Abstract
A new environmental challenge for Costa Rica involves the precise and reliable quantification of data from its fossil-fueled transportation sector. In the context of greenhouse gas inventories (measurement), uncertainty assessment, as the best quality parameter of any estimation or measurement, takes on a new relevance by becoming a mandatory requirement on ISO 14064-1:2018. However, a significant limitation has been found by users when quantifying standard (measurement) uncertainties associated with emission factors with asymmetric probability distributions. The present article sought to take advantage of fitting asymmetric distributions to estimate and compare possible standard uncertainties for the official emission factors of Costa Rica, specifically for the fuel sector. Five asymmetric distributions and a “symmetrization” method (symmetric approximation of an asymmetric distribution) were chosen and fitted to the data based on their application and previous use. Standard uncertainties were estimated from each distribution parameters as standard deviations. To evaluate the fit, quantiles of interest were extracted from simulated populations compared with the original data values. A systematically better fit was evidenced for the asymmetric triangular and generalized extreme value distributions, both for CO2 emission factors with less asymmetries and CH4 and N2O emission factors with greater asymmetries. This was not the case for the other distributions, where the log-normal distribution applying the correction factor suggested in the literature showed the worst fit. The use of the former distributions is recommended to estimate the standard uncertainties associated with the emission factors from the official Costa Rican database and other emission factors with similar asymmetries.
Highlights
Costa Rica is recognized worldwide as a pioneer in environmental protection and the fight against climate change (UN Environment Programme, 2019)
Among the accepted methodologies for estimating uncertainty, the application of the law of propagation of uncertainty or Gauss’s formula included in the Guide to the Expression of Uncertainty in Measurement (GUM) stands out. This methodology is based on modeling an output quantity y as a known function of several input quantities and handles the uncertainties associated with the input quantities by modeling them as random variables. This approach uses the standard deviations of these random variables and their correlations to produce an approximate evaluation of the standard uncertainty uy, a measurement uncertainty expressed as a standard deviation associated to the output quantity y (Joint Committee for Guides in Metrology [JCGM], 2012)
The applicability of different approaches of asymmetric distributions to address standard uncertainty estimation of emission factors in greenhouse gas (GHG) inventories according to GUM guidelines was evident, for the Costa Rican official database of these factors in the fuel sector
Summary
Costa Rica is recognized worldwide as a pioneer in environmental protection and the fight against climate change (UN Environment Programme, 2019). Among the accepted methodologies for estimating uncertainty, the application of the law of propagation of uncertainty or Gauss’s formula included in the Guide to the Expression of Uncertainty in Measurement (GUM) stands out. This methodology is based on modeling an output quantity y as a known function of several input quantities and handles the uncertainties associated with the input quantities by modeling them as random variables. It should be highlighted that the conventional technique described in the GUM does not require necessarily that probability distributions of the input quantities to be symmetrical (Possolo et al, 2019)
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