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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: ceb@admin.ulb.ac.be 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, Jean-Luc.De.Meulemeester@ulb.ac.be
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, Jean-Luc.De.Meulemeester@ulb.ac.be 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
Standard errors are in parentheses. ***significant at 0.01 level; ** significant at 0.05 level and * significant at 0.10 level.
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
VariablesCoefficients
(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
Dependentvariable:CO2_ROAD(inktpercapita)
Model5 Model6 Model7
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
Standard errors are in parentheses. ***significant at 0.01 level; ** significant at 0.05 level and * significant at 0.10 level.
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 null hypothesis) at 5% significance.
Table 9: Environmental Kuznets Curve (EKC) regressions of the BRIICS without 2007,
2008 and 2009 data
Standard errors are in parentheses. ***significant at 0.01 level; ** significant at 0.05 level and * significant at 0.10 level.
Constant terms for fixed-effect models include the mean of the estimated country effects (α).
VariablesCoefficients
(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
Dependentvariable:CO2_ROAD(inktpercapita)
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
VariablesCoefficients
(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
Dependentvariable:CO2_ROAD(inktpercapita)
Model5 Model6 Model7
24
RH0 (NRH0): rejection of the null hypothesis (non-rejection of null hypothesis) at 5% significance.
Table 10: Environmental Kuznets Curve (EKC) regressions of the BIICS without 2007,
2008 and 2009 data
Standard errors are in parentheses. ***significant at 0.01 level; ** significant at 0.05 level and * significant at 0.10 level.
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 null hypothesis) at 5% significance.
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.
LMstat. P‐value H.stat. P‐value
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
ModelsLMtest Hausmantest
Conclusion
VariablesCoefficients
(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
Dependentvariable:CO2_ROAD(inktpercapita)
Model5 Model6 Model7
LMstat. P‐value H.stat. P‐value
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
ModelsLMtest Hausmantest
Conclusion
25
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