Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example

Feipeng Guo, Linji Zhang, Zifan Wang and Shaobo Ji
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Feipeng Guo: School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
Linji Zhang: School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
Zifan Wang: School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
Shaobo Ji: Sprott School of Business, Carleton University, Ottawa, ON K1S 5B6, Canada

IJERPH, 2022, vol. 19, issue 14, 1-20

Abstract: Driven by China’s peak carbon emissions and carbon neutrality goals, each region should choose a suitable local implementation path according to local conditions, so it is of great significance to mine and analyze the critical influencing factors of regional carbon emissions. Therefore, this paper integrates grey relation analysis (GRA) and an improved STIRPAT model and selects the Yangtze River Delta region of China as the research object to analyze the factors affecting carbon emissions in four provinces in the region. Firstly, it uses the IPCC method to calculate the energy carbon emissions of each province. Secondly, according to the existing research, the relevant influencing factors of carbon emissions are sorted and summarized as candidate sets and this paper uses GRA to calculate the correlation degree of the above candidate sets. On this basis, this paper combines with the characteristics of the improved STIRPAT model to determine the index selection criteria and filter out the critical factors of each province. Thirdly, an improved STIRPAT model is constructed for each province to explore the influence of critical factors and analyze the influencing factors of carbon emissions in detail. The empirical results show that during the period from 2005 to 2019, the carbon emissions of the four provinces in the Yangtze River Delta are significantly different in structure and trend. At the same time, the critical influencing factors of each province are different and the influence of the same factor on different regions is significantly different. Finally, the policy suggestions for the provinces to achieve their peak carbon emissions and carbon neutrality goals are precisely tailored to the different carbon emission influencing factors.

Keywords: energy consumption; Yangtze River Delta; GRA; improved STIRPAT model; carbon emission influencing factors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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