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A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices

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  • Niu, Xinsong
  • Wang, Jiyang
  • Wei, Danxiang
  • Zhang, Lifang

Abstract

With the severe situation of climate and environmental issues, carbon emissions have aroused great attention from the academic community and industry. Building and improving the carbon trading market has become the focus, among which the prediction of carbon emissions trading prices is crucial. However, randomness and instability make it a challenging task to predict price series accurately. To obtain accurate point prediction results, a combined prediction idea is constructed in the study. In addition, considering that the point prediction framework contains less data information, in order to bridge this gap, an interval prediction frame is developed in this research. The optimal distribution of data sequence is obtained by using distribution function and optimization techniques, and successfully achieve different levels of uncertainty prediction according to the point prediction results. Several comparison experiments were carried out using the daily price of carbon emission futures of the European Union Emissions Trading System and the forecasting performance of the proposed forecasting framework was verified through experimental analysis. Experiments and discussion demonstrate the superior performance of the proposed prediction scheme and its excellent forecasting ability for carbon emissions trading price prediction.

Suggested Citation

  • Niu, Xinsong & Wang, Jiyang & Wei, Danxiang & Zhang, Lifang, 2022. "A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices," Renewable Energy, Elsevier, vol. 201(P1), pages 46-59.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:46-59
    DOI: 10.1016/j.renene.2022.10.027
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    References listed on IDEAS

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    2. Dinggao Liu & Liuqing Wang & Shuo Lin & Zhenpeng Tang, 2025. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
    3. Zeng, Liling & Hu, Huanling & Song, Qingkui & Zhang, Boting & Lin, Ruibin & Zhang, Dabin, 2024. "A drift-aware dynamic ensemble model with two-stage member selection for carbon price forecasting," Energy, Elsevier, vol. 313(C).
    4. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    5. Yang, Yi & Xing, Qianyi & Wang, Kang & Li, Caihong & Wang, Jianzhou & Huang, Xiaojia, 2024. "A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy," Applied Energy, Elsevier, vol. 356(C).
    6. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    7. Han, Te & Gu, Xiaoyang & Li, Dan & Chen, Kaiyuan & Cong, Rong-Gang & Zhao, Lu-Tao & Wei, Yi-Ming, 2025. "Causal neural network for carbon prices probabilistic forecasting," Applied Energy, Elsevier, vol. 397(C).
    8. Zhao, Yang & Wang, Jianzhou & Wang, Shuai & Zheng, Jingwei & Lv, Mengzheng, 2025. "Using explainable deep learning to improve decision quality: Evidence from carbon trading market," Omega, Elsevier, vol. 133(C).
    9. Liu, Shuihan & Xie, Gang & Wang, Zhengzhong & Wang, Shouyang, 2024. "A secondary decomposition-ensemble framework for interval carbon price forecasting," Applied Energy, Elsevier, vol. 359(C).
    10. Ma, Jinjin & Yang, Lin & Wang, Donghan & Li, Yiming & Xie, Zuomiao & Lv, Haodong & Woo, Donghyup, 2024. "Digitalization in response to carbon neutrality: Mechanisms, effects and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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