Integrated Assessment Models

In the context of climate change, integrated assessment models typically incorporate a climate model of moderate or intermediate complexity with models of the economic system (especially the industrial and energy sectors), land use, agriculture, ecosystems, or other systems or sectors germane to the question being addressed. Rather than focusing on precise projections of key system variables, integrated assessment models are typically used to compare the relative effectiveness and implications of different policy measures (see Chapter 17). Integrated assessment models have been used, for instance, to understand how policies designed to boost production of biofuels may actually increase tropical deforestation and lead to food shortages (e.g., Gurgel et al., 2007) and how policies that limit CO2 from land use and energy use together lead to very different costs and consequences than policies that address energy use alone (e.g., Wise et al., 2009a). Another common use of integrated assessments and integrated assessment models is for "impacts, adaptation, and vulnerability" or IAV assessments, which evaluate the impacts of climate change on specific systems or sectors (e.g., agriculture), including their vulnerability and adaptive capacity, and explore the effectiveness of various response options. IAV assessments can aid in vulnerability and adaptation assessments of the sort described in Theme 3 above.

An additional and valuable role of integrated assessment activities is to help decision makers deal with uncertainty. Three basic approaches to uncertainty analysis have been employed by the integrated assessment community: sensitivity analysis, stochastic simulation, and sequential decision making under uncertainty (DOE, 2009b; Weyant, 2009). The aim of these approaches is not to overcome or reduce uncertainty, but rather to characterize it and help decision makers make informed and robust decisions in the face of uncertainty (Schneider and Kuntz-Duriseti, 2002), for instance by adopting an adaptive risk-management approach to decision making (see Box 3.1). Analytic characterizations of uncertainty can also help to determine the factors or processes that dominate the total uncertainty associated with a specific decision and thus potentially help identify research priorities. For example, while uncertainties in climate sensitivity and future human energy production and consumption are widely appreciated, improved methods for characterizing the uncertainty in other socioeconomic drivers of environmental change are needed. In addition, a set of fully integrated models capable of analyzing policies that unfold sequentially, while taking account of uncertainty, could inform policy design and processes of societal and political judgment, including judgments of acceptable risk.

Enhanced integrated assessment capability, including improved representation of diverse elements of the coupled human-environment system in integrated assessment models, promises benefits across a wide range of scientific fields as well as for supporting decision making. A long-range goal of integrated assessment models is to seamlessly connect models of human activity, GHG emissions, and Earth system processes, including the impacts of climate change on human and natural systems and the feedbacks of changes in these systems on climate change. In addition to improved computational resources and improved understanding of human and environmental systems, integrated assessment modeling would also benefit from model intercom-parison and assessment techniques similar to those employed in models that focus on Earth system processes.

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