Circular Economy Design towards Zero Waste: Laying the foundation for constructive stakeholder engagement on improving construction, renovation, and demolition (CRD) waste management
Bibliographic record
Abstract
Abstract The city of Montréal is one of many cities worldwide who strive to cut the amount of waste they generate and advance towards zero waste in an effort to meet the Paris Agreement goals. Construction, renovation, and demolition (CRD) waste is a major contributor to urban waste streams but also an area where waste diversion and innovative waste management approaches could deliver significant reductions in waste. One such promising approach is that of circular economy which envisions a future where CRD waste is designed-out of the built environment by keeping construction materials in use. This paper presents a series of methods used to collect and organize data towards advancing circular thinking within CRD material management decision-making in Montréal and mobilizing engagement with the relevant data. Methods includes a detailed literature review, semi-structured interviews with stakeholders from across the building sector value chain followed by a thematic analysis. Collected data is mapped to the internationally recognized United Nations Sustainable Development Goals (SDGs) framework in an aim to provide globally significant methodologies which cities worldwide can use to showcase contributions to the UN SDGs. Advancing towards zero CRD waste in Montréal will require the input of multiple stakeholders. The work presented in this paper is part of a larger research effort which works to deliver an initial but vital first step in the collection, integration, and dissemination of data towards a circular, more sustainable, built environment.
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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 | 0.049 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".