Analyzing the spatial network structure of agricultural greenhouse gases in China.

Investigating the regional correlation and factors affecting agricultural greenhouse gas (GHG) emissions can help establish a regional mechanism for the synergistic reduction of emissions and produce chain-like reductions. Different from the traditional geographical relationship analysis framework, linear analysis ideas, we use social network analysis to discern the regional correlations in agricultural GHG emissions from a relational network viewpoint, clarify the network functions of each node, and explain agricultural GHG correlation from a spatial, economic, and technological viewpoint by nonparametric regression. The results indicate that (1) the emission network is stable and there is a relationship of control between regions, (2) Central China is the most important region in agricultural GHG networks; however, the importance of the northwest and southwest has increased; the northeast has remained relatively independent, (3) influencers are mainly concentrated in the middle of the Yangtze River and the northwest, while dependentors are concentrated in municipalities such as Beijing and Tianjin, and the coastal regions in the southeast, and (4) the interprovincial agricultural GHG correlation can be enhanced by shortening the spatial distance, strengthening economic ties, and increasing the diffusion of technology. Implementing a "leader-follower" strategy according to the role of each region and enhancing the intermediator's "conduit" role will ultimately lead to the formation of an interprovincial interactive and cooperative emission reduction mechanism.