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

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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).
    8. 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.
    9. Carvalho, Carlos & Nechio, Fernanda & Tristão, Tiago, 2021. "Taylor rule estimation by OLS," Journal of Monetary Economics, Elsevier, vol. 124(C), pages 140-154.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. Dekle, Robert, 2020. "Robots and industrial labor: Evidence from Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    Full references (including those not matched with items on IDEAS)

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    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).
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    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).

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