Synergizing Spatial and Temporal Dynamics for Carbon Price Forecasting: A Heterogeneous Ensemble Approach
炭素価格予測のための空間・時間ダイナミクスの相乗効果：不均一アンサンブルアプローチ (AI 翻訳)
Yantong Zhao, Gaoxiu Qiao
🤖 gxceed AI 要約
日本語
本論文は、欧州炭素価格の予測精度向上を目的とし、LASSOによる変数選択、MEMD-ARIMAX-mLSTMによる時間ダイナミクス、GWnet-attnによる空間依存性の捕捉を統合した不均一アンサンブルフレームワークを提案する。実証結果は、既存手法を大幅に上回る予測性能を示し、エネルギー-炭素連環の重要性を明らかにした。
English
This paper introduces a heterogeneous ensemble framework for European carbon price forecasting, integrating LASSO for variable selection, MEMD-ARIMAX-mLSTM for temporal dynamics, and GWnet-attn for spatial dependencies. Empirical results show significant improvements over state-of-the-art benchmarks, highlighting the energy-carbon nexus and offering insights for market risk management.
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
日本でも排出量取引制度の本格化が検討されており、炭素価格予測モデルはリスク管理や政策設計に有用。ただし本論文は欧州市場を対象としており、日本市場への直接適用には調整が必要。
In the global GX context
Accurate carbon price forecasting is crucial for global climate policy and market risk management. This paper's hybrid spatial-temporal approach provides a robust benchmark for modeling carbon markets, with potential applications to other emission trading systems worldwide.
👥 読者別の含意
🔬研究者:The heterogeneous ensemble framework offers a new methodological baseline for carbon price forecasting, integrating spatial and temporal dependencies effectively.
🏢実務担当者:Trading desks and risk managers can adopt the proposed model to improve carbon price predictions and hedge against market volatility.
🏛政策担当者:Regulators can use the framework to assess market dynamics and the impact of structural shocks on carbon prices.
📄 Abstract（原文）
ABSTRACT Accurately forecasting European carbon prices is essential for effective climate policy and market risk management yet remains challenging due to the coexistence of exogenous macroeconomic shocks, endogenous market momentum, and pronounced spatial and temporal dependencies across related markets. Conventional forecasting models often fail to accommodate such spatial–temporal heterogeneity, leading to unstable predictive performance and limited economic interpretability. To address these challenges, we introduce a heterogeneous ensemble framework that integrates the strengths of variable selection, frequency‐domain decomposition, and graph‐based spatial learning. Using 41 variables, we employ LASSO to extract endogenous market momentum, implement the MEMD‐ARIMAX‐mLSTM framework to model temporal dynamics, and adopt GWnet‐attn to capture spatial interdependencies. Through the ensemble approach, the framework effectively combines these distinct market dynamics. Empirical results demonstrate that the proposed model significantly outperforms recent state‐of‐the‐art benchmarks. Ablation studies and robustness tests demonstrate that integrating spatial and temporal information substantially reduces forecasting errors and confers strong robustness, while highlighting the criticalness of the energy–carbon nexus, offering critical insights for market design and risk management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/for.70196first seen 2026-07-28 05:13:13
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