Frameworks for Dealing with Climate and Economic Uncertainties in Integrated Assessment Models

IAMs connect physical and social science models to address cross-disciplinary questions, such as how does climate affect economies. There are different types of IAMs. One category of IAMs, for example, derives scenarios for future population, economies, technology and greenhouse gas (GHG) emissions, and explores how these may influence climate variables and, subsequently, the biosphere. These IAMs tend to be large, complex and computationally intensive. As a result, they are typically deterministic, as there is often insufficient information available to define probability distributions for the thousands of parameters of the model. Even if this could be done, computational power is often not sufficient to run large numbers of simulations with varying parameters. Examples of such IAMs include the Integrated Model to Assess the Global Environment (IMAGE) (van Vuuren et al., 2011, p. 6) and the Global Change Assessment Model (GCAM) (Wise et al., 2009). A different category of IAMs primarily estimates the economic costs and benefits of climate change, and then uses cost-benefit analysis (CBA) to assess the relative desirability of different GHG emissions as well as adaptation policies. These IAMs tend to be much smaller and simpler, which means that uncertainties can be explored via Monte Carlo analysis. IAMs in this group sometimes use GHG and socioeconomic scenarios produced by IAMs in the previous category as exogenous inputs, and then generate their own estimates of temperature, sea-level rise (SLR) and the associated economic impacts. Alternatively they may generate the input scenarios themselves using simple internal models, and then use these in other components of the model. Popular examples include the Dynamic Integrated Climate-Economy model (DICE) (Nordhaus, 2017), the Climate Framework for Uncertainty, Negotiation and Distribution (FUND) (Anthoff and Tol, 2014) and Policy Analysis of the Greenhouse Effect (PAGE) (Hope, 2013). This second group of IAMs will o