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Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China

Author

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  • Hao Lv

    (School of Economics & Management, Northwest University, Xi’an 710127, China)

  • Beibei Shi

    (School of Economics & Management, Northwest University, Xi’an 710127, China
    National and Local Joint Engineering Research Center of Carbon Capture and Storage Technology, Xi’an 710069, China
    Shaanxi Key Laboratory for Carbon Neutral Technology, Xi’an 710069, China)

  • Nan Li

    (School of Economics & Management, Northwest University, Xi’an 710127, China
    Carbon Neutrality College (Yulin), Northwest University, Xi’an 710127, China)

  • Rong Kang

    (School of Economics & Management, Northwest University, Xi’an 710127, China
    National and Local Joint Engineering Research Center of Carbon Capture and Storage Technology, Xi’an 710069, China
    Shaanxi Key Laboratory for Carbon Neutral Technology, Xi’an 710069, China)

Abstract

Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China’s industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.

Suggested Citation

  • Hao Lv & Beibei Shi & Nan Li & Rong Kang, 2022. "Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15538-:d:981621
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    Cited by:

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    3. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    4. Liu, Lei & Rasool, Zeeshan & Ali, Sajid & Wang, Canghong & Nazar, Raima, 2024. "Robots for sustainability: Evaluating ecological footprints in leading AI-driven industrial nations," Technology in Society, Elsevier, vol. 76(C).
    5. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    6. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    7. Han, Wang-Zhe & Zhang, Yi-Ming, 2024. "Carbon reduction effect of industrial robots: Breaking the impasse for carbon emissions and development," Innovation and Green Development, Elsevier, vol. 3(3).
    8. Yang Shen & Zhihong Yang, 2023. "Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    9. Agnieszka Sękala & Tomasz Blaszczyk & Krzysztof Foit & Gabriel Kost, 2024. "Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots," Energies, MDPI, vol. 17(3), pages 1-23, January.

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