Beyond Volume: The Differential Impact of ESG Disclosure Quantity and Quality on Corporate Tail Risk
量を超えて：ESG開示の質と量が企業のテールリスクに与える異なる影響 (AI 翻訳)
Xingyu Chen
🤖 gxceed AI 要約
日本語
本論文は、ESG開示の量と質を区別し、企業のテールリスクへの影響を分析。中国A株上場企業4044社のデータを用い、機械学習でESG開示を定量化、極値理論でテールリスクを推定。結果、開示の量と質の両方がテールリスクを有意に低減し、汚染産業、財務制約の高い企業、小規模企業で効果が大きい。経路として、ESG関連インシデントの減少、情報非対称性の緩和、ステークホルダー関係の強化を特定。
English
This paper distinguishes between ESG disclosure quantity and quality and examines their impact on corporate tail risk. Using a dataset of 4,044 Chinese A-share listed firms from 2011 to 2022, it employs machine learning to quantify ESG disclosure and extreme value theory for tail risk. Results show both quantity and quality significantly reduce tail risk, with stronger effects for polluting industries, financially constrained firms, and smaller firms. Channels include reduced ESG incidents, lower information asymmetry, and enhanced stakeholder relationships.
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SSBJ基準や有報でのESG開示義務化が進む中、開示の「質」と「量」の効果を実証した本知見は、企業の開示戦略や投資家対応に示唆を与える。中国市場の分析だが、日本企業のリスク管理にも応用可能。
In the global GX context
As global ESG disclosure regulations (ISSB, CSRD, SEC) evolve, this paper provides empirical evidence that both the quantity and quality of ESG disclosure reduce corporate tail risk. It highlights the importance of credible disclosure for market stability, supporting regulatory efforts to enhance disclosure standards.
👥 読者別の含意
🔬研究者:Provides a novel distinction between ESG disclosure quantity and quality, with machine learning methodology applicable to other markets.
🏢実務担当者:Demonstrates that improving both the volume and credibility of ESG disclosure can lower tail risk, guiding corporate disclosure strategy.
🏛政策担当者:Offers evidence that mandatory ESG disclosure regulations can enhance market stability by reducing tail risk.
📄 Abstract（原文）
ABSTRACT This paper investigates how ESG disclosure affects corporate tail risk by distinguishing between disclosure quantity and quality. Using a dataset of 4044 Chinese A‐share listed companies from 2011 to 2022, I employ a machine‐learning approach to quantify ESG disclosure from corporate textual data, while tail risk is estimated using extreme value theory. The empirical results show that both ESG disclosure quantity and quality significantly reduce firms' tail risk. The impact is more pronounced for firms in polluting industries, those with high financial constraints, and smaller firms. I identify three potential channels: decreased ESG‐related incidents, reduced information asymmetry between managers and outside investors, and enhanced stakeholder relationships. These findings highlight the importance of comprehensive and credible ESG disclosure in promoting market stability, with implications for sustainability reporting regulations and corporate risk management strategies.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1002/mde.70010first seen 2026-05-05 19:08:06
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