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Life Cycle Operating Carbon Impact of a Standardized Passive House in Various Canadian Climates

2020· article· en· W3014664150 on OpenAlexaffabout
Patrick Werner Andres, Russell Richman

Bibliographic record

VenueJournal of Architectural Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGreenhouse gasPassive houseEnvironmental scienceCarbon fibersLife-cycle assessmentCarbon neutralityMainstreamEfficient energy useEngineeringComputer scienceEcology

Abstract

fetched live from OpenAlex

Over the past 10 years in Europe and North America, passive house standards have become the leading high-performance building standards. Although site energy metrics are paramount in these standards, the roots are in reducing carbon emissions. This article describes the influence that using regional, rather than broad (i.e., continent-wide), energy supply carbon emission factors can have on the life cycle operating carbon emission outcome for single-family dwellings designed according to a passive house standard for three climatic locations across Canada, using two mechanical systems with different fuels. All three cities significantly differ when using regional carbon emissions factors, compared with a broad average. Recommendations are made to generate a hybrid carbon intensity factor balancing national and regional electrical grid carbon intensities. As the passive house standards become more and more mainstream paths to reducing the carbon impact of buildings, understanding the differences between emissions factors becomes increasingly important.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2020
Admission routes2
Has abstractyes

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