University of Tasmania
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Artificial intelligence investments reduce risks to critical mineral supply

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posted on 2024-05-15, 04:25 authored by Joaquin VespignaniJoaquin Vespignani

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.

History

Series

Discussion Paper Series N 2024-02

Pagination

31

Department/School

Finance

Publisher

University of Tasmania

Place of publication

Hobart

Rights statement

Copyright 2024 University of Tasmania

Notes

JEL Classification: Q02, Q40, Q50

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    Tasmanian School of Business and Economics

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