Yu-Cong Jiang, Yan-Ying Li, Shun-Ping Wang, Yu-Xin Yang
{"title":"[基于减排水平指标的中国区域碳排放影响因素分析]。","authors":"Yu-Cong Jiang, Yan-Ying Li, Shun-Ping Wang, Yu-Xin Yang","doi":"10.13227/j.hjkx.202408126","DOIUrl":null,"url":null,"abstract":"<p><p>China has put forward the strategic goal of achieving carbon peak by 2030 and carbon neutrality by 2060, and study of the CO<sub>2</sub> emission reduction potential is crucial for China and provincial regions to realize the dual-carbon goal. In this study, 30 provinces (autonomous regions and municipalities directly under the central government) in China are used as the research objects, and a regional carbon emission reduction potential evaluation system is constructed. Based on the BP neural network under the CRITIC coefficient of variation method, the provincial emission reduction index is calculated, combined with the cluster analysis of the differences in regional emission reduction potentials, and the SSA-XGBoost model is set up to investigate the factors influencing regional carbon emissions in China and their degree of influence. The results of the study include the following: ① The regional carbon emission reduction level index has a significant spatial correlation. The average carbon emission reduction value is higher in the southeastern coastal provinces, and the inland provinces with more backward economic development have more low values. ② China's 30 provincial-level regions are divided into four emission reduction potential categories, including Shandong, Guangdong, Hebei, Jiangsu, Zhejiang, Fujian, Henan, Hubei, Hunan, and Sichuan with good and excellent emission reduction potentials, which are the main driving forces for realizing the \"double carbon\" goal, and 14 provinces with average emission reduction potential. The degree of influence on carbon emissions has the following order: energy structure > digital structure > infrastructure structure > industrial structure > resource structure > population structure > economic structure. Energy structure as an influencing factor has the strongest potential to reduce carbon emissions in the industry category, infrastructure structure has a higher degree of influence in the optimization of the environment category, and population structure is more important than digital structure in the social category. The results show that China's carbon emission reduction level is characterized by uneven development regionally, cleaner energy, and a higher influence of social digitalization. To realize benign and efficient transformation and green development of the provinces and the country, it is suggested that the characteristics of provinces with strong emission reduction capacity should gradually be extended to the average and poorer provinces.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 9","pages":"5415-5427"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Analysis of Influencing Factors of Regional Carbon Emissions in China Based on Emission Reduction Level Indexes].\",\"authors\":\"Yu-Cong Jiang, Yan-Ying Li, Shun-Ping Wang, Yu-Xin Yang\",\"doi\":\"10.13227/j.hjkx.202408126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>China has put forward the strategic goal of achieving carbon peak by 2030 and carbon neutrality by 2060, and study of the CO<sub>2</sub> emission reduction potential is crucial for China and provincial regions to realize the dual-carbon goal. In this study, 30 provinces (autonomous regions and municipalities directly under the central government) in China are used as the research objects, and a regional carbon emission reduction potential evaluation system is constructed. Based on the BP neural network under the CRITIC coefficient of variation method, the provincial emission reduction index is calculated, combined with the cluster analysis of the differences in regional emission reduction potentials, and the SSA-XGBoost model is set up to investigate the factors influencing regional carbon emissions in China and their degree of influence. The results of the study include the following: ① The regional carbon emission reduction level index has a significant spatial correlation. The average carbon emission reduction value is higher in the southeastern coastal provinces, and the inland provinces with more backward economic development have more low values. ② China's 30 provincial-level regions are divided into four emission reduction potential categories, including Shandong, Guangdong, Hebei, Jiangsu, Zhejiang, Fujian, Henan, Hubei, Hunan, and Sichuan with good and excellent emission reduction potentials, which are the main driving forces for realizing the \\\"double carbon\\\" goal, and 14 provinces with average emission reduction potential. The degree of influence on carbon emissions has the following order: energy structure > digital structure > infrastructure structure > industrial structure > resource structure > population structure > economic structure. Energy structure as an influencing factor has the strongest potential to reduce carbon emissions in the industry category, infrastructure structure has a higher degree of influence in the optimization of the environment category, and population structure is more important than digital structure in the social category. The results show that China's carbon emission reduction level is characterized by uneven development regionally, cleaner energy, and a higher influence of social digitalization. To realize benign and efficient transformation and green development of the provinces and the country, it is suggested that the characteristics of provinces with strong emission reduction capacity should gradually be extended to the average and poorer provinces.</p>\",\"PeriodicalId\":35937,\"journal\":{\"name\":\"环境科学\",\"volume\":\"46 9\",\"pages\":\"5415-5427\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"环境科学\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.13227/j.hjkx.202408126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202408126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
[Analysis of Influencing Factors of Regional Carbon Emissions in China Based on Emission Reduction Level Indexes].
China has put forward the strategic goal of achieving carbon peak by 2030 and carbon neutrality by 2060, and study of the CO2 emission reduction potential is crucial for China and provincial regions to realize the dual-carbon goal. In this study, 30 provinces (autonomous regions and municipalities directly under the central government) in China are used as the research objects, and a regional carbon emission reduction potential evaluation system is constructed. Based on the BP neural network under the CRITIC coefficient of variation method, the provincial emission reduction index is calculated, combined with the cluster analysis of the differences in regional emission reduction potentials, and the SSA-XGBoost model is set up to investigate the factors influencing regional carbon emissions in China and their degree of influence. The results of the study include the following: ① The regional carbon emission reduction level index has a significant spatial correlation. The average carbon emission reduction value is higher in the southeastern coastal provinces, and the inland provinces with more backward economic development have more low values. ② China's 30 provincial-level regions are divided into four emission reduction potential categories, including Shandong, Guangdong, Hebei, Jiangsu, Zhejiang, Fujian, Henan, Hubei, Hunan, and Sichuan with good and excellent emission reduction potentials, which are the main driving forces for realizing the "double carbon" goal, and 14 provinces with average emission reduction potential. The degree of influence on carbon emissions has the following order: energy structure > digital structure > infrastructure structure > industrial structure > resource structure > population structure > economic structure. Energy structure as an influencing factor has the strongest potential to reduce carbon emissions in the industry category, infrastructure structure has a higher degree of influence in the optimization of the environment category, and population structure is more important than digital structure in the social category. The results show that China's carbon emission reduction level is characterized by uneven development regionally, cleaner energy, and a higher influence of social digitalization. To realize benign and efficient transformation and green development of the provinces and the country, it is suggested that the characteristics of provinces with strong emission reduction capacity should gradually be extended to the average and poorer provinces.