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Record W1490310362 · doi:10.1596/1813-9450-5767

Municipal Solid Waste Management in Small Towns: An Economic Analysis Conducted in Yunnan, China

2011· book· en· W1490310362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Bank eBooks · 2011
Typebook
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsChinaSolid waste managementEconomic analysisEnvironmental planningGeographyBusinessMunicipal solid wasteAgricultural economicsWaste managementEconomicsEngineeringArchaeology

Abstract

fetched live from OpenAlex

Municipal solid waste management continues to be a major challenge for local governments in both urban and rural areas across the world, and one of the key issues is their financial constraints. Recently an economic analysis was conducted in Eryuan, a poor county located in Yunnan Province of China, where willingness to pay for an improved solid waste collection and treatment service was estimated and compared with the project cost. This study finds that the mean willingness to pay is about 1 percent of household income and the total willingness to pay can basically cover the total cost of the project. The analysis also shows that the poorest households in Eryuan are not only willing to pay more than the rich households in terms of income percentage in general, but also are willing to pay no less than the rich in absolute terms where no solid waste services are available; the poorest households have stronger demand for public solid waste management services while the rich have the capability to take private measures when public services are not available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.028
GPT teacher head0.247
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it