Spatial association network structure of agricultural carbon emission efficiency in Chinese cities and its driving factors

Abstract

In light of the Chinese government’s dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2001 to 2022. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China. Additionally, the quadratic assignment procedure is employed to investigate the driving factors. The findings indicate that: (1) The carbon emission efficiency of cities agriculture in China displays substantial spatiotemporal heterogeneity, characterized by marked regional clustering. Central cities generally exhibit higher efficiency levels, while the surrounding cities tend to have lower efficiency. (2) The carbon emission efficiency of city agriculture in China displays multidimensional, complex, and organic characteristics, with potential for enhanced network stability. (3) Agricultural regions in central and southeastern China dominate the spatial network, while regions with weaker agricultural sectors, like Beijing, Shanghai, and Ningxia, occupy peripheral positions. (4) Within the spatial association network of city agricultural carbon emission efficiency in China, Intra block spatial correlations are low, while interblock spatial correlations are strong, exhibiting significant spillover effects. (5)Variations in agricultural development levels and mechanization significantly enhance the formation of networks related to city agricultural carbon emission efficiency in China. Conversely, differences in industrial structure and fertilizer application levels exert a substantial negative influence on these networks.