Integrating Circular Economy in Canada’s Construction Sector: A comprehensive Review of Opportunities and Execution Status
Bibliographic record
Abstract
Abstract In Canada, the Architecture, Engineering, and Construction (AEC) industry generates approximately 3.4 million tons of Construction, Demolition, and Renovation Waste (CDRW) annually, presenting a critical environmental challenge. While global studies highlight the potential of Circular Economy (CE) principles to address this issue, their application in Canada’s AEC industry remains limited due to fragmented information, which hinders understanding and execution of CE principles. The study aims to assess the state of CE strategies in Canada’s AEC industry via a literature review. It identifies key challenges and opportunities and offers insights to enhance the integration of CE principles within the Canadian context. These are categorized into four key areas: technical, economic, socio-cultural, and governmental. Finally, the execution status of each opportunity within Canada is evaluated and classified as either already executed, in transition, or yet to be executed. The findings reveal 16 critical opportunities with the potential to enhance circularity within Canada’s AEC sector, with 25% yet to be executed, 62% in transition, and 13% already executed. These insights underscore the potential for technological innovation, socio-cultural shifts, and regulatory reforms. This research provides strategic guidance for policymakers, industry professionals, and stakeholders, offering a roadmap for Canada’s AEC industry.
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".