新興炭素市場向けの説明可能で政策認識型AIによる炭素クレジット価格予測：研究フレームワーク
Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets (原題)
Summaiya Unnisa Begum, Mohammed Nadeem Ullah, Mohammed Abdul Ghani Khan
🤖 gxceed AI 要約
日本語
炭素市場の価格予測は難しい。既存研究はEU・中国に偏り、政策テキストを単純な感情スコアに圧縮し、説明安定性を欠く。本研究は10のギャップを整理し、市場系列と政策テキストをクロスアテンションで融合するEPA-CarbonNetを提案。11年分のS&P炭素指数で検証した結果、ランダムウォークに負けるなど否定的結果が多く、政策アテンションは規制イベントと一致しなかった。方向的中率58.6%はベースラインを上回る。
English
Carbon price forecasting is hard; existing work clusters on EU/China, compresses policy text into sentiment, and lacks calibration or explanation stability. This paper distills ten gaps into an impact-feasibility matrix and proposes EPA-CarbonNet, a six-layer architecture fusing market series with policy text via cross-attention and calibrated intervals. Tested on 11 years of S&P carbon index data, results are largely negative: random walk beats the model on 5-day RMSE, SHAP rankings are unstable, and policy attention never aligns with regulatory events. Directional accuracy of 58.6% leads all baselines.
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
日本では炭素クレジット市場（J-Credit）が始動し、価格形成メカニズムの理解が重要。本論文の政策テキストと市場データの融合手法は、日本の排出量取引制度やクレジット市場の分析に応用可能。ただし、予測性能の限界を認識し、政策評価や市場監視に活用する視点が求められる。
In the global GX context
As global carbon markets expand beyond EU and China, this paper's framework for integrating policy text into price prediction is timely. It offers a rigorous, negative-result study that cautions against overclaiming AI accuracy in carbon markets, relevant for ISSB-aligned disclosure and transition finance where carbon price assumptions matter. The open-source artifacts support reproducibility and further research.
👥 読者別の含意
🔬研究者:Provides a structured gap analysis and a reproducible negative result for AI-based carbon price prediction, highlighting the need for calibrated and explainable models.
🏢実務担当者:Offers a framework for incorporating policy signals into carbon price forecasting, but warns of current model limitations; useful for risk assessment in carbon-intensive portfolios.
🏛政策担当者:Demonstrates the difficulty of predicting carbon prices and the importance of transparent, policy-aware models for market oversight and stability.
📄 Abstract（原文）
Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses regulatory text into a sentiment score, and reports accuracy without calibration or explanation stability. We distil ten recurring gaps into an impact-feasibility matrix and propose EPA-CarbonNet, a six-layer architecture that fuses market series with policy text by cross-attention and calibrated intervals alongside policy-attributed explanations. We then build and test it on eleven years of daily S and P carbon index data. The findings are largely negative, and reported as measured: a random walk beats the model on five-day RMSE (0.0365 against 0.0475), SHAP rankings agree at rho = 0.54 across resampled backgrounds, and policy attention never coincides with documented regulatory events. Directional accuracy, at 58.6 percent, leads every baseline. Code, data documentation and all result artifacts are available at https://github.com/Kimalice/Toward-Explainable-and-Policy-Aware-AI-for-Carbon-Credit-Price-Prediction
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2609.01765first seen 2026-09-05 05:12:47
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