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Record W4285167361 · doi:10.5327/z2176-94781277

Assessing circular economy in Brazilian industries through the analytical hierarchy process

2022· article· en· W4285167361 on OpenAlexaff
Priscila Rodrigues Gomes, L. Yusem Carstens, Mara Christina Vilas-Boas, Maria Fernanda Kauling, S. Cruz, Maurício Dziedzic

Bibliographic record

VenueRevista Brasileira de Ciências Ambientais · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Northern British Columbia
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsAnalytic hierarchy processMultidisciplinary approachSustainabilitySustainable developmentBusinessSample (material)Process (computing)Circular economyInvestment (military)HierarchyAction (physics)Index (typography)Environmental economicsIndustrial organizationEconomicsEngineeringComputer sciencePolitical scienceMarket economyOperations research

Abstract

fetched live from OpenAlex

Sustainable development has been pursued by organizations around the world ever since environmental and social issues were introduced into institutional agendas. In the various sectors of the economy, the factors that influence sustainable decisions are multidisciplinary and systemic, and address the concept of Circular Economy (CE). This study aimed to develop a method to measure the level of commitment of companies and sectors to CE. The method allows investigating institutional factors associated with sustainable development and assessing the depth of CE practices. A circularity index is originated that can assist decision makers in the development of specific strategies, investment plans, and policies to guide organizations towards the achievement of a CE. The proposed method was then applied to 75 Brazilian companies recognized for their sustainability initiatives, analyzing practices associated with CE actions, as well as their depth. The results, using the Analytic Hierarchical Process (AHP), indicate that the sectors analyzed do not have a significant difference among them and that the majority of the companies analyzed (80%) do not practice any circular action despite claiming the opposite. Therefore, CE is still incipient in Brazil. The application of the proposed method to a large sample showed its potential for global use, and that it can also be employed to guide actions of single companies or entire sectors towards sustainable development using a CE path.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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.

Study designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevista Brasileira de Ciências AmbientaisSame topicSustainable Supply Chain ManagementFrench-language works237,207