A Comparative Bibliometric Analysis on Plastic Waste Recycling
Bibliographic record
Abstract
The aim of this study is to explore the application trend of the circular economy and research on plastic waste recycling by comparing results from selected databases.The methodology involves developing a bibliometric study based on data from Scopus and the Web of Science Journals & Country Rank, spanning from 2014 to 2023.A total of 2,083 articles were retrieved from these two research databases, with 1,108 and 975 articles coming from Scopus and WoS, respectively.Descriptive bibliographic maps and strategic charts, generated by OriginPro, Excel, and VOSViewer, are presented.The Circular Economy (CE) is a model that eliminates waste, adopts sustainable practices, closes loops in industrial ecosystems, and turns end-of-life products into resources for others.This stands in contrast to the linear economy, which disposes of waste through landfill or incineration.Currently, plastic production is still supported by a resource-intensive paradigm that decouples economic growth from resource consumption.The annual consumption of plastic materials and fossil fuel is projected to triple by 2050, a trend that has attracted significant attention.The introduction of CE has drastically reduced resource consumption.This study compares the Scopus and Web of Science databases regarding current plastic use and recycling of plastic waste.Moreover, it identifies the future contribution of the degrowth economy in managing plastic waste for recycling.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.207 | 0.270 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".