Mitigating UHI effects in Canadian communities using nature based solutions
Bibliographic record
Abstract
Driven by the global emission of greenhouse gases, Canada’s climate has changed and will continue to develop, where on average the warming experienced in Canada has been approximately double the magnitude of global warming. In addition, increases in anthropogenic heat emissions and a reduction of green spaces and surface albedo due to rapid land transformation has directly impacted the energy balance of urban environments. As a result, urban areas are experiencing elevated temperature relative to their rural surroundings otherwise known as the urban heat island (UHI) effect. The federal government of Canada released the strengthened climate plan in 2020, which emphasizes using nature-based solutions (NbS) for their ability to reduce the UHI effect and the potential of overheating while providing one of more desired eco-system services. This report evaluates the potential effectiveness of NbS at reducing the risk of overheating under Canadian climate conditions by completing a review of studies investigating the use of surface greenery (SG) and surface reflectivity (SR) to reduce overheating within Canada. In addition, a review of Canadian bylaws and policies that specify the use of SG and SR to reduce urban temperatures is completed. Combined, the findings from this review could be used to assist in estimating the percent coverage/adoption of NbS techniques on buildings and urban scales, providing a benchmark on the parameters to be assessed within a particular domain. These coverage/adoption estimates could then be used in future simulations to investigate the efficacy and benefits of these techniques. Outgrowth from this work may be able to aid in the development of tools and design guidelines that policy makers and urban planners could use when implementing NbS in their communities to reduce the risk of overheating.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".