Abstract
In the context of carbon peaking and neutrality goals, combined with the pursuit of high-quality agricultural economic development, examining the spatial disparities and convergence of agricultural green and low-carbon transformation is critical for protecting the ecological environment and enhancing national agricultural ecological security. This study estimates agricultural carbon emissions across five dimensions: farmland use, rice cultivation, livestock production, farmland soils, and crop residue burning. Using the EBM-GML model, the study measures the agricultural green and low-carbon transformation index for 30 Chinese provinces from 2005 to 2023. The Dagum Gini coefficient, standard deviation ellipses, kernel density estimation, and spatial Durbin models are employed to analyse the spatial disparities and convergence of China's agricultural green and low-carbon transformation. The findings reveal that the level of agricultural green and low-carbon transformation across provinces and the three major economic zones has increased over time, showing a spatial pattern of "high levels at the periphery and low levels at the centre," with inter-zonal disparities gradually widening. Regional dynamics in this transformation vary significantly, with northern regions growing faster than southern regions. Nationally, the transformation expanded markedly over the sample period, accompanied by growing divergence within the eastern region. At both the national and major sub-regional levels, the agricultural green and low-carbon transformation shows no δ-convergence but demonstrates absolute and conditional β-convergence. This suggests that although regional divergence in the agricultural green and low-carbon transformation does not consistently decline over time, areas with higher transformation levels experience faster reductions than those with lower levels. Consequently, the gap between the two groups gradually narrows, ultimately converging toward a common steady-state level.