Municipal solid waste management in rural areas and small counties: An economic analysis using contingent valuation to estimate willingness to pay for Yunnan, China
Bibliographic record
Abstract
Municipal solid waste management (SWM) is a major challenge for local governments in rural China. One key issue is the low priority assigned by the local government which is faced with limited financing capacity. We conducted an economic analysis in Eryuan, a poor county in Yunnan, China, where the willingness- to- pay (WTP) for an improved solid waste collection and disposal service was valuated and compared with project cost. Similar to most previous studies in developing countries, this study found that the mean WTP is approximately 1% of the household income. The economic internal rate of return of the project is about 5%, which signifies the estimated social benefit to be already higher than the project cost. Moreover, we believe our estimation of social benefit to be a conservative one since our study only focuses on the local people who will be directly served by the project; wider positive externality of the project, such as CO2 emission reduction and groundwater pollution alleviation, etc., whose impact most probably surpass the frontier of Eryuan county, are not considered explicitly in our survey. The analysis also reveals that the poorest households are not only willing to pay more than the rich households in terms of percentage income but are also willing to pay no less than the rich in terms of absolute value in locations where solid waste services are unavailable. This result reveals the fact that the poorest households have stronger demands for public SWM services, whereas the rich may have the ability to employ private solutions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".