The Impact of Glazing as a Form of Building Envelope on Carbon Emissions in Toronto
Bibliographic record
Abstract
In light of the decarbonization targets for Toronto City to mitigate Climate Change and Carbon Emissions by 2030, 2040 and 2050, this study aims to develop an understanding of the real impact of glazing used in buildings' facades on carbon emissions by assessing the whole life carbon (WLC) of a high-rise office building as a case study in downtown Toronto. The objective is to answer the main research question, "What is the impact of different glazing systems as a form of building envelope on total carbon emissions of a high-rise office building in Toronto?" Four types of glazing systems were assessed in One Click LCA for comparing the Embodied Carbon (EC) across multiple scenarios of Window-to-Wall Ratio (WWR) for the office building. They were also modelled in OpenStudio/EnergyPlus for simulating the annual operational energy use and calculating the Operational Carbon (OC) accordingly. The Whole Life Carbon (WLC) of the embodied and operational carbon combined was analyzed in relation to the glazing systems type, WWR, and the thermal transmittance (U-factor) of each system. The results highlighted that embodied and operational carbon are in a direct relationship with the U-factor and WWR for different glazing systems considered. In general, as the WWR or the U-factor reduced, so did the WLC; a reduction ranging from 16% - 40% was addressed from the base case with the highest WLC (47.3 kgCO2e/m2/yr) to the lowest WLC (28.41 kgCO2e/m2/yr), due to the 42% reduction in the Energy Use Intensity (EUI) from (359 kWh/m2) to (207 kWh/m2) by reducing the amount of glazing to 25% and changing the glazing system to a lower U-factor. However, more reductions between 9.4% and 10.8% of WLC could be achieved if the electricity carbon factor gradually decreased in the next 30 years to zero afterward when the electricity is assumed to become clean in 30 years. Finally, the most significant reduction of 17.0% - 41.8% in the WLC is achievable if the building's operations were electrified in 10 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".