| Issue |
Sci. Tech. Energ. Transition
Volume 80, 2025
Emerging Advances in Hybrid Renewable Energy Systems and Integration
|
|
|---|---|---|
| Article Number | 8 | |
| Number of page(s) | 17 | |
| DOI | https://doi.org/10.2516/stet/2024096 | |
| Published online | 06 January 2025 | |
Regular Article
The impact of AI on enterprise energy management: from the perspective of carbon emissions
1
School of Economics and Finance, Xi’an Jiaotong University, No. 74 YanTa West Road, Shaanxi, Xi’an, 710000, China
2
Ankang High Tech School, No. 1 XueFu Road, Huayuan Avenue, High-tech Zone, Ankang, Shaanxi, 725000, China
* Corresponding author: yangwanping666@126.com
Received:
24
August
2024
Accepted:
21
October
2024
Given the significant impact of Artificial Intelligence (AI) technology on corporate energy management and the lack of research in this area, this paper employs text mining techniques to objectively assess the relative level of AI adoption among Chinese listed companies. Using econometric modelling methods, we verify these hypotheses and investigate both the direct and indirect effects of AI on corporate carbon emission intensity. Our research finds that the carbon emission intensity of Chinese enterprises significantly decreased in the early stage, then stabilized, and has notably decreased again in recent years. The average level of AI among listed Chinese enterprises shows an overall upward trend, but the growth rate has slowed down. The level of AI in private enterprises is significantly higher than that in other types of enterprises, while the level of AI in state-owned enterprises is relatively lower. The level of AI in enterprises has a significant negative impact on carbon emission intensity, presenting an “S”-shaped relationship, characterized by initial emission reduction, mid-term rebound, and subsequent emission reduction. AI technology reduces the level of carbon emissions in enterprises by enhancing their green development standards and promoting technological innovation. There are significant differences in the impact of AI levels on carbon emission intensity across different types and regions of enterprises. The empirical conclusions remain robust after addressing endogeneity issues or variable substitution. This study provides important insights for corporate energy transitions and sustainable development, as well as for the formulation of government energy policies.
Key words: Artificial intelligence / Carbon emissions / Energy management / Mechanisms
© The Author(s), published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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