IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article

Optimal climate policy: Uncertainty versus Monte Carlo

  • Crost, Benjamin
  • Traeger, Christian P.

The integrated assessment literature frequently replicates uncertainty by averaging Monte Carlo runs of deterministic models. This Monte Carlo analysis is, in essence, an averaged sensitivity analyses. The approach resolves all uncertainty before the first time period, drawing parameters from a distribution before initiating a given model run. This paper analyzes how closely a Monte Carlo based derivation of optimal policies is to the truly optimal policy, in which the decision maker acknowledges the full set of possible future trajectories in every period. Our analysis uses a stochastic dynamic programming version of the widespread integrated assessment model DICE, and focuses on damage uncertainty. We show that the optimizing Monte Carlo approach is not only off in magnitude, but can even lead to a wrong sign of the uncertainty effect. Moreover, it can lead to contradictory policy advice, suggesting a more stringent climate policy in terms of the abatement rate and a less stringent one in terms of the expenditure on abatement.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0165176513002565
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 120 (2013)
Issue (Month): 3 ()
Pages: 552-558

as
in new window

Handle: RePEc:eee:ecolet:v:120:y:2013:i:3:p:552-558
Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. John Y. Campbell, 1993. "Understanding Risk and Return," NBER Working Papers 4554, National Bureau of Economic Research, Inc.
  2. Simon Dietz, 2009. "High impact, low probability? An empirical analysis of risk in the economics of climate change," GRI Working Papers 9, Grantham Research Institute on Climate Change and the Environment.
  3. Philippe Weil, 1990. "Nonexpected Utility in Macroeconomics," The Quarterly Journal of Economics, Oxford University Press, vol. 105(1), pages 29-42.
  4. Ravi Bansal & Amir Yaron, 2000. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," NBER Working Papers 8059, National Bureau of Economic Research, Inc.
  5. Emi Nakamura & Jón Steinsson & Robert Barro & José Ursúa, 2010. "Crises and Recoveries in an Empirical Model of Consumption Disasters," NBER Working Papers 15920, National Bureau of Economic Research, Inc.
  6. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
  7. Martin L. Weitzman, 2010. "What Is The "Damages Function" For Global Warming — And What Difference Might It Make?," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 57-69.
  8. Keller, Klaus & Bolker, Benjamin M. & Bradford, D.F.David F., 2004. "Uncertain climate thresholds and optimal economic growth," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 723-741, July.
  9. Ravi Bansal & Dana Kiku & Amir Yaron, 2010. "Long Run Risks, the Macroeconomy, and Asset Prices," American Economic Review, American Economic Association, vol. 100(2), pages 542-46, May.
  10. Traeger, Christian P., 2010. "Intertemporal risk aversion – or – wouldn’t it be nice to tell whether Robinson Crusoe is risk averse?," CUDARE Working Paper Series 1102, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  11. Pycroft, Jonathan & Vergano, Lucia & Hope, Chris & Paci, Daniele & Ciscar, Juan Carlos, 2011. "A tale of tails: Uncertainty and the social cost of carbon dioxide," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 5, pages 1-29.
  12. Kopp, Robert E. & Golub, Alexander & Keohane, Nathaniel O. & Onda, Chikara, 2012. "The influence of the specification of climate change damages on the social cost of carbon," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 6, pages 1-40.
  13. Traeger, Christian P, 2008. "Why uncertainty matters - discounting under intertemporal risk aversion and ambiguity," CUDARE Working Paper Series 1092R2, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy, revised Jan 2012.
  14. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
  15. Epstein, Larry G & Zin, Stanley E, 1989. "Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework," Econometrica, Econometric Society, vol. 57(4), pages 937-69, July.
  16. Annette Vissing-Jørgensen & Orazio P. Attanasio, 2003. "Stock-Market Participation, Intertemporal Substitution, and Risk-Aversion," American Economic Review, American Economic Association, vol. 93(2), pages 383-391, May.
  17. Ackerman, Frank & Stanton, Elizabeth A. & Bueno, Ramón, 2010. "Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE," Ecological Economics, Elsevier, vol. 69(8), pages 1657-1665, June.
  18. Millner, Antony, 2013. "On welfare frameworks and catastrophic climate risks," Journal of Environmental Economics and Management, Elsevier, vol. 65(2), pages 310-325.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:120:y:2013:i:3:p:552-558. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.