Temporal Reliability of Willingness to Pay for Payments for Environmental Services: Lessons from Lombok, Indonesia
Bibliographic record
Abstract
Willingness to pay (WTP) for payments for environmental services (PES) can be temporarily reliable if contingent valuation (CV) studies are embedded in an accurate survey population, yield low measurement errors, and are based on a correct assumption of no change in sociodemographic factors affecting buyer preferences. These pre-conditions are assumed in PES schemes applying temporal reliability of WTP. This study tests these conditions in CV-PES studies from 2001, 2003, and 2011 in Lombok, Indonesia, by comparing them with a new CV-PES study in 2015. Our results show that the CV-PES studies would not meet the pre-conditions due to inclusion of non-PES buyers, potential measurement errors implied by a lack of validating information and high WTP estimate, and/or failure to test the condition of no change of socio-economic factors. Results contribute to identifying pragmatic challenges and lessons for applying temporal reliability of WTP to PES implementation.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".