The social costs of pesticides: a meta-analysis of the experimental and stated preference literature
Bibliographic record
Abstract
Pesticide use poses major public health risks and raises environmental concerns globally. We synthesize three decades of stated preferences and experimental approaches that estimate the social costs of pesticide use through consumer and farmer willingness-to-pay (WTP) to prevent or reduce the risks involved. We contribute to the existing literature by demonstrating that the social costs of pesticides vary significantly depending on risk types and levels, where they occur, who is exposed and their risk aversion. The main conclusion is that there exists no single global value estimate for the social costs of pesticide use, there is widespread variation in existing value estimates. Consumers and farmers worldwide share concerns about pesticide risks to their health and the environment. However, there is a need to raise awareness about actual risk exposure levels and public health impacts. Leaving this information out in valuation studies significantly reduces WTP. Equally important is the need to further harmonize stated and revealed preference valuation research design and reporting to facilitate the application of previous study findings to new policy and decision-making contexts.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".