Financial compensation and uncertainty: using mean-variance rule and stochastic dominance to derive conservation payments for secondary forests
Bibliographic record
Abstract
The expected opportunity costs of conserving a specific land use are usually considered adequate as financial compensation. However, a “conservation premium” is sometimes proposed as an added incentive, i.e., compensation greater than the expected opportunity costs. This paper discusses various methodological opportunities for deriving effective compensation under uncertainty. Based on cumulative distribution functions of possible opportunity costs (a Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plantation was considered the alternative to conserving a Chilean secondary forest), generated through Monte Carlo simulations, we derived an inclusive range of possible compensations from 77 up to 375 US$·ha–1·year–1. If we assumed that the two land-use alternatives were mutually exclusive and independent from other risky investments, a compensation of 375 US$·ha–1·year–1 was necessary to convince every decision maker to maintain the secondary forest. However, only 77 US$·ha–1·year–1 was enough for a risk-averse decision maker (given average opportunity costs of 113 US$·ha–1·year–1). Yet, it turned out that the greatest possible opportunity costs would already be compensated for with 199 US$·ha–1·year–1, given an error probability of 0.05. Compensating for the last 5% of possible opportunity costs would thus require an additional 176 US$·ha–1·year–1. Our approach had two main limitations, namely we did not consider portfolio effects, which would allow diversifying away unsystematic risks, and we did not take into account the different systematic risks of the compared alternatives. These limitations may have led to an overestimation of effective compensation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".