The Role of Entrepreneurial Clusters in Advancing Circular Bioeconomy and Innovation: A Case Study from Romania
Bibliographic record
Abstract
This paper explores how bioeconomy-specialized clusters function as systemic intermediaries that support innovation, coordination, and business transformation in the transition to a circular bioeconomy. Using a qualitative case study methodology, we analyze how bioclusters in Romania enable SME development, knowledge transfer, and cross-sector innovation, ultimately strengthening regional entrepreneurial ecosystems. Findings highlight the role of clusters in supporting innovation management, access to financing, and policy alignment in the transition toward a circular bioeconomy. By fostering entrepreneurial networks, facilitating skill development, and supporting SME-driven innovation, clusters act as catalysts for inclusive and resilient business growth. This study highlights how clusters create an enabling environment by providing access to financing, technology, and market expansion opportunities. Although focused on a specific regional biocluster ecosystem, the findings underscore the scalability and replicability of this model across Europe, reinforcing its contribution to entrepreneurship, innovation, and regional economic development. This study contributes to the literature on entrepreneurial ecosystems and circular bioeconomy governance, emphasizing the role of clusters as intermediary organizations that facilitate business-led sustainability transitions. The findings offer practical insights for policymakers, industry leaders, and researchers, outlining strategies to strengthen cluster-based innovation, enhance SME competitiveness, and embed circular practices into national and regional bioeconomy strategies. This paper’s added value lies in combining the Technological Innovation Systems (TIS) framework with empirical evidence from Romanian clusters—a context underexplored in current literature on circular bioeconomy transitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".