Public Preferences for Forest Restoration in Togo
Bibliographic record
Abstract
ABSTRACT This study employs a conditional logit model to analyse the preferences of the Togolese population regarding a degraded forest restoration program. Based on a choice experiment method, the analysis draws on a sample of 255 respondents and a total of 3825 observations. Two models are estimated: a baseline model (Model 1) and a model incorporating interactions with sociodemographic variables (Model 2). The results indicate that respondents assign statistically significant value to three main attributes: carbon sequestration, erosion reduction and biodiversity improvement. In contrast, cost and the type of organisation do not exhibit statistical significance. The rejection of the status quo reflects a strong desire for environmental change. The interaction model highlights preference heterogeneity linked to education level, income, interest in forestry practices and perception of forest conditions. For instance, individuals holding a doctoral degree place less importance on carbon sequestration and erosion reduction, whereas farmers and forestry practitioners are more sensitive to these attributes. Finally, estimates of the marginal willingness to pay (MWTP) confirm that respondents are willing to pay more to combat erosion (5357 FCFA) and enhance biodiversity (3389 FCFA) than for carbon sequestration (200 FCFA), a finding consistent with results from similar studies conducted in Africa and elsewhere.
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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".