Artificial intelligence and enterprise pollution emissions: From the perspective of energy transition

Xiaotong Niu, Changao Lin, Shanshan He and Youcai Yang

Energy Economics, 2025, vol. 144, issue C

Abstract: In the digital transformation era, artificial intelligence (AI) has emerged as a formidable driving force behind enterprises' energy transitions and pollution emission mitigation, leveraging its unique advantages. This study examines the effect of AI on enterprise pollution emissions within the framework of energy transitions, utilizing data from Chinese A-share listed enterprises spanning from 2007 to 2022. Findings of this study reveal that the application of AI significantly reduces enterprise pollution emissions. This conclusion remains robust even after a series of rigorous tests. Further analysis elucidates that AI achieves emission reductions mainly by enhancing energy efficiency, rather than optimizing energy structures. Furthermore, heterogeneity analysis shows disparate effects across different types of enterprise, particularly pronounced in enterprises with low production efficiency, low digital transformation levels, or located in regions with high air pollution. This study enriches the understanding of AI's influence on enterprise pollution emissions, offering pivotal recommendations for enterprises to integrate AI for energy efficiency gains and sustainable development, thereby aligning with and advancing China's strategic goals for “carbon neutrality and peaking emissions” within the realm of energy economics.

Keywords: Artificial intelligence; Energy transition; Pollution emissions (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001732

DOI: 10.1016/j.eneco.2025.108349

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