Pathways of Economic Development in an Uncertain Environment: A Finite Scenario Approach to the U.S. Region Under Carbon Emission Restrictions

Prediction of future economic behavior is increasingly important for both public and private economic planning. This prediction is, however, increasingly fraught with difficulties because of the uncertainty surrounding the future state of so many key economic parameters. In this paper we consider how stochastic programming may be a valuable tool in the analysis of these kinds of problems. Using the U.S. region of Alan Manne and Richard Richels Global 2100 five region world trade model and a set of eight future state-of-the-world scenarios, we observe how the development paths of several key variables predicted by stochastic programming differ in interesting ways from the paths predicted using deterministic methods. We conclude that the explicit way in which stochastic programming models uncertainty may prove useful to economic analysis efforts and provide additional insight into the nature of economic development in an uncertain environment.