1. EU Workshop on Uncertainties in GHG inventories Uncertainty estimation of MS Anke Herold, ETC-ACC Suvi Monni, VTT Technical Research Centre, Finland Sanna Luhtala, Ministry of the Environment, Finland 5 September 2005

2. 2 Approach to collect MS information Questionnaire prior to workshop on uncertainties Response from 18 MS from which 17 had carried out a quantitative uncertainty analysis MS national inventory reports In some slides slightly different figures as in background paper distributed prior to the workshop information for 2 MS was added

3. 3 Status of uncertainty estimation I In total 18 EU MS have performed an uncertainty assessment. 14 from EU-15 MS have carried out an uncertainty estimation (Luxembourg missing), 4 new MS have provided an uncertainty analysis. Good coverage of sectors and gases in MS’ uncertainty analysis –8 MS included all sectors –7 MS included all sectors expect LULUCF –1 MS included all sectors except solvents –2 MS did not include F-gases

4. 4 Status of uncertainty estimation II (responses from questionnaires) Coverage of years: –All MS except one had carried out a quantitative uncertainty analysis for the most recent year (typically 2002 or 2003) –1 MS provided a quantitative uncertainty assessment for 1988 - 1998 –11 MS provided estimates for the base year. –Only 2 MS provided estimates for all years from 1990 to 2003, and some MS also for selected years between the base year and the most recent year. –Most MS do not have plans to cover earlier years except the base year (UNFCCC Guidelines and IPCC GPG do not provide clear guidance whether the years between the base year and the most recent year should be covered.)

5. 5 Status of uncertainty estimation III (responses from questionnaires) Updating of uncertainty estimation –14 MS have updated or plan to update the uncertainty assessment annually. One of these MS plans to update only Tier 1 analysis annually and Tier 2 analysis less frequently. –One MS is planning to update uncertainty assessment periodically, i.e. after methodological changes in the inventory. –One MS is planning to update uncertainty assessment regularly, e.g. every 2 years, depending on the available time and on the changes carried out on the GHG emission inventory. –Three MS do not know/have not planned how often to update the analysis.

6. 6 Status of uncertainty estimation IV (responses from questionnaires) Coverage of years –All MS had carried out a quantitative uncertainty analysis for the most recent year (typically 2002 or 2003) –11 MS had also provided estimates for the base year. –Only 2 MS provided estimates for all years from 1990 to 2003, and some MS also for selected years between the base year and the most recent year. –Most MS do not have plans to cover earlier years except the base year –(UNFCCC Guidelines and IPCC GPG do not provide clear guidance whether the years between the base year and the most recent year should be covered.)

7. 7 Correlations between inventory parameters I The extent whether and how correlations are taken into account varies: –7 MS took correlations into account –8 Member States did not take correlations into account. –Most often correlations were taken into account in the disaggregation level of uncertainty analysis (i.e. uncertainty analysis was done at a level at which cross-sectoral correlations could mainly be avoided) –Emission factors were assumed to be fully correlated across years, as suggested in the IPCC Good Practice Guidance.

8. 8 Correlations between inventory parameters II Energy Total fuel consumption and sectoral shares were considered as correlated For CO 2 emission factors for fuels were considered as correlated with those for the same data sources used in different source categories Activity data was considered as uncorrelated CO 2 offshore emissions were considered as not correlated across years when they are based on separate studies using emission factors appropriate for the time. Gas leakage emissions were considered to be fully correlated across years

9. 9 Correlations between inventory parameters III Waste Activity data through the time series was considered as correlated because in the FOD method the emissions of one year depend on the waste disposed during the 25 years before; Emission factors in FOD model were considered to be correlated, CH 4 recovery was not considered to be correlated. It was assumed that the resulting degree of correlation reflects the reduction (emissions reduced by 63%, correlation assumed as 37%); EF for sewage treatment were considered to be correlated;

10. 10 Correlations between inventory parameters IV Agriculture Correlation between years was assumed for N 2 O and CH 4 from Manure Management and for N 2 O from Agricultural Soils; EF for animals were considered as partly correlated across years for a given species; LULUCF LUCF emissions were considered as correlated

11. 11 Correlations between inventory parameters V Industrial Processes Correlation between years was assumed for Consumption of HFC and SF6 (2F) CO 2 process emissions from blast furnaces, coke ovens and ammonia plant were considered as uncorrelated. Nitric acid production emission factors were considered as uncorrelated Adipic acid production emissions were considered as uncorrelated. Process emissions from blast furnaces, coke ovens and ammonia plant were considered as uncorrelated.

12. 12 Uncertainty Tier methods used for combining uncertainties

13. 13 Methods used for uncertainty estimation I

14. 14 Methods used for uncertainty estimation II

15. 15 Methods used for uncertainty estimation III For EF uncertainties, IPCC default values are widely used in addition to expert judgement

16. 16 Relative uncertainties for MS GHG inventories Uncertainty expresses as bounds of 95% confidence interval relative to the mean value

17. 17 Source categories contributing to level uncertainty In most MS (8 out of 11), N 2 O from agricultural soils was most important contributor to total level uncertainty 2 MS: N 2 O from combustion was the most important contributor 1 MS: CH 4 from landfills was the most important contributor Other important contributors: CH 4 from manure management, CO 2 from fuel combustion in Other Sectors and N 2 O from Nitric Acid Production When MS included LULUCF, important contributing categories changed, e.g. C stock change in living biomass in forest land became most important contributor

18. 18 Trend uncertainties

19. 19 Utilisation of uncertainty analysis 15 MS identified actions to reduce uncertainties –Methodological improvements –QA/QC –Data collection Peer reviews focusing on categories with high uncertainties Specific projects to reduce uncertainties.

20. 20 Feedback from UNFCCC review process Most comments due to incompleteness of uncertainty assessment. Transparency of documentation of uncertainty analysis was also an issue. Encouragement to use uncertainty assessment for Tier 2 key source analysis. One MS had much lower estimates of uncertainty for direct and indirect N 2 O emissions from agricultural soils than the IPCC defaults; the MS revised figures according to suggestions of the ERT in the next year's submission.

21. 21 Difficulties encountered 5 MS did not report any difficulties with the analysis Other MS reported difficulties related to –Expert judgement Identification of cooperative experts Lack of transparency Uncertainty of using expert judgement –Lack of data Lack of default IPCC uncertainties Limited empirical data as basis for uncertainties Data from studies or plant operators are supplied without uncertainties –Correlations LULUCF and agriculture sectors were found most challenging, followed by waste

22. 22 Co-operation between MS and the EU Areas for co-operation identified: –Exchange of information and experiences –Share of studies –Clarify approaches for expert judgement to exclude subjective approaches –Example or case study as a practical guide for emission inventory compilers

23. 23 Thank you for your attention