Valuation approaches to ecosystem goods and services for the National Botanical Garden, Bangladesh
Bibliographic record
Abstract
Abstract The main attractions of national parks include their scenic beauty, security, wildlife and trees. For preserving and maintaining national parks, an appropriate pricing policy can be used. The current study focuses on using the travel cost method (TCM) and contingent valuation method (CVM) as a non-market valuation technique to value the National Botanical Garden in Bangladesh, a developing country where little or no previous works of this kind has been conducted before. The main objective of the paper was to suggest an appropriate entrance fee for the park by assessing the willingness to pay (WTP) from the TCM and CVM; by determining a revenue maximizing entrance fee from the CVM; and by considering socio-demographics, the characteristics of visits and the motivation of the visitors to preserve the National Botanical Garden. The study sampled 100 visitors. These visitors participated in a survey which consisted of closed questions followed by a semi structured in-depth interview. For data processing, SPSS and Microsoft Excel were used. Based on the travel cost demand function using the TCM, the study found that the amount respondents were willing to pay for entrance was 0.955 US dollars and yearly consumer surplus was 593634.5 USD. From the CVM, it was estimated that the WTP was 0.225 USD for the entrance and revenue maximizing entrance fee was 0.376 USD. Finally, the entrance fee suggested for National Botanical Garden was around 0.225 USD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".