Policy Analysis pubs.acs.org/est
Replacing Gasoline with Corn Ethanol Results in Significant Environmental Problem-Shifting Yi Yang,§ Junghan Bae,† Junbeum Kim,‡ and Sangwon Suh*,§ †
Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, Minnesota 55108, United States Department of Man, Environment and Information Technology, University of Technology of Troyes, Troyes 10010, France § Bren School of Environmental Science and Management, University of California, Santa Barbara, California 93106-5131, United States ‡
S Supporting Information *
ABSTRACT: Previous studies on the life-cycle environmental impacts of corn ethanol and gasoline focused almost exclusively on energy balance and greenhouse gas (GHG) emissions and largely overlooked the influence of regional differences in agricultural practices. This study compares the environmental impact of gasoline and E85 taking into consideration 12 different environmental impacts and regional differences among 19 corngrowing states. Results show that E85 does not outperform gasoline when a wide spectrum of impacts is considered. If the impacts are aggregated using weights developed by the National Institute of Standards and Technology (NIST), overall, E85 generates approximately 6% to 108% (23% on average) greater impact compared with gasoline, depending on where corn is produced, primarily because corn production induces significant eutrophication impacts and requires intensive irrigation. If GHG emissions from the indirect land use changes are considered, the differences increase to between 16% and 118% (33% on average). Our study indicates that replacing gasoline with corn ethanol may only result in shifting the net environmental impacts primarily toward increased eutrophication and greater water scarcity. These results suggest that the environmental criteria used in the Energy Independence and Security Act (EISA) be re-evaluated to include additional categories of environmental impact beyond GHG emissions.
1. INTRODUCTION Earlier studies have shown that corn ethanol, which accounts for more than 90% of the current total bioethanol production in the U.S.,1 generally exhibits a modest reduction in well-to-wheel Greenhouse Gas (GHG) emissions as compared with gasoline.2−8 More recent studies have challenged these conclusions however, suggesting that the increased use of corn for ethanol production results in alterations in the supply and demand balance of various commodities, particularly those of agro-food sectors. Among the possible consequences of such alterations, the potential for land use change, such as the conversion of forest land to cropland to make up for the reduced food supply, has been singled out as a likely source of additional GHG emissions.9,10 If such impacts of land use change are taken into consideration, studies suggest that increasing biofuel production can more than negate its GHG benefits through the release of carbon stored in vegetation and the soil.9,10 The concept of indirect land use change (iLUC) has been a point of dispute however, because of the difficulty in establishing a cause−effect relationship between biofuel production and global LUC dynamics.11−14 Although these previous studies have followed the general framework of life cycle assessment (LCA), they have focused almost exclusively on quantifying the energy balance and GHG emissions associated with biofuels production. Such a narrow © 2012 American Chemical Society
scope in environmental impact contradicts one of the core principles of LCA: whereby the results of an analysis are meant to prevent problem-shifting between different life-cycle stages and among different environmental impact categories.15 The singular emphasis on energy balance and the carbon footprint neglects numerous other impacts associated with biofuels production including those related to irrigation, fertilizer application, and pesticide use for feedstock growth.16,17 In a similar way, the full breadth of impacts attributable to conventional gasoline production, such as natural resource consumption and hazardous pollutant emissions, must also be considered in order to make balanced comparisons to biofuels. Few studies have endeavored to make such comparisons between gasoline and corn ethanol on the basis of such a comprehensive set of environmental impact categories,18,19 with no study in particular including more than a few environmental impact categories and an assessment of their relative importance. This is the first study that has been conducted which makes use of a life cycle inventory (LCI) database that the current Received: Revised: Accepted: Published: 3671
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different sets of weights that reflected the value choice of different stakeholders surveyed in ref 30. This sensitivity analysis was used to evaluate whether the overall conclusions drawn from the study were robust. In Figure 1 and the
authors recently compiled under a project funded by the U.S. Department of Agriculture (USDA). This new LCI database covers a wide array of environmental pressures associated with biofuels production including releases of toxic substances and criteria pollutants to air, water, and land, emissions of nutrients that cause eutrophication, consumption of water, and occupation of land. In this study, we compared the suite of environmental impacts attributable to gasoline with that of corn ethanol produced in 2005. Also included are the GHG emissions as a consequence of possible indirect land use change (iLUC), as well as measures of the uncertainty associated with the calculations. In recognition of the fact that local climate, soil, and topography can all significantly influence the environmental impacts attributable to corn production,7,17,20 we conducted an LCA of corn ethanol at the state level and for each of 19 corn-growing states surveyed by the USDA.21 We also utilized a set of weights developed by an expert panel under the National Institute of Standards and Technology (NIST) to aggregate the environmental impacts for an overall comparison. The objective of this study was to provide a comprehensive comparison of the environmental impacts of corn ethanol and gasoline by considering a broader range of environmental impacts and regional disparity in farm input and yield.
Figure 1. Process flow diagrams of corn ethanol (E85) and gasoline.
2. METHODS This study analyzed the full life-cycle of both gasoline and corn ethanol, including feedstock production, shipment of the feedstock to the refinery, refining/conversion, shipment of the fuel to the refueling station, and vehicle operation. For gasoline, this study reflects the U.S. context in which crude oil is to a large extent imported and refined domestically. For corn ethanol, we conducted LCA at the state level, taking into consideration 19 program states.21 Performing the analysis at this level is essential because the input structure, rate of nutrient loss, and yield from corn cultivation all depend on local climatic, soil, and topographic conditions.20 In addition, we used the amount of fuel needed for 1 km vehicle driven as the basis for comparison. Accordingly, we chose two types of vehicle modeled in the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model developed by the Argonne National Laboratory:22 an average passenger car running on conventional gasoline and a flexible-fuel vehicle (FFV) running on E85 (85% ethanol and 15% conventional gasoline by volume). We used the Ecoinvent database23 and the U.S. Life Cycle Inventory (U.S. LCI)24 for most nondurable inputs. For a more complete delineation of the system boundaries,25 the Comprehensive Environmental Database Archive (CEDA) of the U.S.26 was used to compute the environmental impacts associated with durable goods (e.g., refinery capital) throughout the supply chain. The GREET model was used to cover the vehicle operation stage for both products. When using non-U.S. LCI data, we made our best effort to adapt the inventory flow values to reflect U.S. conditions. For example, on-site environmental emissions such as fertilizer runoff were estimated based on studies specific to U.S. context.27,28 Following the ISO standard,29 characterization, normalization, and weighting were performed. The weights used for aggregating environmental impacts were developed by a panel of experts under a study conducted by NIST of the U.S. government.30 Because of the inherent subjectivity in the weighting process, we conducted a sensitivity analysis using
following subsections we describe details related to the methods and data used to construct the process flows for both systems. 2.1. Corn Production. The key input data, including yield and the on-farm usage of commercial fertilizers and pesticides, were taken from the National Agricultural Statistics Service (NASS) survey database under the U.S. Department of Agriculture (USDA).21 The associated energy usage data for the cultivation and harvesting (including irrigation) of corn were obtained from the Economic Research Service (ERS).31 The major on-site environmental pressures at this life-cycle stage include (1) soil N2O, ammonia, and NOx emissions from the use of nitrogen (N) fertilizer; (2) various air emissions from the combustion of fuels such as diesel and natural gas; (3) nutrient accumulation in water bodies due to the loss of N and P fertilizer; (4) toxic effects from the application of pesticides, (5) the use of water for irrigation; and (6) land occupation. We derived the soil N2O emission values from Ogle et al.,27 a source of state-level estimates for the direct emissions from mineral N additions and for the indirect emissions from N losses through the volatilization, leaching, and runoff of N compounds. With a lack of state-specific data available, we chose to apply a generic ammonia and NOx emission factor for all 19 states based on their N-fertilizer application intensity.32,33 The air emissions from energy combustion were calculated using the emission factors from the GREET model.22 Information on the loss of N and P fertilizers to water bodies was obtained from a study of N and P cycle performed by the Natural Resources Conservation Service (NRCS) of USDA.28 Seven geographic regions were included in the study, which identified loss rates of N varying from 10% to 47% and of P varying from 5% to 21%. We applied these loss rates to each of the 19 states according to their location relative to the seven geographic divisions. Pesticides after application may vaporize, runoff into water, or reside in the soil, each posing different magnitudes of toxic impacts.34 We approximated the relative amounts of pesticides entering these three pathways by using information from relevant studies.35,36 3672
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However, the amount of feed production actually avoided as a result of the additional availability of DGS is difficult to quantify because it involves changes in the balance of supply demand and in prices. Another approach to addressing allocation among multiple outputs is partitioning approach, which uses the physical, chemical, or monetary value of the multiple outputs of a process.42 In partitioning approach, the monetary value of the outputs is commonly used as a basis of allocation42,45 with the rationale for this assumption being that the economic values of the process outputs are the drivers behind the production process. The responsibilities for the environmental impact of the process are therefore defined as being proportional to the value of the outputs.46 The disadvantage of this approach is the fact that the allocation factors change as the market prices of the outputs change. We used economic value-based allocation as the default method in this study, with the average market values of ethanol and coproducts in the past five years being used as the basis of allocation.47 Despite having used this as the default, an additional substitution method was also employed to check the sensitivity of the results to the method of allocation.48 2.3. Logistics of Ethanol. Logistics consists of the shipment of corn and the associated products from farms to plants, from plants to blend terminals, and from terminals to refueling stations. The average distance between farms and plants was assumed to be 61.2 km, and heavy-duty trucks were assumed to represent the primary transport mode.49 Ethanol shipped from refinery plants to blend terminals was assumed to be transported by both rail and truck. Rail was assumed to perform 66% of the task of shipment over an average distance of 482.8 km, and truck was assumed for 34% of the task with an average distance of 128.7 km50,67 (see more discussion in SI). The distribution of ethanol from the blend terminals to the refueling stations was assumed to be performed by trucks over an average distance of 16.1 km.50 2.4. Gasoline LCI. An assessment of the U.S. LCI database24 has shown that the database contains only domestic crude oil extraction and refining data for the U.S. However, a significant amount of crude oil is imported from other countries and crude oil extraction in different areas involves significantly different magnitudes of environmental impact. For instance, GHG emissions (CO2 equivalent) for extracting 1 kg of crude oil vary from 0.72 in Nigeria to as low as 0.02 in Britain.23 To best reflect the life-cycle impact of U.S. gasoline given that U.S. gasoline spans the global supply chain, we supplemented the U.S. LCI database with the Ecoinvent database.23 Ecoinvent includes crude oil data for a number of countries that export crude oil to the U.S. including Saudi Arabia and Russia. We derived a weighted average of these crude oil data, based on the origin of the oil and its share in the U.S. total crude oil demand. We also included the U.S. data from the U.S. LCI. We then connected this weighted average with the refining data from the U.S. LCI, in which average values for transportation and allocation between different petroleum products are included. 2.5. Vehicle Operation. The information for an average passenger car consuming conventional gasoline and a flexiblefuel vehicle (FFV) running on E85 with their emission factors was selected from the GREET model, with fuel economy of both vehicle types being 9.95 km per liter.22 Using the low heating value by GREET, it was assumed that driving 1 km requires about 0.1 L of gasoline or about 0.14 L of E85 for an FFV (0.12 L anhydrous ethanol and 0.02 L gasoline).
Irrigation data by state were taken from the Farm and Ranch Irrigation Survey for the years 2003 and 2008.37 We averaged the irrigation intensity (liter per kg of corn) over the two years to reflect the conditions occurring in 2005 and to diminish the influence of varying factors such as climatic conditions. Land occupation was defined as land intensity: the acreage of land needed to produce one bushel of corn. We used the average of land intensity for corn from 2003 to 2008; an approach similar to that used for irrigation intensity. In addition to these values for on-site emissions, we extracted the corresponding LCI information from Ecoinvent for all inputs such as fertilizer and pesticides at this stage to account for the off-site emissions that were associated with them and which occurred elsewhere along the supply chain. In this study, we treated the carbon uptake by corn grain as a carbon credit to corn ethanol system, and we assumed that the same amount of carbon will be released as CO2 during the vehicle operation phase. We did not take carbon uptake by corn stover as a carbon credit to the corn ethanol system, because cultivation of corn, unlike that of perennial grasses with deep roots such as switchgrass, is reported to add no soil organic carbon (SOC).39 Our treatment of carbon uptake by corn grains, with carbon emissions included at vehicle operation stage (Section 2.5), has the same effect with previous studies counting neither carbon uptake nor emissions.7,8,68,69 More discussion about SOC can be found in Supporting Information (SI). 2.2. Ethanol Conversion and Allocation. A representative conversion technology was chosen for all program states. The dominant conversion technology in the U.S. uses a dry mill powered by natural gas. Such systems are considered to produce over 80% of corn ethanol in the U.S.40 Detailed information on the technology specific natural gas, electricity, and water usage as well as coproduct output, was extracted from a survey by University of Illinois on dry mill plants.40 We believe that the refinery plants investigated in this 2005 survey adequately represent enhanced conversion technologies that have since become widely applied.7 All process inputs such as enzymes that the survey did not cover were supplemented using information from another study that explored the magnitudes of process inputs in the overall life-cycle impacts of ethanol.41 Data on capital investment were taken from Hill et al.,5 the only study known to us that has quantified durable material requirements. We applied the CEDA database to calculate the environmental impacts of capital goods, primarily concrete and steel, throughout the supply chain. These values were then amortized on a per-liter-ethanol basis according to the plant size and lifetime assumptions made in Hill et al.5 Using an approach similar to that used for the corn-production stage, we computed on-site air emissionsprimarily from combustion of natural gasusing associated emission factors.22 When a process produces more than one useful output, the environmental impacts associated with the process and its inputs should be assigned to these multiple outputs. This procedure is referred to as allocation within the LCA community.42,43 The process of ethanol conversion produces not only ethanol but also distiller grains with solubles (DGS) requiring an allocation. A commonly used method of allocation in ethanol-LCA studies has been system expansion or substitution.5,7,42,44,68 DGS can displace other feed, presumably reducing the need for the production of feed. It is therefore reasonable to reward ethanol production with some credit equivalent to the impact avoided by the displacement. 3673
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Figure 2. Comparison of gasoline and E85 by impact category on the basis of per km driven: GW = global warming (with dotted line representing iLUC GHGs), HHC = human health cancer, ACD = acidification, HHR = human health respiratory, HHNC = human health noncancer, OLD = ozone layer depletion, Eut. = eutrophication, SF = smog formation, ET = ecological toxicity, FEC = fossil energy consumption, WU = water use, LO = land occupation. The vertical axis is the weighted environmental impact, a dimensionless variable (see the Methods section, and the numerical information underlying this figure can be found in SI). The error bars show the range of values for a given impact category and reflect the differences among the 19 corn-growing states.
2.6. Indirect Land Use Change. Additional feedstock for biofuels can be produced (1) by increasing yield (intensification) or (2) by expanding the cropland for the feedstock (extensification). Improving agricultural yields may relieve some of the environmental impacts associated with future biofuel production; however, it may also exacerbate eutrophication and water consumption.51 Increases in global agricultural yields have slowed down over the last a few decades,52 suggesting that further increases in intensification are not likely to provide significant benefits. The effect of extensification depends on the state of the land to be converted for biofuel feedstock production. Conversion of abandoned land, for instance, would result in a similar or higher magnitude of impact per liter of ethanol compared to that of the current conditions. Given that cropland has limited potential for expansion within the U.S., increasing corn acreage for biofuel production is likely to involve the conversion of land elsewhere, a process which is referred to here as indirect land use change (iLUC).9 The issue of iLUC has, however, become a point of controversy because of the large uncertainties involved in establishing the exact cause-and-effect chain triggered by U.S. biofuel policies in complex global agricultural markets and associated land use dynamics.13 Quantifying global iLUC has been further challenged by Kim et al.,14 who questioned the extent to which the environmentally conscious biofuel developers in the U.S. should be held accountable for adverse consequences such as the clearing of forest on other continents. Overall, quantifying the responsibility for global land use change and its environmental consequences associated with a nation’s biofuel production has proven to be challenging, increasing model complexity and data requirements.13 In this study, we used the work by Plevin et al.53 to represent a possible case of iLUC. In their work, Plevin and colleagues drew system boundaries to include all key parameters affecting iLUC GHG emissions based on prior studies by, for example,
the U.S. EPA and the California Air Resource Board (CARB).54,55 By assigning probability distributions to all parameters, they estimated that iLUC GHG emissions ranged from 10 to 340 g CO2 equiv MJ−1, with a 95% confidence interval from 21 to 142 CO2 equiv MJ−1. Whereas this 95% confidence interval varies by order of magnitude, with the higher bound close to the initial estimate of iLUC by Searchinger et al.,9 successive studies on iLUC with updated agroeconomic models have converged to the lower bound.54−57 A more recent publication by Wang et al.58 came up with 14 CO2 equiv MJ−1 for iLUC GHG emission. Therefore, we chose the lower bound of Plevin’s 95% confidence interval, 21 CO2 equiv MJ−1 as our iLUC scenario. Given the relatively high uncertainty, we distinguished iLUC emissions from the rest in our results. 2.7. Characterization, Normalization, and Weighting. Characterization, normalization, and weighting are three steps within life cycle impact assessment (LCIA) aimed to help better interpret LCI. The cumulative LCI of our study covers more than 1200 environmental flows in different compartments. Characterization first aggregates LCI results into a dozen categories grounded in their relative impact to that of an indicator chosen for each category. Then, through normalization and weighting, characterized results can be further reduced into a single score based on the relative significance of each impact category. The computation used for this normalization and weighting process is expressed in eq 1 E=
∑ i
Wi × Ci Ni
(1)
where C represents the characterized results, W represents the weighting factors, N represents the normalization references, and i represents each impact category. Characterization factors for LCI results are primarily from the Tool for the Reduction and Assessment of Chemical and 3674
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conversion process, the contribution of irrigation makes lifecycle water use for E85 in CO total about 100 L per km driven, whereas that for gasoline is only 0.35 L. E85’s significant water usage because of irrigation constitutes a threat to local ecosystem functions through stresses on water availability.64 Irrigation also entails additional energy use, particularly electricity and natural gas, for pumping, resulting in higher energy use for states that heavily irrigate in comparison with states with little need of irrigation. These additional uses lead to assorted air emissions and ultimately to the aggravation of E85’s overall life-cycle impact. Our analysis of global warming (GW), inclusive of iLUC, shows that use of E85 on average saves GHG emissions by about 20% when displacing gasoline. This result, which is in agreement with that of a recent study by Wang et al.,58 meets the 20% threshold of the new Renewable Fuel Standard (RFS2) set for renewable fuel.54 If iLUC is excluded from consideration in view of its great uncertainty,53 E85 generates 30−50% less GW impact for most states as compared with gasoline, a result that is in accord with previous research that did not address land use change.7 Some of the earlier studies2,5,6 concluded that corn ethanol’s GHG saving was only 10−20% even without considering iLUC, but Liska et al.7 ascribed the conclusion to the use of outdated data. The result also shows significant variation in E85’s global warming impact among different states. Without accounting for iLUC, E85 in TX shows a global warming impact 93% that of gasoline, which is significantly higher than those of the rest. This difference was not confirmed by the result of Liska et al.:7 corn ethanol’s GHG impact from TX was higher than that of other states by only a few percentage points. This difference is due largely to the way soil N2O was accounted for by the two studies. Liska et al. used a simple linear relationship involving nitrogen application intensity and emission factors from IPCC.33 In contrast, our analysis relied on the work by Ogle et al.,27 which took into account regional differences in topographic and climate conditions when computing N2O emissions from corn growth. As a result, the soil N2O emissions in TX are much higher than those in other states in our study.27 3.2. Overall Comparison. An overall comparison between gasoline and E85 using weighted impact is shown in Figure 3, with the error bar indicating regional variations for E85. In conclusion, despite some trade-offs, E85 generates between 16% and 118% (33% on average) greater aggregate environmental impacts than gasoline when iLUC is taken into consideration. Even if iLUC is not considered, the overall environmental impact of E85 is still 6% to 108% (23% on average) higher than that of gasoline. E85’s heavy impacts on eutrophication, water use, and land occupation outweigh its modest benefits from other impact categories, such as global warming, ecological toxicity, and fossil energy consumption. Given the subjectivity associated with the weighting factors used in this study, a Monte Carlo simulation was performed to check the robustness of the overall conclusion against different choices of weighting factors. The result confirms that the general conclusion that gasoline is more favorable than E85 is robust (see Figure S1). We also applied alternative allocation method based on substitution, and found that it did not change the overall conclusions emerging from our work (Figure S2 for details). 3.3. Discussion. In this study, we compared gasoline and ethanol in terms of their environmental impact by considering a wide spectrum of environmental impacts. Considering all
Other Environmental Impacts (TRACI) model developed for the LCA studies in the U.S.34 by the EPA. TRACI, however, does not cover land occupation and water use, for which characterization factors were adapted from an alternate set of characterization methods.42,59 Normalization references (NRs) for the U.S.,60 with our own updates,66 were incorporated with that for the other two categoriesland occupation and water usebased on total U.S. water usage and surface area.61,62 Weighting factors were taken from Gloria et al.,30 developed by NIST for supporting environmentally preferable purchasing in the U.S. 2.8. Sensitivity Analysis. We tested the sensitivity of the weights on the overall aggregated results using Monte Carlo simulation. The range of each weight was determined based on different sets of weights developed by NIST representing the choice value of different stakeholders. A uniform distribution was used, and the model was run 1000 times (see Table S1 and Figure S1 in SI). As discussed earlier, sensitivity analysis was also conducted for the choice of allocation methods (Table S2 and Figure S2).
3. RESULTS AND DISCUSSION 3.1. Comparison by Impact Category. Figure 2 shows the weighted life-cycle impact of gasoline and E85 per km driven. Overall, gasoline significantly outperforms E85 with respect to eutrophication, water use, and land occupation, with only moderate outperformance being achieved with respect to smog formation and acidification effects. E85 however, has an apparent advantage in terms of fossil energy consumption and global warming, and it also moderately outperforms gasoline with respect to ecological toxicity. The impacts of both gasoline and E85 on ozone layer depletion and cancer and noncancer human health are marginal compared with that on other categories. Additionally, their influences on human health respiratory effect are comparable. The error bars in Figure 2 show the range of environmental impacts across the 19 states considered in this study. The results reveal significant regional variation associated with corn production. This variation is especially marked for eutrophication, water use, land occupation, and global warming: aspects of environmental impact whose variation is largely attributable to differences in regional agricultural practices and to differences in climatic and topographic conditions. As an example of this, note the long error bar for eutrophication shown in Figure 2, which is caused mainly by varying regional nitrogen (N) and phosphorus (P) application intensities and loss rates (Figure S3). The application rates (kg nutrient/kg corn) for N and P in 2005 ranged from 0.014 to 0.026 and from 0.001 to 0.006, respectively, and their rate of loss into water bodies ranged from 10% to 47% and from 5% to 21% per kg nutrient applied. As a result, around 0.94 and 0.10 million tons of N and P, respectively, associated with corn production in the 19 states was estimated to be lost into various aquatic systems, exclusive of other routes of nitrogen loss such as volatilization. These losses accounted for 20% and 14% of the total N and P, respectively, used in these states for corn production in 2005. Another impact category in our study which exhibited substantial variation with respect to E85 was water use; a finding which reinforces those of previous studies.17,63 In states having large impacts on water use, irrigation is identified as the predominant contributor (Figure S4). For example, Colorado (CO) requires more than 400 L of irrigation water to produce one kg of corn. Coupled with the water use in the ethanol 3675
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may play an important role in planning future biofuel feedstock production at minimum environmental costs. The current study only considers dry mills, which is the dominant conversion technology in the U.S.40 Including other ethanol conversion technologies in the analysis may provide further insights into the environmental performance of corn ethanol and its regional variation across states. Given the importance of cultivation stage in the total life-cycle impact of corn ethanol, however, the inclusion of other conversion technologies in the analysis will not affect the central message of our study: that replacing gasoline with corn ethanol may result in problem shifting, especially to eutrophication and local water scarcity. This study highlighted the environmental impacts of corn ethanol in comparison with gasoline. Biofuel policy, however, needs to consider not only the aggregate environmental impacts but also other socio-economic and political issues which were not analyzed in this study. Biofuel’s potentially positive impacts toward, e.g., energy independence, energy security, job creation, and revitalization of rural economy and stabilization of gasoline price, as well as its other adverse impacts toward, e.g., increasing food price, should be duly considered in biofuel policy.
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Figure 3. Aggregate environmental impacts of gasoline and E85, with the error bar reflecting the regional variations for E85. GW = global warming, Eut. = eutrophication, ET = ecological toxicity, FEC = fossil energy consumption, WU = water use, LO = land occupation. Lumped in the The rest category are acidification, smog formation, ozone layer depletion, and human health cancer, noncancer, respiratory effects.
ASSOCIATED CONTENT
S Supporting Information *
Additional material as described in the text. This information is available free of charge via the Internet at http://pubs.acs.org.
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impact categories and their relative importance, our results favor gasoline over ethanol, even after excluding the iLUC effect. Based upon the impact category weighting scheme developed by the NIST panel, E85’s stress on water quality and availability significantly increases its overall impact. Our results also show meaningful regional variation for corn ethanol, thus confirming the need to incorporate spatial attributes in evaluations of agricultural products. According to our study, increasing production of corn ethanol, as mandated by EISA, may exacerbate environmental impacts, especially eutrophication and water availability, which confirms a recent study by Donner and Kucharik.65 The GHG emissions from iLUC considered in this study are inherently uncertain. With iLUC excluded, the difference in the overall environmental impact is dominated by eutrophication, water use, land occupation, and fossil energy consumption. Overall, the environmental impact of E85 is 23% (excluding iLUC) to 33% (including iLUC) higher than that of gasoline. Our study seeks to draw attention to the importance of impacts other than climate change in renewable-energy policies, including Energy Independence and Security Act (EISA), that use narrow environmental criteria. A careful evaluation of environmental impacts other than GHG emissions in those policies would be important to minimize the unintended consequences of biofuel development. Particularly, our analysis shows that addressing eutrophication and water consumption impacts is essential in limiting environmental degradation due to biofuel development. Achieving substantial reductions in the nutrient runoff and water consumption associated with biofuel feedstock production are identified as priorities in the effort to mitigate the overall environmental impact of corn ethanol. For this, our analysis indicates that the significant regional variability in eutrophication and water consumption impacts
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]; phone: (805) 893-7185; fax: (805) 893-7612. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This research was supported in part by the USDA/CSREES and the U.S. Department of Energy under Grant 68-3A75-7614. We are thankful to Mr. Eric Fournier and the three anonymous reviewers for their helpful comments and suggestions.
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REFERENCES
(1) FAPRI. World Agricultural Outlook Database; Food and Agricultural Policy Research Institute: Ames, IA, 2010; http://www. fapri.org/tools/outlook.aspx. (2) Wang, M.; Wu, M.; Huo, H. Life-cycle energy and greenhouse gas emission impacts of different corn ethanol plant types. Environ. Res. Lett. 2007, 2, 024001. (3) Pimentel, D. Ethanol fuels: Energy balance, economics, and environmental impacts are negative. Nat. Resour. Res 2003, 12 (2), 127−134. (4) USDA. The Energy Balance of Corn Ethanol, An update; U.S. Department of Agriculture: Wasthington, DC, 2002; http://www. transportation.anl.gov/pdfs/AF/265.pdf. (5) Hill, J.; Nelson, E.; Tilman, D.; Polasky, S.; Tiffany, D. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl. Acad. Sci., U. S. A. 2006, 103 (30), 11206. (6) Farrell, A.; Plevin, R.; Turner, B.; Jones, A.; O’Hare, M.; Kammen, D. Ethanol can contribute to energy and environmental goals. Science 2006, 311 (5760), 506. 3676
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