Accurate carbon price forecasting is essential for market stability, regulatory assessment, and risk management in emission trading systems. However, existing approaches find it challenging to extract informative signals from high-dimensional multi-source data, integrate mixed-frequency influencing factors, and maintain robustness in multistep forecasting. This study proposes a unified multi-step interval-valued carbon price forecasting system that integrates a multi-objective reinforcement learning (MORL)-based mechanism for feature selection, a multi-source mixed-frequency data sampling (M-MIDAS) module, and a leading-expert dynamic residual correction network (LE-DRCN). MORL performs adaptive feature selection by jointly considering multiscale temporal characteristics and feature redundancy. M-MIDAS aligns multi-source heterogeneous factors into a unified representation, whereas LE-DRCN captures structured residual patterns and dynamically adjusts expert contributions through attention-based integration, thereby improving forecasting accuracy and stability across horizons. The proposed system consistently outperforms benchmark models in multistep forecasting on China Emission Allowance market data. Ablation analysis confirmed the importance of mixed-frequency representation and dynamic residual correction, and statistical tests further verified the robustness of the performance gains. Interpretability analysis revealed horizon-dependent expert contributions, reflecting adaptive model behavior under varying market conditions. Overall, the proposed system provides a robust and interpretable solution for multi-step interval-valued carbon price forecasting under complex multi-source and mixed-frequency environments.
Li et al. (Mon,) studied this question.