Interplay between metal and oil markets in the renewable energy transition: An internal and external connectedness perspective

Öz

This study investigates the information spillover between critical metals and oil markets within the context of the global transition to renewable energy. Metals such as copper, aluminum, cobalt, nickel, and zinc are essential for renewable energy technologies, and their price movements are closely linked to energy markets. Using the connectedness decomposition approach, this study analyzes internal and external information flows between metal and oil markets. The findings reveal that copper and aluminum are the strongest information transmitters, while oil prices, particularly Brent and WTI, become more sensitive to metal markets during crisis periods. As the energy transition accelerates, critical metals are playing an increasingly influential role in commodity markets, shaping energy price dynamics. These results provide valuable insights for sustainable energy policies and risk management strategies, emphasizing the growing interdependence between energy and metal markets.

Anahtar Kelimeler

• Renewable energy
• Oil markets
• Metal prices
• Information spillover
• Connectedness analysis

Kaynakça

1. [1] Vidal, O., Goffé, B., & Arndt, N. (2013). Metals for a low-carbon society. Nature Geoscience, 6(11), 894–896. https://doi.org/10.1038/ngeo1993
2. [2] Achzet, B., & Helbig, C. (2013). How to evaluate raw material supply risks-an overview. Resources Policy, 38(4), 435–447. https://doi.org/10.1016/j.resourpol.2013.06.003
3. [3] Kilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99(3), 1053–1069. https://doi.org/10.1257/aer.99.3.1053
4. [4] Kilian, L., & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267–1287. https://doi.org/10.1111/j.1468-2354.2009.00568.x
5. [5] Sovacool, B. K., Ali, S. H., Bazilian, M., Radley, B., Nemery, B., Okatz, J., & Mulvaney, D. (2020). Sustainable minerals and metals for a low-carbon future. Science, 367(6473), 30–33. https://doi.org/10.1126/science.aaz6003
6. [6] Watari, T., Nansai, K., & Nakajima, K. (2021). Contraction and convergence of in-use metal stocks to meet climate goals. Global Environmental Change, 69(October 2020), 102284. https://doi.org/10.1016/j.gloenvcha.2021.102284
7. [7] Calderon, J. L., Bazilian, M., Sovacool, B., Hund, K., Jowitt, S. M., Nguyen, T. P., Månberger, A., Kah, M., Greene, S., Galeazzi, C., Awuah-Offei, K., Moats, M., Tilton, J., & Kukoda, S. (2020). Reviewing the material and metal security of low-carbon energy transitions. Renewable and Sustainable Energy Reviews, 124(February). https://doi.org/10.1016/j.rser.2020.109789
8. [8] Zakeri, B., Paulavets, K., Barreto-Gomez, L., Echeverri, L. G., Pachauri, S., Boza-Kiss, B., Zimm, C., Rogelj, J., Creutzig, F., Ürge-Vorsatz, D., Victor, D. G., Bazilian, M. D., Fritz, S., Gielen, D., McCollum, D. L., Srivastava, L., Hunt, J. D., & Pouya, S. (2022). Pandemic, War, and Global Energy Transitions. Energies, 15(17), 1–23. https://doi.org/10.3390/en15176114

9. [9] Kilian, L., & Zhou, X. (2018). Modeling fluctuations in the global demand for commodities. Journal of International Money and Finance, 88, 54–78. https://doi.org/10.1016/j.jimonfin.2018.07.001
10. [10] Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54–74. https://doi.org/10.2469/faj.v68.n6.5
11. [11] Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
12. [12] Islam, M. S., Islam, M. M., Ahmed, F., Rehman, A. ur, Alam, M. F., & Islam, M. A. (2025). Exploring Nexus Between Oil Price Shocks and Copper Production: Analysing the Role of Mineral Prices and Geopolitical Factors in Saudi Arabia. Computational Economics, 0123456789. https://doi.org/10.1007/s10614-025-10916-1
13. [13] Nwonye, N. G., Onuselogu, O. C. O., Anisiuba, C. A., Ezeaku, H. C., & Egbo, O. P. (2023). Dynamics of green metal price volatility in times of geopolitical tensions: Effects of oil price shocks and carbon emissions futures. Journal of Cleaner Production, 412(April), 137383. https://doi.org/10.1016/j.jclepro.2023.137383
14. [14] Aydoğdu, A., & Uyar, U. (2025). Enerji ve Kıymetli Metal Piyasaları Arasında Yayılım Etkisi: Wavelet Uyum Analizine Dayalı DCC-GARCH Yaklaşımı. Sosyoekonomi, 33(64), 557-585. https://doi.org/10.17233/sosyoekonomi.2025.02.24
15. [15] Büyükşahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38–70. https://doi.org/10.1016/j.jimonfin.2013.08.004
16. [16] Goutte, S., & Mhadhbi, M. (2024). Analyzing Crisis Dynamics: How metal-energy Markets influence green electricity investments. Energy Economics, 134(May), 107614. https://doi.org/10.1016/j.eneco.2024.107614
17. [17] Charteris, A., Obojska, L., Szczygielski, J. J., & Brzeszczyński, J. (2025). Energy market connectedness: A tale of two crises. Energy Economics, 108787.
18. [18] Olivetti, E. A., & Cullen, J. M. (2018). Toward a sustainable materials system. Science, 360(6396), 1396–1398. https://doi.org/10.1126/science.aat6821
19. [19] Nansai, K., Tohno, S., Chatani, S., Kanemoto, K., Kagawa, S., Kondo, Y., Takayanagi, W., & Lenzen, M. (2021). Consumption in the G20 nations causes particulate air pollution resulting in two million premature deaths annually. Nature Communications, 12(1), 1–6. https://doi.org/10.1038/s41467-021-26348-y
20. [20] Saadaoui, J., Smyth, R., & Vespignani, J. (2025). Ensuring the security of the clean energy transition: Examining the impact of geopolitical risk on the price of critical minerals. Energy Economics, 142, 108195.
21. [21] Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63-71.
22. [22] Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438.
23. [23] Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
24. [24] Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
25. [25] Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
26. [26] Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
27. [27] D’AGOSTINO, R. B. (1970). Transformation to normality of the null distribution of g 1. Biometrika, 57(3), 679–681. https://doi.org/10.1093/biomet/57.3.679
28. [28] Anscombe, F. J., & Glynn, W. J. (1983). Distribution of the kurtosis statistic b2 for normal samples. Biometrika, 70(1), 227–234. https://doi.org/10.1093/biomet/70.1.227
29. [29] Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
30. [30] Elliott, B. Y. G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4), 813–836.
31. [31] Fisher, T. J., & Gallagher, C. M. (2012). New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association, 107(498), 777–787. https://doi.org/10.1080/01621459.2012.688465
32. [32] Baumeister, C., & Hamilton, J. D. (2019). Structural interpretation of vector autoregressions with incomplete identifcation: Revisiting the role of oil supply and demand shocks. American Economic Review, 109(5), 1873–1910. https://doi.org/10.1257/aer.20151569
33. [33] Geman, H. (2019). Commodities and Commodity Derivatives: Modelling and Pricing for Agriculturals, Metals and Energy (2nd ed.). Wiley Finance.
34. [34] Ajmi, A. N., Hammoudeh, S., Nguyen, D. K., & Sato, J. R. (2015). On the relationships between CO2 emissions, energy consumption and income: The importance of time variation. Energy Economics, 49, 629–638. https://doi.org/10.1016/j.eneco.2015.02.007