A systematic review of non-market ecosystem service values for biosecurity protection
Bibliographic record
Abstract
While quantified environmental benefits from biosecurity protection programmes are available, they remain scarce, patchy, and context-specific. This contributes to the oversight of non-market economic values such as recreation and conservation in practical decision-making. To better understand this situation, we conducted a systematic review focused on studies that estimated non-market values. Our systematic literature review identified and described the body of knowledge on non-market values of current and future biosecurity protection initiatives worldwide. We identified 75 studies completed between 2000 and 2020 that examined biosecurity protection values across different ecosystems, including forests, freshwater, and marine environments. The results indicated that the three main quantified ecosystem service values were biodiversity conservation and enhancement, recreation, and bundled forest ecosystem services. Among the economic valuation methods, the survey-based stated preference method called choice experiment was the most widely used. This method provides a detailed approach to estimating multiple environmental values derived from biosecurity protection. We identified some significant advancements within the subfield of biosecurity protection, particularly in the valuation methods employed. These advancements include the integration of multiple approaches, such as combining economic valuation with spatial and psychological methods. We envision that our findings will inform the design of future NMV research. This, in turn, will better equip decision-makers to develop more effective, collaborative, and inclusive policies addressing biosecurity issues. These policies will account for the multiple values associated with biosecurity programmes.
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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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".