Economic Valuation of Nature-Based Tourism Object in Rawapening, Indonesia: An Application of Travel Cost and Contingent Valuation Method
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.
Bibliographic record
Abstract
The purpose of this study is to measure the economic value in Rawapening. This study is expected to be able to see how far the role of nature tourism is seen as an environmentally sound tourist attractions. Because the benefits of natural attractions usually have a variety of natural resources such as biodiversity, benefit directly, and indirectly related to important ecological functions that are not only considered as a tourist attraction.This study was used primary data. The primary data obtained from field surveys to the perpetrator who was visiting tourist Rawapening. The analytical method used two methods. There are travel cost method and contingent valuation method.The study was found significant factors the determinant of the probability of individuals to be willing to pay a certain nominal value for environmental quality improvement are nominal amount bid, income, and education. Then, the determinant of the number of visits are an experience to visit, travel costs, income, age, and perception. The economic value of ecotourism was estimated at Rp 7,41 billion for consumer surplus and Rp 1,65 billion for total benefit per year. This implies that the significant economic value of nature based tourism will be lost from any large scale development by degrading natural environment.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it