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Policy Analysis

The Climate Impacts of Bioenergy Systems Depend on Market and Regulatory Policy Contexts
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Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
* Corresponding author e-mail: [email protected]; phone: 510-642-1640; fax: 510-642-1085.
†Energy and Resources Group, University of California, Berkeley.
‡Environmental Science, Policy, and Management, University of California, Berkeley.
§Stanford University.
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2010, 44, 19, 7347–7350
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https://doi.org/10.1021/es100418p
Published September 13, 2010

Copyright © 2010 American Chemical Society. This publication is available under these Terms of Use.

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Abstract

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Biomass can help reduce greenhouse gas (GHG) emissions by displacing petroleum in the transportation sector, by displacing fossil-based electricity, and by sequestering atmospheric carbon. Which use mitigates the most emissions depends on market and regulatory contexts outside the scope of attributional life cycle assessments. We show that bioelectricity’s advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions. The electricity displaced depends upon existing infrastructure and policies affecting the electric grid. These findings demonstrate how model assumptions about whether the vehicle fleet and bioenergy use are fixed or free parameters constrain the policy questions an analysis can inform. Our bioenergy life cycle assessment can inform questions about a bioenergy mandate’s optimal allocation between liquid fuels and electricity generation, but questions about the optimal level of bioenergy use require analyses with different assumptions about fixed and free parameters.

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Copyright © 2010 American Chemical Society

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Synopsis

Whether bioelectricity generation or biofuels production avoids more greenhouse gas emissions depends on the electricity source that bioelectricity generation displaces.

Introduction

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A biomass resource can contribute to greenhouse gas (GHG) emission goals through several pathways. First, converting the biomass to liquid fuels can displace fossil fuels in transportation. Second, biomass can be combusted to generate heat and electricity that can displace fossil fuel sources of heat and electricity. Third, the biomass can be left in place, thereby sequestering carbon that could otherwise be released to the atmosphere. Models’ predicted optimal use of biomass resources for climate mitigation depend on assumptions about relative production costs, the cost of GHG emissions, the direct and indirect effects of alternative biomass uses, and the values affected by biomass utilization.
Several studies have suggested that using biomass for heat and electricity may be more cost-effective at mitigating CO2 emissions than is using biomass for biofuels (1-4). Nonetheless, bioenergy policies recently enacted in the U.S. and Europe aim to reduce GHG emissions from the transportation sector through markets and performance standards based on the life cycle GHG emissions from fuels (5-7). Life cycle assessment (LCA) characterizes the environmental effects of a product by aggregating flows of energy and materials across all phases of production, use, and disposal of the product (8). Two styles of LCA have emerged in the literature: attributional LCA is a static analysis based on a product’s supply chain, whereas consequential LCA considers the net environmental effects induced by a change in production (9). Both styles of LCA have been used in recent regulations aiming to reduce GHG emissions from the transportation sector. Through its static nature and its focus on a product’s supply chain, attributional LCA tempts analysts to draw conclusions that ignore the market conditions that affect ultimate environmental outcomes. Consequential LCA, in contrast, recognizes that environmental effects are not limited to a single supply chain’s impacts and generally depend on policy and market contexts, though the results of consequential LCAs can also mislead analysts if presented without useful framing and sensitivity assessments.
We use the case of bioelectricity and biofuels to demonstrate how the results of comparative LCA are sensitive to model assumptions about factors outside the scope of attributional LCA. While a change in production may affect that product’s supply chain in ways that do not depend strongly on the larger market and policy context, its effect on GHG emissions may depend strongly on how that change in production impacts the production of other goods and services. In particular, we show that the GHG intensity of the electricity displaced by bioelectricity determines whether bioelectricity is less carbon-intensive than biofuels. The model assumptions used by LCA-based policies can therefore affect long-lasting investment in energy infrastructure and feedstock development. More broadly, we argue that whether modelers treat the vehicle fleet and bioenergy use as fixed or free parameters strongly influences model results and the questions they can address. When developing a portfolio of climate policies that might affect both electrified vehicle adoption and biomass resource allocation, policymakers should consult models that assume that both the vehicle fleet and bioenergy use are free parameters that can respond to policy decisions.

Bioelectricity versus Biofuels: The Importance of Assumptions about Displacement

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A recent life cycle assessment suggested that converting biomass into electricity for electrified vehicles abates more GHG emissions than does converting biomass into liquid fuels for use in today’s conventional vehicles (10). This analysis has been used to argue for a redirection of biofuel subsidies toward electric vehicle adoption (11). Crucially, these results assume that bioelectricity generation displaces gasoline. However, under existing institutional and technical arrangements, bioelectricity production does not cause a reduction in gasoline use. Electricity from all sources flows into the grid and is used to meet instantaneous system-wide demand. Bioelectricity could only displace gasoline if its generation were to increase charging by electrified vehicles already in the fleet or if its generation caused vehicle purchases to shift from gasoline-fueled vehicles to electrified vehicles. Yet owners of existing electrified vehicles are unlikely to vary their charging habits according to the quantity of bioelectricity on the grid (12). Further, decisions to purchase electrified vehicles and decisions to use biomass as a primary fuel for electricity are made by different people, at different times, in different places, and without knowledge or concern for the others’ decisions. Unless vehicle purchasers expect additional bioelectricity generation to substantially reduce electricity prices (13), it is difficult to imagine plausible technological or policy mechanisms that would link these decisions such that each increase in biomass electricity production is met with an equal expansion of electrified vehicle charging as well as a decline in gasoline vehicle fueling. A more realistic assessment of GHG mitigation benefits would recognize that an increase in bioelectricity generation in fact displaces other sources of electricity. The specific benefits depend on the type of electricity replaced, which in turn depends on the structure of the regional electricity market.
We estimate the GHG abatement due to bioelectricity and biofuels produced from corn grain and from switchgrass (see Supporting Information). These results depend not only on the direct emissions from producing biofuels and bioelectricity but also on the emissions avoided by displacing some other energy carrier (Figure 1). In line with the aforementioned study’s results, we find that bioelectricity can abate approximately 6 Mg CO2e ha−1 yr−1 more GHGs than do biofuels when using the unrealistic assumption that bioelectricity displaces gasoline; however, more realistic displacement scenarios produce different results (14). First, we find that converting a given feedstock to bioelectricity or to biofuels produces similar levels of abatement if bioelectricity displaces electricity generated from natural gas, but bioelectricity from switchgrass abates more GHGs in this displacement scenario than does ethanol produced from corn. Second, we find that the bioelectricity pathway abates approximately 9 Mg CO2e ha−1 yr−1 more GHGs than does the biofuels pathway when bioelectricity displaces coal-fired electricity, and the GHG savings are greater when comparing switchgrass bioelectricity to corn ethanol. Because biomass can be cofired with coal, this may be the most likely short-term displacement scenario in the absence of policies constraining renewable energy or greenhouse gas emissions. Third, if bioelectricity displaces low-carbon power sources such as wind, bioelectricity generation may actually increase net GHG emissions by approximately 6 Mg CO2e ha−1 yr−1. Bioelectricity generation may displace low-carbon sources if, as in many U.S. states, the electricity sector must satisfy a Renewable Portfolio Standard that specifies a minimum share of generation to come from designated renewable sources. In this case, additional bioelectricity generation may decrease other renewable energy installations. Fourth, if electricity sector emissions are subject to a binding GHG cap that is not adjusted to reflect anticipated vehicle electrification, then using biomass for electricity production should not cause any net change in GHG emissions. Thus, under this type of GHG cap, which conversion pathway abates more GHGs depends solely on whether biofuels are more or less GHG-intensive than gasoline.

Figure 1

Figure 1. Net GHG savings per area of cropland are sensitive to displacement assumptions. The X marker shows ethanol displacing gasoline. The red circle follows ref 10 in assuming that bioelectricity is used to power electrified vehicles and displaces gasoline. The diamond, square, and triangle (coal, natural gas combined cycle, and wind electricity) show the GHG benefit (or cost) when bioelectricity displaces each of these types of power. See Supporting Information for details.

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Recent analyses suggested that production of biofuels can affect commodity prices and thus induce conversion of land to replace displaced food crops. The size and existence of this effect is highly uncertain, but the resulting GHG emissions could dominate the other life cycle emissions for these products (15-18). Assumptions about indirect land use change (ILUC) emissions can affect absolute emissions from biofuels and bioelectricity, reducing their benefits (or increasing their penalties) relative to the business-as-usual scenario (Figure 2). For instance, under the assumption of no ILUC emissions, corn ethanol provides GHG savings relative to gasoline, and bioelectricity from corn provides GHG savings relative to other forms of electricity unless it displaces wind power. Under ILUC emission assumptions greater than or equal to the one adopted for the California Low-Carbon Fuel Standard (30 g CO2e (MJ ethanol)−1) (5), ethanol no longer provides GHG savings relative to gasoline, and electricity from corn grain achieves a net GHG reduction only if the displaced electricity comes from coal-fired plants.

Figure 2

Figure 2. Net GHG savings per area of cropland under varying assumptions about emissions from indirect land use change due to cultivation of corn for biofuels or bioelectricity. The X marker shows ethanol displacing gasoline. The red circle follows ref 10 in assuming that bioelectricity is used to power electrified vehicles and displaces gasoline. The diamond, square, and triangle (coal, natural gas combined cycle, and wind electricity) show the GHG benefit (or cost) when bioelectricity displaces each of these types of power. See Supporting Information for details.

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Policy Questions Should Guide Assumptions about Decision-Making Contexts

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While assumptions about product markets and regulatory systems can affect the conclusions of a study, higher-level assumptions constrain the policy questions to which a study’s conclusions may apply. We develop a typology of modeled assumptions in bioenergy analyses that includes two dimensions of variability: whether the vehicle fleet is fixed or free, and whether the magnitude of overall bioenergy use is fixed or free (Figure 3). Different combinations of these assumptions affect the form of an analysis as well as the questions it can answer.

Figure 3

Figure 3. Assumptions in a bioenergy- and transportation-focused analysis about whether bioenergy use and the vehicle fleet are fixed (taken as given) or free (allowed to vary) strongly influence the questions modelers can address and the results they obtain. For each pair of assumptions, the cells contain examples of the types of questions that analyses can answer and examples of the types of analyses that can contribute to answering the questions.

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An analysis that compares the GHG benefits of bioelectricity and biofuel conversion pathways assumes that bioenergy use is fixed (column A in Figure 3). Analyses like the one in the previous section do not ask how much biomass energy should be used but instead ask whether a unit of biomass delivers more GHG abatement if converted to electricity or to a liquid fuel. To assume that bioelectricity displaces gasoline as in ref 10, one must not only assume that gasoline fueling and bioelectricity generation are linked but also that the vehicle fleet can adjust to include vehicles that use electricity (cell A2 in Figure 3). If the vehicle fleet is instead fixed because the chosen time horizon is too short or vehicle electrification policies are not under the decision-maker’s control, then the fleet will lack electrified vehicles (cell A1 in Figure 3). In this case, as we have seen in Figure 1, the relative GHG abatement from using bioelectricity and biofuels depends on the electricity source displaced.
If we treat the quantity of bioenergy produced as a free parameter (column B in Figure 3), the analysis can extend beyond simply allocating bioenergy resources between bioelectricity and biofuels. Determining the socially optimal level of bioenergy utilization is important and has long-term consequences because the potential costs of bioenergy and associated global land-use changes are multidimensional and include effects on biodiversity, water use, food prices, carbon sequestration rates, and regional climate stability (15, 19-21). If the vehicle fleet is fixed (cell B1 in Figure 3), then assessing the level of bioenergy use requires an assessment of other fuel options for abating transportation sector GHGs. A study produced for the California Low-Carbon Fuel Standard considered the GHG emissions from several conventional and unconventional feedstocks for liquid fuels compatible with the current vehicle fleet (22). Absent policies that reduce the demand for vehicle kilometers traveled, biofuels are one of the few fuel alternatives with the potential to reduce the current vehicle fleet’s GHG emissions to levels targeted by recent policies. Thus, biomass holds promise for decarbonizing transportation in the near-term vehicle fleet, but these benefits depend on complementary land use policies and must be considered in light of the many other values affected by bioenergy feedstock production (23).
Finally, we may assume that both the vehicle fleet and bioenergy use are free parameters (cell B2 in Figure 3). The resulting analysis would jointly consider the form of transportation and both the form and amount of bioenergy use (24). Policymakers developing a portfolio of climate policies could rely on such an analysis for insight into whether the government should promote electrified vehicles and for insight into how to design a bioenergy policy in light of changing vehicle technologies. The conclusions would depend on the cost of GHG abatement across both the transportation and electricity sectors (25), on how the time scales of investment and technological change compare to the time scales of GHG emission targets, and on the depth of reduction sought (26). Previous analyses have indicated that advanced biofuels might provide interim GHG abatement (27), that vehicle electrification promises medium-term abatement (28, 29), and that bioelectricity could accrue long-term importance from its ability to provide net negative GHG emissions when combined with carbon capture and sequestration (30, 31).
The choice of fixed and free parameters determines whether a model can inform policy decisions about the design, the magnitude, or the appropriateness of a bioenergy mandate. How such a bioenergy mandate should weight bioelectricity versus biofuels depends on the GHG intensity of the electricity that would be displaced. How much biomass should be used for energy depends on the other options for decarbonizing liquid fuels and electricity supply and on the broader social costs of producing energy crops and altering land use patterns. Finally, whether a climate policy portfolio should include policies specifically aimed at promoting bioenergy and/or electrified vehicles depends on the costs, benefits, and feasibility of such policies relative to other abatement options. The chosen combination of fixed and free parameters not only affects the model’s results but also constrains the set of policy questions to which the results apply.

Supporting Information

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Complete description of the Energy Displacement Model. This material is available free of charge via the Internet at http://pubs.acs.org.

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Author Information

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  • Corresponding Author
    • Daniel M. Kammen - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220;  Email: [email protected]
  • Authors
    • Derek M. Lemoine - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
    • Richard J. Plevin - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
    • Avery S. Cohn - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
    • Andrew D. Jones - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
    • Adam R. Brandt - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220
    • Sintana E. Vergara - Energy and Resources Group, 310 Barrows Hall, University of California, Berkeley, California 94720-3050, Environmental Science, Policy, and Management, 137 Mulford Hall no. 3114, University of California, Berkeley, California 94720, and Department of Energy Resources Engineering, Stanford University, Green Earth Sciences 065, 367 Panama Street, Stanford, California 94305-2220

Acknowledgment

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We acknowledge the support of the Robert and Patricia Switzer Foundation Environmental Fellowship Program (to D.M.L.), the Energy Biosciences Institute (to A.S.C.), a U.S. Environmental Protection Agency Science to Achieve Results (STAR) fellowship (to A.D.J.), and a National Science Foundation Graduate Research Fellowship (to S.E.V.). We thank the Energy Foundation and the Karsten Family Foundation Endowment of the Renewable and Appropriate Energy Laboratory (to D.M.K.).

References

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This article references 31 other publications.

  1. 1
    Azar, C.; Lindgren, K.; Andersson, B. A. Global energy scenarios meeting stringent CO2 constraints—cost-effective fuel choices in the transportation sector Energy Policy 2003, 31, 961– −976
    Google Scholar
    There is no corresponding record for this reference.
  2. 2
    Grahn, M.; Azar, C.; Lindgren, K.; Berndes, G.; Gielen, D. Biomass for heat or as transportation fuel? A comparison between two model-based studies Biomass Bioenergy 2007, 31, 747– 758
    Google Scholar
    2
    Biomass for heat or as transportation fuel? A comparison between two model-based studies
    Grahn, Maria; Azar, Christian; Lindgren, Kristian; Berndes, Goeran; Gielen, Dolf
    Biomass and Bioenergy (2007), 31 (11-12), 747-758CODEN: BMSBEO; ISSN:0961-9534. (Elsevier Ltd.)
    In 2 different energy economy models of the global energy system, the cost-effective use of biomass under a stringent C constraint was analyzed. Gielen et al. conclude that it is cost-effective to use biofuels for transportation, whereas Azar et al. find that it is more cost-effective to use most of the biomass to generate heat and process heat, despite the fact that assumptions about the cost of biofuels prodn. is similar in the models. The authors compare the 2 models with the purpose of finding an explanation for these different results. Both models suggest that biomass is most cost-effectively used for heat prodn. for low C taxes (<50-100 USD/tC, depending on the year in question). But for higher C taxes, the cost-effective choice reverses in the BEAP model, but not in the GET model. The reason for this is that GET includes H from C-free energy sources as a technol. option, whereas that option is not allowed in the BEAP model. In all other sectors, both models include C-free options above biomass. Thus, with higher C taxes, biomass will eventually become the cost-effective choice in the transportation sector in BEAP, regardless of its technol. cost parameters.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXht1Gis7zJ&md5=6c22c26d789ed70a9777b28fc7a60f36
  3. 3
    Grahn, M.; Azar, C.; Lindgren, K. The role of biofuels for transportation in CO2 emission reduction scenarios with global versus regional carbon caps Biomass Bioenergy 2009, 33, 360– 371
    Google Scholar
    3
    The role of biofuels for transportation in CO2 emission reduction scenarios with global versus regional carbon caps
    Grahn, Maria; Azar, Christian; Lindgren, Kristian
    Biomass and Bioenergy (2009), 33 (3), 360-371CODEN: BMSBEO; ISSN:0961-9534. (Elsevier Ltd.)
    This study analyzes how international climate regimes affect cost-efficiency of fuel choices in the transportation sector. The anal. is carried out with a regionalized version of the Global Energy Transition model, GET-R 6.0. Two different carbon dioxide (CO2) redn. scenarios are applied, both meeting an atm. CO2 concn. target of 450 ppm by the year 2100. The first scenario, "global cap" (GC), uses a global cap on CO2 emissions, and global emissions trading is allowed. In the second scenario, "regional caps" (RC), industrialized regions start to reduce their CO2 emissions by 2010 while developing regions may wait several decades and emission redns. are not tradable across regions. In this second scenario, CO2 emissions are assumed to meet an equal per capita distribution of 1.0 tC/capita, in all six regions, by 2040; emissions then follow a common redn. path, toward approx. 0.2 tC/capita by 2100. Three main results emerge from our anal.: (i) the use of biofuels in the industrialized regions is significantly higher in RC than in GC; (ii) the use of biofuels in RC actually increases the weaker (i.e., higher) the CO2 concn. target (up to 550 ppm); and (iii) biofuels never play a dominant role in the transportation sector. We find that biofuels may play a more important role in industrialized countries if these take on their responsibilities and reduce their emissions before developing countries start reducing their emissions, compared to the case in which all countries take action under a global cap and trade emission redn. regime.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXitlGntbs%253D&md5=17099911120d45d81b00650e817ed3a6
  4. 4
    Wise, M.; Calvin, K.; Thomson, A. M.; Clarke, L.; Bomb-Lamberty, B.; Sands, R.; Smith, S.; Janetos, A.; Edmonds, J.The Implications of Limiting CO2 Concentrations for Agriculture, Land Use, Land-Use Change Emissions and Bioenergy; Technical Report; Pacific Northwest National Laboratory: Richland, WA, 2009, p 44.
    Google Scholar
    There is no corresponding record for this reference.
  5. 5

    California Air Resources Board. Proposed Regulation to Implement the Low Carbon Fuel Standard, Volume I, Staff Report: Initial Statement of Reasons

    ; 2009; p 374.
    Google Scholar
    There is no corresponding record for this reference.
  6. 6
    European Parliament. Renewable Energy Directive; 2009/28/EC; 2009.
    Google Scholar
    There is no corresponding record for this reference.
  7. 7

    USEPA. Renewable Fuel Standard Program (RFS2) Notice of Proposed Rulemaking

    , 2009.
    Google Scholar
    There is no corresponding record for this reference.
  8. 8

    ISO. ISO 14040: Environmental Management - Life Cycle Assessment - Principles and Framework

    ; 2006.
    Google Scholar
    There is no corresponding record for this reference.
  9. 9
    Ekvall, T.; Weidema, B. P. System boundaries and input data in consequential life cycle inventory analysis Int. J. Life Cycle Assess. 2004, 9, 161– 171
    Google Scholar
    There is no corresponding record for this reference.
  10. 10
    Campbell, J. E.; Lobell, D. B.; Field, C. B. Greater transportation energy and GHG offsets from bioelectricity than ethanol Science 2009, 324, 1055– 1057
    Google Scholar
    There is no corresponding record for this reference.
  11. 11
    Ohlrogge, J.; Allen, D.; Berguson, B.; DellaPenna, D.; Shachar-Hill, Y.; Stymne, S. Driving on biomass Science 2009, 324, 1019– 1020
    Google Scholar
    11
    Driving on Biomass
    Ohlrogge, John; Allen, Doug; Berguson, Bill; DellaPenna, Dean; Shachar-Hill, Yair; Stymne, Sten
    Science (Washington, DC, United States) (2009), 324 (5930), 1019-1020CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    There is no expanded citation for this reference.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXms1GisL0%253D&md5=28109fe731092f698889c32105cbe3cc
  12. 12
    Lemoine, D. M.; Kammen, D. M.; Farrell, A. E. An innovation and policy agenda for commercially competitive plug-in hybrid electric vehicles Environ. Res. Lett. 2008, 3014003
    Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Lemoine, D. M. Valuing plug-in hybrid electric vehicles’ battery capacity using a real options framework Energy J. 2010, 31, 113– 143
    Google Scholar
    There is no corresponding record for this reference.
  14. 14
    Larson, E. D. A review of life-cycle analysis studies on liquid biofuel systems for the transport sector Energy Sustainable Dev. 2006, 10, 109– 126
    Google Scholar
    14
    A review of life-cycle analysis studies on liquid biofuel systems for the transport sector
    Larson, Eric D.
    Energy for Sustainable Development (2006), 10 (2), 109-126CODEN: ESDEFY; ISSN:0973-0826. (International Energy Initiative)
    A review. Given current oil prices and the heavy dependence of many countries on imported oil, the potential for producing or importing liq. transportation fuels made from bio mass is attracting keen interest in many developing and industrialized countries. There is also some interest in biofuels for climate change mitigation. This article reviews the rich literature of published life-cycle analyses (LCAs) of liq. biofuels, with a focus on elucidating the impacts that prodn. and use of such biofuels might have on emissions of greenhouse gases. Reviews of LCAs for "conventional" liq. biofuels (biodiesel and sugar/starch bioethanol) and potential "future" liq. biofuels (Fischer-Tropsch fuels, di-Me ether, and cellulosic bioethanol) are included. Striking features of the LCAs reviewed include almost exclusive contextual focus on Europe or North America, wide ranges in net energy balance results and GHG impacts among different biofuels and even for the same biofuel, and a lack of focus on evaluating GHG impacts per unit of land area. The wide range of reported LCA GHG results is due in part to the wide range of plausible values for key input parameters, among which the four most significant parameters exhibiting the greatest variability and/or uncertainty are (1) the climate-active species included in the calcn., (2) assumptions around N2O emissions, (3) the allocation method used for co-product credits, and (4) soil carbon dynamics. Finally, from a comparison of GHG impacts of biomass used for transportation fuels against those for stationary applications one concludes that under some conditions biofuels will provide greater GHG mitigation benefits, but under other conditions, biopower will be favored. It is difficult to make broad and unequivocal statements on this point. Case-specific anal. is required.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xos1yktrk%253D&md5=a7b5f53201ed9a3c2635b1581920656f
  15. 15
    Searchinger, T.; Heimlich, R.; Houghton, R. A.; Dong, F.; Elobeid, A.; Fabiosa, J.; Tokgoz, S.; Hayes, D.; Yu, T. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change Science 2008, 319, 1238– 1240
    Google Scholar
    15
    Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change
    Searchinger, Timothy; Heimlich, Ralph; Houghton, R. A.; Dong, Fengxia; Elobeid, Amani; Fabiosa, Jacinto; Tokgoz, Simla; Hayes, Dermot; Yu, Tun-Hsiang
    Science (Washington, DC, United States) (2008), 319 (5867), 1238-1240CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Most prior studies obsd. that substituting biofuels for gasoline reduces greenhouse gases since biofuels sequester C through the growth of the feedstock. These analyses failed to count C emissions which occur as farmers respond to higher prices and convert forest and grassland to new cropland to replace grain (or cropland) diverted to biofuels. Using a worldwide agricultural model to est. emissions from land-use change, the authors detd. corn-based ethanol, instead of producing a 20% savings, nearly doubles greenhouse emissions over 30 years and increases greenhouse gases for 167 years. Biofuels from switchgrass, if grown on US corn lands, increase emissions 50%. This result raises concerns about large biofuel mandates and highlights the value of using waste products.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXisVSksLg%253D&md5=2296fcdc224e70c0e7da4d9af5115d41
  16. 16
    Liska, A. J.; Perrin, R. K. Indirect land use emissions in the life cycle of biofuels: regulations vs science Biofuels, Bioprod. Biorefin. 2009, 3, 318– 328
    Google Scholar
    16
    Indirect land use emissions in the life cycle of biofuels: regulations vs science
    Liska, Adam J.; Perrin, Richard
    Biofuels, Bioproducts & Biorefining (2009), 3 (3), 318-328CODEN: BBBICH; ISSN:1932-104X. (John Wiley & Sons Ltd.)
    A review. Recent legislative mandates have been enacted at state and federal levels with the purpose of reducing life cycle greenhouse gas (GHG) emissions from transportation fuels. This legislation encourages the substitution of fossil fuels with 'low-carbon' fuels. The burden is put on regulatory agencies to det. the GHG-intensity of various fuels, and those agencies naturally look to science for guidance. Even though much progress has been made in detg. the direct life cycle emissions from the prodn. of biofuels, the science underpinning the estn. of potentially significant emissions from indirect land use change (ILUC) is in its infancy. As legislation requires inclusion of ILUC emissions in the biofuel life cycle, regulators are in a quandary over accurate implementation. In this article, we review these circumstances and offer some suggestions for how to proceed with the science of indirect effects and regulation in the face of uncertain science. Besides investigating indirect deforestation and grassland conversion alone, a more comprehensive assessment of the total GHG emissions implications of substituting biofuels for petroleum needs to be completed before indirect effects can be accurately detd. This review finds that indirect emissions from livestock and military security are particularly important, and deserve further research.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXms12rurw%253D&md5=4c1691f6e89ce36c37328018bb81b70d
  17. 17
    Melillo, J. M.; Reilly, J. M.; Kicklighter, D. W.; Gurgel, A. C.; Cronin, T. W.; Paltsev, S.; Felzer, B. S.; Wang, X.; Sokolov, A. P.; Schlosser, C. A. Indirect emissions from biofuels: How important? Science 2009, 326, 1397– 1399
    Google Scholar
    There is no corresponding record for this reference.
  18. 18
    Hertel, T. W.; Golub, A. A.; Jones, A. D.; O’Hare, M.; Plevin, R. J.; Kammen, D. M. Effects of US maize ethanol on global land use and greenhouse gas emissions: Estimating market-mediated responses BioScience 2010, 60, 223– 231
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Foley, J. A.; et al. Global consequences of land use Science 2005, 309, 570– 574
    Google Scholar
    19
    Global Consequences of Land Use
    Foley, Jonathan A.; DeFries, Ruth; Asner, Gregory P.; Barford, Carol; Bonan, Gordon; Carpenter, Stephen R.; Chapin, F. Stuart; Coe, Michael T.; Daily, Gretchen C.; Gibbs, Holly K.; Helkowski, Joseph H.; Holloway, Tracey; Howard, Erica A.; Kucharik, Christopher J.; Monfreda, Chad; Patz, Jonathan A.; Prentice, I. Colin; Ramankutty, Navin; Snyder, Peter K.
    Science (Washington, DC, United States) (2005), 309 (5734), 570-574CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food prodn., maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXmsFChtrs%253D&md5=6c5699de2c42011adcebf8b4c9589b2c
  20. 20
    Fargione, J.; Hill, J.; Tilman, D.; Polasky, S.; Hawthorne, P. Land clearing and the biofuel carbon debt Science 2008, 319, 1235– 1238
    Google Scholar
    20
    Land Clearing and the Biofuel Carbon Debt
    Fargione, Joseph; Hill, Jason; Tilman, David; Polasky, Stephen; Hawthorne, Peter
    Science (Washington, DC, United States) (2008), 319 (5867), 1235-1238CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Increasing energy use, climate change, and CO2 emissions from fossil fuels make switching to low-C fuels a high priority. Biofuels are a potential low-C energy source, but whether biofuels offer C savings depends on how they are produced. Converting rain forest, peatland, savanna, or grassland to produce food crop-based biofuels in Brazil, southeast Asia, and the US creates a biofuel C debt by releasing 17-420 times more CO2 than the annual greenhouse gas (GHG) redns. these biofuels would provide by displacing fossil fuels. In contrast, biofuels made from waste biomass or from biomass grown on degraded and abandoned agricultural lands planted with perennials incur little or no C debt and can offer immediate, sustained GHG advantages.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXisVSksLs%253D&md5=236c65084d106d27e3624b2374f2511e
  21. 21
    Gerbens-Leenes, W.; Hoekstra, A. Y.; van der Meer, T. H. The water footprint of bioenergy Proc. Natl. Acad. Sci. 2009, 106, 10219– 10223
    Google Scholar
    21
    The water footprint of bioenergy
    Gerbens-Leenes, Winnie; Hoekstra, Arjen Y.; van der Meer, Theo H.
    Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (25), 10219-10223, S10219/1-S10219/10CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    All energy scenarios show a shift toward an increased percentage of renewable energy sources, including biomass. This study gives an overview of water footprints (WFs) of bioenergy from 12 crops that currently contribute the most to global agricultural prodn.: barley, cassava, maize, potato, rapeseed, rice, rye, sorghum, soybean, sugar beet, sugar cane, and wheat. In addn., this study includes jatropha, a suitable energy crop. Since climate and prodn. circumstances differ among regions, calcns. have been performed by country. The WF of bioelectricity is smaller than that of biofuels because it is more efficient to use total biomass (e.g., for electricity or heat) than a fraction of the crop (its sugar, starch, or oil content) for biofuel. The WF of bioethanol appears to be smaller than that of biodiesel. For electricity, sugar beet, maize, and sugar cane are the most favorable crops [50 m3/GJ (GJ)]. Rapeseed and jatropha, typical energy crops, are disadvantageous (400 m3/GJ). For ethanol, sugar beet, and potato (60 and 100 m3/GJ) are the most advantageous, followed by sugar cane (110 m3/GJ); sorghum (400 m3/GJ) is the most unfavorable. For biodiesel, soybean and rapeseed show to be the most favorable WF (400 m3/GJ); jatropha has an adverse WF (600 m3/GJ). When expressed per L, the WF ranges from 1,400 to 20,000 L of water per L of biofuel. If a shift toward a greater contribution of bioenergy to energy supply takes place, the results of this study can be used to select the crops and countries that produce bioenergy in the most water-efficient way.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXot1Gkur8%253D&md5=c373dc11bcf85b36345fceac0cdd58f0
  22. 22
    Farrell, A. E.; et al.A Low-Carbon Fuel Standard for California. Part 1: Technical Analysis; Technical Report; University of California, 2007; p 179.
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Tilman, D.; Socolow, R.; Foley, J. A.; Hill, J.; Larson, E.; Lynd, L.; Pacala, S.; Reilly, J.; Searchinger, T.; Somerville, C.; Williams, R. Beneficial biofuels-the food, energy, and environment trilemma Science 2009, 325, 270– 271
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Eickhout, B.; van den Born, G. J.; Notenboom, J.; van Oorschot, M.; Ros, J. P. M.; van Vuuren, D. P.; Westhoek, H. J.Local and global consequences of the EU renewable directive for biofuels. Testing the sustainability criteria; Technical Report 500143001/2008; Milieu en Natuur Planbureau, 2008; p 70.
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Lutsey, N.; Sperling, D. Greenhouse gas mitigation supply curve for the United States for transport versus other sectors Transportation Res., Part D 2009, 14, 222– 229
    Google Scholar
    There is no corresponding record for this reference.
  26. 26
    McCollum, D.; Yang, C. Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications Energy Policy 2009, 37, 5580– 5596
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Farrell, A. E.; Plevin, R. J.; Turner, B. T.; Jones, A. D.; O’Hare, M.; Kammen, D. M. Ethanol can contribute to energy and environmental goals Science 2006, 311, 506– 508
    Google Scholar
    27
    Ethanol Can Contribute to Energy and Environmental Goals
    Farrell, Alexander E.; Plevin, Richard J.; Turner, Brian T.; Jones, Andrew D.; O'Hare, Michael; Kammen, Daniel M.
    Science (Washington, DC, United States) (2006), 311 (5760), 506-508CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    A review. To study the potential effects of increased biofuel use, we evaluated six representative analyses of fuel ethanol. Studies that reported neg. net energy incorrectly ignored coproducts and used some obsolete data. All studies indicated that current corn ethanol technologies are much less petroleum-intensive than gasoline but have greenhouse gas emissions similar to those of gasoline. However, many important environmental effects of biofuel prodn. are poorly understood. New metrics that measure specific resource inputs are developed, but further research into environmental metrics is needed. Nonetheless, it is already clear that large-scale use of ethanol for fuel will almost certainly require cellulosic technol.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmvVynuw%253D%253D&md5=68f1b83261d55cfb198e33045272ef99
  28. 28
    Samaras, C.; Meisterling, K. Life cycle assessment of greenhouse gas emissions from plug-in hybrid vehicles: Implications for policy Environ. Sci. Technol. 2008, 42, 3170– 3176
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Kammen, D. M.; Arons, S. M.; Lemoine, D. M.; Hummel, H. Cost-effectiveness of greenhouse gas emission reductions from plug-in hybrid electric vehicles. In Plug-in Electric Vehicles: What role for Washington?Sandalow, D. B., Ed.; Brookings Institution Press: Washington, DC, 2009.
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Azar, C.; Lindgren, K.; Larson, E.; Möllersten, K. Carbon capture and storage from fossil fuels and biomass - Costs and potential role in stabilizing the atmosphere Climatic Change 2006, 74, 47– 79
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Uddin, S. N.; Barreto, L. Biomass-fired cogeneration systems with CO2 capture and storage Renewable Energy 2007, 32, 1006– 1019
    Google Scholar
    There is no corresponding record for this reference.

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  3. Corinne D. Scown, Michael Taptich, Arpad Horvath, Thomas E. McKone, and William W. Nazaroff . Achieving Deep Cuts in the Carbon Intensity of U.S. Automobile Transportation by 2050: Complementary Roles for Electricity and Biofuels. Environmental Science & Technology 2013, 47 (16) , 9044-9052. https://doi.org/10.1021/es4015635
  4. Roland Geyer, David Stoms, and James Kallaos . Spatially-Explicit Life Cycle Assessment of Sun-to-Wheels Transportation Pathways in the U.S.. Environmental Science & Technology 2013, 47 (2) , 1170-1176. https://doi.org/10.1021/es302959h
  5. S. Pacca and J. R. Moreira . A Biorefinery for Mobility?. Environmental Science & Technology 2011, 45 (22) , 9498-9505. https://doi.org/10.1021/es2004667
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  7. Jason Hill. Life Cycle Analysis of Biofuels. 2024, 829-833. https://doi.org/10.1016/B978-0-12-822562-2.00334-0
  8. Tyler J. Lark, Nathan P. Hendricks, Aaron Smith, Nicholas Pates, Seth A. Spawn-Lee, Matthew Bougie, Eric G. Booth, Christopher J. Kucharik, Holly K. Gibbs. Environmental outcomes of the US Renewable Fuel Standard. Proceedings of the National Academy of Sciences 2022, 119 (9) https://doi.org/10.1073/pnas.2101084119
  9. Jiangfeng Huang, Muhammad Tahir Khan, Danilo Perecin, Suani T. Coelho, Muqing Zhang. Sugarcane for bioethanol production: Potential of bagasse in Chinese perspective. Renewable and Sustainable Energy Reviews 2020, 133 , 110296. https://doi.org/10.1016/j.rser.2020.110296
  10. Derek Lemoine. Escape from Third-Best: Rating Emissions for Intensity Standards. Environmental and Resource Economics 2017, 67 (4) , 789-821. https://doi.org/10.1007/s10640-016-0006-6
  11. Richard J. Plevin, Mark A. Delucchi, Michael O’Hare. Fuel carbon intensity standards may not mitigate climate change. Energy Policy 2017, 105 , 93-97. https://doi.org/10.1016/j.enpol.2017.02.037
  12. Deepak Rajagopal. A Step Towards a General Framework for Consequential Life Cycle Assessment. Journal of Industrial Ecology 2017, 21 (2) , 261-271. https://doi.org/10.1111/jiec.12433
  13. Deepak Rajagopal. On mitigating emissions leakage under biofuel policies. GCB Bioenergy 2016, 8 (2) , 471-480. https://doi.org/10.1111/gcbb.12262
  14. Alexandra Purkus. Introduction. 2016, 1-25. https://doi.org/10.1007/978-3-319-31135-7_1
  15. Sandhya Nepal, Marco Contreras, George Stainback, John Lhotka. Quantifying the Effects of Biomass Market Conditions and Policy Incentives on Economically Feasible Sites to Establish Dedicated Energy Crops. Forests 2015, 6 (11) , 4168-4190. https://doi.org/10.3390/f6114168
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  18. Daniel L. Sanchez, James H. Nelson, Josiah Johnston, Ana Mileva, Daniel M. Kammen. Reply to 'Emissions accounting for biomass energy with CCS'. Nature Climate Change 2015, 5 (6) , 496-496. https://doi.org/10.1038/nclimate2634
  19. Prativa Shrestha, G. Andrew Stainback, Puneet Dwivedi, John M. Lhotka. Economic and Life-Cycle Analysis of Forest Carbon Sequestration and Wood-Based Bioenergy Offsets in the Central Hardwood Forest Region of United States. Journal of Sustainable Forestry 2015, 34 (3) , 214-232. https://doi.org/10.1080/10549811.2014.980894
  20. Jeffrey M. Bergthorson, Murray J. Thomson. A review of the combustion and emissions properties of advanced transportation biofuels and their impact on existing and future engines. Renewable and Sustainable Energy Reviews 2015, 42 , 1393-1417. https://doi.org/10.1016/j.rser.2014.10.034
  21. Krish Homagain, Chander Shahi, Nancy Luckai, Mahadev Sharma. Biochar-based bioenergy and its environmental impact in Northwestern Ontario Canada: A review. Journal of Forestry Research 2014, 25 (4) , 737-748. https://doi.org/10.1007/s11676-014-0522-6
  22. Puneet Dwivedi, Madhu Khanna. Wood-based bioenergy products — land or energy efficient?. Canadian Journal of Forest Research 2014, 44 (10) , 1187-1195. https://doi.org/10.1139/cjfr-2014-0210
  23. Puneet Dwivedi, Madhu Khanna, . Abatement Cost of GHG Emissions for Wood-Based Electricity and Ethanol at Production and Consumption Levels. PLoS ONE 2014, 9 (6) , e100030. https://doi.org/10.1371/journal.pone.0100030
  24. Alessandra ZAMAGNI, Jeroen GUINÉE, Reinout HEIJUNGS, Paolo MASONI, Andrea RAGGI, Atsushi INABA, Jun NAKATANI. Lights and Shadows in Consequential LCA. Journal of Life Cycle Assessment, Japan 2014, 10 (3) , 213-229. https://doi.org/10.3370/lca.10.213
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  28. D. Joshua Qualls, Kimberly L. Jensen, Christopher D. Clark, Burton C. English, James A. Larson, Steven T. Yen. Analysis of factors affecting willingness to produce switchgrass in the southeastern United States. Biomass and Bioenergy 2012, 39 , 159-167. https://doi.org/10.1016/j.biombioe.2012.01.002
  29. Roger Sathre, Mikhail Chester, Jennifer Cain, Eric Masanet. A framework for environmental assessment of CO2 capture and storage systems. Energy 2012, 37 (1) , 540-548. https://doi.org/10.1016/j.energy.2011.10.050
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  • Figure 1

    Figure 1. Net GHG savings per area of cropland are sensitive to displacement assumptions. The X marker shows ethanol displacing gasoline. The red circle follows ref 10 in assuming that bioelectricity is used to power electrified vehicles and displaces gasoline. The diamond, square, and triangle (coal, natural gas combined cycle, and wind electricity) show the GHG benefit (or cost) when bioelectricity displaces each of these types of power. See Supporting Information for details.

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    Figure 2

    Figure 2. Net GHG savings per area of cropland under varying assumptions about emissions from indirect land use change due to cultivation of corn for biofuels or bioelectricity. The X marker shows ethanol displacing gasoline. The red circle follows ref 10 in assuming that bioelectricity is used to power electrified vehicles and displaces gasoline. The diamond, square, and triangle (coal, natural gas combined cycle, and wind electricity) show the GHG benefit (or cost) when bioelectricity displaces each of these types of power. See Supporting Information for details.

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    Figure 3

    Figure 3. Assumptions in a bioenergy- and transportation-focused analysis about whether bioenergy use and the vehicle fleet are fixed (taken as given) or free (allowed to vary) strongly influence the questions modelers can address and the results they obtain. For each pair of assumptions, the cells contain examples of the types of questions that analyses can answer and examples of the types of analyses that can contribute to answering the questions.

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  • References

    This article references 31 other publications.

    1. 1
      Azar, C.; Lindgren, K.; Andersson, B. A. Global energy scenarios meeting stringent CO2 constraints—cost-effective fuel choices in the transportation sector Energy Policy 2003, 31, 961– −976
      There is no corresponding record for this reference.
    2. 2
      Grahn, M.; Azar, C.; Lindgren, K.; Berndes, G.; Gielen, D. Biomass for heat or as transportation fuel? A comparison between two model-based studies Biomass Bioenergy 2007, 31, 747– 758
      2
      Biomass for heat or as transportation fuel? A comparison between two model-based studies
      Grahn, Maria; Azar, Christian; Lindgren, Kristian; Berndes, Goeran; Gielen, Dolf
      Biomass and Bioenergy (2007), 31 (11-12), 747-758CODEN: BMSBEO; ISSN:0961-9534. (Elsevier Ltd.)
      In 2 different energy economy models of the global energy system, the cost-effective use of biomass under a stringent C constraint was analyzed. Gielen et al. conclude that it is cost-effective to use biofuels for transportation, whereas Azar et al. find that it is more cost-effective to use most of the biomass to generate heat and process heat, despite the fact that assumptions about the cost of biofuels prodn. is similar in the models. The authors compare the 2 models with the purpose of finding an explanation for these different results. Both models suggest that biomass is most cost-effectively used for heat prodn. for low C taxes (<50-100 USD/tC, depending on the year in question). But for higher C taxes, the cost-effective choice reverses in the BEAP model, but not in the GET model. The reason for this is that GET includes H from C-free energy sources as a technol. option, whereas that option is not allowed in the BEAP model. In all other sectors, both models include C-free options above biomass. Thus, with higher C taxes, biomass will eventually become the cost-effective choice in the transportation sector in BEAP, regardless of its technol. cost parameters.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXht1Gis7zJ&md5=6c22c26d789ed70a9777b28fc7a60f36
    3. 3
      Grahn, M.; Azar, C.; Lindgren, K. The role of biofuels for transportation in CO2 emission reduction scenarios with global versus regional carbon caps Biomass Bioenergy 2009, 33, 360– 371
      3
      The role of biofuels for transportation in CO2 emission reduction scenarios with global versus regional carbon caps
      Grahn, Maria; Azar, Christian; Lindgren, Kristian
      Biomass and Bioenergy (2009), 33 (3), 360-371CODEN: BMSBEO; ISSN:0961-9534. (Elsevier Ltd.)
      This study analyzes how international climate regimes affect cost-efficiency of fuel choices in the transportation sector. The anal. is carried out with a regionalized version of the Global Energy Transition model, GET-R 6.0. Two different carbon dioxide (CO2) redn. scenarios are applied, both meeting an atm. CO2 concn. target of 450 ppm by the year 2100. The first scenario, "global cap" (GC), uses a global cap on CO2 emissions, and global emissions trading is allowed. In the second scenario, "regional caps" (RC), industrialized regions start to reduce their CO2 emissions by 2010 while developing regions may wait several decades and emission redns. are not tradable across regions. In this second scenario, CO2 emissions are assumed to meet an equal per capita distribution of 1.0 tC/capita, in all six regions, by 2040; emissions then follow a common redn. path, toward approx. 0.2 tC/capita by 2100. Three main results emerge from our anal.: (i) the use of biofuels in the industrialized regions is significantly higher in RC than in GC; (ii) the use of biofuels in RC actually increases the weaker (i.e., higher) the CO2 concn. target (up to 550 ppm); and (iii) biofuels never play a dominant role in the transportation sector. We find that biofuels may play a more important role in industrialized countries if these take on their responsibilities and reduce their emissions before developing countries start reducing their emissions, compared to the case in which all countries take action under a global cap and trade emission redn. regime.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXitlGntbs%253D&md5=17099911120d45d81b00650e817ed3a6
    4. 4
      Wise, M.; Calvin, K.; Thomson, A. M.; Clarke, L.; Bomb-Lamberty, B.; Sands, R.; Smith, S.; Janetos, A.; Edmonds, J.The Implications of Limiting CO2 Concentrations for Agriculture, Land Use, Land-Use Change Emissions and Bioenergy; Technical Report; Pacific Northwest National Laboratory: Richland, WA, 2009, p 44.
      There is no corresponding record for this reference.
    5. 5

      California Air Resources Board. Proposed Regulation to Implement the Low Carbon Fuel Standard, Volume I, Staff Report: Initial Statement of Reasons

      ; 2009; p 374.
      There is no corresponding record for this reference.
    6. 6
      European Parliament. Renewable Energy Directive; 2009/28/EC; 2009.
      There is no corresponding record for this reference.
    7. 7

      USEPA. Renewable Fuel Standard Program (RFS2) Notice of Proposed Rulemaking

      , 2009.
      There is no corresponding record for this reference.
    8. 8

      ISO. ISO 14040: Environmental Management - Life Cycle Assessment - Principles and Framework

      ; 2006.
      There is no corresponding record for this reference.
    9. 9
      Ekvall, T.; Weidema, B. P. System boundaries and input data in consequential life cycle inventory analysis Int. J. Life Cycle Assess. 2004, 9, 161– 171
      There is no corresponding record for this reference.
    10. 10
      Campbell, J. E.; Lobell, D. B.; Field, C. B. Greater transportation energy and GHG offsets from bioelectricity than ethanol Science 2009, 324, 1055– 1057
      There is no corresponding record for this reference.
    11. 11
      Ohlrogge, J.; Allen, D.; Berguson, B.; DellaPenna, D.; Shachar-Hill, Y.; Stymne, S. Driving on biomass Science 2009, 324, 1019– 1020
      11
      Driving on Biomass
      Ohlrogge, John; Allen, Doug; Berguson, Bill; DellaPenna, Dean; Shachar-Hill, Yair; Stymne, Sten
      Science (Washington, DC, United States) (2009), 324 (5930), 1019-1020CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      There is no expanded citation for this reference.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXms1GisL0%253D&md5=28109fe731092f698889c32105cbe3cc
    12. 12
      Lemoine, D. M.; Kammen, D. M.; Farrell, A. E. An innovation and policy agenda for commercially competitive plug-in hybrid electric vehicles Environ. Res. Lett. 2008, 3014003
      There is no corresponding record for this reference.
    13. 13
      Lemoine, D. M. Valuing plug-in hybrid electric vehicles’ battery capacity using a real options framework Energy J. 2010, 31, 113– 143
      There is no corresponding record for this reference.
    14. 14
      Larson, E. D. A review of life-cycle analysis studies on liquid biofuel systems for the transport sector Energy Sustainable Dev. 2006, 10, 109– 126
      14
      A review of life-cycle analysis studies on liquid biofuel systems for the transport sector
      Larson, Eric D.
      Energy for Sustainable Development (2006), 10 (2), 109-126CODEN: ESDEFY; ISSN:0973-0826. (International Energy Initiative)
      A review. Given current oil prices and the heavy dependence of many countries on imported oil, the potential for producing or importing liq. transportation fuels made from bio mass is attracting keen interest in many developing and industrialized countries. There is also some interest in biofuels for climate change mitigation. This article reviews the rich literature of published life-cycle analyses (LCAs) of liq. biofuels, with a focus on elucidating the impacts that prodn. and use of such biofuels might have on emissions of greenhouse gases. Reviews of LCAs for "conventional" liq. biofuels (biodiesel and sugar/starch bioethanol) and potential "future" liq. biofuels (Fischer-Tropsch fuels, di-Me ether, and cellulosic bioethanol) are included. Striking features of the LCAs reviewed include almost exclusive contextual focus on Europe or North America, wide ranges in net energy balance results and GHG impacts among different biofuels and even for the same biofuel, and a lack of focus on evaluating GHG impacts per unit of land area. The wide range of reported LCA GHG results is due in part to the wide range of plausible values for key input parameters, among which the four most significant parameters exhibiting the greatest variability and/or uncertainty are (1) the climate-active species included in the calcn., (2) assumptions around N2O emissions, (3) the allocation method used for co-product credits, and (4) soil carbon dynamics. Finally, from a comparison of GHG impacts of biomass used for transportation fuels against those for stationary applications one concludes that under some conditions biofuels will provide greater GHG mitigation benefits, but under other conditions, biopower will be favored. It is difficult to make broad and unequivocal statements on this point. Case-specific anal. is required.
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    15. 15
      Searchinger, T.; Heimlich, R.; Houghton, R. A.; Dong, F.; Elobeid, A.; Fabiosa, J.; Tokgoz, S.; Hayes, D.; Yu, T. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change Science 2008, 319, 1238– 1240
      15
      Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change
      Searchinger, Timothy; Heimlich, Ralph; Houghton, R. A.; Dong, Fengxia; Elobeid, Amani; Fabiosa, Jacinto; Tokgoz, Simla; Hayes, Dermot; Yu, Tun-Hsiang
      Science (Washington, DC, United States) (2008), 319 (5867), 1238-1240CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Most prior studies obsd. that substituting biofuels for gasoline reduces greenhouse gases since biofuels sequester C through the growth of the feedstock. These analyses failed to count C emissions which occur as farmers respond to higher prices and convert forest and grassland to new cropland to replace grain (or cropland) diverted to biofuels. Using a worldwide agricultural model to est. emissions from land-use change, the authors detd. corn-based ethanol, instead of producing a 20% savings, nearly doubles greenhouse emissions over 30 years and increases greenhouse gases for 167 years. Biofuels from switchgrass, if grown on US corn lands, increase emissions 50%. This result raises concerns about large biofuel mandates and highlights the value of using waste products.
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    16. 16
      Liska, A. J.; Perrin, R. K. Indirect land use emissions in the life cycle of biofuels: regulations vs science Biofuels, Bioprod. Biorefin. 2009, 3, 318– 328
      16
      Indirect land use emissions in the life cycle of biofuels: regulations vs science
      Liska, Adam J.; Perrin, Richard
      Biofuels, Bioproducts & Biorefining (2009), 3 (3), 318-328CODEN: BBBICH; ISSN:1932-104X. (John Wiley & Sons Ltd.)
      A review. Recent legislative mandates have been enacted at state and federal levels with the purpose of reducing life cycle greenhouse gas (GHG) emissions from transportation fuels. This legislation encourages the substitution of fossil fuels with 'low-carbon' fuels. The burden is put on regulatory agencies to det. the GHG-intensity of various fuels, and those agencies naturally look to science for guidance. Even though much progress has been made in detg. the direct life cycle emissions from the prodn. of biofuels, the science underpinning the estn. of potentially significant emissions from indirect land use change (ILUC) is in its infancy. As legislation requires inclusion of ILUC emissions in the biofuel life cycle, regulators are in a quandary over accurate implementation. In this article, we review these circumstances and offer some suggestions for how to proceed with the science of indirect effects and regulation in the face of uncertain science. Besides investigating indirect deforestation and grassland conversion alone, a more comprehensive assessment of the total GHG emissions implications of substituting biofuels for petroleum needs to be completed before indirect effects can be accurately detd. This review finds that indirect emissions from livestock and military security are particularly important, and deserve further research.
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    17. 17
      Melillo, J. M.; Reilly, J. M.; Kicklighter, D. W.; Gurgel, A. C.; Cronin, T. W.; Paltsev, S.; Felzer, B. S.; Wang, X.; Sokolov, A. P.; Schlosser, C. A. Indirect emissions from biofuels: How important? Science 2009, 326, 1397– 1399
      There is no corresponding record for this reference.
    18. 18
      Hertel, T. W.; Golub, A. A.; Jones, A. D.; O’Hare, M.; Plevin, R. J.; Kammen, D. M. Effects of US maize ethanol on global land use and greenhouse gas emissions: Estimating market-mediated responses BioScience 2010, 60, 223– 231
      There is no corresponding record for this reference.
    19. 19
      Foley, J. A.; et al. Global consequences of land use Science 2005, 309, 570– 574
      19
      Global Consequences of Land Use
      Foley, Jonathan A.; DeFries, Ruth; Asner, Gregory P.; Barford, Carol; Bonan, Gordon; Carpenter, Stephen R.; Chapin, F. Stuart; Coe, Michael T.; Daily, Gretchen C.; Gibbs, Holly K.; Helkowski, Joseph H.; Holloway, Tracey; Howard, Erica A.; Kucharik, Christopher J.; Monfreda, Chad; Patz, Jonathan A.; Prentice, I. Colin; Ramankutty, Navin; Snyder, Peter K.
      Science (Washington, DC, United States) (2005), 309 (5734), 570-574CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food prodn., maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.
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    20. 20
      Fargione, J.; Hill, J.; Tilman, D.; Polasky, S.; Hawthorne, P. Land clearing and the biofuel carbon debt Science 2008, 319, 1235– 1238
      20
      Land Clearing and the Biofuel Carbon Debt
      Fargione, Joseph; Hill, Jason; Tilman, David; Polasky, Stephen; Hawthorne, Peter
      Science (Washington, DC, United States) (2008), 319 (5867), 1235-1238CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Increasing energy use, climate change, and CO2 emissions from fossil fuels make switching to low-C fuels a high priority. Biofuels are a potential low-C energy source, but whether biofuels offer C savings depends on how they are produced. Converting rain forest, peatland, savanna, or grassland to produce food crop-based biofuels in Brazil, southeast Asia, and the US creates a biofuel C debt by releasing 17-420 times more CO2 than the annual greenhouse gas (GHG) redns. these biofuels would provide by displacing fossil fuels. In contrast, biofuels made from waste biomass or from biomass grown on degraded and abandoned agricultural lands planted with perennials incur little or no C debt and can offer immediate, sustained GHG advantages.
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    21. 21
      Gerbens-Leenes, W.; Hoekstra, A. Y.; van der Meer, T. H. The water footprint of bioenergy Proc. Natl. Acad. Sci. 2009, 106, 10219– 10223
      21
      The water footprint of bioenergy
      Gerbens-Leenes, Winnie; Hoekstra, Arjen Y.; van der Meer, Theo H.
      Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (25), 10219-10223, S10219/1-S10219/10CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      All energy scenarios show a shift toward an increased percentage of renewable energy sources, including biomass. This study gives an overview of water footprints (WFs) of bioenergy from 12 crops that currently contribute the most to global agricultural prodn.: barley, cassava, maize, potato, rapeseed, rice, rye, sorghum, soybean, sugar beet, sugar cane, and wheat. In addn., this study includes jatropha, a suitable energy crop. Since climate and prodn. circumstances differ among regions, calcns. have been performed by country. The WF of bioelectricity is smaller than that of biofuels because it is more efficient to use total biomass (e.g., for electricity or heat) than a fraction of the crop (its sugar, starch, or oil content) for biofuel. The WF of bioethanol appears to be smaller than that of biodiesel. For electricity, sugar beet, maize, and sugar cane are the most favorable crops [50 m3/GJ (GJ)]. Rapeseed and jatropha, typical energy crops, are disadvantageous (400 m3/GJ). For ethanol, sugar beet, and potato (60 and 100 m3/GJ) are the most advantageous, followed by sugar cane (110 m3/GJ); sorghum (400 m3/GJ) is the most unfavorable. For biodiesel, soybean and rapeseed show to be the most favorable WF (400 m3/GJ); jatropha has an adverse WF (600 m3/GJ). When expressed per L, the WF ranges from 1,400 to 20,000 L of water per L of biofuel. If a shift toward a greater contribution of bioenergy to energy supply takes place, the results of this study can be used to select the crops and countries that produce bioenergy in the most water-efficient way.
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    22. 22
      Farrell, A. E.; et al.A Low-Carbon Fuel Standard for California. Part 1: Technical Analysis; Technical Report; University of California, 2007; p 179.
      There is no corresponding record for this reference.
    23. 23
      Tilman, D.; Socolow, R.; Foley, J. A.; Hill, J.; Larson, E.; Lynd, L.; Pacala, S.; Reilly, J.; Searchinger, T.; Somerville, C.; Williams, R. Beneficial biofuels-the food, energy, and environment trilemma Science 2009, 325, 270– 271
      There is no corresponding record for this reference.
    24. 24
      Eickhout, B.; van den Born, G. J.; Notenboom, J.; van Oorschot, M.; Ros, J. P. M.; van Vuuren, D. P.; Westhoek, H. J.Local and global consequences of the EU renewable directive for biofuels. Testing the sustainability criteria; Technical Report 500143001/2008; Milieu en Natuur Planbureau, 2008; p 70.
      There is no corresponding record for this reference.
    25. 25
      Lutsey, N.; Sperling, D. Greenhouse gas mitigation supply curve for the United States for transport versus other sectors Transportation Res., Part D 2009, 14, 222– 229
      There is no corresponding record for this reference.
    26. 26
      McCollum, D.; Yang, C. Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications Energy Policy 2009, 37, 5580– 5596
      There is no corresponding record for this reference.
    27. 27
      Farrell, A. E.; Plevin, R. J.; Turner, B. T.; Jones, A. D.; O’Hare, M.; Kammen, D. M. Ethanol can contribute to energy and environmental goals Science 2006, 311, 506– 508
      27
      Ethanol Can Contribute to Energy and Environmental Goals
      Farrell, Alexander E.; Plevin, Richard J.; Turner, Brian T.; Jones, Andrew D.; O'Hare, Michael; Kammen, Daniel M.
      Science (Washington, DC, United States) (2006), 311 (5760), 506-508CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      A review. To study the potential effects of increased biofuel use, we evaluated six representative analyses of fuel ethanol. Studies that reported neg. net energy incorrectly ignored coproducts and used some obsolete data. All studies indicated that current corn ethanol technologies are much less petroleum-intensive than gasoline but have greenhouse gas emissions similar to those of gasoline. However, many important environmental effects of biofuel prodn. are poorly understood. New metrics that measure specific resource inputs are developed, but further research into environmental metrics is needed. Nonetheless, it is already clear that large-scale use of ethanol for fuel will almost certainly require cellulosic technol.
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    28. 28
      Samaras, C.; Meisterling, K. Life cycle assessment of greenhouse gas emissions from plug-in hybrid vehicles: Implications for policy Environ. Sci. Technol. 2008, 42, 3170– 3176
      There is no corresponding record for this reference.
    29. 29
      Kammen, D. M.; Arons, S. M.; Lemoine, D. M.; Hummel, H. Cost-effectiveness of greenhouse gas emission reductions from plug-in hybrid electric vehicles. In Plug-in Electric Vehicles: What role for Washington?Sandalow, D. B., Ed.; Brookings Institution Press: Washington, DC, 2009.
      There is no corresponding record for this reference.
    30. 30
      Azar, C.; Lindgren, K.; Larson, E.; Möllersten, K. Carbon capture and storage from fossil fuels and biomass - Costs and potential role in stabilizing the atmosphere Climatic Change 2006, 74, 47– 79
      There is no corresponding record for this reference.
    31. 31
      Uddin, S. N.; Barreto, L. Biomass-fired cogeneration systems with CO2 capture and storage Renewable Energy 2007, 32, 1006– 1019
      There is no corresponding record for this reference.

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