Integrating Geodetector and GTWR to Unveil Spatiotemporal Heterogeneity in China’s Agricultural Carbon Emissions Under the Dual Carbon Goals

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) targets. To reveal the spatiotemporal evolution characteristics and complex driving mechanisms of agricultural carbon emissions (ACEs), this study constructs a comprehensive accounting framework for agricultural carbon emissions based on provincial panel data from China spanning 2000 to 2023. The framework encompasses three major carbon sources—cropland use, rice cultivation, and livestock farming—enabling precise quantification of total agricultural carbon emissions. Furthermore, spatial-temporal distribution patterns are characterized using methodologies including standard deviational ellipse (SDE) and spatial autocorrelation analysis. For driving mechanism identification, the Geodetector and Geographically and Temporally Weighted Regression (GTWR) models are employed. The former quantifies the spatial explanatory power and interaction effects of driving factors, while the latter enables dynamic estimation of factor influence intensities across temporal and spatial dimensions, jointly revealing significant spatiotemporal heterogeneity in driving mechanisms. Key findings: (1) temporally, total ACEs exhibit fluctuating growth, while emission intensity has significantly decreased, indicating the combined effects of policy regulation and technological advancements; (2) spatially, emissions display an “east-high, west-low” pattern, with an increasing number of hotspot areas and a continuous shift of the emission centroid toward the northwest; and (3) mechanistically, agricultural gross output value is the primary driving factor, with its influence fluctuating in response to economic and policy changes. The interactions among multiple factors evolve over time, transitioning from economy-driven to synergistic effects of technology and climate. The GTWR model further reveals the spatial and temporal variations in the impacts of each factor. This study recommends formulating differentiated low-carbon agricultural policies based on regional characteristics, optimizing industrial structures, enhancing modernization levels, strengthening regional collaborative governance, and promoting the synergistic development of climate and agriculture. These measures provide a scientific basis and policy reference for achieving the “dual carbon” goals.