gxceed
← 論文一覧に戻る

Stock Exchange ESG Disclosure Guidance and Carbon Mitigation: International Evidence

証券取引所のESG開示ガイダンスと炭素削減：国際的エビデンス (AI 翻訳)

Yan, Jiamian

プレプリント2025-11-20#開示インフラOrigin: Global
DOI: 10.25949/30141763
原典: https://doi.org/10.25949/30141763

🤖 gxceed AI 要約

日本語

証券取引所のESG開示ガイダンスの採用が企業の炭素削減に与える影響を実証。6つの主要フレームワーク（GRI、SASB、TCFD等）に注目し、ガイダンスのある取引所に上場する企業ほど温室効果ガス排出削減効果が高いことを示す。特にScope1排出に顕著。同時に複数フレームワークを推奨すると効果は弱まる。自主的な開示、発展途上国、低炭素集約産業などで効果が大きい。短期的収益性に影響なく、長期的市場価値とESGパフォーマンスを向上。

English

This paper provides empirical evidence that companies listed on stock exchanges offering ESG disclosure guidance achieve greater carbon mitigation, especially for Scope 1 emissions. The effect is strongest when disclosure is voluntary, in developing countries, and in less carbon-intensive industries. Endorsement of multiple frameworks weakens the effect. No short-term profitability impact, but long-term value and ESG performance improve.

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基準の開発が進む中、証券取引所の開示ガイダンスが企業の脱炭素に実質的な影響を与えるという本知見は、任意開示から強制開示への移行や、複数フレームワークの整理に示唆を与える。

In the global GX context

This study directly informs the global debate on the effectiveness of disclosure frameworks (TCFD, ISSB) and the role of stock exchanges in driving corporate climate action. It highlights that multiple frameworks may dilute impact, a key consideration for harmonization efforts like the ISSB's work.

👥 読者別の含意

🔬研究者:This paper offers robust empirical evidence on the causal effect of stock exchange ESG guidance on emissions reduction, with nuanced findings on framework multiplicity and contextual factors.

🏢実務担当者:Corporate sustainability teams should note that listing on exchanges with ESG guidance correlates with better emissions performance and long-term value; engaging with exchange guidance can be strategic.

🏛政策担当者:Regulators and stock exchanges can use these findings to design or refine ESG disclosure guidance, considering the trade-offs of endorsing multiple frameworks and the importance of voluntary vs. mandatory approaches.

📄 Abstract（原文）

This study investigates the tangible impact of the adoption of environmental, social, and governance (ESG) disclosure guidance by stock exchanges on corporate carbon mitigation. The focus is on six major reporting frameworks commonly cited in stock exchange guidance documents: the Global Reporting Initiative, the Sustainability Accounting Standards Board, the Task Force on Climate Related Financial Disclosures, the International Integrated Reporting Council, the Climate Disclosure Standards Board, and the CDP. Using a sample of large international companies, this thesis provides empirical evidence that companies listed on stock exchanges that provide ESG disclosure guidance achieve greater greenhouse gas emissions mitigation than those listed on exchanges without such guidance. Notably, this mitigation effect is particularly pronounced for Scope 1 (direct) emissions. When multiple disclosure frameworks are endorsed concurrently, the effect persists but diminishes in strength. Finally, the effect is stronger when ESG disclosure is voluntary, in developing countries, in less carbon-intensive industries, and among firms governed by civil law rather than common law systems. While short-term profitability is not significantly affected, ESG disclosure guidance enhances firms’ long-term market value and overall ESG performance. Robustness tests confirm the consistency of the results across alternative model specifications, including alternative measures of the dependent variable, country-level exclusions, additional control variables, industry×year fixed effects, and lead-lag models. The findings highlight the nuanced role of stock exchange endorsement of ESG disclosure guidance on corporate behaviour and ESG performance, emphasising the need for harmonized frameworks to optimize their environmental impact.

🔗 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。