Green Rhetoric, Grey Reality: Algorithmic Detection and ESG Oversight in Financial Markets
グリーンレトリック、グレーリアリティ：金融市場におけるアルゴリズム検出とESG監視 (AI 翻訳)
Umberto Nizza
🤖 gxceed AI 要約
日本語
本論文は、EUの新指令2024/825を踏まえ、欧州主要銀行の非財務開示における環境主張の曖昧性をアルゴリズム分析で検出。自然言語処理を用いて未検証で将来志向のESG記述が横行していることを明らかにし、アルゴリズム監査を予防的コンプライアンスと規制執行のツールとして提案する。
English
This paper investigates the opacity and vagueness of environmental claims in non-financial disclosures of major European banks. Using NLP, it identifies widespread use of unverifiable ESG statements that may violate EU law, particularly under Directive 2024/825. It proposes algorithmic auditing as a preventive compliance and regulatory enforcement tool, advocating for semantic supervision to align rhetoric with commitments.
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
本論文はEU指令を基盤とするが、日本でも金融庁が求める「有価証券報告書」におけるESG開示の質的向上に示唆を与える。SSBJ基準が求める「検証可能性」や「透明性」を実現するためのアルゴリズム監査手法は、日本の開示実務にも応用可能。
In the global GX context
This paper is highly relevant to global GX discourse as it addresses greenwashing detection in financial markets under the EU's new Directive 2024/825. The proposed algorithmic auditing framework offers a scalable approach for regulators worldwide—including the SEC and ISSB adopters—to enforce disclosure quality and combat greenwashing.
👥 読者別の含意
🔬研究者:Provides empirical evidence and a methodological framework for detecting greenwashing in ESG disclosures, advancing the field of algorithmic auditing.
🏢実務担当者:Offers a tool for corporate sustainability teams to self-assess the verifiability of their ESG statements and avoid regulatory pitfalls.
🏛政策担当者:Highlights the need for semantic supervision and presents a model for integrating algorithmic auditing into disclosure regulation.
📄 Abstract（原文）
Abstract This paper investigates the systemic opacity and semantic vagueness characterizing environmental claims in the non-financial disclosures of major European banking institutions. By cross-referencing the emerging European legal framework – in particular after the introduction of the new Directive 2024/825 – with empirical findings obtained through algorithmic analysis of consolidated sustainability reports, the paper exposes widespread use of vague, unverifiable, and prospective ESG statements that could qualify as misleading under EU law. Using a Python-based natural language processing tool, the study identifies recurrent linguistic patterns that diverge from the transparency, verifiability, and measurability standards now mandated. It argues for the adoption of algorithmic auditing both as a preventive compliance mechanism and as a regulatory enforcement tool, proposing a shift towards a new model of semantic supervision capable of aligning rhetorical ESG narratives with demonstrable corporate commitments.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.15375/zbb-2026-0104first seen 2026-05-15 18:55:05
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