Artificial intelligence investments reduce risks to critical mineral supply

Joaquin Vespignani and Russell Smyth

No 2024-02, Working Papers from University of Tasmania, Tasmanian School of Business and Economics

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.

Keywords: artificial intelligence; critical minerals; risk premium (search for similar items in EconPapers)
JEL-codes: Q02 Q40 Q50 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published by the University of Tasmania. Discussion paper 2024-02

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Related works:
Journal Article: Artificial intelligence investments reduce risks to critical mineral supply (2024)
Working Paper: Artificial Intelligence Investments Reduce Risks to Critical Mineral Supply (2024)
Working Paper: Artificial intelligence investments reduce risks to critical mineral supply (2024)
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