The rapid adoption of electric vehicles (EVs) presents critical infrastructure challenges, particularly in the strategic deployment of electric vehicle supply equipment (EVSE). In this study, we apply a GIS-based Multi-Criteria Decision Analysis (MCDA) framework to optimize EVSE allocation in an urban setting, using Philadelphia as an example. While this framework provides a structured approach to complex spatial decision-making, it is also subject to methodological uncertainties, including sensitivity to weighting schemes and the scale of the spatial unit selection. These challenges are further compounded for decision-makers who are not experts in MCDA methodologies. Through detailing the workflow of MCDA and conducting a comparative analysis of different methods and parameter choices, we assess how these decisions influence site suitability outcomes and decision reliability. Our goal is not only to support the deployment of EVSE in Philadelphia but also to highlight the strengths and limitations of GIS-MCDA in smart city planning. We offer practical insights for planners, policymakers, and researchers seeking to enhance the transparency, replicability, and effectiveness of GIS-MCDA applications. Our results indicate that methodological differences become particularly pronounced when alternatives have very similar overall scores across multiple criteria, resulting in greater variability in rankings. Additionally, extreme values in the criteria can further impact ranking stability. Uncertainties and challenges arise in the selection of criteria and weighting schemes, propagating through the process of criteria aggregation and parameter definition. A multi-scale approach that integrates multiple methods would enable a more balanced and resilient EVSE deployment strategy in Philadelphia.
Zhou et al. (Tue,) studied this question.