Urban stormwater resilience: Global insights and strategies for climate adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid urbanization combined with increasing extreme precipitation driven by climate change poses significant challenges to urban infrastructure. This study analyzes stormwater management practices across 11 cities in North America, Europe, and Australia, emphasizing strategies for climate change adaptation. Drawing on a review of published documents and interviews with city officials, we assess regulatory frameworks, policies, and design guidelines. This review identifies a critical gap in integrating stormwater management with emission reduction policies, essential for synergistic co-benefits and addressing both mitigation and adaptation challenges. This study examines the policies through the lens of blue-green infrastructure (BGI), identifying challenges such as adapting multifunctional designs to local contexts and establishing effective governance frameworks to maximize their potential. From a funding perspective, stormwater fees offer a transparent way to finance climate-resilient initiatives, with affordability and public acceptance addressed through incentives like stormwater credits. Regular updates to design storm criteria, guided by advancing climate science, are vital for long-term resilience. However, design storms should be a starting point, focusing more on adaptive, multifunctional structures based on the safe-to-fail paradigm. This study highlights the urgent need for holistic, integrated stormwater management approaches to enhance urban resilience and sustainability in a changing climate.
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.
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 | 0.000 | 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 | 0.001 | 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 it