Uncertainty

Abstract

Since the uncertainties are large, an analysis of the uncertainties is crucial for the credibility of the results. To begin, one needs to identify and quantify the individual sources of uncertainty. It is appropriate to group them into different categories, even though there may be some overlap:

• 1. data uncertainty, e.g. slope of a dose-response function, cost of a day of restricted activity, and deposition velocity of a pollutant;

• 2. model uncertainty, e.g. assumptions about causal links between a pollutant and a health impact, assumptions about form of a dose-response function (e.g. with or without threshold), and choice of models for atmospheric dispersion and chemistry;

• 3. uncertainty about policy and ethical choices, e.g. discount rate for intergenerational costs, and value of statistical life;

• 4. uncertainty about the future, e.g. the potential for reducing crop losses by the development of more resistant species;

• 5. idiosyncrasies of the analyst, e.g. interpretation of ambiguous or incomplete information.