IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

On Modeling and Interpreting the Economics of Catastrophic Climate Change

  • Weitzman, Martin L.

With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of low-probability, high-impact catastrophes. Even when updated by Bayesian learning, uncertain structural parameters induce a critical “tail fattening†of posterior-predictive distributions. Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. This paper shows that the economic consequences of fat-tailed structural uncertainty (along with unsureness about high-temperature damages) can readily outweigh the effects of discounting in climate-change policy analysis.

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://dash.harvard.edu/bitstream/handle/1/3693423/Weitzman_OnModeling.pdf
Download Restriction: no

Paper provided by Harvard University Department of Economics in its series Scholarly Articles with number 3693423.

as
in new window

Length:
Date of creation: 2009
Date of revision:
Publication status: Published in Review of Economics and Statistics
Handle: RePEc:hrv:faseco:3693423
Contact details of provider: Postal: Littauer Center, Cambridge, MA 02138
Phone: 617-495-2144
Fax: 617-495-7730
Web page: http://www.economics.harvard.edu/

More information through EDIRC

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. Richard S.J. Tol, 2000. "Is the Uncertainty about Climate Change Too Large for Expected Cost-Benefit Analysis?," Working Papers FNU-3, Research unit Sustainability and Global Change, Hamburg University, revised Sep 2000.
  2. Bellavance, Franois & Dionne, Georges & Lebeau, Martin, 2009. "The value of a statistical life: A meta-analysis with a mixed effects regression model," Journal of Health Economics, Elsevier, vol. 28(2), pages 444-464, March.
  3. Scott Barrett, 2008. "The Incredible Economics of Geoengineering," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 39(1), pages 45-54, January.
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:hrv:faseco:3693423. 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: (Ben Steinberg)

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.