Developing A <scp>Z‐ESG</scp> Score Model for Assessing Corporate <scp>ESG</scp> Performance
企業ESGパフォーマンス評価のためのZ-ESGスコアモデルの開発 (AI 翻訳)
Edward I. Altman, Francesco Baldi, Claudia D'Ippolito, Antonio Salvi
🤖 gxceed AI 要約
日本語
本研究では、AltmanのZスコアのロジックをESG評価に応用した独自のESG格付けモデル「Z-ESG」を開発。欧州上場企業325社のデータを用いた判別分析とロジスティック回帰により、ESGパフォーマンスをスコア化し格付けに変換。資産運用者、リスク管理者、銀行など多様な実務応用が可能。
English
This study develops a novel ESG rating model called Z-ESG, applying Altman's Z-score logic to environmental, social, and governance indicators. Using discriminant analysis and logistic regression on 325 European listed firms, it produces ESG scores and rating classes. The model has multiple practical implications for asset managers, risk managers, banks, and corporate sustainability officers.
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や統合報告書の普及に伴い、客観的なESG評価手法へのニーズが高まっている。本モデルは企業の自己評価や銀行の融資判断に活用でき、日本の開示実務を補完する可能性がある。
In the global GX context
Globally, this model addresses the need for transparent ESG ratings that can be replicated and validated, potentially complementing frameworks like TCFD, ISSB, and CSRD by providing a quantitative scoring tool for ESG performance assessment.
👥 読者別の含意
🔬研究者:The discriminant analysis and logistic regression approach offers a replicable methodology for constructing ESG ratings; researchers can further refine or extend the model to other regions or sectors.
🏢実務担当者:Corporate sustainability officers can use the Z-ESG model for self-assessment and to design ESG strategies; asset managers can identify investment opportunities; banks can price green loans based on the score.
📄 Abstract（原文）
ABSTRACT In this study we develop a novel, unique ESG rating model that exploits the logic of the Z‐score by Altman (1968) to discriminate between ESG performing and non‐ESG performing firms using indicators of ESG performance for each of the three pillars (Environmental, Social, Governance) in place of financial ratios. We name our model the Z‐ESG rating model. Based on a sample of 325 European listed firms, we build a multiple discriminant analysis model to estimate the Z‐ESG score for each firm and confirm these results by employing a logistic regression to determine respective probabilities of being ESG performing. Z‐ESG scores are then converted into agency‐equivalent rating classes through a Z‐ESG rating matrix. Multiple implications can be envisaged for researchers and practitioners: asset managers may use the Z‐ESG rating model to identify new investment opportunities and build appropriate ESG‐performing portfolios; risk managers may exploit the Z‐ESG metrics to assess the current ESG positioning of a firm and monitor its evolving path; credit risk managers can link the Z‐ESG score of a firm to its Z‐score to measure the impact of its ESG performance on its probability of default; bank managers may better price green (or ordinary) loans based on the Z‐ESG score of the borrowers; chief sustainability officers of companies can self‐assess the degree of their ESG performance and design a sustainability strategy that targets a desired Z‐ESG rating; corporate boards may include the Z‐ESG metrics as an additional element of their executive compensation policy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/ijfe.70249first seen 2026-07-18 06:40:32
- semanticscholar https://doi.org/10.1002/ijfe.70249first seen 2026-07-18 07:47:45
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