Journal of Geographical Sciences ›› 2018, Vol. 28 ›› Issue (4): 495-513.doi: 10.1007/s11442-018-1486-9

• Research Articles • Previous Articles     Next Articles

Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

Wensong SU1,2,3(), Yanyan Liu4(), Shaojian WANG5, Yabo ZHAO6(), Yongxian SU7, Shijie LI5   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Zhongguancun Development Group Co., Ltd., Beijing 100080, China
    4. School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China
    5. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    6. School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China
    7. Guangzhou Institute of Geography, Guangzhou 510070, China
  • Received:2017-05-01 Accepted:2017-09-01 Online:2018-03-30 Published:2018-03-30
  • About author:

    Author: Su Wensong (1982-), PhD, specialized in urban geography. E-mail: suws.13b@igsnrr.ac.cn

    *Corresponding author: Liu Yanyan (1985-), PhD, E-mail: liuyyan@mail2.sysu.edu.cn

  • Supported by:
    National Natural Science Foundation of China, No.41601151;Guangdong Natural Science Foundation, No.2016A030310149

Abstract:

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation (Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club’ agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

Key words: carbon emissions, spatial spillover effects, dynamic spatial panel data model, Chinese carbon emission reduction policies, environmental Kuznets curve