Detecting Greenwashing in ESG Disclosure: An NLP-Based Analysis of Central and Eastern European Firms
ESG開示におけるグリーンウォッシングの検出：中央・東欧企業のNLP分析 (AI 翻訳)
A. Davidescu, E. Manta, Ioana Bîrlan, Alexandra-Mădălina Miler, Sorin-Cristian Niță
🤖 gxceed AI 要約
日本語
本研究は、中央・東欧の大企業204社を対象に、NLP技術を用いてESG開示と外部メディア報道の乖離を分析し、グリーンウォッシング深刻度指数（GSI）を構築した。結果、業種・規模を超えて中程度のグリーンウォッシングが広がり、金融・航空・オンライン商業で顕著であることを発見。開示の透明性だけでは信用性確保に不十分で、データ駆動型の外部情報統合監視枠組みの必要性を示唆。
English
This study uses NLP to analyze discrepancies between ESG disclosures and external media coverage for 204 large firms in Central and Eastern Europe, constructing a Greenwashing Severity Index (GSI). It finds moderate greenwashing across sectors, with finance, aviation, and e-commerce showing higher severity. The results suggest transparency alone is insufficient, advocating for data-driven, external-information-integrated monitoring frameworks.
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
日本企業がEU市場で事業展開する際、CSRD等の厳格な開示規制に対応する必要があり、本手法はグリーンウォッシングリスクの評価に活用可能。また、日本国内でもSSBJ開示の信頼性担保に示唆を与える。
In the global GX context
This paper provides a replicable NLP methodology for detecting greenwashing, directly relevant to the EU's CSRD and broader global moves toward mandatory sustainability disclosure. It underscores the need for monitoring beyond self-reported data, informing ISSB and SEC debates.
👥 読者別の含意
🔬研究者:Offers a novel NLP framework (GSI) for quantifying greenwashing, with potential for cross-regional adaptation.
🏢実務担当者:Provides a tool to benchmark own ESG disclosure against external scrutiny, useful for compliance and reputation management.
🏛政策担当者:Highlights gaps in disclosure-only regimes, supporting the case for integrated, data-driven oversight mechanisms.
📄 Abstract（原文）
The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18031486first seen 2026-05-05 23:14:36
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。