Uncertainty estimates obtained from measured emissionsremovals data

This section assumes that good practices are used to obtain the data, as outlined in Chapters 2 and Chapter 6, Quality Assurance/Quality Control and Verification. When estimating uncertainty from measured emissions data, considerations include: (a) representativeness of the data and potential for bias; (b) precision and accuracy of the measurements; (c) sample size and inter-individual variability in measurements, and their implications for uncertainty in mean annual emissions/removals; (d) inter-annual variability in emissions/removals and whether estimates are based upon an average of several years or on the basis of a particular year.

Representative sampling (or sampling design) implies that measurements are made for typical system characteristics, operating conditions, time periods, and/or geographic areas of interest. The precision and accuracy of individual measurements will depend upon the equipment and protocols used to make the measurements. The sample size will often be a trade-off between the desirability for more data and the cost of making measurements. In some cases, such as for continuous monitoring, the sample size may be large enough to effectively serve as a census, rather than a partial sample, of data. In general the variability in the data from one short-term time period (e.g., hour, day, week) to another will depend upon the characteristics of the category. If the goal is to develop an estimate of annual average emissions or removals, then judgement may be required as to whether measurements conducted over a short term are representative of rates over a longer time period and, if not, whether the measurement programme can be expanded to additional time periods. For example, flux measurements (data on emission factors) should represent the entire year. In the AFOLU Sector this is crucial, since emissions are highly dependent on climatic conditions which typically are not the same for the growing period and winter period.

Figure 3.4

Example of uncertainty in emission measurements and mean emission rate

(a) Fitted distribution for inter-unit variability in emissions;

(b) Uncertainty in fitted distribution because of small sample size (n=20);

(c) Uncertainty in mean emission rate.

(a) Inter-Unit Variability

Fitting a Distribution for Example Emission Rate

Fitting a Distribution for Example Emission Rate

/ Lognormal

(b) Uncertainty in Distribution of Variability

Probability Band for Example Emission Rate


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