The regulatory quality in the light of environmental, social and governance framework at world level

Abstract

This article investigates the determinants of Regulatory Quality-RQ for 193 countries in the period 2011–2020.The database used is the World Bank's Environmental, Social, and Governance-ESG dataset. The analysis was conducted through usage of Ordinary Least Squares OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. Results show that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index” while the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, the k-Means algorithm optimized with the Elbow Method has been applied and five clusters were found. In adjunct, eight machine learning algorithms have been confronted to predict the value of RQ. Results show that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of − 1.29%. In the end, a alysis with the Euclidean distance is discussed.