Willingness to Pay for Forest Restoration in Togo: Comparison of Estimates From Choice Experiment, Contingent Valuation and Travel Cost Methods
Bibliographic record
Abstract
ABSTRACT Many regions of the world aim to increase their forest cover to sequester carbon, improve biodiversity, reduce soil erosion, or provide more recreational opportunities. To achieve this, several forest restoration programs have been implemented in developing countries. This study examines the willingness to pay (WTP) of Togolese citizens for the afforestation program in Togo, using the choice experiment (CE) method, the contingent valuation (CV) method, and the travel cost (TC) method. The WTP obtained with the TC method is higher than that with the stated preference methods (CE and CV) ($180.77 for TC vs. $2.95 for CE and $58.76 for CV). Additionally, respondents expressed positive WTP for attributes such as carbon sequestration, erosion reduction, biodiversity improvement and the organisation that manages forest policies. Our results contribute to the literature comparing these methods, and our study is the first conducted in Togo to use all three methods in an environmental context.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".