Skip to main content
Springer Nature Link
Log in

Financial turbulence and decarbonisation: evidence from energy transition materials

  • Original Paper
  • Published:
Mineral Economics Aims and scope Submit manuscript

Abstract

As the global push for clean energy intensifies, the role of energy transition materials (ETMs) such as copper, nickel, aluminium, zinc, iron ore, tin, and lead has grown increasingly critical. However, their vulnerability to financial turbulence raises pressing concerns about supply security and price stability. This study investigates the dynamic and frequency-dependent relationships between financial stress and the price movements of these key transition metals, using quarterly data from 1991Q1 to 2023Q4. Employing a multi-layered wavelet framework, which includes Wavelet Power Spectrum, Wavelet Coherence, Partial Wavelet Coherence, and Vector Wavelet Coherence, we uncover distinct patterns of comovement and causality across various time horizons. Results reveal that copper and nickel exhibit persistent high-power zones and strong coherence with financial stress, especially during major economic crises. At the same time, metals like tin and lead demonstrate more moderate or episodic linkages. Partial coherence estimates confirm these associations even after accounting for geopolitical risks, economic policy uncertainty, and shifts in climate policy. Robustness checks via VWC further validate the findings. These insights highlight the differentiated sensitivities of ETMs to macro-financial shocks and emphasise the urgency of tailored risk mitigation strategies to safeguard the stability of green supply chains in an increasingly volatile global financial system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from €37.37 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Norway)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

Data availability

No datasets were generated or analysed during the current study.

Notes

  1. The availability of FSI data dictates quarterly frequency.

References

  • Ahir H, Dell’Ariccia G, Furceri D, Papageorgiou C, Qi H (2023a) Financial stress and economic activity: evidence from a new worldwide index. International monetary fund

  • Ahir H, Bloom N, Furceri D (2023b) Global economic uncertainty remains elevated, weighing on growth. IMF Blog

  • Ahmed WM, Sleem MA (2022) Time-frequency moment interdependence of equity, oil, and gold markets during the COVID-19 pandemic. Cogent Econ Finance 10(1):2085292

    Article  Google Scholar 

  • Akadiri SS, Özkan O (2025) Energy markets, geopolitical risks, and global trade: a high-stakes tug of war. Geol J

  • Akadiri SS, Ozkan O, Alola AA (2025) Investigating the determinants of load capacity factor in Nigeria: an asymmetric quantile approach on urbanisation, economic growth, FDI, and resource dependency. Resour Policy 104:105586. https://doi.org/10.1016/j.resourpol.2025.105586

    Article  Google Scholar 

  • Aloui C, Hammoudeh S, Ben Hamida H (2015) Global factors driving structural changes in the comovement between sharia stocks and Sukuk in the Gulf Cooperation Council countries. N Am J Econ Finance 31:311–329

    Article  Google Scholar 

  • Apergis N, Bonato M, Gupta R, Kyei C (2018) Do geopolitical risks predict stock returns and volatility of leading defence companies? Evidence from a nonparametric approach. Def Peace Econ 29(6):684–696

    Google Scholar 

  • Armah M, Amewu G, Bossman A (2022) Time-frequency analysis of financial stress and global commodities prices: insights from wavelet-based approaches. Cogent Econ Finance 10(1):2114161

    Article  Google Scholar 

  • Baffes J, Kabundi AN, Nagle PSO, Ohnsorge F (2018) The role of major emerging markets in global commodity demand. World bank policy research working paper, (8495)

  • Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty*. Q J Econ 131(4):1593–1636. https://doi.org/10.1093/qje/qjw024

    Article  Google Scholar 

  • Baker SR, Bloom N, Davis SJ, Terry SJ (2020) COVID-induced economic uncertainty (No. w26983). National Bureau of Economic Research

  • Balcilar M, Gupta R, Kyei C, Wohar ME (2016a) Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Econ Rev 27(2):229–250

    Article  Google Scholar 

  • Balcilar M, Gupta R, Pierdzioch C (2016b) Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resour Policy 49:74–80

    Article  Google Scholar 

  • Batten S, Sowerbutts R, Tanaka M (2020) Climate change: macroeconomic impact and implications for monetary policy. Ecological, societal, and technological risks and the financial sector, 13–38

  • Büyükşahin B, Robe MA (2014) Speculators, commodities and cross-market linkages. J Int Money Finance 42:38–70

    Article  Google Scholar 

  • Caldara D, Iacoviello M (2022) Measuring geopolitical risk. Am Econ Rev 112(4):1194–1225. https://doi.org/10.1257/aer.20191823

    Article  Google Scholar 

  • Cevik EI, Dibooglu S, Kenc T (2016) Financial stress and economic activity in some emerging Asian economies. Res Int Bus Finance 36:127–139

    Article  Google Scholar 

  • Conraria LA, Soares MJ (2011) The continuous wavelet transform: a primer

  • Corbet S, Goodell JW, Günay S (2020) Comovements and spillovers of oil and renewable firms under extreme conditions: new evidence from negative WTI prices during COVID-19. Energy Econ 92:104978

    Article  Google Scholar 

  • Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press

  • Gao J, Zhang L (2024) Climate policy uncertainty and corporate investment: evidence from the US tourism and hospitality sector. J Travel Res 63(2):517–530

    Article  Google Scholar 

  • Gavriilidis K (2021) Measuring climate policy uncertainty (SSRN Scholarly Paper 3847388). https://doi.org/10.2139/ssrn.3847388

  • Gouhier T, Grinsted A, Simko V (2024) R package biwavelet: conduct univariate and bivariate wavelet analyses. (Version 0.20.22), https://github.com/tgouhier/biwavelet

  • Gozgor G, Lau CKM, Ma J (2022) Effects of economic shocks on human behaviour, mental life and the environment: implications for the post-COVID-19 crisis era. Front Psychol 13:922875

    Article  Google Scholar 

  • Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11(5/6):561–566. https://doi.org/10.5194/npg-11-561-2004

    Article  Google Scholar 

  • Helbig C, Bradshaw AM, Wietschel L, Thorenz A, Tuma A (2018) Supply risks associated with lithium-ion battery materials. J Clean Prod 172:274–286

    Article  Google Scholar 

  • Hu Y, Bai W, Farrukh M, Koo CK (2023) How does environmental policy uncertainty influence corporate green investments? Technol Forecast Soc Chang 189:122330

    Article  Google Scholar 

  • International Energy Agency (IEA) (2021) The role of critical minerals in clean energy transitions. Retrieved from https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energy-transitions

  • Jacks DS, O’Rourke KH, Williamson JG (2011) Commodity price volatility and world market integration since 1700. Rev Econ Stat 93(3):800–813

    Article  Google Scholar 

  • Kang W, Ratti RA (2013) Oil shocks, policy uncertainty and stock market return. J Int Financ Mark Inst Money 26:305–318

    Article  Google Scholar 

  • Korsah D, Amewu G, Osei Achampong K (2024) The impact of geopolitical risks, financial stress, and economic policy uncertainty on the returns and volatilities of African stock markets: wavelet coherence analysis. J Humanit Appl Social Sci 6(5):450–470

    Google Scholar 

  • Liu Y, Liang S, X., Weisberg RH (2007) Rectification of the bias in the wavelet power spectrum. J Atmos Ocean Technol 24(12):2093–2102. https://doi.org/10.1175/2007JTECHO511.1

    Article  Google Scholar 

  • Mensi W, Shafiullah M, Vo XV, Kang SH (2022) Spillovers and connectedness between green bond and stock markets in bearish and bullish market scenarios. Finance Res Lett 49:103120

    Article  Google Scholar 

  • Mihanović H, Orlić M, Pasarić Z (2009) Diurnal thermocline oscillations driven by tidal flow around an island in the middle Adriatic. J Mar Syst 78:S157–S168. https://doi.org/10.1016/j.jmarsys.2009.01.021

    Article  Google Scholar 

  • Ng E, W K, C J, Chan L (2012) Geophysical applications of partial wavelet coherence and multiple wavelet coherence. https://doi.org/10.1175/JTECH-D-12-00056.1

  • Organisation for Economic Cooperation and Development (OECD) (2017) Green growth indicators 2017. OECD Publishing. https://doi.org/10.1787/9789264268586-en

    Article  Google Scholar 

  • Oygur T, Unal G (2021a) Vector wavelet coherence for multiple time series. Int J Dynamics Control 9(2):403–409. https://doi.org/10.1007/s40435-020-00706-y

    Article  Google Scholar 

  • Oygur T, Unal G (2021b) Vectorwavelet: vector wavelet coherence for multiple time series. R package version 0.1.0. https://github.com/toygur/vectorwavelet

  • Pastor L, Veronesi P (2012) Uncertainty about government policy and stock prices. J Finance 67(4):1219–1264

    Article  Google Scholar 

  • Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press

  • Rua A (2010) Measuring comovement in the time–frequency space. J Macroecon 32(2):685–691

    Article  Google Scholar 

  • Saint Akadiri S, Ozkan O (2025) Critical minerals and structural oil shocks: evidence from wavelet cross-quantile correlation. Resour Policy 103:105570

    Article  Google Scholar 

  • Sharif A, Aloui C, Yarovaya L (2020) COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: fresh evidence from the wavelet-based approach. Int Rev Financial Anal 70:101496

    Article  Google Scholar 

  • Sieradzki R, Kwiatek S (2025) Forecasting price volatility of non-ferrous metals: a comparison of econometric, machine learning, and AI models. Machine learning and AI models (March 04, 2025)

  • Sovacool BK, Ali SH, Bazilian M, Radley B, Nemery B, Okatz J, Mulvaney D (2020a) Sustainable minerals and metals for a low-carbon future. Science 367(6473):30–33

    Article  Google Scholar 

  • Sovacool BK, Hook A, Martiskainen M, Brock A, Turnheim B (2020b) The decarbonisation divide: contextualising landscapes of low-carbon exploitation and toxicity in Africa. Glob Environ Change 60:102028

    Article  Google Scholar 

  • Tang K, Xiong W (2012) Index investment and the financialisation of commodities. Financial Anal J 68(6):54–74

    Article  Google Scholar 

  • Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79(1):61–78. https://doi.org/10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2

    Article  Google Scholar 

  • Umar Z, Gubareva M, Teplova T, Tran DK (2022) COVID-19 impact on NFTs and major asset classes interrelations: insights from the wavelet coherence analysis. Finance Res Lett 47:102725

    Article  Google Scholar 

  • World Bank (2021) Global economic prospects: commodity markets outlook. Washington, DC

  • Yousfi M, Bouzgarrou H (2024) Geopolitical risk, economic policy uncertainty, and dynamic connectedness between clean energy, conventional energy, and food markets. Environ Sci Pollut Res 31(3):4925–4945

    Article  Google Scholar 

  • Zhang H, Wang X, Tang J, Guo Y (2022) The impact of international rare Earth trade competition on global value chain upgrading from an industrial chain perspective. Ecol Econ 198:107472

    Article  Google Scholar 

  • Zhu H, Huang R, Wang N, Hau L (2020) Does economic policy uncertainty matter for the commodity market in china? Evidence from quantile regression. Appl Econ 52(21):2292–2308

    Article  Google Scholar 

Download references

Acknowledgements

Not Applicable.

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Institute of Graduate Studies and Research, Cyprus International University, Northern Cyprus, TR-10 Mersin, Nicosia, Turkey

    Seyi Saint Akadiri

  2. Tokat Gaziosmanpasa University, Tokat, Turkey

    Oktay Ozkan

  3. ARUCAD Research Centre, Arkin University of Creative Arts and Design, Northern Cyprus, Mersin 10, Kyrenia, Turkey

    Oktay Ozkan

Authors
  1. Seyi Saint Akadiri
    View author publications

    Search author on:PubMed Google Scholar

  2. Oktay Ozkan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

The authors jointly contributed to and supervised this study.

Corresponding author

Correspondence to Seyi Saint Akadiri.

Ethics declarations

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Ethical approval

The Authors mentioned in the manuscript have agreed to authorship, read, and approved the manuscript, and provided consent for submission and subsequent publication of the manuscript.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Appendix

Appendix

Table 2 Details of the dataset
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akadiri, S., Ozkan, O. Financial turbulence and decarbonisation: evidence from energy transition materials. Miner Econ (2025). https://doi.org/10.1007/s13563-025-00559-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1007/s13563-025-00559-x

Keywords

Profiles

  1. Seyi Saint Akadiri View author profile
  2. Oktay Ozkan View author profile