A Bibliometric Analysis of Ocean Plastic Pollution Research: Trends and Future Directions
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of global research on ocean plastic pollution to identify key trends, influential contributors, thematic structures, and future research directions. Bibliometric data were retrieved from the Scopus database, covering the period from 2000 to 2024. Using VOSviewer, the study analyzed co-authorship networks, keyword co-occurrences, and temporal evolution of research themes. The analysis included publication trends, authorship patterns, country collaborations, and research clusters. The results reveal a substantial increase in research output, with dominant themes clustering around marine pollution pathways, microplastic impacts, environmental monitoring, and waste management strategies. Keywords such as “plastic pollution,” “microplastic,” and “environmental monitoring” were central to the field. Influential authors included Jambeck J.R., Law K.L., and Thompson R.C., while the United States, the Netherlands, and Canada emerged as leading countries in terms of productivity and collaboration. Recent research trends show a shift toward sustainability, climate linkages, and circular economy frameworks. The field is transitioning from problem identification to integrated solutions and policy-oriented approaches. Future research should strengthen interdisciplinary integration and promote more inclusive international collaborations, particularly involving regions most affected by marine plastic pollution. This study provides a systematic, visualized mapping of the ocean plastic pollution research landscape and offers strategic insights for academics, practitioners, and policymakers seeking to advance sustainable marine environmental governance.
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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 | 0.062 | 0.279 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".