Review of national policy instruments motivating circular construction
Bibliographic record
Abstract
Building construction, renovation, and demolition (CRD) waste represent about one-third-of global waste production. Implementing circular economy practices in CRD can significantly reduce waste as well as related impacts, such as greenhouse gas emissions. Yet the effect of different types of policies in promoting construction circularity requires further investigation. The objective of this paper is to review the range of policy instruments applied by different countries and investigate how effectively they promote circularity. Based on a literature review, thirty-seven instruments were identified and classified into five types: regulatory, economic, technical, operational, and communicative. The implementation of each of these instruments was investigated for 19 countries, selected to represent each of the world's regions as defined by the World Bank, including: Barbados, Brazil, Bulgaria, Canada, China, Egypt, Germany, India, Israel, Japan, Kyrgyz Republic, Mexico, Morocco, Portugal, Qatar, Republic of Korea, Sri Lanka, Thailand, and United States. In each region, countries were selected to represent the full scope of CRD generated per capita. Economic, technical, operational and specific CRD regulatory instruments are moderately correlated to CRD waste recovery. In high income countries CRD regulations are the most correlated with recovery, whereas in middle income countries technical instrument implementation is more correlated.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".