Assessing the environmental justice implications of seismic risk in Ottawa-Gatineau and Montreal metropolitan areas
Bibliographic record
Abstract
This study investigates distributive environmental injustices in seismic risk exposure across urban areas of Ottawa-Gatineau and Montreal, testing the hypothesis that socially vulnerable communities face disproportionate seismic hazards. Using geographically aggregated data from Canada's probabilistic seismic risk model and the 2021 national census, we examine spatial heterogeneity in the relationships between race/ethnicity, socioeconomic vulnerability, and seismic risk. Social vulnerability was measured through economic insecurity and neighbourhood instability indices. The study uses separate geostatistical models to assess spatial heterogeneity and endogeneity. To address potential endogeneity in global ordinary least squares regression, we apply two-stage least squares regression with instrumental variables (e.g., rural areas, dwelling density) for robust global estimates. Spatial variability has been assessed using multiscale geographically weighted regression for more localized insights. Bivariate local indicators of spatial association cluster mapping further identify risk hotspots and high-risk socioeconomically disadvantaged areas for targeted interventions and disaster risk reduction programs. Findings reveal that recent immigrants, seniors, lone-parent households, and visible minorities are significantly associated with seismic risk in both regions. In Montreal, higher risk correlates with populations living alone, low-income individuals, those without a high school diploma, and non-official language speakers. In Ottawa-Gatineau, seismic risk is more strongly linked to seniors, visible minorities, and lone-parent families. Older housing consistently emerges as a critical built-environmental vulnerability. These results underscore the need for region-specific policies that integrate social and structural risk factors into disaster mitigation. The study contributes to environmental justice and social vulnerability literature, advocating for vulnerability-based risk management and targeted urban resilience strategies. • Socio-environmental inequities in seismic risk exposure are spatially heterogeneous. • Socially vulnerable residents face most severe seismic risk-related inequalities. • Risk hotspots shift locally when hazard integrates with instability or insecurity. • Neighbourhood instability factors exert a stronger influence over high seismic risk. • Recent immigrants experience higher seismic risk-related racial/ethnic inequalities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".