[Structural Characteristics and Driving Factors of Agricultural Digital-green Synergy's Spatial Association Network...

Exploring the structural characteristics and driving factors of the spatial association network of agricultural digital-green synergy from the perspective of scientific exploration of new quality productive forces holds significant implications for optimizing regional resource allocation and promoting high-quality agricultural development. Based on panel data from 31 provinces in China from 2014 to 2022， this study employs the coupling coordination degree model， modified gravity model， social network analysis method， and QAP method to investigate the spatial association network characteristics and driving factors of agricultural digital-green synergy. The results reveal that： ① The level of agricultural digital-green synergy steadily increased during the study period， exhibiting a distinct "policy-driven" surge in time and a spatial pattern characterized by higher levels in the southeastern coastal regions and lower levels in the northwestern inland regions. ② During the study period， the spatial network connectivity of agricultural digital-green synergy decreased， while its stability improved. ③ The spatial association structure of agricultural digital-green synergy at the regional level exhibited the following characteristics： Eastern provinces occupied a central position in the spatial association network， with significantly higher centrality than that in other regions， primarily functioning as element exporters. Western and northeastern provinces had lower centrality， serving as element recipients in the spatial association network. Central provinces played an intermediary role in the network， primarily belonging to the brokerage sector. ④ The driving factors of the spatial association network of agricultural digital-green synergy included economic development level， geographical distance， innovation level， urbanization level， industrial structure， education level， and scientific and technological innovation potential， with innovation level being the primary driving factor.