Integrated assessment models of climate change: Beyond a doubling of CO

Abstract

One of the principal tools in analyzing climate change control policies is integrated assessment modeling. While indispensable for asking logical “what if” questions, such as the cost-effectiveness of alternative policies or the economic efficiency of carbon taxes versus R&D subsidies, integrated assessment models (IAMs) can only produce “answers” that are as good as their underlying assumptions and structural fidelity to a very complex multi-component system. However, due to the complexity of the models, the assumptions underlying the models are often obscured. It is especially important to identify how IAMs treat uncertainty and the value-laden assumptions underlying the analysis.In particular, IAMs have difficulty adequately addressing the issue of uncertainty inherent to the study of climate change, its impacts, and appropriate policy responses. In this chapter, we discuss how uncertainty about climate damages influences the conclusions from IAMs and the policy implications. Specifically, estimating climate damages using information from extreme events, contemporary spatial climate analogs and subjective probability assessments, transients, “imaginable” surprises, adaptation, market distortions and technological change are given as examples of problematic areas that IA modelers need to explicitly address and make transparent of IAMs are to enlighten more than they conceal.