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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

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    Cited by:

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    2. 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.
    3. 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).
    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).

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