What question did this study set out to answer?

The study aims to identify the predictors of corporate carbon disclosure in China using advanced machine learning models rather than traditional linear methods.

April 19, 2026SustainabilityOpen Access

Predicting Corporate Carbon Disclosure in China: Evidence from Interpretable Machine Learning

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The study aims to identify the predictors of corporate carbon disclosure in China using advanced machine learning models rather than traditional linear methods.
Analyzed data from 48,187 Chinese A-share firms from 2012 to 2024.
Developed an interpretable machine learning model for predicting carbon disclosure.
Compared performance of machine learning models with linear regression and other algorithms like GBDT and XGBoost.
Conducted heterogeneity tests across different firm categories.
Ensemble methods outperformed linear models in both in-sample and out-of-sample predictions.
GBDT achieved the best out-of-sample performance with an R2 of 0.5191.
Key predictors include firm size, media attention, and environmental policy intensity.
Regulatory factors are more significant for heavily polluting and state-owned firms, while market factors are crucial for private firms.

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Implication

Uncovers key predictors of corporate carbon disclosure in China, suggesting improved prediction through advanced machine learning techniques.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69e47250010ef96374d8e6e1 https://doi.org/10.3390/su18084022

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Implication

Uncovers key predictors of corporate carbon disclosure in China, suggesting improved prediction through advanced machine learning techniques.

Predicting Corporate Carbon Disclosure in China: Evidence from Interpretable Machine Learning

Key Points

Discussion

Implication

Cite This Study

Predicting Corporate Carbon Disclosure in China: Evidence from Interpretable Machine Learning

Key Points

Discussion

Implication

Cite This Study

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