IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i12p7219-d837522.html
   My bibliography  Save this article

The Impact of Industrial Intelligence on Energy Intensity: Evidence from China

Author

Listed:
  • Xiekui Zhang

    (China-ASEAN Collaborative Innovation Center for Regional Development Co-Constructed by the Province and Ministry, Guangxi University, Nanning 530004, China
    School of Economics & China-ASEAN Institute of Financial Cooperation, Guangxi University, Nanning 530004, China)

  • Peiyao Liu

    (School of Business Administration, South China University of Technology, Guangzhou 510641, China)

  • Hongfei Zhu

    (School of Business, Guangxi University, Nanning 530004, China)

Abstract

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve the productivity and energy efficiency of factories and governments. Meanwhile, the social ecological environment change has also caused widespread social concern in recent years, and energy efficiency, which is related to climate change, has forced almost every country to reduce their carbon emissions for bettering environmental quality. However, there is little research that has studied this problem from the perspective of industrial robots, even though they are an indispensable part in modern industrial systems. In order to promote the development of artificial intelligence and its application in industrial fields effectively and raise the energy consumption efficiency of production, this paper investigates the impact of industrial intelligence on energy intensity in China, as it is the largest manufacturing and energy consumption country in the world, and we also hope that the experimental results in this study can guide relevant departments and governments to formulate reasonable policies to enhance the utilization efficiency of energy and improve the environmental quality synchronously. For the sake of the rigor of this research and the accuracy of the experimental results, this study explores the corresponding effect mechanisms of industrial intelligence on China’s energy intensity from 2008 to 2019 by using the classical linear regression model OLS (Ordinary Least Squares) and WLS (Weighted Least Squares) separately, which were applied in the previous studies. The results of this study reveal three major findings. The first is that it further proves that the application of artificial intelligence can indeed reduce energy intensity, and the wide applications of artificial intelligence can reduce energy intensity significantly by reducing energy consumption. Besides, the ownership structure of state-owned enterprises will have a positive impact on energy efficiency. The environmental performance of state-owned enterprises is better than that of foreign-funded and private enterprises. Finally, the models further verify the significant impact of the enterprise scale effect on energy intensity. It will bring about the improvement of economic efficiency, and the larger the enterprise, the more obvious the economies of scale effect and the lower the energy intensity.

Suggested Citation

  • Xiekui Zhang & Peiyao Liu & Hongfei Zhu, 2022. "The Impact of Industrial Intelligence on Energy Intensity: Evidence from China," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7219-:d:837522
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/12/7219/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/12/7219/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zheng, Yingmei & Qi, Jianhong & Chen, Xiaoliang, 2011. "The effect of increasing exports on industrial energy intensity in China," Energy Policy, Elsevier, vol. 39(5), pages 2688-2698, May.
    2. Earnhart, Dietrich & Lizal, Lubomir, 2006. "Effects of ownership and financial performance on corporate environmental performance," Journal of Comparative Economics, Elsevier, vol. 34(1), pages 111-129, March.
    3. 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).
    4. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    5. Karen Fisher-Vanden & Yong Hu & Gary Jefferson & Michael Rock & Michael Toman, 2016. "Factors Influencing Energy Intensity in Four Chinese Industries," The Energy Journal, , vol. 37(1_suppl), pages 153-178, January.
    6. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    7. Cette, Gilbert & Devillard, Aurélien & Spiezia, Vincenzo, 2021. "The contribution of robots to productivity growth in 30 OECD countries over 1975–2019," Economics Letters, Elsevier, vol. 200(C).
    8. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    9. Dekle, Robert, 2020. "Robots and industrial labor: Evidence from Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    10. Eskeland, Gunnar S. & Harrison, Ann E., 2003. "Moving to greener pastures? Multinationals and the pollution haven hypothesis," Journal of Development Economics, Elsevier, vol. 70(1), pages 1-23, February.
    11. Lin, Boqiang & Chen, Yu & Zhang, Guoliang, 2018. "Impact of technological progress on China's textile industry and future energy saving potential forecast," Energy, Elsevier, vol. 161(C), pages 859-869.
    12. Carvalho, Carlos & Nechio, Fernanda & Tristão, Tiago, 2021. "Taylor rule estimation by OLS," Journal of Monetary Economics, Elsevier, vol. 124(C), pages 140-154.
    13. 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).
    14. Sharma, Rajesh & Shahbaz, Muhammad & Kautish, Pradeep & Vo, Xuan Vinh, 2021. "Analyzing the impact of export diversification and technological innovation on renewable energy consumption: Evidences from BRICS nations," Renewable Energy, Elsevier, vol. 178(C), pages 1034-1045.
    15. 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.
    16. Bu, Maoliang & Li, Shuang & Jiang, Lei, 2019. "Foreign direct investment and energy intensity in China: Firm-level evidence," Energy Economics, Elsevier, vol. 80(C), pages 366-376.
    17. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    18. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    19. Schleich, Joachim, 2009. "Barriers to energy efficiency: A comparison across the German commercial and services sector," Ecological Economics, Elsevier, vol. 68(7), pages 2150-2159, May.
    20. Zhu, Junming & Niu, Limin & Ruth, Matthias & Shi, Lei, 2018. "Technological Change and Energy Efficiency in Large Chinese Firms," Ecological Economics, Elsevier, vol. 150(C), pages 241-250.
    21. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    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. Beata Milewska & Dariusz Milewski, 2023. "The Impact of Energy Consumption Costs on the Profitability of Production Companies in Poland in the Context of the Energy Crisis," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Wang, Jiangquan & Nghiem, Xuan-Hoa & Jabeen, Fauzia & Luqman, Adeel & Song, Malin, 2023. "Integrated development of digital and energy industries: Paving the way for carbon emission reduction," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    3. 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).
    4. 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).
    5. Kunkel, S. & Neuhäusler, P. & Matthess, M. & Dachrodt, M.F., 2023. "Industry 4.0 and energy in manufacturing sectors in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    6. Chi Gong & Xianghui Yang & Hongru Tan & Xiaoye Lu, 2023. "Industrial Robots, Economic Growth, and Sustainable Development in an Aging Society," Sustainability, MDPI, vol. 15(5), pages 1-12, March.
    7. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(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. 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.
    2. Zhang, Dayong & Li, Jun & Ji, Qiang, 2020. "Does better access to credit help reduce energy intensity in China? Evidence from manufacturing firms," Energy Policy, Elsevier, vol. 145(C).
    3. 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).
    4. Huang, Junbing & Wang, Yajun & Guo, Lili, 2022. "Energy intensity and energy-specific technological progress: A case study in Guangdong province of China," Renewable Energy, Elsevier, vol. 184(C), pages 990-1001.
    5. Xiekui Zhang & Hongfei Zhu, 2023. "The Impact of Industrial Intelligence on Carbon Emissions: Evidence from the Three Largest Economies," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    6. Huang, Junbing & Lai, Yali & Wang, Yajun & Hao, Yu, 2020. "Energy-saving research and development activities and energy intensity in China: A regional comparison perspective," Energy, Elsevier, vol. 213(C).
    7. Wu, Shu & Ding, Song, 2021. "Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector," Energy Economics, Elsevier, vol. 99(C).
    8. Shuxing Chen & Xiangyang Du & Junbing Huang & Cheng Huang, 2019. "The Impact of Foreign and Indigenous Innovations on the Energy Intensity of China’s Industries," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    9. Adom, Philip Kofi, 2015. "Business cycle and economic-wide energy intensity: The implications for energy conservation policy in Algeria," Energy, Elsevier, vol. 88(C), pages 334-350.
    10. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    11. 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).
    12. Xiekui Zhang & Baocheng Yu, 2023. "The Impact of Ownership Structure on Technological Innovation and Energy Intensity: Evidence from China," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    13. Le Tang, 2020. "Energy prices and investment in energy efficiency: evidence from Chinese industry 1997–2004," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 34(2), pages 93-105, November.
    14. Trinh, Hai Hong & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Vo, Diem Thi Hong, 2022. "Examining the heterogeneity of financial development in the energy-environment nexus in the era of climate change: Novel evidence around the world," Energy Economics, Elsevier, vol. 116(C).
    15. Adom, Philip Kofi, 2015. "Asymmetric impacts of the determinants of energy intensity in Nigeria," Energy Economics, Elsevier, vol. 49(C), pages 570-580.
    16. 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).
    17. Huang, Junbing & Luan, Bingjiang & He, Wanrui & Chen, Xiang & Li, Mengfan, 2022. "Energy technology of conservation versus substitution and energy intensity in China," Energy, Elsevier, vol. 244(PA).
    18. 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).
    19. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Jiao, Zhiming & Han, Cuicui, 2019. "How do industrialization and trade openness influence energy intensity? Evidence from a path model in case of Bangladesh," Energy Policy, Elsevier, vol. 133(C).
    20. Bo Chen & Dong Tan, 2023. "Industrial Robots and the Employment Quality of Migrant Workers in the Manufacturing Industry," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

    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:jsusta:v:14:y:2022:i:12:p:7219-:d:837522. 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.