Growing greener cities – The potential for engineered wood construction to lower Montreal’s environmental impact
Bibliographic record
Abstract
Deep decarbonization of buildings and construction is required to reduce the 40% of global carbon emissions produced by this sector. Mass-timber construction that substitutes carbon-capturing wood for carbon intensive materials like steel and concrete can assist in this transition. However, most studies of material use and embodied carbon in the built environment are deficient in that they rarely analyze the city-scale, and they seldom capture connections between the city and hinterlands that supply most construction materials.As such, we lack knowledge to effectively decarbonize new construction in cities and do not know the potential impacts, such as deforestation, of large-scale mass-timber construction in cities. We address these knowledge gaps through a city-wide assessment of three key construction materials - steel, concrete, and wood - in the city of Montreal, Canada. We combine bottom-up material accounting of the building stock with life cycle assessment to analyze the carbon emissions and land change implications of future development scenarios in the city. We compare the “status quo” construction reliant on concrete and steel to the use of renewable, regionally available materials, such as mass timber at the neighborhood and city scales.This thesis provides much-needed insights to aid the construction sector in strategically implementing low-carbon development that decreases the environmental impacts of urbanization both in cities and in their hinterlands. We find the average embodied carbon impact of modern residential housing on the Montreal Agglomeration to be 2.7 T CO2eq./capita. We estimate that agglomeration wide transition to engineered wood construction and/or increased settlement density does not necessarily decrease this footprint across each individual municipality/arrondisement. We do find that scale up of engineered wood construction could be supported by Quebec’s harvestable forests
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".