GHG Uncertainty Analysis

Definition

GHG Uncertainty Analysis (or Assessment) is an important part of the effort of compiling an inventory of anthropogenic emissions and removals of GHGs (GHG Inventory) and to understand changes over time.

Using Monte Carlo

The principle of Monte Carlo analysis is to select random values of emission factor, Activity Data and other estimation parameters from within their individual probability density functions, and to calculate the corresponding emission values. 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 category level, for aggregations of categories or for the inventory as a whole. Like all methods, Monte Carlo analysis only provides satisfactory results if it is properly implemented. This requires the analyst to have scientific and technical understanding of the inventory. Of course, the results will only be valid to the extent that the input data, including any expert judgements, are sound.

The Monte Carlo approach consists of four clearly defined steps. Only the first of these requires effort from the user. The emission inventory calculation, the PDFs, and the correlation values should be set up in the Monte Carlo simulation framework.

• Step 1: Specify category uncertainties. This includes estimation parameters and activity data, their associated means and PDFs, and any correlations.
• Step 2: Select random variables. Select input values. Input values are the estimates applied in the inventory calculation. This is the start of the iterations. For each input data item, a number is randomly selected from the PDF of that variable.
• Step 3: Estimate emissions and removals. The variables selected in Step 2 are used to estimate annual emissions and removals based on input values. Correlations of 100 percent are easy to incorporate, and good Monte Carlo packages allow other correlations to be included. Since the emission calculations should be the same as those used to estimate the national inventory, the Monte Carlo process could be fully integrated into the annual emission estimates.
• Step 4: Iterate and monitor results. Iterate and monitor results. The calculated total from Step 3 is stored, and the process then repeats from Step 2. The results from the repetitions are used to calculate the mean and the PDF.