A spatial shift-share decomposition of energy consumption changes in China. (December 2019)

• Record Type: Journal Article
• Authors: Lin, Gang; Jiang, Dong; Fu, Jingying; Wang, Di; Li, Xiang
• Is Part Of: Energy policy. Volume 135(2019)
• Publication Date: 2019-12
• Subjects: Energy consumption -- Spatial analysis -- Neighborhood effects -- Spatial weight matrix; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2019.111034
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: The objective of this paper is to investigate the changes in the regional energy consumption at the provincial level in China from 2007 to 2016 by introducing a spatial shift-share decomposition approach to measure the neighborhood effect on the regional energy efficiency change through the establishment of a spatial weight matrix considering the geographical and economic proximity. Using this decomposition method, the regional advantage (or disadvantage) in energy efficiency can be revealed, excluding the influence of neighborhood performance in terms of energy efficiency. The results indicate that a shift from energy intensive industrial productions toward the less energy intensive service sector occurs in almost all provinces of China over the period observed; there is significant spatial differentiation on the spatial decomposition of the efficiency change component across provinces, and the changes in energy efficiency in provinces of Jing-Jin-Ji, Yangtze River Delta, Chengdu-Chongqing, Guanzhong Plain, Pan-Pearl River Delta city agglomeration and the three provinces in Northeast China are mainly affected by the neighborhood effect. Our empirical findings suggest that energy policy making should be more concerned about the spatial correlation and coupling effects of energy consumption, especially in the national urban agglomeration of China. Highlights: A spatial shift-share decomposition for analysing energy consumption changes is proposed. Spatial weights matrix is structured based on economic and geographical factors. Neighborhood effects on regional energy efficiency performances are detected.