A review of spatial targeting methods of payment for ecosystem services
Bibliographic record
Abstract
Payments for Ecosystem Services (PES) have been studied extensively over the past decade as an important policy tool for coordinating ecological protection and regional socioeconomic development. One of the greatest challenges of PES implementation is to understand where to pay, i.e., spatial targeting, which can directly impact PES effectiveness and efficiency. In this study, we conducted a systematic review of spatial targeting methods based on literature analysis using Citespace. Firstly, peer-reviewed articles related to spatial targeting of PES were selected from the Web of Science database based on keywords. Cases applying PES spatial targeting methods were then chosen and analyzed after all articles were read. In total, 70% of the chosen cases focused on improving the compensation efficiency of biodiversity or another single environmental objective, whereas the remaining cases focused on coordinating trade-offs between equity and efficiency or multiple environmental objectives. The main PES spatial targeting approaches included cost-benefit analysis, multi-objective optimization, data envelope analysis and other methods aimed at specific issues. Of these, cost-benefit analysis has been most widely applied at different scales, including county, regional and watershed scales. Significant differences among the different PES spatial targeting methods were found, including in PES spatial targeting dimensions, efficiency optimization approaches and method application conditions. The practice of PES spatial targeting requires the selection of appropriate methods based on contextual biophysical and socioeconomic conditions as well as relevant environmental issues. The combined application of PES spatial targeting methods, compensation willingness of stakeholders and dynamic implementation of PES spatial targeting should be considered in future research.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".