{"title":"[Formation Mechanism of Digital Economy on the Spatial Correlation Network Structure in Carbon Emission and Its Optimization Strategy].","authors":"Dan Yan, Shao-Xuan Huang, Jia-le He, Wan-Li Zhang","doi":"10.13227/j.hjkx.202403239","DOIUrl":null,"url":null,"abstract":"<p><p>The development of the digital economy has broken the \"spatiotemporal barriers\" in traditional production connections, leading to more extensive cross-regional spatial flows of carbon emissions. Based on provincial panel data from 2010 to 2021, this study uses social network analysis to reveal the structure and spatiotemporal evolution of regional carbon emission spatial correlation networks. A QAP model is constructed to explain the mechanisms by which digital economy development influences these networks through infrastructure effects, structural optimization effects, technological innovation effects, and resource allocation effects. The results showed that: ① In terms of distribution patterns, the spatial correlation of national carbon emissions exhibited a \"multi-center driven\" network structure, with connections strengthening from south to north and east to west, with Jiangsu, Zhejiang, and Shandong as the main nodes. ② Provinces, such as Beijing, Tianjin, Shanghai, and Zhejiang acted as central players in the network, forming a \"net beneficiary\" sector that exerted a siphon effect on other regions, thereby playing a dominant role in the correlation network. ③ The level of digital economy, adjacency relationships, environmental regulations, and government-business relations were common driving factors promoting the formation of carbon emission spatial correlation networks. ④ Regarding the impact mechanism of the digital economy, increasing differences in digital inclusive finance, digital innovation applications, and digital infrastructure have led to complex multi-dimensional spatial linkages of carbon emissions. The larger the digital industry development gap, the more detrimental it is to the formation of inter-provincial carbon emission network structures.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2852-2864"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-08","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.202403239","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
The development of the digital economy has broken the "spatiotemporal barriers" in traditional production connections, leading to more extensive cross-regional spatial flows of carbon emissions. Based on provincial panel data from 2010 to 2021, this study uses social network analysis to reveal the structure and spatiotemporal evolution of regional carbon emission spatial correlation networks. A QAP model is constructed to explain the mechanisms by which digital economy development influences these networks through infrastructure effects, structural optimization effects, technological innovation effects, and resource allocation effects. The results showed that: ① In terms of distribution patterns, the spatial correlation of national carbon emissions exhibited a "multi-center driven" network structure, with connections strengthening from south to north and east to west, with Jiangsu, Zhejiang, and Shandong as the main nodes. ② Provinces, such as Beijing, Tianjin, Shanghai, and Zhejiang acted as central players in the network, forming a "net beneficiary" sector that exerted a siphon effect on other regions, thereby playing a dominant role in the correlation network. ③ The level of digital economy, adjacency relationships, environmental regulations, and government-business relations were common driving factors promoting the formation of carbon emission spatial correlation networks. ④ Regarding the impact mechanism of the digital economy, increasing differences in digital inclusive finance, digital innovation applications, and digital infrastructure have led to complex multi-dimensional spatial linkages of carbon emissions. The larger the digital industry development gap, the more detrimental it is to the formation of inter-provincial carbon emission network structures.