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• W4386491249 endingPage "118870" @default.
• W4386491249 startingPage "118870" @default.
• W4386491249 abstract "Based on data from 335 cities in China, this study employs the standard deviation ellipse method to portray unbalanced and differential spatiotemporal evolution patterns of environmental emissions and socioeconomic elements. A logarithmic mean Divisia index analysis and in-depth discussion are carried out to disclose the main driving factors and underlying reasons for the differences. Decoupling trends exist among carbon emissions, gross domestic product (GDP) and population in terms of their gravity center migrations. The standard deviation ellipse direction of carbon emissions gradually changed from ‘northeast‒southwest’ to ‘northwest‒southeast’, and the standard deviation ellipse areas of carbon emissions and air pollution continuously expanded over time; at the same time, that of GDP contracted. Economic growth has always been the main driver of carbon emissions and air pollution nationally, but its role has weakened. Moreover, decreases in the energy intensity and carbon and pollution intensities are the main factors contributing to emissions reductions. Differentiated spatiotemporal economic structure evolution, regional heterogeneities in the energy intensity and efficiency, and cross-region power energy transmissions are identified as the underlying reasons for the unbalanced spatiotemporal patterns of the environmental emissions and socioeconomic elements. Based on these findings, policy suggestions can be made to address the imbalances and promote carbon mitigation, air quality improvement and high-quality social-economic development at the city level." @default.
• W4386491249 created "2023-09-07" @default.
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• W4386491249 date "2023-11-01" @default.
• W4386491249 modified "2023-10-18" @default.
• W4386491249 title "Exploring the spatiotemporal pattern evolution of carbon emissions and air pollution in Chinese cities" @default.
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• W4386491249 doi "https://doi.org/10.1016/j.jenvman.2023.118870" @default.
• W4386491249 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37678024" @default.
• W4386491249 hasPublicationYear "2023" @default.
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