Optimizing EV Charging Infrastructure: A Spatial and Investment – Driven Approach in Istanbul

In response to the global acceleration of electric vehicle (EV) adoption, this study repositions EV charging stations as strategic urban real estate assets rather than mere technical infrastructure. It highlights the untapped potential of charging infrastructure to contribute to sustainable urban mobility, spatial efficiency, and real estate value generation when optimally located. Recognizing current limitations in site selection—often driven by technical feasibility alone—the research proposes a location-based, investment-oriented site selection model that integrates urban planning, spatial accessibility, and economic feasibility into a cohesive decision-support framework. Focusing on Istanbul as a case study, the model operates under a fixed investment budget, aiming to identify optimal locations that maximize usage potential and return on investment (ROI). The analytical framework is grounded in the DIKW (Data–Information–Knowledge–Wisdom) hierarchy and applies a four-stage methodology: data collection, spatial analysis, multi-criteria decision-making (AHP, Fuzzy AHP, SWARA), and portfolio-based evaluation. Geographic Information Systems (GIS) were used to process and visualize key indicators, including population density, transportation networks, land values, and existing charging station locations. The study’s criteria are categorized into urban, economic, and environmental dimensions. While energy infrastructure data were limited, the model is designed to be scalable and adaptable for future data integration. Rooted in location theory, Highest and Best Use (HBU) analysis, and portfolio management principles, the model frames EV charging stations as components of a broader urban investment strategy. Ultimately, the research offers a spatially explicit, data-driven tool for public and private stakeholders, facilitating strategic decision-making in EV infrastructure deployment. The model’s flexible structure allows for adaptation across diverse urban contexts, contributing to both economic and spatial sustainability in EV infrastructure planning.