Public perception and valuation of UV health risks in low and high risk countries
Bibliographic record
Abstract
This paper is concerned with valuing the health risks of sun exposure due to ultra violet (UV) radiation. The study's main objective is to assess the public perception and valuation of UV health risks in countries in which the scientifically established risk levels are significantly different. Using a multi-country survey, we investigate the relationships between risk awareness, perception, behaviour and willingness to pay for risk reductions. These risk reduction premia are evaluated both for a private good (a new sun protection product) and a public good (a global fund for reducing global emissions of stratospheric ozone-depleting substances). We find that public awareness of risks is highest in New Zealand. Interestingly, the health effects of sunbathing are perceived as more detrimental in South Europe (Portugal and Greece) than in North Europe (England and Scotland), even though the actual risks are higher in the latter. We furthermore find that a substantial market exists for higher protection sunscreens, as evidenced by willingness to pay in the private good case. These willingness to pay amounts can be fairly well explained using a model incorporating both exogenous risk factors and risk-reducing behaviour. In terms of the public good, the policy message, which emerges is that public willingness to contribute to the Multilateral Fund established under the Montreal Protocol is highly variable across the countries studied.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".