68 / 2023-12-29 13:01:42

Research on the structural evolution and driving factors of spatial correlation network of carbon emissions in China：evidence from 260 cities

Carbon emissions, spatial correlation network, social network analysis, structural characteristics, driving factors.

Abstract: China is facing enormous pressure to reduce emissions currently, and an effective way to realize regional synergistic emission reduction is to comprehensively consider the spatial linkage effect of carbon emissions. Based on the data of 260 cities from 2001 to 2021, we measured the structural characteristics of the spatial correlation network (SCN) of regional carbon emissions in China using the social network analysis (SNA) methodology, exploring the evolutionary characteristics and driving factors of the network in the temporal and spatial dimensions. The results of the study show that: (1) China's urban carbon emission network has a complex spatial structure, with the overall tightness of the network gradually strengthening, the hierarchical structure gradually weakening, and the network connection gradually stabilizing. (2) The network is characterized by a clear "core-edge" structure, with cities such as Shanghai, Suzhou, Hangzhou, Changsha, Wuxi, Shenzhen and so on at the core of the network, and they are important controllers or bridges in the network. (3) The network can be categorized into four regional blocks, with a clear geographic distribution of blocks and an increasing size of inflow blocks. (4) Cities with adjacent space or similar urbanization levels are more likely to establish carbon emission linkages, and the widening gap in economic development, industrial structure, and technological innovation dynamics is the key to driving the formation of linkages. The results of this paper provide references for developing more equitable, efficient, and targeted regional cooperative emission reduction strategies.