How Do Determinant Factors and Stakeholders Shape the Future of Sustainable Blue Economy Development? A Literature-Based Content Analysis
Bibliographic record
Abstract
The sustainable blue economy has emerged as a global framework integrating economic growth, environmental protection, and social inclusion.However, there remains a gap in understanding the key determinants and stakeholder dynamics that shape its sustainability.This study aims to identify the multi-level factors influencing sustainable blue economy performance and to analyze the roles of stakeholders involved.A systematic literature review and content analysis were conducted following the SPAR-4-SLR protocol.Articles published between 2010 and 2024 were retrieved from the Scopus database through Publish or Perish, using keyword combinations related to "sustainable blue economy."Based on inclusion and exclusion criteria, 64 articles were selected from an initial pool of 164 papers.The results reveal three levels of determinant factors (macro, meso, and micro) that influence sustainability outcomes.Key stakeholders include governments, the private sector, local communities, NGOs, academics, and international organizations, each with distinct yet interconnected roles.The review highlights the increasing global attention to blue economy research, particularly since 2020.This study contributes by offering a structured synthesis of multi-level factors and stakeholder interactions, providing a clearer analytical framework for guiding future research and policy development in sustainable blue economy practices.
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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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".