Blue Economy and Corporate Value Creation: A Bibliometric Analysis of Global Research Trends
Bibliographic record
Abstract
The blue economy has emerged as a key strategy for sustainable development. This study employs a bibliometric approach to analyze the development and research trends of the blue economy, specifically its relationship with economic performance and firm value. Conceptualized as a sustainable development strategy, the blue economy integrates economic, social, and environmental dimensions centered on marine and coastal resources. Data from the Scopus database (2010-2025) was analyzed using VOSviewer software to map collaboration networks and thematic evolution. The results reveal a substantial increase in publications since 2016, peaking in 2024, highlighting the topic’s growing global relevance. China is the dominant contributor, led by institutions like the Ocean University of China and the Ministry of Natural Resources, followed by universities from the UK, the Netherlands, Australia, and Canada. The analysis identified three primary research clusters, sustainability, marine aspects, and economic and social sectors within the blue economy. The study confirms the blue economy’s significant contribution to economic growth, resource efficiency, and social welfare. However, its success is contingent upon effective governance, technological innovation, and strong integration between the public and private sectors, outlining critical areas for future development and policy focus.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.166 | 0.258 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".