Data Mining in Environmental, Social, Governance (ESG) Analysis: An Overview

Ali Fuseini *

Computer Science Department, Takoradi Technical University, Ghana.

Nana Kweku Nkyekyer

Computer Science Department, Takoradi Technical University, Ghana.

Kelvin Kojo Bennin

Computer Science Department, Takoradi Technical University, Ghana.

Richard Essah

Computer Science Department, Takoradi Technical University, Ghana.

*Author to whom correspondence should be addressed.


Abstract

ESG (environmental, social, and governance) considerations are now essential standards for evaluating the ethical and sustainable effects of investments. This study examines data mining's use in ESG analysis, emphasizing how it may be used to glean useful insights from sizable and varied datasets. It looks at data mining techniques, resources, and applications for trend identification, ESG compliance assessment, and decision support. Future directions, ethical issues, and difficulties in incorporating cutting-edge technologies into ESG analysis are also covered.

Keywords: ESG analysis, data mining, sustainability, environmental performance, social equity, governance transparency, machine learning, text mining, clustering algorithms, predictive, predictive modeling, sentiment analysis, big data, standardization of ESG metrics, real-time ESG monitoring, IOT in ESG, blockchain for governance, ESG reporting, sustainable investments, anomaly detection, greenwashing


How to Cite

Fuseini, Ali, Nana Kweku Nkyekyer, Kelvin Kojo Bennin, and Richard Essah. 2025. “Data Mining in Environmental, Social, Governance (ESG) Analysis: An Overview”. Asian Journal of Research in Computer Science 18 (2):46-49. https://doi.org/10.9734/ajrcos/2025/v18i2560.