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Artificial intelligence investments reduce risks to critical mineral supply

Author

Listed:
  • Joaquin Vespignani

    (Tasmanian School of Business and Economics, University of Tasmania, Australia)

  • Russell Smyth

    (Department of Economics, Monash University, Clayton, Australia)

Abstract

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and nontechnical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself through shortening the duration of mining projects and the required rate of investment through reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

Suggested Citation

  • Joaquin Vespignani & Russell Smyth, 2024. "Artificial intelligence investments reduce risks to critical mineral supply," Monash Economics Working Papers 2024-08, Monash University, Department of Economics.
    • Handle: RePEc:mos:moswps:2024-08
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    References listed on IDEAS

    as
    1. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
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    5. Onifade, Moshood & Adebisi, John Adetunji & Shivute, Amtenge Penda & Genc, Bekir, 2023. "Challenges and applications of digital technology in the mineral industry," Resources Policy, Elsevier, vol. 85(PB).
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Critical Minerals; Artificial Intelligence; Risk Premium;
    All these keywords.

    JEL classification:

    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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