A simple state-contingent pricing rule for complex intertemporal externalities
Some externalities, such as global warming, involve complex relationships between emissions and an environmental state variable, with effects over lags of uncertain length. Coming up with theoretically-motivated and practical policy options in such cases has proven difficult. Deterministic intertemporal general equilibrium models yield what appear to be feasible optimal price paths, but only by assuming away many key uncertainties, nor do they specify how the possibility of new information should affect the policy path. Bayesian models allow limited uncertainty and optimal learning based on observed effects of policy changes, but suggest a discouraging delay before optimal policy can be identified. A full insurance model suggests that risk aversion and 'fat-tailed' probabilities of catastrophe imply an implausibly (or at least impractically) large risk premium, implying that practical policy decisions depend so critically on uncertain parameters as to be unavoidably arbitrary. This paper proposes an entirely new approach based on the observation that the situation giving rise to a complex intertemporal externality also yields an observable state variable that contains information relevant to the identification of the optimal policy path. I derive a simple transformation by which the state variable can yield a good approximation to the optimal externality price. I outline assumptions sufficient to yield the transformation, and present numerical examples that illustrate its ability to follow linear and nonlinear first-best price paths. A specific application to greenhouse gases is proposed.
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.
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.
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.:
- Andrew J. Leach, 2004.
"The Climate Change Learning Curve,"
Cahiers de recherche
04-03, HEC Montréal, Institut d'économie appliquée.
- 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.
- Tol, Richard S. J., 2005. "The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties," Energy Policy, Elsevier, vol. 33(16), pages 2064-2074, November.
- Martin L. Weitzman, 2009.
"On Modeling and Interpreting the Economics of Catastrophic Climate Change,"
The Review of Economics and Statistics,
MIT Press, vol. 91(1), pages 1-19, February.
- Weitzman, Martin L., 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," Scholarly Articles 3693423, Harvard University Department of Economics.
- Richard S. J. Tol & Gary W. Yohe, 2006. "A Review of the Stern Review," World Economics, World Economics, Economic & Financial Publishing, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 7(4), pages 233-250, October.
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:33:y:2011:i:1:p:111-120. 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.