Version 1
: Received: 25 December 2022 / Approved: 27 December 2022 / Online: 27 December 2022 (01:30:08 CET)
How to cite:
Laureti, L.; Costantiello, A.; Leogrande, A. The Role of Renewable Energy Consumption in Promoting Sustainability and Circular Economy. A Data-Driven Analysis. Preprints2022, 2022120496. https://doi.org/10.20944/preprints202212.0496.v1
Laureti, L.; Costantiello, A.; Leogrande, A. The Role of Renewable Energy Consumption in Promoting Sustainability and Circular Economy. A Data-Driven Analysis. Preprints 2022, 2022120496. https://doi.org/10.20944/preprints202212.0496.v1
Laureti, L.; Costantiello, A.; Leogrande, A. The Role of Renewable Energy Consumption in Promoting Sustainability and Circular Economy. A Data-Driven Analysis. Preprints2022, 2022120496. https://doi.org/10.20944/preprints202212.0496.v1
APA Style
Laureti, L., Costantiello, A., & Leogrande, A. (2022). The Role of Renewable Energy Consumption in Promoting Sustainability and Circular Economy. A Data-Driven Analysis. Preprints. https://doi.org/10.20944/preprints202212.0496.v1
Chicago/Turabian Style
Laureti, L., Alberto Costantiello and Angelo Leogrande. 2022 "The Role of Renewable Energy Consumption in Promoting Sustainability and Circular Economy. A Data-Driven Analysis" Preprints. https://doi.org/10.20944/preprints202212.0496.v1
Abstract
In this article we investigate the role of “Renewable Energy Consumption” in the context of Circular Economy. We use data from the World Bank for 193 countries in the period 2011-2020. We perform several econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our 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 “GHG net emissions/removals by LUCF” and “Mean Drought Index”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we compare eight different machine learning algorithms to predict the value of Renewable Energy Consumption. Our results show that the Polynomial Regression is the best algorithm in the sense of prediction and that on average the renewable energy consumption is expected to growth of 2.61%.
Keywords
Environmental Economics; General; Valuation of Environmental Effects; Pollution Control Adop-tion 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.
