A0733
Title: The ESG score and the sustainable development: A machine learning analysis
Authors: Susanna Levantesi - Sapienza University of Rome (Italy)
Gabriella Piscopo - University of Naples Federico II (Italy) [presenting]
Kevyn Stefanelli - Sapienza University of Rome (Italy)
Abstract: In line with the values of a sustainable economy, companies are progressively implementing strategies that aim to balance profitability with environmental, social, and governance (ESG) commitments. The financial sector's heightened sensitivity to climate and environmental risks accentuates the imperative of advancing sustainable investment practices. Within this framework, sustainability, integrating ESG factors, stands as a central strategic focus. The aim is to investigate the relation between ESG score and sustainable practice of some listed companies. To this end, the results of the classical regression framework are compared with those of advanced machine learning techniques, including random forest and gradient boosting machine algorithms.
