IDEAS home Printed from https://ideas.repec.org/a/eee/ecanpo/v83y2024icp548-561.html
   My bibliography  Save this article

Does artificial intelligence reduce corporate energy consumption? New evidence from China

Author

Listed:
  • FU, Yunyun
  • SHEN, Yongchang
  • SONG, Malin
  • WANG, Weiyu

Abstract

Artificial intelligence is playing a significant role in addressing the energy crisis. This study selected data from manufacturing companies listed on China's A-share market from 2011 to 2022 and calculated the total energy consumption for the first time. The data include the usage of coal, natural gas, gasoline, diesel and water consumption, electricity usage, and centralized heating. The data were then matched and merged with robot usage data from the International Federation of Robotics to empirically study the impact and mechanism of artificial intelligence on energy consumption levels. Our findings reveal that energy consumption decreases by 0.20 % with a one-unit increase in artificial intelligence applications by a corporation, indicating artificial intelligence can significantly reduce energy consumption. The mechanisms by which artificial intelligence affects energy consumption include technological innovation and digital transformation. Additionally, a heterogeneity analysis revealed that applying artificial intelligence in state-owned enterprises, high-tech companies, and non-heavy-pollution industries can further reduce energy consumption. Our study also provides important practical implications for formulating and optimizing global energy policies to achieve sustainable development goals.

Suggested Citation

  • FU, Yunyun & SHEN, Yongchang & SONG, Malin & WANG, Weiyu, 2024. "Does artificial intelligence reduce corporate energy consumption? New evidence from China," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 548-561.
    • Handle: RePEc:eee:ecanpo:v:83:y:2024:i:c:p:548-561
    DOI: 10.1016/j.eap.2024.07.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0313592624001723
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eap.2024.07.005?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    3. Yuan, Chaoqing & Liu, Sifeng & Wu, Junlong, 2010. "The relationship among energy prices and energy consumption in China," Energy Policy, Elsevier, vol. 38(1), pages 197-207, January.
    4. Wang, Zhaohua & Yang, Zhongmin & Zhang, Yixiang & Yin, Jianhua, 2012. "Energy technology patents–CO2 emissions nexus: An empirical analysis from China," Energy Policy, Elsevier, vol. 42(C), pages 248-260.
    5. 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).
    6. Kong, Dongmin & Yang, Yiwei & Wang, Qin, 2023. "Innovative efficiency and firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 52(C).
    7. Joseph S. Shapiro & Reed Walker, 2018. "Why Is Pollution from US Manufacturing Declining? The Roles of Environmental Regulation, Productivity, and Trade," American Economic Review, American Economic Association, vol. 108(12), pages 3814-3854, December.
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    9. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    10. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    11. Hübler, Michael, 2012. "Carbon tariffs on Chinese exports: Emissions reduction, threat, or farce?," Energy Policy, Elsevier, vol. 50(C), pages 315-327.
    12. Apergis, Nicholas & Payne, James E., 2010. "Coal consumption and economic growth: Evidence from a panel of OECD countries," Energy Policy, Elsevier, vol. 38(3), pages 1353-1359, March.
    13. Sadorsky, Perry, 2011. "Trade and energy consumption in the Middle East," Energy Economics, Elsevier, vol. 33(5), pages 739-749, September.
    14. Gan, Jiawu & Liu, Lihua & Qiao, Gang & Zhang, Qin, 2023. "The role of robot adoption in green innovation: Evidence from China," Economic Modelling, Elsevier, vol. 119(C).
    15. Tang, Maogang & Liu, Yinlin & Hu, Fengxia & Wu, Baijun, 2023. "Effect of digital transformation on enterprises' green innovation: Empirical evidence from listed companies in China," Energy Economics, Elsevier, vol. 128(C).
    16. Seth G. Benzell & Laurence J. Kotlikoff & Guillermo LaGarda & Jeffrey D. Sachs, 2015. "Robots Are Us: Some Economics of Human Replacement," NBER Working Papers 20941, National Bureau of Economic Research, Inc.
    17. Shi, Xunpeng & Sun, Sizhong, 2017. "Energy price, regulatory price distortion and economic growth: A case study of China," Energy Economics, Elsevier, vol. 63(C), pages 261-271.
    18. Yingjia Zhong & Hongyan Zhao & Tianbao Yin, 2023. "Resource Bundling: How Does Enterprise Digital Transformation Affect Enterprise ESG Development?," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    19. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    20. Apergis, Nicholas & Payne, James E., 2010. "Renewable energy consumption and economic growth: Evidence from a panel of OECD countries," Energy Policy, Elsevier, vol. 38(1), pages 656-660, January.
    21. Christian Matt & Thomas Hess & Alexander Benlian, 2015. "Digital Transformation Strategies," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(5), pages 339-343, October.
    22. Wang, Fangjun & Wang, Xuanzi & Li, Boying & Liu, Yang S., 2023. "Ownership structure and eco-innovation: Evidence from Chinese family firms," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    23. Nada Kobeissi & Iftekhar Hasan & Bo Wang & Haizhi Wang & Desheng Yin, 2023. "Social capital and regional innovation: evidence from private firms in the US," Regional Studies, Taylor & Francis Journals, vol. 57(1), pages 57-71, January.
    24. Li, Ke & Lin, Boqiang, 2015. "Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1107-1122.
    25. Wang, Rui & Yang, Shijie, 2023. "Credit ratings and firm innovation: Evidence from sovereign downgrades," Journal of Banking & Finance, Elsevier, vol. 148(C).
    26. Matt, C. & Hess, Thomas & Benlian, Alexander, 2015. "Digital Transformation Strategies," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 75202, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. Feng, Taiwen & Sun, Linyan & Zhang, Ying, 2009. "The relationship between energy consumption structure, economic structure and energy intensity in China," Energy Policy, Elsevier, vol. 37(12), pages 5475-5483, December.
    28. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    29. Abdulaziz Aldoseri & Khalifa N. Al-Khalifa & Abdel Magid Hamouda, 2024. "AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact," Sustainability, MDPI, vol. 16(5), pages 1-25, February.
    30. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    31. Junshi Chen & Jing Chi & Hamish Anderson, 2023. "CEO happiness curve and firm innovation: evidence from China," Applied Economics Letters, Taylor & Francis Journals, vol. 30(19), pages 2814-2818, November.
    32. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    33. Wang, Lei & Zhou, Yahong & Chiao, Benjamin, 2023. "Robots and firm innovation: Evidence from Chinese manufacturing," Journal of Business Research, Elsevier, vol. 162(C).
    34. Andrea Chiarini, 2021. "Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 3194-3207, November.
    35. Song, Malin & Xie, Qianjiao & Shen, Zhiyang, 2021. "Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints," Energy Policy, Elsevier, vol. 153(C).
    36. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    37. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    38. Shen, Yongchang & Fu, Yunyun & Song, Malin, 2023. "Does digital transformation make enterprises greener? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1642-1654.
    39. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    40. Akhmat, Ghulam & Zaman, Khalid, 2013. "Nuclear energy consumption, commercial energy consumption and economic growth in South Asia: Bootstrap panel causality test," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 552-559.
    41. Dowlatabadi, Hadi & Oravetz, Matthew A., 2006. "US long-term energy intensity: Backcast and projection," Energy Policy, Elsevier, vol. 34(17), pages 3245-3256, November.
    42. Zheng, Wei & Walsh, Patrick Paul, 2019. "Economic growth, urbanization and energy consumption — A provincial level analysis of China," Energy Economics, Elsevier, vol. 80(C), pages 153-162.
    43. Gutiérrez, Emilio & Teshima, Kensuke, 2018. "Abatement expenditures, technology choice, and environmental performance: Evidence from firm responses to import competition in Mexico," Journal of Development Economics, Elsevier, vol. 133(C), pages 264-274.
    44. Fangfang Hou & Congshan Li, 2023. "Reverse innovation and firm value in emerging markets: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 161-198, March.
    45. Freire-González, Jaume, 2011. "Methods to empirically estimate direct and indirect rebound effect of energy-saving technological changes in households," Ecological Modelling, Elsevier, vol. 223(1), pages 32-40.
    46. Matt, C. & Hess, Thomas & Benlian, Alexander, 2015. "Digital Transformation Strategies," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 75002, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    47. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    48. Lin, Boqiang & Moubarak, Mohamed, 2014. "Renewable energy consumption – Economic growth nexus for China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 111-117.
    49. Zhou, Di & Qiu, Yuan & Wang, Mingzhe, 2021. "Does environmental regulation promote enterprise profitability? Evidence from the implementation of China's newly revised Environmental Protection Law," Economic Modelling, Elsevier, vol. 102(C).
    50. Andrew Kusiak, 2018. "Smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 508-517, January.
    51. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    52. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    53. David Mhlanga, 2023. "Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review," Energies, MDPI, vol. 16(2), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pu, Guifang & Xie, Yanxiang & Wu, Lidong & Wang, Kai, 2024. "Industrial robots and corporate risk-taking value," Finance Research Letters, Elsevier, vol. 70(C).
    2. Agata Jaroń & Anna Borucka, 2024. "Analysis of Energy System Transformations in the European Union," Energies, MDPI, vol. 17(23), pages 1-18, December.

    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. Zhou, Zhongsheng & Li, Zhuo & Du, Shanzhong & Cao, June, 2024. "Robot adoption and enterprise R&D manipulation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    3. Lin, Boqiang & Xu, Chongchong, 2024. "Enhancing energy-environmental performance through industrial intelligence: Insights from Chinese prefectural-level cities," Applied Energy, Elsevier, vol. 365(C).
    4. Zhang, Aili & Zhu, Han & Sun, Xinyu, 2024. "Manufacturing intelligentization and technological innovation: Perspectives on intra-industry impacts and inter-industry technology spillovers," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    5. 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).
    6. 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).
    7. Zhang, Zhenhua & Zhang, Yunpeng & Wu, Huangbin & Song, Shunfeng & Pan, Yuxi & Feng, Yanchao, 2024. "Dual effects of automation on economy and environment: Evidence from A-share listed enterprises in China," China Economic Review, Elsevier, vol. 88(C).
    8. 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).
    9. Han, Wang-Zhe & Zhang, Yi-Ming, 2024. "Carbon reduction effect of industrial robots: Breaking the impasse for carbon emissions and development," Innovation and Green Development, Elsevier, vol. 3(3).
    10. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    11. Sun, Huaping & Edziah, Bless Kofi & Kporsu, Anthony Kwaku & Sarkodie, Samuel Asumadu & Taghizadeh-Hesary, Farhad, 2021. "Energy efficiency: The role of technological innovation and knowledge spillover," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    12. Tae Wan Kim & Fabrizio Maimone & Katherina Pattit & Alejo José Sison & Benito Teehankee, 2021. "Master and Slave: the Dialectic of Human-Artificial Intelligence Engagement," Humanistic Management Journal, Springer, vol. 6(3), pages 355-371, December.
    13. Naude, Wim, 2019. "The race against the robots and the fallacy of the giant cheesecake: Immediate and imagined impacts of artificial intelligence," MERIT Working Papers 2019-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Liu, Yunxin & Cao, Yuqiang & Lu, Meiting & Shan, Yaowen & Xu, Jiangang, 2024. "Automating efficiency: The impact of industrial robots on labor investment in China," Economic Modelling, Elsevier, vol. 140(C).
    15. Li, Jianjun & Wu, Zhouyi & Yu, Kaijia & Zhao, Wei, 2024. "The effect of industrial robot adoption on firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 60(C).
    16. Shurong Zi & Ziyuan Pan & Yanjie Chen & Xinheng Liu & Qi Ban, 2025. "Intelligent Manufacturing and Pollution Emissions from Chinese Manufacturing Firms: Theories and Mechanisms," Sustainability, MDPI, vol. 17(2), pages 1-21, January.
    17. Wang, Hua & Liao, Lingtao & Wu, Ji (George), 2023. "Robot adoption and firm's capacity utilization: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    18. Peng Liang & Xinhui Sun & Luzhuang Qi, 2024. "Does artificial intelligence technology enhance green transformation of enterprises: based on green innovation perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 21651-21687, August.
    19. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    20. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.

    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:ecanpo:v:83:y:2024:i:c:p:548-561. 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.journals.elsevier.com/economic-analysis-and-policy .

    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.