Mapping the scientific landscape of organic fraction of municipal solid waste recycling: A bibliometric analysis (2005–2024)
Bibliographic record
Abstract
This bibliometric analysis portrays the recycling of the organic fraction of municipal solid waste over a 20-year period (2005 to 2024). The great challenges posed by the ever-increasing generation of organic waste in municipal garbage encouraged us to review the state of scientific knowledge to identify gaps, major research themes and influential research that can enable decision-makers to make more informed decisions about organic waste recycling in the future. Using 1,842 scientific documents from the Scopus database, this research applies performance analysis and scientific mapping to highlight research trends, specific subtopics, and other structural features as well as to understand the importance of certain articles and researchers. The results show a strong increase in the production of scientific corpus beyond 2015, with emerging topics such as the circular economy, sustainable development and innovative technologies for the treatment of organic waste. Composting and anaerobic digestion are the most proven technologies, but pyrolysis, insect bioconversion and biorefineries are gaining ground. The bibliometric analysis reveals a number of gaps, including socio-technical integration, consumer behaviour and compost contamination control. This research goes beyond describing current knowledge, charting a course for future research. • Municipal organic waste recycling has progresses significantly since 2015, incorporating principles of the circular economy and technological innovations. • Composting and anaerobic digestion remain the predominant processing methods, while pyrolysis is gaining attention. • Significant gaps include socio-technical integration and compost contamination. • Life cycle analyses often fail to consider integrated approaches to waste management. • Empirical research on policy incentives and scaling of waste technologies is lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.167 | 0.256 |
| 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, 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".