ArticleInternational journal of environmental research and public health2022
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.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Dissertation title: decoupling analysis of building carbon emissions and economic growth based on Tapio model and EKC curve.Carbon balance and management · 2025Article
- Spatial distribution characteristics and influencing factors of youth soccer specialty schools in China.Scientific reports · 2024Article
- The race between global economic growth and carbon emissions: based on a comparative study of developed and developing countries.Environmental science and pollution research international · 2024Article
- Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends.Environmental science and pollution research international · 2024Article
- Retracted: Unveiling the synergy: Green finance, technological innovation, green energy, and carbon neutrality.PloS one · 2024Article
- The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China.International journal of environmental research and public health · 2022Article
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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.
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