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A predictive model for the security and stability of the lithium-ion battery industry chain based on price modal combinations

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  • Jian-fei, Li
  • Han, Peng
  • Xiao-yu, Luo

Abstract

Prices act as crucial market signals within industrial chains, and the smooth transmission of prices has significantly impacts on the safety and stability of the entire chain. This paper considers the lithium-ion battery industry chain as a complex system and uses prices as modal signals to construct an FEEMD-NAR-HMM industrial chain safety and stability prediction model. The research findings are as follows: (1) The lithium-ion industry chain exhibits a typical "price-stability" dissipative structure, where the consistency of price fluctuations within the industry chain has a significant impact on its overall safety and stability. (2) When products at different stages of the lithium-ion industry chain experience simultaneous price increases or decreases, the safety and stability of the industry chain are at a high level. When the transmission smoothness of price fluctuations across different stages of the lithium-ion industry chain is low, the safety and stability of the industry chain are at a low level. (3) In the near future, the lithium-ion industry chain will continue to exposed to volatility risks. The government should implement macroeconomic control measures to stabilize the lithium-ion market and increase research investment in lithium resource recycling to prevent a crisis of lithium resource shortages.

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  • Jian-fei, Li & Han, Peng & Xiao-yu, Luo, 2024. "A predictive model for the security and stability of the lithium-ion battery industry chain based on price modal combinations," Resources Policy, Elsevier, vol. 99(C).
  • Handle: RePEc:eee:jrpoli:v:99:y:2024:i:c:s0301420724007396
    DOI: 10.1016/j.resourpol.2024.105372
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    1. Belaïd, Fateh & Mikayilov, Jeyhun I., 2024. "Closing the Efficiency Gap: Insights into curbing the direct rebound effect of residential electricity consumption in Saudi Arabia," Energy Economics, Elsevier, vol. 135(C).
    2. Mahsa Ghorbani & Edwin K P Chong, 2020. "Stock price prediction using principal components," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    3. Nguyen-Tien, Viet & Dai, Qiang & Harper, Gavin D.J. & Anderson, Paul A. & Elliott, Robert J.R., 2022. "Optimising the geospatial configuration of a future lithium ion battery recycling industry in the transition to electric vehicles and a circular economy," Applied Energy, Elsevier, vol. 321(C).
    4. Guo, Xueyi & Zhang, Jingxi & Tian, Qinghua, 2021. "Modeling the potential impact of future lithium recycling on lithium demand in China: A dynamic SFA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    5. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries," Papers 2001.09769, arXiv.org.
    6. Fikru, Mahelet G. & Belaïd, Fateh & Ma, Hongyan, 2024. "Carbon capture and renewable energy policies: Could policy harmonization be a puzzle piece to solve the electricity crisis?," Energy Economics, Elsevier, vol. 136(C).
    7. Hanjiro Ambrose & Alissa Kendall, 2020. "Understanding the future of lithium: Part 2, temporally and spatially resolved life‐cycle assessment modeling," Journal of Industrial Ecology, Yale University, vol. 24(1), pages 90-100, February.
    8. Li, Lin & Dababneh, Fadwa & Zhao, Jing, 2018. "Cost-effective supply chain for electric vehicle battery remanufacturing," Applied Energy, Elsevier, vol. 226(C), pages 277-286.
    9. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    10. Mu, Dong & Ren, Huanyu & Wang, Chao & Yue, Xiongping & Du, Jianbang & Ghadimi, Pezhman, 2023. "Structural characteristics and disruption ripple effect in a meso-level electric vehicle Lithium-ion battery supply chain network," Resources Policy, Elsevier, vol. 80(C).
    11. Jiehui Yuan & Zhihong Liu & Ting Zhou & Xiaoming Tang & Juan Yuan & Wenli Yuan, 2023. "Sustainable Development of Lithium-Based New Energy in China from an Industry Chain Perspective: Risk Analysis and Policy Implications," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    12. Ren, Zhijun & Li, Huajie & Yan, Wenyi & Lv, Weiguang & Zhang, Guangming & Lv, Longyi & Sun, Li & Sun, Zhi & Gao, Wenfang, 2023. "Comprehensive evaluation on production and recycling of lithium-ion batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    Full references (including those not matched with items on IDEAS)

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