Driving Factors of CO 2 Emissions in China’s Power Industry: Relative Importance Analysis Based on Spatial Durbin Model

Yuanying Chi, Wenbing Zhou, Songlin Tang and Yu Hu
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Yuanying Chi: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Wenbing Zhou: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Songlin Tang: Economic School, Shandong Technology and Business University, Yantai 264005, China
Yu Hu: School of Economics and Management, Beijing University of Technology, Beijing 100124, China

Energies, 2022, vol. 15, issue 7, 1-15

Abstract: The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China’s CO 2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO 2 emissions in China’s power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO 2 emissions in China’s power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO 2 emissions. The energy demand structure and CO 2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO 2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO 2 emissions than that of construction and transportation. For achieving the CO 2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors.

Keywords: low-carbon transformation; power industry; driving factor analysis; spatial Durbin model; relative importance analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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