A major issue that has arisen time and time again in managing the environment is how to regulate emissions where there are potentially important irreversible environmental consequence and where there is great uncertainty about the magnitude of the consequences. The tension is often between controlling pollution now in order to avoid potential irreversible effects, versus waiting to learn more about the potential irreversibilities and better mitigation technologies. Global warming is the most recent in a long series of environmental issues characterized by the debate of whether to take immediate action or wait until there is more information. Other recent environmental issues would include acid rain and the use of pesticides and herbicides. The purpose of this research is to analyze the effect of the rate of learning about a global warming, or alternatively about the rate at which uncertainties about the problem a resolved, on optimal decisions regarding emissions of greenhouse gases. Various theoretical models will be developed and tested to answer this question, focusing on different ways uncertainty and learning can be represented. While this project focuses on theory and in advancing our understanding of these learning processes, an ultimate application of the research is in allowing the development of better policy models to help shape U.S. and world options and strategies for managing global warming.
