gxceed
← 論文一覧に戻る

THE IMPACT OF ESG DISCLOSURE QUALITY ON CORPORATE FINANCIAL PERFORMANCE UNDER IFRS SUSTAINABILITY STANDARDS

IFRSサステナビリティ基準下におけるESG開示の質が企業財務パフォーマンスに与える影響 (AI 翻訳)

Shodiyeva Malika Shermatovna, Abdullayev Xurshidjon Nazrullayevich

Zenodoデータセット2026-07-04#ESGOrigin: Global経営インパクト: 資金調達対象セクター: cross_sector
DOI: 10.5281/zenodo.21186073
原典: https://zenodo.org/records/21186073
📄 PDF

🤖 gxceed AI 要約

日本語

本研究は、ISSBが2023年に公表したIFRS S1・S2基準を踏まえ、ESG開示の質が財務パフォーマンスに与える影響を2018~2024年の24カ国・2,847企業年データで分析。固定効果パネル回帰の結果、ESG開示とROA・トービンのQとの間に有意な正の関係、資本コストとの間に負の関係を確認。監査の質が調整変数として作用し、欧州社債市場では15.2bpの「グリー二ウム」効果も観察された。

English

This study analyzes the impact of ESG disclosure quality on corporate financial performance under IFRS S1/S2, using panel data of 2,847 firm-years across 24 jurisdictions from 2018-2024. Fixed-effects regressions show significant positive associations with ROA and Tobin's Q, and negative association with cost of capital. Audit quality moderates the relationship, and a 15.2 bps greenium is found in European corporate bond markets.

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

IFRS S1・S2は日本のSSBJ基準の基礎であり、本稿の実証結果は日本企業がESG開示の質向上により資本コスト低減や市場評価向上を期待できることを示唆。監査品質の調整効果は、日本の監査実務にも示唆を与える。

In the global GX context

This global empirical study validates the economic benefits of high-quality ESG disclosure under ISSB standards, providing evidence that firms can lower cost of capital and improve market valuation. The findings support the transition to mandatory sustainability reporting globally, including under CSRD and SEC rules.

👥 読者別の含意

🔬研究者:Provides robust cross-jurisdictional evidence on the ESG-financial performance link using post-ISSB framework data.

🏢実務担当者:Demonstrates that improving ESG disclosure quality can reduce cost of capital and enhance market value, guiding corporate strategy.

🏛政策担当者:Offers empirical support for mandating high-quality ESG disclosures under IFRS S1/S2, showing benefits for capital markets.

📄 Abstract(原文)

The introduction of IFRS S1 and IFRS S2 Sustainability Disclosure Standards by the International Sustainability Standards Board (ISSB) in June 2023 represents a transformative milestone in global corporate sustainability reporting. This study examines the impact of ESG disclosure quality on corporate financial performance within the framework of these newly adopted standards. Drawing upon a comprehensive analysis of 2,847 firm-year observations across 24 jurisdictions from 2018 to 2024, we employ panel data regression models with fixed effects to investigate the relationship between ESG disclosure quality and multiple dimensions of financial performance, including Return on Assets (ROA), Return on Equity (ROE), Tobin's Q, and Weighted Average Cost of Capital (WACC). Our findings reveal a statistically significant positive association between ESG disclosure quality and corporate financial performance, with a coefficient of β  = 0.2111 (p < 0.01) for the ESG-ROA relationship and β  = 0.341 (p < 0.001) for the ESG-Tobin's Q nexus. The results demonstrate that firms with higher ESG disclosure scores experience lower costs of capital, with environmental disclosure showing the most pronounced effect on debt financing costs (coefficient = -0.011, p < 0.01). Furthermore, we identify audit quality as a significant moderating variable that amplifies the positive relationship between ESG disclosure and financial performance. The study also documents a significant "greenium" effect of 15.2 basis points in European corporate bond markets. These findings provide robust empirical evidence supporting the theoretical predictions of stakeholder theory, legitimacy theory, and signaling theory, while offering practical implications for corporate managers, investors, and policymakers navigating the evolving landscape of sustainability reporting under IFRS S1 and S2 standards.  

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。

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。