Coupling and coordination of food security and agricultural carbon emission efficiency: Changing trends, influencing factors, and different government priority scenarios

Seeking a balance between food security and carbon mitigation is key to achieving sustainable agricultural development. This study evaluates the coupling coordination degree (CCD) between the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in China from 2010 to 2021 using the coupled coordination model. By adjusting the model coefficients, different government priority scenarios are simulated to explore their impact on CCD. The Geodetector method is employed to identify the influencing factors of CCD, investigate their interactions, and assess the differences in these factors across various government priority settings. The average CCD between FSI and ACEE exhibits a notable upward trend, rising from 0.4583 in 2010 to 0.6595 in 2021. Furthermore, regional disparities are widening, particularly in the major production areas. Catch-up effects exist within regions. Policy simulations showed staged interactions between food security and agricultural carbon efficiency, shifting from food security to balanced production and ecology, then to prioritizing low-carbon production for food security. Adjusting policy priorities can effectively improve coupling coordination in the short term, with increasing impact as priority shifts. CCD is influenced by policy, technology, economy, and society, varying with policy priorities. In the baseline scenario, key factors for CCD include the urban-rural income gap, technological advancement, urbanization, and farmers' education level. When the government prioritizes food security, the impact of narrowing income gaps and agricultural industry agglomeration becomes more pronounced. Conversely, emphasizing carbon emission efficiency enhances the influence of technological advancements and urbanization on CCD. Tailoring agricultural production strategies to local conditions and emphasizing interactive effects among factors is crucial for achieving environmentally friendly and high-quality agricultural development goals.