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A Sustainable Model for Forecasting Carbon Emission Trading Prices

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  • Jiaqing Chen

    (School of Economics and Finance, Xi’an Jiao tong University, Xi’an 710061, China
    Xiamen Tiandi Development and Construction Group Co., Ltd., Xiamen 361013, China)

  • Dongpeng Peng

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Zhiwei Liu

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Lingzhi Wu

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

  • Ming Jiang

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China)

Abstract

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Suggested Citation

  • Jiaqing Chen & Dongpeng Peng & Zhiwei Liu & Lingzhi Wu & Ming Jiang, 2024. "A Sustainable Model for Forecasting Carbon Emission Trading Prices," Sustainability, MDPI, vol. 16(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8324-:d:1485055
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    References listed on IDEAS

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    1. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    2. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).
    3. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    4. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    5. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    6. Zahraee, Seyed Mojib & Shiwakoti, Nirajan & Stasinopoulos, Peter, 2022. "Application of geographical information system and agent-based modeling to estimate particle-gaseous pollutantemissions and transportation cost of woody biomass supply chain," Applied Energy, Elsevier, vol. 309(C).
    7. María Inmaculada Rodríguez-García & María Gema Carrasco-García & Javier González-Enrique & Juan Jesús Ruiz-Aguilar & Ignacio J. Turias, 2023. "Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
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