A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. (April 2021)
- Record Type:
- Journal Article
- Title:
- A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. (April 2021)
- Main Title:
- A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions
- Authors:
- Magazzino, Cosimo
Mele, Marco
Schneider, Nicolas - Abstract:
- 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. Highlights: Renewable energies, coal consumption, economic growth, and CO2 we examine. We investigate the causal predictedAbstract: 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. Highlights: Renewable energies, coal consumption, economic growth, and CO2 we examine. We investigate the causal predicted relationship on China, India, and the USA. An advanced methodology in Machine Learning is used. India has a prediction of an increase in emissions of CO2 . China and the U.S. present a more promising path to sustainability than India. … (more)
- Is Part Of:
- Renewable energy. Volume 167(2021)
- Journal:
- Renewable energy
- Issue:
- Volume 167(2021)
- Issue Display:
- Volume 167, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 167
- Issue:
- 2021
- Issue Sort Value:
- 2021-0167-2021-0000
- Page Start:
- 99
- Page End:
- 115
- Publication Date:
- 2021-04
- Subjects:
- Wind energy -- Solar energy -- Coal consumption -- CO2 emissions -- Machine learning -- China -- India -- USA
C45 -- Q2 -- Q4
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2020.11.050 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 15498.xml