Artificial Intelligence Models for Detecting Greenwashing in UK ESG and Green Finance Projects
英国のESGおよびグリーンファイナンスプロジェクトにおけるグリーンウォッシュ検出のための人工知能モデル (AI 翻訳)
Bernard Wilson, Godiya Mallum Shallangwa, Samson Lamela Mela
🤖 gxceed AI 要約
日本語
本研究は、英国のESG・グリーンファイナンスプロジェクトにおけるグリーンウォッシュ（見せかけの環境主張）をAIで検出する手法を開発。BERTやClimateBERTなどのNLPモデルとXGBoost等の機械学習を組み合わせ、487社のサステナビリティ報告書を分析。86.34%の精度でグリーンウォッシュリスクを識別し、企業規模やガバナンスが重要な予測因子であることを発見。FCAの反グリーンウォッシュ規制に対応した実用的な枠組みを提供。
English
This study develops an AI framework to detect greenwashing in UK ESG and green finance projects, using NLP (BERT, ClimateBERT) and machine learning (XGBoost, Random Forest) on 487 firms' sustainability reports (2018-2024). It achieves 86.34% accuracy in identifying greenwashing risk and reveals firm size, governance, and financial constraints as significant predictors. The methodology supports regulatory bodies and investors in enhancing transparency under the UK FCA's anti-greenwashing rule.
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、2024年から有報でのサステナビリティ情報開示が義務化され、グリーンウォッシュへの規制強化が課題となっている。本論文のAI検出手法は、日本企業のESG開示の質を監視するツールとして応用可能性があり、金融庁や投資家にとって示唆に富む。ただし、データセットが英国企業に限定されているため、日本市場への適用には追加の検証が必要。
In the global GX context
As global regulators (FCA, SEC, ESMA) tighten anti-greenwashing rules, this paper offers a scalable AI methodology to detect discrepancies between ESG disclosures and actual performance. It contributes to the growing field of AI-driven assurance in sustainable finance, relevant for the credibility of transition finance and net-zero commitments worldwide.
👥 読者別の含意
🔬研究者:Provides a reproducible AI framework for greenwashing detection and identifies key predictors (firm size, governance) for further academic investigation.
🏢実務担当者:Offers a practical tool for corporate sustainability teams to audit their own disclosures and for investors to assess greenwashing risk in portfolios.
🏛政策担当者:Demonstrates the feasibility of AI-based regulatory monitoring to enforce anti-greenwashing rules, supporting transparency in green finance markets.
📄 Abstract（原文）
This study examines the application of artificial intelligence models for detecting greenwashing practices in UK Environmental, Social, and Governance projects and green finance initiatives. The research addresses the growing concern over misleading sustainability claims in light of the UK Financial Conduct Authority's anti-greenwashing rule implemented in May 2024. Employing a mixed-methods approach, this study develops a comprehensive framework integrating Natural Language Processing techniques, specifically transformer-based models including BERT and ClimateBERT, with machine learning algorithms such as XGBoost and Random Forest for quantitative prediction and classification. The methodology incorporates a dataset of UK-based companies' sustainability reports, ESG disclosures, and green finance documentation from 2018 to 2024, comprising 487 firms across multiple sectors. The quantitative analysis utilizes a dual approach: textual analysis through NLP models achieving 86.34% accuracy in identifying greenwashing risk patterns, and financial-ESG divergence analysis using optimized machine learning models with R² values of 0.9790. Key findings reveal that AI models can effectively identify discrepancies between ESG disclosure scores and actual environmental performance, with firm size, governance structure, and financial constraints emerging as significant predictors of greenwashing behaviour. The study contributes to the literature by providing a robust, scalable methodology for regulatory bodies and investors to enhance transparency in sustainable finance markets, ultimately supporting the UK's commitment to achieving net-zero emissions targets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.30574/wjarr.2026.29.1.0177first seen 2026-05-15 18:57:42
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