AI-Verified Carbon Credit Intelligence for High-Integrity Climate Finance and Net-Zero Market Governance
高インテグリティ気候金融とネットゼロ市場ガバナンスのためのAI検証炭素クレジットインテリジェンス (AI 翻訳)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
本モノグラフは、AI検証による炭素クレジットの信頼性向上のための統合アーキテクチャを提案する。不確実性を考慮した測定、因果的追加性分析、機械学習、地理空間分析、デジタルMRV、市場ガバナンスを組み合わせ、検証を多層的な推論問題として扱う。土地、エネルギー、産業、除去、金融ポートフォリオ、公共ガバナンスの各セクターに適用し、高インテグリティなネットゼロ市場設計のためのモデル、指標、監査プロトコルを提供する。
English
This monograph proposes an integrated architecture for AI-verified carbon credit intelligence, combining uncertainty-aware measurement, causal additionality analysis, ML, geospatial analytics, digital MRV, and market governance. It treats verification as layered inference, covering land, energy, industry, removals, finance, and governance, offering models, metrics, and audit protocols for high-integrity net-zero market design.
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、カーボンクレジット市場（J-クレジット）の信頼性向上と、SSBJ開示におけるスコープ3排出量の検証が課題。本フレームワークは、AIによる検証プロセスを強化し、日本の市場ガバナンスと開示制度に示唆を与える。
In the global GX context
Globally, this addresses the credibility gap in carbon markets, aligning with ICVCM and VCMI principles. It offers a rigorous framework for AI-based verification that can support high-integrity climate finance and net-zero governance, relevant to ISSB and CSRD disclosure requirements.
👥 読者別の含意
🔬研究者:Provides a comprehensive research architecture for AI-verified carbon credit intelligence, offering reusable models and metrics for further study.
🏢実務担当者:Offers audit protocols and governance artifacts to enhance the credibility of carbon credit portfolios and digital MRV systems.
🏛政策担当者:Highlights the need for AI-based verification standards to ensure market integrity and support net-zero governance frameworks.
📄 Abstract（原文）
Abstract Carbon credits transform quantified mitigation or removal claims into financial instruments, yet their credibility depends on evidence that is temporally consistent, causally defensible, environmentally complete, and governable across heterogeneous markets. This monograph develops an integrated research architecture for AI-verified carbon credit intelligence, combining uncertainty-aware measurement, statistical explanation, causal additionality analysis, machine learning, geospatial analytics, reproducible digital measurement-reporting-verification systems, and market governance. The framework treats verification as a layered inference problem rather than a single classification task: observed activity must be linked to atmospheric outcomes, counterfactual baselines must be interrogated, leakage and reversal risks must be represented, model confidence must be calibrated, and every decision must remain traceable to evidence provenance. Subsequent chapters connect causal estimation to predictive risk scoring, anomaly detection, multimodal learning, scalable data engineering, MLOps, cybersecurity, and portfolio-level climate-finance decisions. Sectoral pathways cover land systems, energy and industry, engineered removals, financial portfolios, and public governance, with comparative relevance to South Asia, Europe, Africa, and the Americas. The resulting monograph offers reusable models, metrics, audit protocols, governance artifacts, and deployment strategies for research programs and high-integrity net-zero market design. Keywords carbon credit intelligence, climate finance, net-zero governance, AI verification, carbon accounting, measurement uncertainty, additionality, counterfactual baselines, leakage risk, permanence, reversal risk, digital MRV, causal inference, geospatial analytics, anomaly detection, probabilistic calibration, explainable AI, data provenance, MLOps, market integrity, portfolio risk, climate governance, responsible AI
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.62311/nesx/rb2jy-978-81-689097-8-6first seen 2026-08-13 05:54:48
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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。