{"title":"[Spatiotemporal Evolution and Influencing Factors of Carbon Emissions in China's Four Major Urban Agglomerations].","authors":"Hai-Peng Wu, Qiao-Xing Li","doi":"10.13227/j.hjkx.202506331","DOIUrl":null,"url":null,"abstract":"<p><p>Guided by the national strategic goals of carbon peaking and carbon neutrality, it is of great significance to investigate the spatiotemporal evolution characteristics and driving factors of carbon emissions in China's major urban agglomerations. Based on the integrated nighttime light index and energy statistical data, this study estimates the carbon emissions of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CC) urban agglomerations from 2000 to 2022. By analyzing carbon emission intensity and per capita carbon emissions, the spatiotemporal evolution patterns and regional disparities are explored. Furthermore, the logarithmic mean Divisia index (LMDI) decomposition method is employed to explore the dynamic changes of the influencing factors. The results reveal that total carbon emissions in the four urban agglomerations experienced rapid growth in the early stage, followed by a noticeable slowdown. Spatially, carbon emissions expanded from densely concentrated core cities to surrounding regions, with intra-regional emissions gradually becoming more balanced. Emission intensity generally showed a downward trend, with steady declines in the YRD and PRD regions and a more significant decrease in the BTH Region, although it continued to maintain a relatively high level. Per capita carbon emissions demonstrated diversified trajectories along with economic development: The YRD and BTH regions underwent inter-quartile transitions, while the PRD Region maintained a low and stable level, and the CC region exhibited greater internal variability. Economic growth remained the primary driver of carbon emission increases, whereas the effect of population scale had weakened. Improvements in industrial energy efficiency and the optimization of industrial structure played a major role in curbing emissions, and the contribution of energy structure adjustment had become increasingly prominent. In summary, the evolution of carbon emissions in China's four major urban agglomerations was characterized by decelerated growth, spatial diffusion, and persistent regional heterogeneity. To promote a low-carbon transition, region-specific and coordinated strategies should be strengthened moving forward.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 9","pages":"5889-5906"},"PeriodicalIF":0.0000,"publicationDate":"2026-09-08","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.202506331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
Guided by the national strategic goals of carbon peaking and carbon neutrality, it is of great significance to investigate the spatiotemporal evolution characteristics and driving factors of carbon emissions in China's major urban agglomerations. Based on the integrated nighttime light index and energy statistical data, this study estimates the carbon emissions of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CC) urban agglomerations from 2000 to 2022. By analyzing carbon emission intensity and per capita carbon emissions, the spatiotemporal evolution patterns and regional disparities are explored. Furthermore, the logarithmic mean Divisia index (LMDI) decomposition method is employed to explore the dynamic changes of the influencing factors. The results reveal that total carbon emissions in the four urban agglomerations experienced rapid growth in the early stage, followed by a noticeable slowdown. Spatially, carbon emissions expanded from densely concentrated core cities to surrounding regions, with intra-regional emissions gradually becoming more balanced. Emission intensity generally showed a downward trend, with steady declines in the YRD and PRD regions and a more significant decrease in the BTH Region, although it continued to maintain a relatively high level. Per capita carbon emissions demonstrated diversified trajectories along with economic development: The YRD and BTH regions underwent inter-quartile transitions, while the PRD Region maintained a low and stable level, and the CC region exhibited greater internal variability. Economic growth remained the primary driver of carbon emission increases, whereas the effect of population scale had weakened. Improvements in industrial energy efficiency and the optimization of industrial structure played a major role in curbing emissions, and the contribution of energy structure adjustment had become increasingly prominent. In summary, the evolution of carbon emissions in China's four major urban agglomerations was characterized by decelerated growth, spatial diffusion, and persistent regional heterogeneity. To promote a low-carbon transition, region-specific and coordinated strategies should be strengthened moving forward.