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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".