A Machine Learning Approach on the Relationship Among Solar and Wind Energy Production, Coal Consumption, GDP, and CO2 Emissions

Renewable Energy, Volume 151, May 2020, Pages 829-836, DOI: 10.1016/j.renene.2020.11.050, ISSN: 0960-1481

Posted: 18 Feb 2021

See all articles by Cosimo Magazzino

Cosimo Magazzino

University of Rome III; Royal Economic Society; Italian Economic Association

Marco Mele

Luspio University - School of Political Sciences

Nicolas Schneider

Centre d'Économie de la Sorbonne (CES) - Center of Economics of the Sorbonne (CES); University of Paris 1 Pantheon-Sorbonne, Centre d'Economie de la Sorbonne (CES), Students

Date Written: 2020

Abstract

China, India, and the USA are the world’s biggest energy consumers and CO2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO2 emissions. This indicates that curbing CO2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal.

Keywords: Wind energy; Solar energy; Coal consumption; CO2 emissions; Machine learning; China; India; USA

JEL Classification: C45; Q2; Q4

Suggested Citation

Magazzino, Cosimo and Mele, Marco and Schneider, Nicolas, A Machine Learning Approach on the Relationship Among Solar and Wind Energy Production, Coal Consumption, GDP, and CO2 Emissions (2020). Renewable Energy, Volume 151, May 2020, Pages 829-836, DOI: 10.1016/j.renene.2020.11.050, ISSN: 0960-1481, Available at SSRN: https://ssrn.com/abstract=3745072

Cosimo Magazzino (Contact Author)

University of Rome III ( email )

Via Ostiense 139
Rome, RM 00154
Italy

HOME PAGE: http://scienzepolitiche.uniroma3.it/cmagazzino/

Royal Economic Society ( email )

London
United Kingdom

Italian Economic Association ( email )

Piazzale Martelli, 8
Ancona, AN 60121
Italy

HOME PAGE: http://www.siecon.org/online/en/

Marco Mele

Luspio University - School of Political Sciences ( email )

Rome
Italy

Nicolas Schneider

Centre d'Économie de la Sorbonne (CES) - Center of Economics of the Sorbonne (CES) ( email )

106-112 bd de l'Hôpital
Paris, 75642
France

University of Paris 1 Pantheon-Sorbonne, Centre d'Economie de la Sorbonne (CES), Students ( email )

106-112 Boulevard de l'hopital
Paris Cedex 13
France

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