Candidate Network Composition and Locational Fairness in Electric Vehicle Charging Placement: Evidence to Guide Low-Adoption-Phase Planning in Sapporo, Japan

Equitable distribution of public EV charging infrastructure is a priority as cities electrify transport, yet placement optimization often treats equity as secondary. This study develops a multi-objective framework treating demand coverage and locational fairness as simultaneous objectives across Sapporo’s ten districts, using private car travel as a demand proxy for the low-adoption phase. A hybrid genetic algorithm with NSGA-II generates Pareto-optimal station and charger configurations across six investment levels, JPY 450 million to JPY 7000 million, using 706,249 synthetic agents with trip origins in 30,359 meshes. Three candidate networks are evaluated: the existing EV charging stations, 450 gas station-centered candidates, and 1830 expanded multi-facility candidates, with a reachable mesh restriction separating spatial from allocation-driven exclusion. The solutions found for the gas station-centered network are fairer than those found for the expanded network, which contains every one of them, so the gap reflects the search and not the fairness the expanded network can attain. The expanded network leaves 44.3% of origin meshes with no trip destination within 1 km of a candidate station, so closing this gap requires new sites near the trip destinations. District-level decomposition exposes disparities concealed by aggregate metrics, guiding infrastructure allocation under Sapporo’s Green Transformation (GX) planning.