Spatial correlation network of China's carbon emissions and its influencing factors: Perspective from social network analysis

Illustrating the features of the spatial correlation network and influencing factors of carbon emissions is crucial for achieving “carbon peaking” and “carbon neutrality” goals. This study constructs a spatial correlation network for China's inter-provincial carbon emissions from 2013 to 2022 using a modified gravity model. Social network analysis (SNA) is applied to delineate network features across overall, individual, and clustering dimensions. A nonlinear quadratic assignment procedure (QAP) is employed to investigate the influencing factors. Our findings revealed that: (1) Based on overall and individual network features analysis, our findings revealed significant spatial interdependencies among provincial administrative regions regarding carbon emissions, displaying a “core-periphery” structure within the spatial network. (2) According to spatial cluster analysis, the provincial administrative regions are categorized into four distinct clusters. Most coastal developed regions are grouped into the “isolate” cluster, while the majority of central and western regions are classified within the “sycophant” cluster. The capital city and its vicinity, as well as the mid-lower reaches of the Yangtze River, constitute the “primary” block, and most of the northwest as well as northeast regions fall into the “broker” block. (3) According to QAP analysis, geographical adjacency, economic development levels, opening-up levels, industrial structures, and population density are all correlated with spatial carbon emission correlations. Consequently, in managing carbon emissions, it is imperative to adopt a regional collaborative governance approach, focusing not only on individual province emissions reduction efforts but also on the interconnected carbon emission dynamics among provinces.