On the Economic Effects of a Res Local Industry Deployment in Morocco: A Case of Study Defining Scenarios from a Survey to Stakeholders
Ramon Mahia () and
Rafael de Arce ()
Additional contact information
Ramon Mahia: Department of Applied Economics (Econometrics), University Autonomous of Madrid, 28049 Madrid, Spain
Sustainability, 2020, vol. 12, issue 17, 1-16
The aim of this article is to simulate the economic impact on Gross Domestic Product (GDP) and employment of renewable energy sources investment in Morocco over the next 40 years. In this sense, several potential scenarios of energy component evolution have been used based on the results of a specific survey to sector stakeholders. We obtain accurate results, avoiding speculative/theoretical assumptions in terms of scenario design. As usual in the sector, a Dynamic Input–Output Model (DI–O) is used to estimate the direct and indirect effects of such a large investment and, avoiding the criticism of this type of model in the context of long-term simulations, the alternative of de Arce et al. (2012) is used. In this framework, substantial results derive from the three scenarios considered: the increase in Moroccan GDP as a result of this investment could be around 1.2–1.7 points and, on average, 42,000 new jobs could be created.
Keywords: RES; investments; economic impact; CSP and component dependency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:17:p:6811-:d:402450
Access Statistics for this article
Sustainability is currently edited by Prof. Dr. Marc A. Rosen
More articles in Sustainability from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().