Effects of electricity consumption on carbon intensity across Chinese manufacturing sectors
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This research aims to study the effect on industrial carbon intensity by decomposing electricity consumption into electricity consumption volume and electric power intensity by using panel data of 27 China’s manufacturing sectors. An improved STIRPAT model is also developed by this article to identify the unexplored potential influencing factors. The research findings suggest that there exists a long-run equilibrium relationship between electricity consumption and carbon intensity and unidirectional causality from electricity consumption volume to carbon intensity. Regression results indicate that electricity consumption volume has a significantly negative effect on industrial carbon intensity for the full sample. However, due to the significantly positive influence that electric power intensity has on carbon intensity, we conclude that energy consumption and industrial economy failed to achieve the decoupling effect. The impacts of electricity consumption volume and electric power intensity have industrial heterogeneity. Electric power intensity impacts carbon intensity the most for resource intensive sectors. The effects of subgroups are further examined for sectors with high/low carbon emission volume and carbon intensity. Foreign direct investment (FDI) is conducive to reducing carbon intensity for sectors with high volume and sectors with high intensity. Industrialization level demonstrates a significantly positive effect on improving carbon intensity for sectors with low volume and sectors with low intensity. Finally, we put forward specific suggestions on the basis of these empirical findings.
KeywordsElectricity consumption Decoupling effect Improved STIRPAT model Carbon intensity Manufacturing sectors
This work has been supported by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, Scientific Research and Cultivation Project for Young Teachers of Nanjing Audit University in 2019 (Grant No. 19QNPY007), National Natural Science Foundation of China (Grant No. 71573136), National Social Science Foundation of China (Grant No. 17ZDA096), Annual Project of the National Social Science Fund (Grant No. 18BJL124), Scientific Research Projects of the Fifth Phase of “333 Project” in Jiangsu Province (Grant No. BRA2017460), and the “Blue Project” in Colleges and Universities of Jiangsu Province.
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