Assessing complex policy problems such as those that are the target of integrated environmental health impact assessments inevitable involves major uncertainties.  Doing an assessment without considering these uncertainties is a pointless task; likewise, presenting the results of an assessment without giving information about the uncertainties involved makes the results more-or-less meaningless.  The uncertainties likely to be encountered therefore need to be considered at the feasibility testing stage, in order to ensur  that an assessment is worthwhile, that it is better to undertake an assessment now rather than wait for better information (so that the uncertainties can be reduced), and - if an assessment is to proceed - to work out how to deal with the uncertainties involved. 

The dimensions of uncertainty

Uncertainties arise at every step in an assessment: from the initial formulation of the question to be addressed to the ultimate reporting and interpretation of the findings.  They also take many different forms, and are not always immediately obvious.  It is therefore helpful to apply a clear and consistent framework for identifying and classifying uncertainties, in order to ensure that key sources of uncertainty are not missed and that users can understand how they arise and what their implications might be.  In particular, it is useful to distinguish between two essential properties of uncertainty:

• location - where within the analytical process (and embedded causal) chain it arises
• level - its magnitude or degree of importance

(A link to a detailed explanation of this framework is given in the panel to left).

Describing uncertainties

It is also important to describe and, where possible, quantify the uncertainties that arise during the assessment.   The best way of doing this is likely to vary, depending on the issue and the assessment methds being used.  In some cases, rigorous statistical methods can be used, epecially where the main uncertainties relate to sampling or measurement error.  In other cases, more qualitative methods are more appropriate.  To describe all the main sources of uncertainty in a large and complex assessment may need some combination of these techniques.  The problem of this is that it can then be  difficult to compare the degree of uncertainty inherent in different parts of the process, or to derive an overall measure for the assessment as a whole.  In selecting a method for describing uncertainty, therefore, we need to consider not only its scientific rigour, but also its appicability to the sorts of data and types of uncertainty involved.  In the end, it may be more informative to use a simple yet consistent method, which includes all the main uncertainties in the assessment, rather than a more sophisticated one which only considers some of them.

The link below gives access to further information on methods of uncertainty analysis, and to a range of useful tools.