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regulating through Clean Air Act –R&D-focused policies have dominated since 1988 Australia –Passed carbon tax in 2011 –Repealed in 2014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 CO 2 Emissions (Deterministic)

Global Mean Temperature Change (Deterministic)

Global Mean Temperature Change Uncertainty

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? If $10 million investment is required, would you do it? What about $60m?  = $30m

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 CO 2 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

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 2 o C

Realized Temperature Change for 2 Degree Target

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…

If We Learn in 2020

Simulating Learning: Stochastic Programming

If We Learn in 2030

If We Learn in 2040

If We Never Learn

Summary – Effect of Learning Later with 2 o target

Decision Under Uncertainty With Partial Learning

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

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