As one of the major sources of carbon emissions, the significant spatial disparities in agricultural carbon emissions (ACE) pose a serious challenge to coordinated regional carbon reduction efforts. In order to precisely identify the sources of these ACE differences, this study estimates the ACE of China from 2005 to 2020 across four main emission sources and applies the bidimensional decomposition method of the Gini coefficient to measure and decompose their spatial disparities. Finally, the key factors driving ACE disparities are analyzed using the Quadratic Assignment Procedure (QAP). The results show that China’s total ACE initially declined, followed by an upward trend over the study period. Spatially, emissions were higher in eastern regions compared to western regions, and higher in southern regions compared to northern regions. The differences in paddy field emissions between the central and western regions were identified as the primary contributor to east–west disparities, while differences in agricultural materials emissions between northern and southern regions were the dominant source of north–south disparities. Furthermore, regional differences in agricultural development levels and mechanization capacity were found to be the strongest drivers of spatial ACE disparities. This study provides empirical evidence for formulating region-specific and source-targeted carbon reduction policies. Our findings highlight the importance of addressing regional imbalances, particularly in paddy field management and agricultural material usage, to promote more coordinated and sustainable agricultural carbon reduction across China.

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution.