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

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  • 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
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    as
    1. 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.
    2. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, , vol. 32(2), pages 59-80, April.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    15. 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.
    16. 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.
    17. 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).
    18. 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.
    19. Wang, Lei & Zhou, Yahong & Chiao, Benjamin, 2023. "Robots and firm innovation: Evidence from Chinese manufacturing," Journal of Business Research, Elsevier, vol. 162(C).
    20. 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.
    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. 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.
    23. 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).
    24. Kong, Dongmin & Yang, Yiwei & Wang, Qin, 2023. "Innovative efficiency and firm value: Evidence from China," Finance Research Letters, Elsevier, vol. 52(C).
    25. 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).
    26. 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).
    27. 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.
    28. 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.
    29. 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).
    30. 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.
    31. 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.
    32. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    33. 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.
    34. Andrew Kusiak, 2018. "Smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 508-517, January.
    35. 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.
    36. 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.
    37. 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.
    38. Wang, Rui & Yang, Shijie, 2023. "Credit ratings and firm innovation: Evidence from sovereign downgrades," Journal of Banking & Finance, Elsevier, vol. 148(C).
    39. 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.
    40. Hübler, Michael, 2012. "Carbon tariffs on Chinese exports: Emissions reduction, threat, or farce?," Energy Policy, Elsevier, vol. 50(C), pages 315-327.
    41. 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.
    42. 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.
    43. Sadorsky, Perry, 2011. "Trade and energy consumption in the Middle East," Energy Economics, Elsevier, vol. 33(5), pages 739-749, September.
    44. 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.
    45. 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).
    46. 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.
    47. 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.
    48. 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).
    49. 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).
    50. 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.
    51. 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.
    52. 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.
    53. Dowlatabadi, Hadi & Oravetz, Matthew A., 2006. "US long-term energy intensity: Backcast and projection," Energy Policy, Elsevier, vol. 34(17), pages 3245-3256, November.
    54. 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).
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