Environmental implications of higher ethanol production and use in the U.S.: A literature review. Part II – Biodiversity, land use change, GHG emissions, and sustainability

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Environmental implications of higher ethanol production and use in the U.S.: A literature review. Part II – Biodiversity, land use change, GHG emissions, and sustainability ⁎

S. Kent Hoekman , Amber Broch Division of Atmospheric Sciences, Desert Research Institute (DRI), 2215 Raggio Parkway, Reno, NV 89512, USA

A R T I C L E I N F O

A BS T RAC T

Keywords: Ethanol E20 Biodiversity Land use change GHG emissions Sustainability

To address issues of energy security and greenhouse gas (GHG) mitigation, substantial amounts of corn-derived ethanol are being used in U.S. gasoline. Currently, ethanol comprises 10% of the U.S. gasoline pool (E10), but there is interest in increasing this – possibly doubling the amount currently used. Production of corn ethanol raises several concerns with respect to environmental and ecological impacts. This paper reviews the available literature regarding impacts on biodiversity, land use change (LUC), greenhouse gas (GHG) emissions, and overall sustainability. A companion paper addresses impacts on water, soil, and air quality. We emphasize recent information appearing since comprehensive reports on these topics were issued by the U.S. EPA and NRC/NAS in 2011. The principal environmental and ecological concerns arise from the intensive agricultural activities associated with corn cropping. Expansion of these activities promotes regional mono-culture, which is accompanied by reduced plant and animal biodiversity, and diminished ecosystem functions and services. Extensification of corn cropping into Conservation Reserve Program (CRP) lands is occurring, which raises concerns about erosion, nutrient runoff, and other adverse environmental impacts. Estimating the impacts of increased ethanol on GHG emissions requires sophisticated life cycle assessment (LCA) modeling approaches to account for emissions resulting from land use change (LUC). Although considerable uncertainty remains, recent regulatory modeling by EPA and CARB estimate modest GHG reductions of about 20% from corn ethanol as compared to gasoline. Being a major commodity in global food/feed markets, displacing large amounts of corn to produce ethanol strengthens demand and increases prices. To date, these effects have been small, but there are growing concerns about increased use of staple food products to produce fuels within the context of an expanding global population that faces severe calorie shortages. Tradeoffs regarding environmental, ecological, and social impacts raise questions about the overall sustainability of such an approach.

1. Introduction Ethanol has been utilized as a gasoline blend stock in the U.S. since the early 1980s, although initial volumes were quite small. Greater use of ethanol occurred following passage of the Clean Air Act Amendments (CAAA) of 1990, which created mandates for gasoline oxygenates to address wintertime carbon monoxide (CO) problems and summertime ozone (O3) problems in some urban areas. Ethanol usage increased further in the late 1990's when the other major fuel oxygenate, methyl tertiary butyl ether (MTBE) was phased-out due to environmental concerns and policy changes. Driven by concerns about energy security, the Energy Policy Act (EPAct) of 2005 established a national Renewable Fuel Standard

⁎

(RFS), which boosted demand for ethanol, as it mandated an increase in renewable fuels from 4.0 billion gallons per year (bg/y) in 2006 to 7.5 bg/y in 2012. However, just two years later, the Energy Independence and Security Act (EISA) was adopted, which modified the original RFS requirements (henceforth called RFS2) and further increased the volumetric requirements for renewable fuels [1]. RFS2 requires the total volume of renewable fuel to reach 36 bg/y by the year 2022, while capping the volume of “conventional biofuel” at 15 bg/y. The category of “conventional biofuel” consists nearly exclusively of ethanol derived by fermentation of corn starch (so-called “corn ethanol”). Today's production level of corn ethanol exceeds 14 bg/y, and is sufficient to provide a nationwide supply of E10, which consists of a 10 vol% blend of ethanol in gasoline. Recently, interest has been

Corresponding author. E-mail address: [email protected] (S.K. Hoekman).

http://dx.doi.org/10.1016/j.rser.2017.05.052 Received 7 May 2017; Accepted 12 May 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Hoekman, S.K., Renewable and Sustainable Energy Reviews (2017), http://dx.doi.org/10.1016/j.rser.2017.05.052

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With respect to corn ethanol, however, there is widespread concern of increasing mono-culture, more intensive agriculture, general decline of biodiversity, and other ecological costs [17–20]. In addition, it is believed that land use change, such as conversion of grassland to crops, is a major contributor to loss of diversity [21]. Given these concerns, the Ecological Society of America organized a conference in 2008 on “Ecological Dimensions of Biofuels” [22]. This conference, as well as a subsequent publication by Dale et al. [23], provides a good background on a range of ecosystem and biodiversity issues. In addition, the EPA Triennial Report and the NRC/NAS report on Renewable Fuel Standards are excellent starting points for these discussions [9,10]. In the following sections, the main issues regarding ethanol's impacts on biodiversity and ecosystems are discussed.

expressed in considering higher ethanol blends, such as E20 and above, for purposes of improving fuel economy and reducing greenhouse gas (GHG) emissions [2–5]. If such an increase were to occur in the near term (by 2025), it is clear that additional corn ethanol would be required, as the capacity for producing large amounts of cellulosic ethanol does not exist [6,7]. We estimate that to satisfy both the food/ feed uses of corn and nationwide E20 fuel would require an additional 6 billion bushels of corn, bringing the total annual corn production to about 20 billion bushels, which represents an increase of over 40% from today's level [8]. Production and use of corn-based ethanol as a motor fuel raises numerous concerns regarding environmental and natural resource consequences. Many of these concerns are discussed in two welldocumented reports from 2011: (1) NRC/NAS report, “Renewable Fuel Standard: Potential Economic and Environmental Effects of U.S. Biofuel Policy,” [9] and (2) EPA's report, “Biofuels and the Environment, First Triennial Report to Congress” [10]. Most of the environmental and ecological concerns associated with corn ethanol are attributed to the feedstock production stage; that is, the agricultural production of corn. Lesser impacts of concern are attributed to other stages of the biofuel supply chain – including feedstock transport, biofuel production, biofuel distribution, and final fuel use. The impacts and concerns resulting from today's level of ethanol (~14 bg/y) are reasonably well understood and documented. Many of these issues are summarized and discussed here, with qualitative extrapolation being used to characterize potential impacts resulting from doubling this volume to supply E20 nationwide. In the companion paper, we focused on environmental issues pertaining to water, soil, and air quality [8]. In this paper, we address the implications of increased corn ethanol with respect to biodiversity, land use change, GHG emissions, and sustainability. EPA's Triennial Report provides a convenient framework for systematic evaluation and discussion of the environmental and resource issues of concern related to corn ethanol. This report, which was required under the 2007 EISA, represents not only EPA's understanding of biofuels’ impacts, but also includes input from the U.S. Department of Agriculture (USDA) and the U.S. Department of Energy (DOE). Taken together, the Triennial Report and the aforementioned NRC/NAS report provide an excellent review and summary of environmental and resource conservation issues across the biofuel supply chain as reported in over 500 peer reviewed publications through 2010. In this paper, we follow a similar structure in discussing these issues, but also include more recent information. Furthermore, the EPA Triennial Report does not address the issue of life-cycle GHG impacts of biofuels, even though an important function of EISA is the establishment of GHG reduction thresholds. Rather, life-cycle GHG impacts of biofuels under EISA are described in EPA's Regulatory Impact Analysis (RIA) report, which is considered complementary to the EPA Triennial Report [11]. Here we also review and summarize information regarding life-cycle GHG impacts of ethanol as described in the RIA report and other sources.

2.1. Terrestrial biodiversity Overall terrestrial biodiversity is generally increased when a variety of different plants are growing in one area [20,23]. Hence, any shift towards mono-culture raises concerns about loss of species diversity, particularly loss of vertebrate species. Using a meta-analysis for biofuel crops in the U.S., Fletcher et al. found that vertebrate abundance and diversity were significantly higher in Conservation Reserve Program (CRP) lands than in nearby row crops (corn and soybeans) [24]. (CRP lands have been retired from active agricultural production, and are planted with an approved ground cover.) Furthermore, birds of conservation concern suffered negative effects in corn-cropping areas compared to CRP lands. Dale et al. have shown that several bird species and ducks that had been declining in recent decades increased in abundance once CRP lands were established [23]. Thus, the potential for taking CRP lands out of retirement to grow corn raises biodiversity concerns, although various agricultural management practices could be implemented to mitigate these effects [25]. The National Wildlife Federation (NWF) has focused on the Prairie Pothole Region as being of particular concern with respect to the effects of increased corn agriculture on wildlife and habitat. As shown in the map of in Fig. 1, this region includes parts of Iowa, Minnesota, North Dakota, and South Dakota [26]. The Prairie Pothole Region is of vital importance for waterfowl breeding and migratory bird habitat, and is the main location of remaining prairie lands in the U.S. Driven by incentives to produce more corn ethanol, an additional 3.2 million acres were put into corn production in the Prairie Pothole Region between 2005 and 2007. The NWF study showed that counties experiencing the largest increases in corn also suffered the greatest loss of grassland bird species. In a more recent NWF publication, it was reported that 7.3 million acres were converted from native prairie,

2. Biodiversity and ecosystem effects Biodiversity refers to the “variety and variability among living organisms and the ecological complexes in which they occur” [9,12]. A formal definition of biodiversity developed in 2010 by the Conference on Biological Diversity (CBD) states, “biological diversity means the variability among living organisms from all sources including, inter alia terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species and of ecosystems” [13]. In principle, growth of agricultural feedstocks for biofuels could either enhance or threaten biodiversity, depending upon feedstock type, agricultural management practices, and land cover changes [14]. In addition, biofuel feedstock production can have either direct or indirect effects on biodiversity and ecosystems [15,16].

Fig. 1. Prairie Pothole Region. Source: Brooke et al., National Wildlife Federation (2009) [26].

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[39]. It was concluded that “expansion of biofuel cropping systems in North America is likely to have extensive and in many cases complex impacts on arthropod communities in North American landscapes. Some of these impacts will be beneficial, others harmful, and many will take years to be fully realized.” Besides the direct effects of these insects on the agricultural plants themselves, there are numerous secondary or indirect effects involving birds and other consumers of insects. These authors emphasized the importance of integrated pest management (IPM) systems in dealing with biofuel crop pests. Meehan et al. developed a biocontrol index (BCI) to reflect the ability of natural enemies to control pests in different cropping systems, and determined how BCI changes in relation to expansion of energy crops [40]. BCI is increased by the expansion of perennial energy crops, but is decreased when annual crops (like corn) replace perennial grasslands. The practical outcome of low BCI values is that greater use of pesticides is required to maintain acceptable crop quality and yields. This work was recently extended by Skevas et al., who incorporated BCI values in a spatially-explicit bio-economic model and showed that including insecticide costs increased the price of annual crop residues (corn stover) as compared to perennial crops [41]. A recent report by researchers from the USGS highlighted potential loss of habitat for managed honey bee colonies due to expansion of biofuel crops in the Northern Great Plains (NGP) [42]. This region is used extensively by commercial beekeepers because of the abundance of uncultivated pasture and rangelands, and presence of alfalfa, sunflower, canola, and other desirable forage crops. For reasons of colony health and quality of honey, beekeepers avoid proximity to large-scale corn cropping. These researchers found that between 2006 and 2014, biofuel crop expansion totaling 1.2 million ha occurred around the 18,000 apiaries in North and South Dakota, raising concerns about the decline in suitable habitat for ongoing beekeeping activities. The issue of invasive species is of some concern with respect to biofuel feedstocks [43,44]. The U.S. government defines an invasive species as an “alien (non-native) species whose introduction does, or is likely to cause economic or environmental harm, or harm to human health.” Although the varieties of corn used today are not native to the U.S., there is little risk of these plants becoming invasive. However, there is an indirect relationship between modern corn varieties and the spread of other invasive species. Most modern corn is genetically engineered to resist glyphosate, a commonly used herbicide. It is known that glyphosate resistance has developed in several agricultural weeds, leading to greater invasive behaviors, and requiring the use of more toxic herbicides to control [45]. Of greater concern with respect to invasive species are herbaceous energy crops intended for 2nd generation biofuels – such as switchgrass, miscanthus, sorghum, etc. It has been pointed out that the desirable traits these biofuel crops possess – including rapid growth rates, high yields with minimal inputs of fertilizers and pesticides, drought resistance, and tolerance of poor soils – also typify invasive species [46,47].

rangeland, wetlands, and forest into cropland between 2008 and 2012, with much of this being driven by the increasing demand for corn ethanol [27]. Numerous others have discussed concerns about increased corn production causing stresses on wildlife habitat in general, and bird species in particular [25,28–31]. A recent study in Wisconsin showed bird communities to be reduced in number and diversity in cornfields and grass monocultures as compared to mixed grass fields [32]. Furthermore, birds designated as species of greatest conservation need (SGCN) were particularly deficient in cornfields. These authors stated, “Our results concur with previous studies that suggest an increase in corn production to meet bioenergy demand would be detrimental to grassland bird populations. Bird species of conservation concern may experience greater negative impacts of increased corn production than more common species.” 2.2. Aquatic biodiversity While terrestrial biodiversity concerns are due primarily to habitat disruption resulting from land use change (LUC), aquatic biodiversity concerns are more strongly linked to agricultural management issues involving drainage, sedimentation, and runoff of nutrients and pesticides. An important factor is the extensive network of drainage systems installed in the U.S. Midwest over the past 100 years. As described in a comprehensive review by Blann et al., this network carries sediments, nutrients, and pesticides away from croplands and into surface and ground waters [33]. Another important effect of this artificial drainage is the reduction (or elimination) of wetland and riparian habitats, with resulting impacts on both terrestrial and aquatic species occupying these areas. It has been estimated that wetland losses in the Prairie Pothole Region have reduced populations of wetland-dependent wildlife by at least 50%. A recent study by Muturi et al. found significant differences in mosquito production between corn and miscanthus cropping [34]. This demonstrates the potential for biofuel crops to modify the chemistry of aquatic habitats in ways that may influence mosquito production, and thereby the risk of exposure to mosquito-borne diseases. The major issues of eutrophication and hypoxia resulting from nutrient runoff, and their effects on aquatic biodiversity, are addressed in the companion paper [8]. In addition, high phosphorous concentrations are linked to declines in invertebrate communities [35], and high ammonia nitrogen concentrations are toxic to aquatic animals [10,36]. Blann et al. have concluded that the cumulative aquatic ecosystem effects resulting from extensive agricultural drainage include three major developments: (1) widespread declines in many intolerant species, (2) dramatic shifts in the composition of aquatic communities, and (3) homogenization of aquatic faunal assemblages toward more tolerant, generalist species [33]. As pointed out by NRC/NAS, most of these effects causing loss of aquatic biodiversity derive from many years of agricultural practices, and are not a unique result of bioenergy feedstock production [9]. Nevertheless, a dramatic increase in the amount of corn cropping necessary to produce more ethanol raises concerns about worsening these effects.

2.4. Ecosystem functions and services Ecosystem functions are described as the processes within an ecosystem, while ecosystem services are the benefits that humans receive from ecosystem functions [48,49]. Examples of ecosystem services include water and climate regulation, soil formation, nutrient cycling, and food production [50]. While difficult to quantify the value of these services, it is important to do so in cases involving environmental damage assessment and compensation. Costanza et al. have estimated the annual value of ecosystem services globally to be $33 trillion, which is higher than the value of global gross national product [49]. The United Nations Environment Program has recognized the importance of ecosystem and biodiversity by establishing an international effort called the Economics of Ecosystems and Biodiversity [51]. Boyd and Wainger have defined a process to estimate the value of these ecosystem services using a series of ecosystem benefit indicators (EBIs)

2.3. Other ecosystem effects Several other ecosystem impacts of concern with respect to corn ethanol have been identified by EPA [10]. For example, soil erosion can lead to increased wetland sedimentation, which can prevent germination of otherwise viable seeds. In aquatic ecosystems, additional sediment can also increase water turbidity and temperature, and limit habitat for cold water fish [37]. Continuous cropping can also increase the density of agricultural pests, such as the corn rootworm, thus requiring even greater use of pesticides [38]. In a recent review, Landis and Werling summarized the literature regarding potential impacts of biofuel production upon insect pests 3

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3. CRP and land use change (LUC)

[52]. This approach illustrates the spatial variability of EBIs and highlights the need to evaluate each situation based on its unique set of physical, social, and economic conditions. Dale et al. emphasized the importance of considering the impacts of bioenergy crops on ecosystem services at a regional scale, as this is where the ecological, social, and economic impacts of sustainability are most affected [53]. Several groups have attempted to assess the value of agriculturerelated ecosystem services in the U.S. For example, Losey and Vaughan estimated that insects provide ecosystem services valued at more than $57 billion/year, with $4.5 billion of this being attributed to natural pest control in agricultural crops [54]. More recent work by Landis et al. suggests that these values may be too low. They investigated the reduction of biocontrol services for soybean aphids resulting from increased corn production in four Midwestern states [55]. They found that soybean yields were reduced and more pesticide use was required as corn production increased. The authors concluded that “increased reliance on corn as a biofuel feedstock will have negative impacts on biocontrol services in agricultural landscapes.” In 2000, the U.N. called for a Millennium Ecosystem Assessment (MEA) to understand the consequences of ecosystem change for human well-being, and to establish the scientific basis for actions needed to enhance the conservation and sustainable use of ecosystems [56]. The final report defined ecosystem services in four subcategories: provisioning, regulating, cultural, and supporting. Examples of each type are given in Table 1. The MEA has a global focus, and does not explicitly discuss biofuels, yet many of its findings are relevant to the expansion of corn ethanol in the U.S. One significant finding is that over the past 50 years, humans have changed ecosystems more rapidly and extensively than in any comparable period of time. This is driven largely by growing demands for food, fresh water, timber, fiber, and fuel. In parallel, considerable degradation of ecosystem services has occurred during these 50 years, with further degradation expected during the next 50 years. The major driver for damages in terrestrial systems is changes in land cover – particularly conversion to cropland. For freshwater ecosystems, the main drivers for damage include modifications of water regimes, spread of invasive species, and pollution – particularly high levels of nutrient loadings. These factors are highly relevant to the potential expansion of corn cropping for increased production of ethanol. Some reports have suggested that landscapes for bio-energy feedstock production could be designed to maximize ecosystem benefits [9,57,58]. Jordan et al. described the concept of agricultural multifunctionality, which is defined as “the joint production of standard commodities (e.g., food or fiber) and ecological services” [17]. Defining an optimum balance between bioenergy crop yield and ecosystem services was also explored by Anderson-Teixeira et al., who described the situation as “land sharing” vs. “land sparing” [59]. Polasky et al. utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess relationships between land use changes and ecosystem impacts in Minnesota [60]. Scenarios that maximized agricultural expansion provided the greatest return to landowners, but generated the lowest net societal benefit. In particular, maximizing agricultural expansion resulted in loss of stored carbon, negative impacts on water quality, and a decline in terrestrial biodiversity.

Producing sufficient ethanol to provide nationwide E20 gasoline would require considerable increases in corn production, likely involving both agricultural intensification and extensification. Intensification refers to higher corn production from the same land that is already being used. This can be achieved by use of advanced plant strains; optimization of fertilizer, water, and pesticides; double cropping; and other agricultural practices. Extensification refers to expansion of corn cropping into lands that are currently used for other purposes, thus resulting in land use change (LUC). Both intensification and extensification can have adverse environmental effects [61,62]. A 2011 report from the USDA Economic Research Service illustrates the complexity of LUC, which depends upon individual farmer's decisions, and can vary from expectations and modeled predictions [63]. By means of a detailed Agricultural Resources Management Survey (ARMS), farm-level information was obtained to understand LUC trends involved with the expansion of U.S. corn production from 2000 to 2009. A complex picture emerged, indicating that there were several major contributors to increased corn production – including conversion of other crops (especially soybeans) to corn, conversion of uncultivated land to crops, and the expansion of double cropping. On a global scale, there is concern about large increases in U.S. biofuels resulting in indirect (or induced) LUC (ILUC) in other areas, where agricultural activities may need to expand to compensate for the decline in available commodities from the U.S [64–67]. The issue of ILUC will be addressed in a later section, in connection with life-cycle GHG emissions associated with corn ethanol. In this section, we focus on LUC in the U.S. that is occurring (or predicted to occur) as more corn is grown to increase production of ethanol. 3.1. Conservation reserve program (CRP) One category of LUC that is of particular concern with respect to environmental impacts is CRP land. CRP is a voluntary program, administered by USDA, in which contracts are established with agricultural producers and landowners to retire certain croplands and pasture from production for a period of 10–15 years [68]. When first established in 1985, the primary purpose was to retire lands that were highly erodible. In 1990, eligibility for CRP was broadened to include water quality concerns, and bids to enroll new lands were ranked based on an environmental benefit index (EBI). In 1996, protection of wildlife habitat was included as a criterion for CRP selection, and the EBI ranking system was modified to place equal weight on erosion control, water quality, and wildlife habitat. The amount of land included in CRP has varied over the years. While the original legislation envisioned the program retiring 40–45 million acres, enrollment authority was capped at 38 million acres (about the size of Iowa) in 1996 [68]. In 2002, CRP's enrollment authority was increased to 39.2 million acres. When land is enrolled in the CRP, it is retired from agricultural production, planted with approved ground cover, and managed using approved conservation practices. The most common ground cover adopted by CRP is grasses – especially in corn growing regions. At the end of the CRP contract period, these lands can readily be converted back to crop production or grazing. While there have been some concerns about possible adverse economic and social effects in rural areas, CRP is widely regarded as a success in reducing soil erosion, enhancing wildlife populations, and protecting soil quality. Additional benefits likely accrue to recreational areas, public works, and industrial operations that are positively affected by reduced sediment loadings, landscape amenities, and surface/ground water improvements [69]. In a 2004 review of CRP's impacts, it was determined that approximately 34% of the total economic benefits of CRP were realized in the Corn Belt, although only 14.7% of CRP lands were located in this region [68]. An

Table 1 Categories of Ecosystem Services. (Taken from Millennium Ecosystem Assessment [56]). Provisioning

Regulating

• Food • Climate regulation water • Fresh and fiber • Flood regulation • Wood • Fuel • Disease regulation • Water purification

Cultural

Supporting

cycling • Aesthetic • Nutrient Soil formation • Spiritual • • Educational • Primary • Recreational production

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Recently, Clark et al. utilized the DOE/NREL Biomass Scenario Model (BSM) to assess possible LUC involving CRP lands to meet the ethanol requirements in RFS2 [73]. It was found that converting CRP to conventional biofuel feedstocks (corn and soybeans) had more severe negative environmental impacts than did conversion to lignocellulosic feedstocks. At the same time, converting CRP lands to row crops did not provide any benefits in terms of biofuel production.

econometric assessment was also conducted to determine LUC that would occur if the CRP program were to expire. Modeling results predicted that 51% of total land enrolled in CRP would return to crop production within one year, with an 80% return rate to cropland in the Corn Belt. This highlights the concern that increased demand for corn ethanol will lead to retirement of CRP lands, and thus loss of the environmental benefits currently provided by these lands. In a later USDA report, the roles of economics and policy upon LUC and its environmental effects were examined [70]. The authors point out that while total U.S. cropland acreage has remained nearly constant over the past 100 years, (at about 130 million acres) there has been a considerable amount of LUC, as individual lands go into and out of crop production. They also highlighted the off-setting incentives between different agricultural policies. In particular, the CRP incentivizes removal of cropland from production to achieve environmental goals, while crop insurance programs incentivize expansion of cropland into environmentally sensitive areas. This study also concluded that lands enrolled in CRP are generally less productive than lands shifting into and out of crop production, and if they were returned to cultivation, these lands would be more vulnerable to erosion. In 2009, Secchi et al. examined the potential loss of CRP lands in Iowa to increased corn cropping in response to higher ethanol production [18]. CRP land supply curves were constructed for various corn prices, and environmental impacts of increased cropping were estimated using the Environmental Policy Integrated Climate (EPIC) model. (EPIC provides edge-of-field estimates of soil erosion, nutrient loss, carbon sequestration, and other environmental indicators.) Adverse impacts of soil erosion and nutrient loss increased dramatically as incrementally higher corn prices bring into production more and more environmentally fragile land. To avoid this, the authors concluded that higher CRP rental rates would be necessary to keep more lands in the CRP program, and suggested that a shift from “whole field” enrollment to a focus on high priority “buffer” practices would be necessary. A follow-up study investigated the land use impacts of ethanol expansion in Iowa on both the intensive and extensive margins, and the environmental consequences of such LUC [71]. [Intensive margin refers to currently cropped land being converted from non-corn (mostly soybean) to corn; extensive margin refers to non-cropped land (mostly CRP land) being converted to corn cropping.] A field-level discrete economic model of crop rotation and land use management was used to predict LUC under three different corn price scenarios: $108, $142, and $167 per tonne (roughly $2.74, $3.60, and $4.24 per bushel). The LUC results were then input into a field level environmental model (EPIC) to estimate impacts such as erosion, nutrient loss, and carbon sequestration in the soil. Modeling results showed that as the price of corn increased, greater corn production occurred on both the intensive and extensive margins, while all environmental indicators worsened. The authors concluded that “returning CRP land into production has a vastly disproportionate environmental impact, as non-cropped land shows much higher negative marginal environmental effects when brought back to row crop production.” Fargione et al. reported that between 2006 and 2007, U.S. corn acreage increased by 6.2 million ha, while soybean acreage decreased by 4.4 million ha [66]. Thus, it appears that most LUC to accommodate increased corn production occurred through crop switching, which is believed to have minimal adverse environmental consequences. However, in the fall of 2007, nearly 1 million ha left the CRP program, raising concerns about the potential for more substantial biodiversity and environmental harm [65,72]. More recent data show that between 2007 and 2016, CRP enrollment dropped from 37 to 24 million acres [27]. Fargione et al. also laid out a framework for assessing the impacts of increased biofuels on wildlife, and suggested a variety of mitigation measures [72]. They pointed out the clear benefits that CRP lands currently provide for birds, fish, and freshwater ecosystems in general, and cautioned that these benefits will erode as CRP land is lost to increased corn cropping.

3.2. LUC assessments In a 2011 paper, Langpap and Wu integrated economic and physical models to assess how increased corn commodity prices (driven by increased ethanol demand) would affect land use and cropping systems in the Midwest [74]. This study determined that increases in corn prices would result in widespread conversion of non-cropland to cropland (both CRP and other lands) as well as substantial increases in continuous corn rotation acres. With corn prices at $6/bushel, 50% of the region's pasture and range land would be converted to cropland, and total corn acreage would increase by 23%. The authors also assessed the environmental harm caused by such LUC, noting in particular increased use of fertilizer and pesticides, increased erosion and nutrient runoff, and loss of SOC. To predict the locations of LUC to accommodate increased corn cropping, Evans et al. utilized species distribution models (SDM), which are typically employed to estimate habitat suitability for plant and animal species in natural areas [75]. The models function by overlaying location records for the species of interest across a series of GIS layers containing socio-economic and environmental information. The authors found that such models were able to predict (on a countyscale basis) the 24% increase in corn cropping that occurred in the U.S. between 2006 and 2007. A detailed EPA study regarding LUC in the Midwest driven by biofuel mandates was published by Mehaffey et al. in 2012 [76]. A spatially explicit classification of the Midwest landscape was combined with an econometric model from FAPRI (Food and Agricultural Policy Research Institute). This provided a baseline to develop future landscapes that would accommodate increased biofuels production. The objective was to define changes in agricultural practices and land use that would enable transition from the 2001 baseline situation to a 2020 biofuel target. (While not explicitly stated, the 2020 biofuel target appears similar to the 36 bg/y biofuels requirement under the EISA.) This EPA study included three important assumptions: (1) the majority of the mandated ethanol would come from corn grain, (2) conversion to continuous corn would occur first in areas with the most fertile soil, and (3) shifts in crop rotational practice would occur before any shifts to CRP or pastureland occurred. Predicted changes in cropping acres and corn production in the top 12 corn-producing states are presented in Table 2. Dramatic increases in continuous corn cropping were predicted, such that total corn yield in many states doubled between the 2001 base case and the 2020 biofuel target case. Very little change in use of CRP lands was predicted. Nevertheless, the authors cautioned that continuous corn planting will create its own set of environmental concerns. For example, removing legumes as part of the rotation will likely require increased fertilizer application. Without greater use of conservation practices, this would be expected to increase nutrient loads to streams and GHG emissions to the atmosphere, degrading both water and air quality. Wright and Wimberly investigated land cover/land use change (LCLUC) patterns from 2006 to 2011 in the Western Corn Belt (WCB), which consists of five States: North Dakota, South Dakota, Minnesota, Iowa, and Nebraska [77]. They examined conversion of grassland to crops and conversion of crops to grassland using the USDA Cropland Data Layer (CDL), which is a digital land use/land cover map produced annually from high-resolution satellite imagery. While significant LCLUC occurred in both directions, the net change in each state was a loss of grasslands. In the five states combined, a total of 1.3 million 5

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within cotton growing states expanded by 1.3 million ha. Both cotton and corn are considered to be “high impact crops.” It is generally thought that the environmental impacts of LUC when converting from one high impact crop to another are negligible [63]. These authors showed that this is largely true when considering average corn in the Midwest and average cotton in the South. However, due to the poor suitability of southern soils and climate for corn, most environmental impacts are worsened when cotton is replaced with corn. This study illustrates the importance of considering marginal LUC impacts, not just average impacts. Babcock and Iqbal examined LUC on a more global level, comparing average land use in 2004–2006 to land use in 2010–2012 [81]. They determined that other than in Argentina, Indonesia, and some African countries, the LUC that occurred between these two periods was dominated by intensification, not extensification. In other words, expansion into new land areas was limited, and the primary change was to use existing land resources more effectively – by double cropping and crop shifting strategies. The authors pointed out that in the U.S., the amount of land enrolled in the CRP declined by 4 million hectares from 2007 to 2013, which is a much larger area than required for new crop production, so presumably, there is now an abundance of ex-CRP land that is available for future planting. Lark et al. used USDA's CDL to investigate LUC throughout the U.S. from 2008 to 2012 [82]. By utilizing additional data sources, these researchers were able to attain much greater crop detail and higher spatial specificity than in earlier studies. It was found that during this time period, total net cropland increased by 2.98 million acres, while gross land conversion was nearly four times this amount. About 7.3 million acres that had been uncultivated since 2001 or before were converted to crop production, with corn being the most common crop planted directly on these new lands. Some specific areas of crop expansion are cause for environmental concern – including steeply sloped lands in southern Iowa and northern Missouri, heavily irrigated regions of western Kansas, and other areas above the rapidly-depleting Ogallala aquifer. This analysis indicated that up to 42% of the crop expansion may have come from land exiting the CRP. Subsequently, further analysis of the same dataset revealed that much of the conversion of grassland to cropland was concentrated in locations near ethanol production plants [83]. The authors stated that their results, along with those from a similar study by Motamed et al. [84] “provide corroborating evidence that accelerated ethanol development under RFS2 was an important driver of recent grassland loss.” A recent study by EPA and USDA researchers used a field-level CRP database along with USDA's CDL to determine the fate of expiring CRP parcels in a 12-state Midwestern region [85]. (This is the same region studied by Mahaffey et al. [76] as discussed above and summarized in Table 2.) It was determined that during the period of 2010–2013, about 30% of the expiring CRP land returned to agricultural production, with most of it being used for corn and soy beans; 0.45 and 0.53 million acres, respectively. These results are quite different from the earlier prediction of Mehaffey et al. that the use of CRP lands could be reduced while meeting an aggressive biofuel target in 2020.

Table 2 Predicted change in acres and corn production to satisfy 2020 biofuel requirements. Source: Mehaffey et al. (2012) [76]. Change in Planted Acres from 2001 to 2020, million acres

Corn Grain Production, million bushels

Corn

Wheat + Soybeans

CRP lands

2001

2020

Corn Belt Illinois Indiana Iowa Missouri Ohio

3.57 1.53 6.24 1.08 2.05

−3.08 −1.38 −3.22 −0.84 −1.17

0.11 −0.01 0.12 −0.13 0.02

1654 725 2017 383 376

3044 1292 4209 676 870

Central Plains Kansas Nebraska

1.50 2.55

−0.53 −0.24

0.14 0.04

317 857

577 1393

Lake States Michigan Minnesota Wisconsin

0.24 2.73 0.53

−0.46 −2.42 −0.23

−0.05 0.04 −0.09

286 1029 463

460 2023 775

Northern Plains North Dakota South Dakota

1.87 1.38

−0.12 −0.48

−0.40 −0.06

200 327

522 623

Total

25.27

−14.17

−0.37

8634

16,464

acres of grassland was lost from the WCB during this period. These authors pointed out that the observed rates of grassland conversion to crops (1.0–5.4% per year) are higher than has occurred since the 1920's and 1930's, when mechanized agriculture was introduced. Furthermore, the specific locations of the LCLUC are of concern. Cropland expansion was observed onto marginal lands characterized by high erosion risk and vulnerability to drought – particularly in Nebraska and the Dakotas. In Minnesota, much of the LCLUC occurred near wetlands, creating concerns about wildlife habitat disruption. Resumption of cropping on CRP lands was a factor in all states, but the total expansion of cropping extended beyond CRP lands. Plourde et al. also utilized USDA's CDL to perform a somewhat similar analysis of LUC within several states in the continental U.S [78]. Although the total area under production of major crops (corn and soybeans) increased only slightly between the periods of 2003– 2006 and 2007–2010, rotation patterns changed substantially, with a general trend towards more continuous corn cropping. The authors identified numerous concerns related to this increased intensification of corn, including greater amounts of chemical inputs required to maintain high crop production. In addition, several general benefits of crop rotation – such as positive effects on soil quality, invasive species control, reduced GHG emissions, and crop disease protection – are reduced. Johnston used CDL information to examine recent LUC in the Dakota Prairie Pothole Region (DPPR) [79]. Corn and soybean cropping expanded by 27% between 2010 and 2012, an areal increase larger than the State of Connecticut. Johnston pointed out the difficulty in tracking LUC over time because multiple land uses are constantly being interchanged with each other. During the period of study, a considerable amount of the corn/soybean expansion occurred at the expense of grassland, but an even larger share came from conversion of wheat and other small grains, which has implications for food security. Yang and Suh considered LUC in which land previously used for cotton in the South was converted to corn [80]. From 2005 to 2009, cotton area harvested was reduced by 1.8 million ha, while corn area

4. Life-cycle greenhouse gas (GHG) emissions Current increases in ethanol use in the U.S. are driven by targets within the federal RFS2 and California Low Carbon Fuel Standard (LCFS) regulations. Both policies dictate that ethanol (and other biofuels) must meet certain GHG reduction targets in order to qualify for use. Within these policies, net GHG emissions are quantified through life-cycle assessment (LCA) modeling, which can be used to estimate environmental impacts of a product from its “cradle to grave.” To determine the GHG impacts of a biofuel, an LCA is conducted in comparison to the fuel's conventional petroleum counterpart, and emissions of CO2, nitrous oxide (N2O) and methane (CH4) that arise from each life cycle stage are assessed. These GHGs are converted to a 6

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amounts of stored carbon [100]. Land disturbances to create space for crops or pasture can impact GHG emissions in a variety of ways:

CO2 equivalent basis (CO2,eq) using 100-year global warming potential (GWP) factors established by the Intergovernmental Panel on Climate Change (IPCC). (CH4 and N2O have CO2 equivalency factors of 26, 296, and 1, respectively [86].) All GHGs are summed to determine the net carbon intensity (CI) of a fuel pathway, which is normalized by the energy contained within the final unit of fuel (e.g. g CO2,eq/MJfuel) for comparison among different fuels. A full LCA of a biofuel includes inputs and requirements for feedstock planting, growth, harvesting, fuel production, distribution, and combustion - as well as all intermediate transportation steps. Coproducts generated throughout the life cycle are included. For example, distiller's grains are co-produced with corn ethanol, and can be used as an animal feed. Some of the GHG emissions over the fuel pathway can be allocated to such co-products, thereby reducing the net GHGs attributed to ethanol. For these reasons, LCA modeling is data intensive, requiring supporting inputs for each life cycle stage and use of various modeling tools and databases. The GREET model has been widely applied to determine energy and emissions effects of ethanol and other biofuels [64,87]. It is used extensively within the LCFS regulations, and partially used within the RFS2 regulations. GREET allows users to investigate fuel and vehicle combinations on a full-fuel cycle basis, evaluating consumption of resources, CO2-equivalent GHG emissions, and seven other criteria pollutants for over 100 fuel pathways and 80 vehicle/fuel systems. It was originally developed in 1996 by ANL, and continues to evolve to incorporate improved data and additional pathways [88,89]. The current version, GREET 1.8d includes more recent information about ethanol plant operations, and incorporates the effects of LUC using updated GTAP (Global Trade and Analysis Project) and CCLUB (Carbon Calculator for Land Use Change from Biofuels Production) results. With these updates, reduced carbon intensity (CI) values have been reported for corn ethanol as compared to use of previous GREET versions. For example, in 2007, Wang et al. reported that on average, corn ethanol reduced GHG emissions by 19% relative to gasoline [90]. In 2011, Wang et al. reported an average corn ethanol CI value of 24% lower than baseline gasoline [91]. In 2013, the ANL group reported a further reduction in corn ethanol's CI due to a smaller contribution from LUC [92]. While the final metrics of various LCAs are similar (e.g. CI values), the models, approaches and assumptions used vary significantly, which can lead to widely differing results. For example, regional or state specific modeling can produce different results than modeling of larger geographic regions, even when using similar pathways [93]. The method of treating co-products is important [91,94,95], and assumptions about fertilizer use and conversion of nitrogen inputs to nitrous oxide (N2O) can have significant impacts, since N2O is a highly potent GHG [95–97]. Many assumptions are applied throughout the LCA process, with each introducing variability and uncertainty. Consequential LCA approaches have been used in regulatory applications involving biofuels, in addition to the more limited attributional LCA [98,99]. A consequential LCA is meant to expand the system boundaries to model actual changes in the real world; for example, to incorporate indirect land use change (ILUC) effects. Such approaches introduce a large amount of uncertainty. Because the RFS2 and LCFS regulations include ILUC effects within their CI calculations, this area has been quite contentious, and modeling practices have continued to evolve to improve estimations.

• • •

Removal of above and below ground vegetation (roots) releases the carbon stored in the vegetation as CO2. The CO2 may be released slowly over time as vegetation is left to decompose, or can occur in a rapid burst if burned (which also results in emissions of CH4 and N2O as combustion products). Existing vegetation helps maintain a balance between carbon uptake and emissions from the soil. As soils are disturbed, this balance is disrupted. It can take many years to establish a new equilibrium. An annual uptake of CO2 occurs during growth of vegetation (via photosynthesis). If vegetation is removed, future CO2 sequestration by the growing biomass is eliminated. This is called foregone sequestration. Also, replacement of dense vegetation, such as forests, with less dense vegetation results in a diminished capacity to sequester CO2.

Any type of LUC can release substantial amounts of GHGs to the atmosphere, which generally occurs as a large initial release with continuing impacts over a longer period of time as a new equilibrium is established. Conversion of natural lands, particularly forests and peatlands results in the most significant releases. Conversion of one crop to another also has GHG impacts, as well as reversion of lands (e.g. abandoned pasture land can be reverted to natural grasslands). Biofuel-induced LUC is often classified as either direct or indirect. If LUC occurs to directly produce the biofuel crop, and this change can be traced to the biofuel, it is called a direct-land use change (DLUC). Indirect (or induced) land use changes (ILUC) result from shortages of crops that influence economics through supply and demand fluctuations. Resulting price increases create incentive to produce the displaced crop elsewhere in the world, which may eventually result in cropland expansion and LUC. As explained in a recent European Commission report, direct and indirect LUC are intertwined in reality [101]. Both direct and indirect LUC have significant potential to contribute GHGs, and therefore are included in policy evaluations of a fuel's CI. LUC effects cannot be measured directly, and must be modeled to predict contributions to fuel CI. Additional details about the amount of land changed as well as the carbon stocks and emission factors of various land types in various regions are required. The IPCC provides guidelines for “Land-use, Land-use Change and Forestry,” which sets default values for above-ground LUC depending on the specified land category and vegetation type [102]. IPCC estimates that ~1.5 billion tons of carbon are emitted to the atmosphere each year from forest and grassland clearing, which accounts for 20% of annual CO2 emissions [103,104]. ILUC modeling requires predictors of economic response that are not generally included in standard LCA models such as GREET. To incorporate LUC effects, agro-economic equilibrium models (such as GTAP) are used to determine the amount and type of LUC that will occur by location and land type. These results are then linked to emission factor (EF) databases to determine the resulting GHG emissions. In recent years, agro-economic models have been adapted and improved to better predict the LUC response to increases in biofuel use or “shocks” [105,106]. Resulting emissions are heavily dependent on the amount of land that is converted, what type of land is converted (grassland, forestland, etc.), where land is converted, and the carbon stocks of the original land. This requires detailed data on historical and future trends in crop growth and LUC patterns, economic market and price fluctuations, predictions about yield changes, and estimations of carbon stocks of converted lands. In addition, since LUC emissions occur over extended periods of time, some type of time accounting is normally applied. An example of the modeling process to predict an overall CI of a fuel, including ILUC, is illustrated in Fig. 2. Since there are many options for each of the models and databases that can be used, CI results attributed to ILUC are often quite different.

4.1. Land use change and LCA modeling Higher ethanol usage is expected to increase corn agriculture through intensification, displacement, and expansion. Intensification is likely to contribute additional GHGs from increased use of fertilizers and pesticides, and from degradation of soils. Displacement of other crops and expansion into natural lands also have GHG implications. Converting from forests, pastures or CRP lands to cropland results in the removal of biomass and disruption of soil, which can release large 7

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Fig. 2. Modeling flow to predict total biofuel life cycle carbon intensity including ILUC.

Fig. 3. Indirect Land Use Change (ILUC) impacts with respect to total Carbon Intensity of RFS2 and LCFS policies for different ethanol pathways.

The “carbon neutrality” principle is generally applied in LCA modeling of biofuels. This assumes that CO2 emissions produced during combustion of the fuels are completely offset by the biogenic CO2 uptake during plant growth. Using this assumption, it is only necessary to model production-related emissions for direct CI. Recent reports by DeCicco, however, question carbon neutrality, suggesting that it is not valid for many real-world biofuel production processes [111–113]. DeCicco argues that LCA models do not properly reflect the dynamics of the terrestrial carbon cycle. In particular, it is inappropriate to attribute all carbon uptake during plant growth to the biofuel produced, because some degree of uptake occurs on all lands, regardless of whether they are used for biofuels.

Searchinger et al. were among the first to introduce the concept of ILUC. They estimated that including ILUC would more than double the CI value of corn ethanol, and result in a carbon payback period as long as 167 years [64]. Searchinger applied the GREET model along with LUC results from FAPRI, tied with emission factors from the Woods Hole database. At that time, the concept of ILUC was fairly new, and the models that Searchinger et al. used were adapted for this purpose, but were not well suited to accurately predict the economic responses of biofuels. However, this work caused many to take notice, and the concept was rapidly adopted into the LCFS and RFS2 regulations. Recent attempts have been made to validate the predicted amount of LUC that has occurred due to increased ethanol use. Babcock and Iqbal investigated FAOSTAT data and found that much of the increase in demand for biofuel crops has been met through yield increases rather than LUC or expansion [81]. A similar investigation reviewing land expansion around the world between 2000 and 2010 found that the impact of biofuel expansion is minimal [107]. Both studies found that models have not adequately accounted for yield increases and multiple cropping effects. Approaches for modeling ILUC continue to be debated [108]. For example, Kim, et al. suggest that a nutritional allocation for ILUC emissions be applied, as displaced crops could be replaced by different food/ feed options, and biofuels should not be held accountable for emissions arising from consumers’ dietary preferences [109]. Searchinger et al. argue that current models predict GHG benefits of growing crops to produce biofuels only if global food consumption is reduced [110]. These authors calculated that roughly 25–50% of total calories in corn and wheat that is diverted to ethanol are not replaced. This reduces GHG emissions from LUC, but also adversely affects people and livestock. They also argue that biofuels from crops that would have been grown anyway do not provide a valid offset for combustion CO2.

4.2. LCA and ILUC modeling results in policy EPA and CARB both incorporate ILUC effects within LCA modeling to determine the overall CI values of biofuels. While the two regulatory approaches are somewhat different, ILUC is estimated in both cases by combined economic and emission factor models and is included as an “adder” to the direct CI that is calculated from more traditional LCA models. As work continues in the area of ILUC, these models continue to evolve. Recently, CARB adopted revisions to the LCFS which include updates to ILUC modeling. The contributions of ILUC to total CI of ethanol fuels as determined by EPA and CARB are illustrated in Fig. 3. The modeling approaches applied in the RFS2 and LCFS are described below. 4.2.1. EPA approach to LCA and ILUC in the renewable fuel standard (RFS2) Under the RFS2 requirements, a conventional renewable fuel, such as corn ethanol, must meet a 20% GHG reduction relative to the 2005 petroleum baseline to generate Renewable Identification Numbers 8

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Fig. 4. System boundaries and modeling flow chart for biofuel LCA in EPA RFS2 [201].

2017, and 2022. These results indicate that GHG emissions associated with LUC are projected to decrease significantly from 2012 to 2022. As shown in Fig. 3, when the direct and indirect CI contributions are combined, the 2012 corn ethanol result lies well above the typical gasoline CI range of 93–100 gCO2eq/MJ. The projected CI value for corn ethanol in 2017 lies near the high end of the gasoline range, while the 2022 projection lies 15–20% below the gasoline range. A more recent assessment of corn ethanol's CI value was conducted by ICF on behalf of the USDA [115]. While the EPA's RFS2 methodology was followed, numerous input updates were incorporated, based on newer sources of information since EPA's assessment in 2010. For example, corn yields were increased, fertilizer inputs per bushel of corn were reduced, GHG emissions from operation of corn ethanol plants were reduced, and an updated version of the GREET model was used. However, the most influential changes had to do with reduced LUC in South America and Africa. Consequently, the ILUC contribution to total ethanol CI was reduced from ~30 gCO2-eq/MJ in EPA's original assessment for the year 2022 to approximately 8 gCO2-eq/MJ. The overall CI value for corn ethanol in 2022 estimated by EPA is ~78 gCO2-eq/MJ (see Fig. 3). This updated ICF/USDA study projects a total value of ~48 gCO2-eq/MJ, which is nearly 40% lower than the previous EPA estimate, and 50% lower than the gasoline baseline. However, it is important to recognize that all these assessments are based on future year scenarios having considerable uncertainty. The situation today is quite different, with many “grandfathered” corn ethanol plants still operating, without the requirement of meeting even the minimal 20% GHG reduction target of the RFS.

(RINs). However, this target only applies to facilities that commenced construction after December of 2007; thus facilities producing ethanol before that date are exempt, or “grandfathered.” EPA's approach to determine CI of biofuels within RFS2 employs an intricate linkage of numerous models and databases, as illustrated in Fig. 4. The methodologies, data inputs, assumptions, etc. used in the EPA RFS2 analysis underwent substantial peer review, and have not been modified significantly since the RFS2 was approved in 2010. Emission factors from the GREET model are used to determine upstream emissions and the MOVES model is used for vehicular emissions. Two different agro-economic models are used in combination with emission factor databases to predict international and domestic LUC. Within RFS2, ILUC is not clearly defined, and the outputs of the models are unique to the RFS2 approach. For our analysis purposes, we considered EPA's international effects to be indirect LUC, while domestic LUC is classified as direct LUC. The CI of each fuel pathway is determined by summing all the outputs listed on the right hand side of Fig. 4. Further details about EPA's determination of domestic and international LUC have been presented elsewhere [114,115]. EPA's assessments of LUC and CI for most biofuels are based on 2022 scenarios, a year corresponding to the anticipated conclusion of the RFS2 fuel phase-in period. However, EPA also conducted two intermediate year evaluations for corn ethanol: 2012 and 2017. Less information about these intermediate scenarios is provided in the EPA Regulatory Impact Analysis (RIA) document describing the inputs for the LCA, making it difficult to understand the details of this modeling or the assumptions that resulted in differences in LUC and GHG emissions. Nevertheless, by utilizing available spreadsheet data contained within the RIA docket, we have determined both domestic and international cropland area changes (and their associated GHG impacts) in these EPA assessments. Table 3 provides the results for various ethanol pathways, including corn ethanol in the years 2012,

4.2.2. California Air Resources Board – low carbon fuel standard (LCFS) California's LCFS was implemented with the Governor's Executive Order S-01–07 in January 2007, approved in April 2009, and went into effect in January 2010 [116]. The LCFS requires 9

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Table 3 RFS2 changes in domestic cropland and international land used for biofuel production in 2022 scenarios - with Resulting GHG Emissions resulting GHG emissions. Source: EPA RFS2 Regulatory Impact Analysis (2010) [201] (pp 350, 362, 363); Docket information EPA-HQ-OAR-2005-0161. Scenario

Domestic Cropland

Corn Ethanol−2022 Corn Ethanol−2017a Corn Ethanol−2012a Sugarcane Ethanol−2022 Switchgrass Ethanol−2022 Corn Stover Ethanol−2022 a

International Cropland

Total ILUC GHG (kg CO2 eq mmBTU-1)

Cropland Area Change (million acres)

30-year annualized GHG from LUC (kg CO2 eq mmBTU-1)

Cropland Area Change (million acres)

30-year annualized GHG from LUC (kg CO2,eq mmBTU-1)

1.4 1.4 0.8 N/A 4.2

−4.0 −1.3 0.1 N/A −2.5

2.0 4.0 3.5 1.1 3.4

31.7 53.8 76.1 4.3 15.1

27.7 52.5 76.2 4.3 12.6

0.6

−10.8

–

–

−10.8

Summaries of intermediate year evaluations are not included in the RIA, but some information can be found in docketed reports or through manipulation of spreadsheets therein.

Following review and advice from an expert work group, CARB implemented several modifications during their 2015 re-adoption of the LCFS regulations [116]. These modifications include updates to the GTAP-BIO model that specifically incorporate information about biomass/biofuels to improve analysis of LUC [105]. Results from a study by Tyner showed that with these updates, considerably lower ILUC emission values were predicted compared to previous estimates [105,124]. The revised GTAP-BIO model also incorporated a new carbon stock database called the AEZ-EF [124,125]. This database provides weighted average soil and biomass carbon for 203 unique geographic regions, which are combinations of 19 GTAP regions and 18 AEZs. This database relies on IPCC GHG inventories and default values, augmented with more recent data as they become available [126]. In addition, CARB adopted the GREET 2.0 model in place of the earlier 1.8b version that was used in its 2009 regulation. This work was discussed at various public workshops and hearings, where revised ILUC results and corresponding CI values were presented as part of the LCFS re-adoption process [106,127]. Default values for direct CI of fuels are no longer provided in the 2015 regulations, as fuel producers are expected to determine their own values, then apply the ILUC “adder” value determined by CARB. In Table 4, example 2015 pathway values are shown in comparison with CARB's 2009 default values. The total CI value for an example 2020 corn ethanol pathway as determined in the 2015 LCFS revised process is 80.09 gCO2eq/MJ, which is 19% lower than the applicable gasoline baseline and 9.6% lower than the 2009 default value for corn ethanol. [Note that because allocations of the direct CI values by lifecycle stage were not provided for this corn ethanol pathway, it is not possible to show this type of breakdown in Fig. 3].

a 10% reduction in CI of the total transportation fuel pool by 2020, with the reduction being assessed through LCA modeling. In 2009, CARB developed CI look-up tables for numerous fuel pathways before the regulation went into effect. In this early version, direct emissions from the biofuel pathways were estimated using the GREET 1.8 model adapted for California, while indirect LUC emissions were estimated using the GTAP economic model and the Woods Hole emission factor (EF) database. Data and reports on results of the LCFS and CI modeling are scattered in many locations, including the CARB Staff Report [117] and its appendices [118], as well as documents for each of several individual pathways [119–122]. The GTAP model originally used in the LCFS rulemaking process determined amounts of land use change (in pasture, cropland and forests) within 18 different AgroEconomic Zones (AEZs) in response to policy changes. For corn ethanol pathways, a 15 bg “shock” was modeled. The land area output of the GTAP model is linked to GHG emission factors (EFs) developed from the Woods Hole Database [123]. This database is coarser than the GTAP output, having only 10 regions, which must be matched to 18 AEZs from GTAP. The Woods Hole database includes EFs for forest lost (to crops), forest gained (from pasture) and grassland lost (applied to livestock and pasture) in Mg CO2,eq ha1 over a 30-year time period. The emission factors are multiplied by the land area output from GTAP for a specific type of conversion to determine the net GHG emissions from ILUC. As shown in Fig. 3, results from CARB's 2009 modeling of a corn ethanol scenario gave an overall CI value within the typical gasoline range, with an ILUC contribution of 30 gCO2eq/MJ. Table 4 Comparison of 2009 and 2015 LCFS Regulation CI Values for 2020 Scenarios. Sources: CARB Staff Report (2014) [106] and Public Workshop (2015) [127] Fuel (pathway no. in parentheses)

2009 Regulation Direct CI, gCO2e/MJ

CARBOB CaRFG Corn Ethanol (ETHC004) Sugarcane Ethanol (ETHS001) Sorghum Ethanol (ETHG001)

2015 Regulation

ILUC, gCO2e/MJ

Total CI, gCO2e/MJ

CI Reduction vs. CaRFG

Direct CI, gCO2e/MJ

98.38 98.95 59.71a

30.00

98.38 98.95 89.71

27.40

46.00

58.51a

30.00

CI Reduction vs. CaRFG

CI Change from 2009 to 2015, gCO2e/MJ

ILUC, gCO2e/MJ

Total CI, gCO2e/MJ

−9.3%

100.53 99.11 60.29

19.80

100.53 99.11 80.09

−19.2%

+2.15 +0.16 −9.62

73.40

−25.8%

41.43

11.80

53.23

−46.3%

−20.17

88.51

−10.6%

67.29

19.40

86.69

−12.5%

−1.82

a Existing corn and sorghum Direct CI values were adjusted from 68.40 and 66.24 g/MJ, respectively to reflect improvements in energy consumption, ethanol and DDGS yields, and DGS-livestock feed displacement ratios.

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[131]. Fig. 5 illustrates the results for corn ethanol and switchgrassderived cellulosic ethanol. Corn ethanol (with ILUC) has a mean CI value of 110 g CO2eq/MJ, which is about 20% higher than baseline gasoline. However, this value includes considerable uncertainty, with the 90% confidence interval ranging from 70 to 160 g CO2eq/MJ. Fig. 5 also illustrates the major impact of ILUC uncertainty in driving overall CI results, as the probability distributions without ILUC are much narrower than the distributions that include ILUC. More recently, Plevin et al. examined uncertainty in ILUC-derived CI values by performing model simulations that linked a GTAP-based economic model with the AEZ-EF carbon emission factor model, which is the approach used in CARB's LCFS [132]. Expert judgement was used to define distributions for a variety of parameters within the GTAP-BIO-ADV and AEZ-EF models. Monte Carlo simulations were performed to determine probability ranges of ILUC CI values for corn ethanol, sugarcane ethanol, and soybean biodiesel. As illustrated in the results shown in Fig. 6, the mean ILUC CI value for corn ethanol is 33 g CO2eq/MJ, with a central 95% distribution range from 18 to 55 g CO2eq/MJ. (These values are from scenarios in which the amount of food is fixed in the developing world.) This mean value is similar to EPA's international LUC value in 2022 of 30 g CO2eq/MJ, but is considerably higher than CARB's recently adopted value of 19.8 g CO2eq/MJ. Plevin et al. also determined that most of the uncertainty in the ILUC CI values was contributed by the economic model (GTAPBIO-ADV), not by the emission factor model (AEZ-EF). The regulatory applications of corn ethanol CI by both EPA and CARB are based on scenarios that assume a volume “shock” of 15 bg ethanol. However, the response of CI to increased ethanol volumes is not expected to be linear. If the amount of corn ethanol were doubled to produce nationwide E20, CI values would probably increase. Direct CI would likely increase due to expansion of corn cropping in the U.S. Midwest into more marginal and/or CRP lands that require greater use of fertilizer and pesticides. Indirect CI would likely increase due to greater global conversion of environmentally sensitive lands into agriculture. To our knowledge, the response of CI values to larger biofuel volume changes has not been thoroughly studied.

Fig. 5. Probability distributions for CI of ethanol from corn and switchgrass, with and without ILUC. [from Kocoloski et al. [131] Reprinted with permission from Energy Policy 56, 41-50, 2013, Elsevier.].

Fig. 6. ILUC emission factors for different biofuels. Boxes represent interquartile range, cross marks on whiskers represent central 95% of distribution. FF = food fixed in developing world. [from Supporting Information of Plevin et al. [132] Reprinted with permission from Environ. Sci. Technol.49, 2656-2664, 2015, Amer. Chem. Soc.].

4.3. Uncertainty in lifecycle CI of ethanol For regulatory purposes, it is generally desirable to define and use single point values for CI of biofuels. However, a single CI value for all corn ethanol pathways is not justifiable, as some pathways are more carbon intensive than others. A fundamental issue with respect to regulatory applications of LCA for CI estimation is that the modeling approaches used are deterministic, and do not account for any uncertainty in the outcomes [128]. Clearly, all parameters used in such LCA modeling have some degree of uncertainty. In such situations, it is common to apply statistical modeling methods using probability distributions to represent each parameter in the lifecycle model. Monte Carlo simulations can then be performed to generate final CI results in the form of probability distributions. One of the first systematic applications of such a probability-based approach for assessing the CI of U.S. corn ethanol expansion was published by Plevin et al. in 2010 [129]. These researchers determined a “bounding range” of 10–340 g CO2eq/MJ, and a 95% “central interval range” of 21–142 g CO2eq/MJ. The upper ends of these ranges are several times higher than CI values for gasoline. These authors also noted that the ILUC CI estimates used (at that time) by EPA (34 g CO2eq/MJ) and CARB (30 g CO2eq/MJ) were near the low end of the plausible range. Mullins et al., investigated the issue of uncertainty, and determined that CI values for corn ethanol have a very wide distribution of 50– 250 g CO2eq/MJ [130]. This large variability was driven mainly by ILUC emission factors, which have extremely high uncertainty. Based on this analysis, the authors concluded that corn ethanol has only a 10% probability of achieving the RFS2 requirements of 20% reduced CI compared to baseline gasoline. In subsequent work, the same research group performed an uncertainty analysis for CI as part of a hypothetical nationwide LCFS program (structured like the current California LCFS)

5. Food vs. fuel Of all the concerns with respect to increased production and use of corn ethanol, perhaps none is more controversial than the issue of “food vs. fuel.” This is a complex area, which is often presented as part of a bigger picture involving global social justice and ethical decisions. The issue is not simply one of diverting U.S. grown corn from food/feed uses to ethanol production, thereby reducing the supply of corn for food/feed. In fact, it has been argued that increasing U.S. corn ethanol actually increases the overall food/feed market by production of additional DDGS, corn syrup, corn oil, and other products [133,134]. Rather, the principal concerns are that increased corn production for ethanol could displace other crops that are more directly connected to global food supplies, and the potentially severe consequences this could have on the world's poor, for whom food insecurity is already a major issue. 5.1. Initial food vs. fuel debate “Food vs. fuel” concerns have been raised since the early 2000's, when substantial increases in corn ethanol were first anticipated. As early as 2007, the International Monetary Fund (IMF) predicted that an increase in biofuel production would lead to increased food prices [135]. In the same year, Rajagopal et al. concluded that unlike other alternative energy technologies, the impacts of biofuels will be greater on food prices than on energy prices [136]. They also warned that “without adequate safeguards, further expansion of biofuels will mean an unpalatable trade-off between cars for the rich and starvation for the poor.” 11

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global issues: energy, food, land use, and development. It was pointed out that biofuels can relate both positively and negatively with each of the four dimensions of food security: availability, access, utilization (nutrition) and stability. The Committee recommended that governments adopt a coordinated food and energy security strategy, which addresses the land, water, and resource implications of biofuel policies. Kline et al. also point out the complexity of the relationships between food security and biofuel policies, and argue that when considered within the context of specific local situations, it is possible for biofuels to positively address both energy and food security concerns [152]. Also of concern is that increasing food prices (due to diversion of food/feed to biofuels) leads to detrimental changes in human nutrition and reduction of total food intake. Scovronick and Wilkinson modeled 2020 food/diet scenarios for Brazil, China, and the U.S. in which biofuel-induced food price inflation was either “moderate” or “high” [153]. Compared to a 2009 baseline, the moderate inflation scenario reduced 2020 daily per capita food energy by 1–3%, while the high inflation scenario resulted in a 3–8% reduction. These authors also examined potential health impacts of biofuel production and use, concluding that emissions of air pollutants and increased food prices were the two biofuel factors most likely to have adverse health effects [154]. The issue of reduced food consumption resulting from increased use of biofuels has also been highlighted by Searchinger et al. [110]. These authors explained that there are three possible responses to the diversion of crops from food/feed to biofuels: (1) cropland may expand into new areas, (2) more crops may be produced from existing cropland, and (3) some food/feed may not be replaced. Agro-economic models used to estimate the impacts of increasing biofuels predict that all three responses occur. By examining data and outputs from the three major agro-economic models used in biofuel regulations (GTAP by CARB, FAPRI by EPA, and MIRAGE by the EC) Searchinger et al. “estimate that roughly 25–50% of the net calories in corn or wheat diverted to ethanol are not replaced but instead come out of food and feed consumption.” It is likely that these models overestimate actual food reduction, thus they underestimate LUC and the resulting GHG emissions that would be required to make-up more of the displaced food/feed. Recent reports from the World Resources Institute (WRI) investigated implications of crop-based biofuels for the future supply of food [155,156]. It was pointed out that to feed the world's anticipated population in 2050 will require approximately 70% more crop calories than were available in 2006. This problem is exacerbated by the increasing land competition for growing biofuel feedstocks. If foodderived biofuels were to increase to comprise 10% of global transportation fuels, the “calorie gap” would increase further to 90%. To achieve a sustainable food future, it was recommended that biofuels made from waste feedstocks be encouraged, while those from crops (or from the dedicated use of land) should be discouraged. Rather than promoting expansion of biofuels, the WRI recommends that “the world should instead move in the opposite direction and give up the use of cropbased biofuels for transportation … a strategy more in line with a sustainable food future.” Rulli et al. recently examined the food-energy-water nexus on a global basis, and assessed the displacement of water and land use for biofuels production [157]. They pointed out that while biofuels may enhance energy security, reduce GHG emissions, and improve agricultural profitability in some situations, the calories being devoted to biofuels are not available for nutritional purposes. The authors concluded that “about 280 million people (i.e., more than one fourth of the malnourished population of the world) could be fed with the crop calories used for biofuels in 2013”. Of course, problems of food distribution and malnourishment are very complex, and this conclusion is not meant to suggest that they can be solved by elimination of biofuels. Nevertheless, these findings illustrate the magnitude of potential food resources being devoted to fuels, which is part of the

The debate intensified in 2008, when agricultural commodity prices spiked to 30-year highs in a matter of months. The increased use of corn for ethanol production was targeted as one of the primary reasons for this price spike. Subsequent investigations into the food crises showed that the increased biofuel market may have been responsible for 30–70% of the increase in corn prices [137,138]. Although these price spikes are now attributed to multiple factors – including the high price of oil, speculation in commodity markets, regional drought, and others – the increased demand for corn ethanol is still believed to have played an important role in the increased corn prices from 2006 to 2008 [9,139,140]. For example, the Congressional Budget Office attributed 28–47% of the increase in corn prices between 2007 and 2008 to domestic ethanol demand [141]. An assessment by the International Food Policy Research Institute (IFPRI) also determined that the demand for biofuels was a strong factor influencing the increase in corn prices [142]. Although the food vs. fuel debate in the U.S. diminished with falling corn prices in 2009–2011, it was reignited due to the severe Midwest drought during the summer of 2012, which significantly reduced the total corn crop [143–145]. Some of the controversy about the role of ethanol production in increased food prices stems from imprecise and contradictory terminology. The NRC/NAS report of 2011 provides helpful clarification of certain terminology by clearly distinguishing between the effects of ethanol on agricultural commodity prices and the effects on retail food prices [9]. Twelve different studies were cited that had estimated the effects of biofuels on agricultural commodity prices during 2007–2009. Because these studies investigated different scenarios and used different assumptions, direct comparisons are not possible. Nevertheless, they all determined that ethanol production had a positive effect on the price of corn, showing a range of 20–75% price increase. The NRC/NAS Committee chose to use a lower range of 20–40% in their estimation of how this translates to price increases of retail food. (The upper end of this range is in good agreement with an independent assessment by Babcock and Fabiosa, who determined that the expansion of corn ethanol could account for 36% of the commodity corn price increase from 2006 to 2009 [146].) Based on the Committee's assessment, a 1–2% increase in the price of grocery food products could be attributed to corn ethanol, with somewhat higher increases in prices of animal products that use corn feed. For example, a 6–12% increase in the retail price of broiler meat was attributed to ethanol. Apart from these concerns about short-term spikes in food prices, there are longer-term concerns about the general relationship between increased corn ethanol and food prices. As increased ethanol demand leads to an increase in the price of corn, this affects many aspects of the global food market. For example, Hayes et al. estimated that for every $1/bushel increase in the price of corn, the cost of meat, dairy products, and total food increases by 2.9%, 1.7%, and 0.8%, respectively [147]. Harrison also examined evidence from multiple sources and concluded that higher corn prices resulting from demand for corn ethanol would contribute to food price inflation [148]. Dicks et al. determined that compliance with the 36 bg/y biofuel requirement of EISA (including 16 bg/y of cellulosic ethanol) would result in loss of over 30% of the domestic beef cattle herd, thereby raising the price of all meats [149]. A report prepared for the National Council of Chain Restaurants concluded that the RFS2 corn ethanol requirements result in average annual cost increases of about $3000 to $18,000 per restaurant, due to higher food prices [150]. 5.2. Global food security The issue of global food security and its connection to biofuels has recently attracted considerable attention, as highlighted in a 2013 report by the High Level Panel of Experts on Food Security and Nutrition of the U.N. Committee on World Food Security [151]. This report emphasizes the challenge of analyzing relationships between biofuels and food security, which occur at the intersection of four major 12

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North America was among the worst in terms of both water and energy usage, giving an average result of 110 L water required to produce 1 MJ of fuel, as compared to 0.14 L/MJ for gasoline. A critical review paper that assessed environmental and sustainability factors associated with “next generation biofuels” relative to corn ethanol and biodiesel was published by Williams et al. in 2009 [175]. This comprehensive assessment included life-cycle-based factors of GHG emissions, air pollutants, soil health, water usage, water quality, waste streams, biodiversity, and LUC. The authors concluded that future generation biofuels will outperform current biofuels with respect to nearly every factor investigated, although lack of specific data create a high degree of uncertainty. While no comparisons were made to a baseline gasoline case, the methodology outlined in this paper laid the foundation for a comprehensive and systematic approach by which to compare environmental and health impacts across all life-cycle stages for any fuel of interest. In 2010, de Vries et al. compared ecological sustainability of first generation biofuel production from 9 major feedstocks [176]. The sustainability indicators included resource use efficiency, net energy ratio, GHG emissions, soil erosion, eutrophication, SOC, pesticide usage, and water usage. Attributing equal weight to each indicator, the authors determined that corn ethanol was relatively positive with respect to soil erosion and maintaining SOC, but was unfavorable with respect to most other indicators. They concluded that “first generation biofuels from wheat and maize ethanol appear not sustainable, especially since they hardly meet their prime goals: reduction of fossil energy use and GHG emissions.” Also from a European perspective, Lankoski and Ollikainen applied an economic-ecological modeling framework to biofuel feedstocks and concluded that the positive GHG benefits of ethanol (and other biofuels) may be offset by other negative environmental impacts [177]. In 2011, Raghu et al. discussed the need to consider multiple ecological impacts when assessing the overall benefits of biofuels [178]. They defined a term called net ecological and energetic benefit (NEEB) and showed how a multi-dimensional ranking of different biofuel crops provided much more information than single-dimensional ranking. They also suggested that the concept of bio-complexity provides a good framework for dealing with the multi-dimensional issues of biofuels, as this involves a systems approach for exploring the spatial, temporal, and organizational dimensions of complexity. A group from Oak Ridge National Laboratory (ORNL) defined bioenergy sustainability as “the capacity of biofuel development, production, distribution and use to proceed while maintaining options for future generations” [179]. In addition to a broad range of environmental indicators (e.g., soil carbon, erosion, GHG emissions, biodiversity, air quality, and water quality) socio-economic factors were also identified – such as food security, employment, farm income, rural life style, and others. It was pointed out that land management decisions regarding biofuel feedstocks often involve trade-offs among environmental, social, and economic effects. To maximize the benefits of biofuels, this group called for development of “multi-metric, spatialoptimization models” to identify land management practices that consider and compare all these factors. ..Subsequently, a broader group of ORNL researchers defined a set of 19 specific, measureable indicators to use in evaluating the sustainability of bioenergy systems [180]. These indicators cover 6 categories of environmental concern: (1) soil quality, (2) water quality and quantity, (3) GHGs, (4) biodiversity, (5) air quality, and (6) productivity. The indicators are meant to serve as a basis for evaluating a wide variety of bioenergy systems, and capturing impacts related to feedstocks, locations, management practices, and conversion pathways. No specific biofuel examples were provided in this paper, though it was suggested that a multivariate analysis of the 19 indicator values should be conducted as the basis for discussion about a particular system's sustainability. Others have emphasized the importance of considering the context of a particular

justification for the European Parliament's 2015 modification of the Renewable Energy Directive (RED) to include a 7% cap on transportation fuels derived from crop-based feedstocks [158]. The literature is replete with other papers and reports that use ethical and moral arguments as the basis for questioning increased use of biofuels. For example, the Nuffield Council on Bioethics has laid out a set of six ethical principles that should be met for biofuels development to be permissible [159]. These principles include issues of human rights, environmental sustainability, global climate change, fair trade practices, and equitable distribution of costs and benefits. Some authors have emphasized the potential land use conflicts arising from increasing use of biofuels [160], while others focus on the disproportionate effects of biofuel-derived food price increases on the world's poor [19,161,162]. Many of these ethical/moral arguments are similar to those recently expressed in Pope Francis’ Encyclical Letter, “On care for our common home” [163]. To summarize, a statement from Bonin and Lal is appropriate: “Biofuel production using feedstocks that are also staple foods is not a long-term solution in the search for renewable energy from biomass” [48]. 6. Tradeoffs and sustainability Any major change to fuels entails trade-offs of various resource and environmental effects. For example, considering the increased water requirements for corn ethanol, Service stated that “an increased reliance on biofuels trades an oil problem for a water problem” [164]. Also, in their assessments of land requirements for growing corn to produce ethanol, Abbasi et al. concluded that “four times more land is needed to fuel an automobile than to feed one person” [165]. Several other authors have also addressed the general issue of trade-offs inherent in biofuel systems [166–170]. 6.1. Multi-variable assessments When comparing the overall impacts of different fuel systems, it is important to consider a wide range of environmental and ecological effects through multi-variable assessments. In 2007, Von Blottniz and Curran reviewed 47 published reports that compared bio-ethanol and conventional fuel systems on a life-cycle basis, though only 7 of these considered impacts other than energy balances and GHGs [171]. These 7 addressed impacts such as fossil resource depletion, acidification, eutrophication, human toxicity, ecological toxicity, and others. The bioethanol feedstocks included sugar beet, wheat, potato, cassava, and agricultural wastes. While the results across all 7 studies were quite variable, in most cases bio-ethanol was predicted to have greater adverse effects on acidification, ecological toxicity, and human toxicity compared to gasoline. The authors cautioned against basing biofuel production policy on LCA studies that consider only GHG and fossil fuel depletion, without considering a broader range of environmental impacts. Kim and Dale investigated the life-cycle impacts of corn ethanol produced by a dry mill process in the U.S. Midwest and determined that when used as E10, ethanol led to systematic dis-benefits in terms of acidification, eutrophication, and photochemical smog [172]. The dominant factors responsible for these adverse impacts were nitrogen losses from the soil (N2O, NOx, and NO3-), which resulted from use of fertilizers in growing corn. Kreider and Curtis evaluated the potential impacts of displacing gasoline with ethanol, with respect to land availability, water requirements, energy ratios, and life-cycle GHG emissions [173]. They concluded that displacing 50% of current gasoline with corn ethanol was theoretically possible but logically impossible, as this would require use of all U.S. cropland, demand more water than is available, and provide no GHG benefit. Singh et al. integrated energy use and water consumption factors to assess the amount of water required to produce 1 MJ of biofuel [174]. They determined that corn ethanol in 13

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situation when determining which indicators to use in assessing biofuel sustainability [181]. The context includes the purpose of the assessment, the specific biofuel production and distribution system, policy conditions, stakeholder values, and other issues. More recently, a group from ORNL compared the potential environmental effects of gasoline and 1st generation ethanol, with respect to spatial and temporal scales [182]. Detailed descriptions of processes and environmental effects were given for each step in the fuel supply chain. While largely qualitative, the authors concluded that the adverse effects of ethanol production may impact less area and result in more easily reversed effects of shorter duration than the adverse effects of gasoline production.

ranked twelve energy systems for powering all U.S. on-road vehicles [187]. Eleven impact categories were considered, including GHG, air pollution mortality, energy security, water supply, land use, wildlife, resource availability, and water pollution. Weighting factors were applied to each of the 11 impact categories to determine a weighted average score for each energy system. The systems having the most favorable rankings were powered by wind energy – including battery electric vehicles (BEV) and hydrogen fuel cell vehicles (HFCV), while the two systems with the poorest rankings were corn ethanol and cellulosic ethanol.

6.2. Comprehensive environmental assessment (CEA)

Numerous authors have addressed the topic of biofuels sustainability in general, and corn ethanol sustainability in particular. Definitions of sustainability vary, but most can be traced back to concepts outlined in the Brundtland Report of 1987, which stated that sustainable development “meets the needs of the present without compromising the ability of future generations to meet their own needs” [188]. Numerous other definitions and concepts of sustainability have been discussed [189–192]. Generally, sustainability is understood to have three dimensions: social, environmental, and economic - sometimes described as the 3Ps of people, planet, and profit. One of the earliest EPA reports that addressed sustainability of bio-based products was published by Curran in 2003 [193]. This paper presented different LCA approaches to evaluate the environmental trade-offs of bio-based alternatives to petroleum-based products. While the results of the study were quite general – especially in the case of biofuels – they showed that the reality behind widely-held beliefs regarding “green” issues is often more complex than expected. As pointed out by Davis et al., defining environmental sustainability for bioenergy is particularly complicated, as compared to conventional energy, because it requires engagement from many diverse scientific (and non-scientific) disciplines and economic sectors [194]. With respect to environmental sustainability, these authors described a wide variety of indicators that they categorized into six dimensions: feedstock type, feedstock location, feedstock management practices, spatial extent of feedstock, original condition of the land, and ecosystem services. The first international consensus on indicators for sustainable bioenergy production arose from the Food and Agriculture Organization (FAO) of the United Nations (UN), as documented in their 2011 report, The Global Bioenergy Partnership Sustainability Indicators for Bioenergy [195]. More recently, the FAO issued a comprehensive report on Biofuels and the Sustainability Challenge, in which a sustainable biofuel production system is defined as “one that is economically viable, conserves the natural resource base and ensures social well-being” [196]. The three core dimensions (economic, environmental, and social) are inter-linked and should be applied holistically. Economic sustainability requires long-term profitability, minimal competition with food products, and competitiveness with fossil fuels. Environmental sustainability encompasses broad global issues (e.g., GHG emissions and climate change) as well as local/ regional issues (e.g., water management, erosion, soil quality, and air pollutants). Social sustainability is highly location-specific, and includes such elements as food security, poverty, employment, and worker health. This FAO study concludes that from a sustainability perspective, biofuels offer both advantages and risks. Ridley et al. analyzed over 1600 peer-reviewed articles between 2000 and 2009 that addressed several dimensions of biofuel sustainability – including environmental impacts, human well-being, economics, and geography [197]. They determined that the amount and quality of information was variable across these dimensions. In particular, considerable information is available regarding GHG emissions, fuel production, and feedstock production; but relatively little information about international trade, human health, and biodiversity. With such

6.3. Sustainability

Using oxygenated gasoline as an example, Davis and Thomas outlined a systematic approach to evaluate various environmental trade-offs among fuel options [183]. They emphasized the importance of considering both a multi-media effect perspective (air, land, and water) and a life-cycle product perspective (cradle-to-grave). This approach, called comprehensive environmental assessment (CEA), combines LCA with risk assessment (RA). The objective of CEA is not necessarily to arrive at a collective judgment about which fuel option is best, but to identify and understand an array of potential impacts associated with each option, so that more informed decisions can be made. These authors emphasized that due to the complexity of the ecological and health relationships, and the non-quantifiable nature of some trade-offs, the outcomes of CEA must be qualitative. Powers et al. presented an EPA perspective on CEA in a 2012 paper [184]. They emphasized that CEA provides both a systematic framework for organizing complex information and a structured process for reaching transparent judgements about the implications of such information. By following a meta-assessment approach, CEA combines existing assessment methods (such as LCA, decision-support techniques, and cost-benefit analysis) within the basic risk assessment (RA) paradigm. These authors also explained how CEA could be applied to the issue of biofuel sustainability. Gasparatos et al. outlined a framework to systematically review the trade-offs of biofuels with respect to ecosystem services and human wellbeing [185]. Their methods attempt to explicitly identify trade-offs associated with biofuel production and use by examining linkages between ecosystem functions and human wellbeing. It was concluded that biofuels can have both positive and negative consequences in many areas of human wellbeing – including food security, freshwater, erosion regulation, biodiversity, rural development, energy security, and health. While this paper is quite conceptual and international in focus, the authors did identify several issues of particular concern with respect to first-generation biofuels, such as corn ethanol. These include over-reliance on fertilizers and agrochemicals, competition with food production, water demands, water pollution, soil erosion, and loss of biodiversity. Recently, several researchers have applied CEA elements to compare and contrast the overall effects of biofuel options. Yang et al. compared a wide variety of environmental impacts of gasoline and E85, taking into account regional differences among 19 corn-growing states [167]. These impacts were then aggregated by applying weighting factors developed by the National Institute of Standards and Technology (NIST) [186]. Larger adverse impacts were calculated for E85 compared to conventional gasoline in most environmental categories – particularly acidification, eutrophication, water use, and land use. Overall, the average of these weighted impacts was 23% higher for E85 compared to gasoline, and even greater if the impacts of ILUC are considered. The authors concluded that “replacing gasoline with corn ethanol may result in problem shifting, especially to eutrophication and local water scarcity.” Using a somewhat similar approach, Jacobson evaluated and 14

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cropping is increasing, leading to development of a mono-culture and reduction of plant and animal biodiversity. This is increasing stresses on wildlife habitat in general and bird species in particular. This loss of biodiversity, which also reduces ecosystem functions and services, is expected to worsen with further increases in corn agriculture. While difficult to quantify their value, ecosystem services provide essential benefits in areas of water purification, climate regulation, soil formation, nutrient cycling, food production, recreation, and other areas.

Table 5 Sustainability Objectives/Metrics for Assessing Bioenergy Systems. Source: Adapted from Dale and Ong (2014) [190]. Environmental

• Decrease compared to

Social GHGs

alternative system

Economic

large amounts economic • Produce • Provide of energy, beyond that benefits for local required to obtain the

communities

bioenergy or improve economic • Maintain • Provide profits without Maintain or increase net soil health • incentives or nutritional services or improve • Maintain subsidies currently provided by air quality land resources or improve • Maintain water quality social benefits • Provide for local communities or improve • Maintain water supplies plant and • Enhance wildlife biodiversity sensitive • Protect ecosystems • Minimize waste

7.2. CRP and land use change Increased demand for corn-ethanol is expected to promote agricultural intensification and extensification – both of which can have adverse environmental effects. Extensification into CRP lands is particularly worrisome, as these lands have been removed from active agriculture to provide erosion control, reduction of nutrient runoff, protection of biodiversity, and enhancement of ecosystem services. It is generally recognized that CRP lands are less productive than actively cropped lands. Returning these lands into active crop production is likely to result in disproportionate adverse environmental impacts. Modeling studies have shown that sufficient corn could be grown in the U.S. to supply nationwide E20 fuel without utilizing CRP lands. However, recent observations indicate that actual corn cropping is expanding rapidly into former CRP lands and other environmentally fragile lands.

uneven coverage of critical topic areas, assessments of trade-offs and overall sustainability should be made with caution. Assessing sustainability of biofuels is exceedingly difficult owing to the complexity and multiplicity of indicators, which cover a range of spatial and temporal scales. In addition, as pointed out by Sheehan, sustainability issues are commingled with technical and ethical questions [189]. Endres examined sustainability requirements for biomassto-bioenergy as established by different nations, states, and regional organizations, and pointed out that lack of definitional harmonization among these groups is a serious hindrance to international trade [198]. Bosch et al. have also emphasized the need for an internationallyagreed biomass sustainability governance framework [199]. Dale and Ong reviewed numerous approaches of defining sustainable bioenergy systems and identified a set of critical sustainability objectives/indicators that are most frequently used [190]. Besides these metrics, shown in the three categories of Table 5, an overarching general requirement is that sustainable bioenergy systems must limit their reliance on key inputs that are themselves nonrenewable, or are unsustainably produced. More recently, Dale et al. published a framework for selecting indicators of bioenergy sustainability [200]. Rather than defining a rigid set of indicators for all situations, this framework emphasizes flexibility. Through a process of stakeholder engagement, a specific set of goals is defined, followed by determination of sustainability indicators that are most suitable for achieving the goals in the case being considered. While perhaps appropriate in some situations, this general approach seems problematic when considering nationwide plans and regulations requiring specific volumes of biofuels.

7.3. Life-cycle GHG emissions The RFS2 and LCFS regulations of EPA and CARB, respectively, both stipulate that GHG emissions attributable to biofuel pathways be evaluated using a life-cycle assessment (LCA) approach that includes the effects of land use change (LUC). These regulations also require that a specific level of reduction in GHG intensity (g CO2eq/MJ) be achieved for a particular biofuel (or set of biofuels) to be acceptable. GHG emissions associated with indirect land use change (ILUC) are a controversial and highly uncertain component of a biofuel's total life-cycle impact. Estimation of ILUC carbon intensity requires the combination of a global agro-economic model (to assess the location, amount, and type of LUC in response to a biofuel volume shock) and a carbon emissions factor model (to assess the amount of GHG emitted from each type of LUC). Regulatory LCA approaches used by EPA and CARB have estimated that corn ethanol provides a modest GHG reduction benefit (~20%) in future year scenarios. With use of newer information sources becoming available, the GHG reduction benefits of corn ethanol appear to be improving. However, taking into account the uncertainties in these models and their inputs, some current corn ethanol pathways may not offer significant GHG benefits compared to gasoline. 7.4. Food vs. fuel

7. Conclusions Corn is a major commodity in the global food/fuel market. Displacing significant amounts of corn to produce biofuels results in strengthened demand and price increases for this commodity. However, an increase in this commodity price translates to a much smaller increase in the price of grocery food products. The fraction of the U.S. corn crop that is used in corn ethanol production has increased from < 10% in 2000 to over 40% today. This is a major concern to many individuals and organizations that have strong ethical/moral objections to using a staple food product in the production of a fuel. The connections between biofuels and global food security are of increasing concern – particularly in light of a growing world population that is estimated to require 70% more global food production by 2050. A significant issue requiring additional study is that LCA regulatory modeling approaches currently used to assess GHG benefits of corn ethanol assume that 25–50% of the net calories diverted to fuel are not replaced elsewhere in the food/feed system.

Currently, over 14 bg/y of corn-ethanol is used in U.S. gasoline, with nearly all of this in the form of E10. For a variety of reasons, there is interest in exploring an increase in the amount of corn-ethanol to achieve a nationwide level equivalent to E20. Even at today's production level, there are numerous concerns regarding the overall impacts of corn-ethanol. At higher production levels, most of these concerns are exacerbated. In Part II of this literature review study, we have summarized the main issues of concern associated with biodiversity and ecosystems, land use change, GHG emissions, food vs. fuel, and sustainability. The key findings and conclusions are given below, categorized by major topic area. 7.1. Biodiversity and ecosystems To produce additional corn ethanol, the practice of continuous corn 15

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7.5. Tradeoffs and sustainability [10]

Increased production and use of corn-ethanol clearly involves tradeoffs of various resource and environmental impacts. Compared to a gasoline baseline, corn-ethanol reduces petroleum usage and may provide modest reductions in life-cycle GHG emissions. However, these benefits are offset by dis-benefits with respect to nutrient runoff, water quality, water availability, soil quality, air pollutants, land use change, and loss of biodiversity. Biofuel policies that are based solely on GHG impacts may result in overall environmental harm. The sustainability of corn-ethanol is a topic of open debate. Besides the resource and environmental issues described in this report, sustainability includes social and economic dimensions. Assessing sustainability of biofuels is exceedingly difficult, owing to the complexity and multiplicity of indicators, which cover a range of spatial and temporal scales. In addition, sustainability issues are commingled with technical and ethical questions. The ability to produce sufficient corn-ethanol in the U.S. to supply nationwide E10 fuel is clearly demonstrated today, although this has raised numerous environmental, ecological, and social concerns. Increasing the production to supply nationwide E20 appears to be technically possible, but raises serious questions about overall sustainability.

[11] [12] [13] [14] [15]

[16]

[17]

[18]

[19]

[20] [21]

7.6. Summary conclusion Use of corn ethanol as a fuel provides both benefits and dis-benefits. Benefits include rural economic development, enhanced employment, reduction of non-renewable fossil-based fuels, production of valuable by-products (e.g. DDGS), and modest reductions of GHG. Dis-benefits include potential water pollution, water shortages, soil degradation, loss of biodiversity, increased air pollutants, greater food insecurity, and diminished sustainability. While mitigation efforts are being developed and employed to address most of these dis-benefits, these concerns are likely to worsen if sufficient increases of corn ethanol production were to occur to provide nationwide E20 fuel.

[22] [23] [24]

[25]

[26]

[27]

Acknowledgement

[28]

This work was partially supported by the American Petroleum Institute (API). The data collection, analysis, and interpretation were performed by the authors alone. The results and conclusions presented are those of the authors and do not necessarily reflect those of API. Preparation of the manuscript was done entirely by the authors.

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