Economic Valuation of Forest Restoration Programs in Togo: A Contingent Valuation Approach
Bibliographic record
Abstract
This study assesses Togolese households’ willingness to pay (WTP) for forest restoration programs across the five economic regions of Togo: Maritime, Plateaux, Centrale, Kara, and Savanes. Forests provide essential ecosystem services, including carbon sequestration, water regulation, biodiversity conservation, and soil protection, yet these services are often undervalued in economic decision-making. The assessment was conducted using the contingent valuation (CV) method, with labor contributions as the payment vehicle, subsequently converted into monetary equivalents. A total of 238 valid responses were analyzed after removing protest responses. The WTP values were examined using a Tobit regression model to identify the main sociodemographic determinants. The results reveal significant regional disparities: the Savanes region exhibits the highest average WTP (65,625 CFA), whereas the Maritime region shows the lowest (37,261 CFA). Young adults and women are generally more willing to contribute, although patterns vary across regions. Urban residence tends to reduce WTP in the Maritime and Kara regions, likely due to a lower perception of the direct benefits of forest ecosystem services. Overall, this study provides crucial empirical evidence to inform forest restoration planning and sustainable forest policy in Togo, demonstrating the value of contingent valuation for designing context-specific environmental policies in developing 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".