Version 1
: Received: 11 December 2022 / Approved: 12 December 2022 / Online: 12 December 2022 (04:40:25 CET)
How to cite:
Laureti, L.; Costantiello, A.; Leogrande, A. The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective. Preprints2022, 2022120193. https://doi.org/10.20944/preprints202212.0193.v1
Laureti, L.; Costantiello, A.; Leogrande, A. The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective. Preprints 2022, 2022120193. https://doi.org/10.20944/preprints202212.0193.v1
Laureti, L.; Costantiello, A.; Leogrande, A. The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective. Preprints2022, 2022120193. https://doi.org/10.20944/preprints202212.0193.v1
APA Style
Laureti, L., Costantiello, A., & Leogrande, A. (2022). The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective. Preprints. https://doi.org/10.20944/preprints202212.0193.v1
Chicago/Turabian Style
Laureti, L., Alberto Costantiello and Angelo Leogrande. 2022 "The Impact of Renewable Electricity Output on Sustainability in the Context of Circular Economy: A Global Perspective" Preprints. https://doi.org/10.20944/preprints202212.0193.v1
Abstract
In this article we investigate the impact of “Renewable Electricity Output” on green economy in the context of circular economy for 193 countries in the period 2011-2020. We use data from World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Our results show that Renewable Electricity Output is positively associated, among others, to “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, to “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of 4 clusters. Finally, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries.
Keywords
environmental economics; general; valuation of environmental effects; pollution control adoption and costs; recycling
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
