Proximity planning for urban electrification: Walkable access to EV charging infrastructure in Montreal
Bibliographic record
Abstract
As cities pursue low-carbon mobility transitions, equitable access to electric vehicle (EV) infrastructure remains a persistent planning challenge, particularly for aging populations with reduced mobility. This study evaluates walkable access to public EV charging stations on Montréal Island, with a focus on elderly care facilities, highlighting that equitable, age-sensitive charger placement is increasingly vital as the city’s aging population risks being overlooked in current infrastructure planning. Using GIS-based spatial analysis, we model accessibility to public EV charging infrastructure for both the general adult population and older adults, applying age-adjusted walking speeds within a 15-minute threshold. The results show that walk-up accessibility is systematically lower for older adults and is strongly associated with median age and the spatial concentration of urban amenities. We identify “double-burden” zones where demographic vulnerability (higher shares of older residents) intersects with infrastructure gaps, underscoring the limitations of proximity-based planning when it is decoupled from equity considerations. Building on these findings, our focused analysis of elderly care facilities and their surrounding walkable environments exposes a critical infrastructure gap: nearly half of these sites have no public EV charging stations within a reasonable walking distance. To inform targeted interventions, we overlay areas of poor charger accessibility with point-of-interest (POI) density and apply a simple greedy siting heuristic, identifying priority zones for deployment that maximize both need and broader community benefit. To contextualize these disparities, we develop a causal loop diagram that links charger deployment, equity objectives, and market dynamics, framing policy levers for equity-based planning of EV charging infrastructure. This research offers a transferable framework for cities aiming to align EV infrastructure with inclusive, proximity-based urban planning goals, ensuring that the electrification transition does not leave aging populations behind
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".