IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v401y2025ipas0306261925013376.html

AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms

Author

Listed:
  • Wu, Chuntao
  • Li, Haoran
  • Yuan, Bingbing

Abstract

Artificial intelligence (AI) offers unprecedented opportunities for energy management and optimization through data-driven, precision decision-making. This paper investigates how AI development influences energy efficiency (EE) by analyzing an unbalanced panel dataset of 54,657 observations from 4453 listed firms in China over the period 2007 to 2023. Studies have found that the development and diffusion of AI can significantly enhance EE at the firm level, while also inducing short-term energy rebound effects. Mechanistic analysis suggests that AI enhances EE mainly through innovative inputs and green innovation outputs. However, the relationship between AI and labor—where AI serves as a substitute for human labor—limits the role of human capital investment in fostering innovation. Further, heterogeneity analysis reveals that firms in non-high-tech, labor-intensive, and privately owned sectors, as well as those located in the Midwest or with established market positions, are particularly likely to benefit from AI-driven EE improvements. This study not only extends the application of technology diffusion theory to the domain of AI, but also draws comparative insights from other BRICS nations, aiming to offer actionable guidance for China's transition toward intelligent manufacturing and a low-carbon economy.

Suggested Citation

  • Wu, Chuntao & Li, Haoran & Yuan, Bingbing, 2025. "AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925013376
    DOI: 10.1016/j.apenergy.2025.126607
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925013376
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.126607?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    3. Ren, Xiaohang & Zeng, Gudian & Sun, Xianming, 2023. "The peer effect of digital transformation and corporate environmental performance: Empirical evidence from listed companies in China," Economic Modelling, Elsevier, vol. 128(C).
    4. Patnaik, Priyadarsini & Bakkar, Mahmoud, 2024. "Exploring determinants influencing artificial intelligence adoption, reference to diffusion of innovation theory," Technology in Society, Elsevier, vol. 79(C).
    5. 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).
    6. Xie, Yu & Wu, Desheng, 2024. "How does competition policy affect enterprise digitization? Dual perspectives of digital commitment and digital innovation," Journal of Business Research, Elsevier, vol. 178(C).
    7. Guo, Xiaochuan & Li, Mengmeng & Wang, Yanlin & Mardani, Abbas, 2023. "Does digital transformation improve the firm’s performance? From the perspective of digitalization paradox and managerial myopia," Journal of Business Research, Elsevier, vol. 163(C).
    8. Katja Mann & Lukas Püttmann, 2023. "Benign Effects of Automation: New Evidence from Patent Texts," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 562-579, May.
    9. Chao Ding & Jing Ke & Mark Levine & Jessica Granderson & Nan Zhou, 2024. "Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    10. 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).
    11. Lee, Chien-Chiang & Wang, Chang-song & He, Zhiwen & Xing, Wen-wu & Wang, Keying, 2023. "How does green finance affect energy efficiency? The role of green technology innovation and energy structure," Renewable Energy, Elsevier, vol. 219(P1).
    12. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    13. Li, Juan & Ma, Shaoqi & Qu, Yi & Wang, Jiamin, 2023. "The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China," Resources Policy, Elsevier, vol. 82(C).
    14. Yujie Wu & Mingyue Wang & Yingming Li & Junbi Zhou & Xiangjie Cao, 2024. "Can the construction of a research and development platform encourage innovation in green technology by private enterprises? Evidence from China," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 742-763, February.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Wang, Yongpei & Yan, Qing, 2023. "Is cleaner more efficient? Exploring nonlinear impacts of renewable energy deployment on regional total factor energy efficiency," Renewable Energy, Elsevier, vol. 216(C).
    17. Wilbur Chen & Suraj Srinivasan, 2024. "Going digital: implications for firm value and performance," Review of Accounting Studies, Springer, vol. 29(2), pages 1619-1665, June.
    18. Chen, Jia & Wang, Ning & Lin, Tongzhi & Liu, Baoliu & Hu, Jin, 2024. "Shock or empowerment? Artificial intelligence technology and corporate ESG performance," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 1080-1096.
    19. Calabrese, Armando & Costa, Roberta & Tiburzi, Luigi & Brem, Alexander, 2023. "Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    20. Peng, Hui & Lu, Yaobin & Wang, Qunwei, 2023. "How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy," Energy, Elsevier, vol. 268(C).
    21. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    22. Ma, Rufei & Deng, Liqian & Ji, Qiang & Zhai, Pengxiang, 2022. "Environmental regulations, clean energy access, and household energy poverty: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    23. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    24. Guo, Yuanyu & Xie, Wenlan & Yang, Yang, 2024. "Dual green innovation capability, environmental regulation intensity, and high-quality economic development in China: Can green and growth go together?," Finance Research Letters, Elsevier, vol. 63(C).
    25. Shuping Cheng & Lingjie Meng & Weizhong Wang, 2022. "The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    26. Quintana-Garci­a, Cristina & Benavides-Velasco, Carlos A., 2008. "Innovative competence, exploration and exploitation: The influence of technological diversification," Research Policy, Elsevier, vol. 37(3), pages 492-507, April.
    27. Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3265-3315, National Bureau of Economic Research, Inc.
    28. Lee, Chi-Chuan & Fang, Yuzhu & Quan, Shiyun & Li, Xinghao, 2024. "Leveraging the power of artificial intelligence toward the energy transition: The key role of the digital economy," Energy Economics, Elsevier, vol. 135(C).
    29. Yang, Shubo & Jahanger, Atif & Hossain, Mohammad Razib, 2023. "Does China's low-carbon city pilot intervention limit electricity consumption? An analysis of industrial energy efficiency using time-varying DID model," Energy Economics, Elsevier, vol. 121(C).
    30. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    31. Cheng, Manli & Wang, Junbo & Yang, Shanlin & Li, Qiang, 2024. "The driving effect of technological innovation on green development: From the perspective of efficiency," Energy Policy, Elsevier, vol. 188(C).
    32. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    33. Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning [Machine learning methods that economists should know about]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3265-3315.
    34. J. P. Eggers & Sarah Kaplan, 2009. "Cognition and Renewal: Comparing CEO and Organizational Effects on Incumbent Adaptation to Technical Change," Organization Science, INFORMS, vol. 20(2), pages 461-477, April.
    35. Hanchen Wang & Tianfan Fu & Yuanqi Du & Wenhao Gao & Kexin Huang & Ziming Liu & Payal Chandak & Shengchao Liu & Peter Katwyk & Andreea Deac & Anima Anandkumar & Karianne Bergen & Carla P. Gomes & Shir, 2023. "Scientific discovery in the age of artificial intelligence," Nature, Nature, vol. 620(7972), pages 47-60, August.
    36. Wang, Ying & Deng, Xiangzheng & Zhang, Hongwei & Liu, Yujie & Yue, Tianxiang & Liu, Gang, 2022. "Energy endowment, environmental regulation, and energy efficiency: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    37. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    38. Hanchen Wang & Tianfan Fu & Yuanqi Du & Wenhao Gao & Kexin Huang & Ziming Liu & Payal Chandak & Shengchao Liu & Peter Katwyk & Andreea Deac & Anima Anandkumar & Karianne Bergen & Carla P. Gomes & Shir, 2023. "Publisher Correction: Scientific discovery in the age of artificial intelligence," Nature, Nature, vol. 621(7978), pages 33-33, September.
    39. Tao, Weiliang & Weng, Shimei & Chen, Xueli & ALHussan, Fawaz Baddar & Song, Malin, 2024. "Artificial intelligence-driven transformations in low-carbon energy structure: Evidence from China," Energy Economics, Elsevier, vol. 136(C).
    40. Jaeho Choi & Anoop Menon & Haris Tabakovic, 2021. "Using machine learning to revisit the diversification–performance relationship," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1632-1661, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Shao, Mingxing & Wen, Lei & Li, Sifei & Huang, Binyue, 2025. "Exploring the role of artificial intelligence as a catalyst for energy technology innovation," Energy Economics, Elsevier, vol. 147(C).
    3. Zhu, Qing & Che, Jianhua & Liu, Shan & Wu, Liangzheng & Zhang, Jigang & Li, Yuze, 2025. "How can artificial intelligence technology applications accelerate energy innovation in China? Evidence from provincial regional data," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 484-502.
    4. Zeng, Ming & Zhang, Weike, 2024. "Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development," Energy Economics, Elsevier, vol. 139(C).
    5. Xi, Kang & Shao, Xuefeng, 2025. "Impact of AI applications on corporate green innovation," International Review of Economics & Finance, Elsevier, vol. 99(C).
    6. Zhang, Wei & Zhang, Yunjia & Lan, Xuling & Song, Malin, 2025. "“Green BRICS”: How artificial intelligence can build the explicit structure and implicit order of energy transition," Energy Economics, Elsevier, vol. 149(C).
    7. Lin, Boqiang & Zhou, Dengli, 2025. "A new green transition driver: How does artificial intelligence affect the green economic efficiency," Energy, Elsevier, vol. 334(C).
    8. Xi, Zhen & Zhang, Yichen & Zhao, Jing, 2025. "Digital strategy ripples: Understanding the digital transformation cascade along the supply chain," Finance Research Letters, Elsevier, vol. 80(C).
    9. Lee, Chien-Chiang & Li, Jiangnan & Yan, Jingyang, 2025. "Can artificial intelligence contribute to the new energy system? Based on the perspective of labor supply," Technology in Society, Elsevier, vol. 81(C).
    10. Luo, Heng & Sun, Ying & Tao, Xiaosha & Tan, Wenwu & Kamarudin, Fakarudin, 2024. "Effects of global value chains on energy efficiency in G20 countries," Energy, Elsevier, vol. 313(C).
    11. Li, Fei & Zhang, Nana, 2025. "Digital transformation, innovation investment and quality," Finance Research Letters, Elsevier, vol. 75(C).
    12. 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.
    13. Guo, Qingbin & Peng, Yanqing & Luo, Kang, 2025. "The impact of artificial intelligence on energy environmental performance: Empirical evidence from cities in China," Energy Economics, Elsevier, vol. 141(C).
    14. Junguo Shi & Wenyi Yan & Yan Li & Qian Wang & Shanshan Dou, 2025. "Balancing Environmental Regulation and Marketization: A Quantile Analysis of Energy Efficiency in China’s Provinces," Energies, MDPI, vol. 18(7), pages 1-21, April.
    15. Zhao, Xiaomeng & Zhao, Jun & Taghizadeh-Hesary, Farhad, 2025. "Is energy system resilience improved in the energy transition? Evidence from China," Energy Economics, Elsevier, vol. 146(C).
    16. Huo, Da & Gu, Wenjia & Guo, Dongmei & Tang, Aidi, 2024. "The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling," Energy Economics, Elsevier, vol. 140(C).
    17. Wang, Kai-Hua & Jiang, Xin-Yu & Tang, Yun, 2025. "Artificial intelligence, cloud computing, blockchain, and the energy market in the era of energy transition," Energy Economics, Elsevier, vol. 151(C).
    18. Wei, Huiru & Zhang, Jie & Yuan, Kuiran, 2025. "Digital horizons, green futures: How does new-generation artificial intelligence pilot zone drive corporate low-carbon transformation?," Journal of Asian Economics, Elsevier, vol. 100(C).
    19. Xueyun Rong & Zhongkai Yang & Yutao Sun, 2024. "Inventors’ brokerages dynamic and exploratory innovation: the moderating role of knowledge diversity," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(5), pages 2533-2558, May.
    20. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925013376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.