The carbon footprint of future engineered wood construction in Montreal
Bibliographic record
Abstract
Abstract Engineered wood (EW) has the potential to reduce global carbon emissions from the building sector by substituting carbon-intensive concrete and steel for carbon-sequestering wood. However, studies accounting for material use and embodied carbon in buildings rarely analyse the city-scale or capture connections between the city and supplying hinterlands. This limits our knowledge of the effectiveness of decarbonising cities using EW and its potential adverse effects, such as deforestation. We address this gap by combining bottom-up material accounting of construction materials with life cycle assessment to analyse the carbon emissions and land occupation from future residential construction in Montreal, Canada. We compare material demand and environmental impacts of recent construction using concrete and steel to future construction using EW at the neighbourhood, urban scales under high- and low-density growth scenarios. We estimate that baseline embodied carbon per capita across the Agglomeration of Montreal is 3.2 tonnes per carbon dioxide equivalents (CO 2 eq.), but this ranges from 8.2 tonnes CO 2 eq. per capita in areas with large single-family housing to 2.0 tonnes CO 2 eq. per capita where smaller homes predominate. A Montreal-wide transition to EW may increase carbon footprint by up to 25% under certain scenarios, but this varies widely across the city and is tempered through urban densification. Likewise, a transition to EW results in less than 0.1% land transformation across Quebec’s timbershed. Moreover, sustainable logging practices that sequester carbon can actually produce a carbon-negative building stock in the future if carbon in the wood is not re-emitted when buildings are demolished or repurposed. To decarbonise future residential construction, Montreal should enact policies to simultaneously promote EW and denser settlement patterns in future construction and work with construction firms to ensure they source timber sustainably.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".