15.023j / 12.848j / esd.128j global climate change ...effect of uncertainty and learning on...
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MIT OpenCourseWare http://ocw.mit.edu
15.023J / 12.848J / ESD.128J Global Climate Change: Economics, Science, and PolicySpring 2008
For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
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Effect of Uncertainty and Learning on Decisions
Mort WebsterMIT Joint Program on the Science and Policy of Global Change
15.023-12.848-ESD.128
April 23, 2008
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Course so far…
• Projecting climate impacts
• Costs of GHG emissions reductions
• Analysis under certainty
• Descriptions of uncertainty– Probability distributions
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Outline
• Motivation: Why uncertainty matters
• Effect of learning on decisions
• Application of decision analysis to climate policy
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Motivation IThe “Question”
Since climate change is so uncertain, shouldn’t we just wait until we know more?
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Why Does Uncertainty Matter?
Revenues ($ Million)
0 20 40 60 80 100
Pro
babi
lity
Den
sity
0.00
0.05
0.10
0.15
0.20
0.25
0.30
If $10 million investment is required,
would you do it?What about $60m?
μ = $30m
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Why Does Uncertainty Matter?
• If no learning is possible and no risk aversion– Make decision based on expected value
• If you can learn and revise along the way– May want to do more or less at first (hedging)
• If you are risk-averse– You care about more than mean outcomes
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What is “Risk-Aversion”?
Choose:A) A gamble with 50% chance of paying $100
and 50% chance of paying $0orB) Pay $49 for sure
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Risk-Averse Utility Function
Monetary outcome (Billion $)
0 2 4 6 8 100.0
0.2
0.4
0.6
0.8
1.0U
tility
Risk-Neutral
Risk-Averse
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Why Does Learning Matter?
• Why would you do something different today if you can learn tomorrow?
• Answer: if the outcome is irreversible
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Arrow-Fisher Irreversibility
• Problem: – Two time periods t=1, 2– Total forest area = 100– Cost of cutting forest C(x)– Benefits of cutting forest B(x) - UNCERTAIN– Choose x1, x2 to Max (B-C).
• What is x1 if you can’t learn?• What is x1 if you can learn between t1 and t2?
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Irreversibilities in Climate Change
• GHG Concentrations– Temperature Change– Climate Damages
• Capital Stock / Economic Investments
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DICE Model of Climate and Economy
• Simple Model of Economy:– Capital and Labor Make GDP– GDP split between Savings and Consumption– CO2 Emissions from Production
• Simple Climate Model– Carbon Cycle – Emissions to Concentrations– Radiative Forcing– Energy Balance
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DICE (Continued)
• Policy Variable: – Reduce Emissions by Fraction (μ)– Abatement Costs C = f(μ)– Damage Costs D = f(ΔT)
• Maximize Discounted Utility (Consumption)• Two Types of Analysis
– Cost-Benefit (balance abatement costs with damage costs)
– Cost-Effectiveness (set absolute constraint)
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Focus of this Study:Climate Sensitivity
• One of the critical uncertainties• Defn:
– Amount of global mean temperature change from a doubling of CO2 at equilibrium.
• Meaning: – Represents net effect of feedbacks in the
atmosphere.
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Current Uncertainty in Climate Sensitivity
Climate Sensitivity (oC)0 2 4 6 8 10
Pro
babi
lity
Den
sity
0.00
0.05
0.10
0.15
0.20
0.25
0.30
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Alternative Analysis Frameworks
• Cost-Benefit– Use damage function to monetize climate impacts– Find economically efficient path of abatement
• Cost-Effectiveness– Pick some target (e.g., concentration or
temperature stabilization)– Find least cost way to meet target
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Effect of Learning on Efficient Policy
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
50
100
150
200Perfect InformationNever LearnComplete Resolution in 2040
CS=8.0
CS=5.0
CS=3.0
CS=1.5
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Effect of Learning underCost-Benefit
Time When Learning Occurs
2020 2030 2040 2050 2060 never
Opt
imal
Tax
in 2
015
17.0
17.2
17.4
17.6
17.8
18.0
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Hedging Policy Under Uncertainty Cost-Benefit Case
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
50
100
150
200
Perfect InformationNever Learn
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What Determines the Optimal Hedge?
Mean of Climate Sensitivity (oC)
2 3 4 5 6 7
Opt
imal
Car
bon
Tax
in 2
015
($/to
n)
10
15
20
25
30
35
Answer: The Shape of the Probability Distribution
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Decision-Making under Uncertainty
Some Simple Starting Points:1) What should you do if you KNOW?2) What should you do if you will NEVER
LEARN?3) What should you do if you don’t know, but
WILL LEARN at time T?4) What should you do if you don’t know and
will reduce your uncertainty at time T?
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2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
CS = 1.5CS = 3.0CS = 5.0CS = 8.0
Illustration: Optimal Carbon Tax for Temperature Target of 2oC
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2000 2020 2040 2060 2080 2100 2120 2140
Glo
bal M
Ean
Sur
face
Tem
pera
ture
Cha
nge
(o C)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
CS = 1.5CS = 3.0CS = 5.0CS = 8.0
Realized Temperature Change for2 Degree Target
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QUESTION: What should we do now if we are uncertain?
• Wait (do nothing until we know more)?
• Implement Highest Tax (worst case)?
• Implement Lowest Tax (best case)?
• Something in the Middle? – Where in the middle?
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Relative to Best Policy in Each Case…
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
Perfect Information
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If We Learn in 2020
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
Perfect InformationLearn in 2020
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Simulating Learning:Stochastic Programming
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
CS = 1.5CS = 3.0CS = 5.0CS = 8.0
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If We Learn in 2030
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
Perfect InformationLearn in 2020Learn in 2030
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If We Learn in 2040
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
Perfect InformationLearn in 2020Learn in 2030Learn in 2040
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If We Never Learn
2000 2020 2040 2060 2080 2100
Car
bon
Tax
($/to
n)
0
100
200
300
400
Perfect InformationLearn in 2020Learn in 2030Learn in 2040Never Learn
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Summary – Effect of Learning Laterwith 2o target
Time When Learning Occurs
2020 2030 2040 2050 2060 never
Opt
imal
Tax
in 2
015
30
40
50
60
70
80
90
100
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Time When Learning Occurs
2020 2030 2040 2050 2060 never
Opt
imal
Tax
in 2
015
0
20
40
60
80
100
120
140Temp <= 2.0Temp <= 2.5Temp <= 3.0Temp <= 3.5
Effect of Learning for Several Temperature Targets
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Decision under uncertainty with partial learning
2000 2020 2040 2060 2080 2100 2120 2140
Car
bon
Tax
($/to
n)
0
100
200
300
400
Near-term hedgingunder uncertainty
Revised policy afterreducing uncertainty
Perfect Informationafter 2100
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Reduction in Standard Deviation
0.0 0.2 0.4 0.6 0.8 1.0
Car
bon
Tax
in 2
015
($/to
n)
0
20
40
60
80
100
120
140 Target = 2oLearning in 2020, Perfect Info by 2030
Target = 2oLearning in 2020, Perfect Info by 2040
Target = 2.5oLearning in 2020, Perfect Info by 2060
Effect of Partial Learningon Optimal Hedging
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Summary
• Wait to Learn?– No, start now, adjust later.– How much depends on your expectations.
• If I can learn– Do a little less now– Depends on when you will learn– Depends on your goal (stabilization vs balancing
costs and benefits)