Integration of Data Elements and Artificial Intelligence for Synergistic Pollution and Carbon Reduction in 275 Chinese Cities

Abstract

China’s ecological civilization construction and the “dual-carbon” strategy highlight the urgent need for coordinated governance of pollution and carbon reduction. Whether data elements and artificial intelligence integration (DEAII) can serve as a new pathway to achieve this goal remains to be explored. This study investigates the dynamic effects of DEAII on pollution and carbon reduction using panel data from 275 prefecture-level cities in China during 2009–2021. An evaluation index system and a modified coupled coordination degree model are developed to measure DEAII, while an ordinary least squares (OLS) fixed effects model is applied to assess its impacts. The results show stage-specific effects of DEAII, including the phenomenon of “pollution reduction but carbon increase”. Mechanism analysis indicates that improvements in green energy technology efficiency (GETE) and optimization of urban spatial structure are the main channels for achieving synergy. Heterogeneity analysis reveals that although government attention to environmental protection strengthens pollution control, it has limited effects on short-term carbon reduction. Moreover, the carbon reduction benefits of green energy transition pilots exhibit a time lag, and the “digital intelligence divide” generates negative spatial spillovers. These findings provide new evidence for the dilemma of “environmental protection without low-carbon benefits” and suggest policy directions for enhancing the coordinated governance of pollution and carbon reduction.