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# DeComposition Analysis & Extrapolation Application for Forward-Looking Services 1.

## Presentation on theme: "DeComposition Analysis & Extrapolation Application for Forward-Looking Services 1."— Presentation transcript:

DeComposition Analysis & Extrapolation Application for Forward-Looking Services 1

Outline 2 1. Methodology 2. Example of ex-post DCA usage in Austria 3. Example of ex-ante DCA usage in Austria 4. Possible ex-ante usage of DCE for Forward-Looking Services

1. Methodology 3

Principles of DeComposition Analysis (DCA) 4 Design of policy instruments and assessment/monitoring of measures require knowledge of driving forces (drivers) and factors (indicating components) influencing an environmental parameter, like GHG emissions The composition analysis delivers a methodical approach to quantifying the effects of these driving forces Therefore the parameter, for example the GHG emissions, are decomposed into a product of relevant factors In order to determine the respective contribution of these factors, the changes in GHG emissions are examined

Exemplary procedure of the Composition Analysis 5 The first step of a composition analysis is to identify the primary drivers (e.g. GDP, population, energy consumption on GHG emissions) The factors then can be built by one or two drivers like an indicator The effect of a certain factor within the chosen period (e.g. 1990..2009) is quantified by calculating the single effect of a change of this factor on the total change of the parameter (e.g. energy related GHG emissions), while leaving this factor constant. Each individual difference of the effect by the change of all other factors to the total change of the parameter represents the contribution of this factor in respect to the principle of additionality and a top-down approach. If all factor changes over the period under observation, the result has to be equivalent to the overall change in GHG emissions, and represents the sum of the effects of all factors

Field of application 6 => Such an analysis can be done for any specific field or sector, but with the assumption of the continuities change of conditions without a basic change of the concerned system => The relevance of the result will depend on the appropriateness of the selected drivers for the relevant field or sector Disclaimer But DCE should be never seen as a quantification tool like a verified bottom-up model, because it’s a top-down data analysis to identify the relevance of chosen factors (indictors), describing indicative the change of conditions. Wildcards, like economical crises, natural disasters or wars, and fundamental change of conditions (e.g. relevant drivers), like new social developments, unknown technologies or new economical or political structures are not easy to be taken into account in a DCE. Methods concerned on risks and discontinuities or qualitative approaches should be used additionally in respect to look at the change and uncertainty of the relevant current system and knowledge.

Main calculation steps of DCA 7

DCA Example in 8 Steps 8

DCA Step 1: Choice of parameter 9 p Energy-related Total GHG Emissions Parameter includes energy-related GHG emissions from EIONET countries w/o LUCF, national counting by JI/CDM and international bunkers

DCA Step 2: Choice of relevant main drivers 10 d1Population d2GDP, real prices 2005 d3Final Energy d4Final Fuel Energy d5Fossil Fuel Energy

DCA Step 3: Data compilation and gap filling 11 Data compilation Time series from base year 1990 to last data year 2009 Data sources EUROSTAT, EEA Gap filling method Icelandic data was supplemented by Norwegian data based on per capita values Liechtenstein data was filled up by Swiss data based on per capita values Missing GDP data was completed by dividing the EIONET 32 into two economical groups and assigning the yearly overall change in drivers from the remaining countries with available data to countries without data (for each group)

DCA Step 4: Definition of factors 12 Definition of factors containing all relevant main drivers contributing to changes in parameter f1 (d1)Population Population f2 (d2/d1) GDP per inhabitantSpecific economic performance F3 (d3/d2) Energy IntensityFinal Energy Intensity F4 (d4/d3) Fuel IntensityShare of Fuels in Final Energy F5 (d5/d4) Fossil Fuel IntensityShare of Fossil Fuels in all Fuels F6 (p/d5) Carbon Intensity w/o LUCFCarbon Intensity of Fossil Fuels

Factors contributing to changes 13 ∆ GHG = ∆ pop *∆ (GDP/pop) * ∆ (ENE cons /GDP) * ∆ (ENE fuel use / ENE cons ) * ∆ (ENE fossil fuel use / ENE fuel use ) * ∆ (GHG/ENE fossil fuel use ) GHG……..……..……...greenhouse gas emissions pop……….....................population GDP……..……..……...gross domestic product GDP/pop………………describes economic development ENE cons ……..................final energy consumption ENE cons /GDP………….describes energy intensity ENE fuel use ………...........final fuel energy use ENE fuel use /ENE cons ……describes the share of fuels in final energy consumed ENE fossil fuel use ………....fossil fuel energy use ENE fossil fuel use /ENE cons..describes the share of fossil fuels in final fuel energy consumed GHG/ENE fossil fuel use …...describes the emission intensity of fossil fuels

DCA Step 5: Change of factors to the base year 14 Unit: Base year percentage points of the respective factor Method: The product of all changed factor percentages equals the parameter’s changed value, expressed in base year percentage points

DCA Step 6: Induced share of total parameter change per factor 15 Unit: Base year percentage points of the parameter Method: The product of all changed factor percentages, excluding the considered factor, is subtracted from the parameter’s changed value expressed in base year percentage points Explanation: As one factor is left out, the resulting difference is a plausible indicator for the factor’s contribution to the parameter change

DCA Step 7: Weight of parameter change per factor 16 Unit: percent Method: Each factor’s induced share of total parameter change is weighted by the sum of all absolute singular factor change effects Remark: The absolute sum is different from the sum with plus and minus values

DCA Step 8: Change of parameter per factor 17 Unit: Base year percentage points of the parameter Method: Each weight is divided by the sum of all singular factor weights and multiplied by the parameter’s total change, expressed in base year percentage points Remark: The resulting sum is equal to the parameter’s change

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Decomposition Extrapolation DCE The Baseline scenario (Business as usual) 19 A Decomposition analysis applied on the concerned parameter deliver the data base for the extrapolation of the factors Estimation of total factor change from base year to target year 2050 Expectation value Minimum Maximum Drivers and parameter for target year are calculated out of expected factor change to build up the PaM scenario Normal DeComposition Analysis (DCA) is used to derive the influence of the factors on the expected change of the parameter

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Decomposition Extrapolation DCE The Policy and Measures (PaM) scenario 21 Policies, measures and their interdependencies are considered Choice of two representative PaMs for the example I. Fast market diffusion of renewables by subsidies, obligations, R&D and information II. Strengthening of efficiency technologies through research, subsidies, obligations and information Estimations on increasing or decreasing effect on each factor caused by one single measure

Decomposition Extrapolation DCE The Policy and Measures (PaM) scenario 22 Estimation of contrary reduction or synergetic reinforcement of each single effect on factors through other PaMs at target year regarding to the principle of additionality Drivers and parameter for target year 2050 are calculated out of estimated PaM-effects on factor change by all PaMs to build up the PaM scenario DeComposition Analysis (DCA) is used again to derive the influence of the factors on the parameter in this scenario

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24 Relative GHG emission effect from 1990 to 2050 of both scenarios induced by each factor change and the total change!

2. Example of ex-post DCA usage in Austria 25

Ex-post DCA for the total GHG emissions of Austria: Main drivers for trends from 1990-2009 26

3. Example of ex-ante DCA usage in Austria 27

28 Model based DCA for households in Austria: Main drivers for GHG emission trends from 1990-2050

4. Possible ex-ante use of DCE for Forward-Looking Services 29 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Possible applications of DCA & DCE projects 30 Confidence range of results Effect by the change of driving forces Design of effective and controllable measures First estimation for a scenario quantification Backcasting from political targets to the needed change of components Estimation of trends, policies, measures and uncertainties Trends and extrapolation of driversGood estimation practice Key issues of policy Key indicators DCA ErrorError propagationpropagation Design elements Aim Method DCE Ex-ante: Ex-post: Additionality

Please don’t hesitate to contact us, if you are interested in applying DCA/DCE in an European project looking at PaMs regarding energy and GHG emissions in the building sector! Alexander Storch alexander.storch@umweltbundesamt.at ++43 (0)1 313 04/5965 31 Environment Agency Austria, Vienna www.umweltbundesamt.at EIONET/FLIS Ljubljana ■ February 17 th 2012

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