Dynamics of Transition to Territorialized Circular Economy: Governance & Adapted Models
Bibliographic record
Abstract
The transition to a territorialized circular economy (TCE) is imperative to achieving sustainability tailored to the unique environmental, socio-economic, and cultural characteristics of regions. This study investigates TCE in Quebec through the analysis of specific cases, including industrial symbiosis (Bécancour, Rivière-du-Nord), circular cities (Montreal, Victoriaville), the Québec Circulaire platform, and urban metabolism (Quebec City). These cases have yielded critical insights into governance practices, business models, collaboration mechanisms, and localized performance indicators. The findings indicate that the successful implementation of TCE necessitates adaptive governance frameworks that foster active collaboration among public administrations, businesses, NGOs, and citizens. In the examined cases, the role of local governments as facilitators was found to be pivotal in aligning regional policies with community-specific initiatives. The analysis further reveals that customized business models, such as eco-design, recycling, and shared-resource platforms, are more effective in addressing local needs. Additionally, territorial metabolism emerges as a valuable tool for quantifying resource flows and measuring the impact of circular initiatives. The study also uncovers key challenges, including regulatory fragmentation between provincial and federal levels, and diverging priorities among stakeholders, which hinder the integration of circular projects. Notwithstanding these challenges, the study underscores the transformative potential of TCE when governance structures and business models are adapted to local realities. This research offers actionable recommendations to enhance TCE's economic, social, and environmental outcomes, providing a roadmap for stakeholders to overcome barriers and optimize circular transitions in diverse regional contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".