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This method is intended to assist in characterizing uncertainties in emissions data for the Mileubalans and to identify critical issues related to uncertainty. The method assesses both quantitative and qualitative dimensions of uncertainty. Quantitative uncertainties are expressed by assigning probability distributions to the main emissions parameters and propagating those uncertainties via Monte Carlo simulations. Qualitative dimensions of uncertainty are expressed by use of a pedigree method, which provides rankings on a variety of qualitative attributes of emissions parameters. The general features of the method are described below as a series of steps. The first steps attempt to make explicit the structure of the system in which the emissions data is collected by disaggregating the data. A series of steps follow that which are designed to identify the main assumptions employed and key sources of error. These steps help to calibrate the analyst in providing qualitative and quantitative uncertainty estimates in sections 9 and 10. The final sections cover the use of sensitivity analyses and communication of results. Much of the data generated with the method is organized via a Monte Carlo / Pedigree spreadsheet, which is implemented with the '@RISK' software package. Note that the method is designed to provide a fairly rapid overview and diagnosis of uncertainty so that it can be adapated and used as a standard in a range of emissions studies. It should help structure uncertainty analysis on sets of data where this has not yet been done, or provide a convenient form to represent uncertainties where more detailed analysis on uncertainties has already been completed. The method also adds features that are not normally included in conventional uncertainty analyses. It is intended to convey the most salient uncertainties, and to provide guidance on where to put effort to improve the quality of emissions estimates. The method encourages a consistent characterization of uncertainty, avoiding the use of more precision than is justified by available knowledge. Finally, the method does not attempt to be exhaustive in characterizing all possible uncertainties. In particular, it does not provide a lot of focus on 'structural' uncertainties, which are assumed to play a more modest role in the assessment of emissions data than they do in other domains.
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