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

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  • 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
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    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. 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).
    4. 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).

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