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Coping with the Uncertainties of Climate Change

Prof. Charles D. Kolstad Stanford University SIEPR, PIE & Economics

April 8, 2013

Uncertainty is Complex

• “There are known knowns: there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know."

• Famous contemporary philosopher (2003)

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Dimensions of Uncertainty • Uncertainty and risk

– Some uncertainty quantifiable; some not – Uncertainty multifaceted: natural science, damage, costs – Uncertainty is different from stochasticity – Who is uncertain, who learns and does it matter? Scientists? Regulator? Farmer? Big business?

• Fat tails – As ΔT increases, damage grows more rapidly than π(ΔT) declines expected damage grows with ΔT

• Learning – Over time, uncertainty changes as we learn; implications for timing of action – Some learning can be directed by investing (eg, in R&D)

• How to direct R&D budgets and incentivize innovation? • Measuring the value of information

• Risk perception – People perceive risks differently, generating paradoxes

• Irreversibilities – Some actions are irreversible; uncertainty and learning suggests biasing current action towards precaution

• Evolution of future technology highly uncertain – Endogenous – depends on regulatory action adopted – Key to costs and damages (though adaptation)

• Managing risk and uncertainty – Decentralized

• Insurance markets • Catastrophe bonds and other derivatives

– Centralized • Government investments in infrastructure • Mitigation actions tempered by uncertainty and learning

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Uncertainty and Risk

• Subjective vs Objective probabilities (Frank Knight) – Objective (“Knightian risk”)

• Probabilities can be assigned to states-of-world • Data can be used to generate probabilities • Examples:

– coin flip; probability of rain in Des Moines on May 1, 2013 – Subjective (“Knightian uncertainty”)

• Probabilities less well known • Possibly little data on which to base estimates • Examples:

– Probability of West Antarctic Ice Sheet collapsing in 2014 – Probability of North Atlantic THC shutdown in 21st Century

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Uncertainty over Climate Sensitivity (from IPCC AR4,Fig 9.20)

What kind of uncertainty is this?

CO2 (ppm) Pre-industrial: 280 Current: 390

Fat Tails • Probability distributions

– Thin tailed – finite support – Medium tailed – tails drop exponentially (normal distribution) – Fat tailed – tails drop more slowly

• The big issue is the likelihood of extreme events (in probability sense) • Consider two events (Nordhaus, 2011):

– Height of American women • Mean: 64”; standard deviation of 3”; normally distributed • Probability of 11’ woman (23 sigma): 10-230

– Earthquakes (ie, energy released in earthquake) • Seismologists tell us energy released in earthquakes follows fat tailed power law distribution • If we had assumed a normal distribution, 2011 Japan quake should occur once every 10-13

years (using historical data), rather than 10-2 (based on power law distribution). • Fat tails and climate change

– Assume: damage from climate change has fat tail, risk aversion significant – THEN expected value of damages is negative infinity – Which implies all resources should be devoted to mitigating climate change

• Implications for climate change policy still a matter for debate

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Learning • Learning: time passes, information is acquired. • Learning about a parameter may not reduce the variance of your estimate

– The estimate of modal/mean climate sensitivity largely unchanged over 100+ years (2.5-3 degrees C)

• Who learns? – Individual agents (eg, farmers) who must act in an environment of changing uncertainty

and learning – Regulators who make decisions about mitigation, adaptation and other actions, in

environment of uncertainty and learning – Multi-agent decisions (IEAs)

• How does learning take place? – Passively, with the passage of time – Active experimentation (update prior on climate sensitivity with one more

year of data) – Active acquisition of information (R&D)

• What is the process of active learning? – R&D is a fundamental process of information acquisition – spend more and

learn more (in expectation) – Emit more or less GHG will change the strength of the climate signal

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Simple Model of Passive Learning

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A is prior Truth is B, C or D

Example: Regulator Learning

• Regulator uncertain about climate sensitivity • Each year provides more data on climate

(distribution of weather) • Regulator decides on regulatory actions with

– Uncertainty regarding costs, benefits, consequences – Knowing she will know more in a decade – One action is deferring action

• Regulator can also take action to change learning – Change climate change R&D budget – Promulgate regulations to accelerate learning (eg, fuel

economy standards or banning incandescents)

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Example: Farmer Learning Value of ag land relative to US mean

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One view of adaptation and adjustment

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•Farmers can instantly change cropping in response to climate change •Climate is unobserved: Problem is that farmers do not observe a change in the climate •Only after experiencing a change in climate for a number of years will farmers adapt and change •Impacts consist of losses after long run adaptation PLUS transient costs (“adjustment costs”) •Some action may be precautionary: install irrigation from RISK of climate becoming drier

Annual costs of a change in climate (observed vs. unobserved)

12 Source: Kelly, Kolstad & Mitchell, “Adjustment Costs from Environmental Change,” J. Env. Econ. & Mgmt., 50:468-95 (2005).

What is the source of these adjustment costs?

• Weather rarely is “average” • When weather turns out differently than

expected, farm output and profits can suffer • When the climate has changed and farmer

doesn’t realize it, farmer suffers more losses • After suffering adverse weather for a period of

time, farmer realizes climate has changed • In the end, everything re-equilibrates

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Empirical implementation • Observe annual profits as a function of

– Farm characteristics – Weather expectations (climate from historical record) – Actual weather

• Estimate profit function (of weather and climate) – Use panel (time series & cross section) to estimate – Data drawn from midwest, 1974-2000

• Examine counterfactual – Unobserved change in climate (consistent with IPCC forecasts) – Slow realization that climate has changed (Bayesian farmer) – Measure impacts, adjustment costs and adaptation

• Results for median county – Impacts $3.07 per acre gain (5.2% of annual profit) – Adjustment costs $1.38 per acre – Net impact: $1.69 per acre gain

14 For application, see: Kelly, Kolstad, Mitchell, 2005

Perceptions of risk

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Figure from Kolstad (2011), adapted from Slovic (1979)

Irreversibilities • Examples:

– Build dam, flood irreplaceably beautiful canyon – Increase CO2 concentration in atmosphere – Invest in abatement capital to mitigate emissions

• In each case, action is difficult to quickly reverse • Learning key to the issue

– Take action under uncertainty about state of world – Learn true state of the world – Try to reverse action under particular states of the world

• Literature: – Abatement equipment irreversibility favors deferring action – CO2 in atmosphere favors accelerating action

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Active Learning: R&D • Much climate policy hinges on decarbonizing our economy

through innovation and invention • Two primary mechanisms

– Direct R&D spending, primarily from federal government – Incentives for R&D: carbon taxes, R&D tax incentives, prizes (eg,

Golden Carrot), patent protection (IP) – Mechanisms and empirical characteristics of these approaches

poorly understood • R&D spending should be driven by value of information: in

which areas would improved knowledge be most valuable – Hunch: economics and the costs of mitigation, adaptation and

residual damage

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Managing Risk and Uncertainty I. Decentralized

• Insurance markets – Some risks insurable (eg home fire), some not (eg,

unemployment) – Sometimes institutional change is needed (eg, flood insurance in

US vs. Bermuda) – Government provision of insurance plagued with difficulties

• Financial markets – Catastrophe bonds (eg, pays $1 if global average temperature

rises to x in given year; or if Cat V hurricane hits Gulf in given year) • Real options

– Investing in technology which reduces risk (eg, irrigation for farming insures against drought risk)

• Decentralization of risk management generally preferred

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Managing Risk and Uncertainty II. Centralized

• Mitigation – Obvious way to reduce risk – Best if accompanied by carbon price incentive

• Adaptation – Public investments to ameliorate possible consequences of

climate change • Institutional reform

– Subsidized flood insurance tends to magnify damage to US Gulf Coast

– Water allocation in California tends to magnify water shortage consequences from Climate Change in Calif.

• International agreements – Uncertainty may make agreement easier – verdict still out

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Treatment of Uncertainty in Climate Analysis

• Several types of uncertainty in climate models – Parametric uncertainty (parameter has a value but investigator is unsure about it) – Structural uncertainty (true model structure is uncertain to investigator) – Stochasticity – noise that is unknowable ex ante

• Simplest (and most common analysis) – Probability distribution stipulated for key parameters and distribution of outcomes

computed – Monte Carlo analysis – any endogenous decision making implicitly deterministic – Structural uncertainty difficult to analyze

• More difficult (uncommon for complex models) – Decision-making under uncertainty endogenous to model – Uncertainty changes over time as learning takes place (active or passive learning)

• In fairness, it is hard enough to treat the problem deterministically; uncertainty is much harder

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Conclusions

• Risk, uncertainty and learning pervades climate problem

• Don’t look for “most likely” consequences of a doubling of GHG – look at “tail events”

• Let the value of information drive further research – Focus more on cost of mitigation/adaptation and

consequences of a changed climate

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