Food security is the cornerstone of national security, and exploring the resilience of the grain industry chain is particularly important under the current complex and changing external environment. The study is to evaluate the grain industry chain resilience and identify its influencing factors. Based on the panel data of 30 provinces (cities) in China from 2011 to 2022, this paper adopted Partial Least Squares structural equation modeling method to carry out comprehensive evaluation, applied Dagum Gini coefficient to analyze spatial differences, and used geo-detectors to identify the influencing factors. The results showed that (1) from an overall perspective, the resilience of China's grain industry chain showed an overall upward trend, and each region had been greatly improved; (2) from the perspective of spatial differences, the overall spatial variability of China's grain industry chain is relatively small, and the gap mainly originates from inter-regional differences; (3) from the perspective of influencing factors, the level of grain foreign trade, regional market size and agricultural innovation level were important factors. In order to enhance the resilience of China's grain industry chain, based on the heterogeneous characteristics, the coordinated development of each region should be integrated, and the endogenous power should be given full play to enhance the resilience of the industry chain.

Carbon emissions reduction is gaining increasing attention worldwide. Digital agriculture has a carbon emissions reduction effect. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emissions reduction.To this end, the spatial characteristics, spillover effects and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model. The results show that digital agriculture development reduces agricultural carbon emissions.The results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region, while agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Furthermore, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emissions reduction effect.