Implementation and Education of Circular Economy in Community Solid Waste Management: A Systematic Literature Review
Bibliographic record
Abstract
This paper aims to offer strategic management recommendations for the incorporation of circular economy principles into municipal solid waste management and to disseminate knowledge regarding the implementation of this concept within local communities. This research combined systematic literature reviews with qualitative methods, utilizing content and descriptive analysis to evaluate the findings. The study indicates that municipal waste management in communities encounters numerous challenges, including insufficient funding, inadequate infrastructure, and noncompliance from the populace, all of which hinder effective municipal garbage management. In addition, a more significant concern is the lack of efficient techniques for recycling and waste segregation. Moreover, as urban populations increase, waste generation escalates, exerting strain on current disposal facilities. Implementing a circular economy strategy in municipal waste management has numerous benefits, including the reduction of landfill trash, the conservation of natural resources, and the generation of employment opportunities through material recycling and repurposing. Circular economy enhances environmental sustainability by reducing pollution and facilitating the transition to a more resilient, resource-efficient system. Moreover, educating people about circular economy principles enhances their understanding of sustainable practices, leads to less waste and resource conservation, and enhances economic prospects by generating green employment and fostering local innovation in waste management techniques.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".