Energy impacts of heat island reduction strategies in the Greater Toronto Area, Canada
Bibliographic record
Abstract
In 2000, the Toronto Atmospheric Fund (TAF) embarked on an initiative to quantify the potential benefits of Heat Island Reduction (HIR) strategies (shade trees, reflective roofs and pavements) 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 Laboratory (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. Results show potential annual energy savings of over $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 direct and indirect effects of HIR strategies. Of that total, about 88 percent was from the direct impact roughly divided equally among reflective roofs, shade trees and wind-shielding, and the remainder (12 percent) from the indirect impact of the cooler ambient air temperature. The residential sector accounts for over half (59 percent) of the total, offices 13 percent and retail stores 28 percent. Savings from cool roofs were about 20 percent, shade trees 30 percent, wind shielding of tree 37 percent, and indirect effect 12 percent. 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 percent resulted from wind-shielding, 16 percent from shading by trees, and 5 percent from cool roofs. Potential annual electricity savings were estimated at about 150GWh or over $12M, of that about 75 percent accrued from roofs and shade trees and only 2 percent from wind shielding. The indirect effect was 23 percent. Potential peak-power avoidance was estimated at 250MW with about 74 percent attributed to the direct impacts (roofs about 24 percent, shade trees 51 percent and wind-shielding a small negative percent) and the remainder (26 percent) to the indirect impact. The greatest part of avoided peak power (about 83 percent) was because of the effects of the residences and the rest shared by offices (7 percent) and retail stores (9 percent).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".