Equitability of Policies to Address the Urban Heat Island Effect A Comparison of Portland and Vancouver
Bibliographic record
Abstract
"Urban heat island effects raise temperatures in cities, creating particularly harsh conditions for vulnerable populations. This study compares the approaches of Portland, USA and Vancouver, Canada, in mitigating the urban heat island effect, drawing on a literature review, spatial analysis, critical policy review and a discussion of policy findings. Both cities experienced devastating heatwaves in 2021, which accelerated policy responses and heightened attention to equity. Spatial analysis revealed inequities in access to green space and cooling infrastructure, with the highest vulnerability found in older, denser and less-vegetated neighborhoods. Policy analysis indicated partial alignment with United Nations guidelines but identified persistent gaps, including limited adoption of materials-based cooling measures, unstable funding and weak integration of cooling requirements into zoning and development codes. The findings highlight the need for a comprehensive, justice-oriented approach that combines physical cooling interventions with socially inclusive planning and stable, cross-sector governance. Embedding these principles into core urban policy frameworks can reduce heat exposure and help ensure that no community is left behind as cities work to adapt to rising urban temperatures. "@eng
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".