Application of the Contingent Valuation Method to Abidjan’s Banco National Park
Bibliographic record
Abstract
Banco National Park, a protected area, is the subject of incessant attacks through anthropogenic actions. This study aims to determine the willingness to pay (WTP) of visitors to safeguard the Banco national park. To achieve this, we used the contingent valuation method. The estimation of the simple tobit econometric model revealed the factors that significantly influence WTP. Thus, preferentially for young people, adults and seniors are willing to provide more resources to contribute to the protection of the Banco park. Concerning marital status, the results reveal that compared to singles, relevant visitors of other marital statuses other than married express low WTP. Income positively increases the WTP expressed by visitors. The comparative study of the average WTP of visitors according to origins and income reveals that visitors from Côte d'Ivoire have an average income of 876 772 FCFA and those from other developing countries with an average income of 898 207 FCFA demonstrate non-significantly different average WTP. The same goes for visitors from Europe and other developed countries in North America and Japan with average incomes of 1 824 479 FCFA and 2 490 000 FCFA respectively. On the other hand, there is a significant difference between the means of visitors from developing countries including Côte d'Ivoire and those from developed countries. In view of these results, we recommend that entrance fees to the park be discriminated based on the origins of visitors. Thus, those from developing countries must pay lower park entrance fees than those from developed countries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".