Abstract:

Based on panel data from 30 provinces, autonomous regions, and municipalities in China from 2014 to 2023, this study employs the entropy weight method, social network analysis, and exponential random graph models to systematically examine the structural characteristics and driving factors of the spatial correlation network of agricultural resilience in China. The results indicate that (1) Agricultural resilience shows a steady upward trend, although development across dimensions remains uneven. Economic and social resilience perform relatively well, whereas production and ecological resilience lag behind. (2) The spatial correlation network of agricultural resilience exhibits a flat development trend. Although the overall network remains highly accessible, close correlations have not yet formed, and most provinces, autonomous regions, and municipalities maintain one-way correlations. (3) The spatial correlation follows a “west-central-east” radiation path. Net spillover sectors are mainly concentrated in the central and western regions, whereas net benefit sectors are primarily distributed in the central and eastern regions, and the broker sector still requires substantial improvement. (4) The formation of spatially correlated networks of agricultural resilience in China results from the interplay of endogenous structures, actor attributes, and external environments. Endogenous structures such as reciprocity and circularity promote the formation of the agricultural resilience network. Economic level, production conditions, industrial structure, and extreme heavy rainfall are the core driving forces for the formation of the agricultural resilience network, while geographical proximity and trade are important external driving forces.

Key words: agricultural resilience, spatial correlation network, social network analysis, exponential random graph model