[Spatial Correlation Network Structural Characteristics and Driving Factors of Agricultural "Carbon...

Synergistic advancement of carbon reduction， pollution reduction， green expansion， and growth in agriculture constitutes an inevitable strategic choice for driving agricultural green transformation and achieving China's carbon peaking and carbon neutrality goals. This study constructed an evaluation index system consisting of four dimensions and 32 basic indicators to measure the synergistic level of agricultural carbon reduction-pollution reduction-green expansion-growth in 30 provinces of China from 2011 to 2022 and employed a modified gravity model and social network analysis to reveal the structural characteristics and driving factors of the spatial correlation network for this synergistic development. The findings showed that： ① From 2011 to 2022， the synergy level of agricultural carbon reduction-pollution reduction-green expansion-growth increased from 0.326 to 0.441， transitioning from mild imbalance to near imbalance. However， a prominent regional imbalance persisted， forming a gradient differentiation pattern of "eastern > central > western." ② The spatial correlation network for the synergistic development of agricultural carbon reduction-pollution reduction-green expansion-growth formed and was becoming increasingly interconnected. Network connectivity and stability continued to improve， with a noticeable flattening trend in the network structure. However， the system had not yet reached an optimal spatial correlation state. ③ Shanghai， Jiangsu， Beijing， and Guangdong constituted the core layer of the network， serving as both central actors and intermediaries/bridges， while western and northeastern provinces remained in the peripheral zones of the network. ④ The Beijing-Tianjin Region and the Yangtze River Delta constituted stable net beneficiary blocks， functioning as "benefit highlands" within the network. Guangdong， Zhejiang， and Fujian formed broker blocks， while central， western， and northeastern provinces composed net spillover blocks， creating a depression effect characterized by "factor inflows exceeding outflows." ⑤ Geographical adjacency， differences in urbanization levels， and technological innovation capabilities positively drove the formation of spatial correlation networks， while differences in agricultural human capital acted as a hindrance. The facilitating effect of differences in transportation infrastructure intensified over time， the impact of differences in digital infrastructure had an inverted "U" shape， differences in market maturity and land transfer rate maintained a positive driving force， and the impact of differences in agricultural fiscal support and environmental regulation intensity remained relatively marginal.