Over the past few decades, scholars have employed a wide range of methodologies to determine the factors influencing firms’ voluntary carbon disclosure. Most of these studies have been conducted in advanced markets. This article aims to examine the trend of voluntary carbon disclosure in the Korean financial market by utilizing machine learning models such as Random Forest and Gradient Boosted Decision Tree. Based on a set of hand-collected carbon disclosure data, we initially demonstrated significantly better performance of machine learning models compared to the traditional logistic model. Regarding the factors influencing disclosure, we consistently find the importance of environmental scores, emphasizing the role of the emerging mega-trend of ESG management practices in disclosure decisions. However, in contrast to recent studies, we do not find that the unique Korean governance structure, chaebol, has any significantly different implications in terms of prediction performance and variable importance in carbon disclosure decisions.

中文翻译：

---

韩国市场碳披露的机器学习方法：环境绩效的作用

在过去的几十年里，学者们采用了多种方法来确定影响企业自愿碳披露的因素。这些研究大部分是在先进市场进行的。本文旨在利用随机森林和梯度提升决策树等机器学习模型来研究韩国金融市场自愿碳披露的趋势。基于一组手工收集的碳披露数据，我们最初证明了机器学习模型与传统逻辑模型相比具有明显更好的性能。关于影响披露的因素，我们始终认为环境评分的重要性，强调ESG管理实践的新兴大趋势在披露决策中的作用。然而，与最近的研究相比，我们没有发现韩国财阀独特的治理结构在碳披露决策的预测绩效和变量重要性方面有任何显着不同的影响。