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Assessing Climate Change Disclosure and Its Governance Drivers: Insights From the European Utilities Sector

気候変動開示とそのガバナンス要因の評価：欧州公益事業セクターからの洞察 (AI 翻訳)

C. Boța-Avram, Adriana Tiron Tudor, Liana Stanca

Corporate Social Responsibility and Environmental Management📚 査読済 / ジャーナル2026-03-02#TCFDOrigin: EU
DOI: 10.1002/csr.70523
原典: https://doi.org/10.1002/csr.70523

🤖 gxceed AI 要約

日本語

本研究は、欧州の公益事業企業における気候変動開示の程度に影響を与えるガバナンス要因を、TCFDフレームワークに基づき分析した。機械学習を用いて自動構築した気候関連スコア（TCFD_Score）を用い、回帰分析の結果、取締役会の多様性や規模、CEOの二重性、外部保証などが開示水準に正の影響を与えることが示された。一方、取締役会の独立性や報酬連動などは有意な影響を示さなかった。

English

This study examines governance drivers of climate change disclosure in the European utilities sector using a TCFD-aligned score built via machine learning. Findings show board characteristics (gender diversity, size), CEO duality, and external assurance positively influence disclosure, while board independence and sustainability-linked compensation do not.

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

欧州公益事業セクターの分析ではあるが、TCFDに基づく開示スコアの自動構築手法やガバナンス要因の特定結果は、SSBJ適用を進める日本企業の開示実務や取締役会設計にも示唆を与える。特に、外部保証の質が重要である点は日本でも注目すべきである。

In the global GX context

This paper provides empirical evidence on how governance mechanisms drive TCFD-aligned disclosure quality, relevant for global implementation of ISSB standards and CSRD. The automated scoring methodology can be replicated in other jurisdictions, and the findings on board diversity and external assurance are timely for disclosure reforms worldwide.

👥 読者別の含意

🔬研究者:Offers a replicable machine-learning approach to construct TCFD disclosure scores and identifies significant governance predictors for climate reporting in a regulated sector.

🏢実務担当者:Highlights that board diversity and external assurance by accounting professionals can enhance climate disclosure quality, informing corporate governance strategies.

🏛政策担当者:Suggests that mandating or encouraging external assurance and specific board characteristics may improve compliance with TCFD/ISSB frameworks.

📄 Abstract（原文）

This study aims to enhance academic understanding of the factors influencing the disclosure practices of climate change among European utility companies, specifically in the context of their sustainability reporting. The primary objective is to explore, through a multi‐theoretical framework, the governance drivers that significantly affect the extent of climate change disclosures in the European utilities sector. Data were collected from LSEG Workspace, formerly known as Refinitiv Eikon, which included a sample of 71 European utilities companies operating during the financial year 2024. Initially, in accordance with the Climate‐Related Financial Disclosures recommendations of the Task Force (TCFD) and aligned with Sustainable Development Goal 13 (SDG 13: climate action), we established a climate‐related score (TCFD_Score) to evaluate the compliance of sustainability reports with the TCFD framework. This score was developed using an automated structured process that incorporates a machine learning approach, specifically designed in the Python programming language within the Google Colab environment. Subsequently, an ordinary least squares regression analysis was used to identify which governance drivers significantly affect the climate‐related score. The findings indicate that the characteristics of the board (such as gender diversity and board size), CEO duality, external assurance of sustainability reporting, and the qualifications of external assurance providers in the audit‐accounting field positively and significantly influence the climate disclosure score. On the contrary, factors such as board independence, audit committee independence, sustainability‐based compensation, and dedicated sustainability committees did not show a significant impact. By examining these determinants, this research seeks to provide valuable information on the mechanisms underlying transparency and accountability in environmental reporting by utilities providers.

🔗 Provenance — このレコードを発見したソース

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