Journal
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
Volume 31, Issue 5, Pages 1728-1752Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jedc.2006.06.001
Keywords
climate change; Bayesian learning; environmental regulation; growth; pollution; dynamic programming; precautionary principle
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The purpose of this paper is to confront economic models of climate change with the reality that limited information exists with which to form expectations about the evolution of the climate. A key element in the tension between those who believe we should impose aggressive climate change mitigation policies and those who do not is the question of if we are merely in a long period of shock-induced, above average temperatures or if observed increases in temperature are a result of carbon emissions. This paper characterizes learning dynamics resulting from the use of observations of temperature to update beliefs about two key characteristics of global climate: the persistence of natural trends and the sensitivity of temperature to atmospheric carbon levels. This paper shows that, contrary to predictions in the literature that uncertainty may be resolved very quickly, the time to learn the true processes may be in the order of thousands of years. Further, this paper shows the effects of uncertainty on the likelihood that observations from the statistical record lead to important estimate and policy errors. (C) 2006 Elsevier B.V. All rights reserved.
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