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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
A Multi-Model Regional Decomposition of CO2
Emissions: Socio-Economic Developments vsEnergy Efficiency and Carbon Intensity
Improvements
Michele Maurizio Malpede∗
*Fondazione Eni Enrico Mattei (FEEM)email: [email protected]
International Energy WorkshopAbu-Dhabi June 3, 2015
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Overview
Objectives and Motivation
Data
Methodology
SSP Scenarios
Results-Regional ∆CO2
Results-Model Uncertainty
Appendix
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Objectives of the Study
The study has 2 goals:
1. To explore regional distribution of CO2 emission variationsand their drivers from 2010 to 2100 and across differentsocio-economic scenarios
2. To examine how different integrated assessment modelspredict regional distributions of CO2 emissions
Motivation
• To determine the implications of each of the 4 drivers inevaluating the impacts of socio-economic scenarios on globaland regional economic systems and exploring the differencesbetween short-term (2030 vs 2010), medium-term (2050 vs2010) and long term (2100 vs 2010) effects
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Data
Models
• AIM-CGE
• GCAM
• MESSAGE
• IMAGE
• REMIND
• WITCH
Time
2010-2100
Regions of the world
Advanced Economies: EUROPE, USADeveloping Economies: R5ASIA, R5MAF, R5LAM
Socioeconomic Scenarios
5 SSPs developed by the National Center for AtmosphericResearch (NCAR)
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Methodology• Kaya and Yokoburi (1997) decompose carbon dioxide
emissions into 4 components, namely Population, GDP percapita, Energy Efficiency and Carbon Intensity:
CO2 = POP ∗ GDP
POP∗ PE
GDP∗ CO2
PE(1)
• As in Ang (2000) a difference in an aggregate indicator can bedecomposed into the sum of the effects by differences inexplaining factors and residual terms. Hence defining:∆CO2 = CO2t − CO20
∆CO2 = ∆POP+∆
(GDP
POP
)+∆
(PE
GDP
)+∆
(CO2
PE
)+∆res
(2)
• Each driver contributes in the following form (where t is theyear (i.e 2030, 2050, 2100), 0 is the starting year (i.e. 2010), istands for the region and j refers to the scenario):
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Log Mean Divisia Index Method (LMDI)
∆POP =CO2t,i,j − CO20,i,j
lnCO2t,i,j − lnCO20,i,j
ln
(POPt,i ,j
POP0,i ,j
)(3a)
∆GDP
POP=
CO2t,i,j − CO20,i,j
lnCO2t,i,j − lnCO20,i,j
ln
GDPt,i,j
POPt,i,j
GDP0,i,j
POP0,i,j
(3b)
∆PE
GDP=
CO2t,i,j − CO20,i,j
lnCO2t,i,j − lnCO20,i,j
ln
PEt,1,j
GDPt,i,j
PE0,i,j
GDP0,1,j
(3c)
∆CO2
PE=
CO2t,i,j − CO20,i,j
lnCO2t,i,j − lnCO20,i,j
ln
CO2t,i,j
GDPt,i,j
CO20,i,j
PE0,i,j
(3d)
∆res = 0 (3e)
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
The Shared Socio-economic Pathways (SSPs)
Figure: Conceptual framework and names of new global Shared Socio-economic
Pathways (SSPs) for climate change research as defined in O’Neill et al.(2012).
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Results of the decomposition of ∆CO2 - SSP2
Characterizations of the SSP2 scenario:
• Middle of the Road. This is the most adopted SSP foranalysis.
• Medium Technology Growth
• Environmental Awareness
• Medium Energy Demand
• Medium Economic Growth
• Medium Population Growth
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Results of the decomposition of ∆CO2 - SSP2
0
.25
.5
.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2030vs2010 - SSP20
.51
1.5
22.
53
3.5
4C
O2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2050vs2010 - SSP2
0
.5
1
1.5
2
2.5
3
3.5
4C
O2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2100vs2010 - SSP2
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Contribution of each driver in explaining ∆CO2 - SSP2
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
What We Deduce from the LMDI Decomposition for SSP2
R5MAF and R5ASIA ∆CO2 emissions Over Time
In such framework, R5ASIA and R5MAF countries will experiencethe same ∆CO2 for the period 2010-2050. After 2050 a divergenceis shown, largely due to a greater impact of Population growth andGDP per capita on R5MAF carbon dioxide emissions.
Europe, USA and R5LAM ∆CO2 emissions Over Time
These 3 regions will once again manage, to keep CO2 emissionsconstant over time. This partially due to a slow population growthand GDP per capita.
Role played by each factor
Population and GDP per capita will affect positively CO2
emissions, while Energy Efficiency and Carbon Intensity will play anegative role in terms of carbon dioxide pollution.
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
General Conclusion I
• Over the period 2010 to 2050 R5ASIA and R5MAF willpollute more than EUROPE, R5LAM and USA. While from2050 to 2100 R5MAF will experience a divergence in CO2
Emissions, R5ASIA will keep CO2 emissions at the same levelsof 2050, this is mainly due to a decrease of population and aminor increase of GDP per capita.
• This can be also seen by taking the Average yearly GrowthRates of CO2
log(1+gCO2 ) = log(1+gPOP)+log(1+g GDPPOP
)+log(1+g PEGDP
)+log(1+g CO2PE
)
(4)
where gx =(
xt+n
xt
) 1n − 1
For small values of x we obtain
gCO2 = gPOP + g GDPPOP
+ g PEGDP
+ g CO2PE
(5)
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Results Average Yearly Growth Rates
-1
-.75
-.5
-.25
0
.25
.5
.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
Ave
rage
Yea
rly C
O2
Var
iatio
ns %
EUROPE R5ASIA R5LAM R5MAF USA
Average Yearly Growth Rates - 2010-2050 - CO2 - SSP2
-1
-.75
-.5
-.25
0
.25
.5
.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
Ave
rage
Yea
rly C
O2
Var
iatio
n %
EUROPE R5ASIA R5LAM R5MAF USA
Average Yearly Growth Rates - 2050-2100 - CO2 - SSP2
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
General Conclusion II: Uncertainty Among Models
• The source of uncertainty among the 6 models considered wasassessed by using Principal Component Analysis
• Considerable degree of homogeneity among models. Howeverthe PCs associated to IMAGE and MESSAGE differ fromthose associated to WITCH, REMIND, GCAM and AIM
• Results of the PCA show a greater difference among modelsin assessing Energy Efficiency in 2020 with respect to 2050,suggesting that the assumptions that models make on PrimaryEnergy consumption are different, while converging over time.
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Example: R5ASIA - SSP2 - Primary EnergyComponent/Total Primary Energy
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Fossil Components
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Thanks for your attention
Corresponding author
email: [email protected]
AcknowledgementThe research leading to these results has received funding from theEuropean Union Seventh Framework Programme (FP7/2007-2013)under grant agreement n◦ 308481 (ENTRACTE).
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Appendix:
Results of Regional ∆CO2 for the rest of SSPs
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Results of the Decomposition SSP1
-1
-.75
-.5
-.25
0
.25
.5
.75
1
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2030vs2010 - SSP1
-.5
0
.5
1
1.5
2
2.5
3
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2050vs2010 - SSP1
-.5
0
.5
1
1.5
2
2.5
3
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2100vs2010 - SSP1
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
In depth: The contribution of each driver in explaining∆CO2 - SSP1
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Characterizations of the SSP1 scenario:
• Most Optimistic Scenario in terms of Challenges to Mitigationand Adaptation
• Rapid Technology
• High Environmental Awareness
• Low Energy Demand
• Medium-High Economic Growth
• Low Population
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
What We Deduce from the LMDI Decomposition for SSP1
Middle East and Africa ∆CO2 emissions Over Time
R5MAF countries will experience a greater increase in CO2
emissions over time than the other regions. This is largely due toan increase in GDP per capita.
Europe, USA and R5LAM ∆CO2 emissions Over Time
These 3 regions will manage, in this Scenario, to keep CO2
emissions constant over time. This partially due to a slowpopulation growth and GDP per capita. Low primary Energydemand will also play a large impact on pushing down CO2 forR5LAM.
Role played by each factor
Energy Efficiency will play the largest role in pushing CO2
emissions down over time, while carbon intensity effect will bemore slightly consistent over time
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
LMDI - ∆CO2 - Regional Comparison - Over Time - SSP3
0
.25
.5
.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2030vs2010 - SSP3
0
.5
1
1.5
2
2.5
3
3.5
4
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2050vs2010 - SSP3
0
.5
1
1.5
2
2.5
3
3.5
4C
O2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2100vs2010 - SSP3
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
In depth: The contribution of each driver in explaining∆CO2 - SSP3
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Characterizations of the SSP3 scenario:
• Worst Scenario in terms of Economic Growth
• Slow Technology
• Little Environmental Awareness
• High Energy Demand
• Low/Negative Economic Growth
• Reduced Trade
• Very High Population
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
LMDI - ∆CO2 - Regional Comparison - Over Time - SSP4
-1-.
75-.
5-.
250
.25
.5.7
51
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2030vs2010 - SSP4
-.5
0
.5
1
1.5
2
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2050vs2010 - SSP4
-.5
0.5
11.
52
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2100vs2010 - SSP4
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
In depth: The contribution of each driver in explaining∆CO2 - SSP4
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Characterizations of the SSP4 scenario:
Notes: For SSP4, No Data Available for IMAGE, MESSAGE andREMIND.
• Low Level of Challenges to Mitigation, but High Level ofChallenges to Adaptation
• Slow Technology
• High Inequality
• Low Energy Demand
• Low Economic Growth
• High Population
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
LMDI - ∆CO2 - Regional Comparison - Over Time - SSP5
0.25.5
.751
1.251.5
1.752
2.252.5
2.753
3.253.5
3.754
4.254.5
4.755
5.255.5
5.756
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2030vs2010 - SSP50
.51
1.5
22.
53
3.5
44.
55
5.5
6C
O2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2050vs2010 - SSP5
0
.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
CO
2
EUROPE R5ASIA R5LAM R5MAF USA
LMDI - CO2 - 2100vs2010 - SSP5
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
In depth: The contribution of each driver in explaining∆CO2 - SSP5
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Objectives and Motivation Data Methodology SSP Scenarios Results-Regional ∆CO2 Results-Model Uncertainty Appendix
Characterizations of the SSP5 scenario:
Notes: For SSP5, No Data Available for IMAGE, MESSAGE andREMIND.
• Worst Scenario in terms of CO2 Emissions
• High Level of Challenges to Mitigation, but Low Level ofChallenges to Adaptation
• Rapid Technology for Fossil
• High Energy Demand
• High Economic Growth
• Low Population
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