Machine Learning-Based Classification and Feature Analysis of Heterogeneous Environmental Sustainability Disclosure
機械学習に基づく異質な環境持続可能性開示の分類と特徴分析 (AI 翻訳)
F. Lin, Chin-Chiu Lee, Te-Nien Chien
🤖 gxceed AI 要約
日本語
本研究は、機械学習を用いて台湾企業の環境開示データを分析し、高・低ESGパフォーマンス企業の開示特性を分類・比較する。CatBoostなどのアンサンブルモデルが従来手法を上回る性能を示し、炭素排出、エネルギー効率、廃棄物管理が主要な特徴であることが明らかになった。再生可能エネルギー変数の重要性が時間とともに増加していることも示され、環境開示の非線形・多次元的性質が確認された。
English
This study develops an ML-based framework to classify environmental disclosure characteristics of high vs. low ESG performers in Taiwan (2022-2024). Ensemble models (CatBoost) outperform traditional ones, with carbon emissions, energy efficiency, and waste management as dominant features. The importance of renewable energy variables increases over time, highlighting the nonlinear and multidimensional nature of environmental disclosure.
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
台湾企業を対象としているが、日本企業の開示分析にも応用可能な機械学習手法を提供。SSBJや有価証券報告書における非財務情報の質的評価に役立つ可能性がある。
In the global GX context
This paper demonstrates the power of machine learning in analyzing heterogeneous environmental disclosure, relevant for global frameworks like ISSB and TCFD. It shows how ensemble models can identify key disclosure features, aiding investors and regulators in assessing disclosure quality beyond simple ESG scores.
👥 読者別の含意
🔬研究者:Provides a reproducible ML methodology for analyzing environmental disclosure heterogeneity and feature importance.
🏢実務担当者:Offers a benchmark for using ML to evaluate and compare corporate environmental disclosure quality, useful for investment screening.
🏛政策担当者:Highlights which disclosure elements (carbon, energy, waste) are most discriminative, informing regulatory focus and standard-setting.
📄 Abstract（原文）
Environmental sustainability disclosure has become increasingly critical as climate risks intensify and regulatory and investor demands for transparent, decision-useful information continue to grow. It plays a key role in reducing information asymmetry and supporting capital allocation, risk assessment, and regulatory oversight. However, prior studies predominantly rely on aggregated ESG indicators and linear models, which often fail to capture the structural heterogeneity and nonlinear relationships inherent in environmental data. This study develops a machine learning-based analytical framework to examine environmental disclosure using corporate data from the Taiwan Economic Journal (TEJ) from 2022 to 2024. A polarized sampling design is employed by selecting firms in the top and bottom 20% of ESG performance to identify and compare the distinctive disclosure characteristics of companies with high versus low environmental performance. Five models are evaluated using Accuracy, Precision, Recall, F1-score, and AUROC. The results show that ensemble models outperform traditional approaches, with CatBoost achieving the most robust performance. Feature importance analysis reveals a concentrated structure dominated by carbon emissions, energy efficiency, and waste management, while the importance of renewable energy variables increases over time. These findings highlight the nonlinear and multidimensional nature of environmental disclosure and demonstrate the value of machine learning in enhancing environmental sustainability analysis, investment decision-making, and regulatory effectiveness. As this study is based on a single-country dataset (Taiwan), future research may incorporate cross-country datasets to improve external validity.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18126206first seen 2026-06-29 08:35:45
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