Strategic Dynamics of Circular Economy Initiatives in Food Systems: A Game Theory Perspective
Bibliographic record
Abstract
This paper analyses how strategic interactions between actors influence the development of circular economy (CE) initiatives in food systems. Using a case study from Saint-Hyacinthe, a mid-sized and agri-food technopole in Québec (Canada), we investigate how cooperation, competition, and power asymmetries shape CE adoption across the supply chain. Drawing on game theory and a typology of management dynamics, the study identifies four patterns: negotiated management, constrained leadership, hierarchical relationships, and competitive behaviour. Empirical data were collected through two collaborative workshops involving public, private, and community-based actors, resulting in 244 coded entries across 12 boards. These allowed us to assess actors’ interests, attitudes, and capacities in relation to CE strategies at upstream, midstream, and downstream stages. The results show that strategies aligned with dominant interests and existing capacities are more likely to be supported, while those requiring structural change are tolerated or marginalized. Findings highlight the role of incentive mechanisms, institutional flexibility, and coordination in enabling more transformative circular initiatives. By adopting a stage-sensitive perspective, this study also fills a gap in the literature by examining how actor dynamics differ across upstream, midstream, and downstream segments of the food system, contributing to CE research by applying game theory to actor configurations and interaction dynamics in food systems. It calls for further exploration of interdependencies and contextual conditions that either facilitate or hinder the emergence of effective, inclusive, and systemic CE transitions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".