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Artificial intelligence investments reduce risks to critical mineral supply

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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

  • Vespignani, Joaquin & Smyth, Russell, 2024. "Artificial intelligence investments reduce risks to critical mineral supply," Working Papers 2024-02, University of Tasmania, Tasmanian School of Business and Economics.
    • Handle: RePEc:tas:wpaper:25814770
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    References listed on IDEAS

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    1. Speirs, Jamie & McGlade, Christophe & Slade, Raphael, 2015. "Uncertainty in the availability of natural resources: Fossil fuels, critical metals and biomass," Energy Policy, Elsevier, vol. 87(C), pages 654-664.
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    3. Nwaila, Glen T. & Frimmel, Hartwig E. & Zhang, Steven E. & Bourdeau, Julie E. & Tolmay, Leon C.K. & Durrheim, Raymond J. & Ghorbani, Yousef, 2022. "The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach," Resources Policy, Elsevier, vol. 78(C).
    4. 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.
    5. Vincent Moreau & Piero Carlo Dos Reis & François Vuille, 2019. "Enough Metals? Resource Constraints to Supply a Fully Renewable Energy System," Resources, MDPI, vol. 8(1), pages 1-18, January.
    6. 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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    Cited by:

    1. Jamel Saadaoui & Russell Smyth & Joaquin Vespignani, 2024. "Ensuring the security of the clean energy transition: Examining the impact of geopolitical risk on the price of critical minerals," Monash Economics Working Papers 2024-19, Monash University, Department of Economics.

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    More about this item

    Keywords

    artificial intelligence; critical minerals; 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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