Costs of Lost opportunities: Applying Non-Market Valuation Techniques to Potential REDD+ Participants in Cameroon
Bibliographic record
Abstract
Reduced Emissions from Deforestation and Forest Degradation (REDD+) has been systematically advanced within the UN Framework Convention on Climate Change (UNFCCC). However, implementing REDD+ in a populated landscape requires information on local costs and acceptability of changed practices. To supply such information, many studies have adopted approaches that explore the opportunity cost of maintaining land as forest rather than converting it to agricultural uses. These approaches typically assume that the costs to the smallholder are borne exclusively through the loss or gain of the production values associated with specific categories of land use. However, evaluating the value of land to smallholders in incomplete and messy institutional and economic contexts entails other considerations, such as varying portfolios of land holdings, tenure arrangements, restricted access to capital, and unreliable food markets. We suggest that contingent valuation (CV) methods may provide a more complete reflection of the viability of REDD+ in multiple-use landscapes than do opportunity cost approaches. The CV approach eliminates the need to assume a homogenous smallholder, and instead assumes heterogeneity around social, economic and institutional contexts. We apply this approach in a southern rural Cameroonian context, through the lens of a hypothetical REDD+ contract. Our findings suggest local costs of REDD+ contracts to be higher and much more variable than opportunity cost estimates.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".