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Annual Review of Resource Economics

Volume 9, 2017

Managing Climate Change Under Uncertainty: Recursive Integrated Assessment at an Inflection Point

Abstract

Uncertainty is critical to questions about climate change policy. Recently developed recursive integrated assessment models have become the primary tools for studying and quantifying the policy implications of uncertainty. We decompose the channels through which uncertainty affects policy and quantify them in a recursive extension of a benchmark integrated assessment model. The first wave of recursive models has made valuable, pioneering efforts at analyzing disparate sources of uncertainty. We argue that frontier numerical methods will enable the next generation of recursive models to better capture the information structure of climate change and to thereby ask new types of questions about climate change policy.

Keyword(s): carbon, climate, dynamic programming, greenhouse gas, insurance, learning, precaution, prudence, uncertainty
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2017-10-05
2024-04-24
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Literature Cited

  1. Ackerman F, Stanton EA, Bueno R. 2010. Fat tails, exponents, extreme uncertainty: simulating catastrophe in DICE. Ecol. Econ. 69:1657–65 [Google Scholar]
  2. Anthoff D, Tol RSJ. 2013. The uncertainty about the social cost of carbon: a decomposition analysis using FUND. Clim. Change 117:515–30 [Google Scholar]
  3. Anthoff D, Tol RSJ, Yohe GW. 2009. Risk aversion, time preference, and the social cost of carbon. Environ. Res. Lett. 4:024002 [Google Scholar]
  4. Baker MB, Roe GH. 2009. The shape of things to come: Why is climate change so predictable?. J. Clim. 22:4574–89 [Google Scholar]
  5. Barthelmann V, Novak E, Ritter K. 2000. High dimensional polynomial interpolation on sparse grids. Adv. Comput. Math. 12:273–88 [Google Scholar]
  6. Becker GS, Murphy KM, Topel RH. 2010. On the economics of climate policy. B. E. J. Econ. Anal. Policy 10:19 [Google Scholar]
  7. Boyd JP. 2000. Chebyshev and Fourier Spectral Methods Mineola, NY: Dover, 2nd ed..
  8. Breeden DT. 1979. An intertemporal asset pricing model with stochastic consumption and investment opportunities. J. Financ. Econ. 7:265–96 [Google Scholar]
  9. Brumm J, Scheidegger S. 2016. Using adaptive sparse grids to solve high-dimensional dynamic models Work. Pap., Karlsruhe Inst. Tech., Ger.
  10. Cai Y, Judd KL. 2012. Dynamic programming with shape-preserving rational spline Hermite interpolation. Econ. Lett. 117:161–64 [Google Scholar]
  11. Cai Y, Judd KL. 2014. Advances in numerical dynamic programming and new applications. Handbook of Computational Economics 3 K Schmedders, KL Judd 479–516 Amsterdam: Elsevier [Google Scholar]
  12. Cai Y, Judd KL, Lenton TM, Lontzek TS, Narita D. 2015. Environmental tipping points significantly affect the cost-benefit assessment of climate policies. PNAS 112:4606–11 [Google Scholar]
  13. Cai Y, Judd KL, Lontzek TS. 2012. DSICE: a dynamic stochastic integrated model of climate and economy Work. Pap. 12-02, Cent. Robust Decis. Mak. Clim. Energy Policy, Univ. Chicago
  14. Cai Y, Judd KL, Lontzek TS. 2013. The social cost of stochastic and irreversible climate change NBER Work. Pap. 18704
  15. Cai Y, Lenton TM, Lontzek TS. 2016. Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nat. Clim. Change 6:520–25 [Google Scholar]
  16. Carroll CD, Kimball MS. 1996. On the concavity of the consumption function. Econometrica 64:981–92 [Google Scholar]
  17. Crainich D, Eeckhoudt L, Trannoy A. 2013. Even (mixed) risk lovers are prudent. Am. Econ. Rev. 103:1529–35 [Google Scholar]
  18. Crost B, Traeger CP. 2013. Optimal climate policy: uncertainty versus Monte Carlo. Econ. Lett. 120:552–58 [Google Scholar]
  19. Crost B, Traeger CP. 2014. Optimal CO2 mitigation under damage risk valuation. Nat. Clim. Change 4:631–36 [Google Scholar]
  20. Cyert RM, DeGroot MH. 1974. Rational expectations and Bayesian analysis. J. Polit. Econ. 82:521–36 [Google Scholar]
  21. Datta M, Mirman LJ, Schlee EE. 2002. Optimal experimentation in signal-dependent decision problems. Int. Econ. Rev. 43:577–607 [Google Scholar]
  22. Dietz S, Gollier C, Kessler L. 2015. The climate beta Work. Pap. 215, Cent. Clim. Change Econ. Policy, Grantham Inst. Clim. Change Environ., London
  23. Dietz S, Stern N. 2015. Endogenous growth, convexity of damages and climate risk: how Nordhaus’ framework supports deep cuts in carbon emissions. Econ. J. 125:574–620 [Google Scholar]
  24. Dongarra JJ, van der Steen AJ. 2012. High-performance computing systems: status and outlook. Acta Num. 21:379–474 [Google Scholar]
  25. Drèze JH, Modigliani F. 1972. Consumption decisions under uncertainty. J. Econ. Theory 5:308–35 [Google Scholar]
  26. Eeckhoudt L, Gollier C, Schneider T. 1995. Risk-aversion, prudence and temperance: a unified approach. Econ. Lett. 48:331–36 [Google Scholar]
  27. Epstein LG, Zin SE. 1989. Substitution, risk aversion, and the temporal behavior of consumption and asset returns: a theoretical framework. Econometrica 57:937–69 [Google Scholar]
  28. Fernández-Villaverde J, Ramírez JFR, Schorfheide F. 2016. Solution and estimation methods for DSGE models NBER Work. Pap. 21862
  29. Fisher AC, Narain U. 2003. Global warming, endogenous risk, and irreversibility. Environ. Resour. Econ. 25:395–416 [Google Scholar]
  30. Fitzpatrick LG, Kelly DL. 2017. Probabilistic stabilization targets. J. Assoc. Environ. Resour. Econ. 4:611–57 [Google Scholar]
  31. Gillingham K, Nordhaus WD, Anthoff D, Blanford G, Bosetti V. et al. 2015. Modeling uncertainty in climate change: a multi-model comparison NBER Work. Pap. 21637
  32. Gollier C. 2001. The Economics of Risk and Time Cambridge, MA: MIT Press
  33. Gollier C. 2010. Ecological discounting. J. Econ. Theory 145:812–29 [Google Scholar]
  34. Gollier C. 2012. Evaluation of long-dated investments under uncertain growth trend, volatility and catastrophes CESifo Work. Pap. 4052, Munich
  35. Gollier C, Jullien B, Treich N. 2000. Scientific progress and irreversibility: an economic interpretation of the `Precautionary Principle.’. J. Public Econ. 75:229–53 [Google Scholar]
  36. Golosov M, Hassler J, Krusell P, Tsyvinski A. 2014. Optimal taxes on fossil fuel in general equilibrium. Econometrica 82:41–88 [Google Scholar]
  37. Greenstone M, Kopits E, Wolverton A. 2013. Developing a social cost of carbon for US regulatory analysis: a methodology and interpretation. Rev. Environ. Econ. Policy 7:23–46 [Google Scholar]
  38. Hansen J, Russell G, Lacis A, Fung I, Rind D, Stone P. 1985. Climate response times: dependence on climate sensitivity and ocean mixing. Science 229:857–59 [Google Scholar]
  39. Heutel G, Moreno-Cruz J, Shayegh S. 2015. Solar geoengineering, uncertainty, and the price of carbon NBER Work. Pap. 21355
  40. Heutel G, Moreno-Cruz J, Shayegh S. 2016. Climate tipping points and solar geoengineering. J. Econ. Behav. Organ. 132:19–45 [Google Scholar]
  41. Hope C. 2006. The marginal impact of CO2 from PAGE2002: an integrated assessment model incorporating the IPCC's five reasons for concern. Integr. Assess. J. 6:19–56 [Google Scholar]
  42. Howarth RB. 2003. Discounting and uncertainty in climate change policy analysis. Land Econ. 79:369–81 [Google Scholar]
  43. Hwang IC. 2016. A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations. Comput. Econ. In press. https://doi.org/10.1007/s10614-016-9583-2 [Crossref]
  44. Hwang IC, Reynès Tol F. 2017. The effect of learning on climate policy under fat-tailed uncertainty. Resour. Energy Econ. 48:1–18 [Google Scholar]
  45. Hwang IC, Tol R, Hofkes M. 2013. Active learning about climate change Work. Pap. 65-2013, Dep. Econ., Univ. Sussex, UK
  46. Hwang IC, Tol R, Hofkes M. 2016. Fat-tailed risk about climate change and climate policy. Energy Policy 89:25–35 [Google Scholar]
  47. Jensen S, Traeger CP. 2014. Optimal climate change mitigation under long-term growth uncertainty: stochastic integrated assessment and analytic findings. Eur. Econ. Rev. 69:104–25 [Google Scholar]
  48. Jensen S, Traeger CP. 2016. Pricing climate risk Work. Pap., Sch. Econ. Bus., Nor. Univ. Life Sci., As, Nor.
  49. Jones RA, Ostroy JM. 1984. Flexibility and uncertainty. Rev. Econ. Stud. 51:13–32 [Google Scholar]
  50. Judd KL. 1998. Numerical Methods in Economics Cambridge, MA: MIT Press
  51. Judd KL, Maliar L, Maliar S, Valero R. 2014. Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain. J. Econ. Dyn. Control 44:92–123 [Google Scholar]
  52. Judd KL, Solnick A. 1994. Numerical dynamic programming with shape-preserving splines Work. Pap., Stanford Univ., Stanford, Calif.
  53. Kelly DL, Kolstad CD. 1999a. Bayesian learning, growth, and pollution. J. Econ. Dyn. Control 23:491–518 [Google Scholar]
  54. Kelly DL, Kolstad CD. 1999b. Integrated assessment models for climate change control. International Yearbook of Environmental and Resource Economics 1999/2000: A Survey of Current Issues H Folmer, T Tietenberg 171–97 Cheltenham, UK: Edward Elgar [Google Scholar]
  55. Kelly DL, Kolstad CD. 2001. Solving infinite horizon growth models with an environmental sector. Comput. Econ. 18:217–31 [Google Scholar]
  56. Kelly DL, Tan Z. 2015. Learning and climate feedbacks: optimal climate insurance and fat tails. J. Environ. Econ. Manag. 72:98–122 [Google Scholar]
  57. Kimball MS. 1990. Precautionary saving in the small and in the large. Econometrica 58:53–73 [Google Scholar]
  58. Kopp RE, Golub A, Keohane NO, Onda C. 2012. The influence of the specification of climate change damages on the social cost of carbon. Econ. Open Access Open Assess. E-J.6
  59. Krueger D, Kubler F. 2004. Computing equilibrium in OLG models with stochastic production. J. Econ. Dyn. Control 28:1411–36 [Google Scholar]
  60. Leach AJ. 2007. The climate change learning curve. J. Econ. Dyn. Control 31:1728–52 [Google Scholar]
  61. Leland HE. 1968. Saving and uncertainty: the precautionary demand for saving. Q. J. Econ. 82:465–73 [Google Scholar]
  62. Lemoine D. 2016. The climate risk premium: how uncertainty affects the social cost of carbon Work. Pap. 15-01, Univ. Ariz., Tucson
  63. Lemoine D, Traeger C. 2014. Watch your step: optimal policy in a tipping climate. Am. Econ. J. Econ. Policy 6:137–66 [Google Scholar]
  64. Lemoine D, Traeger CP. 2016a. Ambiguous tipping points. J. Econ. Behav. Organ. 132:5–18 [Google Scholar]
  65. Lemoine D, Traeger CP. 2016b. Economics of tipping the climate dominoes. Nat. Clim. Change 6:514–19 [Google Scholar]
  66. Litterman B. 2013. What is the right price for carbon emissions?. Regulation 36:38–43 [Google Scholar]
  67. Lontzek TS, Cai Y, Judd KL, Lenton TM. 2015. Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy. Nat. Clim. Change 5:441–44 [Google Scholar]
  68. Lontzek TS, Narita D, Wilms O. 2016. Stochastic integrated assessment of ecosystem tipping risk. Environ. Resour. Econ. 65:573–98 [Google Scholar]
  69. Lucas RE Jr.. 1978. Asset prices in an exchange economy. Econometrica 46:1429–45 [Google Scholar]
  70. MacRae EC. 1975. An adaptive learning rule for multiperiod decision problems. Econometrica 43:893–906 [Google Scholar]
  71. Maliar L, Maliar S. 2014. Numerical methods for large-scale dynamic economic models. Handbook of Computational Economics 3 K Schmedders, KL Judd 325–477 Amsterdam: Elsevier [Google Scholar]
  72. Maliar L, Maliar S. 2015. Merging simulation and projection approaches to solve high-dimensional problems with an application to a new Keynesian model. Quant. Econ. 6:1–47 [Google Scholar]
  73. Manne A, Richels R. 1995. The greenhouse debate: economic efficiency, burden sharing and hedging strategies. Energy J. 16:1–37 [Google Scholar]
  74. Miranda MJ, Fackler PL. 2002. Applied Computational Economics and Finance Cambridge, MA: MIT Press
  75. Nordhaus W. 2013. Integrated economic and climate modeling. Handbook of Computable General Equilibrium Modeling 1 PB Dixon, DW Jorgenson 1069–131 Amsterdam: Elsevier [Google Scholar]
  76. Nordhaus WD. 1992. An optimal transition path for controlling greenhouse gases. Science 258:1315–19 [Google Scholar]
  77. Nordhaus WD. 1994. Managing the Global Commons: The Economics of Climate Change Cambridge, MA: MIT Press, 1st ed..
  78. Nordhaus WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies New Haven, CT: Yale Univ. Press
  79. Nordhaus WD. 2016. Projections and uncertainties about climate change in an era of minimal climate policies NBER Work. Pap. 22933
  80. Nordhaus WD, Popp D. 1997. What is the value of scientific knowledge? An application to global warming using the PRICE model. Energy J. 18:1–45 [Google Scholar]
  81. Peck SC, Teisberg TJ. 1993. Global warming uncertainties and the value of information: an analysis using CETA. Resour. Energy Econ. 15:71–97 [Google Scholar]
  82. Pizer WA. 1999. The optimal choice of climate change policy in the presence of uncertainty. Resour. Energy Econ. 21:255–87 [Google Scholar]
  83. Puterman ML, Shin MC. 1978. Modified policy iteration algorithms for discounted Markov decision problems. Manag. Sci. 24:1127–37 [Google Scholar]
  84. Pycroft J, Vergano L, Hope CW, Paci D, Ciscar JC. 2011. A tale of tails: uncertainty and the social cost of carbon dioxide. Econ. OpenAccess Open Assess. E-J.5:1–29
  85. Raper SCB, Gregory JM, Stouffer RJ. 2002. The role of climate sensitivity and ocean heat uptake on AOGCM transient temperature response. J. Clim. 15:124–30 [Google Scholar]
  86. Roe GH. 2009. Feedbacks, timescales, and seeing red. Annu. Rev. Earth Planet. Sci. 37:93–115 [Google Scholar]
  87. Roe GH, Baker MB. 2007. Why is climate sensitivity so unpredictable?. Science 318:629–32 [Google Scholar]
  88. Rudik I. 2016. Optimal climate policy when damages are unknown Work. Pap., Iowa State Univ., Ames
  89. Sandsmark M, Vennemo H. 2007. A portfolio approach to climate investments: CAPM and endogenous risk. Environ. Resour. Econ. 37:681–95 [Google Scholar]
  90. Sibley DS. 1975. Permanent and transitory income effects in a model of optimal consumption with wage income uncertainty. J. Econ. Theory 11:68–82 [Google Scholar]
  91. Smolyak S. 1963. Quadrature and interpolation formulas for tensor products of certain classes of functions. Sov. Math. Dokl. 4:240–43 [Google Scholar]
  92. Stokey N, Lucas RE Jr. 1989. Recursive Methods in Economic Dynamics. Cambridge, MA: Harvard Univ. Press
  93. Tol RSJ. 1999. Safe policies in an uncertain climate: an application of FUND. Glob. Environ. Change 9:221–32 [Google Scholar]
  94. Traeger C. 2014. A 4-stated DICE: Quantitatively addressing uncertainty effects in climate change. Environ. Resour. Econ. 59:1–37 [Google Scholar]
  95. Traeger CP. 2015. Analytic integrated assessment and uncertainty Work. Pap., Univ. Calif., Berkeley
  96. Urban NM, Keller K. 2009. Complementary observational constraints on climate sensitivity. Geophys. Res. Lett. 36:L04708 [Google Scholar]
  97. Weil P. 1990. Nonexpected utility in macroeconomics. Q. J. Econ. 105:29–42 [Google Scholar]
  98. Weitzman ML. 2013. Tail-hedge discounting and the social cost of carbon. J. Econ. Lit. 51:873–82 [Google Scholar]
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Managing Climate Change Under Uncertainty: Recursive Integrated Assessment at an Inflection Point
Annual Review of Resource Economics 9, 117 (2017); https://doi.org/10.1146/annurev-resource-100516-053516
/content/journals/10.1146/annurev-resource-100516-053516
/content/journals/10.1146/annurev-resource-100516-053516
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  • Article Type: Review Article

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