[Spatial Network Structure and Drivers of Carbon Emissions in County-level Regions of Shaanxi Province， China]

Accurately grasping the structural characteristics of the spatial correlation network of county-level carbon emissions and its influencing factors is of great significance for promoting regional collaborative carbon emission reduction and low-carbon sustainable development. Based on carbon emissions data in county-level regions of Shaanxi Province， this study quantitatively evaluates the carbon ecological carrying coefficient， systematically examines the structural characteristics of the spatial correlation network of carbon emissions in county-level regions of Shaanxi Province using social network analysis and the exponential random graph model and analyzes the key driving factors behind the network formation. The results show that： ① The spatial carbon emission network exhibited high connectivity and gradually increasing stability， but the spatial correlation remained weak， with persistent issues such as structural looseness and redundancy. ② Key nodes in the spatial correlation network of carbon emissions were occupied by counties such as Gaoling District and Fengxian County， while regions like Yanliang District and Fengxian County played the role of "central actors" being less susceptible to influence or control by other counties. Counties such as Yanliang District and Yangling District demonstrated significant intermediary roles， exerting strong control over carbon emissions in other counties within the network. ③ The spatial correlations of carbon emissions in southern Shaanxi， Guanzhong， and northern Shaanxi all exhibited the characteristic of "inter-plate connections being stronger than intra-plate connections，" with widespread "carbon emission transfer" phenomena. Guanzhong displayed complex spatial spillover relationships， southern Shaanxi lacked tight connections and requires enhanced coordination， while northern Shaanxi showed significant cross-regional correlations and pronounced mutual influences. ④ The self-organizing regulatory effect was found to weaken over time， shifting from bidirectional interlocking to unidirectional dominance. The influence of social selection behavior factors─such as carbon sink pressure， economic development， energy efficiency， industrial structure， environmental protection efforts， technological level， and urbanization rate─on carbon emission correlations exhibited phased changes： Carbon sink pressure and economic factors dominated in the early stage， while environmental protection and industrial restructuring played prominent roles in the later stage， though the driving force of technology remains insufficient. Meanwhile， the constraining effects of geographical distance and county adjacency relationships gradually diminished.