Carbon Credit Market Pricing in a Big Data Financial Environment
ビッグデータ金融環境における炭素クレジット市場価格設定 (AI 翻訳)
Luping Yu, Yongshan Zhang, Zihao Wang, Li Zhou, Qian Cui
🤖 gxceed AI 要約
日本語
本研究は、炭素クレジット市場の価格予測と政策調整の課題に対し、ベイズ深層強化学習フレームワークを提案する。不確実性を考慮した確率的価格モデリングと制約ベースの強化学習を組み合わせ、5つのデータセットで誤差28%削減、性能25%向上、取引利益22%増加を達成した。ストレステストで堅牢性も確認され、不確実性を意識した学習が炭素市場の安定性と効率性を高めることを示した。
English
This paper proposes a Bayesian Deep Reinforcement Learning framework for carbon credit pricing that combines probabilistic modeling, uncertainty propagation, and constraint-based reinforcement learning. Experiments across five datasets show a 28% error reduction, 25% performance improvement, and 22% increase in trading profits while maintaining compliance. Stress tests confirm robustness, demonstrating that uncertainty-aware learning enhances carbon market stability and efficiency.
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J-クレジット制度や東証カーボンクレジット市場が稼働しており、価格形成の効率性は企業のカーボンプライシング戦略や開示（SSBJ等）に直結する。本手法は不確実性を明示的に扱う点で、日本の排出量取引制度の設計や検証にも示唆を与える。
In the global GX context
As global carbon markets expand under Article 6 and compliance schemes like EU ETS and China's national ETS, accurate and robust pricing models are critical for market stability and transition finance. This paper introduces uncertainty-aware machine learning that outperforms existing models, offering practical improvements for traders, regulators, and carbon credit project developers worldwide.
👥 読者別の含意
🔬研究者:This paper provides a novel Bayesian deep reinforcement learning framework that integrates uncertainty into carbon pricing models, advancing the intersection of AI and carbon market design.
🏢実務担当者:Energy traders and carbon credit portfolio managers can adopt the proposed model to improve trading profitability while ensuring regulatory compliance in volatile markets.
🏛政策担当者:Regulators can leverage the model's stability and compliance features to design more resilient carbon market mechanisms and stress-test policies under uncertainty.
📄 Abstract（原文）
Global carbon markets face challenges in forecasting prices and aligning policies due to volatility, regulatory constraints, and uncertainty. While existing models improve predictions, they fail to incorporate uncertainty into decision-making. To address this, the authors propose a Bayesian Deep Reinforcement Learning framework for Carbon Pricing, which combines probabilistic price modeling, uncertainty propagation, and constraint-based reinforcement learning. Experiments across five datasets show that the model reduces error by 28%, improves performance by 25%, and boosts trading profit by 22%, while maintaining high emissions compliance. Stress-test results confirm the model's robustness, demonstrating that uncertainty-aware learning enhances the stability and efficiency of carbon credit markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.4018/joeuc.405808first seen 2026-05-05 22:47:01
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。