THE CLIMATE POLICY DILEMMA

1. THE CLIMATE POLICY DILEMMA Robert S. Pindyck M.I.T. December 2012

2. INTRODUCTION • Question: Should stringent climate policy be an immediatepriority for environmental economists? • Waning political support for stringent GHG abatement. • Economic argument for stringent policy far from clear. • Disagreement over likelihood of alternative climate outcomes and impacts, as well as nature and extent of uncertainty over those outcomesand impacts. • Disagreement over framework to use to evaluate benefits from abatement, including social welfare function and discount rate. • Makes climate policy difficult to evaluate, and a hard sell for the public at large.

3. INTRODUCTION (Con‘t) • Why is it so difficult to apply cost-benefit analysis to GHG abatement policy? • Long time horizon: key parameters (discount rate, IRRA) become crucial. • Uncertainty over climate change, hard to even characterize the uncertainty. • Uncertainty over impact of climate change: we know almost nothing. • Last 20 years has seen many quantitative studies of climate policy, including a variety of IAMs. • Claim: We can’t make the case for immediate adoption of stringent policy based on consensus distributions, IAMs. • Must consider catastrophic outcomes.

4. OVERVIEW • Policy evaluation: uncertainties and areas of disagreement. • Welfare function, key parameters. • Uncertainty over climate outcomes. • Uncertainty over impact of climate change. May be “unknowable.” • Implies IAMs not useful for policy evaluation. • Case for stringent policy must be based on catastrophic outcomes. • “Plausible” probabilities and impacts. • What about non-climate catastrophes? • Conclusion: Can a convincing case be made for stringent GHG abatement now?

5. Economic Evaluation of Climate Policy • Standard approach: Calculate NPV of current and expected future costs and benefits. • This is essence of IAM-based analysis. • Considerable uncertainties, hard to characterize nature and extent. • No consensus on social welfare measurement. • Assume we agree on CRRA utility: U(C) = C1-η/(1-η) • What rate to discount future utility (rate of time preference), δ? • What value for index of risk aversion, η? • Can’t simply choose δ and ηto get desired answer.

6. DISCOUNT RATE • Financial and macro data: δ is 2% to 5%. • Even 2% makes PV of future welfare gains from GHG abatement too small to justify any policy. • Lower rate for intergenerational comparisons, i.e., time horizon of 50 or 100 years? Stern Review sets δ close to zero. • Should δ = 0on “ethical” grounds? Economists have little to say. John versus Jane. • δ is a policy parameter. Reflects values of policy makers. Might or might not reflect voters. • δ might be positive, zero, or even negative. • Problem: if δ is an arbitrary parameter, hard to make a case for (or against) stringent policy. Must also make case for value of δ.

7. INDEX OF RELATIVE RISK AVERSION • The IRRA, η, also critical. Two effects: • Large η implies U’(C) declines rapidly as C rises, reducing benefits from preventing future loss of C. • Large ηimplies high risk aversion, increasing benefits if future C uncertain. • Unless risk aversion or uncertainty is extreme, first effect will dominate. • What is “correct” value for η? Behavioral or policy parameter, not an ethical one. • If behavioral, range is 1 to 4. If policy, 1 to 3. • If η≥ 2, welfare gains from policy too small. • Stern Review sets η= 1. • Problem: if η is arbitrary, hard to make case for (or against) stringent policy.

8. UNCERTAINTY OVER TEMPERATURE • In JEEM (2012), I estimated WTP to ensure that T ≤ 3°C. • Growth rate of C: g = g0 ─γT, with γ calibrated to IAMs. • Temperature: • T2100 is distributed as gamma, mean = 3°, SD = 2.1° • Fitting to mean and SD implies r = 3.8, λ = 0.92, and θ = -1.13. • For δ = 0, η ≥ 1.5, WTP < 2% of GDP. • Can get WTP of 3% or more if δ = 0, ηclose to 1. • Can get higher WTP by doubling impact parameter γ. • Do other distributions for T imply higher WTP? • Calculate WTP using Frechet (GEV Type II) and Roe-Baker distributions. Both are fat-tailed.

9. WTP USING ALTERNATIVE DISTRIBUTIONS • Frechet: where z = (T-μ)/σ, k > 0, and T ≥ μ – σ/k. • Calibrating to mean = 3°, SD = 2.1° gives k = 0.28, μ = 2.15, σ = 0.195. • Roe-Baker: where z = T + θ. Calibrating to mean = 3°, SD = 2.1° gives = 0.797, σf= .0441, and θ = 2.13. (The feedback parameter in the Roe-Baker model is normally distributed with mean and SD and σf respectively.) • Graphs compare gamma, Frechet, and Roe-Baker distributions for T2100 and resulting WTP.

10. THREE DISTRIBUTIONS FOR T2100

11. THREE DISTRIBUTIONS: HIGH T2100

12. IMPLICATIONS FOR WTP

13. WTP WITH HIGH IMPACT

14. UNCERTAINTY OVER IMPACT • Why so difficult to estimate economic impact of climate change? • Very little data on which to base empirical work. • Little or no economic theory explaining impact of higher temperatures. • Climate change is slow, creating potential for adaptation. How much adaptation will occur? • Our understanding of economic impact unlikely to improve in next 20 years. • May be in the realm of the “unknowable.” • Most IAMs posit ad hoc loss function relating T to GDP. • E.g., DICE Model has L(T) = 1/(1 + π1T + π2T2 ) • Weak tool for policy.

15. CATASTROPHIC CLIMATE CHANGE • Case for stringent policy must be based on chance of catastrophic outcome. • Not a climate outcome– a catastrophic impactof whatever climate change might occur. • Outside the realm of IAMs and WTP estimates. • What to do? Roughly estimate probabilities of large climate changes and distributions for impact, as in studies of “consumption disasters.” • Find “plausible” range of catastrophic outcomes, measured by decline in productive capital. Find plausible probabilities. • Calculate WTP to avert outcomes or to reduce probabilities. • Is WTP large and robust to range of δ and η? • This does not have perceived precision of IAM-based analysis. But that precision is illusory. • Given “unknowables,” can only rely on the “plausible.”

16. MULTIPLE CATASTROPHES • Suppose analysis based on “plausible” outcomes and probabilities yields high WTP, e.g., 10% of GDP. Are we home? • Maybe not. Must consider other potential catastrophes: nuclear or biological terrorist attack, mega-virus, non-climate environmental catastrophe ... (use your imagination). • Calculate WTPs for these catastrophes the same way. • Problem: WTPs not additive. When taken as a group, WTP for each potential catastrophe (including climate) will fall. • Non-climate catastrophes reduce expected GDP growth, increasing expected future marginal utility before climate catastrophe occurs. This increases WTP to avoid climate change. • With more catastrophes, large fraction of GDP needed to keep us safe. This “income effect” reduces WTP for climate. It dominates.

17. CONCLUSIONS • Should we push for early adoption of a stringent GHG abatement policy? I have not answered this. But: • Case cannot be made based on “likely outcomes,” i.e., distributions for temperature consistent with IPCC, and economic impact functions used in most IAMs. • Greatest uncertainty is with impact of climate change. Economic loss functions for most IAMs are ad hoc. Not surprising given how little we know. • Economic impact of climate change may be in the realm of the “unknowable.” • Case for stringent abatement must be based on the possibility of catastrophic outcome. This means economic outcome, not climate outcome. • Need “plausible” estimates of probabilities of various climate outcomes, and impacts from those outcomes. • Must consider other potential catastrophes as well.