Acta Geographica Sinica ›› 2019, Vol. 74 ›› Issue (6): 1131-1148.doi: 10.11821/dlxb201906005

• Urban and Regional Development • Previous Articles     Next Articles

Spatial spillover effect and driving forces of carbon emission intensity at city level in China

WANG Shaojian,HUANG Yongyuan   

  1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Received:2018-04-04 Revised:2019-03-11 Online:2019-06-25 Published:2019-06-20
  • Supported by:
    National Natural Science Foundation of China(41601151);Guangdong Special Support Program;Pearl River S&T Nova Program of Guangzhou

Abstract:

Since the Paris Climate Change Conference in 2015, reducing carbon emission and lowering carbon intensity has become a global consensus to deal with climate change. Due to different economic development stages, carbon intensity is regarded as a better index to measure regional energy-related carbon emissions. Although previous scholars have made great efforts to explore the spatiotemporal patterns and key driving factors of carbon intensity in China, the results lack the perspective from city level because of limited availability of statistical data of city-level carbon emission. In this study, based on carbon intensity of 283 cities in China from 1992-2013, we used the kernel density estimation, spatial autocorrelation, spatial Markov-chain and quantile regression panel model to empirically reveal its spatial spillover effects and explore the critical impact factors of carbon intensity at the city level. Our result indicates that although the total carbon emission increased during the study period, carbon intensity saw a gradual decline and regional differences were shrinking. Secondly, the city-level carbon intensity presented a strong spatial spillover effect and diverse regional backgrounds exerted heterogeneous effects on regions. Thirdly, quantile panel data analysis result showed that for low-intensity cities, on the one hand, FDI and transport sector were main contributing factors, and economic growth, technical progress and high population density negatively affected carbon intensity. On the other hand, industrial activity, extensive growth of investment and urban sprawl were key promoting factors for high-intensity cities, and population density was beneficial to emission reduction task. Furthermore, technological advance has not exerted negative influence on carbon intensity in high-intensity cities. At last, we suggested that Chinese government should take different carbon intensity levels into full consideration before policy making.

Key words: city level, carbon emission intensity, spatial spillover effect, spatial Markov-chain, quantile regression panel model