Life cycle carbon assessment of reinforced concrete, structural steel, and mass-timber buildings in Canada
Bibliographic record
Abstract
The growing urban population drives demand for more buildings, increasing carbon emissions. To meet the 2030 Emissions Reduction Plan targets, the Canadian government is encouraging renewable construction materials such as mass-timber (MT) to decarbonize the construction sector. However, comparative assessments of embodied carbon emissions of various construction materials, building heights, and locations remain scarce. This study addresses this gap by conducting a comprehensive whole building life-cycle assessment (WBLCA) of functionally equivalent three and six-storey reinforced concrete (RC), structural steel (SS), and MT buildings in Vancouver, Montreal, and Toronto, Canada, using Athena Impact Estimator for Buildings (IE4B) software tool. Results show that excluding biogenic carbon, MT reduces Global Warming Potential (GWP) by 33–36% and 27–32% for three and six-storey buildings compared to RC in phases A–C (cradle-to-grave), and by 14–19% and 5– 14% compared to SS, respectively. Including phase D (beyond end-of-life) and accounting for biogenic carbon, MT further reduces GWP by 99–102% and 109–118% for the three and six-storey buildings compared to RC, and by 99–105% and 118–139% compared to SS, respectively. Although MT outperformed RC and SS overall, the study further explores how material type, building height, and seismicity influence GWP across different phases of the WBLCA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".