IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i4p2091-d748302.html
   My bibliography  Save this article

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China

Author

Listed:
  • Jun Liu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
    Institute of Free Trade Zone, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Yu Qian

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Yuanjun Yang

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Zhidan Yang

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2091-:d:748302
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/4/2091/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/4/2091/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. Zhu, Junpeng & Lin, Boqiang, 2022. "Economic growth pressure and energy efficiency improvement: Empirical evidence from Chinese cities," Applied Energy, Elsevier, vol. 307(C).
    4. Vélez-Henao, Johan-Andrés & García-Mazo, Claudia-Maria & Freire-González, Jaume & Vivanco, David Font, 2020. "Environmental rebound effect of energy efficiency improvements in Colombian households," Energy Policy, Elsevier, vol. 145(C).
    5. 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).
    6. 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).
    7. Nicholas Bloom & Christos Genakos & Ralf Martin & Raffaella Sadun, 2010. "Modern Management: Good for the Environment or Just Hot Air?," Economic Journal, Royal Economic Society, vol. 120(544), pages 551-572, May.
    8. Popp, David C., 2001. "The effect of new technology on energy consumption," Resource and Energy Economics, Elsevier, vol. 23(3), pages 215-239, July.
    9. Varun Rai & Adam Douglas Henry, 2016. "Agent-based modelling of consumer energy choices," Nature Climate Change, Nature, vol. 6(6), pages 556-562, June.
    10. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    11. 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.
    12. Ni, Bin & Obashi, Ayako, 2021. "Robotics technology and firm-level employment adjustment in Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    13. Fisher-Vanden, Karen & Jefferson, Gary H. & Jingkui, Ma & Jianyi, Xu, 2006. "Technology development and energy productivity in China," Energy Economics, Elsevier, vol. 28(5-6), pages 690-705, November.
    14. Saunders, Harry D., 2008. "Fuel conserving (and using) production functions," Energy Economics, Elsevier, vol. 30(5), pages 2184-2235, September.
    15. Macharia, Kenneth Kigundu & Gathiaka, John Kamau & Ngui, Dianah, 2022. "Energy efficiency in the Kenyan manufacturing sector," Energy Policy, Elsevier, vol. 161(C).
    16. Tang, Liwei & He, Gang, 2021. "How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China," Energy, Elsevier, vol. 235(C).
    17. 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).
    18. Lemoine, Derek, 2020. "General equilibrium rebound from energy efficiency innovation," European Economic Review, Elsevier, vol. 125(C).
    19. Avi Goldfarb & Daniel Trefler, 2018. "AI and International Trade," NBER Working Papers 24254, National Bureau of Economic Research, Inc.
    20. Su, Bin & Goh, Tian & Ang, B.W. & Ng, Tsan Sheng, 2022. "Energy consumption and energy efficiency trends in Singapore: The case of a meticulously planned city," Energy Policy, Elsevier, vol. 161(C).
    21. Liu, Haomin & Zhang, Zaixu & Zhang, Tao & Wang, Liyang, 2020. "Revisiting China’s provincial energy efficiency and its influencing factors," Energy, Elsevier, vol. 208(C).
    22. Welsch, Heinz & Ochsen, Carsten, 2005. "The determinants of aggregate energy use in West Germany: factor substitution, technological change, and trade," Energy Economics, Elsevier, vol. 27(1), pages 93-111, January.
    23. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    24. Vlačić, Ernest & Dabić, Marina & Daim, Tugrul & Vlajčić, Davor, 2019. "Exploring the impact of the level of absorptive capacity in technology development firms," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 166-177.
    25. Boubakri, Narjess & Cosset, Jean-Claude & Saffar, Walid, 2013. "The role of state and foreign owners in corporate risk-taking: Evidence from privatization," Journal of Financial Economics, Elsevier, vol. 108(3), pages 641-658.
    26. Curtis, E. Mark & Lee, Jonathan M., 2019. "When do environmental regulations backfire? Onsite industrial electricity generation, energy efficiency and policy instruments," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 174-194.
    27. He, Yong & Fu, Feifei & Liao, Nuo, 2021. "Exploring the path of carbon emissions reduction in China’s industrial sector through energy efficiency enhancement induced by R&D investment," Energy, Elsevier, vol. 225(C).
    28. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    29. Jin, Sang-Hyeon, 2007. "The effectiveness of energy efficiency improvement in a developing country: Rebound effect of residential electricity use in South Korea," Energy Policy, Elsevier, vol. 35(11), pages 5622-5629, November.
    30. Ma, Hongmei & Gao, Qian & Li, Xiuzhen & Zhang, Yun, 2022. "AI development and employment skill structure: A case study of China," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 242-254.
    31. Sowa, Konrad & Przegalinska, Aleksandra & Ciechanowski, Leon, 2021. "Cobots in knowledge work," Journal of Business Research, Elsevier, vol. 125(C), pages 135-142.
    32. Jung, Jin Hwa & Lim, Dong-Geon, 2020. "Industrial robots, employment growth, and labor cost: A simultaneous equation analysis," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    33. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    34. 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.
    35. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    36. Adha, Rishan & Hong, Cheng-Yih & Firmansyah, M. & Paranata, Ade, 2021. "Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia," MPRA Paper 110444, University Library of Munich, Germany.
    37. Nick Bloom & Christos Genakos & Ralf Martin & Raffaella Sadun, 2010. "In brief: Modern Management: Good for the environment or just hot air?," CentrePiece - The magazine for economic performance 303, Centre for Economic Performance, LSE.
    38. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    39. 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).
    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. Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).
    2. 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).

    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. Shao, Jun & Wang, Lianghu, 2023. "Can new-type urbanization improve the green total factor energy efficiency? Evidence from China," Energy, Elsevier, vol. 262(PB).
    2. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    3. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    4. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Yin, Zi Hui & Zeng, Wei Ping, 2023. "The effects of industrial intelligence on China's energy intensity: The role of technology absorptive capacity," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. 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).
    7. Yang, Jun & Zou, Ran & Cheng, Jixin & Geng, Zhifei & Li, Qi, 2023. "Environmental technical efficiency and its dynamic evolution in China's industry: A resource endowment perspective," Resources Policy, Elsevier, vol. 82(C).
    8. Huang, Geng & He, Ling-Yun & Lin, Xi, 2023. "Deterioration or improvement? Intermediate product import and enterprises' environmental performance," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 139-150.
    9. Su, Chi-Wei & Yuan, Xi & Umar, Muhammad & Lobonţ, Oana-Ramona, 2022. "Does technological innovation bring destruction or creation to the labor market?," Technology in Society, Elsevier, vol. 68(C).
    10. Fan, Pengda & Qian, Xuepeng & Wang, Jian, 2023. "Does gender diversity matter? Female directors and firm carbon emissions in Japan," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    11. Huang, Geng & Lin, Xi & He, Ling-Yun, 2023. "Good for the environment? Foreign investment opening in service sector and firm's energy efficiency," Energy Economics, Elsevier, vol. 127(PA).
    12. Zhao, Yueyang & Mao, Jinzhou, 2022. "Energy effects of non-energy policies: Minimum wage standard and enterprise energy efficiency in China," Resources Policy, Elsevier, vol. 79(C).
    13. 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).
    14. He, Ling-Yun & Huang, Geng, 2023. "Can importing improve the energy efficiency? Theory and evidence from Chinese industrial firms," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 451-469.
    15. Chen, Maozhi & Sinha, Avik & Hu, Kexiang & Shah, Muhammad Ibrahim, 2021. "Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    16. 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).
    17. Rui Ding & Tao Zhou & Jian Yin & Yilin Zhang & Siwei Shen & Jun Fu & Linyu Du & Yiming Du & Shihui Chen, 2022. "Does the Urban Agglomeration Policy Reduce Energy Intensity? Evidence from China," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    18. Garrone, Paola & Grilli, Luca & Mrkajic, Boris, 2017. "The energy-efficient transformation of EU business enterprises: Adapting policies to contextual factors," Energy Policy, Elsevier, vol. 109(C), pages 49-58.
    19. Xi Lin & Ling‐Yun He, 2023. "‘Going global’ and pollution in home country: Evidence from Chinese industrial firms," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(4), pages 1135-1174, October.
    20. Leitão, João & Ferreira, Joaquim & Santibanez-González, Ernesto, 2022. "New insights into decoupling economic growth, technological progress and carbon dioxide emissions: Evidence from 40 countries," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

    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:gam:jijerp:v:19:y:2022:i:4:p:2091-:d:748302. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.