| A Monte Carlo model develops estimates of uncertainty for
inventory source and sink categories based on (a) mathematical models used to estimate emissions/removals for each category; (b) category specific input parameters and emission/removal estimates; and (c) the statistical properties underlying the input parameters and estimates. The principle of Monte Carlo analysis is to select random values of each parameter (e.g., emission factors and activity data) from within their individual probability density functions, and to calculate the corresponding values (e.g., emissions). This procedure is repeated many times, using a computer, and the results of each calculation run build up the overall emission probability density function. Monte Carlo analysis can be performed at the sub-category level, at the category level, for aggregations of categories, or for the inventory as a whole. There are multiple commercial software packages available to perform Monte Carlo simulations, including packages that integrate easily with spreadsheets (e.g., Microsoft Excel). Monte Carlo analysis can also deal with probability density functions of any physically possible shape and width, can handle varying degrees of correlation (both in time and between categories) and can deal with more complex models.
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