Chinese Urban Carbon Emission Correlation Network: Construction, Structural Characteristics, and Driving Factors

Feixue Sui, Xiaoyi Shi () and Chenhui Ding ()
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Feixue Sui: College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
Xiaoyi Shi: College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
Chenhui Ding: School of Economics and Management, Dongguan University of Technology, Dongguan 523808, China

Sustainability, 2025, vol. 17, issue 17, 1-22

Abstract: Against the backdrop of carbon reduction and sustainable development, cities play a central role in carbon emissions. These emissions are interconnected through economic, demographic, technological, and other factors, forming a complex network. This study investigates the structural characteristics and driving factors of carbon emission linkages among Chinese cities, with the aim of providing theoretical support and practical guidance for the development of sound regional carbon reduction policies. An improved gravity model was used to measure both the presence and intensity of linkages between cities. Social Network Analysis (SNA) was applied to examine network features such as density, centrality, and hierarchical structure. In addition, the Quadratic Assignment Procedure (QAP) was employed to test the effects of geographical proximity, economic disparities, demographic differences, and technological gaps on carbon emission linkages. Based on these methods, the study constructs the Chinese Carbon Emission Correlation Network (CECN), which shows high connectivity, a clear hierarchical structure, and a strengthened role of core cities. Cities with extensive linkages are mainly located in the eastern coastal region and political centers, forming a spatial pattern with stronger connections in the east than in the west, and more along the coast than inland. The network can also be divided into five distinct sub-groups. Moreover, geographical proximity, population differences, economic affluence, and technological disparities were found to significantly shape the spatial correlation of carbon emissions. These findings offer valuable guidance for designing targeted carbon reduction policies, which are essential for fostering regional coordination and advancing sustainable urban development.

Keywords: carbon emission; spatial correlation networks; influencing factors; social network analysis; Chinese carbon emission correlation network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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