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Oct 27, 2010 - Renewable and low carbon fuel standards being developed at federal and state levels require an estimation of the life cycle carbon inte...
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Environ. Sci. Technol. 2010, 44, 8773–8780

Characterizing Model Uncertainties in the Life Cycle of Lignocellulose-Based Ethanol Fuels S A B R I N A S P A T A R I * ,† A N D HEATHER L. MACLEAN‡ Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States, and University of Toronto, Department of Civil Engineering and School of Public Policy and Governance, 35 St. George Street, Toronto, Ontario Canada M5S 1A4

Received June 20, 2010. Revised manuscript received October 4, 2010. Accepted October 5, 2010.

Renewable and low carbon fuel standards being developed at federal and state levels require an estimation of the life cycle carbon intensity (LCCI) of candidate fuels that can substitute for gasoline, such as second generation bioethanol. Estimating the LCCI of such fuels with a high degree of confidence requires the use of probabilistic methods to account for known sources of uncertainty. We construct life cycle models for the bioconversion of agricultural residue (corn stover) and energy crops (switchgrass) and explicitly examine uncertainty using Monte Carlo simulation. Using statistical methods to identify significant model variables from public data sets and Aspen Plus chemical process models, we estimate stochastic life cycle greenhouse gas (GHG) emissions for the two feedstocks combined with two promising fuel conversion technologies. The approach can be generalized to other biofuel systems. Our results show potentially high and uncertain GHG emissions for switchgrass-ethanol due to uncertain CO2 flux from land use change and N2O flux from N fertilizer. However, corn stoverethanol, with its low-in-magnitude, tight-in-spread LCCI distribution, shows considerable promise for reducing life cycle GHG emissions relative to gasoline and corn-ethanol. Coproducts are important for reducing the LCCI of all ethanol fuels we examine.

Introduction Ethanol produced from domestic biomass may become an important near-term transportation fuel due to its potential to contribute to energy security and greenhouse gas (GHG) abatement goals. In accordance with recent U.S. national policy objectives for developing lignocellulose-based fuel technologies by 2013 (1) and state-level policies to reduce the life cycle carbon intensity (LCCI) of transportation fuels (2), lignocellulosic ethanol offers a means of meeting these objectives through a suite of emerging bioconversion technologies (3-5). Biochemical conversion of lignocelluloseto-ethanol consists of enzymatic hydrolysis and fermentation, preceded and followed by pretreatment, and fractionation, * To whom correspondence should be addressed. Phone: 215571-3557; E-mail: [email protected]. † Department of Civil, Architectural and Environmental Engineering, Drexel University. ‡ University of Toronto, Department of Civil Engineering and School of Public Policy and Governance. 10.1021/es102091a

 2010 American Chemical Society

Published on Web 10/27/2010

respectively, all of which are developing at semicommercial scales. Therefore, there is much uncertainty regarding preferredprocesses,andresultingenvironmentalperformance. While well-to-wheel (WTW) analyses (the term used for life cycle (LC) studies of fuel/vehicle pathways) of ethanolblended fuels produced through biochemical conversion have been conducted for lignocellulosic feedstocks (6-10), including some as meta-analyses (11), most studies focused on energy and selected environmental impacts and in a few cases, health implications caused by air quality risks (12, 13). With few exceptions (14, 15), most WTW studies have considered a single ethanol conversion technology and there has been very limited (16, 17) investigation of uncertainty over the LC, although both of these aspects are changing in recent literature (18). Because the conversion technologies under development have different product yields and process conditions, therefore demanding unique feedstock and process inputs (19), they have varying energy and environmental performance, an aspect that has not been transparently discussed in the WTW literature. Moreover, while several WTW models (e.g., refs 20 and 21,) have the capacity for undertaking Monte Carlo (MC) simulations, most often default (predefined) parameters are used with these models, the distributions are often generalized and not well documented, and in some cases the distributions are aggregated for an entire LC step (e.g., fuel conversion), or the models may not reflect specific production pathways. The above is in spite of each step in the LC being subject to significant uncertainties. Applying MC simulation is challenging, as it requires the utilization of plausible probability distributions for variables, which necessitates investigating specific nuances of the fuel system under investigation, an aspect that is not elaborated upon in modeling tools, their documentation, or the ISO 14040 standards (22) for life cycle assessment (LCA). Although some of the uncertainties in the different processing steps utilized for converting lignocellulose to ethanol have been documented in the literature (5, 23, 24), these have largely not been transferred into WTW models. Instead, most analyses assume that the process for converting lignocellulose to ethanol exhibits high ethanol yields as would be expected from “nth plants” that have overcome cost and performance penalties of first-of-a-kind facilities; and they treat the conversion process as a “black box”. Widely cited North American (20) and European (17) studies assume bioconversion yields from the National Renewable Energy Laboratory (NREL) (25). In many cases, fuel production technologies under development vary considerably from NREL’s design and have different WTW GHG emissions performance, aspects that need to be incorporated into future studies, particularly since low carbon and renewable fuels policies require characterizing the LCCI of fuels. As bioconversion technologies for converting lignocellulose to ethanol are still emerging, it is prudent to identify significant variables in WTW models to then explicitly examine using uncertainty analysis, employing the best available data. In the case of the pretreatment/hydrolysis methods and fermentation organism selection, the best publicly available data at this time come from laboratory and process development (PDU) scale measurements. Our prior analysis (19) focused on uncertainty in ethanol bioconversion and illustrated how performance variation (e.g., ethanol and coproduct yields) and uncertainty affect LC environmental metrics. The objective of the current work is to combine the stochastic biorefinery model (whose development is detailed in ref 19) with a stochastic model of feedstock production, and therefore investigate the extent VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Summary of WTW Pathway Characteristics pathway namea

feedstock

vehicleb

fuel consumption (combined city/highway)c

electricity (R)

E85

8.6 L/100 km, adjusted to be functionally equivalent to RFG-N based on energy density

electricity (R) electricity (R) electricity (R) midterm (MT) pathways

E85 E85 E85

conversion technology

coproducts and credits

near-term (NT) Pathways: DA-CS

Corn Stover

DA-SSCF

AFEX-CS DA-SG AFEX-SG

Corn Stover Switchgrass Switchgrass

AFEX-SSCF DA-SSCF AFEX-SSCF

CBP-R

Switchgrass

adv-AFEX-CBP-R

CBP-I

Switchgrass

RFG-N (20) RFG-M (20)

Crude oil Crude oil

adv-AFEX-CBP-I electricity (I) literature comparison pathwaysa: N/A N/A

corn (20)

Corn grain

corn (26)d

Corn grain

corn (27)d

Corn grain

CS (20)

Corn stover herbaceous crop (Switchgrass)

SG (20)e

87.5% dry mill; 12.5% wet mill

electricity (R)

coproducts displace: corn, soybean meal, nitrogen (urea), and soybean oil

E85

7.2 L/100km adjusted to be functionally equivalent to RFG-M based on energy density

E85 RFG RFG E85

identical to co-products in (20) 80% dry mill; 20% wet mill SSCF

identical to co-products in (20) electricity

E85

SSCF

electricity

E85

6.2 L/100 km 5.2 L/100 km 8.6 L/100 km, adjusted to be functionally equivalent to RFG-N based on energy density

dry DGS coproduct

a Fuel characteristics of the reformulated gasoline (RFG) portion of each E85 fuel were taken from (20). b E85 consists of 81% ethanol and 19% gasoline by volume, although it is marketed as “E85”: the additional gasoline component is based on the presence of the denaturant. All vehicles are displacement-on-demand spark ignition conventional drive vehicles as described in ref 18. E85 vehicles are flexible fuel vehicles. c Fuel economy for near-term vehicles taken as the Corporate Average Fuel Economy (CAFE) standard (28) for model year (MY) 2015 passenger vehicles; we assume that CAFE standards will increase by 2% per year between 2015 to 2025 based on discussion with industry experts, and thus assume fuel economies for the midterm MY 2025 vehicles to be 7.1 L/100km. We adjust the fuel economy for E85 fuels to reflect their lower energy density compared to reference RFG vehicles. Notes: All SSCF technologies use enzymatic hydrolysis; N/A ) not applicable; DA ) dilute acid; AFEX ) ammonia fiber explosion; SSCF ) simultaneous saccharification and cofermentation; R ) Rankine cycle; I ) integrated gasification combined cycle; -N ) near-term, -M ) midterm; DGS ) distillers grains and solubles. d Estimates for ref 26 and ref 27 include indirect land use change (ILUC) adders of 30 and 104 g CO2e/MJ, respectively, estimated by those sources for corn E85 and adjusted to the vehicles we examine. e The herbaceous biomass feedstock in ref 20 has input nutrient requirements and yields similar to switchgrass.

to which model uncertainties throughout the fuel production cycle (feedstock and fuel), affect the LCCI and fossil energy for second generation ethanol. Using the best available data, we use MC simulation to demonstrate the importance of quantifying uncertainties (and gaps described above) in WTW studies of biofuels. We discuss the significance of our results for biofuel policy development.

Methods Model Development. We develop WTW models to analyze fossil energy input, GHG (CO2, CH4, and N2O), and air pollutant [nonmethane organic gases (NMOG), carbon monoxide (CO), nitrogen oxides (NOx), particulate matter less than 10 µm (PM10), and sulfur oxides (SOx)] emissions associated with lignocellulosic E85 fuels and their use in lightduty vehicles (LDV) and compare them with reformulated gasoline (RFG) and corn- and lignocellulose-based E85 fueled vehicles from literature. The pathways are summarized in Table 1. The models assume the lignocellulose-to-ethanol technologies are at commercial scale in the U.S. and technologies are modeled for two time frames; near-term (c. 2015), and midterm (c. 2025), assuming mature technology develops for the latter time frame. The models include 8774

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activities associated with feedstock production, ethanol conversion, fuel blending, all transportation steps throughout the LC, and use of E85 in LDVs. Fossil energy and GHG emissions are normalized to 1 MJ and include the full WTW pathway (fuel cycle and vehicle operation with vehicle fuel economies adjusted according to fuel energy density, in accordance with methods used by 14, 16, 20). The fuel intensity (per MJ) functional unit is chosen as recent policy initiatives (e.g., California’s Low Carbon Fuel Standard/LCFS) have adopted this basis. Energy and emissions associated with infrastructure and capital equipment are not included in the analysis. Air pollutants are estimated per kilometer of travel, taking into consideration fuel energy density and vehicle cycle differences, and are discussed in the Supporting Information (SI). Below we discuss key assumptions on the E85 fuels and uncertainty analysis. See ref 19 for details on the biorefinery models; we summarize key variables and assumptions in the SI. Near-Term Well-to-Tank Models. Two agricultural feedstocks, a crop residue, corn stover (CS) and an energy crop, switchgrass (SG) are examined. To investigate cases in which biofuels do not interfere with high productivity agricultural lands needed for food production, and those that would not

likely induce “indirect” land use change (ILUC) (26), we investigate use of marginal lands including Conservation Reserve Program (CRP) lands and pasture grasslands, that could be converted to perennial SG cultivation. The selected lands are deemed to be marginal due to being erodible or needing special conservation practices. Conversion of such lands to energy crops could result in significant initial direct CO2 emissions (11, 29). To capture direct LUC CO2 emission effects for SG, we consider the land conversion conditions adopted in California’s LCFS (27) for cellulosic crops grown on these marginal lands (0-3.7 Mg CO2 ha-1 year-1, see SI discussion and Table S2) and SG yields from ref 30. Moreover, we estimate direct and indirect N2O emissions from N fertilizer application, residue remaining on the land, and roots using IPCC methods (31). As a crop residue, CS is not likely to induce ILUC CO2, as it does not displace existing crops, nor does it release CO2 directly through its removal. However, in some cases, removing CS can lead to a decline in soil organic carbon (SOC), which can lead to a decline in corn yields (32). We do not consider changes to SOC (sequestration or loss) in this analysis and assume the biogenic carbon sequestered through photosynthesis in the CS biomass is released via the fuel over a short cycle. Only a fraction of the CS is assumed to be removed for fuel production (between 25%-70%), the fraction remaining on the land maintains soil organic carbon (SOC) and prevents soil erosion. We account for replacement inputs of N, P, and K that substitute for quantities in the CS removed for ethanol production and apply a coproduct credit for avoided N2O emissions from the residue removed from the field. See SI (Table S2) for discussion on CS yield, removal fraction, nutrient replacement, and coproduct assumptions. The conversion technology, scaled to 2000 dry Mg day-1, is based on biorefinery models we constructed consisting of two possible pretreatment systems, a dilute acid (DA) system developed at NREL and the ammonia fiber explosion (AFEX) technology developed at Michigan State University. The enzymatic hydrolysis-fermentation technology known as simultaneous saccharification and cofermentation (SSCF) is assumed. SSCF converts pretreated biomass to fermentable sugars using enzymes, and the sugars to ethanol using the organism, Z. mobilis; we assume the enzymes are purchased, however they could be produced on-site. Electricity generated via a Rankine boiler is coproduced (5). We apply system expansion coproduct allocation and assume the coproduct electricity displaces that from the U.S. national average grid because we make no specific assumptions about the locations of the ethanol facilities. However, error is introduced with this assumption due to our not crediting each marginal unit of electricity displaced from the electricity grid served; therefore, we examine our results with and without coproduct credits. Midterm Well-to-Tank Models. Two fuel pathways for the midterm assume mature technologies, a larger capacity facility (5000 dry Mg day-1) and a higher yielding, advanced SG feedstock (33), which result in higher ethanol and electricity yields. SG yields are projected by some researchers to increase by 1.5 to 3.5 dry Mg ha-1 by 2030 depending upon location in the U.S (34).; although others (35) project more conservative yield increases. We assume an average SG yield (15 dry Mg ha-1) and range (10 to 30 dry Mg ha-1) as noted in ref 36 for projections to 2030 and LUC CO2 emissions (0-0.92 Mg CO2 ha-1 year-1, SI discussion and Table S2). An advanced AFEX pretreatment system; an advanced hydrolysis-fermentation design known as consolidated bioprocessing (CBP), with in situ enzyme production; and electricity coproduct generated through either Rankine (R) or integrated gasification combined cycle (I) technology are examined, both equipped with air pollutant emission controls that were not (but could have been) incorporated in the near-term

facilities. We apply system expansion coproduct allocation as described above and assume the same electricity mix as in the near-term, making no assumptions about the future composition of the US mix; and examine mature pathways with and without the electricity credit. Life cycle model variables, statistics and assumptions are discussed in SI. Tank-to-Wheel. We model the use of E85 fuels in LDVs, but do not include vehicle production. We select E85 blends as this is the highest proportion of ethanol that can be utilized in vehicles on the road in the U.S. today and is relevant for comparing with reference gasoline LDVs. Vehicle characteristics are summarized in Table 1, with further details in SI. New vehicles entering use in 2015 are required to meet the U.S. EPA Tier 2 standards (37); we assume that near and midterm vehicles meet Tier 2 Bin 5 and Bin 2 standards, respectively, over an assumed 193 000 km vehicle lifetime. Uncertainty Analysis. We use MC simulation (10 000 iterations) to generate stochastic estimates of the LC fossil energy and GHG emissions for the E85 fuel pathways developed in this research. The resulting estimates represent likely ranges corresponding to the production cycle (WTT) of E85 fuels; we include the vehicle use stage to compare with RFG vehicles, but do not examine vehicle in-use uncertainty. We compare our near-term stochastic models with stochastic estimates of RFG, E85 (corn), and E85 (CS and SG)from literature (20). We exclude a MC simulation of air pollutants (AP) due to insufficient data for assigning statistical parameters across the LC. Factorial design multivariable sensitivity analysis was used to identify significant model parameters and interactions among them for the fuel conversion stage (38). Significant parameters that contribute to feedstock production/collection uncertainty were identified using single-variable sensitivity analysis given the expected low and high ranges observed in literature (see SI). Statistical data that describe the probability distributions for all parameters used in the MC simulation were taken from published literature for the feedstocks and from laboratory and other experimental data for fuel conversion. The above is summarized in SI Tables S2 and S3, which also list literature comparison parameters. The performance of the midterm pathways is only sensitive to uncertain feedstock parameters since ethanol yield is assumed to be high and to not vary significantly as a result of maturity.

Results and Discussion GHG Emission Uncertainties in the Feedstock-Fuel Production Cycle. To understand the influence of significant model parameters prior to aggregation with RFG for the E85 fuel, we compare the variability and uncertainty in GHG emissions for the E100 (100% ethanol) fuel production pathways (well-to-plant (exit)-gate, WTG), disaggregated by major LC stage (Figure 1). All results are presented over a 95% confidence interval (CI). Feedstock Production/Collection and Transport. Feedstock production (for SG) and collection (for CS) is responsible for the largest portion (median range, 16-84 g CO2e MJ-1 for near and midterm models, 60-90% excluding coproduct credits) of WTG GHG emissions. Feedstock transport emissions are small with little variability for all models shown (2-3 g CO2e MJ-1 over the 95% CI). Comparing the two feedstocks, GHG emissions (Figure 1a) for near-term SG are higher and have the larger ranges over the 95% CI (130 g CO2eq MJ-1 and 94 g CO2e MJ -1 with DA and AFEX pretreatments, respectively), due in large part to high N2O emissions associated with N-fertilizer application (magnified by the high global warming potential of N2O), and to the direct LUC CO2 flux for converting marginal lands to SG managed lands. The long tail from the 50th to 97.5th percentile for SG is due to the LUC CO2 flux distribution, whose peak, low and high values are 0.92, 0, and 3.7 Mg CO2 VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Stochastic WTG GHG emissions for E100 produced from CS and SG feedstocks using near-term DA and AFEX technologies; and SG for midterm CBP technologies. Results (a) through (d) show the individual contributions and (e) and (f) sum the WTG GHG emissions from individual contributions including and excluding the electricity credit, respectively. Stochastic estimates based on MC simulation are presented as box and whisker plots. The top of the box represents the 75th percentile, the middle line represents the median (50th percentile) and the bottom of the box represents the 25th percentile. The whiskers correspond to the 2.5th and 97.5th percentiles. Because the magnitude of GHG emissions for the stages noted above differ, they are not presented in one consistent scale. Y-axis scale ranges for (a), (d), (e), and (f) are shown in 20 unit increments, while ranges for (b) and (c) are shown in 2 unit increments. ha-1 year-1, respectively. N2O emissions have a wide span when considering the uncertainty range for perennial grasses estimated using IPCC methods; these emissions widen the feedstock GHG range, particularly for SG. The CS median values are much lower (∼60 g CO2 MJ-1) than SG and have much tighter ranges over the 95% CI due to lower N-fertilizer requirements (for nutrient replacement), credit for CS removal, and that there is no LUC component associated with this feedstock. There is considerable uncertainty in setting appropriate values for LUC CO2 flux for SG on marginal lands as field tests on different marginal soils are ongoing (39) but few 8776

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studies have been completed. LUC CO2 flux is difficult to predict because of the range of soil types, depths and moisture contents expected on marginal sites. Our models assume the SG yields measured by ref 30 on lands that could qualify for the CRP as the best estimates of SG yields on marginal lands. Feedstock yield directly impacts the magnitude of LUC CO2 flux per unit of fuel produced. Over time, as land productivity increases, SG, having a deep rooted plant structure, will recover the CO2 released initially when managed SG cultivation was implemented. Higher assumed SG yield and lower LUC CO2 flux for the midterm compared to near-term pathways factor into the lower median values

and tighter 95% CI and interquartile ranges. A positive CO2 flux could result for a short (10) or long (29) period if marginal lands are put into biomass production; although (29) shows no or very little carbon debt when Prairie biomass replaces abandoned cropland. Hence, there may be cases for marginal lands whereby there is no or little CO2 released when abandoned (marginal) cropland is converted to energy crop production. For this case, the median GHG emission for SG production (for both near- and midterm cases) would be much lower than our estimate, and the uncertainty range may be smaller. The midterm cases are examples of low LUC flux. Sequestration of SOC depends on many factors (land use history, crop rotation requirements, etc.) pertaining to the land on which the SG is grown. Fuel Conversion. GHG emissions associated with ethanol conversion (Figure 1c, medians range from 2-12 g CO2e MJ-1) have little variability over the 95% CI (3-4 g CO2e MJ-1 spread for all models), and contribute less to overall WTG GHG emissions (on the order of 8-33% excluding the coproduct credit) and uncertainty than does feedstock production. The low emissions are due to the assumed use of the lignin fraction to supply on-site steam and electricity for facility operations. If instead, fossil-based energy were required for these operations, then emissions could be considerably higher (assuming no carbon capture). Emissions primarily result from the production of pretreatment and conditioning chemicals, and enzymes that are included in our models, unlike a number of other studies that exclude these (see ref 40). Although fuel conversion is not significant, ethanol yield variability (SI, Table S3) determines the amount of biomass needed per MJ of fuel produced (feedstock production, Figure 1a) and the magnitude of the coproduct credit (Figure 1d), both of which factor into GHG emissions for the feedstock and credits for the electricity coproduct. There is little difference between feedstock (SG or CS with fixed technology) at the fuel conversion stage; only differences among technology (DA vs AFEX) are significant, with the higher yielding AFEX emitting less (2 g CO2e MJ-1) and having a tighter range. Electricity Coproduct. Coproduct credits improve the GHG performance of ethanol due to the displacement of electricity from the highly coal dependent U.S. grid mix. When included, the coproduct credit lowers WTG GHG emissions by a significant margin (median emissions decline by 30-88 g CO2e MJ-1). Variability in the credit is significant for nearterm pathways (25-50 g CO2e MJ-1) due to dependence on feedstock composition (SI, Table S1) and pretreatment, hydrolysis, and fermentation yields (see SI, Table S2 and ref 19); but not for midterm pathways (6-10 g CO2e MJ-1), which exhibit tight ranges for all parameters. The lower ethanol yields of the DA technology assumed in this study (compared with AFEX) lead to larger coproduct credits. While the lignin portion of the feedstock contributes most of the boiler feedstock (for both DA and AFEX), the residual cellulose/hemicellulose from pretreatment and unfermented sugars in a syrup comprise the remaining portion of the boiler feedstock, a portion that increases with decreasing ethanol yield (SI Figure S3 and ref 19). When electricity credits are included in the WTG results, the net GHG intensities are negative for the CS pathways and midterm CBP-I for the entire 95% CI range and for up to the 75th percentile for CBP-R, meaning that GHGs are avoided. The electricity is correlated with ethanol yield: at low ethanol yields the coproduct credit contributes to the wide WTG GHG spread. In the absence of the electricity credit (Figure 1f), the net WTG results are all positive with tighter spreads, compared to the cases with the credits that are dominated by the influence of feedstock production variables. The effect of system expansion coproduct crediting is that the coproduct (under the assumptions of the current study) influences the LCCI more so than the ethanol product.

Comparing DA and AFEX technologies, the median GHG emission of AFEX-CS is lower than DA-CS without the credit, but higher than AFEX-SG when the credit is included. Electricity credit spread depends only on variability in the material balance of biomass conversion (19). Other aspects such as the lignin boiler’s performance at commercial scale, and variation in the electricity mix that is displaced and how this may change over time are not considered. Additionally, the selection of coproduct(s) a facility produces is outside of the scope of this study. While the performance of bioconversion of lignocellulosic feedstock to ethanol is uncertain due to a lack of real-world industrial-scale experience, overall, in the processes we examine, it is the feedstock production and the electricity credit that contribute the most to fuel cycle GHG emissions uncertainty. WTW Comparison of Lignocellulosic E85-fueled LDV with Literature. We compare the WTW GHG emissions (Figure 2a) and fossil energy (Figure 2b) associated with using the E85 fuels in LDVs with results from literature for RFG and corn, CS, and SG E85 fueled LDVs. All pathways are presented and compared over a 95% (CI) (also see SI Table S8). Results are shown on the basis of fuel intensity (per MJ); however, we present results per kilometer of travel in the SI as is customary for WTW analyses. When coproduct credits are excluded from our results, the median GHG emissions for the DA-SG case is higher than RFG; and higher, on par with or lower than corn E85 results from literature (20, 27, 26), respectively. The latter two cited literature studies of E85 include ILUC effects. Our SG pathways have a significantly wider uncertainty range compared with those from literature (20) whether electricity credits are included or not (over the 95% CI, AFEX SG span is 76 g CO2eMJ-1; DA SG span is 109 g CO2eMJ-1), due to large uncertainties in feedstock LUC CO2 and N2O emissions. The wide uncertainty range for our SG models with either technology results in a high LCCI compared to RFG between the 75th and 97.5th percentiles. When including coproduct credits the near-term SG pathways range from lower than to slightly higher than RFG and corn E85. CS GHG emissions are considerably lower than those of SG, RFG, and corn, both with and without the coproduct credit. Even with the expected uncertainties and exclusion of coproduct credits, CS E85 can reduce the LCCI of transportation fuels. While there may be limitations to using residues (e.g., restricted removal rates to maintain SOC, low spatial density across the landscape), the fact that removing CS requires low nutrient replacement input (SI Table S2), which incurs a small release of N2O compared to the energy crop, and that its removal also reduces N2O emissions from the crop residue fraction that would otherwise be left on the land, leads to CS being the lowest-C feedstock we examine. The uncertainty ranges associated with our estimates (with DA or AFEX) are much wider than those in ref 20 due to our higher N2O emissions from N replacement estimated using methods from ref 31, and that synthetic N addition depends on the fraction of CS removed: 5 kg N are added for every Mg CS removed; at the high end of CS removal (70%), crop residue N2O emissions are low, but N-fertilizer addition is high; at low CS removal, the N2O emissions from crop residues are high and dominate N2O emissions, but synthetic N addition is low (kg N ha-1). The compared case from literature (20) does not account for variation in CS removal, nor the dependence of N-addition on varying CS removal (see SI for further discussion). Literature WTW GHG emissions for SG E85 from (20) are low compared to ours due to their model incorporating a large negative CO2 flux (5.3 g CO2eq MJ-1) related to the herbaceous crop’s deep-rooted plant structure, which greatly offsets GHG emissions from N2O; and their assumption that land for newly cultivated SG will come from pasture (61%) VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Stochastic WTW (a) GHG emissions and (b) fossil energy inputs for near-term E85 fuel pathways developed in this work; RFG and corn, CS, SG E85 pathways taken from literature (20); and midterm (mature) E85 fuel pathways developed in this paper compared with RFG from literature (20). Our E85 pathways estimate the WTW GHG emissions with coproduct credit (box-plot appearing first) and without coproduct credits (shaded box-plot appearing second); box-plots for each case are adjacent to one another in the figure. All literature pathways include coproduct credits noted in Table 1. The box and whisker plot shows the 95% confidence interval and interquartile range. The top of the box represents the 75th percentile, the middle line represents the median (50th percentile) and the bottom of the box represents the 25th percentile. The whiskers correspond to the 2.5th and 97.5th percentiles. Pathways are as denoted in Table 1. and cropland (39%), and that they do not account for ILUC effects, which would be expected with the assumption of using cropland rather than marginal lands not in current use (e.g., CRP land). If ILUC uncertainty were factored into the corn E85 result from ref 20, its median GHG emission and range would be higher and wider respectively, than shown. California’s LCFS applied an ILUC “adder” of 30 g CO2e MJ-1 to account for market-induced effects for corn-based E100 fuels. When combined with different mixes of dry and wet milling, production location (e.g., Midwest or California), coproducts sold (wet or dry distillers’ grains), and boiler fuel (natural gas, coal, or biomass cofiring), their estimate for corn E85 could range from 81-104 g CO2e MJ-1 when used in the LDV we examine (SI Table S9). The ILUC adder (104 g CO2e MJ-1) 8778

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estimated by ref 26 results in the highest WTW GHG emission of all pathways (177 g CO2e MJ-1). Fossil energy input results for the E85 fuels are only sensitive to the electricity credit, as indicated by the narrow range when credits are excluded. On the whole, the DA technologies show a wider spread in fossil energy use and GHG emissions relative to the AFEX technologies. In comparing the two, it is the higher variability in sugar generation for the DA technology that causes this difference: compared to the AFEX models, the DA models have lower mean ethanol yields and slightly higher standard deviations (SI Tables S3 and S4), resulting in a more pronounced GHG spread. With the maturing of ethanol conversion technologies and SG feedstocks, even assuming positive CO2 fluxes to the atmosphere from uncertain LUC and N2O emissions, com-

pared to RFG, E85 fuels can reduce the LCCI intensity by 60-106%. Life cycle fossil energy input and GHG emissions can both decline with advances in technology. The advances needed to reach the projections for mature CBP technology require continued research on all aspects of the technology (41). However, as an example of mature technology, uncertainties aside, the CBP-I model demonstrates the best performance (low-LCCI and high ethanol yield) of all ethanol pathways we analyze. WTW Air Pollutant (AP) Emissions. We examined the effect of biorefinery AP emissions in Aspen Plus chemical process designs that include (33), and did not include (5) emission controls and found that WTW APs for E85 vehicles are comparable to RFG vehicles when the biorefinery is equipped with AP controls (See SI Table S12). Installation of emission controls with commercially available equipment is an important step for improving the WTW performance of next generation biofuels, and would be required in the biorefinery’s permitting process. Contrary to Hill’s (12) assumptions, it is unlikely that a biorefinery would be allowed to emit all uncontrolled volatiles, CO or NOx. AP controls increase both the energy and cost of production, but would not significantly impact the fuel’s LCCI. Prospects for Low Carbon Bioethanol. There are many types and sources of uncertainty that influence the LCCI of lignocellulosic ethanol; we examine model uncertainty and demonstrate that direct LUC CO2 in the case of SG, N2O from N fertilizer, and coproduct credits are the most important determinants of LCCI. The biorefinery is contained in space and therefore it is easier, in comparison to the feedstockrelated activities, to control its GHG performance through advances in R&D. As agricultural technologies improve (energy efficiencies, N application, etc.) and new knowledge on the dynamics of biomass CO2 flux is gained, stochastic WTW models should be updated to reflect those changes in LCFS policy. MC simulation is a robust tool for determining whether a fuel will satisfy a regulatory standard and tracking its performance over time, but the feasibility of its practical use in such settings would have to be further investigated. SG ethanol is shown to have higher and more uncertain LCCI compared to CS ethanol. To some extent, N2O emissions from fertilizer usage can be controlled through technology (e.g., by developing crops with low input requirements (8)). LUC-CO2, which can be large and uncertain, is not possible to control through technology. At best, it can be controlled through appropriate attention to land choice. In some cases, energy crops could induce ILUC since most land suitable for growing the crops, even if marginal, could be used for other agricultural purposes. Therefore, careful approaches to land selection for energy crops (to minimize LUC CO2) and improved understanding of C-N flux from marginal lands have potential to partially address these issues, reduce LUC uncertainty, and be updated in WTW models. Lignocellulosic ethanol has advantages over corn ethanol because (1) it can be produced from feedstocks that do not compete for prime agricultural cropland needed for food production; (2) the feedstocks require comparatively lower fertilizer inputs (and therefore have lower N2O emissions); and, (3) the conversion process can make use of the energy in the biomass itself (lignin), thereby substantially lowering net fossil energy inputs. However, lignocellulosic ethanol requires continued research and development to be commercialized. While this study examined energy use and emissions for several promising ethanol production pathways, the method should be applied to other feedstock/conversion technology pathways and additional considerations such as economics, other environmental metrics, and production scale effects must be examined in an overall evaluation of the attractiveness of lignocellulosic ethanol.

Most published LC studies have examined a single conversion technology performing under optimistic conditions not always measured experimentally. We have examined uncertainty in conversion technology and find that even with performance representative of early “semi-commercial” technology, lignocellulosic ethanol is expected to be a promising low-carbon alternative to RFG and corn E85.

Acknowledgments We thank Natural Sciences and Engineering Research Council of Canada, Government of Ontario Early Researcher Award, General Motors, and AUTO21 Network Centre of Excellence for financial support and Dr. Yimin Zhang for comments on the research.

Supporting Information Available Detailed discussion on data, methods, and findings are available free of charge via the Internet at http://pubs.acs.org.

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