1132581 (Xu). This project has two major goals. First, a spatially-explicit agent-based LCA framework will be developed to improve the standard LCA modeling technique by overcoming the issues involved with analyzing emerging technologies with dynamic and evolving supply chains. The achievement of this goal will advance LCA methodology and provide a new tool for environmental sustainability analysis. Second, this improved LCA modeling framework will be applied to the U.S. switchgrass bioenergy system (biofuels and biomass electricity) to examine the future supply chain dynamics and evolution under a variety of policy scenarios and evaluate the associated life cycle environmental impacts. The completion of this work will provide a roadmap for the nation and selected states to achieve bioenergy development goals while meeting market demand and minimizing environmental impact. The proposed spatially-explicit agent-based LCA modeling framework will integrate conventional LCA modeling with an agent-based modeling technique to allow feedback between the environmental intervention database and the dynamics and evolution of supply chain. The model will be spatially-explicit by incorporating geospatial data and tools. The proposed framework addresses some of the grand challenges identified by the LCA community in both bioenergy LCA and LCA methodology in general, and will be generally applicable to other systems beyond the bioenergy case study. The success of this project will improve the state of the LCA method to analyze dynamic, emerging systems. In addition to this methodological advancement, results of this project may directly impact decisions pertaining to bioenergy development. Through this project, we will be able to 1) understand how the entire bioenergy supply chain will respond to different policy interventions, 2) provide decision support information for the development and deployment of biofuels and biomass electricity at national and state levels, 3) guide the biofuel and biomass electricity industries to advance technology development, and 4) educate the next generation of engineers and policy makers for understanding complex issues in the bioenergy system and obtaining multidisciplinary skills. The impacts of this research are potentiallly broader than providing critical information about switchgrass bioenergy development and its environmental impacts. The techniques established using a coupled agent-based model with LCA can be used for any developing system. The method developed in this research can address one of the key challenges of trying to predict the environmental impacts of a non-established system, adding dynamic components that assist in the understanding of developing complex adaptive systems and the aggregate effect of a technology change. The educational impact of this project includes integration of undergraduate and graduate courses and curricula. One PhD student will be trained working on cutting edge research on a topic of intense international interest. Master's students will see aspects of this research integrated into the curriculum of the Engineering Sustainable Systems dual degree program between the UM College of Engineering and School of Natural Resources and Environment. The project team will also participate in the UM UROP program which provides mentoring and research experiences for underrepresented minority undergraduate students. To broadly disseminate research results for educational purposes, the team will develop and submit relevant course modules to the Center for Sustainable Engineering's electronic library for peer review. The models developed in this project will be shared with the scientific community through the OpenABM Consortium.