Forecasting European Union Electronic Trading Systems Phase 4 Spot Prices Using Data‐Driven Hybrid Deep Learning Models: Integrating Energy and Market Activity as Controls
エネルギー市場活動とマクロ経済変数を制御変数としたデータ駆動型ハイブリッド深層学習モデルによるEU ETS第4フェーズスポット価格予測 (AI 翻訳)
Noman Arshed, Shajara Ul‐Durar, Younes Ben Zaied, Marco De Sisto
🤖 gxceed AI 要約
日本語
本研究はEU ETSの炭素スポット価格予測のため、経験的モード分解とBiLSTM、アテンション機構を統合したハイブリッド深層学習フレームワークを提案する。非定常な炭素価格を複数のIMFに分解し、各周波数成分の時系列依存性を学習。マクロ経済変数と政策ショックを外生変数として組み込み、従来のLSTMより高い精度（RMSE 4.59）を達成した。SHAP分析により重要特徴量を特定し、予測の信頼性を可視化した。
English
This study proposes a hybrid deep learning framework integrating empirical mode decomposition, BiLSTM, and attention mechanism for forecasting EU ETS carbon spot prices. The model decomposes nonstationary prices into IMFs to capture temporal dependencies, incorporates exogenous macroeconomic variables and policy shocks, and achieves superior accuracy (RMSE 4.59) over conventional LSTM. SHAP analysis identifies key features, and confidence intervals confirm forecast reliability.
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
日本でも東京都や埼玉県の排出量取引制度が存在するが、本論文はEU ETSを対象としている。それでも、炭素価格予測の高度な手法は、日本のGX政策における将来の炭素市場設計や企業のカーボンプライシング対応に示唆を与える。また、深層学習と信号分解の組み合わせは、日本の研究者や実務者にとって技法面での参考になる。
In the global GX context
This paper advances carbon price forecasting by integrating signal decomposition with attention-based deep learning, directly relevant to the EU ETS Phase 4 and global carbon markets. The methodology offers transferable insights for other cap-and-trade systems, including emerging ones in Asia and North America. The use of SHAP enhances model interpretability, aligning with regulatory expectations for transparent climate finance tools.
👥 読者別の含意
🔬研究者:Provides a state-of-the-art hybrid forecasting framework for carbon prices, combining EMD, BiLSTM, and attention, with robust performance evaluation.
🏢実務担当者:Offers a practical tool for risk management and compliance teams to forecast carbon costs, with interpretable feature contributions via SHAP.
🏛政策担当者:Demonstrates the value of incorporating macroeconomic and policy variables into carbon price models, aiding market stability analysis.
📄 Abstract（原文）
Amid the focus on climate change mitigation, this study explores carbon market forecasting. This study uses a hybrid forecasting framework that integrates empirical model decomposition, bidirectional long short‐term memory (BiLSTM) network, and attention mechanism to enhance the predictive performance of carbon spot prices within the European Union (EU) Emissions Trading System (ETS). The model decomposes the nonstationary carbon prices to multiple intrinsic mode functions (IMF) representing each distinct frequency component. The forecasting at IMF level enables learning of temporal dependence and volatility. The final model reconstructs the signals to present overall prediction. The multiple iterations that include a selection of macroeconomic variable led to the final root mean square errors (RMSE) value of 4.59, which shows that the BiLSTM outperforms a conventional long short‐term memory (LSTM) setup. This study also improves the model by including exogenous macroeconomic variables and policy shocks to enhance predictive accuracy. Shapley additive explanations (SHAP) analysis also identified the important features and variables. The visualized confidence interval confirms the reliability of the forecasts. The findings of the study highlight the effectiveness of integrating signal decomposition with deep learning and inclusion of exogenous factors. This study offers practical insights for regulators and researchers who are engaged in the emissions market and climate finance.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/for.70152first seen 2026-05-15 17:05:17
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。