The spatio-temporal evolution and driving mechanisms of synergistic reduction of atmospheric pollution and carbon emissions in Chinese cities

Amid rapid urbanization, China faces dual challenges of air pollution (AP) and carbon emissions (CE), urgently requiring synergistic governance—an area that remains underexplored due to limited dynamic and heterogeneous perspectives in existing literature. To address this gap, this study integrated spatiotemporal transition analysis with a two-way fixed-effects panel quantile regression model, identifying key synergistic drivers based on a novel classification of cities by pollution-carbon reduction urgency. Key findings revealed: (1) Distinct spatiotemporal co-evolution patterns: Annual PM2.5 concentration (APC) in Chinese cities followed an “increase-then-decline” trajectory, while per capita carbon emissions (PCCE) showed “rapid growth followed by moderation.” Both exhibited significant and intensifying spatial agglomeration, with CE demonstrating greater rigidity and path dependence. Three governance zones were identified, with priority zones—concentrated mainly in northern China—requiring the most urgent integrated action; targeted zonal strategies were accordingly proposed. (2) Complex synergistic reduction mechanisms: Drivers were classified into four categories—fully synergistic, non-synergistic, context-dependent synergistic, and one-dimensional. Based on this, a differentiated policy framework emphasizing “categorized implementation and systemic integration” was developed, offering theoretical and practical support for advancing the synergistic reduction of atmospheric pollution and carbon emissions (SRAPCE).