A unified multi-criteria equity metric for data-driven decision-making: Application to tract-level electric vehicle charging station planning

This paper introduces a unified, data-driven multi-criteria equity metric designed to address inequities in Electric Vehicle Charging Station (EVCS) accessibility and Electric Vehicle (EV) adoption. Existing studies predominantly focus on macro-level factors and rely on single-criterion or context-specific approaches, limiting their applicability to dynamic, multi-dimensional challenges. Additionally, current equity evaluations often involve inherent subjectivity, complicating decision-making processes. Focusing on the Dallas-Fort Worth area, this study advances the field by transitioning from city-level to census tract-level analysis. The proposed framework utilizes dimension reduction techniques to construct a unified equity metric and high-resolution clustering analysis to identify EV-related socio-demographic typologies. These typologies—EV developed, EV developing, and EV underdeveloped/disadvantaged—are validated against disadvantaged communities (DACs) defined by the Climate and Economic Justice Screening Tool (CEJST), mapped EV/EVCS distributions, and their corresponding socio-demographic radar plots. The data-driven equity metric has advantages in unity, objectivity, adaptability, and robustness compared to existing metrics. By identifying micro-level inequities, the framework provides tailored policy recommendations for each typology, supporting equitable EV infrastructure planning.