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Artificial intelligence and public environmental concern: Impacts on green innovation transformation in energy-intensive enterprises

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  • Zhao, Xiongfei
  • Li, Shuangjie

Abstract

With rising energy consumption and environmental awareness, AI offers both challenges and opportunities for energy-intensive enterprises' low-carbon transition. This paper, grounded in green innovation theory, develops an assessment method to evaluate the pressure on energy-intensive enterprises’ green innovation transformation, focusing on three dimensions: R&D cost savings, innovation level enhancement, and environmental sustainability promotion. Additionally, this paper examines the impact of AI adoption and public environmental concern on the green innovation transformation of these enterprises. The study finds that the pressure for green innovation transformation in energy-intensive enterprises primarily arises from R&D cost savings and environmental protection. Both AI adoption and public environmental concern negatively affect the efficiency of green innovation, but AI adoption can mitigate the negative impact of public environmental concern on green innovation efficiency. AI adoption primarily reduces green innovation efficiency by increasing the pressure for R&D cost savings, whereas public environmental concern reduces efficiency by increasing both R&D cost savings and carbon reduction pressure. Finally, the results of heterogeneity under different transformation pressure characteristics further prove this point. This study not only challenges existing literature but also provides practical significance for the strategic adjustment of green innovation transformation paths in energy-intensive enterprises.

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  • Zhao, Xiongfei & Li, Shuangjie, 2025. "Artificial intelligence and public environmental concern: Impacts on green innovation transformation in energy-intensive enterprises," Energy Policy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:enepol:v:198:y:2025:i:c:s0301421524004890
    DOI: 10.1016/j.enpol.2024.114469
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