Municipal solid waste management in small towns: an economic analysis conducted in Yunnan, China
Bibliographic record
Abstract
Municipal solid waste management \n continues to be a major challenge for local governments in \n both urban and rural areas across the world, and one of the \n key issues is their financial constraints. Recently an \n economic analysis was conducted in Eryuan, a poor county \n located in Yunnan Province of China, where willingness to \n pay for an improved solid waste collection and treatment \n service was estimated and compared with the project cost. \n This study finds that the mean willingness to pay is about 1 \n percent of household income and the total willingness to pay \n can basically cover the total cost of the project. The \n analysis also shows that the poorest households in Eryuan \n are not only willing to pay more than the rich households in \n terms of income percentage in general, but also are willing \n to pay no less than the rich in absolute terms where no \n solid waste services are available; the poorest households \n have stronger demand for public solid waste management \n services while the rich have the capability to take private \n measures when public services are not available.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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 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".