Estimating the impact of building retrofit measures on the operational greenhouse emissions of medium office buildings – A case study in Ontario, Canada
Bibliographic record
Abstract
The aim of this study is to evaluate the impact of using hourly Marginal Emission Factors (MEF) for electricity consumption instead of annual Average Emission Factors (AEF) to estimate the operational GHG emission reductions of building retrofit measures in medium office buildings in Ontario, Canada. Considering the availability of MEFs, three cities located in different climate zones within the province of Ontario were selected for this study, i.e. Toronto (CZ5), Ottawa (CZ6), and Timmins (CZ7A). Operational carbon emissions were calculated by multiplying an emission factor by the annual consumption of each energy source required by the building, as specified in common carbon accounting methods such as the GHG Protocol and ISO 14064. Results show that the retrofit measures that have the highest potential to reduce operational GHG emissions of medium office buildings located in Ontario are: replacing the HVAC system, replacing windows, and reducing air leakage rate. The obtained results also indicate that the annual electricity MEF can be significantly higher than the AEF reported in the National Inventory Reports. This means that indirect emissions are significantly underestimated when using AEFs, compared to MEFs. Additionally, in cases where natural gas is used as primary heating fuel, the use of AEF overestimates the percentage of GHG emission reduction associated with fuel switching by up to 23% compared to cases where MEFs are used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".