Title: Agricultural pollution control and carbon reduction in China: Spatiotemporal heterogeneity, synergy, and drivers
Abstract: Promoting the synergistic governance of pollution control (PC) and carbon reduction (CR) in the agricultural sector was an important way for the Chinese government to implement the “dual carbon” initiative and respond to climate change. Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022, this paper constructs a framework consisting of spatiotemporal evolution, synergy effect measurement, differences in contributions across regions, and influencing factors analysis to reveal the relationship between agricultural PC and CR. The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85% and 0.79%, respectively. However, the annual decline rates of their emission intensities were 3.14% and 4.32%, respectively. This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results, that the effect of PC was weaker than that of CR and had an obvious “policy node effect.” Simultaneously, the synergy between PC and CR evolved from “basic coordination” to “basic imbalance.” The contribution of inter-regional differences was relatively large, while intra-regional differences were smaller, highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR. The basic conditions, industrial structure, input intensity, and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR, and the influence of these significant factors exhibited clear spatiotemporal heterogeneity. These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.
Keywords: pollution control; carbon reduction; spatiotemporal heterogeneity; synergy; influencing factors
DOI: https://doi.org/10.1007/s11442-026-2460-6
