,708 0,689 0,713 0,725 0,698 0,693 0,693 0,674 0,771 (0,104)*** (0,104)*** (0,108)*** (0,101)*** (0,106)*** (0,103)*** (0,109)*** (0,097)*** (0,075)*** Share of the VA of Industry in a S.S. on total VA 0,013 0,007 -0,029 -0,011 -0,023 -0,014 -0,014 (0,013)** -0,008 (0,008)*** Share of the VA of Services on total VA 0,013 0,018 -0,007 0,013 0,013 0,012 0,011 -0,012 -0,011 -0,011 (0,007)** (0,007)* (0,006)* (0,006)* Number of patents per thousand of inhabitants -0,288 -0,320 -0,250 -0,296 -0,258 -0,313 -0,271 -0,202 -0,198 (0,087)*** (0,086)*** (0,089)*** (0,085)*** (0,088)*** (0,085)*** (0,089)*** (0,078)*** (0,078)** Share of the urban sorted waste on the total urban waste 0,012 0,012 0,010 0,012 0,010 0,013 0,009 0,013 0,012 (0,004)*** (0,004)*** (0,004)*** (0,004)*** (0,004)*** (0,004)*** (0,004)** (0,003)*** (0,003)*** Exports of Industry in a S.S. on total VA of Industry in a S.S.

0,109 0,103 0,036 0,112 0,029 0,107 0,005 0,183 0,158

-0,084 -0,084 -0,087 -0,084 -0,087 -0,083 -0,087 (0,080)** (0,077)** North-East 0,373 0,378 0,317 0,344 0,331 0,362 0,387 0,227 0,233 (0,106)*** (0,106)*** (0,111)*** (0,104)*** (0,109)*** (0,101)*** (0,107)*** (0,089)** (0,089)*** Centre 0,248 0,253 0,241 0,234 0,249 0,254 0,237 (0,112)** (0,113)** (0,118)** (0,112)** (0,117)** (0,113)** (0,118)**

217

(1) (2) (3) (4) (5) (6) (7) (8) (9) Explanatory variable POLS 0 POLS 1a POLS 1b POLS 2a POLS 2b POLS 3a POLS 3b POLS 4 POLS 5 South -0,102 -0,048 -0,071 -0,088 -0,055 -0,052 -0,057 -0,140 -0,138 -0,144 -0,136 -0,141 -0,137 -0,144 Islands -0,082 -0,053 -0,049 -0,108 -0,024 -0,064 0,005 -0,167 -0,167 -0,175 -0,164 -0,170 -0,166 -0,174 Milano 2,509 2,413 0,920 2,302 0,922 2,363 0,962 2,048 2,050 (0,480)*** (0,479)*** (0,448)** (0,474)*** (0,448)** (0,468)*** (0,449)** (0,460)*** (0,460)*** Savona 1,982 1,992 1,924 1,938 1,945 1,979 1,979 1,872 1,877 (0,306)*** (0,306)*** (0,321)*** (0,305)*** (0,318)*** (0,305)*** (0,321)*** (0,295)*** (0,295)*** Brindisi 1,923 1,897 1,890 1,900 1,889 1,894 1,903 1,825 1,773 (0,295)*** (0,295)*** (0,309)*** (0,295)*** (0,309)*** (0,295)*** (0,310)*** (0,293)*** (0,291)*** Taranto 0,487 0,449 0,256 0,385 0,278 0,425 0,346 0,422 -0,349 -0,350 -0,365 -0,348 -0,363 -0,346 -0,364 -0,344 Caltanisetta -0,417 -0,345 -0,365 -0,393 -0,344 -0,332 -0,432 -0,457 -0,580 -0,338 -0,337 -0,353 -0,336 -0,351 -0,336 -0,353 -0,329 (0,319)* Cagliari 0,525 0,528 0,626 0,489 0,647 0,528 0,642 0,433 -0,349 -0,350 (0,366)* -0,350 (0,364)* -0,350 (0,368)* -0,347 Constant coefficient -17,976 -18,437 -19,856 -16,528 -20,748 -18,653 -18,977 -15,394 -15,018 (9,693)* (9,714)* (10,168)* (9,652)* (10,064)** (9,697)* (10,201)* -9,644 -9,627 Number of observations (province * year) 515 515 515 515 515 515 515 515 515 R-squared 0,540 0,540 0,490 0,540 0,490 0,540 0,490 0,530 0,530 AIC 1.011,126 1.012,757 1.059,101 1.013,342 1.057,519 1.011,024 1.062,222 1.015,616 1.014,145 BIC 1.117,231 1.114,617 1.156,717 1.110,957 1.150,890 1.108,639 1.155,593 1.100,499 1.090,540 Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S. Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

218

Table A4.9 – Specification S-1: sign and statistical significance of the coefficients

Dependent variable: logarithm of the quantity of waste of Industry in a S.S. per value-added of Industry in a S.S.

1998-2004 2000-2004

Explanatory variable sign significance

level sign

significance level

Log(VA Industry in a S.S.) + ** + ** [Log(VA Industry in a S.S.)]^2 - * - * Log(VA Industry in a S.S. per UL) - *** - *** [Log(VA Industry in a S.S. per UL)]^2 - *** - *** UL density of Industry in a S.S. - *** - *** Energy consumption of Industry in a S.S. per unit of VA

+ *** + ***

Share of the VA of Services on total VA + + Number of patents per thousand of inhabi-tants

- *** - ***

Share of the urban sorted waste on the total urban waste

+ *** + ***

Exports of Industry in a S.S. on total VA of Industry in a S.S.

+ *** + **

North-East + *** + *** Milano + *** + *** Savona + *** + *** Brindisi + *** + *** Caltanisetta - *** - **

Standard error in brackets. The symbols *,**,*** show statistical significance at levels of 10%, 5% and 1%

219

5. THE PRODUCTION OF WASTE OF INDUSTRY IN A STRICT

SENSE: A SIMULATION

5.1 Introduction

The econometric model previously tested is the basis on which the simulation of the current chapter

lies: the aim is simulating the behaviour of the dependent variable in a hypothetically growing economy

framework, in order to investigate whether the EKC trend can observed, for the Italian provinces, also

for the future. After the economic crisis that has stricken the world since 2008, the performance of the

Italian economy has gone down, with negative growth rates across the years 2010, 2011 and 201240.

Therefore, two different scenarios of economic growth have been created, based on real and hypotheti-

cal growth estimates, for the period 2006-2010, that were available in some reports of two different re-

search centres (Centro Studi Unioncamere and Prometeia41), and based on ad hoc hypothesis. In the pre-

sent chapter both the adopted scenarios will be detailed, and the model will be simulated according to

the two specifications given in Chapter 4.

5.2 The hypothesis of the simulation of a EKC relationship: growth rates of the

Italian local economies

The simulation of both the specifications covers a span of time of five years, 2006-2010, and the hy-

pothesis of growth have been given at a regional level. A general framework of all the hypothesis has

been reported in Table 5.1, while in Table A5.1, A5.2, A5.3 and A5.4 in the Appendix the several

growth rates have been detailed for every province.

The adopted scenarios simulate the different dynamics of the Italian economy, as regards the variables

used in the model. Under the first scenario42 (called F-1), the Italian economy were going through a pe-

riod of changes and transformations, and the main drivers of the economic growth were supposed to

be the exports and the investments: if, on one side, the exports were supposed to be increasing thanks

to an exogenous factor, the augmented world demand for goods and services, an endogenous factor as

the industrial complex restructuring , on the other side, were seen to be able to generate positive effects

on the export, other than to be able to produce wealth thanks to the investments on productive capital.

40 See www.istat.it for the details.

41 Centro Studi Unioncamere (a cura di), 2007, Scenari di Sviluppo delle Economie Locali Italiane 2007-2010, july 2007, Roma, and Centro Studi Unioncamere (a cura di), 2007, Scenari di Sviluppo delle Economie Locali Italiane 2007-2010, november 2007, Roma.

42 Centro Studi Unioncamere (a cura di), 2007, Scenari di Sviluppo delle Economie Locali Italiane 2007-2010, july 2007, Roma.

220

Table 5.1 – Scenarios for the simulations: percentage variations (%) with respect to the previous year

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

NORTH-WEST

Value-added

Industry 2,7 2,6 1,3 2,0 2,3 2,7 2,3 1,4 1,7 2,2

Services 1,7 2,5 2,0 1,7 1,9 1,7 2,1 1,7 1,6 1,8

Total 1,8 2,4 1,7 1,7 1,9 1,8 2,2 1,5 1,6 1,8

Workers of industry 1,1 0,0 0,5 0,3 0,6 1,3 0,1 0,7 0,4 0,5

Exports 3,1 3,2 4,5 4,4 4,5 3,1 3,9 3,0 3,8 4,0

NORTH-EAST

Value-added

Industry 3,6 3,0 1,7 1,5 1,6 3,6 2,7 1,8 1,3 1,5

Services 1,8 2,1 2,2 2,1 2,1 1,8 2,2 2,0 2,1 2,1

Total 2,0 2,3 2,0 1,8 1,9 2,0 2,3 1,8 1,7 1,8

Workers of industry 1,6 0,6 0,6 0,6 0,9 1,9 0,7 0,9 0,7 0,8

Exports 4,1 4,9 3,2 3,3 3,5 4,1 4,6 1,7 2,7 3,0

CENTRE

Value-added

Industry 1,4 2,5 1,1 1,8 2,1 1,3 1,6 1,1 1,6 2,0

Services 1,5 1,5 1,9 1,7 1,8 1,5 1,6 1,7 1,6 1,7

Total 1,6 1,8 1,8 1,7 1,8 1,6 1,7 1,6 1,6 1,7

Workers of industry 0,6 0,2 0,0 0,0 0,4 0,8 0,3 0,3 0,1 0,4

Exports 7,7 4,2 4,0 4,0 4,2 7,7 2,3 2,5 3,4 3,6

SOUTH

Value-added

Industry 1,4 1,4 1,2 1,4 1,9 1,6 0,9 1,2 1,0 1,8

Services 1,4 1,7 1,9 1,7 2,0 1,4 1,3 1,6 1,6 1,9

Total 1,2 1,8 1,8 1,6 1,9 1,2 1,4 1,5 1,5 1,8

Workers of industry 1,8 0,6 0,0 0,2 0,2 0,7 0,7 0,5 0,5 0,3

Exports 1,4 2,2 2,7 2,9 3,2 1,4 1,9 1,2 2,3 2,7

ITALY

Value-added

Industry 2,6 2,5 1,3 1,7 2,0 2,6 2,1 1,4 1,5 1,9

Services 1,6 2,0 2,0 1,8 2,0 1,6 1,8 1,7 1,7 1,9

Total 1,7 2,1 1,8 1,7 1,9 1,7 1,9 1,6 1,6 1,8

Workers of industry 1,3 0,3 0,3 0,3 0,6 1,3 0,4 0,7 0,5 0,5

Exports 4,0 3,7 3,8 3,8 4,0 4,0 3,6 2,3 3,2 3,5

Note: under both the scenarios, in the same period, the UL density, the number of patents and the percentage of the sorted waste collec-tion have been kept as constant; the energy consumptions per unit of value-added have been supposed to increase by +0,5 yearly

Source: Centro Studi Unioncamere – Prometeia

221

The forecasts were foreseeing a nation GDP growth of +2,0% in 2007, mainly caused by exports,

which national increase was supposed to be by +3,7% (less than in 2006). In 2008, the Italian GDP was

supposed to rise by +1,7%, while the value-added was registering a decrease in its growth by -0,3%

with respect to the previous year, thus increasing by +1,8%. For the years 2009 and 2010, the supposed

growth of that scenario was about +1,7% and 1,8%: the world demand was supposed to push the Ital-

ian exports to values around +4,0% yearly, while the foreign investments would have driven the inter-

nal demand. As regards the total value-added of Italy, the forecasts were similar to those concerning the

GDP, a sign that the capability to create value would have been the same as the previous years, while

the sectorial growth rates were thought to be different: Industry would have experienced a higher

growth rate of +2,6% in 2007, while Services would have had an increase ranging from about +1,6% in

2006 to +2,0% in 2010. As concerns the employment, the scenario sees a growth rate of the unit of la-

bour of +1,0%, against a slowdown in the three years after (2008-2010), where the rate is between

+0,8% and +0,9%. As regards the macro-regional data43, it can be noted that the main variable of the

waste analysis model, the value-added, has been supposed to be increasing in time and in all regions,

with a +2,4% peak in 2007 in the North-West regions, while the industrial value-added growing (to

quote the highest rates only) by +3,0% in the North-East in 2007, +2,5% in the Centre 2007, and

+1,9% in the South, in 2010.

To sum up, under the first scenario, F-1, the little economic recovery of Italy which started in 2006 has

not had a stop, thus being confirmed during the period 2007-2010: such recovery would be stronger in

the northern regions, and slower in the southern ones, where, at the beginning, the rate would be below

the national average, whence, during the years 2008-2010, the GDP growth would be as much as the

national rate.

The second scenario under examination44 foresees a slowdown of the entire Italian economy, thus re-

flecting the first bad consequences that the 2008 crisis would have caused, partly due to the crisis which

has been started by the US sub-prime mortgages affair, and partly to the reduced internal demand.

In the two years 2009-2010, the supposed growth of the Italian economy would have been around

+1,6% and 1,7% respectively, with the total value-added increasing by +1,8% in 2010. In the same pe-

riod, a recovery of the Italian exports has been hypothesized, with growth rates of +3% and more for

the following years. The employment market has been supposed to raise its number of workers of

+0,7% in 2007, to decrease the same by -0,7% in 2008, and to increase it again by +0,8% in 2009 and

2010. At a macro-regional model, the industrial value-added grows more in the northern regions, with

the North-East topping the ranking in 2007 and 2008 (respectively, +2,7% and +2,8%), and the North- 43 For all the data forecasts: Centro Studi Unioncamere (a cura di), 2007, Scenari di Sviluppo delle Economie Locali Italiane 2007-2010, July 2007, Roma.

44 Centro Studi Unioncamere (a cura di), 2007, Scenari di Sviluppo delle Economie Locali Italiane 2007-2010, november 2007, Ro-ma.

222

West in 2009 and 2010 (+1,7% and +2,2%). With reference to the industrial employment situation, it

has been supposed that the growth or workers would be stronger in the North-East (+0,9% in 2008),

while in the South the increment is little during all the period. The North-West, moreover, has the

highest growth rate for the exports, reaching +4,0% in 2010, followed by the +3,6% growth in the ex-

ports of the Centre.

As regards the second scenario called F-2, therefore, the hypothesis is consistent with a little economic

recovery in 2006, a slowdown in 2007 and 2008, and a new beginning of a recovery in 2009 and 2010.

The most dynamic area would be the northern one, while the exports sector would be the leading one.

Last, as concerns the energy variable, both the scenarios, F-1 and F-2, keeps the same hypotheses45: in

the period 2005-2016, along with these data, the estimates would have led to an increase in the demand

of energy, with an average growth rate of +1,5% yearly, corresponding to a demand of 389 TWh in

2016. In 2010, the intermediate year of this span of time, the demand of electricity was supposed to be

around 358,9 TWh, with an average growth rate of +1,7% in the period 2005-2010, and +1,3% in the

period 2010-2016. As regards the energy intensity, given by the ratio between Energy consumption and

value-added, an increase of +0,5% has been hypothesized, directly provided by the estimates of Terna

agency for the examined period.

5.3 The evolution of the production of waste: the simulation of the model

The model developed and tested in the previous sections, against the vast majority of past studies on

polluting emissions quoted in the literature review, has been able to exploit a rich database with panel

data disaggregated at a provincial level. In order to be able to present a simulation coherent with the de-

tail level given in the database, but without having forecasts at a provincial level, the hypotheses col-

lected at a regional level have been extended to a provincial level, having provinces growing at the same

growth rates in all the given regions. For some variables some ad hoc growth rates have been used,

based on the trends observed by the analysis of the historical data 1998-2004. Table 5.2 sums up the

rules used in the two scenarios for the simulations, and it provides the source of those estimated rates.

The results of the two simulations for each province have been reported in Table A5.5, A5.6, A5.7 and

A5.8 in the Appendix, whose main insights will be given here below.

45 Terna (a cura di, 2006), Previsioni della domanda elettrica in Italia e del fabbisogno di potenza necessario 2006 – 2016, Roma.

223

As regards the scenario F-1, the numbers of the simulation highlight a level of waste intensity generally

higher46 in the first specification, S-1, rather than in the second specification, S-2, but with a lot of ex-

ceptions: such a higher value in the Specification S-1 is clear for the North of Italy, where there are cit-

ies where it has been estimated higher for all the respective values of the Specification S-1, in compari-

son with the Specification S-2 (e.g., Torino, Milano, Bolzano and Venezia), while in some other prov-

inces the indicator is higher in some years, and lower in others (Vercelli and Lecco), and in others its

value results higher with the Specification S-2 than with the S-1 (it is the case of all the cities of the

Emilia Romagna). As for the Centre and the South of Italy, such a greater value, there where it exists, is

more geographically “scattered”: thus, together with provinces where the indicator is greater in the sec-

ond specification (it is the case of the Marche), some provinces exist where there is an alternation of

such a difference across the years (as an example, Taranto and Reggio Calabria), or provinces too where

the difference between the value according to S-1 and the one according to S-2 is always clearly positive

(Crotone, Caltanissetta and Sassari, for example). According to the fact that the ratio between the

value-added per local unit, or the value-added per worker, is considered as a measure of the economic

efficiency of a province, the simulation gives results that vary among the +30% and the -20%, with re-

spect to the Specification S-1: that is, the Specification S-1 gives higher values of the indicator up to a

+30%, or lower values of it up to a -20%, in comparison with the Specification S-2.

As regards the production of waste in connection with the province’s own economic capability (the

waste and value-added ratio), both the specifications see a reduction in the indicator in some northern

provinces, in the period 2005-2010 (Torino, Milano and Bergamo, for example), while there is a general

increase in the central regions, and an even more marked increase in the southern ones, even if the dif-

ferences do not disappear: according to Specification S-1, as an example, Imperia would have a growth

of the indicator of abut +22% in 2006, but the S-2 calculates a decrease of almost -1%, instead; the

same can be seen for Reggio Calabria (+33% under S-1, but -1,3% under S-2), or for Caltanissetta

(+5,17% under S-1, but -9,35 for S-2).

The Scenario F-2 gives similar results in terms of differences between Specification S-1 and S-2: some

provinces have higher values in all the years (Torino, Milano, Lodi and Massa Carrara, e.g.) some others

start from positive differences between S-1 and S-2, to end up with negative differences of the indicator

(Vercelli, Isernia and Taranto, as an example), while others show their values of the indicator under S-2

being higher than the values under S-1 (for example, Pordenone, Ferrara, Ravenna and Siracusa), but in

general the Specification S-1 gives higher results than the Specification S-247.

46 Such a result is justified by the total sum, year after year, of the differences of the indicator of every province between the Specification S-1 and S-2, under the Scenario F-1.

47 Such a result is justified again by the total sum, year after year, of the differences of the indicator of every province be-tween the Specification S-1 and S-2, but this time they are under the Scenario F-1.

224

Last, by doing a comparison between the values given by the scenarios, according to each specification,

it has to be noted how the Scenario F-1, which states a more dynamic economic growth than F-2, has

percentage variations of the waste-intensity indicator, for the period 2006-2010, higher in absolute val-

ues than those under the Scenario F-2: the values of the Specification S-1 under the Scenario F-1 vary

in percentage terms more than under F-2, both in positive, and in negative terms, and such variations

usually report the same signs under both scenarios. Analogously, the values of the Specification S-2

vary in a similar way across both the scenarios, F-1 and F-2, with the values under the Scenario F-1

varying, in percentage terms, more than those under the Scenario F-2, in the same period.

Table 5.2 – Growth hypotheses of the variables for both the specifications (S-1 and S-2)

Variable Specification S-1 Specification S-2

VA Industry in a Strict Sense Regional growth rates of the VA of Industry Source: Centro Studi Unioncamere

Regional growth rates of the VA of Industry Source: Centro Studi Unioncamere

VA Industry in a Strict Sense per UL Regional growth rates of the VA of Industry Source: Centro Studi Unioncamere

---

VA Industry in a Strict Sense per worker

---

Ratio between the regional growth rates of the VA of Industry and those of the workers of Industry Source: Centro Studi Unioncamere

Density of the UL of the RE Constant Constant

Consumptions of Electricity of Industry in a Strict Sense per unit of VA

Constant +0,5% annual growth rate for all the provinces

Constant +0,5% annual growth ra-te for all the provinces

Share of the VA of Services on the total VA

Ratio between the regional growth rates of the total VA and those of the Services Source: Centro Studi Unioncamere

Ratio between the regional growth rates of the total VA and those of the Services Source: Centro Studi Unioncamere

Number of patents per thousand of in-habitants

Constant Constant

Share of the urban sorted waste collec-tion on the total urban waste collection

Constant Constant

Ratio between the value of Exports of Industry in a Strict Sense and its VA

Ratio between the regional growth rates of the Exports of Industry in a Strict Sense and those of its VA Source: Centro Studi Unioncamere

Ratio between the regional growth rates of the Exports of Industry in a Strict Sense and those of its VA Source: Centro Studi Unioncamere

225

5.3.1 The results of the simulation: the five randomly selected provinces

Just as an example, Table 5.3 reports the values of the waste intensity related to the five randomly se-

lected provinces (L’Aquila, Genova, Milano, Roma and Venezia).

The simulation for this set of provinces shows (under both the scenarios, F-1 and F-2, and with both

the specifications, S-1 and S-2) that the provinces of Milano, Venezia and Roma all lie on the increasing

part of the curve, that is, for these provinces, the increase of the economic efficiency goes together with

a relative decrease of the waste intensity. Genova, on the other hand, sees its indicator going alterna-

tively up and down in the various years, under all the scenarios, and according all the specifications:

considering the whole period, only with the Specification S-1 and under the Scenario F-1, the percent-

age variation has a positive value (+0,40%), while, according to the other specifications under the Sce-

nario F-2, as well as according to the Specification S-2 under the Scenario F-2, it is negative. L’Aquila,

in the end, shows a little percentage growth of the indicator with the Specification S-1, under both the

scenarios (respectively, +1,62% and +0,92%), while in the Scenario S-2 the simulations gives a strong

decrease (-10,47% and -9,58%).

All the results of the simulation are given in the Table 5.3.

226

Table 5.3 – Waste intensity of the five provinces, under both the scenarios, and according to both the specifications

Waste / value-added Absolute variations Percentage (%) variations Variations (%) during all the period Scenario F-1 Specification S-1 Province Region 2006 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2006-2010 Milano Lombardia 74,03 70,27 68,93 66,62 63,72 -3,76 -1,34 -2,31 -2,90 -5,08 -1,91 -3,35 -4,36 -13,93 Venezia Veneto 344,94 340,64 339,33 337,91 336,50 -4,29 -1,32 -1,41 -1,41 -1,24 -0,39 -0,42 -0,42 -2,45 Genova Liguria 237,92 238,83 239,75 239,63 238,87 0,91 0,93 -0,13 -0,76 0,38 0,39 -0,05 -0,32 +0,40 Roma Lazio 36,93 34,92 34,62 33,88 33,02 -2,01 -0,30 -0,74 -0,86 -5,43 -0,87 -2,14 -2,55 -10,59 L'Aquila Abruzzo 173,49 174,16 174,80 175,55 176,30 0,67 0,64 0,76 0,74 0,39 0,37 0,43 0,42 +1,62 Specification S-2 Province Region 2006 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2006-2010 Milano Lombardia 65,81 61,70 60,42 58,18 55,80 -4,12 -1,28 -2,23 -2,38 -6,26 -2,07 -3,70 -4,10 -15,22 Venezia Veneto 328,85 323,88 323,07 322,23 323,17 -4,97 -0,81 -0,84 0,94 -1,51 -0,25 -0,26 0,29 -1,73 Genova Liguria 236,60 235,81 236,65 234,44 232,23 -0,79 0,84 -2,20 -2,21 -0,33 0,35 -0,93 -0,94 -1,85 Roma Lazio 37,16 33,63 33,02 31,88 30,91 -3,54 -0,60 -1,15 -0,97 -9,51 -1,79 -3,47 -3,05 -16,83 L'Aquila Abruzzo 157,56 156,25 150,68 146,92 141,07 -1,31 -5,57 -3,76 -5,85 -0,83 -3,56 -2,49 -3,98 -10,47 Scenario F-2 Specification S-1 Province Region 2006 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2006-2010 Milano Lombardia 75,05 71,72 70,11 68,00 65,14 -3,32 -1,62 -2,10 -2,86 -4,43 -2,26 -3,00 -4,21 -13,20 Venezia Veneto 347,10 343,70 340,77 340,00 338,51 -3,40 -2,93 -0,77 -1,48 -0,98 -0,85 -0,23 -0,44 -2,47 Genova Liguria 241,29 240,85 241,07 241,09 240,28 -0,44 0,21 0,02 -0,81 -0,18 0,09 0,01 -0,34 -0,42 Roma Lazio 37,33 36,35 35,94 35,37 34,55 -0,98 -0,42 -0,57 -0,82 -2,62 -1,14 -1,59 -2,31 -7,45 L'Aquila Abruzzo 174,59 175,16 175,06 175,63 176,19 0,56 -0,09 0,57 0,56 0,32 -0,05 0,33 0,32 +0,92 Specification S-2 Province Region 2006 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2006-2010 Milano Lombardia 67,37 63,61 62,39 60,23 57,62 -3,77 -1,22 -2,16 -2,62 -5,59 -1,92 -3,45 -4,34 -14,48 Venezia Veneto 332,49 327,74 326,27 326,00 326,05 -4,74 -1,48 -0,26 0,04 -1,43 -0,45 -0,08 0,01 -1,94 Genova Liguria 241,77 240,35 241,91 240,06 237,55 -1,42 1,56 -1,85 -2,51 -0,59 0,65 -0,77 -1,04 -1,74 Roma Lazio 37,65 34,33 33,92 32,87 31,87 -3,31 -0,41 -1,06 -1,00 -8,80 -1,19 -3,11 -3,03 -15,34 L'Aquila Abruzzo 155,73 154,57 149,70 146,60 140,81 -1,15 -4,87 -3,10 -5,79 -0,74 -3,15 -2,07 -3,95 -9,58

227

5.4 Simulation’s results: a discussion

Besides the numerical values of the simulations on the provincial data reported in the Appendix, the

analysis of the simulated values, under both the scenarios, and according to both the specifications, by

the means of the assumptions, made in the previous sections, on the growth rates of the drivers of the

models, has casted a light on a very interesting qualitative element, which is quite new in the specialized

literature about waste.

Figure 5.1 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-1, Specification S-1 (VA/UL) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros)

Simulated independent variable: (logarithm of) value-added per local unit (millions of 1995 euros per UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 1

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 4,50 5,00 5,50 6,00 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005

2006

2007

2008

2009

2010

Particularly interesting are, indeed, the nature of the relationship between the measurement of pollution

(the waste intensity) simulated by the model and the measures of economic efficiency of the industrial

sector (waste per value-added, and waste per worker). By drawing a graph, in two dimensions only

(while the relationship is among a dependent variable and many drivers), for the data of the period

2005-2010, and putting on the y-axis the dependent variable measuring waste (waste per unit of value-

added, in logarithmic terms) and on the x-axis the respective measure of economic efficiency (in Figure

5.1 and 5.2, the value-added per local unit, in Figure 5.3 and 5.4, the value-added per worker, and all the

plots in logarithmic terms48), it can be said that, with relation to the estimated values, and according to

48 All the plots in this chapter reports the measures in logarithmic terms: i twill be not repeted in the course of the text.

228

the final specification of the model used in the simulation, an Environmental Kuznets Curve for the

Italian special waste of Industry in a Strict Sense can be observed. This claim is coherent under both

the adopted scenarios, and both by considering the (logarithm of the) value-added per local unit (Speci-

fication S-1, Figure 5.1 and 5.2) and the (logarithm of the) value-added per worker (Specification S-1,

Figure 5.3 and 5.4) as efficiency indicator.

In Figure 5.1 and 5.2, the relationship between the (logarithm of the) waste per value-added and the

value-added per local unit, all the other variables being constant, under the Scenario F-1, and according

the Specification S-1. In Figure 5.2, it can be seen how the scatter plot of the simulated values shows a

reverse U-shaped behaviour along with the value-added per local unit, thus confirming the classical

trend of an EKC: this indicates that the production of waste of Industry in a Strict Sense (weighed by

the economic value of the productive activities of the same sector, in the same province) increases with

the increase of the value-added, up to a point from where it starts to decrease with the increase of the

value-added of the local units of the province. Such a result means that the industrial fabric of Italy,

once it has reached a certain value of economic efficiency per local unit (the tuning point of the EKC),

shows a trend of decrease in production of waste, along an increase of the province’s economic per-

formances.

Figure 5.2 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 1

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 4,50 5,00 5,50 6,00 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005-2010

Quadratica

Cubica

Quinto grado

229

In Figure 5.3 and 5.4, related to the Specification S-2, a similar relationship can be seen: the scatter

plots resulting from the simulation of the waste production per value-added (the waste intensity) show

again, for the period 2005-2010, a bell-shaped curve with the increase of the variable on the x-axis,

which is the value-added per worker, and the graphs also report the interpolating curves (the curve fit-

ting) inside the cloud of the data. Also in this case, there is the confirmation of the hypothesis under

which, after a first period of worsening of the environmental conditions due to the increase of wealth,

then the richer, in per worker terms (thus, the economic efficiency per worker is higher), the sector of

Industry of Strict Sense becomes in the province, the lower its waste intensity is, and the higher the en-

vironmental efficiency is.

Moreover, the analysis of the data of the separate years, for both the graphs, shows how non only such

bell-shaped fitting line exists for every year, but that line shifts more to the right of the plot along with

the passing of time: this means that the same value of environmental efficiency can be reached, on av-

erage and year after year, thanks to a higher degree of economic per local unit or per worker efficiency.

Figure 5.3 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-1, Specification S-2 (VA/worker)

Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros) Simulated independent variable: (logarithm of) value-added per worker (millions of 1995 euros per worker)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / Unità di Lavoro): previsioni 2005-2010, Scenario 1

2,00

3,00

4,00

5,00

6,00

7,00

8,00

3,20 3,40 3,60 3,80 4,00 4,20 4,40

log (migliaia di euro del 1995 / Unità di Lavoro)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005

2006

2007

2008

2009

2010

From Figure 5.3 and 5.4 it can also be noted that those reverse U-shaped curves keep the same bell

shape with the x-axis variables even if the fitting curve comes form a different polynomial specification

230

of the function: the results do not change if the fitting line is a third degree polynomial function, or a

fifth degree one, thanks to which it can be verified whether N-shaped curves can be found (the N-

shaped curves lead to an novel increase of the waste pollution after a first phase of declining value for

it, while the per local unit or per worker value-added increases) instead of reverse U-shaped ones. Such

a result, related to the simulation years, shows that quick threshold effects cannot be achieved with

these data: threshold effects usually happen when, after a certain value of the measure of wealth, the re-

verse U-shaped curve has a new flex in its decreasing arm, and thus it rises again (the test is usually veri-

fied by using third degree and fifth degree polynomials), and such a case is not present here. It can be

said, therefore, that the general tendency of the industrial waste intensity of the Italian provinces, dur-

ing the considered period, is a decreasing one, with the increase of the measure of its value-added.

Such qualitative (i.e., graphical) results do not change if both the specifications, S-1 and S-2, under the

Scenario F-2 are taken into account (as reported in Figure 5.5, 5.6, 5.7 and 5.8), thus confirming the ro-

bustness of the analysis to changes in the dependent variables.

Figure 5.4 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy.

The EKC for waste: Scenario F-1, Specification S-2 (VA/worker)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / Unità di Lavoro): previsioni 2005-2010, Scenario 1

2,00

3,00

4,00

5,00

6,00

7,00

8,00

3,20 3,40 3,60 3,80 4,00 4,20 4,40

log (migliaia di euro del 1995 / Unità di Lavoro)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005-2010

Quadratica

Cubica

Quinto grado

231

Figure 5.5 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-2, Specification S-1 (VA/UL) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros)

Simulated independent variable: (logarithm of) value-added per local unit (millions of 1995 euros per UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 2

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 4,50 5,00 5,50 6,00 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ m

ilio

ni d

i eu

ro d

el 1

995)

2005

2006

2007

2008

2009

2010

Figure 5.6 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy.

The EKC for waste: Scenario F-2, Specification S-1 (VA/UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 2

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 4,50 5,00 5,50 6,00 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005-2010

Quadratica

Cubica

Quinto grado

232

Figure 5.7 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy.

Results of the simulation (2005-2010): Scenario F-2, Specification S-2 (VA/worker) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros)

Simulated independent variable: (logarithm of) value-added per worker (millions of 1995 euros per worker)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / Unità di Lavoro): previsioni 2005-2010, Scenario 2

2,0

3,0

4,0

5,0

6,0

7,0

8,0

3,20 3,40 3,60 3,80 4,00 4,20 4,40

log (migliaia di euro del 1995 / Unità di Lavoro)

log

(t

/ mil

ion

i di e

uro

del

199

5) 2005

2006

2007

2008

2009

2010

Figure 5.8 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy.

The EKC for waste: Scenario F-2, Specification S-2 (VA/worker)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / Unità di Lavoro): previsioni 2005-2010, Scenario 2

2,0

3,0

4,0

5,0

6,0

7,0

8,0

3,20 3,40 3,60 3,80 4,00 4,20 4,40

log (migliaia di euro del 1995 / Unità di Lavoro)

log

(t

/ mili

on

i di e

uro

de

l 19

95)

2005-2010

Quadratica

Cubica

Quinto grado

233

The indication that can be gained from the qualitative analysis of the graphs is that Industry in a Strict

Sense in the Italian provinces shows a gradual decreasing tendency for its waste intensity, in the long

run. Indeed, some provinces lie on the increasing arm of the curve, while others have already passed

the turning point of this bell-shaped curve, and they lie on the decreasing arm: not only, but according

to the data from the various simulations49, it can be also observed that some provinces go from the

“negative” to the “positive” (in terms of judgment, and not in mathematical terms) side during the con-

sidered span of time (2005-2010). Such is the case, for example, of Ancona under the Scenario F-1 and

according to the Specification S-1, or of Terni under F-1 and according to S-2, which both see the year

2006 as the turning point years, when they reach the highest waste intensity value, and then they regis-

ter a gradual decrease for this indicator.

Figure 5.9 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense,

Northern and Central Italy. The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 1 Centro-Nord Italia

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,50 4,70 4,90 5,10 5,30 5,50 5,70 5,90 6,10 6,30 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ mili

on

i di e

uro

del

199

5)

2005-2010

Quadratica

Cubica

Quinto grado

Such an advantageous relationship of the evolution of the waste intensity is different if the analysis is

focussed separately on the Centre and the North of Italy together, and the South of Italy. Figure 5.9

and 5.10 report the simulated data of the model under the Scenario F-1 and according to the specifica-

tion S-1, and they exhibit the strong difference between the two considered macro-regions: in Figure

49 See the tables in the Appendix.

234

5.9 the situation for the Centre-North shows provinces mostly having concluded, or being about to

conclude, the transition from the increasing stage of the Italian national reverse U-shaped curve to the

decreasing stage, thus going along a virtuous path in environmental terms50 other than in economic

ones. The Northern and Central provinces have, therefore, exceeded that point of their economic de-

velopment that allow their citizens to take care of the environmental “good” (indicated by the diminish-

ing of the pollution indicator), and, according to the simulations, their future economic development

do not appear as dangerous as their past development51.

Figure 5.10 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Southern Italy.

The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

Log (Rif IndSS / VA IndSS) su log (VA IndSS / UL): previsioni 2005-2010, Scenario 1 Sud Italia

2,00

3,00

4,00

5,00

6,00

7,00

8,00

4,00 4,50 5,00 5,50 6,00 6,50

log (migliaia di euro del 1995 / UL)

log

(t

/ mil

ion

i di e

uro

de

l 199

5)

2005-2010

Quadratica

Cubica

Quinto grado

A different situation appears, on the contrary, in the South and in the Islands. Figure 5.10 shows that

the Southern Italian provinces are mostly in the ascending stage of the reverse U-shaped curve: by the

observation of the cubic and the fifth degree interpolation, a more or less marked N-shaped relation-

ship emerges for those provinces. This might bring to think about the existence of those dangerous

50 In terms of Figure 5.9, they are on the “roof” and on the “downhill” of the Italian national curve.

51 In terms of Figure 5.9, such relationship implies that the reverse U-shaped fitting curve interpolating the values of the provinces of this macro-region only is more “flattened” than the Italian national fitting curve (which includes all the prov-inces, and not only the Northern-Central ones), that is its curvature is very little marked.

235

threshold effects that lead to a further growth of the quantity of waste with the increase of wealth (the

x-axis), after a little initial descending stage of this indicator.

The simulations, therefore, and their plots show the presence of a reverse U-shaped behaviour for Italy

as a whole, but the analysis of the two big macro-areas, Centre-North and South, leads to observe a

marked difference between the two areas, if not in terms of functional relationship, for sure in terms of

the position on the Italian national curve depicting this relationship: it can thus be noted that in the de-

scending arm mostly lie the several provinces of the North and of the Centre, while the regions of the

South are mainly localized along the ascending arm, and this reveals a break between the two economic

realities. Such a situation can be explained by the difference of the economic development between the

two areas, and by their different industrial composition. The variable on the x-axis in the figures 5.1 up

to 5.10 (which is the adopted performance indicator) highlights the different degree of evolution of the

industrial sector of the several provinces: the main indication, deriving from it, for Italy as a whole is

that the background tendency is a future decrease of the waste intensity along with a growth of the per-

formances of the economy, even if in the considered period some provinces still lie on the ascending

arm of the reverse U-shaped curve (and Figure 5.10 highlights that they are mostly southern provinces).

Summing up, depending on its own economic development and on the variables that influence it, In-

dustry in a Strict Sense of the Italian provinces lies on a path which makes its respective waste intensity

increase and then decrease in time, thus bringing its waste production to be economically sustainable

during the years: the firms in the provinces grow, and the waste produced, after an initial period of

growth, tend to decline, thanks to the economic development too. The simulations, consistently with

the graphs of the historical values in Chapter 4, show a situation where for some provinces (mostly in

the South) the economic development goes with an even greater production of waste, and these prov-

inces lie on the ascending arm of the Italian reverse U-shaped curve, while for other provinces (mostly

in the North and in the Centre) the development contributes to create less waste, and these provinces

lie on the decreasing arm of the same curve.

Such a measure of waste intensity, however, does not say anything about the effective environmental

sustainability of such a production: the simulated waste intensity indicates that the produced waste is

about to decrease, on the basis of the economic development that has produced it, and that they are

“compatible” with this development, while the goal of further analysis will be to state whether this

waste, even if compatible with the produced wealth, is compatible with the environment and with the

society that are forced to cope with such waste.

236

5.5 Conclusions

The simulations done by the tested model have been carried out on the basis of different scenarios of

growth of the independent variables. Depending on the scenario and the specification, the model gives

different estimates for the same province, but the background tendency is for an increase of the waste

intensity for those provinces which still have a low economic efficiency, and for a decrease of the waste

production there where the economic efficiency is quite high: such behaviour allows to state that the

waste intensity of Industry in a Strict Sense of the Italian provinces has a reverse U-shaped relationship

with the economic development (all the other variables being equal), and this result confirms for the

first time the existence of an Environmental Kuznets Curve for waste studies.

237

APPENDIX – A5

Table A5.1 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, North-West

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

PIEMONTE Value-added Industry 2,3 2,2 1,2 2,0 2,4 2,3 2,0 1,3 1,8 2,3 Services 1,3 2,0 1,3 1,2 1,5 1,3 1,8 1,1 1,1 1,4 Total 1,5 2,2 1,4 1,4 1,7 1,5 2,0 1,2 1,3 1,6 Workers of industry 2,5 -0,3 1,4 0,3 0,6 2,7 0,1 1,4 0,4 0,5 Exports 3,0 3,9 5,1 5,0 5,0 3,0 4,6 3,6 4,4 4,5 VALLE D'AOSTA Value-added Industry 4,0 3,5 0,0 1,1 1,8 4,0 3,2 0,1 0,8 1,7 Services 0,4 1,4 1,2 1,1 1,5 0,4 1,2 0,9 1,1 1,4 Total 1,7 2,5 1,6 1,5 1,8 1,7 2,3 1,4 1,5 1,7 Workers of industry -2,9 0,7 7,9 2,7 2,4 -2,7 0,0 9,3 2,8 2,2 Exports 13,5 -1,2 0,6 1,0 1,5 13,5 -0,5 -0,9 0,4 0,9 LOMBARDIA Value-added Industry 2,8 2,6 1,3 1,9 2,3 2,8 2,4 1,3 1,7 2,2 Services 2,0 2,8 2,3 2,0 2,2 1,9 2,4 2,1 2,0 2,1 Total 2,0 2,6 1,8 1,8 2,1 1,9 2,3 1,7 1,8 2,0 Workers of industry 0,3 0,0 0,1 0,2 0,6 0,5 0,1 0,4 0,3 0,5 Exports 3,6 2,7 4,1 4,1 4,2 3,6 3,5 2,6 3,5 3,7 LIGURIA Value-added Industry 4,5 4,2 2,3 2,8 2,9 4,5 3,9 2,3 2,4 2,7 Services 0,9 1,8 1,7 1,3 1,5 1,8 1,2 0,9 0,8 1,1 Total 1,2 2,1 1,7 1,4 1,7 2,1 1,6 1,1 1,0 1,3 Workers of industry 6,5 0,4 1,1 0,6 0,9 6,7 0,2 1,7 0,7 0,8 Exports -6,2 7,1 8,0 7,5 7,3 -6,2 7,8 6,4 6,9 6,7

Note: under both the scenarios, in the same period, the UL density, the number of patents and the percentage of the sorted waste collec-tion have been kept as constant; the energy consumptions per unit of value-added have been supposed to increase by +0,5 yearly

Source: Centro Studi Unioncamere – Prometeia

238

Table A5.2 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, North-East

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

TRENTINO - ALTO ADIGE Value-added Industry 4,5 3,7 0,3 1,4 2,1 4,5 3,4 0,4 1,1 2,0 Services 2,0 2,3 2,1 1,9 2,2 1,5 2,5 2,0 1,9 2,2 Total 2,1 2,0 1,4 1,5 2,0 1,8 2,0 1,3 1,5 1,9 Workers of industry -1,9 0,6 0,2 0,1 0,4 -1,7 0,6 0,4 0,2 0,3 Exports 3,4 6,0 4,2 4,2 4,3 3,4 5,7 2,6 3,5 3,7 VENETO Value-added Industry 3,6 3,0 1,6 1,6 1,6 3,6 2,7 1,7 1,3 1,5 Services 1,9 2,1 2,4 2,2 2,2 1,6 2,3 2,2 2,2 2,2 Total 2,1 2,3 2,0 1,9 1,9 1,9 2,3 1,9 1,8 1,8 Workers of industry 1,1 0,6 0,5 0,5 0,9 1,3 0,6 0,8 0,7 0,8 Exports 2,4 5,2 3,5 3,6 3,8 2,4 5,0 2,0 3,0 3,2 FRIULI - VENEZIA GIULIA Value-added Industry 4,3 3,5 0,8 1,5 1,6 4,3 3,3 0,9 1,2 1,5 Services 1,8 2,0 2,1 2,0 2,0 1,3 2,2 1,9 2,0 1,9 Total 2,4 2,3 1,7 1,8 1,8 2,1 2,4 1,6 1,7 1,8 Workers of industry 2,6 0,8 0,8 0,6 0,8 2,8 0,8 1,0 0,7 0,7 Exports 8,2 3,8 2,3 2,5 2,8 8,2 3,6 0,8 1,9 2,3 EMILIA ROMAGNA Value-added Industry 3,4 2,8 2,2 1,5 1,6 3,3 2,5 2,3 1,3 1,5 Services 1,7 2,1 2,2 2,1 2,1 2,2 2,1 1,8 2,0 2,0 Total 1,9 2,4 2,2 1,8 1,9 2,2 2,3 1,9 1,7 1,7 Workers of industry 2,6 0,7 0,8 0,7 1,0 2,8 0,7 1,1 0,9 0,9 Exports 5,0 4,6 2,9 3,1 3,3 5,0 4,3 1,4 2,5 2,8

Note: under both the scenarios, in the same period, the UL density, the number of patents and the percentage of the sorted waste collec-tion have been kept as constant; the energy consumptions per unit of value-added have been supposed to increase by +0,5 yearly

Source: Centro Studi Unioncamere – Prometeia

239

Table A5.3 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, Centre

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

TOSCANA Value-added Industry 0,5 1,4 0,8 2,1 2,3 0,4 1,1 0,9 1,8 2,2 Services 2,1 1,8 2,1 1,7 1,9 1,7 1,7 1,9 1,7 1,8 Total 1,7 1,7 1,7 1,7 1,9 1,5 1,5 1,6 1,7 1,9 Workers of industry -4,3 0,2 0,7 0,2 0,4 -4,1 0,2 1,0 0,3 0,3 Exports 6,4 5,0 4,6 4,6 4,7 6,4 3,1 3,1 4,0 4,1 UMBRIA Value-added Industry 2,9 4,0 2,5 2,5 2,6 2,7 2,4 2,6 2,2 2,5 Services 0,9 0,8 1,7 1,6 1,7 1,7 1,2 1,3 1,4 1,5 Total 1,6 1,6 1,8 1,7 1,9 2,2 1,5 1,5 1,6 1,7 Workers of industry 5,1 0,1 -0,9 -0,4 0,2 5,3 0,2 -0,7 -0,3 0,2 Exports 8,0 3,2 3,0 3,2 3,4 8,0 1,4 1,5 2,6 2,8 MARCHE Value-added Industry 1,2 2,5 1,4 1,7 2,3 1,6 1,9 1,4 1,4 2,2 Services 1,1 1,2 1,8 1,6 1,7 2,6 1,4 1,3 1,4 1,5 Total 0,9 1,7 1,6 1,6 1,8 2,0 1,7 1,3 1,4 1,6 Workers of industry 2,3 0,5 -0,8 -0,3 0,3 2,6 0,7 -0,5 -0,1 0,2 Exports 15,0 1,8 1,8 2,1 2,4 15,0 0,0 0,3 1,5 1,9 LAZIO Value-added Industry 2,4 3,6 0,8 1,5 1,7 1,9 1,8 0,9 1,2 1,6 Services 1,4 1,5 1,9 1,7 1,8 1,2 1,6 1,7 1,7 1,7 Total 1,6 1,8 1,9 1,7 1,8 1,4 1,8 1,6 1,7 1,7 Workers of industry 6,0 0,1 0,0 0,2 0,6 6,3 0,2 0,3 0,3 0,6 Exports 4,0 5,1 4,9 4,8 4,8 4,0 3,2 3,3 4,2 4,3

Note: under both the scenarios, in the same period, the UL density, the number of patents and the percentage of the sorted waste collec-tion have been kept as constant; the energy consumptions per unit of value-added have been supposed to increase by +0,5 yearly

Source: Centro Studi Unioncamere – Prometeia

240

Table A5.4 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, South and Islands

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

ABRUZZO Value-added Industry 0,4 0,5 1,7 1,6 1,9 0,6 0,5 1,7 1,3 1,8 Services 0,9 1,1 1,7 1,7 1,9 1,7 0,6 1,3 1,5 1,7 Total 0,7 1,4 1,8 1,7 1,9 1,2 1,1 1,6 1,5 1,7 Workers of industry -1,2 -0,1 -0,3 0,1 -0,3 -2,2 0,0 0,2 0,4 -0,2 Exports 0,2 -0,1 0,6 1,0 1,5 0,2 -0,3 -0,8 0,4 1,0 MOLISE Value-added Industry 1,2 1,1 -0,1 0,8 1,5 1,5 1,1 -0,1 0,5 1,3 Services 1,2 1,6 1,9 1,4 2,0 2,5 1,0 1,4 1,2 1,8 Total 0,7 1,2 1,2 1,1 1,7 2,0 0,7 0,8 0,9 1,5 Workers of industry -0,9 1,7 0,5 0,6 0,9 -1,9 1,7 1,0 0,9 1,0 Exports -4,2 3,9 4,2 4,2 4,3 -4,2 3,7 2,7 3,6 3,7 CAMPANIA Value-added Industry 1,8 0,7 1,1 1,9 2,3 1,6 0,7 1,2 1,6 2,2 Services 0,9 1,5 1,8 1,7 1,9 0,9 1,1 1,5 1,6 1,8 Total 1,1 1,7 1,9 1,8 2,0 1,0 1,5 1,7 1,7 1,9 Workers of industry 5,3 0,6 0,2 0,6 0,7 4,2 0,8 0,6 0,9 0,8 Exports 4,4 3,7 4,0 4,0 4,1 4,4 3,4 2,4 3,4 3,6 PUGLIA Value-added Industry 2,3 0,4 1,2 1,6 2,3 3,0 0,3 1,2 1,2 2,1 Services 1,6 2,1 1,9 1,5 2,1 1,6 1,7 1,6 1,4 1,9 Total 0,8 1,5 1,5 1,3 1,9 1,1 1,2 1,2 1,2 1,7 Workers of industry 2,0 0,5 0,0 0,2 0,7 0,9 0,6 0,5 0,5 0,7 Exports -6,5 5,8 5,8 5,6 5,6 -6,5 5,5 4,3 5,0 5,0 BASILICATA Value-added Industry 3,4 2,1 0,3 0,2 0,6 1,1 2,5 0,7 0,2 0,7 Services 1,1 2,2 1,6 1,5 1,7 2,5 1,4 1,0 1,2 1,5 Total 1,3 1,8 1,3 1,3 1,5 1,7 1,4 1,0 1,1 1,4 Workers of industry 0,8 1,1 0,0 -0,2 0,1 -0,2 1,1 0,3 0,1 0,1 Exports 47,4 -9,1 -7,4 -6,2 -4,9 47,4 -9,3 -8,8 -6,7 -5,4 CALABRIA Value-added Industry 2,2 4,0 0,1 0,2 2,0 0,8 4,2 0,3 0,1 2,0 Services 1,8 1,7 1,8 1,5 1,7 1,8 1,2 1,5 1,4 1,6 Total 1,7 1,5 1,5 1,3 1,7 1,3 1,3 1,3 1,3 1,6 Workers of industry 0,6 1,7 0,2 0,0 0,1 -0,4 1,9 0,5 0,2 0,2 Exports -2,9 5,1 5,3 5,1 5,1 -2,9 4,9 3,7 4,5 4,6

241

Scenario F-1 Scenario F-2 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010

SICILIA Value-added Industry 0,7 3,3 1,8 0,6 1,1 1,5 0,7 1,7 0,3 0,9 Services 1,7 1,8 2,0 1,8 2,1 1,3 1,5 1,8 1,8 2,0 Total 1,5 2,3 2,0 1,6 1,9 1,2 1,8 1,9 1,6 1,8 Workers of industry 1,3 0,4 -0,6 -0,3 -0,9 0,2 0,8 0,0 0,1 -0,9 Exports -3,1 2,4 2,9 3,0 3,3 -3,1 2,2 1,3 2,4 2,7 SARDEGNA Value-added Industry 0,1 0,6 0,9 2,0 2,2 1,3 0,4 0,7 1,5 2,0 Services 1,7 1,5 2,2 2,1 2,2 1,7 1,1 1,9 2,0 2,0 Total 1,2 1,5 1,9 1,9 2,0 1,4 1,2 1,7 1,8 1,9 Workers of industry -1,6 0,1 0,8 0,5 0,6 -2,6 0,0 1,3 0,8 0,6 Exports 8,3 0,6 1,2 1,5 2,0 8,3 0,3 -0,3 0,9 1,4

Note: under both the scenarios, in the same period, the UL density, the number of patents and the percentage of the sorted waste collec-tion have been kept as constant; the energy consumptions per unit of value-added have been supposed to increase by +0,5 yearly

Source: Centro Studi Unioncamere – Prometeia

242

Table A5.5 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-1, Specification S-1 (VA/UL)

Province Region 2006 2007 2008 2009 2010 Torino Piemonte 136,71 133,58 132,99 130,57 127,18Vercelli Piemonte 465,41 462,93 466,64 466,20 463,27Novara Piemonte 116,37 113,49 113,00 110,80 107,73Cuneo Piemonte 189,40 186,25 186,32 184,08 180,58Asti Piemonte 147,38 148,33 150,00 151,24 152,11Alessandria Piemonte 115,47 114,17 114,48 113,64 112,13Biella Piemonte 63,28 62,36 62,35 61,68 60,62Verbano Cusio Ossola Piemonte 209,10 211,38 213,82 216,17 218,27Aosta Valle d'Aosta 340,60 333,43 335,34 335,06 333,40Varese Lombardia 87,65 84,85 84,11 82,49 80,29Como Lombardia 92,81 90,75 90,35 89,23 87,60Sondrio Lombardia 84,21 83,25 83,14 82,60 81,74Milano Lombardia 74,03 70,27 68,93 66,62 63,72Bergamo Lombardia 183,13 176,03 173,99 169,86 164,37Brescia Lombardia 389,81 380,53 379,20 374,46 367,31Pavia Lombardia 285,46 281,89 282,22 280,84 278,16Cremona Lombardia 240,59 232,61 230,08 225,16 218,66Mantova Lombardia 377,21 363,67 360,14 352,45 342,02Lecco Lombardia 259,63 251,07 248,45 243,27 236,38Lodi Lombardia 139,23 134,59 133,02 130,12 126,33Bolzano - Bozen Trentino Alto Adige 116,70 115,96 117,26 117,63 117,34Trento Trentino Alto Adige 239,95 234,96 237,37 236,88 234,38Verona Veneto 405,56 396,89 393,19 389,43 385,71Vicenza Veneto 218,83 213,52 211,29 209,05 206,87Belluno Veneto 111,00 106,78 104,78 102,80 100,85Treviso Veneto 175,45 171,59 169,93 168,25 166,61Venezia Veneto 344,94 340,64 339,33 337,91 336,50Padova Veneto 186,06 184,25 183,65 183,00 182,35Rovigo Veneto 205,31 206,80 207,94 208,99 210,01Udine Friuli Venezia Giulia 403,13 394,86 396,26 394,54 392,54Gorizia Friuli Venezia Giulia 344,01 337,08 338,37 336,92 335,35Trieste Friuli Venezia Giulia 279,71 272,37 273,02 271,19 269,13Pordenone Friuli Venezia Giulia 260,06 249,97 249,43 246,07 242,47Imperia Liguria 16,41 17,05 17,74 18,51 19,29Savona Liguria 2.033,90 2.070,88 2.110,54 2.146,58 2.178,59Genova Liguria 237,92 238,83 239,75 239,63 238,87La Spezia Liguria 340,70 339,86 338,80 336,16 332,64Piacenza Emilia Romagna 116,76 115,23 113,72 113,02 112,21Parma Emilia Romagna 107,72 105,22 103,00 101,82 100,50Reggio Emilia Emilia Romagna 174,48 170,17 166,29 164,33 162,16Modena Emilia Romagna 212,30 206,47 201,32 198,60 195,61Bologna Emilia Romagna 115,42 111,37 107,94 105,99 103,89Ferrara Emilia Romagna 322,40 318,70 314,97 313,68 312,05Ravenna Emilia Romagna 444,10 432,47 422,61 417,23 411,24Forlì - Cesena Emilia Romagna 164,95 162,89 160,83 159,83 158,66Rimini Emilia Romagna 92,99 93,43 93,52 93,81 94,06Massa Carrara Toscana 768,03 788,42 806,30 828,59 851,49Lucca Toscana 347,75 352,48 358,03 360,83 362,95Pistoia Toscana 53,79 54,77 55,55 56,66 57,78Firenze Toscana 71,03 71,50 72,14 72,22 72,16Livorno Toscana 285,58 286,98 289,70 288,90 287,30

243

Province Region 2006 2007 2008 2009 2010 Pisa Toscana 149,13 149,00 149,60 148,23 146,45Arezzo Toscana 109,75 111,08 112,61 113,36 113,92Siena Toscana 204,66 207,39 210,15 212,30 214,19Grosseto Toscana 1.577,80 1.623,04 1.653,58 1.716,40 1.784,73Prato Toscana 31,31 32,00 32,58 33,35 34,13Perugia Umbria 191,40 191,51 191,59 191,41 190,90Terni Umbria 709,96 679,50 663,31 646,81 628,98Pesaro e Urbino Marche 266,03 267,78 269,29 270,79 272,09Ancona Marche 156,71 153,18 151,73 149,76 146,59Macerata Marche 127,49 128,46 129,24 130,04 130,76Ascoli Piceno Marche 120,20 120,72 121,27 121,80 122,14Viterbo Lazio 206,97 211,64 213,52 215,94 218,42Rieti Lazio 55,37 56,15 57,14 58,03 58,89Roma Lazio 36,93 34,92 34,62 33,88 33,02Latina Lazio 81,87 77,61 77,50 76,31 74,80Frosinone Lazio 175,65 170,27 170,67 169,46 167,73L'Aquila Abruzzo 173,49 174,16 174,80 175,55 176,30Teramo Abruzzo 178,85 179,23 179,72 180,22 180,67Pescara Abruzzo 67,91 68,11 68,33 68,53 68,70Chieti Abruzzo 172,49 171,90 169,39 167,29 164,83Campobasso Molise 438,44 443,09 443,81 448,11 453,68Isernia Molise 143,87 145,29 148,39 150,32 151,78Caserta Campania 170,52 171,51 172,60 173,66 174,54Benevento Campania 68,78 69,94 71,67 74,50 77,91Napoli Campania 79,35 80,25 81,38 82,82 84,36Avellino Campania 140,41 141,74 143,24 144,86 146,41Salerno Campania 137,28 138,93 141,07 143,99 147,23Foggia Puglia 386,34 390,91 399,93 411,15 426,15Bari Puglia 225,69 228,54 232,66 237,31 242,80Taranto Puglia 1.625,76 1.659,58 1.679,78 1.691,54 1.688,12Brindisi Puglia 1.948,45 1.983,95 2.027,82 2.074,80 2.126,50Lecce Puglia 120,54 122,20 125,64 129,98 135,92Potenza Basilicata 247,67 242,12 239,82 238,30 236,89Matera Basilicata 130,22 129,13 127,49 126,30 125,84Cosenza Calabria 50,10 53,83 54,01 54,28 56,23Catanzaro Calabria 53,07 56,71 56,90 57,18 59,09Reggio di Calabria Calabria 49,55 56,46 56,83 57,40 61,32Crotone Calabria 468,27 498,88 501,23 504,33 520,70Vibo Valentia Calabria 26,30 28,62 28,79 29,01 30,29Trapani Sicilia 396,82 422,44 437,34 443,27 453,19Palermo Sicilia 226,84 233,38 237,09 238,74 241,23Messina Sicilia 300,65 319,00 330,43 336,07 344,47Agrigento Sicilia 27,69 29,70 30,87 31,31 32,07Caltanissetta Sicilia 1.663,95 1.687,22 1.705,64 1.722,37 1.739,86Enna Sicilia 42,47 45,31 46,92 47,51 48,54Catania Sicilia 87,59 91,94 94,58 95,84 97,72Ragusa Sicilia 76,98 82,40 85,63 86,99 89,20Siracusa Sicilia 946,82 933,64 943,37 971,36 995,68Sassari Sardegna 626,38 632,41 640,78 656,09 672,62Nuoro Sardegna 192,17 193,82 196,03 200,06 204,32Cagliari Sardegna 1.609,05 1.626,94 1.646,66 1.658,70 1.670,66Oristano Sardegna 96,76 97,56 98,72 101,10 103,66

244

Table A5.6 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-1, Specification S-2 (VA/worker)

Province Region 2006 2007 2008 2009 2010Torino Piemonte 124,95 121,10 123,06 121,08 118,51Vercelli Piemonte 446,62 450,69 462,52 470,18 476,82Novara Piemonte 106,09 103,75 106,08 105,30 104,10Cuneo Piemonte 174,45 170,86 174,60 173,45 171,59Asti Piemonte 146,28 147,78 150,72 152,91 154,86Alessandria Piemonte 110,08 109,47 111,70 112,09 112,12Biella Piemonte 61,25 61,97 63,14 64,08 64,91Verbano Cusio Ossola Piemonte 207,07 209,37 212,83 215,64 218,25Aosta Valle d'Aosta 285,79 270,37 315,72 327,26 334,11Varese Lombardia 83,54 81,29 81,09 80,12 78,94Como Lombardia 87,71 85,56 85,37 84,44 83,32Sondrio Lombardia 79,10 77,43 77,12 76,28 75,41Milano Lombardia 65,81 61,70 60,42 58,18 55,80Bergamo Lombardia 175,81 171,62 171,57 169,97 167,85Brescia Lombardia 378,19 371,64 372,80 370,94 367,95Pavia Lombardia 269,98 264,08 264,04 261,81 259,09Cremona Lombardia 220,33 212,81 211,08 207,23 203,10Mantova Lombardia 357,17 349,21 349,62 346,97 343,53Lecco Lombardia 249,83 245,29 245,30 243,54 241,30Lodi Lombardia 122,84 116,87 115,05 111,82 108,56Bolzano - Bozen Trentino Alto Adige 106,99 104,70 106,41 106,18 105,14Trento Trentino Alto Adige 219,26 213,18 216,60 215,56 212,71Verona Veneto 393,96 387,84 386,49 385,13 385,76Vicenza Veneto 219,29 219,25 220,21 221,19 222,79Belluno Veneto 115,29 115,74 116,41 117,09 118,13Treviso Veneto 178,76 179,11 179,96 180,81 182,06Venezia Veneto 328,85 323,88 323,07 322,23 323,17Padova Veneto 182,31 180,68 180,55 180,40 180,93Rovigo Veneto 207,58 210,09 211,96 213,80 215,94Udine Friuli Venezia Giulia 410,14 404,33 409,48 410,42 412,06Gorizia Friuli Venezia Giulia 312,28 313,25 318,28 321,32 325,06Trieste Friuli Venezia Giulia 280,77 276,15 280,34 281,06 282,46Pordenone Friuli Venezia Giulia 274,14 272,78 275,95 277,20 278,87Imperia Liguria 17,03 16,90 17,00 16,76 16,56Savona Liguria 2.038,12 2.025,46 2.036,64 2.010,08 1.986,76Genova Liguria 236,60 235,81 236,65 234,44 232,23La Spezia Liguria 332,18 324,19 320,84 310,12 300,60Piacenza Emilia Romagna 123,25 122,82 122,33 122,71 123,36Parma Emilia Romagna 113,60 112,69 111,84 111,93 112,28Reggio Emilia Emilia Romagna 187,65 186,81 185,68 186,23 187,14Modena Emilia Romagna 226,77 225,13 223,32 223,58 224,23Bologna Emilia Romagna 122,99 120,00 117,65 116,93 116,59Ferrara Emilia Romagna 349,74 356,00 359,74 364,38 369,29Ravenna Emilia Romagna 474,05 467,96 463,40 463,23 464,42Forlì - Cesena Emilia Romagna 175,03 176,94 177,87 179,22 180,66Rimini Emilia Romagna 94,47 95,28 95,68 96,38 97,21Massa Carrara Toscana 590,09 593,39 607,27 601,74 595,56Lucca Toscana 306,16 308,64 315,68 313,99 311,80Pistoia Toscana 53,49 54,06 54,78 55,12 55,39Firenze Toscana 61,15 60,89 61,68 60,49 59,20Livorno Toscana 218,67 217,16 221,01 215,58 210,02

245

Province Region 2006 2007 2008 2009 2010Pisa Toscana 127,12 126,52 128,13 125,72 123,15Arezzo Toscana 113,45 115,46 117,94 119,21 120,29Siena Toscana 195,99 198,16 201,84 202,42 202,72Grosseto Toscana 1.421,93 1.424,77 1.439,61 1.433,49 1.426,27Prato Toscana 30,37 30,72 31,24 31,37 31,45Perugia Umbria 192,12 192,15 191,43 190,22 188,86Terni Umbria 718,47 686,97 658,46 634,42 615,55Pesaro e Urbino Marche 275,18 278,03 281,19 283,78 285,78Ancona Marche 160,14 159,50 159,23 158,79 157,86Macerata Marche 133,24 135,61 138,18 140,42 142,41Ascoli Piceno Marche 126,72 128,20 129,93 131,38 132,51Viterbo Lazio 204,95 204,04 204,81 205,08 205,63Rieti Lazio 52,22 51,86 52,80 53,43 54,18Roma Lazio 37,16 33,63 33,02 31,88 30,91Latina Lazio 84,69 79,57 79,56 78,48 77,70Frosinone Lazio 176,22 172,11 173,21 173,06 173,19L'Aquila Abruzzo 157,56 156,25 150,68 146,92 141,07Teramo Abruzzo 180,72 181,16 181,84 182,46 182,86Pescara Abruzzo 64,67 64,44 63,24 62,41 60,96Chieti Abruzzo 172,54 172,16 170,56 169,60 168,04Campobasso Molise 422,17 427,89 433,24 436,97 440,75Isernia Molise 139,24 142,77 147,20 150,44 153,55Caserta Campania 165,01 165,98 165,90 165,18 163,87Benevento Campania 78,27 78,86 80,06 81,71 83,60Napoli Campania 83,51 84,14 84,64 84,83 84,79Avellino Campania 141,54 142,99 144,12 144,93 145,43Salerno Campania 141,17 142,23 142,86 143,06 142,89Foggia Puglia 390,33 393,76 396,95 399,98 402,96Bari Puglia 225,51 228,40 230,61 232,34 233,40Taranto Puglia 1.604,53 1.654,24 1.696,25 1.735,06 1.769,43Brindisi Puglia 1.933,55 1.976,00 2.013,13 2.047,94 2.080,35Lecce Puglia 132,23 133,30 136,55 140,12 144,02Potenza Basilicata 239,57 232,22 228,40 225,20 222,56Matera Basilicata 130,43 126,53 123,78 121,71 120,30Cosenza Calabria 50,09 49,76 49,91 49,93 49,40Catanzaro Calabria 53,74 53,95 54,12 54,19 53,99Reggio di Calabria Calabria 51,02 50,78 51,07 51,22 50,72Crotone Calabria 415,93 409,27 411,86 412,44 403,74Vibo Valentia Calabria 27,14 27,54 27,69 27,82 27,92Trapani Sicilia 401,88 410,46 415,78 418,44 421,69Palermo Sicilia 222,72 217,35 212,33 210,76 206,19Messina Sicilia 299,08 300,74 301,66 303,64 304,18Agrigento Sicilia 27,89 28,34 28,54 28,63 28,68Caltanissetta Sicilia 1.603,87 1.542,24 1.487,83 1.476,15 1.430,15Enna Sicilia 42,84 44,07 44,75 45,00 45,36Catania Sicilia 87,78 88,18 88,41 88,85 88,96Ragusa Sicilia 78,85 81,74 83,78 84,77 86,26Siracusa Sicilia 1.073,76 1.065,35 1.080,50 1.121,85 1.153,45Sassari Sardegna 565,24 566,10 568,98 567,84 566,26Nuoro Sardegna 165,17 164,86 165,70 163,80 161,66Cagliari Sardegna 1.613,51 1.622,75 1.643,81 1.629,82 1.615,29Oristano Sardegna 86,98 86,97 87,43 87,25 87,00

246

Table A5.7 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-2, Specification S-1 (VA/UL)

Province Region 2006 2007 2008 2009 2010 Torino Piemonte 139,52 137,08 135,79 133,63 130,29Vercelli Piemonte 477,54 477,33 478,61 478,53 475,56Novara Piemonte 119,02 116,79 115,63 113,68 110,63Cuneo Piemonte 193,65 191,46 190,54 188,60 185,14Asti Piemonte 150,20 151,56 152,75 153,96 154,80Alessandria Piemonte 117,91 117,13 116,90 116,19 114,70Biella Piemonte 64,44 63,79 63,51 62,93 61,89Verbano Cusio Ossola Piemonte 211,65 214,23 216,28 218,53 220,59Aosta Valle d'Aosta 342,79 336,79 337,12 337,02 335,13Varese Lombardia 88,87 86,49 85,42 83,97 81,80Como Lombardia 94,00 92,32 91,61 90,60 88,99Sondrio Lombardia 84,83 84,08 83,81 83,35 82,51Milano Lombardia 75,05 71,72 70,11 68,00 65,14Bergamo Lombardia 185,83 179,73 176,97 173,23 167,81Brescia Lombardia 395,19 387,68 384,96 380,78 373,74Pavia Lombardia 289,15 286,62 285,98 284,80 282,13Cremona Lombardia 242,99 236,06 232,89 228,50 222,15Mantova Lombardia 382,91 371,40 366,35 359,42 349,11Lecco Lombardia 262,59 255,21 251,80 247,15 240,39Lodi Lombardia 140,63 136,59 134,65 132,03 128,32Bolzano - Bozen Trentino Alto Adige 118,24 117,67 118,41 118,84 118,48Trento Trentino Alto Adige 242,84 238,45 239,71 239,65 237,12Verona Veneto 408,36 401,02 395,18 392,42 388,66Vicenza Veneto 220,72 216,23 212,57 210,92 208,68Belluno Veneto 111,79 108,10 105,44 103,88 101,96Treviso Veneto 176,81 173,55 170,86 169,61 167,92Venezia Veneto 347,10 343,70 340,77 340,00 338,51Padova Veneto 187,25 185,84 184,39 184,00 183,28Rovigo Veneto 206,16 207,71 208,32 209,35 210,26Udine Friuli Venezia Giulia 404,96 397,29 396,15 395,05 392,90Gorizia Friuli Venezia Giulia 346,11 339,64 338,14 337,04 335,15Trieste Friuli Venezia Giulia 280,94 274,10 273,00 271,75 269,64Pordenone Friuli Venezia Giulia 261,33 251,83 249,45 246,81 243,20Imperia Liguria 16,71 17,68 18,34 19,04 19,77Savona Liguria 2.069,24 2.113,28 2.146,39 2.179,94 2.209,50Genova Liguria 241,29 240,85 241,07 241,09 240,28La Spezia Liguria 343,85 340,37 338,54 336,28 332,81Piacenza Emilia Romagna 116,28 115,01 112,97 112,35 111,52Parma Emilia Romagna 107,30 105,15 102,40 101,37 100,08Reggio Emilia Emilia Romagna 173,55 169,87 164,90 163,15 160,98Modena Emilia Romagna 211,29 206,26 199,86 197,44 194,49Bologna Emilia Romagna 114,93 111,41 107,30 105,58 103,53Ferrara Emilia Romagna 320,82 317,86 312,45 311,31 309,60Ravenna Emilia Romagna 442,80 432,81 420,99 416,38 410,63Forlì - Cesena Emilia Romagna 164,33 162,60 159,85 158,95 157,77Rimini Emilia Romagna 92,58 93,03 92,77 93,00 93,19Massa Carrara Toscana 766,27 782,20 797,56 819,32 842,72Lucca Toscana 362,43 366,43 370,79 374,66 377,59Pistoia Toscana 52,99 53,78 54,51 55,60 56,80Firenze Toscana 73,61 74,00 74,44 74,75 74,85Livorno Toscana 299,36 301,10 303,28 303,73 302,93

247

Province Region 2006 2007 2008 2009 2010 Pisa Toscana 157,14 157,09 157,29 156,53 155,10Arezzo Toscana 114,16 115,20 116,32 117,38 118,16Siena Toscana 209,06 211,26 213,48 216,04 218,35Grosseto Toscana 1.476,98 1.511,86 1.542,24 1.596,81 1.662,01Prato Toscana 31,03 31,57 32,08 32,83 33,65Perugia Umbria 192,12 192,33 192,21 192,24 191,90Terni Umbria 717,06 699,98 680,38 666,42 649,00Pesaro e Urbino Marche 267,41 267,92 268,46 269,65 270,72Ancona Marche 157,08 153,69 151,42 149,76 146,59Macerata Marche 128,13 128,51 128,86 129,50 130,12Ascoli Piceno Marche 120,83 120,81 120,89 121,30 121,55Viterbo Lazio 206,92 209,63 211,39 213,59 216,10Rieti Lazio 55,91 56,52 57,21 58,07 58,91Roma Lazio 37,33 36,35 35,94 35,37 34,55Latina Lazio 83,17 81,22 80,66 79,83 78,39Frosinone Lazio 177,50 175,38 175,15 174,54 173,01L'Aquila Abruzzo 174,59 175,16 175,06 175,63 176,19Teramo Abruzzo 179,54 179,85 179,90 180,26 180,60Pescara Abruzzo 68,04 68,22 68,36 68,54 68,69Chieti Abruzzo 173,84 173,07 169,47 167,55 164,96Campobasso Molise 439,66 444,24 446,32 449,80 454,71Isernia Molise 144,64 145,99 147,81 149,65 150,93Caserta Campania 172,12 173,13 174,00 175,01 175,89Benevento Campania 68,84 70,01 71,83 74,24 77,51Napoli Campania 80,66 81,57 82,56 83,84 85,33Avellino Campania 142,89 144,24 145,44 146,92 148,43Salerno Campania 138,92 140,59 142,60 145,18 148,31Foggia Puglia 391,49 395,53 404,22 413,18 427,00Bari Puglia 228,57 231,28 234,93 238,86 243,92Taranto Puglia 1.635,87 1.671,51 1.685,76 1.703,26 1.701,57Brindisi Puglia 1.977,40 2.012,24 2.050,41 2.092,38 2.140,58Lecce Puglia 122,63 124,09 127,39 130,84 136,30Potenza Basilicata 251,50 245,37 241,92 240,17 238,49Matera Basilicata 129,34 128,47 126,66 125,30 124,80Cosenza Calabria 48,86 52,76 53,11 53,29 55,23Catanzaro Calabria 51,87 55,68 56,04 56,23 58,13Reggio di Calabria Calabria 47,49 54,53 55,22 55,59 59,41Crotone Calabria 459,42 491,55 495,13 497,43 513,80Vibo Valentia Calabria 25,60 28,01 28,27 28,44 29,70Trapani Sicilia 403,53 409,82 423,45 426,89 435,02Palermo Sicilia 228,68 230,51 234,01 235,13 237,32Messina Sicilia 305,94 311,50 321,68 325,49 332,55Agrigento Sicilia 28,18 28,66 29,73 29,98 30,60Caltanissetta Sicilia 1.670,40 1.686,99 1.704,49 1.720,45 1.737,97Enna Sicilia 43,16 43,81 45,31 45,63 46,47Catania Sicilia 88,87 90,14 92,51 93,34 94,94Ragusa Sicilia 78,34 79,76 82,72 83,56 85,38Siracusa Sicilia 963,82 984,28 981,84 1.010,65 1.034,23Sassari Sardegna 629,58 634,07 640,06 651,72 666,47Nuoro Sardegna 193,75 195,02 196,75 199,87 203,74Cagliari Sardegna 1.558,50 1.575,18 1.586,12 1.597,89 1.607,45Oristano Sardegna 97,92 98,47 99,35 101,14 103,45

248

Table A5.8 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-2, Specification S-2 (VA/worker)

Province Region 2006 2007 2008 2009 2010 Torino Piemonte 128,95 126,27 127,60 125,76 122,88Vercelli Piemonte 463,52 470,69 479,92 487,53 493,69Novara Piemonte 109,82 108,48 110,27 109,56 108,09Cuneo Piemonte 180,46 178,48 181,33 180,30 178,03Asti Piemonte 150,30 152,49 154,79 156,94 158,79Alessandria Piemonte 113,53 113,70 115,39 115,83 115,70Biella Piemonte 62,85 63,83 64,74 65,68 66,48Verbano Cusio Ossola Piemonte 210,71 213,61 216,52 219,28 221,78Aosta Valle d'Aosta 289,59 270,57 321,97 333,27 338,29Varese Lombardia 85,35 83,50 83,16 82,23 80,86Como Lombardia 89,48 87,72 87,41 86,51 85,21Sondrio Lombardia 80,13 78,67 78,47 77,66 76,62Milano Lombardia 67,37 63,61 62,39 60,23 57,62Bergamo Lombardia 179,80 176,50 176,01 174,47 172,00Brescia Lombardia 386,10 381,38 381,52 379,82 376,23Pavia Lombardia 275,50 270,78 270,40 268,24 264,92Cremona Lombardia 224,13 217,44 215,98 212,29 207,60Mantova Lombardia 365,58 359,38 359,04 356,44 352,17Lecco Lombardia 254,30 250,73 250,42 248,73 246,05Lodi Lombardia 125,14 119,65 118,17 115,03 111,34Bolzano - Bozen Trentino Alto Adige 109,26 106,96 108,30 108,05 106,66Trento Trentino Alto Adige 223,63 217,53 220,37 219,39 215,84Verona Veneto 398,57 392,81 390,29 389,58 389,13Vicenza Veneto 222,13 222,41 221,92 222,97 224,01Belluno Veneto 116,55 117,05 117,21 117,87 118,61Treviso Veneto 180,80 181,39 181,14 182,02 182,87Venezia Veneto 332,49 327,74 326,27 326,00 326,05Padova Veneto 184,20 182,75 181,98 182,04 182,15Rovigo Veneto 208,83 211,33 212,64 214,35 216,17Udine Friuli Venezia Giulia 413,42 407,63 410,28 411,00 411,47Gorizia Friuli Venezia Giulia 315,29 316,13 318,28 320,58 323,15Trieste Friuli Venezia Giulia 283,20 278,44 281,33 281,82 282,23Pordenone Friuli Venezia Giulia 276,29 274,93 276,07 276,99 277,90Imperia Liguria 17,53 17,45 17,68 17,45 17,18Savona Liguria 2.094,76 2.082,52 2.107,31 2.082,58 2.053,69Genova Liguria 241,77 240,35 241,91 240,06 237,55La Spezia Liguria 337,92 329,75 330,12 319,66 309,01Piacenza Emilia Romagna 122,58 122,10 121,27 121,65 121,98Parma Emilia Romagna 112,98 112,07 110,89 111,03 111,09Reggio Emilia Emilia Romagna 186,17 185,38 183,24 183,71 184,09Modena Emilia Romagna 225,14 223,61 220,68 220,92 220,99Bologna Emilia Romagna 122,34 119,43 116,81 116,28 115,57Ferrara Emilia Romagna 346,83 352,89 354,28 358,14 362,24Ravenna Emilia Romagna 472,32 466,16 461,21 461,60 461,54Forlì - Cesena Emilia Romagna 173,89 175,75 175,80 176,89 178,04Rimini Emilia Romagna 93,89 94,64 94,66 95,26 95,88Massa Carrara Toscana 613,70 612,58 625,98 620,67 612,28Lucca Toscana 321,11 321,62 328,06 326,62 323,51Pistoia Toscana 54,53 54,89 55,48 55,83 56,04Firenze Toscana 64,08 63,53 64,30 63,18 61,70Livorno Toscana 223,69 221,27 225,96 220,85 214,52

249

Province Region 2006 2007 2008 2009 2010 Pisa Toscana 131,31 130,15 131,85 129,58 126,65Arezzo Toscana 119,07 120,36 122,20 123,51 124,41Siena Toscana 202,42 203,46 206,59 207,21 207,12Grosseto Toscana 1.415,48 1.413,77 1.430,62 1.424,63 1.415,40Prato Toscana 31,47 31,66 32,09 32,23 32,26Perugia Umbria 193,15 193,01 191,95 190,82 189,42Terni Umbria 727,95 694,25 664,46 640,76 620,92Pesaro e Urbino Marche 276,56 277,87 279,47 281,73 283,58Ancona Marche 161,61 160,02 158,77 158,31 157,13Macerata Marche 133,75 135,29 137,01 138,98 140,94Ascoli Piceno Marche 127,37 128,08 129,00 130,25 131,32Viterbo Lazio 205,67 203,73 204,58 204,82 205,29Rieti Lazio 53,04 52,29 52,99 53,57 54,21Roma Lazio 37,65 34,33 33,92 32,87 31,87Latina Lazio 86,10 81,00 81,05 80,08 79,19Frosinone Lazio 177,99 173,79 174,77 174,73 174,74L'Aquila Abruzzo 155,73 154,57 149,70 146,60 140,81Teramo Abruzzo 181,86 182,14 182,10 182,48 182,69Pescara Abruzzo 64,08 63,89 62,97 62,30 60,89Chieti Abruzzo 174,27 173,67 170,79 169,66 167,76Campobasso Molise 420,71 426,47 433,83 437,61 441,09Isernia Molise 139,56 143,06 147,29 150,36 153,11Caserta Campania 165,74 167,00 167,15 166,76 165,50Benevento Campania 79,32 79,81 80,74 82,11 83,91Napoli Campania 85,54 86,20 86,42 86,64 86,56Avellino Campania 144,60 146,19 147,02 147,88 148,31Salerno Campania 143,25 144,45 144,91 145,23 145,04Foggia Puglia 392,08 395,53 398,52 401,29 403,86Bari Puglia 227,45 230,45 232,11 233,93 234,81Taranto Puglia 1.627,73 1.678,95 1.715,96 1.755,62 1.787,47Brindisi Puglia 1.959,70 2.003,16 2.035,10 2.069,11 2.098,11Lecce Puglia 134,98 135,90 138,12 141,10 144,88Potenza Basilicata 238,75 230,86 226,94 224,27 221,36Matera Basilicata 131,29 127,01 123,68 121,45 119,85Cosenza Calabria 49,56 49,30 49,60 49,69 49,20Catanzaro Calabria 53,18 53,46 53,77 53,89 53,72Reggio di Calabria Calabria 50,50 50,36 50,82 51,05 50,59Crotone Calabria 407,73 402,65 407,58 409,37 401,35Vibo Valentia Calabria 26,90 27,33 27,55 27,68 27,80Trapani Sicilia 405,37 409,90 413,99 415,93 418,86Palermo Sicilia 220,89 215,75 211,96 211,33 206,71Messina Sicilia 300,64 301,31 301,75 303,82 304,05Agrigento Sicilia 28,03 28,14 28,32 28,40 28,43Caltanissetta Sicilia 1.578,50 1.518,61 1.480,37 1.479,78 1.432,58Enna Sicilia 43,21 43,68 44,26 44,42 44,73Catania Sicilia 88,21 88,50 88,57 89,02 89,08Ragusa Sicilia 79,89 81,68 83,29 83,99 85,39Siracusa Sicilia 1.103,59 1.124,67 1.121,46 1.163,15 1.189,98Sassari Sardegna 557,02 556,99 560,14 559,15 556,86Nuoro Sardegna 162,84 162,12 163,88 162,36 160,04Cagliari Sardegna 1.525,76 1.530,90 1.549,05 1.540,07 1.522,53Oristano Sardegna 86,45 86,24 86,94 86,74 86,36

250

Table A5.9 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Average values and median values, 1998-2004

Province Average 1998-2004 Median 1998-2004

Torino 120 125 Vercelli 380 382 Novara 91 97 Cuneo 135 131 Asti 132 135 Alessandria 91 92 Biella 108 110 Verbano Cusio Ossola 181 190 Piemonte 127 133 Aosta 256 234 Valle d’Aosta 256 234 Varese 84 87 Como 84 88 Sondrio 81 82 Milano 59 61 Bergamo 173 178 Brescia 349 382 Pavia 236 219 Cremona 257 254 Mantova 245 222 Lecco 220 228 Lodi 150 135 Lombardia 137 146 Bolzano - Bozen 108 116 Trento 199 212 Trentino Alto Adige 157 167 Verona 437 442 Vicenza 224 234 Belluno 106 105 Treviso 160 169 Venezia 299 312 Padova 171 185 Rovigo 382 304 Veneto 245 252 Udine 346 361 Gorizia 503 505 Trieste 199 160 Pordenone 213 224 Friuli Venezia Giulia 298 307 Imperia 17 16 Savona 1.001 402 Genova 207 197 La Spezia 316 336 Liguria 342 282 Piacenza 201 218 Parma 110 113 Reggio Emilia 157 168 Modena 204 207 Bologna 117 121 Ferrara 337 327 Ravenna 435 449

251

Province Average 1998-2004 Median 1998-2004

Forlì - Cesena 220 197 Rimini 83 87 Emilia Romagna 185 190 Massa Carrara 1.024 1.070 Lucca 388 384 Pistoia 54 57 Firenze 60 59 Livorno 186 176 Pisa 194 200 Arezzo 175 153 Siena 151 152 Grosseto 1.512 1.543 Prato 33 33 Toscana 196 191 Perugia 183 184 Terni 661 646 Umbria 322 335 Pesaro e Urbino 171 158 Ancona 138 142 Macerata 120 123 Ascoli Piceno 105 105 Marche 133 133 Viterbo 162 166 Rieti 121 93 Roma 40 36 Latina 80 81 Frosinone 187 199 Lazio 71 70 L'Aquila 173 176 Teramo 133 144 Pescara 59 68 Chieti 154 151 Abruzzo 133 137 Campobasso 467 495 Isernia 120 130 Molise 349 368 Caserta 135 138 Benevento 52 56 Napoli 69 73 Avellino 136 138 Salerno 129 128 Campania 98 109 Foggia 199 173 Bari 197 204 Taranto 968 954 Brindisi 1.164 1.076 Lecce 87 90 Puglia 433 412 Potenza 241 210 Matera 117 113 Basilicata 210 198 Cosenza 35 25 Catanzaro 31 29 Reggio di Calabria 35 21

252

Province Average 1998-2004 Median 1998-2004

Crotone 226 155 Vibo Valentia 26 25 Calabria 54 32 Trapani 235 206 Palermo 197 192 Messina 151 150 Agrigento 31 28 Caltanissetta 306 87 Enna 26 23 Catania 71 80 Ragusa 61 62 Siracusa 255 198 Sicilia 166 137 Sassari 348 321 Nuoro 164 155 Cagliari 1.196 1.276 Oristano 52 54 Sardegna 745 766

Italy 181 188

253

CONCLUSIONS

The relationship between economic growth and the respective environmental conditions has been

deeply debated about at a local, national and world level.

The effect of economic growth on the environment has been usually shown to be caused by three fac-

tors: the growth of the scale of the economic activity, the changes in the productive structure of the

economy under exam, and the development of that technology by the means of which the different

productive activities have a certain impact. These remarks have led many economists to assert that the

relationship between economy and environment has first a positive sign, and then a negative one: in the

early stages of development, the environmental quality worsens because of the gradual industrialization,

but then, when economic growth reinforces and increases, it shows a higher tendency to improve envi-

ronmental quality. Such a linkage has been named Environmental Kuznets Curve since the early work

of Grossman and Krueger (1991).

After more than two decades of searching for EKC-style curves and patterns, the main message taken

from Grossman and Krueger’s work seems to be changed as regards specifications and causal relation-

ships: there the main message was that trade and higher income levels would operate to get a better en-

vironment, but the recent supporting evidence about newer data is mostly weak, since ever better em-

pirical estimates has not revealed a clear and definitive causal income–pollution relationship (Auff-

hammer and Carson, 2008). As detailed in Chapter 1, there is still little evidence that a stop in growth

would improve pollution levels, while, instead, there is robust evidence that pollution levels typically fall

at high-income levels. This does not mean that an EKC path is a sure and inevitable pattern for coun-

tries, since the research is still finding a common underlying process which could link specific changes

in income to specific changes in pollution, on the timescale of a few years. According to Dasgupta et al.

(2002), the EKC theory just describes an inverted-U relationship between pollution and economic de-

velopment, and its main critics are that empirically estimated curves have their declining portions as a

fake behaviour, either because they are cross-sectional snapshots that mask a long-run race to the bot-

tom in environmental standards, or because industrial societies will always produce new pollutants.

However, recent evidence has raised an optimistic view by suggesting that the curve is actually flatten-

ing and shifting to the right section of the curve: the driving forces of such a change appear to be eco-

nomic liberalization, clean technology diffusion, and new approaches to pollution regulation in devel-

oping countries. Even if it is not a robust forecasting engine, the EKC has proved itself to be a useful

tool to analyze the relationship between wealth and pollution: having based the present analysis on the

EKC literature's findings and implications that have been briefly detailed in Chapter 1, the present

254

work has tried to study the waste production of Italian industrial sector in the view of the issues of the

EKC framework.

Chapter 2 has dealt with the descriptive and the estimation analysis of the relation between the indus-

trial waste in Italy, in the period 1998-2004, and Chapter 3 with the socio-economic factors that may

have been responsible for its generation. Industrial waste was accounting for almost the half of special

waste produced in Italy, during the period 1998-2004. Such a value has continuously risen in that pe-

riod, against a gradual decrease of the share of the value-added of Industry in a Strict Sense over the to-

tal value-added of Italy, together with a stagnation of that sector’s productivity, in the same period. The

territorial and sectorial analysis have shown that the percentage of individuals that present the MUD

statement (over the total of the individuals) is highly diverse, taking into account both a sectorial point

of view, and a geographical point of view. The analysis of the quantities has indicated that the produc-

tion of waste was continuously growing, both in absolute terms, and in relative terms (per worker and

per local unit), while an increase in economic productivity was not taking place at all. In Italy, waste in-

tensity was increasing, while in Europe has decreased within the studied period, 1998-2004.

Chapter 3 has described the drivers that were causing the increase of waste, and these variables have

been used in the subsequent model. From the analysis of the specialized literature, some hypothesis

have been done about the socio-economic variables which are behind the production of polluting emis-

sions, and those drivers which are linked to the economic cycle directly responsible for the production

of waste have been selected. Together with the value-added, which is the main indicator of the eco-

nomic wealth of a society, other variables have been taken into account: energy consumption, urban

waste production, the degree of innovation represented by the number of patents, the value of exports,

population density and local units density, the shares of value-added of Industry in a Strict Sense and of

Service Industry over the total value-added.

For all the variables, a brief description has been provided, in order to be used in the econometric tests

of Chapter 4, where the stages of specification, estimation and analysis of the results of the model have

been formulated into three phases. In the first stage, after having identified the variables to be included

into the basic model, with particular reference to those economic indicators that contribute to deter-

mine the production of waste and that can influence its trend in the course of time, and after having

dealt with the correspondent data, the econometric model to be estimated has been specified, according

to the mainstream EKC literature outlined in Chapter 1. Moreover, the more appropriate methodologi-

cal tools have been chosen, in order to better develop the informative content of the MUD database,

and, according to the literature, the choice has been driven to the use of the pooled OLS estimator. In

the second stage, the best formal specification for the model has been found by repeated tests and by

discarding the less statistically significant variables, in order to come up with the “best” quantitative re-

255

lationship among the variables to be used in the following simulation. In the third stage, the presence of

a reverse U-shaped trend has been studied, both numerically and graphically.

The results obtained in Chapter 4 are quite innovative in the relevant academic and non-academic lit-

erature on industrial waste, and as regards their economic determinants too, both in terms of abun-

dance of the details in the proposed model, and in terms of the contribution of the present research to

the Environmental Kuznets Curve literature: they show that the waste of Industry in a Strict Sense in

the Italian provinces, after an initial growth due to the increase of economic wealth, can decrease,

thanks to the increase in environmental goods that such wealth tends to cause, and therefore waste of

that sector can have a reversed U-shaped behaviour with respect to the value-added of the same sector,

in the relative provinces. This hypothesis, for the non-urban waste, has not been studied yet in previous

research papers in the literature, nor has it been studied for any other country, leaving space for more

research on these topics.

In Chapter 5, the simulations done on the basis of the tested model of Chapter 4 have been carried out

according to different scenarios of growth of the independent variables. Depending on the scenario and

the specification, the model gives different estimates for the same province, but the background ten-

dency is for an increase of the waste intensity for those provinces which still have a low economic effi-

ciency, and for a decrease of the waste production there where the economic efficiency is quite high:

such behaviour allows to state that the waste intensity of Industry in a Strict Sense of the Italian prov-

inces has a reverse U-shaped relationship with the economic development (all the other variables being

equal), and this result confirms for the first time for waste studies the existence of a (general) Environ-

mental Kuznets Curve based upon special waste in Italy.

The research presented here shows that, as regards industrial waste at an aggregated level, an increasing

dynamics of the link between special waste and value-added can be observed in Italy, for the years

1998-2004. By reducing the territorial level of the analysis, results have been obtained that confirm the

hypothesis that, in presence of some structural factors, the relationship between waste and value-added

can decrease, after a first, unavoidable increase. The present work shows, for the first time for Italian

data on special waste, that this kind of link, indeed, can decrease because of three main reasons: first, if

the level of economic efficiency per local unit (represented by the value-added per local unit) overtakes

a certain threshold. Second, the quoted relationship decreases if the density of local units goes beyond a

certain level, showing the positive role of what can be a sort of true and effective industrial district

scheme. Third, the link decreases if the technological innovation capability (represented by the number

of registered patents) grows. The relation between waste and value-added, instead, increases with the

increase of the energy intensity, while a very weak role is played by the structural composition of the

economy, there where it is shown that, when the ratio of services sector on the economy increases, the

waste over value-added ratio does not decrease.

256

The results of the present work are in line with the recent data on waste production: according to the

recent Fondazione Symbola-Unioncamere report (2013), in the period 2008-2010, waste production has

experienced a decrease of -13,6% in Italy, while the average EU growth rate in that span of time is

lower in absolute terms (-2,7%). The most environmentally efficient sectors in 2008-2010 period, both

in emissions’ terms and in waste terms, have been the same ones studied in the present research: manu-

facturing industry and constructions respectively shows a -16,6% and -14,9% decrease rate in Italy,

while the same rates for the EU are -19,5% and +0,1% (Figure C.1). The present work was forecasting,

under the light of the estimated model, a decrease for the measure of the global special waste in Italy,

for the same period, and not in a generally decreasing economy, as Italy still is since 2008, but in a hy-

pothetical framework of increasing economic drivers.

Figure C.1 – Growth rates of waste production in EU, 2008-2010.

* Excluding Ireland, Croatia, Luxembourg and Malta ** Italy, Germany, France, Spain and United Kingdom Source: Fondazione Symbola-Unioncamere (2013)

From the main results of this work, it can be concluded that, first, the production of industrial waste

per unit of value-added is not generally increasing, as it could have been inferred at a first glance, but it

shows a reverse U-shaped tendency: in other words, for some Italian provinces, an increase of the

value-added is coupled with a diminishing quantity of waste, while for some others the contrary holds.

The several provinces of the analysis shift from the increasing path of the EKC to the decreasing one,

together with the increase of the value-added that is produced within their boundaries, that is, with the

increase of wealth: therefore, those provinces with a higher degree of economic development are also

capable to slow down the production of waste.

The forecasts simulated on the basis of different scenarios do confirm the tendency of Italian prov-

inces, and thus of the Italian economic system, to shift towards the decreasing path of the EKC, and

towards a decoupling between economic growth and waste production. Another conclusion is that a

257

sort of structural rift between North and South in Italy does exist, as regards waste production (per

value-added) too, because the dynamics of the link between waste and value-added is increasing mainly

for the South: therefore, tough and rigorous national environmental guidelines are needed, but they

must be adapted to the local and regional level, in order to overcome the local characteristics.

These results do confirm the hypothesis that the Environmental Kuznets Curve still holds, that, for the

first time, it has been found for Italian data of special waste (related to a quite long span of time), and

that the modernization of the existing industrial structure may help in reducing the negative effect of

the economic activity upon the environment. According to this view, this research work contributes

with an original light to that field of studies dealing with theoretical models and empirical evidence on

EKC, and tries to give an answer to the current debate, excluding an a priori acceptance of either the

idea that economic growth always leads to environmental unsustainable pollution, or the assumption

that a steady economic growth automatically leads to a future and unavoidable steady improvement of

environmental conditions per se.

258

ACRONIMI – ACRONYMS

1) AIC: Akaike Information Criterion 2) Ateco: codici delle Attività Economiche 3) BIC: Bayesian/Schwartz Information Criterion 4) CER: Catalogo Europeo dei Rifiuti (Direttiva 75/442/CEE) 5) DEFRA: Department for Environment, Food and Rural Affairs (UK) 6) DTI: Department of Trade Industry (UK) 7) EEA: European Environment Agency (Agenzia Europea per l'Ambiente) 8) EKC: Environmental Kuznets Curve (Curva di Kuznets Ambientale) 9) EWC: European Waste Code 10) GHG: greenhouse gas (gas serra) 11) Gt: gigatonnellata (1.000 kg) 12) HDI: Human Development Index (Indice di Sviluppo Umano) 13) Ind.S.S.: Industria in Senso Stretto (Industry in a Strict Sense) 14) IPAT: Impact, Population, Affluence, Technology model 15) Istat: Istituto Nazionale di Statistica 16) MSW: Municipal solid waste (rifiuti urbani) 17) MUD: Modulo di Dichiarazione Unica 18) Nace: European Classification of Economic Activities (Nomenclatura delle Atti

vità Economiche) 19) OECD: Organisation for Economic Co-operation and Development (Organizza-

zione per la Cooperazione e lo Sviluppo Economico, OCSE) 20) PHH: pollution heaven hypothesis (ipotesi dei paradisi d'inquinamento) 21) RE: Register of Enterprises 22) RI: Registro delle Imprese 23) UL: Unità locale (Local unit) 24) UNDP: United Nations Development Program 25) VA: Valore aggiunto (Value-added) 26) WKC: Waste Kuznets Curve (Curva di Kuznets dei Rifiuti)

259

LIST OF FIGURES

Figure 1.1 – The relationship between environmental degradation and income: the Environmental Kuznets Curve (from Kaika and Zervas, 2013)

18

Figure 1.2 – Examples of different patterns between environmental pressure and economic wealth (income per capita), from Wang (2007)

20

Figure 1.3 – Optimal pollution-income paths, from Andreoni and Levinson (2001) 27 Figure 1.4 – The Green Solow Model and the EKC: the transitional dynamics to-

wards the steady state (from Brock and Taylor, 2010) 33

Figure 1.5 – Conflicting dynamics of the EKC, from Agras and Chapman (1999) 42 Figure 2.1 – Value-added: Industry in a Strict Sense and Total, Italy, 1998-2004 (mil-

lions of euros of 1995) 61

Figure 2.2 – Value-added: share of the Industry in a Strict Sense on the total value-added, Italy, 1998-2004

62

Figure 2.3 – Special waste in Italy, for each type, MUD database, 1998-2004 (tons) 67 Figure 2.4 – Special waste production, Italy, MUD database, 1998-2004 (tons) 67 Figure 2.5 – Special waste production, for each macro-sector, Italy, 1998-2004 (tons) 68 Figure 2.6 – Coverage (%) of the MUD local units with respect to the RE local units,

as for Industry in a Strict Sense, Italy: 1998 77

Figure 2.7 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, Italy: 2004

78

Figure 2.8 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, Italy: variations, 1998-2004

78

Figure 2.9 – Waste production (tons), Industry in a Strict Sense, Italy: 1998-2004 81 Figure 2.10 – Waste production (tons), Industry in a Strict Sense, North of Italy, by

label: 1998-2004 84

Figure 2.11 – Waste production (tons), Industry in a Strict Sense, North-West of Italy, by label: 1998-2004

85

Figure 2.12 – Waste production (tons), Industry in a Strict Sense, North-East of Italy, by label: 1998-2004

85

Figure 2.13 – Waste production (tons), Industry in a Strict Sense, Centre of Italy, by label: 1998-2004

86

Figure 2.14 – Waste production (tons), Industry in a Strict Sense, South of Italy and Islands, by label: 1998-2004

86

Figure 2.15 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 1998-2004

Figure 2.16 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 1998-2004

88

Figure 2.17 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 1998

89

Figure 2.18 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, 2004

89

Figure 2.19 – Waste production (tons), Industry in a Strict Sense, Italy: per local unit values, variations (%), 1998-2004

90

Figure 2.20 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 1998

90

Figure 2.21 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, 2004

91

Figure 2.22 – Waste production (tons), Industry in a Strict Sense, Italy: per worker values, variations (%), 1998-2004

91

260

Figure 2.23 – Waste production, total of Manufacturing (Ateco 15 to 36), year 2004 97 Figure 3.1 – Value-added (millions of euros of 1995, €), Industry in a Strict Sense, It-

aly: 1998-2004 125

Figure 3.2 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, Italy: 1998

127

Figure 3.3 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, Italy: 2004

128

Figure 3.4 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, Italy: difference between 2004 and 1998

129

Figure 3.5 – Waste production and value-added, Industry in a Strict Sense and total, Italy: 1998-2004 indices

130

Figure 3.6 – Energy consumption (millions of kWh), Industry in a Strict Sense, Italy: 1998-2004

134

Figure 3.7 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: 1998

135

Figure 3.8 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: 2004

136

Figure 3.9 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, Italy: difference 2004-1998

137

Figure 3.10 – Share (%) of the urban sorted waste over the total urban waste, Italy: 1998-2004

138

Figure 3.11 – Share (%) of the urban sorted waste over the total urban waste, prov-inces of Italy: 1998

139

Figure 3.12 – Share (%) of the urban sorted waste over the total urban waste, prov-inces of Italy: 2004

140

Figure 3.13 – Share (%) of the urban sorted waste over the total urban waste, prov-inces of Italy: difference 2004-1998

141

Figure 3.14 – Total number of patents per thousand of inhabitants, Italy: 1998-2004 142 Figure 3.15 – Exports (millions of euros of 1995, €), Industry in a Strict Sense, Italy:

1998-2004 144

Figure 3.16 – Share (%) of the value-added of Industry in a Strict Sense and of Ser-vice Industry on the total value-added, Italy: 1998-2004

146

Figure 4.1 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-1, 1998-2004, logarithms. Dependent variable: (logarithm of) waste per value-added (tons per millions of euros of 1995) Independent variable: (logarithm of) value-added (millions of euros of 1995)

199

Figure 4.2 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-1, 1998-2004, absolute levels. Dependent variable: waste per value-added (tons per millions of euros of 1995) Independent variable: value-added (millions of euros of 1995)

199

Figure 4.2 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-2, 1998-2004, absolute levels. Dependent variable: waste per value-added (tons per millions of euros of 1995) Independent variable: value-added (millions of euros of 1995)

200

Figure 4.3 – Goodness of fit: scatter plots, historical (blue) and estimated (red) values, specification S-2, 1998-2004, logarithms. Dependent variable: (logarithm of) waste per value-added (tons per millions of euros of 1995) Independent variable: (logarithm of) value-added (millions of euros of 1995)

200

Figure 5.1 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-1, Specification S-1 (VA/UL) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros)

227

261

Simulated independent variable: (logarithm of) value-added per local unit (millions of 1995 euros per UL)

Figure 5.2 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

228

Figure 5.3 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-1, Specification S-2 (VA/worker) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros) Simulated independent variable: (logarithm of) value-added per worker (millions of 1995 euros per worker)

229

Figure 5.4 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. The EKC for waste: Scenario F-1, Specification S-2 (VA/worker)

230

Figure 5.5 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-2, Specification S-1 (VA/UL) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros) Simulated independent variable: (logarithm of) value-added per local unit (millions of 1995 euros per UL)

231

Figure 5.6 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. The EKC for waste: Scenario F-2, Specification S-1 (VA/UL)

231

Figure 5.7 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2005-2010): Scenario F-2, Specification S-2 (VA/worker) Simulated dependent variable: (logarithm of) waste per value-added (tons per millions of 1995 euros) Simulated independent variable: (logarithm of) value-added per worker (millions of 1995 euros per worker)

232

Figure 5.8 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. The EKC for waste: Scenario F-2, Specification S-2 (VA/worker)

232

Figure 5.9 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Northern and Central Italy. The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

233

Figure 5.10 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Southern Italy. The EKC for waste: Scenario F-1, Specification S-1 (VA/UL)

234

Figure C.1 – Growth rates of waste production in EU, 2008-2010 256

262

LIST OF TABLES

Table I.1 – Waste generation projections for 2025, region by income (from Hoorn-weg and Bhada-Tata, 2012)

5

Table I.2 – Italian expenditure for waste management, 1997-2007 (millions of €, cur-rent-prices; from CMCC, 2010)

5

Table 1.1 – Estimated elasticity of urban waste production to income of some early studies

48

Table 1.2 - Main studies on the Waste Kuznets Curve hypothesis 58 Table 2.1 – MUD: number of local units (UL) in the Industry in a Strict Sense, Italy,

1998-2004 64

Table 2.2 – Share of the local units of the Industry in a Strict Sense on the total num-ber of the UL, Italy, 1998-2004

64

Table 2.3 – Number of MUD statements in Industry in a Strict Sense, with respect to the total number of statements of the economic activities, Italy, 1998-2004

69

Table 2.4 – Ateco divisions relevant to this study 70 Table 2.5 – Coverage (in % of local units of the Registro delle Imprese, RI) of the

MUD database, Industry in a Strict Sense, 1998-2004 74

Table 2.6 – Coverage (%) of the MUD local units with respect to the RE local units, as for Industry in a Strict Sense, 2004: 5 randomly sampled provinces

77

Table 2.7 – Waste production (tons), Industry in a Strict Sense, Italy, by label: 1998-2004

82

Table 2.8 – Waste production, Industry in a Strict Sense, Italy, by label: indices, 1998-2004

82

Table 2.10 – Waste production (tons), Industry in a Strict Sense, Italy, by macro-regions: absolute value and percentage variation, 1998-2004

83

Table 2.9 – Waste production, Industry in a Strict Sense, Italy, by label: composition (%), 1998-2004

83

Table 2.11 – Waste production (tons), Industry in a Strict Sense, Italy: per worker and per local unit values, 1998-2004

87

Table 2.12 – Waste production (tons), Industry in a Strict Sense, 5 provinces: per local unit values, 1998-2004

92

Table 2.12 – Waste production (tons), Industry in a Strict Sense, 5 provinces: per local unit values, 1998-2004

92

Table 2.14 – Waste production (tons), Industry in a Strict Sense, Italian regions: 1998-2004

93

Table 2.15 – Waste production, Industry in a Strict Sense, Italian regions: indices, 1998-2004

94

Table 2.16 – Waste production, Industry in a Strict Sense, Italian regions: percentage composition, 1998-2004

95

Table 2.15 – Waste production, Industry in a Strict Sense, 5 provinces: 1998-2004 96 Table 2.16 – Waste production (tons), Industry in a Strict Sense, Italy, by Ateco divi-

sions: 1998-2004 100

Table A2.1 – Share of the Value-added of Industry in a Strict Sense on the total Value-added of the geographical unit (%)

101

Table A2.2 – Waste production (tons), Italy, by Ateco divisions: 1998-2004 104 Table A2.3 – Waste production (tons), Industry in a Strict Sense, Italy, by provinces

and regions: 1998-2004 106

Table A2.4 – Local Units of MUD, Local Units of RI, and coverage (in % of local units of the Registro delle Imprese, RI) of the MUD database, Industry in a Strict

109

263

Sense, by provinces and regions: 2004 Table A2.5 – Coverage (in % of local units of the Registro delle Imprese, RI) of the

MUD database, Industry in a Strict Sense, by provinces and regions: 1998-2004 112

Table A2.6 – Ranking of the most pollutant provinces, as regards waste production (tons), by Ateco divisions: 2004

115

Table A2.7 – Waste production per local unit (tons per UL, t/UL), Industry in a Strict Sense, Italy, by provinces and regions: 1998-2004

117

Table A2.8 – Waste production per worker (tons per worker, t/add), Industry in a Strict Sense, Italy, by provinces and regions: 1998-2004

120

Table 3.1 – Waste production per unit of value-added (in 1995 euros, €), Industry in a Strict Sense, Italy: ranking 2004, ranking 1998, and its difference

131

Table 3.2 – Waste production per unit of value-added (tons per million of euros of 1995, t/€), Industry in a Strict Sense, 5 random provinces: 1998-2004

133

Table 3.3 – Energy intensity (kWh per euros of 1995, kWh/€), Industry in a Strict Sense, 5 random provinces: 1998-2004

137

Table 3.4 – Share (%) of the urban sorted waste over the total urban waste of the 5 random provinces: 1998-2004

141

Table 3.5 – Total number of patents per thousand of inhabitants, Italy, 5 randomly selected provinces: 1998-2004

143

Table 3.6 – Ratio of exports over value-added, Industry in a Strict Sense, 5 randomly selected provinces: 1998-2004

144

Table 3.7 – Population density (inhabitants per Km2), the 5 randomly selected prov-inces: 1998-2004

145

Table 3.8 – Local units density (units per Km2), the 5 randomly selected provinces: 1998-2004

145

Table 3.10 – Share (%) of the value-added of Industry in a Strict Sense on the total value-added, 5 randomly selected provinces: 1998-2004

147

Table 3.9 – Share (%) of the value-added of Service Industry on the total value-added, 5 randomly selected provinces: 1998-2004

147

Table A3.1 – Waste production per unit of value-added (tons per million of euros of 1995), Industry in a Strict Sense, Italy: 1998-2004

151

Table A3.2 – Energy intensity (kWh per euros of 1995), Industry in a Strict Sense, It-aly: 1998-2004

154

Table A3.3 – Share (%) of the urban sorted waste over the total urban waste, prov-inces of Italy: 1998-2004

157

Table A3.4 – Total number of patents per thousand of inhabitants, provinces of Italy: 1998-2004

160

Table A3.5 – Ratio of exports over value-added, Industry in a Strict Sense, provinces of Italy: 1998-2004

163

Table A3.6 – Population density (inhabitants per Km2), provinces of Italy: 1998-2004 166 Table A3.7 – Local units density (units per Km2), provinces of Italy: 1998-2004 169 Table A3.8 – Share (%) of the value-added of Service Industry on the total value-

added, provinces of Italy: 1998-2004 172

Table 4.1 – Variables and their descriptive statistics (1998-2004) 177 Table 4.2 – Regressions results. Dependent variable: waste per value-added. Specifica-

tion S-1, 1998-2004 185

Table 4.3 – Regressions results. Dependent variable: waste per value-added. Specifica-tion S-1, 2000-2004

187

Table 4.4 – Regressions results. Dependent variable: waste per value-added. Specifica-tion S-2, 1998-2004

189

Table 4.5 – Regressions results. Dependent variable: waste per value-added. Specifica-tion S-2, 2000-2004

191

264

Table 4.6 – Specification S-1: value-added per UL of Industry in a Strict Sense as per-formance driver

196

Table 4.6 – Specification S-2: value-added per worker of Industry in a Strict Sense as performance driver

197

Table A4.1 – Regressions results. Dependent variable: waste per worker. Specification S-1, 1998-2004

202

Table A4.2 – Regressions results. Dependent variable: waste per worker. Specification S-1, 2000-2004

204

Table A4.3 – Regressions results. Dependent variable: waste per worker. Specification S-2, 1998-2004

206

Table A4.4 – Regressions results. Dependent variable: waste per worker. Specification S-2, 2000-2004

208

Table A4.5 – Regressions results. Dependent variable: waste per worker. Specification S-3, 1998-2004

210

Table A4.6 – Regressions results. Dependent variable: waste per worker. Specification S-3, 2000-2004

212

Table A4.7 – Regressions results. Dependent variable: waste per value-added. Specifi-cation S-3, 1998-2004

214

Table A4.8 – Regressions results. Dependent variable: waste per value-added. Specifi-cation S-3, 2000-2004

216

Table A4.9 – Specification S-1: sign and statistical significance of the coefficients 218 Table 5.1 – Scenarios for the simulations: percentage variations (%) with respect to

the previous year 220

Table 5.2 – Growth hypotheses of the variables for both the specifications (S-1 and S-2)

224

Table 5.3 – Waste intensity of the five provinces, under both the scenarios, and ac-cording to both the specifications

226

Table A5.1 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, North-West

237

Table A5.2 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, North-East

238

Table A5.3 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, Centre

239

Table A5.4 – Simulation scenarios (F-1 and F-2): regional rates of variation, Italy, South and Islands

240

Table A5.5 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-1, Specification S-1 (VA/UL)

242

Table A5.6 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-1, Specification S-2 (VA/worker)

244

Table A5.7 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-2, Specification S-1 (VA/UL)

246

Table A5.8 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Results of the simulation (2006-2010): Scenario F-2, Specification S-2 (VA/worker)

248

Table A5.9 – Waste per value-added (tons per million of 1995 euros), Industry in a Strict Sense, Italy. Average values and median values, 1998-2004

250

265

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