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Life Cycle Analysis (LCA) has long been utilized for decision making about the sustainability of products. LCA provides information about the total emissions generated for a given functional unit of a product, which is utilized by industries or consumers for comparing two products with regards to environmental performance. However, many existing LCAs utilize data that is representative of an average system with regards to life cycle stage, thus providing an aggregate picture. It has been shown that regional variation may lead to large variation in the environmental impacts of a product, specifically dealing with energy consumption, related emissions and resource consumptions. Hence, improving the reliability of LCA results for decision making with regards to environmental performance needs regional models to be incorporated for building a life cycle inventory that is representative of the origin of products from a certain region. In this work, we present the integration of regionalized data from process systems models and other sources to build regional LCA models and quantify the spatial variations per unit of biodiesel produced in the state of Indiana for environmental impact. In order to include regional variation, we have incorporated information about plant capacity for producing biodiesel from North and Central Indiana. The LCA model built is a cradle-to-gate. Once the region-specific models are built, the data were utilized in SimaPro to integrate with upstream processes to perform a life cycle impact assessment (LCIA). We report the results per liter of biodiesel from northern and central Indiana facilities in this work. The impact categories studied were global warming potential (kg CO2 eq) and freshwater eutrophication (kg P eq). While there were a lot of variations at individual county level, both regions had a similar global warming potential impact and the northern region had relatively lower eutrophication impacts.

Spatial Life Cycle Analysis of Soybean-Based Biodiesel Production in Indiana, USA Using Process Modeling