Nature Based Solutions to mitigate urban heat island effects in Canadian cities: literature review
Bibliographic record
Abstract
Canada is warming at double the rate of the global average caused in part by a fast-growing population and extensive land transformations, where buildings significantly contribute to the urban heat island (UHI) phenomenon (Jandaghian & Berardi, 2020a). The federal government released a strengthened climate plan in 2020, which emphasizes using Nature Based Solutions (NBS) to combat the effects of the UHI. Accordingly, two NBS techniques are proposed: increasing surface reflectivity (ISR) of horizontal building surfaces (e.g., roofs) and increasing surface greenery/vegetation (ISG) on both vertical and horizontal building envelope components (Berardi, Jandaghian, & Graham, 2020). Figure 1 shows the NBS strategies to mitigate UHI effects. ISR and ISG lead to a decrease in air temperature and temperature-dependent atmospheric chemistry that controls photochemical reaction rate of ozone production (Jandaghian & Akbari, 2019). Direct benefits of NBS on the built environment include, decreasing cooling loads of buildings in summertime, reducing the rate of heat-related mortality (HRM), and improving urban climate and air quality (Jandaghian & Akbari, 2021). In addition, the implementation of NBS generates a range of co-benefits, such as carbon sequestration, water purification, green growth, biodiversity of habitats, recreational facilities, mental well-being and job creation. Given the apparent accelerating pace of climate change and the need to improve the resilience of buildings and communities, greater efforts and investment are now required to facilitate the uptake of NBS for urban centers located in Canada that are intent on mitigating the effects of UHI in the future. This literature review presents a snapshot of the current understanding of NBS and how they affect building performance, the simulations conducted to evaluate NBS and UHI effects and modeling approaches used, the primary knowledge gaps as well as the future steps needed to reduce the effects of UHI in urban agglomerations across Canada. Accordingly, the next section provides a condensed summary of previous research on ISR and ISG. Then the modeling approach is described followed by a road map to assist policymakers to take action against the adverse impacts of UHI in cities. The road map consists of I) providing approaches to reduce the UHI influences with respect to the implications of warming/changing climate, urban canopy/landscape characteristics and building design II) development of the best management practices (BMP) for NBS – UHI for Canadian communities and III) development of guidelines for the design of NBS–UHI solutions to mitigate UHI in accordance with Canadian design climatic load.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".