Modeling climate change impacts on urban population exposure to heat stress dynamics in Vancouver and Victoria, Canada
Bibliographic record
Abstract
• High-resolution WRF-UCM projects heat exposure in Vancouver and Victoria. • Population growth drives future heat exposure more than climate change. • Heat stress days triple under SSP5-8.5 relative to SSP2-4.5 by 2100. • Multi-index analysis shows Humidex underestimates local heat exposure. The Urban Heat Island effect, a direct consequence of urban design and human activities, significantly alters urban areas’ micro-climates. This phenomenon leads to uneven thermal distributions, impacting some communities more than others. The effect is compounded by climate change, posing challenges to climate justice and equity. Factors such as land-sea breeze introduce additional complexities in coastal cities like Vancouver and Victoria (BC, Canada), which are the focus of this study. This study utilizes the high-resolution Weather Research and Forecasting model coupled with a Single-layer Urban Canopy Model to assess the impacts of climate change on the number of heat stress days experienced by the population, in Vancouver and Victoria. We analyze two climate change scenarios based on the Coupled Model Intercomparison Project Phase 6, covering near- and far-future projections under SSP2-4.5 and SSP5-8.5 pathways. The model exhibits a good alignment with stationary and satellite observations for our base year (2018). The results show that population exposure to heat stress under SSP5-8.5 would be three times higher than under SSP2-4.5 by the end of the century. The results highlight the importance of greenhouse gas emission mitigation in reducing population exposure to heat stress, while urban population growth is the main contributor to the increased population exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".