Payments for Environmental Services: Evolution Toward Efficient and Fair Incentives for Multifunctional Landscapes
Bibliographic record
Abstract
Payments for environmental services (PES), the non-provisioning part of ecosystem services, target alignment of microeconomic incentives for land users with meso- and macroeconomic societal costs and benefits of their choices across stakeholders and scales. They can interfere with or complement social norms and rights-based approaches at generic (land-use planning) and individual (tenure, use rights) levels; they interact with macroeconomic policies influencing the drivers to which individual agents respond. In many developing country contexts, community scale factors strongly influence land users' decisions, whereas unclear land rights complicate the use of market-based instruments. PES concepts need to adapt. Multiple paradigms have emerged within the broad PES domain. Evidence suggests that forms of “coinvestment in stewardship” (CIS) alongside rights are the preferred entry point. Commodification of environmental services (ES) and ES markets might evolve later on, but require strong government regulation to set and enforce rules of the game. We frame hypotheses for wider testing and “no-regrets” recommendations for practitioners.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".