Circular Economy Design towards Zero Waste: Laying the foundation for constructive stakeholder engagement on improving construction, renovation, and demolition (CRD) waste management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it