Regional heterogeneity of agricultural carbon emission reduction potential in China

Agriculture, a critical industry highly susceptible to climate change, requires thorough analysis of its carbon reduction potential and priority exploration to advance towards green and sustainable development. Therefore, this study employs a variable coefficient panel model to examine the regional heterogeneity of influencing factors. It also uses a PSO-BP neural network model to simulate changes in China's agricultural carbon intensity and total emissions under three distinct scenarios. The findings revealed that (1) under the baseline scenario and aggressive scenario, most Chinese provinces and cities can achieve a 30% reduction in agricultural carbon intensity by 2030, and the advanced economic development in the eastern coastal regions positions them favorably for achieving peak carbon emissions. (2) Economic interventions are the main driving force for most Chinese provinces and cities to achieve their agricultural carbon intensity reduction targets, followed by technological interventions and agricultural population adjustment. (3) Eight provinces and cities can be used as emission reduction benchmarks, while Xinjiang, Inner Mongolia, and Henan are challenging points in attaining national emission reduction targets.