Creating Territorial Value Through Circular Economy: Why Proximities Matter?
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".