Whole-life Carbon Analysis of Residential Buildings Retrofit Projects in Ontario
Bibliographic record
Abstract
Life cycle assessments (LCAs) of buildings provide a means to understanding the whole life carbon of the built environment, which can facilitate decision-making about efficient building systems and low carbon design approaches. This paper conducted LCAs on two existing social housing buildings undergoing retrofits. The two buildings were assessed in terms of operational and embodied carbon emissions in three scenarios, the existing building, a retrofit following the Ontario Building Code requirements, and a retrofit approach designed by TCHC. This research examined ways to balance embodied and operational energy consumption and assessed retrofit options to reduce the whole-life carbon of buildings. Energy Plus and One Click LCA software were used to conduct building simulations. The results suggest that while the TCHC retrofit has the lowest whole-life carbon emissions, the OBC design using an air source heat pump (ASHP) also significantly reduces emissions. The low emission factor of electricity in Ontario contributes to the OBC with ASHP's low carbon emissions compared to natural gas-powered systems. Therefore, electrification in Ontario’s context can be a viable pathway to reducing carbon emissions in the built environment. The paper also highlights the importance of electricity grid emissions reductions and how the energy factors of electricity can directly impact building whole-life carbon.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".