Research on the Spatio-Temporal Evolution and Dynamic Prediction of Agricultural Carbon Emission Efficiency: A Case Study of 24 Counties in the Dabie Mountain Region of China

Agriculture has become a major source of greenhouse gases in China. Agricultural carbon emission efficiency (AECC), as a key indicator of agricultural greening and sustainability, holds significant importance for advancing synergistic pollution reduction and carbon emission reduction in China and implementing the “dual carbon” strategy in China, also providing ideas for agricultural emission reduction in developing countries around the world. Taking 24 counties (cities/districts) in the Dabie Mountains, a typical traditional agricultural mountainous region in central China that is currently facing agricultural green transformation, as the research objects, this study constructed an AECC measurement index system, collected 2010–2022 data, used a super-efficiency SBM model with undesirable outputs under the CRS mode to measure AECC, analyzed its spatiotemporal evolution, and applied a BP neural network for dynamic prediction. Our results show the following: (1) during the study period, Dabie Mountains’ overall AECC was low with fluctuations, showing significant but low-dispersion spatial differences; (2) temporally, the overall AECC showed an upward trend over the years, though the distribution pattern remained relatively dispersed; (3) spatially, a prominent “core-periphery” structure exists, with high-value areas showing a trend of spreading from block-like to patch-like, while overall spatial disparities converged; (4) in the coming years, the AECC in the Dabie Mountains will rise significantly with steady growth and shrinking spatial differences, and the spatial pattern will evolve into a “bipolar → tripolar → multipolar” structure.