Environmental product declaration (EPD) usage in early building design stages: Review of effects on the environmental life cycle of a multi-residential building
Bibliographic record
Abstract
The building sector has seen recent growth in the number of published environmental product declarations (EPDs). EPDs share environmental data of construction materials, which can help building practitioners prioritize products with lower environmental impacts. However, EPDs rely on varying assumptions within their life cycle assessment (LCA). This study aimed to evaluate the use of EPDs as a data source instead of a generic data source and its effect on the life cycle impacts of a multi-residential building. This study focused on 19 North American EPDs of structural wood products. The impact assessment results found in the EPDs were compared to the Ecoinvent V3.8 database. The findings of the present study suggest that EPDs can generally be used without distinction compared to the data in the Ecoinvent V3.8 database. However, a few data were found to be outliers. In addition, EPDs of structural wood products only disclosed its manufacturing stage. Other life cycle stages, such as transport of the construction product, can have a significant impact on the building’s LCA. Therefore, using EPDs to assess building impacts is recommended over their direct comparison for practitioners to make more comprehensive decisions towards embodied impacts of buildings.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".