Accounting for Time-Dependent Effects in Biofuel Life Cycle

Aug 14, 2009 - A particularly important application of TCFs may be in LCAs of renewable energy technologies that require intensive investment of mater...
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Environ. Sci. Technol. 2009, 43, 7142–7147

Accounting for Time-Dependent Effects in Biofuel Life Cycle Greenhouse Gas Emissions Calculations ALISSA KENDALL* Dept. of Civil and Environmental Engineering, One Shields Ave. Davis, California 95616 BRENDA CHANG Institute of Transportation Studies, One Shields Ave. Davis, California 95616 BENJAMIN SHARPE Dept. of Civil and Environmental Engineering, One Shields Ave. Davis, California 95616

Received April 15, 2009. Revised manuscript received July 21, 2009. Accepted August 4, 2009.

This paper proposes a time correction factor (TCF) to properly account for the timing of land use change-derived greenhouse gas emissions in the biofuels life cycle. Land use change emissions occur at the outset of biofuel feedstock production, and are typically amortized over an assumed time horizon to assign the burdens of land use change to multiple generations of feedstock crops. Greenhouse gas intensity calculations amortize emissions by dividing them equally over a time horizon, overlooking the fact that the effect of a greenhouse gas increases with the time it remains in the atmosphere. The TCF is calculated based on the relative climate change effect of an emission occurring at the outset of biofuel feedstock cultivation versus one amortized over a time horizon. For time horizons between 10 and 50 years, the TCF varies between 1.7 and 1.8 for carbon dioxide emissions, indicating that the actual climate change effect of an emission is 70-80% higher than the effect of its amortized values. The TCF has broad relevance for correcting the treatment of emissions timing in other life cycle assessment applications, such as emissions from capital investments for production systems or manufacturing emissions for renewable energy technologies.

Introduction Recent biofuels policy developments have incorporated life cycle assessment (LCA) concepts to calculate the greenhouse gas (GHG) emissions intensity of fuels (1-4). These developments are likely a response to findings from recent studies that indicate GHG emissions from biofuels production are larger than previously thought (5), particularly when researchers account for the effects of land use change (LUC) (6, 7). The incorporation of LUC emissions into estimates of emissions intensity has revealed shortcomings in LCA methodology with respect to accounting for GHG emissions timing. Current methods treat emissions identically regard* Corresponding author phone: 530-752-5722; fax: 530-752-7872; e-mail: [email protected]. 7142

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less of when they occur in a product’s life cycle, leading to a miscalculation of their true climate change effects. LUC emissions occur in, or near, the first year of biofuel feedstock cultivation as carbon that is naturally sequestered in vegetation and soil is released during clearing of vegetation and perturbation of the soil. With the exception of one recent study by O’Hare et al. (8), studies estimating LUC emissions from biofuels have implemented a straight-line amortization of those emissions, assigning each crop generation equal responsibility for the LUC event over a specified, relatively arbitrary, time horizon (6, 9). Amortization spreads out an emission that occurs over a short period of time, over a longer time horizon. The cumulative effect of a GHG increases with its time in the atmosphere, thus the practice of straight-line amortizing LUC emissions that occur at the outset of biofuel production significantly underestimates their true climate change effect. The goal of this study is to develop a method to accurately represent the climate change effect of amortized GHG emissions in estimates of life cycle emissions intensity, and to apply this method to estimates of LUC emissions from biofuels production. Others (8) have identified this same shortcoming of straight-line amortization in biofuel GHG emissions estimates and proposed a metric that measures of a fuel’s global warming potential relative to the fossil fuel it replaces. This study takes a different approach and proposes a scaling factor referred to as a time correction factor (TCF) that adjusts the value of an amortized emission to reflect its true climate change effect. The product of the amortized value and the TCF can be incorporated directly into emissions intensity calculations. The TCF ensures that by the end of the amortization time horizon, the climate change effect of an amortized emission is equivalent to that of the actual emission that occurs at the outset of production. Based on previous studies that report CO2 to be the dominant land use related GHG (5, 10, 11), the TCFs developed in this study apply only to CO2 emissions, but equivalent factors could be calculated for methane (CH4) and nitrous oxide (N2O). The TCF is analogous to the global warming potentials (GWPs) developed by the Intergovernmental Panel on Climate Change (IPCC) that convert non-CO2 GHGs to a CO2 equivalent (CO2e) based on their relative impact (12). Both GWP and TCF estimate the relative climate change effect of a gas based on its cumulative radiative forcing (CRF) over some time horizon. However, where GWPs provide equivalency factors for the climate change effect of different GHGs relative to CO2, the TCF provides an equivalency factor for the relative effect of emissions occurring at different times, tailored to the condition where emissions are amortized over a time horizon. The TCF was specifically developed to address proper handling of LUC-related emissions for biofuels. However, the problem of GHG emissions timing and the concept of TCF has application more broadly in LCA, where historically the timing of emissions has been overlooked. A particularly important application of TCFs may be in LCAs of renewable energy technologies that require intensive investment of materials and manufacturing emissions during their production, but produce low emissions during operation, such as photovoltaic technologies. Like biofuels, life cycle GHG evaluations of these technologies often report outcomes in units of emissions intensity, but have not considered emissions timing in their calculations. 1.1. Greenhouse Gas Accounting Practices and Life Cycle Assessment. LUC emissions occur as the carbon that 10.1021/es900529u CCC: $40.75

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is naturally sequestered in vegetation and soil is released during clearing of vegetation and perturbation of the soil. This process may occur directly if land is converted for biofuel feedstock cultivation, or indirectly as market signals or diversion of crops to biofuel production induce LUC elsewhere. The loss of this stored carbon to the atmosphere occurs in, or near, the first year of biofuel feedstock production. LUC is a process required to produce biofuels, just as more traditional capital investments such as the construction of factories or the production of machinery are required to produce manufactured goods. Both LUC and capital investments result in emissions that occur early or in advance of a product’s life cycle, but are required to produce the product. Because generations of future products depend on these early emissions, a method for allocating emissions over time is required. In previous life cycle studies of biofuels, the question of including capital goods such as machinery and farm buildings has been debated, and when included, the capital investments have been straight-line amortized over the expected lifetime of the good (13). This treatment of emissions from capital investments is common practice in LCA, and because temporal aspects have largely been ignored in the characterization of impacts (14), the practice of straight-line amortization has faced limited scrutiny. Some previous LCA studies that have identified the importance of emissions occurring now versus later have explored the use of discount rates in the context of GHG emissions and calculation of carbon equivalency (15). The discounting of emissions generally reflects the logic that GHGs will cause a climate response, in turn causing climate change impacts with consequent economic damages to people and property, which can then be discounted using traditional economic methods. While the selection of an appropriate discount rate has been debated, particularly for economic flows occurring in the distant future (16), the most significant barrier to implementing such a method is not the selection of a discount rate, but the process of translating GHG emissions into specific impacts and then into economic damage costs. This translation process is highly uncertain. Each step in the chain of GHG effects must be modeled and the difficult task of assigning monetary values to climate change effects ranging from biodiversity loss, to forced migration of peoples, to loss of human life must be undertaken as well. The recent work of O’Hare et al. (8) uses CRF and develops alternatives to simple discounting and summing of GWPs over the life cycle after straight-line amortization of emissions. They propose new metrics, a fuel warming intensity (FWI) and a fuel warming potential (FWP), as well as equivalent metrics that combine CRF and economic discounting. These metrics use the relative CRF of biofuels and their fossil fuel counterparts to provide a clear indication of when biofuels perform better or worse than their conventional counterpart. Because of the difficulty of predicting the monetary costs of climate change, O’Hare et al. argue for using CRF as a proxy for climate change effects and apply a discount rate to these CRF estimates. Their conclusions emphasize that discounting merely increases the importance of near-term GHG emissions. At least one additional study has considered the problem of emissions timing, though in the context of comparing carbon sequestration to avoided carbon. Moura Costa and Wilson (17) used a method analogous to the GWP and dependent on CRF to develop an “equivalence time” for sequestered versus avoided GHG emissions, which could also be expressed as an equivalence factor. The FWP, FWI, and Moura Costa and Wilson’s equivalence factor are founded on the same physical concepts as those used to develop the TCF, but their purpose and application are distinct. The FWP and FWI metrics facilitate comparison between biofuels and conventional fuels, relevant at a broader

policy level, and they provide a way to examine the effect of economic discounting in decisions about biofuels. The concept of an equivalence time is tailored to the problem of assigning carbon credits to sequestration versus avoided emissions, particularly relevant in the context of carbon markets. In contrast, the TCF is developed to address the specific challenge of correctly representing the climate change effect of amortized CO2 emissions in life cycle emissions intensity estimates. 1.2. Biofuel-Induced Land Use Change. In February 2008, Science magazine published two articles that challenged current estimates of life cycle emissions related to the production of biofuels. Both articles showed that when LUCrelated emissions were included in GHG estimates, biofuel emissions exceeded that of gasoline (5, 6). The conclusions of these articles have led to a new discourse on the GHG implications of biofuels. Searchinger et al. (6) estimated the additional emissions due to LUC from corn-ethanol production in the U.S. and compared them to total fuel cycle gasoline emissions. Their study reported both direct and indirect LUC emissions. The combined effect of direct and indirect LUC results in life cycle GHG emissions for U.S. corn-ethanol of 177 g CO2e/ MJ, nearly double that of gasoline at 92 g CO2e/MJ. Nonland use life cycle GHG emissions estimates were calculated using Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model (9). A partial equilibrium model was used to predict both direct and indirect LUC emissions by estimating the change in area of domestic land due to higher corn demands based on production levels required to meet corn-ethanol policy mandates (56 billion liters of corn-ethanol by 2016). Increased demand for corn for ethanol resulted in displacement of other crops and increased cropland expansion in other countries dependent on U.S.-corn exports, leading to an estimated 10.8 million hectares of additional land in China, India, Brazil, and the U.S. to meet the increased U.S. demand for corn ethanol. The average up-front conversion emissions, 351 t/ha, for the average distribution of ecosystems were then straight-line amortized over a time horizon of 30 years, yielding 11.7 tons/ha/year. While there may be multiple sources of uncertainty in the Searchinger et al. study, one of the most significant assumptions is the time horizon over which LUC emissions are amortized. They cite the Fourth Assessment Report from the Intergovernmental Panel on Climate Change (12) for their selection of a 30-year time horizon, asserting that GHG emissions reductions in a 30 year time horizon “...will be both difficult to achieve and important to avoid irreversible adverse effects from climate change,” and “...because ethanol is typically viewed as a bridge to more transformative energy technologies” (6). Fargione et al. (5) estimated emissions resulting from the direct conversion of various ecosystems to cropland for biofuel feedstock cultivation. Original ecosystems included rainforests, grassland, and existing croplands, which could be converted to biodiesel and ethanol fuel crops such as palm, sugar cane, and corn. Using soil and plant carbon loss estimates and the share of different lands attributed to biofuels through market value, they found that GHG emissions due to land use conversion were 17-423 times more than what the biofuels would displace with respect to gasoline. The authors did not amortize LUC emissions, but instead applied a payback time concept and calculated the time required for biofuel usage to offset the incurred “carbon debts” from the initial conversion. While the concept of payback time avoids the problem of selecting a time horizon, it does not provide a way to incorporate LUC emissions into VOL. 43, NO. 18, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Global Biofuel Policies, Target Years, and Treatment of LUC country or region United States Brazil European Union Germany China Canada Thailand Colombia India Australia

biofuel policy mandates/targets 36 billion gallons (21 billion “advanced biofuels”) mandatory blending in gasoline between 20-25% directive for 10% share of renewables in transport fuels overall biofuels mandate for transportation of 8% 9 million tonnes of ethanol and 15 million tonnes of biodiesel average renewable content: 5% in gasoline, 2% in diesel 10% blending in gasoline and diesel 10% blending in gasoline and 5% in diesel transport fuels to have 20% biofuel content 350 million liter

estimates of biofuel GHG intensity on a per-MJ or per-L basis, which proposed policies require. Regardless of the treatment of time horizon, or criticisms of methods used to model LUC, both articles have spurred the regulatory community to address LUC-related emissions in life cycle GHG calculations of biofuels. 1.3. Biofuels Policies and the Selection of a Time Horizon for Amortization. A variety of policy objectives including decreasing the carbon intensity of transportation fuels, diversifying fuel supply sources, and developing longterm replacements for fossil fuels have motivated governments around the world to promote biofuels. Most of these biofuel policies set specific volumetric mandates, targets, or blending requirements by certain target years. Table 1 summarizes key biofuel policies from the 10 largest ethanol producers in the world (18). The target year could be used as an indicator for the minimum time horizon of biofuels cultivation. That is, any biofuels-driven land use events that occur now, or have occurred previously, are likely to remain under cultivation at least until the target year, and then as long as the target remains in place. Targets distinguish between first and second generation biofuels. “Second generation” or “advanced” biofuels typically refer to biofuels derived from cellulose, hemicellulose, or lignin as well as biofuels made from waste materials, different feedstocks from those currently used for biofuel production. Timelines for adopting second generation biofuels could offer another potential indicator for the expected time horizon of LUC from first generation biofuels. As evident in Table 1, two countries and one international region, the U.S., Germany and the E.U., have included LUC provisions in their biofuel policies. The following briefly summarizes the policies that explicitly address LUC and what attention, if any, is given to the issue of time horizon. The United States passed the Renewable Fuels Standard (RFS) in December 2007 as a part of the Energy Independence and Security Act (EISA) (2). As a condition of the RFS, any renewable fuel produced from new facilities must achieve at least a 20% reduction in life cycle GHG emissions compared to baseline (2005) emissions. Life cycle GHG emissions are defined as the “aggregate quantity of greenhouse gas emissionssincluding direct emissions and significant indirect emissions such as from land use changessas determined by the Administrator of the U.S. Environmental Protection Agency (EPA)...” (2). The RFS attempts to account for domestic LUC onlysthe EPA cites modeling limitations as the reason for not taking any indirect changes to international cropping 7144

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second generation biofuels timeline

explicit LUC provisions

yes

yes

no

no

2020

yes

yes

2015

yes

yes

2020

no

no

2010, 2012

no

no

2012

no

no

2009

no

no

2017

no

no

2010

no

no

target year 2022

patterns into account. Domestic LUC is said to be “well understood,” but it is not clear whether indirect LUC in the U.S. was included in the analysis (2). In addition, while the RFS describes domestic LUC as an input into their analysis models, there is no discussion of time horizon or how this parameter influences the results. The Biofuels Directive of the European Union (EU) has a mandatory target of a 20% share of renewable energies in overall energy consumption by 2020 and a mandatory 10% minimum target to be achieved by all Member States for the share of biofuels in transportation by 2020 (3). The Directive’s methodology for calculating the GHG emissions from the production and use of biofuels has a specific land use component, which attempts to quantify the GHG emissions associated with direct LUC. Emissions caused by LUC are straight-line amortized over 20 years (3). Within the EU, Member States are free to craft individual policies that compliment the Biofuels Directive, and Germany’s Biofuels Sustainability Ordinance (BSO) is one such example. As with the Biofuels Directive, the BSO stipulates that emissions caused by direct LUC effects have to be considered within the GHG balance using a 20-year amortization period (4). In both the Biofuels Directive and the BSO, there is no explanation for the choice of a 20-year time horizon. As with Searchinger et al.’s (6) selection of a 30-year time horizon, policy-makers have somewhat arbitrarily selected a 20-year time horizon. Despite the lack of a conclusive argument for or against it, policy-makers in the European Commission and the German government have established precedence for a 20-year time horizon. As aforementioned, the projected time span of a given land conversion has a significant effect in determining the GHG intensity of a biofuel. The purpose of this study is to identify a solution for properly representing amortized emissions, not to advocate for a specific time horizon. Because there is precedence in the literature and in policy, we use 20 and 30 year time horizons in many of our calculations.

2. Materials and Methods 2.1. Cumulative Radiative Forcing. While the most fundamental concerns surrounding GHGs are their effects on climate change and consequently on humanity and the environment, measuring GHG effects with CRF requires fewer assumptions and presents less uncertainty compared to measuring by temperature change or other metrics further down the consequence chain for GHGs. For this reason, and because others have set a precedence for using CRF in similar

FIGURE 1. Temporal allocation of LUC emissions using straight-line amortization over a time horizon (TH). applications (8, 17), CRF was selected as the basis of temporal equivalency for GHG emissions. The IPCC selected CRF as the basis for calculating the relative impact of GHG emissions in its widely used GWPs. The first step in calculating CRF requires identifying the radiative efficiency of a gas, and the decay function that defines its lifetime in the atmosphere. When multiplied, these two parameters equal the instantaneous radiative forcing (RF) of a gas in the atmosphere. The IPCC’s Fourth Assessment Report (12) defines the radiative efficiency and the decay function of CO2, Ci(t) as follows: Radiative Efficiency ) ac ) 5.35 ln

C C0

FIGURE 2. Decay of CO2in the atmosphere from a pulse emission in year zero versus an equivalent emission amortized over a 20-year time horizon.

(1)

where C is the concentration of CO2 in the atmosphere after some small perturbation and C0 is the initial concentration of CO2 in the atmosphere; Ci(t) ) 0.217 + 0.259e-t/172.9 + 0.388e-t/18.51 + 0.186e-t/1.186 (2) If the Fourth Assessment Report’s (12) background concentration value of 378 ppm and a perturbation of +1 ppm is used, aC ) 1.4135 × 10-5. We will treat radiative efficiency as independent over time, as the IPCC does, though this is a simplification since concentration is expected to increase over time. Figure 1 shows a conceptual representation of the current method of straight-line amortization for LUC emissions over a prescribed time horizon. Figure 1 is merely a conceptual representation, since LUC-related emissions actually occur with a large pulse emission as land is cleared, and then more gradually as the soil releases smaller carbon emissions over a period of some years. The conceptual representation condenses LUC emissions into a single pulse-emission event. While some resequestering of carbon will occur after the period of cultivation ends, this process is not included in Figure 1 nor the calculation of TCFs; see (8) for discussion of this, and modeling options for recovery of converted lands after biofuel cultivation ends. Figure 2 shows the quantity of CO2 related to the LUC event in the atmosphere based on Figure 1’s emissions schedule for the actual emission and the amortized emission assuming a 20-year time horizon. RF is calculated by multiplying the quantity of CO2 in the atmosphere by CO2’s radiative efficiency. Because of the linear relationship between radiative efficiency and atmospheric CO2 concentrations, a plot of RF over time would look identical to that shown in Figure 2, simply scaled by the value of ac. Figure 2 illustrates that allocating LUC emissions using straight-line amortization over a prescribed time horizon results in a different concentration, and consequent RF, compared to a single emission of CO2 of the same size at

FIGURE 3. CRF for a pulse emission in year zero versus an equivalent emission amortized over a 20-year and 30-year time horizons. time zero. This means the current approach to allocating emissions over time distorts the RF, and thus CRF as well. Equation 3 shows the integral of RF over some time horizon (TH) for some gas “i”, the IPCC’s method for calculating CRF (12): CRF )

∫

TH

0

RiCi(t)dt

(3)

Where ai is the radiative efficiency of some gas i, and Ci(t) defines the decay function of gas i from the atmosphere. Figure 3 shows the CRF for a pulse emission of 1 ppm and the same emission allocated by straight-line amortization over 20- and 30-year time horizons. The CRF profiles for the amortized emissions fall well below the CRF profile for the pulse emission, demonstrating the significant underestimation of CRF over time caused by amortization. The selection of a time horizon influences the magnitude of the underestimation of CRF. A comparison of the CRF for the 20 and 30-year time horizons shows that as the time horizon for amortization lengthens, the difference in CRF between the pulse emission and amortized emissions increases. 2.2. Development of a Time Correction Factor using Cumulative Radiative Forcing. Equation 4 shows the IPPC’s methods for calculating the GWP for a specific GHG based on a CO2 equivalent value over a specified time horizon.

GWP )

∫ ∫

TH

0 TH

0

RiCi(t)dt

(4)

RcCc(t)dt

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TABLE 2. TCF Applied to U.S. Corn Ethanol Life Cycle GHG time horizon (years)

time correction factor

U.S. corn-ethanol (gCO2e/MJ)a

10 20 30 40 50

1.73 1.778 1.785 1.775 1.769

612.8 350.4 258.6 211.5 183.4

a Based on values provided in Searchinger et al. (6) Table 1B.

the subscript C refers to CO2, and the subscript i refers to the GHG of concern. Similar to the IPCC’s GWP factor, our TCF factor is defined with the following equation:

TCF )

∫ ∫

TH

0

TH

0

RpCp(t)dt

(5)

RTHCTH(t)dt

the subscript p refers to the pulse emission, and the subscript TH refers to the amortized emission, assumed to be straightline amortized over TH. Important assumptions are embedded in the calculation of the TCF and limit its application: (1)The background atmospheric CO2 concentration remains constant, which means RE remains constant. (2)The TCF for CO2 cannot be applied to emissions values reported as CO2e, because equivalency is calculated based on the relative CRF of gases over a prescribed time horizon, usually 100 years (3). The TCF is only applicable to pulse emissions that are amortized over some time horizon. More complex calculations are required to model the relative impact of flows of GHG emissions over time.

3. Results and Discussion 3.1. Calculation of the Time Correction Factor. Columns 1 and 2 in table 2 show the TCF for CO2 for a range of time horizons. Note that the time horizon, TH, over which we integrate radiative forcing, will always be the same as the time horizon over which the LUC emission is amortized. Table 2 evidences one important characteristic of the TCF, namely that the time horizon selected does not significantly change its value. As a consequence, the amortized LUC emission calculation changes significantly for different time horizons. Equation 6 shows how the TCF is applied to an amortized emission, and demonstrates the influence of time horizon on the value of TCF-adjusted amortized emissions. Annual Adjusted LUC GHG Emissions )

LUCtotal TCF TH (6)

Figure 4 shows the effect of applying the TCF to CO2 emissions amortized over various time horizons. The figure is based on a pulse emission that changes background concentration by 1 ppm in the atmosphere. This number is selected for illustrative purposes only, and is not meant to reflect the value of an actual LUC event. 3.2. Application of the TCF to Corn-Ethanol Greenhouse Gas Estimates. Searchinger et al.’s (6) study estimated corn ethanol’s life cycle GHG emissions using the GREET model (v1.7) (19) for production-related emissions (73 g CO2e/ MJ ethanol) and a 30-year straight-line amortization of LUC emissions (104 g CO2e/MJ for 30 years of production), or 177 g CO2e/MJ ethanol based on their application of straightline amortization of LUC emissions. To facilitate comparison, the same emissions estimates are used here to illustrate the 7146

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FIGURE 4. TCF applied amortized co2 emissions. effect of applying the TCF. Table 2 shows life cycle GHG emissions for corn ethanol with LUC emissions adjusted by the TCF. For the 30 year time horizon, the TCF increases life cycle GHG estimates by 46%. 3.3. Broader Applications of TCF in LCA. Accounting for the timing of up-front emissions in LCAs may be particularly important when applied to products with reasonably long lifetimes where manufacturing or construction emissions may be important. TCFs have particular application to systems whose performance is reported in units of emissions intensity. Potential applications include renewable energy technologies like photovoltaic panels which produce emissions during manufacturing but produce few emissions during operation, and whose GHG-intensity is reported on a per-kWh basis over. A study by Fthenakis and Alsema (20) reports an emissions factor of 25 g CO2e/kWh for CdTe thin film photovoltaic panels installed in European conditions with a lifetime of 30 years. Since virtually all CO2 emissions occur during the panel production stage (21), the perkWh value increases by a factor of approximately 1.785 if a TCF for a 30 year time horizon is applied to the emissions factor, yielding a value of nearly 45 g CO2e/kWh. Note that this assumes that the CO2e quantity is dominated by CO2 emissions, since the TCF applies to emissions of CO2 and not other GHGs. While Fthenakis and Alsema’s study only reported CO2e emissions, an LCA of CdTe panels in the Ecoinvent Database (22), which reports each GHG separately, found that CO2 comprises approximately 95% of the total GWP associated with production. While this calculation relies on simplified assumptions regarding the timing and type of GHG emissions for photovoltaic panels, this preliminary application of a TCF suggests earlier calculations underestimate the per-kWh CO2e emissions by almost 45%. Future research will develop TCFs for non-CO2 GHGs. In addition, since most LCAs report GHGs in units of CO2e, a scaling factor that combines TCFs and GWPs may be preferable and will also be explored.

Acknowledgments We thank Prof. Michael O’Hare for his review of our manuscript; Prof. Michael O’Hare, Richard Plevin, Andy Jones, Dr. Jeremy Martin, and Eli Hopson for sharing their kindred and complementary ideas and insights; Prof. Christopher Cappa for his valuable insights on atmospheric chemistry; and Prof. Deb Niemeier and Dr. Mark Delucchi for their support of our project. This research was made possible by a grant from the UC Davis-Chevron Research Agreement entitled Identifying Appropriate System Boundaries and Capturing Land Use Change Effects for Life Cycle Carbon Calculations of Biofuels (PI Alissa Kendall).

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(12) IPCC Fourth Assessment Report: Climate; Intergovernmental Panel on Climate Change: Geneva. (13) Change 2007 - The Physical Science Basis; Cambridge University Press: Cambridge, 2007. (14) Pimentel, D.; Patzek, T. W. Ethanol production using corn, switchgrass, and wood; Biodiesel production using soybean and sunflower. Nat. Resour. Res. 2005, 14 (1), 65–76. (15) Udo de Haes, H. A.; Jolliet, O.; Finnveden, G.; Hauschild, M.; Krewitt, W.; Muller-Wenk, R. Best available practice regarding impact categories and category indicators in life cycle impact assessment: Background document for the second working group on life cycle impact assessment of SETAC-Europe (WIA-2). Int. J. Life Cycle Assess. 1999, 4 (2), 66–74. (16) Delucchi, M. A. A Lifecycle Emissions Model (LEM): Lifecycle Emissions from Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, Heating and Cooking Fuels, and Materials. Appendix D: CO2 Equivalency Factors, Research Report UCD-ITS-RR-03-17D; UC Davis Institute of Transportation Studies: Davis, CA, 2003. (17) Weitzman, M. L. Why the Far-Distant Future Should be Discounted at Its Lowest Possible Rate. J. Environ. Econ. Manage. 1998, 36, 201–208. (18) Moura Costa, P.; Wilson, C. An equivalence factor between CO2 avoided emissions and sequestrationsDescription and applications in forestry. Mit. Adapt. Stratos. Global Change 2000, 5, 51–60. (19) Renewable Fuels Association. Ethanol Industry Outlook 2007: Building New Horizons; RFA: Washington, DC, 2008. (20) Wang, M. GREET 1, version 1.7; Argonne National Laboratory: Argonne, IL, 2007. (21) Fthenakis, V.; Alsema, E. Photovoltaics energy payback times, greenhouse gas emissions and external costs: 2004-Early 2005 Stuts. Prog. Photovoltaics 2006, 14 (3), 275–280. (22) Ecoinvent Centre. Ecoinvent Data, version 2.0; Swiss Centre for Life Cycle Assessment: Duebendorf, 2008.

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