This goal of this research is to create a framework for a predictive and dynamic Life Cycle Assessment (LCA) tool. This research is anticipated to result in transformational advances in LCA, including the ability to forecast the stochastic nature of developing systems and incorporating spatial and temporal considerations currently lacking in LCA. A developing switchgrass-to-energy system in the southeastern United States will be used as a case study to show how a sophisticated mathematical approach can ultimately lead to a more robust and effective LCA tool. The project will transfer the latest knowledge from decision theory and stochastic modeling to forecast changing land use patterns and the effect on environmental impacts of bioenergy. Bayesian modeling techniques will be used to predict the decision making behavior of individuals within a developing system. Once a system forecast is determined, life cycle inventory data coupled with a Geographic Information System will be used to determine the change in environmental profile over time. Since the uncertainties of developing systems are inherently large, Monte Carlo Analysis will be used to determine the range of possible values and identify system sensitivity. This research has the potential for the creation of a systematic and rational basis for decision-making regarding bioenergy in the southeastern United States. The Southeastern Bioenergy Research Collaborative, an interdisciplinary group of over twenty researchers, economists, and policy makers will use the results of this study in their ongoing investigations of bioenergy opportunities for fuel production. A major aspect of this project will be the creation of a public educational video regarding economic, environmental, and social aspects of bioenergy development.
