Maximizing Returns from Payments for Water‐Based Ecosystem Services: Incorporating Externality Effects of Land Management
Bibliographic record
Abstract
Abstract Given the expansion of payments for water‐based ecosystem services (PWES) worldwide, two relevant issues are as follows: (1) determination of efficient allocations of payments among land managers, and (2) how this might change when paying one manager to implement a best management practice (BMP) to enhance an ecosystem service impacts the cost‐effectiveness of BMPs considered by other land managers not currently involved in PWES. Such externalities may be negative if diminishing returns dominate, or positive if mechanisms such as “social diffusion” dominate. We analyze how a planner should optimally allocate payments, depending on whether the expected externalities are negligible, negative, or positive. We employ (1) an optimal control model to gain insights on the problem’s dynamics, and (2) stochastic dynamic programming to determine optimal funding strategies using a specific application. The study contributes to the literature by identifying dynamically optimal PWES payment patterns, and illustrates how they should change when one accounts for externalities induced by the program. Because such impacts have not been addressed previously in a rigorous way, this treatment provides useful value added for PWES design and 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".