Identifying the Causes of Low Participation Rates in Conservation Tenders
Bibliographic record
Abstract
Conservation tenders are being used as a policy mechanism to deliver environmental benefits through changes in land, water and biodiversity management. While these mechanisms can potentially be more efficient than other agri-environmental and payment for ecosystem service schemes, a key limitation in practice is that participation rates from eligible landholders are often low, limiting both efficiency and effectiveness. In this paper we document and review potential causes of low participation in two categories: those that treat participation as an adoption issue focused on searching for the landholder, farm or practice characteristics that limit participation; and those that treat it as an auction design issue, looking for the different auction, contract or transaction cost elements that limit landholder interest in participation. We then model how landholders make choices to engage and bid in a tender, making three important contributions to the literature on this topic. First, we document the low participation rates in conservation tenders, mostly across developed countries, an issue that has received little attention to date. Second, we explain that a decision to participate in a conservation tender involves three simultaneous decisions about whether to change a management practice, whether to be involved in a public or private program with contractual obligations, and how to set a price or bid. Third, we explain that there are a number of factors that affect each stage of the decision process with some, such as landholder attitudes and risk considerations, relevant to all three. Our findings suggest that decisions to participate in a conservation tender are more complex than simple adoption decisions, involving optimisation challenges over a number of potentially offsetting factors.
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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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".