Evolution of spatial correlation structure of indirect carbon emissions from household consumption in China:Based on social network analysis
Understanding the spatial clustering and structural characteristics of indirect carbon emissions from household consumption is crucial for China to achieve the"carbon peaking and carbon neutrality"goal under its new development framework.This study calculates indirect carbon emissions from household consumption in China and examines the structural characteristics of the spatial correlation network for these emissions from 2013 to 2022 using social network analysis.The findings reveal the following:(1)Indirect carbon emissions from household consumption exhibit an overall upward trend,increasing 1.2-fold over ten years.Emissions from"food","housing","transport and communication",and"education,culture,and entertainment"constitute 75%of the total.(2)Overall network characteristics:The overall network structure,centered on provinces and cities such as Jiangsu Province,Beijing City,Zhejiang Province,and Shanghai City,demonstrates a"core-edge"distri-bution pattern.Network density and the number of associations have declined,while grade gradient and associa-tion intensity have increased.(3)Characteristics of the block model:Regional network characteristics,based on node spillover and reception effects,are categorized into four segments:"net spillover","net benefit","broker",and"two-way spillover",and each segment plays different roles in the field of spatial correlation.(4)Individual network characteristics:Regarding individual network characteristics,provinces such as Shanghai City,Jiangsu Province,and Zhejiang Province,with the highest degree of centrality,occupy the core areas of the correlation network and exhibit significant spatial correlation and outward radiation effects.In contrast,provinces such as Qinghai Province and Heilongjiang Province,located on the periphery,exhibit weaker correlation effects.
indirect carbon emissions from household consumptionspatially associative networkssocial net-work analysisgravitational model
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