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

2. INTRODUCTION Question: Should stringent climate policy be an immediate priority 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 outcomes and 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) = C 1-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 η to 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 = 0 on “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 η implies 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 = g 0 ─ γT, with γ calibrated to IAMs. – Temperature: – T 2100 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, η close 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

10. THREE DISTRIBUTIONS FOR T 2100

11. THREE DISTRIBUTIONS: HIGH T 2100

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 + π 1 T + π 2 T 2 ) – 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 impact of 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.