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Industrial Intelligence and Carbon Emission Reduction: Evidence from China’s Manufacturing Industry

Author

Listed:
  • Tale Mi

    (College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Tiao Li

    (College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

This study delves into the impact of industrial intelligence on corporate carbon performance using micro-level data from 1072 listed manufacturing companies in China’s A-share market from 2012 to 2021. Industrial intelligence, through the integration of advanced technologies such as AI, IoT, and big data analytics applied to industrial robots, significantly improves the corporate carbon performance, measured by the carbon intensity and total emissions. Although the total carbon emissions increase due to the output effect, the efficiency optimization effect of industrial intelligence has a greater impact, reducing carbon intensity and emissions. The reduction effect from increased production efficiency outweighs the increase from the output effect. Heterogeneity tests show significant carbon reduction effects of industrial intelligence in industries with heavy and moderate carbon emissions, but an increase in carbon emissions in industries with light carbon emissions. Regional differences also emerge, with more effective carbon reduction in the Yangtze River Delta and Pearl River Delta regions compared to the Beijing-Tianjin-Hebei region. These findings highlight the carbon reduction potential of industrial intelligence across different industries and regions, offering valuable insights for targeted environmental policies and corporate strategies.

Suggested Citation

  • Tale Mi & Tiao Li, 2024. "Industrial Intelligence and Carbon Emission Reduction: Evidence from China’s Manufacturing Industry," Sustainability, MDPI, vol. 16(15), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6573-:d:1447355
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    References listed on IDEAS

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    1. Ma, Qiang & Murshed, Muntasir & Khan, Zeeshan, 2021. "The nexuses between energy investments, technological innovations, emission taxes, and carbon emissions in China," Energy Policy, Elsevier, vol. 155(C).
    2. Gries, Thomas & Naude, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224623, Verein für Socialpolitik / German Economic Association.
    3. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    4. 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.
    5. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    6. 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).
    7. Hong Cheng & Ruixue Jia & Dandan Li & Hongbin Li, 2019. "The Rise of Robots in China," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 71-88, Spring.
    8. Kenneth Gillingham & James H. Stock, 2018. "The Cost of Reducing Greenhouse Gas Emissions," Journal of Economic Perspectives, American Economic Association, vol. 32(4), pages 53-72, Fall.
    9. Julius J. Andersson, 2019. "Carbon Taxes and CO2 Emissions: Sweden as a Case Study," American Economic Journal: Economic Policy, American Economic Association, vol. 11(4), pages 1-30, November.
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