Urban planning, design and management approaches to building urban resilience: a rapid review of the evidence
Bibliographic record
Abstract
Urban planning, risk governance and resilience have become increasingly important pathways to promote and protect public health at the local level. While climate change, inadequately planned urbanization and environmental degradation have left many cities vulnerable to disasters; the COVID-19 pandemic further highlighted the links between health and urban environments, and the relevance of sustainable and resilient planning. As part of the Protecting environments and health by building urban resilience project led by the WHO European Centre for Environment and Health, we conducted a rapid review of the evidence on urban planning, design and management strategies for increasing preparedness and resilience at the local level. Drawing from six databases (2015–2021), we identified a total of 172 scientific articles. Specific local response strategies were identified for six hazard types and eight cross-cutting issues. Findings suggest that institutional innovation, improving early warning, or understanding risks and cascading effects, are important for all hazards, while urban greening and controlling urban sprawl have synergies and co-benefits across multiple hazard types. This compilation of evidence can support local administrations and communities in further integrating health protection considerations into mainstream urban planning and management and help prepare cities to increase hazard preparedness and become more resilient.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".