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Carbon Price Point and Interval-Valued Prediction Based on a Novel Hybrid Model

Author

Listed:
  • Haoyu Chen

    (CHN ENERGY Investment Group Co., Ltd., Beijing 100011, China)

  • Qunli Wu

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China)

  • Chonghao Han

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China)

Abstract

Accurate carbon price forecasting enables the steady operation of the carbon trading market and optimal resource allocation while also empowering market participants to understand dynamics and make informed decisions, ultimately supporting sustainable development in the carbon market. While early research primarily focused on point forecasting of single-value carbon price, recent studies have shifted towards interval prediction, although there is still a lack of research dedicated to developing models for interval-valued predictions. The importance of interval-valued forecasting lies in its ability to better capture the upper and lower bounds of the carbon price range across different time dimensions, thereby revealing the intrinsic patterns and trends of price fluctuations and assisting in point forecasting to comprehensively capture carbon market volatility. This study offers a novel approach based on a CEEMDAN-CNN-BiLSTM-SENet hybrid model, providing a framework for both point and interval-valued carbon price predictions. The model makes a more comprehensive analysis of the carbon market possible by combining the predictions from these two approaches. In the case study using Hubei market’s data, the mean absolute percentage error for carbon pricing was 0.8125%, with the MAPE for the highest and lowest prices being 1.8898% and 1.7852%, respectively—both outperforming other comparative models. The results demonstrate that this model can measure trends of carbon pricing effectively.

Suggested Citation

  • Haoyu Chen & Qunli Wu & Chonghao Han, 2025. "Carbon Price Point and Interval-Valued Prediction Based on a Novel Hybrid Model," Energies, MDPI, vol. 18(5), pages 1-31, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1054-:d:1596836
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    References listed on IDEAS

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    5. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
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