Environmental and Resource Economics

, Volume 55, Issue 1, pp 21–46 | Cite as

Scientific Ambiguity and Climate Policy

Article

Abstract

Economic evaluation of climate policy traditionally treats uncertainty by appealing to expected utility theory. Yet our knowledge of the impacts of climate policy may not be of sufficient quality to be described by unique probabilistic beliefs. In such circumstances, it has been argued that the axioms of expected utility theory may not be the correct standard of rationality. By contrast, several axiomatic frameworks have recently been proposed that account for ambiguous knowledge. In this paper, we apply static and dynamic versions of a smooth ambiguity model to climate mitigation policy. We obtain a general result on the comparative statics of optimal abatement and ambiguity aversion, and then extend our analysis to a more realistic, dynamic setting, where we introduce scientific ambiguity into the well-known DICE model of the climate-economy system. For policy-relevant exogenous mitigation policies, we show that the value of emissions abatement increases as ambiguity aversion increases, and that this ‘ambiguity premium’ can in some plausible cases be very large. In these cases the effect of ambiguity aversion on welfare is comparable to that of other much studied welfare parameters. Thus ambiguity aversion may be an important neglected aspect of climate change economics, and seems likely to provide another argument for strong abatement policy.

Keywords

Climate change Uncertainty Ambiguity 

JEL Classification

Q54 D81 

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References

  1. Ackerman F, Stanton EA, Bueno R (2010) Fat tails, exponents, and extreme uncertainty: simulating catastrophe in DICE. Ecol Econ 69(8): 1657–1665CrossRefGoogle Scholar
  2. Anscombe FJ, Aumann RJ (1963) A definition of subjective probability. Ann Math Stat 34(1): 199–205CrossRefGoogle Scholar
  3. Arrow KJ, Hurwicz L (eds) (1977) An optimality criterion for decision-making under ignorance. In: Studies in resource allocation processes, p 482. Cambridge University Press, CambridgeGoogle Scholar
  4. Athanassoglou S, Xepapadeas A (2012) Pollution control with uncertain stock dynamics: when, and how, to be precautious. J Environ Econ Manag 63(3): 304–320CrossRefGoogle Scholar
  5. Bassett GW, Koenker R, Kordas G (2004) Pessimistic portfolio allocation and choquet expected utility. J Financ Econom 2(4): 477–492CrossRefGoogle Scholar
  6. Binmore K. (2009) Rational decisions. Princeton University Press, Princeton, NJGoogle Scholar
  7. Bossaerts P, Ghirardato P, Guarnaschelli S, Zame WR (2010) Ambiguity in asset markets: theory and experiment. Rev Financ Stud 23(4):1325–1359Google Scholar
  8. Cai Y, Judd KL, Lontzek TS (2012) DSICE: a dynamic stochastic integrated model of climate and economy. SSRN working paperGoogle Scholar
  9. Camerer C (1999) Ambiguity-aversion and non-additive probability: experimental evidence, models and applications. In: Luini L (eds) Uncertain decisions: bridging theory and experiments. Kluwer, Dordrecht, pp 53–80CrossRefGoogle Scholar
  10. Dasgupta P (2008) Discounting climate change. J Risk Uncertain 37(2): 141–169CrossRefGoogle Scholar
  11. Dow J, da Costa Werlang SR (1992) Uncertainty aversion, risk aversion, and the optimal choice of portfolio. Econometrica 60(1): 197–204CrossRefGoogle Scholar
  12. Ellsberg D (1961) Risk, ambiguity, and the Savage axioms. Q J Econ 75(4): 643–669CrossRefGoogle Scholar
  13. Frame DJ, Faull NE, Joshi MM, Allen MR (2007) Probabilistic climate forecasts and inductive problems. Philos Trans R Soc A Math Phys Eng Sci 365(1857): 1971–1992CrossRefGoogle Scholar
  14. Ghirardato P, Maccheroni F, Marinacci M (2004) Differentiating ambiguity and ambiguity attitude. J Econ Theory 118(2): 133–173CrossRefGoogle Scholar
  15. Gilboa I (2009) Theory of decision under uncertainty, 1st edn. Cambridge University Press, Cambridge, MACrossRefGoogle Scholar
  16. Gilboa I, Postlewaite A, Schmeidler D (2009) Is it always rational to satisfy Savage’s axioms?. Econ Philos 25(3): 285–296CrossRefGoogle Scholar
  17. Gilboa I, Postlewaite AW, Schmeidler D (2008) Probability and uncertainty in economic modeling. J Econ Perspect 22(3): 173–188CrossRefGoogle Scholar
  18. Gilboa I, Schmeidler D (1989) Maxmin expected utility with non-unique prior. J Math Econ 18(2): 141–153CrossRefGoogle Scholar
  19. Gilboa I, Schmeidler D (1995) Case-based decision theory. Q J Econ 110(3): 605–639CrossRefGoogle Scholar
  20. Gollier C (2001) The economics of risk and time. MIT Press, CambridgeGoogle Scholar
  21. Gollier C (2009) Portfolio choices and asset prices: the comparative statics of ambiguity aversion. IDEI working paper 357. http://idei.fr/display.php?a=4812
  22. Gollier C, Gierlinger J (2008) Socially efficient discounting under ambiguity aversion. IDEI working paper 561. http://idei.fr/display.php?a=9848
  23. Gonzalez F (2008) Precautionary principle and robustness for a stock pollutant with multiplicative risk. Environ Resour Econ 41(1): 25–46CrossRefGoogle Scholar
  24. Hansen LP, Sargent TJ (2007) Robustness. Princeton University Press, Princeton, NJGoogle Scholar
  25. Heal G (2009) Climate economics: a meta-review and some suggestions for future research. Rev Environ Econ Policy 3(1): 4–21CrossRefGoogle Scholar
  26. Henry C, Henry M (2002) Formalization and application of the precautionary principle. Columbia University Department of Economics discussion paper series. http://www.columbia.edu/cu/economics/discpapr/DP0102-22.pdf
  27. Herstein IN, Milnor J (1953) An axiomatic approach to measurable utility. Econometrica 21(2): 291–297CrossRefGoogle Scholar
  28. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14(4): 382–401CrossRefGoogle Scholar
  29. Hope C (2006) The marginal impact of CO2 from PAGE2002: an integrated assessment model incorporating the IPCC’s five reasons for concern. Integr Assess 6(1):19–56Google Scholar
  30. Jouzel J, Masson-Delmotte V, Cattani O, Dreyfus G, Falourd S, Hoffmann G, Minster B, Nouet J, Barnola JM, Chappellaz J, Fischer H, Gallet JC, Johnsen S, Leuenberger M, Loulergue L, Luethi D, Oerter H, Parrenin F, Raisbeck G, Raynaud D, Schilt A, Schwander J, Selmo E, Souchez R, Spahni R, Stauffer B, Steffensen JP, Stenni B, Stocker TF, Tison JL, Werner M, Wolff EW (2007) Orbital and millennial Antarctic climate variability over the past 800,000 years. Science 317(5839): 793–796CrossRefGoogle Scholar
  31. Ju N, Miao J (2012) Ambiguity, learning, and asset returns. Econometrica 80(2): 559–591CrossRefGoogle Scholar
  32. Karp L, Zhang J (2006) Regulation with anticipated learning about environmental damages. J Environ Econ Manag 51(3): 259–279CrossRefGoogle Scholar
  33. Keller K, Bolker BM, Bradford DF (2004) Uncertain climate thresholds and optimal economic growth. J Environ Econ Manag 48(1): 723–741CrossRefGoogle Scholar
  34. Kelly DL, Kolstad CD (1999) Bayesian learning, growth, and pollution. J Econ Dyn Control 23(4): 491–518CrossRefGoogle Scholar
  35. Klibanoff P, Marinacci M, Mukerji S (2005) A smooth model of decision making under ambiguity. Econometrica 73(6): 1849–1892CrossRefGoogle Scholar
  36. Klibanoff P, Marinacci M, Mukerji S (2009) Recursive smooth ambiguity preferences. J Econ Theory 144(3): 930–976CrossRefGoogle Scholar
  37. Knight F (1921) Risk, uncertainty, and profit. Houghton Mifflin, New YorkGoogle Scholar
  38. Knutti R (2010) The end of model democracy?. Clim Change 102(3): 395–404CrossRefGoogle Scholar
  39. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23(10): 2739–2758CrossRefGoogle Scholar
  40. Kreps DM, Porteus EL (1978) Temporal resolution of uncertainty and dynamic choice theory. Econometrica 46(1): 185–200CrossRefGoogle Scholar
  41. Lange A, Treich N (2008) Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions. Clim Change 89(1): 7–21CrossRefGoogle Scholar
  42. Lemoine D, Traeger CP (2012) Tipping points and ambiguity in the Economics of climate change. NBER working paper no 18230Google Scholar
  43. Lenton TM, Held H, Kriegler E, Hall JW, Lucht W, Rahmstorf S, Schellnhuber HJ (2008) Tipping elements in the Earth’s climate system. Proc Natl Acad Sci 105(6): 1786–1793CrossRefGoogle Scholar
  44. Maccheroni F, Marinacci M, Rustichini A (2006) Ambiguity aversion, robustness, and the variational representation of preferences. Econometrica 74(6): 1447–1498CrossRefGoogle Scholar
  45. Manne A, Richels R (1992) Buying greenhouse insurance: the economic costs of CO2. The MIT Press, Cambridge, MAGoogle Scholar
  46. McJeon HC, Clarke L, Kyle P, Wise M, Hackbarth A, Bryant BP, Lempert RJ (2011) Technology interactions among low-carbon energy technologies: what can we learn from a large number of scenarios?. Energy Econ 33(4): 619–631CrossRefGoogle Scholar
  47. Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR (2009) Greenhouse-gas emission targets for limiting global warming to 2C. Nature 458(7242): 1158–1162CrossRefGoogle Scholar
  48. Nordhaus WD (2008) A question of balance. Yale University Press, New Haven, CTGoogle Scholar
  49. Savage LJ (1954) The foundations of statistics. Wiley, LondonGoogle Scholar
  50. Schmeidler D (1989) Subjective probability and expected utility without additivity. Econometrica 57(3): 571–587CrossRefGoogle Scholar
  51. Slovic P, Tversky A (1974) Who accepts Savage’s axiom?. Behav Sci 19(6): 368–373CrossRefGoogle Scholar
  52. Smith LA (2002) What might we learn from climate forecasts? Proc Natl Acad Sci USA 99(Suppl 1): 2487–2492Google Scholar
  53. Smith LA (2007) Chaos: a very short introduction, vol 159. Oxford University Press, OxfordGoogle Scholar
  54. Stainforth DA, Allen MR, Tredger ER, Smith LA (2007) Confidence, uncertainty and decision-support relevance in climate predictions. Philos Trans R Soc A Math Phys Eng Sci 365(1857): 2145–2161CrossRefGoogle Scholar
  55. Stern N (2008) The economics of climate change. Am Econ Rev 98(2): 1–37CrossRefGoogle Scholar
  56. Stern NH (2007) The economics of climate change: the Stern review. Cambridge University Press, CambridgeGoogle Scholar
  57. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365(1857): 2053–2075CrossRefGoogle Scholar
  58. Tol R (1997) On the optimal control of carbon dioxide emissions: an application of FUND. Environ Model Assess 2(3): 151–163CrossRefGoogle Scholar
  59. Traeger CP (2009) Recent developments in the intertemporal modeling of uncertainty. Annu Rev Resour Econ 1(1): 261–286CrossRefGoogle Scholar
  60. von Neumann J, Morgenstern O (1944) Theory of games and economic behaviour. Princeton University Press, Princeton, NJGoogle Scholar
  61. Weitzman ML (1976) On the welfare significance of national product in a dynamic economy. Q J Econ 90(1): 156–162CrossRefGoogle Scholar
  62. Weitzman ML (2007) A review of The Stern review on the economics of climate change. J Econ Lit 45(3): 703–724CrossRefGoogle Scholar
  63. Weitzman ML (2009) On modeling and interpreting the economics of catastrophic climate change. Rev Econ Stat 91(1): 1–19CrossRefGoogle Scholar
  64. Weitzman ML (2012) GHG targets as insurance against catastrophic climate damages, ghg targets as insurance against catastrophic climate damages. J Public Econ Theory 14(2): 221–244CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of Agricultural and Resource EconomicsUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Grantham Research Institute on Climate Change and the EnvironmentLondon School of Economics and Political ScienceLondonUK
  3. 3.Department of Geography and EnvironmentLondon School of Economics and Political ScienceLondonUK
  4. 4.Columbia Business SchoolColumbia UniversityNew YorkUSA

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