© 2016 Society of Chemical Industry and John Wiley & Sons, Ltd Correspondence to: Richard Venditti, Department of Forest Biomaterials, College of Natural Resources, North Carolina State University, 1204 Biltmore Hall, Raleigh, NC 27695–8005, USA. E-mail: richard_venditti@ncsu.edu 1 Modeling and Analysis Multi-attribute uncertainty analysis of the life cycle of lignocellulosic feedstock for biofuel production Neethi Rajagopalan, Richard Venditti, Steve Kelley, Jesse Daystar, North Carolina State University, Raleigh, NC, USA Received August 22, 2016; revised October 12, 2016; accepted October 13, 2016 View online at Wiley Online Library (wileyonlinelibrary.com); DOI: 10.1002/bbb.1737; Biofuels, Bioprod. Bioref. (2016) Abstract: Life cycle assessments (LCAs) have become a common tool for measuring the environmental performance of various products and processes. This study reviewed the life cycle of lignocellulosic biomass feedstock-based (pine, eucalyptus, and switchgrass) ethanol biofuels and compared the results to gasoline. Uncertainty analysis of the cradle-to-wheel results was conducted using a pedigree matrix method to address uncertainty of life cycle inventory items. A tool called stochastic multi-attrib- ute analysis for life cycle impact assessment (SMAA-LCIA) was used to interpret the results generated from the LCA. The normalized results showed that gasoline had high environmental impacts in catego- ries such as carcinogenics and global warming, followed by switchgrass. But the use of a single-score metric indicated that all bio-based feedstocks had similar environmental performance, all significantly better than gasoline. The SMAA-LCIA results showed that when all impacts were weighted equally, the preference from highest to lowest was for loblolly pine, eucalyptus, gasoline, and switchgrass. This rank order switched when differing weighting schemes of Producer, User, and LCA Expert were used. Overall, this method highlights the trade-offs associated with the selection of different feedstocks and improves the comparison of overall results amongst fuel sources, which can then be more clearly pre- sented to a decision-maker. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd Supporting information may be found in the online version of this article. Keywords: biofuels; multi-attribute analysis; pedigree matrix; SMAA-LCIA; single score Introduction T he Energy Independence and Security Act of 2007 (EISA) mandated the use of 36 billion gallons of renewable fuels annually by 2022. 1 Te Act also further speci fed that, of the 36 billion gallons, 21 billion gallons must be derived from cellulosic ethanol and other advanced biofuels. Lignocellulosics are the most abundant feedstock available and are commonly found in trees and other plants, including switchgrass. Lignocellulosics can be converted to fuels via a sugar/fermentation pathway or by thermal methods to produce fuels. Biofuels produced from agricultural crops such as corn and soybean (starch and oils starting materials, respectively) cannot meet