Comparing life cycle implications of building retrofit and replacement options
Bibliographic record
Abstract
When is it better to retrofit a building as opposed to demolishing and rebuilding it? Life cycle environmental and economic analyses are used to address this question through the study of a typical four bedroom detached house in Toronto. Three vintages of the reference house are used: 1930s solid masonry; 1960s wood frame; and post oil crisis, 1980s wood frame. Retrofit studies considered include insulating the attic and basement walls and air leakage sealing. Over a 40-year life cycle, the rebuild option has lower life cycle energy, global warming potential, and air pollution, which are predominantly associated with building operation. But the retrofit options have lower water pollution, solid waste generation, and weighted resource use, associated with material flows. The retrofit options also have lower life cycle economic costs than rebuilding. In this respect, the preferred options are basement plus air leakage sealing retrofit for the 1930s house, basement retrofit for the 1960s house, and no change for 1980s house. There are ways to overcome the trade-off in negative environmental impacts between retrofitting and rebuilding, such as use of renewable energy sources or re-use and recycling of deconstruction and demolition materials in new construction.Key words: life cycle assessment, life cycle costing, building retrofits, sustainable development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".