Artificial intelligence development promotes green technology innovation across Chinese cities using panel quantile regression analysis

Green technology innovation is essential for advancing carbon–neutral development, yet existing studies largely rely on mean-based methods that may conceal substantial heterogeneity across cities with different innovation capacities. This study examines the relationship between artificial intelligence (AI) development and green technology innovation using a balanced panel of 180 Chinese prefecture-level cities from 2010 to 2022. AI development is measured by the number of AI-related enterprises, while green technology innovation is captured by approved renewable-energy-related patent applications. A fixed-effects panel quantile regression framework is employed to identify distributional differences across the 10th, 25th, 50th, 75th, and 90th conditional quantiles. The results show that AI is positively and significantly associated with green technology innovation across all examined quantiles, with the strongest effect observed at the median, indicating that cities with intermediate innovation capacity may be particularly well positioned to convert AI-related capabilities into green technological outputs. Heterogeneity analysis further reveals that the AI effect varies across city size, resource dependence, and regional location. The relationship is relatively stronger in large cities, more stable in non-resource-rich cities, and strongest in western China, where substantial renewable-energy potential and greater scope for technological upgrading may increase the marginal benefits of AI development. The main findings remain robust to alternative AI and green innovation measures, exclusions of specific city groups, winsorization, repeated placebo permutations, and supplementary instrumental-variable analysis. Overall, the study provides city-level distributional evidence that AI development is consistently associated with stronger green technology innovation, while the magnitude of this relationship depends on local economic, industrial, and innovation conditions. The findings highlight the need for differentiated AI–green innovation policies rather than a uniform urban strategy.