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5 Environmental Effects and Tradeoffs of Biofuels P etroleum extraction, transport, reﬁning, and combustion have many known negative environmental effects, including disruption of sensitive ecological habitats and high greenhouse-gas (GHG) emissions. Biofuels, too, have their environmental costs (NRC, 2003, 2010a), but displacing petroleum-based fuels with biofuels can reduce the nation’s de- pendence on imported oil and potentially reduce overall environmental harm (Robertson et al., 2008). Each stage in a biofuel’s life cycle uses nonrenewable resources and generates emissions that affect land, air, and water. Hence, the environmental beneﬁts and negative effects over the life cycle of petroleum-based fuels and biofuels would have to be compared against each other so that policymakers can decide which tradeoffs are acceptable. There is neither a simple nor single means of comparing biofuels and petroleum-derived fuels over their full life cycles and over their entire suites of environmental effects, yet decades of research on this topic have revealed that some ways of producing biofuels from certain feedstocks offer distinct advantages over others and thus have greater potential for provid- ing environmental beneﬁts over petroleum-derived fuels. Furthermore, certain stages in the life cycle of biofuels have greater environmental effects than others, and thus deserve particular attention in targeting strategies for optimizing environmental outcomes. This chapter covers the following topics on the potential environmental effects of in- creasing biofuel production: • It provides an overview of the life-cycle assessment methodology typically used to assess environmental effects of biofuel production and use. • It examines the current state of knowledge about key environmental effects. Each environmental effect is discussed, when applicable, in the context of feedstock production, conversion to fuels, and combustion and over the life cycle of bio- fuel production and use. Methods for assessing effects and the anticipated results or observed effects reported in the published literature are presented. Gaps in data availability and deﬁciencies in existing modeling platforms, each of which 181
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182 RENEWABLE FUEL STANDARD contributes to uncertainty in assessing environmental effects, are also pointed out in the following areas: • GHG emissions • Air quality • Water quality • Water quantity and consumptive use • Soil • Biodiversity • Ecosystem services • It uses regional environmental assessments of biofuel production as an illustration because the effects of biofuel production are location-speciﬁc, and conclusions drawn from regional environmental assessments could differ from an assessment of cumulative effects across the nation. • It discusses opportunities to minimize negative environmental effects at the end of the chapter. Although the committee stresses the importance of comparing environmental effects of biofuels to petroleum-based fuels, environmental effects of petroleum-based fuels have been covered in other publications (NRC, 2003, 2010a) and are beyond the scope of this study. LIFE-CYCLE APPROACH FOR ASSESSING ENVIRONMENTAL EFECTS: AN OVERVIEW Biofuels affect the environment at all stages of their production and use. Some effects are easily noticed (for example, odors emanating from an ethanol plant). Others are less apparent, including those that result from activities along the biofuel supply chain (for example, nitrate leaching into surface waters as a result of nitrogen fertilizer application on corn ﬁelds) and those that could occur beyond the supply chain via market-mediated effects (for example, loss of biodiversity upon land-use change induced by higher corn prices). Different effects can occur at local, regional, national, or global scales. Some of these effects are easily quantiﬁed while others are difﬁcult to measure. To better understand the suite of environmental effects associated with biofuels, re- searchers commonly turn to the method of life-cycle assessment (LCA). At the outset, researchers need to deﬁne the goal and scope of LCA. For example, researchers need to consider whether the goal is to assess the effects of biofuel produced at an individual bio- fuel production facility, the average effect of biofuel produced for the entire nation, or the effect of biofuel produced as a result of a policy mandating additional production. Then, an inventory of the resources used and net quantities of substances emitted as a result of biofuel production and use is compiled. This inventory is used to prepare an impact assess- ment that quantiﬁes the ultimate effects on human health, ecosystem function, and natural resource depletion. Numerous methods for compiling inventories and conducting impact assessments exist, all of which have particular strengths and limitations in their modeling of speciﬁc processes and the availability and quality of data used to populate these models. LCA is a valuable tool for quantifying the environmental effects of biofuels, yet wide- spread misinterpretation of the results from studies using different assessment methods has led to great confusion. More often than not, this confusion arises when conclusions from these studies are reported without mention of the particular framework and assumptions under which the analyses were conducted. For example, statements such as “this biofuel releases
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183 ENVIRONMENTAL EFFECTS AND TRADEOFFS OF BIOFUELS less of this pollutant than gasoline” are by themselves meaningless and often misleading unless the goal and scope of the study cited in support of this statement are presented. (See Box 5-1 for a description of the importance of care when reporting results from LCA studies.) A common problem is confusion over two different approaches of LCA—attributional and consequential—and their appropriate use when evaluating biofuels. Attributional LCA, the more traditional form, traces the material and energy ﬂows of a biofuel supply chain and seeks to attribute environmental impact to a biofuel based upon these ﬂows. Consequential LCA, on the other hand, considers the environmental effects of the cascade of events that occur as a result of a decision to produce or not to produce a given biofuel. Many differences between these two approaches of LCA arise because of their distinct ap- plications (Ekvall and Weidema, 2004; Ekvall and Andræ, 2006). Attributional LCA makes use of process-speciﬁc or average data, while consequential LCA uses marginal data. Attri- butional LCA does not consider the market-mediated effects of a given biofuel, such as en- vironmental effects caused by changes in crop or petroleum prices as a result of biofuel pro- duction. Consequential LCA, similar to a cost-beneﬁt analysis, includes market-mediated effects. In essence, attributional LCA takes as a given the total environmental effect of all human activities and seeks to assign responsibility for a portion of the effect to a particular biofuel. Consequential LCA also takes as a given the total environmental effect of all human activities, but it assigns to a particular biofuel the change in total effect caused by a decision and the resulting action of whether to implement, expand, or contract biofuel production. As such, attributional LCA is useful in improving efﬁciency along a biofuel supply chain, and consequential LCA is appropriate in the evaluation of policy and regulation. Both attributional and consequential LCA make use of knowledge of biofuel supply chains, but conducting the latter is far more complicated as it requires marginal data and modeling of market-mediated effects (Kløverpris et al., 2008; Finnveden et al., 2009). In ad- dition, consequential LCA requires preparation of two alternate scenarios (that is, scenarios that represent “yes” and “no” to a decision) whereas attributional LCA requires only one scenario be described (that is, an actual or a projected scenario). Similarly, when measuring the direct environmental effects of supply chains themselves, attributional LCA can rely on actual, measured data, whereas consequential LCA requires that at least one set of data be estimated: When evaluating policies already fully implemented, one set would have to be estimated (that is, the scenario that did not occur) and when evaluating policies with future effects, two sets would have to be estimated (that is, the scenarios for both the “yes” and “no” to a decision). In total, the uncertainty surrounding the results from consequential LCA is compounded compared to attributional LCA, complicating its use in policy deci- sions, even where LCA is mandated such as in the Renewable Fuel Standard as amended in the Energy Independence and Security Act of 2007 (RFS2). This discussion of LCA methodology is important to understanding the environmen- tal effects of biofuels. To date, a large number of studies have used attributional LCA to evaluate individual biofuel production streams and the biofuels industry as a whole. Such studies are helpful for assessing the environmental performance of biofuel supply chains, but they do not consider the broader range of effects from increased biofuel production, such as the effects mediated by markets. Only studies that speciﬁcally estimate the environ- mental effects resulting from the marginal increase in fuel production caused by RFS2 are appropriate for assessing the environmental effects of increasing biofuel production due to its implementation. Studies that have used consequential LCA as a means of quantifying the marginal impact of increased biofuel production are sparse and much needed. In this chapter, results using both methods are presented, with the caveat that what might have been found under one set of circumstances may not hold under other conditions.
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185 ENVIRONMENTAL EFFECTS AND TRADEOFFS OF BIOFUELS GREENHOUSE-GAS EMISSIONS Feedstock Production One of the most debated topics surrounding the environmental effect of biofuels is the net GHG emissions from producing various feedstocks. Potential GHG emissions from bioenergy feedstock production include carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4).1 As elaborated below, the key factors that affect GHG emissions from bioenergy feedstock production are site-speciﬁc and depend on the type of feedstocks pro- duced, the management practices used to produce them, and any land-use changes that their production might incur. Type of Feedstock and Management Practices Potential bioenergy feedstocks mentioned in Chapter 2 can be categorized as annual, herbaceous perennial, short-rotation woody crops (SRWCs), and residue from other sys- tems such as corn stover or forest residue. Choice of feedstock is an important factor in determining the GHG effect of biofuels. For example, perennial herbaceous biomass could increase soil carbon sequestration compared to annual crops (Anderson-Teixeira et al., 2009; Blanco-Canqui, 2010; NRC, 2010b). The GHG implications of a particular feedstock depend on the relationship between that feedstock and site properties such as soil type and climate. As with any agricultural crop, management practices affect the net GHG balance of bioen- ergy feedstock production in several ways: cropping patterns, amount of agrichemical use, tillage practices, and farm equipment use. Farmers and foresters select management practices on the basis of crops grown, soil conditions, precipitation patterns, slope, exposure, available equipment, and their knowl- edge and preferences. In general, choices are made to maximize yield per dollar of input and are not made on the basis of GHG emissions. Yet, choices of management practices have a major inﬂuence on GHG emissions (NRC, 2010b). CO2 released from fossil fuel combus- tion in the manufacturing, transport, and application of agricultural inputs (for example, fertilizers, pesticides, seed, and agricultural lime), N2O released during nitrogen fertilizer production (Snyder et al., 2009), and N2O released because of nitriﬁcation and denitriﬁca- tion stimulated by nitrogen fertilizer application (Bouwman et al., 2010) contribute to GHG emissions. Therefore, producers who choose to cultivate bioenergy feedstocks that require higher agrichemical input in place of crops that require less agrichemical input would incur increases in GHG ﬂuxes. Some bioenergy energy feedstock such as forest residue would have no GHG contribution from agrichemical input. Agricultural soil management accounted for about 68 percent of the total N2O emis- sions in the United States in 2008 (EPA, 2010c). Emission of N2O is predominantly a result of microbial processes of nitriﬁcation and denitriﬁcation; therefore, emission generally increases with nitrogen availability, or the extent to which nitrogen input exceeds crops’ needs (Bouwman et al., 1993, 2002; McSwiney and Robertson, 2005). The type and timing of nitrogen fertilizer used also affects N2O ﬂuxes (Bavin et al., 2009). Technologies for precise application of fertilizers can potentially reduce fertilizer use without compromising yield (Snyder et al., 2009; Gebber and Adamchuk, 2010; Millar et al., 2010), but those technologies are not widely adopted because of socioeconomic, agronomic, and technological reasons 1 Globalwarming potential of a GHG is the warming caused by emission of 1 ton of that GHG compared to 1 ton of CO2 over a speciﬁc time interval. The global warming potentials over a 100-year period are 1 for CO2, 25 for CH4, and 298 for N2O.
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186 RENEWABLE FUEL STANDARD (Robert, 2002; Lamb et al., 2008; USDA-NIFA, 2009). Precision management of nitrogen fertilization can also improve biomass quality for cellulosic biofuels (Gallagher et al., 2011). The environmental beneﬁts of crop rotations include enhanced control of weeds, pests, and diseases; increased availability of nutrients; accumulation of soil carbon; and higher yields (NRC, 2010b). Those beneﬁts, if combined with higher yields, contribute to reduc- ing agrichemical input and GHG emissions. Increased diversity of crops planted in a ﬁeld (either at once or over the course of a year) could also reduce the amount of pesticide appli- cation needed (GAO, 2009). For example, mixtures that include grasses and nitrogen-ﬁxing legumes can also reduce nitrogen fertilizer needs (Tilman et al., 2006; Fornara and Tilman, 2008; NRC, 2010b). Gardiner et al. (2010) compared preexisting corn, switchgrass, and mixed prairie crops in Michigan and found that switchgrass and mixed prairie crops sup- ported greater abundance of arthropod generalist natural enemies of crop pests. Even crop rotation between corn and soybean can help control pests and reduce the use of pesticides by breaking the pattern of pests and disease that can be present in monocultures. Integrated pest management can potentially contribute to reducing pesticide input (Trumble et al., 1997; Reitz et al., 1999; NRC, 2010b). The effect of no-till and reduced tillage on soil organic carbon (SOC) storage is incon- sistent and depends on depth of soil sampling and crop management (Dolan et al., 2006; Baker et al., 2007; Johnson et al., 2007; Luo et al., 2010; Kravchenko and Robertson, 2011). Studies that assess carbon content in the entire soil proﬁle (0-60 cm) did not ﬁnd higher soil carbon in no-till ﬁelds than in conventionally tilled ﬁelds (Blanco-Canqui and Lal, 2008; Christopher et al., 2009). Nonetheless, no-till and reduced tillage may contribute to reduc- ing GHG emissions because those practices require less fossil-fuel inputs for machinery that perform the tilling (Adler et al., 2007) and emissions of N2O might be lower (Omonode et al., 2011). No-till and reduced tillage also have other environmental beneﬁts because they enhance soil water retention and microbial activity and diversity, reduce soil erosion and sediment runoff, and improve air quality compared to conventional tillage (NRC, 2010b). Methods of Assessment Over the past several decades, ecosystem ecologists have estimated carbon storage and GHG consequences of land-use management practices on regional and continental scales, using spatial databases to represent key driving variables, including soils (for example, STATSGO), average climatic data, satellite imagery (for example, MODIS), and current or projected land-use management, combined with simulation models. This strategy has been used to assess consequences of cropping (Campbell et al., 2005; Del Grosso et al., 2005; Izaurralde et al., 2006), forest management (Adams et al., 1999; Sohngen and Sedjo, 2000; Murray et al., 2005; Johnson et al., 2010), and climate change (Paustian et al., 1997; Lu and Zhuang, 2010). Notably, simulation results (and indeed the biological processes re- sponsible for GHG ﬂuxes) are very sensitive to site-speciﬁc factors that are variable. Those site-speciﬁc factors, including fertilization practices, cultivation and residue management, and forest age classes, are rarely available as input data. Thus, potential error increases for scaled-up estimates, based on the presence, accuracy, and spatial resolution of input data, and the ability of simulation models to accurately estimate ﬂuxes. Zhang et al. (2010) used this strategy to assess environmental effects including GHG emissions that might occur based on spatially explicit scenarios of bioenergy feedstock expansion, including annual crops, herbaceous perennial crops, SRWC, and residue har- vest. They predicted locations for different bioenergy crops and management options in a nine-county region in southwestern Michigan that would minimize GHG emissions while maintaining certain minimum yields and maximum nitrate runoff levels. They presented
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187 ENVIRONMENTAL EFFECTS AND TRADEOFFS OF BIOFUELS sample results involving the minimization of GHG ﬂux per unit area, although the ﬂex- ibility of their framework allows for the calculation of other variables of interest, such as GHG ﬂux per unit of energy produced, which may be more useful for integration with full LCAs. In addition, Zhang et al. (2010) noted that their framework could be extended into a spatially explicit LCA in which, for example, optimal locations for bioreﬁneries could be modeled simultaneously with feedstock production locations. Anticipated or Observed Results As mentioned above, the effects of bioenergy feedstock production on GHG emissions depend on feedstock choice, management practices, and changes in land use and land cover so that any quantitative estimates of GHG emissions are site speciﬁc. This section discusses the anticipated or observed effects of feedstock production on GHG emission as organized by major feedstock categories. For corn and soybean production, fertilizer use generates GHGs as a result of fossil- fuel input in manufacturing and transporting fertilizers and of nitrogen from fertilizers not taken up by plants and emitted as N2O. In 2005, about 95 percent of the corn acreage in the United States received nitrogen fertilizer, and the average application rate was about 138 lb/acre (Table 5-1). Soybean requires less inputs (particularly nitrogen fertilizers) to produce than corn on a per-acre basis (Schnepf, 2004). However, a comparison of GHG contribution from fertilizer manufacture and use in feedstock production between biofuels have to account for crop yield per acre,2 conversion yield from feedstock to biofuel,3 and the energy content of biofuel.4 The opportunity offered by the future use of cellulosic feedstocks is that GHG emis- sions could be reduced, but that beneﬁts can only be achieved in some situations. Corn stover, cereal straw, and other crop residues draw on existing crops so that their use as bio- energy feedstock under best management practices might not contribute much additional GHG emissions. However, overharvesting of crop residues could result in additional need for agrichemical inputs and the loss of soil organic matter, which is critical for maintaining soil structure and water retention capacity and for improving nutrient cycling and other soil processes (Wilhelm et al., 2007; NAS-NAE-NRC, 2009; NRC, 2010b). Any additional fuel use for collecting the residues that contributes to GHG emissions would also have to be accounted for. TABLE 5-1 Fertilizer Use for Corn and Soybean Production in the United States Corna Soybeanb Acreage fertilized receiving nitrogen fertilizer (percent) 96 18 Average rate of nitrogen fertilizer application (lbs/acre) 138 16 Acreage fertilized receiving phosphate fertilizer (percent) 81 23 Average rate of phosphate fertilizer application (lbs/acre) 58 46 Acreage fertilized receiving potash fertilizer (percent) 65 25 Average rate of potash fertilizer application (lbs/acre) 84 80 aLatest data from source are for the year 2005. bLatest data from source are for the year 2006. SOURCE: USDA-ERS (2010c). 2 Corn yield per acre is about 4 times higher than soybean yield (USDA-NASS, 2010). 3 About 1 bushel of soybean produces 1.5 gallons of biodiesel, while 1 bushel of corn produces about 2.7 gallons of ethanol. 4 The energy content of corn-grain ethanol is about two-thirds of that of soybean biodiesel.
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188 RENEWABLE FUEL STANDARD Growing perennial dedicated bioenergy crops could have less direct GHG emissions than growing row crops because their root systems contribute to sequestration of carbon. Surveys of common agronomic practices for growing Miscanthus show a broad range in nitrogen fertilizer use, typically around 50-100 lbs per acre per year (Heaton et al., 2004; Khanna et al., 2008). In their review of published literature, Parrish and Fike (2005) reported that data on nitrogen requirements in switchgrass span a range of 0-200 lbs per acre, and that the variations can be partly attributed to different harvest practices, within-plant nitro- gen recycling, and site-speciﬁc soil nitrogen mineralization rates and atmospheric deposi- tion and microbial ﬁxation of nitrogen. Liebig et al. (2008) measured changes in soil organic carbon (SOC) in the top 0-30 cm and 0-120 cm of soil in switchgrass ﬁelds on 10 farms that were previously used for annual crop production in the central and northern Great Plains. They reported accumulation of SOC over time, but the change in SOC varied considerably across sites from –2.2 to 16 Mg CO2 eq per hectare per year in the top 0-30 cm. Garten et al. (2010) found that a single harvest of switchgrass at the end of the growing season increased SOC sequestration and system nitrogen balance on well-drained Alﬁsols in west Tennessee. SOC sequestration rates in the top 15 cm of reconstructed tall grass prairies on previously cultivated land in southern Iowa varied signiﬁcantly with topography and age of the prai- rie stand (Guzman and Al-Kaisi, 2010). Using woody residues as a bioenergy feedstock can result in relatively low GHG emis- sions compared to crops that are planted and harvested exclusively for bioenergy purposes if they are a byproduct of existing harvesting operations and do not require fertilizer input. In some regions of the United States, harvesting dead material from the forest ﬂoor and forest thinning could reduce the potential for wildﬁres (Fight and Barbour, 2005; Busse et al., 2009; Kalies et al., 2010) that also contribute much CO2 to the atmosphere. SRWC can sequester SOC depending on trees grown, soil types, and prior land use, according to a review of literature by Blanco-Canqui (2010). The author noted that nitrogen- ﬁxing trees sequester more SOC than other trees. Fertilization and irrigation can increase SOC sequestration and yield increase, but CO2 emissions associated with these activities may offset some SOC beneﬁts (Blanco-Canqui, 2010). Biofuel-Induced Land-Use Changes Carbon is stored in soil and in above-ground and below-ground vegetation. Soil carbon storage depends on soil characteristics and past disturbances. The amount of carbon stored in vegetation depends on the vegetation type. Therefore, land-use changes that involve removing or planting of vegetation could either release a large amount of carbon from soil or store carbon depending on the conditions of the land prior to use, crop characteristics (Fearnside, 1996; Guo and Gifford, 2002b; Woodbury et al., 2006), and management prac- tices (as discussed above). Similarly, land-use change could disrupt or enhance the future potential of land to store carbon. Land use is deﬁned by anthropogenic activities, such as agriculture, forestry, and urban development, that alter land-surface processes, including biogeochemistry, hydrology, and biodiversity. Land cover is the extent and type of physical and biological cover over the surface of land. Some authors have divided land-use changes into two types when consid- ering biofuel policy: direct land-use change and indirect land-use change. Biofuel-induced land-use changes occur directly when land is dedicated from one use to the purpose of growing biofuel feedstock. Biofuel-induced land-use changes can occur indirectly if land use for production of biofuel feedstocks causes new land-use changes elsewhere through market-mediated effects. The production of biofuel feedstocks can constrain the supply of
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189 ENVIRONMENTAL EFFECTS AND TRADEOFFS OF BIOFUELS commodity crops and raise prices, thus triggering other agricultural growers to respond to market signals (higher commodity prices) and to expand production of the displaced commodity crop. This process might ultimately lead to conversion of nonagricultural land (such as forests or grassland) to cropland. Because agricultural markets are intertwined globally, production of bioenergy feedstock in the United States could result in land-use and land-cover changes elsewhere in the world. If those changes reduce the carbon stock in vegetation, carbon would be released in the atmosphere when land-use change occurs. In particular, transition from forest to cropland or pasture emits a large amount of CO2 be- cause of CO2 releases from decomposition of woody debris and short-lived wood products (NRC, 2010c). Similarly, land-use change could disrupt or enhance the future potential of land to store carbon. Many economic studies have shown the “unintended” consequences of policy (Stavins and Jaffe, 1990; Wu, 2000; Wear and Murray, 2004), and the principle from Wu’s study is relevant to increasing biofuel production in United States. Wu (2000) showed cropland enrolled in the U.S. Department of Agriculture’s (USDA) Conservation Reserve Program (CRP) had a 20-percent slippage. That is, for every 5 acres of cropland enrolled in CRP, 1 acre of noncropland is added to cropland elsewhere. That study did not account for carbon emissions, but it pointed out the rippling effects of shifting land uses. Other studies have linked land-use changes to carbon changes and showed that projects and policies intended to mitigate GHG emissions in the forestry or agricultural sector could lead to “leakage,”5 or responses to those projects and policies by other parties that also cause GHG emissions (Sohngen and Brown, 2004; Murray et al., 2007). Methods of Assessment Land-Use and Land-Cover Changes. Remote sensing using satellite and aircraft sensors can be used to map land cover and land use and provide information on above-ground vegetation and residue cover (NRC, 2010c). Data from remote sensing can be coupled with land monitoring to estimate GHG ﬂuxes from land-use changes (Houghton, 2010; NRC, 2010c; West et al., 2010). Uncertainties of annual carbon ﬂuxes from deforestation, refores- tation, and forest degradation based on remote sensing vary from 25 to 100 percent (NRC, 2010c). Variations in plant residue, along with soil moisture and mineralogy and vegetation cover, are a problem in estimating soil surface carbon. Even so, progress has been made in assessing crop residue coverage using space-borne hyperspectral instruments (Daughtry et al., 2006; NRC, 2010c). Estimates of N2O emissions from managed lands have about 50-per- cent uncertainty even with the best inventory methods, and those estimates are even more uncertain in developing countries than in developed countries (NRC, 2010c). Market-Mediated Effects. A number of different types of economic models have been used to calculate the global indirect effects of increasing biofuel production. An important aspect emphasized by these models is global interaction. For example, shocks to supply and demand in one region have well-deﬁned price effects on global markets, as illustrated by the market price ﬂuctuations as a result of drought in Russia in 2010. Economic models have been developed to capture this phenomenon. The short-term and long-term effects of biofuel policy on global commodity markets are discussed in Chapter 4. A second aspect emphasized by these models is the competition among different land uses. Economic models are often best suited to account for the behavior of different 5 GHG leakage is the term that was introduced to refer to the conditions when an activity displaces GHG emis- sions outside the boundaries of the activity area (Murray et al., 2007). For example, afforestation efforts in one country could lead to market forces that encourage deforestation in another country (Meyfroidt et al., 2010).
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190 RENEWABLE FUEL STANDARD competing demands for land, as well as the supply of land. A number of different economic models, including general-equilibrium and partial-equilibrium models, have been used to study indirect land changes, and the advantages and disadvantages of several approaches have been discussed elsewhere (Kretschmer and Peterson, 2010). The estimates of indirect land changes are then added to direct GHG models, such as GREET,6 to estimate total di- rect and indirect GHG emissions. Although such analyses consider emissions as a result of market-mediated effects on land use, they are not, strictly speaking, consequential LCAs. Rather, they represent a hybrid approach in which marginal data for a speciﬁc parameter (land use) are incorporated into an attributional LCA model. Among many differences, comprehensive consequential LCA would, for example, also consider elasticity of petro- leum markets. GHG Emissions Estimated from Market-Mediated Land-Use Changes. GHG emissions from indirect land-use or land-cover changes can be estimated by coupling estimates of market-mediated land-use or land-cover changes with estimates of GHG emissions from those projected land-use or land-cover changes. The resulting projection of GHG emissions from indirect land-use changes has large uncertainty because of difﬁculty to establish a causal link between direct-use changes and indirect-use changes that are separated spa- tially and temporally. For example, many factors inﬂuence land-use changes, and showing precisely that a price change induced by biofuel policy as the precipitating cause is difﬁcult. Even if an economic linkage can be shown, calculating the carbon change is difﬁcult be- cause there is substantial heterogeneity in carbon on the landscape. If the indirect land-use change involves removing tropical forests, the carbon emissions could be high, but if the indirect land-use change involves converting pasture or fallow land to cropland, then the carbon effects could be smaller. Several concerns have been raised about the existing estimates of the indirect effects of land use. One concern relates to the many steps that need to be undertaken to show indi- rect land-use change and uncertainty associated with all those steps. For example, the ﬁrst step in any analysis of the effects of U.S. policy is to determine what crops besides corn are displaced as a result of increased biofuel production. The second step is to determine how much these changes in U.S. markets inﬂuence prices in other countries (Babcock, 2009; Zil- berman et al., 2010). The key concern with these calculations is that U.S. economists have an idea of U.S. farmers’ responses to price change on the basis of historic trends, but Babcock (2009) argued that the response of farmers in other parts of the world to price changes is much less certain. Similar concerns have been raised by Kim and Dale (2011), who were un- able to ﬁnd correlative evidence between increased demand for corn and land-use change from 2001-2007. O’Hare et al. (2011) argued that Kim and Dale’s analysis was ﬂawed. The committee advocates that additional data and analyses are needed to assess net changes in land use as a result of market-mediated effects of feedstock production for biofuels. A second concern is that simulations from economic models use point estimates of various parameters, each of which varies temporally and spatially (Zilberman et al., 2010). A third concern is that other factors that contribute to land-use change decisions, including cul- tural, political, and ecological factors (Geist and Lambin, 2002; Turner et al., 2007), are not accounted for in economic models. Finally, one response to rising prices is intensiﬁcation of existing croplands. The different models discussed later account for cropland intensiﬁca- tion to different extents. For example, the study by Searchinger et al. (2008) assumes that increased yields from intensiﬁcation will be offset by lower yields on lower-quality lands 6 The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model by Argonne National Laboratory.
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191 ENVIRONMENTAL EFFECTS AND TRADEOFFS OF BIOFUELS brought into production. The results from Hertel et al. (2010) directly incorporate intensiﬁ- cation of crop management as a result of rising prices. Cropland intensiﬁcation helps reduce the overall indirect effects. Anticipated Effects Direct conversion of native ecosystems to producing corn for ethanol releases large amounts of GHG into the atmosphere (Fargione et al., 2008; Gibbs et al., 2008; Ravindra- nath et al., 2009). Based on the deﬁnition in RFS2, only planted crops and crop residue from agricultural land cleared prior to December 19, 2007, and actively managed or fallow on that date are considered compliant feedstocks. This deﬁnition discourages land clearing of native ecosystems for bioenergy feedstock production so that GHG emissions from direct land-use change could be minimized. However, some farmers could use existing cropland to produce bioenergy feedstocks. Conversely, converting from annual to perennial bioenergy crops can enhance carbon sequestration on that piece of land (Fargione et al., 2008). The perennial bioenergy crops are considered RFS-compliant feedstock. However, the carbon storage could be offset by market-mediated effects on land-use and land-cover changes elsewhere as a result of bio- fuel production in the United States. A few authors estimated GHG emissions from indirect land-use change as a result of increasing corn-grain ethanol production in the United States. Their simulations represent changes in GHG emissions from land-use changes with or without U.S. biofuel production. Other drivers of land-use changes were not considered. Searchinger et al. (2008) estimated that GHG emissions from indirect land-use change in Brazil, China, India, and the United States from U.S. corn-grain ethanol production to be 104 g CO2 eq per MJ. Searchinger et al. (2008) projected land-use changes on the basis of historical data from 1990 to 1999. They estimated GHG emissions from the land-use change would be offset by GHG beneﬁts ac- crued from substituting gasoline with corn-grain ethanol only after 167 years. Dumortier et al. (2010) demonstrated that differences in the economic model and data source did not alter the estimate of GHG emission from indirect land-use change much when they used the same assumptions of increase in ethanol production over time and types of land cover converted as Searchinger et al. (2008). In contrast, changes in assump- tions on the type of land converted, net land displacement factor,7 crop yield, and increase in ethanol production had large effects on estimated GHG emissions (Dumortier et al., 2010; Plevin et al., 2010). The model of the Global Trade Analysis Project (GTAP) has been used to estimate biofuel-induced land-use change emission estimates for the California Air Resources Board (Tyner et al., 2010). To evaluate the land-use implications of U.S. ethanol production, they developed three groups of simulations. In the ﬁrst group, they calculated the land-use im- plications of U.S. ethanol production off the 2001 database. This is version 6 of the GTAP global database, which is updated every 2-3 years. This approach isolates effects of U.S. ethanol production from other changes that shape the world economy. In the second group of simulations, Tyner et al. (2010) ﬁrst constructed a baseline that represents changes in the world economy during the time period of 2001-2006. Then they calculated the land-use impact of U.S. ethanol production based on the updated 2006 database. Finally, in the third group of simulations, they used the updated 2006 database obtained from the second group 7 Net land displacement factor is the ratio of land acreage brought into crop production anywhere in the world as a result of market-mediated effects of bioenergy feedstock production to land acreage dedicated to bioenergy feedstock production.
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