Tools
Tools
Winiwarter, W. 
ORCID: https://orcid.org/0000-0001-7131-1496 & Muik, B.
(2011).
Statistical dependence in input data of national greenhouse gas inventories: Effects on the overall inventory uncertainty.
In:
Greenhouse Gas Inventories: Dealing With Uncertainty.
Eds. White, T, Jonas, M ORCID: https://orcid.org/0000-0003-1269-4145, Nahorski, Z, & Nilsson, S,
Dordrecht: Springer.
10.1007/978-94-007-1670-4_3.
Abstract
An uncertainty assessment of the Austrian greenhouse gas inventory provided the basis for this analysis. We isolated the factors that were responsible for the uncertainty observed, and compared our results with those of other countries. Uncertainties of input parameters were used to derive the uncertainty of the emission estimate. Resulting uncertainty using a Monte Carlo approach was 5.2% for the emission levels of 2005 and 2.4 percentage points for the 1990-2005 emission trend. Systematic uncertainty was not assessed. This result is in the range expected from previous experience in Austria and other countries. The determining factor for the emission level uncertainty (not the trend uncertainty) is the uncertainty associated with soil nitrous oxide N2O emissions. Uncertainty of the soil N2O release rate is huge, and there is no agreement even on the magnitude of the uncertainty when country comparisons are made. In other words, reporting and use of N2O release uncertainty are also different between countries; this is important, as this single factor fully determines a country's national greenhouse gas inventory uncertainty. Inter-country comparisons of emission uncertainty are thus unable to reveal much about a country's inventory quality. For Austria, we also compared the results of the Monte Carlo approach to those obtained from a simpler error propagation approach, and find the latter to systematically provide lower uncertainty. The difference can be explained by the ability of the Monte Carlo approach to account for statistical dependency of input parameters, again regarding soil N2O emissions. This is in contrast to the results of other countries, which focus less on statistical dependency when performing Monte Carlo analysis. In addition, the error propagation results depend on treatment of skewed probability distributions, which need to be translated into normal distributions. The result indicates that more attention needs to be given to identifying statistically dependent input data in uncertainty assessment.
| Item Type: | Book Section |
|---|---|
| Research Programs: | Air Quality & Greenhouse Gases (AIR) Mitigation of Air Pollution (MAG) |
| Bibliographic Reference: | In: T White, M Jonas, Z Nahorski, S Nilsson (eds); Greenhouse Gas Inventories: Dealing With Uncertainty; Springer, Dordrecht, Netherlands pp.19-36 |
| Depositing User: | IIASA Import |
| Date Deposited: | 15 Jan 2016 08:45 |
| Last Modified: | 27 Aug 2021 17:21 |
| URI: | https://pure.iiasa.ac.at/9682 |
Actions (login required)
 |
View Item |