What are the Technical and Regulatory Tools to Achieve Decarbonization of the Construction Industry in Canada?
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".