Time-Frequency Dynamics and Cross-Market Integration among ‎Metal, Energy, Carbon, and AI Sectors

  • Authors

    • Monika Research Scholar at Mittal School of Business, Lovely Professional University, Phagwara, ‎Punjab, India
    • Dr. Rupinder Katoch Professor at Mittal School of Business, Lovely Professional University, Phagwara, Punjab, ‎India
    https://doi.org/10.14419/w3dx2d59

    Received date: November 24, 2025

    Accepted date: December 4, 2025

    Published date: December 17, 2025

  • Co-Movement; Wavelet Coherence; Metal Market; Energy Market; Carbon Market; Artificial ‎Intelligence; Financial Integration; Sustainability

  • Abstract

    This study examines the dynamic co-movement and interconnectedness of the Metal, Energy, ‎Carbon, and Artificial Intelligence (AI) markets to understand their evolving interactions in the ‎context of technological progress and sustainability transitions. The study employs wavelet coherence analysis to investigate the interactions between these markets across various time scales, encompassing both short-term variations and long-term trends.  The results ‎demonstrate substantial long-term coherence, especially between Energy and Carbon, as well ‎as between Carbon and AI, signifying robust and enduring interdependencies.  Medium-term ‎correlations demonstrate modest variability, likely influenced by market restrictions and ‎innovation cycles, whereas short-term linkages seem more unstable, reflecting acute shocks ‎and developing technologies.  This paper presents new empirical evidence about the increasing ‎integration of financial and resource-based markets, highlighting the impact of AI ‎advancements and environmental issues on conventional sectors. The study enhances ‎comprehension of cross-market behavior, providing significant insights for investors, ‎policymakers, and researchers investigating market predictability, risk management, and ‎sustainable economic planning‎.

  • References

    1. Adewuyi, A.O., Adeboye, O.S., and Tiwari, A.K. (2024) ‘A New Look at the Connectedness Between Energy and Metal Markets Using a Novel Approach’, American Business Review. Edited by E.J.A. Abakah, 27(1), pp. 116–166. Available at: https://doi.org/10.37625/abr.27.1.116-166.
    2. Balcilar, M. et al. (2025) ‘Technological innovations fuel carbon prices and transform environmental management across Europe’, Journal of Envi-ronmental Management, 373, p. 123663. Available at: https://doi.org/10.1016/j.jenvman.2024.123663.
    3. Balcilar, M., Usman, O. and Agan, B. (2024) ‘On the connectedness of commodity markets: A critical and selective survey of empirical studies and bibliometric analysis’, Journal of Economic Surveys, 38(1), pp. 97–136. Available at: https://doi.org/10.1111/joes.12541.
    4. Ghorbani, Y. et al. (2024) ‘The strategic role of lithium in the green energy transition: Towards an OPEC-style framework for green energy-mineral exporting countries (GEMEC)’, Resources Policy, 90, p. 104737. Available at: https://doi.org/10.1016/j.resourpol.2024.104737.
    5. Gubareva, M., Shafiullah, M. and Teplova, T. (2025) ‘Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets’, Energy Economics, 141, p. 108085. Available at: https://doi.org/10.1016/j.eneco.2024.108085.
    6. Hossain, M.R. et al. (2024) ‘Empowering energy transition: Green innovation, digital finance, and the path to sustainable prosperity through green finance initiatives’, Energy Economics, 136, p. 107736. Available at: https://doi.org/10.1016/j.eneco.2024.107736.
    7. Jiang, W. and Chen, Y. (2022a) ‘The time-frequency connectedness among carbon, traditional/new energy and material markets of China in pre- and post-COVID-19 outbreak periods’, Energy, 246, p. 123320. Available at: https://doi.org/10.1016/j.energy.2022.123320.
    8. Jiang, W. and Chen, Y. (2022b) ‘The time-frequency connectedness among metal, energy and carbon markets pre and during COVID-19 outbreak’, Resources Policy, 77, p. 102763. Available at: https://doi.org/10.1016/j.resourpol.2022.102763.
    9. Li, D., Wang, H. and Wang, J. (2024) ‘Artificial Intelligence and Technological Innovation: Evidence from China’s Strategic Emerging Industries’, Sustainability, 16(16), p. 7226. Available at: https://doi.org/10.3390/su16167226.
    10. Luo, Y. and Hong, S. (2025) ‘Multidimensional Linkages Among Carbon, Energy and Metals Markets: Evidence from Spillover Effects and Co-Movements’. SSRN. Available at: https://doi.org/10.2139/ssrn.5279362.
    11. Marín-Rodríguez, N.J., González-Ruiz, J.D. and Botero Botero, S. (2022) ‘Dynamic Co-Movements among Oil Prices and Financial Assets: A Scien-tometric Analysis’, Sustainability, 14(19), p. 12796. Available at: https://doi.org/10.3390/su141912796.
    12. Onyeaka, H. et al. (2023) ‘Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review’, Sustainability, 15(13), p. 10482. Available at: https://doi.org/10.3390/su151310482.
    13. Raggad, B. and Bouri, E. (2025) ‘Artificial intelligence and clean/dirty energy markets: tail-based pairwise connectedness and portfolio implications’, Future Business Journal, 11(1), p. 29. Available at: https://doi.org/10.1186/s43093-025-00451-8.
    14. Siddik, A.B. et al. (2025) ‘Artificial intelligence as a catalyst for sustainable tourism growth and economic cycles’, Technological Forecasting and Social Change, 210, p. 123875. Available at: https://doi.org/10.1016/j.techfore.2024.123875.
    15. Wang, P. et al. (2022) ‘Carbon neutrality needs a circular metal-energy nexus’, Fundamental Research, 2(3), pp. 392–395. Available at: https://doi.org/10.1016/j.fmre.2022.02.003.
    16. Xiang, D. et al. (2024) ‘Would really long-only climate-transition strategies in commodities bring lower market risk for sustainable markets in the long run? The Islamic sustainable market versus the global sustainability leaders’, Economic Analysis and Policy, 82, pp. 1271–1295. Available at: https://doi.org/10.1016/j.eap.2024.05.012.
    17. Xu, Y., Shao, X. and Tanasescu, C. (2024) ‘How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers’, Energy Economics, 132, p. 107477. Available at: https://doi.org/10.1016/j.eneco.2024.107477.
    18. Yang, Y. (2024) ‘Co-movement of commodity futures and stock markets’, Highlights in Business, Economics and Management, 24, pp. 2608–2617. Available at: https://doi.org/10.54097/gf8fqk26.

  • Downloads

  • How to Cite

    Monika, & Katoch , D. R. . (2025). Time-Frequency Dynamics and Cross-Market Integration among ‎Metal, Energy, Carbon, and AI Sectors. International Journal of Accounting and Economics Studies, 12(8), 513-529. https://doi.org/10.14419/w3dx2d59