説明可能なAIを用いたマルチスケール分解-アンサンブル枠組みによる炭素価格予測とドライバー分析
A Multiscale Decomposition-Ensemble Framework with Explainable AI for Carbon Price Forecasting and Driver Analysis (原題)
Yuanyuan Ma, Siyu Peng, Yun Yu
🤖 gxceed AI 要約
日本語
炭素価格の複雑な非線形ダイナミクスを捉えるため、CEEMDAN、サンプルエントロピー再構成、変動係数アンサンブルを統合したハイブリッド予測フレームワークを構築。SHAPとTVP-SV-VARを用いてスケール別・時変的なドライバー影響を解明し、CEEMDAN-SE-GRUモデルがR2=0.96と高精度を達成。政策提言として多層的な監視・早期警戒システムや適応的政策ツールキットを提案。
English
This study develops a hybrid forecasting framework integrating CEEMDAN, sample entropy reconstruction, and coefficient of variation ensemble to capture multi-scale nonlinear carbon price dynamics. SHAP and TVP-SV-VAR reveal scale-specific and time-varying drivers. The CEEMDAN-SE-GRU model achieves R2=0.96 with over 60% error reduction. Policy recommendations include multi-layered monitoring and adaptive policy toolkits.
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
日本ではカーボンプライシング（GX-ETS、炭素賦課金）の本格導入が進む中、炭素価格の予測・変動要因分析は企業のリスク管理や投資判断に直結する。本手法は日本の排出量取引市場の価格形成メカニズム理解や、市場監視・政策設計に応用可能な示唆を提供する。
In the global GX context
As global carbon markets expand under Paris Agreement frameworks, robust carbon price forecasting and driver analysis are critical for market risk management and policy design. This study's integration of explainable AI with econometric methods offers a transferable approach for understanding carbon price dynamics in other markets, contributing to the literature on carbon market efficiency and climate policy evaluation.
👥 読者別の含意
🔬研究者:Provides a novel hybrid AI-ensemble method for carbon price forecasting and driver analysis, with implications for market efficiency and policy evaluation.
🏢実務担当者:Offers a framework for carbon price risk assessment and early warning systems, useful for corporate risk management and trading strategies.
🏛政策担当者:Recommends multi-layered monitoring and adaptive policy toolkits for carbon market regulation, relevant for designing effective carbon pricing mechanisms.
📄 Abstract（原文）
Accurate carbon price prediction and fluctuation analysis are essential for carbon market risk management and achieving carbon neutrality goals. However, carbon prices exhibit complex multi-scale nonlinear dynamics intertwined with time-varying external factors, hindering reliable forecasting. This study constructs a hybrid prediction framework integrating CEEMDAN, Sample Entropy (SE) reconstruction, and the Coefficient of Variation (VC) ensemble algorithm. SHapley Additive exPlanations (SHAP) quantifies scale-specific nonlinear factor contributions, while TVP-SV-VAR captures dynamic carbon price-driver correlations. Empirical results indicate that the CEEMDAN-SE preprocessing strategy significantly improves the prediction accuracy of baseline models. The CEEMDAN-SE-GRU model achieves optimal performance, with an R2 of 0.96 and over 60% reductions in both MSE and MAE relative to the baseline GRU model. Meanwhile, the VC ensemble outperforms single models and alternative fusion strategies. SHAP identifies scale-heterogeneous drivers: short-run prices follow sentiment and macro outlooks, medium-run trends tie to industrial costs and global markets, long-run paths align with energy transition and global climate governance. The TVP-SV-VAR model uncovers significant time-varying spillover effects on raw carbon prices. Based on these findings, we recommend establishing multi-layered dynamic monitoring and early warning systems, constructing a differentiated and adaptive policy toolkit, refining cross-market risk isolation and buffering mechanisms, and advancing institutional improvements through gradual implementation.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.3390/systems14091090first seen 2026-09-07 00:12:44
- openalex https://doi.org/10.3390/systems14091090first seen 2026-09-08 04:43:35
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