Blue/green infrastructures: a dual solution for urban heat island and urban flooding
Bibliographic record
Abstract
This review critically examines the interplay between green and blue infrastructures, urban heat island (UHI) effects, and urban flooding. By synthesizing recent findings from 2009 to 2024, we explore how using green and blue infrastructures mitigating UHI and urban flood events. We delve into the characteristics and effectiveness of various green and blue infrastructure solutions, including green roofs, bioswales, and wetlands. The analysis reveals that UHI significantly exacerbates urban flooding disasters. Despite advancements in understanding the effect of blue and green infrastructures, significant gaps remain in the literature. Notably, long-term impact assessments and comprehensive evaluations of existing mitigation strategies are scarce. This highlights the urgent need for targeted research and the development of adaptive management practices to enhance UHI and flood prediction and management in urban settings. Future investigative pursuits should integrate the simultaneous ramifications of UHIs and urban pollution islands to proficiently inform urban planning strategies. The review emphasizes the importance of interdisciplinary approaches, integrating hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers, enriching the current body of knowledge and providing clear directions for future investigations in urban disaster management.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".