Publications

Modeling the near-term risk of climate uncertainty : interdependencies among the U.S. states

Backus, George A.; Warren, Drake E.; Tidwell, Vincent C.

Decisions made to address climate change must start with an understanding of the risk of an uncertain future to human systems, which in turn means understanding both the consequence as well as the probability of a climate induced impact occurring. In other words, addressing climate change is an exercise in risk-informed policy making, which implies that there is no single correct answer or even a way to be certain about a single answer; the uncertainty in future climate conditions will always be present and must be taken as a working-condition for decision making. In order to better understand the implications of uncertainty on risk and to provide a near-term rationale for policy interventions, this study estimates the impacts from responses to climate change on U.S. state- and national-level economic activity by employing a risk-assessment methodology for evaluating uncertain future climatic conditions. Using the results from the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report (AR4) as a proxy for climate uncertainty, changes in hydrology over the next 40 years were mapped and then modeled to determine the physical consequences on economic activity and to perform a detailed 70-industry analysis of the economic impacts among the interacting lower-48 states. The analysis determines industry-level effects, employment impacts at the state level, interstate population migration, consequences to personal income, and ramifications for the U.S. trade balance. The conclusions show that the average risk of damage to the U.S. economy from climate change is on the order of $1 trillion over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs. Further analysis shows that an increase in uncertainty raises this risk. This paper will present the methodology behind the approach, a summary of the underlying models, as well as the path forward for improving the approach.