The multi-dimensional spatial network effect on grain production resilience: evidence from China’s provinces

Enhancing grain production resilience is crucial for ensuring national food security and supply stability. This study explores the roles of different types of correlation networks in regional grain production resilience from a generalized spatial distance perspective. By using the panel data of 28 Chinese provinces from 2011 to 2021, we employ spatial autoregressive panel models with convex combinations of weight matrices and Bayesian model averaging for our empirical analysis. Our findings reveal that multi-dimensional spatial networks significantly influence provincial grain production resilience. Notably, industrial distances have the greatest impact, followed by cultural, ecological, and geographic distances. Additionally, we identify positive factors, such as the marketization of agricultural capital, financial development, and the supply of rural healthcare resources, alongside negative factors, such as urbanization, that significantly affect grain production resilience both within and across provinces connected by multi-dimensional spatial networks. The theoretical framework and empirical research presented in this study provide innovative perspectives for studying regional grain production resilience and valuable insights into the mechanisms for synergistic development among regions to enhance overall resilience.