Factors Influencing the Willingness to Pay for Entrance Permit: The Evidence from Taman Negara National Park
Bibliographic record
Abstract
Non-market techniques such as Travel Cost Method (TCM) and Contingent Valuation Method (CVM) are commonly used to estimate the economic benefits of outdoor recreation. This study applied the CVM, with Willingness to Pay (WTP) as the elicitation method, to investigate the pattern of willingness to pay among visitors of Taman Negara National Park (TNNP). In applying CVM, the respondents were asked on the maximum amount they were willing to pay to enter this park. Data were obtained using closed-ended questionnaires through interview. About 196 visitors were involved in the study. This study used multiple regressions (MR) to investigate factors that determine WTP for entrance permit in TNNP. This study found that the WTP was positively related to several important factors; and these factors include nationality, income, education and marital status. All these factors can help to explain the WTP for entrance permit at TNNP. Approach in determining WTP for entrance permit will help park authorities to be more financially self-sufficient. In addition, it will generate more income, and thus more efficiency in operating and maintaining the national parks. Keywords: Willingness to pay, Contingent Valuation Method, multiple regressions, national park, entrance permit
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".