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10.02.2006Kari Grönfors Effects of ETS data and methodology on GHG inventory time series in Finland.

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Presentation on theme: "10.02.2006Kari Grönfors Effects of ETS data and methodology on GHG inventory time series in Finland."— Presentation transcript:

1 Kari Grönfors Effects of ETS data and methodology on GHG inventory time series in Finland

2 Contents Organisations involved in ETS /NAP /GHG inventory in Finland Calculation of GHG emissions in CRF sectors 1 and 2 Confidentiality Data flows in ETS and GHG inventory Recalculation of time series Use of ETS / NAP data in recalculation Emission factors & oxidation factors Effects of recalculation on 1990 emissions Methodological issues Comparison of mass balance approach & energy approach in Iron & Steel industry Conclusions:non-problems problems

3 Organisations involved in ETS /NAP /GHG inventory in Finland Energy Market Authority (EMV): - main responsibility of emission trading data and register - collects data from ETS plants, database (almost) ready Ministry of trade and industry (MTI) / Energy department - collected data from operators for preparation of NAP1 - the same data will be used for NAP2 - database not directly linked to other systems StatFi/Greenhouse gas inventory unit: - main responsibility of GHG inventory - compilation of CRF tables and NIR - calculation of emissions from sector Energy (1AB) and Industrial processes (2A-D)

4 Calculation of GHG emissions in CRF sectors 1 and 2 ILMARI calculation system for energy based emissions - bottom up data (~2000 point sources) - primary data from national emissions register VAHTI - supporting data from energy production and fuel consumption surveys and industrial statistics and from many other sources - special inquiries for certain plants (refineries, iron & steel plants) Bottom up data covers almost all fuel combustion in energy and industrial sectors. Also process emissions are calculated using plant level data, taken either from VAHTI, industrial statistics or in some cases directly from operators.

5 Confidentiality StatFi has legally access to all data collected by authorities; also data collected by branch organisations is widely used in e.g. Energy statistics (based on agreements). All data collected by StatFi is defined as confidential, also the data received from other authorities and organisations; plant or enterprise level data can not be published nor delivered to other authorities; thus only summary reports can be published. There are some subcategories in GHG inventory in CRF 1 and 2 sectors (and also in Energy statistics), where less than 3 enterprises are included. In these cases enterprises could prevent reporting of data, but they have not requested that. Published data (in CRF and NIR) includes the same type of information that is available in their environmental reports. The detailed calculation sheets used in GHG inventory will not be published. In F-gases there are sub-sectors with confidential data.

6 Data flows in ETS and GHG inventory Operators report annual fuels and emissions, including fossil and non-fossil CO2, to VAHTI emission register maintained by regional environment offices Operators report annual fuels and CO2 emissions to ETS database maintained by Energy Market Authority (EMV) StatFi imports data from VAHTI and in the future also from ETS database to ILMARI system In ILMARI CO2 emissions are calculated from each fuel batch using national or plant specific emissions factors Calculated CO2 is compared to reported figures and may be corrected if necessary Thus ILMARI includes original reported CO2 from VAHTI, final CO2 and from 2005 on also reported CO2 from ETS

7 Data flows in ETS and GHG inventory 2

8 Recalculation of time series Recalculation of time series during 2005 (partly still going on) - fuel consumption data: 14 years, ~40000 fuel records - fuel codes (updated fuel classification) - technical data (combustion techniques, fuel capacity, etc.) - CRF categories, NACE classification - non-CO2 emission factors (from separate study by VTT) - CO2 emission factors (national or plant specific); the same EFs for oxidation factors - reallocation of iron & steel process emissions - more accurate system for non-energy use + reallocation

9 Use of NAP / ETS data in recalculation Data collected for NAP1 by MTI was used for checking/identifying missing point sources of process emissions, mainly use of carbonates (in the 2005 submission) Certain plants have produced either latest years calculations or time series using mass balance approach; this data was used in recalculation and reallocation of Iron and Steel process emissions Some non-conventional fuel types were identified from ETS monitoring plans (industrial residues or wastes, mixed fuels, non-specified gases) Some plants technical data was corrected using ETS monitoring plans

10 Emission factors & oxidation factors 1 National fuel classification (official version 4-digit level) was revised to take into account ETS demands (in co-operation with EMV) Especially group mixed fuels is now more detailed than in the previous version; includes 4 sub-categories The list of fuels and definitions is published in website of Stat Fi; it includes default NCVs and CO2 emission factors ( The same classification will be taken to VAHTI emission register from 2005 (some fuel categories are further divided to 5-digit level subcategories) Stat fi updates the list annually, taking into account changes in NCVs and EFs When preparing their ETS monitoring plans, enterprises are obliged to use this fuel classification and published default EFs in the case of lower Tiers For solid fuels, default EFs can be used in any case in the first period of ETS (national decision)

11 Emission factors & oxidation factors 2 Default oxidation factors from MRG were chosen instead of IPCC Guidelines to avoid inconsistency in time series Only a small number (< 5) of plants will be using plant specific oxidation factors Country specific emission factors were developed for certain fuels (natural gas, LPG, peat, heavy fuel oil) instead of IPCC defaults Plant specific emission factors were checked from calculations prepared for ETS (coke, BF gas, coke oven gas) Some non-conventional fuel types have been reallocated to fossil/mixed/non- fossil (e.g. hydrogen may have been reported as either other fossil gas or other non-fossil gas)

12 Effects of recalculation on 1990 emissions Total GHG emissions in 1990 (after recalculation) 71,5 Mt CO2-eq. of which 53,3 Mt CO2 from fuel combustion. - oxidation factor of solid fuels:0,98 => 0,99 + 0,15 Mt - oxidation factor of liquid fuels0,99 => 0,995+ 0,14 Mt - emission factor of heavy fuel oil77,4 => 78,8+ 0,1 Mt - emission factor of natural gas56,1 => 55,04 - 0,1 Mt - corrections in peat consumption - 0,1 Mt - fuels that had not been reported (pet coke etc.)+ 0,6 Mt - removal of double counting (non-energy use of fuels) - 0,3 Mt (total + 1,1 Mt)

13 Methodological issues In general there are no big surprises in monitoring plans; some plants have suggested their own monitoring methodologies. Only a very few plants (<5) use continuous measurement. All Iron and Steel plants use mass balance approach in ETS. In the previous inventories emissions in this sector were calculated using energy approach (starting from coke input in blast furnaces); no calculations were made from other material input and outputs of carbon than limestone Refineries use energy approach in ETS, except for certain processes Some special cases can be seen in refineries: e.g. emissions from catalytic regeneration - TCC coke and FCC coke - have been treated as combustion emissions in the previous inventories (0,5 % of total fuel combustion); comparison RA-SA? All cement kilns use the same methodology (clinker production) Lime kilns use either A or B method

14 Comparison of mass balance approach & energy approach in Iron & Steel industry Old methodology:for each process energy emissions:CO2 = fuel inputs * OF * EF4,51 Mt process emissions:CO2 = 0,44 * limestone use0,28 Mt total emissions:4,79 Mt fuels including:coke and heavy oil for blast furnace (reducing agents) coke oven gas & conventional fuels for each process but excluding:blast furnace gas (to avoid double counting) New methodology: total emissions: CO2 from mass balance approach reported by each process4,94 Mt energy emissions: CO2 = fuel inputs * OF * EF2,89 Mt process emissions: total CO2 - energy emissions2,05 Mt fuels including:conventional fuels, coke oven gas & blast furnace gas but excluding:coke and heavy oil for blast furnace (reducing agents) Difference between the old and new methodology:0,15 Mt (3,0 %)

15 Conclusions: non-problems in our case (but to be considered ) Access to ETS data; close co-operation between EMV and StatFi Using ETS data; no major problems identified so far Confidentiality in publishing CRF tables (except F-gases and military use of fuels) Balancing top-down and bottom-up approaches Consistency between Energy balance / GHG inventory / ETS data (some fine tuning needed) Coverage: in all sectors emissions in ETS <= emissions in GHG inventory

16 Conclusions: problems Different default oxidation factors in IPCC96, MRG1, MRG2, IPCC2006: inventory, AAU reporting, NAP2 Time schedules: NAP2, MRG, AAU, 2006 submission Continuous improvement or stable emission factors during NAP period? Confidentiality in publishing background data of ETS (problem for other emission reporting than GHG inventory) Uncertainty assessment in GHG and ETS: similar or comparable? Minor technical and (slightly bigger) time schedule problems in developing a new module in ILMARI for ETS data

17 Conclusions: more problems Minor problems in getting ETS data and inventory data consistent and comparable: transferred CO2 (depends on the use) different level of reporting in some cases (installation/plant/site) different CRF codes (for example coking plant) continuous measurement: no fuel data reported to ETS database updated default emission factors (2005 ETS data is based on older set) What is the acceptable difference between GHG and ETS data in plant level? Differences in sectoral data between ETS and CRF need to be documented in the NIR, as well as reasons for these differences.

18 Thank you for your attention!

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