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Austrias emission projections

Stephan Poupa & Melanie Sporer, 10th Mai 2010-1Outline2General System

Models and Methods

Results

Outlookreporting, institutional arrangements, who decides the key parameters and which measures are considered in wm and wam, problems in evaluation of measuresmost important key parameters e.g economic growth, oil priceNEC, CLRTAP

21. General System3NISA and EMIPRO reporting

Institutional arrangements

Scenario development

Policies and measures

General System-3Reporting (NISA and EMIPRO)44Submission under UNFCCC National Inventory Report (NIR), Common Reporting Format (CRF) Tables, National CommunicationsSubmission under EC NECNFR Tables for NEC Gases and ReportSubmission under EC GHG Monitoring Mechanism CRF Tables/Short NIR, EMIPRO Report, PaMs & Projections TablesSubmission under UNECE/LTRAP Informative Inventory Report (IIR), Nomenclature for Reporting (NFR) TablesNational Reports to the parliament and for the public

Policies & Measures and ProjectionsUNFCCC + Kyoto ProtocolEnvironmental Control Act EC NEC DirectiveEC GHG Monitoring MechanismUNECE/LRTAP + ProtocolsGeneral System-------------------perform projections to meet reporting requirements and to give advice to policy makers

shows framework for data compilation of inventories within the NISA and projections and policies & measures for GHG and air pollutants

inventory and projections reporting requirements under fairly same legal basis (list on the left) list of deliveries on the right

might be interesting that AT has not ratified the Gothenburg Protocol yet

-exceptional: inventory and projections, GHG and air pollutants elaborated by fairly same team (beneficial effect on consistency, transparencyTCCCA, on which we put strong emphasis)

might also be notable that we host CEIP and ETC/ACC4Institutional arrangements5General SystemBMLFUW: contracting bodyWIFO: general macro-economic data;industrial productionTU Vienna: ERNSTL; input renewablesEnergieagentur: BALMOREL, LEAPUmweltbundesamt: overall coordination, technical support, compilation, waste projections, autoproducer, policies and measures, TU Graz:GLOBEMI, GEORGWIFO, Gumpen-stein: PASMA project communication and coordination is challenging and so is timing: 6 months!5Scenario development6with measures - scenario (wm) and a with additional measures -scenario (wam) for GHG; wm -scenario for air pollutants

the underlying wm- forecast (activity data) takes into account climate change mitigation measures that were implemented under the Austrian Climate Strategies 2002 and 2007 before 8th August 2008

wam forecast takes into account planned policies and measures with a realistic chance of being adopted and implemented in time.General SystemAll additional measures have been defined at expert level in consultation with the Federal Ministry of Agriculture, Forestry, Environment and Water Management (BMLFUW).Air pollutants: NEC asks for wm only.Base year: generally n-2 (2006) except WIFO (macroeconomic energy model) which needs complex input data.

62. Models and Methods7Modeling framework and sectoral approach

Quality management

Uncertainty assessment

Key parameters

Models and Methods-7Modeling framework for emission projections8Energy Forecast (national energy balance, macro-economic model, 3 bottom up , models) Transport Forecast (bottom-up national transport model GLOBEMI) Agricultural Forecast (PASMA model and expert consultations) Waste Forecast(EAA and expert judgements) Forecast Industrial Processes, Solvents, F-Gases(EAA,macro-economic model) Sectoral Emission Projections (Umweltbundesamt-EAA) National Emission Projections (Umweltbundesamt-EAA) Models and methods-modeling framework for air emission projections becomes established

-combines top-down and bottom-up: in order to give a complete overview of the countrys future emissions, we apply the top down macroeconomic approach. For the inclusion of technical measures and policies we use sectoral bottom up models.

The forecast of activities is modelled on the structure of the national inventory. The data structure of activities, input data, emission factors and emission calculations is based on SNAP categories (Selected Nomenclature for sources of Air Pollution). The structure of output data is presented and aggregated in the Common Reporting Format (CRF) of the UNFCCC. EF and sectoral emission calculation methodology consistent with emission inventory

last step compilation of sectoral emission projections to obtain national totals

advantage: level of detail is higher with that external input of sectoral forecasts, drawback: cannot easily develop a great variety of different scenarios and its rather complex to top up additional measures e.g. in order to create what-if-scenarios.

Types of models and institutions:- Energy Forecast, based on the National Energy Balance of Statistics Austria and on a macro-economic model of the Austrian Institute of Economic Research (WIFO: total energy demand and production), supported by calculations with the bottom-up models BALMOREL (public electric power and district heating supply, optimization package cost minimization, GAMS based), LEAP (AEA, electric power demand; simulation tool, demand driven approach) and ERNSTL (EEG, domestic heating and domestic hot water supply, minimize costs objective). Results of the different models are balanced within a few cycles. Umweltbundesamt experts combined the data of the different models and includes additional calculations on energy input for the iron and steel industry, production of electric power and district heating within industry, use of waste as fuel in power plants and industry, energy input of, compressor stations, demand of the energy producing sector, total energy demand.- Transport Forecast, based on a bottom-up, national transport model GLOBEMI (Technical University of Graz, road emission calculation, also fuel export effect). Inputdata derived from transport demand model (population data, motorisation rates, vehicle fleet sizes, economic and income development statistics). Off road emissions from GEORG-model.- Forecast of emissions from industrial processes, of solvent emissions and emissions of fluorinated gases are based on expert judgements of the Umweltbundesamt.- Agricultural Forecast, based on the PASMA model of the Austrian Institute of Economical Research (Sinabell & Schmid 2005) and expert consultations with the Agricultural Research and Education Centre, Gumpenstein (Pllinger 2005, 2008).- Waste Forecast, based on the Umweltbundesamt forecast of the quantity of waste deposited and wastewater handled.

8Quality management

9Questionnaire has been used for checking input data for compliance with the most important data quality requirementsSeveral data consistency checks have been performed e.g. by documentation of data inputs and changes in the calculation files; fixed input form has been used for each sector Often same person responsible for sectoral emission projections and Inventory; some sectors use emission methods based on the verified inventory methodsAn output data check has been carried out by comparing the results of the sectors in detail and checking the plausibility of the emission trendsThere are iterative feedback-loops between modeling teams, sectoral experts, and sectoral inventory experts in which scenarios, assumptions and policies and measures included in the forecasts are discussed

Models and methodsIn general, data quality checks similar to the management system of the Austrian Air Emission Inventory have been performed in each sector.

9Key underlying assumptions

10Models and methods201020152020GDP [bio 2000]256.52287.83321.70Population [1 000]8 4278 5618 672Stock of dwellings [1 000]3 6023 7253 827International coal prices [/GJ]6.597.367.44International oil prices [/GJ]14.9314.9314.93International gas prices [/GJ]9.629.629.62average oil price of the energy model (WIFO): USD120 (USD/ 1,37)Emission projections in this report are based on economic scenarios that were developed before the current financial and economic crisis. Therefore, recent economic developments are not taken into account in the emission projections presented here. The same general key factors are used for both scenarios.

Projections are consistent with the historical emission data of the Austrian Emission Inventory (Inventory Submission January 2008) up to the data year 2006.

The update of the scenarios is underway (up to 2030).

10Uncertainty Assessment

11Sensitivity assessments have been performed for specific (sub-) sectors, analysing the increase and decrease of key factors or of a combination of key factors:

Energy sector: influence of the natural gas price, electricity demand and electricity imports on CO2 emissions of Energy Industries; influence of the oil price on CO2 emissions from Manufacturing Industries and Construction; changes of renovation rate and changes of boiler exchange rates on CO2 emissions from the Residential and Commercial sector; influence of fuel price differences between Austria and neighbouring countries on CO2 emissions from Transport

Agricultural sector: changes of product prices

Models and methodsAll these assessments are based on model results, obtained by calculating effects on energy or live stock. It is necessary to mention that the emission results have in general no linear dependence on changes of an input factor. This is the reason why all presented sensitivity data cannot be seen as a functional dependency with varied parameters. The emission effect can be seen only for the specific value of the parameters given.

The assessments focused on CO2 as the activity forecasts are the basis for both emission projections - GHG and air pollutants.

113. Results12ResultsSensitivity Analysis

Submission under EC NEC Directive

Submission under UNECE/ LRTAP Convention

12Sensitivity analysis transport 13ResultsThe emission calculation for the transport sector is characterised by significant uncertainties. As mentioned in chapter 5.1.3.1 the price-induced fuel export is a great challenge concerning the projection of CO2 emissions. For this reason, two sensitivity scenarios are constructed. The main variable describing the fact of price- induced fuel export is the price difference between Austria and the neighbouring countries. Therefore the scenarios are as follows:Scenario wm: Is the present scenario with fuel price differences held constant from 2007 onwards.Scenario 0: Here the fuel price differences decline in stages until 2010 where they are assumed to be zero.Scenario X2: The fuel price differences are gradually increased so that they are twice as high in 2010 compared to the year 2007.In Figure 25 the results of the three scenarios are illustrated. One can see that the CO2 emissions develop at a lower rate in the scenario where they converge to zero in the year 2010 (scenario 0). The results of the other two scenarios can be summed up as follows:Equalization of fuel price with neighbouring countries: lower emissionsDoubling of fuel price level in comparison to neighbouring countries: higher emissionsFor a detailed discussion of this topic see Hausberger et al. 2009, 2nd fuel export study not published yet.

13Sensitivity analysis residentials 14ResultsThe ERNSTL model provides the energy demand for stationary sources in residential and commercial buildings. For verification of the stability of the modelled wm outcome, a sensitivity analysis examines the changes of following parameters:WM-Sens_foss+30%: price chance of fossil fuels (+30%)WM-Sens_gas+30%: price change of gas (+30%)WM-Sens_bio+20%: price change of biomass (+20%)WM-Sens_renrate+0.3%: change of renovation rate (+0.3%)WM-Sens_renrate-0.3%: change of renovation rate (-0.3%)WM-Sens_boilexrate+1%: change of boiler exchange rate (+1%)WM-Sens_boilexrate-1%: change of boiler exchange rate (-1%)

The variation of the renovation rate and boiler exchange rate at the stated rates shows that there is a low influence of these parameters alone. The greenhouse gas emissions vary at most by 1%. A more significant impact is produced by the alternation of prices. An increase of 30% in the price of fossil fuels will reduce GHGs by about 13% in 2020, whereas an increase in the biomass price will lead to a gain of around 4% in greenhouse gas emissions.

Furthermore, for a deeper analysis minor changes of parameters were studied. On the assumption that very small alternations of input parameter do not influence the output significantly we tested the robustness of the model for these parameters:WM-Rob_renrate+0.02: renovation rate (+0.02 percentage points)WM-Rob_renrate-0.02: renovation rate (-0.02 percentage points)WM-Rob_boilerexrate+0.02: boiler exchange rate (+ 0.02 percentage points)WM-Rob_boilerexrate-0.02: boiler exchange rate (- 0.02 percentage points)WM-Rob_gas+0.02: price change of gas (+ 0.02 percentage points)WM-Rob_gas-0.02: price change of gas (- 0.02 percentage points)WM-Rob_foss+0.02: price change of fossil fuels (+ 0.02 percentage points)WM-Rob_foss-0.02: price change of fossil fuels (- 0.02 percentage points)WM-Rob_bio+0.02: price change of biomass (+ 0.02 percentage points)WM-Rob_bio-0.02: price change of biomass (- 0.02 percentage points)

14EC NEC Directive15 Results19902000200520072010PollutantsAustrian Emission Inventory 2008[kt]Projection Emissions [kt]NOx 192.51204.45239.62220.10198.75SO2 74.3431.6427.1925.6025.80NMVOC273.64176.04178.71179.81167.70NH3 71.1869.2566.1166.4161.45PollutantsEmissions 2010 without tank tourism [kt/a]Ceilings 2010[kt/a]NOX146.20103SO225.7539NMVOC164.31159NH361.3166Table 1: Austrian national total emissions for 1990, 1995, 2000, 2005 and projected emissions for 2010 after implementation of agreed policy for Austria in 1000 tons per year, i.e. [kt/a]

Table 2: Austria's emission projection according to Directive 2001/81/EC and ceilings for 2010

15UNECE/LTRAP Convention16 Results201020152020PollutantsAustria's emission projection based on fuel sold [kt/a] NOX198.75158.80129.66SO225.8024.5323.63NMVOC167.70172.45179.08NH361.4561.1361.01201020152020PollutantsAustria's emission projection based on fuel used [kt/a] NOX146.20125.44110.63SO225.7524.4823.57NMVOC164.31169.15176.13NH361.3161.0460.93Table 1: Austria's emission projection based on fuel sold [kt/a] Table 2: Austria's emission projection based on fuel used [kt/a]

164. Outlook17More flexibility in development of varied reduction scenariosAssess synergies and trade offs between gases (AQ/CC)Update of energy projections 2030Long term scenarios 2050 (maybe other approach e.g. backcasting)Improved uncertainty assessmentNational Emission Projections System Austria (maybe GAINS, Access,.)

OutlookSome ideas and planned improvements.

Discussion: - Relalistic sectoral economic growth; is oil price still sensitive?Sensitivity analysis and uncertainty assessment on a sectoral basis - Discussion maybe: do you have Uncertainty Assessment on a more aggregated level as well?there is definitely potential for improvement in the incorporation of non-technical and behavioural measures, how do others deal with that?

17Contact & InformationStephan Poupastephan.poupa@umweltbundesamt.at

Melanie Sporermelanie.sporer@umweltbundesamt.at

18Umweltbundesamtwww.umweltbundesamt.atJoint TFEIP/EIONET meetingLarnaca 10th MaiMelanie Sporer took over the emissions projections compilation for GHG and air pollutants and reporting from Barbara Muik.18National System

Quality Management System(including Good Practice)

Austrian Air Emission Inventory