The potential of Blue-Green infrastructure as a climate change adaptation strategy: a systematic literature review
Bibliographic record
Abstract
Abstract Blue-Green Infrastructure (BGI) consists of natural and semi-natural systems implemented to mitigate climate change impacts in urban areas, including elevated air temperatures and flooding. This study is a state-of-the-art review that presents recent research on BGI by identifying and critically evaluating published studies that considered urban heat island mitigation and stormwater management as potential benefits. Thirty-two records were included in the review, with the majority of studies published after 2015. Findings indicate that BGI effectively controls urban runoff and mitigates urban heat, with the literature being slightly more focused on stormwater management than urban heat island mitigation. Among BGI, the studies on blue- and blue-green roofs focused on one benefit at a time (i.e. thermal or hydrologic performance) and did not consider promoting multiple benefits simultaneously. Two-thirds of the selected studies were performed on a large urban scale, with computer modelling and sensor monitoring being the predominant assessment methods. Compared with typical Green Infrastructure (GI), and from a design perspective, many crucial questions on BGI performance, particularly on smaller urban scales, remain unanswered. Future research will have to continue to explore the performance of BGI, considering the identified gaps.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".