Economic incentives for the wildlife trade and costs of epidemics compared across individual, national, and global scales
Bibliographic record
Abstract
Abstract The wildlife trade drives biodiversity loss and zoonotic disease emergence, and the health and economic impacts of COVID‐19 have sparked discussions over stricter regulation of the wildlife trade. Yet regulation for conservation and health purposes is at odds with the economic incentives provided by this multibillion‐dollar industry. To understand why the wildlife trade persists despite associated biodiversity and global health threats, we used a benefit–cost approach using simple calculations to compare the economic benefits of the wildlife trade at the individual, national, and global scales to the costs of COVID‐19, severe acute respiratory syndrome (SARS), and Ebola disease across scenarios of epidemic frequency. For COVID‐19, benefits of the wildlife trade outweigh costs at individual scales, but costs far exceed benefits at national and global scales, particularly if epidemics were to become frequent. For SARS and Ebola, benefits outweigh costs at all scales, except if Ebola‐like epidemics were to become frequent. The wildlife trade produces net benefits for people who depend on wildlife for food and income but incurs net costs on stakeholders at larger scales from increased epidemic risk. While our analysis omits a variety of costs and benefits that are difficult to quantify and contrast, our analysis is meant to illustrate the distributional outcomes across stakeholder groups that could result from increased wildlife trade regulation. Importantly, the feasibility of trade regulatory policies will depend on how these benefits and costs compare across groups and would therefore need to involve accessible and attractive alternative sources of food and livelihoods for those who depend on the wildlife trade.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".