Synergistic emission reduction effect of pollution and carbon in China's agricultural sector: Regional differences, dominant factors, and their spatial-temporal heterogeneity

The agricultural sector is pivotal in achieving synergistic reduction effect of pollution and carbon emissions (PCSRE). In this study, a modified coupling coordination degree (CCD) model is employed to measure the PCSRE in China's agricultural sector from 2000 to 2021, focusing on non-point source pollution and carbon emissions. Then, the Dagum Gini coefficient, GeoDetector model, and Panel Geographically-Temporally Weighted Regression (PGTWR) model are used to examine the regional differences and sources, identify the dominant factors, and investigate their spatial-temporal heterogeneity impacts. The results show that: (1) Agricultural PCSRE exhibits an increasing trend, with a “center-periphery” spatial distribution pattern on the main grain-producing areas (GPAs) in the eastern. (2) Inter-regional differences are the main source of the overall differences in agricultural PCSRE, with the highest regional differences between GPAs and main grain-marketing areas (GMAs). The largest intra-regional differences are in the GMAs. (3) The agricultural economic scale, planting structure, agricultural machinery, education level in rural areas, and transportation infrastructure are the dominant factors affecting the spatial differentiation of agricultural PCSRE. (4) The impact of dominant factors on agricultural PCSRE exhibits spatial-temporal heterogeneity. These findings contribute to a comprehensive understanding of the patterns and deep-rooted causes of agricultural PCSRE, providing references for decision-making on the green transformation of agriculture. •Assessing the synergistic reduction effect of pollution and carbon (PCSRE) in agriculture.•The PCSRE shows a rising trend but with a spatial distribution pattern of “center-periphery”.•Regional differences between GPAs and GMAs is the largest source of the overall differences.•Combine the use of GeoDetector identification and PGTWR model.•There is spatial-temporal heterogeneity in the influence of dominant factors on the PCSRE.