Negotiating over payments for wetland ecosystem services
Bibliographic record
Abstract
Abstract This paper proposes and examines the economic efficiency of novel payment schemes for the provision of wetland ecosystem services. By definition, payments for ecosystem services typically involve voluntary transactions between the beneficiaries and providers of ecosystem services. We develop a theoretical model that addresses the role that a third party—such as a social planner or government agency, acting in the interest of society—can play to ensure the optimal provision of ecosystem services. We consider different regulatory frameworks combining payments for ecosystem services with a subsidy that the third party grants to the beneficiaries or providers of ecosystem services. We compare the outcomes of the different policy mixes characterized by different levels of involvement of the third party. Of particular interest is the comparison between the outcomes of payments for ecosystem services subsidy arrangements in which the third party plays decentralized and centralized roles. Our results show, among other things, that the third party is indifferent between a negotiated payment for ecosystem services combined with a subsidy scheme and the constrained first‐best payments for ecosystem services subsidy scheme, in the presence of transaction and administrative costs. However, beneficiaries and providers may have conflicting preferences over the two payments for ecosystem services schemes.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".