Monte Carlo simulation requires the analyst to specify PDFs (see Fishman, 1996) that reasonably represent each model input for which the uncertainty is quantified. The PDFs may be obtained by a variety of methods, as described in Section 3.2.2.4 including statistical analysis of data or expert elicitation. A key consideration is to develop the distributions for the input variables to the emission/removal calculation model so that they are based upon consistent underlying assumptions regarding averaging time, location, and other conditioning factors relevant to the particular assessment (e.g., climatic conditions influencing agricultural greenhouse gas emissions).

Monte Carlo analysis can deal with probability density functions of any physically possible shape and width, as well as handling varying degrees of correlation (both in time and between source/sink categories). Monte Carlo analysis can deal with simple models (e.g., emission inventories that are the sum of sources and sinks, each of which is estimated using multiplicative factors) as well as more complex models (e.g., the first order decay for CH4 from landfills).

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