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• W3096360975 abstract "Regional electricity consumption presents important network structure features. This article applies the social network approach to construct the regional electricity consumption network in China during the period from 2000 to 2018. It analyses global features, local features and the block division of the networks. Then, the dynamic effect of the global and local features on regional electricity intensity is empirically verified through a quantile regression model. The results first show that the spatial correlation of regional electricity consumption has formed a network structure, exhibiting the characteristics of increasing network density, from 0.2 to 0.24, and decreasing network hierarchy and network efficiency, with network hierarchy decreasing from 0.73 to 0.24 and network efficiency decreasing from 0.64 to 0.56; second, the regional electricity consumption network can be divided into four blocks that play different roles and interact to maintain the sustainable evolution of the network; and third, changes in network structure can have a significant effect on regional electricity intensity, and overall, the effect of density is approximately -0.96, and the effects of hierarchy and efficiency are 0.15 and 0.46, respectively. Finally, some policy suggestions beneficial for reducing regional electricity intensity are provided." @default.
• W3096360975 created "2020-11-09" @default.
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• W3096360975 date "2020-01-01" @default.
• W3096360975 modified "2023-10-18" @default.
• W3096360975 title "Spatial Correlation of Electricity Consumption in China Based on Social Network Approach" @default.
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• W3096360975 doi "https://doi.org/10.1109/access.2020.3036220" @default.
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