MétaCan
Menu
Back to cohort

Environmental product declaration (EPD) usage in early building design stages: Review of effects on the environmental life cycle of a multi-residential building

2023· article· en· W4387810386 on OpenAlexaff
Gabrielle Pichette, Pierre Blanchet, Gatien Geraud Essoua Essoua, Charles Breton

Bibliographic record

VenueBioResources · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsHéma-QuébecUniversité Laval
Fundersnot available
KeywordsLife-cycle assessmentEnvironmental impact assessmentProduct (mathematics)EngineeringBusinessProduction (economics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.239
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBioResourcesSame topicSustainable Building Design and AssessmentFrench-language works237,207