Elsevier

China Economic Review

Volume 23, Issue 2, June 2012, Pages 371-384
China Economic Review

Economic development and carbon dioxide emissions in China: Provincial panel data analysis

https://doi.org/10.1016/j.chieco.2012.02.004Get rights and content

Abstract

This paper investigates the driving forces, emission trends and reduction potential of China's carbon dioxide (CO2) emissions based on a provincial panel data set covering the years 1995 to 2009. A series of static and dynamic panel data models are estimated, and then an optimal forecasting model selected by out-of-sample criteria is used to forecast the emission trend and reduction potential up to 2020. The estimation results show that economic development, technology progress and industry structure are the most important factors affecting China's CO2 emissions, while the impacts of energy consumption structure, trade openness and urbanization level are negligible. The inverted U-shaped relationship between per capita CO2 emissions and economic development level is not strongly supported by the estimation results. The impact of capital adjustment speed is significant. Scenario simulations further show that per capita and aggregate CO2 emissions of China will increase continuously up to 2020 under any of the three scenarios developed in this study, but the reduction potential is large.

Highlights

► We construct a provincial panel of China’s CO2 emissions covering the year 1995–2009. ► We investigate driving forces and reduction potential of China’s CO2 emissions. ► The Environmental Kuznets Curve is not strongly supported by the estimation results. ► China’s CO2 emissions will increase up to 2020, but the reduction potential is large.

Introduction

Scientific evidence overwhelmingly indicates that greenhouse gas emitted by human activity is the main cause of global warming. Stern (2007) warns that, if no action is taken to reduce emissions, the overall costs and risks of climate change will be equivalent to at least a 5% of global GDP loss each year. With progress in industrialization and urbanization, China's energy consumption and carbon dioxide (CO2) emissions have increased rapidly in the past few years. In 2009, the total energy consumption of China reached 2.9 billion tons of standard coal, with the total CO2 emissions reaching 7.7 billion tons.1 As one of the largest emitters, China has become the focus of global reduction of CO2 emissions. Thus the following questions need to be addressed. What are the main factors affecting China's CO2 emissions? What are the emission trends for the foreseeable future? How large is the reduction potential?

Almost all of the studies on modeling and forecasting China's CO2 emissions are based on national level time series data or industrial level cross-sectional data, and only a few of them are based on panel data models. It is widely recognized that panel data sets for economic research possess several major advantages over conventional cross-sectional or time series data sets. Panel data usually give the researchers a larger number of data points. More importantly, panel data models are able to capture the individual heterogeneity by introducing an individual specific effect term in the regression model, thus improving the estimation performance (Baltagi, 2005, Hsiao, 2003). A number of previous studies on international CO2 emissions based on cross-country panel data models have taken advantage of panel data econometric models, such as Holtz-Eakin and Selden (1995), Tucker (1995), Schmalensee, Stoker, and Judson (1998), Lantz and Feng (2006), Maddison (2006), Aldy (2007). Most recently, Auffhammer and Carson (2008) attempt to forecast China's CO2 emission path for the foreseeable future by using provincial panel data models, and they find evidence of underestimation in previous studies which are conducted based on time series or cross-sectional data. Their estimation results, however, are not based on a panel data set of CO2 emissions but are based on a panel data set of waste gas emissions.

This paper investigates the driving forces, emission trends and reduction potential of China's CO2 emissions over the next decade, based on a provincial panel data set of CO2 emissions covering the years 1995–2009. Our results show that economic development, technology progress and industry structure are the most important factors affecting China's CO2 emissions, while the impacts of energy consumption structure, urbanization level and trade openness are negligible. The inverted U-shaped relationship between per capita CO2 emissions and economic development level is not strongly supported by the estimation results. The impact of capital adjustment speed on China's CO2 emissions is significant. Scenario simulations further show that per capita and aggregate CO2 emissions of China will increase continuously up to 2020 even with active policy interventions, but the reduction potential is large.

This paper makes two contributions to the literature on investigation of China's CO2 emissions. First, we construct a novel provincial panel data set of CO2 emissions covering the years 1995–2009 for China. This data set not only allows us to capture the advantages of panel models in this study, but also provides a database for future research, such as the allocation of emission rights and reduction obligations among provinces. Second, using this newly constructed panel data set, we investigate the driving forces, emission trends and reduction potential of China's CO2 emissions based on both static and dynamic panel models. We find some interesting results that are different from previous studies.

The rest of the paper is organized as follows. Section 2 reviews existing literature briefly. In Section 3 we estimate CO2 emissions for 29 provinces of China in detail. Section 4 focuses on the econometric model and data description. Section 5 presents the estimation results and specification search. In Section 6, we forecast per capita and aggregate CO2 emissions up to 2020 under three different scenarios. The last section is devoted to conclusion.

Section snippets

Literature review

The existing literature on modeling and forecasting China's CO2 emissions mainly falls into four categories from methodological perspectives. The first category is the index decomposition analysis based on national level time series data, including Ang and Pandiyan, 1997, Zhang, 2000, Wang et al., 2005, Wu et al., 2005, Fan et al., 2007a, Liu et al., 2007, Feng et al., 2009, Zhang et al., 2009a and Zhang, Mu, Ning & Song (2009), etc. A variety of index decomposition methods are used in these

Estimating provincial CO2 emissions

We estimate CO2 emissions for 29 provinces of China from 1995 to 2009, based on the CO2 emission coefficients published by IPCC (2006) and the National Coordination Committee Office on Climate Change and Energy Research Institute under the National Development and Reform Commission (2007).4

Econometric models and data description

We consider the following reduced-form econometric model:yit=δyi,t1+Zitβ+ηi+εitwhere yit is per capita CO2 emissions of province i in year t, and yi,t 1 its first order lag item; δ scalar coefficient while β a vector of parameters; ηi represents the individual effect, capturing the idiosyncratic characters of each province; εit the error term; Zit a vector of exogenous variables, including per capita GDP, industry composition, urbanization level, energy consumption structure, technology

Estimation results and explanations

We estimate nine regression models according to the inclusion of different independent variables based on model (3), with the results listed in Table 4. The advantages of our method are that we can test the robustness of the models by comparing the coefficients of each model and find the best model by specification search. We test the dependent variable ln(per_CO2) for a unit root using Levin, Lin, and Chu's (2002) test and the testing results overwhelmingly reject the null hypothesis of a unit

Forecasting CO2 emissions

In this section, we forecast per capita and aggregate CO2 emissions based on Model V (for more technical details, please refer to the Appendix).15

Conclusion

In this paper, we investigate the driving forces, evolution trends and reduction potential of China's CO2 emissions. A provincial panel data set of China's CO2 emissions covering the years 1995–2009 is constructed, a series of static and dynamic panel regression models are estimated, and then a best forecasting model selected by in-sample and out-of-sample criteria is used to forecast the emission trends and reduction potential up to 2020. The results are summarized as follows.

  • (1)

    Both per capita

Acknowledgments

We truthfully appreciate the comments and suggestions from two anonymous referees and the participants of the 2nd Princeton-China Forum on Energy, Environment and Economic Policy Research. Limin Du gratefully acknowledges the support from the Ministry of Education of the People's Republic of China (09YJC790167), the Fundamental Research Funds for the Central Universities, and the Natural Science Foundation of Zhejiang Province (Q12G030086). Chu Wei gratefully acknowledges the support from the

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