Ecological Economics 70 (2011) 1344–1353
Contents lists available at ScienceDirect
Ecological Economics
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n
Analysis
The impact of urbanization on CO2 emissions: Evidence from developing countries☆
Inmaculada Martínez-Zarzoso a,b,⁎, Antonello Maruotti c,1
a
b
c
Ibero America Institute for Economic Research, Universität Göttingen, Germany
Instituto de Economía Internacional, Universitat Jaume I, Spain
Dipartimento di Istituzioni Pubbliche, Economia e Società, Università di Roma Tre, Via Gabriello Chiabrera, 199–00145 Roma, Italy
a r t i c l e
i n f o
Article history:
Received 18 December 2009
Received in revised form 10 February 2011
Accepted 12 February 2011
Available online 30 March 2011
JEL classification:
Q25
Q4
Q54
Keywords:
CO2 emissions
Developing countries
Panel data
Semi-parametric mixture model
Urbanization
a b s t r a c t
This paper analyzes the impact of urbanization on CO2 emissions in developing countries from 1975 to 2003. It
contributes to the existing literature by examining the effect of urbanization, taking into account dynamics
and the presence of heterogeneity in the sample of countries. The results show an inverted-U shaped
relationship between urbanization and CO2 emissions. Indeed, the elasticity emission-urbanization is positive
for low urbanization levels, which is in accordance with the higher environmental impact observed in less
developed regions. Among our contributions is the estimation of a semi-parametric mixture model that
allows for unknown distributional shapes and endogenously classifies countries into homogeneous groups.
Three groups of countries are identified for which urbanization's impact differs considerably. For two of the
groups, a threshold level is identified beyond which the emission-urbanization elasticity is negative and
further increases in the urbanization rate do not contribute to higher emissions. However, for the third group
only population and affluence, but not urbanization, contribute to explain emissions. The differential impact
of urbanization on CO2 emissions should therefore be taken into account in future discussions of climate
change policies.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Climate change with the attendant need to stabilize contributing
global emissions is one of the most challenging problems of our times,
and a matter of great concern among policy makers. Some aspects of
the projected impact such as global warming, increasing desertification, rising sea levels and rising average temperatures, might have a
disproportionate impact on developing countries, which least contributed to the cause of climate change. The main greenhouse gas in
terms of quantity is CO2, which according to IPCC (2007) accounted
for about 76.7% of total anthropogenic greenhouse gas emissions in
2004. Although the reduction commitments of CO2 emissions were
seen as a task predominantly for developed countries (UNFCCC,
1997), based on the consensus that they are the largest contributors to
global CO2 emissions, there have been recent calls for the developing
countries to play an active role in global emissions reduction (Winkler
et al., 2002). The level of CO2 emissions from developing countries has
☆ I would like to thank the editor and four anonymous referees for their diligent
review, excellent comments, and very valuable suggestions. I acknowledge the support
and collaboration of Proyecto ECO2010-15863.
⁎ Corresponding author at: Ibero America Institute for Economic Research, Universität
Göttingen, Platz der Goetinger Sieben 3, 37077 Göttingen, Germany. Tel.: +49 551399770;
fax: +49 551398173.
E-mail addresses: martinei@eco.uji.es (I. Martínez-Zarzoso),
antonello.maruotti@uniroma3.it (A. Maruotti).
1
Tel.: + 39 06 57335305.
0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolecon.2011.02.009
been rapidly exceeding that of the developed countries and in 2003
accounted for almost 50% of the world's CO2 emissions (Fig. 1). This
trend is expected to grow if the current path in terms of energy
consumption is maintained. Since CO2 is one of the main contributors
to global emissions, it is of great interest to determine which policy
measures will be more effective in curbing CO2 emissions.
While many factors have been adduced for climate change, as
affluence grows, energy consumption is singled out as having the
most adverse impact on the environment. However, this impact is
more severe when accompanied by demographic growth and rural
migration into cities, given that population increases and urban
development lead to increases in energy consumption and, consequently, to greater atmospheric pollution. Especially in developed
regions the process of economic development is accompanied by
agricultural intensification, industrialization and urbanization, with
large scale movements of labor force from rural to urban settlements.
Urban economic activities may influence environmental quality in a
distinct way as industrialization does. On the one hand, higher
densities may place an excessive burden on absorptive capacities of
the local environment. On the other hand, high urban density may
also influence patterns of resource use and global environmental
quality. Urbanization allows for economies of scale in production but
requires transportation. Indeed, urbanization exerts a number of
independent influences in energy use. It constantly displaces
traditional energy with modern energy, which substantially increases
the energy intensity of some activities while decreasing traditional
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
Fig. 1. Carbon dioxide emissions in 2003.
Source: World Development Indicators 2007.
energy use. These changes can ultimately result in increasing
emissions that may contribute to the global warming effect. However,
the process of urbanization also involves concentration of firms in
space reducing the cost to enforce environmental legislation, and can
also encourage the use of mass transport in place of motor vehicles
(Jones, 1991).
Since the early 70s a scientific consensus emerged pointing to
human induced forces driving global warming. The IPAT2 model
in stochastic form, named STIRPAT, has been used by ecologists,
sociologists and economists to assess the impact of population,
affluence and other factors on carbon dioxide loads (e.g. York et al.,
2003; Shi, 2003; Martínez-Zarzoso, Bengochea-Morancho and
Morales-Lage, 2007). The model has been used to assess not only
the impact of the core components population and affluence on
environmental impact, but also to address sociological questions like
the impact of modernization on the environment (York et al., 2003).
In the last two decades a number of researchers, mainly economists,
have investigated the determinants of CO2 emissions within the
framework of the Kuznets Curve hypothesis. It suggests an invertedU shaped curve between economic development and environmental
impact. However, most of the studies do not reach conclusive
evidence in favor of the hypothesis (See Perman and Stern, 2003, for
a survey). More recent developments use decomposition analysis
and efficient frontier methods taking into account not only affluence
but also energy intensity, technical change, and structural change as
explanatory variables. In most cases changes in per-capita CO2
emissions are explained by changes in income per capita, energy
intensity, and structural change in the economy (assuming implicitly
that population has a unitary elasticity with respect to emissions).
Relatively little effort has been devoted to investigating the impact of
demographic factors on the evolution of CO2 emissions and most of
the existing studies assume that this impact is comparable for all
countries and constant over time (Jones, 1991; York et al., 2003;
Parikh and Shukla, 1995; Cole and Neumayer, 2004). Two exceptions
to this general assumption are the studies of Shi (2003), who
grouped countries according to income levels, and Martínez-Zarzoso
et al. (2007), who studied the impact of population growth for old
and new European Union members. Both studies focus specifically on
2
Impact = Population Affluence Technology (IPAT).
1345
the differential impact of population growth on emissions – without
considering urbanization. As in Jones (1991) and Parikh and Shukla
(1995), this paper specifically focuses on the influence of urbanization on global environmental quality, but, as a novelty, it introduces
the assumption of differential impacts across countries. Especially
given the rapid pace of urban growth in middle and low income
countries, there is a need to understand the interactions between
urbanization and global environmental change processes. This paper
contributes to this literature in two different ways. The first novelty
is to focus on the impact of urbanization on CO2 emissions for
developing countries, by introducing non-linearities and dynamics in
the model and by controlling for affluence and technique effects.
Given the revealed persistence of CO2 emissions we use a dynamic
specification which allows us to account for the path-dependent
nature of the distributional pattern. The resulting endogeneity
problem is addressed by using a Least Squares Dummy Variable
Corrected (LSDVC) and several instrumental variables and GMM
estimators, which are recently-proposed panel data techniques
particularly suitable for macro-panels. The second novelty of this
paper regards the estimation technique used to address differential
impacts for different groups of countries. We estimate a semiparametric mixture model that allows for unknown distributional
shapes and endogenously classifies countries into homogeneous
groups. To the best of our knowledge this has not been done before.
The study focuses on developing countries, because few studies have
dealt with this group of countries before (Parikh and Shukla, 1995),
and considers the heterogeneity present in the sample in terms of
variability of the estimated coefficients across different groups of
countries. We specify a model in which CO2 emissions are related to
the level of income per capita, the population size, the percent of
urban over total population, the industrial structure, and the energy
intensity of each country. The study classifies countries into groups
following statistical procedures. The results show that not only
affluence but also urbanization drives emissions following a Kuznets
curve as predicted by the Ecological Modernization Theory (EMT)
developed by sociologists. This impact is not homogeneous, so
countries are classified into three groups according to the differential
impact of the factors influencing emissions.
The paper is organized as follows. Section 2 briefly reviews the
relevant literature. Section 3 presents the theoretical framework and
specifies the model. Section 4 describes the empirical analysis and
discusses the main results and Section 5 concludes.
2. Literature Review
The first studies that considered demographic factors to explain
the sources of air pollution were based on cross-sectional data for only
one time period and consider mainly the effects of population growth
(Cramer, 1998, 2002; Daily and Ehrlich, 1992; Dietz and Rosa, 1997;
Zaba and Clarke, 1994; Cramer and Cheney, 2000). The results from
these studies indicate that the elasticity of CO2 emissions and energy
use with respect to population are close to unity. Some other studies
used panel data and relaxed the assumption of equal impact of
population for all countries. In this line, Shi (2003) found a direct
relationship between population changes and carbon dioxide emissions in 93 countries over the period from 1975 to 1996 that varies
with the levels of affluence. Indeed, he shows that the impact of
population on emissions has been more pronounced in lower-income
countries than in higher-income countries. Also Martínez-Zarzoso
et al. (2007) found a differential impact of population on emissions for
old and new EU members. Total population was the only demographic
variable considered in both studies.
A number of carbon-emissions/energy consumption studies have
included not only total population but also additional demographic
variables — among them urbanization, population density and age
composition. The results from those studies show at best mixed
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I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
results. The first strand of studies (Jones, 1991; Parikh and Shukla,
1995; York et al., 2003) used cross-sectional data for a single year.
Jones (1991) focused exclusively on urbanization and the mechanisms by which it alters energy consumption. He conducted a
regression analysis of 59 developing countries for 1980 to study the
overall impact of urbanization on energy-use. He concluded that a 10%
increase in the proportion of the population living in cities would
increase modern energy consumption per capita by 4.5–4.8%, holding
constant per capita income and industrialization. Parikh and Shukla
(1995) also provided an early analysis of the effect of urbanization on
energy use and greenhouse gas emissions in developing countries.
They estimated two models: a panel data fixed-effects model for
energy consumption per capita over the period 1965–87 and a crosssectional model estimated for the year 1986 for four greenhouse
gasses in per capita terms. The set of explanatory variables were in
both models income per capita, share of agriculture in GDP,
population density and urban population rate. Their results for the
period 1965–87 indicate that a 10% increase in a country's urban
population leads to a 4.7% rise in its per capita total energy
consumption. The effect on CO2 emissions is only calculated with
1986 data and it is somewhat lower: a 10% increase in a country's
urban population leads to a 0.3% rise in its per capita CO2 emissions.
York et al. (2003) tested for a non-linear relationship between
emissions and urbanization for a cross-section of 137 countries using
1991 data and found that the squared term was negative but not
statistically significant. The main shortcoming of those investigations
is that the results may be biased due to unobserved countryheterogeneity stemming from different country regulations or
behaviors that affect emissions differently for different countries
and are not measurable.
A second strand of studies overcome this lacking by estimating the
effect of demographic factors using panel data (Cole and Neumayer,
2004; Fan et al., 2006; York, 2007; Liddle and Lung, 2010). In this
context Cole and Neumayer (2004) considered 86 countries during
the period from 1975 to 1998 and found a positive link between CO2
emissions and a set of explanatory variables including population,
urbanization rate and energy intensity, and a negative link with
household sizes. With respect to urbanization they found that a 10%
increase in the urbanization rate increases CO2 emissions by 7%. They
estimated a model in first differences that included income per capita
and energy intensity as additional regressors. The authors assumed
that the effect of population and urbanization is equal for all income
levels. Fan et al. (2006) and York (2007) both focused exclusively on
developed countries. Whereas the former finds a negative relation
between urbanization and carbon emissions for their OLS regressions,
the latter author reports a positive relationship between urbanization
and energy use for 14 EU countries. Finally, Liddle and Lung (2010)
focused on the impact of age-structure on carbon emissions and also
considered urbanization as an explanatory factor. Their results show a
positive and non-significant effect of urbanization on aggregate
carbon dioxide emissions for a cross-section of 17 developed
countries and 10 periods (panel span 1960–2005 using observations
at 5-year intervals). Summarizing, the results with respect to the
relationship between urbanization and emissions are not conclusive
and the relationship between economic development and urban
environmental performance is still not well understood, especially for
developing countries. We hypothesize that several reasons could be
responsible of this lack of consensus. First, the relationship could be
non-linear and second it could differ by groups of countries.
A different but related research approach examines the “Environmental Kuznets Curve” (EKC) hypothesis suggesting an inverted
U-shape relationship between pollution per capita and economic
development. A few studies considered population density as an
additional explanatory variable (e.g., Cole et al., 1997; Panayotou et
al., 2000; Dinda, 2004). The results obtained within this framework
are far from homogeneous and their validity has been questioned in
recent surveys of the EKC literature (e.g., Stern, 1998, 2004). Most of
the criticisms are related to the econometric techniques and the
presence of omitted-variables bias (Perman and Stern, 2003).
Borghesi and Vercelli (2003) state that the studies based on local
emissions present acceptable results, whereas those concerning
global emissions do not offer the expected outcomes, and therefore
the EKC hypothesis cannot be generally accepted.
A few other researchers considered the existence of an inverted
U-shape relationship between pollution per capita and urbanization
(Ehrhardt-Martinez et al., 2002; York et al., 2003). The main
theoretical justification draws on ecological modernization theory
that basically argues that capitalist economies have the ability to
promote environmental goals. Ehrhardt-Martinez et al. (2002) and
York et al. (2003) argued that urbanization is a good proxy for
modernization and therefore environmental impact should decrease
with higher shares of urban population.
3. Theoretical Framework and Hypothesis
In this paper we have taken the STIRPAT model as the reference
theoretical and analytical framework, but also consider the economic
theories that predict an EKC with respect to income and the
sociological theories that suggest an EKC relative to urbanization,
rather than economic development per se.
Starting from Ehrlich and Holdren's (1971) basic foundation, Dietz
and Rosa (1994, 1997) formulated a stochastic version of the IPAT
equation with quantitative variables containing population size (P),
affluence per capita (A), and the weight of industry in economic
activity as a proxy for the level of environmentally damaging
technology (T). These authors designated their model with the term
STIRPAT (Stochastic Impacts by Regression on Population, Affluence,
and Technology). The model specification for a single year is given by
the following equation:
β
β
β
Ii = αPi 0 Ai 1 Ti 2 ei
ð1Þ
where Ii, Pi, Ai, and Ti denote Impact, Population and Technology in
country i; α and the β's are parameters to be estimated, and ei is the
random error.
The STIRPAT model has been widely used to analyze the
determinants of a variety of environmental impacts (Dietz and Rosa,
1997; Cramer, 1998; Shi, 2003; York et al., 2003; Cole and Neumayer,
2004; among others). York et al. (2003) refined the STIRPAT model
and discussed how it can be expanded to incorporate additional
factors, such as quadratic terms or different components of P, A or T. P
is measured with total population and with the percentage of urban
population. The affluence variable, A, is measured by the gross
domestic product per capita and, as a proxy for measuring T, we have
considered the percentage of industrial activity with respect to total
production and energy efficiency.
On the one hand, the inclusion of a quadratic term of affluence (A)
is in line with economic theories predicting an inverse U-shaped
relationship between economic development and environmental
impact (Andreoni and Levinson, 2001). On the other hand, the
inclusion of a quadratic term of one component of P, population living
in urban areas, is in line with modernization theories (EhrhardtMartinez, 1998). Though urbanization is often associated with greater
levels of pollution it may also facilitate some environmental
improvements, for example through economies of scale in the
provision of sanitation facilities.
We hypothesize that CO2 production is based on demographics but
can shift once efficiency of living in a city is reached. In particular,
urban economic activities may have two separate environmental
effects — apart from those related to higher incomes, higher
consumption levels and industrialization. First, urbanization accelerates the transition to modern fuels changing the patterns of
10
lco2
5
0
resource use. Second, urban areas provide better opportunities to
achieve economies of scale and to use natural resources more
efficiently than rural areas. Indeed, compact cities are usually more
efficient than disperse ones. Therefore, the way in which cities are
designed is an important issue for sustainable development. A higher
urbanization rate can also have a positive impact on emissions due to
the typically more pollution intensive consumption patterns of
individuals in urban areas. Indeed, in developing countries, we
would expect those in urban areas to utilize cars, motor cycles and
busses to a greater extent than those in rural areas. Similarly,
passenger transport in cities is heavily weighted towards fuel-using
modes (Jones, 1991).
1347
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I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
10
4. Empirical Application
15
20
lp
4.2. Model Specification
Following the empirical model formulated by Dietz and Rosa
(1997) we have estimated an extended version of the STIRPAT model
for an unbalanced panel with 1925 observations. The empirical
analysis is also in line with the latest emerging approaches based on
extended EKC models described in the introduction. In our view, the
factors driving changes in pollution should be analyzed in a single
model and under the appropriate quantitative framework, hence
allowing for a more flexible model with variable coefficients across
groups with different behavior.
In order to test whether the evolution of the factors considered in
the STIRPAT model influences the level of CO2 emissions through time
and across countries, we have derived the empirical model by taking
logarithms of Eq. (1) and adding the time dimension as follows:
lnIit = δi + β0 lnPit + β1 lnAit + β2 lnTit + ϕt + εit
ð2Þ
where I, P, A, and T are defined above. The sub-index i refers to
countries and t refers to the different years. δi and ϕt capture country
3
Energy efficiency is available for 31 low-income countries, 39 lower-middleincome countries, 25 upper-middle-income countries.
10
5
0
Our dataset include all developing nations for which data on all
variables are available. This covers 88 countries over the period 1975
to 2003. The sample of countries is considerably reduced when energy
efficiency is included as an explanatory variable since data for this
variable is not available for many developing countries.3 There are
also some countries for which income data is missing and transition
economies only report data since the early 1990s when their
economies began to open up. With illustrative purposes, the countries
under analysis are classified into income groups according to data
from the World Development Indicators (WDI) 2007. Low-income
economies are those in which 2005 GNI per capita was $875 US or less
(54 countries). Lower-middle-income economies are those in which
2005 GNI per capita was between $876 and $3466 (58 countries),
upper-middle-income economies are those in which 2005 GNI per
capita was between $3466 and $10,725 (40 countries). Countries
considered in each group are listed in Table A.1 in the Appendix A
(WDI, World Bank, 2007). A summary of the data, as well as the
simple correlation coefficients between the variables in the model, is
shown in Table A.2 in the Appendix A. In addition, Fig. 2 reports two
scatter plots. The first one shows a clear positive linear relationship
between population and emissions, whereas the second shows a
positive relationship between urbanization and emissions for low
urbanization levels and a negative one for higher levels. We proceed
now with a more sophisticated analysis to investigate this relationship in depth.
15
4.1. Data and Variables
0
20
40
60
80
100
--urban population (% of total)-lco2
Fitted values
Fig. 2. Scatter plot: CO2 and population and CO2 and urbanization. Note: lp denotes
population in natural logs, pupc is the percentage of urban population over total
population and lco2 denotes logged CO2 emissions.
and time fixed effects respectively, and εit is the error term. Since the
model is specified in natural logarithms the coefficients of the
explanatory variables can be directly interpreted as elasticities. The
time effects, ϕt, can be considered as a proxy for all the variables that
are common across countries but vary over time. These effects are
sometimes interpreted as the effects of emissions-specific technical
progress over time (Stern, 2002), but they could account for other
effects related to globalization. Model 2 is augmented with urbanization measured as the percentage of population living in cities. In
addition, we look at the age composition of population by adding two
variables — namely the percentage of population that is above 65 and
the percentage of population that is between 14 and 64 years old. The
inclusion of population density (pdit) instead of population as a
regressor is also considered. Finally, we adopt a less restrictive
functional form that introduces squared terms for affluence and
urbanization. In this way we are able to test for the EKC hypothesis
and the modernization theory. In addition, we consider a dynamic
specification that takes into account the effect of past CO2 emissions
on the current levels. The augmented model is given by
2
lco2it = δi + ϕt + λlco2i;t−1 + γ0 lyhit + γ1 lyhit + γ2 lpit
+ γ3 lpupit + γ4 lpup2it + γ5 lp1564it + γ6 lp65it +
ð3Þ
+ γ7 leiit + γ8 liait + εit
where l denotes natural logarithms, co2 are the CO2 emissions in tons, yh
is the gross domestic product per capita expressed in constant PPP
(purchasing parity prices) (2000 US$), p is the total population, pup is
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I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
Table 1
Panel-unit root test results.
(1975–2003)
Exogenous variables: individual effects
Method
Levin, Lin & Chu test (LLC)
Var levels
Statistic
Prob.***
Cross-sections
Obs
Statistic
Prob.***
Cross-sections
Obs
lco2
lpop
lyh
lei1
lpup
−7.26
−13.40
−15.95
−15.55
−12.14
0.00
0.00
0.00
0.00
0.00
145
148
132
88
148
3892
3641
3101
1996
3854
430.82
1239.35
689.59
473.01
1770.02
0.00
0.00
0.00
0.00
0.00
145
148
132
88
148
3995
4106
3220
2034
4106
145
148
132
88
148
3894
3958
3075
1980
3858
426.48
563.76
334.62
197.97
2429.85
0.00
0.00
0.00
0.12
0.00ns
145
148
132
88
148
3995
4106
3220
2034
4106
PP–Fisher Chi-square
Exogenous variables: individual effects, individual linear trends
lco2
−7.50
0.00
lpop
−25.75
0.00
lyh
−7.63
0.00
lei1
−6.34
0.00
lpup
−5.11
0.00
Notes: Newey–West bandwidth selection using Bartlett kernel. Automatic selection of lags based on SIC. LLC Null: Unit root (assumes common unit root process). PP Null: Unit root
(assumes individual unit root process).*** denotes rejection of the null hypothesis at the 1% level and ns denotes not statistically significant.
the percentage of total population living in urban areas, p1564 denotes
the percentage of population between 15 and 64 years old, p65 denotes
the percentage of population older than 64, ei denotes energy efficiency
measured as GDP at constant PPP prices divided by energy use, where
energy use refers to apparent consumption (production + imports −
exports), and finally ia is the percentage of the industrial activity with
respect to the total production measured by the GDP.
Eq. (3) is estimated for the full sample of countries using paneldata estimation techniques that are appropriate for dynamic models:
LSDVC, fixed-effects instrumental variable and GMM methods. Such a
model specification may however fail to fit the data due to a
misspecification of the model (due to e.g. omitted covariates) or
due to the possible correlation among country observations. We also
adopt a more flexible estimation procedure that overcome these two
Table 2
Regression results for all countries in the sample (1975–2003).
lco2(− 1)
lyh
Lyh2
lp
lpup
lpup2
M1
M2
M3
M4
M5
M6
FE
CLSDV
FE_IV_1
FE_IV_2
FE_IV_3
Diff-GMM
0.726***
18.587
0.675***
2.737
−0.02
−1.378
0.246**
2.255
0.506*
1.791
−0.089*
−1.945
0.790***
51.104
0.282***
8.006
0.680***
9.725
0.424***
4.636
0.678***
9.657
0.429***
4.652
0.674***
9.384
0.412***
4.807
0.521***
3.173
0.715***
4.464
0.198**
2.005
0.586**
2.312
−0.101***
−2.644
0.319**
2.453
0.755***
2.902
−0.121***
−3.064
0.311**
2.431
0.861***
2.899
−0.136***
−3.063
0.357
0.795
1.329*
1.961
−0.209*
−1.853
−0.658***
−4.975
0.112
1.623
Yes
lpd
0.741***
2.89
−0.120***
−3.068
0.336**
2.561
lp1564
−0.283***
−4.277
0.125***
4.143
Yes
−0.283***
−4.273
0.124***
4.089
Yes
0.255
1.335
0.055
0.698
−0.276***
−4.409
0.125***
4.121
Yes
0.821
1831
0.162
784.731
1.272
0.259
0.449
0.503
0.821
1831
0.161
785.798
1.255
0.263
0.833
1831
0.161
786.606
1.156
0.282
lp65
lei
lia
Time dummies
Within R2
Adjusted R2
Nobs
RMSE
Log lik
Hansen test
prob.
Endog. test
prob.
Ar(1) prob.
Ar (2) prob.
−0.244***
−4.636
0.108***
2.918
Yes
0.85
−0.167***
−5.33
0.108***
3.184
Yes
1925
0.163
773.738
1925
1833
N° Instr = 60
1.002
0.988
0.015
0.725
Note: All the variables are in natural logs. GDPpc denotes per capita income. t-statistics are below each estimated coefficient. *, **, *** denote significance at the 10, 5, and 1% level,
respectively. FE denotes fixed effects. IV denotes instrumental variables estimator. Bootstrapped standard errors in brackets (bias correction initialized by Arellano-Bond estimator).
Diff-GMM denotes difference Generalized Method of Moments. The Hansen test results indicate that the instruments used are valid instruments. Ar(1) and Ar(2) tests for
autocorrelation of first and second order respectively, since the model is estimated in first differences we expect to have autocorrelation of first order but not of second order, which
is what the probabilities associated to the tests indicate. RMSE denote Root Mean Squared Error.
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
inconveniences and, in addition allow us to account for the possibility
that some of the parameters of the model may differ across
unobserved subgroups.
A simple way to do so is through latent class (LC) models
(Vermunt and Magidson, 2002), also known as finite mixture models.
Because of their flexibility, LC are being increasingly exploited as a
convenient, semi-parametric way in which to model unknown
distributional shapes (Alfò et al., 2008). This is in addition to their
obvious applications where there is group-structure in the data or
where the aim is to explore the data for such structure (Wedel and
DeSarbo, 1995).
Following this path we assume that the amount of CO2 emissions,
co2it, can be clustered into K groups. Formally, we introduce a latent
variable Z that takes K discrete values, say z1,…zK, with distribution
P(Z = zk) = πk, 0 ≤ πk, ≤ 1, k = 1,…K, and Σk πk = 1. The probability
density function is then given by
2
f ðco2it Þ = Σk πk; f k lco2it jβk ; σk
ð4Þ
where fk(.) denotes the conditional distribution of Iit within the k-th
latent class and πk represents the prior probability of component k. In
our application f(.) is a univariate Gaussian density, with componentspecific parameter vector given by θk = (βkX,σ 2k) and complete
parameters vector equal to ψ = (π1…,πK, θ1,…,θK); in which Eq. (4)
describes a mixture of standard linear regression models (DeSarbo
and Cron, 1988).
In this framework, parameter estimation can be carried out
through an Expectation–Maximization (EM) algorithm (Dempster et
al., 1977) in a maximum likelihood context. Thus, we define the loglikelihood function as
2
log L = Σi Σt logΣk πk; f k lco2it jβk ; σk
ð5Þ
which can usually not be maximized directly.
The EM algorithm maximize Eq. (5) with respect to ψ via an
iterative procedure that first estimates the posterior class probabilities for each observation
h
i
2
2
wik = πk; Πt f k lco2it jβk ; σk
Σk πk; Πt f k lco2it jβk ; σk
=
ð6Þ
It then derives the prior class probabilities as
πk = Σi wik = N
ð7Þ
by maximizing the log likelihood for each component separately using
the posterior probabilities as weights
2
2
max Σi Σt wik log f k lco2it jβk ; σk ; w:r:t βk ; σk
ð8Þ
They are weighted sums of equations for an ordinary linear regression
model.
These steps are alternated repeatedly until the log likelihood
changes by an arbitrarily small amount. For fixed K we retained the
model with the best maximized log likelihood value. Once the
algorithm has reached its end for a given K, we can proceed assuming
K = K + 1 and proceed to parameter estimates for this value of the
number of components. After that, a formal comparison could be done
using penalized likelihood criteria (such as AIC or BIC) or by
employing a formal likelihood ratio test.
The proposed approach allows classification of countries on the
basis of the posterior probabilities estimates wik, which represent a
potentially useful by-product of the adopted approach.
According to a simple maximum a posteriori rule, the i-th country
can in fact be classified in the k-th component if wik = max(wi1,…wiK).
It is worth noting that each component is characterized by homoge-
1349
neous values of estimated latent effects; i.e., conditional on the
observed covariates countries from that group show a similar structure,
at least in the steady state. The latent variables should capture the effect
of missing covariates such as those related to institutions.
Further, by defining a dynamic model we are able to provide an
adequate association structure distinguishing between true and
apparent association (Aitkin and Alfò, 1998). In the former case actual
and future observations are directly influenced by past values, which
cause a substantial change over time in the corresponding distribution. The latter case arises when observations are drawn from
heterogeneous populations (as in LC models). A final remark on this
point concerns that the static LC model may produce a misleading
finding, since the unobserved but persistent heterogeneity can be
interpreted as due to a strong serial dependence.
4.3. Main Results
Before estimating the econometric models, we test whether the
datasets are stationary. If this condition is not met, results could be
showing spurious relationships. Two different panel-based unit root
tests are used: the Levin–Lin-Chu ADF (Levin et al., 2002) and the PPFisher (Maddala and Wu, 1999; Choi, 2001) tests. Both examine the
null of a unit root, but the former examines the null of a common unit
root for all cross-sectional units, whereas the later examines the null
of individual unit roots. The results of both unit root tests are reported
in Table 1 and indicate rejection of the null hypothesis. Hence, since all
the variables are stationary in levels we proceed with the panel
analysis.
Eq. (3) was first estimated for the whole set of countries under
analysis. Table 2 shows the main results obtained by using different
estimation methods. The model was first estimated using a dynamic
model4 with fixed effects and clustered standard errors5 (Model 1).
The results indicate that almost all the variables included are
statistically significant and show, in general, the expected signs. The
exception is the quadratic term of income per capita. The coefficient is
non-significant indicating that we cannot confirm the EKC hypothesis
for developing countries.
By introducing dynamics into the model specification we are able
to calculate short and long run elasticities. The main problem that we
have to correct for the endogeneity bias affecting the lagged
dependent variable remains. Although this bias can be low for panels
with a large time dimension (it is of order 1/T, T being the number of
time periods), it is still important to correct the estimates for the bias
especially when the interest lies in the estimation of long run
elaticities. The recent econometric literature has suggested two ways
to correct for this bias. A way is to use the bias-corrected least squares
dummy variable estimator (LSDV) proposed by Kiviet (1995) and
extended by Bun and Kiviet (2003) and Bruno (2005) for unbalanced
panels. This corrected estimator works remarkably well in terms of
bias reduction while maintaining the high degree of efficiency of the
uncorrected LSDV estimator. The results are reported in Model 2
(Table 2) and are in line with those obtained in the first column.
Alternative dynamic panel data estimators are instrumental variables
4
A dynamic model is used as a means of dealing with autocorrelation, which was
present when a static model was initially estimated. We would like to thank a referee
making this suggestion.
5
The result of the Hausman test indicates that the country effects are correlated
with the residuals and therefore the random-effect estimates are inconsistent, only the
within-fixed-effects estimates are consistent. Since the country and time-specific
effects are statistically significant (as indicated by the respective LM tests) the OLS
estimates with a common intercept are biases and therefore not reported. To account
for the presence of heteroskedasticity, the error structure is assumed to be
heteroskedastic between the groups (panels) and it is estimated using clustered
standard errors, sometimes called panel corrected standard errors.
1350
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
Table 3
Estimation results by country group (1975–2003).
Estimate
Std. Error
Table 4
Country grouping.
z
Long run elasticities
Comp.1
Lco2(− 1)
lyh
lp
lpup
lpup2
lei
lia
Turning point urb.
0.963
0.031
0.040
0.445
−0.062
−0.020
−0.015
36%
0.009
0.013
0.009
0.056
0.010
0.013
0.021
106.761***
2.297**
4.352***
7.923***
−6.280***
−1.478
−0.734
0.818
1.080
11.900
−1.663
−0.523
−0.405
Comp.2
Lco2(− 1)
lyh
lp
lpup
lpup2
lei
lia
0.966
0.061
0.039
0.037
−0.008
0.002
0.036
0.010
0.014
0.013
0.197
0.028
0.012
0.019
96.779***
4.394***
3.041***
0.190
−0.294
0.175
1.918*
1.793
1.096
1.139
−0.244
0.063
1.049
Comp.3
Lco2(− 1)
lyh
lp
lpup
lpup2
lei
lia
Turning point urb.
0.760
0.419
0.268
0.801
−0.108
−0.247
0.116
41%
0.029
0.071
0.042
0.276
0.044
0.084
0.050
26.446***
5.862***
6.354***
2.902***
−2.452**
−2.931***
2.307**
1.747
1.117
3.342
−0.450
−1.028
0.482
K
2
3
4
5
6
logLik
1279.904
1424.36
1500.952
1549.978
1605.462
AIC
BIC
ICL
−2413.81
−2628.72
−2707.9
−2731.96
−2768.93
−2008.73
−2018.32
−1892.19
−1710.92
−1542.58
−2007.58
−2014
−1889.57
−1708.07
−1540.29
Note: K denotes the number of country groups evaluated. AIC, BIC and ICL denote
respectively Akaike Information Criterion, Bayesian Information Criterion and
Integrated Completed Likelihood. *, **, *** denote significance at the 10, 5, and 1%
level, respectively.
and GMM estimators. An advantage is that in this setting we can also
test for the endogeneity of other variables such as income or
urbanization and correct for heteroskedasticity. In cases with small
T and large N, the GMM estimators are less biased than the LSDV
although relatively inefficient. For moderate T and N it is recommendable to restrict the number of instruments used. Models 3 to 5
present the results of a GMM estimator with the variables in levels
and country fixed effects, using as instruments for the lagged
dependent variable its own second and third lag. The final model
presents the differenced-GMM estimator of Arellano and Bond
(1991). The GMM-system proposed by Blundell and Bond (1998)
was also estimated but did not pass the specification tests.
The results obtained using alternative methods are relatively robust
indicating that all the variables included are statistically significant and
show, in general, the expected signs. The significance of additional
demographic variables is tested in Models 4 and 5. In Model 4 we
include population density instead of population as a regressor and the
results concerning urbanization are robust to the inclusion of this
variable. Moreover, in Model 5 we additionally look at the age
composition of population. We add two variables, namely the
percentage of population that is between 14 and 64 and the percentage
of population that is over 65 years old. Adding the age composition
hardly affects the urbanization elasticity. Neither the affluence nor the
technology variables are much affected in this or consecutive estimations and are therefore not discussed further. A higher percentage of the
age group between 14 and 64 years old has a positive impact on
emissions that is not significant at the 5% level. A higher share of elderly
people also has a positive but smaller impact on emissions that is also
N
Group 1
Group 2
Group 3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
Argentina
Bangladesh
Belarus
Brazil
China
Colombia
Croatia
Czech Republic
Egypt, Arab Rep.
El Salvador
Hungary
India
Jamaica
Macedonia
Morocco
Nicaragua
Pakistan
Peru
Poland
Russian Federation
Slovak Republic
South Africa
Syrian Arab Republic
Thailand
Tunisia
Turkey
Uzbekistan
Armenia
Azerbaijan
Bolivia
Botswana
Bulgaria
Chile
Congo, Dem. Rep.
Costa Rica
Dominican Republic
Estonia
Ghana
Guatemala
Honduras
Indonesia
Iran
Jordan
Kazakhstan
Kenya
Latvia
Lebanon
Lithuania
Malaysia
Mexico
Moldova
Mozambique
Nigeria
Oman
Panama
Paraguay
Philippines
Romania
Senegal
Sri Lanka
Sudan
Tanzania
Trinidad & Tobago
Turkmenistan
Ukraine
Uruguay
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
Albania
Algeria
Angola
Benin
Cameroon
Congo, Rep.
Cote d'Ivoire
Ecuador
Ethiopia
Gabon
Georgia
Haiti
Kyrgyz Republic
Namibia
Nepal
Tajikistan
Togo
not significant at the 5% level. Model 4 is the preferred model in terms
of root mean squared error. Considering that the performance of
Models 3 and 4 is very similar in terms of RMSE and R2, we have chosen
instead to interpret the results obtained in Model 3. In this way we can
compare our results with those obtained previously in the literature
which considers population instead of population density.
In the short run the population elasticity is lower than one and
lower than the income elasticity, indicating that in the short term
income growth contributes more to increase emissions than population
growth. We find a non-linear relationship between urbanization and
CO2 emissions that is in accordance with the modernization theory.
Similarly, we have tested for non-linear relationships of the income
variable, but have not found evidence confirming the Kuznets-curvehypothesis. An increase in energy efficiency decreases emissions almost
proportionally. Finally, the effect of the percentage of industrial activity
is positive and small, and the time effects show a negative sign and an
increasing magnitude over time; this could be indicative of global
technical progress over time that is reducing emissions.
As an additional robustness check we control for endogeneity of
the urbanization variable to consider the possibility of an inverse
relationship between urbanization and pollution. We estimate the
model using two stage least squares6 and use as instruments for the
6
The STATA command xtivreg2 and xtabond2 were used for the estimation.
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
Comp 1_UM
Com 2_MUL
Comp 3_ML
30
20
10
0
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
-10
-20
-30
-40
Note: Estimation based on the results shown in Table 3.
Fig. 3. Time effects for different groups.
urbanization variable the first two lagged values of it in levels (Model
3 in Table 2). We also test for endogeneity by using a Hausman-type
endogeneity test, which under the null hypothesis of exogeneity is
distributed as a χ2 with degrees of freedom equal to the number of
regressors tested. The results of the test and the associated
probabilities shown in the last row of Table 2 (Model 3) indicate
acceptance of the null hypothesis. Hence, urbanization can be treated
as exogenous in the estimated model.
To explore further the possibility of heterogeneous effects and the
changing role of the variables explaining emissions across countries, a
finite mixture model is estimated. As already mentioned in the previous
section, this model overcomes the problems due to omitted variables
and the possible correlation among country observations. Additionally
it provides us with an endogenous classification of countries into
homogeneous groups. The obtained posterior probabilities allow us to
divide our sample into three groups of countries. We do this according
to two model selection criteria: the Bayesian Information Criterion
(BIC) and the Integrated Completed Likelihood (ICL).7
The first group contains 27 countries, the second 44 and the third
17 (Table 4). Countries in the first group are mainly upper-middle and
middle-low income countries according to the World Bank classification and are characterized by showing a more than proportional
long run elasticity of population with respect to emissions and lower
income elasticity. In those countries urbanization is inversely (Ushaped) related with CO2 emissions, whereas energy efficiency and
the percentage of industrial activity play no role in explaining the
response variable.
The second group is the most numerous and contains countries in
all stages of development that are characterized as having a very high
7
The BIC picks the candidate model with the highest probability given the data. It is
similar to the AIC but with a more severe penalty for complexity and it is consistent for
the number of components (Keribin, 1998). It is known that BIC does not
underestimate the number of components asymptotically (Leroux, 1992), indeed BIC
may overestimate the number of components when components are not well
separated. For this reason, we also considered ICL as an alternative. Furthermore, in a
range of applications of finite mixture models, model choice based on BIC has given
good results. We also reported the AIC values in Table 3. AIC is not strongly consistent,
though it is efficient (Claeskens and Hjort, 2008). Unfortunately, in this particular
application AIC does not provide any evidence of a specific mixture. The values
decrease for increasing number of components and, hence, in view of the
interpretability of the results, the BIC and the ICL are considered as model selection
criteria.
1351
long run elasticity with respect to income (1.79) and an almost
proportional impact of population growth on emissions. Also the
percentage of industrial activity contributes to increase emissions, but
urbanization and energy efficient are not statistically significant.
Finally, countries in the third group are middle-low and low income
countries8 for which all explanatory factors considered play a role —
especially income and population but also energy efficiency, the
percentage of industrial activity and urbanization. Similar as for group
1, urbanization presents an inverse U-shaped relationship with co2.
Emissions increase up to a turning point which for this group is 41% of
urbanization. To summarize, we confirm the modernization theory for
countries in groups 1 and 3, but not for countries in the second group.
With respect to the EKC hypothesis we also considered the inclusion
of a squared income term as an explanatory variable. It was negatively
signed, but not statistically significant. It was therefore excluded from
the model.
Fig. 3 presents the time effects of the three groups of countries. We
observe an overall slightly decreasing trend in the magnitude of the
estimated coefficients for the three groups over the whole period. For
the first and third component however, most of the year dummies are
not statistically significant. Assuming that these effects can represent
specific technical progress over time common to all countries, the
results indicate that technical progress has contributed to the
decrease in CO2 emissions, especially in developing countries in the
second group.
To sum up, the environmental impact caused by population,
urbanization, and affluence variables (scale effect) seems to be
heterogeneous across countries and this heterogeneity does not
correspond to different categories of income. Other factors are
responsible for the different CO2 emissions paths followed by different
countries and should be a matter of further research. The differential
impact that we have found in this paper should be taken into account
in future climate change negotiations and to design appropriate
mitigation policies.
5. Conclusions
In this paper a multivariate analysis of the determinants of carbon
dioxide emissions in developing countries during the period 1975 to
2003 has been conducted. We have taken the STIRPAT formulation,
the EKC hypothesis and the modernization theories as our theoretical
framework. In the first model population is introduced as a predictor
together with affluence per capita and the level of environmentally
damaging technology (proxied for by the weight of the industrial
sector in the GDP and with energy intensity). Affluence is measured
by the GDP per capita in PPP. We have added urbanization as a
predictor and used several estimation methods in a dynamic paneldata framework.
The results show different patterns for three groups of countries.
For the first and third sets of countries the elasticity emissionurbanization is positive for low and negative for high urbanization
levels. In the second group urbanization is not statistically significant.
This result has a very important policy implication: for some
countries once urbanization reaches a certain level, the effect on
emissions turns out to be negative, contributing to reduced environmental damage.
We obtained some evidence confirming the ecological modernization theory, developed by sociologists, predicting that environmental
impacts may follow a Kuznets curve relative to urbanization.
However, we did not find robust evidence of an EKC with respect to
income per capita, rejecting therefore the EKC hypothesis for
developing countries.
8
According to the World Bank classification (Table A.1 in the Appendix A).
1352
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
Appendix A
Table A.1
Lists of countries classified by income group.
Source: World Development Indicators 2007. Nobs denotes de number of observations per country.
Low-income
Nobs
Lower-middle-income
Nobs
Upper-middle-income
Nobs
Bangladesh
Benin
Congo, Dem. Rep.
Cote d'Ivoire
Ethiopia
Ghana
Haiti
India
Kenya
Kyrgyz Republic
Mozambique
Nepal
Nigeria
Pakistan
Senegal
Sudan
Tajikistan
Tanzania
Togo
Uzbekistan
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
24
29
29
29
23
29
10
29
29
12
24
29
29
29
29
21
12
14
29
12
19
14
29
29
Albania
Algeria
Angola
Armenia
Azerbaijan
Belarus
Bolivia
Brazil
Bulgaria
Cameroon
China
Colombia
Congo, Rep.
Ecuador
Egypt, Arab Rep.
El Salvador
Georgia
Guatemala
Honduras
Indonesia
Iran, Islamic Rep.
Jamaica
Jordan
Kazakhstan
Macedonia, FYR
Moldova
Morocco
Namibia
Nicaragua
Paraguay
Peru
Philippines
Sri Lanka
Syrian Arab Republic
Thailand
Tunisia
Turkmenistan
Ukraine
Ukraine
24
29
19
14
14
12
29
29
24
28
29
29
29
29
29
14
12
29
29
29
29
11
29
12
12
12
29
13
10
29
23
29
29
19
29
29
24
12
13
Argentina
Botswana
Chile
Costa Rica
Croatia
Czech Republic
Dominican Rep
Estonia
Gabon
Hungary
Latvia
Lebanon
Lithuania
Malaysia
Mexico
Oman
Panama
Poland
Romania
Russian Federation
Slovak Republic
South Africa
Trinidad and Tobago
Turkey
Uruguay
Venezuela, RB
29
23
29
29
12
14
29
12
29
24
12
10
12
29
29
29
24
14
16
12
19
29
20
29
21
29
Table A.2
Summary statistics and correlations.
Variable
Obs
Mean
Std. dev.
Min
Max
lco2
lyh
lp
lp1564
lp65
lpd
pup
lpup
lei
lia
4142
3353
4258
4080
4080
4200
4254
4254
2122
3293
8.411
7.673
15.358
4.028
16.998
3.737
41.626
3.578
14.966
3.255
2.573
0.875
1.982
0.103
1.887
1.335
20.330
0.600
0.609
0.461
1.298
5.393
9.888
3.834
12.348
−0.079
3.200
1.163
12.981
0.632
15.237
9.777
20.977
4.259
22.967
6.956
92.480
4.527
16.315
4.488
Correlations
lco2
lyh
lp
lp1564
lp65
lpd
lpup
lei1
lia
lco2
lyh
lp
lp1564
lp65
lpd
lpup
lei
lia
1
0.421
0.686
0.514
0.778
0.239
0.312
−0.032
0.435
1
−0.189
0.658
0.001
−0.048
0.677
0.502
0.447
1
0.103
0.945
0.356
−0.183
−0.046
−0.027
1
0.373
0.340
0.380
0.191
0.294
1
0.419
−0.045
−0.024
0.046
1
−0.274
0.071
−0.223
1
0.333
0.408
1
−0.017
1
I. Martínez-Zarzoso, A. Maruotti / Ecological Economics 70 (2011) 1344–1353
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