Unlocking low-carbon agriculture in China: spatiotemporal dynamics of carbon intensity and multi-configurational pathways for efficiency improvement

Against the backdrop of intensifying global climate governance, improving agricultural carbon emission efficiency has become essential for advancing green agricultural transformation. Using panel data from 30 Chinese provinces from 2013 to 2023, this study measures agricultural carbon emission intensity (ACEI) and employs Moran’s I, the Dagum Gini coefficient, kernel density estimation, and dynamic qualitative comparative analysis to examine its spatiotemporal patterns, regional disparities, and efficiency-improvement pathways. Results show that national ACEI decreased by nearly 48% over the decade, yet pronounced regional heterogeneity persists, with consistently higher levels in central provinces. Dagum Gini decomposition indicates that hyper-variability is the major source of the widening overall imbalance. Spatial analysis further reveals a shift from dispersion to emerging “high–high” and “low–low” clusters, with strengthened spatial dependence. Three configuration pathways are identified—urbanisation-driven, policy-led, and human-capital-oriented—highlighting multiple equivalent mechanisms formed through the interaction of structural, socioeconomic, and institutional factors. These findings enhance understanding of the multi-dimensional dynamics of China’s agricultural low-carbon transition and provide evidence for designing regionally differentiated and cross-regional coordinated emission-reduction strategies.