Addressing Uncertainty in Performance Measurement of Intelligent Systems
Climate Change: Addressing Uncertainty, Inertia, and Equity
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Transcript of Climate Change: Addressing Uncertainty, Inertia, and Equity
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86025 Energy Systems Analysis Arnulf Grubler
Climate Change: Addressing Uncertainty, Inertia, and Equity
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86025 Energy Systems Analysis Arnulf Grubler
The Greenhouse EffectE. Boeker, Environmental Physics
#updated for 2005 ann.average CO2 conc. CDIAC
Gas conc. ppm GWP factor G. Warming (ºK)
H2O 5000 0.2 20.6CO2 380# 1 7.2O3 0.03 3900 2.4N2O 0.3 310 0.8CH4 1.7 21 0.8
HFC-134 ~ 0.03 1000 0.6
Total 32.4
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86025 Energy Systems Analysis Arnulf Grubler
Source: IPCC AR4 WG1 2007
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86025 Energy Systems Analysis Arnulf Grubler
Temperature Change (over 1901-1950 mean): Observations and Modeled by Including all Positive and
Negative Forcings. Source: IPCC AR4 WG1 2007
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EU Regional Climate Variability:Observations (b)modeled for present (c)and future (d)conditions.
Note 2003 heat wave (a) being far outside both observational and model range.
IPCC uncertainty terminology (adopted from Schneider and Moss) :<1% probability =“exceptionally unlikely” (but 2003 happened)
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Height of Pasterze glacier in 1960
Current CC Impacts:80 meters thinning of Pasterze glacier,Austria
But…uncovering 5000 yrold vegetation
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86025 Energy Systems Analysis Arnulf Grubler
Global Carbon & Warming Budgets
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86025 Energy Systems Analysis Arnulf Grubler
Climate Change: Major Uncertainties
• Demographic (growth & composition)• Economic (growth, structure, disparities)• Social (values, lifestyles, policies)• Technologic (rates & direction of change)• Environmental (limits, adaptability)• Valuation (discounting, non-market damages and
benefits)
• Addressing uncertainties:ScenariosResearchSocial choice
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86025 Energy Systems Analysis Arnulf Grubler
A Taxonomy of SRES Scenarios(“baselines”, no climate policies)
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Emissions vs. Energy Use & Technologyin IPCC SRES Scenarios
Uncertainty 1: Population and GDP growth, prices, policies
Un
certa
inty 2: R
eso
urce availa
bility
, tech
no
log
y
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INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC)
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Example Climate Change:Projected Global Mean Temperature Change
and Sources of Uncertainty
IPCC WG1 TAR: “By 2100, the range in the surface temperature response acrossthe group of climate models run with a given scenario is comparableto the range obtained from a single model run with the different SRES scenarios”
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Example Climate Change:Projected Global Mean Temperature Change
and Sources of Uncertainty
Baseline Uncertainty:50 % climate (sensitivity) modeling25% emissions (POP+GDP influence)25% emissions (TECH influence)
Uncertainty on extent and success of climate policies
Minimum committed warming1.5 °C (certainty)
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0
2
4
6
SRES scenarios cumulative emissions 1990 - 2100, GtC
<100 1000 2000 3000
ppm CO2
300 600 1000
70 2.5
° C global mean temperature change1 2 3 4 5 6
MAJOR CLIMATE CHANGE UNCERTAINTIESSocioeconomic (Future Cumulative Emissions SRES Scenarios)
Carbon Cycle (Resulting CO2 Concentration) andClimate Sensitivity (ºC for 2CO2)
Vulnerability: low high
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0
5
10
15
20
199020002010202020302040205020602070208020902100
Carb
on
em
issi
on
s (G
tC y
r -1)
Year
550 ppmv (F = 1.1)
550 ppmv (F = 0.6)
550 ppmv (F = 2.6)
F = 0.6 T2x = 4.5
F = 2.6 T2x = 1.5
(2.6, 1.5)
(2.6, 2.5)
(1.1, 2.5)
(0.6, 2.5)
(0.6, 4.5)
(a)
0.1
1
10
100
1,000
10,000
199020002010202020302040205020602070208020902100
CO
2 sh
ad
ow
pri
ce (
$ p
er
ton
C)
Year
550 ppmv (F = 1.1)
550 ppmv (F = 0.6)
550 ppmv (F = 2.6)
F = 0.6 T2x = 4.5
F = 2.6 T2x = 1.5
(not shown as zero)
(b)
Uncertainties (F=carbon cycle; Δt2x=climate sensitivity)in Stabilizing Climate Change at +2.5 ºC by 2100
Emissions Shadow Prices
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86025 Energy Systems Analysis Arnulf Grubler
North -- South
• Responsibility: Mostly in Annex-I• Vulnerability: Mostly in “South”
• Adaptation capacity: Mostly in Annex-I• Future emission growth: Mostly in “South”
• Near-term mitigation potential: highest in Annex-I
• Near-term mitigation costs: lowest in “South”
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86025 Energy Systems Analysis Arnulf Grubler
1990 Per Capita CO2 by Source vs. Population
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Distribution of Industrial CO2 Emissions since 1800
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86025 Energy Systems Analysis Arnulf Grubler
The Greenhouse “Barometer”
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86025 Energy Systems Analysis Arnulf Grubler
Agricultural Impacts for Alternative Climate Change Scenarios.
Source: IIASA LUCC, 2000.
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86025 Energy Systems Analysis Arnulf Grubler
Environmental Change: Development vs. Climate
• More ecosystems will be destroyed by economic development than by the climate change this development induces
• Far more human lives are threatened by a lack of development than by any climate change resulting from a closure of the development gap
• Baselines: “business-as-usual” + climate control vs sustainability paradigm
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86025 Energy Systems Analysis Arnulf Grubler
Vulnerability to Economic Development vs. Seal Level Rise. Source: IPCC AR4 WG2, 2007
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86025 Energy Systems Analysis Arnulf Grubler
Reducing CC Vulnerabilities
• Economic & Social Developmentun-targeted and asymmetricalpoverty vulnerability: -affluence vulnerability: +
• Adaptation targeted to CC
• Emissions reduction (mitigation)lowering CC but not eliminating it
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86025 Energy Systems Analysis Arnulf Grubler
Vulnerability to CC by 2050 (IPCC AR4 WG2 2007)
A2 current adaptive capacity Improved adaptive capacity
Mitigation only (550 stab) Mitigation + improved adaptation
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86025 Energy Systems Analysis Arnulf Grubler
Mitigation Options• Demographic change• Economic development• Social behavior
• Efficiency Improvements• Low carbon intensity• Zero carbon (solar, nuclear)• Carbon removal
• Non CO2 gases (agriculture&industry)
• End deforestation• Sink enhancements• “geo-engineering”
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86025 Energy Systems Analysis Arnulf Grubler
Emissions & Reduction MeasuresMultiple sectors and stabilization levels
0%
20%
40%
60%
80%
100%
400600800100012001400
CO2 eq. Concentration in 2100, ppm
Sh
are
of
cum
ula
tiv
e e
mis
sio
n r
ed
uc
tio
ns
by
sec
tor
(20
00
-21
00)
B1A2r
Energy & Industry
Forestry
Agriculture
0%
20%
40%
60%
80%
100%
400600800100012001400
CO2 eq. Concentration in 2100, ppm
Sh
are
of
cum
ula
tive
em
issi
on
red
uct
ion
s b
y g
as (
2000
-210
0)
B1A2r
CO2
CH4
N2O
Other Gases
Source: Riahi et al., TFSC 2007
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Costs of Different Baselines and Stabilization ScenariosSource: IIASA, 2000.
Deployment rate of efficiency and low-emission technologies
Σ: The lower baseline emissions (efficiency, clean supply)the easier to achieve (currently unknown) climate targets
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Stabilization Costs(% GDP loss, top,; and carbon price, $/tCO2, bottom) by 2100 as a Function of Baseline,
Model, and Stabilization Level Differences
IPCC AR4 WG3 2007
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Technology as Source and Remedy of Climate Change:IPCC Baselines and 550 ppmv Stabilization Scenarios (in GtC), Source: IIASA, 2002.
BASELINE WITHFROZEN EFFICIENCYAND TECHNOLOGY
Σ: With “frozen” efficiency and technology improvementsemissions grow “through the roof”. Even with continued improvements,additional emission reduction is needed for climate stabilization
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Baseline Emissions vs. Reductions in Illustrative 550 ppm Stabilization Scenarios (Source: Riahi, 2002)
CO2 reductions by source from A1 to A1-550
0
10
20
30
1990 2010 2030 2050 2070 2090
CO
2 e
mis
sio
ns
[GtC
]
Demand reduction
Fuel switching(mainly shifts awayfrom coal)
CO2 scrubbing andremoval
A1
A1-550
CO2 reductions by source from B2 to B2-550
0
10
20
30
1990 2010 2030 2050 2070 2090
CO
2 e
mis
sio
ns
[G
tC]
Demand reduction
Fuel switching(mainly shifts awayfrom coal)
CO2 scrubbing andremoval
B2
B2-550
CO2 reductions by source from A1C to A1C-550
0
10
20
30
1990 2010 2030 2050 2070 2090
CO
2 e
mis
sio
ns
[G
tC]
Demand reduction
Fuel switching(mainly shifts awayfrom coal)
CO2 scrubbing andremoval
A1C
A1C-550
CO2 reductions by source from A2 to A2-550
0
10
20
30
1990 2010 2030 2050 2070 2090
CO
2 e
mis
sio
ns
[G
tC]
Demand reduction
Fuel switching(mainly shiftsaway from coal)
CO2 scrubbingand removal
A2
A2-550
Σ: The higher the baseline emissions, the more reliance on “silver bullet” backstop technologies like CCS
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Emission Reduction Measures Riahi et al. TFSC 2007
Emissions reductions due to climate policies
Improvements incorporated inbaselines
0 500 1000 1500 2000 2500 3000 3500 4000
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Energy Intensity Improvement (Baseline)
Carbon Intensity Improvement (Baseline)
Cumulative contribution to mitigation (2000-2100), GtC eq.
1390 ppm
1090 ppm
970 ppm
820 ppm
670 ppm
590 ppm
520 ppm
480 ppm
0 500 1000 1500 2000 2500 3000 3500 4000
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Energy Intensity Improvement (Baseline)
Carbon Intensity Improvement (Baseline)
B1
B2
A2
820 ppm
670 ppm
590 ppm
520 ppm
480 ppm
Σ: Technological change in Baseline best hedging against target uncertainty
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Emission Reduction Measures Riahi et al TFSC 2007
0 50 100 150 200 250 300 350
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Cumulative contribution to mitigation (2000-2100), GtC eq.
1390 ppm
1090 ppm
970 ppm
820 ppm
670 ppm
590 ppm
520 ppm
480 ppmA2B1 B2
RF = 0.7
RF = 0.3
RF = 0.2
RF = 0.1
RF = 0.5
RF = 0.1
RF = 0.3
RF = 0.7
RF = 1.0 RF = 0.2
(0.9 incl. baseline)
RF = Robustness factor of options across scenario uncertainty is highest for:F-gases and N2O reduction, energy conservation & efficiency, and biomass+CCS “wildcard” (if feasible)
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Mitigation Technology Portfolio Analysis
• Paramount importance of Baseline • Costs matter• Diffusion time constants matter• Differences in where technology is developed
and where it is deployed• Technological interdependence and systemic
aspects important in “transition” analysis• Non-energy, non-CO2 can help, but cannot
solve problem
Σ: Popular “wedge” analysis fails on all above accounts!
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86025 Energy Systems Analysis Arnulf Grubler
Energy Carbon and Climate: How far to go?
• Energy: 20 (5% exergy efficiency)• Carbon: Zero (H2-economy)
• Damages: committed warming (>1.5 C?)• Non-linear (catastrophic) change: ???
• “Collateral damages”-- Geoengineering, e.g. aerosol cooling (white sky)-- sequestration (leakage, marine ecology)-- biomass (soil carbon, biodiversity, agriculture)-- solar (albedo changes)
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86025 Energy Systems Analysis Arnulf Grubler
Policy Conundrums
• Equitable quantitative targets at odds with economics or infeasible
• Cost optimal emission reduction: Start with inefficiencies in DCs but requires new instruments (CDM+)
• Separation of equity and efficiency (e.g. via tradable permit allocation) might be politically infeasible (unprecedented N-S resource transfers)
• Uncertainties cannot be ignored (soil C, avoided deforestation)
• Mitigation technology innovation “recharge” chain broken (declining R&D)
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86025 Energy Systems Analysis Arnulf Grubler
Allocating Emissions Reductions or Access to Global Commons