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Forecasting of interval carbon price in China based on decomposition-reconstruction-ensemble framework
1School of Economics and Management, Anhui University of Science and Technology, Huainan, China. bbhu@aust.edu.cn.
Carbon Balance and Management
|January 9, 2026
Summary
Accurate carbon price prediction is crucial for green energy transition. This study introduces a novel framework using multivariate variational mode decomposition and extreme learning machines to forecast interval carbon prices, improving market management.
Area of Science:
- Environmental Economics
- Computational Finance
- Data Science
Background:
- Effective carbon market management and the global green energy transition necessitate precise carbon price forecasting.
- Existing decomposition and reconstruction methods struggle with the complexities of interval-valued carbon prices.
- Accurate interval carbon price prediction is vital for informed decision-making in environmental policy and energy markets.
Purpose of the Study:
- To develop an advanced framework for accurately predicting interval-valued carbon prices.
- To address the limitations of traditional methods in handling the volatility and interval nature of carbon prices.
- To enhance the robustness and accuracy of carbon price forecasting models by incorporating external influencing factors.
Main Methods:
- A novel decomposition-reconstruction-ensemble framework was proposed.
- Multivariate Variational Mode Decomposition (MVMD) optimized by the Rime Ice Optimization Algorithm (RIME) was used for decomposition and reconstruction.
- Multi-scale Fuzzy Dispersion Entropy (MFDE) was employed for enhanced signal processing.
- A RIME-optimized Multiple Kernel-based Extreme Learning Machine (MKELM) was utilized for sub-series prediction.
- External factors (energy, economy, environment) were integrated into the prediction model.
Main Results:
- The proposed model demonstrated superior prediction accuracy compared to benchmark methods.
- Empirical analysis confirmed the model's robustness in forecasting interval carbon prices.
- The framework effectively captured the fluctuations inherent in interval-valued carbon price data.
- Integration of external factors significantly improved predictive performance.
Conclusions:
- The developed decomposition-reconstruction-ensemble framework offers a significant advancement in interval carbon price prediction.
- The model's accuracy and robustness make it suitable for complex real-world scenarios in carbon markets.
- This approach provides a valuable tool for policymakers and market participants navigating the green energy transition.
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