Predicting Corporate Carbon Disclosure in China: Evidence from Interpretable Machine Learning
中国における企業の炭素開示の予測：解釈可能な機械学習による証拠 (AI 翻訳)
He Peng Yang, Norhaiza Bt. Khairudin, Danilah Salleh
🤖 gxceed AI 要約
日本語
本研究は、中国A株企業48,187件のデータを用い、解釈可能な機械学習モデルで炭素開示の主要予測因子を特定。GBDTが最も高い予測性能（R²=0.5191）を示し、企業規模、メディア注目、環境政策強度、市場集中度、経営陣の財務背景が重要であることを発見。重汚染産業や国有企業では規制・ガバナンス要因が、東部地域や民間企業では市場要因がより重要と判明した。
English
This study uses 48,187 observations of Chinese A-share firms from 2012 to 2024 to develop an interpretable machine learning model predicting corporate carbon disclosure. GBDT achieves the best out-of-sample performance (R²=0.5191). Key predictors include firm size, media attention, environmental policy intensity, market concentration, and executive financial background. Heterogeneity tests show regulatory and governance factors matter more for heavily polluting and state-owned firms, while market factors dominate in eastern and private firms.
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
中国企業の炭素開示を機械学習で予測する手法は、日本のSSBJ開示や有報における気候関連情報の拡充に示唆を与える。特に、企業特性に応じた開示予測因子の違いは、日本企業の開示改善や投資家向け分析に応用可能。
In the global GX context
This paper demonstrates the power of interpretable machine learning for predicting corporate carbon disclosure, relevant to global frameworks like TCFD and ISSB. The findings on regulatory vs. market drivers across firm types offer insights for disclosure policy design worldwide, especially in emerging economies.
👥 読者別の含意
🔬研究者:Provides a robust ML benchmark for carbon disclosure prediction and reveals nonlinear interactions among determinants.
🏢実務担当者:Identifies key factors (e.g., media attention, policy intensity) that firms can leverage to improve disclosure practices.
🏛政策担当者:Highlights the differential importance of regulatory vs. market factors across firm types, informing targeted disclosure mandates.
📄 Abstract（原文）
Corporate carbon disclosure has become increasingly important in China’s transition toward sustainability and low-carbon development, yet existing research often focuses on isolated determinants and relies mainly on linear empirical models. Using 48,187 observations of Chinese A-share firms from 2012 to 2024, this study identifies the key predictors of corporate carbon disclosure. It develops an interpretable machine learning model and compares its predictive performance with that of linear regression, LASSO, decision tree, random forest, support vector machine, GBDT, and XGBoost. The results show that ensemble methods outperform linear models in both in-sample and out-of-sample predictions. GBDT delivers the best out-of-sample performance, with an R2 of 0.5191, suggesting that nonlinear relationships and interaction effects matter in predicting corporate carbon disclosure. The key factors identified are firm size, media attention, environmental policy intensity, market concentration, and executive financial background. The heterogeneity tests show that regulatory and governance factors are more important for firms in heavily polluting industries, state-owned firms, and firms in central and western China, whereas market factors are more important for firms in eastern China, private firms, and firms in less polluting industries. Overall, the paper provides new evidence on the prediction of corporate carbon disclosure and offers practical implications for regulators and firms seeking to improve their sustainability-related disclosure practices.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18084022first seen 2026-05-05 07:41:51
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。