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Exploring market instability of global lithium resources based on chaotic dynamics analysis

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  • Liu, Donghui
  • Gao, Xiangyun
  • An, Haizhong
  • Jia, Nanfei
  • Wang, Anjian

Abstract

Driven by emerging technologies such as batteries in electric vehicles (EVs), global lithium market undergoes significant changes. To explore market instability of global lithium resources subjected to EV development, lithium exploration, and carbon constraints, a model of global lithium markets-electric vehicle development-carbon constraints (GL-EV-CC) is established based on a four-dimensional difference equation feedback system. Accordingly, a detailed analysis strategy by virtue of bifurcation and chaotic theory is investigated, with which some sudden changes and critical points can be obtained to provide some implications in face of the challenges of future market changes. Results show that lithium demand for EVs will evolve from an equilibrium state to a periodic state and finally to a chaotic state with the continuous change of the degree of investment in Research and Development (R&D) of EVs. When the degree of exploration is less than 0.15, attention should be given to the resource exhausted crisis. When the degree of carbon constraint is high, market instability of lithium demand should be fairly concerned, while when the degree of carbon constraint is low, market instability of lithium supply should be focused. Unlike previous studies, chaotic dynamic analysis is employed in this study, and thereby not only a broader sensitivity analysis but also complex dynamic behaviors such as chaotic behavior at the moment of equilibrium can be explored, in which feasible policies can be proposed to control some policy parameters to be within a reasonable range.

Suggested Citation

  • Liu, Donghui & Gao, Xiangyun & An, Haizhong & Jia, Nanfei & Wang, Anjian, 2024. "Exploring market instability of global lithium resources based on chaotic dynamics analysis," Resources Policy, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jrpoli:v:88:y:2024:i:c:s030142072301084x
    DOI: 10.1016/j.resourpol.2023.104373
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    References listed on IDEAS

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    1. Morcillo, José D. & Franco, Carlos J. & Angulo, Fabiola, 2018. "Simulation of demand growth scenarios in the Colombian electricity market: An integration of system dynamics and dynamic systems," Applied Energy, Elsevier, vol. 216(C), pages 504-520.
    2. Zhou, Na & Su, Hui & Wu, Qiaosheng & Hu, Shougeng & Xu, Deyi & Yang, Danhui & Cheng, Jinhua, 2022. "China's lithium supply chain: Security dynamics and policy countermeasures," Resources Policy, Elsevier, vol. 78(C).
    3. Laurence Kavanagh & Jerome Keohane & Guiomar Garcia Cabellos & Andrew Lloyd & John Cleary, 2018. "Global Lithium Sources—Industrial Use and Future in the Electric Vehicle Industry: A Review," Resources, MDPI, vol. 7(3), pages 1-29, September.
    4. Krawiec, Adam & Szydłowski, Marek, 2017. "Economic growth cycles driven by investment delay," Economic Modelling, Elsevier, vol. 67(C), pages 175-183.
    5. Hommes,Cars, 2015. "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems," Cambridge Books, Cambridge University Press, number 9781107564978, November.
    6. Shi, Qing & Sun, Xiaoqi & Xu, Man & Wang, Mengjiao, 2022. "The multiplex network structure of global cobalt industry chain," Resources Policy, Elsevier, vol. 76(C).
    7. Lu, Bin & Liu, Jingru & Yang, Jianxin, 2017. "Substance flow analysis of lithium for sustainable management in mainland China: 2007–2014," Resources, Conservation & Recycling, Elsevier, vol. 119(C), pages 109-116.
    8. Keilhacker, Michael L. & Minner, Stefan, 2017. "Supply chain risk management for critical commodities: A system dynamics model for the case of the rare earth elements," Resources, Conservation & Recycling, Elsevier, vol. 125(C), pages 349-362.
    9. Jones, Ben & Elliott, Robert J.R. & Nguyen-Tien, Viet, 2020. "The EV revolution: The road ahead for critical raw materials demand," Applied Energy, Elsevier, vol. 280(C).
    10. Zeng, Xianlai & Li, Jinhui, 2013. "Implications for the carrying capacity of lithium reserve in China," Resources, Conservation & Recycling, Elsevier, vol. 80(C), pages 58-63.
    11. Koyamparambath, Anish & Santillán-Saldivar, Jair & McLellan, Benjamin & Sonnemann, Guido, 2022. "Supply risk evolution of raw materials for batteries and fossil fuels for selected OECD countries (2000–2018)," Resources Policy, Elsevier, vol. 75(C).
    12. Sverdrup, Harald Ulrik, 2016. "Modelling global extraction, supply, price and depletion of the extractable geological resources with the LITHIUM model," Resources, Conservation & Recycling, Elsevier, vol. 114(C), pages 112-129.
    13. Liu, Donghui & Gao, Xiangyun & An, Haizhong & Qi, Yabin & Wang, Ze & Jia, Nanfei & Chen, Zhihua, 2020. "Exploring behavior changes of the lithium market in China: Toward technology-oriented future scenarios," Resources Policy, Elsevier, vol. 69(C).
    14. Ma, Yutian & Li, Wenwen, 2020. "Application and research of fractional differential equations in dynamic analysis of supply chain financial chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    Full references (including those not matched with items on IDEAS)

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