Spatiotemporal Dynamics and Nonlinear Associations of Agricultural Carbon Emissions in China: Insights from Explainable Machine Learning and GTWR

Agriculture is pivotal to China’s dual-carbon goals, yet the nonlinear and spatiotemporally heterogeneous relationships between agricultural carbon emissions (ACE) and their associated socioeconomic, agricultural-production, public-investment, and climatic factors remain poorly understood. Here, we compile a multi-source provincial ACE inventory covering cropland use, rice cultivation, and livestock production for 2000–2023 and combine spatial trend and autocorrelation diagnostics with explainable machine learning using XGBoost–SHAP and geographically and temporally weighted regression (GTWR). We find that national ACE increased from 254.48 to 270.25 Mt, while emission intensity fell by 59.6%, indicating that the carbon efficiency of agricultural production improved substantially, although total emissions did not achieve an absolute decline. ACE exhibited persistent spatial imbalance, significant spatial clustering, and gradual diffusion of high-emission areas. Model benchmarking showed that XGBoost achieved the best overall performance, with a mean cross-validated R2 of 0.9131 and an independent temporal-test R2 of 0.7056. SHAP importance aggregated across repeated cross-validation identified agricultural public investment (21.3%) and urbanization rate (17.9%) as the two leading factors, followed by precipitation, temperature, agricultural industrial structure, and industrial agglomeration level, with the same six factors consistently identified by the three tree-based models. SHAP dependence plots further revealed nonlinear relationship patterns and approximate transition locations around an agricultural public investment level of CNY 11.84 billion, an urbanization rate of 54.69%, annual precipitation of 698.4 mm, and annual temperature between 13.01 and 22.17 °C. GTWR provided stronger statistical evidence of spatiotemporal nonstationarity for urbanization rate, temperature, and industrial agglomeration level, whereas the coefficient patterns of agricultural public investment, agricultural industrial structure, and, particularly, precipitation received more limited local statistical support. These findings provide support for region-specific agricultural carbon mitigation by improving agricultural input-use efficiency, optimizing the allocation of public investment, facilitating the transition toward low-emission crop and livestock management, and integrating climate adaptation with sustainable agricultural production and food security.