3Energy Impacts of Heat Island Reduction Strategies in Toronto, Canada
Bibliographic record
Abstract
In 2000, the Toronto Atmospheric Fund (TAF) embarked on an initiative to quantify the poten-tial benefits of Heat Island Reduction (HIR) strategies (shade trees, reflective roofs and pave-ments) in reducing cooling energy use in buildings, lowering the ambient air temperature and improve air quality. This report summarizes the efforts of Lawrence Berkeley National Labora-tory (LBNL) to assess the impacts of HIR measures on building cooling- and heating-energy use. We discuss our efforts to calculate annual energy savings and peak-power avoidance of HIR strategies in the building sector of the Greater Toronto Area. The analysis is focused on three major building types that offer most saving potentials: residence, office and retail store. Using an hourly building energy simulation model, we quantify the energy saving potentials of (1) using cool roofs on individual buildings [direct effect], (2) planting deciduous shade trees near south and west walls of building [direct effect], (3) planting coniferous wind-shielding vegetation near building [direct effect], (4) ambient cooling by a large-scale program of urban reforestation with reflective building roofs and pavements [indirect effect], (5) and the combined direct and indirect effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".