DeComposition Analysis & Extrapolation Application for Forward-Looking Services 1.
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Transcript of DeComposition Analysis & Extrapolation Application for Forward-Looking Services 1.
1
DeComposition Analysis & Extrapolation
Application for Forward-Looking Services
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Outline
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
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1. Methodology
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Principles of DeComposition Analysis (DCA) 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
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Exemplary procedure of the Composition Analysis
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
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Field of application
=> 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
DisclaimerBut 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.
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Main calculation steps of DCA
Choice of parameter
Choice of relevant main drivers
Data compilation and gap filling
Definition of factors
Change of parameter per factor
Weight of parameter change per factor
Induced share of total parameter change per factor
Change of factors to the base year
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DCA Example in 8 Steps
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DCA Step 1: Choice of parameter
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
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DCA Step 2: Choice of relevant main drivers d1 Population d2 GDP, real prices 2005 d3 Final Energy d4 Final Fuel Energy d5 Fossil Fuel Energy
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DCA Step 3: Data compilation and gap filling
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)
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DCA Step 4: Definition of factors
Definition of factors containing all relevant main drivers contributing to changes in parameter
f1 (d1) Population Population f2 (d2/d1) GDP per inhabitant Specific economic
performance F3 (d3/d2) Energy Intensity Final Energy Intensity F4 (d4/d3) Fuel Intensity Share of Fuels in Final
Energy F5 (d5/d4) Fossil Fuel Intensity Share of Fossil Fuels in all
Fuels F6 (p/d5) Carbon Intensity w/o LUCF Carbon Intensity of Fossil
Fuels
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Factors contributing to changes∆ GHG = ∆ pop *∆ (GDP/pop) *
∆ (ENEcons/GDP) * ∆ (ENEfuel use / ENEcons) *
∆ (ENEfossil fuel use / ENEfuel use) * ∆ (GHG/ENEfossil fuel use )
GHG……..……..……... greenhouse gas emissions
pop………..................... population
GDP……..……..……... gross domestic product
GDP/pop……………… describes economic development
ENEcons…….................. final energy consumption
ENEcons/GDP…………. describes energy intensity
ENEfuel use………........... final fuel energy use
ENEfuel use/ENEcons…… describes the share of fuels in final energy consumed
ENEfossil fuel use……….... fossil fuel energy use
ENEfossil fuel use/ENEcons.. describes the share of fossil fuels in final fuel energy
consumed
GHG/ENEfossil fuel use…... describes the emission intensity of fossil fuels
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DCA Step 5: Change of factors to the base year
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
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DCA Step 6: Induced share of total parameter change per factor 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
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DCA Step 7: Weight of parameter change per factor 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
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DCA Step 8: Change of parameter per factor
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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En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (1
990
)
GD
P p
er i
nh
ab
itan
t
Po
pu
latio
n
Fo
ssil
Fu
el I
nte
nsi
ty
Fu
el I
nte
nsi
ty
Ca
rbo
n In
ten
sity
En
erg
y In
ten
sity
En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (2
009
)
0
20
40
60
80
100
120B
as
e y
ea
r 19
90
= 1
00
pe
rce
nt
Comparison 1990 and last data year 2009
DeComposition Analysis (DCA) EIONET countries 1990-2009
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Decomposition Extrapolation DCEThe Baseline scenario (Business as usual)
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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En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (1
990
)
GD
P p
er i
nh
ab
itan
t
Po
pu
latio
n
Fu
el I
nte
nsi
ty
Ca
rbo
n In
ten
sity
Fo
ssil
Fu
el I
nte
nsi
ty
En
erg
y In
ten
sity
En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (2
050
)
0
20
40
60
80
100
120
140B
as
e y
ea
r 19
90
= 1
00
pe
rce
nt
Comparison 1990 and target year 2050
DeComposition Extrapolation (DCE) EIONET countries Baseline 1990-2050
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Decomposition Extrapolation DCEThe Policy and Measures (PaM) scenario
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
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Decomposition Extrapolation DCEThe Policy and Measures (PaM) scenario
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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En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (1
990
)
GD
P p
er i
nh
ab
itan
t
Po
pu
latio
n
Ca
rbo
n In
ten
sity
Fu
el I
nte
nsi
ty
En
erg
y In
ten
sity
Fo
ssil
Fu
el I
nte
nsi
ty
En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (2
050
)
0
20
40
60
80
100
120
140B
as
e y
ea
r 19
90
= 1
00
pe
rce
nt
Comparison 1990 and target year 2050
DeComposition Extrapolation (DCE) EIONET countries PaM 1990-2050
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Relative GHG emission effect from 1990 to 2050 of both scenarios induced by each factor change and the total change!
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2. Example of ex-post DCA usagein Austria
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Ex-post DCA for the total GHG emissions of Austria:Main drivers for trends from 1990-2009
En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (1
990
)
GD
P p
er i
nh
ab
itan
t
Po
pu
latio
n
Fu
el I
nte
nsi
ty
Fo
ssil
Fu
el I
nte
nsi
ty
En
erg
y In
ten
sity
Ca
rbo
n In
ten
sity
En
erg
y-re
late
d T
ota
l GH
G
Em
iss
ion
s (2
009
)
0
20
40
60
80
100
120
140
160
180
200
Ba
se
ye
ar 1
99
0 =
10
0 p
erc
en
t
Comparison 1990 and last data year 2009
DeComposition Analysis (DCA) Austria 1990-2009
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3. Example of ex-ante DCA usagein Austria
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0
20
40
60
80
100
120
140
160
180
Em
issi
ons
1990
Num
ber
of
dwel
lings
Ave
rage
are
a pe
r dw
ellin
g
Ene
rgy
use
for
heat
ing/
hot w
ater
per
m²
Ele
ctric
ity
Dis
tric
t hea
ting
Am
bien
t hea
t
Bio
mas
s
Fue
l mix
(fo
ssil)
Hea
ting
degr
ee
days
Em
issi
ons
2050
Decomposition Analysis for Households 1990-2050
comparision 1990 and 2050
bas
e ye
ar 1
990
= 1
00 p
erce
nt
Model based DCA for households in Austria:Main drivers for GHG emission trends from 1990-2050
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4. Possible ex-ante use of DCEfor Forward-Looking Services
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Possible applications of DCA & DCE projects
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
Error propagation
Design elements
Aim
MethodDCEEx-ante:Ex-post:
Additionality
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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
++43 (0)1 313 04/5965
Environment Agency Austria, Viennawww.umweltbundesamt.at
EIONET/FLIS
Ljubljana■ February 17th 2012
