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The Impact of Artificial Intelligence on Green Innovation Efficiency

In: Proceedings of the 2025 4th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2025)

Author

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  • Junjie Yin

    (Central University of Finance and Economics, China Economic and Management Academy)

Abstract

Green innovation efficiency (GIE) is a critical component in achieving sustainable development. Against the backdrop of global low-carbon development, the rapid advancement of artificial intelligence (AI) offers new opportunities to enhance green innovation efficiency. This study aims to investigate the impact of AI on GIE. I conducted a scientific measurement of GIE using panel data from 30 Chinese provinces over the period 2000–2023. Applying the Super-SBM model, I incorporated both positive outputs and negative outputs to provide a comprehensive assessment of GIE. This research constructs both static panel regression models and a system GMM dynamic model. The core independent variable is the number of AI enterprises (AI). Estimations were conducted using ordinary least squares (OLS), fixed effects, random effects, and system GMM methods. Empirical findings reveal a significantly negative relationship between AI development and GIE. The system GMM results confirm the robustness of this conclusion. This study also provides the heterogeneous effects of AI across different regions, which shows that in the western region, an increase in the number of AI enterprises significantly reduces green innovation efficiency. Conversely, the effects in the northeastern, eastern, and central regions are not statistically significant.

Suggested Citation

  • Junjie Yin, 2025. "The Impact of Artificial Intelligence on Green Innovation Efficiency," Advances in Economics, Business and Management Research, in: Qihui Chen & Nazrul Islam & Zulkiflee bin Mohamed & Yahua Xu (ed.), Proceedings of the 2025 4th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2025), pages 543-552, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-916-2_58
    DOI: 10.2991/978-94-6463-916-2_58
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