IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6573-d1447355.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6573/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/15/6573/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).
    2. 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).
    3. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    4. de Vries, Gaaitzen J. & Gentile, Elisabetta & Miroudot, Sébastien & Wacker, Konstantin M., 2020. "The rise of robots and the fall of routine jobs," Labour Economics, Elsevier, vol. 66(C).
    5. Lihua Zhang & Tian Gan & Jiachen Fan, 2023. "Do industrial robots affect the labour market? Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(3), pages 787-817, July.
    6. Klump, Rainer & Jurkat, Anne & Schneider, Florian, 2021. "Tracking the rise of robots: A survey of the IFR database and its applications," MPRA Paper 107909, University Library of Munich, Germany.
    7. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    8. Jurkat, Anne & Klump, Rainer & Schneider, Florian, 2023. "Robots and Wages: A Meta-Analysis," EconStor Preprints 274156, ZBW - Leibniz Information Centre for Economics.
    9. Luca Grilli & Sergio Mariotti & Riccardo Marzano, 2024. "Artificial intelligence and shapeshifting capitalism," Journal of Evolutionary Economics, Springer, vol. 34(2), pages 303-318, April.
    10. 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).
    11. Kaizhi Yu & Yao Shi & Jiahan Feng, 2024. "The influence of robot applications on rural labor transfer," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    12. Belloc, Filippo & Burdin, Gabriel & Landini, Fabio, 2020. "Robots and Worker Voice: An Empirical Exploration," IZA Discussion Papers 13799, Institute of Labor Economics (IZA).
    13. Tao, Weiliang & Weng, Shimei & Chen, Xueli & ALHussan, Fawaz Baddar & Song, Malin, 2024. "Artificial intelligence-driven transformations in low-carbon energy structure: Evidence from China," Energy Economics, Elsevier, vol. 136(C).
    14. Xiaoyu Bian & Guangsu Zhou, 2024. "The effects of robots on internal migration: Evidence from China," Journal of Regional Science, Wiley Blackwell, vol. 64(3), pages 840-865, June.
    15. Ke-Liang Wang & Ting-Ting Sun & Ru-Yu Xu, 2023. "The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises," Economic Change and Restructuring, Springer, vol. 56(2), pages 1113-1146, April.
    16. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    17. Cao, Yuqiang & Hu, Yong & Liu, Qian & Lu, Meiting & Shan, Yaowen, 2023. "Job creation or disruption? Unraveling the effects of smart city construction on corporate employment in China," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    18. Filippo Belloc & Gabriel Burdin & Fabio Landini, 2023. "Advanced Technologies and Worker Voice," Economica, London School of Economics and Political Science, vol. 90(357), pages 1-38, January.
    19. Wang, Hua & Liao, Lingtao & Wu, Ji (George), 2023. "Robot adoption and firm's capacity utilization: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    20. Dongyu Zhao & Man Wang, 2024. "Smart Manufacturing Promotes High-Quality Development of Enterprises in China," Sustainability, MDPI, vol. 16(23), pages 1-20, November.

    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:16:y:2024:i:15:p:6573-:d:1447355. 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.