JOURNAL ARTICLE
Net Zero Emissions in Saudi Arabia by 2060: Least-Cost Pathways, Influence of International Oil Price, and Economic Consequences.
Published In: Energy Journal, 2025, v. 46, n. 5. P. 57 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Durand-Lasserve, Olivier 3 of 3
Abstract
This article analyzes Saudi Arabia’s goal to achieve net zero emissions (NZE) by 2060 through a hybrid forward-looking general equilibrium model that explicitly represents energy technologies and administered energy prices. It examines least-cost NZE pathways under varying international oil price scenarios and carbon capture availability, finding that domestic price reforms alone can reduce emissions by 13–27% by 2060, while full NZE requires large-scale deployment of renewables, clean hydrogen, and especially carbon capture technologies, including direct air capture (DAC). The study highlights that lower international oil prices increase the carbon price needed for NZE and may cause energy system costs to exceed oil export revenues, with hydrogen exports offering limited offset unless hydrogen prices are substantially higher than current projections. Macroeconomic impacts include a potential 3–10% reduction in GDP by 2060 depending on oil prices and carbon capture availability, driven by increased energy expenditures, investment crowding out, and Dutch disease effects, underscoring the importance of coordinated carbon pricing, technology development, and economic diversification in Saudi Arabia’s energy transition.
Additional Information
- Source:Energy Journal. 2025/09, Vol. 46, Issue 5, p57
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2025
- ISSN:0195-6574
- DOI:10.1177/01956574251340006
- Accession Number:187409636
- Copyright Statement:Copyright of Energy Journal is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.