Low-Cost Strategies to Improve Municipal Solid Waste Management in Developing Countries: Experimental Evidence from Nepal
Bibliographic record
Abstract
Abstract Many cities in developing countries lack adequate drainage and waste management infrastructure. Consequently, city residents face economic and health impacts from flooding and waterlogging, which are aggravated by solid waste infiltrating and blocking drains. City governments have recourse to two strategies to address these problems: a) ‘hard’ infrastructure-related interventions through investment in the expansion of drainage and waste transportation networks; and/or, b) ‘soft’, low-cost behavioural interventions that encourage city residents to change waste disposal practices. This research examines whether behavioural interventions, such as information and awareness raising alongside provision of inexpensive street waste bins, can improve waste management in the city. We undertook a cluster randomized controlled trial study in Bharatpur, Nepal, where one group of households was treated with a soft, low-cost intervention (information and street waste bins) while the control group of households did not receive the intervention. We econometrically compared baseline indicators – perceived neighbourhood cleanliness, household waste disposal methods, and at-source waste segregation – from a pre-intervention survey with data from two rounds of post-intervention surveys. Results from analysing household panel data indicate that the intervention increased neighbourhood cleanliness and motivated the treated households to dispose their waste properly through waste collectors. The intervention, however, did not increase household waste segregation at source, which is possibly because of municipal waste collectors mixing segregated and non-segregated waste during collection. At-source segregation, a pre-requisite for efficiently managing municipal solid waste, may improve if municipalities arrange to collect and manage degradable and non-degradable waste separately.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".