Evaluating the Embodied Carbon Emission of Building Wood Waste: A Case Study of Deconstruction vs. Demolition in Canada
Bibliographic record
Abstract
Abstract Circular economy plays a significant role in global carbon emissions, with end-of-life wood waste management posing possible environmental benefits. This study explores the potential for reducing embodied carbon emissions through deconstruction practices, using a case study in the City of Vancouver, BC to provide Canada-specific insights. Life cycle assessment approach was applied to evaluate the embodied carbon emissions considering system boundary stages C1–C4 and beyond-boundary impacts (stage D). Real-world data were collected through an on-site survey of the deconstruction process. 10 scenarios were designed to compare the environmental impacts of different demolition and deconstruction strategies, incorporating various wood waste treatment methods. The results demonstrate that deconstruction scenarios significantly reduce embodied carbon emissions compared to demolition, with reductions of up to 8.0 tonnes CO 2 -eq. More advanced deconstruction strategies, particularly those maximizing material reuse, showed the highest carbon savings, with the most effective scenarios (reuse) achieving reductions between 5 and 6 tonnes CO 2 -eq. Additionally, worker transport was identified as a key influencing factor for deconstruction, with emissions reductions possible through optimized logistics, improved worker efficiency, and deconstruction techniques. These findings underscore the substantial environmental benefits of deconstruction and provide compelling evidence for policies that promote and potentially mandate deconstruction over traditional demolition in urban areas seeking to lower embodied carbon emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".