Can ESG disclosure predict carbon risk? Evidence from machine and deep learning models

Md Abubakar Siddique and Sitara Karim

Finance Research Letters, 2025, vol. 83, issue C

Abstract: As ESG disclosure becomes increasingly shaped by shifting regulatory agendas, its role in predicting carbon risk warrants closer examination. This study employs machine learning, deep learning, and ensemble techniques to assess whether ESG and financial indicators can effectively predict carbon risk. Results demonstrate that advanced AI models significantly outperform traditional regressions by capturing complex, non-linear relationships often overlooked by conventional methods. SHAP analysis further identifies environmental disclosure as the most influential predictor. These findings offer a timely, data-driven framework for investors, firms, and policymakers navigating the uncertainties of evolving sustainability reporting standards.

Keywords: AI predictability; ESG disclosure; Carbon risk; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612325009316
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:83:y:2025:i:c:s1544612325009316

DOI: 10.1016/j.frl.2025.107672

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().