Uncertainty and Learning in Sequential DecisionMaking: The Case of Climate Policy

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Uncertainty in Integrated Assessment of Global
Climate Change
Mort Webster
Department of Public Policy, University of North
Carolina at Chapel Hill
U.S Environmental Protection Agency National Risk
Management Research Laboratory December 21, 2005
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Persons pretending to forecast the future shall
be considered disorderly under subdivision 3,
section 901 of the criminal code and liable to a
fine of 250 and/or six months in prison.
Section 889, New York State Code of Criminal
Procedure
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Outline

• Introduction Climate Policy as Risk Mgmt
• Sequential Decision with Partial Learning
• Estimating the Risk of THC Collapse
• Value of Emissions Trading under Uncertainty

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Introduction

• Climate Policy as Risk Management

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Motivation How should we choose near-term
climate policy?

• Example Senate Energy Bill Debate, Summer 2005
• McCain-Liebermann (20/tonC)
• Bingaman Bill (7/tonC)
• Hagel Resolution (Voluntary 0/ton)
• What is the right level of effort?

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Bottom Line Climate Policy as Risk Reduction

• Need to have a long-term strategy to guide
  short-term decisions
• Climate Policy How can we reduce/manage the risk
  of severe climate change impacts?
• To manage risk need to know the relevant
  uncertainties to best of our current knowledge.

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Cascade of Uncertainties

• Uncertainties in socioeconomic and technological
  trends
• Uncertainties in climate system
• Uncertainties in regional impacts
• Uncertainties/Disagreements in (economic)
  valuation of impacts

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Using Uncertainty Characterizations

• Range of Possible Outcomes
• Assessing the importance of climate change
• Decision-focus
• Use uncertainties to look for Robust Strategies
• NOTE Risk Reduction ? Uncertainty Reduction
• Learning Which uncertainties are likely to be
  reduced (and within what time frame)?
• How does this change the robust (optimal)
  near-term strategy?
• Value of Information
• Which reducible uncertainties will most change
  the policy decisions?

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MIT Integrated Global System Model (IGSM)
Joint Program on the Science and Policy of Global
Change
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
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Uncertainty Analysis of Climate Projections
Uncertain Parameters

• Economic Technological Uncertainties
• Labor Productivity Growth
• Autonomous Energy Efficiency Improvement Rate
• Emissions Factors for Industrial Pollutants
• Climate Uncertainties
• Climate Sensitivity
• Heat Uptake by Deep Ocean
• Aerosol Forcing Strength

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Uncertainty Analysis of Climate Projections
Outcomes

• What is the uncertainty (PDF) in
• Global Mean Temperature Change
• Sea Level Rise
• As a result of
• No Climate Policy
• One Possible Path of GHG Reductions
• How does the risk of extreme outcomes change?

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Sources for Parameter PDFs

• Economic Parameters
• Literature, e.g., std err on regression
  coefficients
• Expert Elicitation
• Climate Parameters
• Constrained by Climate Detection (Forest et al)
• Combined with Expert Judgments

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Uncertainty Propagation through IGSM

• Using Latin Hypercube Sampling, draw samples from
  all parameter PDFs, imposing correlation (n250
  n1000)
• Propagate through EPPA to obtain emissions of all
  GHGs and other relevant substances
• Propagate through climate-chemistry model to
  obtain climate outcomes

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Global CO2 Emissions
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CO2 ConcentrationsMedian and 95 Bounds
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Total Radiative ForcingMedian and 95 Bounds
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Global Mean TemperatureChange Median and 95
Bounds
No Policy
Policy
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Global Mean Temperature Change by 2100
1 in 20 chance of exceeding 3.2o
1 in 20 chance of exceeding 4.9o
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Communicating the Odds of Temperature Change
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Communicating the Role of Policy
Stringent Policy
No Policy
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Topic 1

• Sequential Decision
• Reducing Uncertainty
• from Climate Observations

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Decision-Analytic Framing of Climate Policy
Problem

• Simple Example 2-period decision
• Choose mitigation level for 2010-2030
• Choose mitigation level for 2030-2100
• Use 2 extreme assumptions to bound the range
• No Learning Both decisions made under
  uncertainty
• Complete Learning All Uncertainties resolved
  before second decision

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Decision Analytic Framework
2010 Decision
2030 Decision
Future Uncertainties
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Use Uncertainties in Decision Framework
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Value of Information within Simple Framework
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Using Observations to Reduce Climate Uncertainty

• How to reduce climate uncertainty?
• Experiments and Research
• Observations (e.g., global mean surface
  temperature)
• Using observations, how long until uncertainty
  reduced?
• How might this change todays decision?

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Applying Bayes Rule

• Suppose we observe global mean surface
  temperature increase at some future time (e.g.,
  2050).
• Focus on Climate Sensitivity
• Use Bayes

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Numerical Implementation

• Perform Monte Carlo on Prior PDFs to 2050
• Choose 0.05, 0.5, 0.95 from PDF of DT2050
• For each possible observation, update the prior
  PDF of Climate Sensitivity and obtain new
  posterior PDF
• Use posterior PDF of sensitivity for Monte Carlo
  simulations of temperature change from 2060 to
  2100.

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Compute Temperature Change Conditional on Climate
Sensitivity
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Prior and Posterior Distributions for Climate
Sensitivity (2030)
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Prior and Posterior Distributions for Climate
Sensitivity (2050)
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Posterior also depends on Observed Emissions and
on Climate Noise
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Uncertainty Bounds (5-95) on Temperature Change
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Use Updated Uncertainty in Decision Model
Near-term decision
Revise Policy
Observe Climate
Remaining Uncertainty
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Optimal Mitigation with Partial Learning
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Sensitivity of Near-Term Policy to Climate Damage
Uncertainty
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Improvements to Partial Learning Calculations

• Right now Only climate sensitivity updated,
  based on GMT,
• Better Update the JOINT PDF of sensitivity, heat
  uptake, aerosol forcing, based on spatial pattern
  of temperature in atm. ocean,
• Appropriate treatment of climate noise,
• Update every decade
• Model costs of learning (observations, RD)

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Questions to Explore

• Relative potential for uncertainty reduction in
  economic, climate, impacts
• Better estimates of value-of-information
• Guidance for overall level of effort for
  near-term policy
• What to do about negative learning?

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Topic 2

• Estimating the Risks
• of a Collapse in
• North Atlantic Circulation

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Key Uncertainties Climate Sensitivity and Rate
of Forcing Increase
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Maximum North Atlantic Overturning
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Deterministic Equivalent Modeling Method (DEMM)
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Estimated PDFs of Surface Temperature
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Errors in Estimation Overturning
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Determining the Threshold for Collapse
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Response Surface of Max Ovt(Interpolated from 64
runs)
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Fitting Arctan() with DEMM
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PDFs of Overturning from Arctan-based Fits
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Topic 3

• Value of International
• Emissions Trading Under Uncertainty

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Motivation

• Kyoto Protocol, Future Agreements (?), include
  mechanism for emissions trading
• Climate agreements necessarily set targets
  several years into the future
• Uncertainty in future economic growth energy
  consumption of countries
• Q What is the value of emissions trading as a
  hedge against uncertainty?

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Emissions Trading under KyotoCertainty Case
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Gains from Trading under Certainty
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Partial Equilibrium Gain from trade
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Marginal Abatement Curves from EPPA
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Experimental Design

• Two Allocations
• Kyoto Allocations to CAN, EET, EUR, JPN
• Cost-Effective Allocations
• Using resulting allocations from Kyoto after
  emissions trading (under reference growth)
• Uncertainty in Labor Productivity Growth
• Compare Trade vs. No Trade for each allocation

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Partial Equilibrium under Uncertainty Kyoto
Allocations
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Partial Equilibrium under Uncertainty
Cost-Effective Allocations
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PE Gain from Trading
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GE Gain from Trading
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Gains from Trade to CANCost-effective alloc no
taxes
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Summary of Key ResultsGain in Consumption from ET

• Kyoto (FSU) under certainty 106B
• Kyoto (no FSU) certainty 25B
• PE Kyoto (mean) 7B
• PE effective (mean) 2.4B
• GE Kyoto (mean) 23B
• GE effective (mean) 1.1B

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Conclusions

• Pure value of emissions trading as a hedge
  against uncertainty
• Practically zero on the average
• Swamped by terms-of-trade effects and distortions
• What is the Value of Intl Emissions Trading?
• Reallocation Mechanism
• Wealth Transfer Mechanism to Induce Participation

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Calvins View on Risky Decisions
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Major Challenges for Climate Change Research

• Regional scale impacts
• Regional climate, ecosystems effects,
  hydrological, agricultural, etc.
• Ways to think about technological change
• Ability to model more realistic policies
• Ways to deal with uncertainty and design scenarios

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Uncertainty in Policy Costs
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Challenges to Uncertainty Analysis

• Empirical Challenges
• Past behavior does not fully determine the future
• Sparse data
• Expert judgments required cognitive biases
• Methodological Challenges
• Combining experts
• Model uncertainty

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Challenges to Uncertainty Analysis (II)

• Institutional Challenges
• How to structure formal assessment processes?
• Focus on consensus
• Expert judgments in a political context
• Appropriate venue for uncertainty analysis?
• Philosophical Challenges
• Frequentist vs. Bayesian
• Differing views on future social development

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Possible Next Steps to Improve UA

• Constructing PDFs for socio-econ. parameters
• Using historical data to inform
• Multiple Experts
• assessments across wider range, intercomparisons?
• More focus on impacts (beyond DT)?
• More links between standard scenarios and
  probabilistic information?
• Other ideas?

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Impacts of Period 1 Decisions
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Using Data to Constrain Climate Parameter
Distributions
From Forest et al, Science 295, 113-117
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Uncertainties in Emissions/Costs
Webster et al (2002), Atmos. Env., 36(22),
3659-3670.
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Uncertainties in Climate System
Forest et al (2002), Science 295, 113-117.
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Uncertainties in Impact Valuation
Roughgarden and Schneider (1999). Energy Policy
27 415-429.