Life cycle stage practices and strategies for circular economy: assessment in construction and demolition industry of an emerging economy
Bibliographic record
Abstract
The strategic implementation of circular economy (CE) practices in the construction and demolition (C&D) industry is critical for achieving environmental sustainability goals. Understanding CE practices based on reduce, reuse, recycle, recover, remanufacture, and redesign (6R) principles from the perspective of the whole life cycle can promote the implementation of CE practices in the C&D industry. However, studies that shed light on this subject especially in emerging economies are generally lacking. This study contributes to filling this gap by using a three-phase methodology consisting of a literature review and a hybrid best-worst method and grey relational analysis to give insights into practices and strategies to prioritize CE practice implementation. Specifically, the paper focuses on identifying CE practices based on 6R principles, the significance of the identified CE practices, and understanding how to prioritize the implementation of the significant CE practices. The study's findings established that implementing CE practices based on reduce and recover principles at the design stage contributes significantly to environmental sustainability. Additionally, the study highlights the relevance of both bottom-up and top-down approaches in the implementation of CE practices.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".