Forecasting EU ETS carbon prices using information on CCU/CDR technologies’ investments
EU ETS炭素価格予測におけるCCU/CDR技術投資情報の活用 (AI 翻訳)
Maria S. Kasidoni, Nikolaos D. Adamopoulos, Maria D. Loizidou
🤖 gxceed AI 要約
日本語
本研究は、CCU/CDR技術への投資に注目したプロキシ変数がEU ETS炭素価格の予測に有用かを検証。統計モデル、機械学習、深層学習を用いた予測精度の向上を実証し、政策立案者や市場参加者への示唆を提供する。
English
This paper examines whether an investment-linked proxy for carbon management technologies (CCU/CDR) improves EU ETS carbon price forecasting. Using statistical, ML, and deep learning models, it demonstrates forecast accuracy gains, offering insights for market participants and regulators.
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GX-ETSや炭素税の導入が進む中、炭素価格予測手法の高度化は日本企業のリスク管理に示唆を与える。特に、CCS/CCU投資と炭素価格の関係性は国内政策検討の参考となる。
In the global GX context
This work advances carbon price forecasting by integrating real-economy investment signals, relevant for EU ETS participants and policymakers. The methodology can be adapted to other carbon markets, including emerging Asian schemes.
👥 読者別の含意
🔬研究者:Provides a benchmarking framework for carbon price forecasting with ML and stacking ensembles, and highlights measurement uncertainty in proxies.
🏢実務担当者:Offers a practical tool for compliance teams and traders to anticipate price movements using investment data, improving hedging strategies.
🏛政策担当者:Demonstrates how CCU/CDR investment signals can serve as leading indicators for carbon price dynamics, aiding market design and stability.
📄 Abstract（原文）
We examine the predictive relevance of an investment-linked proxy capturing market attention toward carbon management technologies, including Carbon Capture Utilization and Carbon Dioxide Removal (CCU/CDR) technologies, for EU ETS carbon prices. Building on evidence that EU ETS prices affect investment, this study examines the reverse relationship by testing whether carbon management investment signals can inform price forecasting. Empirically, we demonstrate that incorporating this proxy into the dataset improves forecast accuracy across statistical, machine learning (ML), and deep learning models. Methodologically, we implement a systematic benchmarking framework and hybrid stacking ensembles, and apply a structured predictor-screening workflow with explicit caveats to ensure transparency regarding measurement uncertainty in the proxy. From a policy perspective, our results provide actionable insights for market participants and regulators seeking to anticipate carbon price dynamics. Future research could explore alternative proxies, higher-frequency data, or causal identification strategies to further enhance forecasting and deepen understanding of carbon market behavior.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.esr.2026.102302first seen 2026-07-05 04:49:36
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