Payments for ecosystem services programs: A global review of contributions towards sustainability
Bibliographic record
Abstract
Payments for ecosystem services programs (PESPs) are increasingly being adopted globally to enhance sustainability outcomes. There are also hundreds of studies yearly on various aspects of PESPs, but research on their contributions to sustainability of communities and the ecosystems they depend upon at the global scale are rare. Our global review explores twelve key characteristics of PESPs at three different phases (inputs - implementation - outputs and outcomes) and their relationship types of these characteristics to sustainability outcomes. To do so, we review 376 peer-review journal articles on PESPs, and test three hypotheses related to these relationships. Our findings confirm that the relationships between each of these characteristics and sustainability outcomes are bidirectional and/or multidirectional to some extent and can be positive, negative or both, depending on specific cases and research methods used to study these relationships. The findings also disclose that separating one characteristic as the primary causal factor in any relationship or outcome is not easy as relevant characteristics are linked in a complex network. Thereby, determining key characteristics of PESPs that drive relationships for the sake of sustainability is important. Through analyzing relationships between PESP characteristics, this study offers a series of suggestions to further aid the contributions of PESPs' contributions to sustainability in the future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".