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Review of national policy instruments motivating circular construction

2024· article· en· W4405097142 on OpenAlexafffundabout
Rebecca Dziedzic, Pavithran Pondicherry, Maurício Dziedzic

Bibliographic record

VenueResources Conservation and Recycling · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Northern British ColumbiaConcordia University
FundersMitacs
KeywordsCircular economyEngineeringBusinessConstruction engineeringEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.255
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2024
Admission routes3
Has abstractyes

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