Spatial correlation network of renewable energy consumption and its influencing factors: Evidence from 31 Chinese provinces

Huiping Wang and Peiling Liu

Renewable Energy, 2023, vol. 217, issue C

Abstract: Accurately understanding the relevance of renewable energy consumption (REC) among regions is an important basis for the scientific formulation of energy policies and an important entry point for achieving “carbon peaking and carbon neutrality goals”. Based on the REC data of the 31 provinces in China from 2001 to 2020, the present work uses the social network analysis method to investigate the spatial correlation network of REC and its influencing factors. The REC of the 31 provinces in China has an obvious spatial correlation network structure. From 2001 to 2020, the network density of this spatial correlation network gradually increased, and the network structure was relatively stable. Henan, Hebei and Anhui are in the center of the network, and they play the role of bridges. Within this network, seven northeastern provinces, including Heilongjiang, Jilin and Liaoning, display characteristics of bidirectional spillover blocks and act as “guides”. However, seven western provinces, including Gansu, Qinghai and Tibet, display characteristics of a “net spillover block” and spill over to other blocks. Spatial adjacency and regional differences in industrial structure are conducive to the formation of a spatial correlation network, but regional differences in vehicle ownership and government intervention are not.

Keywords: Renewable energy consumption; Social network analysis; Spatial correlation network; Quadratic assignment procedure (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:217:y:2023:i:c:s0960148123010881

DOI: 10.1016/j.renene.2023.119173

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