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Record W7127957127 · doi:10.22260/crc-csce-2025/0105

What are the Technical and Regulatory Tools to Achieve Decarbonization of the Construction Industry in Canada?

2025· article· W7127957127 on OpenAlexaboutno aff
Adrien Dupire, Claudiane Ouellet-Plamondon

Bibliographic record

Venuenot available
Typearticle
Language
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsConstruction industryIndustry 4.0Production (economics)Electric power industryKey (lock)

Abstract

fetched live from OpenAlex

This article presents a systemic exploration and subsequent discussion of the diverse technical and regulatory measures implemented by the Canadian government to achieve its climate objectives.It also provides a framework detailing how cities are currently being constructed in Canada.The article seeks to elucidate the meaning of sustainable development for Canadian cities and territories, with a view to reducing their carbon footprint effectively.Furthermore, it evaluates the efficiency of current decarbonisation targets and examines existing solutions.Canada's regulatory framework is analyzed, highlighting the limitations and challenges the government encounters, while proposing viable solutions to attain the net-zero target by 2050.The article then delves into the two primary decarbonization frameworks applicable to the building sector: embodied and operational carbon.The study provides key performance indicators that have been achieved, alongside an analysis of the technical and economic challenges the sector faces, particularly concerning the embodied carbon.Finally, the study explores the potential of various existing certifications.The study assesses the scope of these certifications, the qualitative aspects of their implementation, and the experiential outcomes.This provides a comprehensive review of their effectiveness in advancing sustainability goals.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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