Global research trends in zero-waste cities: a comprehensive bibliometric analysis
Bibliographic record
Abstract
Zero-waste cities are one of the important ways to promote resources sustainable development worldwide and have significant implications for promoting the recycling of waste resources and promoting global sustainable development. This study presents a comprehensive bibliometric analysis of zero-waste cities to explore the current research landscape. Web of Science Core Collection (WoSCC), PubMed and Scopus databases were used to retrieve a total of 392 articles containing research on zero-waste cities. These data were analysed by bibliometric methods to draw the research trends and recent advances in zero-waste cities comprehensive overview. The results show that the first article on zero-waste cities was published in 2008, marking the start of research in this area. In 2013, the most cited article was published by Zaman Au et al., and Italy is the country with the most research in this field. Additionally, the scholars and institutions who published the most papers in this field were investigated. Keywords: “circular economy”, “food waste”, “zero waste”, “recycling” and “waste management” remain the most used words in research. Furthermore, the data analysis illustrated that “waste collection” and its related components such as “waste diversion rate” and “waste generation” are emerging topics in zero-waste cities, however, it needs further development and more connections with zero-waste cities. Keywords such as “circular economy”, “sustainable development” and “carbon emissions” may become research hotspots or future trends. Finally, this study presents the future direction of zero-waste cities, thematic focus and research hotspots making a valuable contribution to zero-waste cities field.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical 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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.179 | 0.260 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 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".