Synthesizing the evidence on green and blue infrastructure for urban temperature mitigation in Canada
Bibliographic record
Abstract
Urban green and blue infrastructure (UGBI) is increasingly integrated into cities for its numerous benefits, particularly their cooling effects. As the body of evidence on UGBI cooling ability grows, systematic reviews are essential; however, Canadian studies have been notably absent from global reviews. This study synthesizes the evidence on UGBI cooling effect in Canadian cities, addressing gaps on cooler climates by examining the geographic, climatic, methodological, and UGBI-specific dimensions of the Canadian evidence. Following PRISMA guidelines, we retrieved 1062 articles from Scopus and Web of Science, and after rigorous screening and data extraction, analyzed 43 studies using a systematic review approach. The results reveal a significant increase in studies over time, with a concentration on major cities such as Toronto, Vancouver, and Montreal. Central Canada overwhelmingly represents the evidence base. Most research was conducted in cold climate zones and primarily focused on green infrastructure elements, such as trees, vegetation, and green roofs, primarily focusing on their abundance rather than configurational or functional attributes. Thermal impacts were mainly measured through air temperature, land surface temperature, and energy savings, with cooling effects generally higher during the daytime. Among UGBI types, trees and parks exhibited the strongest cooling effects. Methodologically, simulation and observational approaches dominated, with a significant focus on micro-scale analyses. The review highlights important gaps, including the underrepresentation of smaller cities and regions such as Atlantic Canada, limited research on blue infrastructure, and minimal integration of health outcomes. Addressing these gaps is critical for developing robust guidelines to enhance urban resilience.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".