preliminary iam scenarios based on the rcp/ssp framework · 2019-12-12 · preliminary iam...
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Preliminary IAM scenarios based on the RCP/SSP framework
Keywan Riahi International Institute for Applied Systems Analysis
SSP Drivers: KC Samir, Wolfgang Lutz, Rob Dellink, Marian Laimbach, Jesus
Crespo, Brian O’Neill, Leiwan Jiang… AIM: Shinichiro Fujimori, Toshihiko Masui, Mikiko Kainuma, …
GCAM: Jae Edmonds, Kate Calvin, Stephanie Waldhoff, Steve Smith, … IMAGE: Detlef van Vuuren, Elke Stehfest, David Gernaart …
MESSAGE-GLOBIOM: Volker Krey, Oliver Fricko, Petr Havlik, Shilpa Rao, Nils Johnson, …
ReMIND-MAGPIE: Elmar Kriegler, Nico Bauer, Alex Popp, Benjamin Bodirsky, WITCH-GLOBIOM: Massimo Tavoni, Johannes Emmerling, …
AGCI Workshop 5 August 2014, Aspen, Colorado
Integrated Analysis
Mi0ga0on, Adapta0on, Impacts
SSPs and Integrated Analysis
2
Representa0ve Concentra0on Pathways (RCPs)
Forcing, Emissions, concentra0ons, land
use, land cover
Socio-‐economic Pathways (SSPs)
Descrip0ons of Alterna0ve
Socioeconomic Pathways
Earth System Model Simula0ons (CMIP5)
Climate Change, climate variability
Moss, Edmonds, et al. (2010)
IAM scenarios Mi-ga-on dimension
SSP Process • Conceptual framework and nature of the SSPs established • Quantification of key elements of the SSPs has been completed • Narratives completed • Refined (but not final) IAM reference scenarios developed • Preliminary SPAs defined • Initial climate policy scenarios • Preliminary IAM SSP marker selected
• Still to do: – Continue vetting and development of SSP IAM scenarios – Check SPAs for refined stabilization analyses
The SSP-IAM Kitchen
IAM Models
GDP
POP
Urbanisation
SSP1 SSP2 SSP3 SSP4 SSP5
(Narratives)
Narratives
GDP
POP
Energy
Land-use
Technology, Demand, Life-
styles, Productivity
GHG Emissions
Aerosol/Pollutant Emissions
Urbanization
SSPs (Assumptions) IAM Models
SSP ASSUMPTIONS
Global Driver Assumptions
Lutz & KC, 2014
6000
7000
8000
9000
10000
11000
12000
13000
SSP1
SSP2
SSP3
SSP4
SSP5
Jiang & O’Neill
0
20
40
60
80
100
120
140
160
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
GDP per capits ($/cap -‐PPP) SSP5
SSP3
SSP2
SSP4
SSP1
OECD, PIK, IIASA
Inequality assumptions across SSPs
0
20
40
60
80
100
120
140
160
180
2010 SSP3 SSP2 SSP1 SSP4 SSP5
GD
P/ c
aptia
(PPP
)
Annex1 Non-Annex1
OECD Projections
Inequality ratio: 5.0
1.3
1.2
3.7 1.6
3.1
REFERENCE SCENARIOS (NO CLIMATE POLICY)
All results are still preliminary
Energy – SSP Reference Cases
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP1 (Sustainability)
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass SSP4
(Inequality)
Other renewablesNuclearGasOilCoalBiomass
Other renewablesNuclearGasOilCoalBiomass
Two scenarios where mitigation is relatively easy
Transition away from coal/oil Low demand
High share of poor with low emissions Low/intermediate demand Technology available to the “elite”
IMAGE GCAM
Energy – SSP Reference Cases
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP3 (Regional rivalry)
Other renewablesNuclearGasOilCoalBiomass
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP5 (Fossil-fueled growth)
Other renewablesNuclearGasOilCoalBiomass
Two scenarios where mitigation is relatively difficult
REMIND-MAGPIE AIM
Coal-intensive development Very high demand
Fossil-intensive High poverty Slow technological change Strong fragmentation
Energy – SSP Reference Cases
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP2 (Middle of the road)
Other renewablesNuclearGasOilCoalBiomass
A central scenarios with intermediate mitigation challenge
Balanced Technology Intermediate demand
MESSAGE-GLOBIOM
Energy – SSP Reference Cases
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP1 (Sustainability)
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass SSP4
(Inequality)
Other renewablesNuclearGasOilCoalBiomass
Other renewablesNuclearGasOilCoalBiomass
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP3 (Regional rivalry)
Other renewablesNuclearGasOilCoalBiomass
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP5 (Fossil-fueled growth)
Other renewablesNuclearGasOilCoalBiomass
1850 1900 1950 2000 2050 2100
EJ
0
500
1000
1500
2000
2500Other renewablesNuclearGasOilCoalBiomass
SSP2 (Middle of the road)
Other renewablesNuclearGasOilCoalBiomass
Land-use Change (index 1=2010)
0.6
0.7
0.8
0.9
1
1.1
1.2
SSP1-‐IMAGESSP2-‐MESSAGE-‐GLOBIOMSSP3-‐AIMSSP4-‐GCAMSSP5-‐REMIND-‐MAGPIE
0.6
0.8
1
1.2
1.4
1.6
1.8
SSP1-‐IMAGESSP2-‐MESSAGE-‐GLOBIOMSSP3-‐AIMSSP4-‐GCAMSSP5-‐REMIND-‐MAGPIE
Forest land Cropland
SSP1: sustainability
SSP1: sustainability
SSP3: high pop low productivity
RCP CO2 Emissions, World (Fossil fuels and Industry)
2000 2020 2040 2060 2080 2100
CO
2 em
issi
ons
(GtC
O2)
-20
0
20
40
60
80
100
120
140
160
RCP 8.5
RCP 2.6
RCP 4.5
RCP 6.0
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP5
SSP3 SSP2
SSP1
Fossil fuels and Industry CO2 Emissions, World (Reference Scenarios)
2000 2020 2040 2060 2080 2100
Em
issi
ons|
CO
2|Fo
ssil
Fuel
s an
d In
dust
ry
-20
0
20
40
60
80
100
120
140
SSP4
CH4 Emissions, World Reference Scenarios
2000 2020 2040 2060 2080 2100
Em
issi
ons|
CH
4
0
100
200
300
400
500
600
700
800
900
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP5
SSP3
SSP2
SSP1 SSP4
N2O Emissions, World Reference Scenarios
2000 2020 2040 2060 2080 2100
Em
issi
ons|
N2O
0
5000
10000
15000
20000
25000
30000
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP5
SSP3
SSP2
SSP1
SSP4
Policy Targets (exposure/concentrations) Technological Innovation
Policy Strength
High Income Countries Medium and Low Income
Strong Much lower than current targets in order to minimize adverse effects on both general population, vulnerable groups, and ecosystems.
Comparatively quick catch-up with the developed world (relative to income)
Pollution control technology costs drop substantially with control performance increasing.
Central Lower than current targets
Catch-up with the developed world at income levels lower than when OECD countries began controls (but not as quick as in the strong control case).
Continued modest technology advances.
Weak Regionally varied policies. High emissions levels and/or institutional limitations substantially slower progress in pollution control.
Lower levels of technological advance overall.
Air pollution policy assumptions (Storylines, exposure, targets)
World Emissions|NOx SSP reference scenarios
2000 2020 2040 2060 2080 2100
Em
issi
ons|
NO
x
0
20
40
60
80
100
120
140
160
180
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP5
SSP3
SSP2
SSP1
SSP4
World Emissions|Sulfur SSP Reference Scenarios
2000 2020 2040 2060 2080 2100
Em
issi
ons|
Sul
fur
0
20
40
60
80
100
120
140
160
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
World Radiative Forcing Reference Scenarios
2000 2020 2040 2060 2080 2100
Forc
ing
(W/m
2)
0
2
4
6
8
10
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP5
SSP3, SSP2
SSP1, SSP4
SSP/RCP combinations based on reference IAM scenarios
Forc
ing
leve
l (W
/m2 )
8.5
6.0
4.5
2.6
SSP1 SSP2 SSP3 Shared Socio-economic Pathways
SSP4 SSP5
6 - 7.5 W/m2 >8 W/m2
5.2 – 6.2 W/m2 ~5.8 W/m2
Climate Policy Scenarios
?
CLIMATE POLICY SCENARIOS
All results are still preliminary
Shared Policy Assumptions SPAs SSPs describe worlds with widely different challenges to mitigation due to eg fragmentation, lack of institutions, inequity, lack of technology, etc..
SPAs reflect these differences
Two main SPA dimensions
Accession/ Regional Participation
Effectiveness of land policies
F1: Early Accession, Global collaboration as of 2020 SSP1, SSP4
L1 highly effective SSP1
F2: Some delays, poor regions join in 2030
SSP2,5
L2 Intermediately effective (limited REDD) SSP2,4
F3: Late Accession, poor regions join in 2040 SSP3
L3 Low effectiveness (implementation failures, high transaction costs)
SSP3
Shared Policy Assumptions Accession
2000 2020 2040 2060 2080 2100
GHG emissions (GtCO2 equiv.)
0
10
20
30
40
50
60
70
80No Policy
Extrapolation of current policies
Strong global actiontoward 2ºC
Global GHG emissions
(AMPERE, Kriegler et al)
SSP1,4 SSP2,5
SSP3
World Forcing 6.0
2000 2020 2040 2060 2080 2100
Forc
ing
(W/m
2)
0
2
4
6
8
10
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
World Forcing 4.5
2000 2020 2040 2060 2080 2100
Forc
ing
(W/m
2)
0
2
4
6
8
10
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
World Forcing 3.7
2000 2020 2040 2060 2080 2100
Forc
ing
(W/m
2)
0
2
4
6
8
10
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
World Forcing 2.6
2000 2020 2040 2060 2080 2100
Forc
ing
(W/m
2)
0
2
4
6
8
10
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
SSP/RCP combinations based on reference IAM scenarios
Forc
ing
leve
l (W
/m2 )
8.5
6.0
4.5
2.6
SSP1 SSP2 SSP3 Shared Socio-economic Pathways
SSP4 SSP5
6 - 7.5 W/m2 >8 W/m2
5.2 – 6.2 W/m2 ~5.8 W/m2
Special Issue in GEC SSPs + IAM Scenarios
• Narratives: O’Neill et al (submitted) • Population: KC & Lutz (accepted) • GDP: (1) Leimbach et al, (2) Dellink et al, (3) Crepo
(submitted) • Urbanization: Jiang & O’Neill (submitted) • 5 x IAM marker papers • Crosscut papers:
– Energy – Land-use – Air Pollution/Aerosols
Data availability and resolution
https://secure.iiasa.ac.at/web-apps/ene/SspDb
All data will be publicly available at the SSP database Already available (national data) GDP Population (structure, education, tot) Urbanization IAM scenario data by end of the year Energy Land-use Emissions Forcing & Temperature Other relevant indicators (energy/carbon price, economic feedbacks, etc..) Resolution: 5 World Regions (more details available from IAM teams 10-26 regions) At the moment there are no concrete plans for spatial downscaling (assess on user needs) (individual efforts for downscaling: NCAR, IMPRESSIONs, IIASA, and other projects)
Timeline • Beta-version of IAM scenarios will become
available for comments by end of 2014 • Submission of selected papers (eg
overview paper) around the same time • Parallel community/paper review • Present beta-SSP scenarios at the IPCC
Scenarios Meeting in February (not fixed yet!)
• Finalization of scenarios and SI mid 2015
Additional Slides
Inequality across models Global GINI
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1980
1985
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
IIASA
SSP1
SSP2
SSP3
SSP4
SSP5
Country Groupings • For defining these scenarios we distinguish
among three groups of countries: • High Fertility Countries (HiFert): Countries
with current level of fertility less than 2.9 children per woman (2005-2010).
• Low Fertility Countries (LoFert) Countries with current level of fertility less than or equal to 2.9 not belonging to Rich OECD countries (see below)
• High Income-OECD Countries (Rich-OECD) As per the definition of World Bank.
Education Scenarios • The fast track (FT) scenario is extremely ambitious; it assumes that
all countries expand their school systems at the fastest possible rate, which would be comparable with best performers in the past such as Singapore and South Korea .
• The global education trend (GET) scenario is more moderately optimistic and assumes that countries will follow the average path of school expansion that other countries already somewhat further advanced in this process have experienced.
• The constant enrollment rate (CER) scenario assumes that countries only keep the proportions of cohorts attending school constant at current levels.
• The most pessimistic scenario, constant enrollment numbers (CEN), assumes that no more schools at all are being built and that the absolute number of students is kept constant, which under conditions of population growth means declining enrollment rates.
0
20
40
60
80
100
120
140
160
1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
SSP -‐ Per Capita GDP (billion US$2005PPP / million people)
SSP2 -‐ IIASA -‐ World SSP2 -‐ IIASA -‐ WorldSSP2 -‐ PIK -‐ World SSP2 -‐ PIK -‐ WorldSSP2 -‐ OECD -‐ World SSP2 -‐ OECD -‐ World
0
20
40
60
80
100
120
140
160
1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
SSP -‐ Per Capita GDP (billion US$2005PPP / million people)
SSP1 -‐ IIASA -‐ World SSP2 -‐ IIASA -‐ WorldSSP1 -‐ PIK -‐ World SSP2 -‐ PIK -‐ WorldSSP1 -‐ OECD -‐ World SSP2 -‐ OECD -‐ World
0
20
40
60
80
100
120
140
160
1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
SSP -‐ Per Capita GDP (billion US$2005PPP / million people)
SSP3 -‐ IIASA -‐ World SSP2 -‐ IIASA -‐ WorldSSP3 -‐ PIK -‐ World SSP2 -‐ PIK -‐ WorldSSP3 -‐ OECD -‐ World SSP2 -‐ OECD -‐ World
0
20
40
60
80
100
120
140
160
1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
SSP -‐ Per Capita GDP (billion US$2005PPP / million people)
SSP4 -‐ IIASA -‐ World SSP2 -‐ IIASA -‐ WorldSSP4 -‐ PIK -‐ World SSP2 -‐ PIK -‐ WorldSSP4 -‐ OECD -‐ World SSP2 -‐ OECD -‐ World
0
20
40
60
80
100
120
140
160
1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
SSP -‐ Per Capita GDP (billion US$2005PPP / million people)
SSP5 -‐ IIASA -‐ World SSP2 -‐ IIASA -‐ WorldSSP5 -‐ PIK -‐ World SSP2 -‐ PIK -‐ WorldSSP5 -‐ OECD -‐ World SSP2 -‐ OECD -‐ World
Global GDP levels by scenario
Source: preliminary SSP database 39
Population assumptions consistent with SSP Storylines
Low challenges for adaptation
High challenges for adaptation
China - population for five SSPs
Lutz & KC
0
200
400
600
800
1000
1200
1400
1600
SSP1
SSP2
SSP3
SSP4
SSP5
World 2050
SSP1 Pop=8673 Mio SSP3 Pop=10603 Mio
Lutz & KC
Common interpretation of the SSPs OECD – IIASA - PIK
43
Frontier TFP growth Speed of convergence
SSP1: Sustainability Medium high High
SSP2: Middle of the road Medium Medium
SSP3: Fragmentation Low Low
SSP4: Inequality Medium Low Income: Low
Middle Income: Medium High Income: Medium
SSP5: Conventional development High High
N.B. Quantitative interpretations and methodology differ between models, illustrating the uncertainties in making economic projections
Key SSP elements (three main products + IAV variables)
SSP Narratives
Population (age, sex,
mortality, fertility, education)
Urbanization (national)
Economic development (regional/national)
Quantitative drivers
Energy (technology,
resources, etc)
Emissions (forcing,
temperature)
Land-use (productivity,
diets, etc)
IAM Scenarios
1
2 3
Iterative Process
Air pollutant Uncertainties of the RCPs
BC
OC
CO
SOx
NOx
NMVOCs
RCP 2.6 RCP 4.5 RCP 8.5
Rogelj et al (NCC, 2014)
Sulfur Emissions Uncertainties in RCP 2.6 and 8.5
RCP 2.6
RCP 8.5
Rogelj et al (NCC, 2014)
Original RCP
Current legislation
Original RCP
Current legislation
World Emissions|Sulfur 2.6
2000 2020 2040 2060 2080 2100
Em
issi
ons|
Sul
fur
0
20
40
60
80
100
120
140
160
AIM/CGE GCAM IMAGE MESSAGE-GLOBIOM REMIND-MAGPIE SSP1 SSP2 SSP3 SSP4 SSP5
Assumptions about other drivers
SSP Element Low Med High Low Med High Low Med High Low Med High Low Med High
Traditional Fuel Usecontinued traditional fuel use
Lifestyles low service demands
low
Energy Intensity of ServicesIndustry high
Buildings medium
Transportation mediumlow/mediu
m
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5
Country Income Groupings
Non-‐climate Policies
Energy Demand Side
fast phase-‐out, driven by policies and economic
development
intermediate phase-‐out, regionally diverse speed
continued realiance on traditional fuels
some traditional fuel use among low income housholds
fast phase-‐out, driven by development priority
medium (low for global level/high for local level)
modest service demands (less material intensive)
medium service demands (generally material intensive)
medium service demands (material intensive)
modest service demands
high service demands (very material intensive)
Environmental Awareness high medium low high
low medium high low medium
low medium high low/medium medium
low medium high low high
General Comments some regional diversity retained
Energy demand (access, intensity of services, environmental awareness, etc..)
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5Country Income
Groupings
SSP Element Low Med High Low Med High Low Med High Low Med High Low Med High
CoalMacro-‐economy cost driver neutra l cost reducing cost reducing neutra l cost driver cost reducing
Technology medium medium high medium very high
National & environmental policy
very restrictive supportive very supportive supportive supportive restrictive very restrictive
Conv. HydrocarbonsMacro-‐economy neutra l neutra l neutra l cost driver cost reducing
Technology medium medium medium fast very high
National & environmental policy
restrictive supportive mixed (not supported in MEA/FSU) supportive supportive restrictive very restrictive
Non-‐conv. HydrocarbonsMacro-‐economy neutra l neutra l neutra l cost driver cost reducing
Technology s low medium medium medium very high
National & environmental policy
very restrictive supportive very supportive supportive supportive restrictive very restrictive
GeneralTrade barriers Free Barriers High Barriers Barriers Free
Fossil resources (availability, costs, trade, etc..)
Qualitative descriptions Modeling teams were flexible to make own interpretations
More assumptions…. Energy technologies (Innovation, acceptance, costs, etc…)
Land use change (productivity, regulation, trade, diets, etc…)
SSP Element Low Med High Low Med High Low Med High Low Med High Low Med High
Low Med MedHigh Low Low
High High HighHigh High High
Non-‐bio Renewables ConversionHigh High HighHigh High High
Nuclear PowerLow Low Med High High HighHigh High High High Med Med
CCS (under climate policy only)High High HighHigh Med Med
Conventional and Unconventional Fossil Fuel Conversion (synfuel and syngas in parenthesis if different)
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5Country Income Groupings
Social Acceptance Low Med High HighTechnology Development Med Med Low Med (High)
Low Med
Technology Development High Med Low MedSocial Acceptance Low Med High Med
Technology Development High MedSocial Acceptance High Med
Med
Technology Development Med Med Med
Commercial Biomass Conversion
Social Acceptance Low Med Med Med
Med Low
High
Technology Development Med Med MedSocial Acceptance Low Med
SSP Element Low Med High Low Med High Low Med High Low Med High Low Med High
Land use change regulation
strong medium weak weak medium strong medium
Agriculture
Land productivity growth rapid rapid medium medium slow slow medium rapid rapid
Environmental Impact of food consumption
low medium high medium high
International Trade globalized regionalized regionalizedlimited access
globalized
globalized
globalized
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5Country Income Groupings