Synergizing pollution reduction and carbon mitigation: Evidence from China's national AI innovation pilot zones

Against the backdrop of China's carbon peaking and carbon neutrality strategies, improving urban capacity for coordinated pollution reduction and carbon mitigation has become a central issue in environmental governance. Artificial intelligence (AI), as a new generation of general-purpose technology, may reshape environmental governance through policy guidance, institutional support, and application diffusion. Yet systematic evidence on whether AI policy promotes pollution–carbon synergy remains limited. Using panel data for 271 prefecture-level and above cities in China from 2011 to 2023, this study applies a multi-period difference-in-differences approach to evaluate the environmental effects of the National AI Innovation Pilot Zone policy. The results show that AI pilot policies significantly improve pollution–carbon synergy in pilot cities, and the effect strengthens over time. Mechanism analysis suggests that this effect operates through improved resource allocation, technological progress, and industrial upgrading, while also being accompanied by a potential energy rebound channel through increased electricity demand. Further spatial analysis shows significant spillover effects on neighboring cities. Heterogeneity tests indicate that the policy effect is stronger in cities with stricter environmental regulation, stronger human capital, more developed green finance, and better digital infrastructure, but weaker in cities facing greater economic growth pressure. Overall, this study shows that the environmental effects of AI policy should be understood as a net outcome shaped by governance capacity, local factor endowments, energy constraints, and regional linkages.