A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation

Montassar Kahia, Tarek Moulahi, Sami Mahfoudhi, Sabri Boubaker and Anis Omri ()

Resources Policy, 2022, vol. 79, issue C

Abstract: Improving environmental quality is at the heart of the Saudi Vision 2030. Within this context, this study seeks to extend previous environmental economics literature by examining the relationship between disaggregated energy use, economic growth, and environmental quality in Saudi Arabia using machine learning (ML) techniques. Using data from 1980 to 2020, we found that reducing CO2 emissions cannot be done in Saudi Arabia without a complete transition from fossil to renewable resources and a more viable road to sustainability. ML-based regression and prediction shows that CO2 emissions will continue to grow until 2024. Beginning in 2025 and beyond, the emissions decrease (i.e., reducing CO2 emissions) must be accompanied by an increment use of renewable energies to guarantee stable economic growth.

Keywords: Machine learning; (non)-renewable consumption; Environmental quality; Economic growth (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420722005475
Full text for ScienceDirect subscribers only

Related works:
Working Paper: A Machine Learning Process for Examining the Linkage among Disaggregated Energy Consumption, Economic Growth, and Environmental Degradation (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722005475

DOI: 10.1016/j.resourpol.2022.103104

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().