Advancing ecosystem services auctions: Insights from an international Delphi panel
Bibliographic record
Abstract
Auction theory has made major contributions to overcoming allocation problems involving asymmetric information and common-pool resources, leading to multiple Nobel Prizes and serving as a foundation for multi-billion-dollar markets. Despite evidence that related mechanisms could enhance the performance of payments for ecosystem services (PES), adoption has been sporadic and inconsistent. One possibility is that the relevant peer reviewed literature has low visibility or consensus design elements are not sufficiently accessible to interested experts. To overcome this barrier, we adopt a straightforward approach: we asked the PES auction subfield to describe itself. In collaboration with an expert panel (n = 32) whose affiliations span more than two dozen universities and research bodies across three continents—including top-ranked economists, ecosystem services theorists, and practitioners with experience designing and implementing PES programs with and without auctions—we synthesize a birds-eye view of ecosystem services auctions for an interdisciplinary audience. Through an iterative, mixed-method Delphi consultation, we identify broad consensus about fundamental elements of theory and practice, including what functions auctions tend to perform well, common challenges, and key factors influencing their performance. By selecting topics that panelists appeared to disagree about for further discussion, we also highlight open questions and potential research frontiers. We conclude with a reflection on using the Delphi method to foster exchange between time-constrained experts.
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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 | 0.230 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".