Methods to combine uncertainties

Once the uncertainties in activity data, emission factor or emissions for a category have been determined, they may be combined to provide uncertainty estimates for the entire inventory in any year and the uncertainty in the overall inventory trend over time. Results from sampling theory, as described in Section 2.5.1, Measurement-Based Tier 3 Inventories, of Chapter 2 in Volume 4 for the AFOLU Sector, may be used in cases where sampling is applied for direct measurement of, e.g., carbon stock changes. In these situations, sampling theory provides an estimate of the uncertainty in emissions/removals for a given category, without need to separately characterise an activity and emission factor.

Two approaches for the estimation of combined uncertainties are presented in the following sections: Approach 1 uses simple error propagation equations, while Approach 2 uses Monte Carlo or similar techniques. Either Approach may be used for emission sources or sinks, subject to the assumptions and limitations of each Approach and availability of resources. Complementary step by step explanation of the statistical calculation methods of Approaches is given in Sections 3.7.1 and 3.7.2.

Biases should be addressed prior to applying either Approach 1 or 2, as set out in Section For example, as discussed in Section, an assessment of bias, and potential disagreements among modelling approaches, should be conducted, and any action identified to improve the inventory estimate should be taken. Approaches 1 and 2 focus on quantifying the random component of the uncertainty of the inventory results where known sources of bias have been removed. The inventory estimates may still include unknown bias and in the analysis all errors are assumed behaving as random (Winiwarter and Rypdal, 2001).

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