炭素会計・排出モニタリング・企業気候説明責任のための人工知能：インテリジェントシステムによる透明性・正確性・持続可能性の向上
Artificial Intelligence for Carbon Accounting, Emissions Monitoring, and Corporate Climate Accountability: Enhancing Transparency, Accuracy, and Sustainability through Intelligent Systems (原題)
Neelam Gupta, Smita Goswami
🤖 gxceed AI 要約
日本語
本chapterは、AI（機械学習・NLP・コンピュータビジョン・ブロックチェーン）を炭素会計、排出監査、企業気候説明責任に応用する最新研究を整理する。従来の排出係数・マスバランス・連続監視手法の限界を指摘し、AIが90%超の予測精度やサブ時間単位のScope1-3追跡、異常検知・グリーンウォッシュ検出、デジタル検証を可能にすると論じる。データ品質・アルゴリズムバイアス・標準化ギャップを課題として挙げ、ネットゼロ移行へのAI活用の将来像を示す。
English
This chapter reviews how AI—machine learning, NLP, computer vision, and blockchain—can address limitations in carbon accounting, emissions auditing, and corporate climate accountability. It argues AI enables >90% prediction accuracy, sub-hourly Scope 1-3 tracking, anomaly detection, greenwashing detection, and digital verification, while noting challenges in data quality, algorithmic bias, and standardization gaps.
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
SSBJ基準・有報でのScope3開示・統合報告書作成において、AIによるデータ収集・検証の自動化は日本企業の実務負担軽減と開示品質向上に直結する。特にサプライチェーン排出量の算定精度向上は、投資家対応と政策連動の両面で重要。
In the global GX context
Aligns with global disclosure frameworks (ISSB, CSRD, SEC climate) by showing how AI can operationalize Scope 3 measurement, assurance, and anti-greenwashing—key for transition finance and auditability. Offers a roadmap for integrating intelligent systems into regulated climate reporting.
👥 読者別の含意
🔬研究者:AI×炭素会計の研究動向と未解決課題（データ品質・バイアス・標準化）を俯瞰できる。
🏢実務担当者:Scope3算定や開示データ検証にAIをどう組み込むか、実務導入の方向性を得られる。
🏛政策担当者:AI活用を前提とした開示規制・検証制度の設計や標準化議論に示唆を与える。
📄 Abstract（原文）
Nowadays, with increasing climate change awareness, carbon accounting is becoming more important than ever before. At the same time, there are several limitations and drawbacks concerning the contemporary methods of carbon accounting. This chapter will discuss how artificial intelligence can be applied to address these issues. In particular, the areas of impact include carbon accounting, emissions auditing, and corporate climate responsibility. The chapter will also showcase the latest research and evidence on how machine learning, natural language processing, computer vision, and AI-driven blockchains can help in overcoming the main challenges associated with greenhouse gas accounting and regulation. The existing traditional carbon accounting methods are emission factor-based, mass balance, or continuous monitoring systems, but they all have their own constraints: static assumptions that do not account for the variability of the system in real time with regard to operation, high implementation costs, and there are limited predictability. AI models provide promising alternatives with high prediction accuracy, even above 90% in the application of predicting power generation emissions, and can support tracking Scope 1-3 emissions with sub-hourly accuracy, in almost real time. The chapter outlines the potential features of AI technologies in data collection and data processing (to minimize manual errors and remove duplication in data sources), in predictive analytics and anomaly detection (to improve quality of decision-making and greenwashing), and in digital verifiers (using a combination of blockchain and AI to strengthen transparency and auditability). Corporate Climate Accountability frameworks, supply chain Scope 3 emissions measurement, and how AI has been brought in line with regulatory climate disclosure requirements are given special focus. The chapter draws its conclusions by recognizing the existing challenges, such as data quality, bias in algorithms, and standardisation gaps, and by providing a future outlook of AI-empowered CCSs that contribute to the global transition towards net-zero.
🔗 Provenance — このレコードを発見したソース
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