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AI, FinTech and clean minerals: A wavelet analysis and quantile value-at-risk investigation

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  • Karim, Sitara
  • Husain, Afzol
  • Lim, Weng Marc
  • Chan, Ling-Foon
  • Tehseen, Shehnaz

Abstract

The increasing demand for clean minerals and the rise of new-age technologies present significant challenges and opportunities for sustainable development. This study aims to explore how artificial intelligence (AI) and financial technology (FinTech) affect the exploitation of clean minerals in the pursuit of sustainable development. Employing wavelet analysis and quantile value-at-risk (QVaR), we provide a comprehensive analysis of the dynamic relationships, risks, and returns associated between clean minerals and these technological innovations. Our wavelet findings indicate that there are strong co-movements for aluminum, copper, and zinc with various clean and technological indices while nickel shows weak co-movements. Our QVaR results reveal significant differences in risk and return profiles across indices, underscoring the high-risk, high-reward nature of clean and technological sectors. These insights underscore the importance of incorporating AI and FinTech into regulatory frameworks and industry practices, advocating for a collaborative approach to leverage these technologies to influence the exploitation of clean minerals toward greater sustainability. Therefore, the novelty of this study lies in its comprehensive methodological approach to scrutinize the linkages between clean minerals and new-age technologies, with significant multi-stakeholder implications for policy and practice, aligning with the United Nations Sustainable Development Goals.

Suggested Citation

  • Karim, Sitara & Husain, Afzol & Lim, Weng Marc & Chan, Ling-Foon & Tehseen, Shehnaz, 2024. "AI, FinTech and clean minerals: A wavelet analysis and quantile value-at-risk investigation," Resources Policy, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:jrpoli:v:99:y:2024:i:c:s0301420724006871
    DOI: 10.1016/j.resourpol.2024.105320
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