A Case Study on the Impact of Transportation of Mass Timber Products on the Cradle-to-Gate LCA Results for an Institutional Building
Bibliographic record
Abstract
Mass timber products (MTPs) are being adopted in new building constructions and remodels in the last three decades, credited to their renewable and low carbon footprint characteristics. However, there are no mass timber manufacturers currently existing in Atlantic Canada. Extended distances of transporting MTPs to this region from other Canadian provinces may sacrifice the environmental benefits of using MTPs. This study was aimed to understand, via conducting a cradle-to-gate life cycle assessment (LCA), the environmental impacts of a mid-rise institutional building, which is located in the Province of New Brunswick Canada. By comparing the current steel frame design of this building with an alternative mass timber building design with the same height range and function including the transportations of major building materials. It was found that the mass timber building design could still have environmental advantages over the steel structure, as much as 19.5% lower global warming impact and 16.8% lower ozone depletion impact, even with MTPs delivered from the furthest location considered in this study. However, the disadvantages in other impact categories, such as 31.9% higher smog impact, 13.6% higher acidification impact, and 248.2% higher eutrophication impact were found when using the TRACI impact assessment in this whole building LCA study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".