The impact of AI on enterprise energy management: from the perspective of carbon emissions
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...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Science and Technology for Energy Transition |
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| Online Access: | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240293/stet20240293.html |
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| author | Yang Guilin Yang Guihua Yang Wanping |
| author_facet | Yang Guilin Yang Guihua Yang Wanping |
| author_sort | Yang Guilin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-015ab633baf94a30be3e544238405956 |
| institution | Kabale University |
| issn | 2804-7699 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Science and Technology for Energy Transition |
| spelling | doaj-art-015ab633baf94a30be3e5442384059562025-01-08T11:24:01ZengEDP SciencesScience and Technology for Energy Transition2804-76992025-01-0180810.2516/stet/2024096stet20240293The impact of AI on enterprise energy management: from the perspective of carbon emissionsYang Guilin0Yang Guihua1Yang Wanping2https://orcid.org/0009-0006-6787-9103School of Economics and Finance, Xi’an Jiaotong UniversityAnkang High Tech SchoolSchool of Economics and Finance, Xi’an Jiaotong UniversityGiven 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.https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240293/stet20240293.htmlartificial intelligencecarbon emissionsenergy managementmechanisms |
| spellingShingle | Yang Guilin Yang Guihua Yang Wanping The impact of AI on enterprise energy management: from the perspective of carbon emissions Science and Technology for Energy Transition artificial intelligence carbon emissions energy management mechanisms |
| title | The impact of AI on enterprise energy management: from the perspective of carbon emissions |
| title_full | The impact of AI on enterprise energy management: from the perspective of carbon emissions |
| title_fullStr | The impact of AI on enterprise energy management: from the perspective of carbon emissions |
| title_full_unstemmed | The impact of AI on enterprise energy management: from the perspective of carbon emissions |
| title_short | The impact of AI on enterprise energy management: from the perspective of carbon emissions |
| title_sort | impact of ai on enterprise energy management from the perspective of carbon emissions |
| topic | artificial intelligence carbon emissions energy management mechanisms |
| url | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240293/stet20240293.html |
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