Big Data and Machine Learning in ESG Research*

Abstract

The wide applications of machine learning techniques to big data allow researchers to dig deep into novel large-scale data sets, such as job postings, earnings calls, and news reports. They also equip researchers with powerful tools to study important but subtle/challenging topics that are impossible to explore before on a large scale, such as corporate culture and climate risk exposure. In this review, I survey various applications of different machine learning techniques in ESG research, beginning with foundational methods such as bag-of-words, progressing through topic modeling, word embedding, and BERT, and culminating with generative artificial intelligence (AI) and other advanced machine learning approaches. I conclude by outlining future directions for using big data and machine learning in ESG research.