IDEAS home Printed from https://ideas.repec.org/a/taf/rripxx/v29y2022i3p696-718.html
   My bibliography  Save this article

Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs

Author

Listed:
  • Peter Dauvergne

Abstract

Artificial intelligence (AI) is set to greatly enhance the productivity and efficiency of global supply chains over the next decade. Transnational corporations are hailing these gains as a ‘game changer’ for advancing environmental sustainability. Yet, looking through a political economy lens, it is clear that AI is not advancing sustainability nearly as much as industry leaders are claiming. As this article argues, the metrics and rhetoric of corporate social responsibility are exaggerating the benefits and obscuring the costs of AI. Productivity and efficiency gains in the middle sections of supply chains are rebounding into more production and consumption, doing far more to enhance the profitability of big business than the sustainability of the earth. At the same time, AI is accelerating natural resource extraction and the distancing of waste, casting dark shadows of harm across marginalized communities, fragile ecosystems, and future generations. The micro-level gains from AI, as this article exposes, are not going to add up to macro-level solutions for the negative environmental consequences of global supply chains, while portraying AI as a force of sustainability is legitimizing business as usual, reinforcing a narrative of corporate responsibility, obfuscating the need for greater state regulation, and empowering transnational corporations as global governors. These findings extend the theoretical understanding in the field of international political economy of the hidden dangers of relying on technology and corporate governance to resolve the deep unsustainability of the contemporary world order.

Suggested Citation

  • Peter Dauvergne, 2022. "Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs," Review of International Political Economy, Taylor & Francis Journals, vol. 29(3), pages 696-718, May.
  • Handle: RePEc:taf:rripxx:v:29:y:2022:i:3:p:696-718
    DOI: 10.1080/09692290.2020.1814381
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09692290.2020.1814381
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/09692290.2020.1814381?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shahzad, Umer & Ghaemi Asl, Mahdi & Panait, Mirela & Sarker, Tapan & Apostu, Simona Andreea, 2023. "Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets," Resources Policy, Elsevier, vol. 80(C).
    2. Mingyue Chen & Shuting Wang & Xiaowen Wang, 2024. "How Does Artificial Intelligence Impact Green Development? Evidence from China," Sustainability, MDPI, vol. 16(3), pages 1-23, February.
    3. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).

    More about this item

    Statistics

    Access and download statistics

    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:taf:rripxx:v:29:y:2022:i:3:p:696-718. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rrip20 .

    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.