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Environ. Sci. Technol. 2005, 39, 9750-9758

Life Cycle Assessment of Switchgrass- and Corn Stover-Derived Ethanol-Fueled Automobiles SABRINA SPATARI, YIMIN ZHANG, AND HEATHER L. MACLEAN* Department of Civil Engineering, University of Toronto, 35 St. George St. Toronto, Ontario, Canada M5S 1A4

Utilizing domestically produced cellulose-derived ethanol for the light-duty vehicle fleet can potentially improve the environmental performance and sustainability of the transport and energy sectors of the economy. A life cycle assessment model was developed to examine environmental implications of the production and use of ethanol in automobiles in Ontario, Canada. The results were compared to those of low-sulfur reformulated gasoline (RFG) in a functionally equivalent automobile. Two time frames were evaluated, one near-term (2010), which examines converting a dedicated energy crop (switchgrass) and an agricultural residue (corn stover) to ethanol; and one midterm (2020), which assumes technological improvements in the switchgrass-derived ethanol life cycle. Near-term results show that, compared to a RFG automobile, life cycle greenhouse gas (GHG) emissions are 57% lower for an E85-fueled automobile derived from switchgrass and 65% lower for ethanol from corn stover, on a grams of CO2 equivalent per kilometer basis. Corn stover ethanol exhibits slightly lower life cycle GHG emissions, primarily due to sharing emissions with grain production. Through projected improvements in crop and ethanol yields, results for the mid-term scenario show that GHG emissions could be 25-35% lower than those in 2010 and that, even with anticipated improvements in RFG automobiles, E85 automobiles could still achieve up to 70% lower GHG emissions across the life cycle.

Introduction Fueling United States and Canada light-duty vehicles (LDV, cars and light trucks) with a domestically produced, cellulosederived fuel has the potential to be an attractive route toward improving environmental quality and sustainability of the countries’ energy and transportation sectors. The Province of Ontario is Canada’s largest provincial consumer of energy and highest greenhouse gas (GHG) emitter. In 2003, Ontario’s LDVs represented 37% of Canada’s total LDV fleet (1, 2), which in turn, was responsible for 25% of Canada’s total GHG releases in 2001 (3). As a signatory to the Kyoto Protocol, Canada has pledged to reduce its GHG emissions by 6% below 1990 levels by 2008-2012. However, Canadian GHG emissions have actually risen by 20% since 1990, which means that very significant reductions are required under the Protocol [on the order of 155 Mt/year] (4). Ontario’s LDV * Corresponding author phone: 416-946-5056; fax: 416-978-3674; e-mail: [email protected]. 9750

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fleet’s contribution to GHG emissions may be reduced by gradually substituting fossil fuels with cellulose-derived ethanol. The production and use of cellulosic biomass-derived ethanol have the potential to result in significant abatement of GHG emissions since the carbon released as CO2 from combustion of the fuel would be incorporated into the regrowth of the plant. Cellulosic biomass is the structural material found in herbaceous and woody plants and includes cellulose and hemicellulose, as well as lignin. By use of advanced hydrolysis techniques, the cellulose and hemicellulose fractions of the biomass are converted to sugars, which are then fermented and distilled to ethanol. Lignin is generally assumed to be used as an energy source to drive this process. This paper examines the potential for reducing GHG and selected air pollutant emissions, and fossil fuel energy use associated with Ontario’s LDV fleet through using ethanol derived from an energy crop (a crop grown primarily to provide a feedstock for energy production; in this study, switchgrass) and an agricultural residue (parts of plants not removed from the fields with the primary food or fiber product; in this study, corn stover). It is critical to differentiate cellulose-derived ethanol from the starch-derived ethanol currently produced in the United States/Canada primarily from corn (grain). Starch is a storage polymer consisting of a single sugar (glucose), which is relatively easy to hydrolyze, compared to cellulose, which is a structural polymer and more recalcitrant in nature. Starch-rich grains such as corn do not have the lignin energy source of cellulosic feedstocks such as corn stover, and thus existing starch-based ethanol cannot take advantage of additional energy savings [see refs 5 and 6]. The existing transport fuel infrastructure, which today supports almost exclusively petroleum-based fuels, would require a substantial shift to support significant use of biobased fuels; albeit, ethanol would require fewer changes to the delivery infrastructure than several other options. Uncertainties remain over the attractiveness of ethanol due to issues including biomass availability, net environmental impacts (7), economics, and scalability of new process technologies. A life cycle perspective is useful for determining the net environmental benefits/costs of cellulosic ethanolfueled vehicles compared to other fuel/vehicle alternatives. Existing life cycle-based studies consider pathways for producing ethanol from cellulosic feedstocks and the subsequent use of the fuel in automobiles. Levelton (8) presented a life cycle assessment (LCA) for cellulose-derived ethanol/ gasoline blends in vehicles in Canada and compared these options with gasoline vehicles. MacLean and Lave (9) reviewed a set of LCAs of vehicles fueled with cellulosederived ethanol. Sheehan et al. (10) completed an extensive LCA comparing automobiles fuelled with gasoline/cellulosic ethanol blends with those fueled with gasoline. Fu et al. (11) compared the environmental performance of E10 derived from a balsam fir, with and without energy inputs from lignin, with gasoline and concluded that GHGs can be reduced only if lignin is used for energy in the process (most other studies assume the lignin is used for energy). Overall, all of the studies concluded that there was potential for significant reductions in GHG emissions with the use of cellulosic ethanol-fueled vehicles compared to those fueled with gasoline; however, there was considerable variation in specific results and assumptions in the studies. This is due in part to the lack of commercial cellulosic ethanol production and the lack of cellulosic feedstocks grown specifically for ethanol production. 10.1021/es048293+ CCC: $30.25

 2005 American Chemical Society Published on Web 11/15/2005

FIGURE 1. Life cycle flow diagram for the production of lignocellulosic ethanol and its use in an automobile. Life cycle processes include fuel production, distribution, and use. Cradle-to-gate modules for select process feeds (e.g., electricity and fertilizer) are included in the framework. The current study develops and applies LCA models to cellulosic ethanol production using state-of-the-art data on process technologies applicable to dedicated energy crops and to agricultural residues [although it should be noted that the performance assumed in the study has not been proven at commercial scale (see ref 8 for further details)], and biomass cultivation conditions found in the province of Ontario. Where Sheehan et al. (10) report a high annual production rate of ethanol from a single feedstock (corn stover in the state of Iowa), we expand the scope to examine a region with a more diverse feedstock base and a more northern climate, examining both switchgrass and corn stover-derived ethanol. Unlike the majority of previous studies, we consider multiple time frames and include sensitivity analyses. The LCA model results for emissions of GHGs and criteria air pollutants are presented in this document, while those for energy use are in the Supporting Information (Table S6).

Methodology Life cycle assessment was used to compare the environmental performance of internal combustion engine automobiles fueled with cellulose-derived E85 and low-sulfur (30 ppm) reformulated gasoline (RFG) during two time frames, near(2010) and mid-terms (2020). The near-term time frame is relevant to the stakeholder industries’ near-term planning and GHG emissions abatement regulatory initiatives such as the Kyoto Protocol. The latter, longer-term vision is relevant to the expected transition of the industry, energy security, technological improvements, and in anticipation of future GHG abatement initiatives. We focus on high-level (E85) blends in this work rather than low-level blends such as E10 due to the far more significant fossil fuel displacement and GHG emissions benefits [the latter results are, however, presented in MacLean (12)]. The life cycle models for ethanol derived from switchgrass and corn stover were developed to quantify process inputs and outputs, including energy (total, fossil and renewable),

selected GHG (CO2, CH4, and N2O) and air pollutants (CO, NOx, nonmethane organic compounds (NMOC), SOx, particulate matter (PM) using successive material and energy balances (13). A critical literature review, focused on obtaining high quality data most relevant to the study scope (time frame and geographic location), was presented in MacLean (12). Based on this review, data sets on process flows related to cultivating switchgrass and collecting corn stover, feedstock transportation, fermenting the resulting biomass to produce ethanol, and distributing the ethanol were collected from the scientific literature, and in some cases from specific models on fuel production and conversion, such as GHGenius (14), and GREET (15). In addition, life cycle modules for electricity, industrial fuels, and bulk material transportation were developed for Ontario by the authors. Figure 1 shows the processes considered in the ethanol vehicle LCA models. Near-Term (2010) Model Development for the Production of Ethanol from Switchgrass and Corn Stover. Ethanol production consists of two main sets of processing activities (see Figure 1): those involved in crop production (switchgrass cultivation or corn stover collection in this work) and the activities associated with the production of the ethanol (conversion of the biomass to ethanol). Connecting these two sets of activities is a transportation step for delivering biomass to the ethanol facility, and following conversion, there is distribution of the fuel to demand centers in Ontario. The following section describes the most critical issues in model development, while tables in Supporting Information provide additional details. (A) Feedstock Selection. On the basis of analyses presented in MacLean (12), energy crops and agricultural residues would be significant cellulosic feedstock sources in Ontario, and so a representative energy crop and residue were selected for the work. Switchgrass (Panicum virgatum), a warm-season (C4) perennial grass, has been selected in the United States and Canada as a promising energy crop (16, 17). Switchgrass is native to southern Ontario and is adaptable to most agricultural regions in North America (17) VOL. 39, NO. 24, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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and, on the basis of our evaluation (12), was selected as the energy crop for the present study. Agricultural residues are promising as a feedstock for ethanol production due to their high embodied energy content and the fact that they are a byproduct of conventional crop production. The U.S. Department of Energy has reported that corn stover is expected to become a major resource for bioenergy applications (18). Corn is the grain with the highest annual production in Ontario, and since the amount of residue is approximately equal to that of the corn harvested and less than 1% of the residue is currently removed from the field (19), it has considerable potential as an ethanol feedstock in the Province. However, corn stover is not without its limitations; the current method of collection [a two-pass system, modeled in Sheehan et al. (10)] is not attractive in Ontario due to the wet conditions in the fall after grain harvest. A one-pass collection system that collects the grain and stover together (20) was assumed in this work as it is expected to make stover removal more attractive; however, these systems are not yet commercialized and therefore their performance as well as their economic and environmental implications are uncertain. (B) Electricity and Process Fuel Production Profiles. Life cycle profiles (resource extraction through production) for electricity, process fuels, truck transportation, and agricultural equipment were developed to model activities used in the production of the ethanol. The electricity profile used in all modules in this model was developed specifically for Ontario and based on the breakdown of sources of electricity production taken from the Canadian Electricity Association (21). Table S1 in Supporting Information shows model assumptions, parameters, and data sources for each energy module developed in the life cycle models. (C) Switchgrass Cultivation. This life cycle module, called “crop production”, develops an inventory of energy and related emissions involved in producing switchgrass and associated with the production of process materials such as fertilizer, seeds, herbicide, fuel for operating agricultural machinery, and for truck transportation. Table S2 in Supporting Information provides additional details. Application of N-based fertilizers results in emissions of N2O, a GHG, from soils (direct) and from a more intricate reaction of NOx and NH3 from soils and water (indirect) through the processes of nitrification and denitrification (see ref 22 for further details). In this work, direct and indirect emissions of N2O resulting from fertilizer application were estimated from the Intergovernmental Panel on Climate Change (IPCC) guidelines and calculated as 1.3 and 0.8 kg of N2O ha-1 year-1, respectively, for switchgrass cultivation in Ontario (23). These emissions factors agree with recent studies that have estimated N2O fluxes from northern regions (24). Since switchgrass is not currently cultivated in North America as an energy crop, the resource requirements for its cultivation and yields were estimated on the basis of test plot data and expert judgment. An analysis of data relevant to Ontario is presented in MacLean (12). Yields in test plot areas in Ontario generally range from 7 to 13 ovendry Megagrams per hectare (odMg/ha) (e.g., refs 25 and 26). The yields are highly dependent on agricultural practices and geographic regions of the Province. Agriculture experts suggest that in actual practice crop yields are likely to range between 50% and 80% of test plot yields (27) due to variability in fields, differences in management practices, etc. We consider the 80% value in this study, resulting in a range of 6-10 odMg ha-1 year-1. In this model, we assumed best cultivation and harvesting practices were used throughout the province and set the average yield at 8 odMg ha-1 year-1 (average of the range given above). We assumed the switchgrass (and corn stover in that case) is left to dry on the field by sunlight, as supported by Kim and Dale (28), and 9752

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thus no additional energy was required for drying the feedstock. (D) Agricultural Residue Collection. The “crop production” module for corn stover is essentially a “residue collection” module. The module inventories emissions and energy use associated with collecting the corn stover and applying fertilizer to replace nutrients lost when the stover is removed from the soil. We assume that 62% of the aboveground stover is used for ethanol production and 38% is left on the field to lessen soil erosion and thus assist in maintaining soil carbon [the reader is referred to Lal et al. (29) for additional discussion on soil carbon replacement]. This corn stover removal rate is taken from Shinners et al. (20), which considers practical limitations on stover removal in one-pass harvesting. The rate is slightly more conservative than the 70% of Sheehan et al. (10) but is less conservative than other estimates (e.g., 33%) in the literature (30). Further field studies on one-pass harvesting and on maintaining soil carbon specific to Ontario would be needed to determine whether the 62% is an appropriate removal rate. We allocated the energy and emissions from the one-pass harvesting equipment to the grain and stover in a 1:0.62 ratio. We assumed the replacement of N, P, and K-fertilizer at the rates of 7.5, 2.9, and 12.5 kg/Mg of corn stover removed, as suggested by Kim and Dale (28) for P and K and by Lang (31) for N. Table S2 in Supporting Information provides additional details. (E) Land Use Change and Soil Carbon. The net soil carbon flux, resulting from changes in land use due to the introduction of energy crops, utilized in LCAs of cellulosic ethanol can significantly impact the study results. The studies generally show significant benefits (carbon uptake) resulting from their selection of a net carbon flux parameter. However, the flux of carbon into or out of the soil is guided by a complex set of mechanisms that depend on climate, soil nutrients, existing carbon stock, agricultural management practices, and other factors (29) and developing credible parameter(s) for soil carbon uptake or discharge is challenging. LCAs have generally examined simplified scenarios [e.g., converting idle crop and pasture land to grassland (15) or unimproved farmland to grassland (8)] and estimated a single net carbon flux parameter. In the present study, we assumed, on the basis of analysis in MacLean (12), that portions of current pastureland and land growing conventional crops are converted to switchgrass. It is unlikely that there will be a resulting net carbon flux to the atmosphere from this land use change. In our judgment, it is reasonable to assume no net carbon flux resulting from land use change in developing our LCA model since there is not sufficient data to be confident in modeling the complex process. (F) Feedstock Transportation. We assumed that all feedstock transportation was by diesel truck [based on distances being below 480 km, the cutoff for truck transport being economical (32)]. The switchgrass and corn stover feedstocks are assumed to travel 90 km to the ethanol plant with the truck returning 90 km to the farm carrying an empty load. Primary energy and emissions were taken from NRCan (14), and air pollutant emissions were assumed to remain constant each year. See Table S1 in Supporting Information for further details. (G) Ethanol Production. Biological conversion of cellulosic feedstocks to ethanol is the process receiving the most attention and therefore is the process modeled in our study. The conversion of the dry biomass involves enzyme-catalyzed hydrolysis followed by fermentation and distillation. The ethanol production material balances in our LCA models are based on the most recently projected ethanol conversion technology (for an assumed 2000 dry Mg/day facility) developed by the National Renewable Energy Laboratory (10). This technology was selected due to the study being of high

TABLE 1. Near-Term Life Cycle GHG Emissions for Ethanol (E100) Derived from Switchgrass and Corn Stovera switchgrass

corn stover

GHG comparison

CO2

N2O

CH4

total GHG

CO2

N2O

CH4

total GHG

crop production sequestration transport ethanol production ethanol distribution use net GHG releases

192 -4005 26 2501 5 1509 229

236 0 8 0 2 0 245

12 0 2 0 1 0 15

440 -4005 36 2501 8 1509 489

151 -3986 24 2482 5 1509 185

122 0 7 0 2 0 131

10 0 2 0 1 0 13

283 -3986 34 2482 8 1509 330

a All values are expressed in grams of CO equivalent per liter of ethanol. CO equivalent is calculated based on the 100 year Intergovernmental 2 2 Panel on Climate Change global warming potentials (37); weightings are CO2 (1), N2O (296), and CH4 (23). Totals may not add due to rounding.

quality and peer-reviewed, and due to its transparent presentation of detailed data. The model assumed that sugars produced by both the cellulose and hemicellulose fibers were used for ethanol production. Sheehan et al. (10) report that this assumption should hold for near-term development of this technology at industrial scales. The ethanol conversion facility primarily uses the lignin fraction of the biomass as fuel and a small amount of liquefied petroleum gas. The ethanol facilities produce electricity from the lignin portion of the biomass as a coproduct. Since the model in Sheehan et al. (10) was developed with the assumption of corn stover as the feedstock, we were able to use the data directly for our stover model. For our switchgrass-derived ethanol model, however, we modified the material balance in Sheehan’s model in order to reflect the slight difference in cellulose, hemicellulose, and lignin fractions between switchgrass and corn stover. This modification results in an ethanol yield of 330 L/odMg for switchgrass (compared to 340 L/odMg for corn stover), and CO2 generation of 1065 g of CO2/odMg for switchgrass (compared to 1052 g of CO2/odMg for corn stover). Table S3 in Supporting Information further discusses the ethanol production model. (H) Ethanol Distribution. The distribution of ethanol (end product) to fueling stations was assumed to be by diesel truck and therefore we utilized data similar to that in the feedstock transport modeling. We assumed in the study that all of the ethanol is used in the personal transportation sector in the Province. A transport distance of 46 km was used, based on optimization of the ethanol distribution transport distance in Ontario (33). (I) Use. The use stage of the life cycle model examines the combustion of the fuel in a model year 2006 Chevrolet Impala, a U.S. Environmental Protection Agency (EPA)-designated large size class flexible fuel vehicle (FFV; these vehicles can be fueled with gasoline or any ethanol gasoline blend up to E85). This vehicle will be approximately midway through its lifetime in 2010, the near-term time frame of this study. For the near-term analyses, we utilized the U.S. EPA combined city/highway fuel consumption; 9.0 L/100 km (26 mpg) of gasoline and 12.5 L/100 km (19 mpg) of E85 (the higher fuel consumption is due to ethanol only having about 2/3 the energy density of gasoline). The vehicle is approximately 3% more efficient (on a fuel energy content basis) when running on E85 compared to gasoline. In the mid-term analyses we assumed a 15% improvement in fuel consumption compared to the near-term Impala. The E85 vehicles were fueled with ethanol blended with 30 ppm sulfur RFG. The GHGenius model 2.6B (14) was run to determine the life cycle energy use and emissions from the baseline low-sulfur (30 ppm) RFG vehicles relevant to the two time frames considered. The use-stage GHG emissions for the E85 vehicles were obtained by running GHGenius (14) and specifying relevant vehicle characteristics (e.g., time frame, efficiency, fuel properties). For the regulated vehicle exhaust emissions,

General Motors of Canada Limited provided the authors with Federal Test Procedure emissions certification values [the values reported are those associated with a well-maintained and operating vehicle at the 120 000 mile (402 336 km) useful lifetime] for the Impala when tested with gasoline (30 ppm sulfur) and E85 (34). The vehicle is certified under the U.S. EPA Tier 2 motor vehicle emissions standards (Bin 5): for further information see ref 35. Table S4 in Supporting Information shows the certification levels for the Impala and the Bin 5 standard. Since it is uncertain what the level of emissions will be in 2020, we model only GHG emissions for that time frame. Mid-Term Scenario Development. A mid-term scenario was developed to examine the production of ethanol from switchgrass. Corn stover is not included due to the expectation that energy crops would be the dominant feedstock source for the industry in the longer term. The 2020 time frame considers expected technological advances in both energy crop production and ethanol conversion including increased crop yields, use of genetically modified switchgrass with higher cellulose and hemicellulose fractions based on Wooley et al. (36), higher ethanol conversion efficiency, improved energy efficiency in the production and transportation activities, and lower automobile fuel consumption. Table S5 in Supporting Information summarizes input data for the scenario. Analysis of Parameter Sensitivity. We examined two key uncertainties, feedstock and ethanol conversion yields, and determined the impact on life cycle GHG emissions of varying these parameters over relevant ranges. In the 2010 scenario we assumed a crop yield range of 6-10 odMg ha-1 year-1 with an average yield of 8, which was used in developing the initial model. We assumed ethanol yields of 267-345 L/odMg for switchgrass (from 75% to 95% of the theoretical conversion of cellulose and hemicellulose). In 2020, crop yields were varied from 8 to 13 odMg ha-1 year-1, with an average of 11 (based on the test plot results), and ethanol conversion was varied from 440 to 490 L/odMg (from 85% to 95% of the theoretical conversion of cellulose and hemicellulose), the higher cellulose/hemicellulose contents assumed in 2020 make this higher conversion yield possible.

Results and Discussion Life Cycle Inventory of Ethanol Production. Results for E100 (100% ethanol)-fueled vehicles (although the remainder of our analysis is limited to E85) are presented in Table 1 to illustrate the life cycle results associated solely with ethanol production and use, so as to distinguish these results from those when quantities of gasoline are blended with the ethanol. Table 1 shows the life cycle GHG emissions for E100 derived from switchgrass and corn stover divided among the three global warming gases (CO2, N2O, and CH4). The carbon sequestered during crop growth (4005 and 3986 g of CO2 equiv/L of ethanol for switchgrass and corn stover, respecVOL. 39, NO. 24, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. WTT Comparison of GHG Emissions of University of Toronto (UT), Levelton (7), and Sheehan (10) Models of Cellulosic Feedstock to Ethanol (E100)a feedstock

sequestration crop production transport ethanol production distribution total (WTT)e a

UT switchgrass

UT corn stover

Sheehanb corn stover

Leveltonc switchgrass

Leveltonc corn stover

-4005 440 36 2501 8 -1020

-3986 283 34 2482d 8 -1179

-3974 360 30 2492d 8 -1085

-1609

-1404

b

All values are expressed in grams of CO2 equivalent per liter. Sheehan’s model results were converted from emissions of grams of CO2 equivalent per kilometer to grams of CO2 equivalent per liter by use of a fuel economy of 6.2 km/L for E100, the energy equivalent of their 7.3 km/L for E85. Data were taken from Figures 18 and 19 in ref 10. c WTT results were summed from the following categories: fuel dispensing, fuel storage and distribution, fuel production, feedstock transport, feedstock recovery, land use changes, fertilizer manufacture, emissions displaced by coproducts, and sequestration effects. Since results reported did not all correspond clearly with the production process steps listed in the above table, we do not disaggregate the results in this table. All results were taken from Table 5-7 in ref 8 and converted from grams of CO2 per million British thermal units to grams of CO2 per liter except the sequestration value, which was taken from Table 5-10 in ref 8 and converted from emissions in grams of CO2 per mile to emissions of grams of CO2 per liter by use of the fuel economy 9.0 L/100 km employed by Levelton for 2010. d Differences in GHG emissions for ethanol production between the UT corn stover model and Sheehan’s are due to rounding. e Totals may not add due to rounding.

tively) is calculated on the basis of the CO2 emitted during ethanol production (as a fermentation product and due to combustion of the lignin used for process energy) and ethanol use in the vehicle (combustion). This CO2 released during ethanol production and use is also the largest contributor to GHG emissions across the life cycle but is primarily the result of using the renewable cellulosic feedstock. Since the carbon sequestered during crop growth, the carbon present in the switchgrass and stover (negative values in Table 1), offsets the majority of these emissions, for switchgrass the net contribution to GHG emissions is actually highest from N2O (associated with crop production), while for corn stover, the CO2 contribution is slightly higher. From the perspective of global warming potential, with the assumptions employed in this study, ethanol from corn stover is slightly more attractive than that from switchgrass as reported in Table 1. The life cycle GHG emissions from corn stover-derived ethanol are 330 g of CO2 equiv/L compared to 489 g of CO2 equiv/L for switchgrass-derived ethanol (about 33% reduction). This result is primarily due to the lower demand for energy during crop production for corn stover due to the assumption of the one-pass collection system, which leads to allocating a portion of the collection activities to the grain. Since the stover removal rate is uncertain, we examined a more conservative removal rate (33%) and found that GHG emissions resulting from collecting the stover were not increased significantly (less than 10%) compared to those associated with the 62% stover removal rate. N2O emissions are lower for the corn stover ethanol because less N-fertilizer is required to replace nutrients in the soil when the stover is removed than is required for growing switchgrass. The slightly higher ethanol conversion efficiency of the stover (due to its higher cellulose/hemicellulose content) does not result in significant differences in GHG emissions between the switchgrass and stoverderived ethanol production. We compared our ethanol production [also known as wellto-tank (WTT) analysis] results, which include the process steps crop production, feedstock transport, and ethanol conversion and distribution, with the results of Sheehan (10) and Levelton (8). Table 2 shows E100 WTT results for our switchgrass and corn stover models (UT), Sheehan’s, and Levelton’s. Overall, model results are similar and show large negative values associated with the WTT activities due to the impact of the carbon sequestration associated with the crop production. The comparison shows that the UT results match most closely with Sheehan’s, in part because both studies utilized the same ethanol conversion data. A key difference 9754

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in the results is that we report lower CO2 emissions from crop production of corn stover than does Sheehan. This is due to our assumption of one-pass collection compared to Sheehan’s two-pass system and also due to differences in nutrient replacement assumptions. Other variations in the WTT results are due to differences in model construction, including coproduct allocation procedures, and data sets (ours being specific to Ontario where feasible). Levelton (8) shows larger negative results for GHG emissions than either Sheehan or UT. Sources of the differences include that Levelton’s WTT model reports higher potential for carbon sequestration during crop production (due to assumptions related to carbon uptake due to land use change), lower ethanol conversion energy requirements, and coproduct allocation, all of which lead to lower WTT GHG emissions. Another difference between Levelton and the other studies is that Levelton’s ethanol conversion process is based on the Iogen Corporation process (38), which assumes a higher yield resulting from complete conversion of pentose sugars by the year 2010, unlike Sheehan et al., who posit that 85% of pentose sugars should be convertible in the nearterm. Levelton does not indicate a conversion efficiency for the hexose sugars. A key difference in the Levelton and UT analyses is that the former reports that the production of switchgrass-derived ethanol results in lower emissions of GHG than corn stover-derived ethanol, whereas our results are the opposite. One reason for the variation in the two studies is the differing assumptions about net soil carbon uptake due to land use change. Our study more conservatively assumes no net soil carbon uptake due to land use change while Levelton assumes a considerable benefit (120 g of CO2/L for switchgrass) although the issue is not discussed in detail. Many gaps in research on soil carbon remain (39, 40), and there is considerable uncertainty regarding the impact on soil carbon of converting land in existing uses in Ontario (and elsewhere) to perennial grasslands. This comparison illustrates that assumptions about soil carbon change due to conversion of land to energy crops significantly impact the relative attractiveness of different cellulosic ethanol feedstocks (and could potentially impact the overall attractiveness of cellulosic ethanol) and highlights an important scientific field requiring additional research. Comparison of E85 and Reformulated Gasoline-Fueled Automobiles: Near-Term Scenario. Figure 2 compares the life cycle GHG emissions results for switchgrass- and corn stover-derived E85 and low-sulfur RFG automobiles for the near-term scenario. Figure 2 shows that the E85-fueled automobiles exhibit substantially lower emissions of GHG

FIGURE 2. Life cycle greenhouse gas emission comparison for three fuels: RFG, switchgrass-derived E85 (SG E85), and corn stover-derived E85 (CS E85). Net GHG emissions are 252 g of CO2 equiv/km (RFG), 107 g of CO2 equiv/km (SG E85), and 87 g of CO2 equiv/km (CS E85). Totals may not add due to rounding. as compared with the RFG-fueled automobile. The life cycle GHG emissions of the E85 vehicles are 57% and 65% lower than those of the RFG vehicle for switchgrass- and corn stoverderived ethanol, respectively. The majority of the emissions in all cases are CO2. The emissions associated with the E85 vehicles are largely related to the production and combustion of the gasoline blend portion of the fuel, the application of fertilizer and herbicide during crop growth, and agricultural and transportation activities, which in the model assume the use of diesel fuel. The GHG emissions associated with the gasoline primarily result from the combustion of the fuel and the associated release of carbon, which forms CO2 in the atmosphere. The life cycle air pollutant emissions for the E85- and low sulfur RFG-fueled vehicles are shown in Figure 3. Furthermore, well-to-tank emissions of SOx and PM are presented in Table 3. Vehicle use emissions of SOx and PM are not reported as SOx is not regulated under the Tier 2 standards, and General Motors has a waiver from the U.S. EPA and so is not required to report PM emissions from mainstream (e.g., gasoline and E85) vehicles based on their demonstration of very low emissions from these vehicles (34). Consistently throughout Figure 3 and Table 3, the E85 fuels have higher upstream emissions of air pollutants than RFG, although emissions of NMOC are not significantly different. The NOx and PM predominantly result from the transportation of feedstock by diesel truck to the ethanol production facility. Ethanol production accounts for almost all of the SOx emissions since the model developed by Sheehan et al. (10) and utilized in this study does not include emission controls for sulfur, which is present in the biomass. However, Sheehan et al. state that this is not a deficiency in the ethanol conversion technology since SOx emissions can be reduced through process controls. In addition, the emissions resulting from feedstock transportation could be reduced with appropriate emissions controls and fuel/technology improvements. Emissions from vehicle operation, with the exception of CO from RFG vehicles, are smaller on a grams per kilometer basis than those resulting from upstream activities, emphasizing the importance of a life cycle approach. The certification values for the RFG- and E85-fueled vehicles are very similar and both vehicles easily meet the Tier II Bin 5 emissions standards (see Table S4 in Supporting Information). However, estimating emissions resulting from “realworld driving” (which are the relevant ones for life cycle studies) is very complex. Ross et al. (41) is an example of an oft-cited study that investigated sources of emissions dif-

FIGURE 3. Life cycle air pollutant emissions for the three fuels: RFG, switchgrass-derived E85 (SG E85), and corn stover-derived E85 (CS E85). (a) Net CO emissions are 0.79 g/km (RFG), 1.60 g/km (SG E85), and 1.54 g/km (CS E85). (b) Net NOx emissions are 0.27 g/km (RFG), 1.25 g/km (SG E85), and 1.17 g/km (CS E85). (c) Net NMOC emissions are 0.16 g/km (RFG), 0.20 g/km (SG E85), and 0.19 g/km (CS E85). ferences between certification standards and real-world driving. Our past work discussed challenges of reporting relevant vehicle emissions in life cycle studies (42, 43), and VOL. 39, NO. 24, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Well-to-Tank Emissions of SOx and PM for RFG, E85 (SG), and E85 (CS) SOx (g/km) PM (g/km)

RFG

E85 (SG)

E85 (CS)

0.09 0.02

0.54 0.07

0.53 0.07

research investigating real-world driving emissions is ongoing (44). Due to the unavailability of real-world emissions data, we used test data for the FFV fueled with RFG and E85, assuming that any deviations in real-world driving will have approximately the same effect on both fuels. It is important to be aware, however, of the strict test conditions under which the vehicles are certified and that driving styles, vehicle maintenance, and other factors have been shown to impact emissions. Formaldehyde (HCHO) is the only hazardous air pollutant (air toxic) regulated directly under the Tier 2 standards (see Table S4 in Supporting Information), although the U.S. EPA classifies several other pollutants emitted from motor vehicles as known or probable human carcinogens, including benzene, acetaldehyde, 1,3-butadiene and diesel particulate matter. The U.S. EPA reports that the emissions of the first three of these toxics (which are hydrocarbons) will be reduced through the regulation of hydrocarbons (35). Emissions of these toxics differ between gasoline- and E85-fueled vehicles. Winebrake et al. (45) tested gasoline and E85 vehicles and found that some toxic emissions were lower for E85 (e.g., benzene and 1,3-butadiene) while others were higher (e.g., acetaldehyde). Winebrake et al. weighted the emissions by their relative toxicity and reported that the E85 vehicles demonstrated emissions benefits compared to conventional gasoline, primarily based on benzene being more toxic than acetaldehyde. As additional research becomes available, improvements should be made in estimating real-world emissions from the vehicles. Mid-Term Scenario and Sensitivity Analysis. Table 4 compares the projected life cycle GHG emissions for E85 derived from switchgrass in the mid-term with our nearterm results for E85 from the same feedstock. The table also shows results for the sensitivity analyses for crop and ethanol conversion yields. Using assumptions about technological improvements discussed earlier, we have estimated that by the mid-term time frame, on average, net life cycle GHG emissions for E85 vehicles (switchgrass-derived ethanol) could decline by 27% (from 107 to 79 g of CO2 equiv/km). In analyzing the sensitivity of selected parameters, we find that the variation in crop yields analyzed in this study does not significantly impact the life cycle GHG emissions

in either the near- or mid-term scenarios. Table 4 shows that, in both cases, variations in crop yields caused GHG emissions to change by less than (1% (although the crop yield could significantly impact the economic attractiveness of the feedstock and ethanol production). Ethanol conversion yields have a greater impact on life cycle GHG emissions. For 2010, at the low end of the ethanol conversion yield (75% of the theoretical yield), GHG emissions are 11% higher than the base case; however, on the high end of the conversion efficiency (95% of the theoretical yield), the emissions would be only approximately 1% lower than the base case. This results since the base model assumes an already high conversion efficiency, and therefore there is little room for improving ethanol yields without changing to a feedstock with higher cellulose and hemicellulose compositions. In the mid-term, life cycle GHG emissions are also less sensitive to changes in biomass yields than they are to changes in ethanol yield. The analyses illustrate that, from a process design perspective, sizable reductions in GHG emissions may be achieved through the careful selection of crops having high cellulose and hemicellulose compositions, requiring low energy requirements for production, and that can potentially share those production burdens with other coproducts. The LCAs present a cross-section of the environmental discharges associated with producing ethanol-blended fuels from agricultural cellulosic sources in Ontario. The analyses demonstrate a high potential for reducing GHG emissions and the use of petroleum-based fuels in the personal transportation sector through the production and use of cellulosic ethanol. Even with assumed improvements in efficiency in RFG automobiles in the mid-term, E85 vehicles are estimated to have the potential to reduce life cycle GHG emissions by 70% compared to RFG automobiles. While improvements in the life cycle carbon balance are clearly demonstrated in using ethanol blends, this point is less clear with air pollutant emissions, which would need to improve in both the near- and mid-terms. Utilizing renewable fuels (with low net air pollutant emissions) in the ethanol WTT activities and improving emission controls on all processing steps could address this point. Despite the detail that has been maintained throughout the development and application of the LCA models in this study, there are limitations to the work, which include the uncertainty in the life cycle inventory values associated with energy crops and agricultural residues, due to neither one being utilized for commercial ethanol production, and in the ethanol conversion technology, since it has not been proven at commercial scale and several technology breakthroughs are required to attain the conversion efficiencies assumed in this study. The study also highlights the critical

TABLE 4. Scenario and Parameter Sensitivity Analysis for Life Cycle Greenhouse Gas Emissions for E85 Derived from Switchgrass for Near- and Mid-Terms 2010 parameter variation base case biomass yield base case ethanol yield base case life cycle GHG emissions

8 odMg ha-1 year-1 330 L/Mg

high biomass yield

10 Mg ha-1 year-1

low biomass yield

6 Mg ha-1 year-1

high ethanol yield

340 L/Mg

low ethanol yield

267 L/Mg

a

2010 life cycle GHG emissionsa

2020 parameter variation

2020 life cycle GHG emissionsa

11 odMg ha-1 year-1 470 L/Mg 107.4 g of CO2 equiv/km 107.3 g of CO2 equiv/km (-0.06%) 107.5 g of CO2 equiv/km (+0.1%) 106.1 g of CO2 equiv/km (-1.3%) 119.5 g of CO2 equiv/km (+11.2%)

78.5 g of CO2 equiv/km 13 Mg ha-1 year-1 8 Mg ha-1 year-1 490 L/Mg 440 L/Mg

78.46 g of CO2 equiv/km (-0.03%) 78.54 g of CO2 equiv/km (+0.07%) 77.0 g of CO2 equiv/km (-1.9%) 80.5 g of CO2 equiv/km (+2.5%)

Deviations in life cycle GHG emissions expressed in percent change from the base case are shown in parentheses.

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nature of assumptions regarding net carbon flux due to land use change as well as the importance of allocation procedures between agricultural commodities and the need for further research in these areas. In addition, it is important to consider that other environmental metrics aside from energy use and GHG and air pollutant emissions must be considered in determining the attractiveness of an alternative fuel, and also that economic and social issues need to be analyzed. Further development and commercialization of cellulosic ethanol systems are needed prior to making conclusions regarding the environmental and economic preferability of corn stover and switchgrass-derived ethanol. For cellulose-derived ethanol from agricultural feedstocks to have promise, sufficient quantities of suitable feedstock must be available as well as facilities to convert the feedstock to ethanol within a reasonable distance of the feedstock production area. Feedstock producers would have to be willing to produce energy crops and/or remove a portion of residues from their fields, would have to do so in a sustainable manner, and would have to receive acceptable compensation for the commodities. In addition, the ethanol conversion process must be further developed so as to be able to attract investors for a “first plant” to demonstrate commercial-scale technology and then achieve technological improvements so as to move toward more competitive economics with gasoline and other alternatives (today’s rising gasoline prices are making this more of a reality). Overall, the development of a cellulosic ethanol industry and the use of the fuel in high-level ethanol/gasoline blends in light-duty vehicles are expected to be positive steps in moving to a more sustainable personal transportation sector, a domestic fuel supply, and substantial economic benefits to rural communities. However, realizing these benefits requires the resolution of significant technological, environmental, and, likely most importantly, political uncertainties.

Acknowledgments We thank the Ontario Ministry of Agriculture and Food (OMAF) and the National Science and Engineering Research Council (NSERC) for support. We also thank Aida Perez for research assistance and personnel at OMAF and Natural Resources Canada for their assistance. The authors, however, accept responsibility for the information and views expressed in this article.

Supporting Information Available Data sources for energy modules, model assumptions and parameters for crop production, near-term, model assumptions and parameters for ethanol production from switchgrass and corn stover feedstocks, near-term, Federal Test Procedure 75 emission results for the 2006 Chevrolet Impala E85 flexible fuel vehicle, mid-term (2020) scenario parameters, assumptions, and data sources, and near- (2010) and mid- (2020) term life cycle energy data for E100 fuels derived from switchgrass (SG) and corn stover (CS). This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review November 1, 2004. Revised manuscript received July 29, 2005. Accepted October 12, 2005. ES048293+