Improving Municipal Solid Waste Collection Services in Developing Countries: A Case of Bharatpur Metropolitan City, Nepal
Bibliographic record
Abstract
Municipal solid waste management is one of the major challenges that cities in developing countries are facing. Although waste collection services are critical to build a smart city, the focus of both scholarship and action/activism has been more on the utilization of waste than on collection. We devised a choice experiment to elicit the preferences of municipal residents with regard to the various attributes of solid waste collection services in the Bharatpur Metropolitan City of Nepal. The study showed that households identify waste collection frequency, timing of door-to-door waste collection services, and cleanliness of the streets as the critical elements of municipal waste collection that affect their welfare and willingness to pay. While almost all households (95%) were participating in the waste collection service in the study area, more than half (53%) expressed dissatisfaction with the existing service. Women were the main actors engaged in waste collection and disposal at household level. The results of the choice analysis suggest that households prefer a designated waste collection time with waste collection bins placed at regular intervals on the streets for use by pedestrians who often throw garbage on the streets in the absence of bins. For these improvements, households were willing to pay an additional service fee of 10–28% on top of what they were already paying. The study also finds that municipal waste collection can be improved through the involvement of Tole Lane Committees in designing the timing and frequency of the service and by introducing a system of progressive tariffs based on the number of storeys per house.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".