11/21/02 Draft, Do Not Copy Energy Impacts of Heat Island Reduction Strategies in Toronto, Canada
Bibliographic record
Abstract
The effect of heat-island reduction (HIR) strategies on annual energy savings and peak-power avoidance of the building sector of the Greater Toronto Area is calculated. The analysis is focused on three major building types that offer highest saving potentials: residence, office, and retail store. Using the DOE-2 building energy simulation model, we quantified the energy saving potentials of (1) using reflective roofs on individual buildings, (2) planting deciduous shade trees near the south and west walls of building, (3) planting coniferous wind-shielding vegetation near a building, (4) ambient cooling by a large-scale program of urban reforestation with reflective building roofs and pavements, and (5) the combined effects of 1-4. Results show potential annual energy savings of over CAD$11M (with uniform residential and commercial electricity and gas prices of $0.084/kWh and $5.54/GJ) could be realized by ratepayers from the combined effects of HIR strategies. Of that total, about 88 % was from the direct effects [1-3] and the remainder (12%) from the effects of the cooler ambient air temperature. The residential sector accounts for over half (59%) of the total savings; offices, 13%; and retail stores, 28%. Savings from reflective roofs were about 20%; shade trees, 30%; wind shielding of trees, 37%; and ambient cooling effect, 12%. These results are highly sensitive to the price of gas. Assuming a residential gas price of $10.84/GJ (gas price during December 2001), the net annual savings are reduced to about $10M; about 78 % resulted from wind-shielding, 16 % from shading by trees, and 5 % from cool roofs. Potential annual electricity savings were estimated at about 150GWh and potential peak-power avoidance was estimated at 250MW. 1.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.098 | 0.011 |
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".