Machine learning approach for carbon disclosure in the Korean market: The role of environmental performance

• Published in: Science progress (1916) Vol. 107; no. 1; p. 368504231220766
• Main Authors: Lee, Jeong Hwan, Cho, Jin Hyung, Kim, Bong Jun, Lee, Won Eung
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2024; Sage Publications Ltd
• Subjects: Carbon; Decision trees; Environmental management; Environmental performance; Learning algorithms; Machine learning; Carbon emission; chaebol; CSR; RF; ESG; GBDT; machine learning
• ISSN: 0036-8504; 2047-7163
• DOI: 10.1177/00368504231220766

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.