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A Multi-Step Interval-Valued Carbon Price Forecasting System Based on Multi-Source Mixed-Frequency Information Modeling

マルチソース混合周波数情報モデリングに基づく多段階区間値炭素価格予測システム (AI 翻訳)

Junyuan Li, Wendong Yang

Systems📚 査読済 / ジャーナル2026-05-11#炭素価格Origin: CN
DOI: 10.3390/systems14050545
原典: https://doi.org/10.3390/systems14050545

🤖 gxceed AI 要約

日本語

本研究は、多目的強化学習による特徴選択、マルチソース混合周波数データサンプリング、および動的残差補正ネットワークを統合した、多段階区間値炭素価格予測システムを提案する。中国排出許可市場のデータを用いた実験では、提案システムがベンチマークモデルを凌駕し、混合周波数表現と動的残差補正の重要性を確認した。

English

This study proposes a unified multi-step interval-valued carbon price forecasting system integrating multi-objective reinforcement learning for feature selection, multi-source mixed-frequency data sampling, and a leading-expert dynamic residual correction network. Experiments on China Emission Allowance market data show consistent outperformance over benchmarks, with ablation and robustness tests confirming the importance of mixed-frequency representation and dynamic correction.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

本論文は中国炭素市場を対象としているが、日本でも2026年度の排出量取引制度本格化に向け、高精度な価格予測手法への関心は高い。提案されたマルチソース・混合周波数モデルは、日本の炭素市場データへの応用可能性を検討する際の参考となる。

In the global GX context

This paper provides a robust and interpretable framework for carbon price forecasting using advanced machine learning, with strong empirical validation on the Chinese market. Globally, as carbon pricing mechanisms expand, such methods can inform market monitoring, risk management, and policy evaluation.

👥 読者別の含意

🔬研究者:The novel integration of multi-objective reinforcement learning with mixed-frequency data sampling offers a methodological contribution to carbon price forecasting literature.

🏢実務担当者:Companies exposed to carbon markets can use this system for more accurate price forecasts, aiding compliance cost management and trading strategies.

🏛政策担当者:Regulators can leverage the interpretability analysis to understand carbon price drivers and market dynamics, supporting market design improvements.

📄 Abstract（原文）

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.

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