Road Transport, Economic Growth and Carbon …...Carbon dioxide, the main greenhouse gas, has increased dramatically since 1950 and accounted in 2010 for more than 75% of global GHG

Road Transport, Economic Growth and Carbon Dioxide Emissions in the BRIICS: Conditions For a Low Carbon Economic Development Barakatou Atte-Oudeyi, Bruno Kestemont and Jean-Luc De Meulemeester In this article, we investigate the relationship between economic growth and CO2 emissions per capita due to road transport in order to test the validity of the Environmental Kuznets Curve (EKC) hypothesis. We test an EKC model on a sample of six emerging countries (Brazil, Russia, India, Indonesia, China and South Africa so-called BRIICS) using yearly data from 2000 to 2010. Empirical results reveal an inverted U-shaped EKC curve relating CO2 emissions per capita due to road transport to the level of economic development (level of GDP per capita). In all models tested, the turning point exceeds the current GDP per capita of the richest country of the group, which means that it would happen virtually in a far future or after a strong growth episode. Results show that the turning point of this EKC for road transport depends on population density and the integration of government effectiveness into the BRIICS’s economic development policy. However, when Russia is omitted from the group, the EKC hypothesis does not hold anymore and CO2 emissions per capita are uniformly increasing with per capita GDP. The main policy implication from our results is that policy makers should not base their policy on the EKC hypothesis: increasing the per capita GDP level alone cannot reduce CO2 emissions per capita from road transport and without a significant change in policy, economic growth will exacerbate CO2 emissions. Keywords: BRIICS; Road Transport; Economic Growth; CO2 Emissions; Environmental Kuznets Curve; Panel Data; Pooled OLS Regression Model; Fixed-Effects and Random-Effects Regression Models. JEL Classifications: Q53, Q56, Q58, R41

CEB Working Paper N° 16/023 June 2016

Université Libre de Bruxelles - Solvay Brussels School of Economics and Management

Centre Emile Bernheim ULB CP114/03 50, avenue F.D. Roosevelt 1050 Brussels BELGIUM

e-mail: [email protected] Tel.: +32 (0)2/650.48.64 Fax: +32 (0)2/650.41.88

1

Road Transport, Economic Growth and Carbon Dioxide Emissions in the BRIICS:

Conditions For a Low Carbon Economic Development

Barakatou ATTE-OUDEYI1

Bruno KESTEMONT1,2

Jean-Luc DE MEULEMEESTER1,3

ABSTRACT

In this article, we investigate the relationship between economic growth and CO2 emissions

per capita due to road transport in order to test the validity of the Environmental Kuznets

Curve (EKC) hypothesis. We test an EKC model on a sample of six emerging countries

(Brazil, Russia, India, Indonesia, China and South Africa so-called BRIICS) using yearly data

from 2000 to 2010. Empirical results reveal an inverted U-shaped EKC curve relating CO2

emissions per capita due to road transport to the level of economic development (level of

GDP per capita). In all models tested, the turning point exceeds the current GDP per capita of

the richest country of the group, which means that it would happen virtually in a far future or

after a strong growth episode. Results show that the turning point of this EKC for road

transport depends on population density and the integration of government effectiveness into

the BRIICS’s economic development policy. However, when Russia is omitted from the

group, the EKC hypothesis does not hold anymore and CO2 emissions per capita are

uniformly increasing with per capita GDP. The main policy implication from our results is

that policy makers should not base their policy on the EKC hypothesis: increasing the per

capita GDP level alone cannot reduce CO2 emissions per capita from road transport and

without a significant change in policy, economic growth will exacerbate CO2 emissions.

Keywords: BRIICS; Road Transport; Economic Growth; CO2 Emissions; Environmental

Kuznets Curve; Panel Data; Pooled OLS Regression Model; Fixed-Effects and Random-

Effects Regression Models.

JEL-Codes: Q53, Q56, Q58, R41

1 Solvay Brussels School of Economics and Management, Université Libre de Bruxelles, Belgium. 2 Statistics Belgium, Belgium 3 Contact person: 50 av FD Roosevelt, 1050 Brussels, [email protected]

2

Road Transport, Economic Growth and Carbon Dioxide Emissions in the BRIICS:

Conditions For a Low Carbon Economic Development

Barakatou ATTE-OUDEYI4

Bruno KESTEMONT1,5

Jean-Luc DE MEULEMEESTER1,6

I. INTRODUCTION

Economic growth is a key objective for developing countries. During the past two decades,

both theoretical and empirical analyses have been developed allowing for the identification of

several key determining factors of economic growth7. Besides labour force, physical and

human capital, technology and the role of institutions (Acemoglu 2009) the development of

transport infrastructure – particularly rail infrastructure – has been put forward as a growth

factor in Europe during the Industrial Revolution (Rosenstein-Rodan 1943; 1976; Deane and

Cole 1967) and in the United States in the second half of the nineteenth century (Fogel 1962).

Following a recent survey of the economic history literature on the role of transportation

(Bogart, 2013, p. 174), it should be stressed that a debate concerning the indispensability of

railroads for the more developed economies has existed, stressing “how different transport

modes can serve as substitutes”. Following Bogart (2013) the consensus was greater regarding

the critical character of railways transportation for development in less developed countries

like Mexico and Brazil (Coatsworth 1979; Summerhill 2005). Today and mainly in

developing countries, the impact of road transport on economic growth is stressed,

increasingly displacing the role of other forms of transport, including rail (Fay and Yepes

2003, table 4). The World Bank (1994), in a report devoted to the worldwide development of

infrastructure, described transport infrastructure – and roads in particular – as a key driver of

economic activity and as improving living standards of people, especially those in developing

countries. The relationship between road transport and economic growth has therefore been

the subject of several studies (such as Fernald 1999 and Banerjee, et al. 2012) in the recent

years. Most of these studies agree on a positive relationship.

Following a recent OECD report (2016), the short-run rebound effect of road transportation is

estimated to be 17-18%, whereas the long-run effect would be about 32-33%. Lower income,

4 Solvay Brussels School of Economics and Management, Université Libre de Bruxelles, Belgium. 5 Statistics Belgium, Belgium 6 Contact person: 50 av FD Roosevelt, 1050 Brussels, [email protected] 7 For a recent textbook with connections with environmental issue see Weil (2009).

3

higher gasoline prices and higher density of public transport infrastructure are associated with

larger rebound effects.

During the last fifteen years, a number of new elements have contributed to a change in

perceptions of the role of these infrastructures. The first element is global warming due to

increasing concentrations of greenhouse gases (GHG) such as carbon dioxide (CO2), methane

(CH4) or dioxide nitrogen (NO2) in the atmosphere (Stocker, et al., 2013). Carbon dioxide, the

main greenhouse gas, has increased dramatically since 1950 and accounted in 2010 for more

than 75% of global GHG emissions in the world (OECD, 2012). The second element concerns

the role of transport and particularly road transport in carbon dioxide emissions. In 2010, the

transport sector generated 22% of CO2 emissions in the world (figure 1.A). The majority of

these emissions comes from road transportation, which accounts for 74 % of CO2 emissions

from the entire transport sector (figure 1.B).

Figure 1: Total worldwide CO2 emissions from fuel combustion, 2010.

Source: Authors’ elaboration with data from the IEA (2012)

With these two observations in mind, the main aim of this study is to analyse the link between

CO2 emissions from road transport and economic growth in six emerging countries (BRIICS).

We try to assess whether a low-carbon transport-driven economic development possible.

This study is organised as follows: in section two we review the literature on the relationship

between economic growth and environmental quality through the EKC hypothesis. In section

three we describe the socio-economic and environmental profiles of the BRIICS countries. In

section four we present the theoretical models and data, while empirical results and discussion

are included in section five. Finally, section six summarises the main results and concludes.

Electricity and heat

production + other

industrial energy

46%Manuf. industries

and construction

21%

Transport22%

Residential6%

Other5%

A. By sector

Road74%

Other Transp

26%

B. By mode of transport

4

II. LITERATURE REVIEW

Numerous studies (such as Fernald 1999; Banister and Berechman 2001;McKinnon 2007;

Lakshmanan 2007; Didier et al. 2007 and Banerjee, et al. 2012) confirm the causal impact of

road infrastructure on a country’s economic growth. These studies, however, are limited by

their lack of specific estimates of the costs or damages that these infrastructures have on the

environment (deforestation, loss of biological diversity, air and water pollution). Authors such

as F. K. Rioja (2001) found that an increase in economic activity in response to an increased

mobility of people and more important movements of goods may be detrimental to the

welfare of the population due to environmental degradation. In recent years, scientific

research in different fields has also shown and confirmed that human and economic activity

influences environmental quality. An increase in mobility of people and movement of goods

requires a greater amount of energy consumption, which has a negative influence on

environment due to increased pollutants emissions, particularly carbon dioxide.

The relationship between economic growth and pollutants – carbon dioxide in particular – has

been widely explored in the empirical literature in recent years, mainly through the

Environmental Kuznets Curve (EKC) hypothesis. The concept of EKC, named after Simon

Kuznets (1955) who considered an inverted U-shaped curve between economic development

and income inequality, was projected on the income-environmental degradation relationship

by Grossman and Krueger (1991) in the early nineties.

Figure 2: Environmental Kuznets Curve (EKC) hypothesis

Source: Authors’ elaboration

Following this hypothesis, the emission of pollutants (proxy of environmental quality) is

explained by a quadratic function of the level of per capita income (measure of the level of

Environmental Decay

Turning Point Income

Per Capita Income

Env

iron

men

tal D

eple

tion

Environmental Improvement

5

economic growth). In their study, Grossman and Krueger (1991) used different cross-country

panel data for 1977, 1982 and 1988 on the concentration of three air pollutants (sulphur

dioxide, smoke and suspended particles) in various urban areas of forty-two countries

(including developed and developing countries). They used three samples, forty-two countries

for sulphur dioxide, nineteen countries for smoke (or dark matter) and twenty-nine for

suspended particles. They found that an inverted U-shaped relationship exists between

economic growth and two concentrations of air pollutants (sulphur dioxide and smoke).

Sulphur dioxide and smoke increase with GDP per capita, at low levels of per capita income,

but gradually decrease with GDP per capita at higher income levels.

Table 1: Past studies on different EKC hypothesis.

Source: Authors’ elaboration based on Carvalho and Almeida (2009)

Authors Country/Region Period Dependentvariable Typeofdata Turningpoint Conclusion

GrossmanandKrueger(1991)Urbanareaslocatedin42

countries1977,1982and1988

S02,smokeandSPMconcentrations

PaneldataBetween$5000and$14000

forSO2and$5000forSmoke

EvidencesforanEKCinthecaseofSO2andSmokeemissions.NoneEKCrelationshipisobtainedintheofSPMemissions

Shafik(1994) 149countries 1960‐1986

Lackofsafewater,lackofurbansanitation,SPM,S02,dissolvedoxygenandfecalcoliformsinrivers,deforestation,municipalsolidwasteandC02emissions

Paneldata ‐NoneEKCrelationshipsareobtainedinthecaseofmunicipalsolidwasteandC02emissions.

MoomawandUhruh(1997) 16countries 1950‐1992 CO2emissions Paneldata ‐NoneEKCrelationshipsareobtained.

Coleetal. (1997)7regionsalongtheworld

1960‐1991 CO2emissions Paneldata $25,100

ThefindingsdemonstratethattheglobalimpactofCO2emissionshasprovidedlittleincentiveforcountriesimplementunilateralactionsfortheseemissions

Kaufmannetal. (1998) 23countries 1974‐1989 S02 PaneldataBetween$3000and

$12,500AninvertedUshapedcurvewasfound.

DeBruynetal. (1998)

4countries(Germany,Netherlands,UnitedKingdomand

USA)

1960‐1993CO2,NOxandSO2

emissionsPaneldata ‐

AninvertedUshapedcurvewasnotfound.

AgrasandChapman(1999) 34countries 1971‐1989CO2emissionsand

energyPaneldata

$62,000forenergy

and$13,630forCO2

InvertedUshapedcurvebetweenincomeandenergyandbetweenincomeandCO2

emissions.

DijkgraafandVollebergh(2001) OECDcountries 1960‐1997 CO2emissions PaneldataBetween$13,959and

$15,704

TheexistenceofanoverallinvertedUshapedcurve.ButthefactofthatmanycountriesdonotreflectEKCpatternbecomesparticularlyimprobable.Thecrucialassumptionofhomogeneityacrosscountriesisproblematic.

Lucena(2005) Brazil 1970‐2003 CO2emissions Timeseries ‐EvidencesforanEKCinthecaseofCO2emissions.

Arraesetal.(2006)countries(sample

sizeisnotdefined)

1980,1985,1990,1995and2000

CO2emissionsandotherindicatorsofdevelopment

Paneldata isnotcalculedinthepaperAninvertedUshapedcurveforCO2emissionswasfound.

Olusegun(2009) Nigeria 1970‐2005 CO2emissions Timeseries ‐AninvertedUshapedcurvewasnotfound.

CarvalhoandAlmeida(2009) 167countries 2000‐2004 CO2emissions PaneldataBetween$12,342and

$27,106AninvertedUshapedcurvewasfound.

BoopenandVinesh(2011) Mauritius 1975‐2009 CO2emissions Timeseries ‐AninvertedUshapedcurvewasnotfound.

6

However, contrary to what they observed with sulphur dioxide and smoke, Grossman and

Krueger (1991) discovered that there was no turning point for suspended particles: the curve

was uniformly increasing with GDP per capita. As shown in figure 2 above, the EKC

hypothesis implies a non-constant relation between levels of GDP and environmental

degradation: emission levels grow first, reach a peak (turning point of the curve) and then

start to decline after a threshold level of income has been crossed. As pointed out by Weil

(2009) in his textbook Economic Growth, the logic behind the EKC is fairly straightforward.

“At very low levels of economic development, countries simply do not engage in enough

production to cause significant pollution. As income per capita grows, environmental damage

initially grows with it. At high income level, however a second factor comes to the fore:

People are rich enough to care about pollution and take steps to reduce it. In microeconomic

terms, a clean environment is a luxury good for which people are willing to spend more as

they become wealthier” (Weil, 2009 page 496-497).Consequently, in a highly-developed

country (usually measured by the level of income per capita), an improvement in

environmental quality could be observed, due essentially to structural changes towards

information intensive industries and services, coupled with increased environmental

awareness, enforcement of environmental regulation, better technology, etc as pointed out by

Stern, et al. (1996) and De Bruyn et al.(1998).

The pioneering study of Grossman and Krueger (1991) on the EKC hypothesis has been

confirmed and used in other studies (Shafik 1994; Grossman et Krueger 1995; Kaufmann et

al. 1998). Nevertheless, for some pollutants, in particular carbon dioxide, the results differ

substantially and are inconclusive. Weil (2009) observed also that there is no EKC when it

comes to CO2 emissions. Indeed, Shafik and Bandyopadhyay (1992) have highlighted that the

EKC hypothesis is generally tested for some localised pollution in air and water (Cole et al.

1997), mainly in urban areas whereas the production of urban waste and CO2 emissions

appear to increase uniformly with income and do not seem to face the turning point of the

EKC (Bengochea-Morancho, et al. 2001 and Grubb et al. 2006). According to Weil (2009),

this finding is consistent with the observation that, unlike the effects of other forms of

pollution, the harm done by a given country’s emission of CO2 is concentrated on people who

live far away.

Table 1 above presents several past studies on EKC hypothesis. We see that, among the

various pollutants studied, CO2 emissions seem to occupy an important place and conclusions

do not seem to support the EKC hypothesis for this form of pollution. These studies focused

7

on CO2 emissions at the aggregate level (the total CO2 emitted in a country or region by all the

different sectors). In our study, however, we are going to focus on the link between levels of

economic development and CO2 emissions of one single sector, road transport, for six

emerging economies, the so-called BRIICS countries. The BRIICS countries are classified by

the World Bank as middle-income economies and are therefore seen as the intermediate

economies between high-income economies and low-income economies. If the EKC

hypothesis is verified for the BRIICS, their growth of emissions would reverse in the future,

after their GDP reaches a certain level. In the next section, we present the characteristics of

the BRIICS such as their socio-economic indicators, energy consumption and CO2 emissions

evolutions particularly from road transport.

III. BRIICS

BRIICS is an acronym used to designate the group of the six largest and most important

emerging economies, whose role in the global economy has grown and will continue to grow:

Brazil, Russia, India, Indonesia, China and South Africa. This acronym was used for the first

time in the OECD (2011) report, Economic Policy Reforms: Going for Growth. Some socio-

economic indicators (table 2 below) show that the BRIICS countries are not homogeneous.

They nevertheless have many things in common, such as increasing participation in

international trade, openness of their economies to foreign investment, a steady increase in per

capita income, increasing presence in research and innovation, and dynamism of their

companies on foreign markets.

Table 2: BRIICS Socio-Economic Indicators, 2010.

Sources: World Bank (WDI, 2015)

World 1.16 6,884.35 129,733,917 53.06 4.08 13,134.41

Brazil 0.88 195.21 8,358,140 23.36 7.57 14,659.99

Russia 0.04 142.85 16,376,870 8.7 4.50 21,663.64

India 1.29 1,205.62 2,973,190 405.50 10.26 4,546.59

Indonesia 1.33 240.68 1,811,570 132.86 6.22 8,498.24

China 0.48 1,337.70 9,388,211 142.49 10.63 9,429.50

SouthAfrica 1.46 50.79 1,213,090 41.87 3.04 12,086.92

GDPGrowth(annual%)

GDPperCapita,PPP(contant2011internattional$)

PopulationGrowth(annual%)

Population(million)

LandArea(sq.km)

Populationdensity(people/sq.kmofland

area)

8

As pointed out in the 2012 OECD report, the essential common characteristic of the BRIICS

is their confidence in the future, which nourishes and sustains growth optimism (and maybe

disregard for environmental costs). Goldstein and Lemoine (2013) observed that in 2011, the

total GDP of the BRIC (Brazil, Russia, India and China, but without Indonesia) in purchasing

power parity represents 26% of world GDP against 17% in 2000 (in EU 20% in 2011 against

25% in 2000). As pointed out by Jan-Erik Lane (2013), without energy, economic system

would not accomplish anything. The author observed that the total energy consumption is

linked with total country GDP growth. In this line, the 2012 OECD report highlights that, in

response to their rapid economic growth (coupled with population growth) observed in recent

years, the BRIICS countries will become the largest consumers of energy mainly fossil fuels

and emitters of carbon dioxide in 2050. Data indicate an increase in fossil fuel consumption

from 1,969 million tonnes oil equivalent in 1990 to 4,123 million tonnes oil equivalent in

2010, representing a rise of 109.40% on average growth rate (British Petroleum, 2012).

According to the source of fossil fuels, coal and oil are the dominant energy sources in

BRIICS countries, mainly due to the high consumption of these energy sources by China

(British Petroleum, 2012).

Figure 3: Road sector energy consumption of the BRIICS countries, 1990-2010

Source: Authors’ elaboration with data from the World Bank (WDI, 2013)

Energy consumption in the transport sector follows the same trend. According to the World

Energy Outlook (2012), the energy demand in transport, particularly road transport, will

increase by 40% in 2035. We can observe that total energy consumption by road transport of

all BRRICS and in each country (figure 3.A), and per capita consumption in this sector for all

0

50000

100000

150000

200000

250000

300000

350000

400000

Kg

of

oil

eq

uiv

alen

t

Country/Region

A. Total consumption

19901995200020052010

0

200

400

600

800

1000

1200

1400

1990 1995 2000 2005 2010

Kg

of

oil

eq

uiv

alen

t

Years

B. BRIICS: consumption per capita

9

BRIICS countries (figure 3.B), have dramatically increased in twenty years’ time (by 141%

and 29% respectively from 1990 to 2010 for the BRIICS).

In their paper, K. Saidi and S. Hammami (2015) indicated that there is a bidirectional

causality relationship between energy consumption and economic growth for the four panels.

But their results significantly reject the neo-classical assumption that energy is neutral for

growth. A unidirectional causality running from CO2 emissions to economic growth appears

for some panels (the Latin American and Caribbean ones). Indeed, an increase in fossil fuels

consumption is associated with an increase in emissions of pollutants, in particular carbon

dioxide (CO2). In 2010, carbon dioxide accounted for more than 75 % of greenhouse gas

emissions in the world (OECD, 2012). Between 1990 and 2010, BRIICS countries’ CO2

emissions also increased and in 2010, these countries were among the 20 highest emitters of

CO2 in the world (IEA, 2012). Most of their CO2 emissions are mainly caused by the

production of electricity, energy, manufacturing and construction (81%); only 11% was

associated with transport in 2010 (IEA, 2012). CO2 emissions due to transportation were

largely dominated by CO2 emissions from road transport in the same year (Figure 4.A).

Figure 4: Transport CO2 emissions in 2010

Source: Authors’ elaboration with data from the IEA (2012) and the World Bank (WDI, 2013)

Figure 4.B above illustrates per capita CO2 emissions in tonnes (tCO2) caused by the transport

sector in 2010. We can clearly observe that among the modes of transport in each country, in

the BRIICS countries and in the world, CO2 emissions per capita are largely dominated by

those from road transport. Russia is the only country where per capita CO2 emissions due to

transport are more or less evenly split between road transport and other modes of transport.

This may be partly related to the size of the country which, to some extent, may require other

modes of transport than road (such as rail or airways) to connect the cities. As with most

Road78%

Other Transp

22%

A. BRIICS: Total Transport CO2 Emissions by Mode

0%

20%

40%

60%

80%

100%

Per

cen

tag

e

Country/Region

B. Transport CO2 Emissions per Capita by Mode

Road

Other Transp

10

developing countries, emissions of carbon dioxide due to the road sector in the BRIICS

countries seem to be positively correlated with the rising standard of living. In the next

section, we will proceed to the definition of different models and data that will allow us to

verify this relationship.

IV. THEORITICAL MODEL AND DATA

Model specification

Based on EKC hypotheses and literature (Grossman and Krueger 1991, 1995; Shafik and

Bandyopadhyay 1992; Selden and Song 1994; and Stern 2004), our empirical approach

considers that environmental degradation (EDit), our dependent variable, is related to per

capita income (Yit) and its squared term (Y2it). The inclusion of the squared income will allow

us to verify if there is an inverted U-shaped curve between levels of income and carbon

dioxide emissions per capita. The theoretical expected sign of the squared income coefficient

is negative and should be significant. Following Selden and Song (1994), our basic model is

given by:

1

where i is a country index (i = 1,…, N), t is a time index (t = 1,…, T) and ε is a error term with

mean zero and finite variance. The regression coefficients are (the intercept), and (the

slope parameters).

The ‘‘turning point’’ , the level of income where emissions or concentrations are at a

maximum, is given by the derivative of the functions (1):

2⁄ 2

The sign and the magnitude of and are important and emissions can be said to exhibit a

meaningful Kuznets relationship when 0 and 0 (see Richmond et al. 2006 and

Plassmann and Khanna 2007 for the assessment and the intuition on turning point). It is

important to stress here that a basic issue to address concerns the inclusion of other

explanatory variables than per capita GDP. Additional explanatory variables, mainly socio-

economic variables, (SEit) are added in our extended model. The extended model allows us to

determine what happens to the EKC after other explanatory variables are added and

11

consequently to verify whether these variables have an influence on environmental status. The

extended model therefore is:

´ 3

where ´ is a vector of coefficient ( ,…, )´ of the socio-economic variable. Exogenous

factors omitted from model (3) but affecting road transport CO2 emissions (for instance a

country’s institutions, culture, climate and geography) impact on the idiosyncratic error term

( ) which captures the effects of all relevant omitted variables in the model. The latter could

be correlated across all periods for a particular country (or among countries for a given

period). To address these issues (see Hsiao, 1986), the combined error term ( ) is specified

as follow:

Where , and respectively denote unobserved country effects (i.e. country-specific

effects which is time invariant variables), unobserved time effects (i.e. time-specific effects

which is country/individual invariant variables) and idiosyncratic error term. “The simplest

and most intuitive way to account for individual and/or time differences in behaviour, in the

context of panel data regression problem, is to assume that some of the regression coefficients

(the intercept and the slope parameters) allowed to vary across individual and/or through

time” (Mátyás and Sevestre 1996 chapter 1 and 2).

To control for the country-specific effects ( the time invariant parameters) in by

considering model (4), country-fixed effects and country-random effects regressions are

therefore performed. In the country-fixed effects model, the is absorbed by the intercept

), whereas in the country-random effects model it is treated as component of the random

disturbance.8

Time-specific effects assume that all countries are affected in the same way over the

time. Results show that no time-specific effects are needed in this case. Thus, these results

imply that the combined error term will be rewrite as in the case of this

study where only the country-specific effects will take into account. The extended model (3) is

therefore rewrite as follow:

´ 4

8 See appendix 1 for further details

12

For econometric estimation, two specification tests are therefore applied to evaluate the

significance of these models. One of them allows us testing for the absence of a simple pooled

ordinary least squares (OLS) regression and the other one allows us choosing the most

appropriate model between country-fixed effects and county-random effects regressions when

the absence of a simple pooled OLS regression is rejected.9

Data

o Dependant variable

Carbon dioxide emission due to road transport in kiloton per capita (CO2_ROAD), the

dependent variable, is used as the proxy for environmental degradation in this study. CO2

emissions are defined as those stemming from the burning of fossil fuels by the road transport

sector. Data sources are the International Energy Agency (IEA, 2012), International Transport

Forum (ITF, 2013) and OECD (2013).

o Explanatory variables

The main explanatory variable is per capita GDP (GDP_CAP): since road CO2 emissions are

the result of economic growth, per capita GDP and its quadratic term are specified for the

EKC relationship. Per capita GDP in PPP is the total annual output of a country’s economy in

purchasing power parity which allows international comparison of GDP across years without

interference from the effects of inflation. Data are in constant 2011 international dollars. The

data source is the World Bank, International Comparison Program database (2015).

Population density and the government effectiveness index are used as socioeconomic

variables.

Population Density (POP_DENS): population can be a driving force behind the increase in

emissions, in particular for the road transport sector. Previous studies (Shafik and

Bandyopadhyay 1992; Grossman and Krueger 1995) have put forward that emissions, in

particular associated with transportation (Selden and Song 1994) are actually lower when

people live closer together. Thus, population is introduced in the models through its density.

Population density is therefore defined by mid-year population divided by land area in square

kilometres. The sources for estimates of land area and population data are the Food and

Agriculture Organization (FAO) and World Bank, respectively.

9 See page 11

13

Government Effectiveness Index (GOV_EFF): government effectiveness can positively

influence environmental quality (Dudek et al. 1990; Dietz, et al. 2003; Urwin and Jordan

2008; Weil 2009) through higher performance of better designed policies, and their more

effective implementation. The Government Effectiveness Index is a measure of the quality of

public service provision, the quality of the bureaucracy, the competence of civil servants, the

independence of the civil service from political pressures, the quality of policy formulation

and implementation, and the credibility of the government's commitment to such policies.

This is measured in units ranging from about -2.5 to 2.5 (Kaufmann, et al. 2010), with higher

values corresponding to better governance outcomes. The data source for this variable is from

Kaufmann, et al. (2009) and the Worldwide Governance Indicators (WGI, 2013).

Table 3: Description of the Variables

Following the above basic and extended model specifications one basic equation (5) is

retained for this study:

_ _ _ 5

and two extended equations (6) and (7):

Variables DescriptionExpectedSign

ofβi* DataSources

CO2_ROAD(dependentvariable)

DioxidCarbonemissionsduetoroadtransportoverpopulationbycountry(inktpercapita)

InternationalEnergyAgency(IEA,2012),

InternationalTransportForum(ITF,2013)and

OECD(2013)

GDP_CAPGDPpercapitabasedonpurchasing

powerparity(constant2011international$)

+World Bank, International

Comparison Program database (2015)

GDP_CAP2SquaredGDPpercapitabasedonpurchasingpowerparity(constant

2011international$)‐

WorldBank,InternationalComparisonProgramdatabase(2015)

POP_DENSPopulationoverthegeographical

area(insq.Km)bycountryi.epeoplepersq.kmoflandarea

‐FoodandAgricultureOrganisation(FAO)andWorldBank(WDI,2013)

GOV_EFFGovernmentEfectivenessIndexinunitsrangingfromabout‐2.5(bad)

to2.5(good)‐

Kaufman,etal.(2009)andtheWorldwide

GovernanceIndicators(WGI,2013)

*i=1to4correspondingrespectivelytoeachexplanatoryvariables

14

_ _ _ _ 6

_ _ _ _ _

7

Panel data methodology was applied to estimate models described by formulas (5), (6) and (7)

presented above. The sample consists of the BRIICS countries yearly between 2000 and 2010.

The 2009 data for carbon dioxide emission due to road transport for each country consists in

the mean of 2008 and 2010 data. The basic model (5) allows the comparison of the results

with those obtained by Selden and Song (1994).

As emphasised in most studies, previous econometric estimations, particularly in the case of

panel data (pooled model) with the simple OLS, had some limitations such as unobserved

factors, heterogeneity or omitted variable bias (the latter, for example, occurs when the

omitted variable is correlated with the explanatory variable, and is a determinant of the

dependent variable). This is not easy to detect in a multivariate regression setting. To

overcome the shortcomings of this estimation method, due essentially to individual (country)

effects, techniques of panel data are the most recommended (see Hsiao 1986; Mátyás and

Sevestre 1996; Wooldridge 2002; Kennedy 2003; Stock and Watson 2003; Verbeek 2008 and

Baltagi 2008). To control for country-specific effects, two modelling approaches, namely

fixed-effects model (FE) and random-effects model (RE), are therefore performed. Two

specification tests of individual effects, the Lagrange Multiplier test of Breusch and Pagan

(1980) and the Hausman test (1978), are applied to evaluate the significance of the models

and to allow the choice of the most appropriate model for econometric estimation.10

In this analyse, the pooled OLS regression, fixed-effects models and random-effects regression

are presented for the three models pointing out above. For both the basic empirical model (5)

and the two extended model (6) and model (7), the presence of a simple pooled OLS

regression, fixed-effects or random-effects regression specifications tests are also performed. 11

V. EMPIRICAL RESULTS AND INTERPRETATION

Results for the BRIICS countries

The descriptive statistics used in this study are given in table 4 below.

10 See appendix 2 for further details 11 See below on each regression results table.

15

Table 4: Descriptive statistics

The results of the estimation for model (5), (6) and (7) are shown in table 5 below. Overall,

the results are strong given the small number of observations. Indeed, the results appear to be

quite stable across alternative formulations. Estimates of the main parameters of interest,

and , all have the expected signs (see table 3) and are typically different from zero at high

levels of significance.

Table 5: Environmental Kuznets Curve (EKC) regressions

Standard errors are in parentheses. ***significant at 0.01 level; ** significant at 0.05 level and * significant at 0.10 level.

Constant term for fixed-effect models includes the mean of the estimated country effects (α).

RH0 (NRH0): rejection of the null hypothesis (non-rejection of the null hypothesis) at 5% significance.

Given the method of estimation, the results reveal evidence for an inverted U-shaped EKC for

per capita CO2 emissions due to road transport and per capita GDP when both the basic and

Variables Observations Mean Max Min Std.Dev

CO2_ROAD(kiloton) 66 477.82 986.95 82.16 303.97

GDP_CAP($1000) 66 9.71 22.51 2.55 5.16

POP_DENS(people/sq.kmoflandarea) 66 118.57 405.50 8.67 127.50

GOV_EFF 66 ‐0.04 0.69 ‐0.77 0.35

VariablesCoefficients

(βi)PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

Constant β0 ‐284.9717*** 168.5773*** 143.7892*** ‐253.9308*** 218.5294*** 225.1891*** ‐107.4198 225.3554*** 239.0236***

(36.7753) (10.3084) (34.9728) (72.3940) (12.9669) (29.4304) (75.8842) (22.7709) (32.7784)

GDP_CAP β1 109.5613*** 33.9154*** 37.6279*** 105.7075*** 38.7693*** 40.9956*** 77.4810*** 38.3538*** 40.1064***

(8.8547) (2.1750) (3.9636) (12.1671) (3.0822) (2.9966) (12.0112) (4.7545) (4.2321)

GDP_CAP2 β2 ‐2.4964*** ‐0.1662 ‐0.2596 ‐2.3804*** ‐0.3075* ‐0.3694* ‐1.1264** ‐0.2995 ‐0.3516

(0.3759) (0.1498) (0.2145) (0.4581) (0.1783) (0.1828) (0.4527) (0.2116) (0.2162)

POP_DENS β3 ‐0.0642 ‐0.6752*** ‐0.8507*** ‐0.1779 ‐0.7019*** ‐0.8982***

(0.1248) (0.1560) (0.2955) (0.1235) (0.0744) (0.0760)

GOV_EFF β4 230.9459*** 13.1036 39.0728

(32.8022) (59.6049) (36.0992)

TurningPoint 21,944 102,032 72,473 22,204 63,040 55,489 34,393 64,030 57,034

N 66 66 66 66 66 66 66 66 66

R2 0.8821 0.9914 0.7452 0.8823 0.9916 0.7743 0.9367 0.9916 0.7877

AdjustedR2 0.8784 0.9904 0.7371 0.8766 0.9904 0.7634 0.9326 0.9903 0.7737

Model5 Model6 Model7

Dependentvariable:CO2_ROAD(inktpercapita)

LMstat. P‐value H.stat. P‐valueModel5 68.44 0.00 RHO 8.23 0.02 RHO Fixed‐EffectsModelModel6 72.35 0.00 RHO 4.32 0.23 NRHO Random‐EffectsModelModel7 43.34 0.00 RHO 9.60 0.05 NRHO Random‐EffectsModel

LMtestModels

HausmantestConclusion

16

the two extended models are used. Thus, there appears to be evidence to reject the null

hypothesis that emissions are monotonically increasing in per capita GDP. The estimated

coefficients on the square term of per capita GDP-squared are negative and significant except

for the fixed-effects and random-effects regressions for model (5) and model (7), where the

coefficients are not significant. Moreover, population density typically enters with the

predicted sign and always significantly different from zero at the high levels of significance

except for pooled regression. We can observe that an increase in population density decreases

the turning point.

The government effectiveness coefficients do not have the expected signs and do not seem to

be significant for increasing CO2 emissions from road transport except pooled regression. To

sum up, the estimated coefficients indicate that per capita CO2 emissions due to road transport

increase as the BRIICS countries develop but tend to decrease once a threshold in terms of

GDP per capita is being approached. Perhaps the most interesting findings concern the point

at which an increase in per capita GDP decreases per capita CO2 emissions due to road

transport, the so-called turning point. Our highest estimated turning point exceeds $100,000.

The highest turning point is observed for the fixed-effects regression, whereas the smallest for

the pooled regression. These two extreme thresholds of the turning point are observed for the

basic model (5). However, the Lagrange Multiplier test (LM) of Breusch and Pagan (1980)

and the Hausman test (1978) show that the fixed-effects regression is appropriate for model

(5), where the turning point is $102,032 per capita and the random-effects regression for

models (6) and (7), where the turning is reached at $55,489 and $57,034 per capita

respectively. All the estimated turning points occur on a much higher per capita GDP than the

observed current per capita GDP ($21,663 for Russia, see table 2). For instance, in the case of

the fixed-effects regression for the basic model (5), the turning point in terms of GDP per head

should be high as four times the contemporary Russian GDP per head ($102,032/21,663).

These turning points could be observed in the future without quite high sustained growth

assumptions.

The turning point estimates appear to be strongly sensitive to the inclusion of population

density (as Selden and Song (1994)) and government effectiveness. Both the fixed-effects and

random-effects regressions yield quite qualitatively similar results. When population density

and government effectiveness are included simultaneously in the basic model (i.e. model (7)),

the positive impact of population density on environmental quality decreases slightly but

remains strongly significant. In contrast, the turning point in this model increases slightly

compared to model (6).

17

Robustness check

o Results for the BIICS countries

Table 6: Descriptive statistics without Russia (BIICS)

The inclusion/exclusion of individual countries was tested to check the robustness of these

models. It appeared that the exclusion of Russia (the most developed country in our sample)

changed the conclusion. In this group, as shown in table 2 above, Russia is a larger country in

terms of land area, with a smaller population density (8.7 capita per km2) coupled with a

higher income per capita. These characteristics can therefore explain why Russia is the only

country in this group where per capita CO2 emissions of transport are more evenly split

between road transport and other modes of transport (see figure 4.B). Table 6 above gives the

descriptive statistics for the five countries (BIICS).

The estimation of the EKC model (5), (6) and (7) for the BRIICS without Russia (BIICS) is

shown in table 7 below through the three methods. It is clear that the results obtained for the

estimated coefficients on the square term of per capita GDP-squared are not those expected or

predicted by the EKC theory. These coefficients are not significant for model (5) and (6). The

null hypothesis which states that per capita CO2 emissions from road transport are

monotonous and increasing with per capita GDP may therefore be accepted when Russia is

excluded from the group. Indeed, these results shows that the inverted U-shaped EKC

between the per capita CO2 emissions generated by the road transport sector and per capita

GDP does not exist for these five countries and the results point to a monotone and increasing

curve: a turning point will never occur.

An increase in population density (i.e. when people live closer together) seems to contribute

to improve the quality of the environment associated with road transport as shown by a highly

significant coefficient with the predicted sign. Here also, the results of the impact of

population density on the degradation of the environment related to road transport confirm

Variables Observations Mean Max Min Std.Dev

CO2_ROAD(kiloton) 55 409.06 886.71 82.16 283.70

GDP_CAP($1000) 55 8.04 14.66 2.55 3.61

POP_DENS(people/sq.kmoflandarea) 55 140.52 405.50 20.88 128.93

GOV_EFF 55 0.04 0.69 ‐0.45 0.32

18

those obtained by Selden and Song (1994). When population density and government

effectiveness are included simultaneously in the basic model (model (7)), the impact of

population density decreases slightly but remains strongly significant. Again the estimated

coefficients of government effectiveness do not have the expected signs but are significant

when the pooled and random-effects models are considered. This contrasting result shows the

impact of Russia in the explanation of the inverted U-shaped EKC obtained above for the

entire group as indicated due essentially to its socio-economic characteristics (see table 2).

Again, in order to choose the most appropriate regression model for each models, the

Lagrange Multiplier test (LM) of Breusch and Pagan (1980) and the Hausman test (1978) are

performed. The results show that the fixed-effects model specification is the preferred option

only for model (7), while the random-effects model specification is preferred for the models

(5) and (6).

Table 7: Environmental Kuznets Curve (EKC) regressions without Russia


Constant terms for fixed-effect models include the mean of the estimated country effects (α).

RH0 (NRH0): rejection of the null hypothesis (non-rejection of the null hypothesis) at 5% significance.

o Quality of bureaucracy as measure of government effectiveness


(βi)PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

Constant β0 ‐160.4812** 170.2168*** 161.2213*** ‐60.5024 220.6144*** 236.2685*** 146.9681 268.9481*** 146.9681**

(71.9809) (16.8036) (31.4530) (117.4669) (17.8412) (48.0223) (117.8464) (29.8509) (71.1449)

GDP_CAP β1 67.8738*** 25.0270*** 26.0564*** 52.7995* 31.5627*** 33.8753*** 16.3572 16.1726** 16.3572

(23.3696) (5.1916) (5.6372) (28.9263) (5.3160) (4.5656) (24.1313) (8.0768) (14.8166)

GDP_CAP2 β2 0.3065 0.4852 0.4945 0.9033 0.2005 0.1286 2.2083* 1.0827*** 2.2083

(1.4535) (0.3465) (0.3957) (1.6497) (0.3530) (0.3248) (1.2582) (0.3900) (0.7554)

POP_DENS β3 ‐0.1779 ‐0.5757*** ‐0.7798*** ‐0.3602** ‐0.5491*** ‐0.3602***

(0.1264) (0.0560) (0.2251) (0.1538) (0.0732) (0.1019)

GOV_EFF β4 275.3812*** 91.3428 275.3812***

(31.4517) (88.4922) (11.8689)

TurningPoint ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

N 55 55 55 55 55 55 55 55 55

R2 0.8603 0.9924 0.6917 0.8617 0.9926 0.7195 0.9381 0.9931 0.9381

AdjustedR2 0.8549 0.9914 0.6799 0.8535 0.9915 0.7030 0.9332 0.9919 0.9332



LMstat. P‐value H.stat. P‐valueModel5 40.63 0.00 RHO 5.47 0.06 NRHO Random‐EffectsModelModel6 44.97 0.00 RHO 3.29 0.35 NRHO Random‐EffectsModelModel7 35.03 0.00 RHO 365.38 0.00 RHO Fixed‐EffectsModel

ModelsLMtest Hausmantest

Conclusion

19

We have observed that the estimated coefficients on the government effectiveness variable are

not always significant for both the BRIICS and the BIICS. We check for the robustness of this

result by using one of the government effectiveness variable components, i.e. the quality of

the bureaucracy (BQ). The quality of the bureaucracy (BQ) is measured in units ranging from

about 0 (lowest) to 1 (highest). The data source is the International Country Risk Guide

(ICRG, 2014). Thus, through the extended model (7), the estimation of EKC is provided by

table 8 (appendix 4). For the BRIICS, the results are consistent with the earlier finding. The

main coefficients have all the expected signs and are significant. For the BIICS, the results

diverge compared to earlier results. Both the BRIICS and BIICS, the coefficients estimate of

the quality of the bureaucracy do not have the expected sign and are not always significant as

observed for the aggregate indicator (GOV_EFF) and confirms our earlier results. However,

the turning point observed for the BRIICS by using bureaucracy quality is smaller comparing

to the earlier result obtained with the aggregate indicator ($30,003 against $34,393 per capita

for pooled model, $61,917 against $64,030 per capita for fixed-effects model and $49,134

against $57,034 per capita for random-effects model). The turning point is observed for the

BIICS when the pooled model and random-effect model are considered. The Lagrange

Multiplier test (LM) of Breusch and Pagan (1980) and the Hausman test (1978) show that

fixed-effects regression model (appendix 3, table 8) is preferred for both the BRIICS (where

the turning point is slightly close to the preferred model in the early result) and for the BIICS

(which confirm the earlier results).

o Impact of the world economic crisis 2007-2009

To show that our results are not affected by the recent economic crisis, we dropped 2007,

2008 and 2009 data from our estimations both for the BRIICS and the BIICS. The estimations

of for the BRIICS and the BIICS are to be found in table 9 and table 10 respectively.12 All

estimated coefficients have the expected signs and confirm our earlier finding for the BRIICS.

However, the amount of the turning point for the BRIICS without 2007-2009 data is higher in

fixed-effects and random-effects regression and smaller in the pooled regression compared to

the whole sample. As we can observe the coefficients of squared GDP per capita value for

model (5) and (6) are not significant for the fixed-effects and the random-effects regression

and make their turning point no significant when 2007-2009 are excluded. The turning point

is found for the BIICS only for the pooled regression and fixed-effects respectively for model

(5) and model (7). Again, the Lagrange Multiplier test (LM) of Breusch and Pagan (1980) 12 See appendix 3

20

and the Hausman test (1978) are performed and result show that for both the BRIICS and the

BIICS, the random-effects model regression is the preferred option only for model (6), while

the fixed-effects model specification is preferred for the models (5) and (7).

Discussion

Figure 5 below illustrates this contrasting result by showing the various patterns of different

countries taken individually, from a growing N-shape for Indonesia and Russia to an apparent

beginning of EKC for South Africa, India and China. The densely populated countries (China,

India) tend to have a flatter inverted U-shape, which suggests a more “road-efficient” growth

in densely populated areas, although these countries have ratified the Kyoto Protocol.

Figure 5: BRIICS, link between revenue and CO2 emissions from transport (2000-2010)

Source: Authors’ elaboration with data from the IEA (2012) and the World Bank (WDI, 2015)

The Kyoto Protocol contains a specific compromise for industrialised economies and

economies in transition to reduce their CO2 emissions below their 1990 level throughout the

period 2008-2012 (5.2%). However, no compromise has been made for developing countries,

21

in particular BRIICS countries, grounded on the argument that the industrialisation process

and development should not be limited by any constraint for generating and consuming

energy (Galeotti and Lanza 1999). Although international agreements can be important in the

reduction of greenhouse gases, the emissions need to be targeted according to each country’s

responsibility in the total amount of emissions.

Figure 6: International trade balance of CO2 emissions (1990-2008)

Source: Authors’ elaboration with data from Peters et al. (2011).

As Muradian, et al. (2002), Xu and Dietzenbacher (2014) observed in their recent study that

most emitting industries which are located in developed countries (USA and EU) have

relocated carbon emissions from their countries to developing countries, particularly emerging

countries. The authors observed that this phenomenon of industries relocation increases the

international trade of carbon. Davis et al. (2011) found that, in 2004, 10.2 billion tons CO2

(37%) of global emissions were from fossil fuels traded internationally and an additional 6.4

billion tons CO2 (23%) of global emissions were embodied in traded goods. As shown in

figure 6A above, developing countries are net exporter of CO2 emissions while developed

countries are net importers of such CO2 carriers (Peters et al. 2011). Like developing

countries, the BRIICS and the BIICS are also net exporters of carbon (figure 6B). This

phenomenon can partly explain the absence of EKC in road transport sector observed without

Russia. Additionally, a trend in each BRIICS country (Figure 5) indicates that much remains

to be done at the level of their commitment to reduce CO2 emissions, in particular in road

transport. As pointed out by Weil (2009), without a significant change in policy, economic

-2000

-1500

-1000

-500

0

500

1000

1500

2000

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008M

tCO

2

Years

A. Developed vs. Developing Countries

DevelopedcountriesDevelopingcountries

0200400600800

1000120014001600180020002200

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

MtC

O2

Years

B. BRIICS vs. BIICS

BRIICS

BIICS

22

growth in “poor” countries particularly in emerging countries will greatly exacerbate CO2

emissions.

VI. CONCLUSION

This study is the first we are aware of to have investigated the EKC for just one sector of the

economy, road transport, by analysing panel data from six emerging countries, the so-called

BRIICS countries. Fixed-effects and random-effects models specifications through the

Lagrange Multiplier test and Hausman tests allowed addressing unobserved country

heterogeneity and the associated omitted variable bias. An inverted U-shaped EKC is

observed for economic growth and CO2 emissions per capita due to road transport of the

BRIICS countries. However, the evidence suggests that EKC does not hold when Russia is

dropped from the group. The turning point of the EKC exceeds the current per capita GDP of

the richer country of the group, which means that it would happen virtually in a far future or

after a strong growth episode for the other countries. It is very sensitive to population density

and government effectiveness, but the latter variable is not always significant. The main

policy implication from this empirical study is that policy makers should not base policies on

the EKC hypothesis in the sense that increasing per capita GDP level alone cannot reduce

CO2 emissions, as found in other studies (e.g. Hervieux and Darné 2014). For the BRIICS

reducing emissions of CO2 would mean reducing energy consumption per capita which in turn

means their halting economic growth, (J.E. Lane (2013)). Without a significant change in

BRIICS transport and development policy in general, economic growth will greatly

exacerbate per capita CO2 emissions. This analysis also presents a framework and

methodology that can be useful for further study in EKC hypothesis on other emissions and

sectors. Several dimensions could therefore be developed and undoubtedly enrich these

conclusions.

VII. ACKNOWLEDGEMENTS

We gratefully acknowledge the contributions by Antonio ESTACHE (Prof. at SBS-EM/ULB,

ECARES), Yves DOMINICY (PhD student at ECARES) for their useful comments and

suggestions during this study and Wim BERVOETS for a linguistic review of this paper. We

also acknowledge the ULB (Université Libre de Bruxelles) for the financial contributions. We

are responsible for any errors that remain.

23

VIII. ANNEXES

Table 8: Environmental Kuznets Curve (EKC) regressions with bureaucracy quality



RH0 (NRH0): rejection of the null hypothesis (non-rejection of null hypothesis) at 5% significance.

Table 9: Environmental Kuznets Curve (EKC) regressions of the BRIICS without 2007,

2008 and 2009 data




(βi)PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

Constant β0 ‐515.1107*** 193.3981*** 168.3250** ‐532.8270** 191.1481*** ‐532.8270**(83.5525) (22.7232) (77.3473) (261.0711) (22.8553) (260.5123)

GDP_CAP β1 92.4466*** 39.0572*** 43.9161*** 73.8135*** 31.8549*** 73.8135***(13.1163) (3.3399) (4.9039) (25.3542) (5.1237) (14.4609)

GDP_CAP2 β2 ‐1.5406*** ‐0.3154* ‐0.4469* ‐0.6107 0.1933 ‐0.6107(0.5175) (0.1873) (0.2379) (1.6415) (0.3511) (1.0289)

POP_DENS β3 ‐0.6588** ‐0.6546*** ‐0.9181*** ‐0.9039 ‐0.5536*** ‐0.9039*(0.2506) (0.1405) (0.1022) (0.5402) (0.0572) (0.4578)

BQ β4 710.2340*** 41.2446 90.6516 938.4795 44.1318 938.4795*(197.7800) (45.1874) (123.1958) (567.5067) (46.3752) (534.2819)

TurningPoint 30.003 61,917 49,134 60,434 ‐ 60,434

N 66 66 66 55 55 55

R2 0.9066 0.9916 0.7949 0.8821 0.9926 0.8821

AdjustedR2 0.9005 0.9903 0.7815 0.8727 0.9913 0.8727


BRIICS BIICS

Groupofcountry

LMstat. P‐value H.stat. P‐value

BRIICS 46.85 0.00 RHO 16.34 0.00 RHO Fixed‐EffectsModelBIICS 36.26 0.00 RHO 686.52 0.00 RHO Fixed‐EffectsModel

GroupofcountryLMtest Hausmantest

Conclusion


(βi)PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

Constant β0 ‐296.427*** 277.8926*** 254.0890*** ‐292.7657** 336.1719*** 364.1677*** ‐132.0005 302.3091*** 347.7535***(45.7654) (31.6765) (52.7461) (128.9758) (28.5828) (7.1488) (135.8461) (16.9249) (60.7811)

GDP_CAP β1 117.7243*** 20.0968*** 23.8248** 117.2435*** 24.0327*** 27.5087*** 84.9106*** 26.4061*** 30.3978***(11.9456) (4.3749) (9.3459) (21.7662) (5.7429) (7.1488) (21.9433) (5.1731) (7.2249)

GDP_CAP2 β2 ‐3.0407*** ‐0.0520 ‐0.1486 ‐3.0253*** ‐0.1712 ‐0.2708 ‐1.5150* ‐0.2228* ‐0.3435*(0.5252) (0.0833) (0.2620) (0.8198) (0.1201) (0.1711) (0.8297) (0.1234) (0.1733)

POP_DENS β3 ‐0.0079 ‐0.7009*** ‐1.1234*** ‐0.1329 ‐0.5765** ‐1.1576***(0.2445) (0.1220) (0.1283) (0.2572) (0.2168) (0.1113)

GOV_EFF β4 226.6724*** ‐60.9694* ‐33.1194(35.6161) (31.2291) (31.0496)

TurningPoint 19,358 193,238 80,164 19,377 70,189 50,792 28,023 59,260 44,247

N 48 48 48 48 48 48 48 48 48

R2 0.8636 0.9959 0.6414 0.8636 0.9961 0.7110 0.9263 0.9964 0.7363

AdjustedR2 0.8576 0.9952 0.6255 0.8543 0.9952 0.6913 0.9195 0.9956 0.7118



24


Table 10: Environmental Kuznets Curve (EKC) regressions of the BIICS without 2007,

2008 and 2009 data




IX. REFERENCES

Acemoglu, Daron. 2009. "Introduction to Modern Economic Growth". The MIT Press.

Agras, Jean, and Duane Chapman. 1999. "A dynamic approach to the Environmental Kuznets

Curve hypothesis". Ecological Economics 28 (2): 267–277.

Arraes, R. A., Diniz M. B. and Diniz, M. J. T. 2006. "Curva Ambiental de Kuznets e

Desenvolvimento Econômico Sustentável". Revista de Economia e Sociologia Rural,

Rio de Janeiro, vol. 44, n. 3, p. 525-547, 2006.

Baltagi, Badi. 2008. "Econometric analysis of panel data". Wiley. com.

Baltagi, Badi H., Javier Hidalgo, and Qi Li. 1996. "A nonparametric test for poolability using

panel data". Journal of Econometrics 75 (2): 345–367.


Model5 15.56 0.00 RHO 13.27 0.00 RHO Fixed‐EffectsModelModel6 17.95 0.00 RHO 5.44 0.14 NRHO Random‐EffectsModelModel7 6.33 0.00 RHO 19.55 0.00 RHO Fixed‐EffectsModel


Conclusion


(βi)PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

PooledModel

FixedEffects

RandomEffects

Constant β0 ‐187.0292* 282.4000*** 271.1698*** ‐104.5505 316.3474*** 364.7087*** 133.2703 260.3334*** 133.2703(108.3487) (22.7513) (61.9500) (238.7945) (13.1776) (90.1742) (215.1844) (28.4167) (230.9240)

GDP_CAP β1 79.3971** 6.6922*** 8.3576 65.9861 9.7365** 14.8356** 23.1443 31.5715*** 23.1444(36.9186) (2.3566) (9.3130) (56.4572) (3.7779) (6.8337) (44.3833) (8.6317) (42.4895)

GDP_CAP2 β2 ‐0.3750 0.8456*** 0.8244** 0.1974 0.7112*** 0.5117** 1.7624 ‐0.5605 1.7624(2.4203) (0.3465) (0.3452) (3.2032) (0.1528) (0.1887) (2.2915) (0.6549) (1.8816)

POP_DENS β3 ‐0.1492 ‐0.3458** ‐0.8765*** ‐0.3670 ‐0.4707*** ‐0.3670***(0.2891) (0.1379) (0.1542) (0.3031) (0.1272) (0.3706)

GOV_EFF β4 284.9205*** ‐111.0742** 284.9205***(34.3765) (52.7867) (29.9335)

TurningPoint 105,863 ‐ ‐ ‐ ‐ ‐ ‐ 28,164 ‐

N 40 40 40 40 40 40 40 40 40

R2 0.8371 0.9957 0.5508 0.8379 0.9958 0.6000 0.9272 0.9963 0.9272

AdjustedR2 0.8283 0.9950 0.5265 0.8244 0.9948 0.5667 0.9188 0.9954 0.09188




Model5 8.47 0.00 RHO 8.29 0.02 RHO Fixed‐EffectsModelModel6 10.72 0.00 RHO 3.72 0.29 NRHO Random‐EffectsModelModel7 8.28 0.00 RHO 582.01 0.00 RHO Fixed‐EffectsModel


Conclusion

25

Banerjee, Abhijit, Esther Duflo, and Nancy Qian. 2012. "On the road: Access to

transportation infrastructure and economic growth in China". National Bureau of

Economic Research. Working Paper 17897.

Banister, David, and Yossi Berechman. 2001. "Transport investment and the promotion of

economic growth". Journal of transport geography 9 (3): 209‐18.

Bengochea-Morancho, Aurelia, Francisco Higón-Tamarit, and Inmaculada Martínez-Zarzoso.

2001. "Economic Growth and CO2 Emissions in the European Union". Environmental

& Resource Economics 19 (2): 165‐172.

Bogart, D. 2013. "The Economic History of Transportation, in: Whaples R. and R.E. Parker

(eds.), Routledge Handbook of Modern Economic History". Routledge (Taylor and

Francis), pp. 167-176.

Boopen, Seetanah, and Sannassee Vinesh. 2011. "On the relationship between CO 2

emissions and economic growth: the Mauritian experience". University of Mauritius.

British Petroleum (BP) statistical review of world energy. June 2012.

Breusch, Trevor Stanley, and Adrian Rodney Pagan. 1980. "The Lagrange multiplier test and

its applications to model specification in econometrics". The Review of Economic

Studies 47 (1): 239–253.

Carvalho, Terciane Sabadini, and Eduardo Almeida. 2009. "The global environmental

Kuznets curve and the Kyoto Protocol". CEP 36036: 330.

Coatsworth, J. 1979. "Indispensable railroads in a backward economy: the case of Mexico".

The Journal of Economic History, 39 (04): 939-60.

Cole, Matthew A., Anthony J. Rayner, and John M. Bates. 1997. "The environmental Kuznets

curve: an empirical analysis". Environment and development economics 2 (04): 401–

416.

Davis, Steven J., Glen P. Peters, and Ken Caldeira. 2011. "The supply chain of CO2

emissions". Proceedings of the National Academy of Sciences 108 (45): 18554‐59.

Deane, Phyllis, and W. A. Cole. 1967. "British Economic Growth, 1688-1959: Trends and

Structure". London: Cambridge University Press.

De Bruyn, Sander M., Jeroen CJM van den Bergh, and Johannes B. Opschoor. 1998.

"Economic growth and emissions: reconsidering the empirical basis of environmental

Kuznets curves". Ecological Economics 25 (2): 161–175.

Didier, Michel, Rémy Prud’homme, R. Guesnerie, and Hugues Bied-Charreton. 2007.

"Infrastructures de transport, mobilité et croissance". La Documentation française.

26

Dietz, Thomas, Elinor Ostrom, and Paul C. Stern. 2003. "The struggle to govern the

commons". Science 302 (5652): 1907–1912.

Dijkgraaf, Elbert, and Herman R.J Vollebergh. 2005. "A Test for Parameter Homogeneity in

CO2 Panel EKC Estimations". Environmental and Resource Economics 32 (2): 229–

239.

Dudek, Daniel J., and Alice LeBlanc. 1990. "Offsetting new CO2 emissions: a rational first

greenhouse policy step". Contemporary Economic Policy 8 (3): 29–42.

Fay, Marianne, and Tito Yepes. 2003. "Investing in Infrastructure: What is Needed from 2000

to 2010?" World Bank Policy Research Working Paper (3102).

Fernald, John G. 1999. "Roads to prosperity? Assessing the link between public capital and

productivity". American Economic Review 89 (3): 619–638.

Fogel, Robert William. 1962. "A quantitative approach to the study of railroads in American

economic growth: a report of some preliminary findings". The Journal of Economic

History 22 (2): 163–197.

Galeotti, Marzio, and Alessandro Lanza. 1999. "Richer and cleaner? A study on carbon

dioxide emissions in developing countries". Energy Policy 27 (10): 565–573.

Goldstein, Andrea, and Françoise Lemoine. 2013. "L’économie des BRIC: Brésil, Russie,

Inde, Chine". La Découverte. Paris.

Grossman, Gene M., and Alan B. Krueger. 1991. "Environmental Impacts of a North

American Free Trade Agreement". National Bureau of Economic Research. Working

Paper 3914

———. 1995. "Economic growth and the environment". The quarterly journal of economics

110 (2): 353–377.

Grubb, Michael, Lucy Butler, and Olga Feldman. 2006. "Analysis of the relationship between

growth in carbon dioxide emissions and growth in income". Oxbridge Study on CO2-

GDP Relationships, Phase 1: 19.

Hausman, Jerry A. 1978. "Specification tests in econometrics". Econometrica 1251–1271.

Hervieux, Marie-Sophie and Olivier Darné. 2014. "Production and consumption-based

approaches for the Environmental Kuznets Curve in Latin America using Ecological

Footprint".http://halshs.archives-ouvertes.fr/hal-00958692/.

Hsiao, Cheng. 1986. "Analysis of panel data". Cambridge university press. Vol. 34.

IEA. 2012. World Energy Outlook 2012. OECD/IEA, Paris.

27

Kaufmann, Robert K., Brynhildur Davidsdottir, Sophie Garnham, and Peter Pauly. 1998. "The

determinants of atmospheric SO2 concentrations: reconsidering the environmental

Kuznets curve". Ecological Economics 25 (2): 209-20.

Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2009. "Governance matters VIII:

aggregate and individual governance indicators, 1996-2008". World bank policy

research working paper (4978).

Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. "The Worldwide Governance

Indicators: Methodology and Analytical Issues". SSRN Scholarly Paper ID 1682130.

Rochester, NY: Social Science Research Network.

Kennedy, Peter. 2003. "A guide to econometrics". The MIT press.

Kuznets, Simon. 1955. "Economic growth and income inequality". The American economic

review 45 (1): 1–28.

Lakshmanan, T. R. 2007. "The wider economic benefits of transportation: an overview".

OECD Publishing.

http://internationaltransportforum.org/jtrc/DiscussionPapers/DiscussionPaper8.pdf.

Lucena, A. F. P. 2005. "Estimativa de uma Curva de Kuznets Ambiental Aplicada ao Uso de

Energia e suas Implicações para As Emisões de Carbono no Brasil". Unpublished

Master Thesis, Faculdade de Engenharia, Universidade Federal do Rio de Janeiro.

Mátyás, László, and Patrick Sevestre. 1996. "The econometrics of panel data". Springer.

http://link.springer.com/content/pdf/10.1007/978-3-540-75892-1.pdf.

McKinnon, Alan C. 2007. "Decoupling of road freight transport and economic growth trends

in the UK: An exploratory analysis". Transport Review 27 (1): 37‐64.

Moomaw, William R., and Gregory C. Unruh. 1997. "Are environmental Kuznets curves

misleading us? The case of CO2 emissions". Environment and Development

Economics 2: 451–463.

Muradian, Roldan, Martin O’connor, and Joan Martinez-Alier. 2002. "Embodied pollution in

trade: estimating the ‘environmental load displacement’of industrialised countries".

Ecological Economics 41 (1): 51‐67.

OECD. 2012. "OECD Environmental Outlook to 2050: The Consequences of Inaction".

http://www.oecd.org/env/indicators-modelling-

outlooks/oecdenvironmentaloutlookto2050theconsequencesofinaction.htm.

Olusegun, Omisakin. 2009. "Economic Growth and Environmental Quality in Nigeria: Does

Environmental Kuznets Curve Hypothesis Hold?" Environmental Research Journal 3

(1): 14–18.

28

Plassmann, Florenz, and Neha Khanna. 2007. "Assessing the precision of turning point

estimates in polynomial regression functions". Econometric Review 26 (5): 503‐28.

Peters, Glen P., Jan C. Minx, Christopher L. Weber, and Ottmar Edenhofer. 2011. "Growth in

emission transfers via international trade from 1990 to 2008". Proceedings of the

National Academy of Sciences 108 (21): 8903‐8.

Richmond, Amy K., and Robert K. Kaufmann. 2006. "Is there a turning point in the

relationship between income and energy use and/or carbon emissions?" Ecological

economics 56 (2): 176‐89.

Rioja, Felix K. 2001. "Growth, welfare, and public infrastructure: A general equilibrium

analysis of latin american economies". Journal of Economic Development 26 (2): 119–

130.

Rosenstein-Rodan, Paul N. 1943. "Problems of industrialisation of eastern and south-eastern

Europe". The Economic Journal 53 (210/211): 202–211.

———. 1976. "The Theory of the Big Push". CM. Meir, Leading Issues in Economic

Development.

Selden, Thomas M., and Daqing Song. 1994. "Environmental quality and development: is

there a Kuznets curve for air pollution emissions?" Journal of Environmental

Economics and management 27 (2): 147–162.

Shafik, Nemat. 1994. "Economic development and environmental quality: an econometric

analysis". Oxford Economic Papers: 757–773.

Shafik, Nemat, and Sushenjit Bandyopadhyay. 1992. "Economic growth and environmental

quality: time series and cross section evidence." Policy Research Working Paper No.

WPS904, the World Bank, Washington, DC, USA.

Stern, David I. 2004. "The rise and fall of the environmental Kuznets curve." World

development 32 (8): 1419–1439.

Stern, David I., Michael S. Common, and Edward B. Barbier. 1996. "Economic growth and

environmental degradation: The environmental Kuznets curve and sustainable

development." World Development 24 (7)

Stock, James H., and Mark W. Watson. 2003. "Introduction to econometrics." Addison Wesley

Boston. Vol. 104.

Stocker, T. F., D. Qin, and G. K. Platner. 2013. "Climate Change 2013: The Physical Science

Basis. Working Group I Contribution to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change." Summary for Policymakers (IPCC,

2013).

29

Summerhill, W.R. 2005. "Big Social Savings in a small laggard economy: railroad-led

growth in Brazil." The Journal of Economic History 65 (01): 72-102.

Urwin, Kate, and Andrew Jordan. 2008. "Does public policy support or undermine climate

change adaptation? Exploring policy interplay across different scales of governance."

Global environmental change 18 (1): 180–191.

Verbeek, Marno. 2008. "A guide to modern econometrics." Wiley.com. 3rd.

Weil, David N. 2009. "Economic Growth." Pearson International Edition, Pearson/Addition

Wesley, Boston: 495–505.

Wooldridge, Jeffrey M. 2002. "Econometric analysis of cross section and panel data." The

MIT press.

WorldBank. 1994. "World development report 1994: infrastructure for development." 13184.

The World Bank.

http://documents.worldbank.org/curated/en/1994/06/17390707/world-development-

report-1994-infrastructure-development.

Xu, Yan, and Erik Dietzenbacher. 2014. "A structural decomposition analysis of the

emissions embodied in trade." Ecological Economics 101: 10‐20.

Documents

Road Transport, Economic Growth and Carbon …...Carbon dioxide, the main greenhouse gas, has increased dramatically since 1950 and accounted in 2010 for more than 75% of global GHG