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Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting

Author

Listed:
  • Rujie Liu

    (Three Gorges Electric Power Co., Ltd., Wuhan 430021, China)

  • Wei He

    (Three Gorges Electric Power Co., Ltd., Wuhan 430021, China)

  • Hongwei Dong

    (Three Gorges Electric Power Co., Ltd., Wuhan 430021, China)

  • Tao Han

    (Three Gorges Electric Power Co., Ltd., Wuhan 430021, China)

  • Yuting Yang

    (Three Gorges Electric Power Co., Ltd., Wuhan 430021, China)

  • Hongwei Yu

    (Institute of Quality Development Strategy, Wuhan University, Wuhan 430072, China)

  • Zhu Li

    (Electronic Information School, Wuhan University, Wuhan 430072, China)

Abstract

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals.

Suggested Citation

  • Rujie Liu & Wei He & Hongwei Dong & Tao Han & Yuting Yang & Hongwei Yu & Zhu Li, 2024. "Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting," Energies, MDPI, vol. 17(15), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3662-:d:1442624
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    References listed on IDEAS

    as
    1. Tang, Bao-jun & Shen, Cheng & Gao, Chao, 2013. "The efficiency analysis of the European CO2 futures market," Applied Energy, Elsevier, vol. 112(C), pages 1544-1547.
    2. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    3. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    4. Cheng, Zishu & Li, Mingchen & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," Energy Economics, Elsevier, vol. 134(C).
    5. Liudmila Reshetnikova & Natalia Boldyreva & Anton Devyatkov & Zhanna Pisarenko & Danila Ovechkin, 2023. "Carbon Pricing in Current Global Institutional Changes," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Bredin, Don & Muckley, Cal, 2011. "An emerging equilibrium in the EU emissions trading scheme," Energy Economics, Elsevier, vol. 33(2), pages 353-362, March.
    7. Jensen, Jesper & Rasmussen, Tobias N., 2000. "Allocation of CO2 Emissions Permits: A General Equilibrium Analysis of Policy Instruments," Journal of Environmental Economics and Management, Elsevier, vol. 40(2), pages 111-136, September.
    8. Daskalakis, George, 2013. "On the efficiency of the European carbon market: New evidence from Phase II," Energy Policy, Elsevier, vol. 54(C), pages 369-375.
    9. repec:dau:papers:123456789/6791 is not listed on IDEAS
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