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Artificial intelligence and green development: Evidence from China on efficiency and equity

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  • Wu, Tielong

Abstract

This study investigates the impact of Artificial Intelligence (AI) policy on urban green development, focusing on both efficiency and equity perspectives. We first apply a green growth model to analyze the green development. Then, using panel data from 286 cities in China between 2000 and 2023, we assess the Green Development Efficiency (GDE) of cities through Data Envelopment Analysis and analyze the impact of AI policy on efficiency with a Difference-in-Differences model. Finally, we do convergence analysis to explore the equity of green development across cities. The results show that AI policies lead to a “dividend” in the form of enhanced overall GDE, driven by improvements in technical and scale efficiency, increased patent outputs, and urban agglomeration. However, the study also reveals a “divide”, as the benefits are not equally distributed across cities. Convergence analysis reveals no global convergence across cities but identifies six convergent clubs and one divergent club. Ordered probit analysis shows that AI policies do not significantly affect a city's likelihood of transitioning between clubs. In the meantime, regional heterogeneity is observed, with the positive impact of AI policy being more pronounced in eastern regions, while smaller improvements are observed in central and western regions. Negative spillover effects are also found in cities within a 50 km radius of AI policy, where GDE decreases due to resource competition. While AI policies improve efficiency, they do not promote equitable green development. Future AI policies should focus on a more equitable green transition.

Suggested Citation

  • Wu, Tielong, 2026. "Artificial intelligence and green development: Evidence from China on efficiency and equity," Energy Economics, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:eneeco:v:155:y:2026:i:c:s0140988326000800
    DOI: 10.1016/j.eneco.2026.109201
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