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A new green transition driver: How does artificial intelligence affect the green economic efficiency

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  • Lin, Boqiang
  • Zhou, Dengli

Abstract

As energy shortages and environmental pressures intensify, promoting green economic development provides a key approach to fostering sustainable growth and breaking away from the resource-dependent development model. Artificial intelligence (AI), as a strategic technology leading future development, is being progressively embedded in critical domains such as industrial upgrading and energy transformation. To effectively assess the AI development of cities, this paper constructs a comprehensive evaluation index from the three dimensions: AI policy guidance, AI development environment, and AI technology innovation. Based on the panel data of 269 prefecture-level cities in China from 2008 to 2022, this paper utilizes a two-way fixed effects model to study the impact of AI development on green economic efficiency (GEE) and the transmission mechanism. The results show that: (1) AI development significantly improves the GEE, and this conclusion still holds after the endogeneity test and robustness test. AI development also exerts a positive spatial spillover effect on the GEE of neighboring regions. (2) Mechanism analysis shows that AI development can promote urban green development by improving urban energy efficiency, promoting green technology innovation, and accelerating industrial upgrading. (3) Heterogeneity analysis reveals that AI development promotes the GEE more significantly in non-Yangtze River Economic Belt cities, resource-based cities, and key cities for environmental protection. Based on the results of the study, this paper puts forward targeted policy recommendations for promoting urban green economic development from the perspectives of strategic planning, transmission pathways, and differentiated development.

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  • Lin, Boqiang & Zhou, Dengli, 2025. "A new green transition driver: How does artificial intelligence affect the green economic efficiency," Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:energy:v:334:y:2025:i:c:s0360544225034504
    DOI: 10.1016/j.energy.2025.137808
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