Estimating the Value of Forestry Eco-tourism Products by Contingent Valuation Method
Bibliographic record
Abstract
CVM is a popular method of value evaluation in the world now, which functions on the public goods with non-use value such as environmental products etc. This paper aims to research the availability of CVM in Chinese forestry eco-tourism products value evaluation. We start from the advantages and limitation of CVM, hypothize the survey of WTP(willing to pay)in Yi Chun forestry eco-tourism area, which is performed in Harbin., and design the survey method, content and proceeding of questionnaire and the attentive problems when applying. After exploring the limitation of estimating Chinese forestry eco-tourism products in practice by CVM, we draw the conclusion that CVM can be widely spread in Chinese forestry eco-tourism products value evaluation. Key words: CVM, Consumer surplus，Forestry eco-tourism products value, WTP Resume: La MEC est une methode populaire d’evaluation de la valeur dans le monde d’aujourd’hui, qui s’applique aux biens publiques a valeur de non-usage tels que les produits environnementaux, etc. L’article present vise a etudier la faisabilite de la MEC dans l’evaluation de valeur des produits eco-touristiqes sylvicols de Chine. Commencant par les avantages et les limites de la MEC, nons supposont l’enquete de WTP(willing to pay) dans la zone eco-touristique forestiere Yi Chun accomplie au Harbin, elaboront la methode d’enquete, les questions du questionnaire et la procedure, et prevoyont les problemes potentiels dans l’application. Apres l’etude des restrictions de l’evaluation des produits eco-touristiques sylvicols chinois par la MEC, nous arrivons a la conclusion que la MEC peut etre largement appliquee dans l’evaluation de valeur des produits eco-touristiques sylvicols en Chine. Mots-Cles: MEC, surplus de consommation, valeur des produits eco-touristiques sylvicols, WTP
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".