Household Waste Generating Factors and Composition Study for Effective Management in Gorkha Municipality of Nepal
Bibliographic record
Abstract
Municipal solid waste is a growing concern in cities of developing countries and households are the main contributor. Lack of reliable data sources remain one of the major drawbacks for deciding on effective waste management option. The study area Gorkha municipality is selected because it is one of the highly under-researched and least resource intensive municipalities in Nepal. However, continued growth in municipal waste if left unattended will only intensify the problem and thus demands proactive action. Therefore, the objective of this study is to analyze waste composition and to evaluate the socioeconomic factors impacting household waste generation for effective management. Using stratified sampling method, 401 households were selected from all 15 municipal wards. Socioeconomic factors impacting household waste generation were assessed using Ordinary Least Square regression model. The rate of household waste generation in Gorkha municipality is found to be 0.24 kg/capita/day and estimated total household waste generation of 9.4 tonnes/day. Household size and income are found to have positive impact on waste generation, both statistically significant at 1% and thus can be important indicators to forecast solid waste generation trend. Household waste composition was 47.25% organic waste, 37.52% recyclable waste that comprised of 10.38% paper and paper products, 9.88% glass, 6.92% metal, 5.39% plastic, 3.57% textile and 1.38% rubber and leather, and rest 15.23% other waste. Organic waste has the highest share and if not managed properly, creates serious health and environmental hazards. It could be managed efficiently by composting at household and local government level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".