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Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value

Author

Listed:
  • Malin Song

    (Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu)

  • Heting Pan

    (Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu)

  • Zhiyang Shen

    (IESEG School of Managementg, LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Kristine Tamayo-Verleene

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

In the context of sustainable development, the enhancement of energy efficiency (EF) for achieving cleaner production has become a prominent area of academic interest. Accordingly, this study explores the correlation between artificial intelligence (AI) investments and corporate EF to strike a balance between economic growth and ecological products value realization. In light of the "double carbon" target constraints and economic challenges, addressing this question holds paramount theoretical and practical significance. This study primarily utilizes data from Chinese listed companies from 2007 to 2021 to gauge the influence of AI on corporate EF. Results of our benchmark regression analysis reveal that a 1 percentage point increase in AI investment can lead to a corresponding 0.0228 percentage point improvement in enterprise EF. Additionally, employing the Heckman model, our study establishes that the enterprise EF data examined herein has no sample selection bias. Furthermore, no endogenous selection issues were observed within the scope of our study. Exploring the mechanisms of this relationship, our analysis demonstrates that the number of independent green patent applications and the sustainability accounting index strengthen the positive impact of AI on corporate EF. Thus, this paper offers valuable insights and reference points for businesses aiming to enhance their energy conservation and emissions reduction efforts.

Suggested Citation

  • Malin Song & Heting Pan & Zhiyang Shen & Kristine Tamayo-Verleene, 2024. "Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value," Post-Print hal-04552684, HAL.
    • Handle: RePEc:hal:journl:hal-04552684
    DOI: 10.1016/j.eneco.2024.107392
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    Cited by:

    1. Wang, Yong & Zhao, Wenhao & Ma, Xuejiao, 2024. "The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities," Energy, Elsevier, vol. 312(C).
    2. Lee, Chien-Chiang & Zou, Jinyang & Chen, Pei-Fen, 2025. "The impact of artificial intelligence on the energy consumption of corporations: The role of human capital," Energy Economics, Elsevier, vol. 143(C).
    3. Mijit, Razia & Hu, Qianlin & Xu, Jingxuan & Ma, Guangrong, 2025. "Greening through AI? The impact of Artificial Intelligence Innovation and Development Pilot Zones on green innovation in China," Energy Economics, Elsevier, vol. 146(C).
    4. Ren, Baoping & Qiu, Zhaoxuan & Liu, Bei, 2025. "Supply chain decarbonisation effects of artificial Intelligence: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    5. Niu, Niu & Ma, Junhua & Zheng, Deyuan & Lu, Yang & Zhang, Bin, 2025. "Extreme weather and the green transition of energy firms: The moderating effect of digital technology and digital inclusive finance," Research in International Business and Finance, Elsevier, vol. 76(C).
    6. Luo, Kaikai & Wang, Fen & Chen, Xuezhen, 2025. "Impact of artificial intelligence on energy efficiency in Chinese enterprises," International Review of Economics & Finance, Elsevier, vol. 103(C).
    7. Niu, Xiaotong & Lin, Changao & He, Shanshan & Yang, Youcai, 2025. "Artificial intelligence and enterprise pollution emissions: From the perspective of energy transition," Energy Economics, Elsevier, vol. 144(C).
    8. Sai, Rockson & Yuan, Hongping & Kwabena Takyi, Ebenezer & Abudu, Hermas & Agyeman, Stephen, 2025. "Evaluation of transport carbon efficiency, reduction potential, and influencing factors in Africa," Transport Policy, Elsevier, vol. 162(C), pages 65-83.
    9. Cosma, Simona & Galletta, Simona & Mazzù, Sebastiano & Rimo, Giuseppe, 2024. "Banks' fossil fuel divestment and corporate governance: The role of board gender diversity," Energy Economics, Elsevier, vol. 139(C).
    10. Wan, Yaqi & Li, Zhensheng, 2025. "Environmental governance shock and industrial intelligence upgrading: Insights from machine-labor substitution," International Review of Financial Analysis, Elsevier, vol. 102(C).
    11. Zhengang Zhang & Peilun Li & Liangxiong Huang & Yichen Kang, 2024. "The impact of artificial intelligence on green transformation of manufacturing enterprises: evidence from China," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-36, August.
    12. Jun Liu & Hengxu Shen & Junwei Chen & Xin Jiang & Abdul Waheed Siyal, 2025. "Artificial Intelligence and Carbon Emissions: Mediating Role of Energy Efficiency, Factor Market Allocation and Industrial Structure," Energies, MDPI, vol. 18(5), pages 1-18, February.
    13. Zhu, Qingyuan & Sun, Chenhao & Xu, Chengzhen & Geng, Qianqian, 2025. "The impact of artificial intelligence on global energy vulnerability," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 15-27.
    14. Liu, Tie-Ying, 2025. "Do industrial robots optimize the energy structure? Evidence from fossil energy consumption," Energy Economics, Elsevier, vol. 148(C).
    15. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    16. Song, Malin & Du, Juntao, 2024. "Mechanisms for realizing the ecological products value: Green finance intervention and support," International Journal of Production Economics, Elsevier, vol. 271(C).
    17. Wang, Yafei & Shi, Ming & Liu, Junnan & Zhong, Min & Ran, Rong, 2025. "The impact of digital-real integration on energy productivity under a multi-governance framework: The mediating role of AI and embodied technological progress," Energy Economics, Elsevier, vol. 142(C).
    18. Li, Xin & Li, Shiyuan & Cao, Jifeng & Spulbar, Andrei Cristian, 2025. "Does artificial intelligence improve energy efficiency? Evidence from provincial data in China," Energy Economics, Elsevier, vol. 142(C).
    19. Li, Lingxiao & Wen, Jun & Li, Yan & Mu, Zi, 2025. "Supply chain challenges and energy insecurity: The role of AI in facilitating renewable energy transition," Energy Economics, Elsevier, vol. 144(C).
    20. Peiran Zhang & Hongmin Li, 2025. "Sustainable Transformation Paths for Value Realization of Eco-Products Empowered by New Quality Productivity: Based on Provincial Panel Data in China," Sustainability, MDPI, vol. 17(11), pages 1-28, May.
    21. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).

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