Download Optimal Climate Policies under the 2°C Constraint Using a

Probabilistic assessment of optimal
policies under climate uncertainty
The 2◦ C constraint
V. Bosetti, L. Drouet, M. Tavoni
Euro-Mediterranean Center on Climate Change,
CIP Division, Italy
Fondazione Eni Enrico Mattei, Italy
IEW 2013
Paris, 21 June 2013
Uncertainty in Integrated Assessment
Incomplete understanding of climate change in many aspects
(Heal and Millner, 2013):
• scientific uncertainty
• Models’ parameters
• Various representations
• socio-economic uncertainty
• future emissions (GDP, productivity, time preference,
technology change. . . )
• economic impacts
Uncertainty is partially represented in current IAMs
Decision makers need support tools with
• representations and analyses of uncertainty,
• assessment of robust policies
Uncertainties propagation in IAM
Socio-economic
scenarios
E
Climate
system
T°
Global economic
impacts
Uncertainties
Socio-economic
drivers
Geophysical
parameters
Impact estimates
Probabilistic approach
Quantify the uncertainty using distribution rather than a fix values.
• Assign weights to future scenarios
• Measure the likelihood of model’s parameters
• Combine distributions through the whole IAM
Previous studies
• Probabilistic Integrated Assessment of “Dangerous” Climate
Change (Mastrandea and Schneider, 2004, Science)
• Probabilistic cost estimates for climate change mitigations
(Rogelj et al., 2013, Nature)
WITCH
• Hybrid integrated assessment model (Bosetti et al., 2009)
• Economy: Ramsey-type optimal growth (inter-temporal)
• Energy: Energy sector detail (technology portfolio)
• Climate: Damage feedback (global variable)
• Global, with the world divided in 13 regions
• Optimization model: maximization of utility function
In this work
• The Climate module is deactivated
• The damages are not computed
• All countries act cooperatively to address the climate change
issue
Socio-economic challenges
for mitigation
Shared Socio-economic Pathways
SSP5:
SSP3:
(Mitigation Challenges Dominate)
(High Challenges)
Conventional Development
Fragmentation
SSP2:
(Intermediate Challenges
Middle of the Road)
SSP1:
SSP4:
(Low Challenges)
(Adaptation Challenges Dominate)
Sustainability
Inequality
Socio-economic challenges
for adaptation
from (O’Neill et al, 2012)
SSPs implementation: BAU scenarios
World Population
World GDP (via TFP)
SSP3
SSP5
12
12
SSP4
10
SSP2
World GDP (2005=1)
World Population [Billions]
14
SSP1
8
SSP2
SSP4
4
8
SSP3
SSP5
SSP1
2000
2025
2050
2075
2100
SSPs’ likelihood
Equal likelihood (no studies yet)
2000
2025
2050
2075
2100
Mitigation scenarios
WITCH computes optimal emissions trajectories to comply with
given carbon budgets.
→ Covering the space of mitigation.
Kyoto GHG budgets over the 21st century
Range [GtCO2 -eq]
SSP1
SSP2
SSP3
SSP4
SSP5
1115–3805
1115–3717
1115–3447
1115–3624
1115–3971
GHG emissions trajectories
CO2 emissions [GtC]
20
ssp
SSP1
SSP2
SSP3
SSP4
10
SSP5
0
2020
2040
2060
2080
2100
Climate model SNEASY
SNEASY (Urban and Keller, 2010) is a simple Earth System Model
composed of
• a climate module based on DOECLIM,
• a carbon-cycle model, including feedbacks from CO2
concentration and temperature,
• a Atlantic Meridional Overtuning Circulation (AMOC)
Boxmodel
• Timestep: 1 year
• Inputs (provided by WITCH):
• CO2 emissions,
• non-CO2 radiative forcing components.
Parameters’ estimation
Climate:
Carbon-cycle:
• Climate sensitivity
• Thermocline transfer rate
• Ocean vertical diffusivity
• Respiration sensitivity
• Aerosol forcing scaling
• Carbon fertilization
Observation errors:
CO2 concentrations, temperature and Ocean heat.
Parameters are estimated from past observations using a
Bayesian inversion technique based on a Monte-Carlo Markov
Chain algorithm.
The chain is used to compute probabilistic temperature projections
Hindcast calibration period
Atmospheric CO2 concentration [ppm]
Surface temperature anomaly [K]
1.0
375
350
0.5
325
0.0
300
95% chain
1900
1950
2000
Ocean heat anomaly [10^21J]
1850
30
1
20
0
10
−1
0
−2
−10
−3
−20
−4
1900
1950
2000
Atm−ocean carbon flux [GtC]
mean chain
Observations
1950
1960
1970
1980
1990
2000
1986
1990
1994
Posterior marginal distributions
Climate Sensitivity [C/Wm2]
Ocean vertical diffusivity [cm2/s]
Probability densities
2
4
6
Respiration Sensitivity
1
2
3
4
5
Initial temperature [C]
−0.2
−0.1
0.0
0.1
Aerosol forcing scale
2
4
6
8
Carbon fertilization factor
0.0
0.5
1.0
1.5
Thermocline transfer rate [m/y]
0.25
0.50
0.75
1.00
Initial ocean heat [10ZJ]
0
10 20 30 40 50
Initial CO2 concentration [ppm]
−60
−40
−20
0
280
282
284
286
288
Corridors of cost-efficient policies
SSP1 cost-efficient corridors
20
CO2 emissions [GtC]
Prob < 2C
< 0.05
15
0.05 − 0.1
0.1 − 0.33
0.33 − 0.5
10
0.5 − 0.66
0.66 − 0.9
0.9 − 0.95
0.95 − 0.99
5
0
2025
2050
2075
2100
Scenario classification with a 2°C threshold
Scenarios' likelihood to stay below 2°C in the 21st century
Unlikely [<33%]
Medium Likelihood [33−66%]
Very Likely [>66%]
CO2 emissions [Gt]
20
ssp
SSP1
SSP2
SSP3
SSP4
10
SSP5
0
2025
2050
2075
2100
2025
2050
2075
2100
2025
2050
2075
2100
Mitigation cost distributions
Mitigation cost distributions per likelihood to stay below 2C
< 0.33
0.33−0.66
> 0.66
0
−20
% GDP
Proba < 2C
< 0.33
−40
0.33−0.66
> 0.66
−60
−80
2050
2100
2050
2100
2050
2100
Economic impacts
Tol (2009) reviews estimates of economic impact of climate change
from 14 studies
• Mean estimates: Nordhaus(1994a), Frankhauser(1995), Tol(1995),
Nordhaus and Young(1996), Mendelshon et al. (2000), Nordhaus
and Boyer (2000), Maddison(2003), Rehdanz and Maddison(2005)
• Skewed distribution: Nordhaus(1994b), Plambeck and Hope(1996),
Hope(2006)
• Normal distribution: Tol (2002), Nordhaus (2006)
Analysis:
• Various methods for estimation: aggregation, statistical
evaluation
• Positive impact for low warming
• Uncertainty increases rapidly with warming
Impact function fitting
We perform Monte-Carlo regressions of a quadratic function:
I = β1 T + β2 T 2 ,
where I is the the economic impact in % GDP and T the
temperature increase in °C
Marginal distributions of regression coefficients
beta 1
beta 2
Probability density
0.4
0.75
0.3
0.50
0.2
0.25
0.1
0.0
0.00
0.0
2.5
5.0
7.5
−4
−3
−2
−1
0
Economic impacts as a function of temperature
5
●
●
●
●
●
●
0
●
●
●
●
●
●
●
% GDP
95% CI
●
−5
50% CI
−10
−15
0
1
2
3
Temperature increase since preindustrial level [°C]
4
●
mean estimates
●
Tol (2009) fit
Economic impacts distributions
Impact distributions from the expected temperature increase
5
0
+
+
+
+
+
+
2070
2080
2090
2100
% GDP
−5
−10
−15
−20
−25
2050
2060
Economic impacts distributions
Impact distributions per likelihood to stay below 2C
<0.33
0.33−0.66
>0.66
5
0
●
●
●
●
●
2050
2100
2050
2100
●
−5
% GDP
−10
−15
−20
−30
2050
2100
Conclusion
Summary
The probabilistic approach in IA allows to report and analyse the
uncertainty.
Further works
• Risk management
• Use the probabilistic information in the optimization
frameworks
• cost-effective analysis
• cost-benefit analysis
Remark
Probabilistic approach is not always applicable (Kunreuther, 2013)
Thanks!
[email protected]
The research leading to these results has received funding from the European
Community’s Seventh Framework Programme FP7/2007-2013 under grant
agreement n°282846 (LIMITS)
We thank K. Keller from the Pennsylvania State University for supplying the
SNEASY code.