ECONOMICS, MEASUREMENT AND FUTURE OUTLOOK OF THE EXTERNAL COSTS OF

PROMETEIA

FUTURE OUTLOOK OF THE EXTERNAL

COSTS OF TRANSPORT IN ITALY

2006

ECONOMICS, MEASUREMENT AND

Italy is the nation, in Europe and the rest of the world, with the greatest tradition

and experience in the construction and management of motorway infrastructure

financed through the tolling system.

Accordingly, our country has faced many of the problems and issues that are

today of current interest in the European Community well before other countries have,

availing itself of professional help both from the motorway sector as well as from the

academic one. This professional help is not only based on abstract theories but rather

on experience that has been acquired on the field.

It thus seems only natural that Aiscat, in representation of the operators of the

Italian motorway network in concession, should have commissioned a report on the

external costs of transport, a subject which, further to its inclusion in European

directive no. 2006/38, and the deadlines set by the European Parliament, has become of

current interest; no longer therefore only a matter of academic discussion, but rather

an important element and parameter of assessment for European Transport Policy.

Although representing the motorway sector, Aiscat has taken into consideration

the valuable indications provided by the European Authorities concerning the need of

not limiting the analysis to external costs vis-à-vis individual modes of transport but

rather coherently analyse these modes in terms of mobility and transport as a whole.

Based on the above understanding and approach, this report has been prepared

with the support of Prometeia - a highly qualified research and consulting firm - , and

the contribution of export researchers whose background and skills guarantee the

rigour and depth of analysis that were the key to this work from the very outset.

I am therefore pleased and honoured to make this report available to all those

who shall take part in establishing a common position on the subject, and trust that this

contribution may help deal with all open issues in an analytical and rigorous manner,

as should rightly be expected for all projects destined to affect the future of Europe and

its people.

Fabrizio Palenzona Chairman AISCAT

I

This work was commissioned to Prometeia SpA, and has been fully financed by

AISCAT.

Original contributions from members of the workgroup were provided during

2006.

I made a number of simplifications to the source material in order to standardise

this work in terms of its technical aspects. For ease of reference a number of

particularly important sections have been boxed in italics.

Contributions to this work were made by: Fabio Nuti, Anna Montini,

Massimiliano Mazzanti (Università di Bologna), Marco Stampini (Scuola Superiore

Sant’Anna, Pisa), Marco Ponti, Marco Brambilla (Politecnico di Milano), Giorgio

Casoni (Politecnico di Milano), Iacopo Caropreso (Sinopsis Lab), Leonardo Catani,

Claudia Sensi (Prometeia).

Mr. Massimo Schintu, General Secretary of Aiscat, provided a most valuable

contribution and without his support we would have been unable to present this report.

Particular thanks goes to Prof. Marco Ponti, Prof. Fabio Nuti and

Mr. Leonardo Catani, who, in addition to writing parts of the work, were responsible

for its overall presentation.

The subject matter of this report, although addressed by a significant number of

works, is still very unsettled. Accordingly, observations and comments to the results did

not receive unanimous consensus from the workgroup. Different emphases and opinions

continue to exist, also in terms of a number of definitions. This said, the material

hopefully represents a just compromise between the different viewpoints. Finally, I take

all responsibility for any mistakes and inaccuracies that should be found in parts of this

work that are not attributed to individual authors.

Mariano Bella PROMETEIA

October 2006

III

PROMETEIA

CONTENTS

INTRODUCTION AND SUMMARY OF MAIN FINDINGS 1

PART ONE

THEORY AND PRACTICE IN THE VALUATION OF THE SOCIAL COSTS OF

TRANSPORT

1. THE EXTERNAL EFFECTS OF TRANSPORT ACTIVITIES 18

1.1. GENERAL CONCEPTS 18

1.1.1. The concept of externality 18

1.1.2. Origin of externalities: real (non-monetary) and monetary 24

1.1.3. Solutions to the problem of externalities 26

1.1.4. Externalities in the determination of the cost of transport 29

1.1.5. External benefits 32

1.1.6. Efficiency and equity 32

1.2. GENERAL PRINCIPLES USED IN THE DETERMINATION OF TRANSPORT PRICES 35

2. METHODS OF ECONOMIC VALUATION OF NON-MARKET GOODS 38

2.1. METHODS BASED ON DEMAND CURVES 38

2.1.1. Stated preference techniques 38

2.1.1.1. Contingent valuation method 43

2.1.1.2. Conjoint analysis methods 44

2.1.2. Benefit transfer method (Btm) 47

2.1.3. Revealed preferences methods 49

2.1.3.1. Travel cos s method 50 t

2.1.3.2. Hedonic pricing method 51

2.2. METHODS NOT BASED ON DEMAND CURVES 52

2.2.1. Averting behaviour method 52

2.2.2. Shadow project method 52

2.2.3. Dose-response or physical bond method 53

V

PROMETEIA

3. TYPES AND DIMENSION OF TRANSPORT EXTERNALITIES 54

3.1. CONGESTION 54

3.1.1. Definitions 54

3.1.2. The value of travel time: opportunity cost 61

3.1.3. Valuation methods 62

3.1.4. The value of travel time in available literature 64

3.1.5. Calculating congestion in private road transport 67

3.1.6. Congestion for other means of transport 69

3.1.7. Congestion in the transport of goods 69

3.2. ENVIRONMENTAL EXTERNALITIES 76

3.2.1. The economic definition of environment 76

3.2.2. Environmental externalities caused by transport activities 79

3.2.3. Infrastructure 80

3.2.4. Road transport 81

3.2.5. Air transport 84

3.3. ACCIDENT RISK AND THE VALUE OF HUMAN LIFE 107

3.3.1. General considerations 107

3.3.2. Valuation methods 111

3.3.2.1. The method of human capital 111

3.3.2.2. The method of insurance 113

3.3.2.3. The method of willingness to pay 114

3.3.2.4. The method of averting behaviour 115

3.3.2.5. The trade-off between risk and money 115

3.3.2.6. The method of cost-effectiveness 119

3.3.2.7. The method of cost-utility 119

3.3.2.8. Definition and calculation of QALY (qual ty adjusted life years) 120 i

3.3.3. Accidents 124

3.3.4. The value of statistical life 126

3.3.4.1. Methods for estimating the marginal external costs of accidents 130

3.3.4.2. Average or marginal costs 131

3.3.4.3. Quantifying the damage caused by accidents other than by road transport 132

3.3.5. Survey of available literature on the estimates of external costs per accident 133

3.4. NOISE 153

3.5. EFFECTS ON ARTISTIC, HISTORICAL AND MONUMENTAL PROPERTIES 162

3.5.1. Significance of the problem 162

VI

PROMETEIA

3.5.2. Valuation 164

3.5.3. Survey of available literature 165

PART TWO

VALUATION OF SOCIAL COSTS, INTERNAL COSTS AND EXTERNAL COSTS OF

TRANSPORT TO 2020

FOREWORD

1. MOBILITY ESTIMATES AND FORECASTS 173

1.1. RECONSTRUCTION OF BASE DATA USED TO FEED THE PASSENGER AND

FREIGHT TRAFFIC PER MODE OF TRANSPORT ANALYSIS AND FORECASTING

MODEL 173

1.1.1. Airplane 174

1.1.2. Train 174

1.1.3. Ship 174

1.1.4. Road: ordinary and motorway network 175

1.1.5. Tram and Underground (only passengers) 176

1.1.6. Bus (only passengers) 176

1.2. THE REFERENCE ECONOMIC SCENARIO 177

1.3. THE PASSENGER AND FREIGHT TRAFFIC PER MODE OF TRANSPORT

FORECASTING MODEL 178

2. ESTIMATES AND FORECASTS OF SOCIAL COSTS 185

2.1. ACCIDENTS 185

2.1.1. Extrapolation to 2020 of the number of fatalities and injuries on the Italian tolled

motorway network 185

2.1.2. Reconstruction of the historical series of general and fatal accidents on

ordinary roads 198

2.1.3. Extrapolation of the consequences of accidents suffered by people per different

mode of transport other than road transport 205

2.1.4. Summary of the consequences of accidents suffered by people per mode of

transport 206

2.2. AIR POLLUTION AND THE GREENHOUSE EFFECT PER MODE OF TRANSPORT 208

2.2.1. Analysis and forecasting models of the different make-up of the vehicle fleet:

automobiles per fuel type, engine size and technology 208

VII

PROMETEIA

2.2.2. Analysis and forecasting models of the different make-up of the vehicle fleet: other

vehicles per fuel type, engine size and technology 211

2.2.3. Description of the calculation of emission externalities: air pollution and

greenhouse effect for road transport 213

2.2.4. Description of the calculation of emission externalities: air pollution and

greenhouse effect for other modes of transport 219

2.3. NOISE POLLUTION PER MODE OF TRANSPORT 219

2.4. FROM EXTERNALITIES TO SOCIAL COSTS AND TECHNIQUES FOR

BENEFIT TRANSFER 220

2.4.1. Assessing the statistical life and social costs of the consequences of road accidents

on people 220

2.4.2. Assessing the social cost of air pollution and the greenhouse effect per mode of

transport 221

2.4.3. Assessing the social cost of noise pollution per mode of transport 223

2.4.4. Capitalisation of social costs at year 2020: assumptions 223

2.5. CURRENT AND FORECASTED VALUES OF SOCIAL COSTS PER MODE OF TRANSPORT

AND TYPE OF EXTERNALITY IN THE TWO SCENARIOS OF GROWTH OF THE

ECONOMY AND DEMAND FOR MOBILITY 224

2.5.1. Social costs in the two scenarios of growth of the economy and mobility 225

2.5.2. Costs of congestion 231

2.6. A MONTE CARLO METHOD SIMULATION EXERCISE TO ASSESS THE CONFIDENCE

INTERVALS OF THE ESTIMATED SOCIAL COSTS OF TRANSPORT IN 2020 233

APPENDIX TO CHAPTER 2 239

A – COPERT III 239

B – INTERVALS FOR UNITARY EXTERNAL COSTS 241

C – FREQUENCY DISTRIBUTIONS PER SIMULATION 243

D – CONFIDENCE INTERVALS PER SIMULATION 256

E – SUMMARY OF THE ANALYSIS OF THE EXTERNAL EFFECTS OF MOBILITY IN ITALY 257

VIII

PROMETEIA

3. VALUATION OF THE EXTENT OF INTERNALISATION IN 2004 AND FUTURE

OUTLOOK 259

3.1. MARGINAL COST OF TRAFFIC FOR INTERNALISING RESOURCES -

OBSERVATIONS 259

3.2. VALUATION OF RESOURCES HAVING AN INTERNALISING EFFECT PER MODE OF

TRANSPORT 263

3.2.1. Road transport 263

3.2.1.1. Road transport: Motorway 268

3.2.1.2. Road transport: Ordinary network 269

3.2.2. Local public transport 271

3.2.3. Railway transport 272

3.2.4. Air transport 274

3.2.5. Maritime transport 275

3.3. EXTRAPOLATION OF THE VALUATION OF INTERNALISING RESOURCES

AS OF 2020 275

3.4. SOCIAL, INTERNAL AND EXTERNAL COSTS AS OF 2020 279

BIBLIOGRAPHY 283

IX

PROMETEIA

INTRODUCTION AND SUMMARY OF MAIN FINDINGS

This work aims to present a realistic picture of the current and medium term

forecast of social and external costs of transport in Italy, vis-à-vis the different

modes of transport that satisfy – although in a number of cases we should say

do not sat sfy – an increasing demand for mobility. i

The work is divided into two parts: part one concerns the theory of valuation

and addresses what should be counted and how it should be counted when

human action is considered, particularly in the transport sector. Part two

applies the evidence produced in part one to measure the social and external

costs of transport.

The following distinction is made between social costs and external costs:

social cost is the monetised amount of utilised resources required to produce

one unit of a good or service; external cost is the monetary value of an

externality, i.e. of the effect on the production or consumption of a second

party by a first party, without there being any direct economic consideration.

Specifically, social cost is the sum of external costs and internal costs. The

latter comprise production costs plus internalising monetary resources.

Production costs, including the entrepreneur’s normal profit, are totally

internal, and are met by the party that enjoys or subsidises the service. It is

for this reason that for internal cost we have always only considered the

internalising consideration part. In other words, the quota of social costs that

refers to production costs has not been considered since it does not affect the

amount of external costs – the calculation of which is the aim of this work.

In this sense there are social costs in the production of a restaurant meal or a

pair of shoes. However, within such a scope the concept is irrelevant because

all social costs are internal, i.e. paid by the party that enjoys the service or

consumes the good. All the social costs are internal while external costs, equal

to overall or social costs less internal costs, are equal to zero. In other words

an exchange takes place where the price is, theoretically, and assuming the

existence of competitive markets, equal to the marginal social cost.

1

PROMETEIA

In the case of transport, the presence of an externality – pollution, congestion,

accidents or other – is common and therefore social costs and internal costs

rarely coincide: it is common opinion that the difference between the two

amounts is a positive one, i.e. that the costs of transport are, in part, external.

This is also the result of the calculations performed in this work. However,

the difference in external costs and benefits generated by different modes of

transport is so large that the above conclusion is not very meaningful.

In order to consider the important relations between infrastructure quality and

the characteristics of the transport service, we have always distinguished

between air, train and maritime transport but also, within road transport,

distinguished between the ordinary network and the tolled motorway network:

road transport has therefore been treated as two different forms of transport.

Lack of sufficient historical data has not allowed a similar breakdown for the

railway network sector (medium-long haul transport, for example, compared to

local and regional transport).

Anticipating a significant finding of this work, we can say that the ordinary

road network is the mode of transport that generates the highest external

costs while motorways generate negative external costs for the system as a

whole. All other forms of transport currently fall somewhere between these

two extremes. Extrapolations carried out for the 2020 horizon emphasise these

absolute and relative positions.

In order to achieve our aim of accurately calculating social costs and external

costs, a survey of the most recent and authoritative literature available was

carried out, with the aim of analysing the various phases leading to the

definition of monetary values. The methods, most significant international

experience and findings of the same have been analysed in order to apply the

benefit transfer criterion: this is the practice of using the findings of third

parties’ original work to apply the same within different contexts. The

European Commission (Green Paper, Towards the correct and effective

determination of prices in the transport sector, 1995) recommended the

creation of databases containing valuations on different types of externalities

so as to provide useful and usable terms of comparison against which to carry

2

PROMETEIA

out valuations in areas where time and/or resource constraints do not make a

new valuation economically viable. The literature survey contained in this work

indicates that in the presence of reliable methods - duly described in their

technical details – the benefit transfer criterion produces reliable, non

distorted, fully usable results, at least as a starting point for the building of

more accurate tools of analysis.

Statistical life values for the calculation of social costs (millions of euro 2004; average values have been used in this work) minimum average maximum

fatal accident 0.37 2.12 8.67

serious injury 0.04 0.22 0.41

light injury 0.016 0.02 0.04

An assessment of the consequences of human action, whether private or

collective, requires the acquisition of important methodological hypotheses:

the primary one adopted in this work is that the opinion of people is relevant

also for matters in which the definition of the subject of the valuation is

complex, and that such opinions may be suitably valuated according to criteria

based on the willingness to pay or to accept. We therefore assume that the

techniques’ difficulties, if underpinned by a valid idea and theoretical method,

are not an acceptable justification to safe (but invariably wrong) shortcuts.

For example, the criterion of stated preferences, suggested by theory and

developed in the subject-matter’s literature, leads to a valuation of statistical

life – which then generates the external cost of accidents in terms of the value

of human life that is lost – that is significantly higher than those based on

criteria of lost production. After all, one should easily agree that the cost of an

injury or acquired disability cannot be calculated economically on the basis of

revenue lost as a result of the accident. The stress, suffering, physical and

psychological pain of the casualties and their families have a broader and

deeper side to them and, correctly, have an economic value that is much

higher than that of lost revenue: at the very most the latter is a measurement

of cost, and therefore not comparable to the marginal benefit that should be

the criterion used to assess a policy’s costs and benefits.

3

PROMETEIA

For a number of reasons, the theory of valuation, underlying cost-benefit

analysis, as well as cost benefit analysis itself, has only recently found a

broader albeit still insufficient application in our country. Academics involved in

this work are of the view that the cost-benefit analysis has often been applied

using inappropriate techniques.

Scepticism or prejudice are generally shown towards this technical tool: all too

often there has been a fear of uncovering exorbitant costs vis-à-vis a project’s

benefits if a broad valuation of costs – first among which, the loss of human

life in accidents – were adopted, as has been in this work. A high value given

to human life implies significant attention to be given to the related costs and,

accordingly, a high level of investment required to avoid these costs, that is

when this investment is, of course, directed towards reducing risk - in this case

the risk of accidents. Therefore, in a number of circumstances a suitable

valuation of a project’s costs and benefits could in fact be in the interest, if we

may use this expression, of those parties who have historically paid less

interest to, and are very concerned by, valuation through cost-benefit analysis.

Another principle that has been adopted and which is discussed in the

theoretical part of this work and applied in the calculations provided in part

two, is the one whereby any variance in the national and international

estimates of a specific cost or marginal benefit received by the user-citizen is

not sufficient enough a reason to exclude these cost or benefit line items from

the overall calculation of social costs. Rather, it should be an incentive to carry

out further analysis to be added to the literature and empirical evidence that

may perhaps be used in other contexts through application of the benefit

transfer criterion.

Unitary external cost estimates for various forms of externality show significant

differences in available literature; ascribable to different contexts and

techniques. Excluding all extreme values and selecting the empirical evidence

admissible according to valuation theory, average values have been computed

and applied. However, to reflect the objectively complex and uncertain nature

of the calculation and avoid giving the impression of false accuracy, medium-

term estimates and forecasts have been carried out for two very different

4

PROMETEIA

scenarios of growth of the economy and demand for transport: the

approximate forecast that is of greatest interest for the implementation of

policies concerning social and external costs may be confidently identified as

falling between the values of these two scenarios. Furthermore, most

estimates are provided with confidence intervals obtained with the Monte Carlo

simulation method. In this work we have thus preferred reliable uncertainty to

unrealistic accuracy.

Through the acquisition of average values for different externalities inferred

from available literature, the use of simplifying hypothesis for the dynamics of

variables concerning technology and the use of updated forecasts on the

future size of mobility per mode of transport, separately for passenger and

freight traffic, an estimate for social costs has been calculated. Netting this

estimate for internalising monetary resources - that every form of transport

generates through taxes, toll payments, subsidies and tariffs - gives us a

valuation of the external costs of transport.

5

PROMETEIA

Summary of the level and modal distribution of passenger and freight mobility in the two forecast scenarios (% share and cumulative % change)

BASE CASE SCENARIO (GDP +0.5% Avg. Yr.)

PASSENGERSlevel % share level % share cum. avg. year

Air 28398 2,9 37438 3,5 31,8 1,7Train 51196 5,2 48060 4,5 -6,1 -0,4Ship 6645 0,7 7074 0,7 6,4 0,4Tram+undergorund 5895 0,6 7480 0,7 26,9 1,5Bus 98942 10,1 105242 9,8 6,4 0,4Road Ordinary Network 679964 69,7 746980 69,6 9,9 0,6Road Motorway Network 104396 10,7 120229 11,2 15,2 0,9TOTAL 975436 100,0 1072503 100,0 10,0 0,6

FREIGHTlevel % share level % share cum. avg. year

Air 656 0,1 735 0,2 12,0 0,7Train 23369 5,2 25033 5,2 7,1 0,4Ship 230974 51,1 246645 51,3 6,8 0,4Road Ordinary Network 85307 18,9 86697 18,0 1,6 0,1Road Motorway Network 111582 24,7 121215 25,2 8,6 0,5TOTAL 451889 100,0 480324 100,0 6,3 0,4

HIGH CASE SCENARIO (GDP +2.1% Avg. Yr.)


Air 28398 2,9 67610 4,7 138,1 5,6Train 51196 5,2 46982 3,2 -8,2 -0,5Ship 6645 0,7 10524 0,7 58,4 2,9Tram+undergorund 5895 0,6 3349 0,2 -43,2 -3,5Bus 98942 10,1 118376 8,1 19,6 1,1Road Ordinary Network 679964 69,7 1033206 71,1 52,0 2,6Road Motorway Network 104396 10,7 172663 11,9 65,4 3,2TOTAL 980290 100,0 1452709 100,0 48,2 2,5



2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

6

PROMETEIA

Keeping the conditions of infrastructure unchanged, the mobility forecasts

show continuous demand pressure on infrastructure, with significant

differences according to transport mode. In 2004, the base year for all

calculations in this work, road transport – both on ordinary and motorway

networks – accounts for more than 80% of passenger mobility and just under

45% of freight mobility (considering all forms of freight net of oil pipelines,

and not only terrestrial freight, as is preferred by some analysts). In the

summary table we may note, among others, how ship freight is almost ten

times the size of railway freight.

Clearly, the dynamics of the economy in the two scenarios directly affects the

overall growth expectations of the demand for transport. In the low case

scenario passengers and freight are expected to grow by 10% and 6%,

respectively. In the high case scenario passengers and freight are expected to

grow by almost 50% and 40%, respectively. The future demand for mobility

may thus reasonably be expected to fall somewhere between these values.

If in the next 15 years our economy shall grow only slightly, in line with the

average growth observed between 2001-2005, no substantive change in the

modal mix of transport should be observed (except for small increases in air

and motorway traffic). However, even in such a hypothetical context, we must

ask ourselves whether a continuation of current transport policies, that all too

often leave mobility to be governed by congestion, is acceptable.

If the average annual growth change in GDP throughout the period of the

forecast should be closer to that recently observed in main European countries

(approximately 2%), road transport would reach an 83% share of total

passenger traffic and make up half of all freight traffic. Light and heavy

motorway transport mobility would increase by 65% and 80%, respectively. Air

traffic would almost double its share of total mobility.

Estimated social costs per unit of transported passenger and freight (in 2004)

identifies road transport as the form of transport that generates the highest

social costs. However, the inclusion of subsidies as externalities of railway

transport and the significant variability of the estimates concerning passenger

transport on ships, suggest caution when comparing these results with others

available in literature, which do not in any event vary significantly from those

presented herein.

7

PROMETEIA

Average social costs per mode of transport

ON Motorway Train Ship Air Total2004 - Passengers - € per 1000 pkm

Accidents 47,9 13,8 0,6 1,8 0,2 38,9

0,0 1,8 5,3Infrastructure maintenance 0,0 0,0 0,0 0,0 0,0 0,0Subsidies 3,9 0,0 29,4 0,0 0,0 4,6Total 84,7 40,1 53,0 91,8 44,7 75,6

2004 - Freight - € per 1000 TkmAccidents 15,8 5,1 0,0 0,0 0,0 4,2Air Pollution 101,4 64,9 4,6 2,5 1,5 36,7Greenhouse Effect 33,6 37,1 3,1 1,1 146,5 16,4Noise 8,1 6,4 3,2 0,0 8,9 3,3Infrastructure maintenance 56,9 0,0 0,0 0,0 0,0 10,7Subsidies 0,0 0,0 64,3 0,0 0,0 3,3Total 215,7 113,7 75,2 3,6 156,9 74,7

Air Pollution 12,4 10,9 15,2 64,0 1,3 12,4Greenhouse Effect 14,7 10,9 4,0 26,0 41,5 14,5Noise 5,7 4,5 3,9

The social costs of transport – summary of monetary valuations of externalities (billions of euro – 2004 indexed)

ON Motorway Train Ship Air Total2004

Accidents 37,1 2,5 0,0 0,0 0,0 39,6Air Pollution 17,9 8,7 0,9 1,0 0,0 28,6Greenhouse Effect 13,8 5,6 0,3 0,4 1,3 21,5Noise 4,9 1,3 0,3 0,1 6,6Infrastructure maintenance 4,9 4,9Subsidies 2,9 3,0 5,9Total 81,6 18,2 4,5 1,4 1,4 107,1

Low Case Scenario - 2020Accidents 36,6 2,6 0,0 0,0 0,0 39,2

llution 4,9 1,4 0,9 1,1 0,1 8,4reenhouse Effect 14,2 6,3 0,3 0,5 1,8 23,0ise 6,0 1,6 0,3 0,1 8,0astructure maintenance 4,9 4,9

bsidies 2,1 2,2 4,3

Air PoGNoInfrSuTotal 68,7 11,9 3,7 1,7 1,9 87,8

High Case Scenario - 2020Accidents 59,6 4,0 0,0 0,0 0,0 63,7Air Pollution 8,2 2,4 1,2 1,9 0,1 13,8Greenhouse Effect 17,4 8,6 0,4 0,8 4,1 31,2Noise 10,0 3,4 0,4 0,1 13,9Infrastructure maintenance 4,9 4,9Subsidies 2,1 2,2 4,3Total 102,2 18,4 4,2 2,7 4,3 131,8 Note: the accidents line item refers to the cost of human injury; social costs do not include the costs of production.

8

PROMETEIA

In the base year, the social costs of transport amounted to 107.1 billion euro, including

subsidies, i.e. resources not directly paid by the user. This value represents 7.7% of

GDP (but, as shall be seen later on, and conversely from what occurs in other sectors

of the economy, the phenomenon is largely internalised). Almost 50% of the social

d for 2004, even as a percentage of GDP (falling to

.8%). This difference may be explained by technological improvements in the overall

esent 7.1% of GDP, therefore a slight

of social costs:

costs of transport are attributable to pollution and the greenhouse effect.

Technological innovations embedded in vehicles used in road transport determine

drastic reductions in the future outlook of pollution costs under both scenarios. These

trends are already observable in historical data. Conversely, without the introduction of

specific containment and reduction policies, the greenhouse effect is posed to increase.

The social cost of accidents is significant and amounted to almost 40 billion euro in

2004, representing 37% of total social costs, all of which are attributable to road

transport (accidents relating to other modes of transport are infrequent and small in

size).

In the 2020 low case scenario social costs amount to 87.8 billion euro and are

therefore lower than those estimate

5

vehicle fleet that reduces polluting emissions despite increased traffic. Technological

improvement in the high case scenario – an improvement that reduces pollution per

unit of traffic – is unable to offset the effects of increased traffic and of the cost

components whose value grows at the same rate of real income. In this scenario social

costs amount to 131.8 billion euro and repr

reduction compared to current levels. The share of social costs attributable to the

transport of passengers is higher than the 68% estimated for 2004, and very close to

75% in both forecasted scenarios.

We thus observe the ordinary road network to be clearly responsible today, and ever

more so under the two forecasted scenarios, for the vast majority

76.1% in 2004 and approximately 78% in 2020 - without significant differences

between the two scenarios. Thus, if we should agree on these findings, we shall have

defined our scope in terms of required action.

This forecast is another warning signal in terms of the ordinary road network’s future

ability to even maintain its current (poor) service level. The result of this hypothesis is

further emphasised by the very low possibility for modal diversification available to

Italy based on the current composition of its infrastructure. In fact, the motorway

9

PROMETEIA

network is today condemned to support growing traffic on a local and sub-regional

basis, in order to make up for the ordinary network’s poor capacity.

is important to note that by service level we refer to a number of different factors,

s

external cost since it is a club externality, i.e. an externality that is exclusively

borne by those who ta p y, two summary

of social costs are , one with c on costs and without

here the version is the o hat is generally commentated

conc on the external costs of travel have been drawn.

er, for clear reasons o y, congestion must be caref en into

essing opt riff definition.

It

ranging from commercial speed to rate of accident; congestion is not considered a

being an

ke part in the trans ort system. Accordingl

versions provided ongesti one

congestion costs, w latter ne t

and based on which our lusions

Howev f efficienc costs ully tak

account when addr imal ta

Summary of road accident rates: fatalities and injuries /billions of vehicle km Ordinary Network (ON) Motorway Network Total

FATALITIES 2004 13.7 6.4 12.5 2020 Low scenario 8.5 5.3 8.0 2020 High scenario 7.9 4.6 7.3

INJURIES 2004 740.5 208.1 653.6 2020 Low scenario 691.2 181.8 605.5 2020 High scenario 649.0 140.4 557.5

Road accidents represent an extremely high cost for society and the recent provisions

concerning the penalty-points driving licence have attained very positive results. In our

00 units. This, in substance, could define the

riority and intensity of actions aimed at containing road transport externalities,

future forecasts, accident rates fall, both for fatalities as well as for injuries, for both

scenarios. The fatality rate from motorway accidents in 2004 starts at a rate that is less

than half that of the rate of fatalities for accidents on the ordinary road network: this

should make us think. If in the base year the fatality rate of accidents on the ordinary

road network were equal to that of the motorway network we would have a saving, in

terms of human lives, of almost 3,0

p

carefully assessing the importance of the problems vis-à-vis the ordinary road network

and the motorway network.

10

PROMETEIA

The motorway’s substitute role for the rest of the road network, the variability of traffic

density per motorway stretch and the complexity of the distribution of fatality rates

from accidents, do however suggest that that the motorway network also be constantly

monitored in terms of accidents and that the penalty-points driving licence effect may

run the risk of turning into a short-lived positive step of a phenomenon whose

dangerous growth may be restored.

% relationship between share of social costs and share of traffic – passenger and freight mobility in the two forecasted scenarios

2004 YEAR 2020 YEAR 2020

PASSENGERS LOW CASE SCENARIO HIGH CASE SCENARIO

100,0 100,0 100,0

ORDINARY NETWORK 123,3 122,5 120,0

MOTORWAY NETWORK 69,7 63,8 56,4

TOTAL ROAD 116,2 114,3 110,9

OTHER 33,6 39,4 46,8

TOTAL

FREIGHT



TOTAL ROAD 209,7 203,0 181,4

OTHER 15,3 21,4 22,3

TOTAL 100,0 100,0 100,0

If we therefore consider the magnitude of the rate of accidents on the ordinary road

network we may come to understand how, according to our calculations, it is here that

r

safety should be prioritised. A similar conclusion is commonly heard today by experts

and institutional representatives and the fact that we have reached this same

conclusion not only through traffic assessments, but indirectly too, through estimates

of social and external costs, should underline the importance and urgency of the

political action that should follow. In terms of introducing toll systems on the ordinary

road network, careful thought should be given, we believe, to considering this

possibility as a pre-condition for effective investment towards improved safety on the

ordinary road network.

In terms of the social costs of different forms of oad transport, it is worth noting that

in the scenario of high economic growth passenger air traffic will play an important role

11

PROMETEIA

in the determination of social costs, with its size increasing fourfold compared to the

base year.

Social costs deriving from forms of transport other than road transport are rather low.

The greenhouse effect and noise are the sources of increasing social costs for all

modes of transport and in both scenarios and thus warrant suitable addressing: unlike

air pollution, there does not currently appear to be any explicit movement towards the

containment of these forms of externality, even though technological progress shall

play an important role in terms of making policies of containment for these

externalities soon possible.

For these externalities, which are monetised in the social costs metrics, users pay - by

way of toll payments, tariffs and subsidies (through taxes) - for a number of resources

dy,

articularly for the saying (now quite old) that, since the aim of fuel taxes is a general

though

that are netted off from the social costs in order to establish how external to the

universe of transport these effectively are compared to the costs generated. In this

respect, the calculations are based on certain technical assumptions. The optimal

situation (maximum social surplus) would exist when all and only social costs (short-

term marginal costs) are paid by those who generate them. Subsidies that are

provided for distributive purposes do not affect this approach: all subsidies generate

surplus losses, which may be accepted “a posteriori” for distributive or environmental

reasons, but the trade-off between efficiency and equity must be carefully measured.

These considerations are valid whatever the stated aim of the taxation or subsi

p

tax one and not an internalising one, it is not possible to include this line item in

environmental policy analysis. In this report excise duties and indirect taxes, such as

VAT, in addition to direct payments and insurances, have, in accordance with economic

theory, been considered to be internalising monetary resources (considerations), that

are to be deducted from social costs in order to determine external costs. For tariffs

and toll payments, in addition to tax revenue, the costs per user beyond the marginal

costs deriving from traffic have also been considered to be internalising, even

this is used to sustain part of the fixed costs of the infrastructure and extraordinary

maintenance, without which the physical efficiency of the infrastructure capital would

be lost.

It must then be remembered how the indifference observed in terms of neglect of

subsidies to less polluting forms of transport and the levying of taxes on the most

12

PROMETEIA

polluting forms of transport should take into account the direct and cross elasticities of

demand for mobility of different forms of transport. This should be particularly the case

ternalising monetary resources are deducted from social costs and projected in

gesting that at least part of investment costs should also be paid by users,

e. moving away from the theory of tariffs at marginal costs even if only for this

specific aspect. The crucial question, however, is that this move away from the

since the above observations seem to ignore the question of long-term price signals,

which, in the case of subsidies, would generate problems of over-consumption for

subsidised forms of transport. Subsidies are therefore considered to be social costs –

or rather, to be more precise, external costs – for the transport mode that receives

them, and taxes are accordingly considered to be internalising monetary resources.

Once in

the two scenarios of different GDP growth rates we obtain estimated external costs of

transport in Italy in 2004 of 33.1 billion euro, equal to less than 2.4% of the year’s

GDP. 81% of these external costs are generated by road transport, i.e. 1.8% of GDP.

However, whilst the ordinary road network costs the overall transport system more

than 30 billion euro, the motorway network appears to generate net external negative

costs of 3.7 billion euro. This result may be explained by two considerations.

Firstly, toll payments, based on a pay-per-use principle, make the user perceive the

cost of the service and thus internalise external costs. In general, every form of

transport that sustains fixed infrastructure and maintenance costs aimed at

safeguarding the physical efficiency of the capital asset (requiring internalising

monetary resources that are greater than marginal costs) should have a structure of

external costs that shows negative values: something that in Italy, today, only occurs

for motorway transport.

It must be clear however that this does not challenge the requirements of the

efficiency theory, according to which prices should be equal to marginal costs. Rather,

it integrates the theory and makes it realistic within the contexts of regulation of

natural monopolies. The question of tariffs at marginal costs for infrastructure (which

are very low), compared to tariffs at average costs (often very high since they include

investments and fixed operating costs) is, after all, a matter of debate, and not only in

Italy.

It remains true, however, that tariffs set at average costs are theoretically inefficient.

This said, regulatory, tax and distributive arguments do exist for tariffs set at average

costs, sug

i.

13

PROMETEIA

prescribed approach should at the very least concern all transport infrastructure in a

standard manner, and not only motorways and airports, as is presently the case. In

fact, if every form of transport were to internalise all externalities (i.e. paid all the

rginal costs due to the inclusion of infrastructure

costs, would be lower. In other words, if we all paid the same percentage of the costs

of investment, then modal equilibrium, at least, would be safeguarded.

The second consideration concerns total and fatal accident rates on the motorway

network compared to the ordinary road network, and the way in which insurance

premiums are built. Basically, third-party liability takes into consideration the average

number of kilometres that the insured party will likely travel. However, it is soon

understood that the risk level of kilometres travelled on the motorway network is much

lower than the risk level of ordinary road network travel. The internalising monetary

resource derived from insurances, for each kilometre of motorway that is travelled,

largely goes towards covering the costs generated from the risks of the ordinary road

network. This is one of the sources of what we have defined as external motorway

benefits.

Looking forward, in the low growth scenario external costs tend to reduce both in

absolute terms as well as in terms of GDP percentage, and by 2020 would be below

1.8% of GDP.

In the scenario of high economic growth, external costs are seen to grow in absolute

terms but remain constant in terms of GDP percentage. In this case too, the motorway

would show negative external costs.

social costs that are generated through travel) the inefficiencies in terms of the

distribution of traffic between different modes of transport, that would exist by

adopting prices that are higher to ma

14

PROMETEIA

Social costs, internalising monetary resources and external costs (billions of euro – 2004 indexed) SOCIAL COSTS

ON Motorway Railway Ship Air TotalTOTAL

2004 81,6 18,2 4,5 1,4 1,4 107,1Low Scenario - 2020 68,7 11,9 3,7 1,7 1,9 87,8High Scenario - 2020 102,2 18,4 4,2 2,7 4,3 131,8

PASSENGERS2004 63,2 5,5 2,7 0,6 1,3 73,3Low Scenario - 2020 56,6 4,7 2,3 0,7 1,8 66,2High Scenario - 2020 87,5 6,9 2,6 1,3 4,0 102,3

FREIGHT2004 18,4 12,7 1,8 0,8 0,1 33,8Low Scenario - 2020 12,1 7,1 1,4 1,0 0,1 21,7High Scenario - 2020 14,7 11,5 1,5 1,4 0,2 29,4

INTERNALISING MONETARY RESOURCESON Motorway Railway Ship Air Total

TOTAL2004 51,1 21,9 0,3 0,1 0,6 74,0Low Scenario - 2020 42,1 17,8 0,3 0,1 0,8 61,0High Scenario - 2020 53,3 30,0 0,3 0,2 1,5 85,2



EXTERNAL COSTS = SOCIAL COSTS - INTERNALISING MONETARY RESOURCESON Motorway Railway Ship Air Total

TOTAL2004 30,5 -3,7 4,1 1,3 0,8 33,1Low Scenario - 2020 26,6 -5,9 3,4 1,5 1,1 26,8High Scenario - 2020 48,9 -11,6 3,9 2,6 2,8 46,5

PASSENGERS2004 20,2 -4,3 2,4 0,5 0,7 19,4Low Scenario - 2020 20,9 -2,5 2,1 0,6 1,0 22,1High Scenario - 2020 41,2 -6,2 2,4 1,1 2,6 41,0

FREIGHT2004 10,3 0,6 1,8 0,8 0,1 13,6Low Scenario - 2020 5,7 -3,4 1,4 1,0 0,1 4,7High Scenario - 2020 7,7 -5,4 1,5 1,4 0,2 5,5

15

PROMETEIA

We then have the greenhouse effect, responsible for the rapid growth of external costs

in air transport whilst a no change assumption in the management of traffic revenues

and subsidies to the railway system implies a slight reduction in absolute external costs

(at 2004 indexed prices) for this form of transport.

This work, just as other works recently completed both nationally and internationally,

discards any fanciful hypothesis based on modal re-equilibrium and the successful

redistribution of benefits and costs across the different modes of transport.

Mobility is a subject where dreams are very expensive.

Notwithstanding the complexity of this report, replete with simplifying assumptions and

largely based on observed average and marginal values in different and heterogeneous

contexts, our conclusions in terms of the size and forecasted dynamics of the social

and external costs of transport appear to be, overall, robust. The most significant

finding derived from our calculations is that the vast majority of external costs arise

from the ordinary road network, through total and fatal accident rates. Although

recognising the positive effects brought by the penalty-points driving licence, the

strategy of leaving things as they are cannot be an acceptable one. Making the

ordinary road network safe must therefore be a priority. The possibility of introducing

the tolling system to large parts of the ordinary network, if this is recognised as a pre-

condition to the reduction of risk levels, should be taken into serious consideration,

possibly very quickly and according to clear procedures.

16

PROMETEIA

PART ONE

THEORY AND PRACTICE IN THE

VALUATION OF THE SOCIAL COSTS OF

TRANSPORT

17

PROMETEIA

1. THE EXTERNAL EFFECTS OF TRANSPORT ACTIVITIES

1.1. GENERAL CONCEPTS

1.1.1. THE CONCEPT OF EXTERNALITY

An external effect (or externality) is, in general, an effect produced on the production

and consumption of a second party by a first party, without their being any direct

monetary payment between the two parties. Economists usually say that externalities

influence the functions of production or utility of the party on whose account or for

whose benefit they are produced. While in normal market transactions the exchange of

money is essential, in externalities such exchanges does not take place: we therefore

say that we are dealing with effects that are external to the market (hence the term,

externality).

For example, an activity that releases polluting substances into the atmosphere may

generate costs to other activities (e.g. agriculture, industry or tourism) in the form of

lower output or lower value. Likewise, it may determine lower productivity of

individuals that are part of the production process, thus influencing (indirectly) the

value of production. These are known as external effects on production.

The same activity may determine a lower enjoyment of commodities such as silence,

rest, contemplation etc. In such cases we find ourselves dealing with a (negative)

externality on consumption, since the activity that is being capped or impeded is the

consumption of a commodity (silence, rest, etc.).

A classic example of a positive externality on production is the case of a farmer who, in

disinfesting his own field, also provides a benefit to neighbouring farmers without

however being compensated for this. An example of a positive externality in the

transport sector is the so called Mohring effect, to the extent that those taking

advantage of this effect are producers. A positive externality on consumption is

generally represented by one party’s increased enjoyment in consuming a certain

activity as a result of activities that are carried out by other subjects to whom the

former subject has no obligation whatsoever (e.g. the greater value of a house in the

countryside produced by the presence of public or private gardens in the

neighbourhood).

18

PROMETEIA

Available literature provides a number of slightly different definitions to

externality. We have adopted the following:

1) by externali y we shall refer to the real aspect of the phenomenon while by t

external cos we shall refer to i s monetary quantification; t t

2) human action generates internal and external market effects and all

consequences of human ac ion may be mone ised.t t

3) social costs (of human action) are the value, determined in some manner, of all

resources consumed to produce it;

4) social cos s = internal costs + external costs; t

5) social cos s = monetary costs + non monetary effects; t

6) market costs = monetary costs;

7) non market effects= non-monetary effects -

In this work we assess human action on the basis of price or value indices that are

somehow comparable; if moral issues are raised – for example, human life is priceless

– we fall out of the field of economics and therefore out of the field of this work; in

reality, as shall be clear, in the above example it is not human life that we are

valuating but rather the alterna ive projects aimed at improving or sparing human life. t

Definitions 3) and 4) are the most impor ant ones: although absolutely general they t

are the delimitating boundary of our analysis; definitions 5), 6) and 7) are useful

because they combine monetary and non-monetary elements, naturally only under a

conceptual and not ope ative standpoint since the items on the right of the equation r

may not be summed (and are therefore called effects and not costs) i not only afterf

having been transformed into costs.

When claims are made about transport activities generating negative environmental

externalities a correct statement is being made; however, this does not imply that the

party tha is polluting should pay more than it is already paying: this depends on t

valuation of the speci ic circumstances (i.e. the eal circumstances under examination). f r

It may in fact be possible that a tax system makes the pollu ing party bare the full costt

of i s action and tha therefore the quantity of externality generated is optimal to he t t , t

exten that the damage caused to third parties and the polluting par y’s payment are t t

equal. The question of how the internalising tax proceeds are utilised concerns the

fairness of the internalising policy, but efficiency has been reached.

19

PROMETEIA

When an increase in value (for example, the value of a property) is generated by the

proximity to infrastructure we have a pecuniary externality: the property’s price

appreciation transfers the benefit to the property owner without him having to

compensate anybody for this. We shall now look at real external effects, i.e. those

effects that do not generate price changes to goods or services as opposed to

pecuniary externalities (the latter being very more frequent but also less problematic to

tackle).

Externalities are the undesired secondary effects of main activities: but we certainly

cannot say that those who produce these effects are unaware of them (and, therefore,

that these effects are involuntary, as some authors write), nor that to some extent at

least the party producing these effects does not accept them or discount them.

POLLUTION AND CONGESTION: DIFFERENCES AND SIMILARITIES

Pollution and congestion are perhaps the most obvious cases of negative externalities.

We may distinguish between two fundamental cases1:

(a) pure pollution (one or more parties generate the effect, whilst other parties

suffer the consequences of the same in a more or less passive manner);

(b) pure congestion (all parties carry out similar activities, generating similar effects

on each other).

PURE POLLUTION

Let’s assume that in Fig. 1 the BM curve depicts the situation of a polluting producer

(P) and a citizen (C) who suffers the damage caused by the pollution. The X-axis (Emi)

is the quantity of harmful emissions caused by activity P and suffered by C. We assume

that the quantity of emissions is directly proportional to the quantity of production of

the main activity P. The Y-axis (€) represents monetary values. The BM curve depicts

the producer’s marginal benefits: the curve reaches a monetary value of zero at the

point where the producer no longer has an interest to produce more (= producer’s

point of equilibrium; the producer’s marginal benefit is equal to zero when maximum

profit is reached: this point is represented by K). The DM curve depicts the marginal

damage (expressed in monetary terms) suffered by C. It is assumed that this value

tends to increase unlimitedly as emissions increase.

1 According to the classification provided by J. Rothenberg (1970).

20

PROMETEIA

Fig. 1 – The bargaining solution to a problem of pollution

€ DM

E

BM

L Emi

K

If bargaining costs may be kept at zero, it may be demonstrated that parties P and C

will meet at point E (at a level of pollution L) regardless of which of the two parties

holds the legal right to its behaviour.

Let’s assume that the law gives C the right of not suffering the consequences of

ve a net profit. This will lead the two parties to move towards a quantity L. At

, on the other hand, it were C having to pay P to convince it to not carry out

re determined by

C and P at a point L, regardless of which of the two parties holds the right of law. It

should be noted that L is a positive value.

pollution. In this case, we would find ourselves on the left of L: in the absence of any

initiative by P, we would finally place ourselves at a level of zero production, with zero

pollution. But P will be prepared to pay C an amount equal to the difference between its

marginal benefits and C’s marginal costs in order to have the right to produce since P’s

benefits are greater than the damage suffered by C, and therefore P can indemnify C

and still ha

levels of production that are higher than L, C’s valuation of the damage suffered is

greater than P’s receivable benefit, and therefore P will have no interest to buy a further

right to produce from C.

If

production, C could lead P to forsake production for units in excess of L. Indeed, only

from L upwards does C’s valuation of the damage suffered exceed P’s valuation of its

product. Accordingly, C could induce L not to produce only if production were to

exceed L. Conclusion: once again, P shall chose to produce up to point L (and not

beyond).

The final result is that the levels of production and, hence, pollution, a

21

PROMETEIA

Bargaining among the parties has not reduced pollution to zero, nor could it have given

that it is the level of compromise that satisfies the interests of two parties. This should

not come as a shock to environmentalists. Many pollution generating activities are also

source of collective benefits. Forcing pollution to disappear is almost never the best

solution. All efforts towards a goal have an opportunity cost: it is necessary to achieve

the desired result whilst paying the lowest possible opportunity cost.

PURE CONGESTION

Often, the source of congestion is a condition of limited carrying capacity, as in the

case of a motorway, an art gallery or a theatre. But the different forms of pollution

may also be considered congestion phenomena since they involve the excessive use of

a resource: where excessive means greater than capacity allows.2

2 How can pollution be compared to congestion phenomena in the strict meaning of the word? Well, it can be in the sense that as there is no price to be paid to use the atmosphere, we are all free to use it as a repository of various waste: dust and smoke from production activities, exhaustion from vehicle engines, home heating, stubble fires, the smells that come with these activities, etc. The complete absence of a price means that the quantity of emissions unloaded into the atmosphere is far greater than the atmosphere’s capacity to regenerate itself through air currents.

22

PROMETEIA

Fig. 2 – Private cost and social cost of the journey

Economics argues that congestion generally is the effect of the lack of a rationing

mechanism. Given that the most obvious of these mechanisms is the price mechanism,

we may see how economists identify the absence of prices as being the most frequent

cause for congestion phenomena, from traffic (both urban and suburban) to the

distribution of environmental and natural resources. Individuals are not aware of the

fact that they too generate costs for others: this is what happens when a traveller sets

out on the road, based on his/her perception of the cost of the journey. In Fig. 2, the

traveller only perceives the curve of his/her own marginal costs (CMP=private marginal

costs); these costs are identical to average social costs (CSAvg)3. Ex ante, the

journey’s costs are given by the average journeying time, which we assume the

3 The value that the marginal user allocates to road usage is determined by observing the average costs borne by the other users: this is the average social cost (where the adjective “average” may indeed cause some confusion). The private cost (marginal) cannot therefore be greater to the average social cost.

P (€ )

Volume of traffic

CSMarg

CMP =

CSAvg

P 0 P 1

HC

JD

L

q0 q1

0

23

PROMETEIA

traveller is able to correctly convert into monetary earnings4, and the consumption of

materials such as petrol, oil, etc. at a given speed. In so doing however, the traveller is

neglecting the contribution that he/she is personally making to increasing journey time,

and accordingly increasing average costs (the greater journeying time being a cost),

increasing (albeit only marginally) motorway congestion. His/her contribution shall

increase the marginal social cost (CSMarg), or cost ex post, of all travellers.

We may also reason differently: if the journey’s price were estimated to be p1 rather

than p0 the volume of traffic (= number of travellers) would be Oq1. However, on the

basis of social costs (a function of motorway capacity as well as other features) traffic

volume should not exceed Oq0. The triangular area C (=HLJ) is the sum of costs not

accounted for by travellers, known as deadweight loss 5.

Fig. 2 depicts a possible remedy. By introducing a tax equal to the difference between

marginal social costs and private costs, travellers would be made aware of the real

costs of the journey and a number of them would be induced not to make the journey:

if a tax p1-p0 were introduced, all travellers for whom demand would be – at that given

price – less than cost (=supply), i.e. those for whom the cost would be greater than

the private cost plus the tax, and especially those from q1 to q0, who would have

accessed the motorway in the absence of the tax 6, would no longer make the journey.

1.1.2. ORIGIN OF EXTERNALITIES: REAL AND MONETARY

The origin of externalities may be explained by considering their close similarity to a

public good.7 This is the case when the goods/services are non-rival in terms of

4 In a number of cases this calculation is rather easy. A freelance knows exactly how much an additional hour of his/her work is worth. However, when saved journey time does not correspond to increased work activity, but is rather a simple form of satisfaction, a monetary quantification will require greater effort. 5 If the quantity consumed is q0 rather than q1, society as a whole loses the quantity represented by the vertices q0 q1LH, but part of this area (q0 q1JH) is in fact freed for other uses (= it isn’t lost). The real loss is therefore only HLJ. 6 Although the travellers consume quantity q1 at a price – net of taxes – of p2, their actual willingness to pay (including the tax) is p1. 7 Some may rightly argue that this is actually a public problem (i.e. a good with a negative sign): but this does not affect our reasoning. In technical terms (and not legal terms!), a public good is a good that (1) is non-rival in terms of consumption and (2) is non-excludable in terms of consumption. In other words, (1) the consumption of a given good by consumer x does not affect the quantity of the same good consumed by consumers y, w, z, …; (2) there are no efficient mechanisms whereby those who are not prepared to pay the price of the good may be excluded from consuming it.

24

PROMETEIA

consumption. For goods and services that have no price but are rival we are no longer

dealing with public goods but common goods, which, if priced, would make them true

and proper market goods (=private goods). Public and common goods are typically

characterised by the lack or inadequacy of rights (property or user rights) assigned to

the goods being considered 8. Because of this, certain effects, both positive and

negative, occur without it being possible for them to be affected by the principles of

market exchange: particularly the obligation of paying a price. Hence, the expression

external effect (or externality), which refers to the fact that the effect remains external

to the market. The absence or inadequacy of rights assigned to these goods may

derive from historical practice, tradition, political calculation or the true and proper

technical difficulty, or impossibility, to exclude non-payers from enjoying a certain good

or service (for example, it is impossible to impede parties responsible for cross-border

pollution to use their own atmosphere).

Any action or relation between individuals or groups may, therefore, have an effect on

others. Usually, however, these effects lead to a payment: a party receiving a benefit

pays the value of the benefit to the party generating this benefit. A party that harms a

party must pay an amount to the harmed party. Accordingly, it may be said that the

effects get transmitted by the price mechanism. However, because of this very reason,

these are examples of external effects in a rather improper sense (pecuniary external

effects or externalities that are (merely) pecuniary in nature), since the exchange of

money between parties actually occurs. The fact that the effect of the events consists

in a change to the goods’ prices, and not in a change to the functions of production or

utility of the party suffering the externality, makes it something deeply different from

an actual externality.

Transport offers a number of examples of pecuniary external effects. For example,

increased income generated in a given region by greater traffic is an effect that may be

perfectly measured in monetary terms (i.e. greater income).

8 According to current definition, a property right is represented by exclusive title to property or use provided for by the law. Refer, for example, to McTaggart, Findlay and Parkin, 1992, p. 468.

25

PROMETEIA

1.1.3. SOLUTIONS TO THE PROBLEM OF EXTERNALITIES

In a market economy externalities are very common. This suggests that the traditional

rules for optimal allocation of resources may not hold true in the presence of

externalities. It is therefore appropriate to analyse the possible solutions to correcting

the distortions that may exist.

SOCIAL PRACTICE

One way of dealing with negative externalities relies on social practice and tradition.

According to one view, “certain social practices may be seen as attempts to force

individuals to take into account the externalities that they themselves generate”.

Tradition may also play a similar function: “the recognition of certain signals and

suitable measures [that follow] are instilled in the culture”9. An example that is usually

used to support this argument is awareness – developed in the process of education –

of the costs of pollution on other individuals. There are nonetheless two strong

counterarguments: the possibility of free riding, that is to say, of situations where the

implicit individual cost of virtuous behaviour exceeds the satisfaction derived from such

behaviour and the possibility that practice and tradition may not work on groups in the

same way that they are believed to work on individuals.

INCORPORATIONS

Another possible solution to the problems of externalities is represented by

incorporation of one part of the equation into the other (or by merging the two parts

into one). For example, an airport company may acquire the properties of private

individuals who are being harmed by noise or air pollution. In this case we are looking

at a solution that may have a greater possibility of succeeding if applied to groups or

companies rather than to individuals. In general, however, this solution too seems to

be of limited practical significance.

9 Wills, (1995-96), p. 59.

26

PROMETEIA

REGULATIONS

Regulations, or, in other words, the imposition of rules that limit human activities and

the imposition of sanctions on parties generating externalities beyond these limits,

represent one of the most common methods of solving the problems of externalities.

The solution is a good one in terms of its simplicity, but may cause inefficiencies.

Fig. 3 - Regulating pollution

d

X a Xb XpX*

PMC+d

PMC

MBa

MBb

Pollution (tons)

Euro (per year)

Fig. 3 depicts the situation of two companies, a and b, having identical costs (PMC),

but different marginal benefit curves (MBa and MBb). Perfectly competitive market

equilibrium (=absence of regulations) would be reached at a quantity of pollution Xp,

i.e. where the marginal benefit curve intersects the private marginal cost curve (PMC).

However, when companies consider the damage (d) that is generated by their action,

the optimal quantity of pollution for each company moves to Xa and Xb, respectively. If

we assume different marginal benefit curves, government’s actions to force all

companies to reduce pollution by the same amount, or to cap pollution at a set

quantity such as X*, may not be optimal. Accordingly, regulations and sanctions may

look like practical solutions but are not necessarily efficient. Their practical application

27

PROMETEIA

may further be limited by a monitoring issue – monitoring being essential to identify

violations to the rules.

Fig. 4 – Pigouvian tax

Euro per year

PIGOUVIAN (OR CORRECTIVE) TAX

Another possible solution to the problem of negati

imposing corrective taxes aimed at modifying the b

say, aimed at encouraging producers themselves to

the optimal social level. A pigouvian tax is a “tax le

corresponds to exactly the same amount of the marg

the optimal point of production”.

As may be seen in Fig. 4, perfect competitive equ

point where demand curve (D) intersects the pri

(PMC), i.e. at quantity Xp. However, the optimal qu

point where marginal social cost (SMC) that takes in

the production of the good is equal to D. The pigouv

i.e., equal to the marginal damage (d) produced by

of production. By introducing the tax, companies s

level of the sum (cost of production + tax). As a co

will have increased, and demand for the good will

production is reached at price P1 and quantity X*

28

Pollution (tons)

ve externalities is represented by

ehaviour of producers: that is to

limit the production of goods to

vied on each unit of pollution that

inal damage caused to society, at

ilibrium would be attained at the

vate marginal cost of production

antity of production is Q*, i.e. the

to account damage (d) caused by

ian tax will need to be equal to (t),

the externality at the optimal point

ee their supply curve shift to the

nsequence, the price of the good

have fallen. The efficient level of

. Accordingly, the existence of an

PROMETEIA

externality induces a remedy that itself decreases supply of the main good produced

by the polluting party. Pigouvian taxes are more effective than regulations as a way to

obtain efficiency of allocation. However, they have the same shortfalls. In order to tax

externalities, the government or agency that is assigned this task needs to be able to

identify the activities that cause the externalities and the substances that produce

them, and then be able to satisfactorily calculate the value of the damage. Since this

information is usually only obtainable in the form of best estimates, the tax will have

the effect of pushing towards the optimal position rather than achieving the optimal

position. Of course, as more and more knowledge of the phenomena is gathered, the

degree of success may improve.

RIGHTS

An alternative solution to the problem of externalities is given by the definition of rights

(rights of property or use). The existence of a recognised and safeguarded right

enables the victims of externalities to prosecute those who are responsible for the

damage and claim damages. We address yet another aspect in the following point.

THE COASE THEOREM AND NEGOTIATION

Another way in which the definition of rights may help attain efficiency of allocation is

establishing a place for negotiation. The central issue is some form of assignment of

rights among the parties. The so called Coase theorem states that, in the presence of

externalities, the efficient solution to a problem of allocation will be reached regardless

of which party shall be assigned the rights, as long as the rights are assigned to a

party. The shortfalls of this solution concern the invariable presence of bargaining costs

and the practical difficulties that exist in identifying and quantifying the damage that is

caused.

1.1.4. EXTERNALITIES IN THE DETERMINATION OF THE COST OF TRANSPORT

The use of resources such as space, the environment and safety may be governed by

prices or specific rules. The first solution is consistent with the general principles of

market economies. Externalities are a significant component of the social cost of using

any form of transport, and need to be added to the private cost in order to reconstruct

the total cost.

In order to determine the marginal social cost of transport one must analyse the

29

PROMETEIA

following items:

Infrastructure: according to most available literature, externalities that are caused by

infrastructure (for example, the construction works of a motorway, or railway) cannot

be included among the effects caused by transport; this is, among other things, an

important aspect of decisions concerning the infrastructure’s construction10; in this

work we do not consider this type of effect.

Congestion: with this term we refer to the cost suffered by the marginal user and the

other users of the form of transport, as a consequence of the marginal user’s decision

to use a certain freely accessible form of transport.

Scarcity: the definition is similar to that provided for congestion costs, but refers to

forms of transport to which access is regulated (ex., slot).

Accidents: this category concerns the increase or decrease of the statistical risk of

harm/damage to people or property.

Environment: this item includes all those effects that are in essence related to air

pollution (borne by people and property; this includes the important - albeit

controversial – greenhouse effect, the pollution of land and water and noise).

In this work we do not calculate any possible external effects of infrastructure,

since, as observed, these are not related to the activities of transport.

Congestion is dealt with in great detail, but as is commonly noted in available

literature, its monetary quantification in terms of external cost is not summed to other

external cos s, since it is a club ex ernality (except for paragraph 2.5.2. of part two, t t

and only for the sake of completion) This returns us to the problem of the fairness or.

equity of the consequences of human action and public policies (who pays? who should

pay, once it has been established that there is an amount to be paid?).

Congestion is a club externality in he sense that it is not felt by third parties t

(parties not belonging to the club) therefore, if third parties who a e external to : r

transport users are not harmed, and transport users are taxed in order to generate the

optimal quantity of externality, he tax proceeds mus somehow go towards t t

compensating the same transport users).

, In the case of a corrective tax (pigouvian) in Fig. 4 the tax proceeds (P0-P1)X* 10 After all, if considerations that are born from the presence of an externality (for ex., environmental) have the effect of decreasing the supply of infrastructure, this will affect the costs of congestion and scarcity (defined below) that are generated by the use of the very infrastructure.

30

PROMETEIA

should go towards compensating users from Xp to X* who have been expe led from l

the club and who have thus suffered a welfare loss (whilst those who are in the club

enjoy better conditions of mobility (speed and therefore time, condi ion of traffic flow) t

than available before the tax.

It is useful to remember that congestion charges that are today a matter of

great discussion come with collection charges that are at times similar to the social

surplus lost before the tax is levied (as mentioned for example in Infras-IWW 2004, pp.

18-19). This aspect has to do with the inelasticity of the demand for mobility for each

form of transport, which is also a consequence of the lack of any realistic alternatives.

If the demand for mobility is perfectly inelastic (i.e. is parallel to the y-axis) the

pigouvian tax has no ef icient effects, then, in consideration of the fact that no par y f t

abandons the market and no par y that is in the market gains any benefit, the t

compensation should go entirely to the market users (all of them and all taxed

equally).

If the demand for mobility is only slightly elastic, then we often find ourselves

in situations where the cost of levying congestion charges is equal, i not greater than, f

the loss of surplus that it aims to eliminate (which is small since the a ea defined by r

points HLJ in Fig. 2 is reduced because the demand curve shifts to the right around

point J). What really happens, as is well known, is that congestion charges, whose

distorting effects are small to the exten that the absolu e value of he elasticity of t t t

demand is small (i.e. the more inelastic the less the distortion), are o en used as localft

tax revenue tools and the tax proceeds are used for reasons other han economics-t

based regulation of mobility. Clearly, in such cases, there is great inequity suffered by

those who make up the taxed mobility system. It is useful to remember that a number

of empirical estimates identify road t ansport to be the form of transpor cha acterisedr t r

by the lowest direct p ice elasticity (in absolute values) whilst the elasticities of otherr

modes of transport are significantly higher. These observa ions are true for Italy and t

refer to the 1990s and year 2000. Elasticities obviously change with time, also as a

function of any significant infrastructural change.

1.1.5. EXTERNAL BENEFITS

Positive external effects generated by transport activity are not to be found in available

literature. A part from the enjoyment that some observers of transport activity may

31

PROMETEIA

have (for example, those who enjoy seeing trains or airplanes go by)11, and the safety-

curtain function on large metropolitan roadways mentioned by K. Button12, the only

example that is cited is that known as the Mohring effect13. The Mohring effect consists

of the benefits to users derived from the increased service supply through an increase

in traffic. This hypothesis too, however, only results in a metaphorical use of the term.

1.1.6. EFFICIENCY AND EQUITY

We must remember that according to economic theory, social welfare is the sum of

goods and services that are available to a population at a given time (the goal of

efficiency, or profit maximisation), and the social allocation of these resources, the

distribution of which may be retraced to the political goal of equity or fairness. It

would in fact be difficult to argue that a community where all wealth were held by a

small minority enjoyed successful social welfare. The efficiency goal is measurable and

may be judged rather easily (a condition of more resources is better than one with

fewer resources), the equity goal is likewise measurable (the distribution of income is

well known), but the judgement that may be made is a function of political decision

making (for example, greater or lesser equality, or meritocracy, etc.).

Efficiency and equity are measurable goals, but cannot be easily separated. A century

ago economic theory believed that the two goals were separable (Pareto), whilst today

we know that such a separation is only illusory: no public decision is able to make all

things better for all parties and therefore what is sought are strategies that determine

overall income increases (efficiency) that exceed any damage generated to a few

parties, compensating these harmed parties when this is deemed to be fair and is

possible (equity). For example, tariffs to internalise external environmental costs are,

by definition, efficient (they maximise social surplus), but certainly hit poorer classes

harder (those who have old cars and live in suburban areas because of housing costs).

Following the introduction of the environmental tax, wealthier polluters would pay and

continue to travel, whilst poorer polluters would travel on collective forms of transport.

If the proceeds collected from the environmental tax were used to improve collective

forms of transport, certain equity aspects would also be solved. However, there is

another social category for which equity aspects are very relevant: those who unjustly 11 Compare Verhoef (1996). 12 Button, (2002), p. 93. 13 Mohring, (1972).

32

PROMETEIA

suffer the damage generated by others. Tariffs are fair as well as efficient for the

following reason: they often allow compensation to those who are damaged by

pollution.

There are no simple solutions to achieving both efficiency and equity: they must both

be carefully assessed and acceptable trade-offs between various aspects must be

decided. The only thing that should not be allowed is the right of not measuring:

relying only on political judgement that is not corroborated by best possible

quantitative analysis is always a source of manipulation and highly arbitrary (more so,

the very concept of judgement is significantly weakened).

An essential component of environmental politics is therefore the use that is made of

the tariff proceeds: this use holds the solutions to most of the equity issues.

Another relevant aspect concerns the different possibilities available to the social

categories to avoid the provisions (let’s think of the effects of prohibiting the use of

cars in city centres on Sundays for those who can spend the weekend away from the

city and those who cannot…or the availability of private parking in central areas for the

traffic limiting provisions). This aspect must also be included in the public action’s

assessment programme.

ROAD ACCIDENTS

Accidents generate high costs both to individuals as well as society: many of these

costs are borne by those who directly gene ate them (personal injury suf ered or r f

damage to one’s own vehicle), others are generated on third parties, but are largely

covered by insurance premiums; finally, a share is covered by the national health

service (but if they implied additional costs fo the health service they would be in r ,

terms of this additional component external costs). Therefore, if the majority of total,

costs are covered by the par ies who generate them, accidents represent a partial club t

externality; so it makes sense (is efficient) to invest significant public resources to

reduce them, but it would not be fair nor efficient to increase insurance o other tarif s. r f

This is the same reason for the low relevance of home accidents on socie y as a whole,t

in spite of the fact tha home accidents generate more casualties than road acciden s: t t

the external component of these cos s is small. t

For road accidents, a share of the cost is made up of an externality when the event

generates stress, pain, anguish, loss of production for people related to the victim.

Since it is very difficult to breakdown social costs of accidents into s rictly external t

33

PROMETEIA

costs club external cos s and internal costs, in this work we shall not consider the idea, t

of club externality, which shall however be considered when looking at congestion.

On the one hand this allows (as shall be done in part two, chapters 2-3) better

comparability of the results obtained in this work compared to other similar works, also

withou having to introduce excessive complications deriving from the definition of t

externality (in a strict sense and in terms of club) into our calculations; on the other

hand, it is appropriate at this point to once again state that a significant share of costs

connected to the rate of accidents should not be treated by means of efficiency tariffs

since part of the social costs are ef ectively a club externali y. Howeve , the Europeanf t r

Commission suggests internalising the cost of accidents by developing a more accurate

system of insurance, partially in contras with the consequences deriving from the t

adoption o the club externality concept. f

TAXES ARE BETTER THAN PROHIBITIONS

Economic heory demonstrates ra her clearly how environmental policies that a e t t r

based on tariffs have positive effects on efficiency: environmental goods are consumed

only up to he point when the benefits of this consumption exceed social costs without , t

society being hurt. Conversely, prohibition cannot select among consumers those who

have a greater utility to consume environmental goods (it is right for a surgeon to fly

by helicopter in order to carry out an operation in remote areas, even if flying by

helicopter pollutes a lot…). Even i tariffs hit lower incomes more, the use of the f

proceeds of tarif s allows, at least in theory, to compensate bo h those who are f t

harmed by the pollution as well as those pollu ers who, because of the tariffs, may be t

pushed to change their behaviour This is not possible through a policy of prohibition. .

Therefore, when it is possible to introduce a tax without there being prohibitive

implementation costs a tax strategy is bet er than a policy of prohibition, both in terms , t

of equity as well as in terms of efficiency – the latter not being guaranteed at all by a

policy of prohibition.

34

PROMETEIA

1.2. GENERAL PRINCIPLES USED IN THE DETERMINATION OF

TRANSPORT PRICES

Transport prices for various forms of transport should reflect the marginal social cost

(CMS) of the transport. There seem to be no satisfying alternatives to this solution,

even if many different definitions, especially for practical purposes, have been

proposed14. This principle however creates a number of problems of (a) theoretical

nature and (b) practical nature. The former concerns the actual definition of marginal

social cost, and, particularly, identification of the building blocks of this concept. The

latter concerns the existence of payment forms that satisfactorily reflect marginal social

costs. Problems of a practical nature also address the convenience of using payment

forms having high implementation costs without however producing suitable benefits.

An efficient price for the use of a form of transport should include three components.

Firstly, a component of variable costs (consumption of infrastructure and cost of

service per km travelled); a second component relating to capital costs; and lastly, a

tax on congestion; this component should be equal to the cost of congestion for its

various forms: greater journeying time imposed on other travellers as an effect of an

additional km travelled, environmental externalities and rate of accidents (in prevailing

current practice, only the first of these three forms of congestion is considered to be a

true and proper payment for the use of the infrastructure).

The marginal social costs (CMS) principle tends to make cost components of transport

activities that are not perceived by users as being real costs, very explicit. If faced with

CMS (rather than merely private costs), users would adjust their behaviour (if and how

much to travel, how to travel…) so as to maximise their net benefits, without making

others bare the costs of their decisions. As a consequence, society as a whole would

maximise its net benefits. In practice, this means including in the CMS those

externalities that are not accounted for in private costs and excluding any transfers

(especially any subsidies). For modal choice, the CMS principle should be applied to all

forms of transport.

14 For full converge refer to Quinet (2005).

35

PROMETEIA

A rather rich array of literature argues tha the Marginal Social Cos principle, if t t

properly applied to each and all forms of transport, would confirm today’s modal

breakdown between public t ansport and private transport. In particular, if some r

change we e possible, i would concern urban areas (in favour of forms of transport r t

other than private automobiles) whilst for medium and long journeys road transport

would be further emphasised, i.e. forms of private transport The rationale, in a .

nutshell is the following: the benefits of private transport are so high that if subsidies,

were eliminated from public transport the superiority of private transport over public

transport would be even more evident: so evident that the problem of including this or

that component in the CSM would be somewhat futile.

PAYMENT FORMS

Possible forms of payment are necessarily tied to the type of externality. However, a

general overview may be possible.

The problem of finding forms of payment that may reflect – and thus satisfactorily

compensate – CMS is a practical one, the final solution to which has not yet been

identified in available literature.

In general, CMS strongly depend on context: they may vary according to the

characteristics of the receiving environment, the form of transport considered, and

casual factors (such as weather conditions, etc.).

In terms of infrastructure use (this essentially concerns roads), payments that are

commensurate with vehicle type, volume being transported (tons-km) and load are

solutions that some consider to be satisfactory in theory and, according to many

contributions, also in practice.

The most common form of payment among those that hit the users of certain forms of

transport, that is to say fuel taxes, has no clear relation to the consumption, for

example, of road infrastructure and associated services (particularly: there is no

proportional relationship between the weight of a vehicle, the consumption of fuel and

the wearing effect on the road, except for, and then again only in relative terms, heavy

vehicles). Accordingly, fuel taxes cannot be said to be taxes that are levied on

infrastructure use (=construction + maintenance ), neither from the point of view of

an individual nor from that of society. Moreover, fuel taxes may represent acceptable

solutions as a first approximation.

36

PROMETEIA

In terms of congestion, since this is a phenomenon that varies widely according to type

of road and time of the day, the optimal solution rests in applying mechanisms that are

capable of recording these changes. These mechanisms exist and have been

experimented in different places (Singapore and Oslo, Hong Kong and Trondheim,

Cambridge (UK) and London). It is worth recalling that these experiments have proved

to be encouraging – at least when carried out in their less attenuated forms, and

exploiting existing technology as much as possible – since the effects on local

reduction of congestion have been significant.

We cannot therefore, at this stage, not observe that congestion charges can work if

immediate available alternative forms of transport exist. If this is not the case, then, as

noted, congestion charges are a form of tax that hide some other objective. In these

cases paying members of the public should, at the very least, be made aware of the

use that is made of these additional taxes that are paid in order to travel.

37

PROMETEIA

2. METHODS OF ECONOMIC VALUATION OF NON-MARKET

GOODS

The value of market goods is given by price. There are, however, many cases of goods

that are recognised to have an economic value but do not have a price. Over the last

decades economic analysis has developed a number of methods based on direct or

indirect observation of individual behaviour, in real and hypothetical contexts.

The methods that have so far been used in attempting to measure the benefit or

damage that occurs outside normal market exchanges essentially fall into two main

categories: methods that are not based on demand curves to estimate the value of

environmental goods and methods that are based on demand curves. In the first case,

price measurements are mainly proposed, whilst in the second case measurements of

value are estimated15. Methods that are based on demand curves may further be

broken down into:

- stated preference techniques;

- revealed preference methods;

- Benefit Transfer methods.

Fig. 6 is a partial diagram of the most common techniques of valuation available

today.

2.1. METHODS BASED ON DEMAND CURVES

2.1.1. STATED PREFERENCE TECHNIQUES

These are the so called direct methods that base the valuation of goods on surveys

attained through questionnaires in which respondents are asked to state their

preferences within hypothetical markets, recognising that a value is attached to a good

by virtue of a number of specific attributes. A car, for example, has a specific value

because of certain features such as colour, fuel consumption, design and price.

Individuals reveal preferences for these specific attributes and are willing to accept

trade-offs between these: an individual may, for example, be willing to buy a car 15 Ever since the days of Adam Smith economists have recognised that the price of a good does not necessarily reflect its value. We may all observe the disparity that exists in the price of water (generally low, although increasing rapidly in the more industrialised countries) and its value (very high).

38

PROMETEIA

having a relatively higher price, but with lower fuel consumption levels compared to

other cars. Similarly, an environmental good is characterised by a number of features

such as environmental quality, the cost associated to its direct or indirect enjoyment,

the presence of substitutes and so on.

Fig. 6 - The main techniques of valuation of non-market goods

Shadow project method

Valuation methods for

environmental goods

Methods based on demand curves

Methods not based on demand curves

Averting behaviour method

Dose-response method

Stated Preference techniques

Benefit Transfer methods

Revealed Preference methods

Travel costs method

Hedonic pricing method

Contingent valuation method

Conjoint analysis methods

In the field of environmental goods valuation stated preference techniques or direct

methods may be distinguished according to the number of attributes of the

environmental good: if only one attribute is considered (price, or a tax), we refer to

Contingent Valuation Method (hereafter, VC); if the valuation is a multi-quality one we

refer to a Conjoint Analysis. A fundamental concept underlying these methods is the

willingness to pay.

THE WILLINGNESS TO PAY AND ACCEPT

According to general principles of economic valuation, benefits must be calculated by

using the willingness to pay (hereafter, DAP) principle. This is the pivoting concept of

economic methods that may be used to monetise positive external effects. In the case

39

PROMETEIA

of negative externalities however, which is the case we are particularly interested in,

the concept to be used is the one of the willingness to accept (DAA). This distinction is

an important one and shall be addressed in detail later on in this report. DAP and DAA

mirror consumers’ market behaviour (DAP is a synonym for market demand). In a

market economy they represent the only measurement of value that is consistent with

the principles on which economic exchange is based. DAP is clearly linked to an

individual’s ability to pay and, for this reason, has been criticised in terms of its equity.

These criticisms however, have no reason to exist when addressing efficiency.

Prevailing theory usually assumes that the willingness to accept - that is to say, the

compensation demanded ex post for the damage that has been suffered – following a

negative change to an individual’s wellbeing is equal to the willingness to pay for a

similar increase. However, a number of studies have demonstrated DAA to be

systematically greater than DAP for the same or similar good. Further to observations

and experiments, the case for contradicting results between normative theory of

consumer choice and actual consumer behaviour is widely recognised by economists,

but has only recently been given serious credit.

PROSPECT THEORY

One of the most surprising findings (having radical consequences) of D. Kahnemann’s

and A. Tversky’s works is the so called Prospect Theory. This shows the existence of

asymmetries between gains and losses compared to a given initial situation. According

to this theory, given equal absolute changes (measured in amounts of money won or

lost), “losses are perceived more intensely than gains”. This contradicts the axiom of

traditional theory, according to which, given a certain initial wealth, a certain gain (ex.

$ 100) generates a change in utility (in this case, an increase) which is identical – in

absolute terms – to that of a decrease in utility caused by a loss of $100.

40

PROMETEIA

Fig. 7 - Prospect Theory

Note: WTP=DAP;WTA=DAA.

Prospect Theory assumes a utility function such as the one in Fig. 7a, whilst traditional

theory assumes the function in Fig. 7b. The difference may be better understood if the

two graphs are converted into a more intuitive form. Figures 7c and 7d depict the

utility function of traditional theory in the case of (c) gains and (d) losses, respectively.

Conversely, Prospect Theory shows a clear asymmetry between gains (=marginally

decreasing) and losses (=marginally increasing; figures 7e and 7f). The consequence

of this result – which has been verified through a number of tests showing converging

results – is that valuation based on willingness to pay (appropriate when we are

dealing with gains) may not give the same result of valuation based on the willingness

to accept (appropriate when dealing with losses). Given an equal absolute change,

individuals would seem to perceive a loss much more than a gain, because a loss

41

PROMETEIA

implies separation from an object for which some form of attachment or identification

already exists – something which has obviously not (yet) occurred for an object that

has only just been gained. The same may be said for a sum of money not gained – this

will be perceived to hurt less than if the same sum of money were lost.

This is significant in terms of quantifying environmental damage. Environmental

damages are loss situations that are to be valuated based on the willingness to accept;

but such valuations would run the risk, according to the new vision of the problem, of

involving amounts that are significantly higher than those based on the willingness to

pay. The divergence is not in itself irrational but may produce infinite values (unlike

s not limited by the ability to pay: the former willingness to pay, willingness to accept i

is by definition finite, the latter isn’t), and this would mean insurmountable difficulties

in determining the actual damages of losses or damage suffered.

What has actually happened is that a number of authors have, since some time now,

proposed a conceptually acceptable way out. If there is a cost involved to supply a

good this cost must be borne by all users of the good. The direction of payments

should always be from the users to the suppliers, not the other way round. In this way

the reduction in quality or quantity of a given good would always come with a payment

by users, and not a payment made to the users. In the case of deterioration of

environmental quality, individuals would always measure their loss of wellbeing under

the form of a forced payment increase to be made should they wish to re-establish the

initial level of quality. This enables valuation of environmental damage to be structured

in terms of the willingness to pay rather than in terms of the somewhat dangerous

willingness to accept.

42

PROMETEIA

2.1.1.1. CONTINGENT VALUATION METHOD

First proposed in the 1960s but only first applied in the early 1970s, this is today the

hases and sales records can be observed, then it may be possible to create

n hypothetical market in which individuals may express their DAP. The hypothetical

uestionnaire that defines the components and

e questionnaire allow a definite,

between

willingness to pay and ability to pay. In other words, VCs would allegedly measure the

latter rather than the former. One cannot deny the existence of a relationship between

willingness to pay and ability to pay. In principle, it is not plausible to believe that

individuals may be able to completely disassociate their DAP from knowledge of the

simplest and most popular method among stated preference techniques. In VC only

two attributes (the cost/price and the non-market good) and two choices (pay the

cost/price and enjoy the good or refuse to pay the cost/price and not enjoy the good)

are identified. Estimates of the value of environmental goods are obtained directly

through surveys that elicit respondents to provide a monetary valuation to the decision

being made («how much would you be willing to pay for…»). The underlying idea of

this method is that if one wants to know the value of a non-market good for which no

actual purc

a

market is built by administering a q

conditions of the exchange. The questionnaire describes the good, how the good is

enjoyed, the form and terms of payment, and the conditions in which the valuation is

made.

The VC is based on the contingent market notion, i.e. a market in which a given event

is proposed and for which individuals shall have to pay or receive compensation. It

should be noted that the preferences for the good being valuated are identified by

analysing the choices made by respondents within this artificial market in an

hypothetical context. The estimates obtained with this method are all inclusive in the

sense that they take into account all the good’s value components, including the value

of existence – something that is neglected by indirect methods and approaches that

are not based on demand curves. These features have, over time, made VC the most

common method of valuation for non-market goods, such as environmental goods, but

also human life and health. It is important that th

complete and standard idea to be formed among all respondents. The questionnaire is

administered to a sample of the population interested by the change to environmental

quality.

According to critics, VC has the serious shortfall of neglecting the relationship

43

PROMETEIA

resources that they may actually have available. The problem, in any case, possesses

no general solution. The solution shall depend on the decisional context that is

adopted.

2.1.1.2. CONJOINT ANALYSIS METHODS

e good or service

the consumer’s/user’s decision-making process, and also assesses which are the

the

980s, but in a systematic manner only since the 1990s, these techniques have been

applied to the valuation of environmental goods too.

The main advantage of CA lies in the similarity between its distinguishing procedural

flow and the consumer’s actual thought-process when making a choice and/or

purchasing a market good: the interviewee assesses the good in its entirety, without

necessarily explaining his/her preferences for each single feature of the good.

In CA studies the disaggregation of values that make up the overall value of an

environmental good is dealt with by means of an hedonic function estimate. This

estimate is based on data obtained through the choices of a sample of interviewees.

The method consists in a number of stages, as summarised in Fig. 8.

1. Characterisation of the decision problem: through focus groups, literature surveys,

and expert interviews it is possible to characterise the decision problem in terms that

may be understood by the interviewees. Specifically, it is necessary to understand:

i) how individuals define the valuation dimensions of the good or service to be

valuated, ii) how individuals search for information on alternatives and on the good’s

These are multivariate methods of analysis first used and applied to marketing

research. They are based on overall opinion expressed by the users of goods or

services with respect to a number of complex alternatives and consist in breaking

down the original valuations into values of utility, separately for each feature of the

good or service, so that overall preferences may be determined. A sample of users is

administered a number of forms, each of which describes the good (or service) to be

valuated; these descriptions are based on combinations of a number of features of the

good (the attributes). Starting with the opinions given to these descriptions by the

interviewees, conjoint analysis then ranks the various features of th

in

best alternatives, among those proposed, for each of the features mentioned16. In

1

16 The algorithm for the analysis of data obtained from the experimental context consists in a system of multiple regressions where each stated preference is a dependent variable whose value is explained by the attribute levels - independent variables of the system.

44

PROMETEIA

attributes, iii) how to assemble the set off choices to be submitted for valuation, and

iv) how individuals make their decisions. These elements become essential in

formulating the problem of valuation in such a way that it be consistent with the

thought-process that characterises actual decision making.

Fig. 8 – The stages of the stated preferences model based on attributes

Characterisation of the

Design of

decision problem questionnaire

Selection of attributes of the

good to be

Sampling and data collection

valuated

Preparation of descriptions based

on possible combinations

Model estimates

2. Selection of the attributes of the good to be valuated: based on the information

obtained in the previous stage, it now becomes possible to define the number and

value of the levels of each attribute of the good to be valuated. This stage of the

process is often conducted at the same time as stage 1. The attributes are usually

presented to the interviewees in verbal or numerical form, and at times in graphical

form.

3. Preparation of descriptions based on possible combinations: once the attributes and

associated levels have been determined, the researcher prepares a number of

descriptions that are based on the possible combinations.

45

PROMETEIA

4. Design of questionnaire: the questionnaire, containing one or more choice

descriptions and a number of questions aimed at mapping the profile of interviewees

may be completed by the respondents by themselves or be presented and

administered by an interviewer.

5. Sampling and data collection: this addresses the usual considerations relating to

how representative the selected sample is, vis-à-vis the costs of the research.

to explore their

questionnaires,

number of methodological as well as statistical-econometric value added factors are

e to be made in a satisfactory way.

Secondly, the way the eliciting form is built through repeat choice is complex, but very

flexible. The degree of complexity of the choices may vary from case to case, the

disaggregation of the good into attributes and levels may be more or less analytical,

and the number of choices made by each individual may be varied.

6. Model estimates: starting with the data collected in the previous stage, the

econometric techniques that are usually adopted are the ones of a multinomial type

according to the criteria of maximum likelihood estimate.

The use of Conjoint Analysis methods encourages respondents

preferences, highlighting any trade-offs existent in their choice horizons, and enables

the preparation of valuation scenarios for environmental goods that are based on DAA

as well as DAP; furthermore, it allows a more complete valuation since it considers

several attributes of the good and not only those that are of a strictly monetary nature.

In any case, Conjoint Analysis models are only a recent approach and need to be

tested and assessed across different contexts of valuation. Although there is a strong

degree of complementarity between CA models and Contingent Valuation, as

techniques aimed at eliciting preferences through the administration of

a

observed for the CA technique.

Firstly, the monetary factor – the implicit price of the good – is deemphasised, since it

is only one of several attributes describing the good’s offer. We therefore avoid directly

asking interviewees what their willingness to pay is. The marginal willingness to pay

(accept) vector is uncovered indirectly, it being obtained by the relationship between

non-monetary and monetary coefficients. However, this means that the scopes of CA

and VC (and, in general, of traditional economic analysis, such as cost-benefit analysis)

are different, and to some extent not comparable: CA takes into consideration a much

broader type of choice, within which there may be different choice criteria. Conversely,

VC strictly refers to the economic profile of a given decision, and contains principles

and rules that allow decisions of this typ

46

PROMETEIA

Thirdly, repeat choices allow for the building of databases that store more information,

with the same number of interviewed individuals; therefore, statistical-econometrical

surveys may use techniques that are alternative and/or of varying degree of

complexity. In fact, econometric analysis has more scope to test alternative models

and specs compared to VC (both for discrete as well as for continuous data). These

advantages however need to be analysed and considered vis-à-vis the probably higher

costs of the analysis – both in the stages of preparation and administration of the

questionnaire as well as in the stage of analysis of the collected data.

2.1.2. BENEFIT TRANSFER METHOD

The Benefit Transfer Method (BTM) estimates the demand functions of a specific

he entire valuation model. In the first case,

prim tal good.

good are the ones

t the monetary

lu ntire demand functions

environmental good by using the findings of other valuations. There are two possible

types of transfer: the one that transfers a value from a pilot or representative study

(primary study) and the one that transfers t

the average values of some unit of good or service that have been estimated in a

ary study are used to calculate the value of the change of the environmen

This means that the quantitative and qualitative changes of the

observed in the new study being conducted (secondary study) whils

va ations come from other studies. In the second case, the e

estimated elsewhere are transferred17.

This method has at least two important advantages. Firstly, transfer costs are lower

than the costs that would be incurred for an ad hoc study. Secondly, the time required

to carry out the transfer, and thus the study, is shorter than for any other valuation

method. It is clear that the quality of the results of the transfer will depend on how

well the primary study was carried out and on the degree of affinity between primary

and secondary studies. The research stage of the primary study from which necessary

data is transferred is an extremely important one, in that situations as close as possible

to the reference context should be identified. Furthermore, the good to be valuated

must be the same good that is observed in the pilot study. The characteristics of the

17 The E.U. White Paper on environmental responsibility (2000) gives way to the use of valuation methods for non-market goods, and particularly contingent valuation methods. However, the White Paper underlines how specific studies are recommended only for very large cases, since for minor cases the costs and timing of the valuation would be excessive vis-à-vis the damage that is to be compensated. For such minor cases, the White Paper encourages the use of the BTM.

47

PROMETEIA

population shall also need to be similar even though it may be possible to correct a few

differences during the elaboration of data extracted from the primary study.

One of the main problems of using the benefit transfer method is understanding if, and

to what extent, the transfer causes a distortion of estimates. In order to check the

aspects are not described or because only the researchers who

conduc d the primary study are aware of the details of the procedures adopted. If

at are closely related to the secondary study

ecause

of the technical complexity of certain transfer procedures, as well as the skill required

to compare primary and secondary studies, the benefit transfer method should only be

used by researchers who are suitably skilled in both valuation techniques and required

statistical tools.

Despite the difficulties involved with using this method its usefulness must be given

due consideration since the possibility of using the results of primary studies conducted

in different contexts increases the value of pilot research. More so, when research of

this kind is conducted with great accuracy and use of resources, its cost-benefit

relationship increases; the creation of standard protocols to be applied for the use of

data and transparency of the valuation procedures is encouraged; finally, a possibility

reliability of data it is appropriate to verify:

- the theoretical significance of the transfer (for example, is the value estimated in

the primary study exactly the same as the one of the secondary study?);

- the proper collection and handling of the transferred data;

- the suitability of the statistical techniques that have been applied;

- the intrinsic properties of the valuation model that is used.

All this information is not always easily inferable from analysis of the primary study

since certain qualitative

te

there are a number of primary studies th

then it may be possible to carry out the benefit transfer by means of meta-analysis.

This consists in grouping the results of a number of valuations so as to obtain more

reliable transfer values. One form of meta-analysis consists in assuming that the

parameters being estimated in every primary study come from a “mother distribution”.

In this case meta-analysis takes into account the differences that exist in samples and

standard errors of each primary study, and gives greater weight to studies involving a

large sample size and having smaller standard errors. Another possibility is that of

using regression techniques to explain the variability found in primary studies. B

48

PROMETEIA

is given to carry out studies that make use of the benefit transfer in contexts where

resources are limited18.

2.1.3. REVEALED PREFERENCE METHODS

Revealed preference methods (indirect methods) do not rely on surveys to estimate

the value of a given environmental good, but rather turn to the market for signals that

allow to estimate an individual’s willingness to pay (DAP). The two most common

methods use the prices of property as indicators of the value of an area’s specific

environmental attributes and the willingness to pay to visit, say, a park, wood, or lake

as proxy of an individual’s DAP to enjoy the place. The two methods use price systems

that are expressed by the market that are then suitably calculated and corrected; their

reliability is therefore affected by the overall efficiency of the reference market.

18 The strengths of this method, which do not however eliminate the need to carry out and finance representative pilot studies, may be seen by the creation of a number of databases that enable extraction of transfer value for specific environmental goods. One of these is the Environmental Valuation Reference Inventory (EVRI) developed under the direction of a panel of international valuation experts from Environment Canada (Canadian government); a second is Envalue - Australia’s Environment Protection

aim of these databases is to provide an easily accessible, low cost, repository of primary studies from which to extract values that may be transferred to secondary studies. With the incentive given toBTM we are moving in the direction of creating an archive of shadow prices, where

Authority (EPA) (see http://www.ec.gc.ca/envhome.html, and http://www.epa.nsw.gov.au/envalue). The

using every study that is

rried out contributes not only to valuation of the damage in question, but generates information that ay be used in future studies. Of course, for this to be possible, it is necessary that each valuation study e carried out and described with scientific honesty so as to allow potential users of the study to nderstand what has been done and what the underlying assumptions affecting the value may be.

cambu

49

PROMETEIA

2.1.3.1. TRAVEL COSTS METHOD This method is suitable for the valuation of non-market goods for which the cost of

transport is a significant component of the consumption decision. Historically, the

model first appeared in the United States in the late 1940s. Harold Hotelling, professor

of mathematics at the University of North Carolina, rediscovered a number of works by

French economist Jules Dupuit (1844), and argued that the social value of a national

park may be measured by the area underpinning the good’s demand curve. The

method is especially applied in the environmental field: particularly for the valuation of

the area’s loss of value may be studied by the change in

e that must be covered to reach the

green areas and natural parks that may attract visitors living both close and far away.

The method’s application to cultural events, such as art exhibitions, also seems very

appropriate. The underlying idea is that travel costs (comprising both monetary as well

as implicit costs, such as travelling time) may be analysed in order to estimate the

demand curve of the environmental good. It is necessary to possess data on the

number of visits (per unit of time, e.g. per year) and on the cost of each visit made by

different individuals (or coming from different areas, in the zone version). From these

data it is then possible to infer how the total number of visits (the quantity of the

demand function) would change as entry price changed (which often, in the case of

natural parks, is initially null or fixed at a symbolical level). Once the demand curve has

been built, the good’s DAP can be calculated. This entails that if an accident damages

or destroys a green area,

DAP, through analysis of data on the number of visits and travel costs before and after

the accident. This change in the DAP is a measurement of the value of the

environmental damage. For national parks, the cost of utilising these specific locations

falls, among other things, by the distanc

recreational site. In general, it may be said that an individual’s decision process for a

particular recreational site depends on the travel costs, the location’s physical

attributes (for example, its quality in terms of physical components) and a number of

socioeconomic variables. According to Hotelling, starting from these theoretical

assumptions, it may be possible to build a model capable of defining individual demand

functions with which to identify, given certain conditions that shall appear clearer later

on, the economic benefits that arise from these recreational activities.

50

PROMETEIA

2.1.3.2. HEDONIC PRICING METHOD This method too is essentially applied in the environmental field. It exploits the

relationship of complementarity between environmental quality and properties

purchase or rented on the property market: apartments that share similar functional

haracteristics but that are located in areas possessing different environmental

tistical studies of the

lationship between the prices of different properties and the functional and

me may enable price estimates to be made for

ay be obtained from the result of an algorithm

ased on a two-stage process: the first stage is estimating the hedonimetric function

prices with their relative characteristics through a

isolating the real reasons explaining property

rice differences and the choice of the most suitable specification form to use.

urthermore, the lack of a lot of significant property data makes the relationship

etween the prices of private goods and environmental characteristics discordant.

onsequently, real market conditions, as described by the model, are not very realistic.

or example, it seems somewhat unreasonable to assume perfect information,

c

qualities are purchased or rented at different prices. Sta

re

environmental characteristics of the sa

different components of environmental quality.

For example, houses are purchased and sold on organised markets, and one of the

factors affecting the individual sell or buy decision is the level of noise and air pollution.

This has suggested that there may be a way to determine the value of environmental

services by analysing the housing property market. The model aims to define a

demand function expressing the relationship between quality of the environmental

good (for example, the level of noise/peacefulness) and the marginal willingness to

pay. This demand function estimate m

b

by a sample of housing selling

functional specification capable of approximating the sampled data; the second stage

uses the marginal price of each characteristic as a dependent variable in estimating the

inverse demand function.

One possible application of the method concerns the estimate of the damage created

by a waste dump. A variation to the method, applied to wages, has been used to study

the compensation that is requested by workers in order to accept higher risk works or

worse work environments (hedonic wages).

There have been a number of criticisms to the hedonic pricing model. A first difficulty

is posed by the identification of the explicative variables to be included in the price

function, especially in relation to the problems arising in terms of multicollinearity

among the variables, the possibility of

p

F

b

C

F

51

PROMETEIA

territorial mobility, no costs for information gathering and relocation. Lastly, the model

s ce of delays in market’s price

where. A second possibility involves calculating the cost of a replacement

project that may be able to carry out, for example, the same functions of the

ecosystem or the environmental good that has been destroyed or damaged in the

course of the new project and acquire this estimated value with the benefits deriving

from the new project’s implementation. The two approaches are not alternative. This

method appears somewhat difficult to implement due to the difficulty of rebuilding

ecosystems that possess the same functionalities of the original ones, and the

uniqueness of a number of environmental goods which therefore rule out the

possibility of creating shadow projects.

finds ome difficulty to account for the existen

adjustments to demand changes and the difficulties of perception and valuation of

environmental qualities in the eyes of consumers remain.

2.2. METHODS NOT BASED ON DEMAND CURVES

2.2.1. AVERTING BEHAVIOUR METHOD

This method analyses potential or actual protection expenditure required to remove

environmental damage (think of a family that fits double glazing windows against

traffic noise, the construction of noise-barriers, or the building of tunnels covering

roadways or railways that have intense traffic). This method makes it possible to

estimate the social willingness to pay to reduce annoyance levels to acceptable levels.

The method is very useful; by looking at a number of market signals it allows to rapidly

and quite cheaply identify a minimum threshold for the value that individuals associate

to environmental goods and services.

2.2.2. SHADOW PROJECT METHOD

If new projects threaten natural habitats, the shadow project method of valuation

looks at the possibility (in terms of sustained costs) that another environmental good

be rebuilt else

52

PROMETEIA

2.2.3. DOSE-RESPONSE OR PHYSICAL BOND METHOD

(for example, biological or engineering)

at may exist between the good (such as an environmental good) and the consumer.

his estimation procedure does not attempt to elicit the preferences of economic

utes a so called dose-response relationship.

This method analyses the technical relationship

th

T

agents, but rather comp

In other words, when we find ourselves dealing with some type of environmental

damage and when this damage is related to a cause, the cause-effect relationship is

referred to as a dose-response relationship. The relationship between the physical

damage produced (effect) and the level of pollution (cause) is defined by a

mathematical function known as the damage function. Multiplying the physical damage

function by an appropriate monetary price gives us the relative monetary damage

function. Practical applications of this non-behavioural technique address, for example,

estimates of environmental degradation expressed in terms of the depreciation of

buildings, damage to agricultural produce, cost estimates of individual morbidity or the

value of life. A number of criticisms have been made to this approach. These

highlight, in particular, the fact that damage functions may not be directly related to

individual utility functions and, as such, not be able to correctly describe the behaviour

of economic agents on the hand, and not allow valuation of the value of existence

component of the Total Economic Value (VET) of a public environmental good on the

other hand.

53

PROMETEIA

3. TYPES AND DIMENSION OF TRANSPORT EXTERNALIT

3.1. CONGESTION

IES

Individuals make their own decisions about travelling and mode of travel and take into

account, above all, monetary cost and travel time, followed by other characteristics

costs that are due to the longer travel time of people and goods, and may have other

effects (the risk of accidents, stress, less comfort…). Congestion is an externality of

individual travel decisions: that is to say, it is an unexpected effect of t ese decisions,

connected to interaction among individuals. In this regard, the analyst must essentially

determine two elements: the amount of time and the monetary value of time. The first

of these two elements is data, and its measurement poses no theoretical problem.

Conversely, monetary valuation of a unit of time has been the subject of argument for

activities is the economic value of the time spent (the opportunity cost of time). This

value will have to be weighed, so to say, to take into account certain qualitative

aspects that are not always clearly perceived through the decisions of individuals.

Accordingly, there are two main elements that allow us to determine the value of time

lost due to congestion: the opportunity cost of travel time and the value of the quality

of travel time.

The opportunity cost is the value of the benefit related to the activity that the

individual is foregoing. As such, this value does not depend on the chosen mode of

transport, nor does it depend on travelling conditions.

3.1.1. DEFINITIONS

(sometimes considered just as important) of the form of travel (safety, comfort, etc.).

Individual travel decisions may cause inefficient allocation of resources, since

individuals are not necessarily inclined to considering the effects of their own decisions

on others. For example, individuals may not consider the negative external effect that

their decisions may have on the travel time of others. Congestion generates external

h

more than forty years, with no unanimous agreement yet.

In dedicating time to our travel needs, we all choose, either explicitly or, as is more

often the case, implicitly, to forego alternative activities which are usually source of

enjoyment or, to some extent, income generating. The value of these foregone

54

PROMETEIA

Conversely, the value of the quality of time, is related to the utility or disutility of the

journey’s experience. It depends on the form of transport, duration of travel and the

conditions in which the journey is made. Congestion produces stress, uncertainty about

the time of arrival and physical effort – at least for the driver.

In addressing the problem of congestion, we shall primarily refer to private road

transport. Many of the observations that follow may, however, be directly generalised

in terms of journeys made by bus, train, ship or plane. In these cases however, we

may distinguish between two types of congestion: one type being due to individual

travel decisions, the other type being due to the decisions made by the service

operator. When the means of transport does not provide for a maximum capacity an

individual’s choice may become, especially beyond a certain threshold, rival to the

choice of other individuals. Excessive use produces overcrowding and disutility for all.

d,

e road allows the flow of a certain number of vehicles per unit of time. When a new

ehicle joins the traffic the travelling speed falls20. This interaction has two

onsequences. Firstly, each vehicle that joins the traffic must expect a longer travelling

me than the time first expected (at the time the decision was taken) and that existed

r other vehicles before the new vehicle joined. Secondly, the entry of a new vehicle

creases travel time for all others. The increased travel time imposed on all other

ehicles is a true and proper externality.

For the sake of simplicity, let’s assume that the cost of a journey is essentially given by

the time spent (we thus exclude all other cost types, such as operating costs of the

vehicle, costs arising from accidents, etc.). In the model which we shall represent

This type of congestion is similar to the type caused by excessive use of the road.

Conversely, if every unit of transport has a maximum capacity, congestion does not

occur within the means of transport but between the means of transport. In this case

the problem is related to the planning decisions of the service operator19.

The traditional congestion model goes back to the 1960s and is known as power

function model (US Bureau of Public Roads, 1964). This model assumes the starting

point to be a suburban road without entries or exists. Given a certain travelling spee

th

v

c

ti

fo

in

v

19 In forms of transport that have controlled entry we may define congestion as being the marginal variance of the probability of traffic problems; this variance is calculated over a short interval at the point of the capacity constraint. 20 The model is known as a power model because time and marginal cost are assumed to increase exponentially as the number of vehicles increases. In such a context the relationship between number of vehicles and road capacity is non-linear.

55

PROMETEIA

graphically, this is the equivalent of assuming that operating costs/km are constant.

it of time spent for

the transport, and the length in kilometres of the journey of each individual. This

implies that we may indifferently talk about journey time and cost; it also implies that

we may indifferently talk about the number of individuals travelling and the volume of

traffic expressed in man-kilometres. Each individual decides if and how to travel based

on the journey’s cost. Since all individuals that find themselves on the road at a certain

moment in time spend the same time for the journey, the individual cost is the average

cost of the journey. However, the entry of a new car-driver increases the journeying

time of all others. Accordingly, the marginal social cost of a new entry is greater than

the average cost21. If marginal cost is greater than average cost, and remains so as

more and more users join, the average cost will necessarily have to increase. (Fig. 9).

A first definition of congestion uses the journeying time necessary in the (unrealistic)

situation in which the road were completely available to only one vehicle as the

reference level. In Fig. 9, this situation corresponds to an average travel cost of AC(0).

When traffic increases, the individual cost increases (because each individual spends

AC22. The optimal private point, resulting from

For simplicity, we shall also normalise to one the value of each un

more time), as shown by the curve

short-sighted decision making, is point F, i.e. where the quantity of traffic (qi) is equal

to the average individual cost and the marginal willingness to pay for the transport.

21 By way of example, assume a vehicle were able to make a journey in 10 minutes. If a

of transport, AC is the individual marginal cost and MC is the social marginal cost.

second vehicle enters the road, assume the average time to make the journey increased to 15 minutes. Normalising to one the value of a unit of time, implies that the average cost goes from 10 to 15. The marginal total cost which previously was 10, is now 20 for the second (equal to the relationship between the total cost increase, which goes from 10 to 30, and the increase in the volume of traffic, which goes from 1 to 2). 22 We must be careful of the particular use – to some extent a dangerous one – that is made of microeconomic terminology. In the classic economics textbook example of a negative externality, a distinction is made between individual marginal cost and social marginal cost (equal to individual marginal cost plus the value of the marginal external damage). In the case

56

PROMETEIA

Fig. 9 – Congestion according to the power function model

The total cost of the time spent – by all – is given by AC(q

H AC(0) z J

WTP'

0 q q Man km

Euro MCV

i)*qi. Having assumed

s i

MC(q i ) G

MC(q s ) E AC

FAC(q s) AC(q i )

complete absence of traffic as the reference level, the congestion cost, the part of cost

that is due to congestion is [AC(qi)-AC(0)]*qi (equal to the rectangular area enclosed

by points AC(0), J, F e AC(qi)). For each individual the cost of the congestion is [AC(qi)-

AC(0)] = JF.

Alternatively, congestion may be defined assuming an arbitrary speed of travel as

being the point of reference, or the average speed indicated by engineers as being the

speed that may maximise the hourly flow of vehicles.

Lastly, congestion may be defined as being traffic in excess of the optimal volume for

society (Prud’Homme, 2001). This social optimum is represented by point E,

corresponding to a quantity of traffic (qs) that equates marginal social cost to marginal

willingness to pay for the journey. Each unit of traffic in excess of qs is sub-optimal,

since it produces a marginal benefit (the individual’s willingness to pay) that is less

than the marginal social cost (given by the time spent by the individual himself, plus

the time that the individual makes those who are already travelling lose). The so-called

net loss for society, in terms of welfare, associated to the individual’s optimum level F

rather than social optimum level E, is the area enclosed between the lines delimited by

57

PROMETEIA

points EGF. According to this definition, this is the total cost of congestion to society.

The marginal cost of the congestion that is generated by the last individual that enters

utable to every

individual (and given our assumptions, to every vehicle or kilometre or journey), is

equal to the ratio between area EGF and the quantity of traffic qi.

Whatever the definition used, in order to analyse quantity and value of congestion it is

necessary to estimate the relationship between volume of traffic and average travel

time. This time must then be given a value so as to obtain a measurement of cost. If a

definition of congestion based on economic efficiency is used, it will then be necessary

to estimate the marginal social cost function (not a complicated calculation once the

average cost function has been determined) and the transport demand function.

When we talk about time lost due to travel, we need to make two clarifications. Firstly,

the calculation needs to exclude the time during which the individual, although blocked

by congestion, still manages to carry out the activity that he/she would have chosen as

an alternative. This may be the case because the individual is able to work with the

same productivity that he/she would have had in the office (if this is not so, an

adjustment needs to be made), or because the individual may carry out some form of

a

reached, after which it starts to increase, as shown in Fig. 10. For traffic volumes that

are lower than q’, individual and social marginal costs are equal. Low intensity traffic

does not generate external costs. However, all the observations that we have made

before in terms of the power function model, and concerning optimum private and

the road is GF. The average cost of the congestion that may be attrib

recreational activity (listen to music, read a book, see a film).

Secondly, if the congestion has effects on the individual’s productivity before his/her

departure or after his/her arrival, the work time that is lost must be added to the

congestion quantity. The same is true for any effects on the individual’s ability to enjoy

any free time. For simplicity, these two elements are not considered in Fig. 9.

The validity of the power function model has recently been questioned by a number of

authors. Two main criticisms are made of the model. Especially in urban traffic (but in

suburban traffic too, if we relax the assumption of no entry/exit roads), congestion is

not only a function of the number of vehicles circulating: for example, every bottleneck

may generate slow-ups or complete traffic stalls. It is also possible that the entry of

new vehicle onto the road will increase the travel time of all others only after a certain

traffic volume threshold is exceeded. In such a case, the AC demand curve will be

more of a linearly broken line, constant up to a level AC(0) until the threshold q’ is

58

PROMETEIA

social points remain valid even if individual and social costs have a broken linear

function.

Fig. 10 – Congestion according to the linearly broken model

The reasoning is somewhat different when we look at “point-to-point” forms of

transport such bus, train, airplane or ship transport. Arrival delays for these forms of

transport are largely due to infrastructure network congestion. With the exception of

buses, for which road congestion is due to interaction with other vehicles, delays are

due to error in the scheduling of departures and arrivals, or errors in managing the

interaction between means of transport, or lastl , due to the unsuitable network size.

In line with the first definition of congestion, the external cost may here also e

calculated as the product of lost time and value of time. Lost time is easily quantifiab

s

y

b

le,

since it corresponds to the total delay of all passengers; however, any adjustments

oncerning productivity before, during and after the journey still need to be made.

different type of congestion occurs inside the means of transport, especially when no

aximum capacity is provided for: conditions of over-crowding make the journey less

leasant23. More so, this type of congestion is not included in the definition of

c

A

m

p

In fact, for some forms of transport, a higher number of passengers may make the journey

more pleasant or less boring, as it may provide the possibility for conversation or gathering of information. However, beyond a certain threshold, it is generally recognised that crowding increases a journey’s disutility.

Euro MC

MC(q i )

MC(q s ) E

AC

FH

AC(0) JWTP'

23

0 q' q s q i Man km

AC(q ) AC(q s) i

59

PROMETEIA

congestion provided above. If a crowded bus arrives on time, the models that we have

so far considered recognise zero congestion. However, an external effect is generated

by the crowding and this affects the quality of time.

Particular attention must be paid not to confuse (and apply to the wrong context) the

total change in the value of the externality (which interests project analysis) with that

of the marginal value in the optimal situation (which interests the definition of

transport efficiency).

The policy maker who is interested in efficient allocation of resources will not aim at

eliminating congestion, but rather at bringing congestion to its optimal level. If we

refer to Fig. 9, this corresponds to reducing traffic volume from qi to qs. This objective

may be attained by introducing a pigouvian toll equal to the difference between

marginal social cost (MC) and individual cost (AC) at the optimal point. In Fig. 9, this

toll is represented by the length of the segment EH and must be paid by all individuals

that use the road.

Conversely, when assessing the social convenience of a project that reduces the level

of traffic, one must look at the effect on the average cost and marginal social cost of

transport, i.e. the effects on curves AC and MC. The project’s benefit will be given by

the change in social welfare (which in Fig. 9 is initially equal to VZE-EGF). Both

decisions also have distributive effects. A pigouvian toll, for example, increases overall

social welfare, but for many individuals the price to be paid will be higher than the

external cost borne. If this occurs primarily for less wealthy individuals, then improved

efficiency has a regressive effect in terms of equity. Therefore, when the social

opportunity of carrying out a project or introducing a pigouvian toll is assessed, it is

important to measure costs and benefits for different population groups separately.

60

PROMETEIA

3.1.2. THE VALUE OF TRAVEL TIME: OPPORTUNITY COST AND THE VALUE OF

COMFORT

Let’s imagine a world in which free time generated decreasing marginal utility, and a

(or

monetary benefits), net of taxes.

r the individual, every hour spent working has a lower value than the net hourly

H an hour’s work, for society as a whole, is not equal to the

lost due to

world in which journeying and work were not related to any particular disutility, as if

travelling by car or working in the office were equivalent to resting. In the neoclassical

analysis model of economic decisions, the individual may chose how many hours

fractions of an hour) to work. This individual will ask for free time up to the point in

which the marginal benefit will be greater or equal to the opportunity cost of his/her

decision not to work (payment foregone) and will work for the remaining time: to be

more precise, the opportunity cost of one hour of free time will be given by the hourly

wage (plus monetary and non

Fo

wage. owever, the value of

marginal benefit that the worker would gain should he/she not work. Rather, for

society that hour of work is as valuable as the goods that may be produced – i.e. the

marginal value of work production (VPML). A company takes on work up to the point

where VPML is greater or equal to the marginal cost of the work, which is given by the

gross wage (plus monetary and non-monetary benefits). Consequently, in a world

where working is not unpleasant, the value of an hour of work that is

congestion has a greater or equal value (at the margin) to the hourly gross wage.

In summary, every hour of (free) time that is lost to congestion, and which would

otherwise be dedicated to non-working activities, is at least as valuable as the net

hourly wage. Every hour of work that is lost to congestion is at least as valuable as the

gross hourly wage.

Commuting time is exactly at the margin of work time and free time. Accordingly, the

value of an hour of commuting is equal to the net hourly wage.

Conversely, if work generated a utility or disutility the above results would change as

follows: if utility is generated, then the individual will offer to work up to the point

where the marginal benefit of free time is equal to the net wage plus the value of work

enjoyment. If work generated a disutility then the individual would offer to work up to

the point where the marginal benefit of free time is equal to net wage less the value of

the disutility that is related to the work. In this second case, the supply of work is

obviously lower.

61

PROMETEIA

In the case of unpaid work, such as voluntary work or family work (housecleaning) the

VPML is positive but the wage is zero. Goodwin (1976) underlines the need to consider

the longer travel time. As well as the journey

ith the

ated preference method, individuals make hypothetical decisions based on proposed

scenarios. The main advantage of the revealed preference method is that the valuation

is based on actual decisions that may be observed. Its main shortfall is the reverse side

of the coin: the analyst is forced to base his/her study on limited elements, where not

all combinations of time, monetary cost and comfort may be observed. With stated

preference techniques the exact opposite is true. More elements may be made

available and trade-offs different from the ones actually observed may be analysed:

however, all choices are hypothetical rather than actual. Combining the two methods

within the same study offers an interesting opportunity. Revealed preferences may be

used to confirm the results of stated preferences. The latter may then be used, with all

time that is dedicated to activities such as housecleaning as work time.

In order to class lost time (due to congestion) as being either work time or free time, it

is not relevant whether a given interval (e.g. an hour) be officially part of work time. In

fact, an individual may react to congestion by reorganising his/her day’s schedule.

Lastly, the value of comfort depends on the means of transport that is used. Comfort

reflects the value of the change in the level of utility associated to the journey vis-à-vis

the alternative activity that the individual would carry out if he/she weren’t travelling. A

number of empirical estimates suggest that different means of transport possess very

different levels of discomfort.

3.1.3. VALUATION METHODS

The valuation of an externality that is due to congestion requires an estimate of the

average journeying cost function, which in turn depends on travel time and the value

of every unit of time. The value of time is different for each individual and depends on

the activity that is impeded by

undertaken and the time spent, it is then theoretically necessary to identify individual

characteristics, the type of activity that is lost, and the comfort differential.

The time value estimate may be calculated in two ways; stated preference or revealed

preference methods. Both methods aim to value a unit of time by measuring the rate

at which travel time and monetary cost of the journey are exchanged. With the

revealed preference method, the actual journeying decisions are analysed. W

st

62

PROMETEIA

their characteristics of flexibility, to explore combinations of travel time and monetary

based on answers given by workers unless the

it is better to use

does not have the necessary time and resources to carry out an

cost that may not otherwise be observed.

If revealed preferences are not directly observed, but rather reported by individuals ex

post, then the problem posed by the difference between real time and perceived time

remains. Goodwin (1976, p. 39) shows how individuals tend to report lower than real

times when the activity is pleasant and higher than real times when the activity is

unpleasant: for example, in a bus journey individuals reported a waiting time of fifteen

minutes (perceived), when in fact the waiting time was five minutes. Yet another

problem is given by individuals’ inability to perceive time savings that are below a

certain threshold. This makes the valuation of projects that reduce congestion times by

only a few minutes for many individuals difficult. It is also worth underlining that the

valuation of work time cannot be

workers are self-employed. Indeed, a dependent worker does not consider VMPL,

neither in the revealed nor in the stated preferences decisions, but only considers the

marginal benefit to himself/herself. Therefore, in these cases

statistical data on the average production of workers having the same characteristics.

The same goes for unpaid work.

When the researcher

ad hoc study, it may be possible to use the Benefit Transfer Method (BTM) which,

despite its many problems, is almost an obligatory tool in many cases and comes with

the significant advantage of its low cost. When the socio-economic context is

sufficiently similar to the one being observed and the study is recent, then researching

for the value of time in available literature may be the most convenient choice.

63

PROMETEIA

3.1.4. THE VALUE OF TRAVEL TIME IN AVAILABLE LITERATURE24

Tables 1-2 show the results of empirical studies of the value of journeying time. Table

1 looks at travelling without congestion whilst Table 2 looks at cases with congestion.

ing), means of transport, context

In Table 1 the value of time is generally expressed as a fraction of gross hourly wage.

When gross is not mentioned, it means that the original work or survey from which the

value was taken is unclear. It is however improbable that they refer to net wage. In

terms of congestion, the value of time is expressed as a fraction of the value of

journeying time in normal traffic conditions (without congestion). For every study ,the

tables provide details of the author, year of publication, value of time and, if indicated,

the type of journey time (work or other, commut

(urban/suburban) and type of study (revealed preferences (RP), stated preferences

(PR) or available literature survey).

The best surveys of value of journeying time probably are Wardman (1998) and

Delucchi (2004c). As of today, the best work that has been applied and which may be

used to guide new ad hoc work is probably Small et al. (1999).

The value of journeying time is a function of the alternative activity that is being

foregone and there is a fundamental difference between foregoing work time and

foregoing free time.

Let’s start from travel time that is not connected to work activities (including

commuting). Available literature tells us that one hour of travel time without

congestion has a value of between twenty and eighty percent of gross hourly wage.

The average value of this interval - 50% of the gross hourly wage – is indicated by

numerous authors as being a reference level (Small, 1992; Waters, 1992; Barnes,

1995; Delucchi, 2004c). Available literature underlines the fact that the value of time

depends on income. Accordingly, applying an average value to all individuals may lead

24 Most available literature focuses on the value of one hour of journeying time by car in flowing traffic conditions. Attention is focused on costs to passengers rather than freight. An important distinction is made between work time and free time. In both cases the valuation is often expressed as a percentage of hourly wage. A current of recent literature focuses on the value of travel time given conditions of congestion, and the effects of uncertainty with regard to arrival time. Unfortunately, in a number of cases the original work has been impossible to find. The value of time that we indicate is sourced from surveys

these shortfalls make comparative analysis of different valuations very complex, the f each study have been provided and all unknown parameters are indicated. Luckily, the

that make reference to the results of previous literature. In these cases especially, the underlying assumptions to the value of time estimates are often not provided in detail. For the purposes of this work it is important to underline that the definition of congestion is not a uniform one, and is often not clearly specified. Sincecharacteristics oanalyst can rely on a set of results that are relatively consistent among the different studies and on general agreement on the key elements that should be considered in carrying out any new ad hoc estimates.

64

PROMETEIA

to wrong conclusions. It is necessary to apply different values for different user groups.

The value of time expression as a fraction of hourly wage takes this criticism into

account (at least in part).

Recent studies have suggested that the fraction of gross hourly wage decreases as

income increases. Haight (1994) argues that when income increases threefold, the

value of time increases by only 40 percent. Small et al. (1999) estimate that the value

of travel time is 50% of the gross hourly wage when family income is 15,000

dollars/year, and falls to 24% if annual income increases to 95,000 dollars. We suggest

the existence of a decreasing relationship between income and the fraction of gross

hourly wage representing the value of travel time.

When the time spent in conditions of congestion is lost in terms of work activities, the

loss to society is VPML – i.e. greater than or equal to the gross hourly wage that the

individual would have earned, increased by the monetary and non-monetary benefits

that come with the work. If we assume the individual were to lose his least productive

work hour, we can state that there exists a relationship of equivalence. Hensher et al.

ongestion, find no significant differences compared to the estimate

(1990) estimated VPML to be 1.34 times the gross wage. Delucchi (2004d) uses a

value of 1.20. We must then add the individual’s change in comfort value, i.e. the gain

or loss of utility due to the fact of being in a car rather than in an office. This element

changes from case to case, according to the nature of one’s work and individual

preferences. Hensher (1997) estimates that work time spent in conditions of

congestion has a value of 20 percent of the gross hourly wage, which is the same

value given to commuting time, but a lower value than the value reported by

individuals when foregoing recreational activities. Nuti et al. (2003), in a context

characterised by c

of free time substitution. However, except for cases where individuals are independent

workers, it is improbable that workers would assign a value to time that may be

indicative of VPML. In this case it is more appropriate to carry out an estimate based

on marginal gross hourly wage, and then possibly make an adjustment to take into

account the comfort differential related to the substitution of work time by travel time.

The value of travel time is also affected by the journey’s disutility function. We shall

now address the means of transport, the duration of the journey and the level of

congestion.

The value of travel time varies according to the means of transport that is used.

Bruzelius (1979) estimates that the time spent travelling by foot or waiting for public

65

PROMETEIA

transport weighs two or three times more than time travelled by car. This result is

consistent with the fact that individuals perceive time that is spent waiting for public

transport as being greater than the time this actually involves (Goodwin, 1976). When

a “point-to-point” means of transport is used, journeying time is made up of waiting

time, time onboard the vehicle and disembarking time. Traffic congestion affects each

of these time elements and decreases the service’s reliability. Train, airplane and ship

may be comparable to the car in terms of comfort, but give the passenger the

possibility of working or carrying out some form of recreational activity (this is not

possible for the car driver). For these forms of transport it thus seems reasonable to

use car values of time provided by literature, but suitably adjusting them for the lost

me estimate in order to take into account the fraction of production or recreational

ide less comfort than

s time savings is lower in percentage terms (compared to the entire journey).

sistent with estimates by

European Union finds the value to be 50%. Brownstone et al. (2003) estimate that

travel time with congestion has a value of 88% of gross hourly wage. Assuming that

ti

activity. Buses, however, (certainly in an urban context), prov

cars. For this reason, it seems appropriate to use the value provided in available

literature for foot travelling. For all “point-to-point” forms of transport the suggestion is

to give waiting time a higher value than that given to onboard time.

The value of journeying time varies according to the journey’s duration. Hensher

(1997) and Small et al. (1999) argue that the willingness to pay to reduce journeying

time by one unit is lower for longer journeys. This is explained by the fact that for long

journey

This result is not necessarily consistent with expectations, since a longer journeying

time has two implications that should lead to a higher valuation. Firstly, the greater the

journey’s duration, the greater the marginal utility of the activities that the individual is

forced to forego (opportunity cost). Secondly, longer journeys produce, at the margin,

greater disutility.

Beyond the elements already considered, we may say that due to physical effort, stress

and uncertainty about the time of arrival, the value of travel time varies according to

congestion. Bradley et al. (1986) and MVA Consultancy et al. (1987), estimate that in

the united Kingdom, travel time in conditions of congestion has a value of 30-40%

more than travel time without congestion. This result is con

Train (1976) and Bates et al. (1987) for the United States. MVA Consultancy and ITS

Leeds (1992), estimate that in Holland, travel time with congestion has a value of 50-

290% more than travel time without congestion. The PETS D7 (1998) study for the

66

PROMETEIA

travel time without congestion has a value of 50% of gross wage, this implies an

additional cost of 80%. Finally, Small et al. (1999) estimate travel time with congestion

to have a value 150% greater than travel time without congestion. Nuti et al. (2003),

referring to Italy, find the average willingness to pay for one hour of reduced

journeying to be approximately 40 euro (2001), and that this value does not change

of interviewees. In general, to assume a travel time value with congestion of

PRIVATE ROAD TRANSPORT

much with the type of alternative activity that the individual is forced to forego

(whether it be work or free time). This value is approximately twice the gross hourly

wage

more than 50% compared to the case without congestion seems to be conservative.

This implies a travel time with congestion value of 75% the gross hourly wage.

3.1.5. CALCULATING CONGESTION IN

In order to calculate the congestion costs of private road transport, the first element

that needs to be estimated is the value of an hour of time (or other unit). For one hour

of time the average national gross hourly wage is assumed. One hour of free time has

an average value of 50% of gross wage (w). In order to take into account congestion

disutility, an adjustment of 50% may be made. Accordingly, for free time that is lost to

travelling in conditions of congestion a value of 75% of the gross hourly wage may be

used.

An estimate or assumption needs to be made for the share of the travel time that is

actually worked. If we indicate this fraction of time as β, then, on average, the value of

one hour of congestion is given by: p = [(1-β)*0.75+β]* w. The hourly value of time p

is the basis for the estimate of the function of average costs of journey AC (in Fig. 9)25.

The average cost depends on journey operating cost and the time required for the

journey and is a function of the volume of traffic, expressed as man-kilometres in

Fig.9. In the case where traffic is measured in vehicle-kilometres, the average rate of a

vehicle’s occupancy needs to be estimated. In the case where not all of the travel time

is lost, since the individuals may, for example, be able to carry out work or recreational

activities, the amount of time needs to be suitably adjusted.

Point F in the figure is the current point of equilibrium, i.e. the observed combination

of volume of traffic and average journeying cost: qi is data; AC(qi) is given by the sum

of the operating cost and the value of time spent to travel one kilometre. (The area

This part is based on Prud’Homme (in ACI-ANFIA, 2001).

25

67

PROMETEIA

enclosed in the box delimited by points 0, qi, F and AC(qi), less the sum of operating

costs, is the total value of the time spent for the transport. The area enclosed in the

box delimited by points AC(0), J, F and AC(qi) is the total value of time lost to

interaction between car drivers and corresponds to one of the definitions of congestion

cost).

The average cost to travel one kilometre when there is only one vehicle on the road

AC(0) needs to be estimated; it is given by the sum of operating cost and value of

time.

Points F and Z are used to estimate the AC function. At times the function is assumed

to be linear. Alternatively, a power function with non-linear growth may be estimated.

The marginal journeying cost function MC is calculated starting from AC. It represents

the place of social marginal costs.

Starting from point F, the transport demand curve (WTP’) may be estimated by

assuming a linear or constantly elastic form. In either case, the underpinning

assumption concerns the value of the elasticity at point F. The MC and WTP’ curves

determine the optimal social point E; the net loss associated to excessive traffic is

measured as the area delimited by the lines identified by points EGF; this is the cost of

congestion in terms of social welfare; the quantity of congestion is the difference

between q (the optimal private point F) and q (corresponding to the optimal social i s

point E).

Since the parameters vary significantly according to context, it is appropriate, if

possible, to estimate cost of congestion separately for non-motorway urban traffic,

motorway urban traffic, non-motorway suburban traffic and suburban motorway traffic.

68

PROMETEIA

3.1.6. CONGESTION FOR OTHER MEANS OF TRANSPORT

For suburban buses, trains, ships and airplanes, all excess travel time with respect to

official times may be considered as congestion time. The quantity of congestion may

be obtained by the following formula: q = N*r*α, where N is the number of

passengers, r is the average delay in hours and α is the fraction of travel time in

.1.7. CONGESTION IN THE TRANSPORT OF GOODS

he mode of transportation for goods is mainly affected by the following elements:

onetary cost, value of the goods, risk of damage or loss, travel time and reliability

mall et al., 1999). Congestion (and the risk of congestion) increases the uncertainty

f delivery time, reducing the reliability of the service. Empirical studies demonstrate

at the shipper is prepared to accept longer transportation times provided these

orrespond to a lower risk of late delivery (Wilson et al., 1986). The traditional

pproach for the valuation of the social cost of late delivery of goods due to congestion

based on the opportunity cost of the frozen capital. In practice, this method consists

calculating the interest matured on the value of the goods in transit in relation to the

reater travel time. However, except for very long journeys, this method yields very

w valuations, which are contradicted by empirical literature (Nash and Sansom,

999). An upper limit to damages due to delays may be calculated by looking at the

onetary cost differential of alternative means of delivery that would have guaranteed

quicker delivery but were not chosen. This approach does not take into due

onsideration the issue of transit time reliability. A consignee may not be interested in

fast delivery, and yet a delay with respect to expected delivery may produce great

amage, far greater damage in fact than what should have been spent to obtain a

mely delivery by using a more expensive means of transport.

excess – vis-à-vis official times – that is really lost (because not dedicated to neither

work nor leisure) during the journey. The unitary value of the congestion may be given

by the formula provided for car transport, suitably adjusting parameters β and w.

These parameters may change significantly for users of different means of transport.

For urban buses things are different. In fact, congestion may lengthen both time

onboard the bus as well as waiting time at the bus stop. Available literature estimates

the value of waiting time to be twice that of travel time. This time may therefore be

calculated as 1.5 times the gross hourly wage.

3

T

m

(S

o

th

c

a

is

in

g

lo

1

m

a

c

a

d

ti

69

PROMETEIA

70

Providing indications of the value of the external cost of congestion with respect to the

transport of goods - to be applied through the benefit transfer method - is an almost

impossible task. The valuation fundamentally depends on the value of the goods and

the n eds of the consignee. It is therefore necessary to carry out ad hoc valuations

using stated or revealed preference methods.

Whe is not possible to carry out an ad ho udy that takes into account the

chara te tics of the goods transported and ements of the productive

system, th e traditional approac based on ortunity cost of frozen capital

must be used. In practice, this meth interest matured on the

value of the goods in ansit in re er travel time. It is useful to note

that this approach is not consistent wit e ed for congestion damage in

passenger transport. In fact, no reference de and we are

unaware of e that t using a structural theory

e e tra

e

n it

c

arablcomp

c st

the requir

the opp

great

one us

ris

en th h

od requ

tion

s the tr

ires calculating the

the

to social welfare loss is ma

ansport of goods

tr

tur

th

la

t

t of

to

h th

any litera

to t

rea

hat of nspor passengers.

PROMETEIA

Tab. 1 – Available literature on the value of travel time without congestion (V eans of transport (autom mixed); C = context (urban, suburban); M = method (survey, SP = stated preferences, RP = revealed nown.)

Author (year) Unitary value of time Notes

= vehicle, m obile, bus, preferences). NC = not k

Lisco (1967) f hourly wage 5,000 USD 1997

me/35.000)*50% of hourly wage ual income is <35,000 USD 1997

V = NC. C = NC. M = NC Type of time: commuting Gross/Net wage: not specified

50% oIf annual income is >=3 (incoIf ann

Bruzelius (1979) On foot or waiting tim200-300% of the va

e of public transport lue of travel time by car

e

Automobile (in vehicle), commuting, 20-30% of hourly wage

C = NC. M = Survey of available literatur Gross or net wage: not specified

Miller et al. (1985) aximum 80%) ey of available literature 55% of hourly wage (average, minimum 30%, m V = NC. C = NC. M = SurvGross/net wage: not specified

Miller (1989) V = NC. C = NC. M = Survey of available literature 60-80% of gross hourly wage Small (1992) 50% of hourly wage V = NC. C = NC. M = Survey of available literature

Gross/net wage: not specified Waters (1992)

age: not specified 50% of hourly wage

V = NC. C = NC. M = Survey of available literature ross/net wG

Barnes (1995) 50% of hourly wage

V = NC. C = NC. M = Survey of available literature Gross/net wage: not specified

Hensher (1997) tion of gross hourly wage journey

ge

uting with own car

ing with company car

Journey during work 0.10 0.20

torway. M = SP. Country: Australia.

A fracType of Min AveraMax

mCom0.13 0.22 0.61 Commut0.14 0.27

.71 0

V

= Automobile. C = urban mo

71

PROMETEIA

Author (year) Unitary value of time Notes

0.71 Recreational activity 0.26 0.31

0.22

0.42 Other personal activity

0.44 1.07

Calfee and Winston 20% of hourly wage V = Automobile. C = suburban. M = SP Gross/net wag(1998)

e: not specified

Wardman (1998) Work time: 12.9 pounds/hour (average); 1.6 minimum; 41.0 maximum

V = mixed, in ban and suburban. M = Survey of 105 studies SP (6%) and RP (94%) during the period 1980-9Country: United Kingdom

Non-work: 3.6 pounds /hour 0.6 minimum; 11.1 maximum

-vehicle. C = ur6.

Small et al. (1999) AnnuUSD/hour

Table S-1 – p.3 (except for third column) al family income (USD)

Fraction of gross hourly wage(a)

0

V = automobile. C = suburban. M = SP. Country: USA The value is a function of: work or non-work time, gross annual family income, total duration of journey, peak hour or not. (a) Fraction of 5 hours of work per family per year, consistent with Small et

15,000 2.64 0.50 35,000 3.99 0.33 55,000 5.34 0.28 75,000 6.70 0.26 95,008.05 0.24

the gross hourly wage is calculated assuming 2,86al.. (1999, p. 39).

INFRAS and IWW (2000)

18.62 USD/hour European AverV = mixed. C

age = mixed. M = NC

72

PROMETEIA

Tab. 2 – Available literature on the value of travel time with conditions of congestion Author (year) itary value of time Un Notes Train (1976) of the value of the normal journeying time 130%

V = automobile. C = NC. M = NC

Bradley et al. (1986)

of the value of the normal journeying time : United 130% V = NC. C = NC. M = NC. CountryKingdom

Bates et al. (1987)

of the value of the normal journeying time 133% V = NC. C = NC. M = NC

MVA Consultancy et al. (1987)

of the value of the normal journeying time 140% V = NC. C = NC. M = NC. Country: United Kingdom

MVA Consultancy et ITS Leeds(1992)

90% of the value of the normal journeying time

150-3 V = NC. C = NC. M = NC. Country: Holland

PETS D7(1998)

of the value of the normal journeying time 150% V = mixed. C = mixed. M = NC

Small et al. (1999)

V = automobile. C = suburban. M = SP. Country: USA (a) Additional cost. Monetary value of the welfare loss due to substituting one minute of travel time in normal conditions with one minute of travel time in conditions of congestion. Function of the duration of the journey. (b) Value of one hour of travel in conditions of congestion, as a fraction of the average gross wage (15.7 USD/hour, calculated by dividing average income of 45,000 USD/year by the number of

Table S-2 – p. 3 (except for the third column) Duration of journey (minutes) Increased cost (USD/min) (a)

Value of one hour of congestion; a fraction of average gross hourly wage (b)

10 0.79 4.02 15 0.52 2.99

73

A

74

Unitary value of time Notes 26 0.30 2.15 30 0.26

45

3 (except for the third column) pe of journey and Level of income

SD per average square minute off) of one square hour average off; fraction of the gross

)

high income(c)

1.89 Non-work, high income(c)

0.21 0.52

working hours of 2,865). The value of time for a 10 minutes journey in conditions of congestion is, for example, equal to 4.02*(10/60)=0.67 USD. (c) “High income” if annual family income is greater than 45,000 USD

ur off (a measurement of uncertainty) the duration of the

4.44 USD has been assumed (gross annual family come of 70,000 USD). For low incomes an hourly oss wage of 6.98 USD has been assumed (gross nual family income of 20,000 USD).

1.99

0.17 1.65 60 0.13 1.50 Table S-3 – p. TyReliability level (UValue average wage (dWork, 0.26 0.64 Work, low income(c)

0.22

PROMETEI

Author (year)

(d) Value of one square average ho

journey. For high incomes an hourly gross wage of 2ingran

A

75

o )uth r (year Unitary value of time Notes Non-wor , low incomek (c)

0.17 1.46

Brownstonal. (2003)

n re et 88% of hourly wage V = automobile. C = suburban. M = RP.

Cou try: USA. Note: medians compa ed.

Nuti et(2003) i

Value (Euro/hour) r

e

e

e ge

e n t y.

al. Table 3 – p. 78 Type of t me

All f ee-time45.29 Mainly free-time 38.15 All work tim 43.17 Mainly work-tim 42.91 Total/Av ra41.72 Total/Median 25.85

V = mix d. C = urba . M = SP. Coun ry: Ital

V = vehicle, me a m ixed); C = c , sub M , RP = revealed preferences). N n

urban); = method (survey, SP = stated preferencesontext (urban

PROMETEI

A

ans of transport ( uto obile, bus, mC = ot known.

PROMETEIA

3.2. ENVIRONMENTAL EXTERNALITIES

3.2.1. THE ECONOMIC DEFINITION OF ENVIRONMENT

According to the definition provided in the E.U. Commission’s Green Paper (1993), “the

environment includes natural resources […] such as air, water, soil, fauna and flora,

the interaction between these factors, the goods that make up cultural heritage and

the characteristic aspects of the landscape”. As already stated by the Italian

Constitutional Court (judgement no. 641 dated 30/12/1987), the environment, as

“unitary immaterial good”, is an indivisible entity and not simply the sum of its

constituent parts. This definition highlights the interdependency between the different

components as an essential aspect of the concept of environment. In the inevitably

anthropocentric vision adopted by economical sciences, the environment has a value

since it contributes to the welfare of individuals in a number of different ways. Firstly,

individuals enjoy the environment through direct use of the same (the enjoyment of

going for a walk in a natural park or the view of a beautiful landscape as seen through

one’s window at home).

The environment contributes to individual welfare even in a completely independent

rm from any present or future use. This happens when a site or area is of historical,

evance, giving the site/area a so called “value of

existence”.

fo

cultural, religious or spiritual rel

THE TOTAL ECONOMIC VALUE OF ENVIRONMENTAL GOODS

The categories that have been listed make up the total economic value (VET) of the

environment as a good; these are often classified differently by different authors,

which attests to the somewhat hybrid nature of a number of the components. A simple

and coherent classification is provided in Fig. 11. The services that are generated by a

natural resource may be classified as use and non-use services and the value may, in

turn, be split into use and non-use value. Furthermore, following recognition of the

importance of the interconnections between the economic system and ecosystems a

third value type has been introduced. We shall call this third value type “ecological

function” and it is directly connected to ecosystem functions.

76

PROMETEIA

Fig. 11 - Components of VET for an environmental good

Ecological function

Value of use

Value of non-use

Direct value of use

Indirect value of use

Value of option

Bequest value

Value of existence

Total economic value

THE ECOLOGICAL FUNCTION

c subsystem and ecosystem, which characterises the

basins, safeguarding the genetic diversity of biological resources, etc. Forests, for

example, have important beneficial effects that guarantee the supply of environmental

goods that are necessary for the economic productive system: in conditions of very

intense rainfall a forest that is in good condition is able to prevent the water’s surface

flow and therefore surface wash-away; trees’ roots hold rocks and stones together,

impeding landslides.

The relationship between economi

functioning of both systems, is one of complementarity. Within the context being

studied, the valuation of an environmental good implies taking into consideration the

ecological services or functions that support life, whose functioning and/or

maintenance are an essential prerequisite to enjoying the environmental goods or

services that are used as inputs to the economic (sub)system. Among the ecological

functions of natural environments are climate regulation, the protection of water

77

PROMETEIA

THE VALUE OF USE

The value of use of an environmental good is defined by the characteristics of the

entific progress does not allow us to understand all possible uses of the

public good for the

good’s consumption. We thus distinguish between: the value of direct use, which

derives from direct consumption of the good that is exchanged on the market; the

secondary or indirect value of use, which results from behaviour that does not

necessary pass through the market; the value of option, which defines the willingness

to pay in order to have a right to use an environmental good for which there is no

substitute (for example, the economic value that may be given to the option of

consuming a natural park in the future)26; the value of quasi-option,27 again related to

the future use of an environmental good, but for currently unknown goals. The current

status of sci

animal and plant species that exist in the ecosystem of the tropical forest; however, it

is known that the biodiversity that exists there has in past allowed identification of

active principles used in the treatment of very serious illnesses; biodiversity may be

assigned a quasi-option value, since one wishes to safeguard the possibility of using it

in the future for goals not yet identified, but which scientific progress will, in time,

identify.

THE VALUE OF NON-USE

The value of non-use is made up of the value of existence and the value of bequest.

Value of existence takes into account the possibility that an individual need not

necessarily consume a good to obtain utility: simply knowing that the good will

continue to exist, regardless of the benefits and direct, indirect, present and future

uses, is reason of satisfaction. The bequest value assumes that an individual may have

a willingness to pay for the conservation of an environmental

The value of option is the value that individuals give to the existence of a specific natural

me in

its current conditions. The health of individuals may be put at risk by a number of forms of pollution: this may lead to a range of possible environmental damages. 27 Arrow and Fisher, (1974); Conrad, (1980); Freeman, (1993); Nuti, (2001); Casoni and Polidori, 2002.

26

resource that they currently do not use, in order to conserve the right to use it sometithe future. It may indeed be possible to gain welfare, and thus assign a value to the right of option to sometime in the future carry out an activity that gives direct enjoyment of the environmental good (such as observing animal species in their natural habitat), even if the direct use is excluded for the time being. This naturally requires that the good be conserved in

78

PROMETEIA

benefit of current and future generations. The bequest value may also be thought of as

an indirect value of use if the bequest is considered to be a transfer of resources to

administration of questionnaires is it possible

extract the value that parties associate to the pure and simple existence of a good

mediate or future consumption assumed. It may be argued

of enabling the use of

.2.2. ENVIRONMENTAL EXTERNALITIES CAUSED BY TRANSPORT ACTIVITIES

he types of environmental damage generated by transport activities are air

ollution, noise, greenhouse effect, soil and water pollution, and nuclear risk28.

current or future generations that shall consume the good directly.

We have thus seen that VET is expressed as the sum of three components even if in

practice only the values of use (VALUS) and existence (VALES) are used, since the

ecological function may be included in the values of use and existence provided there

is enough information about the parties who report their willingness to pay. We

therefore have the following: VET=VALUS+VALES. Even if the definition of VET is

somewhat simple, its empirical identification is not. Indeed, the VET calculation has a

high risk of error and therefore of distorted estimates. Compared to approaches that

use market revealed values the VET concept must take into account the value of

existence.

It is worth noting that the only way to identify the value of existence is to use direct

techniques of valuation. Only through the

to

without their being any im

that direct methods of valuation are perhaps the most complete, at least in theory,

since they are the only methods of valuation allowing to identify the VET of a given

environmental good. Their application, however, is not easy, especially in terms of how

the questionnaire needs to be structured, the cognitive context surrounding the

interviewee, and the preparation of the interviewers who take on a true and proper

active role in the survey. Compared to direct methods indirect methods only enable

identification of a good’s value of use, but have the advantage

data that may be sourced on the market. Indirect methods also have a number of

statistical and econometrical problems but avoid all the problems related to the use of

hypothetical contexts that re typical of direct valuation methods.

3

T

p

28 This last type warrants an explanation: it refers to the possibility of pollution from radiations produced by electricity plants used for certain forms of transport.

79

PROMETEIA

Health effects, that may often be considered to be the indirect effects of those

enerated on the environment, are considered separately.

lems are encountered in determining most of these

e average

sts

trans

appro allow to obtain useful information for the correct

cin

often

the f

stron

of th

which

true

ructure are not usually considered in valuations of the

nsport, which rather focus on the external factors related to use

t two of this

g

In general, serious prob

types of damage. The top down approach consists in calculating th

co for the main categories and allocating these to the various types of

port or user. This is the easiest and therefore the most common

ach, but it does not

pri g of the various activities. Bottom up calculations have therefore been

attempted, i.e. calculated by looking at disaggregated data presented in

orm of truly marginal values29. Other significant difficulties derive from the

gly context-specific nature of the damage, i.e. from the fact that the size

e damage essentially depends on the characteristics of the situation in

it is produced. This is certainly the case for noise pollution, but holds

for other forms of pollution too.

3.2.3. INFRASTRUCTURE

The costs and benefits of infrast

external effects of tra

of the infrastructure. However, for most modes of transport, with varying degrees of

significance, an important role is played by negative externalities that are produced

from the environmental impact of infrastructures, in terms of biodiversity, use of non-

renewable natural resources - such as land and materials - and structural impact in

terms of change in the eco-geological equilibrium of the area.

The potential irreversibility associated to uncertainty takes us back to the concept of

quasi-option value – a central theme of cost-benefit analysis which, however, is only of

marginal value here since the calculations that will be performed in par

report only concern the social costs related to the use of the infrastructure.

29 For a summary refer to P. Bickel (2005).

80

PROMETEIA

3.2.4. ROAD TRANSPORT

Externalities associated to road transport fall into the following categories:

* local air pollution: primarily due to emissions of PM10 (particulate matter, local air

pollution, fine dusts, CO (carbon monoxide) and NMVOC (Non Methane Volatile

Organic Compounds)30;

* global air pollution (climatic change, so called Greenhouse Effect): concerns

emissions such SO2 and NOx, that are responsible for supranational and

al external cost. Rothengatter

countries, which we may

as follows: Firstly bal a resents 32% of total

of road transport, wi is percentage s equally be loc

llution. Noise accounts for 14%, whilst accidents account fo . U

produced by Maddiso ) provide similar values.

alues may vary sig ntly depending on whether only impa

health is considered (morb nd fatalities), ether a bro aggr

account the negati ects on buildin ltural good rvest

and productio ls in other eco sectors is dere

tion techniques that are used are primarily dose onse and

transfrontier effects, such as acid rain (mainly from the energy sector), and CO2,

(which is the matter of focus of most available literature and policies due to its

quantitative significance); it is important to underline the complementarity that

exists between local and global environmental externalities: at given

technologies, the production or reduction of these emissions are associated with

a fixed coefficient of correlation between local and global emissions;

* rate of accidents;

* acoustic pollution (noise);

* congestion.

For road transport, Maddison et al. (1996) report different cost indications for the

above categories based on studies of European countries, and ultimately provide a

percentile estimate for these categories in terms of tot

(1994) provides empirical evidence on 17 European

summarise , local and glo ir pollution rep

external costs th th hared tween al and

global air po r 54% S and

German data n et al. (1996

The estimated v nifica the ct on

human idity a or wh ader egate

that takes into ve eff gs, cu s, ha s and

agricultural produce n leve nomic consi d. For

health, the valua -resp 30 r for example to the NAMEA a ing structure w ovides dat e m

heric emissions per sector. Refe

atmospccount hich pr a on th ain 10

81

PROMETEIA

contingent/experimental valuation methods, whilst for damage to buildings hedonic

pricing or stated preference techniques are used.

Lastly, total cost estimates as a percentage of national GDP may be very

heterogeneous, and usually high, from 2.7% in Norway to 5.7% in Belgium (Italy

3.8%). Please note that it is essential to distinguish between external costs that are

calculated for aggregates, or as a percentage of GDP, and marginal or average

external costs. It is possible, and theoretically plausible, that externality categories or

al

ffects show a small and rather atypical significance in the studies that have been

analysed. The weighting of noise too, from 1-9%, is compatible with the data provided

above (9-10%). Accidents range from 8-15% (Tab. 3).

Tab. 3 – Valuation of road transport externalities

Category min (€cent/km) max (€cent/km) % of total

means of transport that are associated with high costs in the aggregate, will rank

differently if measured in terms of marginal external costs.

An interesting preliminary study of externalities is the one by Harrington and McConnell

(2003), in which minimum and maximum values are reported for different

environmental externalities (not in an aggregate form), including infrastructure, energy

and parking safety, from which the percentage shares of total external costs for each

category may then be inferred. Although not comparable to the previous data, since

there are more externality categories here and the measurement is in a non-aggregate

form, but rather per kilometre, we observe that the weight of air pollution falls

between 10-23% (which is comparable to the values reported above, where 5 of the 9

categories that are looked at here were not considered). In particular, global extern

e

Infrastructure 2.61 6.10 23% 10%

Congestion 3.48 13.06 31% 22%

Air pollution 0.87 12.19 8% 21%

Climate change 0.26 0.96 2% 2%

Noise 0.09 5.23 1% 9%

Water pollution 0.09 2.61 1% 4%

Accidents (external) 0.87 8.71 8% 15%

Energy safety 1.31 2.26 12% 4%

Parking 1.74 7.84 15% 13%

Total 11.32 58.96 100% 100%

Note: 2004 actualised values (euro); source: Harrington and McConnell (2003).

82

PROMETEIA

For air pollution, estimates are more difficult to calculate than they are for congestion

ncinerators, draws attention to the

and noise31. This is due to the different factors into which the external cost of air

pollution may be broken down and calculated. The various factors addressed in

available literature, rather heterogeneously, are the type of transport (motorcycle,

automobile, bus, etc.), fuel (diesel, petrol, type of petrol, etc.), type of driving (urban,

suburban, motorway), scale of the impact (local and regional). Additionally, values may

be provided for average and marginal costs32, per passenger or per vehicle.

Harrington and McConnell (2003) draw attention to an additional type of externality,

caused by vehicles: vehicle parts and other materials and substances (e.g. fuel) at the

end of the vehicles’ economic life33. Although analysis of external effects of the use of

vehicles demonstrates that this is only a minor share of the total, high and increasing

relationships between vehicles in circulation and population, in countries such as Italy,

and the lower recyclable rate of newer vehicles34, increasing scarcity of lands and

resistance to the construction of waste dumps and i

external costs of the social management of vehicles post usage. In theory, it would

be possible to assign a fixed external cost to each, based on its features, technology

and possibility of recovery and management during the period. This fixed cost would

obviously not affect marginal external costs, but only affect average external costs.

31 The first source of heterogeneity is the type of environment (e.g. urban, rural, etc.), in

congestion are thus the most suitable cases for this type of marginal valuation. Externalities due to accidents are less suitable for marginal valuations since it is not possible to define a base level for the externality. In any case, the difference between marginal and average value is only significant where the marginal cost curve is very steep: i.e. in cases where the externalities are particularly serious (atmospheric pollution in urban centres, noise in urban centres and close to airports). In other situations, the assumption of equality between the two figures (Cm=CMe) is plausible. Available literature seems to highlight, in line with what we have observed above, marginal values greater than average values. 32 For a treatment of the environmental criticalness of management and European policies addressing the subject matter (End of Life Vehicle Directive) refer to Mazzanti and Zoboli (2005, 2006). The recovery of tyres, with the prohibition of disposing of these at the landfill is an issue addressed by the Landfill Directive of 1999. 34 The last years have witnessed a growing substitution of steel with plastic. On the one hand this has led to lighter cars, which, given equal technology, are less polluting. On the other hand this has drastically reduced the potential for recovering and recycling car parts. Lighter vehicles may also be associated to higher negative effects of accidents.

addition to the type of transport. 32 The difference between marginal costs and average costs varies from one externality to the other. In our view, the notion of average costs is more consistent with an externality associated to a certain initial “level” of status quo/reference. Atmospheric pollution, noise and

83

PROMETEIA

3.2.5. AIR TRANSPORT

Air transport has a number of specificities compared to other modes of transport.

Above all, the significance of global emissions (first of which, as always, CO235) is

relatively greater compared to the local impact share. The latter is of a direct nature

for emissions and congestion - through noise pollution close to airports and

electromagnetic pollution - and of an indirect nature in terms of the development of

. Looking at the UK situation for example, which shows the

or freight, especially express freight which represents

railway and roadway infrastructure - which is a consequence of the building or

expansion of airports. The quality of air in the local system is strongly influenced by

the existence of the airport even though this is in large part, as we have seen, due to

reasons that are not directly imputable to air transport itself – which has its greatest

effects on a global level36. In terms of its effects on the environment, an airport

may be compared to an industrial area. Externalities are high in the

infrastructure’s surrounding area, with problems of allocation efficiency as well

as regional distribution of benefits and costs37.

The significance of the direct environmental impact of airports is felt above all ot the

global level, and depends on the sector’s increasing development, in terms of both

passengers and goods transported, as well as in terms of new airport infrastructure or

the expansion of existing infrastructure. Data point to a constant growth in transported

passengers and goods

highest growth rates in Europe (Bishop and Grayling, 2003), a transported passenger

growth of 78% is observed in the 1990s whilst growth projections for 2030, compared

to 2000, are more than 100% (from 200 to 500 million passengers). Even higher

growth rates are forecasted f 35 The air sector accounts for more than 10% of CO2 increases, with forecasts, ceteris paribus, of growth to 30% in 2050. For policy making it is important to note that, in the absence of regulations, more efficient energy dynamics in the sector run the risk of being more than offset, in terms of their net effects, by the high increase in air traffic forecasted over the next decades. 36 Planes are powered by kerosene. By looking at the OCSE tax energy database it may be noted how, within environmental taxes, those levied on kerosene tend to be somewhat lower

energy are subject to the instruments of fiscal economy. The only price impact is the one that derives from market changes to the prices of fossil fuels.

than those levied on other fossil fuels. In this way, neither the externality nor the use of

37 A large airport may be compared to a large industrial site: As an integrated system, Heathrow is the second emitter of NMVOC after the BASF - Teesside; Heathrow contributes for 60-70% of the production of NOx, 66% of SO2, 50% of NMVOC and 40% of PM10, across an area of 6x8 km from the airport site (the same argument may hold true for other major European airports). By 2016 these percentages are forecasted to increase for Heathrow to 83%, 73%, 60% and 61%, respectively (Bishop and Grayling, 2003, p.55).

84

PROMETEIA

20% of the current market. This has led to the need to analyse the externalities of air

efficiency in the use of resources and policies

at may provide constant incentives for technology improvement (energetic

depend strongly on economic and geographic context, distance from the site and the

time of the day when the valuation of noise reductions, for example, are performed.

Similarly to road transport, most heterogeneous results occur, as may be easily

expected, for local external effects. Indeed local effects are more sensitive to socio-

traffic to eventually introduce policies such as environmental taxes or economic

instruments aimed at promoting greater

th

efficiency), sufficient to compensate the impacts of ever increasing energetic

consumption in this sector.

The marginal costs of the effects of air traffic must first be differentiated between

passenger transport (pkm38) and freight (tkm). In this regard a few numbers related to

the sector’s impact on global pollution may be provided. In 1990 the share of CO2

emissions imputable to the sector (all sources) in Great Britain was 6%, in 2000 11%

(Bishop and Grayling (2003, p.43), with constant growth forecasts up to 2050 which, in

the absence of policy interventions or technological changes will more than likely mean

that the cumulative reductions in national CO2 emissions will not be attained. The air

sector would be one of the sectors most responsible for the missed or only partial

decoupling between CO2 emissions and economic growth. Bishop and Grayling (2003,

p. 46) observe how the airplane/train comparison highlights how emissions per

passenger are four times greater for air transport and, using the same metrics, local

emissions of CO, NOx, and NMVOC are lower for railway transport, whilst SOx

emissions are greater.

Valuations relating to noise pollution and use of the territory in terms of impact and

opportunity cost are more ambiguous. Due to the impact of CO2 in particular, total

external costs per pkm are twice those of railway transport and are ten times greater

for freight (tkm) (EEA, 2001).

For air transport too, even though at a lesser degree than environmental effects of

road transport, the heterogeneity of the estimates poses serious problems. In this case

the greatest heterogeneity may be observed for noise pollution, whose valuation may

38 From here onwards the following abbreviations are used: pkm for passenger kilometre (i.e. average number of kilometres travelled by each passenger transported in the unit of time) and likewise, tkm for ton kilometre and vkm for vehicle kilometre.

85

PROMETEIA

86

infrastructure

type and vehicle than are global effects.

Despite the level of external costs and the sector’s forecasted growth, no specific

regulatory provisions seem to characterise the air transport industry at all, except for

certain congestion charges introduced at the urban level in a number of European and

non-European cities, and infrequent instances of metropolitan transport emission

trading (Los Angeles). The sector is subject to various pressures related to energetic

taxation, but this cannot be properly considered to be of an environmental nature since

it is only occasionally related to the level of external costs. Accordingly, energetic

taxation which hits air transport quite significantly, does not, however, provide any fuel

substitution incentives (based on carbon content, for example, as is the case of the

carbon tax), technological innovation and fuel efficiency. It may produce generic

incentives for technological innovation and fuel consumption reduction, but without

any relation to the specific externalities for which an environment tax should be levied.

For air transport, the difficulties of introducing environmental taxes is greater given the

significant global level (CO2) externalities, for which regulation is harder. In Great

Britain especially, where air transport growth is highest and where environmental

policies to counter CO2 have been very strong, it is possible to perceive growing

attention to the possibility of introducing specific forms of environmental regulation,

such as, for example, including air transport within the emission trading scheme39.

Although less than in the past (due to a broadened and new offer segmentation) air

transport still enjoys direct and indirect subsidies. Furthermore, unlike other forms of

transport air transport has no specific policy for externalities. Due to there being no

decision on where to allocate greenhouse gas emissions (country of the plane’s arrival,

39 Although policy is not the centre of focus of this work, emission trading schemes are interesting since they are market mechanisms that reveal the marginal cost of (reducing) the emissions involved. In conditions of equilibrium, and assuming competitive markets, continuity and usual marginal cost and marginal benefit curves, the marginal cost of reducing emissions is equal to the marginal external cost. Accordingly, the market that is created by the policy is an environment that takes into account the social value of the externality. Refer to Mazzanti, Pontoglio and Zoboli (2005) for a complete survey of international emission trading schemes and a critical discussion of theoretical aspects and implementation. As for environmental taxes, the economic valuation of external costs is instrumental to define an environmental tax that is equal to the marginal cost at the optimal local point (as prescribed by theory).

economic characteristics of the territory and the interaction between

PROMETEIA

87

ture, etc.) the sector does not fa otocol of 1997 and,

equently, falls outside of the European schem

depar

cons

ll under the Kyoto pr

emission trading e.

PROMETEIA

Tab. 4 – Survey of external cost estimates (gross of internalising monetary resources) of air pollution at the local level per mode and means of transport (1), (4): ROAD (Polluting agents: NOx, SOx, Benzene, Lead, O3…) Road Goods Road Passengers

Two wheels Buses Light Veh. Heavy Vehicles Cars

Affuso L., Masson J., Newbery D. (2003) Source: Powell, Marlee (2000) – AREA: UNITED KINGDOM

GBP high1.06, 0.61

Pence/km 1999(2)

medium 0.83, low

Bickel P., Enei R., Leone G., Schmid S. (2002) AREA: CASE STUDIES FOR INTERCITY ROADS

Dies -->

0.35 o), local an-

(B o) local im 0.329

(Bologna-Brennero);

lo -

re 75 (Bol cal impact 0.020 (Bologna-

Brennero)

Cents/vkm 1998

regional

impact 2.886 (Milan-

Chiasso), local impact 1.782

(Milan-Chiasso);

imp 25 (Bol na-

Brenn local

Br )

Euro Cents/vkm

1998

Regional impact 0.505 (Milan-

Chiasso), local

impact 1.905 (Milan-

Chiasso);

regional impact 0.568

(Bologna-Brennero)

local impact 0.403

(Bologna-Brennero)

Euro Cents/vkm 1998

regional impact 3.879 (Milan-Chiasso),

local impact 2.837 (Milan-Chiasso);

mpact 4.468 (Bologna-

impactBrennero)

Euro Cents/vkm 1998 el

regional impact 7(Milan-Chiass

impact 1.555 (MilChiasso);

regional impact 0.400

ologna-Brennerpact

petrol--> regional impact 0.153

(Milan-Chiasso), cal impact 0.097 ((Milan

Chiasso);

gi 0.1ogna-Brennero), lo

onal impact

Euro

regional

a .3ct 3og

ero),impact 0.377

(Bologna-ennero

,

regional i

alBrennero),

0.600 gna-loc (Bolo

(1) Unless otherwise indicated costs are to be understood as beinates found in literature

g marginal he terms low, h are taken fro Powell and Marlee (2000) to take int h variability of cost estim ; (3) it is unclear whether public ansport is in es he unit of measurement of thbeen taken into considerat iginal report every estimate is labe so as to id edium or low confidence interval b sed on how faestimate is from the real value; ♠ = average costs; ♣ = total costs per mode and means of

; (2) t medium and higcluded here; (4) all valuentify that it belongs to a m

m o ge study that has

r off the

account the hitr are reported in t

ion; (5) in the or lled atransport.

88

PROMETEIA

Road Passengers Road Goods

Two wheels Cars Buses Light Veh. Heavy Vehicles

Bickel P., Schmid S., Krewitt W. Friedrich R. (1997), AREA: PARIS, MILAN, STUTTGART, BARNSLEY, AMSTERDAM

mEcu/vkm 1995(5)

Diesel Cars:

Paris 559.13; Stuttgart 63.02; Amsterdam 84.8; Barnsley 103.85;

Stutt.-Mannheim 28.21; Tiel drive 36.1.

Petrol Catalytic Cars:

Paris 72.92; Stuttgart 9.22; Amsterdam 4.5; Barnsley 9.72;

Stutt.-Mannheim 7.82; Tiel drive 4.51

Calthorp E., Joha n D., Pearce D.,

AREA: UNITED KINGDOM

GBP Pence/vkm 1993

GBP Pen cle litre

33 diesel; 43 leaded petrol

Pence/vehicle

GBP Pence/vkm

1993:

Pen

GBP Pence/vkm 1993:

GBP Pe cle lit

91 (together with light vehicles)

nsson O., Litman T., MaddisoVerhoef E. (1996)

2 (auto diesel).

ce/vehi1993

GBP vkm Pence/1993: 36.

GBP

litre 1993 132

4.

GBP ce/vehicle

litre 1993 91 (together wit y h heavvehicles)

30.

nce/vehire 1993

Calthorp E., Johansson O., Litman T., Maddison D., Pearce D., rhoef E. (199Ve 6),

Gote ity) AREA: SWEDEN

SEK/km 1993 0.06 (average in borg), 0 (outside c

SEK/km 1993 1.0 (average in

Goteborg)

Danielis R., Rotaris L. (2001) Source: Pierson, Skimer and Vickerman (1994)


Lir 4

SO2=18.83; VOC=10.608; PM10=56.533

a 9 per vkm 1993-19Nox=54.497;

89

PROMETEIA



De Borger ., Proost S,

AREA: BELGIUM

Euro/vkm 1998

0.9 - rways; 4.5 - 6.7 large city; 1.7 - 2.5 small city;

Euro/vkm 1998

0.

gium Euro

640 million

B., De Nocker L., Mayeres I., Int Panis LVandercruyssen D., Wouters G. (2001)

1 moto

0.6 - 0.7 non urban area

0.95 bus euro0;

0.30 bus2001(euro1,

euro2); 21 bus euro3

�Bel 1998

Enei R., Leone G. (2001)

Euro Cents /vkm 1998

PM10: diesel non cat.

diesel 1.17; diesel

Benz cat.

petrol cat.0.145

B

v

es 0 Benz el 0.00 AREA: FLORENCE

Euro/1000vkm 1998 (including

moped)

Benzene: 2.449

3.04; EUROI

EUROII 0.87.

ene: petrol non 0.681,

Euro/1000Vkm 1998

enzene: diesel

0.00

Euro/1000 km 1998

Benzene: di el 0.0

Euro/1000 vkm 1998

ene: dies

Henry A. and Godart S. (2002b)

♠ Euro/vkm 1998 AREA: LUXEMBOURG 0.020

Henry A., Godart S. (2002a) AREA: BELGIUM motorcyc es 0.014

♠( vkm 1998:

.026

♠( v

♠(variable) Euro/vkm 1998

l

variable) Euro/

0.013 0

♠( ) variableEuro/Vkm 1998

♠( ) variableEuro/Vkm 1998

0.076

variable) Euro/km 1998

0.051

Himanen V., Idstroem T., Karjalainen J., Otterstroem T., Tervonen J. (2002)

AREA: FINLAND

♠(variable) Euro/vkm 1998 0.009

♣ Million Euro 1998

Motorways 56, Intercity Roads 30, Urban Roads 137

♠(variable) Euro/Vkm 1998

0.069


Motorways 6,

Intercity Roads 3,

Urban Roads 51

♠( le)

0.012


Motorways 6,

Intercity Roads 4,

Urban Roads 36

♠(variable) Euro/ vkm 1998

0.045

♣ Million Euro 1998 Motorways 45,

Intercity roads 11, Urban Roads 84

variabEuro/Vkm 1998

90

PROMETEIA



INFRAS (2000) AREA: EU15, SWITZERLAND, NORWAY

Euro per 1000 pkm 1995 14

Euro 1995 Euro per 1000 pkm 1995

4-25

Euro per 1000 Tkm 1995

28-118

Euro per 1000 tkm 1995 14-50

per 1000 pkm 5-17


Euro per 1000 pkm 2000 3.2

Euro per 1000 pkm 2000

12-18

Euro per 1000 tkm 2000 15-100

Euro per 1000 tkm 2000 Euro per 1000 pkm 2000

5.7-44.9 33.5

Korizis D., Tsamboulas D., Roussou A. (2002) AREA: GREECE


Motorcycles 0.000

♣ Million Euro 1998Motorways 0.3, Other Roads 75

♠(vari 1998 ♠(variable)

Euro/vkm 1998 0.012

♣ Million Euro

1998 Motorways 0.4, Other Roads 4


0.003


Motorways 5, Other Roads

507

♠( vkm

8

able) Euro/vkm 0.007


Motorways 4, Other Roads 310

variable) Euro/1998 0.006



Ma k H., Stewart L. (2002)

AREA: PORTUGAL

♠( le)

motorcycles 0.013

♠(varia 1998 ♠( le)

0.018

♠( le) Euro/vkm 1998

0.0019

cário R., Carmona M., Caiado G., Rodrigues A., Martins P., Lin variabEuro/vkm 1998

ble) Euro/vkm0.013

variabEuro/vkm 1998

variab ♠(variable) Euro/vkm1998 0.002

Makie P., Nash C., Shires J., Nellthorp J. (2004) Source: Samson (2002)


m 1998

70

GBP Pence per vk

0.34-1.

91

PROMETEIA



Nash C and Mathews B. (2005) AREA:CASE STUDIE INTERCITY AREAS.

Vkm 2003. an Cases cars ---> nki 0.12,

tgart 0.25, Berlin 0.15,

Florence 0.01 (Florence excludes Nox, SO2, Ozone

and NMVOC); Diesel cars ---> Helsinki n.a.,

Stuttgart 1.45,

Floren es Nox,

side

Mila o 0.25; Bologna-Brennero 0.20;

Diesel Cars ---> Helsinki-Turku n.a.,

Basel-Karlsruhe 0.63, Strasbourg-

Neubrandenburg (outside built area) 0.26,

Strasbourg-Neubrandenburg (within

built area) 0.38, Milan-Chiasso 1.9;

Bologna-Brennero 0.73

Euro per Vkm 2003

Stut .52, Be

No d

(

Milan-Chiasso 6.72; Bologna-Brennero 5.07

S FOR URBAN AND

Euro per bUr

PetrolsiHel

Stut

Berlin 0.75, Florence 0.26 (per

ce excludSO2, Ozone and NMVOC).

Intercity cases Petrol cars --->

Helsinki-Turku n.a., Basel-Karlsruhe 0.37,

Strasbourg-Neubrandenburg (out

built area) 0.12, Strasbourg-

Neubrandenburg (within built area) 0.11,

n-Chiass

Urban Cases Helsinki n.a.,

art 17tgrlin 10.19,

Florence 4.69 (Florence excludes

2, Ozx, SO one anNMVOC).

tercity cases In

Helsinki-Turku 2.09, Basel-Karlsruhe 6.91,

Strasbourg-Neubrandenburg

(outside built area) 3.89,

Strasbourg-Neubrandenburg

n built areawithi )7.46,

92

PROMETEIA



Morisugi H. (1997) AREA: USA, JAPAN, FRANCE

USD cents / vkm(3)

1.90 Japan, 0.38-5.75 USA,

0.8

9 France

Nääs O., Lindberg G. (2002)

♠(variable)

Local im act and Global impact

together: Suburban roads

incl ing Motorways 0.006, Urban Roads 0.019


Suburban Roads including

Motorways 2, Urban Roads 2



Local impact and Global

Motor rban

♣ M 98

Motorw , Urban Roads 126

Local impact and Gl

impatogether: Suburban

Roads including Mo ays

Urba oads

♣ Million Euro

1998 Suburban

Roads including Motorways 11, Urban Roads

17

♠

♠

im and Global impact

together: Suburban

Roads incl ing

Mo ays

Urba oads 0.040

♣ Million

Euro 1998 Suburban

Roads including

Motorways

Ur an Ro 21

0.037


Local impact and Global impact

together: Suburban Roads

including torways

Urban Roads 0.124


AREA: SWEDEN


motorcycles 0.005

Euro/vkm 1998 p

ud

impact together: Suburban Roads including

ways 0.007, URoads 0.014

illion Euro 19

Suburban Roads including ays 113


0.024


obal ct

torw0.028,

n R0.073

(variable) Euro/vkm

1998 0.008

variabl( e)

Euro/vkm 1998 Local pact

udtorw

0.011, n R

17, b

ads


Mo0.056,

Suburban Roads incl ysuding Motorwa

107, Urban Roads 41

93

PROMETEIA



Ricci A., Enei R., Esposito R., Fagiani P.wart L., Bickel P. (2002) ALY

♠(variable) Euro/vk

motorcycles 0.013

♣ Million Euro 1998:

Motorways 6, Intercity Roads

Urban Roads 422

able) Euro/0.012


Motorways 818, Intercity Roads 1384,

oads 1804

♠(variable) m 19


Motorways 65, Intercity Roads

46, Urban Roads

44

♠(variable) Euro/vkm

1

♣ Million Euro 1998 Motorways

75, Intercity

Roads 187, Urban R ads

301

♠(vari 1998

0.053

� Million Euro 1998 Motorways 760,

Intercity Roads 614, Urban Roads 532

, Giammichele F., Leone G., Pellegrini D., Link H., Ste

A: ITARE

m 1998 ♠(vari

171, Urban R

vkm 1998 Euro/vk

0.06698 998

0.018

o

able) Euro/vkm

Tánczos K., Legeza E., Magyar I., Bokor Z. ., Ki gy Z., Rónai P., (2002) - AREA:

RY

♠ Euro/Vkm 1998 0.061

, Farkas G., Kövári Bss B., Békefi Z., Duma L., Na

HUNGA

Tervonen J., Hämekoski K., Otterström T., Peter A., Bickel P., Schmid, S. (2002) AREA: FINLAND

Euro cent/vk EUROII cars -->

health 0.115 collec. and materials 0.008

EUROIII cars --> health 0.09

collec. and materials 0.005

m 1998

Tervonen J., Hämekoski K., OttersSchmid

tröm T., Peter A., Bickel P., , S. (2002)

AREA: FINLAND

EEUROII

collec. an als

EUROII

coll

uro cent/vkm 1998 42t -->

health 1.98, collec. and materials

0.11; EURO health 2.146,

d materi

II 60t -->

0.177; I 42t -->

health 1.3, collec. and materials

0.108; III 60t --> EURO

health 1.42 ec. and materials

0.118

94

PROMETEIA

Tab. 5 - Survey of external cost estimates (gross of internalising monetary resources) of air pollution at the local level per mode and means of transport (1), (2): RAILWAY, AIRPLANE, SHIP (polluting agents: NOx, SOx, Benzene, Lead, O3…) Railway Passengers Railway Freight Airplane Passengers Airplane Freight Ship

Passengers Ship Freight

Affuso L., Masson J., Newbery D. (2003)

Source: Powell and Marlee (2000) AREA: UNITED KINGDOM

GBP pence per km 1999

0.18

Bickel P., Enei R., Leone G., Schmid S. (2002)

AREA: CASE STUDIES FOR INTERCITY ROADS

Euro Cent per Train km 1998

High speed 42.756; intercity 31.650;

local 23.261.

Euro Cent per Train km 1998

14.758 (Milan-Chiasso); 18.334 (Bologna-Brennero)

Bickel P., Schmid S. (2002)

B

Euro per plane (direct emissions at takeoff, fligh

41.51

AREA: CASE STUDY FOR AIRLINE ERLI 7-400 N-LONDON BOEING 73

t and landing) (4)

Bi ., Hämekoski K tröm T., Anton

P., E Van Do 3)

olluting unit) fluvial transport(4)

Nox--> Rotterdam-Nijmegen

3.1, Nijme

Duisburg 3.9;

PRotterda

145.Nijmegen-Duisburg

69.2, Duisburg-Mannheim

68.7; SO2-->

Rotterdam-Nijmegen 9.1, Nijmegen-Duisburg 6.5,

ckel P., Schmid S., Tervonen J., Ott rse

nei R., Leone G., aar P., Carmigchelt nsel H. (200

AREA: CASE STUDY FOR FLUVIALNETWORK ROTTERDAM-

MANNHEIM

M2.5--> m-Nijmegen

Euro/kg (marginal cost per p

gen-Duisburg 2.5, -M eimannh

6,

95

PROMETEIA

Railway Passengers Railway Freight Airplane Passengers Airplane Freight Ship Passengers Ship Freight

Duisburg-Mannheim 5.4;

Benzene--> Rotterdam-Nijmegen

0.8, Nijmegen-Duisburg

0.4, Duisburg-Mannheim

0.4; NMVOC-->

Rotterdam-Nijmegen 1.5,

Nijmegen-D rg 1.5,

Duisburg-Mannheim 1.8.

uisbu

Henry A. and Godart S. (2002b) AREA: LUXEMBOURG

♠ Euro/Train km 1998 0.48 (including buses)

ment 1998 ♠(variable) Euro/vkm 1998 (fluvial) 3.01

♠ Euro/Plane move 32.350

Henry A., Godart S. (2002a) ♠(variable) Euro/Train km 1998 0.202

♠(variable Euro/Plane movement 1998 ♠(variable) Euro/vkm 1998 (fluvial) 2.00 AREA: BELGIUM

) 35.889

Him T., Karjalainen J., Otterstroem T.,

Tervonen J. (2002) AREA: FINLAND

♠(variable) Euro/Train km 1998 0.11


anen V., Idstroem

INFRAS (2000) AREA: EU15, SWITZERLAND, Euro per 1000 pkm 1995

2-24 Euro per 1000 tkm 1995

1-6.8 Euro per m 1995

0.8-2

Euro per 1000 tkm 1995

0.8


4.5 NORWAY

1000 pk

INFRAS (2004) AREA: EU15, SWITZERLAND,

NORWAY

Euro per 1000 pkm 2000 5.1

Euro per 1000 tkm 2000 7.4

Euro per 1000 pkm 2 0 0.2 tkm 2000

1.8 tkm 2000

8.8

00 Euro per 1000

Euro per 1000

Korizis D., Tsamboulas D., Roussou A. (2002) - AREA: GREECE


Macário R., Carmona M., Caiado G., Ro

♠(variable) Euro/Train km

♠(variable) Euro/Train km

drigues A., Martins P., Link H.,

Stewart L. (2002) AREA: PORTUGAL

1998 0.0127

1998 0.178

96

PROMETEIA


Nash C. and Mathews B. (2005)

Euro Cents per Plane km (4) Eu (4)

AREA: CASE STUDIES FOR URBAN AND INTERCITY AREAS

5 cents Berlin-London

Boing737-400

12 Helsinki-Tallin

ro per Ship km

Nääs O., Lindberg G. (2002) ♠(variable) Euro/Train km ♠(variable) Euro/Train km ♠ Euro/Plane Movement 1998

AREA: SWEDEN 1998 0.02

1998 0.08 2.13

Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., Leone

G., Pellegrini D., Link H., Stewart L., Bickel P. (2002) - AREA: ITAL

♠ ariable) Euro/Train km 1998 0.42

♠(variable) Euro/Train km 0.28

♠ Euro/Pl ent 1998

Y

(v 1998 ane Movem 76.74

Schipper Y. (2004) AREA: EUROPE

ECU 1995 (discount r e 3%)(3)

Acute mortality 116250; mortality 84330.

5/ton per illness a102:x: Cs: 761;CO: 3.

CU 199(aver

PM

at

Chronic

Ege values) : 7463;

SONo

2666; 2731;

VO

Tánczos K, Legeza E., Magyar I., Bokor Z., Farkas G., Kövári B., Kiss

B., Békefi Z., Duma L. Nagy Z., Rónai P. (2002)

♠ Euro/Train km 1998

0.466

iable) Euro/Plane Movement 1998

22.433 AREA: HUNGARY

♠(var

TervOtterström T., Peter A., Bickel P.,

Schmid, S. (20AREA: BA

Euro cent per Ship km (maritime transport) (4)

local impact--> morbidity 16.3

61.2;

onal impact-->morbidity 346.0;

onen J., Hämekoski K.,

02) LTIC SEA

;

mortality

regi

97

PROMETEIA


mortality 694.6; collec. and

88.9 materials(1 ) All value it of t n inThe discount rate is addres hipper (2004) to calculate the cost that orted here; for details r to the chapter on ext costs of air pollutio is report; (4) The year in which the costs were calculated is unclear; � = average costs; ♣ e and

) Unless otherwise indicated costs are to be understood as being marginal; (2sed in the function used by Sc

s are reported in the un measurement of the s udy that has been take to consideration; (3) is rep refe ernal n in th

= total costs per mod means of transport.

Tab. 6 - Survey of external cost estimates (gross of internal onetary reso s) of air po n at the loca vel (1) (Poll

Ship

ising m urce llutio l leuting agents: NOx, SOx, Benzene, Lead, O3… )

Road Railway Airplane

AA.AA. (2003) AREA: UNITED KINGDOM

Millions of Pounds 2000

119-136 (passenger transport)

Calthorp E , Verhoef

(2)

Nox --> 0.49 (low), 2.57 (m );

(medium), 0.09 (high);

)

0.9-1.2

0.01 (high)

Billions of Dfl

Nox -->

V

., Johansson O., Litman T., Maddison D., Pearce D.E. (1996)

Source: Bleijenberg (1994); Bonenschansker (1995). AREA: HOLLAND

Billions of Dfl (Dutch guilders) 1994

edium), 3.28 (highSO2 --> 0.01(low), 0.03

VOCs --> 0.23 (low), 1.80 (medium 4 (high), 2.6

Billions of Dfl 1995: 0.01 (medium),

0.01 (high)

Billions of Dfl 1994 Nox -->

0.01 (medium),

1994

0.04 (low), 0.22 (medium),

0.28 (high); SO2 -->

0. 01 (high);O -> Cs -

Calthorp E., Johansson O., Litman T., Maddison D., Pearce D., Verhoef E. (1996) - AREA: UNITED KINGDOM

Billions of Pounds 1993 19.7

De Borger B., De Nocker L., Mayeres I., Int Panis L., Proost S, Vandercruy sen D., Wouters G. (2001) - AREA: BELGIUM

Billions of Euro 1998 s 2.4


uro 1998 3 (incl. buses)

Million Euro 1998 1.4

(Fluvial ransport)

0.09

Million Euro 1998 61

Million EMillion Euro 1998

T

98

PROMETEIA

Road Railway Airplane Ship

Henry A., Godart S. (2002a) AREA: BELGIUM

Million Euro 1998 Million Euro 1998 19

Million Euro 1998 11 1671

Himanen V., Idstroem T., Karjalainen J., Otterstroem T., Tervonen J. : FINLAND

illi(2002) - AREA


Million Euro 1998 7

Million Euro 1998 M 4

on Euro 1998 0.4

INFRAS (2004) - AREA: EU15, SWITZERLAND, NORWAY 4447 4235 Million Euro 20

1652 Million Euro 2000

164282 Million Euro 2000 Million Euro 2000 00

Korizis D., Tsamboulas D., Roussou A. (2002) - AREA: GREECE 98 Million Euro 1998 6

Million Euro 1998 6


Million Euro 19978

Macário R., Carmona M., Caiado G., Rodrigues A., Martins P., Link H., 2002) 98 Million Euro 1998

22 Million Euro 1998

106 Stewart L. (AREA: PORTUGAL

Million Euro 19 472

Nääs O., LindbAR

erg G. (2002) EA: SWEDEN

98 Million Euro 1998 5

Million Euro 1998 2 Million Euro 19

456

Orfeuil J.P. (1997) AREA: FRANCE Billion of Francs 1991

16-37

Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., Leone G., Pellegrini D., Link H., Stewart L., Bickel P. (2002)

ITALY



Million Euro 1998 5 AREA:


Tánczos K, Legeza E., Magyar I., Bokor Z., Farkas G., Kövári B., Kiss B.Békefi Z., Duma L. Nagy Z., Rónai P. (2002)

AREA: HUNGARY

98 Million Euro 1998 41

Million Euro 1998 2

Million Euro 1 98 (fluvial)

97

, Million Euro 191163

9

(1) Value measurement of the s udy under consideration; (2 and high refer to threshol calculated by authors acc rding to various assumpti s and estim

s are reported in the unit of t ) Low, medium ds o onates.

T ost estimates (gross of i etary r pollution at the global level

rt (1), (4) ROAD (Polluting a O)

Road Goods

ab. 7 - Survey of external cper mode and means of transpo

nternalising mon resources) of ai: gents: CO and 2 C

Road Passengers Two wheels Light vehicles Heavy vehicles Cars Buses

Affuso L., Masson J., Newbery D. (2003) Pence per k 0.35, low 0.19

AREA: UNITED KINGDOM GBP m 1999(2) high 0.56, medium

99

PROMETEIA

Road Passengers Road Goods Two wheels Cars Buses Light vehicles Heavy vehicles

Bickel P., Enei R., Leone G., Schmid S. (2002)

AREA: CASE STUDIES FOR INTERCITY ROADS

0.359 (Bologna-Brennero); petrol-->

0.356 (Milan-Chiasso), 0.356 (Bologna-Brennero)

Euro Cents/Vkm 1998


Euro Cents Vkm 1998


Euro Cent vkm 1998 2.162 (Milan-Chiasso),

2.162 (Bologna-Brennero)

Euro Cent/vkm 1998 diesel--> 0.359 (Milan-Chiasso), / /

Bickel P., Schmid S., Krewitt W. Friedrich R. (1997),

mECU/vkm 1995(5)

Diesel Car Paris 2.97;

Ba ey 3.45;

Stutt-Mannheim 2.38;

AREA: PARIS, MILAN, STUTTGART, BARNSLEY, AMSTERDAM

Stuttgart 2.28; Ams 2.70; terdam

rnslStutt.-Mannheim 1.99;

Tie 2.30. l drive Petrol Catalytic Cars

Paris 3.58; Stuttgart 2.98;

; Amsterdam 3.20Barnsley 3.48;

Tiel drive 2.49

Calthorp E., Johansson O., Litman T., Maddison D., oef E. (1996) 0.08 of 0.1 Pearce D., Verh

AREA: SWEDEN

SEK/km 1993 (given a consumption

litres/km)

Danielis R., Rotaris L. (2001)

Li 1994 ( nd

R 01)


re per vkm 1993-Source: studies in Danielis a

otarsi 20CO=0.197; CO2=14.836

De Borger B., De Nocker L., Mayeres I., Int

1.7 - l cities; 0.6 - 0.7 suburban area 0.21 euro3

Euro 1998 640 million

Panis L., Proost S, Vandercruyssen D., Wouters G. (2001) AREA: BELGIUM

Euro cent per vkm 1998 0.9 - 1 Motorways;

4.5 - 6.7 large cities; 2.5 smal

Euro cent per vkm 1998 0.95 eruo0;

0.30 bus 2001 (euro1 and euro2);

♣

Enei R., Leone G. (2001) AREA: FLORENCE

Euro per 1000Vkm

CO: 0.021;

CO2: moped 1.5; motorcycles 2.5.

Euro per 100CO: petrol non cat.

petrol cat. 0.003, diesel 0.001; CO2: petrol unregulated EURO 8.8,

petrol EUROI and EUROII 11.9; diesel unregulated EURO 5.8,

Euro per 1000Vkm 1998

CO: diesel 0.004; CO2: diesel unregulated

EURO 6.7, diesel EUROI and EUROII

Euro per 1000 vkm

CO2: unregulated

EURO 17.6, EUROI and EUROII

(moped) 1998 0vkm 1998

0.043, 1998

100

PROMETEIA


diesel EUROI and EUROII 4.3 8.3 20.0


♠ Euro/vkm 1998 0.012


♠(variable) Euro/vkm 1998 motorcycles 0.004

♠(variabl 98 ♠(variable) Euro/vkm 1998 0.010


♠( vkm e) Euro/vkm 190.005 0.022

variable) Euro/1998 0.017

Himanen lainen J., Otterstroem T., Tervonen J. (2002)

AREA: FINLAND

♠(va ♠(variable) Eur vkm 1998 0.02

♣ Euro million 1998 Motorways 3,



♣ Euro million 1998

Motorways 5, Intercity Roads 3, Urban Roads 12

♠(v m

M 6, Int 8,

V., Idstroem T., Karja

riable) Euro/vkm 1998 0.0039



o/ 1998

0.007

ariable) Euro/vk1998 0.022

♣ Euro million 1998 rwaysoto 2

ercity RoadsUrban Roads 24


Euro per 1000 pkm 1995 9.6

Euro per 1000 pkm 1995 5.5-11

Euro per 1000 tkm 1995 125-134

Euro per 1000 pkm 1995 12-25

Euro per 1000 tkm 1995 15-18


Euro per 1000 pkm 2000 1.7-11.7

Euro/1 0 Euro per 1000 pkm 2000 0.7-9.5

Euro per 1000 tkm 2000 8.2-57.4

Euro/1000 tkm 2000 1.8-12.8

000 pkm 2001.7-27

Kori u A. (2002) - AREA: GREECE

♠(variable) Euro/vmotorcycle♣ Euro million 1998

Motorways 0.1, Other Roads 24

vkm 1998


Other Roads 101

♠(variable) Eur vkm 1998 0.003

♣ Euro million 1998 Motorways 0.1, Other Roads 1






Motorways 0.8, Other Roads 23

zis D., Tsamboulas D., Rousso

km 1998 ♠(variable) Euro/s 0.000; 0.003

o/ 1998

0.0041998 0.006

Macário R., Carmona M., Caiado G., Rodrigues A., Martins P., Link H., Stewart L.

(2002) - AREA: PORTUGAL

♠(variable) Euro/vkm 1998 motorcycles 0.004





Makie P., Nash C., Shires J., Nellthorp J. (2004) Source: Samson (2002)


GBP Pence per vkm 1998 0.15-0.62

101

PROMETEIA


Nash C. and Mathews B. (2005) AREA: CASE STUDIES FOR URBAN AND

INTERCITY AREAS

Euro per vkm 2003, Urban cases, petrol cars ---> Helsinki 0.35,

Be 7, Helsinki-Turku n.a.,

Stu 31,

Helsinki-Turku n.a.,

Strasbourg-Neu nburg (outside

Strasbourg-Neubrandenburg (within built area) 0.47,

Bologna-Brennero 0.36, diesel cars ---> Stras side

builtStrasbour ithin built

B

Urb s

In ses

rg-

(within built area)

Bo 2.16

Stuttgart 0.47, Euro per vkm 2003 rlin 0.4

an caseHelsinki n.a.,

Stuttgart 3.28, Berlin 3.28,

Basel-Karlsruhe 0.37; Diesel cars ---> Helsinki n.a.,

ttgart 0. Helsinki-Turku 2.40, Basel-Karlsruhe 2.18.

tercity caBerlin 0.31,

Strasbourg-Basel-Karlsruhe 0.32. Neubrandenburg Intercity cases, petrol cars ---> brande (outside built area)

2.03, Strasbou

built area) 0.34,

Neubrandenburg

bourg-Neubrandenburg (out 3.28, logna-Brennero area) 0.25,

g-Neubrandenburg (warea) 0.31;

ologna-Brennero 2.16

Morisugi H. (1997) Pence 1991 per vehic0.61 Japan, 0.05-0.19 USA, 0.53 France

le km(3) GBP AREA: USA, JAPAN, FRANCE

102

PROMETEIA


Nääs O., Lindberg G. (2002) SubAREA: SWEDEN

♠(variable) Euro/vkm 1998 motorcycle

♠(variable) Euro/vkm 1998 Local impact and Global

impact togurban Roads includMotorways 0.006, Urban Roads 0.019

uro million 1998an Roads

MotorwayUrban Roads 1


♠(variable vkm 1998

mpact and Global impact togetSuburban Roads including Motorways

Urban Roads 0.014

♣ Euro mi 1998 rban R Motorways

Ur 112

1998 0.016

le) Euro/1998

al impact Global impact

together: Suburban Roads

including Motorways 0.028,

Urban Roads 0.073

♣ Euro million 1998 Suburban Roads

torways 8,oads 10

♠(variable) Euro/Vkm 1998 0.00

able) Euro/vkm 1998

Local impact and Global impact to

Suburban Roads including Motorways 0.011, Urban Roads 0.040


Suburban Roads including Motorways 17, Urban Roads 13

vkm 1998 0.023

♠(v vkm 1998

Local im act and Global impact

toge her:

including Motorways 0.056,

Urban Roads 0.124 ♣ Euro m ion 1998

including Motorways 77,

Urban Roads 16

s 0.002

ether:

ing

Local i

♣ E Suburb including Subu

s 1,

0.004

) Euro/her: Loc

0.007,

llion

oads including129,

ban Roads

♠(variable) Euro/vkm ♠(variable) Euro/

♠(variab vkm

and

including MoUrban R

6

♠(vari

gether:

ariable) Euro/

p

tSuburban Roads

illSuburban Roads

Ricci A., Enei R., Esp , Giammichele F., Leone G., Pellegrini D.,

Link H., Stewart L., Bickel P. (2002)

) Euro/vkm 1998 motorcycles 0.002 ♣ Euro million 1998

Motorways 2, rcity Roads 28,

oads 59

♠(variable vkm 1998 0.004



) Euro/vkm 1998 0.018

Euro million 1998 Motorways 21,







Int 7,

osito R., Fagiani P.♠(variable

AREA: ITALY InteUrban R

) Euro/ ♠(variable

♣ Motorways 260, ercity Roads 19

Urban Roads 88

Tánczos K, Leg

Duma L. Nagy ZAREA: HUNGARY

♠ Euro/vkm 1998 0.010

eza E., Magyar I., Bokor Z., Farkas G., Kövári B., Kiss B., Békefi Z.,

., Rónai P. (2002)

Tervonen J.Peter A., Bicke

r vkm 1998 --> 0.354;

-> 0.349

, Hämekoski K., Otterström T., l P., Schmid, S. (2002)

AREA: FINLAND

Euro cent peEUROII cars

EUROIII cars-

Tervonen J., Hämekoski K., Otterström T.,

Eu t per vkm 1998 EUROII 42t --> 2.40; EUROII 60 --> 2.64;

EUROIII 42t --> 2.46; EUROIII 60 --> 2.71

Peter A., Bickel P., Schmid, S. (2002) AREA: FINLAND

ro cen

(1) Unless d as being marginal; terms low, mediu gh are taken from P d Marlee (2000) to to account the high variliterature; luded here; (4) a e reported in th easurement of th hat has been taken onsideration; (5) in the original repo estimate is labelled so as to identify that it belongs to a medium or low confidence interval based on how far off the estimate is from the real value; ♠ = average costs; ♣ = total costs per mode and means of transport.

otherwise indicated costs are to be understoo (3) it is unclear whether public transport is inc

(2) the m and hi owell an take in ability of cost e tes found in stimart everyll values ar e unit of m e study t into c

103

PROMETEIA

Tab. 8 - Survey of external cost estimates (net of internalising monetary resources) of air pollution at the global level per mode of trans RAILWAY, AVI

Rail ght Airp ngers Airp ht Ship Passengers Ship Freight

port (1), (2): ATION, NAVAL (Polluting agents: CO2 and CO)

way Passengers Railway Frei lane Passe lane Freig

AA.AA. (2003) AREA: UNITED KINGDOM GBP Pounds 2000

70/Ton COx GBP Pounds 2000

70/Ton COx

Affus 03) o L., M D. (20asson J., Newbery


GBP pence per km 1999 0.26

Bickel P., Enei R., Leone G., Schmid S. (2002) AREA: CASE STUDIES FOR INTERCITY ROADS

Euro Cent/Train km 1998

Hi 731;

lo 407.

in km

ro)

gh speed 0.intercity 0.554;

cal 0.

Euro Cent/Tra8 199

0.149 (Milan-Chiasso); 0.185

( na-BologBrenne

Bick 02) AREA: CASE STU ERLIN-LONDON

BOEING 737-400

dur at el P., Schmid S. (20DY FOR AIRLINE B

Euro per plane (directemissions at takeoff,

ilanding) (4)ng flight and

221.61

Bickel P., Schmid S., Tervonen J., Hämekoski K., , Anton P., Enei R., Leone G.Otterström T. , Van Donselaar

AREA: CAS NETWORK

Euro/kg (marginal cost per pollu ng unit)

fluvial transport (4)

n

Duis annheim

P., Carmigchelt H. (2003) E STUDY FOR FLUVIAL

ROTTERDAM-MANNHEIM

CO-->Rotterdam-Nijmege0.0012;

Nijmegen-Duisburg 0.0006; burg-M

ti

0.0006. Henry A. and Godart S. (2002b)

AREA: LUXEMBOURG ♠ Euro/Train km 1998 0.14 (including buses)

♠ Euro/Plane Movement 1998 ♠(variable) Euro/vkm 1998 fluvial 34.661 0.63



♠(variable) Euro/Plane Movement 1998 378.462

♠(variable) Euro/vkm 1998 fluvial 0.59

Himanen V., Idstroem T., Karjalainen J., Otterstroem T., Tervonen J. (2002) - AREA: FINLAND

♠(variable) Euro/Train km 1998

0.14


0.24


Euro per 1000 pkm 1995

4.2-8.9


4.2-5.3

Euro per 1000 pkm 1995 36-42

Euro per 1000 tkm 1995 117

Euro per 1000 Ton km 1995 4.7

104

A

105

Railway Passengers Railway Freight Airplane Passengers Airplane Freight Ship Ship Freight Passengers

INFRAS (2004) AREA: EU15, SWITZE

Euro per 1000 pkm Euro per 1000 tkm Euro per 1000 pkm Euro per 1000 tkm Euro per 1000 tRLAND, NORWAY 2000

0.3-7.1 2000

0.4-5.3 2000

6.6-46.2 2000

33.7-235.7 km 2000

4.3

Korizis D., Tsamboulas D., Rouss A. (2002) AREA: GRE

♠(variable) Euro/Train km 1998 0.124 ou

ECE

Macário R., Carmona M. ins P., Link H., Stewart L. (2002)


♠(variable) Euro/Train

0.0

, Caiado G., Rodrigues A., Mart

AREA: PORTUGAL 0.0015 km 1998

021

Nääs O., LiAREA: SWEDEN


0.02

♠(variabl

0.14

♠ Euro/Pla 1998 69.36

ndberg G. (2002) e) Euro/Train

km 1998 ne Movement

Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., ini D., Link H., Stewart L., Bickel P.

♠(variable) Leone G., Pellegr

(2002Euro/Train km 1998

0.17

♠(variakm 1998

♠ Euro/Plane Movement 1998 196.35

) - AREA: ITALY

ble) Euro/Train

0.16

SchipARE

ECU 1995 per(discount rate 3

66 for CO2

325 for CH4; 6005 for N2

per Y. (2004) A: EUROPE

ton %)(3)

;

O

Tánczos K, Legeza E., Magya as G., Kövári B., Kiss B., Békefi Z., Duma L. Nagy Z., Rónai P.

(2002) - AREA: H

♠ Euro/Train km 1998 0.068

♠(variable) Euro/Pla ovement 1998 r I., Bokor Z., Fark

UNGARY

ne M33.650

PROMETEI

Tervonen J., Hämekos

Euro cents/k ship (maritime trans ort ) (4)

593.5.

ki K., Otterström T., Peter A., Bickel P., Schmid, S. (2002) AREA: BALTIC SEA

m per

p

(1) Unless otherwise indicated costs are to be understood as being marginal; (2) All in the function used by Schip

values it of measure that ha consideratiodiscount rate is addressed per (2004) to calculate the cost that is repor here; for details refer to the pter on external costs o air pollution in this report; (4) The year in which the costs w verage costs; ♣ = total costs per m ransport.

are reported in the un ment of the study s been taken into n; (3) The ted cha f

ere calculated is unclear; ♠ = a ode and means of t

A

106

a o of air p n obal l l (1) (P

Ship

b. 9 - Survey f external cost estimolluting agents: CO

ates (net of internalis

ing monetary resourc

Road

es) ollutio at the

Railway Airplane

gl

eve2 and CO)

AA.AA. (2003 AREA: UNITE KINGDOM

)D

o 000 Billi ns of poun1.4

ds 2

Cal p d D., V B

i l(

s of Dfl 1994 al transport) 06 (low), (medium), 74 (high)

thor E., Johansson O., Litman T., Ma dison D., PearceSource: leijenberg (1994)

AREA: HOLLAND

erhoef E. (1996)Billions of Dfl 1994(3)

0.95 (low), 2.62 (medium),

11.66 (high)

B llions of Df 1994 0.01 medium),

0.05 (high)

Billion(Fluvi

0.0.17

0.

De o i ., Pro t S, Va ercru ., G. (2001) - L Borger B., De N cker L., Mayeres I., Int Pan s L os

Wouters AREA: BE GIUMnd yssen D Billion of Euro 1998

2.4

Henry A. and Go art S. (2002b) REA: LUXEMBO RG

dA U

Million M n Euro 1998 al transport) 0.02

Euro 1998 36

illion Euro 1998 0.90 (incl. buses)

Million Euro1.5

1998 Millio(Fluvi


Million M 1998 Euro 1998 625

illion Euro 1998 11

Million Euro116

Himanen V , Idstroem T., K rjalainen J , Otterstro m T.AREA: FINLAND

. a . e , Terv (2002) Million M 1998 n Euro 1998 5

onen J. Euro 1998 221

illion Euro 1998 6

Million Euro17

Millio

INFRAS 2004 AREA: EU15, SWITZ RLA D, N RWA

( )E N O Y

ion Eu 2000sce

1 3

00 (high

n (2)n Euro 2000 scenario) (2)

506

Mill ro (high nario)(2)

1238

Million Euro 2000 (high scenario) (2)

2894

Million Euro 20sce ario)

79931

Millio(high

Korizis D., Tsam as ., Roussou A. (2AREA: GREECE boul D 002) Million Million 1998 Euro 1998

320 Euro 1998

2 Million Euro

0.03 M i P., Lin

EA: PMillion Million 1998 acário R., Carmona M., Caiado G., Rodr gues A., Martins

(2002) - AR ORTUGAL k H., Stewart L. Euro 1998

483 Euro 1998

3 Million Euro

50 Nääs O., indb rg G. (2002)

AREA: SWEDEN L e Million Million 1998 Euro 1998

383 Euro 1998

3 Million Euro

65 Orfe il J.P. (1997)

AREA: F ANC u

R Eillions B Francs 1991

4-14

Ri F G m eone e t t i P ITAL

Million Million 1998 Million Euro 1998 1

cci A., Enei R., Esposito R., agiani P., iam ichele F., LLink H., S ewar L., B ckel . (2002) - AREA:

G., P llegrini D., Y

Euro 1998 2324

Euro 1998 61

Million Euro197.0

Tá I k G., Kö ri B., K B., ,. ( ) UNGA

Million Million 1998 Million Euro 1998 (Fluvial Transport)

12

nczos K, Legeza E., Magyar ., Bo or Z., Farkas váDuma L. Nagy Z , Rónai P. 2002 - AREA: H

iss Békefi Z. RY

Euro 1998 191

Euro 1998 6

Million Euro3

(1) e e study under co ideratio he tot s u d INFRA 0 d a shadow value of CO2 equal to 140 h o s s calc h ing to i timate i u

S (20 4) are those calculate withs in l terat re.

al co t val es reporte by various assumpt ons and es

ns n; (2) Tulated by the aut ors accord

PROMETEI

T

All values are reported in the unit of measur ment of theuro/Ton; (3) Low, medium and igh refer t thre hold

PROMETEIA

3.3. ACCIDENT RISK AND THE VALUE OF HUMAN LIFE

3.3.1. GENERAL CONSIDERATIONS

the damage (which will obviously depend on how damage is defined

ility and

care e

similar

ideal fo

seen if

premiu

as wou

differen for ex. by a share of the

Transport activities can cause accidents, leading to death, reduced chances of survival

or limitation of functional abilities. Similarly accidents may cause acute or chronic

illness or various other health problems. The effects of transport activities on health

may be direct (for ex., professional illnesses suffered by vehicle drivers, illnesses due

to exposure to polluting agents, such as asbestos) or may be due to contact with the

surrounding environment (infection of eyes or lungs imputable to air pollution, stress

or neurological disorders due to noise, and so on).

“Accident risk” is probably the most accurate expression encompassing all forms in

which the quantity and quality of human life are affected in the course of activity in the

public sector. Accident risk may concern events of varying degrees of seriousness, up

to the extreme case of death. Sometimes, however, when dealing with situations

where risk is related to the chances of survival, we may wish to refer to value of life

rather than mere accident risk.

The logical way to internalise the externalities of accident risk requires both a

quantification of

and the components of same) as well as the identification of a means of payment that

may (a) be able to provide, at least approximately, the proceeds for the quantified

damage and (b) be able to induce travellers to behave properly so as to minimise

accident risk.

Ideally, the payments that would be able to internalise these externalities should be

directly proportional to the number of kilometres travelled within a certain unit of time

(one year) by the marginal traveller and inversely proportional to the effort (ab

) xercised by the same to avoid accidents. It would thus be something very

to an accidents insurance. In other words, accidents insurances could be the

rm of pigouvian tax for this specific type of externality. It would still need to be

in all countries where such a scheme were to be applied, the insurance

ms paid would be sufficient to cover the costs generated. If in some countries,

ld seem to be the case, the premiums were lower than the damage, the

ce could be covered by other general tax components:

107

PROMETEIA

taxe le

travelle

One tra

rejecte

human

loss of proach appears very less

actually trying to value is not

e. Few of us would be

sult in terms of saved lives, or

nfinitely escalating costs from a certain level of safety

may not be very useful, and may in fact be misleading as different conclusions

may be drawn depending on whether marginal or total values are used; since

the decisions that are assessed produce their effects at the margin, this is the

value which we are interested in (it often happens that the practice used to

quantify these values leads to a top down approach and thus to the

quantification of average rather than marginal values);

s vied on fuel. More so, since fuel taxes are paid in proportion to the distance

d they possess one of the features of the ideal tax.

ditional standpoint, until recent times particularly popular in project designing,

d the idea of measuring goods such as accident risk claming that “the value of

life cannot be measured” or “there is no sum of money that may pay for the

health”, or similar arguments. But such an ap

convincing when we come to realise that what we are

human life itself, but rather the use of the resources that produce effects on human

life. When decisions concerning the utilisation of public resources need to be made, we

cannot not ask ourselves whether the use that we are making of these resources is the

one that gives us the maximum possible benefit – i.e. if it is effective – and if it is

compatible with our other objectives and needs of social lif

favourable to allocating very significant amounts of money to projects having a limited

scope, and in so doing preclude the possibility of pursuing other projects (some of

these may in turn appear to be extremely important; others still may have effects on

the life of citizens: thus, it would have no sense to allocate significant sums of money

to increase the safety of air transport when the same re

a better result, may be attained by concentrating expenditure on improving road or

railway safety).

The fact that reduction of accident risk is – almost always - characterised by

decreasing marginal yields, and that therefore, reducing, say, the risk in a given

context by 0.001 may imply i

onwards, only exacerbates the problem.

As in other sectors, a number of criteria are available, all of which may be potentially

shared and all equally biased (and therefore not acceptable as absolute values). The

following are a number of general warnings that are valid for all applications:

- the analyst is usually interested in marginal values; knowing the global value

108

PROMETEIA

- in the case of human life and accident risk, choosing the output unit of

measurement of decisions is difficult, and yet crucial; no unit of measurement is

ideal: the number of days confined in bed, the valuations provided by doctors

or patients in terms of their functional abilities, or conditions such as pain,

anxiety, etc. are all elements of a measurement which, because of its nature, is

usually the sum of a number of different attributes.

In this respect a number of considerations are in order:

1) measurements of input or throughput are not to be used (for ex., number of hours

of medical assistance, or quantity of treatments supplied) in order to measure output,

even when the temptation to use such an approach may be strong, as they may be

perceived as attractive shortcuts; in fact, these are cost measurements that may often

can only be carried out on population

luation42. Using the

significantly underestimate the real benefits of the decisions;

2) the multi-dimension problem needs to be addressed by the use of suitable

measuring instruments; in principle it may be possible to use a number of different

instruments, from multi-attribute analysis to true and proper economic analysis; choice

of instrument will in any case need to be weighted and justified; monetisation of the

output is a step that standardises the different effects;

3) most of the valuation methods that have been proposed are of a lab nature,

meaning that they involve processes that

samples or observation groups; the results cannot therefore be applied to society as a

whole other than through some form of extrapolation; this underlines their nature as

average (or median) values40.

In his pioneering work of 1968, T. Schelling41 argued that the value of life within the

scope of public decision making is not a value of the life of specific individuals, but

rather of statistical lives, i.e. probabilities of life (or risks of death). Whereas the life of

single individuals possesses, in his eyes, an infinite value, the probabilities of death (or

survival) of non-identified individuals may well be the subject of va

40 This may raise a number of doubts; however, we must confess that in economics any price may take on the meaning of average value since it represents, in any case, the result of operating a mechanism of extrapolation (or, as happens on the private goods markets, of negotiation); accordingly, although it is true to say that the price of a good (especially a good such as health) will not necessarily be shared by all individuals, this must not created embarrassment: the meaning of economic prices is above all to guide the decisions of individuals; in this sense an average price is perfectly acceptable. 41 T.C. Schelling, (1968), p. 133. 42 Schelling, work cited.

109

PROMETEIA

words of more recent authors, “an individual shall make different decisions with regard

to his death if he does not expect to die…Likewise, social decisions that concern

intensive treatment medical units shall be seen in a different light only when they refer

to anonymous patients. An anonymous condition is a way to transform life and death

decisions into a decision-making matter for consumers”43. Accordingly, what is being

alued are the (small44) changes in accident risk: in the case of life, in particular, what

of so called statistical lives.

accident) and the number of members of the population

mpedes or makes very difficult the calculation of often

ssential components, such as pain, self-esteem, the valuation of consequences of a

or physical limitation, anxiety (b elf as well as for others), etc.

v

we wish to quantify is the value

A statistical life, or life expectancy, may be defined as the relationship between the

incremental risk of death (or

among whom the risk is distributed. In this way, for example, the expected value of

the loss of a life when the incremental risk is 10-3 and population is 1,000 units is 145.

Schelling’s contribution cleared the field of all the most radical (and less grounded)

objections raised against exercises of valuation wherever human life is involved.

However, this has not settled the ongoing debate on which approach should be

preferred. Indeed, Schelling’s approach would also seem to imply the valuation of costs

much more than real benefits, and therefore seems to be somewhat in contrast with

the only method that is consistent with the principles of market economy i.e. the one

requiring the determination of individuals’ willingness to pay (or to accept) for (small)

changes to their original welfare conditions.

Schelling’s proposed solution i

e

given illness oth for ones

All these components are often decisive in order to determine the value of the change

(for better or for worse) of an individual’s original condition: for ex., not all road

accidents that have the same degree of seriousness, and that produce the same

disablements (described in strictly functional terms), are identical, but rather will differ

significantly different due to different degrees of collateral effects.

43 G. Tolley, D. Kenkel and R. Fabian, Overview, in Tolley et al.,(1993), p.4. 44 Looking at small changes is preferable since it is generally recognised that large changes give rise to non linear valuation changes. 45 It should further be noted that this value is the same in the case of an incremental risk of 10-6

and a population of one million units: this feature has given rise to criticisms.

110

PROMETEIA

It must be noted that the concept of statistical life, originally coined without reference

to the utilitarian aspects of the notion of life (and thus, as previously seen, not very

suitable to reflect certain important aspects of this notion), has since evolved to include

these components, as seems evident by the definition of VOSL in the section on

accidents.

3.3.2. VALUATION METHODS

Some methods do not rely on monetisation at all, but given their very limited scope we

shall ignore them here.

The methods that provide for the monetisation of the effects of interventions to protect

life and the heath of human beings may be distinguished according to the broadness of

the scope of the valuation. A few are confined to determining the costs deriving from

the consumption of resources or loss of production that the illness or death may cause.

Others may go as far as monetising intangible elements, such as pain, anxiety, etc.

ay, human machines”47. Accordingly, the real result of any action aimed at

protecting (or improving) the quality and quantity of human life shall consist in

allowing the individual benefiting from this action to avoid the expense or loss of

income that the illness or death gives rise to. Costs may be broken down into direct

and indirect costs, depending on whether they are costs due to the accident or income

that is lost due to the accident. This income is made up of the capitalised flow of the

The two main approaches are the method of human capital and the principle of

willingness to pay. The former seems very more easier and direct in terms of its

application. However, this method appears somewhat unable to take into account a

number of components of the full value of life, and has a number of other less

significant shortfalls too. The same reservations may be made with regard to the so

called method of insurances.

3.3.2.1. THE METHOD OF HUMAN CAPITAL

Valuation methods related to the so called human capital approach, also known as

gross product, aim to determine the damage that is directly imputable to the damaging

event46. The underlying assumption of these methods is that “people are producers,

that is to s

46 Among the best known works using this approach are Mushkin, (1962), Weisbrod, (1971), Cooper and Rice, (1976), Mushkin (1979). 47 M. Berger et al., in Tolley et al., cited work, p.23.

111

PROMETEIA

income that the individual would have earned from the time of the accident (death or

invalidating illness) until the end of one’s life, for non treatable (or non treated) cases,

or from the beginning to the end of the period of inability to work, for cases that are

actually treated48. Other economic items such as the damage to property49 may then

state expenditure).

he method under examination comes with a number of well known problems. The

xclu t yet entered the labour market (those

xpenditure and therefore considered a benefit! In the case of unpaid work, such as

rk ca bstacles to including these

be added to this amount.

From a practical point of view, calculation of the value of life and health may be used

by applying either one of the two fundamental methods: (a) dividing all the costs

borne to attain these aims by the number of cases in the valuation, or (b) projecting

the cost values of the cases that have been observed to the total of the population

considered. For both methods, it is important to have a correct definition of the case.

In general, the values obtained through either of these two methods will be average

values whereas for public decision-making it is marginal values that we are interested

in; only marginal values can tell us the extent of the benefit attributable to an increase

in state expenditure (or the cost associated with a reduction of

T

method e des all individuals who have no

who are too young and those who are looking for their first employment), those who

are unemployed, pensioners, housewives, etc. Furthermore: studies that are based on

this method tend to exclude income that is not related to work, such as, for example,

family allowances, unemployment subsidies, etc. By using this approach, the death of

elderly people who receive a pension would be recognised as a reduction to state

e

housewo rried out by housewives, there are no real o

incomes in the calculation since they possess an easily calculable opportunity cost,

given by the value that these services would have if there were to be sourced on the

market50.

48 Rice and Cooper, (1967), p. 1954. This method gives the judge the right to determine the valuation of the harm suffered by the individual involved in the accident. This method also has its ambiguities: first of all, the one of providing estimates which, as is the case in some countries, come half way between damages and punishment. 49 At times an amount (which is calculated as a percentage of the total but in a rather arbitrary way) is added to take into account immaterial costs deriving from pain, suffering, etc. 50 In a few studies based on the method under examination the costs arising from non-work time lost (that usually arises together with lost work time) are neglected. Such an omission is the same as saying that non-work time has a zero value. This is consistent with the cost of

112

PROMETEIA

In general it has been observed that the reduction in risk is desired by most individuals

as a good in itself, rather than as a condition allowing one to limit income losses. This

gives way to a divergence between the social and individual values of policies aimed at

reducing accident risk51.

In conclusion, there is reason to believe that the approach under examination

does not provide results that are systematically correlated with those that may

THE METHOD OF INSURANCE

re, conceptually very different.

.3.2.3. THE METHOD OF WILLINGNESS TO PAY

ting to calculate the contribution of life, or health, to the global product of

overall society is certainly a step forward compared to the idea of having this value

only be an ethical one, and allows the capturing of a significant portion of the effects

of the action aimed at safeguarding the life and/or health of individuals. However,

there remains doubt as to whether, in certain cases, the subjective standpoint counts

be obtained by approaches based on the willingness to pay principle, but

rather tends to give lower values due to the omission of a number of

components as well as to an inbuilt tendency to underestimate the size of a

number of the components considered. The human capital method’s greater

practical application for the calculations that are necessary therefore comes, at

times, at the expense of accuracy. To consider the averting expenditure made

by individuals in the overall cost calculation of the illness is a significant move

towards the calculation based on the willingness to pay.

3.3.2.2.

It is infrequent to find valuations that use value of life estimates used by insurance

companies for the payment of damages following invalidating or fatal accidents.

However, it is clear that from a theoretical point of view these measurements do not

represent value of life as such, but rather the amount that the insured party wishes to

leave to his beneficiaries: something, therefo

3

Attemp

illness approach but would not be justifiable under a utilitarian outlook, such as the willingness to pay approach. The omission takes place both for individuals who do not work at all (youngsters, elderly), as well as for individuals who are only temporarily unable to work. 51 A further limit, which is not an infrequent occurrence in the studies carried out according to the cost of production method, is that they tend to exclude costs that are not directly attributable to the activity carried out to reduce accident risk. In this way, the cost of transporting patients undergoing prolonged treatment outside the hospital and homes are, by many authors, neglected in the overall costs calculation.

113

PROMETEIA

at all. On subjective, or utilitarian standpoint will aly a llow the calculation to include

nt that all are interested in the wellbeing of the individual in

uestion53.

e c er subjects, is given by

elements such as pain, anxiety, social alarm, etc. which have no market value, but for

which individuals may have a willingness to pay52. Furthermore, in many cases a

utilitarian standpoint seems more appropriate than a paternalistic one to capture the

significance of certain aspects describing the conditions of health and functional ability

of individuals.

The willingness to pay principle is the only principle that is consistent with the general

principles of valuation for market economies. Replacing this principle with different

ones pulls these principles apart. By adopting a utilitarian principle, the aim of the

valuation does not necessarily end with the life (health, organic functioning) of the

individual addressed by the survey: in fact, it may address other physical aspects of

the condition of survival or health of the individual, subjective elements, such as pain,

fear, anxiety, self-esteem, social alarm etc., conditions of physical and psychological

wellbeing of family members, the individual’s friends and even society’s welfare in

general, to the exte

q

After all, th orrect notion of benefit in this subject, as in oth

the valuation given by individuals to the result of their decisions i.e. the individuals’

willingness to pay in order to achieve this result. This notion includes all the

components of the same result, including those that appear to have no monetary

value, and, more so, is an ex ante concept.

possible to obtain estimates (probably underestimates) of components

through the purchase of analgesics (see Mushkin, 1979). It is probably an underestimate due

suffering from lower levels of production following an accident.

52 In some cases it issuch as physical pain by calculating individuals’ expenditure to avoid or eliminate pain, for ex.

to the possible existence of a difference between the market price that the individual asks for and the full value of the analgesic, a difference that characterises health systems that are based on the third-party payer principle. For similar reasons, an upward distortion may exist in cases where an individual tends to consume excessive analgesics for purely precautionary reasons or due to the effects of addictiveness. 53 According to similar considerations, we say that the value of a programme to improve road safety may also be based on – and usually will be based on – reduced social alarm that the condition of danger generates. The valuation of these components of the benefits of the intervention is additional to strictly economical valuations, such as, for ex, the lower risk of

114

PROMETEIA

3.3.2.4. THE METHOD OF AVERTING BEHAVIOUR

Individuals spend to avoid, or reduce, the risk of accidents. The amount of this

expenditure is clearly related to individuals’ willingness to pay for health and life, even

though it is not easy to explain this relationship with certainty. According to this

approach, the value of an intervention that may have effects on human life may be

measured by considering the costs necessary to avoid that individuals’ lives have

certain consequences. For ex. to avoid serious consequences from a road accident, it

may be sufficient to sustain the implicit costs of the installation of seatbelts or airbags.

Similarly, the DAP to avoid the consequences of a vehicle’s capsizing may be focused

on the cost (at market prices) of fitting a stronger cabin. These are so-called averting

expenditures. However, this method brings with it the risk of confusing costs with

benefits. Furthermore, it isn’t always easy to put this expenditure in direct relation with

the specific results obtained: for example, installing a more effective breaking system

on a vehicle reduces a number of different externalities (harm to people, but damage

to property as well).

3.3.2.5. THE TRADE-OFF BETWEEN RISK AND MONEY

If methods based on DAP/DAA are used, the value of life will be made up of the sum

of money that individuals, based on their opinions, personal values, and expectations,

shall consider right to pay to the public administration to reduce the risk of death,

accidents and so on. More precisely, the valuation shall concern the trade-off, or value

of exchange, between quantity of money and (small) changes to the probability of

incurring increases (or decreases) to the length of life and/or of the quality (health and

its consequences) of life. This sum shall be represented by individuals similarly to how

it is represented for other public goods. An eliciting process will thus be necessary, i.e.

a process of extraction of the preferences of interested parties by means of suitable

survey techniques.

In general, it is believed that an individual will always exchange a change to his/her

probability of survival or the probability of increasing his/her quality of life for a sum of

money: goods such as life, health etc., shall be the subject of a valuation that the

individual shall make starting from his representation of the probability of the different

outcomes of the public intervention. Since these outcomes of the public intervention

(for ex., reduced risk of death or of an invalidating injury, or contamination) are future

events, the normal condition in which the exchange of money and risk will take place is

115

PROMETEIA

one characterised by uncertainty. Literature addressing this subject prefers to refer to

risk rather than probability, in the sense that the probabilities of different outcomes are

nown, or believed to be known. Empirically, the quantity of money that an individual

risk he/she is exposed to, or to improve

v l of wealth and where each individual stands

in his/her life

to substitute future umpt

g

of life for additional quantity of life.

the resources of o

from n

e

marginal reducti

the risk of dying at 20 which is greater than that which a 45 year old individual will

have for a marginal reduction of the risk of dying at 45 (decreasing DAP for marginal

increases in the length of life beyond a certain threshold). In general, therefore,

individuals appear to be aware of the fact that their ability to consume is destined to

k

will be willing to pay to reduce the accident

the quality and/or length of his/her life would seem to depend above all on the

elasticity of consumption as referred to the entire existence of the individual, and,

subordinately, to factors such as the le e

cycle54. By elasticity of consumption we mean the ability of each person

cons ion with current consumption: the greater this ability, the

greater is the person’s willingness to pay for mar inal increases to the length of his/her

life. In a certain sense, one can say that a certain quantity of years (=incremental

duration in years) are purchased at the expense of an inferior quality (current), where

quantity and quality of life, in this use of the term, correspond to levels of a good’s

consumption. It has been empirically estimated that this elasticity has an upper limit

that falls between the absolute values of 0.25-0.40, which would indicate a generally

low propensity to exchange current quality

Other important elements, for which their explicative nature has been verified, concern

each individual at a given m ment in time, the ability to enjoy utility

consumption (an element that is desti ed to change in time, and which is

included in the concept of elasticity) and the degree of exposure to risk factors (the

probability of dying at a given age). In general, it would seem that younger people are

less willing to pay to extend the length of their lives, which may be explained by the

effect of the discount of benefits deriving from a certain additional number of years of

life. Similarly, the DAP to reduce a future risk will, ceteris paribus, be lower than that of

a more imminent risk. By comparing the DAP of individuals in different stages of their

life cycle, we observe that whilst a subject who is, say 20 years old, will have a great r

DAP to reduce the marginal probability of dying at 20, compared to the DAP for a

on at age 45, he/she will also have a DAP for a marginal reduction of

54 Refer to Rosen (1993), in Tolley et al., p. 221.

116

PROMETEIA

diminish as years pass, and this alone is sufficient to explain a marginally decreasing

DAP.

CONTINGENT VALUATIONS OF ACCIDENT RISK

The interviewee is asked to provide his/her valuations assuming that he/she is part of the

population that shall receive the policies or project. The valuation may include strictly personal

components, such as suffering, stress, etc.55.

This method belongs to the family of methods based on the willingness to pay principle, and

contingent valuations are able – at least in principle – to also capture those components hat t

would not be considered by a cost of illness approach. This is further appreciated since money

losses due to medica expenditure or the interruption of work are not always the consequencl es

that individuals consider to be the most serious (decreased comfort is observed as the greatest

source of disutility among those cited in certain works)56. In general, the studies that have been

carried out to date have concluded that a change to the consumer’s surplus given by a

contingent valuation exceeds the value of the project or the policy as may be estimated usin g

the cost of illness metho . There are, however, no elements that allow to establish the extent d

of the gap between the two measurements.

In terms of accident risk, the contingent valuation tool is affected by most of the problems that

are typical of this method. These are problems that may have significant consequences on the

survey’s design and, in particular, on aspects such as the definition of the valuation. For

example, the definition of symptoms may be problematic, or it may be hard for the interviewee

to distinguish them. Furthermore, measures against air pollution, such as provisions aimed at

reducing urban traffic, have consequences on the health of individuals, since they reduce the

seriousness of certain symptoms (eye irritations, breathing problems) and, at the same time,

increase the visibility and reduce damage to monuments. Accordingly, interviewees may

encounter some difficulty in allocating the effects of a phenomenon of pollution pro quota to

one category rather than another, and being induced to provide global valuations, where the

value of each individual effect may not differ significantly to the value given to the full policy 57.

A further problem concerns the possibility that the interviewee has no familiarity with the

pathological condition that may derive from the event in question. In this case, the accuracy of

the survey’s results ends up crucially depending on the accuracy of the descriptions provided by

Shechter et al. (1988); Shechter and Kim, (1991).

V. Kenkel, Berger and Blomquist (1993) in Tolley et al., cit., p.100.

Refer to Brookshire et al. (1979); Loehman, Boldt and Chaikin, (1981).

55

56

57

117

PROMETEIA

the interviewer with regard to the suffering and functional limitations connected to such a

condition.

In the field of individual valuations on which cost-benefit analysis are based, a number of non-

economists have made important criticisms. According to these, when we are dealing with

decisions relating to health or accident risk, individuals’ value judgments will differ so greatly to

make all aggregation procedures necessary to obtain global values for the interventions

meaningless. This is undeniable. We must nonetheless observe that any decisional procedure,

and more generally, any political procedure, must carry out a number of delicate preliminary

decisions based on weighting expressions of willingness of individuals. The weighting

mechanism that is implicit in the use of the DAP (DAA) concept is that it is based on income as

being equivalent to utility. However, we cannot say that this problem applies only to this field.

The correct notion of costs to be used for the valuation of interventions affecting the quality

and quantity of human life is that of opportunity cost. This may raise questions or problems. For

example, for decisions that concern life and accident risk, it may be possible to distinguish a

number of different definitions of opportunity cost, according to which perspective is adopted:

The individual’s viewpoint, or that of his/her family, the viewpoint of the service provider, that

of the third-party payer, and the viewpoint of society. For public decision-making society’s point

of view needs to be used.

The problem of extracting willingness to pay on the basis of individual judgments and the

problem posed by the aggregation of valuations are not reason enough to give up the method

of stated preferences within the scope of cost-benefit analysis. If anything, they imply a

greater effort in promoting the application of these methods.

118

PROMETEIA

3.3.2.6. THE METHOD OF COST-EFFECTIVENESS

Cost-effectiveness analysis (ACE) consists in evaluating the relationship between unit

of product, measured in physical terms, and production costs for each alternative. This

analysis greatly simplifies the evaluator’s task, since it allows the replacement of

measurements of benefits and costs with simpler expressions, which may be non-

monetary and which, in practice, tend not to be, since they consist of numerical

expressions of the projects’ product: a part from QALY, which we shall look at soon,

the results of certain actions may be expressed in terms of the quantity of CO2

eliminated by a traffic reducing project, or the number of lives saved by a certain

project per unit of invested capital. In a case such as the latter, the chosen project

would be the one that allows the saving of most lives per euro invested.

In general, we may say that analysts with a non-economics background, and especially

representatives of the medical professions, tend to prefer the ACE method to the ACB

one, particularly because it avoids the controversial step of monetisation.

he ideal scope of ACE exists when the output of the intervention is not a composite

reby ggregation of output elements.

t

the number of years of life gained thanks to therapeutic treatment. The QALY (Quality-

Adjusted Life Year) method expresses the result of a health decision through the

number of years that the individual gains in the situation without intervention

compared to the situation with intervention. These years are then weighted to take

into account the quality of each year. Furthermore, they are generally expressed in a

standard form, i.e. without discriminating between individuals or groups. Since the

T

one, the eliminating the need to carry out any a

However, cases when the output of a decision or event that concerns the health of an

individual is standardised (or one-dimensional) are very rare.

3.3.2.7. THE METHOD OF COST-UTILITY

Cost-utility analysis (ACU) has evolved interestingly. At first, ACU was an alternative to

cost-benefit analysis. It must however be recognised that in contrast to he physical

units with which ACE measurements are made, ACU uses expressions that most

literature has recognised as measurements of utility. In fact, these measurements may

be considered to be expressions of effectiveness, and not utility.

With this approach, the unit of measurement is a non-monetary one, such as, for ex.

119

PROMETEIA

source of QALY calculations is individual valuations, we may understand the hesitation

that exists in classifying this unit of measurement within ACE.58.

If the indicators of the quality of life may be identified in characteristics of health

condition that may be defined in some objective form (for ex., a fractured thigh-bone,

or a 20% reduced breathing ability), the cost-utility analysis boils down to an

his common metrics shall need to be

QALY, or QUALY are the best known and most popular units of measurement in cost-

effectiveness analysis, and have become particularly popular after their use by the

reformed British National Health Service. Weinstein and Stason define the expected

QALY number (∆E) produced by a certain treatment as the standardised number of

expecte years of life ( Y), plus quality of life improvements due to prevention or

reduction of morbidity

application of cost-effectiveness analysis. If, conversely, quality is measured as

subjective individual valuations of health conditions (for ex.,: ‘on a scale of 0 to 100

what value do you give to a reduction of breathing ability?”, there is strong affinity

with the cost-benefit analysis, an affinity that may actually become identical if the

valuations are expressed in terms of DAP. However, even with an objective approach it

will be necessary to adopt common metrics which may allow comparative analysis

between policies that can produce heterogeneous physical effects and, subsequently,

the possibility of obtaining a unitary valuation. T

obtained by applying numerical values to different situations, or health conditions,

through a simple ranking by importance, or utility judgments.

3.3.2.8. DEFINITION AND CALCULATION OF QALY

59

60 61

d ∆

∆Y ∆Y, less any collateral effects of the treatment morb EC. In

health applications, if an individual having a life expectancy of 80 years dies of lupus at

age 60, the damage he suffered is 20 years. Conversely, if the effect of suitable

treatment is to extend the individual’s life by 10 years, and each of these 10 years is

lived in conditions that are deemed equivalent, in terms of wellbeing, of 0.5 years,

Ashmore, Mulkay and Pinch, (1989) and Siegrist and Junge, (1989), p. 463. A clear and balanced discussion of the problem is found in Butler (1992), p. 143. 59 QALY were officially introduced by Zeckhauser and Shepard, (1976). A summary bibliography may include the following: Torrance, (1976); Weinstein and Stason (1977), Torrance, (1986); Torrance and Feeney, (1989); Carr-Hill, (1989); Loomes and Mc Kenzie, in Baldwin, Godfrey and Propper, (1990); Lockwood; Wagstaff, (1991). 60 These were used, among others, in the famous Oregon Experiment. 61 Work cited.

58 In this regard please refer to Culyer, in Culyer and Wright (1978), p. 9; Mooney, (1986);

120

PROMETEIA

then the effective gain would be 5 years (and the total damage would be 15 years).

The possibility of applying this concept to saved lives outside the health scope, as

eir health between themselves (for ex., the rich cannot buy interventions from the

tion of ponent ty of n a matter ong

There consen the fact th ition shoul east

ical fu lity t er y

dded. The main ques s to avoid dou nting: when a physical

ans that a certain individual will no le to carry out a

the individual’s he ll be expres inability to out

transport for example, is not addressed in available literature. However, it is clear that

the method is easily applicable to conditions of morbidity resulting from phenomena

induced by transport activity (for ex., illness or disorders caused by pollution).

The idea of QALY assumes that individuals are able to exchange quality and quantity of

life among each other. The next step, which consists in aggregating the individual

valuations so obtained, is necessary in order to obtain an opinion of the project’s

acceptability, but this step need not necessarily take the form of aggregated individual

DAPs (as we have seen, this step would transfer cost-utility analysis into pure cost-

benefit analysis). What really happens is that it may limit itself to summing the years of

life gained, weighted by qualitative indexes that have been chosen to express

differences between various health conditions.62

The source of the information from which QALY measurements are obtained is, in final

analysis, individuals’ judgments normally obtained through experimental methods. As

observed by Wagstaff 63, the subjective source of the information should not lead one

to conclude that individuals are the arbiters of the valuation process (which remains in

the hands of the valuator) or that individuals may exchange interventions destined to

th

poor).

The defini the com s of the quali life has bee of str

debate64. is broad sus on at this defin d at l

include phys

easily be a

nctiona , social functionali

tion i

y and mental health. Oth

ble cou

aspects ma

problem me t be considered ab

certain work, alth sha sible as an carry

certain operations, and not as the sum of the disablement itself and the inability to

perform the operations in question. It will in any case be necessary to distinguish the

62 In this regard an interesting debate was held: Zeckhauser, (1975), p. 419, and Shepard and Fuchs, in Fuchs, p. 255. 63 Wagstaff, in Layard and Glaister, p. 428. 64 Refer to Wiklund et al., (1992), p. 187.

121

PROMETEIA

various degrees of seriousness of the disablement, according to the type of functional

impediment that this disablement determines.

If a health programme leads to the improved health (quality of life) of individual X from

0.40 QALY to 0.55 QALY, then the value of the programme is 0.15 QALY. This means

that for individual X, the improvement is equivalent to gaining 15/100 of one year of

healthy life. Let’s look at another example: a health programme extends the life of

individual Y by three years with health conditions of 0.33 QALY, this second

improvement has a value of 0.99 QALY. This means that for individual Y, three years of

life in disabled conditions are equivalent to one year of life in perfect health and that

he/she would be prepared to exchange three additional years of life lived in disabled

conditions with one year of additional life lived in optimal health65. If the two

improvements are the effect of the same programme we may say that the programme

generated 1.14 QALY of benefits. Since health (or the lack of health) has a number of

dimensions to it, it would seem natural to express the various conditions with regard to

which an individual may be called to give his preferences. For example, conditions 1, 2,

3 and 4 in tab. 10 may each be characterised as the sum of five attributes, having

different values according to the condition itself, and which together give us a picture

of a certain individual’s condition66. Each box is given a certain value in terms of QALY.

Tab. 10 – Example of definitions of health conditions for QALY calculation 1 2 3 4

Mobility perfect difficult almost none none Physical activity

perfect limited very limited none

Social relations

normal require occasional help

dependent on the help of others

impossible

Painful symptoms medicines are taken

none bearable bearable provided unbearable

Self-esteem full limited very limited none

These values are scaled, usually on a scale from 1 (or from 100) to 0, where 1

represents the optimal condition (the top left corner box) and 0 represents the

individual’s death. Situations that are worse than death can be considered too, and

these will have a negative value. As may be seen, tables such as the one above may

65 Refer to Torrance, (1986), p. 1, and Weinstein and Stason (1977), p. 716 66 Refer to Kaplan, Bush and Berry (1976), p. 61.

122

PROMETEIA

be very subjective, and accordingly it may be possible to have very different tables to

represent the various conditions of a same illness. The tables that have so far been

developed are characterised by a few rather arguable specificities, such as the fact of

not taking into account the effects that each condition, corresponding to a particular

box, may have on the individual’s income generating ability. Thus, certain health

conditions may not only be caused by the disutility as such but also true and proper

conomic costs. This omission may make procedures for the aggregation of the

obtained through the use of QALY difficult.

hese values to future decisions concerning individuals not included in the

itional goods.

LY categories

according to a few fundamental characteristics, but independent from socio-economic

data, such as, for example, age. As more and more discriminations based on socio-

e

experiment’s results

Aggregation of QALYs also presents a problem. For example, to argue that a

programme that gives 1/1,000 of a QALY per 100 persons has the same value of a

programme that gives 1/10 of a QALY to only one person is equivalent to suggesting

that QALY is a linear measurement, where in fact there may be reason to assume a

marginally decreasing value. In practice, there would be no difficulty to conclude that

the aggregation of values that are expressed in terms of QALY may be possible by

taking into account their mean or median values. However, this is where yet another

disagreement exists, and concerns the nature of QALY measurement as a political

process. The construction of QALY tables is very expensive, in terms of both money

and time, and may be justified only to the extent that the values that are obtained end

up being adopted almost constantly, as if they were true and proper prices of the

services in question. However, to impose average values obtained from a sample and

apply t

original sample, may be seen as a rather forced aggregation. The outcome of this

aggregation would be very different from the one that would result from individuals’

market bargaining such as the one determining the price of trad

In the original intentions of Zeckhauser and Shepard, QALY should have been the

same for all parties, without any discrimination based on individual conditions or

characteristics of social groups. The principle to justify this strictly equalitarian

assumption is that the days of life in perfect conditions of health have the same value

to all, regardless of economic conditions, age, sex and any other characteristic67. A

softer approach provides for the possibility to build differentiated QA

67 V. Chen and Bush, (1976), p. 215; Kaplan and Bush, (1982), p. 61.

123

PROMETEIA

economic characteristics are introduced, the gap between QALY and contingent

(in terms of social, ethnic, spatial,

entiation, and also betw

rmination of Q n

what one would be led to believe a priori68.

3.3.3. ACCIDENTS

e nspor e

avera o ch

however, the traveller the

ent nt

represents the cost th user

of a means of transpo

ty

internal. When the us

that are paid and an traffic violation tickets. The marginal cost of an accident is

efore given by the economic value of the change in accident risk (for the user,

other users and other people in general) when an additional user enters the traffic

flow.

(

In a numbe e literat , o

accidents are divided into direct economic costs, indirect economic costs and the value

of sa t

valuations widens. In terms of interpersonal comparatives, a number of interesting

studies have allowed us to learn that the variability

etc. differ een patients and population in general) of the

subjective valuations on which the dete ALY may be based, is lower tha

A traveller who decid

ge accident risk d

s to use a means of tra

efined for that specific mode

t exposes himself/herself to th

f transport. When deciding whi

means of transport to use the traveller internalises this risk. At the same time,

may influence the accident risk for all the other users of

same form or differ forms of transport. The marginal external cost of the accide

at remains after internalisation69. In other words, when the

rt becomes the victim of an accident, the only externality is the

cost borne by socie and due to his/her travel decision, whilst the value of the risk is

er is injured, all the costs are external except for the damages

y

ther

The cost components that need to be considered in order to calculate social costs of

accidents are: the value of the accident risk, the loss of human capital, medical

treatment, administrative costs, damage to property tab. 11).

r of cases addressed by availabl ure the cost components f

fety itself. People value their own safety for differen reasons and often far in

excess of their lost production capacity. Therefore safety has a value in itself, and

society recognises this to have a specific value; this value is at times referred to as

risk value and, more in general, corresponds to the value of human life.

68 Refer, for example, to Kaplan, Bush and Berry, (1979), p. 501; Balaban, Fagi, Goldfarb and Nettler, (1986), p. 973. 69 This definition is provided by UNITE (2001).

124

PROMETEIA

Tab. 11 – Cost components for the valuation of the social costs of accidents adjusted from Infras-IWW (2004))

Effect Deaths

(Source:

Injured

of accident risk/ value of safety in itself

Victim’s loss of utility, family and friends suffering.

Pain and suffering of the person injured in the accident, as well as

mily

Value

the pain and suffering of faand friends.

Loss of human capital Net production loss due to reduced work time (actual value of lost production), substitution costs.

Medical treatment and funeral expenses

Costs of medical treatment before death. Funeral expenses.

Costs for medical treatment until the person has fully recovered from the accident.

Administrative costs Police, ambulance, administrative, legal and insurance costs. External costs only include those that are not borne by the transport users.

Damage to property/materials

Damage to vehicles, infrastructure, buildings, natural environment. These are usually not considered among external costs because material damages are paid by those who make up the transport users in the form of insurance premiums (but this isn’t always the case).

Congestion damage Congestion costs are not always considered. Congestion should be considered only if separated from other congestion and only if exclusively caused by accidents.

Accidents may cause pain and suffering, but often, in addition to these, they shorten the lives

of their victims. This naturally represents a welfare loss that may be seen as a cost that needs

to be quantified in monetary terms. The UNITE (2001) 70 methodology used to define external

costs (marginal) argues that at least a share of the risk value must be considered to be internal

because travellers make a decision whether to travel or not and in addition may chose which

method of transport to use according to a level of accepted risk. One may counter-argue

(INFRAS-IWW, 2004) that the chances of accident are so small that a degree of risk perception

allowing one to make a rational decision on which form of transport to use is improbable, if not

impossible. Consequently, Infras-IWW (2004) considers all the risk value to be part of the social

cost. The value of accident risk for injured parties is usually estimated as a share of the value of

the accident risk for death. Recent thinking (Lindberg, 2005) suggests that the optimal values

70 UNIfication of accounts and marginal costs for Transport Efficiency. The UNITE project, financed by the European Commission, was devised to help policy makers develop price and tax policies for the transport sector. It addresses three aspects: the accounts of the transport sector, marginal costs and the integration of approaches used at the E.U. level.

125

PROMETEIA

for a serious injury and a slight injury may be represented by 13% and 1% of the accident risk

for death, respectively. It is recommended that all risk value be considered as social cost and

that the 13% and 1% risk of death caused by accident be applied to cases of serious and slight

injury, respectively (as has been done in Part Two).

3.3.4. MONETISING THE VALUE OF HUMAN LIFE: CONCEPT OF STATISTICAL LIFE

The monetisation of human life is of great importance to calculate cost per accident.

This monetisation usually takes the form of value of statistical life (VOSL): this concept

provides the valuation of a (marginal) change in risk. It is therefore a probabilistic

he level of risk the

willingness to pay to stop it from increasing). The DAP (or DAA) may be

y valued through methods of analysis of revealed or stated preferences. For

d preferences, the contingent valuation is often used (respondents indicate their

sk reduction, for ex., the reduction of a fatal accident risk occurring during

year from 4/100,000 to 2/100.000). The value of statistical life is

equently estimated as the arithmetic mean of individual marginal rates of

substitution. For revealed preferences actual market transactions that may indirectly

express the value of safety are analysed. The most commonly used technique to

analyse revealed preferences concerns studies on wage-risk relationship, which

estimate the wage premium associated to a fatal work accident risk.

concept, ex ante, and not a valuation of the loss of life of specific individuals, which

would be an ex post measurement. Literature on the subject is home to a number of

authors who reject the idea that individuals may determine an accurate trade-off

between journey’s safety and money (for ex., refer to Hauer - 1994). In general, in

order to reach a valuation of human life, the following valuation of risk change is

applied. A statement that fatal accident risk is 1:100,000 means that in statistical terms

there is one fatality every 100,000 people per year. A change to this risk means a

change to the number of lives saved and this can be given an economic value. This is

the marginal rate of substitution (SMS) of money for the risk of death, due to any

specific cause. The value of statistical life is therefore simply the average of a number

of SMS, where the average is obtained through a suitable estimator of the underlying

average (unobserved) of the population.

The marginal DAP for a certain reduction in the probability of a fatal accident is

usually considered to be an increasing function of the initial level of risk (it may be

interpreted as a sort of safety demand function: the greater t

greater the

empiricall

state

DAP for a ri

the next

cons

126

PROMETEIA

VOSL examples calculated on the basis of human capital are also found in literature.

ro for human life in

its safety cost-benefit analysis; this value was deter sis of an output

analysis approach that considers an estimate ro n l owever, the

one million to account the willingnes ay t pain and

suffering. In the Csst-Istiee/Anfia-Aci (2001) repo lu 72 1999) for

ere obtained on the basis of analysis of

t estimates as

ell as in terms of ranges), which, for revealed preferences, are as far as 65% lower

The European Union, for example, uses the value of one million eu

mined on the ba

of gross p ductio ost. H

euro does not take in s to p o avoid

rt a va e of € 0,000 (€

human life was used, and values of €75,000 and €15,000 for a serious injury and a

slight injury, respectively. These values w

production capacity lost 71.

In studies that estimate the value of statistical life, the methods that are used and the

assumptions that are made have significant effects on the results that are obtained72. A

recent survey and meta-analysis (de Blaeij et al., 2000) of VOSL estimates obtained

with methods based on revealed or stated preferences in contexts where user demand

for road safety is analysed proved just how difficult it is to compare VOSL estimates of

different studies. This is because the VOSL estimate depends on the method that is

used and is directly related to both the initial fatal accident risk as well as the

formulated decreased risk assumptions. The VOSL estimates thus depend on the

methodology that is used and the assumptions that are made; it must however be

underlined that in general a significant difference is observed between estimates that

are obtained through stated preferences and those obtained through revealed

preferences. The de Blaeij et al. (2000) survey, which we have updated with estimates

from more recent studies, shows average values (both in terms of poin

w

than those obtained with stated preference analysis (tab. 12).

At 2004 prices the values for human life, serious injury and slight injury are 822,000, 86,000 nd 17,000 euro, respectively.

A recent work (Alberini, 2004) has examined the factors that may affect VOSL estimates btained by means of contingent valuation surveys (which fall within the methods of analysis

of stated preferences) which elicit the willingness to pay for a reduction in mortality risk.

71

a 72

o

127

PROMETEIA

Tab. 12 – Average VOSL values (meta-analysis) (M€2004)

Average value* Method

Point est. Min. Max.

Revealed preferences 2.058 0.742 4.950

Stated preferences 3.170 2.042 10.059

* Minimum and maximum age of the minimua range is pr lues.

d on the rest of society and the additional willingness to pay of

relatives and friends as suggested, for example, in the estimates of Carthy et al.

society. This rest of society

ab. 13 - VOSL and possible additional components (millions of euro)

Death Serious injury Slight injury

values are the aver m and maximum values where ovided; they are not point va

Further considerations which may add some degree of heterogeneity to the estimates

concern whether or not the VOSL used to estimate accident costs includes the

additional costs impose

included in Lindberg (2005), and according to which the VOSL based on DAP is the

dominant element of accident costs and would include a part of the direct and indirect

costs too, which would therefore not need to be added. A cost that would need to be

added to the VOSL based on DAP is the cost for the rest of

cost includes the costs for hospital treatment paid through the general taxation system

as well as the costs for the net production lost which would have been enjoyed by

society had the fatal accident not occurred. In Sweden, the additional costs for the rest

of society would represent 8% of DAP in the case of death and 20% in the case of

serious or slight injury. The additional DAP of relatives and friends is estimated to be

40% of the VOSL based on the DAP to avoid the risk of death (tab. 13).

T

VOSL 1.765 0.24 0.02

VOSL plus the additional cost of the accident for the rest of society 1.906 0.27 0.02

VOSL plus the additional cost of the accident for the rest of society and additional DAP of relatives and friends 2.612 0.36 0.02

Source: Lindberg (2005)

128

PROMETEIA

Tab. 14 – Average VOSL values (millions of euro)

M€2004

Min Avg. Max

VOSL 0.367 2.329* 8.673

VOSL per seriously injured 0.040 0.216 0.406

VOSL per slightly injured 0.016 0.020 0.040

seriously injured and slightly injured (at 13% and 1% of the case of death, respectively) were applied to this value (as suggested in Lindberg, 2005 from ECMT (1998)) we obtain values of 0.275M€ and 0.021M€

*If extreme values are excluded the average is 2.115 M€2004; if the VOSL calculation per

16-20) are shown in

ses analysis of 24 works having a VOSL point estimate,

s have a VOSL range estimate and 10 works with VOSLs for a serious injury

for sligh OSL point estimate.

L to recommend for a simulation. It is

geneous estimates and because the

OSL is of so the total accident cost 74. When ad hoc

udies re n n available literature,

ggest 13% and 1% of this value (0.275 M€

injured and slightly injured, respectively.

73.

The summary data of the full literature survey (detailed in tables

table 14. This literature compri

21 work

and a t injury in addition to a V

It is therefore not easy to chose which VOS

difficult because literature provides very hetero

V great significance in quantifying

st a ot possible the average of the values provided i

excluding any extreme values, may be used. Doing so gives us a value of 2.115 M€;

the most recent indications in literature su

and 0.021 M€)75 for seriously

73 According to Lindberg (2005) recent evidence does not suggest that these numbers should

seriously injured group were divided into two, separating permanently seriously injured from temporary s ly injured, as these two groups have significant DAP differences.

be updated even though there may be an important improvement in the estimates if the

erious

74 In Infras-IWW (2004 and 2000) and ECOPLAN (2002) VOSL is also used for self-caused accidents. 75 For studies carried out in Portugal and Finland within the scope of the UNITE project, 15% of the risk of death was used for seriously injured and 1% for slightly injured.

129

PROMETEIA

3.3.4.1. METHODS FOR ESTIMATING THE MARGINAL EXTERNAL COSTS OF ACCIDENTS

A method that is applicable to all forms of transport is the risk-elasticity approach: this

travelling on foot). The approach also

of relatives and friends

c = costs, mainly material, for the rest of society

ginal r a volume of traffic (Q) for a certain

average cost. In the case of accident rate,

approach takes into consideration the risks that a user subjects himself/herself to, as

well as the risks he/she subjects other travellers to (whether they are using the same

form of transport or a different form, including

includes the elasticity of risk, i.e. the manner in which these risks vary when an

additional unit of traffic is added. The method assumes that in his/her decision, the

victim internalises the value of the risk. The three main parameters that this

methodology considers are risk, elasticity and differences in the legal system. The key

function that ultimately determines the magnitude of the external cost of the accident

is given by the relationship between risk and number of users, i.e. elasticity 76. The

total annual cost (CT) of accidents may be expressed as follows:

CTaccident = A (a+b+c)

where:

A = number of accidents

(a+b+c) = sum of the cost components

a = value of statistical life (VOSL)

b = DAP

The mar cost (CM) of the accident fo

category of vehicles will thus be given by:

CM accident = dA/dQ (a+b+c)

so as to give:

CME accident = CM accident – CMP accident

Where CME and CMP are the marginal external cost and the marginal private cost

already internalised, respectively.

This expression referred to the external cost is equivalent to the more traditional

externality of congestion. However, in congestion all users suffer equally and the

internalised individual cost is equal to the

we may say that not all users, but only the victims, suffer the effects of the accident.

The CMP accident will therefore be different for the victim of the accident and the

76 As noted, the marginal cost of an accident is given by the economic value of the change in accident risk that is observed when a new user enters the traffic flow.

130

PROMETEIA

individual responsible for the accident; the victim may internalise the VOSL (a) and

possibly (b) while the CMP accident of the individual responsible for the accident would be

null (van de Bossche et al., 2001 and Lindberg, 2005).

The results of the marginal external cost estimate will depend on the level of accident

risk, the elasticity of the risk (of the accident vis-à-vis the volume of traffic), the

economic values and the marginal private cost.

The UNITE report (2001) argues that although accident risk possesses a certain degree

of uncertainty, it still is one of the most reliable pieces of information for external

marginal cost estimates. Reliable estimates of risk elasticity are more difficult to come

by and this makes estimates of the marginal external cost per accident more difficult

compared to other cost categories. To overcome this problem the UNITE (2001) report

o risk

.e. CMP accident = 0).

proposes use of the best estimate and should this be uncertain a somewhat low value

(a risk elasticity of zero) and one somewhat high value (a 33% increase of the best

estimate). Risk value is the main cost component and is greater than all other values

both in terms of size as well as degree of uncertainty. As for marginal private cost,

UNITE (2001) assumes that this cost consists of the user’s expected risk value plus

that of relatives and friends. As a minimum level (which will give the highest marginal

external cost estimate) the limit case is used where the user assumes n

(i

3.3.4.2. AVERAGE OR MARGINAL COSTS

The theory of marginal external costs of accidents has only recently been developed

and therefore empirical knowledge of the same is somewhat poor. In road transport,

for example, the marginal cost of accidents is the cost induced by an additional vehicle

that uses the road network, causing positive or negative effects. It may be that drivers

are disturbed by the increased traffic and that therefore accidents increase more than

proportionally, or average speed may decrease due to the greater traffic and therefore

the increase in the number of accidents is lower than the increase in traffic volume, or

that there is a shift from serious accidents to less serious accidents due to the lower

average speeds on traffic intense roads.

The Infras-IWW (2004) study calculates the average cost of accidents (in addition to

marginal costs) whilst the previous report (Infras-IWW, 2000) looked at marginal

costs. Infras-IWW (2000) demonstrates that in average traffic volumes the marginal

external costs of accidents are slightly lower or equal to the average external costs,

131

PROMETEIA

suggesting that additional vehicles reduce speed and therefore reduce the probability

of an accident and the seriousness of the same. In Part two of this work (Chap. 2)

empirical evidence will be provided for motorway traffic: the relationship between rate

akes a like study impossible).

marginal costs cannot but be extremely fragmentary. It is for this reason

xternal accident costs in transport requires the availability of a database

ber

001), damage to persons as well as damage related to

hydrocarbon spills (maritime pollution) have been accounted for. The expected value

of accident and traffic density per lane and direction at first increases as traffic

increases and then falls beyond 10-12000 average theoretical vehicles per day per lane

and direction. In any case, the relationship progresses in a rather jumpy way for

classes of traffic density and accordingly, since we are not dealing with a monotone

function, it is not possible to make any further generalisation about the result, and in

any event such generalisations must always be made in relation to a specific type of

traffic (only motorway traffic, since the lack of data for the ordinary road network

m

Given the current phase of development of the subject-matter’s literature, comparative

studies of

that in the analysis of the values obtained in our analysis of national and international

literature greater emphasis will be given to average external costs of accidents per

mode of transport (where minimum, maximum and average values are provided). As

for marginal external costs, we shall only present minimum and maximum values since

the average value would have to be calculated on the basis of very limited data.

3.3.4.3. QUANTIFYING DAMAGE CAUSED BY ACCIDENTS OTHER THAN BY ROAD TRANSPORT

Estimates of e

of the number and type of accidents for each mode of transport. For road transport

this data is easily available; for other forms of transport, given the lower frequency of

accidents, reference is usually made to historical data and average accident values

over relatively long time periods. For railway transport, for example, since the num

of annual accidents varies considerably, this number is calculated (in Infras-IWW,

2004) as an average over the period 1994-2000 (estimated results refer to the year

2000). Accidents that occur at railway-crossings, mainly caused by the road users, are

usually considered to be road accidents; suicides are not considered. For maritime

transport, a transport sector in which accidents often attract great attention by mass

media, average accident costs vary significantly depending on whether or not the risk

of tanker spills into the sea are considered. In the maritime transport section of

AdT/Fs-Confit (2000-2

132

PROMETEIA

of spills for the Italian traffic has been calculated on the basis of global spills, in turn

calculated on the basis of estimated expected spill per accident, observing a twenty-

year historical series.

It isn’t always easy to derive the average cost of an hospitalisation, by degree of

injury, particularly if one should wish to deduct the amount paid in terms of insurance

premiums by the individuals involved in the accident from this amount. Likewise,

rehabilitation costs, getting the injured individual back into work, and the individual’s

substitution for a short or long period of time, is not an immediate calculation.

However, available literature does provide estimates for the productivity loss that the

individual involved in the accident could have generated had he/she continued to work.

types. The greatest differences certainly concern external costs that are

3.3.5. SURVEY OF AVAILABLE LITERATURE ON THE ESTIMATES OF EXTERNAL COSTS PER

ACCIDENT

Available literature provides a vast number of heterogeneous pieces of work in terms

of methodology and results. The difficulties encountered in carrying out a comparative

analysis are represented by the often unclear definition of external cost (see Orfeuil,

(1997), the strong differences between estimates and the weight that are given to

various cost

imputable to a death or serious/slight injury. The measurement of this cost thus

depends on the components that are considered to be external, as well as the method

that is used.

In Infras-IWW (1994) all costs that are not covered by insurances are considered to be

external costs; therefore, there is an a priori exclusion of damage to property whilst

administrative, medical treatment not covered by insurance, substitution and

reintegration costs of the injured, lost production and human value are included.

In Infras-IWW (2000) the cost components are the usual ones: medical treatment,

opportunity cost for society, cost of pain and suffering. The VOSL has been fixed at 1.5

million euros; marginal costs are the same as average costs and no specific relation is

assumed to exist between vehicle per kilometre and accident rate; insurance payments

have been considered in order to calculate the external cost component. The estimates

thus obtained differ from country to country, just as the costs per type of road differ:

the costs associated to urban roads are 4-5 times greater than those associated with

motorway accidents.

133

PROMETEIA

In Infras-IWW (2004) the estimate is based on the previous work. The method used to

determine the identified components of external cost is essentially based on the

availability to pay to reduce the risk of accidents. The external part of the costs is then

identified in those components not covered by individual insurance: particularly

opportunity costs and costs related to pain and suffering. The VOSL is 1.5 million euro,

as suggested in the UNITE studies and used in Infras-IWW (2000), adjusted on a

country-by-country basis according to GDP pro capita. Furthermore, since the VOSL

corresponds to an average value of numerous and different studies of WTP, no

ccident responsibility to the drivers of the various

nsider external

terial

mber of accidents. In

ddition to these primary determinants, others are taken into consideration by the

adjustment is necessary for the year 2000 from the value used in the previous (Infras-

IWW, 2000).

In order to determine accident damage in the fourth report of “Amici della Terra”

(2002), given the absence of any better information on the parties awarded the

damages, a procedure assigning a

means of transport was used. The authors argue that this may lead to an

“underestimation of the share of damage imputable to freight vehicles”. Here, as in

other works, the insurance premiums of risk to persons and the National Health Service

tax on the premiums are deducted from the cost calculation so as to co

costs only.

According to the UNITE project, external costs are those costs that are borne by the

State and private individuals but not those borne by the road users and insurance

companies. The cost components of accidents considered in the analysis are: ma

damages, administrative costs, medical treatment for rehabilitation and reintegration,

lost production, risk costs (specific items are provided for each category). The cost

estimate for Italy in the UNITE project differs from UNITE’s general recommendations

only in terms of allocation, since the data available does not allow to build a matrix

showing who bares the cost and responsibility. The only country for which costs

associated to foot-travellers and cyclists were possible to calculate and incorporate in

the costs borne by society, rather than the user of the vehicle, was Sweden: external

accident costs are higher compared to other countries addressed in the same project.

For airplane accidents, most of which occur between private individuals – who then

bare the costs of the same accidents – the external component of the costs is very

small. In Link et al. (2000) two primary sources are identified for the determination of

accident costs: number and seriousness of the victims and nu

a

134

PROMETEIA

UNITE project on the basis of a marginal costs approach: infrastructure type, vehicle

ment, rehabilitation), reintegration

irst aid and ambulance, accident and emergency, patient’s treatment, non-hospital

aretaking, help and equipment), lost production (substitution costs, net current and

and pain of friends and

roach

compares ideal approach e for the pilot studies (Link

ith e

different factors, the rep

different risk factors if ne

In UNITE the value of used is also 1.5 million euro; this is a

e co

upper b

given by an approach based on the valuation of gross prod

k et al

involved in the accident a a valuation

the risk value (of accide

Where available, damage

The external share of cos

Borger et al. (2001) the

contingent valuation, a c n and standard gambling

determine accident costs

insurance. This model includes both monetary and non-mo

dividual chooses his consumption basket by maximising his/her

wn utility function, within which are numerous items such as accident risk. The model

ssumes that individual decisions on how many kilometres to travel and the measures

f safety to be taken, affect accident risk (De Borger et al 2001, pp.84). The role

layed by liability rules and insurance are addressed when the authors ask themselves

they are able to generate the optimal social level of accidents.

type, driver characteristics etc. More exactly, the cost elements considered in UNITE

(Link et al., 2000) are: material damages (property, infrastructure), administrative

costs (police, justice, administration, medical treat

(f

c

future output losses), risk value (individual risk value, suffering

relatives).

With regard to app es suggested for cost estimates

es and adopted approaches feasibl

of pilot projects, table 15

et al 2000, pp.39). W regard to uncertainties and th

ort suggests that sensitivity tes

sensitivity of estimates to

ts be carried out applying

cessary.

the VOSL that is

conservative estimat

recognise as an

mpared to the 2.5 million euro that literature seems to

705 thousand euro and is

uction lost.

ound. The lower bound value is

For Germany (Lin ., 2000), accident costs related t

re calculated through

o the health of the person

in terms of lost production,

nt) and, medical and non-medic

to property and administrative co

ts is given by the cost that the us

al rehabilitation treatment.

sts are also included.

er imposes on society. In De

ree different ways: through value of life is determined in th

ombination of contingent valuatio

(risk) and lastly through the choice experiment method. Pa

does not explicitly take into con

rticularly, the first model to

sideration liability rules and

netary costs of accidents. It

is assumed that an in

o

a

o

p

if

135

PROMETEIA

Table 16 provides a summary of available literature in terms of average and marginal

external costs of accidents. This shows value intervals that are comparable to the most

recent European estimates per mode of transport.

Tab. 15 – Cost estimate methods (UNITE methodology)

Ideal approach Approaches used in pilot studies

Material damages Use of the accounts of insurance firms (use of average accident costs, if necessary).

Same as the ideal approach

Administrative costs Police: average wage per man-hour spent dealing with the accident. Justice: costs of the legal system.

the insurance firms.

If the ideal approach isn’t possible average costs per accident have been used per class of seriousness of the

Administration: annual report of accident.

Medical treatment, rehabilitation and reintegration

Actual costs incurred by the health sector. Estimates of costs related to accidents.

If the ideal approach isn’t possible average unitary costs per victim found in past studies or in other countries have been used.

Lost Production Substitution costs: number of permanent injuries and fatalities for substitution costs. Net current and future output losses: number of fatalities and their average age, duration of temporary working capacity loss and application of the net potential production method. Future costs of consumption: must be deducted from the loss of production capacity.

Substitution costs: the unitary cost of replacement must be taken from specific studies (only for employed persons). Net current and future output losses: ideal approach for all working age victims. Future costs of consumption: average future consumption per person.

Risk Costs (Risk Value) Risk value according to willingness to pay,preferenc

Multiplication of victims and based on revealed es.

willingness to pay according to degree of seriousness. The risk value is changed by Infras-IWW.

Source: Link et al. (2000)

136

PROMETEIA

137

the Infras-IWW (2004) estimates are taken as reference levels and average

ternal costs are considered, then, in our case, the average values that are

ned are slightly lower for two-wheel vehicles (178 euro per 1000

senger/km against 188.6)77, whilst the values are slightly higher for cars

d buses (32.7 euro against 30.9 and 36 euro against 24, respectively). For

freight transport a broad variability in average external costs is observed for

light vehicles where a number of outliers exist at the higher levels, which are

the reason of average costs per 1000 tkm of 72.5 euro, more than twice the 35

euro in Infras-IWW (2004). Average external costs for industrial vehicles are

rather consistent (4.4 euro per 1000 tons/km in our case compared to 4.75

Euro in the Infras-IWW (2004) benchmark). For railway transport the literature

analysis shows an average value of average external cost of 1.6 euro per 1000

pkm, almost twice that of the benchmark (0.74 euro x 1000 pkm) whilst for

airplane transport a value of 1 euro per 1000 pkm is obtained compared to

0.37 euro in Infras-IWW (2004). For maritime or fluvial freight the literature

analysis indicates an average value of the average external cost per accident

of 0.8 euro per 1000 tkm vis-à-vis the negligible cost provided in Infras-IWW

(2004). It is clear that in observing the summary of literature data we must

bare in mind the different techniques used here and the geographic and

temporal diversity of the contexts of the studies. Nonetheless, the overall

results show average external cost levels imposed by different forms of

transport in their usual descending order, from road transport to railway and

airplane transport. For freight transport we go from the higher levels of road

transport with light vehicles, to heavy vehicles and then to lower levels for

railway and maritime freight.

77 It must however be underlined that INFRAS/IWW (2004) only considers motorcycles whilst a number of studies in literature which we have looked also consider mopeds.

an

pas

obtai

ex

If

P METEIA RO

138

PROMETEIA

139

Tab. 16 – Average and marginal external co cident i ssenger transport (

Cycles/motorcycles Cars se

sts per ac n pa road €2004)

Bu s

Transport Value Average

cost x pkm ave

cost

nal x

ge x

al

eragost x vkm

rginal cost xvkm

averagospkm

marginal costpk

marginal cost xpkm

rage costx vkm vkm

margi averacostpkm

margincost x pkm

av e mac c

e t x x

m

average cost x vkm

marginal cost x vkm

Min 0,132 9 0, 63 38 0,03 012 0,1 0,01 0,0109 ,004 0 0,010 0,0024 0,00 0 0711 ,0 0,036 Average 0,178 0, 27 ,036 0,0036 0 89211 0,03 0 ,0 Passengers

Max 0,205 0, 3 86 0,685 907 0,72 0,03 0,0981 ,081 0 0,145 0,0064 0,02 0 1243 ,4 0,158 Light vehicle vy les s Hea vehic

Transport Value verage

cost x tkm ve

cost

nal x

ge x

l erage ost xvkm

Marginost xvkm

amarginal

x acost tkm

rage x vkm

costvkm

margi averacosttkm

marginacost x tkm

avc c

al

Min 0,0381 9 0, 5 35 8 ,002 0,0180,010 006 0,00 0,00 0,000 0 Average 0, 44 ,0290,0725 015 0,00 0 Freight

Max 0,1021 0, 8 51 9 ,072 0,1580,1909 032 0,15 0,00 0,012 0 Railway e luvial Air Maritim and F

Transport Value average cost x pkm

or tkm ave

cost

nal x

ge x or

al x r

erage ost x vkm

margincost x vkm

erostkmtk

marginal costpkm or

tkm

marginal cost x pkm or

tkm rage costx vkm vkm

margiaveracostpkm

tkm

margincost pkm o

tkm

avc

al cav age

x p o

m

x

average cost x vkm

marginal cost x vkm

Min 0,0005 0, 00 1 730 031 0 0, 03 0 ,1 0

Average 0,0016 0, ,515* 0 2 18 197 54 0, 010 ,3 Passengers

Max 0,0035 0, 36 0,0019 0,0012 3 40 0,0012 329 0,00 ,2 0,018Min 0,0006 0, 04 0 1 73 023 0 0,00 ,1

Average 0,0009 0, 08 ,515* 2 18 172 0,00 54 ,3 Freight Max 0,0011 0, 21 0 3 40 329 0 0,00 ,2

Note: For marginal costs the average was n ed gi num values le rature; per po .ot calculat ven the limited ber of availab in lite * rt call, single data

Tab. 17 - VOSL Calculation – Literature survey

Year VOSL M€2004 (4) Authors Country

CURRENCY and original

prices P d

Study type

S ublishe Data (3) Human life Min. Max. eriousinjury

Slight injury

NOTE

Atkinson and Halvorsen (*) USA 1996 US$(1) 1990 1986 RP 4,338 road mortality Baker (*) USA 1996 US$(1) RP lity 1973 1973(2) 0,798 11,973 road mortaBlomquist (*) USA 1996 US$(1) 1979 1978 RP 0,496 road mortality Blomquist and Miller (*) USA 1996 US$(1) RP 1992 1987 1,386 5,361 road mortality Cohen (*) USA 1996 US$(1) 1980 1974 RP 0,367 road mortality Dreyfus and Viscusi (*) USA 1996 US$(1) RP 1995 1987 3,891 road mortality Ghosh, Lees and Seal (*) RP U.K. 1996 US$(1) 1975 1974 0,784 road mortality

Hansen and Scuffham (*) New Zealand 6 US$(1) ) 0, 6 199 1994 1994(2 RP 64 0,672 road mortality Jondrow, Bowes and Levey (*) USA 1996 US$(1) ) ad mortality 1983 1983(2 RP 2,475 roMorrall (*) USA 1996 US$(1) ad mortality 1986 1984 RP 0,138 1,794 roBeattie et al. (*) U.K. 1996 US$(1) 1998 1996 SP 1,264 14,281 road mortality Carthy et al. (*) U.K. 1996 US$(1) 1999 1997 SP 1,548 2,015 ty road mortaliDesaigues and Rabl (*) France 1996 US$(1) 1995 1994 SP 0,820 19,065 road mortality Johannesson, Johansson and O'Conor (*) en $(1) Swed 1996 US 1996 1995 SP 2,928 5,760 road mortality Jones-Lee, Hammerton and Abbott (*) U.K. 1983 1982 SP 0,547 9,354 1996 US$(1) road mortality Kidholm (*) Denmark $(1) tality 1996 US 1995 1993 SP 0,731 1,088 road morLanoie, Pedro and Latour (*) Canada 1996 US$(1) 1995 1986 SP 1,643 2,940 road mortality Maier, Gerking and Weiss (*) Austria S$(1) ad mortality 1996 U 1989 1989(2) SP 1,432 3,954 roMcDaniels (*) USA S$(1) ad mortality 1996 U 1992 1986 SP 7,959 28,852 roMelinek (*) U.K. 1996 US$(1) 1974 1974(2) SP 0,726 road mortality Miller and Guria (*) New Zealand S$(1) ad mortality 1996 U 1991 1990 SP 1,024 1,636 roPersson and Cedervall (*) Sweden 1996 US$(1) 1991 1987 SP 1,320 28,005 road mortality Persson et al. (*) Sweden S$(1) ad mortality 1996 U 1995 1993 SP 3,288 3,754 roSchwab Christe (*) Switzerland road mortality 1996 US$(1) 1995 1993 SP 0,846 Viscusi, Magat and Huber (*) $(1) ) ad mortality USA 1996 US 1991 1991(2 SP 8,673 roPersson et al. - da Alberini (2004) en oad mortality Swed M$ 2001 2001(2) SP 2,435 rCarthy et al. - da Lindberg (2005) M€ 1999 1997 SP 1,765 0,235 0,018 VOSL U.K.

Carthy et al. - Lindberg (2005) U.K. M€ 1999 1997 SP 1,906 0,271 0,024

VaOSL including the ccident cost for the

rest of society and e WTP of friends

nd relatives tha

140

Year VOSL M€2004 (4)

Carthy et al. - Lindberg (2005) U.K. M€ 1999 1997 SP 2,612 0,365 0,024

VOSL including the accident cost for the rest of society and the WTP

Jenkins, Owens, and Wiggins - Viscusi and

per head wound -9 years) Aldy, (2003) USA M$ 2000 2001 2000 1,229 2,546

Fatal accident risk

(5

Jenkins, Owens, and Wiggins - Viscusi and

Fatal accident risk er head wound 10-14 years) Aldy, (2003) USA M$ 2000 2001 2000 1,054 2,459

p(

Jenkins, Owens, and Wiggins - Viscusi and Aldy, (2003) USA M$ 2000 2001 2000 1,844 3,776

t risk Fatal accidenper head wound (20-59 years)

Johannesson et al. --- from Alberini (2004) den th Swe M$ 1997 1997(2) SP 4,141 All causes of dea

Krupnick et al. from Alberini (2004) Canada 2002 2002(2) SP 0,790 1,844 M Can$ Alberini et al. --- from Alberini (2004) USA M$ 2004 2004(2) SP 0,563 1,239

ACI (2005) Italy M€2004 2005 2004 1,282 0,040 0,016 biological damage

Values based on Italian court parameters to estimate moral and

de Bleaej and van Vuuren (2001) Holland

guilder) 2001 ? pending; 1 guilder MDfl (Dutch= 0,45 €

DoT (UK Department of Transport) - Jones-Lee, Loomes and Philips (1995) UK £1990 1995 1990 1,349

Jones-Lee, Loomes and Philips (1995) UK £1990 1995 1990 0,152 DoT (UK Department of Transport) - Jones-Lee, Loomes and Philips (1995) UK £1993 1995 1993 0,162 DoT (UK Department of Transport) - Jones-Lee, Loomes and Philips (1995) UK £1993 1995 1993 0,162

Ashenfelter, Greenstone (2004) USA M$ 1997 2004 1997 0,948 1,509

Infras-IWW (2000) 17 EU countries M€1995 2000 1995 1,756 0,234 0,018

INFRAS/IWW (2000) Italy M€1995 2000 1995 2,155

Infras-IWW (2004) 17 UE countries 2004 1,756 0,234 0,018

Infras-IWW (2004) Italy 2004 2,155

UNITE Italy M€1998 2001 1998 1,751

AdT/Fs-Confitarma (2000-2001) Italy M€1995 2001 1995 3,981

ExternE/CE (1999) Italy M€1995 1999 1995 3,981

141

Year VOSL M€2004 (4)

Csst-Istiee/Anfia-Aci, (2001) Italy M€1995 2001 1995 3.981

Csst-Istiee/Anfia-Aci, (2001) Italy € 1999 99 0.822 0.086

Human capital methodology, loss in

uction capacity 2001 19 0.017 prod

ETSC from Ac Anfia, p. 3 accident s MECU 1990 1990 3.222 i/ ection Italy

Federtransport (2002) Italy M€2000 2002 2000 3.100 0.447 0.019-0.039 1.500

0.216-

Ashenfelter (2006) USA M$ 1997 2006 1997 1.509 5.448

Highways Economic Note, no. 1 (2004) UK £june2004 2004 2004 0.229

Includes lost production, medical expenses and

ce and osts 2.042 0.018

ambulanhuman c

ICF consulting (2003), p. 11 UE 200 €2002, UK prices 2003 2 1.615 0.158 0.016

ICF consulting (2003), p. 13 UE €2002, UK prices 2003 2002 1.586

estion costs Including cong

Ministry for Transport, NZ (2004) NZ M$ J 2004 st une 2004 2004 2.287 0.406 0.040 Average social co

A urce Transport Re 5 UK Euro 2002 2003 2002 1.878 A.AA. (2003) ICF: sosea 99rch Laboratory 1

UNITE, Lindberg (2002) Sweden per person per

nt Euro 2002 1999 1.467 0.230 0.017 maritime accide(*) Evidence collected from the de Blaeij et al (2000) survey; (1) had originally b ed to exchange local rrency in year of e study rather than eference year of the data, du ssing information; (3) SP refer study based on analysis of stated preferences; RP rs to

vealed preferences; (4) GDP and PPP deflators for 2004 have been used to exchange local currency in €2004.

. GDP and PP ators for 1996P defle to mi

een us cu US$1996; (2) Refers to the publication of th

studies based on analysis of rethe r s to a refe

142

Tab. 18 – Average or marginal external cost due to accidents per mode and means of tra D (1), (2)

Road Passengers Road Freight

nsport: ROA

Two wheels Car bus

Public transport

trolleybus) (bus, tram and Light Vehicles Heavy vehicles

C

GBP pence per vkm 1993(3), (4)

0.7-2.3

althorp E., Johansson O.,Litman T., Maddison D., Pearce D., Verhoef E.

(1996) AREA: UK

Ca ., Li

s

993

areas

SEK per vkm 1993 ely pop

popa

vehicles and hea

lthorp E., Johansson Otman T., Maddison D., Pearce D., Verhoef E.

(1996) - AREA: SWEDEN

SEK per vkm 1993 0.3 densely populated areas; 0.075 scarcely populated area

SEK per vkm 11.2 densely populated

areas; 0.3 scarcely populated

1.2 dens0.3 scarcely

ulated areas;ulated areas (no

distinction is m de between light vy vehicles)

Danieli R., Rotaris L. (2001) Source: Green, D. Jones, D., Delucchi M. (1997)

AREA: FRANCE

ECU per vkm 1992 0.82

ECU per vkm 1992 0.9

ECU per vkm 992 1

0.73

ECU per vkm 10.73

992

Dan L.

Sourc 994) ♠ 991 ♠ECU cen 1000 pkm 1991 nt per 100 91

14.3

ielis R., Rotaris(2001)

e: UIC-INFRAS (1AREA: EU15,

SWITZERLAND, NORWAY

ECU cent per 1000 pkm 123.3

t per

4.2

♠ECU ce 0 tkm 19

Da L. e per p

Suburban roads 2.83 her u

Lire per pkm 1994

Suburb s 1,13 London plus other urban

nielis R., Rotaris (2001),

Source: Pierson, Skimer and Vikerman (1994)

AREA: UK

Lir km 1994

London plus ot rban centres 77.16

an road

centres 36.59

Danielis R., Rotaris L. (2001),

Source: T&E (Ecoplan 1993)

AREA: EUROPE

♠

9.9

♠Eu 1000

tkm 1993

Euro cent per 1000 pkm 1993

ro cent per

2.5

Adt/FS-Confit (2000-2001) Source: Federtransport (2002) - AREA: ITALY

♠ Lire per pkm 1997 214

♠ 1997

♠ Lire per ♠ Lire 1997

♠ Lire ♠ Lire per vkm 1997 257

Lire per pkm 61.8

♠ Lire per vkm 1997 108

♠ Lire per pkm 1997 4 tkm 1997

166

per tkm 6.9

per vkm 1997 43

143


Two wheels Car bus

Public transport (bus, tram and

trolleybus) Light Vehicles Heavy vehicles

Infras-Iww/IC (2000) a

(20 LY

♠ LSource: Federtransport

02) - AREA: ITA



ire per vkm 1997 82

Infras-Iww/IC (2000) b Source: Federtransport (2 Y

♠ Lire per vkm 1997 70.4

♠ Lire per 23.9002) - AREA: ITAL

vkm 1997

He S. ♠ Euro/vkm 1998 0.018

nry A. and Godart (2002b)

AREA: LUXEMBOURG

Henry A., Godart S. ♠(variable) Euro/vkm

1998 ♠(variable) Euro/vkm 1998

0.010 ♠(variable) Euro/vkm 1998

0.006

♠(variable) Euro/vkm 1998 (2002a) - AREA: BELGIUM motorcycles 0.122 0.009


0.002 Himanen V., Idstroem T.,

Karjalainen J., Otterstroem T., Tervonen J. (2002)

AREA: FINLAND

♠( o/vkm

motorcycles 0.022 vehicles for freight transport)

♠(variable km 1998 0.009

0.007

variable) Eur1998

♠(variable vkm 1998 ) Euro/ 0.004 (including cost of light

) Euro/v


INFRAS (2000) AREA: EU15,

SWITZERLAND, NORWAY

Euro per 1000 passengers km 1995

Euro per 1000

(Sour

1000 passengers 1995

79-360

pkm 1995 Euro per 11-54 km

ce: Miller 1994) 1-5

Euro per 1000 tkm 1995 44-163

Euro per 1000 tkm 1995 2.3-11


SWITZERLAND, NORWAY

Euro per 1000 p 36-

0 pkm 2000 Euro per 1000 pkm 2000 km 2000 Euro per 100629 10-90 1-7

Euro per 1000 vkm 2000 10-110

Euro per 1000 tkm 2000 0.7-11.8


SWITZERLAND, NORWAY

♠ 0 ♠ 0 ♠Euro per 1000 pkm 2000 188.6

♠Euro per 1000 pkm 2000 30.9

♠Euro per 1000 pkm 2000 2.4

Euro per 100tkm 2000

35.01

Euro per 100tkm 2000

4.75 IV Report “Amici della

Terra-FS” (2002) - AREA: ITALY

Euro cent per pkm 1999 17.13



Euro cent per tkm 1999

6.75

Euro cent per tkm 1999

0.4 ACI-Anfia (2001) euro x 1000 pkm (1998) 17,1

Korizis D., Tsamboulas D., Roussou A. (2002)

AREA: GREECE

♠(va 98 ♠(va 98

♠ E

♣ Euro million


motorcycles 0.010

♣ Euro million 1998 Motorways 1.2, Other roads 120



Motorways 39.5, Other roads 2311





0.008



(variable)uro/vkm 1998

0.007

1998 Motorways 3.5, Other roads 77

144


Two wheels Car bus


trolleybus) Light Vehicles Heavy vehicles

Lindberg G. (2002) AREA: SWEDEN

♠ Euro per 1000 Vehicle km

64 (Swede

♠ Euro per 1000 Vehicle km 10 (Sweden)

♠ Euro per 1000 vehicle km 43 (Sweden)

♠ Euro per 1000 ♠ Euro per 1000

n)

vehicle km 17 (Sweden)

vehicle km 56 (Sweden)

Lindberg G., (2005) AREA: SWEDEN

Euro per vkm 1999 0.02 (including heavy t, foot travellers and

e vehicles for freigh

cyclists)

Euro per 1000 vkm 1999

11.41 in averagLighter category

2.84 Heavier category

34.92 Macário R., Carmona M., Caiado G., Rodrigues A.,

Martins P., Link H., Stewart L. (2002)

AREA: PORTUGAL


motorcycles 0.250



♠(variable)

Euro/Vkm 1998 0.005


0.002

Makie P., Nash C., Shires J., Nellthorp J. (2004)

Source: Samson (2002) AREA: UK

GBP pence per vkm 1998 0.06-078 (fully allocated costs);

0.82-1.40 (net of insurance payments)

Morisugi H. (1997) AREA: USA, JAPAN,

FRANCE

Dollar cent 1991 per vkm(3)

5.40 Japan, 1.58 USA, 1.06 France

Nääs O., Lindberg G. (2002)

AREA: SWEDEN


motorcycles 0.096;


1998

Suburban roads incl. Motorways 0.064,

Urban roads 0.146;


Suburban roads incl. Motorways 30,

Urban roads 42


0.016


Suburban roads including Motorways 0.010, Urban roads 0.027


Suburban roads including

Motorways 369, Urban roads 561


0.060


Suburban roads including Motorways 0.043, Urban roads 0.083


Suburban roads including

Motorways 28, Urban roads 40


0.022

♠(variable) Euro/vkm 1998 Suburban roads

including Motorways 0.017, Urban roads 0.032

♣ Euro million

1998 Suburban roads incl. Motorways

53, Urban roads 57


0.066

♠(variable) Euro/vkm 1998 Suburban roads incl. Motorways

0.056, Urban Roads

0.109

♣ Euro million ‘98 Suburban roads incl. Motorways

185, Urban roads 83

145


Two wheels bus


trolleybus) Light Ve y vehicles Car hicles Heav

Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., Leone G., Pellegrini D., Link H., Stewart L., Bickel

P. (2002) AREA: ITALY



Motorways 300, Intercity roads 2129,

an roa

♠( o/Vkm 1 0.008


Motorways 0, Intercity roads 15,

15

♠(variable) 8

0.005

♣ illion

5

♠(variable)

0.003


Urb ds 1357

variable) Eur 998

Urban roads

Euro/Vkm 199

Euro m

1998 Motorways 28,Intercity roads

120, Urban roads 5

Euro/Vkm 1998

Motorways 18, Intercity roads

84, Urban roads 25

146


Two wheels Car Bus trolleybus)

Light vehicles Heavy vehicles Public Transport (bus, tram and

Marti M., Sommer H, Suter S. (2002)

AREA: SWITZERLAND

Euro per vkm 1998 Motorways-->

Motorcycles: -0.006 (average internalised risk cost); 0.002 (average non-

Roads outside inhabited centre-->


internalised risk cost by non-responsible victim);

Roads within inhabited centre->



♠ Euro per vkm 1998

bicycles 0.07, motorcycles 0.12 (average

internalised risk cost); bicycles 0.09,

motorcycles 0.16 (average non-internalised risk cost by

non-responsible victim)


-0.001 (average internalised risk cost);

responsible victim); Roads outside inhabited

centre--> -0.019 (average internalised

risk cost); 0.016 (average non-


Roads within inhabited centre->

-0.003 (average internalised risk cost);

0.042 (average non-internalised risk cost by non-

responsible victim);

♠ Euro per vkm 1998 0.01 (internalised risk cost);

0.03(non-internalised risk cost by non-responsible

victim)


-0.011 (average internalised risk cost),

non-responsible victim); Roads outside inhabited

centre -0.195 (average

internalised risk cost), 0.208 (average non-

internalised risk cost by non-responsible victim); Roads within inhabited

centre-> -0.098, (average

internalised risk cost), 0.774, (average non-


♠ Euro per vkm 1998

0.05 (internalised risk cost); 0.39 (non-

internalised risk cost by non-responsible victim)

Euro per vkm 1998

Roads outside inhabited centre

0.031 (average internalised risk

cost); 0.039

cost by non-responsible

victim) Roads within

inhabited centre-->

-0.002 (average internalised risk

cost); 0.047 (average non-

internalised risk cost by non-responsible

victim)

♠ Euro per vkm 1998 0.01

(internalised risk cost); 0.07 (non-


victim)

cost), 0.003 (average non-internalised

victim); Roads outside

inhabited centre-->

-0.020 (average internalised risk

cost), 0.021 (average non-internalised risk cost by non-


Roads within inhabited centre--

> -0.002 (average internalised risk

cost), 0.053 (average non-internalised risk cost by non-


♠ Euro per vehicle km 1998

0.01 (internalised

Euro per vkm 1998

Motorways--> -0.003 (average

internalised risk cost), 0.003 (average non-

le victim);

Roads outside inhabited centre-->

-0.026 (average internalised risk cost), 0.027 (average non-internalised risk cost by non-responsible

victim); Roads within inhabited

centre--> -0.004 (average

internalised risk cost), 0.107 (average non-internalised risk cost by non-responsible

victim); ♠ Euro per vehicle km

1998 0.01 (internalised risk

cost); 0.05 (non-internalised

risk cost by non-responsible victim)

internalised risk cost by non-responsible victim) 0.003 (average non-

internalised risk cost by non- 0.009 (average non-

internalised risk cost by (average non-

internalised risk

risk cost by non-responsible

internalised risk cost by non-responsib

Euro per vkm 1998

Motorways-->-0.004 (average internalised risk

147


Two Car Public Transport (bus, tram and

trolleybus) Light vehicles eavy vehicles wheels Bus H

risk cost); 0.03 (non-


victim)

(1) rgin a e t of the study under consideration; (3) It is unclear whether the costs calc ransport; (4) clear wh = averag s; ♣ = total cost per mode and means of transport

Costs are to be assumed to be ma al unless otherwise indicIt is un

ted; (2) All values are reportether the cost culation includ

d in the unit of measuremenes freight tr ort too; ♠ ulation includes buses and public t s cal ansp e cost s

148

Tab. 19 -Margi al or average ex ccide and m t: T PL (1),(2)

Railway Freight Airplane Passengers Airplane Cargo Ship Passengers Ship Freight

n ternal costs of a nts per mode eans of transpor RAIN, AIR ANE, SHIP

Railway Passengers Affuso L., Masson J., Newbery D.

(2003) Source: Powell, Marlee (2000)

AREA: UK

GBP pence/km 1999 0.23

Danielis R., Rotaris L. (2001) e: UIC-I FRAS (1994) Sourc

NORWAY

N

AREA: EU15, SWITZERLAND,

♠ECU cent per 1000pkm 1991

1.1

Danielis R., Rotaris L. (2001), Source: Pierson, Skimer and

Vikerman (1994) AREA: UK

Lire per pkm 1994 3.59

Danielis R., Rotaris L. (2001), Source: T&E (Ecoplan 1993)

AREA: EUROPE

♠Eu pkm ♠Eur ro cent per

1993 1.1

♠Euro centi per tkm 19930.8

o cent per pkm 19930.2

Adt/FS-Confit (2000-2001) Source: Federtransport (2002)

AREA: ITALY

♠Lire 1997 ♠Lire per tkm

Negligible

per pkm 3.2

♠Lire per tkm 1997 3.4

♠Lire per pkm 1997 3.1 1997

1.5

Infras-Iww/IC (2000) Source: Federtransport (2002)

AREA: ITALY

♠Lire 1997 0.82

♠Lire 1997 0.9

♠Lire 1997

♠

♠Lire per pkm 1997 1.43

per pkm

per vkm

per pkm 1.43

Lire per vkm 19971.6



0.021

♠(variable) Euro/Plane movement

1998 2.936

H .,

Tervonen J. (2002) AREA: FINLAND


Fluvial 13.5, Maritime 0.5

imanen V., Idstroem TKarjalainen J., Otterstroem T., ♠(variable)

Euro/Train km 1998 0.120

INFRAS (2000) AREA: EU15, SWITZERLAND,

Euro per 1000 pkm Euro per Euro per 1000

NORWAY 1995 0-1

Euro per 1000 tkm 1995 0

Euro per 1000 pkm 1995 0-1

1000 tkm 1995

0

tkm 1995 0

Infras-IWW (2004) AREA: EU15, SWITZERLAND,

NORWAY

♠Euro per 1000 pkm 2000 0.74

♠Euro per 1000 pkm 2000 0.37

♠Euro per 1000 pkm 2000 0

Fourth “Rapporto Amici della Terra”-Fs (2002) - AREA: ITALY

Euro cent pkm 1999 0.16

Euro cent per tkm 1999 0.05


149

Railway Passengers Railway Freight Airplane Passengers Airplane Cargo Ship Passengers Ship Freight

Korizis D., Tsamboulas D., Roussou A. (2002) - AREA: GREECE


0.2

Lindberg G., (AREA: SWEDEN

ssing 1999

0.30 o 0.58 the ated

model and used

2005)

depending on elasticity estim

SEK/railway cro

byMacário R., CarRodrigues A., Ma

Stewart L. (2002)


0.257

mona M., Caiado G., rtins P., ink H., L

AREA: PORTUGAL

Nääs O., Lindberg G. (2002) ♠(variable)

AREA: SWEDEN Euro/Train km 0.3

1998 ♠Euro t

1.07

♠Euro/Port C1998 49.73

/Plane movemen1998

all

Ricci A.Fagiani P., Gia

G., Pellegrini D., Link H., Stewart L., B

variable) Euro/Train km 1998

0.0266

♠(va ♠Euro/Plane movement 1998 2.1

, Enei R., Esposito R., ♠(mmichele F., Leone

ickel P. (2002) - AREA: ITALY

riable) Euro/Train km 1998 0.0230 9

Schipper Y. (2004) ECU pkm 1995 0.97x10^

0.38x10^-3AREA: EUROPE -3 and

Marti M.

AREA: SWIT

ain /km 1998

0.04 (internalised t);


victim)

♠

0.30 (non lised risk cost by non-

, Sommer H, and Suter S.

(2002) risk cos0.30 (non-ZERLAND

♠ Euro per Tr Euro per Train /km 1998

0.04 (internalised risk cost);

-interna

responsible victim)

(1) Costs are to be as nal unless otherwise indic unit of measurem t of the study under consideration; ♠ = average costs; ♣ = total costs per mode and means of transport.

sumed to be margi ated; (2) All values are reported in the en

150

151

Tab. 20 – Total external cost (gross of internalising monetary resources) due to accidents(1)


Calthorp E., Johansson O., Litman T., Maddison D., Pearce D., Verhoef E. (1996)

AREA: UK

Million pounds 1993 fatal 3,780;

serious 2,997; slight 2,133

Calth D.,

Source: Bleijenberg (1994) AREA: HOLLAND Accident injuries: 0.48 (low), 1.87

Billion Dfl 1994

(fluvial transport) 0.01

orp E., Johansson O., Litman T., MaddisonPearce D., Verhoef E. (1996)

(medium), 4.33 (high) (2)

Billion Dfl 1994 Fatal accident: 0.73 (low), 1.80

(medium), 2.02 (high); Negligible

Calthorp E., Johansson O., Litman T., Maddison D., Pearce D., Verhoef E. (1996)

Source: Bonenschansker (1995) - AREA: HOLLAND

Billion Dfl 1995 2.3-3.8 negligible

Billion Dfl 1995

(fluvial transport) negligible



Henry A., Godart S. (2002a) - AREA: BELGIUM Million Euro 1998 877

Million Euro 1998 2


Himanen V., Idstroem T., Karjalainen J., Otterstroem T., Tervonen J. (2002) - AREA: FINLAND





Infras-IWW (2004) AREA: EU15, SWITZERLAND, NORWAY

Million Euro/Year 2000 155588




Korizis D., Tsamboulas D., Roussou A. (2002) AREA: GREECE


Million Euro 1998 4

Million Euro 1998 (Maritime Transport)

30

Lindberg G., (2005) AREA: SWEDEN

Million Euro 1999 6265 (excluding buses and light vehicles for freight transport and

including cyclists and foot travellers)

Macário R., Carmona M., Caiado G., Rodrigues A., Martins P., Link H., Stewart L. (2002)

AREA: PORTUGAL




Nääs O., Lindberg G. (2002) - AREA: SWEDEN Million Euro 1998 963 Million Euro 1998 32 Million Euro 1998 1 Million Euro 1998 6

Orfeuil J.P. (1997) - AREA: FRANCE Billion Francs 1991 45

152


Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., Leone G. i H l

(


Million Euro 1998 Million Euro 1998 Million Euro 1998 , PellegrinP. 2002

D., Link ., Stewart L., Bicke ) - AREA: ITALY 10 2.2 0.5

Marti M e S T L

M Euro9 rage e

rnal cge

e n n-res v

n (

., SommAREA:

r H, and uter S. (2002) SWI ZER AND

55.7

2285.8 (xter al co

illion (aveexte

averast no

1998 internalis d ost);

non-internalised ponsible ictim)

Euro Millio 1998.4 average intern

external cost);49.6 (average no

internalised external cost non-responsible vic

8 alised n-

tim)

(1) All values a d the t t d ratio m ulated by th on the basis of various assumptions a e l

re reportend estimat

in uni of measuremen of the stu y under cos found in iterature.

nside n; (2) Low, edium and high refer to thresholds that have been calc e authors

3.4. NOISE

Noise is considered to be an important external cost, particularly in urban areas. At the

time of writing harmful effects of noise may be broken down as follows:

effects on man

general annoyance: this occurs when individuals feel that their everyday activities and

ion can cause an increase in illnesses due to arterial hypertension,

ut also ischemic heart disease (heart attacks, angina pectoris, etc.)78. Furthermore,

there is scientific evidence pointing to the fact that noise may have harmful effects on

mentally disturbed children and elderly persons.

effects on property

monetary damages: the aforementioned physical damage drives people to take leave

(temporarily or permanently) from work. This loss of production comes with a further

negative effect on property prices: houses that are exposed to noise will be in lower

demand compared to properties that are in peaceful neighbourhoods, with

consequential loss of value of purchase prices and rents. It is however necessary to

avoid double-counting noise externalities: if the loss in value is due to the fact that the

location has become an unpleasant working or living area, the cost of this annoyance

cannot be added to the property’s loss in value; this loss already includes that cost, if

there are no physical disablements.

Most noise is today attributed to road, air and railway traffic. For maritime and fluvial

affic, navigation noise (largely due to the auxiliary power units required for lighting

rest is being disturbed;

disruption to activity: noise affects activities that are mentally demanding,

interpersonal communication and sleep;

personal injury: may concern the auditory system and other organs. Hearing damage

can occur through exposure to noise levels that are higher than 80 dB(A) for several

hours a day over a period of at least ten years. Among non-auditory effects on the

human body we note those that harm the cardiovascular, digestive and breathing

systems. Noise pollut

b

tr

78 According to the environment agency, 3% of cardiac infarctions in Germany may be attributed to noise pollution (Infras-IWW, 2004)

153

and other onboard services during port calls) is only one of the many sources of noise

in port locations. There is evidence found in literature that the population is largely

exposed to the road traffic around the port area and the movement of heavy vehicles

and railways carriages entering and leaving the port, whilst the auxiliary power units of

ships have a significant impact only in few cases. Accordingly, only a very limited

percentage of the population (an overall negligible number) is primarily exposed to

noise produced by ships.

Other possible sources of noise are industrial, artisan, agricultural and commercial

activities (especially shops/activities open to the public, etc.), worksites and other

temporary activities related to transport infrastructure.

Data currently available on the population’s exposure to noise is scarce and often not

comparative due to different surveying techniques and analyses used. Recent data that

measures the population’s exposure to noise through an estimate of the percentage of

the population exposed to noise levels exceeding established thresholds is provided in

Infras-IWW (2004).

Fig. 12 – Italian population exposed to transport noise (in millions)

Source: Infras-IWW (2004) on data from Schade (2003) and the Ministry for the Environment (1997)

18.40

12.1012.00 14.00 16.00 18.00

7.018.00 10.00

2.110.57

1.470.47 0.18 0.19

3.592.59

1.35

0.00 2.00 4.00 6.00

1.06 0.42 0.18

20.00

55-6060-6565-7070-75 >75 55-6060-6565-7070-75 >75 55-6060-6565-7070-75 >75

Road Airplane Railway

154

Fig. 12 shows the distribution of the Italian population exposed to various ranges of

uals are exposed to

sign noise (from 55 dB(A) to more than 75 dB(A)).

Tables t a number of representative studies of economic v ise

m stated preference techniques.

y values are e 2004 prices, per family and dB(A). The tables show

ty , this va y be

explained by different levels of initial noise, different levels of income of the

population, different levels of education, different methods and techniques of valuation

adopted an ocial costs are included or not.

s based on revealed preferences, those that are based on stated

do not use e value of a specific enviro l good,

er observe th ation of individuals’ DAP.

ods pricing to noise is

follo

lue noise provided by the

populat luations only reflect the values of those

wh eir

2) since no estimate of the cost function for all levels of noise is available, it is

assumed that the margi wever, the function is probably a

rgi noise increases, because th ter the

the less it is tolerated79);

f a me hedonic pr s applied

ss of the value d, thus, its occupant’s income.

of t the h c price

es with the ce that represents the disturbance

noise generated by urban mobility. Overall, almost 52 million individ

ificant levels of

21-22 lis aluation of no

generated by road

Monetar

obility and area according to

xpressed in

significant variabili in monetary estimates. In general riability ma

d whether s

Unlike method

preferences

but rath

surveys to estimate th

e market for signals that allow estim

nmenta

Among these meth

conditional to the

is hedonic pricing. The application of hedonic

wing assumptions:

1) the monetary va

entire resident

should correspond to the valuation of

ion, but in fact the va

o have bought th homes;

nal cost remains constant. Ho

non-linear one (ma

noise

nal DAP increases as e grea

3) in the absence o

regardle

lternative empirical evidence, the sa

of the housing an

ice i

In the absence

coincid

analytical data, it is assumed tha

average hedonic pri

edoni

caused vis-à-vis all types of activity carried out at home.

79 This assumption is necessary since most studies assume linear cost functions for hedonic

timates. Hall and Welland (1987) and Feitelson et al. (1996) have looked at non-linear ecifications and concluded that these are not any better than linear ones.

price essp

155

Tab. 21 - ferences studies for road traffic noise

StudLocation (scenario description)/year of study

WTP )

year 4

Stated pre

y (valuation

method)

/family/DB(A

in €200

Pommer 8

(C

Basil (CH) (50% reduction in noise

leve

102.33 ehne 199

VM) l)/1988

Sogu Neuchat

level)/

62.02-73.39

Sælensminde &

(CVM and CE)

Count 49.77-102.72

(CVM) (elimination of noise disturbance) / 1995

Helsinki (F nation of noise

disturbanc

.08

1995 Oslo and Ulle on of

noise distur

2

Norway-National study (elimination of noise

disturbance)/1996

VM) Oslo (Norway) sures >55 dB and

elimination o nce)/1999

3.89

Arsenio et al. 2000 Lisbon (Portugal)/1999

Barreiro et al. 2000 Pamplona (Spain) (elimination of noise

disturbance)/

Rhône Alpes Region (France) (elimination of

nce)/2000

el 1994 (CVM) el (CH) (50% reduction in noise

1993

y of Oslo and Akershus (Norway) (50%

Hammer 1994, reduction in noise level) / 1993

Sælensminde 1999

Wibe 1995 Sweden- National study 29.41

Vainio 1995, 2001

(CV)

inland) (elimi

e)/1993

6.06–9

Thune–Larsen

(CVM and CE)

nsaker (Norway) (eliminati

bance)/1994

20.1

Navrud 1997 (CVM) 2.12

Navrud 2000 (C (expo

f disturba

24.36-3

(CE)

54.95

(CVM) 1999

2-3

Lambert et al. 2001 7.37

(CVM) noise disturba

CVM = contingent valuation method; CE = choice experiment; Source: adapted from Navrud

tween

0.3% and less than 2%.

(2002).

Many of the noise studies based on hedonic prices give results in terms of an

index of the sensitivity to depreciation caused by noise (Noise Depreciation

Sensitivity Index - NDSI) which provides the average percentage change in

property values per decibel. Table 24 shows how these variations fall be

156

Tab. 22 – Stated preferences studies for airplane traffic noise

Study (valuation method) Location (scenario description)/year of study WTP/family/DB(A)

year in €2004

Pommerehne 1998

(CVM)

Basil (CH) (50% reduction in noise) /1988 44.5

Thune-Larsen 1995

(CVM and CA)

Residents close to Fornebu airport (Oslo)

(50% reduction in noise) / 1994

201.2–1015.6

Faburel 2001 (CVM) Residents close to Paris-Orly airport

(elimination of the disturbance)/1999

8.4

CVM = contingent valuation method; CA = Conjoint Analysis Source: adapted from Navrud (2002)

Tab. 23 - Studies based on hedonic prices as a % of noise depreciation sensitivity index (NDSI)

Study Country Result (% NDSI per m2) Pommerehene (1987) Switzerland 1.29 NDSI

Soguel (1994) Switzerland 0.91 NDSI

Vainio (1995) Finland 0.36 NDSI

Renew (1996) Australia 0.36 NDSI

Grue et al. (1997) Norway Min: 0.24 NDSI

Max: 0.54 NDSI

) USA 0.40 NDSI Nelson (1982

Uyeno et al. (1993) Canada Min: 0.65 NDSI

Max: 1.66 NDSI

INRETS (1994) Various countries Min: 0.3 NDSI

Max: 1.00 NDSI

Hidano et al. (1992) Japan 0.7 NDSI

ACI/ANFIA (2001) Italy 0.65 NDSI

Source: adjusted from Navrud (2002)

The use of revealed preference methods may appear simple since it involves a spatially

defined impact with short-term effects. However, there are a number of problems that

make the calculation of the economic value of noise rather complicated among which

we may highlight a) the need to identify the main variables that affect the prices of

housing properties b) the presence of distortions in the property market and c) the

existence of restrictions or the scarce propensity in residential housing mobility.

157

Fig. 13 – External costs valuation model (average and marginal) of noise in the INFRAS-IWW (2004) study

In a recent EC workshop (Vaino and Paque, 2002), an attempt was made to define the

value (in euro) per dB(A) per person suffering the noise impact. Notwithstanding the

recognised difficulties to calculate such an estimate the workshop suggested that the

value may fall between the €5.00-€50.00 interval per person per dB(A) per year.

Subsequently, the Working Group on Health and Socio-Economic Aspects published a

position paper on the monetary valuation of noise through stated and revealed

preferences methods. The workgroup suggested an average value of the benefit

deriving from a reduction in noise of €25 per family/decibel/year.

Lastly, there are other authors who estimate the cost of noise according to expenditure

towards protection from noise taking into consideration expenditure towards noise

reduction at the source, collective protection expenditure (for example, noise barriers

along transport infrastructure) and, lastly, private protection expenditure (e.g. double

glazing). In other words, these are techniques that analyse potential or actual

defensive expenditure incurred to remove the impact of the damage caused by noise,

thus making it possible to estimate social willingness to pay to reduce the disturbance

Number of people Disturbed by the traffic

Number of people Suffering heart attacks

Number of people disturbed by the level of vehicle traffic noise >65dB(A)

WTP per person disturbed

Number of acute heart attacks caused by transport noise

Death risk valuation Medical costs caused by transport noise

Total external costs of noise

Allocation between different modes of transport (road, railway, air)

Average and marginal costs per pKm and tKm

158

159

tion of a minimum threshold for the value that individuals associate to

onmental goods and services by using market signals. It must be noted that in

of acoustic pollution are of a psychological nature, and therefore

ervable through averting expenditure calculation.

-IWW (2004) uses a top-down method to calculate average and marginal

al costs of noise, summing individual WTP for a reduction in noise, biological

ts (health costs) and costs associated to common disturbances. In the first

he method of the value of the damage caused by noise mortality and morbidity is

used (through the WTP to reduce the risk of death, in addition to value added losses

and the costs of health structures), whilst in the second case contingent valuation

extended to all individuals is used (Fig. 13). The damage values assumed in the study

reflect the assumption of a linear trend according to increasing noise classes, starting

from an assumed null damage threshold for all values below 55 dB(A). The analysis of

marginal external costs of noise, in units of measurement per vkm, pkm and tkm, has

been differentiated in order to take into account the context of the traffic (urban, sub-

urban and rural), the time of the day (daytime and night) and the traffic density (light

traffic or dense traffic), thus obtaining a broad value interval. For cars, this goes from a

minimum of 0.06 €/1000 vkm for dense daytime traffic in a rural context to 34.81

€/1000 vkm in an urban context with almost no traffic (tab. 24). For railway traffic, the

external costs of noise appear to be higher and fall within an interval going from a min.

15.76 €/1000 vkm to a maximum of 992.71 €/1000 vkm (tab. 25). On the basis of the

three train types observed, (high-speed, inter-regional and freight) the value interval

for passengers falls between 0.05 and 3.41 €/1000 pkm, whilst for freight trains values

fall within 0.03-1.87 €/1000 tkm. Marginal costs per tkm transported by road are

higher than those observed for rail transport. The same conclusion is drawn for

passenger transport. Marginal costs of road transport in urban areas are almost one-

hundred times higher than the values observed in rural areas. This is explained mainly

by population density, transport infrastructure and different traffic densities. For air

transport marginal external cost estimates have been simplified (they do not taken

time of the day nor traffic density into consideration, but only consider demographic

density close to the airport). The value interval for passenger air transport is 0.1- 4.1

case t

damage cos

extern

Infras

not obs

most cases the effects

envir

to acceptable levels. This method is useful in that it allows rapid and relatively cheap

identifica

euro/1000 pkm, whilst the value interval for air freight is 0.3-19.6 euro/1000 tkm (tab.

26).

160

161

Heav .Light 0,14 0,28 0,69 0,69 1,27 0,07 0,25 0,05 24Dense 0,06 0,13 0,32 0,32 0,58 0,03 0,11 0,02 ,11Light 0,25 0,50 1,26 1,26 2,31 0,13 0,45 0,08 44Dense 0,12 0,23 0,58 0,58 1,06 0,06 0,21 0,04 ,20

Light 1,19 2,39 5,97 5,97 10,99 0,63 2,13 0,40 19,91 ,07Dense 0,43 0,86 2,14 2,14 3,94 0,23 0,77 0,14 7,14 ,74Light 2,18 4,35 10,88 10,88 20,01 1,14 3,88 0,73 36,26 ,78Dense 0,78 1,56 3,90 3,90 7,18 0,41 1,39 0,26 13,01 35

Light 18,49 36,98 92,45 92,45 170,11 13,21 33,02 4,62 308,18 10Dense 7,63 15,25 3,13 38,13 70,16 5,45 13,62 1,91 127,11 24Light 33,68 67,35 168,38 168,38 309,82 24,05 60,14 8,42 561,26 46Dense 13,89 27,78 69,45 69,45 127,79 9,92 24,80 3,47 231,50 11

Source: Infras-IWW (2004)

Tab. 26 - Marginal Costs of noise for railway traffic in different traffic conditions

Euro / 1000 tKmArea Time Traffic Density HS I

y Veh0,00,0

2031,

32,13,58,24,

Tab. 25 - Marginal Costs of noise for road traffic in different traffic conditions

Area Time Traffic density Cars Motorcycles/mopeds Bus Light Veh. Heavy Veh. Cars Motorcycles/mopeds Bus Light Veh.2,301,054,191,92

R FT HS IR FTLight 25,20 28,80 30,30 0,08 0,12 0,06Dense 15,30 17,40 18,40 0,05 0,07 0,03Light 45,90 52,40 55,20 0,15 0,21 0,10Dense 27,80 31,80 33,50 0,09 0,13 0,06

Light 174,00 198,00 209,50 0,56 0,80 0,40Dense 106,10 121,20 127,70 0,34 0,49 0,24Light 316,90 362,20 381,50 1,01 1,46 0,72Dense 193,10 220,70 232,50 0,62 0,89 0,44

Light 0,00 0,00 424,80 0,80Dense 0,00 274,80 322,50 1,11 0,61Light 569,60 821,20 963,80 1,82 3,31 1,82Dense 347,10 500,40 587,40 1,11 2,02 1,11

Legend: HS=High Speed train; IR = Inter-regional train; TM = Freight Transport Train


Urban Day

Night

Rural Day

Night

Sub-urban Day

Night

Scenario Marginal Costs per train km Marginal costs per passenger/ ton kmEuro / 1000 vKm Euro / 1000 pKm

Euro / 1000 vKm Euro / 1000 pKmScenario Euro / 1000 tKmMarginal costs per vehicle km Marginal costs per passenger / ton km

Rural Day

Night

Day

Night

Sub-urban

Day

Night

Urban

PROMETEIA

Tab. 26 – Marginal cost of airplane noise for different traffic situations

Marginal cost (Min.)

Marginal cost (Max.)

Scenario Passengers

(€2004 / 1000 pKm)

Freight (€2004 / 1000

tKm)

Passengers (€2004 / 1000

pKm)

Freight (€2004 / 1000

tKm) Low (Austria, Sweden) 0.10 0.31 0.21 0.62 High (Italy, the Netherlands) 2.07 9.82 4.13 19.64 Total EU 17 0.10 0.31 4.13 19.64

Source: Infras-Iww (2004)

Given the limits of this study, the marginal cost estimates of noise are based on a

benefits transfer approach. Even greater difficulty was encountered for large scale

noise estimates (urban and national), for which spatial and temporal data are

incomplete and may be inaccurate due to limited scope for data comparison.

3.5. EFFECTS ON ARTISTIC, HISTORICAL AND MONUMENTAL

PROPERTIES

3.5.1. SIGNIFICANCE OF THE PROBLEM

Goods that have an historical, artistic or cultural value in general, and that are in public

or private hands, are inputs to numerous economic activities, such as tourism, but may

ultimately be subject to the negative effects of their exploitation; effects that are often

not accounted for in market prices. The negative effects are related to two factors: (a)

congestion effects from tourism flows which, beyond critical thresholds, may reduce

the quality and quantity of the cultural resources, decreasing their value and thus

generating demand counter-effects; such congestion effects are usually localised and

their significance is often observed at the level of specific cultural sites; (b) net effects

due to sources of pollution (waste, emissions) deriving from economic activities (not

only related to tourism), which may deteriorate the cultural sites and reduce the value

of the visiting experience.

162

PROMETEIA

For both these effects a correlation may be traced to the greater use of public and

private transport, which may be non-optimal from a social point of view. If the

negative external effects on the value of cultural property were accounted for as well

as the negative effects on health, then the use of highly polluting forms of transport

(especially of a local type) may be seen to be cause of significant damage.

The monetisation of these effects may be pursued through different channels: by

calculating the costs of restoration, through market price effects (property prices, when

the property is private), or through techniques based on expressed preferences in

order to determine the willingness to pay vis-à-vis different restoration and/or

maintenance scenarios for monuments, property, and sites.

The monetary values derived from different techniques vary considerably: accordingly,

different techniques are to be used according to the aim of the study or policy, the

availability of data, and the type of problem.

City buildings or isolated buildings outside the city, individual works outside or within

buildings or monumental complexes, museums, galleries, entire historical centres,

archaeological sites, etc. may suffer external effects80. Social costs, measurable in

terms of a site’s deterioration, and thus capital loss, may be a signal of their

unsustainable operating. A greater physical deterioration of the good generates

additional costs in terms of investment (public and semi-public) in order to recover and

maintain the capital. Alternatively, institutions that have power over user flows could, if

the good is technically excludible, use economic instruments such as prices in order to

reduce social costs and return user flows within manageable thresholds.

Air pollution is a very important factor of material deterioration. Monuments that are

exposed to polluting agents are particularly hit but other works, apparently better

protected, are also hit.

One of the greatest problems involved with protecting cultural goods is the ability to

determine critical values, i.e. limits of acceptable exposure. More so, threshold values,

where these exist, are not constants that may be applied directly to the artefacts, but

depend on the historical damage the monument has already accumulated, climate and

80 A proposal for the external danger classification was presented under law 84/90 promoting a research project on “Carta del Rischio del Patrimonio Culturale”. This document identified three danger categories making up risk. The three risk categories are: static risk, environmental-air risk and risk related to anthropic factors

163

PROMETEIA

microclimate parameters, and the flows of polluting agents that deposit on the surfaces

of monuments, which may vary according to the latter’s incline and exposure.

3.5.2. VALUATION

Economic valuation of external costs is necessary to support strategies for the

economic conservation and management of the artefacts and sites. Similarly, public

policy interventions aimed at ensuring sustainability of historical-cultural properties

through suitable income schemes are necessary. In summary, short-term income

deriving from the economic activities must avoid damaging the capital and must

safeguard long-term income streams from the capital. These incomes depend on a

site’s value of use (valorisation) and value of non-use (conservation). With use and

non-use on the one hand and short-term and long-term issues on the other hand, a

number of difficult trade-offs may take place.

The most immediate and possibly simplest way to calculate the values being sought is

the one based on compensation costs, which may be used to estimate the impact of

atmospheric pollutants on sites and monuments. The method calculates the cost

savings that are implicit in a reduction of the external effects of polluting agents, and

thus quantifies value by measuring the reduction in conservation costs. According to

many authors this is however an approximate and only partial measurement (Pearce

and Mourato 1998). Willingness to pay must be considered the proper theoretical

measurement to be used (Grosclaude and Soguel, 1994).

The two methods (restoration costs and willingness to pay) generally give different

monetary values, since they have different objectives. However, similarly to what we

have already said about accident risk, the two methods may, to a certain extent, be

complementary; for ex. within a cost-benefit analysis of interventions, the first method

would be used to calculate conservation costs, whilst the second method would be

used to calculate social benefits.

The limit of the method based on WTP associated to contingent scenarios is that it

gives results which, because of their intrinsic microeconomic approach and emphasis

on benefits (and thus opportunity cost of the interventions related to social preferences

for different policy objectives, and not the absolute costs of the intervention) are not

usable if the aim is the valuation of objects that cannot be described one-

164

PROMETEIA

dimensionally. In such cases, a valuation of restoration costs based on a technical-

engineering approach may be more useful and effective.

3.5.3. SURVEY OF AVAILABLE LITERATURE

Economic literature addressing the economic valuation of damages caused to cultural

properties by pollution is largely based on works that use the willingness to pay

methodology. There are no known valuation studies based on restoration costs

following general damage or damage related to transport activities.

In general, there is a disaggregation problem of the external effect related and

imputable to transport within the overall effect recorded, for example, for a local level

polluting agent (e.g. PM10). The effect may only be disaggregated through a breaking-

down of the overall pollution effects recorded for each polluting agent using, for

example, NAMEA sector accounting, which contains information on the polluting agents

of each sector from 1990 to 2002.

Possibly, the most pertinent work on external effects of transport (unfortunately not

very recent), is Grosclaude and Sobel (1994). This work evaluates the benefits

generated by the reduction in traffic pollution in the city of Neuchatel, Switzerland. The

benefits are estimated through a contingent valuation carried out on residents, where

individuals had to express their WTP annual contributions into a fund that would

finance the maintenance of sixteen of the city’s most historical monuments. The WTP

was estimated by means of an iterative auction system and was found to fall within an

interval of 77-86$, with 43% of the WTP being equal to zero. It is worth noting that

respondents were shown sets of images which depicted the sites in two very different

conditions (status quo and with maintenance). The difficulty of separating the effects

of traffic generated pollution from total pollution must be underlined. The authors

argue that other sources of pollution in urban centres are marginal, but we believe that

this is an aspect that should be assessed on a case-by-case basis.

The Pollicino and Maddison (2001) study is interesting from a methodological point of

view, even though it is not confined to the effects of local transport. The authors focus

on a specific site and one monument in particular, Lincoln Cathedral (UK), to obtain the

WTP for a scenario that is alternative to the status quo and associated with greater

maintenance of the site (from 10 to 40 years’ time of the status quo, for each

hypothetical scenario).The higher costs of public financing are compared to the

165

PROMETEIA

aggregate benefits deriving from the individual WTP estimates. Both scenarios are

presented with the aide of montage or retouched photos in order to give respondents a

visual representation of the effects of the site under the two options at half-way, i.e. at

half their maintenance cycle (5 years and 20 years, respectively). The WTP estimates

were the same at an annual average value between £ 15-23, a value that differs under

different surveying formats carried out with preference eliciting techniques. The means

of payment is a local additional tax. The aggregate values for the reference population

give a gross benefit estimated between 5.9 and 8.8 million pounds for the improved

maintenance option; a value which, if annualised, amounts to 0.5 million pounds. The

geographical limit of the WTP (the economic jurisdiction) is calculated as 40-53 miles

of the site. The work is interesting for an understanding of how a study aimed at

monetisation of the benefits (and not restoration costs) should be structured,

evidencing strengths and weaknesses. Furthermore, compared to the effects on health

and the environment, the work demonstrates how cultural goods are not a top priority

for citizens/consumers.

Maddison and Mourato (1999) analyse the impact of road infrastructure policies on

cultural sites. Their study concerns road appraisal; we are thus in the context of

valuation (ex ante) of costs and benefits of the construction of transport infrastructure

having an impact on cultural sites. In the case in question the status quo is a site

adjacent to two roads which are aesthetically damaging and may be cause of visitors’

lost welfare deriving from the congestion and pollution of the roads. The alternative

involves the construction of a tunnel, which would avoid the aesthetic damage and

pollution to the visitors but could reduce the benefits of those who may experience the

view of Stonehenge whilst en route. This is a cost-benefit analysis where an attempt is

made, perhaps only a theoretical one, to account for every incremental cost and

benefit associated to changing the status quo. The WTP estimated by means of a

range of payment levels gives an average value of 20-23£ for each British citizen

interviewed at the site, 6-11£ for citizens not interviewed at the site and 0.3-2£ for

foreigners interviewed at the site, with 0-bidders estimated between 55-65%. The

means of payment is represented by a national tax levied on British citizens, and an

entry fee for foreigners, used to finance the cost of the tunnel (£ 2000m). Some

interviewees prefer the status quo: 54% of non-users, 42% of visitors, which thus

explains the need to compare the WTP of the two groups. For those who do not prefer

166

PROMETEIA

the tunnel, the WTP is related to keeping the current roads. We thus observe an

interesting external benefit deriving from infrastructure, the use of which is associated

to the indirect enjoyment of a cultural site. The point is significant in terms of

theoretical definition of the overall external benefits related to road and non-road

infrastructure. In the study, payment is required for two years following the

infrastructure’s construction. The aggregated values are discounted at 6%. For UK

residents, this two-year discounted value is 25£ (265 million in total) for those who

prefer the tunnel (46%), 9.3£ for those who prefer the status quo (116 million in

total), with a net benefit difference of 149 million £ in favour of the tunnel option.

Morey et al. (1997, 1999, 2000) present an economic valuation on the damage caused

by acid rain, deriving from SO2 pollution, on 100 marble artefacts in Washington DC,

using experimental multi-attribute analysis. Even though 75% of respondents (in

Boston and Philadelphia) claim to have visited the artefacts, the survey shows that a

higher significance of social benefit is related to non-use value: conservation of the

artefacts for future generations. Having defined three levels of intervention to reduce

the effects of acid rain, the authors find average WTPs to fall between 33-69$ for the

three options, each characterised by a different level of intervention and impact on the

artefacts, and associated to a one-time payment aimed at reducing the deterioration of

the same. The focus of the valuation may be either interventions to reduce the sources

of pollution (emissions, and generating sources of emissions) or interventions to

conserve and maintain the sites/artefacts. Although the latter are a less structural

solution to the problem posed by external effects the authors underline the fact that

focus on the sources of emission may generate distortions to the estimates of benefits,

and cause joint effects of emission reductions (health, environment, monuments etc.).

The authors thus prefer that the aim of the intervention be the maintenance of specific

cultural goods.

Returning to the question of WTPs averages and medians, these fall between the

intervals of 33-69$ and 25-57$, respectively. The minimum and maximum values for

the three options are 10 and 100$, 15 and 156$, 20-251$. Assuming a discount rate of

7%, the one-time payments have actual annual average values between 2.29 and

4.79$ and medians between 1.76 and 3.96$. These are the social benefits to be

related to the costs of the site’s maintenance options. Further comparative analysis

may be possible by using an alternative range of payment levels, which gives average

167

PROMETEIA

and median WTP measurements that are not entirely consistent with those attained

using the choice experiment. The variability of the estimates makes it necessary to

associate several WTP estimate methods to each study or at least run a sensitivity

analysis on the results of the subsequent cost-benefit analysis for different parameter

values and medians.

In summary the studies show:

* the potential of analyses based on expressed preferences (contingent valuation and

experimental multiple choice formats);

* the differences between studies aimed at monetising the cost of restoration (not

observed in applied literature) and studies aimed at estimating benefits (generally

benefits achievable through different options of reduction of the external effects on

property, direct maintenance interventions and interventions on the sources of

emissions);

* the difficulty in distinguishing the external effects on property that derive from

pollution solely generated by transport activities from that generated by all other

sources.

168

PROMETEIA

PART TWO

VALUATION OF

COSTS AND EXTERNAL COSTS OF TRANSPORT

TO 2020

SOCIAL COSTS, INTERNAL

169

PROMETEIA

170

FOREWORD

In part two of this report the rules and instructions of monetary valuation that

were observed in part one are applied (making wide use of the techniques of

benefits transfer, according to suggestions that were also proposed within an

EC scope).

Of course, we need to collect all missing “ingredients” required to calculate the

main values in the above figure, such as “estimate and forecast of social costs”

and “2004 estimate and 2020 forecast of the net external costs of transport”.

The missing elements concern (a) forecast of demand for mobility, (b) forecast

Structure of the Work

Economy’s macro-system

[1.2.]

Traffic estimates

and forecast

(mobility)[1.]

EXTERNALITY [2.]

Social costs estimates

and forecast[2.]

Valuation criteria of the externalities and external costs, synopsis of literature and techniques of benefits transfer (monetary valuations per average and marginal cost unit) [PART ONE]

Estimate and forecast of the monetary resources

which internalise all or part of the external costs of

transport [3.]

2004 estimate and 2020 forecast of net external costs of transport [3.]

[PART TWO]

Estimates and forecasts of the quantity of accessory factors needed to determine the externality (circulating fleets per type of means of transport) [2.]

PROMETEIA

of the volume of externalities, according to theoretical suggestions in part one

suitably adjusted to said demand for mobility, which then translates into traffic

(and thus into external effects), and lastly (c) forecasts of social costs and

external costs. This last phase is carried out by applying the monetary

valuations of the externalities gathered in part one of this work, deducting

from these the monetary value of the resources that have an internalising role;

this is done by assuming two schematic scenarios of economic development

and the confidence intervals built according to recognised levels of probability.

The part that addresses accidents, particularly road accidents, has been looked

at in a little more detail. Even though a significant part of road accident costs

are, as shall be seen, internalised, road accidents are the source of the most

significant social costs among all cost types related to transport. It follows

quite simply that the transport system already pays - in large part – for the

accidents that it causes, but that public policies may, in any case, have

significant impacts in terms of improved welfare: these policies should not

however be paid once more by users (users already bare the costs).

With specific regard to the motorway sector, and as is already well known, but

perhaps detailed a little more in this work, motorways largely internalise the

social costs of transport and are already – and shall be even more so in the

future – generators of net resources for the system (i.e. users shall pay for the

internal costs of motorway transport, for the related external costs, plus an

additional quota - not clearly justified in terms of economic efficiency, in large

part for the difference between total accident rates and fatal accident rates

vis-à-vis the ordinary road network, because the insurance system does not

distinguish between kilometres travelled on the motorway and kilometres

travelled on the far more dangerous ordinary road network). However, as

noted, this is a result that comes as no surprise to the business community

and academic world.

The Monte Carlo simulations to find plausible confidence intervals for the total

value of social costs to 2020 are used to somehow “free” the results of the

work from pure and abstract numerical bolts. It is the intervals between two

values, often quite distant one from the other, which provide a correct

indication of the subject’s complexity. Wide intervals are not however an

171

PROMETEIA

indication of arbitrary interpretation: although wide, the intervals are

positioned so as to suggest increasing internalisation of the social costs of

transport and thus are, precisely because of their reliable uncertainty, an

indication of a clear policy: except for the ordinary road network, which has

significant externalities and which needs immediate and deep interventions,

the motorway network is characterised by negative external costs (it

internalises costs) whilst railway, air and ship transport show rather low, albeit

increasing externalities, which however appear to be controllable through

focused transport policy interventions. The strong variability in absolute

positions related to mode of transport compared to the proportions of social

costs and external costs implies that any policy implemented though suitable

instruments take these significant differences into account. If these differences

are not taken into account inefficiency levels may increase further, with unfair

effects on the distribution of costs and benefits over the entire transport

system.

In this second part of the work, the first chapter and the first four paragraphs

of the second chapter describe the technical aspects which have made it

possible to estimate and forecast the unitary value of externalities and the

quantity of externalities; the product of these terms gives a first estimate of

social costs, summarised in paragraph 2.5. Paragraph 2.6 and the appendices

to the second chapter provide estimates of the confidence intervals within

which the “real” social cost per mode of transport and type of externality is

likely to be found (by applying recognised confidence levels).

As noted, a value interval appears to be methodologically correct for such a

particularly articulate and complex estimate and forecast exercise. Likewise,

for reasons of exposition, our summary and concluding discussions shall be

based on average point values, but this must not lead us to forget the

problematic dimension and the estimates by approximation of each and all

forecasted social and external cost values. The third chapter provides

monetary valuations of social costs, internalising monetary resources and

external costs, in the year of reference and in the two forecasted scenarios.

172

PROMETEIA

1. MOBILITY ESTIMATES AND FORECASTS

The objective of extrapolating consistent values for mobility from which to forecast

externalities to the 2020 horizon implies the availability of historical series that are

suitable enough to build a schematic traffic-economic analysis and forecasting model.

To satisfy this need it has been necessary to rebuild and/or partially interpolate

discontinuous series and series with missing data. In a few occasions we decided to

interpolate data – well knowing that this procedure adds nothing to the genuine

information of the original data set – only to facilitate use of the same data set. We

now briefly address the operations carried out on the database used for the valuation

forecasts of mobility, externalities and social costs.

1.1. RECONSTRUCTION OF BASE DATA USED TO FEED THE

PASSENGER AND FREIGHT TRAFFIC PER MODE OF TRANSPORT

ANALYSIS AND FORECASTING MODEL

Starting from the official data available from a number of sources (Ministry for

Infrastructure and Transport, Istat, Aiscat) a database of passenger traffic (in terms of

passenger kilometres) and freight traffic (in terms of ton kilometres) was built for all

modes of transport in Italy over the period 1980-2004. For passenger transport, the

following are considered: total airplane (national + international share), total train,

total ship (coastal navigation + international share + internal), ordinary road network,

motorway road network, tram and underground, bus (urban + suburban). For freight,

tons transported by total airplane, total train, total ship, ordinary road network,

motorway road network are looked at separately. We shall now summarise, for each

mode and traffic type, the source data used and the reconstructions that were carried

out - these have been necessary since there is no suitably comprehensive and

standard historical data available from official sources.

1.1.1. AIRPLANE

The reference data is national air traffic plus a share of passenger and ton kilometres

related to international traffic with arrivals or departures to/from an Italian airport.

Ministry for Infrastructure and Transport source data (integrated with ENAC and

Assoaeroporti source data) makes available updated data on passengers (number) and

173

PROMETEIA

tons transported from 1990 to 2004. For the period 1980-1990 data is available only

for the years 1980-1985 and 1990: annual historical series have been linearly

interpolated. In order to calculate traffic data in terms of kilometres travelled old series

(source: Civilavia) for passenger kilometre of paying national traffic were used. From

the average kilometres of national travel calculated from this data we have been able

to use passenger numbers to estimate passenger kilometre traffic (for years after 1999

the average journey length has been kept unchanged at the 1999 level of 530Km). For

freight, an average travel distance of 800km has been assumed.

1.1.2. TRAIN

The reference data is total railway traffic (Trenitalia + regional traffic). Ministry for

Infrastructure and Transport source data (integrated with Trenitalia source data)

makes available updated data on passenger kilometre and tons kilometre transported

from 2000 to 2004. Old historical series from the Ministry for Infrastructure and

Transport and Istat have been used to reconstruct earlier years using annual dynamics.

However a few problems related to heterogeneity of data collection methods exist,

particularly from year 2000.

1.1.3. SHIP

The reference data is total traffic in national waters given by the sum of internal

navigation, coastal navigation and a share of international traffic. Ministry for

Infrastructure and Transport data provides passenger kilometre and ton kilometre for

costal navigation from 1995 to 2004; for previous years linear interpolations of the 5-

year periods were carried out. The same was done for internal navigation, the only

difference being that data was only available for years 1980, 1985, 1990, 1992 and the

period 2000-2004 (for intermediate years the series were interpolated). For

international navigation no data is available in terms of kilometres travelled; this has

been reconstructed from the number of passengers and tons transported (arrived +

departed), using an average travel distance of 500 kilometres.

174

PROMETEIA

1.1.4. ROAD: ORDINARY AND MOTORWAY NETWORK

PASSENGERS

For road transport, estimates (source: Istat) of private car transport are made available

(including cars + motorbike, ordinary road network + motorway network). These

estimates are only available for a few years (last year: 2002) and have been integrated

with APAT and “Amici della Terra” estimates to obtain a longer historical series of total

road passenger transport (collective transport estimates are also made available and

are broken down – see Bus estimates). Furthermore, for the first years (period 1980-

1990) old estimates from the Ministry for Infrastructure and Transport are available,

and consistent with more recent year levels. Data of motorway traffic is made available

by Aiscat (this comes in terms of light vehicle kilometre; passenger kilometre data is

then obtained by using a coefficient of occupancy of 1.7 passengers per vehicle).

Ordinary road traffic data is obtained as the difference between the total passenger

kilometre estimate and estimates obtained from Aiscat’s motorway data.

FREIGHT

Istat sources provide a sample survey on freight traffic in terms of ton kilometre (this

considers vehicles in excess of 3.5tons, registered in Italy). This source allows us to

obtain total road traffic (freight traffic by vehicles that weigh less than 3.5tons is only

6% of overall freight tons transported on the ordinary and motorway networks (Amici

della terra, 1999)). In 2004 the sample was changed and this explains the 13%

increase from 2003. We decided to use the 2004 estimate as reference estimate and to

interpolate the estimate between 2002 and 2004. Correction to the 2003 estimate is

useful in order to avoid the otherwise significant jump in the historical series.

Furthermore, for the first years (period 1980-1990) old Ministry for Infrastructure and

Transport estimates are available, and these are substantially consistent with the levels

of more recent years. Aiscat sources make available data on heavy vehicle kilometre

(commercial + industrial vehicles). Using suitable and constant occupancy coefficients

(5.8 tons per average heavy vehicle) we have used this data to obtain ton kilometres

for the motorway network; ordinary road network valuations are obtained as a

difference.

175

PROMETEIA

1.1.5. TRAM AND UNDERGROUND (only passengers)

Ministry for Infrastructure and Transport sources provide data on tram (separately for

urban and suburban transport) and underground services, in terms of passenger

kilometre from 1990 to 2004. The historical series for tram + underground for the

period 1980-1990 has been reconstructed on the basis of railway traffic dynamics.

Table 1 – Mobility valuations for different modes of transport

Passenger transport (millions of passenger km) var.% traffic

2000 2001 2002 2003 2004 2001 2002 2003 2004Airplane 24274 23944 24050 26571 28398 -1,4 0,4 10,5 6,9Train 49572 50076 49304 50291 51196 1,0 -1,5 2,0 1,8Ship 7184 6848 6691 6715 6645 -4,7 -2,3 0,4 -1,0Tram+underground 5608 5589 5897 5939 5895 -0,3 5,5 0,7 -0,7Bus 94203 95952 96996 97645 98942 1,9 1,1 0,7 1,3Ordinary Road Network 706084 694379 688436 688403 679964 -1,7 -0,9 0,0 -1,2Motorway Network 93364 97241 99840 102802 104396 4,3 2,5 3,0 1,6TOTAL 980290 974209 971214 978367 975436 -0,6 -0,3 0,7 -0,3

Freight transport (millions of ton km) var.% traffic2000 2001 2002 2003 2004 2001 2002 2003 2004

Airplane 599 578 589 623 656 -3,4 1,8 5,7 5,4Train 25053 24451 23147 22552 23369 -2,4 -5,3 -2,6 3,6Ship 197307 197450 202039 209981 230974 0,1 2,3 3,9 10,0Ordinary Road Network 86844 85258 88252 87274 85307 -1,8 3,5 -1,1 -2,3Motorway Network 98257 101252 104426 107498 111582 3,0 3,1 2,9 3,8TOTAL 408060 408989 418454 427928 451889 0,2 2,3 2,3 5,6 note: Ordinary and Motorway road networks include vehicle kilometre of cars and motorbikes for passenger transport and vehicle kilometre of commercial and industrial vehicles for freight transport.

1.1.6. BUS (only passengers)

The reference data concerns urban and suburban bus transport, including public and

private transport services. Ministry for Infrastructure and Transport sources provide

historical series for passenger kilometre of local public transport (urban and suburban).

Private transport estimates are based on Istat sources (estimates of collective urban

and suburban transport) compared to estimates by Apat and Amici della Terra.

Tab. 1 summarises the most recent observations on passenger and freight traffic, at

the base of valuation forecasts of mobility and related externalities.

176

PROMETEIA

1.2. THE REFERENCE ECONOMIC SCENARIO

Two forecast hypotheses for the Italian economy to 2020 have been traced,

determining different traffic, and hence externality, scenarios. The two scenarios differ

in terms of their underlying GDP growth assumption: in the low case scenario, the

average real annual growth rate is 0.5% whilst in the high case scenario the rate is

2.1%. These annual growth rates produce cumulative real growths to 2020 of 8% and

37%, respectively. These two very different growths clearly determine different

impacts on traffic and emissions generated.

In terms of price a neutral and conservative scenario was chosen; price indices for the

different transport modes change in line with the general index of consumer prices. No

particular assumptions have thus been made on modal re-equilibrium or the possible

escalating trends in oil prices.

Thus, mobility demand differences per mode of transport in the two scenarios depend

exclusively on the underlying GDP growth assumptions, fully respecting of course the

different elasticities of demand that characterise different modes of transport

(separately for passenger and freight transport). The different unit elasticity

parameters together with different GDP growth rates do however provide rather

articulate and interesting scenarios, whilst at the same time avoiding excessive

complexity in interpreting the results (which have been based on extremely simplified

assumptions).

It would seem quite natural, given the existing international scenario, to ask ourselves

what the effect on transport and its externalities would be if the price of oil were to

reach or exceed 100 dollars per barrel. However, such an hypothesis is not easy to

incorporate into a model such as ours, which is based on pure extrapolation. Other

hypotheses on the euro-dollar exchange rate would also be required. However, it is

certainly possible to express an “a priori” judgment of what the effects would be: the

higher the price of oil, and without change to the competitive scenario of refining,

wholesale and retail distribution of oil products, the greater its internalising effect of

social costs related to transport, considering that it would not be sensible to assume

significant changes to the regime of indirect taxes and excise duties that go into

determining the ultimate selling price of fuel.

177

PROMETEIA

Therefore, the valuations on internalising social costs through excise duties and

indirect fuel taxes in chapter 3, would need to be considered as minimum values

compared to those that may be obtained from scenarios in which the prices of oil

products traded on the international markets were significantly higher.

1.3. THE PASSENGER AND FREIGHT TRAFFIC PER MODE OF

TRANSPORT FORECASTING MODEL

Annual data broken-down between freight and passenger traffic over the period 1980-

2004 was used to carry out the total traffic forecast in Italy per mode of transport. The

traffic estimates and forecasts were carried out in two phases; a first step allowed us

to estimate and forecast total passenger kilometre values, and then a second step

allocated the kilometre estimates and forecast of the first step to the various modes of

transport by means of an allocation system.

For freight transport only one level of forecast was used; tons transported per mode of

transport.

ESTIMATE AND FORECAST OF TOTAL PASSENGER TRAFFIC (ALL MODES) The econometric model used is of the Error Correction Model (ECM) type, which falls

among the broader context of VAR methodology and cointegration analysis, which

allows us to consider both short-term and long-term dynamics. The idea that underlies

the model is that a long-term relationship exists between the variables and that in the

short-term the process undergoes certain adjustments. The model is described as

follows:

Where:

Y = total passenger km per inhabitant

X = real GDP pro capita

P = synthetic price index for total transport (average prices of individual modes of

transport) deflated by the consumer price index

))()()(())()(()()(

1111 pLogXLogYLogXLogXLogYLogYLog

tttt

t

γβαρβ −−−+−+=

−−−

α = long-term function intercept,

178

PROMETEIA

1β = short-term parameter,

ρ = correction coefficient (adjustment to long-term function),

=γβ , long-term parameters of the GDP pro capita logarithm and deflated price

logarithm, respectively. The output of the long-term regression is the following:

α α1 β γ

coeff -1.12 0.09 1.33 -0.27

t-stat -4.0 5.0 12.8 -2.8

R2 0.99, where α1 is a differentiation on the constant for years 1990 and onwards

which de-emphasis the role played by the reconstructions carried out on the historical

series in terms of the structure of the most significant elasticities.

The short-term regression output, in which the above long-term function is inserted, is

the following:

β1 ρ

coeff 1.63 -0.44

t-stat 9.7 -2.5,

R2 0.99, where β1 is the parameter relating to the difference before the GDP in the

short-term part. The parameters are significant and somewhat expected; the ρ

coefficient indicates a rapid process of adjustment (a little more than two units of time,

in years).

ESTIMATE AND FORECAST OF PASSENGER TRAFFIC PER MODE OF TRANSPORT

Traffic estimates and forecast per mode of passenger transport (airplane, train, ship,

ordinary road network, motorway road network, tram) have been obtained by means

of a static demand system – of the “Almost ideal” demand system type. In order to

analyse the allocation of passenger traffic km among various modes of transport we

used a demand system expressed in expenditure shares per mode of transport; the

expenditure index is obtained by multiplying the price index of the ith mode of transport

(price proxy) by the total km of the same mode. This demand system gives us an

arbitrary first order approximation of any demand system; it satisfies the axioms of

exact choice, perfectly aggregates consumer data, may be estimated without having to

use non-linear estimate methods and may be used to test certain important properties

179

PROMETEIA

of the demand function dictated by consumer economic theory. The system’s generic

equation is:

7)1,..K(ij, log)log( *1

==⎟⎠⎞

⎜⎝⎛++= ∑

= Pypw t

ijt

K

jijiit βγα

where: )7(,..,1

1

===

∑=

kixp

xpw K

iii

iii

is the expenditure share of traffic related to the ith mode of transport, xi is the number

of kilometres travelled by the ith mode of transport, pi is the price index of the ith mode

of transport and y = is the index of total transport expenditure, built in such a

way so that in the base year (where the price indices are equal to 1) the expenditure

index is equal to the sum of the kilometres travelled by all modes of transport being

considered.

Table 2 - Demand elasticity for passenger mobility (2004)

∑=

K

iii xp

1

GDP Price

Airplane 2.6 -0.4

Train 0.4 -0.3

Nave 1.3 -0.7

Tram _ underground -4.4 -0.6

Bus 0.7 -0.3

Ordinary road network 1.5 -0.1

Motorway road network 1.6 -0.7

Total Passenger traffic 1.3 -0.2

Note: cross-elasticities are not shown

The system was estimated using the traditional restrictions of homogeneity and

symmetry; the equations have been estimated by using the transformed logarithm of

the Stone price index Stone ( ∑=k

kk pwLogP log* ) which allows for a linear system

representation of the parameters. The estimated system provides for seven equations

for each mode of transport considered (1=airplane, 2=train, 3=ship, 4=ordinary road

180

PROMETEIA

network, 5=bus, 6=motorway road network, 7=tram-metro). The system’s estimate is

obtained through a SURE type estimator (for apparently non-correlated equations)

excluding the last equation (the one related to the seventh mode of transport). The

two-level passenger traffic analysis produced the elasticities shown in Table 2.

It is highly likely that the GDP elasticity of the tram_underground mode of transport

(unlike buses, only urban journeying is observed) has not been accurately estimated

due to distortions in the base data which, despite the reconstructions that were carried

out, were not successfully eliminated.

Overall however, the elasticities show how important mobility preferences are to Italian

consumers.

ESTIMATE AND FORECAST OF FREIGHT TRAFFIC PER MODE OF TRANSPORT

Estimates and forecasts of freight traffic were carried out in a single step through a

panel type analysis for the different modes of transport. The data used for the

estimates are broken-down per mode of transport (airplane, train, ship, ordinary road

network and motorway road network) and are available in historical series from 1980-

2004 for Italy as a total.

The estimated model is as follows:

where:

Yi = ton kilometre per transported good per inhabitant per mode,

X = Gross Domestic Product pro capita,

Pi = price index per mode of freight transport over the GDP deflator,

di = dummy of the ith mode of transport.

In order to solve space-time heteroskedasticity problems that are very common in

panel type analysis, and given that the OLS estimator in not efficient in this case, the

parameter estimate was obtained by using a GLS estimator (the output estimate of the

chosen model is shown in Table 3).

The elasticities derived from the model are shown in Table 4 and the related forecasts,

modelled on two possible scenarios, are shown in Table 5.

)5(,..1 )()(1 1

==+⎟⎠

⎞⎜⎝

⎛+++= ∑ ∑

= =

KiPXLogddYLog i

K

i

K

iiiiii γββαα

181

PROMETEIA

Table 3 – Mobility demand model per mode of transport.

Coeff. Value t-stat

Reference constant -5.65 -53.3

Diff. airplane constant -4.59 -32.2

Diff. train constant 3.00 22.0

Diff. ship constant 5.08 37.7

Diff. Ordinary network (ON) constant 4.36 32.8

GDP pro capita reference 2.20 71.6

Diff. GDP pro capita airplane -0.18 -3.4

Diff. GDP pro capita train/ship/Road ON -1.51 -30.6

Price (all modes of transport) -0.21 -4.3

R2 = 0.99.

Note: diff. is a differentiation of the reference parameter for which the parameter’s value is obtained by the algebraic sum between diff. and the reference parameter.

The greater demand elasticity of motorway traffic compared to that of the ordinary

road network reflects increasing traffic mode selectivity based on client-passenger

objectives: city congestions and government traffic provisions already show up in the

more recent historical data of the model, where a higher growth of motorway network

traffic compared to the ordinary road network is observed. In this sense, the purpose

of the ordinary road network for freight is to allow for the final stretch of the delivery,

whilst the longer stretch is more and more covered by dedicated modes of transport.

Table 4 – Demand elasticities for freight traffic (2004)

GDP Price

Airplane 2.02 -0.21

Train 0.69 -0.20

Ship 0.69 -0.21

Ordinary road network 0.69 -0.22

Motorway road network 2.20 -0.22

Total freight traffic 1.06 -0.22

182

PROMETEIA

Table 5 – Mobility forecasts per mode of transport: passengers kilometre (million) and tons kilometre (millions) in the two scenarios of economic growth BASE CASE SCENARIO (GDP +0.5% Avg. Yr.)


Air 28398 2,9 37438 3,5 31,8 1,7Train 51196 5,2 48060 4,5 -6,1 -0,4Ship 6645 0,7 7074 0,7 6,4 0,4Tram+undergorund 5895 0,6 7480 0,7 26,9 1,5Bus 98942 10,1 105242 9,8 6,4 0,4Road Ordinary Network 679964 69,7 746980 69,6 9,9 0,6Road Motorway Network 104396 10,7 120229 11,2 15,2 0,9TOTAL 975436 100,0 1072503 100,0 10,0 0,6



HIGH CASE SCENARIO (GDP +2.1% Avg. Yr.)


Air 28398 2,9 67610 4,7 138,1 5,6Train 51196 5,2 46982 3,2 -8,2 -0,5Ship 6645 0,7 10524 0,7 58,4 2,9Tram+undergorund 5895 0,6 3349 0,2 -43,2 -3,5Bus 98942 10,1 118376 8,1 19,6 1,1Road Ordinary Network 679964 69,7 1033206 71,1 52,0 2,6Road Motorway Network 104396 10,7 172663 11,9 65,4 3,2TOTAL 980290 100,0 1452709 100,0 48,2 2,5



2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

2004 2020 var. % 2005-2020

183

PROMETEIA

We are naturally “stretching” the concept a little since, for example, the

average motorway travel distance tends to decrease, and yet the substitution

role played by the motorway network for the rest of the road network

emphasises the mobility developments made by the former network compared

to those observed (and forecasted) by the latter network, phenomena that

reflect on the elasticity parameters. Additionally, we must mention the

phenomena of urban transport mode selectivity generated by urban traffic

restrictions (with the consequent inefficiencies described in the first part of

this work).

These aspects also suggest that freight and passenger transport forecasted mobility

rates of change will be more reduced for the ordinary road network than for the

motorway network.

184

PROMETEIA

2. ESTIMATES AND FORECASTS OF SOCIAL COSTS

2.1. ACCIDENTS

2.1.1.EXTRAPOLATION TO 2020 OF THE NUMBER OF FATALITIES AND INJURIES ON

THE ITALIAN TOLLED MOTORWAY NETWORK

Unlike the ordinary road network, for the motorway road network Promoteia possesses

(thanks to the collaboration with Aiscat) specific traffic and accident analysis and

forecast instruments (also shared and widely used by operators of the sector for

different objectives).

Within the context of transport externalities the motorway network is obviously

responsible for only a minority share of social costs. However, since motorway

transport is the mode for which most traffic data is available a detailed analysis is

believed useful to try to obtain important information concerning possible traffic-social

cost relationships to be used, taking all appropriate cautions, for the ordinary road

network - for which very limited data are available. The lack of such ordinary road

network data is particularly unfortunate given the increasing interest in transport

systems, and particularly road transport systems, shown by the business community

and policy makers. Perhaps, a first step that needs to be taken in order to make

informed decisions on mobility and social costs would be to acquire knowledge on how

much our ordinary road network is used in terms of passenger and freight kilometres.

This may be done through simple criteria of classification (per large road types, per

working and non-working days, peak-times and non-peak times…).

However, the relationship between economy, mobility, network congestion and rates of

accident is both broad and complex even for modes such as motorway transport for

which all traffic details are known.

With regard to the economy-mobility relationship, we have already described the

different models that generate traffic forecasts (the analytical and forecasted data of

the simplified long-term models are in any case consistent, for the first years of the

forecast, with data from more sophisticated traffic models - not treated here as they

fall outside the scope of this work).

185

PROMETEIA

The relationship between mobility, congestion and rates of accident needs to be

clarified. Since congestion is usually defined in terms of objective capacity standards,

without adding anything in terms of behavioural description, we may study the

relationship between mobility (traffic) and rates of accident directly. Indeed, the

relationship between observed mobility and capacity is always a derived indication of

the level of congestion on a given point of the network at a given period of time.

Intuitively, we may expect that higher traffic density (vehicles compared to road

capacity) may generate greater risk of accidents because it worsens mobility

conditions, but at the same time, if higher traffic density implies significant speed

reduction, the risk of accident or coefficient of damage may reduce (on average, fewer

serious injuries or a lower number of fatalities given the same number of accidents).

Infras (2004) also underlines that “The influence of traffic volumes on accident risks

and accident costs is unclear. Different studies indicate that accident risks fall with

increasing traffic volumes.” Now, without considering the fact that the probabilities of a

decreasing accident risk as traffic increases would imply a positive traffic externality

(the marginal user enters the road aware of his accident risk but lowers this risk for all

inframarginal users, thus producing a benefit for them), it would seem that the issue of

mobility and accident rates cannot be looked at in abstract terms but only in context

specific terms: i.e. with reference to as complete as possible a parameterisation of the

situations in which the relationship is analysed.

In this respect, the Aiscat-Prometeia works that refer to the tolled motorway network

have brought an innovative approach to the attention of operators. We shall briefly

look at the salient features of this approach, with the aim of obtaining an extrapolation

to 2020 of the main rates of accident, then used for estimates and forecasted social

and external costs of transport in general and of the motorway transport in particular.

At the same time, these are instruments to produce empirical evidence of the

relationship between traffic and accident rates, limitedly for motorway transport.

In the Aiscat-Prometeia model 55 tolled motorway stretches are taken into

consideration over the period 1985-2005; traffic levels and the number of accidents

(total and fatal) are observed on these 55 stretches on a quarterly basis. The model

then looks at objective parameters of each stretch: mountain and tunnel stretches,

points of conflict, service stations, number of lanes per traffic direction, lane width,

existence of emergency lanes. Furthermore, a number of key variables have been

186

PROMETEIA

taken into consideration: such as, traffic density, quality of the circulating fleet of

vehicles in different territorial zones and the make-up of light-heavy traffic. All these

elements are combined in order to determine a level and direction of change of the

parameters of accident rates. According to this approach, which we may define as

relativistic, all safety valuation parameters are conditional to the observation of a level

of motorway traffic.

The functions used to estimate total and fatal accident rates are negative exponential

and negative binomial, respectively. The negative binomial function is a generalisation

of the Poisson distribution function (where mean and variance are the same) and is

thus also suitable to capture the dynamics of rare events. All the aforementioned

variables contribute to defining the expected value of the distributions that are used

(then fully determined by known relationships between means and variances).

Let’s start by looking at Fig. 1. This figure shows real and interpolated values of the

two simple regressions for both total accident rate and fatal accident rate. In the two

graphs, the historical time is absorbed within the observed traffic values and accident

rate on a quarterly basis for the 55 stretches; this is used to highlight an hypothetical

relationship between traffic density and accidents. Of course, the two simple

regressions do not have much explanatory value. A part from a generally positive

estimate for the total accident rate and a generally negative one for the fatal accident

rate the relationships certainly need to be analysed in greater depth.

One hypothesis that has given good results right from the start is that accidents are a

phenomenon characterised by “jumps” vis-à-vis different traffic densities. Without

considering the historical period, we have grouped the observations on motorway

accidents and traffic according to traffic density. TableTable 6 summarises a series of

regressions carried out.

187

PROMETEIA

188

Fig. 1 Simple Regressions between rate of accident and traffic density on

stretches of the Italian tolled motorway (number of observations= 4400)

The rate of accident regressions carried out on a constant and traffic density per lane

and direction of traffic are used to select the 4400 observations, regardless of the

stretch and time of occurrence, in contexts within which the coefficient that ties rate of

Total fatal accident ratekt = 0.011 - 0.03*vtmgpkt - 0.00005*trend

vtmgp

Fa

tal

acc

ide

nt

rate

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0 50000 100000 150000

0.0

5.0

10.0

0 50000 100000 150000

obsfit

0.0

50.0

100.0

150.0

200.0

250.0

0 50000 100000 150000

Total accident ratekt = 0.51 + 0.81*vtmgpkt - 0.00126*trend

4 0 . 0

4 5 . 0

5 0 . 0

5 5 . 0

6 0 . 0

0 5 0 0 0 0 1 0 0 0 0 0 1 5 0 0 0 0

obsfit

vtmgp

To

tal acc

iden

tra

te

PROMETEIA

accident to traffic density is not significantly different from zero, i.e. defining a range of

traffic density within which the rate of accident is constant or, at least, independent of

traffic density (the process to establish the limits of the intervals is empirical and is

based on adding to the regressions a number of observations of greater traffic density

and identifying the relationship’s jump as soon as the coefficient that ties the rate of

accident to traffic density acquires statistical significance; at this point we move to a

new regression that groups the successive observations in terms of traffic density until

satisfying all available observations, as may be seen in Table 6; the classes are the

same for both total and fatal accidents).

Table 6 – Simple regressions between total rate of accident and traffic density on stretches of the Italian tolled motorway (number of observations=4400)

CLASSES (VTMG_c) NO. OBS. BETAUp to 1409 149 NOT SIGN.from 1409 to 2937 388 NOT SIGN.from 2937 to 3928 422 NOT SIGN.from 3928 to 4386 213 NOT SIGN.from 4386 to 4481.4 46 NOT SIGN.from 4481.4 to 5111 255 NOT SIGN.from 5111 to 5988 338 NOT SIGN.from 5988 to 7794 467 NOT SIGN.from 7794 to 8198 93 NOT SIGN.from 8198 to 9213 229 NOT SIGN.from 9213 to 10010 161 NOT SIGN.from 10010 to 10660 139 NOT SIGN.from 10660 to 12920 364 NOT SIGN.from 12920 to 15110 249 NOT SIGN.from 15110 to 21696 247 NOT SIGN.more than 21696 74 NOT SIGN.

Figures 2-3 are the lines connecting the points whose coordinates are the average

density and the average rate of accident in each of the identified classes. The

implications of these representations are rather important. First of all, the curves (or

more properly broken lines representing sample points on a more dense continuum)

are not monotone and thus the graph’s curve shows both rates of increase and

decrease. Increases mean that the rate of accident increases with density: in other

words accidents increase more than traffic. It must be noted that in this representation

189

PROMETEIA

changes to the rates of accident are a function of density. More explicitly, the graphs

are telling us that a stretch of motorway that has a low traffic density – on the left of

the graph – may be rapidly affected by an increase in accidents due to the fact that

traffic increases and therefore denotes a jump in the rate of accident parameter that

characterises it, since it moves rightwards along the x axis. Again: in figures 2-3, a

motorway stretch may have a quarterly observation pertaining to a group that is

characterised by a certain accident rate while observation in another quarter may

pertain to a different group. In this respect the graphs are to be considered an

approximation of the traffic-accident relationship ceteris paribus, historical time of

observation included (which indeed is not shown).

Secondly, we may note how the decreasing parts of the curve are the most interesting;

a decreasing rate does not imply a decreasing number of accidents. The condition for

which movement between two values of the rate of accident implies a decreasing

number of accidents is clearly |ε(z/v,v)|>1, i.e. it must be elastic (an absolute value of

elasticity greater than 1) with respect to the curve of the rate of accident (z/v).

Intuitively, the negative slant must be steep. However, the two graphs show that

average contiguous points are characterised by significantly different y values and

therefore it isn’t possible to suggest a general trend.

Lastly, with regard to practical implications, the graph suggests that forecasts of the

total rate of accident depends on the parameters of the function of the rate of accident

(shown below) together with the real composition of the 55 AISCAT stretches in terms

of traffic density. For example, one difficulty that is often encountered in processes to

reduce the number of fatal motorway accidents – without considering the fact that the

fatal accident rate on motorways is one fifth of that of the ordinary road network,

which would thus be the focus of priority action, regardless of the penalty points

driving licence, which we shall soon discuss – is the fact that most motorway stretches

are today between 5-12000 theoretical vkm per day per lane and traffic direction. This

means that the structural growth of motorway traffic pushes these motorway stretches

towards traffic densities having a constant rate of accident. The section of the curve in

figure 3 between 12000 and 15000 vtmg_lane is problematic because the curve isn’t

steep enough to offset the increase in accidents that are due to increased traffic.

190

PROMETEIA

191

Fig. 2 Average traffic density and average total rate of accident within each

class (in each of which the rate is independent of traffic density)

Fig. 3 Average traffic density and average fatal rate of accident within each

class (in each of which the rate is independent of traffic density)

As is known, the characterisation of a stretch of motorway with respect to the rate of

accident is both a function of traffic density as well as of a number of structural

variables and driving behaviour.

998

2325

34304158

47755553

6839

7996

8722

9588 11690

443210320

13989

17492

22233

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 5000 10000 15000 20000 25000

998

2325

34304158

47755553

6839

7996

8722

9588 11690

443210320

13989

17492

22233

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 5000 10000 15000 20000 25000

Fata

lac

ciden

tra

te -

aver

age

Traffic density (vtmg_lane class averages)

NOTE: vtmg_lane=(vkl+2*vkp)/(km*days*2*number of lanes)

NOTE: vtmg_lane=(vkl+2*vkp)/(km*days*2*number of lanes)

998

23254158

8722

95887996

116906839

5553

4775

3430

4432

1032013989

17492

22233

35.00

40.00

45.00

50.00

55.00

0 5000 10000 15000 20000 25000

Tota

l ac

ciden

t ra

te -

aver

age

Traffic density (vtmg_lane class averages)

PROMETEIA

Fig. 4 – Results of the regression for the total rate of accident. The MODEL Procedure

Nonlinear OLS Summary of Residual Errors DF DF Adj Equation Model Error SSE MSE Root MSE R-Square R-Sq t_inct 32 4239 183.8 0.0434 0.2082 0.2737 0.2684

Nonlinear OLS Parameter Estimates Approx Approx Parameter Estimate Std Err t Value Pr > |t| a -1.52254 0.0932 -16.34 <.0001 a2 0.254254 0.0460 5.53 <.0001 a15 0.722348 0.2240 3.22 0.0013 a16 0.711066 0.2879 2.47 0.0136 b 310.0979 81.7538 3.79 0.0002 b23 -233.235 63.1631 -3.69 0.0002 b456 -242.037 65.9718 -3.67 0.0002 b78 -253.15 70.8281 -3.57 0.0004 b9 -259.163 72.4762 -3.58 0.0004 b10 -264.766 73.0968 -3.62 0.0003 b11 -276.428 73.8567 -3.74 0.0002 b12 -290.209 74.4071 -3.90 <.0001 b13 -293.141 76.0182 -3.86 0.0001 b14 -296.244 77.0133 -3.85 0.0001 b1516 -347.408 82.4094 -4.22 <.0001 c 0.018218 0.00306 5.95 <.0001 c3 0.027056 0.00686 3.95 <.0001 c4 0.030987 0.00652 4.75 <.0001 c7 0.024275 0.00771 3.15 0.0016 c8 0.01875 0.00870 2.16 0.0311 c13 0.021168 0.0117 1.81 0.0699 c14 0.022616 0.0153 1.48 0.1393 q -0.22063 0.0491 -4.50 <.0001 q1314 0.136766 0.1043 1.31 0.1899 t2 -0.01944 0.0186 -1.04 0.2962 t3 -0.1355 0.0208 -6.51 <.0001 t4 0.104193 0.0174 5.98 <.0001 st 0.255136 0.0747 3.41 0.0006 gal 0.540983 0.0225 24.05 <.0001 barr 0.160577 0.0272 5.90 <.0001 emerg 0.136991 0.0110 12.45 <.0001 pat -0.14318 0.0329 -4.36 <.0001

Number of Observations Statistics for System Used 4271 Objective 0.0430 Missing 129 Objective*N 183.8228

Note – description of parameters: ai= constants for the classes observed (i is the index of the density of class index), bi= traffic density, ci= traffic composition (relationship between light and heavy vkm), qi= quality of the fleet (relationship between fleet of vehicles with engine size greater than and lesser than 1600 cc., in the user area), th= quarter dummy, st= number of service stations per km, gal =number of tunnels per km, barr= toll collection barriers close to the largest urban centres, emerg= width of the emergency lane, pat= penalty-points driving licence effect.

In order to simultaneously take into account all these variables, the model that is used

for the estimate and forecast of total and fatal accidents isn’t linear in the parameters

and variables and is based on the function of expected value: in other words, we

assess how the different factors that potentially have an effect on the rate of accident

come to determine the expected value of a distribution function (negative exponential

192

PROMETEIA

and negative binomial, for total and fatal accidents, respectively) which then in turn

determines the probability of a certain number of accidents occurring.

The outputs of the total and fatal accident models are shown in figures 4 and 5 (the

regression of the rate of accident on the different variables is carried out by pooling on

the 55 AISCAT stretches, with quarterly data from 1985 to 2004).

Not only do we identify a positive and statistically significant impact of traffic on the

rate of accident, both total and fatal, but there is conspicuous empirical evidence that

the traffic-accidents relationship proceeds in jumps (the parameters of differentiation

of the effect of traffic density on the number of total and fatal accidents are

significant). In order to observe changes in accident rates it is necessary to move

beyond certain thresholds of traffic density: intuitively, the chances of committing a

mistake while driving a vehicle varies in a discontinuous way when the traffic pressure

reaches a certain threshold (the accident is a calculation mistake in the sense that the

consequences of an action are significantly different from the expectations of that

action). For example, the technological improvement of vehicles has a greater impact

on the number of fatal accidents, in the sense that on one hand it reduces the overall

number of accidents and on the other hand, thanks to increased safety and quality, the

negative consequences of an accident are reduced (parameter q of the quality of the

fleet in the two regressions is negative – it reduces the rate of accidents or number

thereof – and statistically significant). The width of motorway lanes, ceteris paribus,

also has positive effects on the accident reduction, at least in terms of overall number

of accidents (regardless of the consequences of same).

These elements combine in very different ways depending on traffic density classes (as

seen in figures 2-3): total and fatal accident rates tend to increase, ceteris paribus, on

low density traffic stretches but then remain constant over increasing traffic densities

and only start to decrease slightly when the motorway stretch is significantly

congested. This evidence must be looked at very carefully, even in light of the positive

effects of the penalty-points driving licence. This legislative action has decreased the

number of accidents both on the motorway and ordinary road networks.

193

PROMETEIA

Fig. 5 – Results of the regression for the (expected value of) number of fatal accidents (negative binomial probability distribution) The GENMOD Procedure Model Information Data Set AISC.INC Distribution Negative Binomial Link Function Log Dependent Variable INCM Offset Variable restrict Observations Used 4271 Missing Values 3649 Criteria For Assessing Goodness Of Fit

Criterion DF Value Value/DF Deviance 4247 4492.2334 1.0577 Scaled Deviance 4247 4492.2334 1.0577 Pearson Chi-Square 4247 4226.2271 0.9951 Scaled Pearson X2 4247 4226.2271 0.9951 Log Likelihood 2942.8942

Analysis Of Parameter Estimates Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept 1 -2.0018 0.3133 -2.6158 -1.3878 40.83 <.0001 droll3 1 1.2536 0.3512 0.5651 1.9420 12.74 0.0004 a456 1 1.4126 0.3699 0.6876 2.1376 14.58 0.0001 a78 1 1.3447 0.4128 0.5356 2.1539 10.61 0.0011 droll9 1 1.3094 0.4726 0.3831 2.2357 7.68 0.0056 a1013 1 2.4973 0.3766 1.7592 3.2353 43.98 <.0001 a1416 1 3.0719 0.3928 2.3020 3.8417 61.17 <.0001 b 1 575.4565 127.1327 326.2809 824.6320 20.49 <.0001 b35 1 -391.606 135.5886 -657.355 -125.858 8.34 0.0039 b9496 1 -7.8002 3.2630 -14.1955 -1.4049 5.71 0.0168 b69 1 -424.014 133.9881 -686.626 -161.402 10.01 0.0016 b1013 1 -545.244 128.6568 -797.407 -293.081 17.96 <.0001 b1416 1 -568.743 128.0470 -819.711 -317.776 19.73 <.0001 c4791011 1 -0.0107 0.0101 -0.0304 0.0091 1.12 0.2902 c14 1 -0.0356 0.0166 -0.0681 -0.0031 4.62 0.0316 q 1 -0.8886 0.0849 -1.0550 -0.7222 109.54 <.0001 t2 1 0.0857 0.0365 0.0143 0.1572 5.53 0.0187 t3 1 0.1739 0.0365 0.1024 0.2455 22.68 <.0001 t4 1 0.1643 0.0363 0.0932 0.2354 20.50 <.0001 st 1 -1.2026 0.1748 -1.5453 -0.8600 47.31 <.0001 gal 1 0.6459 0.0986 0.4525 0.8392 42.87 <.0001 pv 1 -0.6821 0.0890 -0.8566 -0.5076 58.71 <.0001 km 1 0.0084 0.0002 0.0080 0.0088 1581.67 <.0001 pat 1 -0.2457 0.0566 -0.3567 -0.1347 18.82 <.0001

Note-description of parameters: ai= constants, bi= traffic density, ci= traffic composition), q= fleet quality, th= quarterly dummy, st, gal, pv, km= structural variables (pv is the number of bridges and viaducts per kilometre), pat= penalty points licence effect.

It has been possible to verify this reduction on the tolled motorway both in terms of

rates of traffic as well as in absolute terms since extremely accurate statistics are

available for these networks, both in terms of mobility (due to the toll system) as well

as in terms of accidents (since only the Road Police may intervene on the motorway).

After the “step” effect of the so called “penalty-points driving licence” demand for

mobility, even though not completely expressed given the safety and quality conditions

194

PROMETEIA

which we addressed above, will tend to align motorways (and road?) along the

substantially constant line of the rate of accident: the phenomenon tends to reproduce

at a lower level too, i.e. as traffic increases the rate of accident does not decrease and

therefore accidents may increase (starting from a lower level). For example, most

motorways today have traffic density within the interval of 5-12000 vtmg per lane and

traffic direction. The rate of accident fluctuates around a value of 75 accidents per 10

billion kilometres travelled; after the penalty-points driving licence was introduced this

value has generally fallen to 65. Given the expected increase in traffic it is highly likely

that most motorway concessionaires will not observe further reductions in the number

of accidents. In fact, an increase is more likely.

AVERAGE ACCIDENT ELASTICITIES TO MAIN VARIABLES

Total Accidents Fatal accidents

Traffic density 0.71 0.14

Traffic composition 0.23 -0.36

Quality of vehicle fleet -0.35 -0.90

Average width of lanes -0.32 -0.34

In terms of the approach that has just been described, the question of whether “the

relationship between traffic and rate of accident is positive, null or negative” is badly

worded, since the answer depends exclusively on the full parameterisation (or as full

as possible) of the mobility system and levels of traffic density. One of the most

significant implications for the near future, as noted, is that although the rate of

accident may decrease (accidents per vehicle kilometre) the absolute number of

accidents may start to grow again (in both scenarios considered in the external cost

calculation). This is explained by the fact that motorways that today have low or

moderate traffic densities will see increases in congestion (or simply in traffic density)

and will move into positions where fatal and total accident rates will be slightly

incremental, not decreasing or only slightly decreasing.

195

PROMETEIA

FORECASTS

The relationship between fatal accidents and number of deaths has been kept constant

according to the fixed ratio that has been historically observed (2004). In order to

address the cause of death (total number of deaths correlated to the accident

occurring even after thirty days of the accident) a similar approach was used: the

coefficient applied to the number of fatalities in order to expand the same to the

“cause of death” is 1.078.

Table 7 and figures 6-8 summarise the results of the extrapolation process (details of

the assumptions, for example those relating to the relationship between total accidents

and number of injured are provided in paragraph 2.1.2. in a unitary form compared to

the ordinary road network).

Through the model of allocation of light and heavy traffic, the two hypothesis of GDP

growth generate expected total motorway traffic density.

The stretch traffic model (not shown) breaks down the total value on the 55

motorways by different forecast horizon.

Tab. 7 – Summary of the extrapolation results to 2020 of rate of accident data on the motorway network: low and high scenarios.

general accidents deaths cause of death injured Total vkm Motorway (million)

HIGH CASE SCENARIO1985 22043 569 734 10916 365221990 30495 688 888 14195 519422004 35878 468 505 16451 790552005 35410 448 483 16236 813772020 41060 574 618 18827 134113

LOW CASE SCENARIO1985 22043 569 734 10916 365221990 30495 688 888 14195 519422004 35878 468 505 16451 790552005 35410 448 483 16236 813772020 35599 444 478 16323 89799

The ratios between fatal accidents, number of deaths and cause of death have been

kept fixed in the forecast too. The cause of death is the broadest aggregate and shall

be used in later chapter as the basis for the calculation of social costs.

196

PROMETEIA

Fig. 6 – Number of general accidents (=total accidents less fatal accidents) per million vkm on the tolled motorway network

0,29

0,34

0,39

0,44

0,49

0,54

0,59

0,64

1985 1990 2004 2005 2020

LOW

HIGH

Fig. 7 – Number of injured per million vkm on the tolled motorway network

0,13

0,15

0,17

0,19

0,21

0,23

0,25

0,27

0,29

0,31

1985 1990 2004 2005 2020

LOW

HIGH

Lastly, the accident rate models described above provide total and fatal accident

forecasts under the two GDP and traffic density scenarios.

197

PROMETEIA

Fig. 8 – Number of deaths (cause of death) per billion vkm on the tolled motorway network

3,5

5,5

7,5

9,5

11,5

13,5

15,5

17,5

19,5

1985 1990 2004 2005 2020

LOW

HIGH

2.1.2. RECONSTRUCTION OF HISORICAL SERIES OF GENERAL AND FATAL

ACCIDENTS ON THE ORDINARY ROAD NETWORK

Base data are represented by Istat-Health Statistics, Istat-Road Accidents 2003-2004

and AISCAT, the latter of course being our source for motorway data. Original data are

shown in table 8.

To carry out an extrapolation of accident rate and fatality values on the ordinary and

motorway road networks we need to adopt two different approaches. For motorway

accidents the AISCAT-Prometeia model is used. For the ordinary road network a simple

regression analysis is carried out, described below, after having carried out the

following steps:

1) subtracting the number of motorway accidents from the total number of

accidents, in order to obtain the number of ordinary road network accidents;

2) distinguishing, again through subtraction, general accidents (i.e. non-fatal) from

fatal ones;

198

PROMETEIA

3) expanding the number of deaths to include the cause of death (source: Istat,

Health Statistics); indeed, in order to obtain as accurate as possible sizing of social

costs it is necessary to consider fatal accidents counting all deaths, found in health

statistics, and not only those deaths occurring within 30 days of the accident (until

1999 within the 7th day) from Accident Statistics sources (Istat collects information

from the Road Police, Carabinieri and Municipal Police); to breakdown total cause of

death (i.e. deaths within the 30th day of the accident) into cause of death occurring on

the motorway network and occurring on the ordinary road network, the ratio of

motorway fatalities and ordinary network fatalities to total cause of death numbers has

been applied for each year; total road accident fatalities in 2002 was 6739 whilst cause

of death was 7119, i.e. 5.7% higher; this latter value was therefore allocated to road

type according to fatalities (deaths within 30 days of the accident) observed on the

motorway and ordinary road networks;

Table 8 – Selected base date DATA ON ACCIDENTS AND CONSEQUENCES SUFFERED BY PERSONS

ALL ROADS MOTORWAY

Year Total accidents

Fatal accidents Deaths Injured cause of

deathTotal

accidentsFatal

accidents Deaths Persons involved

1978 152953 7256 7965 2075561979 162199 7516 8318 2215741980 163770 7684 8537 2228731981 165721 7269 8072 2252421982 159858 6977 7706 2174261983 161114 6916 7685 2197441984 159051 6442 7184 2175531985 157786 6388 7130 216102 22487 444 569 114851986 155427 6330 7076 213159 24199 521 676 119381987 158208 6065 6784 217511 27305 537 665 127451988 166033 6273 6939 228186 28154 531 643 130021989 160828 5766 6410 216329 28852 508 620 126841990 161782 5880 6621 221024 31027 532 688 148831991 170702 6633 7498 240688 9609 32823 569 682 149991992 170814 6578 7434 241094 9645 32240 601 777 155111993 153393 5893 6645 216100 8434 29177 528 664 135651994 170679 5924 6578 239184 8379 31094 517 617 141221995 182761 5819 6512 259571 8054 32844 514 655 147451996 190068 5590 6193 272115 7566 35029 495 619 151101997 190031 5605 6226 270962 7811 36964 544 654 161911998 204615 5788 6342 293842 8092 37486 552 660 196801999 225646 6022 6688 322999 7829 40375 554 677 198022000 229034 6055 6649 321796 7369 39255 470 588 196862001 235409 6074 6691 335029 7370 41204 517 599 189732002 239354 6099 6739 341660 7119 41621 533 624 196062003 231740 5463 6065 327324 37435 469 553 181112004 224553 5082 5625 316630 36278 391 468 16919

4) in order to obtain the number of injured on the ordinary road network alone,

the number of motorway injuries was subtracted from total injured; the latter value

199

PROMETEIA

was obtained as the number of persons involved in motorway accidents (AISCAT

source) net of the number of motorway accident deaths;

5) injuries are broken down into slight injuries and serious injuries, both for

motorway as well as ordinary road accidents. This is done by applying a simple straight

proportion inferred from the (poor) available statistics;

6) finally, the regression analysis is applied to the database estimated from 1985

to 2004, so as to obtain a set of parameters that enable, for the ordinary network, an

extrapolation to 2020 of accident externalities (what we are interested in is the

medium-long term trend and not an accurate annual forecast).

For the period 1991-2002, the correlation between fatalities and cause of death is 0.72

on the ordinary road network and 0.92 on the motorway network. It is worth noting

that the relationship between road mobility and accidents is gross of the number of

kilometres travelled by collective forms of transport (buses); in other words, whilst the

number of deaths and injured includes accidents involving forms of collective transport,

the regressors and denominators of the rate of accident do not include the kilometres

travelled by these means of transport; the error is reduced because in terms of vkm

buses represent a very marginal amount, approximately 1% (but approximately 14.6%

in terms of pkm).

The linear regression of cause of death with respect to fatalities, a linear trend and a

constant (not shown) give us 5837 and 5412 cause of death on the ordinary road

network for 2003 and 2004, respectively; the same result is given by applying an

adjusted coefficient of expansion for the two years 2001-2002 to the number of

fatalities in 2003 and 2004 (1.078 cause of death for each fatality on either the

motorway or ordinary road networks); accordingly, the retropolation of the cause of

death in the period 1985-1990 is carried out with the proportion calculated for the two

years 1991-1992 (approximately 1.28 for both road types) while the construction of

values for the two years 2003-2004 is based on the above proportion of 1.078, which

is related to the two years 2001-2002. The difference between these two coefficients

provides an approximate but robust measurement of the increase in the effectiveness

of emergency road service and improvements in first-aid interventions for road

accident victims. This coefficient of expansion is kept to 2020 to calculate cause of

death numbers starting from the extrapolated value of fatalities.

200

PROMETEIA

The extrapolation is carried out by regression analysis (for each type of road), as

follows: regression of non-fatal accidents with respect to heavy and light traffic (pkm

and tkm), the trend and a dummy taking any value between 1 and 2005 (or otherwise

zero); regression of the number of injured with respect to non-fatal accidents, traffic

composition (relationship between heavy and light vehicles) and a linear trend;

regression of the number of fatalities with respect to the number of injured, traffic

composition and a trend (linear or quadratic). Given the marginal size of the number,

in this process of estimation we do not distinguish between total accidents and non-

fatal accidents. Where useful, the variables have been treated in logarithmic form.

Table 9 highlights selected regressions for the number of accidents as a function of

traffic; positive effects deriving from the amount of traffic on the ordinary road

network are associated to a negative logarithmic trend (the effect of a greater

awareness of driving dangers incurred if the road code is not observed as well as

greater communication on the subject matter). The regression used to extrapolate the

number of accidents to 2020 is the second one in Table 9; tons of freight transported

on the ordinary road network are also shown. Table 10 clarifies the relationship

between number of injured and accidents (with an elasticity slightly less than 0.9). The

regression in Table 10 is used to extrapolate the number of injured on the ordinary

road network to 2020 given passenger and freight forecasts.

Table 9 - Regressions per number of accidents on the ordinary road network dum85-1999 pass lgtrend k

coeff -33958.8 0.2 -14580.5 89755.8t-stat -5.9 2.4 -1.4 3.0R2 0.93

tonn dum85-1998 pass lgtrend kcoeff 1.28 -50062.4 0.14 -15491.7 26520.3t-stat 1.95 -5.10 1.64 -1.65 0.62R2 0.94

201

PROMETEIA

Table 10 - Regression per number of injured on the ordinary network tonn/pass inc k

coeff -225773,2 1,5 67993,9t-stat -3,8 20,7 3,5

R2 0,99

elasticity of the number of injured to the number of accidents (2004)0,89

Table 11 summarises the final phase of the reconstruction process of this important

item, whose social cost, although largely internalised, remains very high.

Among the many exercises carried out, the regression analysis shown in Table 11

appears to be the most satisfying. The traffic composition variable is not observed, and

seems not to have a significant impact in terms of mortality, given the number of

injured.

Table 11 – Regression for the number of fatalities with respect to the number of injured (ordinary road network)

dum91-92 trend nr.feriti kcoeff 730,9 -112,3 0,011 4358t-stat 4,9 -5,3 3,9 8,5

R2 0,81

elasticity of the number of fatalities to the number of injured (2004)0,64

It appears clear, as is today generally recognised – except for what has been discussed

in terms of a possible resurgence in the absolute number of accidents – that the trend

in fatal accidents, given the number of injuries, is a strongly declining one.

As regards the ordinary road network, table 12 summarises the results obtained for the

process of extrapolation of the various accident types.

Figures 9-11 depict the history and forecasted trends of a number of important

accident relationships to traffic (non-weighted sum of light and heavy vkm compared

to private transport) on the ordinary road network.

202

PROMETEIA

Table 12 - Summary of the extrapolation results to 2020 of accident data on the ordinary road network: High case and Low case scenarios

General accidents deaths cause of death injured Total vkm totali ON (ml.)HIGH CASE SCENARIO

1980 156086 8537 11007 222873 1901141990 125407 5933 7650 206829 3101632004 183584 5157 5564 300179 4053532005 176054 4937 5322 289354 3911332010 197358 4750 5120 322559 4624042020 247216 4456 4804 396666 611228

LOW CASE SCENARIO1980 156086 8537 11007 222873 1901141990 125407 5933 7650 206829 3101632004 183584 5157 5564 300179 4053532005 176054 4937 5322 289354 3911332010 179341 4464 4813 295337 4128152020 186901 3517 3791 306976 444106

Figure 9 – Number of general accidents (=total less fatal) per vkm (million) on the ordinary road network

0.3

0.4

0.5

0.6

0.7

0.8

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

ALTO

BASSO

BASSO = LOW ALTO = HIGH

203

PROMETEIA

Fig. 10 – Number of injured per vkm (million) on the ordinary road network

0.55

0.65

0.75

0.85

0.95

1.05

1.15

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

BASSO

ALTO


Fig. 11 – Number of fatalities (cause of death) per vkm (billion) on the ordinary road network

7.0

17.0

27.0

37.0

47.0

57.0

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

BASSO

ALTO


Due to the lack of recent data it has not been possible to specifically evaluate the

effect of recent provisions of law (especially the so called “penalty points driving

licence”; in this respect, the only analysis available is that provided in the paragraph on

204

PROMETEIA

motorway accident rates). Negative trends observed in the regressions that were

carried out do however allow us to capture the effect that communication and new civil

and criminal provisions have had on driving behaviour. There is no significant

difference between fatality rates in the two scenarios, although the number of

fatalities, given the different levels of traffic, does imply higher social costs in the high

case scenario compared to the low case scenario.

2.1.3. EXTRAPOLATION OF THE CONSEQUENCES OF ACCIDENTS SUFFERED BY

PEOPLE PER DIFFERENT MODE OF TRANSPORT OTHER THAN ROAD TRANSPORT

Guided by available literature and consolidated practice on the subject matter, given

the rarity of the accidents, especially fatal accidents, for transport modes other than

road transport and considering the low intensity of the relationship between traffic

levels and accidents, the extrapolation is carried out by keeping the average annual

values of the number of fatalities and injured observed in available historical data,

constant. Specifically, for rail transport, the average has been calculated over the

period 1999-2003, for air transport over the period 2002-2003 (thus excluding the

disaster that occurred at Linate airport in 2001), for maritime and fluvial transport over

the period 1994-2003. For air transport, the number of injuries is set at zero.

Table 13 - Long-term averages for the number of fatalities and injured per mode of transport other than road transport.

Rail Air Maritime and FluvialFatalities Injured Fatalities Fatalities Injured

1994 1 71995 0 51996 10 261997 1 01998 9 21999 7 33 8 62000 21 12 16 02001 8 18 118 0 112002 20 29 3 4 132003 7 21 2 2 54

Calculation of averages to be imputed1999-2003 12,6 22,62002-2003 2,51994-2003 5,1 12,4

(sources: Istat, Ministry of Infrastructure and Transport)

205

PROMETEIA

Table 13 summarises the base data and the calculations carried out; the averages thus

obtained are kept constant, for each and all of the years of the forecast horizon.

2.1.4. SUMMARY OF THE CONSEQUENCES OF ACCIDENTS SUFFERED BY PEOPLE:

NUMBER OF FATALITIES AND INJURIES PER MODE OF TRANSPORT.

Tables 14 and 15 summarise forecasts on accidents and correlated accident rates per

unit of mobility (fatal accidents for train, ship and plane are very low, not significantly

different from zero and are therefore not shown in Table 16).

Table 14 - Summary of accidents: number of events

Road

ON MN Total Rail Maritime

and fluvial Air

FATALITIES

2004 5564 505 6069 12.6 5.1 2.5

2020 Low scenario 3791 478 4269 12.6 5.1 2.5

2020 High scenario 4804 618 5422 12.6 5.1 2.5

INJURED

2004 300179 16451 316630 22.6 12.4 0

2020 Low scenario 306976 16323 323299 22.6 12.4 0

2020 High scenario 396666 18827 415493 22.6 12.4 0

* ON= Ordinary Network; MN= Motorway Network

Tab. 15 – Summary of accidents: fatalities and injured per billion vkm

ON MN Total

FATALITIES 2004 13.7 6.4 12.5 2020 Low scenario 8.5 5.3 8.0 2020 High scenario 7.9 4.6 7.3

INJURED 2004 740.5 208.1 653.6 2020 Low scenario 691.2 181.8 605.5 2020 High scenario 649.0 140.4 557.5

* ON= Ordinary Network; MN= Motorway Network.

206

PROMETEIA

Accident rates are always decreasing: both in terms of fatalities as well as injured, and

in both scenarios. The rate of fatal accidents on the motorway network starts in 2004

at a value that is less than half that of the ordinary network parameter, but in both

scenarios at 2020 the relationship vis-à-vis the ordinary network tends to increase,

suggesting that there is a non-eliminable (or physiological) rate of fatal accidents. With

reference to 2004, if the fatal rate of accidents on the ordinary road network were

equal to that of the motorway network, we would have a life saving of almost 3000

units. Looking forward, in the high growth economic and mobility scenario, which

projects a decreasing number of ordinary network victims but an increasing number of

motorway network victims, by 2020 the possible levelling of fatal accidents would still

imply an annual saving of human lives of more than 2000 units. This, in substance,

may define the priority and intensity of safety action of road transport externalities

paying careful attention to the problems of both the motorway and ordinary road

networks.

As observed, in the high economic and mobility growth scenario the number of fatal

accidents in 2020 may exceed those in 2004. The reasons for this are all to be found in

the unstoppable growth of motorway traffic, continuing the consolidated growth trends

already witnessed in the history of Italian transport (in this respect the model cannot

bring any innovation). The substitution role that the motorway network plays for the

rest of the road network, the variability of traffic density along different motorway

stretches and the complexity of the distribution of fatal accident rates, as observed, for

example, in figure 3, do however suggest that the motorway be constantly observed in

terms of accident rates and that there be recognition of the fact that the penalty-points

driving licence may end up being a positive but only episodic step in the effort to

reduce accidents. By governing traffic through endogenous congestion on the

motorway network, and particularly on the ordinary road network, decision makers run

the risk of losing control on an important source of external transport costs.

207

PROMETEIA

2.2. AIR POLLUTION AND THE GREENHOUSE EFFECT PER MODE

OF TRANSPORT

In order to estimate polluting emissions (an exercise carried out with COPERT III

software, Ntiziachristos and Samaras, 2001) we need to know the make-up of the

existing vehicle fleet (cars, commercial and industrial vehicles, buses, mopeds and

motorbikes) broken-down by fuel type and technology (reference is made to the

Community laws that govern emissions).

In this paragraph we shall present the models and hypothesis used to forecast vehicle

fleet size and make-up in 2020.

2.2.1. ANALYSIS AND FORECASTING MODELS OF THE DIFFERENT MAKE-UP OF THE

VEHICLE FLEET: AUTOMOBILES PER FUEL TYPE, ENGINE SIZE AND TECHNOLOGY

A. OVERALL FLEET SIZE FORECAST

Before describing the forecasting techniques we wish to note that all social costs

valuation work relating to automobile/car fleet data is a Prometeia source. This

valuation has been preferred to that by Aci, which appears somewhat overestimated by

different accounting treatment. This likely overestimation finds confirmation on a

provincial basis in terms of the inconsistency of historical series of fleet sizes, the

number of registered vehicles and the number of scrapped vehicles. It may also be

possible that the Aci data includes a number of older vehicles that are no longer on the

roadworthy.

The first step consists in forecasting the overall automobile fleet until 2020. This has

been carried out by using the Promoteia model, which jointly estimates new

registrations and existing car fleet. The theoretical reference model, summarily

presented hereunder, consists of a system of four equations which tie flows to fleet:

1 12

3

4

1

1

) ( )) ( , , , )

)

) (

*

*

S q z SY f Y i W u

S a Y gPP

S k b S S

t t t tp

t t t t t

t t tp

tt

tT

t t t t

= + − ⋅

= +

= ⋅ + ⋅

= + ⋅ −

−

−∆

)

e

208

PROMETEIA

where: St = fleet at time t (annual average), q = registrations, S* = desired fleet, Yp =

permanent income, Y = disposable income per family, i = interest rate, Wt = total

wealth of family, u = unemployment rate, z = discharged quota, b = fleet adjustment

coefficient to the gap between desired fleet and the fleet existing in the preceding

period, a = multiplier of permanent income on desired fleet, g = multiplier of relative

price of automobile on desired fleet, P = car price index, PT =consumer prices index.

The first equation says that the car fleet at time t is equal to the fleet in the preceding

period net of scrapped cars plus newly purchased car flows. The second equation says

that permanent income is a function of ideal wealth, current disposable income and

expected interest rates and employment market. The third equation says that the

desired fleet is both a function of family permanent income as well as car prices. The

last equation hypothesises that the gap between desired and actual fleets is gradually

closed: problems of asymmetric information on environmental variables and

expectations as well as on the measurement of fleet quality mean that a gradual

adjustment is required.

Looking at the simplified form, we thus obtain:

q k b a f Y i W u b a e b gPP

z b S

S q z S

t t t t t t t t t t t tt

tT t t t

t t t t

= + ⋅ ⋅ + ⋅ ⋅ + ⋅ ⋅ + − ⋅

= + − ⋅

⎧⎨⎪

⎩⎪

−

−

( , , , ) ( )

( )

1

11

We have a system where new registrations and fleet are simultaneously calculated by

estimating parameter z. Therefore, looking forward, if we are given the number of new

registrations and an economically consistent level of scrapings, we are able to obtain a

trend for the future fleet size.

209

PROMETEIA

B. FLEET FORECAST PER FUEL TYPE

The second step consists in breaking down the existing fleet of cars per fuel type

(petrol, diesel oil and other). Based on historical registrations and fleets per fuel type,

the rate of scrapping of petrol, diesel and other fuel type cars was calculated. New

registration forecasts were then carried out per fuel type on the basis of expected

future fuel prices and historical trends. Then, based on historical scrapping rates, fleet

forecasts up to 2020 were produced.

Table 16 – The make-up of automobile/car fleet in 2004 and 2020 Level (000s of units) and percentage of total

Level quota % Level quota %petrol 21397 71,7 13595 43,9 PRE ECE+ECE 4251 14,3 0 0,0 Euro I 4823 16,2 0 0,0 Euro II 6714 22,5 0 0,0 Euro III 5021 16,8 0 0,0 Euro IV 588 2,0 2037 6,6 Euro V 0 0,0 11558 37,3diesel 7736 25,9 14083 45,4 Conventional 0 0,0 0 0,0 Euro I 445 1,5 0 0,0 Euro II 2450 8,2 0 0,0 Euro III 4462 15,0 0 0,0 Euro IV 379 1,3 0 0,0 Euro V 0 0,0 14083 45,4other 694 2,3 3311 10,7 Conventional 269 0,9 0 0,0 Euro I 252 0,8 0 0,0 Euro II 147 0,5 0 0,0 Euro III 25 0,1 0 0,0 Euro IV 1 0,0 470 1,5 Euro V 0 0,0 2841 9,2TOTAL 29828 100,0 30989 100,0

2004 2020

C. FORECAST BREAKDOWN PER ENGINE SIZE AND LAW

In the last step of the estimate a breakdown of the automobile fleet forecast per

engine size and law is carried out. Neutral assumptions are used. For engine size, the

current shares have been assumed constant. In terms of applicable laws, the following

assumptions have been applied:

- cars that are scrapped are assumed to be the ones having the oldest technology;

- new registrations until 2008 are allocated to euro 4 technology, from 2008 onwards

to euro 5 technology; no assumptions are made on further technological progress.

210

PROMETEIA

Table 16 shows the current value of the automobile fleet and the one forecasted for

2020, broken down between engine size, fuel type and technology.

2.2.2. ANALYSIS AND FORECASTING MODELS OF THE DIFFERENT MAKE-UP OF THE

VEHICLE FLEET: OTHER VEHICLES PER FUEL TYPE, ENGINE SIZE AND TECHNOLOGY

In order to estimate and forecast the existing fleet for other vehicle types a number of

correlation models for the demand for new vehicles were built according to main

macro-economic indicators. Then, using the average scrapping rate observed over the

past five years, forecasts for the other type of vehicles fleet were carried out until

2020.

A. COMMERICAL AND INDUSTRIAL VEHICLES

The growth rates of industrial and commercial vehicle registrations were correlated to

investment in machinery, equipment and means of transport, obtaining an elasticity of

0.9. Using this parameter and forecasted investment (source: Prometeia) the fleet is

estimated to be: fleet t+1=fleet t + registrations t+1 – z * fleet t where z is the rate of

scrapping (5.5% for commercial vehicles and 3.9% for industrial vehicles). In terms of

fuel type, petrol currently accounts for a rather modest share of the fleet and is

progressively reducing (14% in 2000, 10% in 2004): the hypothesis is that by the year

2020 the fleet will be entirely composed of diesel vehicles.

B. BUS

The same process as the one described above for heavy vehicles is used. The

estimated elasticity of bus registrations to investment is slightly lower (0.7), as is the

rate of substitution, 3.8. Using these parameters, registrations and total bus fleet are

forecasted. This total forecast is then broken-down into urban and sub-urban quotas to

2020 according to historical trends. Penetration is forecasted to grow from a rate of

19% observed in 2000 to 23% in 2020.

211

PROMETEIA

C. MOPEDS AND MOTORBIKES

Forecasts for moped and motorbike registrations per engine size (up to 50 cc, from 51

to 250cc, from 250 to 750 cc and above 750 cc) are based on correlation models

between demand for new vehicles and income. The average elasticity of demand for

bikes to income is 0.8, with lower values for mopeds and higher values for larger bikes

(the range is between 0.6-1.2). Similarly to that described for other vehicle types, the

fleet has been forecasted by adding new registrations and deducting

scrapped/discharged vehicles using the average rate of scrapping observed over the

last years.

Table 17 – The make-up of the vehicle fleet in 2004 and 2020 per technology (law) Level (000s of units) and percentage of total

level quota % level quota %commercial vehicles 3097 22,8 4033 24,5 Conventional 1330 9,8 0 0,0 Euro I 460 3,4 0 0,0 Euro II 506 3,7 0 0,0 Euro III 801 5,9 4 0,0 Euro IV 0 0,0 222 1,3 Euro V 0 0,0 3807 23,1industrial vehicles 932 6,8 990 6,0 Conventional 571 4,2 0 0,0 Euro I 70 0,5 44 0,3 Euro II 191 1,4 191 1,2 Euro III 100 0,7 100 0,6 Euro IV 0 0,0 110 0,7 Euro V 0 0,0 545 3,3buses 98 0,7 126 0,8 Conventional 44 0,3 0 0,0 Euro I 7 0,1 0 0,0 Euro II 37 0,3 22 0,1 Euro III 9 0,1 9 0,1 Euro IV 0 0,0 16 0,1 Euro V 0 0,0 79 0,5mopeds 4915 36,1 3728 22,6 Conventional 3449 25,4 293 1,8 97/24/EC Stage I 934 6,9 934 5,7 97/24/EC Stage II 532 3,9 2501 15,2motorbikes 4560 33,5 7583 46,1 Conventional 2326 17,1 0 0,0 97/24/EC 2234 16,4 7583 46,1TOTAL 13601 100,0 16460 100,0

2004 2020

212

PROMETEIA

The breakdown of fleets by technology (law) for each vehicle type has been carried out

in the same way as for automobiles/cars, i.e. deducting older vehicles for scrapping

and adding class 4 euro vehicles and later technology for new registrations according

to the year of registration and the first year of effectiveness of the new provision of

law on emissions (as per the COPERT III model). Table 17 summarises current and

forecasted vehicles in 2020 per engine size, fuel type and technology.

2.2.3. DESCRIPTION OF THE CALCULATION OF EMISSION EXTERNALITIES: AIR

POLLUTION AND GREENHOUSE EFFECT FOR ROAD TRANSPORT

Polluting emissions for each mode of transport are calculated using two different

approaches: for road transport, total polluting emissions are calculated by using the

COPERT III model, with a bottom-up calculation procedure. For other modes of

transport pkm and tkm values are considered, thus reference is made directly to the

monetary value of the externalities considered not in terms of units of externality but

rather in terms of units of mobility (mobility units generate externality units which, by

applying the unitary externality values, give monetary values per unit of mobility).

Estimates for the coefficients of emission for other modes of transport inferred from

available literature are sourced from statistics assembled using different

methodologies. The two principle approaches are the top-down one (information flows

from the bottom upwards) and the bottom-up one (information flows from the top

downwards). In the first case, the approach is based on the source emissions that are

examined, whose estimated emissions is carried out in a different way depending on

whether we are dealing with point sources or linear/aerial sources. Once the emissions

have been calculated spatial and time disaggregation is carried out. In the second

approach a summation is carried out, where the summation is weighted according to

different traffic types, quantities and values of the emissions per single category of the

means of transport considered within specific mobility contexts.

The COPERT III model is a bottom-up approach applied to passenger cars, light and

heavy commercial vehicles, buses, mopeds and motorbikes broken-down into different

vehicle classes according to engine size, fuel type and law.

213

PROMETEIA

This methodology has been indicated by the European Agency for the Environment as

being the instrument to use to estimate road transport emissions within the scope of

the CORINAIR programme (CORe INventory AIR) for the building of national emissions

statistics.

The model allows for the estimate of all types of polluting emissions governed by

European law (carbon monoxide, nitrogen oxides, non methane volatile organic

compounds (NMVOC), methane, etc.) and CO2 (which also estimates the contribution

of transport to the greenhouse effect) according to fuel consumption. Emissions of

certain non regulated pollutants such as CH4, N2O, NH3, SO2, heavy metals, aromatic

polycyclic hydrocarbons (IPA), persistent organic pollutants (POP) are also calculated.

Finally NMVOC emissions by single type are provided.

Traffic emissions are calculated using the following formula:

IfE ×=

Where f is the emission factor (per type of pollutant and vehicle) and I is an indicator

of activity.

Emission factors may be referred to three representative variables:

- vehicle class (fuel type and technology);

- engine temperature (when warm and when cold);

- the type of road travelled (urban, suburban and motorway, for different travelling

speeds).

VEHICLE CLASS

CORINAIR 90 is the standard of reference. Under CORINAIR 90, vehicles are divided

into 105 different classes. Macro-classes are defined according to vehicle type and use

and then within these macro-classes a further breakdown is carried out according to

fuel type, weight (for commercial vehicles) or engine size (for cars). The last

breakdown is carried out according to adherence to European emission rules for

vehicles effective from certain dates.

214

PROMETEIA

ENGINE TEMPERATURE AND TYPE OF ROAD TRAVELLED

Depending on engine temperature, the types of emissions that are observed are:

- warm engine emissions (the engine is at its warmest temperature and catalytic

systems are fully functioning);

- cold engine emissions (during the engine’s and catalytic converters’ warm-up

phase);

- emissions from evaporation (due to evaporation of fuel in the vehicle’s fuel tank).

Total emissions per vehicle on each journey are given by the following sum:

ETOT = E warm + E cold + E evaporated

WARM ENGINE EMISSIONS

Warm engine emissions depend largely on the distance travelled by each vehicle, the

speed (road type), age of the vehicle, weight and size of the engine:

E warm = f warm x I warm = f warm x (N_vehicles x travelled_km)

In one unit of time we therefore have:

I warm = N_vehicles x travelled_km

Where the estimate of f warm mainly depends on the speed for each category of vehicle

and each pollutant.

Defining typical journeys allows for grouping of similar speed driving situations in order

to monitor the speed’s trend during the journey. In accordance with CORINAIR

standards, three journey types have been used:

- motorway: characterised by high driving speeds and relatively uninterrupted traffic

flow;

- urban: characterised by low speeds and frequent stops and goes;

- suburban: fits somewhere between the above two situations and is often found on

main and trunk roads.

The equation thus becomes (per unit of time and per type of pollutant):

(E warm ) urban = (f warm ) urban x I warm

(E warm ) suburban = (f warm ) urban x I warm

(E warm ) motorway = (f warm ) urban x I warm

215

PROMETEIA

with (f warm ) urban, suburban, motorway inferable for each vehicle category at the established

speed per type of journey.

COLD ENGINE EMISSIONS Cold engine emissions occur during the engine and catalytic converters warm-up

phase81 and are measured in addition to the emissions that would be obtained if

engines were always at their warmest temperature. The COPERT method considers a

unit time relationship of the following kind:

E cold = B – (A x E warm )

where B represents the actual emissions produced until the engine reaches its warmest

temperature and A is a multiplying factor that avoids duplication of the warm engine

emissions that take place during the warm-up phase. A may be expressed as a fraction

of the kilometres travelled before the engine reaches peak temperature

I cold = I warm x (km_travelled_with_cold_engine / km_travelled) = I warm x A

From which we obtain the following final equation:

E cold = f cold x I cold – (A x E warm ) = A x ( f cold x I cold - E warm) = A x I warm x ( f cold - f warm)

For the percentage of kilometres travelled whilst the engine is cold, COPERT considers

an «A factor» as the average kilometre value of the journey type and environmental

temperature. We have no national data for this kilometre value in Italy and we have

thus assumed an average value of 12 km82; we further assume that cold engine

emissions occur in urban places, since most journeys start in urban places and the

engine warm-up phase is often completed before the urban road is left.

81 Start-up phase is understood as the time interval between the engine’s ignition and the maximum engine temperature. 82 European studies have placed this value between an interval of 5-15 km; the value of 12km is the one recommended for Italy in CORINAIR 90.

216

PROMETEIA

EMISSIONS FROM EVAPORATION

These only concern volatile organic compounds and are added to emissions produced

by internal combustion. These emissions are only estimated for cars fuelled by petrol –

diesel is less volatile and emissions from evaporation are insignificant. An

approximation of emissions from evaporation is also calculated for light commercial

vehicles and motorcycles.

Emissions from evaporation are obtained by summing the following

components:

- diurnal emissions: caused by fuel evaporation due to temperature changes;

- hot and warm soak emissions: produced by the latent heat of the engine (at the

time it is turned off) which in turn determines the evaporation of fuel deposited in

the fuel system;

- running losses: deriving from normal evaporation losses while the vehicle is in

transit

All these components will vary according to external temperature, fuel volatility and

vehicle characteristics; for hot and warm soak emissions and running losses the road

type and driving style are also important.

ESTIMATES AND FORECASTED POLLUTING EMISSIONS IN 2004 AND 2020

The estimates for polluting emissions are obtained on the basis of Prometeia

information on the existing fleet broken-down by vehicle type (passenger

automobiles/cars, light commercial vehicles, buses and heavy commercial vehicles,

mopeds and motorbikes), type of fuel (petrol, diesel, liquefied petroleum gas), age

class, with reference to European rules on the introduction of emission reducing

systems, engine size class (for automobiles/cars) or gross weight (for commercial

vehicles). Each class of vehicles, broken-down on the basis of the above criteria, is

then associated to other information relating to driving conditions such as average

annual kilometres travelled and average speed, broken-down according to the driving

cycle or type of journey (urban, suburban, motorway).

Each pollutant is associated emission and consumption estimation functions according

to travelling speed. These functions represent average emission and consumption

curves obtained from experimental measurements for different vehicle types and

217

PROMETEIA

brands and refer to tests carried out in different European countries on a number of

different urban and suburban driving circuits.

Tables 18-20 show estimated polluting emissions in 2004 and 2020 (low and high case

scenarios), on the basis of assumptions and parameters shown in Appendix A and

broken-down for three different driving circuits (urban, suburban and motorway). In

these tables we may also observe trends for other greenhouse gases a part from

carbon dioxide, namely nitrous oxide (N2O), methane (CH4), and ammonia (NH3).

These gases are undesired by-products of the catalytic converter systems introduced in

the last years to help reduce the main polluting emissions.

Table 18 – Total polluting emissions (tons) per driving cycle in Italy in 2004

CO NOx NMVOC CH4

Urban 1,416,510 167,207 261,306 18,858 Suburban 351,353 162,830 65,778 4,311 Motorway 221,795 183,463 25,551 2,032 TOTAL 1,989,658 513,501 352,635 25,201

PM N2O NH3 CO2

Urban 11,801 6,399 5,539 48,628,516 Suburban 8,898 3,847 7,879 36,364,856 Motorway 11,848 2,348 2,295 34,570,247 TOTAL 32,547 12,594 15,713 119,563,619 ______________________________________________________________________ Legend: CO = carbon monoxide; NOx= nitrogen oxides; NMVOC = non methane volatile organic compounds; CH4=Methane; PM=Fine dust; N2O = Nitrous oxide; NH3 = Ammonia; CO2 = Carbon dioxide.

Table 19 – Low case scenario to 2020: total polluting emissions (tons) per driving cycle.

CO NOx NMVOC CH4

Urban 297,186 27,947 87,064 5,598 Suburban 131,883 33,848 15,158 3,846 Motorway 73,022 43,441 9,067 1,191 TOTAL 502,09 1 105,236 111,288 10,635

PM N2O NH3 CO2

Urban 4,460 815 535 42,824,348 Suburban 966 995 844 38,053,782 Motorway 1,044 1,178 324 35,788,829 TOTAL 6,471 2,988 1,703 116,666,959 ______________________________________________________________________ Legend: same as Table 18.

218

PROMETEIA

Table 20 - High case scenario to 2020: total polluting emissions (tons) per driving cycle.

CO NOx NMVOC CH4

Urban 348,983 31,679 96,033 5,915 Suburban 139,595 36,747 17,120 4,010 Motorway 82,405 60,694 11,548 1,400 TOTAL 570,983 129,121 124,701 11,325 PM N2O NH3 CO2

Urban 6,167 1,025 681 41,812,691 Suburban 1,538 1,238 1,081 36,267,254 Motorway 1,489 1,859 450 38,587,004 TOTAL 9,194 4,123 2,213 116,666,949 ______________________________________________________________________ Legend: same as Table 18.

2.2.4. DESCRIPTION OF THE CALCULATION OF EMISSION EXTERNALITIES: AIR

POLLUTION AND GREENHOUSE EFFECT PER OTHER MODES OF TRANSPORT

For non-road modes of transport, the valuation of externalities is not carried out by

multiplying estimated emissions by the unit cost per ton of polluting agent. Rarely in

available literature do we come across direct estimates of emissions for non-road

modes of transport; what is normally carried out is a monetary valuation of the

polluting effect as a function of pkm and tkm, by using the various heterogeneous

techniques addressed in Part One (paragraph 3.2) of this report. By multiplying the

average valuations (obtained from literature surveys) of the cost per unit of traffic by

the traffic generated or expected we may obtain the total valuation of social cost. This

latter approach is the one used in this work. Accordingly, if from available literature we

estimate that the emission cost of pollutant x is 1 euro per pkm and expected traffic is

10 million pkm, then the social cost of polluting agent x will be 1*10=10 million euro.

2.3. NOISE POLLUTION PER MODE OF TRANSPORT

For noise pollution the same procedure to the one described in paragraph 2.2.4 is

followed for all modes of transport. Noise impact calculations for all modes of transport

have been calculated starting from the unit values of freight and passenger traffic,

according to unit values of monetisation of the external cost of the damage expressed

in Euro2004/tkm and Euro2004/pkm, respectively, and inferred from available literature

219

PROMETEIA

(please refer to Part One, paragraph 3.4). These values have been applied to traffic

volumes to obtain total estimates.

2.4. FROM EXTERNALITIES TO SOCIAL COSTS

The valuation of external transport costs presupposes the application of an impact

reconstruction model and the administration of surveys during the economic valuation

phase to assess the willingness to pay to avoid the various types of impact. However,

when we find ourselves assessing the cost of these surveys, for example, for the

willingness to pay in this specific situation, we invariably end up having to make use of

unit damage values transferred from studies found in available literature. In order to

make the transfer method reliable we have made use of a vast number of case studies

and then identified the best unit monetary values published in European and North

American papers.

The approach that has been used to calculate the economic values to be associated to

externality costs imputable to different modes of transport will, as far as possible, be

based on the average values.

2.4.1. ASSESSING THE STATISTICAL LIFE AND SOCIAL COSTS OF THE

CONSEQUENCES OF ROAD ACCIDENTS ON PEOPLE

Based on a survey of available literature (refer to paragraph 3.3 in Part One) for the

value of statistical life (VOSL), not only for the VOSL associated to a fatal accident but

also the VOSL for a serious or slight injury, we obtain the average, minimum and

maximum values shown in the table hereunder.

220

PROMETEIA

Table 21 - Average VOSL values per fatality, seriously injured and slightly

injured (millions of euro - 2004)

Statistical Life Min Average Max

VOSL 0.367 2.115 8.673

VOSL per serious injury 0.040 0.216 0.406

VOSL per slight injury 0.016 0.020 0.040

In order to simplify the estimate, a single VOSL value per injury was assumed. This

was obtained as the weighted average of serious (25%) and slight (75%) injuries. The

average VOSL value per injury is therefore assumed to be 0.08 million euro (2004).

2.4.2. ASSESSING THE SOCIAL COST OF AIR POLLUTION AND THE GREENHOUSE

EFFECT PER MODE OF TRANSPORT

The unitary values, per ton of pollutant emitted, of the social cost of the (local) air

pollution per modes of road transport are inferred from the ExternE study. These

values are broken down between urban and suburban traffic, with respect to which the

suburban and motorway traffic are combined.

Table 22 – Average external costs of polluting emissions per modes of road transport (euro 2004 per ton)

Urban Suburban Polluting emissionsMin Max Min Max

CO (t) 0.03 0.04 0.01 0.01

Nox (t) 27.76 36.44 28.20 41.46

NMVOC (t) 5.12 6.48 3.03 3.46

PM (t) 1.177.65 1.202.74 740.13 1.024.20

Source: ExternE (1997)

For rail and air transport the values inferred from cases in available literature for pkm

and tkm and for minimum and maximum intervals are used (Table 23) (refer to Part

One, paragraph 3.2, table 5).

221

PROMETEIA

Table 23 – Average external costs of polluting emissions per mode of transport (rail and air) (Euro 2004)

Rail mode Air mode Local Air Pollution Min Max Min Max

Passengers (1000pkm) 2.3 28.1 0.22 2.3

Freight (1000 tkm) 1.17 8.0 0.93 2.0

Source: survey of available literature

Values for ship transport are broken down by type of pollution and unit of freight and

passenger traffic actualised at 2004 values (Table 24).

Among all external cost categories, those related to the so called greenhouse effect are

without doubt the most difficult to define, forecast and (even more so) estimate

economically. Table 25 shows the minimum and maximum intervals per ton of CO2

referred to road transport, whilst for the remaining transport modes Table 26 shows

minimum and maximum tkm and pkm.

Table 24 – Average external costs of polluting emissions: ship transport (euro 2004)

Local Air pollution Passengers (1000 pkm)

Freight (1000 tkm)

CO 0 0.0001

NOx 52.3 2.1

NMVOC 0.5 0.0185

PM 11.2 0.3138


Tab. 25 - Average cost of the greenhouse effect: Road transport (euro 2004)

Polluting emission Min Max

Greenhouse effect - CO2 (t) 115.48 178.78

Source: ExternE (1997)

222

PROMETEIA

Tab. 26 – Average costs of the greenhouse effect per mode of transport (rail, air and ship) (euro 2004)

Rail Air Ship Unit of transport Min Max Min Max Average

values Passengers (1000pkm) 0.30 7.73 7 76 26.0

Freight (1000tkm) 0.43 5.77 37 256 1.1


2.4.3. ASSESSING THE SOCIAL COST OF NOISE POLLUTION PER MODE OF

TRANSPORT

For the external costs of noise reference was made to the values suggested in the

recent Infras-IWW (2004) work. These values are expressed in units of freight and

passenger traffic at 2004 prices (Table 27).

From the table we may see how the average cost of noise pollution generated by

automobiles/cars is among the highest, after air transport of course (particularly air

freight). Bus, air passenger transport and motorcycles generally show lower values.

Table 27 - Average external costs of noise pollution

Noise pollution Average values

(euro 2004)

Automobile/car (1000 pkm) 5.2

Bus (1000 pkm) 1.3

Motorcycle (1000 pkm) 1.6

Light vehicles (1000 tkm) 32.0

Heavy vehicles (1000 tkm) 4.9

Rail- passengers (1000 pkm) 3.9

Rail- freight (1000 pkm) 3.2

Air – passengers (1000 pkm) 1.8

Air freight (1000 tkm) 8.9


2.4.4. CAPITALISATION OF SOCIAL COSTS TO 2020: ASSUMPTIONS

In order to compare 2004 valuations to the forecasts of the two growth scenarios to

2020, we need to standardise monetary values in the two periods. In this respect, all

223

PROMETEIA

2004 monetary values listed in the previous paragraphs (VOSL and costs, whether they

are a function of emissions or unit of traffic directly) have been capitalised to 2020 by

applying the GDP growth rate of the two scenarios. This reflects the need to adjust the

value of human life and the cost components that may somehow reduce it (e.g. air

pollution) to the increased value of the same life in the future, expressed as the

expected ability to generate greater income (captured by the expected GDP growth),

even though this may not necessarily be the most consistent part included in the VOSL

estimate by means of stated preferences (which is the main method used in literature

from which the value used in this work is inferred).

The monetary values at constant prices of all cost components are therefore different

in the two scenarios; in the low case scenario to 2020 the monetary values are 1.08

times those of 2004 while in the high case scenario they are 1.37 times those of 2004.

These growth parameters from 2005 to 2020 of 8% and 37% are net of purely

monetary phenomena (the real GDP change is used). As we have noted, applying real

rates of change enables immediate comparability of the results in the two scenarios

(low and high growth cases) and in each of these two scenarios between time interval

extremes (2004 and 2020).

2.5. CURRENT AND FORECASTED VALUES OF SOCIAL COSTS PER

MODE OF TRANSPORT AND TYPE OF EXTERNALITY IN THE TWO

SCENARIOS OF GROWTHS OF THE ECONOMY AND THE DEMAND

FOR MOBILITY

Having evaluated social costs in monetary units per type of externality and made a

number of simplifying assumptions in order to project the trend of these costs, it may

be possible to summarise overall social costs and social costs per mode of transport.

We must remember that accidents include the social costs relating to damage to

persons, since damage to property is practically entirely privately internalised or

covered by insurance.

Among the components of social costs we also find items relating to maintenance (of

infrastructure) and subsidies. These two items are specifically addressed in the

224

PROMETEIA

following Chapter 3, but for reasons of presentation and calculation completeness

these items are also summarised in the social costs tables in this paragraph.

2.5.1. SOCIAL COSTS IN THE TWO SCENARIOS OF GROWTH OF THE ECONOMY AND

MOBILITY

Table 28 summarises the social costs generated by the model from 2004 to 2020 (high

and low case scenarios). It is worth recalling that social costs, also for the part that

relate to externalities, may be internalised in full or in part – as shall be discussed in

Chapter 3.

Transport externality costs in Italy for 2004 are estimated at 107.1 billion euro, equal

to 7.7% of GDP for the same year and at the same prices (obviously 2004 prices,

which are the basis for the forecasted horizon costs). The most significant social costs

are generated by road transport, which contributes more than 93% of the total cost of

transport. Among the various types of externalities, costs associated to road accidents

and air pollution are the highest (39.6 and 28.6 billion euro, respectively), followed by

the greenhouse effect (21.5 billion euro): overall, the three cost types associated to

externalities represent more than 83% of the social costs of transport.

Estimated social costs in the long-term, to 2020, were obtained by assuming that the

values of the cost components imputable to different external effects grow at the same

rate of GDP (for ease of reference: two scenarios have been defined, one with an

average annual GDP growth of 0.5% (low case) and one with an average annual GDP

growth of 2.1% (high case)).

In the low case scenario social costs are equal to 87.8 billion euro and thus are lower

than those of 2004. This difference is attributable to technology improvements in the

vehicle fleet which favour a reduction in polluting emissions despite traffic increase.

This phenomenon is already historically verifiable and is a shared opinion of all

empirical research on the subject.

In the high case scenario, the technological improvement – which reduces pollution per

unit of traffic – is not however able to offset the effects of increased traffic and the

cost components whose value increases at the same level of real income. Total

external costs in this scenario amount to 131.8 billion euro.

The allocation of social costs to passenger and freight traffic allows us to make a

number of additional observations (Table 29, to be read together with Table 30, which

225

PROMETEIA

summarises forecasts for mobility and breaks down the same between passenger and

freight modal traffic)

Table 28 – Social costs of transport – summary of monetary valuations of the externalities (billions of euros - 2004)

ON Motorway Train Ship Air Total2004

Accidents 37,1 2,5 0,0 0,0 0,0 39,6Air Pollution 17,9 8,7 0,9 1,0 0,0 28,6Greenhouse Effect 13,8 5,6 0,3 0,4 1,3 21,5Noise 4,9 1,3 0,3 0,1 6,6Infrastructure maintenance 4,9 4,9Subsidies 2,9 3,0 5,9Total 81,6 18,2 4,5 1,4 1,4 107,1

Low Case Scenario - 2020Accidents 36,6 2,6 0,0 0,0 0,0 39,2Air Pollution 4,9 1,4 0,9 1,1 0,1 8,4Greenhouse Effect 14,2 6,3 0,3 0,5 1,8 23,0Noise 6,0 1,6 0,3 0,1 8,0Infrastructure maintenance 4,9 4,9Subsidies 2,1 2,2 4,3Total 68,7 11,9 3,7 1,7 1,9 87,8

High Case Scenario - 2020Accidents 59,6 4,0 0,0 0,0 0,0 63,7Air Pollution 8,2 2,4 1,2 1,9 0,1 13,8Greenhouse Effect 17,4 8,6 0,4 0,8 4,1 31,2Noise 10,0 3,4 0,4 0,1 13,9Infrastructure maintenance 4,9 4,9Subsidies 2,1 2,2 4,3Total 102,2 18,4 4,2 2,7 4,3 131,8 Note: the accidents line item refers to the cost of human injury

The share of externalities allocated to passenger transport is greater than 68% in 2004

and tends to reach 75% in both future scenarios. The reason for this may be found in

the dynamics of ordinary road network accidents: whilst in both scenarios pollution

costs tend to reduce drastically for all modes of transport, accidents on the ordinary

road network don’t decrease much, and at times actually increase, thus once again

increasing the externality share due to passenger traffic - today responsible for most of

the costs of general and fatal accidents.

226

PROMETEIA

Table 29 – Social costs of transport – summary of the monetary valuations of externalities broken-down between passenger and freight mobility (billions of euro - 2004)

PASSENGERS ON Motorway Rail Ship Air Total2004

Accidents 35,8 1,9 0,0 0,0 0,0 37,7Air Pollution 9,3 1,5 0,8 0,4 0,0 12,0Greenhouse Effect 11,0 1,5 0,2 0,2 1,2 14,0Noise 4,2 0,6 0,2 0,0 0,1 5,1Infrastructure Maintenance 0,0 0,0Subsidies 2,9 1,5 4,4Total 63,2 5,5 2,7 0,6 1,3 73,3

Low Case Scenario - 2020Accidents 35,3 2,0 0,0 0,0 0,0 37,4Air Pollution 3,0 0,2 0,8 0,5 0,1 4,5Greenhouse Effect 11,0 1,8 0,2 0,2 1,7 14,9Noise 5,2 0,8 0,2 0,0 0,1 6,2Infrastructure Maintenance 0,0 0,0Subsidies 2,1 1,1 3,2Total 56,6 4,7 2,3 0,7 1,8 66,2

High Case Scenario - 2020Accidents 57,6 3,0 0,0 0,0 0,0 60,7Air Pollution 5,1 0,2 1,0 0,9 0,1 7,3Greenhouse Effect 14,0 2,3 0,3 0,4 3,8 20,8Noise 8,7 1,4 0,3 0,0 0,1 10,4Infrastructure Maintenance 0,0 0,0Subsidies 2,1 1,1 3,2Total 87,5 6,9 2,6 1,3 4,0 102,3

FREIGHT ON Motorway Rail Ship Air Total2004

Accidents 1,3 0,6 0,0 0,0 0,0 1,9Air Pollution 8,6 7,2 0,1 0,6 0,0 16,6Greenhouse Effect 2,9 4,1 0,1 0,3 0,1 7,4Noise 0,7 0,7 0,1 0,0 0,0 1,5Infrastructure Maintenance 4,9 4,9Subsidies 0,0 1,5 1,5Total 18,4 12,7 1,8 0,8 0,1 33,8

Low Case Scenario - 2020Accidents 1,3 0,6 0,0 0,0 0,0 1,9Air Pollution 1,9 1,2 0,1 0,7 0,0 3,9Greenhouse Effect 3,2 4,5 0,1 0,3 0,1 8,2Noise 0,8 0,9 0,1 0,0 0,0 1,8Infrastructure Maintenance 4,9 4,9Subsidies 1,1 1,1Total 12,1 7,1 1,4 1,0 0,1 21,7

High Case Scenario - 2020Accidents 2,0 0,9 0,0 0,0 0,0 3,0Air Pollution 3,1 2,3 0,2 1,0 0,0 6,5Greenhouse Effect 3,4 6,3 0,1 0,4 0,2 10,4Noise 1,4 2,0 0,1 0,0 0,0 3,5Infrastructure Maintenance 4,9 4,9Subsidies 1,1 1,1Total 14,7 11,5 1,5 1,4 0,2 29,4 Note: the accidents line item refers to the cost of human injury

227

PROMETEIA

This forecast is another warning signal in terms of the ordinary road network’s future

ability to even maintain its current (poor) service level. The result of this hypothesis is

further emphasised by the very low possibility for modal diversification available to

Italy based on the current composition of its infrastructure. In fact, the motorway

network is today condemned to support growing traffic on a local and sub-regional

basis in order to make up for the ordinary network’s poor capacity (for example, for

freight alone, in the high case scenario to 2020, the motorway would increase from

25% to 33% of total traffic across the Italian territory).

Table 30 - Summary of the modal distribution of passenger and freight mobility in the two forecasted scenarios (% share and cumulative & change)

2004 YEAR 2020 ∆ % cumulative YEAR 2020 ∆ % cumulativePASSENGERS LOW SCENARIO on 2004 HIGH SCENARIO on 2004ORDINARY NETWORK 69,7 69,6 9,9 71,1 52,0MOTORWAY NETWORK 10,7 11,2 15,2 11,9 65,4TOTAL ROAD 80,4 80,9 10,6 83,0 53,7OTHER 19,6 19,1 7,4 17,0 29,2TOTAL 100,0 100,0 10,0 100,0 48,9

2004 YEAR 2020 ∆ % cumulative YEAR 2020 ∆ % cumulativeFREIGHT LOW SCENARIO on 2004 HIGH SCENARIO on 2004ORDINARY NETWORK 18,9 18,0 1,6 16,3 19,6MOTORWAY NETWORK 24,7 25,2 8,6 32,6 83,0TOTAL ROAD 43,6 43,3 5,6 48,8 55,6OTHER 56,4 56,7 6,8 51,2 25,9TOTAL 100,0 100,0 6,3 100,0 38,8

Accordingly, improving the safety of the ordinary road network would appear to be the

priority resulting from these calculations. A similar conclusion is commonly heard today

by experts and institutional representatives and the fact that we have reached this

same conclusion not only through traffic assessments, but indirectly too, through

estimates of social and external costs, should underline the importance and urgency of

the political action that should follow. We believe careful thought should be given to

the possibility of introducing toll systems on the ordinary road network.

228

PROMETEIA

Table 31 - % relationships between share of social costs and share of traffic – passenger and freight mobility in the two forecast scenarios.

2004 YEAR 2020 YEAR 2020

PASSENGERS LOW CASE SCENARIO HIGH CASE SCENARIO



TOTAL ROAD 116,2 114,3 110,9

OTHER 33,6 39,4 46,8

TOTAL 100,0 100,0 100,0

FREIGHT



TOTAL ROAD 209,7 203,0 181,4

OTHER 15,3 21,4 22,3

TOTAL 100,0 100,0 100,0

Table 32 – Comparison between different studies on the external costs of transport in Italy (excluding costs deriving from congestion) Comparison between original values

Road Rail Air Ship Total

FS-Confitarma (1997) 85,8 3,0 2,1 4,7 95,6

Infras-Iww (1997) 73,5 1,3 2,3 77,2

Infras-Iww (2000) 86,5 1,5 7,1 95,2

Federtrasporto

High estimate (2000) 98,4 0,9 2,3 2,0 103,6

Low estimate (2000) 61,7 0,6 1,2 1,1 64,5

Amici della Terra (1999) 98,4 0,9 2,3 2,0 103,6

ACI - ANFIA (2000) 30,5

Prometeia (2004) 99,8 4,5 1,4 1,4 107,1

Comparison between updated values (to 2004 with the nominal GDP change)


FS-Confitarma (1997) 113,5 4,0 2,8 6,2 126,4

Infras-Iww (1997) 97,2 1,7 3,0 101,9

Infras-Iww (2000) 100,7 1,7 8,3 110,7

Federtrasporto

High estimate (2000) 114,5 1,0 2,7 2,3 120,6

Low estimate (2000) 71,8 0,7 1,4 1,3 75,2

Amici della Terra (1999) 121,3 1,1 2,8 2,5 127,7

ACI - ANFIA (2000) 35,5

Prometeia (2004) 99,8 4,5 1,4 1,4 107,1

note - GDP nominal cumulative changes: 1997-2005=32.2%, 1999-2004=23.2%, 2000-2004=16.4

229

PROMETEIA

In terms of the social costs of different forms of road transport, it is worth noting that

in the scenario of high economic growth passenger air traffic will play an important role

in the determination of social costs, with its size increasing fourfold compared to the

base year in the high case scenario.

The social costs deriving from forms of transport other than road transport are rather

low. The greenhouse effect and noise are the sources of increasing social costs for all

modes of transport and in both scenarios and thus warrant suitable addressing: unlike

air pollution, there does not currently appear to be any explicit movement towards the

containment of these forms of externality.

The same observations and current and forecasted critical situations may be seen in

Table 31 where the relationships between shares of social costs and shares of traffic

are shown (these relationships do not have a unit of measurement but rather indicate

the extent of social costs vis-à-vis different modes of transport, broken down between

passenger and freight). Ratios higher than 100 mean that in a given moment and for a

given mode of transport the proportion of social costs is greater than the proportion of

mobility that is satisfied. For the year of reference it appears clear how the ordinary

road network accounts for the largest share of social costs for passenger transport

whilst the motorway appears to be characterised by a strong negative accentuation (in

proportion it satisfies mobility at a low cost). The social costs of other modes of

transport are, in proportion, destined to increase, but in both scenarios maintain a

negative accentuation, meaning that the share of social costs is significantly lower than

the share of traffic that is satisfied. However, the meaning of such a conclusion is set-

back a little by the fact that for passenger mobility, which generates the largest part of

vehicle kilometres travelled, all modes of transport other than road transport put

together account today for only 20% of the traffic. Would the proportion of social

costs, including or excluding congestion, remain the same if a modal re-equilibrium

were to occur? We do not have an answer. This observation is useful to reiterate how

social costs per mode of transport are to be appreciated by taking into account how

much mobility is satisfied: and, for good or for bad, the road supports the greatest

burden of this service.

In terms of freight, our calculations claim that the ordinary road network produces

three times as many social costs compared to the mobility that it satisfies and this

phenomenon may continue to live on in the future. Conversely, and in both forecasted

230

PROMETEIA

scenarios, the motorway network tends to reduce its proportion of costs compared to

the traffic it supports.

Lastly, in Table 32 we summarise the quantitative results of this study compared to

other similar research. The comparisons are presented in terms of original data (the

year of the calculations is shown, naturally at the prices of that year) and in terms of

standardised data that has been revaluated at 2004 prices (and incomes). Values that

have been capitalised at the year of reference of this research are similar. This result

has been obtained by using similar values for average and marginal social costs of the

externalities based on surveys of available literature, and especially by the use of

simplified assumptions for future mobility trends. In the different studies, assumptions

of significant improvement to the vehicle fleet have almost always been made (with a

consequential reduction in the social cost of road pollution) together with an

assumption of higher road mobility growth compared to rail traffic but lower road

mobility growth compared to air traffic. It is important to underline that even the most

recent and accredited European studies have long abandoned the chimera of rapid

modal re-equilibrium, finally turning to more realistic hypothesis.

2.5.2. COSTS OF CONGESTION83

The ASTRA-Italia estimate for 2005 congestion costs is shown in detail in Table 33.

The total value is 3,650 Million Euro, almost half of which is attributed to urban cars.

Table 33 – Estimated costs of congestion estimate for 2005 (millions of

Euro)

Mode Urban Suburban Total Automobile/car 2,010 1,120 3,130 Freight vehicles 615 335 950 Total 2,625 1,455 4,080 Source: ASTRA-Italia model

On the basis of an approximate calculation of traffic breakdown and road size (main

road) between the urban and suburban environment across three geographical areas

(North, Centre and South & Isles), congestions were evaluated per main geographical

areas (Tab. 34).

83 An estimate of congestion costs has been carried out using the ASTRA-Italia model, developed by TRT Trasporti e Territorio. The model produces estimates for the external costs of transport, among which congestion costs. The costs are estimated by associating marginal unit costs inferred from available literature to the traffic volumes calculated by the model.

231

PROMETEIA

As we explained in Part One, congestion is considered to be an externality that only

affects mobility users of a specific network (club) and is not summed to other types of

transport externality. For this same reason, the results of the Astra-Italia model have

not been extrapolated in the projected scenarios to 2020.

Table 34 – 2005 costs of congestion for light and heavy vehicles and geographical area (millions of Euro)

Urban Suburban Total

Area Cars Freight Total Cars Freight Total Cars Freight Total

North 1,000 400 1,400 500 200 700 1,600 700 2,300

Centre 400 100 500 200 100 300 600 200 800

South 500 100 600 400 20 420 900 100 1,000

Source: ASTRA-Italia model

For the sake of completeness, and only for 2004, we provide hereunder a breakdown

of social costs including congestion costs, per mode of transport, deflated at the

nominal GDP rate for the period 2004-2005 (2%): road transport generates 93.4% of

all transport social costs, railway 4% and air and ship transport 1.4% each. It should

be noted therefore, that compared to the data shown in Table 28, only 4 billion euros

of congestion costs have been added to road transport. No adjustment has been made

to the other modes of transport since the data are not reliable. However, as noted in

paragraph 3.1.6. in Part One, for airplanes, ships, trains and buses (which would

however fall under road transport) excess travel time compared to official timetables

may be considered to be congestion time. Accordingly, the aforesaid percentages do

not improve, but rather reduce the reliability of the social costs allocation per mode of

transport, compared to the allocation that excludes congestion costs.

232

PROMETEIA

2.6. A MONTE CARLO METHOD SIMULATION EXERCISE TO

ASSESS THE CONFIDENCE INTERVALS OF THE ESTIMATED

SOCIAL COSTS OF TRANSPORT IN 2020

The aim of the sensitivity test is to assess the validity of the valuation systems that

were used vis-à-vis different assumptions and different value judgements. The tests

are used to highlight if and how the assumptions and values that were used affect the

final estimates. In operational terms, the analysis is carried out by determining the

extreme values (best and worst values) of certain variables of the valuation model.

The sensitivity analysis, and projecting the possible forecast scenarios, is not a

particularly difficult exercise: the same model used to produce the scenarios (low and

high) to 2020 has been used to produce other possible scenarios. What changes is the

input values (vehicle traffic, externality shadow prices, etc.), whilst the calculation

algorithms remain unchanged. Changes are usually made (for example percentage

changes), negative and/or positive, to certain input variables of the valuation model. If

the variables of the valuation model are only a few, then it may be possible, by using

this technique, to verify the robustness of the valuation vis-à-vis any forecasting errors.

However, the so-called one-way sensitivity analysis does have a number of shortfalls:

- the model’s input variables are often numerous and all potentially have a determining

impact on the value estimates of social costs;

- one input variable may differ positively with respect to the expected value, whilst a

second input variable may differ negatively;

- finally, input variables may vary jointly.

As described in detail in Part One, available literature on external costs has highlighted

somewhat variability in the monetary estimates of external effects. In order to obtain a

more accurate description of the uncertainty associated with the input variables of the

valuation model, rather than rely on a simple sensitivity analysis based on a positive

and/or negative percentage change of the input variables, a Monte Carlo simulation

technique has been preferred (how many repeats?).

Monte Carlo simulations have been applied in a number of different fields, including

economics. In economics, what is being simulated using the Monte Carlo method is

the change of certain variables (defined as objective) due to our actions and/or

occurrences that may or may not be dependent on the choice of agents. For example,

233

PROMETEIA

in the corporate world, in order to carry out an economic valuation of a new product

launch it is necessary to estimate the new market demand that the product is able to

generate or, again, the financial advantage to make a property investment is strongly

correlated to the property’s selling price per square metre. In these two examples,

demand and price per square metre are variables that may take different values,

leading to different decisions. A cost-benefit valuation of the investment, followed by a

sensitivity analysis, would in any case leave the decision-maker with having to make a

difficult choice between two scenarios. With Monte Carlo simulation one has the

possibility of giving a likelihood of occurrence to the values that results may take, thus

giving the decision maker a more comprehensive and detailed decision-aiding tool.

The heart of the simulation model’s building rests in assigning appropriate probability

distribution functions to the input variables of the cost-benefit valuation model (for

example, normal, lognormal and triangular): a probability distribution shows the

possible value intervals of the input variables and the relative probabilities of

occurrence. The second step consists in establishing a correspondence between these

uncertain variables and the values whose behaviour we wish to monitor, which in our

case will be represented by the total value of transport damages for the four modes of

transport: road, rail, air and ship. Now, by using functions that generate random

numbers we are able to determine a certain number of possible results (outcome of

the output variables or total monetary damages per mode of transport), and this is

obtained as follows: the random number triggers the corresponding event, which in

turn, being tied to the output by mathematical equations, will determine the value.

This is an iterative exercise, repeated as many times as pre-established. The result will

highlight the relative frequency of output variables, i.e. the probability distribution of

our objective variables (damage per mode of transport and per externality generated).

Assigning of uncertain variables to a probability distribution leaves ample margin for

subjective judgment. Clearly, the choice of the probability distribution function’s type

and form will depend on the type of data being considered. Often, the need to rely on

shortcuts forces one to choose distributions having limited margin for customisation.

This is the case for triangular probability functions: this is an intuitive and manageable

function since it is determined by only three values: a minimum value, a maximum

value and a value indicated as being the most likely.

234

PROMETEIA

Fig. 12 depicts a triangular function with a worst case assumption taking a value of 40,

a most likely value of 100 and a most optimistic value of 150. Figure 13 shows a

breakdown or cumulative probability density function (with continuous and derivable

domain). It is obtained by the definite integral of the probability distribution function

between the lower and higher limits of integration i.e. the worst and best case values,

respectively.

Figure 12 – Example of a triangular probability distribution function (min. value of 40, most probable value of 100 and max. value of 150)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

20 40 60 80 100 120 140 160

Valo

ri x

10^-

2

235

PROMETEIA

Figure 13 – breakdown or cumulative probability distribution function

0

0,2

0,4

0,6

0,8

1

20 40 60 80 100 120 140 160

After having determined the number of iterations, the Monte Carlo process will start

calculating the breakdown function for each input variable and then for each of these

will award a series of random numbers equal to the number of chosen iterations.

Based on the breakdown function, each random number will generate a value on the

x-axis, until the procedure is completed. Each variable input value is fed into the base

case scenario and the project’s key indicator of convenience values are recalculated.

At the end of the iterative process it will be possible to define the relative frequency of

the monetary value of the damage. The result of the iteration will make an interesting

set of data available: the relative likelihoods of the various values obtained by the

Monte Carlo simulation, the degrees of skewness (positive or negative distribution

asymmetry), the value change interval and the degrees of dispersion and lastly, the

confidence intervals for the project’s indicators of convenience. We are therefore

looking at very important information which allows the decision maker to make more

comprehensive and detailed decisions.

Given the strong variability in the monetary value estimates found in available

literature (Part One), we decided that rather than provide only a point estimate of the

damage in the various scenarios, to use the Monte Carlo method to calculate the

236

PROMETEIA

confidence intervals of the monetary values of damage attributable to the four modes

of transport.

Our operational approach to calculate the confidence intervals is as follows:

* for each type of externality three different monetary values were determined

(minimum value, most probable value and maximum value) for externalities generated

by transport, from which a triangular distribution function was defined. The most

probable value coincides with the one used for the first estimate of the value of

damages derived from transport. Should surveys of available literature not provide for

minimum and maximum values a variability of ±25% is assumed with respect to the

base value assumed in the first damage estimate. The tables in Appendix B show the

different values for the different forms of externality generated by the four modes of

transport.

* the various vectors made up of the aforesaid values allow us to define the triangular

distribution function; this function measures the uncertainty associated with the

monetisation of the various externalities.

* The Monte Carlo method allows us, through a suitable number of iterations (10

thousand), to generate values for the damage attributable to each mode of transport

and according to each external effect84.

* the confidence interval calculation allows us to define a value interval (lower and

higher) that includes, with a specific probability, economic value and damage

attributable to the different modes of transport in Italy. This interval is shown for two

scenarios to 2020: low case scenario and high case scenario – relating to the different

GDP growth assumptions.

The values shown in Tables 35-36 highlight the interval within which the damage

estimate falls. Thus, for example, in the low case scenario to 2020, the total damage

has a 95% probability of falling within the interval (60.1-105.0) billion euro2004 or in the

high case scenario between 93.6-165.8 billion euro2004. 90% NO

The Monte Carlo simulation shows how externalities generated by road transport are

the most significant and represent approximately 90% of the total. Road transport on

the ordinary network generates between 70-80% of the externalities.

84 The Monte Carlo simulation was carried out with the software package @Risk version 4.5.

237

PROMETEIA

Tab. 35 – Lower and higher limits* of social cost estimates** per mode of transport in Italy (billions of euro - 2004)

Low scenario High scenario Mode of transport Lower Higher Lower Higher

Road Ordinary network 44.49 85.26 68.05 134.08 Motorway 10.22 14.70 16.10 23.05 Railway 0.98 2.11 1.31 2.72 Ship 1.55 1.93 2.48 3.00 Air 0.81 3.05 1.75 6.86 Total 60.14 104.96 93.61 165.79

* first 5% and last 5% of the frequency distribution of simulated social costs ** excluding items relating to the maintenance of infrastructure and subsidies Tab. 36 – Lower and higher limits* of social costs estimates** of transport in Italy per externality type (billions of euro - 2004)

Low scenario High scenario Category of Externality Lower Higher Lower Higher

Accidents 20.64 65.39 33.86 105.55 Air pollution 7.83 9.08 12.97 14.66 Greenhouse effect 19.34 26.75 26.08 36.34 Noise pollution 7.07 8.99 13.14 16.81 Total 60.14 104.96 93.61 165.79

* first 5% and last 5% of the frequency distribution of simulated social costs ** excluding items relating to the maintenance of infrastructure and subsidies

Finally, the component of externalities relating to accidents is the one with the greatest

impact (between 30-60% of total cost), followed by costs generated from greenhouse

gas emissions.

238

PROMETEIA

239

APPENDIX TO CHAPTER 2

A - COPERT III

Base estimate assumptions of the COPERT III model

1) Estimated emissions in 2004

COPERT III model parameters Assumptions Fuel consumption (t) source: Ministry for Productive Activities Petrol 14.842.000 Diesel 2.212.200 Lpg 1.106.000

Vehicle fleet Prometeia estimates

Average distance Prometeia estimates Temperature Copert III values Average speed km/h Prometeia estimates Urban Suburban Motorway Cars 35 60 110 Light vehicles 35 60 110 Heavy vehicles 35 60 110 Buses 35 60 110 Mopeds 35 50 Motorcycles 35 60 110 % Allocation to driving cycle Prometeia estimates Urban Suburban Motorway Automobiles/cars 44 44 12 Light vehicles 25 35 40 Heavy vehicles 10 30 60 Buses 20 40 40 Urban buses 100 Mopeds 80 20 Motorcycles 50 42 8

PROMETEIA

2) Estimated emissions in 2020

COPERT III model parameters Assumptions

Fuel consumption (t) Growth rate assumptions: to 2009 Prometeia estimates 2010-2020 Unione Petrolifera estimates

Petrol 8,795,000 Diesel 25,273,000 Lpg 2,558,000

Vehicle fleet Prometeia estimates

Average distance Prometeia estimates Temperature Copert III default values Average speed km/h Prometeia estimates Urban Suburban Motorway Cars 35 60 110 Light vehicles 35 60 110 Heavy vehicles 35 60 110 Buses 35 60 110 Mopeds 35 50 Motorcycles 35 60 110 % Allocation to driving cycle Prometeia estimates LOW CASE SCENARIO TO 2020 Urban Suburban Motorway Automobiles/cars 40 46 14 Light vehicles 23 38 39 Heavy vehicles 8 33 59 Buses 20 40 40 Urban buses 100 0 0 Mopeds 80 20 0 Motorcycles 50 42 8 HIGH CASE SCENARIO TO 2020 Urban Suburban Motorway Automobiles/cars 40 46 14 Light vehicles 20 33 47 Heavy vehicles 5 28 67 Buses 20 40 40 Urban buses 100 0 0 Mopeds 80 20 0 Motorcycles 50 42 8

240

PROMETEI

B - C

A

241

OSTS PER UNIT OF EXTERNALITY: MINIMUM, MOST PROBABLE AND MAXIMUM VALUES(*) IN 2020 (EURO AT 2004 PRICES)

Low case scenario ACCIDENTS High case scenario Millions € Min Most prob. Max Millions € Min Most prob. Max

VOSL per fatality 0.40 2.28 9.34 VOSL per fatality 0.50 2.89 11.84

VOSL per injury 0.02 0.07 0.14 VOSL per injury 0.03 0.09 0.18

AIR POLLUTION

Road Low case scenario High case scenario

Ordinary Network Motorway Ordinary Network Motorway Min Most prob. Max Min Most prob. Max Min Most prob. Max Min Most prob. Max

CO (t) 14 19 24 3 3 4 18 24 30 4 5 5 Nox (t) 15443 17859 20274 15692 19379 23065 19572 22634 25695 19887 24559 29231 NMVOC (t) 2846 3224 3602 1685 1806 1926 3607 4087 4565 2136 2289 2440 PM (t) 655217 662196 669175 411789 490817 569843 830387 839232 848076 521880 622035 722189 Rail

Low case scenario High case scenario Min Most prob. Max Min Most prob. Max

Passengers (1000pkm) 2.67 16.37 32.61 Passengers (1000pkm) 3.14 20.75 38.37

Freight (1000 Tkm) 1.36 4.94 9.28 Freight (1000 tkm) 1.60 6.26 10.92

Air Low case scenario High case scenario

Min Most prob. Max Min Most prob. Max

Passengers (1000pkm) 0.24 1.36 2.48 Passengers (1000pkm) 0.30 1.72 3.14

Freight (1000 Tkm) 1.00 1.58 2.15 Freight (1000 Tkm) 1.27 2.00 2.73

Ship Low case scenario High case scenario

Passenger (1000 pkm) Freight (1000 tkm) Passenger (1000 pkm) Freight (1000 tkm)

Min (-25%) Most prob. max (+25%) Min (-25%) Most prob. max

(+25%) Min (-25%) Most prob. max (+25%) Min (-25%) Most prob. max

(+25%) Nox 42.26 56.34 70.43 1.73 2.31 2.88 53.55 71.41 89.26 2.19 2.92 3.65

NMVOC 0.40 0.54 0.67 0.01 0.02 0.02 0.51 0.68 0.85 0.02 0.03 0.03

PM 9.05 12.07 15.08 0.25 0.34 0.42 11.47 15.29 19.11 0.32 0.43 0.54

A

242

Low case scenario GREENHOUSE EFFECT High case scenario

n Most prob. Max Min Most prob. Max

2 137.72 175.47 213.22 CO2 174.54 222.38 270.22

Rail Min Most prob. Max Min Most prob. Max

Passengers (1000pkm) 0.32 4.33 8.33 Passenger (1000pkm) 0.41 5.48 10.55


Min Most prob. Max Min Most prob. Max

Passengers (1000pkm) 7.54 44.71 81.87 Passenger (1000pkm) 9.56 56.66 103.76


Ship Min (-25%) Most prob. max (+25%) Min (-25%) Most prob. max (+25%)

PROMETEI

Road Mi

CO

Passenger (1000pkm) 21.01 28.01 35.01 Passenger (1000pkm) 26.62 35.50 44.37

Freight (1000 ton) 0.89 1.19 1.49 Freight (1000 ton) 1.13 1.51 1.89

Low case scenario NOISE POLLUTION High case scenario € Min (-25%) Most prob. max (+25%) € Min (-25%) Most prob. max (+25%)

Automobile/car (1000 pkm) 4.20 5.60 7.00 Automobile/car (1000 pkm) 5.32 7.10 8.87

Bus (1000 pkm) 1.05 1.40 1.75 Bus (1000 pkm) 1.33 1.77 2.22

Motorcycle (1000 pkm) 12.93 17.24 21.55 Motorcycle (1000 pkm) 16.38 21.84 27.31

Light vehicles (1000 tkm) 25.86 34.47 43.09 Light vehicles (1000 tkm) 32.77 43.69 54.61

Heavy vehicles (1000 tkm) 3.96 5.28 6.60 Heavy vehicles (1000 Tkm) 5.02 6.69 8.36

Rail-passengers (1000 pkm) 3.15 4.20 5.25 Rail- passengers (1000 pkm) 3.99 5.32 6.66

Rail-freight (1000 pkm) 2.59 3.45 4.31 Rail- freight (1000 pkm) 3.28 4.37 5.46

Air-passengers (1000 pkm) 1.45 1.94 2.42 Air – passengers (1000 pkm) 1.84 2.46 3.07

Air freight (1000 tkm) 7.19 9.59 11.98 Air freight (1000 tkm) 9.11 12.15 15.19

(*) social costs of pollution for rail and air modes are presented in an aggregate form (all types of emission) and per unit of traffic (rather than unit of externality).

PROMETEIA C – SIMULATED FREQUENCY DISTRIBUTION

Simulated frequency distribution values of social cost per mode of transport and type

of externality are shown he graphs provide important information: relative

probabilities (towards which the relative simulated frequencies tend) of the various

values obtained from the Monte Carlo simulation occurring, degrees of deviation from

symmetry (positive skew or negative skew distributions), the interval of variation of the

values and degrees of dispersion. The values are expressed in millions of euro at 2004

prices. The scenarios that are considered here are the ones to 2020 with low GDP (low

case scenario) and high GDP (high case scenario) growth assumptions.

LOW CASE SCENARIO TO 2020 - ACCIDENTS

re. The

Rete ordinaria

Mean = 39712,96

X <=23481,385%

X <=57558,4995%

0

0,5

1

1,5

2

2,5

3

3,5

4

10 27,5 45 62,5 80

Values in Thousands

Valu

es in

10^

-5

Ordinary Road Network - Std. Dev 10324 Min. 12452, Mean 39713, Max. 76049,

Asymmetry 0.21

Autostrada

Mean = 3218,943

X <=5008,9695%

X <=1755,825%

0

0,5

1

1,5

2

2,5

3

3,5

4

0 1,75 3,5 5,25 7

Values in Thousands

Valu

es in

10^

-4

Motorway - Std. Dev 996, Min. 869, Mean 3219, Max. 6509, Asymmetry 0.38

243

PROMETEIA

Ferrovia

Mean = 52,2781

X <=97,0995%

X <=18,45%

0

0,0022222

0,0044444

0,0066666

0,0088888

0,011111

0,0133332

0,0155554

0,0177776

0,0199998

0 40 80 120

Values in Thousands

Val

ues

in 1

0^ -3

Railway - Std. Dev 24, Min. 6, Mean 52, Max. 118, Asymmetry 0.47

Nave

Mean = 21,42005

X <=39,5695%

X <=7,715%

0

0,01

0,02

0,03

0,04

0,05

0,06

0 10 20 30 40 5

Values in Thousands

Val

ues

in10

^ -

0

3

Ship - Std. Dev 9, Min. 2, Mean 21, Max. 48, Asymmetry 0.47

Aereo

Mean = 10,01436

X <=18,9195%

X <=3,285%

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,1

0 5 10 15 20 25

Value in Thousands

Val

ues

in 1

0^ -3

Airplane - Std. Dev 4, Min. 1, Mean 10, Max. 23, Asymmetry 0.47

244

PROMETEIA LOW CASE SCENARIO TO 2020 – AIR POLLUTION

Rete ordinaria

Mean = 4897,103

X <=5007,7895%

X <=4784,35%

0

1

2

3

4

5

6

4,65 4,775 4,9 5,025 5,15

Values in Thousands

Valu

es in

10

-3

Ordinary road Network - Std. Dev 68, Min. 4678, Mean 4897, Max. 5097,

Asymmetry –2.15E-02

Autostrada

Mean = 1371,034

X <=1492,395%

X <=1249,445%

0

1

2

3

4

5

6

1,1 1,225 1,35 1,475 1,6

Values in Thousands

Valu

es in

10^

-3

Motorway - Std. Dev 73, Min. 1155, Mean 1371, Max. 1589, Asymmetry -0,02

Ferrovia

Mean = 910,5622

X <=1372,1595%

X <=448,675%

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

0,2 0,6 1 1,4 1,8

Values in Thousands

Valu

es in

10^

-3

Railway - Std. Dev 274, Min. 192, Mean 910, Max. 1616, Asymmetry –7.96E-04

245

PROMETEIA

Nave

Mean = 1224,867

X <=1370,2195%

X <=1073,785%

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

0,9 1,075 1,25 1,425 1,6

Values in Thousands

Valu

es in

10^

-3

Ship - Std. Dev 90, Min. 921, Mean 1224, Max. 1505, Asymmetry –7.53E-02

Aereo

Mean = 51,97823

X <=80,7495%

X <=23,275%

0

0,005

0,01

0,015

0,02

0,025

0,03

10 40 70 100

Values in Thousands

Val

ues

in 1

0^-3

Airplane - Std. Dev 17, Min. 10, Mean 51, Max.93, Asymmetry 6.68E-05

LOW CASE SCENARIO TO 2020 – GREENHOUSE EFFECT

Rete ordinaria

Mean = 14191,78

X <=16278,9195%

X <=12103,95%

0

0,5

1

1,5

2

2,5

3

3,5

11 12,75 14,5 16,25 18

Values in Thousands

Valu

es in

10^

-4

Ordinary Road Network - Std. Dev 1246, Min. 11169, Mean 14191, Max. 17221,

Asymmetry 7.00E-05

246

PROMETEIA

Autostrada

Mean = 6279,906

X <=7203,4795%

X <=5356,015%

0

1

2

3

4

5

6

7

8

4,5 5,375 6,25 7,125 8

Values in Thousands

Valu

es in

10^

-4

Motorway - Std. Dev 551, Min. 4942, Mean 6279, Max. 7620, Asymmetry 7.00E-05

Ferrovia

Mean = 291,4755

X <=431,8995%

X <=150,415%

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

50 175 300 425 550

Values in Thousands

Valu

es in

10^

-3

Railway - Std. Dev 84, Min. 70, Mean 291, Max. 532, Asymmetry 2.09E-02

Nave

Mean = 492,4002

X <=552,295%

X <=432,565%

0

0,0022222

0,0044444

0,0066666

0,0088888

0,011111

0,0133332

0,0155554

0,0177776

0,0199998

350 450 550 650

Values in Thousands

Val

ues

in 1

0^-3


247

PROMETEIA

Aereo

Mean = 1789,762

X <=2737,9195%

X <=840,115%

0

1

2

3

4

5

6

7

0 0,875 1,75 2,625 3,5

Values in Thousands

Valu

es in

10^

-4

Airplane - Std. Dev569, Min. 369, Mean 1789, Max. 3198, Asymmetry –1.60E-03

LOW CASE SCENARIO TO 2020 - NOISE

Rete ordinaria

Mean = 6072,738

X <=6767,8595%

X <=5380,675%

0

1

2

3

4

5

6

7

8

9

10

4,5 5,5 6,5 7,5

Values in Thousands

Valu

es in

10^

-4


Asymmetry 0.02

Autostrada

Mean = 1590,154

X <=1743,895%

X <=1441,055%

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

1,2 1,375 1,55 1,725 1,9

Values in Thousands

Valu

es in

10^

-3

Motorway - Std. Dev 91, Min. 4850, Mean 6072, Max. 7356, Asymmetry 2.43E-02

248

PROMETEIA

Ferrovia

Mean = 288,2183

X <=325,6695%

X <=251,315%

0

0,0022222

0,0044444

0,0066666

0,0088888

0,011111

0,0133332

0,0155554

0,0177776

0,0199998

220 255 290 325 360

Values in Thousands

Val

ues

in 1

0^ -3

Railway - Std. Dev 22, Min. 223, Mean 288, Max. 353, Asymmetry 2.26E-02

Aereo

Mean = 79,64381

X <=92,0995%

X <=67,255%

0

0,01

0,02

0,03

0,04

0,05

0,06

60 70 80 90 100

Values in Thousands

Val

ues

in 1

0^ -3

Airplane - Std. Dev 7, Min. 60, Mean 79, Max. 98, Asymmetry –3.26E-03

HIGH CASE SCENARIO TO 2020 - ACCIDENTS

Rete ordinaria

Mean = 64554,75

X <=93467,0295%

X <=38230,845%

0

0,5

1

1,5

2

2,5

0 35 70 105 140

Values in Thousands

Valu

es in

10^

-5


Asymmetry 0.20

249

PROMETEIA

Autostrada

Mean = 5043,8

X <=7937,1795%

X <=27045%

0

0,5

1

1,5

2

2,5

1 3,5 6 8,5

Values in Thousands

Valu

es in

10^

-4

11

Motorway - Std. Dev 1608, Min. 1278, Mean 5043, Max. 10199, Asymmetry 0.39

Ferrovia

Mean = 66,25436

X <=123,1695%

X <=23,355%

0

0,0022222

0,0044444

0,0066666

0,0088888

0,011111

0,0133332

0,0155554

0,0177776

0,0199998

0 40 80 120

Values in Thousands

Val

ues

in 1

0^ -3

160

Railway - Std. Dev 30, Min. 9, Mean 66, Max. 151, Asymmetry 0.47

Nave

Mean = 27,14658

X <=50,2395%

X <=9,745%

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

0 14 28 42 56

Values in Thousands

Val

ues

in 1

0^ -3

70

Ship - Std. Dev 12, Min. 4, Mean 27, Max. 61, Asymmetry 0.47

250

PROMETEIA

Aereo

Mean = 12,69164

X <=23,9795%

X <=4,165%

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0 10 20 3

Values in Thousands

Val

ues

in 1

0^ -3

0

Airplane - Std. Dev 6, Min. 1, Mean 12, Max. 29, Asymmetry 0.46

HIGH CASE SCEBARIO TO 2020 – AIR POLLUTION

Rete ordinaria

Mean = 8192,699

X <=8365,7495%

X <=8018,765%

0

0,5

1

1,5

2

2,5

3

3,5

4

7,8 8 8,2 8,4 8,6

Values in Thousands

Valu

es in

10^

-3



Autostrada

Mean = 2443,531

X <=2656,9495%

X <=2227,815%

0

0,5

1

1,5

2

2,5

3

2 2,3 2,6

Values in Thousands

Valu

es in

10^

-3

2,9

Motorway - Std. Dev 129, Min. 2052, Mean 2443, Max. 2845, Asymmetry -1,33E-02

251

PROMETEIA

Ferrovia

Mean = 1159,478

X <=1731,495%

X <=588,065%

0

0,2

0,4

0,6

0,8

1

1,2

0,2 0,7 1,2 1,7

Values in Thousands

Valu

es in

10^

-3

2,2

Railway - Std. Dev 342, Min. 246, Mean 1159, Max. 2046, Asymmetry 3,79E-03

Nave

Mean = 1900,169

X <=2095,3695%

X <=1704,365%

0

0,5

1

1,5

2

2,5

3

3,5

1,5 1,7 1,9 2,1 2,3

Values in Thousands

Valu

es in

10^

-3

Ship - Std. Dev117, Min. 1529, Mean 1900, Max. 2269, Asymmetry –1.73E-02

Aereo

Mean = 118,6827

X <=184,395%

X <=53,055%

0

1

2

3

4

5

6

7

8

9

10

20 70 120 170 220

Values in Thousands

Valu

es in

10^

-3


252

PROMETEIA

253

HIGH CASE SCENARIO TO 2020 – GREENHOUSE EFFECT

Rete ordinaria

Mean = 17363,61

X <=19917,5495%

X <=14808,415%

0

0,5

1

1,5

2

2,5

3

13 16 19 22

Values in Thousands

Valu

es in

10^

-4


Asymmetry -3.70E-05

Moto 0, Mean 8581, Max. 10407, Asymmetry-3.70E-05

Autostrada

Mean = 8581,074

X <=9843,2295%

X <=7318,295%

0

1

2

3

4

5

6

6,5 7,5 8,5 9,5 10,5

Values in Thousands

Valu

es in

10^

-4

rway - Std. Dev 753, Min. 675

Ferrovia

Mean = 382,2705

X <=204,455%

0

0,5

1

1,5

2

2,5

3

3,5

4

0 175 350 525

Values in Thousands

X <=558,3795%

700

Valu

es in

10^

-3

Mean 382, Max. 688, Asymmetry -2.25E-03 Railway - Std. Dev 106, Min. 81,

PROMETEIA

Nave

Mean = 812,6181

X <=908,3995%

X <=713,945%

0

1

2

3

4

5

6

7

0,6 0,7 0,8 0,9 1

Values in Thousands

Valu

es in

10^

-3


Aereo

Mean = 4068,234

X <=6257,7695%

X <=1893,155%

0

0,5

1

1,5

2

2,5

3

0 2 4 6

Values in Thousands

Valu

es in

10^

-4

8

Airplane - Std. Dev1303, Min. 901, Mean 4068, Max. 7317, Asymmetry 2.04E-03

HIGH CASE SCENARIO TO 2020 - NOISE

Rete ordinaria

Mean = 10953,39

X <=12264,3895%

X <=9643,945%

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

8,5 9,75 11 12,25 13,5

Values in Thousands

Valu

es in

10^

-4



254

PROMETEIA

Autostrada

Mean = 3505,276

X <=3832,2795%

X <=31725%

0

0,5

1

1,5

2

2,5

2,8 3,15 3,5 3,85 4,2

Values in Thousands

Valu

es in

10^

-3

Motorway - Std. Dev 201, Min. 2806, Mean 3505, Max. 4149, Asymmetry –5.91E-02

Ferrovia

Mean = 408,2067

X <=460,4295%

X <=356,965%

0

0,0022222

0,0044444

0,0066666

0,0088888

0,011111

0,0133332

0,0155554

0,0177776

0,0199998

300 350 400 450 500

Values in Thousands

Val

ues

in 1

0^ -3

Railway - Std. Dev 31, Min. 314, Mean 408, Max. 498, Asymmetry –5.10E-03

Aereo

Mean = 108,7382

X <=125,7495%

X <=91,825%

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

80 100 120 140

Values in Thousands

Val

ues

in 1

0^ -3


255

A

256

ULATION CONFIDENCE INTERVALS

values of the Monte Carlo simulation fall within the higher and lower limits; or rather, on the left of the lower limit falls 5% of the

l cost values of the distribution and on the right of the higher limit falls the last 5% of the distribution.

.0

.8

PROMETEI

D – SIM

90% of the

socia

Low case scenario - 2020 (billions of €2004) Road

Ordinary Network Motorway Rail Ship Air Total

Externality Lower Higher Lower Higher Lower Higher Lower Higher Lower Higher Lower Higher

Accidents 19.2 60.2 1.3 5.2 0.005 0.1 0.002 0.041 0.001 0.019 20.6 65.4

Air Pollution 4.8 5.0 1.2 1.5 0.4 1.5 1.0 1.4 0.02 0.1 7.8 9.1

Greenhouse Effect 11.7 16.6 5.2 7.4 0.1 0.5 0.4 0.6 0.7 2.9 19.3 26.8

Noise 5.3 6.9 1.4 1.8 0.2 0.3 0.07 0.1 7.1 9.0

Total 44.5 85.3 10.2 14.7 1.0 2.1 1.5 1.9 0.8 3.0 60.1 105

High case scenario - 2020 (billions of €2004) Road

Ordinary Network Motorway Rail Ship Air Total

Externality

Lower Higher Lower Higher Lower Higher Lower Higher Lower Higher Lower Higher Accidents 31.7 97.5 1.9 8.2 0.006 0.13 0.003 0.05 0.001 0.02 33.9 105.6

Air Pollution 8.0 8.4 2.2 2.7 0.5 1.8 1.7 2.1 0.0 0.2 13.0 14.7

Greenhouse Effect 14.4 20.4 7.1 10.1 0.2 0.6 0.7 0.9 1.5 6.6 26.1 36.3

Noise 9.4 12.5 3.1 3.9 0.3 0.5 0.1 0.13 13.1 16.8

Total 68.1 134.1 16.1 23.0 1.3 2.7 2.5 3.0 1.8 6.9 93.6 165

PROMETEI

E – SUMMARY OF EXTENRAL EFFECTS OF MOBILITY I

E1

Accidents 27.0

Air Pollution

Greenhou

Noise 10.6

Congestion 12.9

Total 98.7

E2

Accidents 27.4

Air Pollution

Greenhou

Noise 5.3

Total (without congest

Total (without congest(updated to 2000)

A

257

N ITALY

- FS-Confitarma research, 2000-2001 (billions of euro, 1997)


0.1 0.1 1.2 28.4

40.4 0.6 0.5 2.7 44.2

se Effect 7.8 0.2 0.6 0.8 9.4

2.0 1.0 0.0 13.6

0.0 0.0 0.0 12.9

3.1 2.1 4.7 108.6

Total without congestion 85.8 3.0 2.1 4.7 95.6

- Infras-Iww research- estimates for Italy (billions of euro, 1997 and 2000)


0.0 0.0 - 27.5

24.4 0.2 0.0 - 24.7

se Effect 16.4 0.5 1.9 - 18.8

0.6 0.4 - 6.2

ion) 73.5 1.3 2.3 - 77.2

ion) 86.5 1.5 7.1 0.004 95.23

PROMETEI

A

258

E3 - ASTRA-ITALIA study (Federtrasporto, October 2002) External cost estimates of transport in 2000 (billions of euro)

High estimate Low estimate

Road Rail Air Ship Total Road Rail Air Ship Total

Accidents 36.8 0.1 0.2 0.0 37.1 22.5 0.0 0.1 0.0 22.6

Air Pollution and greenhouse effect 54.0 0.7 1.6 1.9 58.3 36.0 0.5 1.1 1.1 38.6

Noise 7.5 0.2 0.5 - 8.2 3.2 0.1 0.1 - 3.3

Congestion - - - - - - - - - -

Total without congestion 98.4 0.9 2.3 2.0 103.6 61.7 0.6 1.2 1.1 64.5

External cost estimates of transport in 2030 (billions of euro)

High estimate Low estimate

Road Rail Air Ship Total Road Rail Air Ship Total

Accidents 58.2 0.1 0.3 0.0 58.6 34.9 0.0 0.1 0.0 35.1

Air pollution and greenhouse effect 22.0 0.9 2.5 2.8 28.2 14.6 0.6 1.7 1.5 18.5

Noise 9.8 0.2 0.9 - 10.9 4.1 0.1 0.1 - 4.3

Congestion - - - - - - - - - -

Total without congestion 90.0 1.2 3.6 2.8 97.7 53.7 0.8 1.9 1.5 57.9

PROMETEIA

3. VALUATION OF THE EXTENT OF INTERNALISATION

IN 2004 AND FUTURE OUTLOOK

The considerations and data in this chapter, together with the quantitative results

observed in the previous chapter, allow us to estimate the extent to which social costs

- net of internalising amounts - generate external costs.

3.1. MARGINAL COST OF TRAFFIC FOR THE CALCULATION OF

INTERNALISING RESOURCES - OBSERVATIONS

In the following part of this chapter we shall address taxes and subsidies. The optimal

situation (maximum social surplus) is obtained when all and only social costs (short-

term marginal) are paid by those who generate them. Hence, the European

Commission’s tax principle known as social marginal cost pricing (where the short-term

is observed, since long-term marginal costs generally coincide with average costs).

Subsidies provided for distribution reasons (to the Local Public Transport or Railways)

do not affect this approach: any subsidy generates a surplus loss, which may, “a

posteriori” be accepted for distributive or environmental reasons. However, the trade-

off between efficiency and equity must be measured carefully since it is widespread

opinion that there are instruments which generate lower inefficiencies with the same

distributive impact.

These observations hold true regardless of the declared aim of the taxes or subsidies,

and especially against the argument (nowadays quite an old one), that since the aim of

fuel taxes is that of a general tax, and not an internalising one, it is not possible to

include this item in analysis supporting environmental policies.

To avoid making any unrealistic conclusions when interpreting the principle of

efficiency, a clarification is in order. This clarification is provided without any scientific

rigour, for which the reader may easily turn to a vast volume of available literature.

The principle of efficiency, which imposes that the user pay only the marginal

cost of production of the good or service, generates, in the case of significant fixed

259

PROMETEIA

infrastructure costs, an unsolvable conflict between the need for efficiency and the

possibility of having the infrastructures. Indeed, one may understand, as in the case of

motorways, that the marginal cost is represented only by that cost which varies

according to the infrastructure’s degree of utilisation and in general this cost is very

low compared to average costs or even compared to the variable maintenance costs

related to the asset’s physical efficiency. Now, nobody would build or manage an

infrastructure at a loss, since the marginal costs do not cover the fixed costs nor do

they cover the costs of maintaining the infrastructure’s physical efficiency at a constant

level.

At one point, the problem was avoided by arguing that fixed costs were to be

covered by general taxation. On one hand this argument tends to hide the fact that in

any case someone is paying for the fixed costs. On the other hand however, the

economics of the past forty years suggests that in the case of general taxation, political

failure was added to market failure due to the lack of any clarity in defining

management instruments by those who should have been safeguarding public interest.

As noted, the theory of regulation helped remove the stalemate and move toward new

forms of regulation based on broad cost valuations (for example, average costs). For

example, the regulating mechanisms lead to second best criteria such as pricing based

on average costs with efficiency incentives on the regulator. Furthermore, the

existence of marginal opportunity costs for public funds (extremely high in the case of

tight budget commitments), would determine social optimum levels different from

neoclassical ones, with positive shadow-prices for taxation and negative shadow-prices

for subsidies (i.e. a different benefit from the one inferable from that of equilibrium

with prices equal to marginal costs). In such a context the efficient tariff of a natural

monopoly would not, in any case, be equal to the marginal cost, but would rather fall

somewhere between the average and marginal costs. This is exactly what happened

with many utilities, and is also what happens for transport infrastructure operated

through concessions. This, however, is not the subject of this work.

Finally, it must be noted how even an inflexible definition of marginal cost may

be misleading and ambiguous. When we look at marginal cost we are looking at the

cost of maintenance vis-à-vis traffic and not the cost of maintenance without traffic

(extraordinary maintenance): these costs depend on the loss of efficiency that age and

the environment, among others, subject any infrastructure to. Can one seriously argue

260

PROMETEIA

that extraordinary maintenance costs do not depend on traffic? The answer must, at

the very least, be uncertain. If at time “t” no extraordinary maintenance is carried out,

then at time “t+1” it will not be possible to provide a service having the same

qualitative characteristics and hence, ceteris paribus, traffic will fall as a result of the

reduced quality experienced (for example, an increase in the rate of accidents). Now

then, if traffic depends on extraordinary maintenance, to a certain extent this is carried

out to satisfy expected traffic (or contractually promised traffic, on the basis of good

probabilities for future traffic demand: something closely related to price cap schemes

which, at least in theory, are often used). Accordingly, the tie between infrastructure’s

management, quality of service and traffic is bidirectional and under an operating

standpoint does not seem to be divisible into fixed costs, extraordinary maintenance

costs or infrastructure management and, lastly, marginal costs that depend exclusively

on traffic.

However, using a calculation method of external costs that were not based on

marginal costs but rather on other types of cost would overly complicate the analysis

and lead us to look at aspects that are not related to this work. Let us all be reminded

that the objective of this work is to determine a proportion between social costs and

external costs for different modes of transport. Therefore, our calculation of

internalising monetary resources to be deducted from social costs, as calculated in the

previous chapter, will stick to the marginal cost criteria.

Accordingly, an efficient breakdown of external costs, for each mode of

transport possessing fixed infrastructure costs and maintenance costs to keep the

asset physically efficient, must be characterised by negative external costs: today, in

Italy, this only applies to motorway transport.

Direct taxes, such as Value Added Tax, play an internalising role in all cases where it

may be assumed that the good which is taxed generates negative externalities. On the

basis of the theoretical considerations noted in Part One, it is easy to imagine that

against a loss of surplus produced by an external cost, the fall in consumption that

invariably follows the levying of a tax reduces both the total surplus (as for all taxes –

net loss, i.e. eliminates economic resources no longer available to any party), but even

before so reduces the loss of surplus that is due to the externality. Whether this is

optimal (environmental) or not, compared to a tax on a good without externalities, one

261

PROMETEIA

levied on a good possessing externalities is greater than the surplus reduction.

Therefore, VAT on fuel or motorway tolls, which may be compared, say, to the VAT

applied on a tin of tuna-fish – levied according to complex criteria that weighs the tax

according to the value added contribution capacity – has the additional function of

compressing the external cost. Whether this is intentional or not this has an effect, and

the effect is significant.

For the sake of completion, the aforesaid argument encompasses another aspect which

needs to be briefly addressed. This aspect has to do with the price of oil, historically

very high (even if below the historical peaks, and taking into account the euro/lira-

dollar exchange rate and inflation). If today’s oil prices are merely a speculative bubble

– and not a reflection of scarcity - then, in terms of a general equilibrium, one may

argue that the internalising share of the tax should be calculated on the difference

between the current price and its equilibrium (scarcity) price. In this case, the

industrial part of the cost that exceeds the equilibrium price would also have an

internalising effect. As may be seen, there is no change in substance. Calculations just

get more arbitrary and complicated: in the following paragraphs we shall use the full

amounts of fuel excise duties and taxes as an internalising monetary resource.

We must also remember that observations on the indifference (who some attribute to

Coarse) between subsidies for less polluting modes of transport and taxes for the most

polluting modes of transport should take into account the direct and cross-elasticities

of the mobility demand for different modes of transport. This is especially true since

the above considerations appear to ignore the question of long-term price signals,

which, in the case of subsidies, would lead to problematic effects of transport over-

consumption for those modes of transport that are most heavily subsidised, since these

modes would not pay for the social costs that they generate. Therefore, in the

following calculations subsidies are treated as social costs for the mode of transport

that receives them whilst taxes are treated as internalising monetary resources.

262

PROMETEIA

3.2. VALUATION OF RESOURCES HAVING AN INTERNALISING

EFFECT PER MODE OF TRANSPORT

In Chapter 2 we had mentioned a further two sources of potentially external social

costs: those related to the payment of subsidies, certainly external, and those related

to the utilisation of infrastructure, for which their external degree must be assessed by

comparing the costs generated and borne by users. Within the scope of this chapter

we have the opportunity to re-address these two items.

3.2.1. ROAD TRANSPORT

Tax proceeds from road transport come from the use of private and commercial

vehicles. The operating costs of the overall vehicle fleet are subject to specific taxes for

various cost components; these cost items are: fuel, lubricants, tyres, maintenance and

repairs, vehicle taxes, third-party liability insurance, motorway tolls, parking, interest

on invested capital. Taxes on capital assets (cars, motorcycles and heavy vehicles) are

not to be considered since they fall under investment goods only for that part of the

tax relating to the vehicle’s depreciation, and only that part of the same which depends

on the vehicle’s use.

Available statistics and bibliography allow us to determine the overall PA tax

collections, starting with the cost items listed above.

The state’s largest source of tax collections is fuel taxes (unleaded petrol, diesel oil and

LPG); the following tables show the absolute value calculated from 2004 recorded

consumptions. Tables 37-41 describe the various steps taken to calculate the tax

proceeds assumed as a base to define the resources having an internalising effect for

the social costs of the externalities, addressed in the previous chapter, per mode of

transport and separately for passengers and freight.

263

PROMETEIA

Table 37 – Fuel consumption in Italy (2004)

Unleaded

petrol

Diesel

oil LPG Total Unit of measurement

Total consumption 14926 24321 1106 40353 Thousands of tons

Specific weight 0.725 0.825 0.520 Kg/Litre

Total consumption 20.588 29.480 2.127 52.195 Millions of litres

Source: ACI (Automobile Club Italia) 2006, Ministry of Productive Activities (2006), www.tecnocentro.it, www.sodigas.com

Table 38 – Total fuel expenditure in Italy (2004)

Unleaded fuel Diesel oil LPG Total Unit of measurement

Average price 1.125 0.940 0.539 Euro/Litre

Total expenditure 23169 27719 1147 52036 Millions of Euro

Source: ACI (Automobile Club Italia) 2006 and Ministry of Productive Activities (2006)

On the basis of the data of the Ministry of Productive Activities, Prometeia estimates

diesel oil consumption for vehicles in 2004 at 10257 thousand tons of fuel; it is then

possible to separate this amount shown in Table 38 into expenditure for private

vehicles and expenditure for commercial vehicles.

Table 39 – Fuel expenditure in Italy per type of fleet (2004, millions of euro) Private vehicles Commercial vehicles

Unleaded petrol Diesel oil LPG Diesel oil Total

Total expenditure 23169 11690 1147 16029 52036

Source: Analysis of data from the Ministry of Productive Activities and Conto Nazionale delle Infrastrutture e dei Trasporti (2004)

The Ministry of Productive Activities has provided a breakdown of the various line items

of the unitary price of fuel for 2004. Based on these values it is possible to calculate

the tax proceeds of fuel taxes.

264

PROMETEIA

Table 40 – Percentage impact of various cost items on fuel prices (2004) Cost item Unleaded petrol Diesel oil LPG

Industrial price 0.3371 0.4045 0.5429

Excise duties 0.4962 0.4288 0.2905

VAT 0.1667 0.1667 0.1667

Source: Ministry for Productive Activities (2006)

Table 41 – Total fuel tax proceeds per type of vehicle fleet (millions of euro, 2004) Private vehicles Commercial vehicles

Unleaded fuel Diesel oil LPG Diesel oil Total

Total proceeds 15359 6962 524 9545 32390

The tax proceeds from all other expenditure items is estimated on the basis of the

recorded value of expenditure borne by private individuals and companies; the switch

from financial to economic costs (and, by measuring the difference, the inland revenue

proceeds) is carried out by using suitable conversion coefficients found in literature.

With regard to value added tax levied on the purchase of vehicles, the following

treatment is observed: tax proceeds from parties that can recover VAT is excluded;

therefore industrial vehicles have been excluded. For motorcycles and cars, use of the

vehicle should be considered, and this may be annually estimated on the basis of new

vehicle registrations, and implicitly assuming that the fleet is kept in its efficient

condition of equilibrium through the purchase of new vehicles equal to those actually

observed. The value of the registrations is the basis from which to calculate the VAT

proceeds, which must then be halved to account for the fact that only 50% of each

vehicles loss in value is due to its use (for marginal costs all calculations need to be

retraced to traffic, both for social costs as well as internalising monetary resources). In

terms of new registrations 2004 appears to be a normal year; 2.2 million newly

registered vehicles fall within the long-term average, with new vehicles representing

7.3% of the existing fleet, implying an average vehicle life of approximately 11 years.

2004 values are therefore taken as a starting point to calculate the tax proceeds as

internalising monetary resource. In Table 42, and Tables 48-49 by means of

extrapolation to 2020, there is a line item described as vehicle purchase and

265

PROMETEIA

accessories and this line item contains an internalising monetary resource of the 50%

of the VAT inferred from 2006 ACI data.

The social costs of accidents are internalised pro quota by insurance premiums. Table

42 shows a line item that refers to internalising monetary resources – Third party

liability insurance. This line item includes the share of insurance premiums that have

the effect of internalising the social costs exactly in proportion to the ratio between

insurance payments for damage to property and people and total third-party liability

insurance payments (source: ISVAP, 2004). In other words, since we have not included

damage to property in the social costs of accidents - necessarily internal from the point

of view of third parties - the share of resources that internalise the externality is

proportional to the amount paid by the insurance companies for damage to people

(approximately 45.6% in 2004). It should be noted that consistency has been assumed

between premiums and indemnification according to the type of damage (to property

or persons).

266

PROMETEIA

Table 42 – Financial costs, economic costs, tax proceeds and total internalising monetary resources of road transport - 2004 (*) billions of euro

2004

Cost itemFinancial costs (*)

Conversion factor

Economic costs (*)

Tax proceeds

(*)

Internalising monetary

resources (*)Private Vehicles

Fuel 36,1 13,2 22,9 22,9Petrol 23,2 0,34 7,8 15,4 15,4

Diesel Oil 11,8 0,40 4,8 7,0 7,0LPG 1,2 0,54 0,6 0,5 0,5

Lubricants 1,1 0,44 0,5 0,6 0,6Tyres 3,1 0,67 2,1 1,0 1,0

Maintenance and repairs 15,1 0,67 10,1 5,0 5,0

Purchase of vehicles and accessories - VAT 8,2 4,1

Vehicle taxes 4,0 0,0 0,0 4,0 4,0Third-Party liability insurance 18,6 0,8 14,7 3,8 3,8

Internalising Resources - Third-party liability insurance

6,7

Motorway tolls 3,3 0,8 2,6 0,7 0,7Internalising resources - Motorways tolls 2,6

Hospitalisations & Parking 5,4 0,8 4,3 1,1 1,1

Interest on invested capital 14,7 1,0 14,7 0,0 0,0

Total private vehicles 101,4 62,3 47,4 52,6

Commercial and Industrial vehicles

Fuel 16,1 0,40 6,5 9,6 9,6Lubricants 4,1 0,44 1,8 2,3 2,3Tyres 5,3 0,79 4,2 1,1 1,1


Interest 7,7 1,00 7,7 0,0 0,0

Taxes 0,7 0,00 0,0 0,7 0,7Third-Party liability insurance 3,7 0,8 2,9 0,8 0,8


1,3


Total commercial vehicles 52,7 35,1 17,6 20,1

Total Road Transport 154,1 97,4 65,0 72,7

Source: Prometeia calculations on Guida all’Analisi costi–benefici dei progetti di investimento (2003) and Conto Nazionale delle Infrastrutture e dei Trasporti (2004); the insurance line item includes the provision for road victims that is paid by the insurance companies.

267

PROMETEIA

3.2.1.1. ROAD TRANSPORT: MOTORWAY

In the motorway sector, toll amounts that exceed infrastructure costs generated

directly by traffic may be referred to amounts for covering fixed operating costs and

investment costs. As such, we are moving away from the principle of efficiency which

requires coverage of all marginal costs only, and may therefore be compared to an

internalising monetary resource (we have already noted how toll payments must cover

a part of fixed costs as well as extraordinary maintenance costs, but adopting a

marginal cost criterion. In order to highlight the breakdown between social costs and

external costs it is useful to give toll payments an internalising role beyond the amount

of the tax proceeds). Given the very small amount of infrastructure marginal costs

attributable to usage caused by traffic (less than 30,000 euro per km, implying

additional externalities for 0.155 billion euro in 2004) almost all motorway toll

payments have an internalising effect and, therefore, just like the share of insurances

that cover damage to persons, we also have a re-charge line item for tolls on

internalising monetary resources (Table 42). The marginal cost of infrastructure usage

has been allocated entirely to commercial and industrial vehicle traffic.

In order to be fully convinced about this last point it may be sufficient to compare a

motorway with an ordinary road: both incur traffic related costs, but in one case the

service is paid, in the other case it is not. Therefore, regardless of the infrastructure’s

ownership or management there should be no doubt about the fact that the

relationship between social costs and the internalising share is to be found in the

amount paid by users to carry out their journeys. The fact that the ordinary road

network is primarily paid for through the general taxation system does not change the

terms of the argument but rather, if anything, highlights the fact that those who enjoy

the service generate costs for others.

We must underline how the output of the motorway service is measured as traffic

given a certain quality of service. In the case in question, this may be approximated by

the combination between commercial travelling time and unit risk of accident per km

travelled. Both these parameters change with the quantity of traffic and keeping them

at constant levels requires a change in effort by the operator through extraordinary

maintenance, which, as noted, by using the criterion based on marginal costs

efficiency, is not taken into consideration.

268

PROMETEIA

Therefore, if the concept of output is changed from one of km travelled to one of km

travelled at certain quality conditions (time and risk) the cost of production could be

significantly higher than the one resulting from a pure valuation of ordinary

maintenance costs related to traffic.

It is appropriate to address another possible objection that may be raised against real

marginal costs of the concessionaire of the motorway infrastructure: if we once again

start from the marginal cost criterion, assumed to be low or even equal to zero, then

efficiency would command a pricing change in terms of a drastic reduction in toll

payments. In so doing, then, in theory, traffic would be drained away from the

ordinary road network – characterised by significant unit risk of accident – towards the

motorway network, with an overall reduction in externalities generated by accidents.

However, the reasoning is based on a false assumption of abstract levels of quality

associated to the two road network types. This is not the case, of course: it is the

average and marginal costs of operating a qualified infrastructure which make certain

risk parameters possible, and these risk parameters would alter unpredictably as soon

as the toll payment were changed. Furthermore, we would lose the relationship

between willingness to pay and consumption, which is given by the very application of

an explicit transport price.

3.2.1.2. ROAD TRANSPORT: ORDINARY NETWORK

Users pay nothing directly for the ordinary road network, but the costs may be

estimated by using the same percentage of maintenance costs that for the motorway

network are dependent on the level of traffic (22.3%, Table 44), and therefore net of

costs relating to quality standards of travel time and risk per km.

Tab. 43 – Length of road networks in Italy

Type of road network Length [km]

Trunk roads 17250

Main roads 151570

Urban and suburban 668673

Total 837493 Source: Conto Nazionale delle Infrastrutture e dei Trasporti – 2004

269

PROMETEIA

Table 44 – Operating costs of motorways - 2005 (euro/km/year)

Average cost

Conversion factor

Average economic

cost

Traffic dependent cost in standard

conditions

Conversion factor

Economic cost

Maintenance costs 122700 0.83 101700 27400 0.83 22700

Other operating costs 41200 0.81 33200

Total 163900 0.82 134900 27400 0,83 22700 Source: Lavori professionali – TRT

Tables 43-44 show base data required to carry out the calculations to obtain the

results in Table 45. The value of 4853 million euro should be considered as an

additional social cost ascribable to the ordinary road network (shown under

infrastructure maintenance in Table 28 (Chapter 2)

Table 45 – Expenditure for the ordinary road network 2004 (millions of

euro)

Expenditure for the ordinary road

network

Total

expenditure

Expenditure exclusively

dependent on traffic

Ordinary maintenance and staff 5015 1120

Extraordinary maintenance 16717 3733

Total 21732 4853

Source: Lavori professionali TRT

These costs, therefore, may conceptually be looked at as unpaid externalities of

ordinary road transport. Finally, it may be assumed that practically all marginal costs of

infrastructure are attributable to heavy vehicle (freight) traffic.

The summarised calculations85 shown in Table 42 shows how 73 billion euro should be

considered as resources to be deducted from the transport social costs in order to

obtain a credible estimate of the external costs of this mode of transport, which we

estimate to be just below 27 billion euro (approximately 1.9% of GDP for 2004 (a 85 It may well be possible that the calculations slightly overestimate the internalising monetary resources of road transport, both on the ordinary as well as the motorway network, and especially for freight mobility. This is because refunds received by freight transport for national health service, a share of fuel duties and a share of motorway tolls have not been considered. In any case these amounts are estimated to be less than one billion euro for 2004.

270

PROMETEIA

summary of all the calculations is provided in Table 50). The overall discussion is

further addressed in paragraph 3.3, after having carried out a valuation of internalising

monetary resources for the other modes of transport as well as an extrapolation of

these resources to 2020 for the two (high and base) growth scenarios.

3.2.2. LOCAL PUBLIC TRANSPORT

Calculations of internalising monetary resources for local public transport, or the

additional external costs generated by subsidies, will be added to the ordinary road

network (even if this may be arguable). Looking at Table 46 it appears clear that Local

Public Transport is heavily subsidised: for these social costs it may be worth taking into

account the costs and benefits of each mode of transport.

Tab. 46 – Costs and revenue of Local Public Transport (millions of euro)

Costs Revenues Subsidies

Buses 5283 2464 2819

Trams 166 78 89

Underground 317 293 25

Total 5766 2835 2932

Source: Conto Nazionale delle Infrastrutture e dei Trasporti (2004)

The value of 2.9 billion euros is shown under the “subsidies” line item for the ordinary

road network, as shown in Table 28 (chapter 2). The separation of VAT from ticket

revenues gives us the share of internalising monetary resources, added directly to the

resources line item in Table 50.

3.2.3. RAILWAY TRANSPORT

The analysis of railway transport data is very complex. The items summarised in Table

47 (sourced from the Italian Railways FFSS Group Financial Statements of 2004) have

a number of grey areas. Capitalisations (the accounting effect of state transfers

towards investment) and payments from regions (for short-distance, regional/sub-

regional travelling) may certainly be classified as subsidies.

271

PROMETEIA

Tab. 47 – Income Statement of the Italian Railway Group - 2004 (millions of euro) Revenues from traffic 3.120

Other revenues 481

Agreements with Regions 1.311

Revenues from State and other admin. bodies 1.806

Capitalisations 958

Manpower - 4.470

Other Costs - 2.646

Depreciation/Amortisation - 676

Funds/Provisions and devaluations - 219

Capital expenditure 8.447

Source: Conto Nazionale delle Infrastrutture e dei Trasporti (2004) on FS data

Revenues from State and other administrative bodies refers to resources that may be

viewed as part of fixed operating costs, and this may lead to an issue of comparative

nature with other infrastructure costs (ports and airports), for which no information is

available at an aggregate level that breaks down fixed, variable or marginal operating

costs. For such infrastructures the fixed part of costs covered by the state are assumed

to be the largest part and therefore tariffs have been assimilated to marginal costs.

These revenues are estimated to be a third of all revenues from the state and other

administrative bodies (approximately 600 million euro per year).

Accordingly, the social cost line item for subsidies for railway transport in 2004

amounts to approximately 2870 million euro (agreements with regions, capitalisations

and one third of revenues from the state and other administrative bodies) to which we

need to add 140 million euro for the accounting deficit (total revenues less total costs),

calculated as an average for the years 2002-2004. We thus reach a subsidies value of

approximately 3 billion euro.

272

PROMETEIA

There are a number of non-explicit subsidies too however: for example, it is a well

known fact that the significant annual losses incurred by the freight sector (and in the

passengers’ sector too last year), are charged to the FS National Railway’s Holding

Company. This enormous debt is rightly considered by the credit system as sovereign

credit (the company is publicly held and cannot go bankrupt). Therefore, it is the

company’s status of public ownership that guarantees the debt, exactly as if it were a

state subsidy; the state remains a residual claimant of the very same debt. In this

regard, we should calculate the annual share of interest that the state pays towards

public debt generated by the railway transport deficit over time (were this really

charged to the company this interest would appear on the income statement as

financing expenses). But this information may only be used in a qualitative manner to

describe the phenomenon; its quantitative impact cannot be calculated, and therefore

it is not included in the calculations that feed into the comparative tables of social costs

and internalising monetary resources per mode of transport (after all, we would then

have had to use a similar procedure to determine the amount of interest paid on the

share of public debt generated by the operating of ordinary road networks and

motorways too, before their privatisation).

Similarly to other modes of transport direct taxes on ticket revenues have been posted

directly to the internalising monetary resources in Table 50.

3.2.4. AIR TRANSPORT

For air transport there appear to be no specific problems in terms of taxation or

subsidies, in some manner or the other retraceable to externalities - very significant -

for this mode of transport. Indeed, airplane fuel (kerosene) is not taxed so as to avoid

competitive refuelling in countries levying lower excise duties (a significant risk given

the international nature of most air transport). The same problem applies to the

maritime sector, and is starting to become important for road freight too (additional

fuel tanks being fitted on heavy vehicles coming from Eastern Europe).

273

PROMETEIA

Subsidies are very high for all airline flag operators, but these always take the form of

lump payments (or rather, one more time, one last time), and therefore cannot be

retraced to subsidies that alter the system’s marginal costs (however, if the subsidies

should be seen to become excessive, one should reconsider this conclusion and

perhaps add an external cost item for air transport, something that we have not done

here).

Similar considerations hold true for infrastructure costs: airport tariffs for the largest

terminals generally cover average costs too, i.e. investment as well, but this is actually

a very complicated situation and each airport would need to be considered on a case

by case basis. Small airports are sometimes unable to cover their operating costs, and

therefore it is impossible (and more so, not significant) to verify whether the final

average result exceeds marginal costs or not. Overall, however, the problem would

appear of little importance because of the low weight of infrastructure costs as well as

the effect, at least partially offsetting, of the aforesaid disequilibrium between large

and small airports.

However, since the airport operating costs are surely higher than the marginal costs,

then, on a qualitative basis, we may confidently say that air transport pays all

infrastructure marginal costs, and probably a share of average costs too, despite not

internalising environmental costs, which are particularly significant given the altitude at

which the emissions are released.

10% VAT on revenue tickets is considered as internalising monetary resources.

3.2.5. MARITIME TRANSPORT

For this sector the same observations just made for air transport apply. There are no

specific taxes, nor subsidies that may affect the marginal costs of infrastructure usage.

Public subsidies, which are very significant, essentially cover investment costs. Similarly

to the air sector, port tariffs certainly cover a part of fixed operating costs, and in some

cases part of investment costs too. Once again however, every port would need to be

considered on a case by case basis according to size, technical characteristics and

operating conditions.

274

PROMETEIA

As for Air transport, the summary tables for maritime transport also have a number of

blanks (Table 28, chapter. 2) for infrastructure costs and subsidies, whilst the VAT

proceeds (10%) posted as internalising resources from ticket revenues are included

(Table 50).

3.3. EXTRAPOLATION OF THE VALUATION OF INTERNALISING

RESOURCES AS OF 2020

The forecast is underpinned by a number of simple and clear assumptions. The goal of

the exercise is to determine the possible boundaries for the relationship between social

costs and internalising monetary resources; using complicated assumptions would only

add elements of confusion to the exercise.

For almost all line items a forecasting factor equal to real GDP for each scenario (low

case and high case) has been used. Namely, this factor has been applied to

expenditure for tyres, ordinary maintenance and repairs, vehicle taxes, third-party

liability and hospitalisation, 50% of VAT proceeds on the sale of motorcycles and

vehicles.

For motorway toll payments the average price per passenger km in 2004 has been

calculated and this has then been multiplied by expected traffic in the two scenarios so

as to obtain expenditure per passenger and freight traffic; we have therefore assumed

that motorway toll payments per unit of traffic remain constant in real terms (it may

increase in nominal terms at the same rate of inflation).

Fuel expenditure has been calculated on the basis of the quantity changes in the

“Forecasted demand for energy and oil in Italy 2006-20020” report by Unione

Petrolifera. This work is particularly significant since it considers a number of variables,

both economic and non-economic (and in particular the increase of energy efficiency),

and assumes a cumulative economic increase that is very close to that of the high-case

scenario forecasted in this work. Accordingly, for this scenario the aforesaid quantity

changes have been used whilst for the low-case scenario conditions have been re-

proportioned for the reduced traffic. The starting point for fuel forecasts was obtained

by multiplying consumptions by average fuel prices, assumed to be constant, in real

terms, in both scenarios.

275

PROMETEIA

Expenditure for lubricants has been applied the same growth rate obtained for fuel.

Finally, allocation of internalising monetary resources to the ordinary road network was

carried out by breaking down the amounts in proportion to traffic generated by the two

modes of transport. Tables 48-49 show individual line items of the results of the

extrapolation of internalising monetary resources per mode of transport. For other

modes of transport, since we are dealing with individual line items – for which

assumptions for the 2020 forecasts have been explained – results are shown in Table

50, which summarises social costs, internalising monetary resources and external

costs.

276

PROMETEIA

Table 48 – Financial costs, economic costs, tax proceeds and total internalising monetary resources for road transport – 2020 LOW CASE SCENARIO - (*) billions of 2004 euro

Low Case Scenario - 2020


Conversion factor

Economic costs (*)

Tax proceeds

(*)



Fuel 21,1 8,1 13,0 13,0Petrol 9,6 0,34 3,2 6,4 6,4

Diesel Oil 10,2 0,40 4,1 6,1 6,1LPG 1,3 0,54 0,7 0,6 0,6




Vehicle taxes 4,3 0,0 0,0 4,3 4,3Third-Party liability insurance

20,0 0,8 15,9 4,1 4,1


0,0 0,0 7,2



Interest on invested capital 15,9 1,0 15,9 0,0 0,0





Interest 8,3 1,00 8,3 0,0 0,0



0,0 0,0 0,0 1,4




Source: Prometeia analysis on data from various sources.

277

PROMETEIA

Table 49 – Financial costs, economic costs, tax proceeds and total internalising monetary resources for road transport – 2020 HIGH CASE SCENARIO - (*) billions of 2004 euro

High Case Scenario - 2020


Conversion factor

Economic costs (*)

Tax proceeds

(*)



Fuel 29,3 11,2 18,1 18,1Petrol 13,4 0,34 4,5 8,9 8,9

Diesel Oil 14,2 0,40 5,7 8,4 8,4LPG 1,7 0,54 0,9 0,8 0,8




Vehicle taxes 5,5 0,0 0,0 5,5 5,5Third-Party liability insurance 25,3 0,8 20,1 5,2 5,2


0,0 0,0 9,2

Motorway tolls 5,4 0,8 4,3 1,1 1,1

Internalising resources - Motorways tolls 4,3


Interest on invested capital 20,1 1 20,1 0,0 0,0





Interest 10,5 1,00 10,5 0,0 0,0



0,0 0,0 0,0 1,8

Motorway tolls 3,1 0,80 2,4 0,6 0,6

Internalising resources - Motorways tolls 2,3



Source: Prometeia analysis on data from various sources.

278

PROMETEIA

3.4. SOCIAL, INTERNAL AND EXTERNAL COSTS AS OF 2020

As noted, internalising monetary resources come from tax proceeds collected on traffic

related goods and services. Before our comments on the final results of this work,

summarised in Table 50, it is worth noting once again that no external cost projections

were carried out based on particular assumptions for the price of oil. However, at the

time of writing the price of oil is approximately 63 dollars per barrel and the

international situation, characterised by a turbulent political scenario and strong

demand for primary energy sources may well not make the anecdotal scenario of oil

prices at 100 dollars per barrel too far fetched. This said, this scenario has not been

elaborated further so as not to overly complicate the analysis, but we can certainly say

that it would have significant effects in terms of internalising monetary resources which

would reduce the weight of social costs well below the external costs shown in this

paragraph.

The social costs of transport amount to 7.7% of GDP in 2004 and would fall to 5.8% of

GDP in 2020 in the low growth case scenario (+0.5% real GDP/year throughout the

forecasted period to 2020), due to constant or reduced accident rates and the

progressive reduction of air pollution costs for all forms of transport. In the high case

scenario social costs would grow in absolute terms due to the strong growth in the

potential demand for mobility – which we have considered also to be actual demand,

even though we would have doubts on the actual capability of the transport system to

sustain the mobility growth in the high growth case scenario without there being

significant qualitative and quantitative improvements to infrastructure.

Even in the high case scenario (GDP +2.1% average per year) social costs would

increase less than mobility and would represent 7.1% of real GDP in 2020 (at 2004

prices).

279

PROMETEIA

Table 50 – Social costs, internalising monetary resources and external costs – billions of euro (2004) SOCIAL COSTS

ON Motorway Railway Ship Air TotalTOTAL

2004 81,6 18,2 4,5 1,4 1,4 107,1Low Scenario - 2020 68,7 11,9 3,7 1,7 1,9 87,8High Scenario - 2020 102,2 18,4 4,2 2,7 4,3 131,8



INTERNALISING MONETARY RESOURCESON Motorway Railway Ship Air Total

TOTAL2004 51,1 21,9 0,3 0,1 0,6 74,0Low Scenario - 2020 42,1 17,8 0,3 0,1 0,8 61,0High Scenario - 2020 53,3 30,0 0,3 0,2 1,5 85,2



EXTERNAL COSTS = SOCIAL COSTS - INTERNALISING MONETARY RESOURCESON Motorway Railway Ship Air Total

TOTAL2004 30,5 -3,7 4,1 1,3 0,8 33,1Low Scenario - 2020 26,6 -5,9 3,4 1,5 1,1 26,8High Scenario - 2020 48,9 -11,6 3,9 2,6 2,8 46,5

PASSENGERS2004 20,2 -4,3 2,4 0,5 0,7 19,4Low Scenario - 2020 20,9 -2,5 2,1 0,6 1,0 22,1High Scenario - 2020 41,2 -6,2 2,4 1,1 2,6 41,0

FREIGHT2004 10,3 0,6 1,8 0,8 0,1 13,6Low Scenario - 2020 5,7 -3,4 1,4 1,0 0,1 4,7High Scenario - 2020 7,7 -5,4 1,5 1,4 0,2 5,5

280

PROMETEIA

Once the internalising monetary resources (Table 50) are deducted from the social

costs – as projected in the two different forecasted scenarios - we observe that in 2004

the external costs of transport are estimated at 33.1 billion euro, just under 2.4% of

the GDP for that year. 81% of those external costs is generated by road transport on

the ordinary road network whilst the motorways are generators of net external benefits

of 3.7 billion euros for the overall economic system. The 4.1 billion euro of external

costs of the railway system derive entirely from the fact that the railway is subsidised

and therefore, for the aforesaid amount, generates welfare losses compared to a

situation of efficiency.

Looking forward, in the low case growth forecast scenario external costs would

decrease both in absolute terms as well as in terms of GDP percentage; in 2020

external costs would be less than 1.8% of GDP with the motorway system still

generating negative external costs – it pays more than it costs – of 5.9 billion euro at

constant prices. In the high case growth forecast scenario external costs would

increase in absolute terms and would remain more or less fixed in terms of percentage

of GDP. Again, the motorway system would contribute negative external costs. The

explanation for this is to be found in the structure of third-party liability insurance. As

described in detail, the amount of risk that is run for each motorway km travelled is

much lower than the risk run per km on the ordinary road network and the current

method of insurance payments is unable to account for these differences (too little is

paid for each km travelled on the ordinary road network and too much is paid per km

travelled on the motorway network).

The greenhouse effect is responsible for the rapid growth of the external costs of air

transport, whilst the unchanged policy assumptions relating to the management of

traffic revenues and subsidies to the railway system imply a slight reduction to external

costs in absolute terms (at 2004 prices) for this mode of transport.

It may be possible to consider, as some observers do, that a part of all accidents

generate costs that are already internalised by the national health system through

general taxation, whilst in these calculations only the part of insurances that refer to

damage to persons has been considered. If this argument is accepted, then the

numbers presented herein are an upper limit of the external costs amount.

281

PROMETEIA

The most significant results of our calculations are represented by the fact that the

vast majority of external costs are generated by the ordinary road network, through

overall and fatal accidents. Accordingly, and with all due consideration given to the

positive effects of the penalty-points driving licence, the strategy of leaving things as

they are cannot be an acceptable one. Safety on the ordinary road network must be a

priority and the possibility of introducing the toll system to large sections of the

ordinary road network should be given quick and serious consideration.

282

PROMETEIA

BIBLIOGRAPHY AA. VV., 2003, Guida all’analisi costi–benefici dei progetti di investimento, disponibile su http://nuval.formez.it/ Aberle G., e Eisenkopf A., 2001, I benefici del trasporto, in Rapporto ACI-ANFIA, I costi e i benefici del trasporto. ACI 2005, I costi sociali degli Incidenti Stradali, Anno 2004, relazione presentata al III Salone Internazionale della Sicurezza Stradale, Riva del Garda 13-15 ottobre 2005. ACI, 2006, Annuario Statistico, disponibile su www.aci.it Affuso L., Masson J., Newbery D. 2003, Comparing investments on new transport infrastructure. Roads vs. Railways, Fiscal Studies, vol. 24, 30. Alberini A. 2004, Robustness of VSL Values from Contingent Valuation Surveys, Nota di lavoro FEEM, 135, Milano. Amador F. J., Gonzalez R. M., De Dios Ortuzar J., 2005, Preference Heterogeneity and Willingness To Pay for Travel Time Savings, Transportations, 32.. Amici della Terra-Ferrovie dello Stato, 2002, I costi ambientali e sociali della mobilità in Italia, Quarto Rapporto, Roma. Anderson D., Mohring H., 1997, Congestion Cost and Congestion Pricing. in Greene e al., cit. ANFIA-ACI, 2001, I costi e i benefici esterni del trasporto, Centro studi sui sistemi di trasporto e ANFIA, Torino. Antoci A., Sacco L., 1996, Il Futuro della Città d’arte: il ruolo della Contribuzione volontaria nelle Politiche di Ammortamento Sociale, Stato e Mercato, 48, 3. Arnott R., de Palma A., Lindsey R., 1990, Economics of a Bottleneck, Journal of Urban Economics, 27. Arnott R., de Palma A., Lindsey R., 1993, A Structural Model of Peak-Period Congestion: a Traffic Bottleneck with Elastic Demand, American Economic Review, 83. Arrow K. e Fisher A., 1974, Preservation, Uncertainty and Irreversibility, in Quarterly Journal of Economics, 87. Ashenfelter O., 2006, Measuring the Value of a Statistical Life: Problems and Prospects, NBER Working Papers, 11916. Ashenfelter O., Greenstone M., 2004, Using Mandated Speed Limits to Measure the Value of a Statistical Life, Journal of Political Economy, 112, 1. Ashmore M., Mulkay M., Pinch T., 1989, Health and Efficiency: A Sociology of Health Economics, Milton Keynes, The Open University Press. Balaban D. J., Fagi P. C., Goldfarb N. I., Nettler S., 1986, Weights for Scoring the Quality of Well-Being Instrument among Rheumatoid Arthritics: A Comparison of General Population Weights, Medical Care, 24. Barnes G. R., 1995, The Values of Waiting Time and a Seat on a Bus: Some Evidence from Singapore. Ph.D. Dissertation, Department of Economics, University of Minnesota, Minneapolis, Minnesota. Bates J., Dix M., May A., 1987, Travel Time Variability and Its Effect on Time of Day Choice for the Journey to Work, Transportation Planning Methods, Proceedings of Seminar C.

283

PROMETEIA

Berger M. e al., 1994, Framework for Valuing Health Risks, in Tolley e al., cit. Bickel P. e al., 2005, Environmental Costs, in C. Nash & B. Matthews, Measuring the Marginal Social Cost of Transport, Elsevier. Bishop S., Grayling T., 2003, The Sky’s the Limit, IPPR, London. Bosetti V., Messina E., 2001, Quasi Option Value and Irreversible Choices, Nota di lavoro FEEM, 14, Milano. Bradley M., Marks P., Wardman M., 1986, A Summary of Four Studies into the Value of Travel Time Savings, Transportation Planning Methods, Proceedings of Seminar M. Brennan M., Gent T., Kollahmthodi S., Watkiss P., 2005, Structure of Cost and Charges Review – Environmental Costs of Rail Transport, AEA Technology Rail, London. Bristow A., Wardman M., 2004, Using Stated Preference to Value Annoyance from Aircraft: A Comparison of Approaches, Applied Environmental Economics Conference 2004, 26 March, The Royal Society, London. Brookshire D. S., D’Arge R. C., Schulze C., Thayer M. A., 1979, Methods Development for Assessing Air Pollution Control Benefits, vol. 2, Experiments in Valuing Non-market Goods: A Case Study of Alternative Benefit Measures of Air Pollution Control in the South Coast Air Basin of Southern California, Washington, D.C., U.S. Environmental Protection Agency. Brownstone D., Ghosh A., Golob T. F., Kazimi C., Amelsfort D. V., 2003, Drivers’ Willingness-to-Pay to Reduce Travel Time: Evidence from the San Diego I-15 Congestion Pricing Project, Transportation Research, 37. Brownstone D., Small K.A., 2005, Valuing Time and Reliability: Assessing the Evidence from Road Pricing Demonstrations, Transportation Research Part A, 39(4). Bruzelius N., 1979, The Value of Travel Time, London: Croon Helm. Butler J. R. G., 1992, Welfare Economics and Cost-utility Analysis, in P. Zweifel e H. E. Frech, Health Economics Worldwide, Dordrecht, Kluwer Academic Publishers. Button, K., 2002, Transport Economics, Edward Elgar, Cheltenham. Calfee J., Winston C., 1998, The Value of Automobile Travel Time: Implications for Congestion Policy, Journal of Public Economics, 69. Calthorp E., Johansson O., Litman T., Maddison D., Pearce D., Verhoef E., 1996, The True Costs of Road Transport, CSERGE Blueprint. Carr-Hill R. A., 1989, Assumptions of the QALYs Procedure, Social Science and Medicine, 29. Casoni G., Polidori P., 2002, Economia dell’ambiente e metodi di valutazione, Carocci, Roma. Chen M. M., Bush J. W., 1976, Maximizing Health System Output with Political and Administrative Constraints Using Mathematical Programming, Inquiry, 13. Cicerchia A., 2000, La carta del rischio: questioni aperte, Economia della cultura, anno X, n. 2. Commissione Europea, 1995, Libro Verde ‘Verso una corretta ed efficace determinazione dei prezzi nel settore dei trasporti’, CEE C Commission. Cooper B. S., Rice D. P., 1976, The Economic Cost of Illness Revisited, Social Security Bulletin, 39.

284

PROMETEIA

Conrad J., 1980, Quasi Option Value and the Expected Value of Information, Quarterly Journal of Economics, vol. 94. Copert III, 2000, COmputer Programme to calculate Emissions from Road Transport, European Environment Agency. Cornes R., Sandler T., 1998, The Theory of Externalities, Public Goods and Club Goods, Cambridge University Press, Cambridge. Creigh-Tyte S., Dawe G., Stock T., 2000, The White Book, Option appraisal for Expenditure Decisions, technical paper n. 2, Department for Culture, Media and Sport, Finance Division, London, www.culture.gov.uk. Culyer A.J., 1978, Need, Values and Health Status Measurement in Culyer A. J., Wright K. G. (a cura di), Economic Aspects of Health Services, London, Martin Robertson. Danielis R., 2001, La teoria economica e la stima dei costi esterni dei trasporti, in ANFIA-ACI I costi e i benefici esterni del trasporto, Torino. Danielis R., Rotaris S., 2001b, Rassegna critica delle stime dei costi esterni dei trasporti, in ANFIA-ACI I costi e i benefici esterni del trasporto, Torino. de Blaeij A., Florax R. J. G. M., Rietveld P., Verhoef E., 2000, The Value of Statistical Life in Road Safety: A Meta-Analysis, Accident Analysis and Prevention 35(6). de Blaeij A., van Vuuren D., 2001, Risk Perception of Traffic Participants. Accident Analysis and Prevention 35(2). De Borger B., De Nocker L., Mayeres I., Panis L., Proost S, Vandercruyssen D., Wouters G., 2001, The External Costs of Transportation. Final Report, TRENEN (2001), Sustainable Mobility Programme Federal Office for Scientific, Technical and Cultural Affairs, Belgium. Delucchi M.A., 2004a, Some Conceptual And Methodological Issues In The Analysis of The Social Cost of Motor-Vehicle Use. Report 2 in the series: The Annualized Social Cost of Motor-Vehicle Use in the United States, Based on 1990-1991 Data, UCD-ITS-RR-96-3 (2). Delucchi M.A., 2004b, Personal Nonmonetary Costs of Motor-Vehicle Use. Report 4 in the series: The Annualized Social Cost of Motor-Vehicle Use in the United States, Based on 1990-1991 Data, UCD-ITS-RR-96-3 (4). Delucchi M.A., 2004c, Motor-Vehicle Goods And Services Priced In The Private Sector. Report 5 in the series: The Annualized Social Cost of Motor-Vehicle Use in the United States, Based on 1990-1991 Data, UCD-ITS-RR-96-3 (5). Delucchi M.A., 2004d, Summary of The Nonmonetary Externalities of Motor-Vehicle Use. Report 9 in the series: The Annualized Social Cost of Motor-Vehicle Use in the United States, Based on 1990-1991 Data, UCD-ITS-RR-96-3 (9). Department of Transport, 2004, Highways Economic Note, no. 1, London. DeSerpa A.C., 1971, A Theory of the Economics of Time, Economic Journal, 81, 828-845. EEA, 2002, Transport environment reporting mechanism (TERM), External Costs of Transport in the EU, EEA, Copenhagen. EFTEC, 2005, Valuation of the Historic Environment, Report to English Heritage, DCMS and Department of Transport, EFTEC, London. Elster J., 1991, Local Justice and Interpersonal Comparisons, in Elster J., Roemer J. E., (a cura di), Interpersonal Comparisons of Well-Being, Cambridge, Cambridge University Press.

285

PROMETEIA

Environmental Assessment Institute, 2005, Motorways Versus Nature, Environmental Assessment Institute, Copenhagen. European 2000, 2000, Habitat Fragmentation Due to Transportation Infrastructure, European Commission, Directorate General Transport. ExternE, 1995, 1999, Externalities of Energy, EU Commission DGXII, Bruxelles Federtrasporto, 2002, Fisco e Pedaggi per Ridurre i Costi del Trasporto: la Metodologia, Bollettino Economico sul Settore dei Trasporti. Feitelson E.I., Hurd R.E., Mudge, R.R., 1996, The Impact of Airport Noise on Willingness-To-Pay for Residences, Transportation Research-D 1 (1). Freeman M. A., 1993, The Measurement of Environmental and Resource Values, Resources for the Future, Washington D.C. Fuchs V., Zeckhauser R., 1987, Valuing Health - A Priceless Commodity, American Economic Review, Papers and Proceedings, 77. Garrod G. D., Scarpa R., Willis K., 2000, Estimating The Benefits of Traffic Calming on Through Routes: A Choice Experiment Approach, FEEM working paper, 7, Milano. Goodwin P.B., 1976, Value of Time, ECMT Round Table 30. Paris: ECMT. Greene D.L., D.W. Jones, Delucchi M.A., (a cura di), 1997, The Full Costs and Benefits of Transportation: Contributions to Theory, Method and Measurement, Berlin, Springer-Verlag. Grue, B., Langeland J. L., Larsen O. I., 1997, Housing Prices - Impacts of Exposure to Road Traffic and Location, TØI report, 351, Oslo. Haight F. A., 1994, Problems in Estimating Comparative Costs of Safety and Mobility, Journal of Transport Economics and Policy, 27(1). Hall F.L., Welland J.D., 1987, The Effect of Noise Barriers on the Market Value of Adjacent Residential Properties, Transportation Research Record, 1143. Harrington W., McConnell V., 2003, Motor Vehicles and the Environment, RFF report, Washington D.C. Hauer E., 1994, Can One Estimate the Value of Life or Is It Better Dead Stuck in Traffic? Transportation Research, 28(2). Henry A., Godart S., 2002a, The Pilot Accounts for Belgium, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds. Henry A. and Godart S., 2002b, The Pilot Accounts for Luxembourg, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds. Hensher D.A., 1997, Behavioral Value of Travel Time Savings in Personal and Commercial Automobile Travel, in Greene e al., cit. Hensher D.A., 2001, Measurement of the Valuation of Travel Time Savings, Journal of Transport Economics and Policy, 35(1). Hensher D. A., Milthorpe F. W., Barnard. P. O., Smith N. C., 1990, Urban Tolled Roads and the Value of Travel Time Savings, The Economic Record, 66. Hidano, N., Hayashiyama Y., Inoue M., 1992, Measuring the External Effects of Noise and Vibration of Urban Transportation by the Hedonic Approach, Environmental Science, 9(3), Tokyo.

286

PROMETEIA

287

Himanen V., Idstroem T., Karjalainen J., Otterstroem T., Tervonen J., 2002, The Pilot Accounts for Finland, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds. Holloway C. A., 1978, Decision Making Under Uncertainty. Models and Choices, Englewood Cliffs, Prentice-Hall. Hook W., 2003, Appraising the Social Costs and Benefits of Road Projects, Institute for Transportation and Development Policy, New York, mimeo. Infras-IWW, Mauch S. e al., (a cura di), 1994, External Effect of Transport, Zurich/Karlsruhe. Infras-IWW, 2000, External Costs of Transport. Accident, Environmental and Congestion Costs in Western Europe, Zurich/Karlsruhe. Infras-IWW, 2004, External Costs of Transport, Update Study, Zurich/Karlsruhe. ISVAP, 2005, Relazione Annuale. Jara-Diaz S.R., Guevara C.A., 2003, Behind the Subjective Value of Travel Time Savings: The Perception of Work, Leisure, and Travel from a Joint Mode Choice Activity Model, Journal of Transport Economics and Policy, 37(1). Jones C.A., 1997, Use of Non-Market Valuation Methods in the Courtroom: Recent Affirmative Precedents in Natural Resource Damage Assessments, INCAE, Costa Rica, mimeo. Jones-Lee M.W, Loomes G., Philips P. R., 1995, Valuing the prevention of non-fatal road injuries: contingent valuation vs. standard gambles, Oxford Economic Papers, 47. Kaplan R. M., Bush J. W., 1982, Health-Related Quality of Life Measurement for Evaluation Research and Policy Analysis, Health Psychology, 1. Kaplan R. M., Bush J. W., Berry C. C., 1976, Health Status: Types of Validity and the Index of Well-Being, Health Services Research, 11. Kaplan R. M., Bush J. W., Berry C. C., 1979, Health Status Index: Category Rating versus Magnitude Estimation for Measuring Levels of Well-Being, Medical Care, 17.. Keeler E. B., Cretin S., 1983, Discounting of Life-Saving and Other Non-Monetary Effects, Management Science, 29. Kenkel D., Cost of Illness Approach, in Tolley e al., cit. Kenkel V. D., Berger M., Blomquist G., 1994, Contingent Valuation of Health, in Tolley e al., cit. Klarman H. E., Francis J., Rosenthal G. D., 1968, Cost Effectiveness Analysis Applied to Treatment of Chronic Renal Disease, Medical Care, 6, (ripr. in Cooper M.H. e Culyer A.J., (a cura di), 1973, Health Economics, Harmondsworth, Penguin Books). Kopp R. e Smith V.K., 1989, Benefit Estimation Goes to Court: The Case of Natural damage Assessments, Journal of Policy Analysis and Management, 8. Korizis D., Tsamboulas D., Roussou A., 2002, The Pilot Accounts for Greece, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), IST, University of Leeds, Leeds. Leftwich, R., e R. Eckert, 1985, The Price System and Resource Allocation (9° ed), The Dryden Press, Sydney.

PROMETEIA

288

Lichfield N., 1999, The British Scene, presented at the symposium “The Urban and Regional Planning Requirements for a Cultural Heritage Conservation Policy”, mimeo, Lindberg G., 2002, Accident Cost Case Studies, Case Study 8b: Marginal External Accident Costs in Stockholm and Lisbon, UNITE, Deliverable 9. Lindberg G., 2003, Estimating external cost, Outline of presentation at IMPRINT seminar. Lindberg G., 2005, Accidents, in Nash C., Mathews B., (a cura di), Measuring the Marginal Social Cost of Transport, Research in Transportation Economics, 14, Elsevier Publisher. Link H., Maibach M., Nellthorp J., Sanson T., Stewart L., 2000, The Accounts Approach, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), Deliverable 2, University of Leeds, Leeds. Lipscomb J., 1989, Time Preference for Health in Cost-Effectiveness Analysis, Medical Care, 27, S233; Lisco T., 1967, The Value of Commuters’ Travel Time: A Study in Urban Transportation. Ph.D. Dissertation, Department of Economics, University of Chicago. Litman T., 2002, Transportation Cost Analysis: Techniques, Estimates and Implications, Victoria Transport Policy Institute (www.vtpi.org). Lockwood M., 1994, Quality of Life and Resource Allocation, in Baldwin S., Godfrey C., Propper C., (a cura di), Quality of Life: Perspectives and Policies, London, Routledge, 33. Loehman T., Boldt D., Chaikin K., 1981, Measuring the Benefits of Air Quality Improvements in the San Francisco Bay Area, Washington, D.C., U.S. Environmental Protection Agency. Loomes G., Mc Kenzie L., 1990, The Scope and Limitations of QALYs Measures, , in Baldwin S., Godfrey C., Propper C., (a cura di), Quality of Life:Perspectives and Policies, London, Routledge, 84. Loomis J. B., 2000, Vertically Summing Public Good Demand Curves: an Empirical Comparison of Economic Versus Political Jurisdictions, Land Economics, 76, 2. Macário R., Carmona M., Caiado G., Rodrigues A., Martins P., Link H., Stewart L., 2002, The Pilot Accounts for Portugal, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds. Maddison D., Mourato S., 1999, Valuing Different Road Options for Stonehenge, CSERGE working paper. Maddison D., Pearce D., Johansson O., Calthrop E., Litman T., Verhoef E., 1996, The True Costs of Road Transport, Earthscan, London. Makie P., Nash C., Shires J., Nellthorp J., 2004, The Economic Efficiency Case for Road User Charging, Report to Department for Transport. Marti M., Sommer H., Suter S., 2002, Accident Cost Case Studies, Case Study 8a: Marginal External Accident Costs in Switzerland, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds. Mazzanti M., 2003, Metodi e strumenti di analisi per la valutazione economica del Patrimonio culturale, Franco Angeli, Milano. Mazzanti M., Pontoglio S., Zoboli R., 2005, Emission trading in Lombardia: studio per una ipotesi di azione a scala regionale, IRER, Programma delle Ricerche Strategiche 2004/2005, Regione Lombardia. Mazzanti M., Zoboli R., 2005, HTEconomic Instruments and Induced Innovation: The Case of End-of-Life Vehicles European PoliciesTH, Nota di lavoro FEEM, 80, Milano, www.feem.it

PROMETEIA

289

Mazzanti M., Zoboli R., 2006, Economic Instruments and Induced Innovation: the European Directive on end of Life Vehicles, Ecological Economics, 58, 2. Mc Taggart D., Findlay C., Parkin M., 1992, Economics , Addison Wesley Publishers Ltd, Sydney. Miller T. R., 1989, 65 mph: Winners and Losers. DOT HS 807 451, National Highway Traffic Safety Administration, U.S. Department of Transportation, Washington, D. C. Miller T. R., Hunter W. W., Waller P. F., Whitman R. D., Whiting B. E., 1985, Development of a Value Criteria Methodology for Assessing Highway System Cost-Effectiveness. FHWA/RD-85/086, Federal Highway Administration, Safety and Design Division, MacLean, Virginia. Ministero dei Beni Culturali ed Ambientali–Istituto Centrale per il Restauro, (1996), Carta del Rischio del Patrimonio Culturale, 1, a cura di A.T.I. MARIS, Bonifica, Roma. Ministero delle Attività Produttive, 2006, Struttura del prezzo medio nazionale dei prodotti petroliferi, disponibile su www.minindustria.it. Ministero delle Infrastrutture e dei Trasporti, 2006, Conto Nazionale delle Infrastrutture e dei Trasporti – Anno 2004, disponibile su www.infrastrutturetrasporti.it. Mishan E.J., 1971, The Postwar Literature on Externalities: An Interpretative Essay, Journal of Economic Literature, 9. Mohring H., 1972, Optimisation and Scale Economies in Urban Bus Transportation, American Economic Review, Papers & Proceedings, 591-604. Mohring H., Schroeter J., Wiboonchutikula P., 1987, The Values of Waiting Time, Travel Time and a Seat on a Bus, Rand Journal of Economics, 18(1). Mooney G. H., 1986, Economics, Medicine and Health Care, Sussex, Wheatsheaf Books. Morey E., Rossmann K. G., 1999, Combining Random Parameters and Classic Heterogeneity to Estimate the Benefits of Decreasing Acid Deposition Injuries to Marble Monuments in Washington D.C., mimeo. Morey E., Rossmann L., Chestnut S., Ragland, 1997, Valuing Acid Deposition Injuries to Cultural Resources, Report for the National Acid Precipitation Assessment Program. Morey E., Rossmann L., Chestnut S., Ragland, 2000, Modelling and estimating DAP for reducing acid deposition injuries to cultural resources: using choice experiments in a group setting to estimate passive use values, mimeo. Morisugi H., 1997, The Social Costs of Motor Vehicle Use in Japan, in Support to the Road Transport Sector, Vol.3 of Environmental Implications of Energy and Transport Subsidies, OECD/GD(97) 156, OECD, Paris. Mourato S., Mazzanti M., 2002, Economic Valuation and Cultural Heritage: Evidence and Prospects, in Assessing the Value of Cultural Heritage, Getty Conservation Institute, Los Angeles. Mushkin S. J., 1962, Health as an Investment, Journal of Political Economy, 70. Mushkin S. J., 1979, Biomedical Research: Costs and Benefits, Cambridge (Mass.), Ballinger. MVA Consultancy, ITS Leeds and Transport Studies Unit of Oxford University, 1987,The Value of Travel Time Savings: A Report of Research Undertaken for the Department of Transport, Newbury, England: Policy Journals. MVA Consultancy, ITS Leeds, 1992, Quality of a Journey: Final Report, Prepared for the UK Department of Transport, Contract No. 02/C/5274.

PROMETEIA

290

Nääs O., Lindberg G., 2002, The Pilot Accounts for Sweden, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), Funded by the 5P

thP Framework RTD Programme, University of Leeds, Leeds.

Nash C., Mathews B., (a cura di), 2005, Measuring the Marginal Social Cost of Transport, Research in Transportation Economics, 14, Elsevier Publisher. Nash C., Sansom T., 1999, Calculating Transport Congestion and Scarcity Costs, Final Report of the Expert Advisors to the High Level Group on Infrastructure Charging, Working Group 2. Navrud S., 2002, The State-Of-The-Art on Economic Valuation of Noise, Final Report to European Commission DG Environment, Bruxelles. Navrud S., Ready S., (a cura di), 2002, Valuing Cultural Heritage Applying Environmental Valuation Techniques to Historic Buildings, Monuments and Artifacts, Edward Elgar, Cheltenham. Nelson J. P., 1982, Highway noise and property values: A survey of recent evidence, Journal of Transport Economics and Policy, 16(2). Ntziachristos L., Samaras Z., 2001, COPERT III, Computer Programme to Calculate Emissions from Road Traffic – Methodology and Emission Factors, Final Report, European Topic Centre on Air Emissions, Thessaloniki, Version 2.2. Nuti F., 2001, La valutazione economica delle decisioni pubbliche. Dall'analisi costi-benefici alle valutazioni contingenti, Torino, Giappichelli Editore. Nuti F., Montini A., Stampini M., 2003, Valutazione economica mediante interviste e-mail: applicazione ad un danno da interruzione dei trasporti, Economia delle Fonti di Energia e dell'Ambiente, 3. Oort O., 1969, The evaluation of Travelling Time, Journal of Transport Economics and Policy, 3. Orfeuil J.P., 1997, Evaluation of the External Costs of Road Transport in France and the Consequences of Costs Internalization, in Support to the Road Transport Sector, Vol.3 of Environmental Implications of Energy and Transport Subsidies, OECD/GD(97), OECD, Paris. Passchier W., 2002, Healthy Airports A Proposal for a Comprehensive Set of Airport Environmental Health Indicators, Universiteit Maastricht, Department of Health Risk Analysis and Toxicology. Penn T.A., 2000, Summary of the natural resource damage assessment regulations under the United States Oil Protection Act, NOAA Report. PETS D7, 1998, Internalisation of Externalities, University of Leeds. Pollicino M., Maddison D., 2001, Valuing the Benefits of Cleaning Lincoln Cathedral, Journal of cultural economics, 25. Pommerherne W. W., 1988, Measuring environmental benefits: comparison of a hedonic technique and CV, in Bros D., Rose M., Seidl C. (a cura di), Welfare and Efficiency in Public Economics, Berlin. Prud’Homme R., 2001, I Costi di Congestione, in ACI-ANFIA, I Costi e i Benefici Esterni del Trasporto, Torino. Putignano C., Pennisi L., 1999, Il costo sociale degli incidenti stradali, Rivista Giuridica della Circolazione e dei Trasporti, Supplemento al N. 3 Maggio-Giugno, 1999, Anno LIII. Quarmby D.A., 1967, Choice of Travel Mode for the Journey to Work: Some Findings, Journal of Transport Economics and Policy, 1. Renew W. D., 1996, The Relationship Between Traffic Noise and House Price, Conference of the Australian Acoustical Society, Brisbane, 13-15th November.

PROMETEIA

291

Ricci A., Enei R., Esposito R., Fagiani P., Giammichele F., Leone G., Pellegrini D., Link H., Stewart L., Bickel P., 2002, The Pilot Accounts for Italy, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency). Funded by the 5P

thP, Framework RTD Programme, University of Leeds, Leeds.

Rice D. P., Cooper B. S., 1967, The Economic Value of Human Life, American Journal of Public Health, 57. Robinson J. C., 1986, Philosophical Origins of the Economic Valuation of Life, Milbank Quarterly, 64. Rosen S., 1993, The Quantity and Quality of Life: A Conceptual Framework, in Tolley e al., cit. Rotaris L., Danielis R., 2001, La valutazione dei costi dell’inquinamento atmosferico e del rumore in Italia, in ACI-ANFIA I costi e i benefici del trasporto, Torino. Rothenberg J., 1970, The Economics of Congestion and Pollution: An Integrated View, American Economic Review, Papers and Proceedings, 60. Rothengatter W., 1994, Do External Benefits Compensate for External Costs of Transport?, Transportation Research, Vol. 28A, 321-328 Rothengatter W., Mauch S., 1994, External effects of transport, Union internationale des chemin de fers, Paris. Rothengatter W., 2003, How Good Is First Best? Marginal Cost and Other Pricing Principles for User Charging in Transport, Transport Policy, 10. Rowe R., Shaw D., Schulze W., 1992, Nestucca Oil Spill, in Ward J., Duffield J., (a cura di), Natural Resource Damages: Law and Economics, John Wiley, New York. Sælensminde K., Hammer F., 1994, Assessing Environmental Benefits by Means of Cojoint Analysis, Institute of Transport Economics, Oslo. Schipper Y., 2004, Environmental Costs in European Aviation, Transport Policy, 11. Schöb R., 2005, The Multi-Mode Ticket: A Pragmatic Solution to Reduce Urban Traffic Congestion, in Arnott R., Rave T., Schöb R., Alleviating Urban Traffic Congestion, Cambridge, MA, MIT Press. Seiler A., 2001, Ecological Effects of Roads. A review, Introductory Research Essay No 9, Department of Conservation Biology, SLU Uppsala. Schelling T.C., 1968, The Life You Save May Be Your Own, in Chase Jr. S. B., (a cura di), Problems in Public Expenditure Analysis, Washington D.C., The Brookings Institution. Schelling T. C., 1984, Choice and Consequence, Cambridge, Harvard University Press. Shechter M., Cohen A., Epstein L., Kim M., Lave L., Mills E., Shafer D., Zeider M., 1988, The Benefits of Morbidity Reduction from Air Pollution Control, University of Haifa, Natural Resource and Environmental Research Center. Shechter M., Kim M., 1991, Valuation of Pollution Abatement Benefits: Direct and Indirect Measurement, Journal of Urban Economics, 30. Shepard D. A., Zeckhauser e R. J., The Choice of Health Policies with Heterogeneous Population, in Fuchs V. R., (a cura di), Economic Aspects of Health, Chicago, University of Chicago Press. Siegrist J., Junge A., 1989, Conceptual and Methodological Problems in Research on the Quality of Life in Clinical Medicine, Social Science and Medicine, 29.

PROMETEIA

292

Small K.A., 1992, Urban Transportation Economics, Harwood Academic Publishers, Chur, Switzerland. Small K.A., Noland R., Chu X., Lewis D., 1999, Valuation of Travel-Time Savings and Predictability in Congested Conditions for Highway User-Cost Estimation. NCHRP Report 431, Washington, DC: National Academy Press. Smith K., Desvouges W. H., 1985, Averting Behavior: Does it Exist?, Working Paper, Nashville, Vanderbilt University, Dept. of Economics. Soguel N., 1994, Evaluation monétaire des atteintes a l’environnement: Une étude hédoniste et contingente sur l’impact des transports, Imprimerie de L’evolve SA Neuchatel. Stokey E., Zeckhauser R., 1978, A Primer for Policy Analysis, New York, W. W. Norton, (ed. it: Introduzione all'analisi delle decisioni pubbliche. Napoli, FORMEZ, 1988). Swanson T., Kontoleon A., 2003, What is the role of environmental valuation in the courtroom? The Us experience and the proposed EU directive, University College, London, mimeo. Thompson M. S., 1986, Willingness to Pay and Accept Risk to Cure Chronic Diseases, American Journal of Public Health, 76. Tinch R., 1995, Valuation of Environmental Externalities, Report to the Department of Transport, London. Tolley G., 1994, Overview, in Tolley e al., cit. Tolley G., Kenkel D., Fabian R., (a cura di), 1994, Valuing Health for Policy. An economic Approach, Chicago, Chicago University Press. Torrance G. W., 1976, Social Preferences for Health States. An Empirical Evaluation of Three Measurement Techniques, Socio-economic Planning Sciences, 10. Torrance G. W., 1986, Measurement of Health State Utilities for Economic Appraisal, Journal of Health Economics, 5. Torrance G. W., Feeney D., 1989, Utilities in Quality-Adjusted Life Years, International Journal of Technology Assessment in Health Care, 5. Train K., 1976, Work Trip Mode Split Models: An empirical Exploration of Estimate Sensitivity to Model and Data Specification, Working Paper No. 7602, Urban Travel Demand Forecasting Project, Institute of Transportation Studies, University of California at Berkeley. Unione Petrolifera, 2006, Previsioni di domanda energetica e petrolifera italiana 2006-2020. UNITE, 2001, D3, Marginal Cost Methodology. US Bureau of Public Roads, 1964, Traffic Assignment Manual, Washington DC: US Bureau of Public Roads. Uyeno D., Hamilton S. W., Biggs A., 1993, Density of Residential Land Use and the Impact of Airport Noise, Journal of Transport and Economics and Policy, 27(1). Vainio M., 1995, Traffic noise and air pollution: Valuation of Externalities with the hedonic price and contingent valuation methods, PhD Thesis, School of Economics and Business Administration, Helsinki. Vainio M., Paque G., 2002, Highlights of the workshop on the State-Of-The-Art in Noise Valuation, European Commission, DG Environment. van de Bossche M., Certan C., Goyal P., Gommers M., Sansom T., 2001, Deliverable 3; Marginal Cost Methodology, UNITE (UNIfication of accounts and marginal costs for Transport Efficiency), University of Leeds, Leeds.

PROMETEIA

293

Verhoef E., 1996, The Economics of Regulating Road Transport, Edward Elgar, Cheltenham. Vickrey W., 1969, Congestion theory and transport investment, American Economic Review Papers and Proceedings, 59. Viscusi W.K., Aldy J. E., 2003, HThe Value of a Statistical Life: A Critical Review of Market Estimates throughout the WorldH, HNBER Working PapersH 9487, National Bureau of Economic Research. Wagstaff A., QALYs and the Equity-Efficiency Trade-Off, Journal of Health Economics, 10. Wardman M., 1998, The Value of Travel Time: A Review of British Evidence, Journal of Transport Economics and Policy, 32(3). Waters II W.G., 1992, Values of Travel Time Savings and the Link with Income, Paper prepared for presentation at the Annual Meeting of the Canadian Transportation Research Forum, Banff, Alberta. Weinstein M. C., Stason W. B., 1977, Allocation of Resources to Manage Hypertension, New England Journal of Medicine. Weinstein M. C., Stason W. B., 1977, Foundations of Cost-Effectiveness Analysis for Health and Medical Practices, New England Journal of Medicine. Weisbrod B. A., 1971, Costs and Benefits of Medical Research: A Case Study of Poliomyelitis, Journal of Political Economy, 79. Wiklund I., 1992, Methods for Assessing Quality of Life in the Cardiac Arhythmia Suppression Trial (CAST), Quality of Life Research, 1. Wills I., 1995-1996, Environmental Problems, Policy, 11. Wilson F. R., Bisson B. G., Kobia K. B., 1986, Factors that Determine Mode Choice in the Transportation of General Freight, Transportation Research Record 1061. Transportation Research Board, National Resesarch Council, Washington DC. Working Group on Health and Socio-economic Aspects, 2003, Valuation of Noise, Position Paper. Zeckhauser R. J., 1975, Procedures for Valuing Lives, Public Policy, 23. Zeckhauser R. J., Shepard D., 1976, Where Now for Saving Lives?, Law and Contemporary Problems, 40. Zeitouni N., Freeman S., 1998, Options, Quasi Options, and the Opportunity to Develop a Resource of Environmental Value, Nota di lavoro FEEM, Milano, www.feem.it.

Documents

ECONOMICS, MEASUREMENT AND FUTURE OUTLOOK OF THE EXTERNAL COSTS OF