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Record W4411634418 · doi:10.1002/bse.70043

Creating Territorial Value Through Circular Economy: Why Proximities Matter?

2025· article· en· W4411634418 on OpenAlexaboutno aff
Chedrak Chembessi, Sébastien Bourdin, André Torre, Christophe Beaurain

Bibliographic record

VenueBusiness Strategy and the Environment · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsCircular economySustainabilityBusinessAdaptabilityValue (mathematics)Corporate governanceResilience (materials science)Resource (disambiguation)Environmental resource managementIndustrial organizationKnowledge managementEconomic systemEconomicsManagementEcology

Abstract

fetched live from OpenAlex

ABSTRACT The circular economy ( CE ) represents a strategic approach to improving business sustainability. Worldwide, many companies have been adopting or implementing circular initiatives. However, the contribution of these circular practices to territorial value creation remains insufficiently explored. This study examines how CE initiatives generate territorial value through the activation of different forms of proximity—geographical, relational and institutional. Based on 70 semistructured interviews with 51 stakeholders in the Kamouraska region (Canada) and in La Rochelle (France), this research discusses how businesses, policymakers and community actors collaborate to optimise resource flows, foster innovation and enhance environmental performance. The findings reveal that CE initiatives strengthen local socio‐economic networks, facilitate knowledge cocreation and improve firms' adaptability to sustainability transitions. The use of proximity mechanisms enables companies to embed CE principles more effectively in their strategic models, which increases resilience and competitiveness. The results emphasise the importance of integrating CE beyond firm‐level practices and recognising the role of territorial governance in supporting sustainable regional or territorial ecosystems.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 designTheoretical or conceptual
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

Citations13
Published2025
Admission routes1
Has abstractyes

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