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Long-term Greenhouse Gas
Stabilization and the Risks of
Dangerous Impacts
M. Webster, C.E. Forest, H. Jacoby, S. Paltsev, J. Parsons, R. Prinn, J. Reilly,
M. Sarofim, A. Schlosser, A. Sokolov, P. Stone, C. Wang
Engineering Systems Division
MIT Joint Program on the Science and Policy of Global Change
Massachusetts Institute of Technology
Society for Risk Analysis
New England Chapter
April 28, 2009
Calvin’s View on
Risky Decisions
Outline
• Motivation
• MIT IGSM Model Framework
• Parametric Uncertainty
• Resulting Uncertainty in Projections
• Exploring Risk-Risk Tradeoffs
Climate Change Policy:
Choosing a Long-Term Target
• UN Framework Convention on Climate Change
– “…stabilization of greenhouse gas
concentrations in the atmosphere at a level
that would prevent dangerous anthropogenic
interference with the climate system.”
• How do we choose this stabilization level?
CCSP Product 2.1a
• Study of GHG Stabilization Scenarios
• Three Models:
– MERGE (EPRI/Stanford)
– MiniCAM (PNNL/UMD)
– IGSM (MIT)
Source: Clarke et al., 2007
Stabilization Scenarios
(Source: U.S. CCSP Product 2.1a)
Global CO2 Emissions (GtC)
25
No Policy
CCSP 750
CCSP 650
CCSP 550
CCSP 450
20
15
10
5
0
2000
2020
2040
2060
Year
2080
2100
Global Mean Temperature Change
(Deterministic)
Global Mean Temperature Change from 2000
5
4
No Policy
Stabilize CO2 at 750ppm
Stabilize CO2 at 650ppm
Stabilize CO2 at 550ppm
3
Stabilize CO2 at 450ppm
2
1
0
2020
2040
2060
2080
Question for this Study
• How can we use information about risks of
exceeding thresholds to guide our choice among
long-run stabilization targets?
• Use the uncertainty in the CCSP results from
one model (MIT IGSM)?
• Objective: Frame the choice of long-term
stabilization target as a risk management
decision
– Consider risks of both climate impacts and abatement
costs
MIT
Integrated
Global
Systems
Model
Uncertainty in Economics
• 110 Uncertain Parameters, including:
– Productivity growth rates (historical data)
– Energy efficiency growth rate (historical data)
– Ease of substituting inputs (historical data)
– Costs of new technologies (expert judgment)
• Main Uncertain Outputs:
– Emissions (GHGs, urban pollutants)
– Costs (consumption loss, carbon prices)
GDP Growth Uncertainty
USA GDP per Capita Growth Rates
GDP per Capita Growth Rate (annual %)
6
Historical
5th Percentile
50th Percentile
95th Percentile
5
4
3
2
1
0
-1
1960
1980
2000
2020
2040
2060
2080
2100
Methodology
• Latin Hypercube Monte Carlo
– 400 random samples of all parameters
– Impose correlation where justified by
empirical data and/or theory
• Impose each CCSP scenario as an
emissions cap over time
– Not a fixed radiative forcing target
– No banking/borrowing
– DO allow GHG trading using GWPs
– DO allow trading between nations each period
Uncertainty in CO2 Emissions
(No Policy)
40
Global CO2 Emissions (GtC)
35
30
90% Bounds
50% Bounds
Stabilization Levels 1-4
IPCC SRES
A1FI
A2
25
20
15
A1B
10
5
0
2000
B1
2020
2040
2060
2080
2100
Carbon Prices in 2020
a) Carbon Price in 2020
Probability Density
0.12
Level 4
Level 3
Level 2
Level 1
Level 4: $5
0.10
0.08
Level 3: $8
0.06
Level 2: $20
Level 1: $71
0.04
0.02
0.00
0
20
40
60
Carbon Price ($/ton CO2)
80
100
Global Welfare Loss (%) in 2020
a) Uncertainty Global Consumption Losses (%) in 2020
Level 4: 0.1%
Probability Density
4
Level 4
Level 3
Level 2
Level 1
Level 3: 0.2%
3
2
Level 2: 0.5%
Level 1: 2.1%
1
0
0.0
0.5
1.0
1.5
2.0
% Loss in Global Consumption
2.5
3.0
SolarWind-Level2
SolarWind-REF
Uncertainty
in Total
Primary
Energy
Sources
2050
Bio-Level2
Bio-REF
Hydro-Level2
Hydro-REF
Nuclear-Level2
Nuclear-REF
Gas-Level2
Gas-REF
Oil-Level2
Oil-REF
Shale-Level2
Shale-REF
Coal-Level2
Coal-REF
0
100
200
300
400
Total Primary Energy (EJ) in 2050
500
Relative Contribution to Variance
Cumulative Global CO2 2000-2100 (Reference)
•Energy Supply
•Energy Demand
•Scale of Economy
•Other Uncertainties
Predict which most
affect cum. CO2,
carbon prices.
Energy Supply
Energy Demand
Scale of Economy
Other
0
10
20
30
40
% Variance Explained
a) Carbon Price in 2020 (Level 2)
Energy Demand
Energy Supply
Other
Scale of Economy
0
10
20
% Variance Explained
30
40
Top Ten Drivers of Uncertainty in
Abatement Cost
ELAS(E,LK)
Markup NGCC
Oil Supply Elas
AEEI
Carbon Price in 2020
(Level 2)
Markup Bio Oil
ESUB(HH)
Elas (L,K)
ELAS(CH4)
GDP
Markup Shale
0
5
10
15
20
25
30
35
% Variance Explained
ELAS(E,LK)
Markup Bio Oil
GDP
Markup Shale
Oil Supply Elas
Carbon Price in 2060
(Level 2)
NGas Supply Elas
ELAS(CH4)
AEEI
Markup NGCC
ELAS(Fuels)
0
5
10
15
% Variance Explained
20
25
Uncertainty in Climate Parameters
• Emissions Uncertainty from EPPA
• Climate Sensitivity
• Heat & Carbon Uptake by Deep Ocean
• Radiative Forcing Strength of Aerosols
• CO2 Fertilization Effect on Ecosystem
• Trends in Precipitation Frequency
Results: Temperature Change
Impacts of Stabilization Paths
Level 4
Level 3
Level 2
Level 1
No Policy
Global Mean Surface Temperature Increase (oC)
(1981-2000) to (2091-2100)
Results: Sea Level Rise
(Excluding Greenland and WAIS)
Cumulative Probability
1.0
0.8
0.6
0.4
No Policy
Level 4
Level 3
Level 2
Level 1
0.2
0.0
0
20
40
60
80
Sea Level Rise 2000-2100 (cm)
(thermal expansion + small glacial melt)
100
Communicating the Odds of
Temperature Change
Communicating the Impact of Policy
No Policy
Stringent Policy
(~550 ppm)
USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL
WARMING for 1980-2100, WITHOUT & WITH A 450, 550, 650 or
750 ppm CO2-equivalent STABILIZATION POLICY?
(400 random samples for economics & climate assumptions)
ΔT > 2oC
ΔT > 4oC
ΔT > 6oC
No Policy
400 in 400
17 in 20
1 in 4
Stabilize at 750
400 in 400
1 in 4
1 in 400
Stabilize at 650
97 in 100
7 in 100
<1 in 400
Stabilize at 550
8 in 10
1 in 400
<1 in 400
Stabilize at 450
1 in 4
<1 in 400
<1 in 400
USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL SEA
LEVEL RISE for 2000-2100, WITHOUT & WITH A 450, 550, 650 or
750 ppm CO2-equivalent STABILIZATION POLICY?
(400 random samples for economics & climate assumptions)
No Policy
Sea Level Rise > Sea Level Rise Sea Level Rise >
0.6m
0.2m
> 0.4m
400 in 400
13 in 20
9 in 100
Stabilize at 750
396 in 400
1 in 5
< 1 in 400
Stabilize at 650
97 in 100
1 in 10
< 1 in 400
Stabilize at 550
9 in 10
1 in 50
< 1 in 400
Stabilize at 450
7 in 10
<1 in 400
< 1 in 400
USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE
LOSS (% change in 2020), WITHOUT & WITH A 450, 550, 650 or
750 ppm CO2-equivalent STABILIZATION POLICY?
(400 random samples for economics assumptions)
ΔWL>1%
ΔWL>2%
ΔWL>3%
No Policy
-
-
-
Stabilize at
750
1 in 100
1 in 400
<1 in 400
Stabilize at
650
3 in 100
1 in 200
<1 in 400
Stabilize at
550
1 in 4
1 in 50
1 in 200
Stabilize at
450
7 in 10
3 in 10
1 in 10
Marginal Reduction in Probability of
Exceeding 5oC Global Temperature Change
Probability of
exceeding
target
Reduction in
Probability
(percentage
points)
Cum. CO2
Emissions
2000-2100
(GtC)
Reduction in
Cumulative
CO2
dProb/dCum
1605.0
-
-
No Policy
54.0%
Stabilize
at 750
2.5%
51.5%
1123.1
481.9
0.107%
Stabilize
at 650
0.3%
2.3%
910.9
212.2
0.011%
Stabilize
at 550
0.0%
0.3%
634.7
276.2
0.001%
Stabilize
at 450
0.0%
0.0%
381.1
253.6
0.000%
Tradeoffs in Choosing Stabilization
Tradeoffs inExpected
Choosing Stabilization
Targets
Targets:
Values
Expected Values
1.6
Expected Global Welfare Loss (%)
CCSP 450
1.4
1.2
1.0
0.8
CCSP 550
0.6
0.4
CCSP 650
0.2
CCSP 750
No Policy
0.0
0
1
2
3
4
5
Expected Global Mean Temperature Change (Degrees C)
6
Risk-Risk Tradeoffs in Choosing
Risk-Risk Tradeoffs in Choosing Stabilization Targets
Stabilization Targets
35
Prob{Global WL > 2%}
30
CCSP 450
25
20
15
10
CCSP 550
5
CCSP 650
CCSP 750
No Policy
0
0
20
40
60
Prob{T>4o}
80
100
120
Risk-Risk Tradeoffs in Choosing
Risk-Risk Tradeoffs in Choosing Stabilization Targets
Stabilization Targets
35
CCSP 450
30
Prob{Global WL > 2%}
Target = 3 Degrees
Target = 4 Degrees
Target = 5 Degrees
Target = 6 Degrees
25
20
15
10
CCSP 550
5
CCSP 650
CCSP 750
No Policy
0
0
20
40
60
80
Prob{Temperature Exceeds Target}
100
120
Key Insights
• Economics
– GDP growth important, not biggest driver
– Energy demand parameters critical
– High returns on reducing uncertainties in AEEI,
elasticities of substitution, etc.
• Climate Science
– Uncertainty still wide
– Mean and upper tails indicate likelihood of significant
impacts without some GHG reductions
Key Insights (II)
• Decision-Making
– Problem is one of risk management
– Risk-risk tradeoffs give different insights than
focusing on mean/reference values
– Suggestive that for a 450ppm, cost risk may
outweigh the reduction in temperature risk
USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE
LOSS (% change in 2050), WITHOUT & WITH A 450, 550, 650 or
750 ppm CO2-equivalent STABILIZATION POLICY?
(400 forecasts with equally probable economics assumptions)
ΔWL>1%
ΔWL>2%
ΔWL>3%
No Policy
-
-
-
Stabilize at
750
1 in 12
3 in 200
3 in 400
Stabilize at
650
1 in 3
1 in 20
3 in 400
Stabilize at
550
9 in 10
3 in 5
1 in 4
Stabilize at
450
98 in 100
96 in 100
85 in 100
Uncertainty in CO2 Emissions
(No Policy)
40
Global CO2 Emissions (GtC)
35
No Policy: 90% Bounds
No Policy: 50% Bounds
IPCC SRES Marker Scenarios
CCSP Product 2.1a Stabilization Scenarios
A1-FI
30
25
20
15
A1-B
10
Level 4 (750ppm)
5
A1-T
Level 2 (550ppm)
0
2000
2020
2040
2060
2080
2100
Why are the probabilities shifted to higher
temperatures than in our previous
calculations (Webster et al, 2003)?
•
•
•
–
–
–
–
–
–
Radiative Forcing Increases?
Emissions (higher lower bound)
Reduced Ocean Carbon Uptake
Additional forcing such as Black Carbon &
Tropospheric Ozone (additional forcing included
but still calibrated by net aerosols in 1990s)
Climate Model Response?
Climate Model Parameters show higher
response
Learning?
Distributions better defined
Distributions shifted higher
IPCC AR4 Temp Chg Uncertainty
Relevant
Comparison
To IGSM
No Policy
Typical Production Function in EPPA
Uncertainty in SO2 Emissions
(No Policy)
Global SO2 Emissions (TgS)
400
Median
50% Probability Bounds
90% Probability Bounds
300
200
100
0
2000
2020
2040
2060
Year
2080
2100
Uncertainty in SO2 Emissions
(No Policy vs. CCSP-550)
Global SO2 Emissions (TgS)
400
No Policy - Median
No Policy - 90% Probability Bounds
Climate Policy - Median
Climate Policy - 90% Probability Bounds
300
200
100
0
2000
2020
2040
2060
Year
2080
2100
Uncertainty in Methane Emissions
Global CH4 Emissions (Mt CH4)
1200
Median
50% Probability Bounds
90% Probability Bounds
1000
800
600
400
200
0
2000
2020
2040
2060
Year
2080
2100
Uncertainty in NOx Emissions
Global NOx Emissions (Tg NO2)
700
Median
50% Probability Bounds
90% Probability Bounds
600
500
400
300
200
100
0
2000
2020
2060
2040
Year
2080
2100
Uncertainty in BC Emissions
Global Black Carbon Aerosol Emissions (Tg)
20
Median
50% Probability Bounds
90% Probability Bounds
15
10
5
0
2000
2020
2040
2060
Year
2080
2100
Zonal Temperature Change
2000-2100 (Median)
Zonal Temperature Change 2000-2100
12
No Policy
Level 4
Level 3
Level 2
Level 1
10
8
6
4
2
0
-80
-60
-40
-20
0
Latitude
20
40
60
80
Zonal Temperature Change
2000-2100 (95th Percentile)
Zonal Temperature Change 2000-2100
18
No Policy
Level 4
Level 3
Level 2
Level 1
16
14
12
10
8
6
4
2
0
-80
-60
-40
-20
0
Latitude
20
40
60
80
PDFs of Global Mean Temp. Chg.
1.2
No Policy
CCSP 750 Stabilization
CCSP 650 Stabilization
CCSP 550 Stabilization
CCSP 450 Stabilization
Probability Density
1.0
0.8
0.6
0.4
0.2
0.0
0
2
4
6
8
Decadal Average Surface Temperature Change
(2090-2100) - (2010-2000)
10
PDFs of Sea Level Rise
(Excluding Greenland and WAIS)
7
No Policy
CCSP 750 Stabilization
CCSP 650 Stabilization
CCSP 550 Stabilization
CCSP 450 Stabilization
Probability Density
6
5
4
3
2
1
0
0.0
0.2
0.4
0.6
0.8
Sea Level Rise 2000-2100 (m)
(thermal expansion + small glacial melt)
1.0
SolarWind-Level2
SolarWind-REF
Bio-Level2
Bio-REF
Global
Electricity
Consumption
by Technology
and Fuel
Hydro-Level2
Hydro-REF
Nuclear-Level2
Nuclear-REF
IGCAP-Level2
IGCAP-REF
NGCAP-Level2
NGCAP-REF
NGCC-Level2
NGCC-REF
Gas-Level2
Gas-REF
Oil-Level2
Oil-REF
Coal-Level2
Coal-REF
0
20
40
60
80
Global Electricity Output (EJ) in 2050
100
a) Carbon Price in 2020 (Level 2)
Energy Demand
Energy Supply
Other
Scale of Economy
0
10
20
30
40
% Variance Explained
b) Carbon Price in 2060 (Level 2)
Energy Demand
Energy Supply
Scale of Economy
Other
0
5
10
15
20
25
% Variance Explained
c) Carbon Price in 2100 (Level 2)
Energy Demand
Scale of Economy
Other
Energy Supply
0
10
20
% Variance Explained
30
40
Cumulative Global CO2 2000-2100
(Reference)
Coal Supply Elas
ELAS(E,LK)
Markup Shale
GDP
Oil Supply Elas
AEEI
Elas (L,K)
Init Urban Emi
Resource Coal
ELAS(CH4)
Markup Coal CCS
Init CH4 Emi
NGas Supply Elas
ELAS(Fuels)
ELAS(N2O)
ESUB(HH)
Markup Syn Gas
ELAS(ELEC,NON)
Resource Oil/Gas
Urban Poll Trends
CCS Expansion
Markup Gas CCS
Resource Shale
Markup Bio Oil
Markup NGCC
Population
ESUB(Wind/Solar)
Markup Bio Elec
Vintaging
Nuclear Expansion
0
5
10
15
20
% Variance Explained
25
30
Global SO2 Emissions (MtS)
400
90% Bounds
50% Bounds
Webster et al. (2002)
300
200
100
0
2000
2020
2040
2060
2080
2100
Historical 1950-2000 (%)
Projected Annual Average Growth
Rate (%) 2000-2100
Region
Mean
Std Dev
0.05
0.5
0.95
USA
2.2
2.3%
1.7
2.1
2.5
CAN
2.3
2.3%
1.7
2.1
2.5
MEX
2.2
5.2%
1.2
2.1
2.9
JPN
4.9
3.5%
1.7
2.2
2.7
ANZ
2.0
1.8%
2.0
2.3
2.6
EUR
2.8
1.6%
1.9
2.1
2.4
EET
1.1
3.9%
2.1
2.8
3.3
FSU
1.1
5.3%
2.0
2.8
3.7
ASI
4.3
4.7%
1.8
2.6
3.3
CHN
4.3
3.7%
2.5
3.1
3.7
IND
2.3
2.7%
2.3
2.7
3.1
IDZ
2.7
5.0%
1.1
2.6
3.9
AFR
1.0
1.8%
2.0
2.3
2.6
MES
2.3
3.3%
1.5
2.1
2.6
LAM
1.7
2.0%
1.7
2.1
2.5
ROW
2.2
3.5%
1.7
2.3
2.8
2.2
2.4
2.6
GLOBAL
a) Fossil Resources
3e-5
Crude Oil
Natural Gas
Coal
Shale
Probability Density
3e-5
2e-5
2e-5
1e-5
5e-6
0
0
5e+5
1e+6
Exajoules
2e+6
2e+6
b) Fossil Fuel Supply Elasticity
1.4
Probability Density
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Price Elasticity of Supply
1.8
2.0
2.2
2.4
14
Global Populations (Billions)
12
90% Bounds
50% Bounds
UN Projections
10
8
6
4
2
0
2000
2020
2040
2060
2080
2100
120
Probability Density
100
SO2
NOx
80
60
40
20
0
-0.08
-0.06
-0.04
-0.02
Time Trend Parameter for Urban Pollutant ()
0.00
Synthetic Oil
Markup
Coal
Gasification
Markup
Advanced Coal
with Carbon
Capture
Natural Gas
with Carbon
Capture
Natural Gas
Combined
Cycle
Fractile
Expert 1
Expert 2
Expert 3
5%
2.0
2.1
2.5
50%
3.5
4.3
4.3
95%
5.0
5.8
6.0
5%
3.4
1.9
3.9
50%
4.3
3.0
5.2
95%
6.5
6.5
6.9
Expert 4
Expert 5
5%
1.1
1.1
50%
1.1
1.2
95%
1.4
1.3
5%
1.1
1.1
50%
1.2
1.2
95%
1.3
1.2
5%
0.8
0.9
50%
0.9
0.9
95%
1.0
1.0
Input Factor Markups
Shale Oil
Coal Gas
Advanced Coal with CCS
Advanced Gas with CCS
Advanced Gas without CCS
Bio-Oil
Bio-Electric
Mean
Std. Dev.
3.20
3.94
1.18
1.15
0.90
3.94
3.94
0.77
0.82
0.10
0.05
0.04
0.82
0.82
0.25
0.20
2.25
1.13
Elasticity of Substitution
Wind and solar
Penetration Rates
New Tech Penetration Rate
Probability Density
3.0
2.5
2.0
1.5
1.0
0.5
0.0
0.0
0.2
0.4
0.6
0.8
Share of Non-Malleable Capital
1.0
Parameter
Correlated Across
(dimensions of matrix)
Correlation
Coefficient
AEEI
Regions (16x16)
0.9
Elasticity of Substitution (L,K)
Sectors (8x8)
0.8
Methane Elasticities (cost)
Regions (16x16)
0.8
N2O Elasticities (cost)
OECD, LCD, FSU, EET (4x4)
0.8
Fossil Resources
Oil, Natural Gas (2x2)
0.9
Urban Pollutant time Trends
Urban Pollutants (7x7)
0.9
Carbon Price Under Level 1 (450ppm)
0.006
IGSM:$233
Probability Density
0.005
0.004
MERGE: $159
0.003
MiniCAM: $129
0.002
0.001
0.000
100
200
300
400
Carbon Price in 2050 ($/ton CO2)
500