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

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics Pub Date : 2025-03-04 DOI:10.1016/j.eneco.2025.108349

Xiaotong Niu, Changao Lin, Shanshan He, Youcai Yang

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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.

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来源期刊

Energy Economics ECONOMICS-

CiteScore

18.60

自引率

12.50%

发文量

524

期刊介绍： Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.