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
Downloads: (external link)
https://figshare.utas.edu.au/ndownloader/files/46311484/1
Our link check indicates that this URL is bad, the error code is: 403 Forbidden (https://figshare.utas.edu.au/ndownloader/files/46311484/1 [302 Found]--> https://s3-ap-southeast-2.amazonaws.com/figshare-production-eu-utas-storage2718-ap-southeast-2/coversheet/46311484/1/202402_Vespignani_Smyth.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIARRFKZQ25CRVZALJA/20250331/ap-southeast-2/s3/aws4_request&X-Amz-Date=20250331T114645Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=c0d13349dff09bd09d7f6965bf67399f7a9ce6b4662ac8b519f20a0095b9c363)
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) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:tas:wpaper:25814770
Access Statistics for this paper
More papers in Working Papers from University of Tasmania, Tasmanian School of Business and Economics Contact information at EDIRC.
Bibliographic data for series maintained by Oscar Pavlov ().