Possibilities and challenges in transfer and generalisation of monetary estimates for environmental and health benefits of regulating chemicals
Bibliographic record
Abstract
This paper reviews and discusses existing methodologies for transferring and extrapolating the economic value of health and environmental impacts across chemicals, and identifies challenges with such value transfer and when it can be suitable. The value transfer methodologies describes can be used to estimate the economic benefits of chemical management regulatory frameworks as a whole, as well as in cost-benefit analyses (CBAs) of risk management measures for individual chemicals. For economic valuation of mortality risks from chemicals, the OECD database of Stated Preference (SP) studies of Value of Statistical Life (VSL) , which should be continuously updated with new valuation studies, has a sufficient number of primary studies internationally to conduct value transfer using meta-analytic regressions. However, the empirical evidence on acute and chronic morbidity endpoints, especially concerning all costs components of chronic illnesses, seems to be scarce. The same is true for chemical-related environmental impacts, especially related to ecosystem services, for the multitude of chemicals. Thus, the main methodological and informational challenge for valid value transfer of environmental and health impacts from chemical regulations seems to be new primary valuation studies of morbidity and ecosystem services impacts caused by exposure to (groups of) chemicals. These new primary valuation studies should be designed with value transfer in mind, and cover several countries, in order to extrapolate and generalise the economic values to evaluate international chemical regulations in CBAs. These new primary studies should ideally cover all relevant scales of the impacts, in order to develop generalised adjustment factors for differences in scale of the impacts between the study sites and the policy site. This would improve the spatial transfer of values. The same is true for the combination of Geographical Information System (GIS) data with existing primary studies of impacts at different scales. Furthermore, these new primary studies should be repeated over time in order to provide more information about how values for the relevant impacts change over time; as preferences, scarcity of the public good and the real income of the affected population change. This would improve temporal transfer.
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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 | 0.245 | 0.517 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".