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The cost of cellulases remains a key issue in the production of cellulosic ethanol, and the impact of enzymes on greenhouse gas ( GHG) emissions of cellulosic ethanol has received little attention. This study evaluates life cycle emissions and cellulase production costs for bioethanol production, considering on-site and off-site production options. A complete enzyme production process was simulated using AspenPlus, generating mass and energy balance information required to calculate GHG emissions and financial metrics. GHG emissions for cellulase production range from 10.2 to 16.0 g CO2 eq g-1 enzyme protein, depending on on-site or off-site production and the method of transportation. Enzyme GHG emissions are predicted to be 258 g CO2 eq. L-1 of ethanol for on-site production, versus 403 g CO2 eq. L-1 for off-site production, based on a 150 MMLY ethanol plant using 11.5 mg enzyme g-1 substrate and a cellulase fermentation yield of 90%. Cellulase production costs were estimated for a range of conditions, including ethanol plant size, enzyme dose and protein yield for on-site production, and enzyme plant size, protein yield and return on investment for off-site production. On-site production costs range between $3.80 and $6.75 kg-1 protein, versus $4.00 to $8.80 kg-1 for off-site production. In both scenarios, the lowest cost corresponds to a 90% protein yield, and a high enzyme demand and production capacity. An enzyme production cost of $4.70 USD kg-1 corresponds to an enzyme cost of 0.46 USD gal-1 ($0.12 L-1) of ethanol in a 150 MMLY plant using 11.5 mg enzyme g-1 substrate. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Highlights • The corn stover ethanol models from GREET and GHGenius were compared. • Life cycle greenhouse gas emissions from baseline models differed by 45 gCO 2 e MJ−1. • Key inputs and assumptions in the models were aligned and made identical. • Life cycle greenhouse gas emissions differed by 1.3 gCO 2 e MJ−1 after alignment. • Variations in emissions persisted for key inputs even after model alignment. Abstract This paper evaluates differences in life cycle greenhouse gas emissions of corn stover ethanol produced in the U.S. Midwest, as estimated by the life cycle-based software GHGenius 4.03a, GREET 2013, and GREET 2015. Life cycle assessments are not typically conducted using more than one software package, but comparisons such as the analysis in this paper provide a critical review of a fuel product system. In particular, differences in the data and assumptions become evident for life cycle stages of the same fuel product when compared between life cycle assessment software packages. Using default settings in the three software packages, life cycle greenhouse gas emissions predictions ranged from 2.75 to 47.8 gCO 2 equivalent per MJ of ethanol (gCO 2 e MJ−1), which presents a difference of as much as 45 gCO 2 e MJ−1. Assumptions regarding nitrogen fertilizer, land management, on/off-site enzyme production, and material/energy inputs included/excluded had substantial effects on life cycle greenhouse gas emissions. A consistent corn stover ethanol pathway using equivalent model assumptions and material/energy inputs was developed and implemented in each of the software packages, resulting in life cycle greenhouse gas emissions ranging from 40.7 to 42.0 gCO 2 e MJ−1. The difference in life cycle greenhouse gas emissions was considerably reduced to 1.3 gCO 2 e MJ−1 at most between software packages; however, individual emissions sources such as nitrogen fertilizer production, fertilizer application, corn steep liquor, glucose, sodium hydroxide, and biomass electricity still exhibit variation between software packages (e.g., up to 7.3 gCO 2 e MJ−1 E100 for equivalent glucose input), mainly due to different emissions factors data. Life cycle greenhouse gas emissions appeared consistent because emissions sources that vary between software packages offset each other. Differences in greenhouse gas emissions determined for corn stover ethanol point to the need to improve the life cycle modelling and replicability of life cycle studies on this biofuel pathway. Such inconsistencies are relevant in a carbon economy because the same product will have a different value in different jurisdictions as a result of differences in life cycle assessment software packages. [ABSTRACT FROM AUTHOR]