Has the development of artificial intelligence promoted urban pollutant and carbon emission reduction? Evidence from China

Driven by the "dual carbon" goals, China's economy is gradually advancing toward green transformation, and leveraging new-generation information technologies to facilitate environmental governance has become a key national strategic priority. Based on the panel data of 282 prefecture-level cities in China from 2009 to 2023, this paper adopts a double machine learning model to systematically investigate the impact of artificial intelligence (AI) on urban pollutant and carbon emission reduction, as well as its underlying mechanisms and regional heterogeneity. The results show that AI significantly promotes urban pollutant and carbon emission reduction, with the core regression coefficient being -0.026. Mechanism analysis reveals that AI exerts its emission reduction effect through three channels: improving green total factor energy efficiency, optimizing industrial structure, and driving green technology innovation. The conclusion remains robust after a series of tests, including excluding municipalities directly under the central government, winsorizing outliers, and resetting the double machine learning model. Heterogeneity analysis indicates that the emission reduction effect of AI is prominent in the Beijing-Tianjin-Hebei urban agglomeration, Chengdu-Chongqing urban agglomeration, as well as the northern coastal, middle Yellow River, southwest and northwest regions of China, while the effect is not significant in the Yangtze River Delta, Pearl River Delta and other regions due to industrial structure constraints and uneven policy implementation. This study verifies the causal effect of AI on urban pollutant and carbon emission reduction at the micro-city level, expands the application boundary of double machine learning in the field of environmental economics, and provides targeted empirical evidence for formulating differentiated "AI for dual carbon initiative" policies in different regions, thus offering important theoretical support and practical reference for advancing the green and low-carbon transformation of China's economy.