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Record W4283373959 · doi:10.1007/s11356-022-21470-w

Life cycle stage practices and strategies for circular economy: assessment in construction and demolition industry of an emerging economy

2022· review· en· W4283373959 on OpenAlexaff
Richard Asante, Daniel Faibil, Martin Agyemang, Sharffudin Ahmed Khan

Bibliographic record

VenueEnvironmental Science and Pollution Research · 2022
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCircular economyReuseSustainabilityDemolitionBest practiceRelevance (law)Demolition wasteLife-cycle assessmentBusinessOrder (exchange)Process managementEnvironmental economicsProduction (economics)EngineeringEconomicsPolitical scienceCivil engineeringManagementWaste managementEcology

Abstract

fetched live from OpenAlex

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.

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)0.005
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0000.001
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 head0.093
GPT teacher head0.393
Teacher spread0.300 · 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
GenreReview

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

Citations45
Published2022
Admission routes1
Has abstractyes

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