Economics Letters 117 (2012) 848–850

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Economics Letters

journal homepage: www.elsevier.com/locate/ecolet

Urbanization and CO2 emissions: A semi-parametric panel data analysisHui-Ming Zhu a,∗, Wan-Hai You a, Zhao-fa Zeng b

a College of Business Administration, Hunan University, Changsha 410082, PR Chinab College of Finance and Statistics, Hunan University, Changsha 410082, PR China

a r t i c l e i n f o

Article history:Received 19 June 2012Received in revised form15 August 2012Accepted 3 September 2012Available online 7 September 2012

JEL classification:C14O5

Keywords:CO2 emissionsUrbanizationEmerging countriesSemi-parametric regressionPanel data

a b s t r a c t

This paper investigates the relationship between urbanization and CO2 emissions in a sample of 20emerging countries over the period 1992–2008 using the semi-parametric panel data model with fixedeffects, proposed by Baltagi and Li (2002). We find little evidence in support of an inverted-U curve,and thus the Kuznets hypothesis is not confirmed by our analysis. Our findings shed new light on theurbanization-CO2 emissions nexus.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

The relationship between urbanization and CO2 emissionshas been extensively investigated in recent years. The empiricalresults, however, are mixed. For example, Cole and Neumayer(2004) and Liddle and Lung (2010) demonstrate a positivecorrelation between urbanization and CO2 emissions, while Fanet al. (2006) find a negative correlation between urbanization andCO2 emissions in developing countries. Poumanyvong and Kaneko(2010) argue that the assumption that the relationship betweenurbanization and CO2 emissions is homogenous for all countriesmay be unreasonable. They examine the effects of urbanization onCO2 emissions for low-, middle-, and high-income group, and findthat while a positive relationship exists for all income groups, it ismost prominent in the middle-income group.

The vast majority of existing literature has assumed thatthere exists a linear relationship between urbanization and CO2emissions. Ehrhardt-Martinez et al. (2002) argue that urbanizationis a good proxy for modernization, and thus the relationshipbetween urbanization and CO2 emissionsmay vary across differentstages of development. Martinez-Zarzoso and Maruotti (2011)

∗ Corresponding author. Tel.: +86 731 88823670; fax: +86 731 88823670.E-mail addresses: zhuhuiming1999@yahoo.com.cn, zhuhuiming@hnu.edu.cn

(H.-M. Zhu).

0165-1765/$ – see front matter© 2012 Elsevier B.V. All rights reserved.doi:10.1016/j.econlet.2012.09.001

find an inverted U-shaped relationship between urbanization andCO2 emissions. To explore a clear relational structure betweenurbanization and CO2 emissions, we re-investigate this topic byemploying the semi-parametric panel fixed effects regressionmodel developed by Baltagi and Li (2002). Within this framework,a no priori parametric functional form is assumed for modelingthe relationship between urbanization and CO2 emissions, whichallows us to obtain the ‘true’ functional form of the urbanization-CO2 emissions nexus. Our empirical results show that therelationship between urbanization and CO2 emissions is not asimple inverted U-shape, and thus our findings are inconsistentwith the results obtained using the conventional polynomialfunctional form specification.

The rest of the paper is organized as follows. Section 2 describesthemodel framework andmethods. Section 3 discusses the results,and Section 4 offers concluding remarks.

2. Theoretical framework and econometric methods

The IPAT model (I = PAT) proposed by Ehrlich and Holdren(1971) has been widely used to assess the impact of populationsize, affluence, and other factors on the environment. Althoughthe IPAT model is a very useful framework, it does have somelimitations (Liddle and Lung, 2010). To address those limitations,Dietz and Rosa (1997) formulate a stochastic version of the IPATmodel, named STIRPAT (Stochastic Impacts by Regression on

H.-M. Zhu et al. / Economics Letters 117 (2012) 848–850 849

Population, Affluence, and Technology). The model specificationfor a single year is as follows

Ii = aPbi A

ci T

di εi, (1)

where I , P , A, and T denote environmental impact, population,affluence, and technology; a, b, c , and d are the estimatedparameters; and ε denotes the disturbance term.

The STIRPAT model can be used to assess the effects onenvironmental impact not only of the core components, populationsize and affluence, but also of other factors, such as modernization(York et al., 2003). To examine the impact of urbanization onthe environment, we have estimated an extended version of theSTIRPATmodel. Thus, the following augmentedmodel is estimatedin the environmental Kuznets curve (EKC) hypothesis framework

ln(CO2it) = αi + β1 ln(Pit) + β2 ln(Ait) + β3 ln(EIit)

+ β4 ln(URBit) + β5 ln(URBit)2+ εit , (2)

where CO2 is the amount of CO2 emissions (in tons per capita) ofcountry i at year t; P denotes the population size; A is per capitaGDP; EI is energy use; URB is the urbanization level and αi is thecountry fixed effect. We also include in our empirical models atime-period specific effect, which is a proxy for the variables thatare common across countries but vary over time. This effect couldbe interpreted as the effect of technical progress over time (Stern,2002).

Yatchew (1998) believes that most economic theories do notidentify a specific functional form in a model for the relationshipbetween the dependent variable and the independent variables. Toavoid possible model misspecification of the parametric domain,a more flexible way of modeling the relationship betweenurbanization and CO2 emissions is to estimate a semi-parametricmodel. In addition, we can obtain a more accurate inference ofmodel parameters in the panel data framework (Hsiao, 2007).Thus, we model the relationship between urbanization and CO2emissions using the semi-parametric panel data model with fixedeffects, developed by Baltagi and Li (2002). The model is given by:

ln(CO2it) = αi + β1 ln(Pit) + β2 ln(Ait)

+ β3 ln(EIit) + f (ln(URBit)) + εit , (3)

where the functional form f (.) is unspecified. The unobservedheterogeneity effects (αi) can be eliminated by taking a firstdifference. To estimate the first difference model consistently,1Baltagi and Li (2002) propose approximating [f (ln(URBit)) − f (ln(URBit−1))] by the following series differences

pk(ln(URBit), ln(URBit−1))

= [pk(ln(URBit)) − pk(ln(URBit−1))], (4)

where pk(ln(URB)) are the first k terms of a sequence of function2

(p1(ln(URB)), p2(ln(URB)), . . .). In our empirical analysis, we usea B-spline regression model with k = 4 (Desbordes and Verardi,2012; Newson, 2001).

3. Data and empirical results

The panel data set consists of cross-country observationscovering the period 1992–2008 for 20 emerging countries.3 Thissample period is selected based on data availability. All of these

1 For a more detailed discussion, see Baltagi and Li (2002).2 Desbordes and Verardi (2012) point out that a typical example of pk series is a

spline.3 Emerging countries are identified as those 20 countries (except Taiwan) listed

in the MSCI: http://www.msci.com/products/indices/country_and_regional/em/.

Table 1Summary statistics (1992–2008, observations = 340).

Variables Mean Standard Deviation Min Max

lnCO2 1.1695 0.8398 −0.2548 2.7037lnP 17.9706 1.2691 16.1219 21.0044lnA 8.8198 0.6874 7.1209 10.1471lnEI 5.2237 0.4483 4.1947 6.34298lnURB 4.0575 0.3245 3.2558 4.4823CountriesN = 20

Brazil, Chile, China, Colombia, Czech Republic,Egypt, Hungary, India, Indonesia, Korea, Malaysia,Mexico, Morocco, Peru, Philippines, Poland,Russia, South Africa, Thailand, Turkey

Table 2Estimation results for CO2 emission models.a

Variables FE Semi-parametric

Constant −34.43067b (4.5871)lnP 0.78642b (0.1941) 0.9488b (0.2465)lnA 1.1216b (0.0682) 1.1995b (0.0906)lnEI 0.9706b (0.0661) 0.9347b (0.0769)lnURB 3.5523c (1.3675)lnURB2 −0.47198c (0.1934)Country dummies Yes YesYear dummies Yes YesR2 0.8915 0.6162a Robust standard errors are reported in parentheses. The time fixed effect is

included but not reported.b denotes the rejection of the null hypothesis at the 1% significance level.c denotes the rejection of the null hypothesis at the 5% significance level.

data are collected fromWorld BankWorld Development Indicators(WDI). The gross domestic product is expressed in constant PPP(2005US$), the urbanization level is measured by the percentageof total population living in urban areas, and energy use (kg of oilequivalent) is measured in per $1,000 GDP (constant 2005 PPP). Allof the variables are converted into natural logarithms. A summaryof the data, as well as a complete list of the sample countries, isshown in Table 1.

The empirical results are reported in Table 2.4 TheHausman testresult (p = 0.0000) suggests the use of the fixed effects model.5Column 1 reports the results of the fixed effects estimator in theEKC hypothesis framework (Eq. (2)). The results indicate that allthe variables included are statistically significant at the 1% or 5%significance level and that they show the expected signs. The re-sults confirm the EKC hypothesis. Column 2 reports the coefficientestimation of the control variables in the semi-parametric paneldata model. The coefficient estimation of all control variables isclose to the parametric model, and it is statistically significant atthe 1% significance level.

Fig. 1 illustrates the semi-parametric estimation of f (·), andshaded areas correspond to 95% confidence intervals. The points inthe graph are partial residuals for CO2 emissions, centered on themean, and the CO2 emissions level has been adjusted for the effectsof the other independent variables.6 The result, in accordance withprevious studies, shows that urbanization has a nonlinear effecton CO2 emissions. However, we find little evidence to support theEKChypothesis. The inverted-Uhypothesis is confirmed onlywhenlnURB reaches 3.5; when lnURB is below this level, CO2 emissions

4 The quadratic term of income is not included in ourmodel because we find thatthe relationship between income and CO2 emissions ismonotone.More specifically,wemodel the relationship between income andCO2 emissions in a semi-parametricframework. We observe that the curve is increasing for all values of income (SeeFig. 2).5 For an excellent textbook treatment of the panel datamodel, see Baltagi (2008).6 In fact, the curve f (·) can be estimated by the following equation: uit =

ln(CO2it ) − ai − β1 ln(Pit ) − β2 ln(Ait ) − β3 ln(EIit ) = f (ln(URBit )) + εit .

850 H.-M. Zhu et al. / Economics Letters 117 (2012) 848–850

Fig. 1. Partial fits of the relationship between urbanization and CO2 emissions.Note: The points in the graph are partial residuals for CO2 emissions in thesemi-parametric model, and the partial residuals are centered on the mean. Thegreen dash line is the curve generated by the parametric model (adjusted for theeffects of the other independent variables). The maroon curve represents the semi-parametric estimation of f (·). Shaded areas correspond to 95% confidence intervals.

Fig. 2. Partial fits of the relationship between CO2 emissions and income. (Non-parametric variable = Income, including control variables in Eq. (2).)

decrease with urbanization. For comparison purposes, we alsoprovide the curve generated by the parametric model (FE modelin Table 2) in Fig. 1 (the green dash line). The result of an invertedU-shaped relationship between urbanization and CO2 emissionsis inconsistent with the result obtained by the semi-parametricanalysis. Thus, our findings shed new light on the urbanizationlevel–CO2 emissions nexus.

4. Conclusion

This paper examines the urbanization-CO2 emissions nexusunder the STIRPAT framework, using the semi-parametric

panel data model with fixed effects proposed by Baltagi and Li(2002). We find a nonlinear relationship between urbanizationand CO2 emissions. However, unlike previous studies, we findlittle evidence to support an inverted-U relationship betweenurbanization and CO2 emissions.

Acknowledgments

The authors wish to thank the editors and an anonymousreferee for very constructive comments. We are also indebtedto Dr. Vincenzo Verardi and Dr. François Libois for providingthe program code and for helpful comments. This research issupported by the National Natural Science Foundation of Chinaunder grants No. 71171075, No. 71221001, and No. 71031004.

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