A Novel Text‐Based Framework for Forecasting Carbon Prices
炭素価格予測のための新しいテキストベースの枠組み (AI 翻訳)
Christian‐Oliver Ewald, Yaoyu Li
🤖 gxceed AI 要約
日本語
本研究は、FinBERTによるニュース感情分析とPCA次元削減を組み合わせたテキストベースの枠組みでEU炭素価格を予測する。2020〜2024年の週次データを用い、CNN-LSTMモデルが従来モデルより優れ、RMSEを約33%削減した。深層学習と次元削減の組み合わせが炭素市場予測に有効であることを示す。
English
This study proposes a text-based framework for forecasting EU carbon prices, combining FinBERT sentiment analysis with PCA dimensionality reduction. Using weekly data from 2020-2024, the CNN-LSTM model with PCA inputs achieves the best performance, reducing RMSE by about 33% compared to the multivariate CNN-LSTM. The findings demonstrate that combining deep learning with dimensionality reduction improves carbon market forecasting.
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
日本では炭素価格予測はまだ発展途上だが、今後の排出量取引制度やカーボンプライシング導入に際し、AIを活用した予測手法は政策立案や企業のリスク管理に示唆を与える。特に、テキストデータと深層学習の組み合わせは、日本の気候関連開示やTCFD対応にも応用可能。
In the global GX context
This paper contributes to global carbon pricing literature by showing that AI-driven text analysis and deep learning can significantly improve carbon price forecasts. For global practitioners, it offers a replicable framework for integrating news sentiment into carbon market analysis, relevant for transition finance and climate risk management.
👥 読者別の含意
🔬研究者:Provides a novel AI-ESG intersection method for carbon price forecasting, combining FinBERT and CNN-LSTM with PCA.
🏢実務担当者:Offers a practical forecasting tool for carbon market participants, potentially improving hedging and investment strategies.
🏛政策担当者:Highlights the potential of AI in monitoring and predicting carbon market dynamics, useful for designing effective carbon pricing policies.
📄 Abstract（原文）
ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT. To address potential noise and redundancy among predictors, principal component analysis (PCA) is applied for dimensionality reduction. The empirical results suggest that deep learning models generally achieve lower forecast errors than traditional models. In particular, the CNN‐LSTM model with PCA‐based inputs achieves the best predictive performance, reducing RMSE by approximately 33% relative to the corresponding multivariate CNN‐LSTM specification. These findings provide evidence that combining deep learning architectures with dimensionality‐reduction techniques can improve forecasting performance in carbon markets.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/for.70202first seen 2026-08-06 05:04:02
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