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Mitigation and Adaptation Under Uncertainty NCAR ASP Uncertainty Colloquium Mort Webster (Penn State) 28 July 2014 Outline of this Talk • Overview of Policy Responses • Overview of Mitigation Policies • Decision-Making Under Uncertainty • Example of Mitigation Under Uncertainty • Mitigation and Adaptation Portfolios Policy Response to Climate Change • Types of Responses: – – – – – Mitigation: Reduce Emissions Adaptation: Reduce Damage from Climate Change Geoengineering: Reduce Climate Change Energy R&D: Develop Technologies for Later Climate Research: Improve Projections • Key Decisions – – – – How to allocate across types How to allocate over time Level of effort for each type Who pays Key Concept: Portfolio • A set of investments, assets, or activities chosen so that in combination an objective is maximized (or minimized) • Example: in finance, portfolio of assets is designed to balance between maximizing expected returns and minimizing variance • Typically, a portfolio is adjusted over time in response to changing information • Main motivation is uncertainty Mitigation • Definition: Reduction of greenhouse gas emissions in the future to a level lower than “what it would have been” • Requires a “counterfactual” • Level of Governmental Entity – – – – International (UN FCCC) National / Regional (U.S., E.U., etc) State City? Necessary Conditions for Emissions Reductions • Someone, somewhere has to consume less energy, and/or • Someone, somewhere has to use energy from a lower carbon source (at higher cost), and/or • Someone, somewhere has to invest resources to lower the cost of low-carbon energy (so that someone will use it in the future) • Question: How can government make incentives for these to occur? Policy Instruments for Mitigation • Emissions Limits – Cap and Trade – Emissions Standards • Increase cost of carbon-intensive energy – Carbon taxes – Remove subsidies on fossil fuels • Incentivize/subsidize low-carbon energy – Production tax credits – Renewable energy standards • R&D-Focused – Direct R&D by Government – R&D Investment Tax Credits Examples of National Policies • European Union – Emissions Trading Scheme (ETS) • U.S. – No federal legislation to date – EPA regulating through Clean Air Act – R&D-focused policies have dominated since 1988 • Australia – Passed carbon tax in 2011 – Repealed in 2014 Global CO2 Emissions (Deterministic) Global CO2 Emissions (GtC) 25 No Policy CCSP 750 CCSP 650 CCSP 550 CCSP 450 20 15 10 5 0 2000 2020 2040 2060 Year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 Global Mean Temperature Change Uncertainty Global Mean Surface Temperature Change Uncertainty 2000-2100 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 Communicating the Odds of Temperature Change Communicating the Impact of Policy No Policy Stringent Policy (~550 ppm) Limitations of this Example? • Correctly states the No Policy case, BUT… – It is NOT a once-and-for-all decision! – What we learn along the way will change the odds. – What we do along the way will change the odds. – What we learn along the way will change what we do, and the converse is also true. Decision Analysis: A Natural Approach • Tools for modeling a decision problem • Decisions under uncertainty • Sequential decisions over time • Effect of resolving uncertainty • Value of information Decision Under Uncertainty If you are uncertain, you should: 1. Ignore it and hope it goes away? 2. Pick the most likely case (middle scenario) and optimize for that? 3. Find best strategy for each scenario and then average the strategies? 4. Plan for the worst case? 5. Something else? The “Question” Since climate change is so uncertain, shouldn’t we just wait until we know more? Why Does Uncertainty Matter? 0.30 If $10 million investment is required, would you do it? What about $60m? Probability Density 0.25 0.20 0.15 m = $30m 0.10 0.05 0.00 0 20 40 60 Revenues ($ Million) 80 100 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 Why Does Learning Matter? • Why would you do something different today if you can learn tomorrow? • One answer: if the outcome is irreversible Irreversibilities in Climate Change • GHG Concentrations – Temperature Change – Climate Damages • Capital Stock / Economic Investments Example Analysis of Decision under Uncertainty and Learning • Focus on 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. • Modeling the problem – – – – Use DICE model instead of MIT Dynamically optimizes over time Stabilize temperature instead of concentrations Carbon price as measure of policy stringency Current Uncertainty in Climate Sensitivity 0.30 Probability Density 0.25 0.20 0.15 0.10 0.05 0.00 0 2 4 6 8 o Climate Sensitivity ( C) 10 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? Illustration: Optimal Carbon Tax for Temperature Target of 2oC 400 Carbon Tax ($/ton) 300 CS = 1.5 CS = 3.0 CS = 5.0 CS = 8.0 200 100 0 2000 2020 2040 2060 2080 2100 3.0 o Global MEan Surface Temperature Change ( C) Realized Temperature Change for 2 Degree Target 2.5 CS = 1.5 CS = 3.0 CS = 5.0 CS = 8.0 2.0 1.5 1.0 0.5 0.0 2000 2020 2040 2060 2080 2100 2120 2140 QUESTION: What should we do now if we are uncertain? • Wait (do nothing until we know more)? • Implement Highest Tax (worst case)? • Implement Lowest Tax (best case)? • Something in the Middle? – Where in the middle? Relative to Best Policy in Each Case… 400 Perfect Information Carbon Tax ($/ton) 300 200 100 0 2000 2020 2040 2060 2080 2100 If We Learn in 2020 400 Perfect Information Learn in 2020 Carbon Tax ($/ton) 300 200 100 0 2000 2020 2040 2060 2080 2100 Simulating Learning: Stochastic Programming 400 Carbon Tax ($/ton) 300 CS = 1.5 CS = 3.0 CS = 5.0 CS = 8.0 200 100 0 2000 2020 2040 2060 2080 2100 If We Learn in 2030 400 Perfect Information Learn in 2020 Learn in 2030 Carbon Tax ($/ton) 300 200 100 0 2000 2020 2040 2060 2080 2100 If We Learn in 2040 400 Carbon Tax ($/ton) 300 Perfect Information Learn in 2020 Learn in 2030 Learn in 2040 200 100 0 2000 2020 2040 2060 2080 2100 If We Never Learn 400 Perfect Information Learn in 2020 Learn in 2030 Learn in 2040 Never Learn Carbon Tax ($/ton) 300 200 100 0 2000 2020 2040 2060 2080 2100 Summary – Effect of Learning Later with 2o target 100 Optimal Tax in 2015 90 80 70 60 50 40 30 2020 2030 2040 2050 2060 Time When Learning Occurs never Decision Under Uncertainty With Partial Learning 400 Perfect Information after 2100 Carbon Tax ($/ton) 300 Revised policy after reducing uncertainty 200 Near-term hedging under uncertainty 100 0 2000 2020 2040 2060 2080 2100 2120 2140 Main Point of this Example • If I am uncertain, but can learn and revise later • The best decision for today is (almost) NEVER: – Do “nothing” – Assume the “worst-case” – Do the “average” decision from a range of scenarios • The optimal “hedge” depends on specific characteristics of your problem Methods for DMUU • Decision Analysis • Dynamic Programming • Stochastic Programming Essence of Dynamic Programming Vt s = max 𝐶 𝑥, 𝑠 + 𝐸 𝑉𝑡+1 (𝑥, 𝑠) 𝑥 • Words: Balance between immediate reward and expected value of being in a better position next time Mitigation and Adaptation • Increasing prominence in policy discussions and in research • Early Treatment of M vs A: – – – – – Substitutes or complements? Framed as a static decision Framed as a deterministic question (no uncertainty) Framed around a generic “policymaker” “Adaptation” as a monolithic concept Realities Surrounding M vs A • Decision-Makers: – Different decisions are made at different levels – Local DMs: can only manage specific adaptation investments – National/Regional: Allocation of resources across general categories • Relative Allocation over Time – Should be a portfolio that is adjusted over time in response to changing conditions and information Realities Surrounding M vs A II • Substitutes vs. Complements? – Mitigation today substitutes for future adaptation – Adaptation today is for climate change already occurring • Adaptation Types – “Flow” Adaptation – requires repeated application – “Stock” Adaptation – long-lived investments – “Option” Stock Adapt. – Build in additional flexibility to infrastructure Mitigation vs Adaptation • Effective response will be a portfolio over time of mitigation and adaptation investments • Precise mix will evolve with new information • Today’s mix should be informed by – Level of near-term climate damages expected – Uncertainties in future climate damage, mitigation costs, and adaptation costs and effectiveness – Expected time to obtain information on the uncertainties Summary • Climate policy decision is a risk management problem • Response should be a portfolio of policies designed to reduce risk as well as minimize expected damages • Near-term response will hedge against the uncertainty • For research, appropriate methods include decision analysis, dynamic programming, and stochastic programming