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Exploring Primary Aluminum Consumption: New Perspectives from Hybrid CEEMDAN-S-Curve Model

Author

Listed:
  • Zhaoshuai Pan

    (Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)

  • Zhaozhi Zhang

    (Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
    Qinghai Salt Lake Industry Co., Ltd., Golmud 816000, China)

  • Dong Che

    (Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China)

Abstract

Aluminum is globally the most used nonferrous metal. Clarifying the consumption of primary aluminum is vital to economic development and emission reduction. Based on the signal decomposition tool and S-curve model, a new hybrid complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-S-curve model is proposed to analyze primary aluminum consumption of different countries for the last 100 years. The results show that: (1) Per capita primary aluminum consumption can be decomposed into low-frequency, medium-frequency, and high-frequency components, contributing over 70%, 2–17%, and less than 9% to variability of consumption series, respectively. This can be interpreted as economic development represented by GDP per capita, shocks from significant events, and short-term fluctuations, respectively. (2) The CEEMDAN-S-curve shows good applicability and generalizability by using this model in different countries. (3) A new strategy is provided to analyze and predict the consumption pattern of primary aluminum. Furthermore, some important topics related to primary aluminum consumption are discussed, such as CO 2 emission and recovery. Based on the results, to meet economic development and achieve sustainable development goals, some measures should be implemented, such as making policies, encouraging resource recovery, and developing new technologies.

Suggested Citation

  • Zhaoshuai Pan & Zhaozhi Zhang & Dong Che, 2023. "Exploring Primary Aluminum Consumption: New Perspectives from Hybrid CEEMDAN-S-Curve Model," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4228-:d:1081311
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    References listed on IDEAS

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    1. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    2. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    3. Jia, Hongxiang & Li, Tianjiao & Wang, Anjian & Liu, Guwang & Guo, Xiaoqian, 2021. "Decoupling analysis of economic growth and mineral resources consumption in China from 1992 to 2017: A comparison between tonnage and exergy perspective," Resources Policy, Elsevier, vol. 74(C).
    4. Du, J.D. & Han, W.J. & Peng, Y.H. & Gu, C.C., 2010. "Potential for reducing GHG emissions and energy consumption from implementing the aluminum intensive vehicle fleet in China," Energy, Elsevier, vol. 35(12), pages 4671-4678.
    5. Trevor Zink & Roland Geyer & Richard Startz, 2018. "Toward Estimating Displaced Primary Production from Recycling: A Case Study of U.S. Aluminum," Journal of Industrial Ecology, Yale University, vol. 22(2), pages 314-326, April.
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