Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (2): 231-252.doi: 10.1007/s11442-019-1594-1

• Research Articles • Previous Articles     Next Articles

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

Shaojian WANG(), Yongyuan HUANG, Yuquan ZHOU   

  1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2018-04-07 Accepted:2018-06-01 Online:2019-02-25 Published:2019-02-25
  • About author:

    Author: Wang Shaojian (1986-), Associate Professor, specialized in urban geography and regional development. E-mail: 1987wangshaojian@163.com

  • Supported by:
    National Natural Science Foundation of China, No.41601151;Natural Science Foundation of Guangdong Province, No.2016A030310149;Pearl River S&T Nova Program of Guangzhou (201806010187)

Abstract:

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

Key words: Chinese cities, kernel density estimation, spatial autocorrelation, spatial spillover effect, spatial Markov chain, quantile regression panel model