Data · dataset · 2002
Measuring Values of Wetland Network in Taiwan
Listed in Harvard Dataverse
This study applies contingent valuation method (CVM) to evaluate the values of two wetland networks-\"reserving all avian species in Taiwan\" and \"reserving all rare and uncommon avian species in Taiwan\", which were constructed by heuristic algorithms and linear-programming optimizations.
Description
The constructing methods and results of wetland networks were introduced to the dichotomous choice with follow-up (DCF) contingent valuation questionnaire.
Cameron\'s expenditure function model and maximum likelihood estimation for DCF data provided by Alberini et al. were used to estimate willingness to pay values for the residents of Taiwan. The significant variables affecting the willingness to pay of \"reserving all avian species\" include income, relative weight of location factor, relative weight of population size factor, and the reason for unwilling to pay is \"conservation work should be done by the government, not me\" (a=5%).
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The significant variables affecting the willingness to pay of \"reserving all avian species\" include he reason for unwilling to pay is \"conservation work should be done by the government, not me\" (a=5%) and relative weight of population size factor (a=10%). The estimated median annual willingness to pay is NT$1,208.94 for \"reserving all avian species in Taiwan\" and NT$1,237.01 for \"reserving all rare and uncommon avian species in Taiwan.\" The confidence interval under a=5% is ( NT$883.98, NT$1664.6) and (NT$903.80, NT$1703.83), respectively, which shows that the willingness to pay for the two networks is not significantly different.
A comparison among various value-evaluation studies on wetlands in Taiwan shows that the values of individual wetland and all wetlands are close to each other, which implies the embedding problem. Therefore, the evaluation of wetlands should be based on all substitution wetlands instead of individual wetland. Keywords: heuristic algorithms, linear- programming optimizations, wetland network, expenditure function, maximum likelihood estimation (MLE), willingness to pay (WTP)
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Where it is published
- Dataverse dataset page dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.6141%2FTW-SRDA-E89002-1 ↗
landing page · from Harvard Dataverse
- DOI doi.org/10.6141/tw-srda-e89002-1 ↗
DOI / persistent id · from Harvard Dataverse
Catalogue records · 1
- Dataverse API dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi%3A10.6141%2FTW-SR… ↗
metadata API · from Harvard Dataverse
Provenance · 1 source records, 5 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Harvard Dataverse | doi:10.6141/TW-SRDA-E89002-1 | 15 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| created_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| description | source · Harvard Dataverse | connector:dataverse@1.0.0 | /description |
| publication_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| title | source · Harvard Dataverse | connector:dataverse@1.0.0 | /name |
| updated_date | source · Harvard Dataverse | connector:dataverse@1.0.0 |