The trade-off between short- and long-lived greenhouse gases under uncertainty and learning

Abstract

To find an optimal climate policy we must balance abatement of different greenhouse gases. There is substantial uncertainty about future damages from climate change, but we will learn more over the next few decades. Gases vary in terms of how long they remain in the atmosphere, which means that equivalent pulse emissions have very different climate impacts. Such differences between gases are important in consideration of uncertainty and learning about future damages, but they are disregarded by the conventional concept of Global Warming Potential We have developed a numerical model to analyze how uncertainty and learning affect optimal emissions of both CO2 and CH4. In the model, emissions of these greenhouse gases lead to global temperature increases and production losses. New information about the severity of the climate problem arrives either in 2010 or in 2020. We find that uncertainty causes increased optimal abatement of both gases, compared to the certainty case. This effect amounts to 0.08 oC less expected temperature increase by year 2200. Learning leads to less abatement for both gases since expected future marginal damages from emissions are reduced. This effect is less pronounced for the short-lived CH4.