EDUCATING FOR A CIRCULAR FUTURE: DIGITAL INNOVATIONS, INTERDISCIPLINARY LEARNING, AND GLOBAL POLICY INSIGHTS IN INDUSTRIAL SYMBIOSIS
Bibliographic record
Abstract
Adapting to a Circular Economy (CE) within the context of ongoing digital transformation calls for a significant rethinking of educational models. Future professionals must be prepared with technical knowledge and the ability to think in systems and adapt to emerging digital environments. This research tends to analyse how different digital tools (i.e., digital platforms, blockchain, digital twins, the Internet of Things, and artificial intelligence) can be effectively integrated into educational practices supporting industrial symbiosis (IS) development. Beyond improving operational workflows and resource exchange, these technologies also serve as pedagogical tools that can deepen learners� understanding of complex sustainability issues. Despite their potential, various challenges hinder widespread adoption. These include the technical intricacies of digital tools, issues of standardisation and compatibility, and broader concerns about infrastructure and social acceptance. As such, the research highlights the importance of embedding digital competence, interdisciplinary learning, and hands-on educational strategies into higher and vocational training. In addition, the study examines global legislative achievements, comparing policy frameworks across the European Union, Canada, the Southern Americas, China, and Vietnam. This comparison reveals how regulatory and institutional cooperation and best practice assessment can drive the adoption of CE and IS principles at scale. Spatial planning and legislative incentives enable such developments and cannot be neglected. The research proposes an integrated educational and policy model to promote a more inclusive, coordinated, and effective shift toward a circular and digitally enabled economy.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".