A sentiment-driven three-stage approach for multi-scale carbon price prediction
Abstract 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 n...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Sustainability |
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| Online Access: | https://doi.org/10.1007/s43621-025-01258-x |
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| author | Yongliang Liu Chunling Tang Aiying Zhou Kai Yang Huaiyu Yuan |
| author_facet | Yongliang Liu Chunling Tang Aiying Zhou Kai Yang Huaiyu Yuan |
| author_sort | Yongliang Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1be5cf71abeb4fc681f7fcca6b9864c6 |
| institution | Kabale University |
| issn | 2662-9984 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-1be5cf71abeb4fc681f7fcca6b9864c62025-08-20T03:27:10ZengSpringerDiscover Sustainability2662-99842025-06-016113210.1007/s43621-025-01258-xA sentiment-driven three-stage approach for multi-scale carbon price predictionYongliang Liu0Chunling Tang1Aiying Zhou2Kai Yang3Huaiyu Yuan4School of Economics, Central South University of Forestry and TechnologySchool of Economics, Central South University of Forestry and TechnologySchool of Economics, Central South University of Forestry and TechnologyShenzhen International Graduate School, Tsinghua UniversitySchool of Economics, Central South University of Forestry and TechnologyAbstract 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.https://doi.org/10.1007/s43621-025-01258-xCarbon trading price forecastingSentiment analysisCEEMDANXGBoostLong short-term memoryDeep learning |
| spellingShingle | Yongliang Liu Chunling Tang Aiying Zhou Kai Yang Huaiyu Yuan A sentiment-driven three-stage approach for multi-scale carbon price prediction Discover Sustainability Carbon trading price forecasting Sentiment analysis CEEMDAN XGBoost Long short-term memory Deep learning |
| title | A sentiment-driven three-stage approach for multi-scale carbon price prediction |
| title_full | A sentiment-driven three-stage approach for multi-scale carbon price prediction |
| title_fullStr | A sentiment-driven three-stage approach for multi-scale carbon price prediction |
| title_full_unstemmed | A sentiment-driven three-stage approach for multi-scale carbon price prediction |
| title_short | A sentiment-driven three-stage approach for multi-scale carbon price prediction |
| title_sort | sentiment driven three stage approach for multi scale carbon price prediction |
| topic | Carbon trading price forecasting Sentiment analysis CEEMDAN XGBoost Long short-term memory Deep learning |
| url | https://doi.org/10.1007/s43621-025-01258-x |
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