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Artificial Intelligence Investments Reduce Risks to Critical Mineral Supply

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  • Joaquin Vespignani
  • Russell Smyth

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," CAMA Working Papers 2024-30, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2024-30
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2024-05/30_2024_vespignani_smyth.pdf
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    References listed on IDEAS

    as
    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.
    2. Alan Carruth & Andy Dickerson & Andrew Henley, 2000. "What do We Know About Investment Under Uncertainty?," Journal of Economic Surveys, Wiley Blackwell, vol. 14(2), pages 119-154, April.
    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).
    7. repec:bla:jecsur:v:14:y:2000:i:2:p:119-53 is not listed on IDEAS
    8. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
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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," CAMA Working Papers 2024-69, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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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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