Abstract

In this chapter, the authors investigate the role of “renewable energy consumption” in the context of circular economy. They assume that the consumption of renewable energy is a proxy for the development of circular economy. They use data from the environmental, social, and governance (ESG) dataset of the World Bank for 193 countries in the period 2011-2020. They perform several econometric techniques (i.e., panel data with fixed effects, panel data with random effects, pooled ordinary least squares [OLS], weighted least squares [WLS]). The results show that “renewable energy consumption” is positively associated among others to “cooling degree days” and “adjusted savings: net forest depletion” and negatively associated among others to “greenhouse gas (GHG) net emissions/removals by land use change and forestry (LUCF)” and “mean drought index.” Furthermore, they perform a cluster analysis with the application of the k-Means algorithm and find the presence of four clusters. Finally, they compare eight different machine-learning algorithms to predict the value of renewable energy consumption.

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