Global Trends in Agricultural Waste-Based Bioplastic Research: A Scientometric Review
Bibliographic record
Abstract
The research of utilizing agricultural waste has been established for over 6 decades, yet problems regarding plastic waste remain the biggest challenge in environmental issues. The aim of this study was to capture the publication, thematic, and collaborative trends in agricultural waste-based bioplastic research through bibliometric or scientometric analysis. Metadata of relevant research was downloaded from the Scopus database as of November 9, 2024, utilizing a pre-determined combination of keywords. We included records from original research and English-written documents, which were further manually screened for relevance. Scienntometric analysis was analyzed using both bibliometrix and VOSviewer. We found 1,451 records being relevant to the scientometric studies, where the publication peaked at 2024 (n = 170). The most relevant source title was the Science of The Total Environment (n=40), followed by polymer and agricultural-related titles. The words “plastic,” “film,” and “mechanical strength” were the most commonly used, with occurrences reaching 3532, 1471, and 1008 times in the abstract. The thematic analysis revealed that the “bioplastic for food packaging” and “starch” are motor and declining themes, respectively. The VOSviewer visualization of keywords co-occurrence revealed “starch” and “bioplastic” as the dominant keywords with total link strengths (TLSs) of 12 and 13, respectively. An Indonesian university, Universitas Sumatera Utara, was the most productive (n=45). However, the country is in 18th position (n=329) with the least average citations per document (5.5). China and the United States were the most productive countries (n=194 and 99, respectively) that received a total of 3523 and 3345 citations, respectively. Collaboration between China and the United States was established with a TLS of 14, with other observed collaborations such as India, the United Kingdom, Canada, Germany, and Brazil. In conclusion, the research is growing rapidly each year, with China-based institutions leading the field, while countries like Indonesia are beginning to gain recognition. The main focus of innovation in this research is on producing bioplastics for food packaging, which is the most reported area. Additionally, there is a trend toward exploring alternative raw materials, indicating a reduced utilization of starch. Future research should aim to optimize bioplastic production by exploring diverse approaches and fostering international collaboration. Doi: 10.28991/HEF-2025-06-01-015 Full Text: PDF
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.205 | 0.270 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".