Exploring artificial intelligence and urban pollution emissions: "Speed bump" or "accelerator" for sustainable development?

Within the framework of the overarching green development objective guiding the transformation and progress of urban green and low-carbon initiatives, the technological advancements stemming from artificial intelligence have introduced fresh paradigms for reducing urban pollution and enhancing environmental governance. A comprehensive exploration into the influence of artificial intelligence on urban pollution emissions has emerged as a pivotal current concern. The findings that the progression of artificial intelligence has effectively curbed urban pollution emissions, assuming the role of a significant "speed bump". Robustness testing corroborated this conclusion. Mechanism testing unveiled that the evolution of artificial intelligence mitigates urban pollution emissions by enhancing production efficiency, minimizing energy consumption, boosting green technology innovation, optimizing industrial structure, and increasing public participation. Heterogeneity analysis underscored substantial variances in the impact of artificial intelligence on urban pollution emissions, influenced by pollutant types, regional disparities, and urban scale differentials. Further scrutiny exposed that while the application of artificial intelligence technology does not entirely mitigate the "local-neighborhood" effect, it does ameliorate the "boundary effect", subsequently reducing pollution emission intensity in cities straddling provincial administrative boundaries. •AI significantly curbs urban pollution emissions, acting as an effective "speed bump".•This study reveals the mechanisms and heterogeneity of the impact of artificial intelligence on pollution emissions.•Although AI does not eliminate the "local-neighborhood effect", it significantly attenuates the "boundary effect".