Determinants of Household Waste Segregation in Gorkha Municipality, Nepal
Bibliographic record
Abstract
Municipal solid waste management is a huge challenge specially in the developing countries. The first and fundamental step to solve the problem of municipal solid waste management is by waste segregation at source and separate collection of waste, which will help to recover recyclable materials and reduce the amount of waste that needs to be disposed at the dumpsite or landfill site. In Nepal, waste segregation at source is mandated by law but the government is not able to implement it successfully. This paper assesses the willingness of the households to practice waste segregation in future if the government enforces the law and various factors that influences the practice. Data was collected from 401 households which was selected using stratified sampling technique from all the municipal wards, the lowest administrative units in Nepal. The finding shows that about 67% of the respondents are willing to segregate waste in future if the government enforces the law. Logit regression model was employed to identify the factors that influence waste segregation practice. The significant variables found from this study are environmental awareness, waste collection service, willingness to pay, make compost, and segregated waste for a week variables, which are statistically significant at 1% level of significance. Income variable is significant at 5% level of significance and gender variable is significant at 10% level of significance. It is recommended that the concerned stakeholders should educate the households on the importance of waste segregation and consider these traits of households before enforcing the law.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".