Spatiotemporal evolution and driving mechanism of synergistic carbon and pollution reduction

Journal article, 2026

Climate change and environmental pollution are key global challenges, with synergistic carbon-pollution reduction a global consensus. As the world's largest developing economy and carbon emitter, China's experience informs global synergistic governance. Spatiotemporal evolution and driving mechanisms of synergistic carbon and pollution reduction from 297 Chinese cities (2000–2023) are examined, applying the coupling coordination degree, k-means clustering, difference-in-differences, and moderating effect models. Findings show that the national coupling coordination degree of synergistic carbon and pollution reduction (CCD-SCPR) exhibits an inverted U-shaped dynamic evolution and gradient spatial differentiation. The “core-periphery” clustering pattern is found, with nearly half of the cities remaining in a dilemma of low CCD-SCPR, underscoring the urgency of synergistic governance. Data element marketization significantly improves CCD-SCPR by breaking information barriers, optimizing resource allocation, and empowering technological innovation. It forms a positive moderating effect with environmental regulation, yielding extra governance benefits. Proposals include differentiated strategies, innovative urban governance, optimized policy coordination, and deeper data element reforms.