Sustainable UAE Through Electric Vehicles by 2030

Abstract

Electric vehicles (EVs) have emerged as a viable solution to mitigate the adverse impacts of global warming and climate change. Despite their growing adoption, the optimal placement of Electric Vehicle Charging Stations (EVCS) in densely populated urban environments presents a significant challenge due to the complex interplay of spatial, social, and environmental factors.
This study presents a systematic methodology for identifying the most suitable locations for EVCS deployment in the United Arab Emirates (UAE), integrating Geographic Information Systems (GIS) with a Multi-Criteria Decision-Making (MCDM) framework. Specifically, the Analytic Hierarchy Process (AHP) and its extension, Fuzzy AHP (FAHP). The research begins with an extensive literature review to compile a comprehensive set of potential site selection criteria. This is followed by a two-tier data collection process, designed to validate and refine the criteria based on expert input and contextual relevance. The process culminates in the identification of 37 expert-validated criteria, categorized and weighted using AHP and FAHP methodologies. These weighted criteria are then applied within the GIS environment to generate a suitability map, highlighting optimal locations for EVCS deployment. The analysis reveals that social factors hold the highest significance in determining suitable locations. These are followed by environmental and transportation factors. The resulting suitability map reveals that the northern coastal regions of the UAE demonstrate the highest potential for EVCS deployment, driven by factors such as population density and infrastructure availability. Conversely, the southern areas are identified as the least suitable due to their low population and challenging accessibility conditions.
This study provides a data-driven approach to infrastructure planning that contributes to sustainable urban development and supports the UAE’s strategic objectives for a sustainable transportation system.

Date of Award	2025
Original language	American English
Supervisor	Abdelrahman Eltoukhy (Supervisor)