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<p>An accurate calculation method of carbon trading price is of great significance to strengthening energy saving and emission reduction. Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new hybrid model for carbon trading price forecasting. The model fuses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks, and leverages SnowNLP to derive sentiment scores from news text and the Baidu Index. To demonstrate the superiority of the proposed model, 5 chinese carbon emissions trading markets are selected for the predictions. The model shows better performance across all markets, improving by 4.20% to 17.89% over the CEEMDAN-LSTM model and outperforming other benchmarks. Furthermore, ablation experiments and parametric sensitivity analyses were carried out to verify the contribution of each component and the overall model’ s robustness. It offers a reliable and stable forecasting tool for stakeholders in the carbon market.</p>

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A sentiment-driven three-stage approach for multi-scale carbon price prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月23日

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  • Yongliang Liu,
  • Chunling Tang,
  • Aiying Zhou,
  • Kai Yang,
  • Huaiyu Yuan

摘要

An accurate calculation method of carbon trading price is of great significance to strengthening energy saving and emission reduction. Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new hybrid model for carbon trading price forecasting. The model fuses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks, and leverages SnowNLP to derive sentiment scores from news text and the Baidu Index. To demonstrate the superiority of the proposed model, 5 chinese carbon emissions trading markets are selected for the predictions. The model shows better performance across all markets, improving by 4.20% to 17.89% over the CEEMDAN-LSTM model and outperforming other benchmarks. Furthermore, ablation experiments and parametric sensitivity analyses were carried out to verify the contribution of each component and the overall model’ s robustness. It offers a reliable and stable forecasting tool for stakeholders in the carbon market.