How Does Artificial Intelligence Empower Urban Pollution Governance?

Against the backdrop of the "dual-carbon" goals and the green development strategy, to explore the role of artificial intelligence (AI) in local pollution and emission reduction, this study employs the Spatial Durbin Model (SDM) and uses data from 274 prefecture-level and above cities in China spanning 2013 to 2022. The explained variable is the environmental pollution index (incorporating industrial wastewater discharge, industrial sulfur dioxide emission, and industrial smoke and dust emission) constructed via the entropy weight method. The core explanatory variable is the logarithmic value of the number of AI enterprises in each city, with control variables including urbanization rate and fiscal decentralization. The results show that: AI development exerts a significantly negative impact on local pollution (the main effect coefficient is -0.011, significant at the 1% level, under both geographic distance matrix and economic-geographic nested matrix); pollution exhibits strong spatial dependence (rho value ranges from 0.828 to 0.876); the spatial spillover effect of AI is heterogeneous; both environmental pollution and AI development demonstrate significant spatial agglomeration (Moran’s I index is significantly positive). This study provides a reference for leveraging intelligence to empower local pollution and emission reduction and facilitate the achievement of "dual-carbon" goals.