Worldwide Nickel Ore Trade, Its Stability and the Characteristics: A Fresh Policy Analysis

Authors

  • Algimantas Laurinavicius Department of Finance, Faculty of Economics and Business Administration, Vilnius University, Vilnius, Lithuania Author
  • Asma Salman College of Business Administration, American University in the Emirates Author
  • Mohamed Elsayed Abdelsalam Ghanem PhD Candidate in Finance, Universitat Oberta de Catalunya Author
  • Antanas Laurinavicius Department of Finance, Faculty of Economics and Business Administration, Vilnius University, Vilnius, Lithuania Author
  • Mohammed Ahmar Uddin Department of Finance and Economics, College of Commerce and Business Administration, Dhofar University, Salalah, Dhofar, Oman Corresponding Author

DOI:

https://doi.org/10.47654/v29y2025i3p85-118

Keywords:

Nickel ore trade, Network analysis, Supply disruption, Scale-free network, Trade stability

Abstract

Purpose: The study is conceptualized to examine structural characteristics and the strength of nickel ore network trading from 2011 to 2025, and focuses on studying major nations' involvement and their risk potential for disruptions in supply.

Design/methodology/approach: Network analysis is utilized to examine the topology of the nickel ore trade network and recognize influential players. Robustness simulations are directed to evaluate the influence of targeted and random disruptions on network stability, with emphasis on vulnerabilities associated with critical nodes.

Findings: The findings reveal that the nickel ore trade network shows a scale-free structure, where a few dominant states exercise disproportionate influence on the trade flows. Simulation findings confirm that disruptions among these key players abruptly decrease network stability, escalate systemic risks, and endanger international supply chains.

Research limitations/implications: The analysis is constrained by data availability and doesn't encompass informal networks of trade or future policy changes that could transform network structure.

Practical implications: Findings provide actionable policy recommendations for governments and industry stakeholders to design strategies that enhance resilience, diversify trade routes, and ensure a sustainable nickel supply.

Originality/value: The originality of this study lies in the application of network robustness simulations to the global nickel ore trade, offering a novel, evidence-based assessment of systemic risks from targeted disruptions—a dimension underexplored in prior literature.

Relevance to Decision Sciences: This research contributes to the field of Decision Sciences by providing a quantitative framework that supports policymakers and corporate strategists in evaluating supply chain vulnerabilities, anticipating risks, and making informed decisions regarding resource security and international trade policy.

References

Ali, W., Gohar, R., Chang, B. H., & Wong, W. K. (2022). Revisiting the impacts of globalization, renewable energy consumption, and economic growth on environmental quality in South Asia. Advances in Decision Sciences, 26(3), 78-98.

Bagadeem, S., Gohar, R., Wong, W. K., Salman, A., & Chang, B. H. (2024). Nexus between foreign direct investment, trade openness, and carbon emissions: fresh insights using innovative methodologies. Cogent Economics & Finance, 12(1), 2295721.

Barigozzi, M., Fagiolo, G., & Garlaschelli, D. (2010). Multinetwork of international trade: A commodity-specific analysis. Physical Review E, 81(4), 046104.

Chang, B. H. (2020). Oil prices and E7 stock prices: an asymmetric evidence using multiple threshold nonlinear ARDL model. Environmental Science and Pollution Research, 27(35), 44183-44194.

Chang, B. H., Auxilia, P. M., Kalra, A., Wong, W. K., & Uddin, M. A. (2023). Greenhouse Gas Emissions and the Rising Effects of Renewable Energy Consumption and Climate Risk Development Finance: Evidence from BRICS Countries. Annals of Financial Economics, 2350007.

Chang, B. H., Channa, K. A., Uche, E., Khalaf, O. I., & Ali, O. W. (2022). Analyzing the impacts of terrorism on innovation activity: A cross country empirical study. Advances in Decision Sciences, 26(Special), 124-161.

Cheng, Y., Hui, Y., Liu, S., & Wong, W. K. (2022). Could significant regression be treated as insignificant: An anomaly in statistics?. Communications in Statistics: Case Studies, Data Analysis and Applications, 8(1), 133-151.

Cheng, Y., Hui, Y., McAleer, M., & Wong, W. K. (2021). Spurious relationships for nearly non-stationary series. Journal of Risk and Financial Management, 14(8), 366.

Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661-703.

Ding, Y., Zhang, M., Chen, S., & Nie, R. (2020). Assessing the resilience of China’s natural gas importation under network disruptions. Energy, 211, 118459.

Dong, D., Gao, X., Sun, X., & Liu, X. (2018). Factors affecting the formation of copper international trade community: Based on resource dependence and network theory. Resources Policy, 57, 167-185.

Dong, G., Qing, T., Du, R., Wang, C., Li, R., Wang, M., ... & Stanley, H. E. (2020). Complex network approach for the structural optimization of global crude oil trade system. Journal of Cleaner Production, 251, 119366.

Elshkaki, A., Reck, B. K., & Graedel, T. E. (2017). Anthropogenic nickel supply, demand, and associated energy and water use. Resources, Conservation and Recycling, 125, 300-307.

Fagiolo, G., Reyes, J., & Schiavo, S. (2009). World-trade web: Topological properties, dynamics, and evolution. Physical Review E, 79(3), 036115.

Gohar, R., Bagadeem, S., Chang, B. H., & Zong, M. (2022a). Do the income and price changes affect consumption in the emerging 7 countries? Empirical evidence using quantile ARDL model. Annals of Financial Economics, 17(04), 2250024.

Gohar, R., Bhatty, K., Osman, M., Wong, W. K., & Chang, B. H. (2022b). Oil prices and sectorial stock indices of Pakistan: Empirical evidence using bootstrap ARDL model. Advances in Decision Sciences, 26(4), 1-27.

Gohar, R., Chang, B. H., Uche, E., Uddin, M. A., & Kalra, A. (2023a). Nexus between energy consumption, climate risk development finance and GHG emissions. International Journal of Financial Engineering, 2350025.

Gohar, R., Osman, M., Uche, E., Auxilia, P. M., & Chang, B. H. (2022c). The economic policy uncertainty extreme dynamics and its effect on the exchange rate. Global Economy Journal, 22(03), 2350006.

Gohar, R., Salman, A., Uche, E., Derindag, O. F., & Chang, B. H. (2023b). Does US infectious disease equity market volatility index predict G7 stock returns? Evidence beyond symmetry. Annals of Financial Economics, 18(02), 2250028.

Gong, X., Chang, B. H., Chen, X., & Zhong, K. (2023). Asymmetric Effects of Exchange Rates on Energy Demand in E7 Countries: New Evidence from Multiple Thresholds Nonlinear ARDL Model. Romanian Journal of Economic Forecasting, 26(2), 125.

Hashmi, S. M., & Chang, B. H. (2021). Asymmetric effect of macroeconomic variables on the emerging stock indices: A quantile ARDL approach. International Journal of Finance & Economics

Hashmi, S. M., Chang, B. H., Huang, L., & Uche, E. (2022). Revisiting the relationship between oil prices, exchange rate, and stock prices: An application of quantile ARDL model. Resources Policy, 75, 102543.

Hashmi, S. M., Chang, B. H., & Rong, L. (2021b). Asymmetric effect of COVID-19 pandemic on E7 stock indices: Evidence from quantile-on-quantile regression approach. Research in International Business and Finance, 58, 101485.

Hashmi, S. M., Chang, B. H., & Shahbaz, M. (2021a). Asymmetric effect of exchange rate volatility on India's cross‐border trade: Evidence from global financial crisis and multiple threshold nonlinear autoregressive distributed lag model. Australian Economic Papers, 60(1), 64-97.

Hou, W., Liu, H., Wang, H., & Wu, F. (2018). Structure and patterns of the international rare earths trade: A complex network analysis. Resources Policy, 55, 133-142.

Hui, Y., Wong, W. K., Bai, Z., & Zhu, Z. Z. (2017). A new nonlinearity test to circumvent the limitation of Volterra expansion with application. Journal of the Korean Statistical Society, 46, 365-374.

Imane, E., Chang, B. H., Elsherazy, T. A., Wong, W. K., & Uddin, M. A. (2023). The External Exchange Rate Volatility Influence on The Trade Flows: Evidence from Nonlinear ARDL Model. Advances in Decision Sciences, 27(2), 75-98.

Ji, Q., Zhang, H. Y., & Fan, Y. (2014). Identification of global oil trade patterns: An empirical research based on complex network theory. Energy Conversion and Management, 85, 856-865.

Jin, X., Chang, B. H., Han, C., & Uddin, M. A. (2024). The tail connectedness among conventional, religious, and sustainable investments: An empirical evidence from neural network quantile regression approach. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.2949

Liu, L., Cao, Z., Liu, X., Shi, L., Cheng, S., & Liu, G. (2020). Oil security revisited: An assessment based on complex network analysis. Energy, 194, 116793.

Lu, M., Chang, B. H., Salman, A., Razzaq, M. G. A., & Uddin, M. A. (2023). Time varying connectedness between foreign exchange markets and crude oil futures prices. Resources Policy, 86, 104128.

Maydybura, A., Gohar, R., Salman, A., Wong, W. K., & Chang, B. H. (2023). The asymmetric effect of the extreme changes in the economic policy uncertainty on the exchange rates: evidence from emerging seven countries. Annals of Financial Economics, 18(02), 2250031.

Mei, L., Chang, B. H., Gong, X., & Anwar, A. (2024). Rising energy demand in emerging countries and the effect of exchange rates: An application of the QARDL model. Energy Efficiency, 17(1), 3.

Mistry, M., Gediga, J., & Boonzaier, S. (2016). Life cycle assessment of nickel products. The International Journal of Life Cycle Assessment, 21(11), 1559-1572.

Mudd, G. M. (2010). Global trends and environmental issues in nickel mining: Sulfides versus laterites. Ore Geology Reviews, 38(1-2), 9-26.

Nacher, J. C., & Akutsu, T. (2015). Structurally robust control of complex networks. Physical Review E, 91(1), 012826.

Nakajima, K., Daigo, I., Nansai, K., Matsubae, K., Takayanagi, W., Tomita, M., & Matsuno, Y. (2018a). Global distribution of material consumption: Nickel, copper, and iron. Resources, Conservation and Recycling, 133, 369-374.

Nakajima, K., Noda, S., Nansai, K., Matsubae, K., Takayanagi, W., & Tomita, M. (2018). Global distribution of used and unused extracted materials induced by consumption of iron, copper, and nickel. Environmental science & technology, 53(3), 1555-1563.

Ni, S., Hou, S., Wang, H., Yu, W., Chen, Q., & Lu, T. (2015). Main factors affecting the nickel price and a preliminary analysis of future nickel prices. Resour. Sci. 40 (1997), 3–6.

Noman, M., Maydybura, A., Channa, K. A., Wong, W. K., & Chang, B. H. (2023). Impact of cashless bank payments on economic growth: Evidence from G7 countries. Advances in Decision Sciences, 27(1), 1-22.

Olafsdottir, A. H., & Sverdrup, H. U. (2021). Modelling global nickel mining, supply, recycling, stocks-in-use and price under different resources and demand assumptions for 1850–2200. Mining, Metallurgy & Exploration, 38(2), 819-840.

Peng, B., Chang, B. H., Yang, L., & Zhu, C. (2022). Exchange rate and energy demand in G7 countries: Fresh insights from Quantile ARDL model. Energy Strategy Reviews, 44, 100986.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.

Reck, B. K., Müller, D. B., Rostkowski, K., & Graedel, T. E. (2008). Anthropogenic nickel cycle: Insights into use, trade, and recycling. Environmental science & technology, 42(9), 3394-3400.

Rungta, P. D., Meena, C., & Sinha, S. (2018). Identifying nodal properties that are crucial for the dynamical robustness of multistable networks. Physical Review E, 98(2), 022314.

Salman, A., Chang, B. H., Abdul Razzaq, M. G., Wong, W. K., & Uddin, M. A. (2023b). The Emerging Stock Markets and Their Asymmetric Response to Infectious Disease Equity Market Volatility (ID-EMV) Index. Annals of Financial Economics, 2350008.

Salman, A., Razzaq, M. G. A., Chang, B. H., Wong, W. K., & Uddin, M. A. (2023a). Carbon Emissions and Its Relationship with Foreign Trade Openness and Foreign Direct Investment. Journal of International Commerce, Economics and Policy, 2350023.

Schneider, C. M., Moreira, A. A., Andrade Jr, J. S., Havlin, S., & Herrmann, H. J. (2011). Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences, 108(10), 3838-3841.

Shi, C. Y., Gao, X. Y., Sun, X. Q., & Hao, X. (2018). Study on the evolution characteristics of international bauxite trade from the perspective of complex network. China Mining Magazine, 27(1), 57-62.

Sole, R. V., & Montoya, M. (2001). Complexity and fragility in ecological networks. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1480), 2039-2045.

Sun, J., Tang, J., Fu, W., Chen, Z., & Niu, Y. (2020). Construction of a multi-echelon supply chain complex network evolution model and robustness analysis of cascading failure. Computers & Industrial Engineering, 144, 106457.

Sun, Q., Gao, X., Zhong, W., & Liu, N. (2017). The stability of the international oil trade network from short-term and long-term perspectives. Physica A: Statistical Mechanics and its Applications, 482, 345-356.

Syed, Q. R., Malik, W. S., & Chang, B. H. (2019). Volatility Spillover Effect of Federal Reserve’S Balance Sheet On The Financial And Goods Markets Of Indo-Pak Region. Annals of Financial Economics, 14(03), 1950015.

Takeyama, K., Ohno, H., Matsubae, K., Nakajima, K., Kondo, Y., & Nagasaka, T. (2016). Dynamic material flow analysis of nickel and chromium associated with steel materials by using matrace. Matériaux & Techniques, 104(6-7), 610.

Tran, V. H., Cheong, S. A., & Bui, N. D. (2019). Complex network analysis of the robustness of the hanoi, vietnam bus network. Journal of Systems Science and Complexity, 32, 1251-1263.

Uche, E., Chang, B. H., & Effiom, L. (2022a). Household consumption and exchange rate extreme dynamics: Multiple asymmetric threshold non‐linear autoregressive distributed lag model perspective. International Journal of Finance & Economics, 28(3), 3437-3450.

Uche, E., Chang, B. H., & Gohar, R. (2022b). Consumption optimization in G7 countries: Evidence of heterogeneous asymmetry in income and price differentials. Journal of International Commerce, Economics and Policy, 13(1), 2250002.

Van den Brink, S., Kleijn, R., Sprecher, B., & Tukker, A. (2020). Identifying supply risks by mapping the cobalt supply chain. Resources, Conservation and Recycling, 156, 104743.

Wang, X., Chang, B. H., Uche, E., & Zhao, Q. (2024). The asymmetric effect of income and price changes on the consumption expenditures: evidence from G7 countries using nonlinear bounds testing approach. Portuguese Economic Journal, 23(1), 35-53.

Wei, W., Samuelsson, P. B., Tilliander, A., Gyllenram, R., & Jönsson, P. G. (2020). Energy consumption and greenhouse gas emissions of nickel products. Energies, 13(21), 5664.

Wong, W. K., Cheng, Y., & Yue, M. (2024). Could regression of stationary series be spurious?. Asia-Pacific Journal of Operational Research, 2440017.

Wong, W.-K., & Pham, M. T. (2022b). Could the test from the standard regression model could make significant regression with autoregressive noise become insignificant – a note. The International Journal of Finance, 34, 19-39.

Wong, W.-K., & Pham, M. T. (2022a). Could the test from the standard regression model could make significant regression with autoregressive noise become insignificant?. The International Journal of Finance, 34, 1–18. https://tijof.scibiz.world/ijof-2022_01

Wong, W.-K., & Pham, M. T. (2023b). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant – a note. The International Journal of Finance, 35, 20-41.

Wong, W.-K., & Pham, M. T. (2023a). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant?. The International Journal of Finance, 35, 1–19. https://tijof.scibiz.world/ijof-2023_01

Wong, W. K., & Pham, M. T. (2025a). Could the correlation of a stationary series with a non-stationary series obtain meaningful outcomes?. Annals of Financial Economics, forthcoming.

Wong, W.-K., & Pham, M. T. (2025b). How to model a simple stationary series with a non-stationary series?. The International Journal of Finance, 37, 1–19.

Wong, W.-K., Pham, M. T., & Yue, M. (2024). Could regressing a stationary series on a non-stationary series obtain meaningful outcomes – a remedy. The International Journal of Finance, 36, 1–20.

Wong, W. K., & Yue, M. (2024). Could regressing a stationary series on a non-stationary series obtain meaningful outcomes?. Annals of Financial Economics, 19(03), 2450011.

Zeng, X., Xu, M., & Li, J. (2018). Examining the sustainability of China’s nickel supply: 1950–2050. Resources, Conservation and Recycling, 139, 188-193.

Zhao, Y., Gao, X., An, H., Xi, X., Sun, Q., & Jiang, M. (2020). The effect of the mined cobalt trade dependence Network's structure on trade price. Resources Policy, 65, 101589.

Zhong, W., Dai, T., Wang, G., Li, Q., Li, D., Liang, L., ... & Jiang, M. (2018). Structure of international iron flow: based on substance flow analysis and complex network. Resources, Conservation and Recycling, 136, 345-354.

Zhu, Z., Dong, Z., Zhang, Y., Suo, G., & Liu, S. (2020). Strategic mineral resource competition: Strategies of the dominator and nondominator. Resources Policy, 69, 101835.

Published

2025-11-16

Issue

Vol. 29 No. 3 (2025): Advances in Decision Sciences

Section

Articles

License

Copyright (c) 2025 Advances in Decision Sciences

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Laurinavicius, A., Salman, A., Ghanem, M. E. A., Laurinavicius, A., & Uddin, M. A. (2025). Worldwide Nickel Ore Trade, Its Stability and the Characteristics: A Fresh Policy Analysis. Advances in Decision Sciences, 29(3), 85-118. https://doi.org/10.47654/v29y2025i3p85-118