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Indirect land use change and biofuels: Mathematical analysis reveals a fundamental flaw in the regulatory approach
b i o m a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 4 0 8 e4 1 2
Available online at www.sciencedirect.com
ScienceDirect http://www.elsevier.com/locate/biombioe
Short Communication
Indirect land use change and biofuels: Mathematical analysis reveals a fundamental flaw in the regulatory approach Seungdo Kim a,b, Bruce E. Dale a,b,*, Reinout Heijungs c,d, Adisa Azapagic e, Tom Darlington f, Dennis Kahlbaum f a
DOE Great Lakes Bioenergy Research Center, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA b Department of Chemical Engineering and Materials Science, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA c Department of Econometrics and Operations Research, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands d Institute of Environmental Sciences, Leiden University, P.O. Box 9518, Leiden, The Netherlands e School of Sustainable Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK f Air Improvement Resource, Inc., 47298 Sunnybrook Lane, Novi, MI 48374, USA
article info
abstract
Article history:
In the Renewable Fuel Standard (RFS2) program, the United States Environmental Protec-
Received 10 May 2014
tion Agency (U.S. EPA) has used partial equilibrium models to estimate the overall indirect
Received in revised form
land use change (iLUC) associated with the biofuel scenario mandated by the Energy In-
12 September 2014
dependence and Security Act of 2007 (EISA). For regulatory purposes, the U.S. EPA “shocks”
Accepted 16 September 2014
(changes) the amount of each biofuel in the economic models one at a time to estimate the
Available online 11 October 2014
threshold values for specific biofuels (single-shock analysis). The primary assumption in the single-shock analysis is that iLUC is a linear process with respect to biofuels, i.e., that
Keywords:
interactions between different biofuels are trivially small. However, the assumption of
Biofuel policy
linearity in the single-shock analysis is not appropriate for estimating the threshold values
Corn ethanol
for specific biofuels when the interactions between different biofuels are not small.
Indirect land use change
Numerical results from the RFS2 program show that the effects of interactions between
Renewable fuel standard
different biofuels are too large to be ignored. Thus, the threshold values for specific biofuels
Soybean Biodiesel
determined by the U.S. EPA are scenario-dependent and value choice-driven. They do not reflect real
Sugarcane ethanol
impacts of specific biofuels. Using scenario-dependent values for regulation is arbitrary and inappropriate. Failure to deal appropriately with interactions between different biofuels when assigning iLUC values to specific biofuels is a mathematical and systematic flaw; it is
* Corresponding author. Department of Chemical Engineering and Materials Science, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA. Tel.: þ1 517 353 6777; fax: þ1 517 336 4615. E-mail address: [email protected] (B.E. Dale). http://dx.doi.org/10.1016/j.biombioe.2014.09.015 0961-9534/© 2014 Elsevier Ltd. All rights reserved.
409
b i o m a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 4 0 8 e4 1 2
not an “uncertainty” issue. The U.S. EPA should find better ways to differentiate the contribution of one biofuel versus another when assigning iLUC values or find better means of regulating the land use change impact of biofuel production. © 2014 Elsevier Ltd. All rights reserved.
1.
Introduction
In climate policy models, indirect land use change (iLUC) is hypothesized to occur when increased demand for crops for biofuel production triggers loss of carbon stocks when lands are subsequently converted to grow more crops [1e4]. These lost carbon stocks are then attributed to the biofuels through a numerical iLUC factor as a greenhouse gas “penalty” to be assessed against the overall life cycle greenhouse gas emissions of the biofuel in question. iLUC cannot be measured directly; instead iLUC has been estimated via global agricultural economic models [1e4]. In renewable fuel policies, the United States Environmental Protection Agency (U.S. EPA) uses the Forestry and Agricultural Sector Optimization Model (FASOM), and Food and Agricultural Policy and Research Institute international (FAPRI-CARD) model in the Renewable Fuel Standard (RFS2) program [1]. The California Air Resources Board (CARB) uses the Global Trade Analysis Project (GTAP) model in the Low Carbon Fuel Standard (LCFS) Program [2]. Such iLUC estimates have been strongly criticized on empirical, technical, and conceptual grounds [5e16]. Here we revisit one of important features in iLUC calculations, namely interactions between different biofuels (or “nonlinearity”), e.g., when competing for land against one another. The nonlinear interactions play important roles in determining the threshold values for specific biofuels. Even though other nonlinear properties in iLUC calculations [4,17e20] are wellknown (e.g., nonlinearity with respect to shock sizes, etc.), very little attention has been paid to these nonlinear interactions between biofuels. We wish to emphasize that we are not considering the validity, precision or adequacy of the agro-economic models involved in estimating iLUC (e.g., FASOM, FAPRI-CARD, GTAP, etc.). In this paper we are only concerned with the following question: what is the magnitude of the nonlinear interactions between different biofuels in estimating iLUC?
2.
Results
In the RFS2 program, the U.S. EPA calculates the overall consequences of iLUC associated with the biofuel scenario mandated by the Energy Independence and Security Act of 2007 (EISA). In this analysis the amount of each biofuel is simultaneously increased in the economic models to simulate the EISA-mandated scenario (referred to as the “overall analysis”), and then a new equilibrium state is established. The model results arising from a new equilibrium state driven by the EISA-mandated scenario are compared to results arising from a reference equilibrium state based on a business-as-
usual scenario in the absences of the EISA. Differences between the new and the reference equilibrium states are the consequences of iLUC associated with the EISA-mandated scenario. The overall analysis can be expressed as Equation (1). DL ¼ L XC1 ; XC2 ; XC3 L XR1 ; XR2 ; XR3
(1)
where L is the model function (e.g., land use change), and X is the volume of the particular biofuel. The subscripts 1, 2 and 3 represent corn ethanol, soybean biodiesel, and sugarcane ethanol, respectively. Superscripts R and C represent the reference and the EISA-mandated scenario (control) states, respectively. When the changes in biofuel volumes are very small (approaching zero), the change in the model function can be written as: DL ¼ dLðX1 ; X2 ; X3 Þ ¼
vL vL vL $dX1 þ $dX2 þ $dX3 vX1 vX2 vX3
(2)
For regulatory purposes, the U.S. EPA attempts to calculate the iLUC effects associated with specific biofuels from the overall iLUC. However, they cannot directly calculate the contributions of individual biofuels because the effects of interactions between different biofuels cannot be attributed to specific biofuels in the models [1]. In order to estimate the effects of individual biofuels, the U.S. EPA shocks the amount of each biofuel in the economic models one at a time (referred to as “single-shock analysis”). When the specific biofuel volume is shocked (changed), the other biofuels are held constant at volumes mandated by the EISA. The central assumption in the single-shock analysis is that iLUC is a linear process with respect to biofuels. If iLUC is a linear process, then differences between the shocked and the EISA-mandated scenario states can appropriately be attributed to changes in that specific biofuel. In fact, linearity is required for regulation so that an estimated iLUC value estimated will be the same for 1 L, 1 dam3 or 1 hm3 of that biofuel. For example, land use change attributed to corn ethanol and the attributed land use change per volume are expressed as Equations (3) and (4), respectively. DL1 ¼ L XC1 ; XC2 ; XC3 L XR1 ; XC2 ; XC3
A1 ¼
L XC1 ; XC2 ; XC3 L XR1 ; XC2 ; XC3 DL1 ¼ XC1 XR1 XC1 XR1
(3)
(4)
where A1 is the land use change per volume attributed to corn ethanol. When the change in corn ethanol volume is very small, the attributed land use change per volume becomes A1 ¼
vL vX1 X2 ;X3 ¼constant
(5)
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The attributed land use change per volume of biofuel can be estimated mathematically from the overall analysis (Equation (2)) as well and becomes A1 ¼
dL vL ¼ dX1 vX1
(6)
If the effects of interactions between different biofuels are essentially zero, then Equations (5) and (6) are identical, and satisfy the mathematical requirement of additivity between the overall and the single-shock analyses. If the interactions are not essentially zero, the attributed land use change per volume in Equation (6) will depend on the volumes of the different biofuels: therefore the overall and the single-shock analyses will not be additive. Note that the attributed land use change per unit volume in Equation (5) is always either a function of corn ethanol production volume or constant regardless of the magnitude of interactions. As mentioned above, this exclusive dependence of iLUC on the volume of corn ethanol is essential to iLUC regulations as they are now constructed. This is the only way a single iLUC factor for corn ethanol can be applied at 1 L, 1 dam3 or 1 hm3 of the fuel. However, the model is nonlinear for a system with nontrivial (nonzero) interactions between different biofuels. Nonlinearity means that the attributed land use change per volume for the specific biofuel varies with amount of biofuel produced, and it is unreasonable, unscientific and arbitrary to regulate based on a single iLUC value. The U.S. EPA states that the single-shock analyses reflect the impacts of specific biofuels. This is true only if the effects of interactions between different biofuels are essentially zero. Otherwise the statement is false. In fact, iLUC values calculated from the FAPRI-CARD model in the RFS2 program show that the effects of interactions between different biofuels are too large to be ignored. Instead of being linear, the actual system is highly nonlinear. Using the FAPRI-CARD model, the international land use change (indirect land use change) that occurs globally as a result of simultaneously shocking the model with all the biofuel volumes is about 7944 km2, while the sum of the singleshock analyses is about 18,631 km2, roughly 2.3 times as large. Furthermore, greenhouse gas (GHG) emissions associated with these indirect land use changes in the overall and the single-shock analyses are 152 Tg CO2 and 282 Tg CO2, respectively, almost a two-fold difference. The U.S. EPA was aware of this very significant discrepancy between the two analyses but simply pointed out that the effects of interactions between different biofuels could not be assigned to individual biofuels [1]. Since the requirement of additivity is not satisfied, indirect land use change that occurs globally is a nonlinear process with respect to biofuels. The iLUC impacts of specific biofuels will vary with biofuel volumes and will not be the same at 1 dam3 versus 1 hm3 of a particular biofuel. Furthermore, the model assumes that iLUC for the specific biofuel in the singleshock analysis occurs when the other biofuels have already reached their EISA-mandated threshold volumes. However, since the system is nonlinear, these threshold values in RFS2 are scenario-dependent. They do not reflect the actual impacts of specific biofuels. Therefore, applying a linear
approach (i.e., single-shock analyses) to this highly nonlinear system is not an appropriate basis for regulations. The Low Carbon Fuel Standard (LCFS) Program of the California Air Resources Board (CARB) employs a similar singleshock analysis to estimate iLUC for specific biofuels. CARB uses the GTAP model [2] to make these estimates, rather than FASOM/FAPRI models used by the US EPA. Since CARB has not published any estimates of the overall iLUC obtained from their modeling runs, we ran the GTAP model (version ARB2011F released October, 6, 2011) [4] with arbitrary scenarios to investigate whether or not the overall and the singleshock analyses are additive, in order to detect nonlinearity. Our results using GTAP reveal approximately a 20% difference between the overall analysis (each biofuel volume shock applied simultaneously) and the sum of the single-shock analyses. Thus it is probable that the California LCFS Program also ignores the effects of interactions between different biofuels. Unfortunately, previous iLUC studies for specific biofuels based on global agricultural economic models used in the RFS2 and the LCFS programs (e.g., Searchinger et al. [3], Tyner et al. [4], etc.) never address interactions between different biofuels. These interactions would be reflected in differences between the sum of the iLUC effects of the individual biofuels and the iLUC value resulting from the overall analysis. Not including these interactions will give unreliable iLUC estimates unless the interactions between different biofuels are essentially zero. Based on our analysis as reported here, these interactions are not essentially zero. Thus the key assumption behind the regulatory approach used by both US EPA and CARB, the assumption of linearity, is invalid. It is not that the results from iLUC calculations are imprecise or uncertain. Precision is not the issue. The modeling approach used by both CARB and EPA to predict iLUC values rests on a faulty assumption; thus the flaw is a mathematical and a systemic one. The iLUC results obtain by CARB and EPA are not simply imprecise or uncertain. They are simply wrong.
3.
Discussion: what to do?
During the review process for this paper, one of the reviewers asked: “what to do?” which we take to mean “if this approach to calculating iLUC is fundamentally flawed, what should we do instead?” The Editor invited us to suggest what we think the EPA and other regulatory bodies might do if the current linear approach to regulating this highly nonlinear system is inappropriate. We appreciate the invitation and will try to respond honestly, directly and clearlydbut we hope, without giving offense. We think there are two basic ways to deal with the issue of potentially increased greenhouse gas generation due to land use change induced by biofuels. The first approach involves rejecting iLUC as a regulatory tool and instead regulating biofuels to incentivize positive outcomes rather than trying to reduce negative outcomes through iLUC. The second approach retains iLUC but focuses on improving iLUC to be more empirically rigorous as well as more fair and honest.
b i o m a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 4 0 8 e4 1 2
3.1. First approach: regulate biofuels for direct environmental improvement We think much of the driving force behind iLUC is a simple desire on the part of some to limit corn-based ethanol (and the underlying corn production system) as an alternative to petroleum fuels, by whatever means necessary. But corn-based ethanol is here to stay, and at very large scale. We frankly think the time and effort of these detractors would be better spent in helping improve the environmental performance of the corn production system. In fact, continued use of iLUC as an arbitrary, deeply-flawed regulatory tool to “punish” corn ethanol actually undermines the cause of genuine environmental improvements for first generation biofuels. iLUC forces us enter into largely unproductive debates instead of actually improving the environment. Instead of using iLUC to regulate against corn ethanol, we could instead regulate for improvements we wish to see occur. For example, it is well known that increased use of cover crops, double crops, riparian buffers, and so forth would do much to improve the environmental performance of corn production.
3.2.
Second approach: improve regulation based on iLUC
Regulators deeply invested in the iLUC approach will probably not abandon their efforts to apply it to biofuel regulation. So we have two suggestions to improve biofuel regulations based on iLUC. The first suggestion is to make iLUC regulation more empirically rigorous by accounting for the interactions between different biofuels. The second suggestion deals with improving the fairness of the regulatory system by treating petroleum fuels as we do biofuels.
3.2.1. First suggestion: improve the empirical rigor of regulations using iLUC We have shown that the system is nonlinear; and that the single shock values do not include the effects of interactions between biofuels. However, the overall analysis (DL in Equation (1)) does include these interactions. We suggest that the overall DL be allocated to the individual biofuels by economic parameters or physical properties (e.g., market prices, cropland requirements, energy contents, etc.) as seen in Equation (7). Li ¼ DL$ai
(7)
where Li is the iLUC associated with the ith biofuel and ai is the allocation factor for the ith biofuel. (The sum of the individual
411
ai is one.) Using the cropland requirements for each biofuel production as allocation factors, we estimated GHG emissions associated with the iLUC for corn ethanol, soybean diesel, and sugarcane ethanol as illustrated in Fig. 1. Most background data used in this calculation (e.g., biofuel yield, co-products, etc.) are obtained from the Greenhouse gases, regulated emissions, and energy use in transportation model [21]. Other allocation approaches could be used but the objective would be to allocate based on DL.
3.2.2. Second suggestion: make the iLUC regulation system more fair and honest We hope that our desire to be clear and honest in our response to the Editor's invitation does not conflict with our wish not to offend. But here is the truth: the current iLUC regulatory system is both unfair and dishonest. It is unfair because it does not treat petroleum fuels and biofuels equally. It is dishonest because it continues to ignore this unfairness, even after the unfairness has been pointed out, repeatedly and over many years. Current iLUC regulation unfairly uses different system boundaries and makes different comparisons for biofuels versus petroleum fuels. For example, petroleum fuel GHGs are based on the average barrel but they are compared with biofuels on the marginal barrel. A more fair comparison would be to compare the marginal barrel of “new” petroleum fuels (e.g., from deep water drilling, hydraulic fracturing or bitumen (“tar sands”)) to the marginal barrel of biofuels. Conventional, on-shore petroleum production is declining, and biofuels should be evaluated based on new sources of petroleum, rather than old, declining sources. These new sources of petroleum are, without any doubt, more polluting, more expensive and more GHG intensive than conventional petroleum. They are the proper comparison with biofuels, not conventional oil. The fact that different system boundaries are used for petroleum fuels versus biofuels is also deeply unfair. Indirect GHG effects are assessed against biofuels, but not against petroleum fuels. We cite here just three examples of indirect effects of petroleum fuels; there are undoubtedly others. First, as petroleum prices rise, other energy sources will be used, when possible, in place of oil, for example, burning coal instead of oil to produce electricity or making synthetic liquid fuels based on coal, with its much higher GHG emissions per megajoule (MJ). Thus, biofuels should be “credited” with their market-mediated reduction in petroleum prices and the resulting reduced use of coal with its attendant high GHG emissions.
Fig. 1 e Single-shock analysis versus allocation [based on the U.S. EPA calculations].
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Second, continued use of fossil fuels drives global warming, especially in the Arctic. As the Arctic tundra and shallow polar seas warm, they may release gigatons of methane from methane clathrates. Evidence of very large methane releases through warming tundra has recently emerged. Substitution of fossil fuels with biofuels could reduce net GHG emissions and thereby reduce warming and the resulting methane release. Biofuels should receive a GHG “credit” for this effect in a truly fair and comprehensive analysis. Third, petroleum exploration in ever more remote areas, such as the Peruvian Amazon, will require road building, which will increase human settlement in lands adjacent to these roads and subsequently more land clearing and agriculture, with its attendant GHG releases. Biofuels will reduce the need for additional petroleum exploration and thereby reduce these indirect GHG releases from increased petroleum exploration. It is time for the US EPA, CARB and European regulatory organizations that claim to account for indirect as well as direct GHG emissions of biofuels to play fair. If they continue to use iLUC as a regulatory tool, then they must account for indirect GHG releases from petroleum exploration and use, for example, by the three pathways outlined above. This is a minimal requirement for fair treatment of biofuels versus petroleum fuels.
Acknowledgments This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC0207ER64494) and DOE OBP Office of Energy Efficiency and Renewable Energy DE-AC05-76RL01830).
references
[1] US EPA. Renewable fuel standard program (RFS2) regulatory impact analysis [Internet]. Washington DC: US Environmental Protection Agency; 2010 [updated 2009 May 26; cited 2014 April 17] Available from:, http://www.epa.gov/ otaq/renewablefuels/420r10006.pdf. [2] California Air Resources Board. Staff Report: Initial Statement of Reasons. [Internet]. Proposed regulation to implement the low carbon fuel standard, vol. I. Sacramento, CA: California Environmental Protection Agency; 2009 [updated 2009 March 5; cited 2014 April 17] Available from:, http://www.arb.ca.gov/fuels/lcfs/030409lcfs_isor_vol1.pdf. [3] Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J, et al. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 2008;319(5867):1238e40. [4] Tyner WE, Taheripour F, Zhuang O, Birur D, Baldos U. Land use changes and consequent CO2 emissions due to US corn ethanol production: a comprehensive analysis [Internet]. West Lafayette, IN: Purdue University; 2010 [updated 2010 April 14; cited 2014 April 17] Available from:, http://www. transportation.anl.gov/pdfs/MC/625.PDF. [5] Mathews JA, Tan H. Biofuels and indirect land use change effects: the debate continues. Biofuels Bioprod Bioref 2009;3(3):305e17.
[6] Liska AJ, Perrin RK. Indirect land use emissions in the life cycle of biofuels: regulations vs science. Biofuels Bioprod Bioref 2009;3(3):318e28. [7] Oladosu G, Kline K, Uria-Martinez R, Eaton L. Sources of corn for ethanol production in the United States: a review and decomposition analysis of the empirical data. Biofuels Bioprod Bioref 2011;5(6):640e53. [8] Kim S, Dale BE. Indirect land use change for biofuels: testing predictions and improving analytical methodologies. Biomass Bioenergy 2011;35(7):3235e40. [9] O'Hare M, Delucchi M, Edwards R, Fritsche U, Gibbs H, Hertel T, et al. Comment on “indirect land use change for biofuels: testing predictions and improving analytical methodologies” by Kim and Dale: statistical reliability and the definition of the indirect land use change (iLUC) issue. Biomass Bioenergy 2011;35(10):4485e7. [10] Kline KL, Oladosu GA, Dale VH, McBride AC. Scientific analysis is essential to assess biofuel policy effects: in response to the paper by Kim and Dale on “indirect land-use change for biofuels: testing predictions and improving analytical methodologies”. Biomass Bioenergy 2011;35(10):4488e91. [11] Kim S, Dale BE. Response to comments by O'Hare et al., on the paper indirect land use change for biofuels: testing predictions and improving analytical methodologies. Biomass Bioenergy 2011;35(10):4492e3. [12] Kim S, Dale BE, Ong RG. An alternative approach to indirect land use change: allocating greenhouse gas effects among different uses of land. Biomass Bioenergy 2012;46:447e52. [13] Broch A, Hoekman SK, Unnasch S. A review of variability in indirect land use change assessment and modeling in biofuel policy. Environ Sci Policy 2013;29:147e57. [14] Finkbeiner M. Indirect land use change e help beyond the hype? Biomass Bioenergy 2014;62:218e21. [15] Warner E, Zhang Y, Inman D, Heath G. Challenges in the estimation of greenhouse gas emissions from biofuelinduced global land-use change. Biofuels Bioprod Bioref 2014;8(1):114e25. [16] Zilberman D, Hochman G, Rajagopal D. Indirect land use change: a second-best solution to a first-class problem. Ag Bio Forum 2010;13(4):382e90. [17] Edwards R, Mulligan D, Marelli L. Indirect land use change from increased biofuels demand: comparison of models and results for marginal biofuels production from different feedstocks [Internet]. Ispra, Italy: European Commission, Joint Research Centre, Institute for Energy; 2010 [cited 2014 July 29] Available from:, http://publications.jrc.ec.europa.eu/ repository/handle/111111111/15324. [18] Al-Riffai P, Dimaranan B, Laborde D. Global trade and environmental impact study of the EU biofuels mandate report [Internet]. Washington DC: International Food Policy Research Institute; 2010 [updated 2010 March 30; cited 2014 July 29] Available from:, http://www.ifpri.org/publication/ global-trade-and-environmental-impact-study-eu-biofuelsmandate. [19] Laborde D. Assessing the land use consequences of EU biofuel policies [Internet]. Washington DC: International Food Policy Research Institute; 2011 [updated 2010 November 4; cited 2014 July 29] Available from:, http://www.ifpri.org/ publication/assessing-land-use-change-consequenceseuropean-biofuel-policies. [20] Khannan K, Crago CL. Measuring indirect land use change with biofuels implications for policy. Annu Rev Resour Econ 2012;4:161e84. [21] Argonne National Laboratory. The greenhouse gases, regulated emissions, and energy use in transportation (GREET) computer model. [Internet]. Argonne, IL: Argonne National Laboratory. [updated 2013 October 25; cited 2014 July 29]. Available from: https://greet.es.anl.gov/.
Available online at www.sciencedirect.com
ScienceDirect http://www.elsevier.com/locate/biombioe
Short Communication
Indirect land use change and biofuels: Mathematical analysis reveals a fundamental flaw in the regulatory approach Seungdo Kim a,b, Bruce E. Dale a,b,*, Reinout Heijungs c,d, Adisa Azapagic e, Tom Darlington f, Dennis Kahlbaum f a
DOE Great Lakes Bioenergy Research Center, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA b Department of Chemical Engineering and Materials Science, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA c Department of Econometrics and Operations Research, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands d Institute of Environmental Sciences, Leiden University, P.O. Box 9518, Leiden, The Netherlands e School of Sustainable Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UK f Air Improvement Resource, Inc., 47298 Sunnybrook Lane, Novi, MI 48374, USA
article info
abstract
Article history:
In the Renewable Fuel Standard (RFS2) program, the United States Environmental Protec-
Received 10 May 2014
tion Agency (U.S. EPA) has used partial equilibrium models to estimate the overall indirect
Received in revised form
land use change (iLUC) associated with the biofuel scenario mandated by the Energy In-
12 September 2014
dependence and Security Act of 2007 (EISA). For regulatory purposes, the U.S. EPA “shocks”
Accepted 16 September 2014
(changes) the amount of each biofuel in the economic models one at a time to estimate the
Available online 11 October 2014
threshold values for specific biofuels (single-shock analysis). The primary assumption in the single-shock analysis is that iLUC is a linear process with respect to biofuels, i.e., that
Keywords:
interactions between different biofuels are trivially small. However, the assumption of
Biofuel policy
linearity in the single-shock analysis is not appropriate for estimating the threshold values
Corn ethanol
for specific biofuels when the interactions between different biofuels are not small.
Indirect land use change
Numerical results from the RFS2 program show that the effects of interactions between
Renewable fuel standard
different biofuels are too large to be ignored. Thus, the threshold values for specific biofuels
Soybean Biodiesel
determined by the U.S. EPA are scenario-dependent and value choice-driven. They do not reflect real
Sugarcane ethanol
impacts of specific biofuels. Using scenario-dependent values for regulation is arbitrary and inappropriate. Failure to deal appropriately with interactions between different biofuels when assigning iLUC values to specific biofuels is a mathematical and systematic flaw; it is
* Corresponding author. Department of Chemical Engineering and Materials Science, Michigan State University, 3815 Technology Boulevard, Lansing, MI 48910, USA. Tel.: þ1 517 353 6777; fax: þ1 517 336 4615. E-mail address: [email protected] (B.E. Dale). http://dx.doi.org/10.1016/j.biombioe.2014.09.015 0961-9534/© 2014 Elsevier Ltd. All rights reserved.
409
b i o m a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 4 0 8 e4 1 2
not an “uncertainty” issue. The U.S. EPA should find better ways to differentiate the contribution of one biofuel versus another when assigning iLUC values or find better means of regulating the land use change impact of biofuel production. © 2014 Elsevier Ltd. All rights reserved.
1.
Introduction
In climate policy models, indirect land use change (iLUC) is hypothesized to occur when increased demand for crops for biofuel production triggers loss of carbon stocks when lands are subsequently converted to grow more crops [1e4]. These lost carbon stocks are then attributed to the biofuels through a numerical iLUC factor as a greenhouse gas “penalty” to be assessed against the overall life cycle greenhouse gas emissions of the biofuel in question. iLUC cannot be measured directly; instead iLUC has been estimated via global agricultural economic models [1e4]. In renewable fuel policies, the United States Environmental Protection Agency (U.S. EPA) uses the Forestry and Agricultural Sector Optimization Model (FASOM), and Food and Agricultural Policy and Research Institute international (FAPRI-CARD) model in the Renewable Fuel Standard (RFS2) program [1]. The California Air Resources Board (CARB) uses the Global Trade Analysis Project (GTAP) model in the Low Carbon Fuel Standard (LCFS) Program [2]. Such iLUC estimates have been strongly criticized on empirical, technical, and conceptual grounds [5e16]. Here we revisit one of important features in iLUC calculations, namely interactions between different biofuels (or “nonlinearity”), e.g., when competing for land against one another. The nonlinear interactions play important roles in determining the threshold values for specific biofuels. Even though other nonlinear properties in iLUC calculations [4,17e20] are wellknown (e.g., nonlinearity with respect to shock sizes, etc.), very little attention has been paid to these nonlinear interactions between biofuels. We wish to emphasize that we are not considering the validity, precision or adequacy of the agro-economic models involved in estimating iLUC (e.g., FASOM, FAPRI-CARD, GTAP, etc.). In this paper we are only concerned with the following question: what is the magnitude of the nonlinear interactions between different biofuels in estimating iLUC?
2.
Results
In the RFS2 program, the U.S. EPA calculates the overall consequences of iLUC associated with the biofuel scenario mandated by the Energy Independence and Security Act of 2007 (EISA). In this analysis the amount of each biofuel is simultaneously increased in the economic models to simulate the EISA-mandated scenario (referred to as the “overall analysis”), and then a new equilibrium state is established. The model results arising from a new equilibrium state driven by the EISA-mandated scenario are compared to results arising from a reference equilibrium state based on a business-as-
usual scenario in the absences of the EISA. Differences between the new and the reference equilibrium states are the consequences of iLUC associated with the EISA-mandated scenario. The overall analysis can be expressed as Equation (1). DL ¼ L XC1 ; XC2 ; XC3 L XR1 ; XR2 ; XR3
(1)
where L is the model function (e.g., land use change), and X is the volume of the particular biofuel. The subscripts 1, 2 and 3 represent corn ethanol, soybean biodiesel, and sugarcane ethanol, respectively. Superscripts R and C represent the reference and the EISA-mandated scenario (control) states, respectively. When the changes in biofuel volumes are very small (approaching zero), the change in the model function can be written as: DL ¼ dLðX1 ; X2 ; X3 Þ ¼
vL vL vL $dX1 þ $dX2 þ $dX3 vX1 vX2 vX3
(2)
For regulatory purposes, the U.S. EPA attempts to calculate the iLUC effects associated with specific biofuels from the overall iLUC. However, they cannot directly calculate the contributions of individual biofuels because the effects of interactions between different biofuels cannot be attributed to specific biofuels in the models [1]. In order to estimate the effects of individual biofuels, the U.S. EPA shocks the amount of each biofuel in the economic models one at a time (referred to as “single-shock analysis”). When the specific biofuel volume is shocked (changed), the other biofuels are held constant at volumes mandated by the EISA. The central assumption in the single-shock analysis is that iLUC is a linear process with respect to biofuels. If iLUC is a linear process, then differences between the shocked and the EISA-mandated scenario states can appropriately be attributed to changes in that specific biofuel. In fact, linearity is required for regulation so that an estimated iLUC value estimated will be the same for 1 L, 1 dam3 or 1 hm3 of that biofuel. For example, land use change attributed to corn ethanol and the attributed land use change per volume are expressed as Equations (3) and (4), respectively. DL1 ¼ L XC1 ; XC2 ; XC3 L XR1 ; XC2 ; XC3
A1 ¼
L XC1 ; XC2 ; XC3 L XR1 ; XC2 ; XC3 DL1 ¼ XC1 XR1 XC1 XR1
(3)
(4)
where A1 is the land use change per volume attributed to corn ethanol. When the change in corn ethanol volume is very small, the attributed land use change per volume becomes A1 ¼
vL vX1 X2 ;X3 ¼constant
(5)
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The attributed land use change per volume of biofuel can be estimated mathematically from the overall analysis (Equation (2)) as well and becomes A1 ¼
dL vL ¼ dX1 vX1
(6)
If the effects of interactions between different biofuels are essentially zero, then Equations (5) and (6) are identical, and satisfy the mathematical requirement of additivity between the overall and the single-shock analyses. If the interactions are not essentially zero, the attributed land use change per volume in Equation (6) will depend on the volumes of the different biofuels: therefore the overall and the single-shock analyses will not be additive. Note that the attributed land use change per unit volume in Equation (5) is always either a function of corn ethanol production volume or constant regardless of the magnitude of interactions. As mentioned above, this exclusive dependence of iLUC on the volume of corn ethanol is essential to iLUC regulations as they are now constructed. This is the only way a single iLUC factor for corn ethanol can be applied at 1 L, 1 dam3 or 1 hm3 of the fuel. However, the model is nonlinear for a system with nontrivial (nonzero) interactions between different biofuels. Nonlinearity means that the attributed land use change per volume for the specific biofuel varies with amount of biofuel produced, and it is unreasonable, unscientific and arbitrary to regulate based on a single iLUC value. The U.S. EPA states that the single-shock analyses reflect the impacts of specific biofuels. This is true only if the effects of interactions between different biofuels are essentially zero. Otherwise the statement is false. In fact, iLUC values calculated from the FAPRI-CARD model in the RFS2 program show that the effects of interactions between different biofuels are too large to be ignored. Instead of being linear, the actual system is highly nonlinear. Using the FAPRI-CARD model, the international land use change (indirect land use change) that occurs globally as a result of simultaneously shocking the model with all the biofuel volumes is about 7944 km2, while the sum of the singleshock analyses is about 18,631 km2, roughly 2.3 times as large. Furthermore, greenhouse gas (GHG) emissions associated with these indirect land use changes in the overall and the single-shock analyses are 152 Tg CO2 and 282 Tg CO2, respectively, almost a two-fold difference. The U.S. EPA was aware of this very significant discrepancy between the two analyses but simply pointed out that the effects of interactions between different biofuels could not be assigned to individual biofuels [1]. Since the requirement of additivity is not satisfied, indirect land use change that occurs globally is a nonlinear process with respect to biofuels. The iLUC impacts of specific biofuels will vary with biofuel volumes and will not be the same at 1 dam3 versus 1 hm3 of a particular biofuel. Furthermore, the model assumes that iLUC for the specific biofuel in the singleshock analysis occurs when the other biofuels have already reached their EISA-mandated threshold volumes. However, since the system is nonlinear, these threshold values in RFS2 are scenario-dependent. They do not reflect the actual impacts of specific biofuels. Therefore, applying a linear
approach (i.e., single-shock analyses) to this highly nonlinear system is not an appropriate basis for regulations. The Low Carbon Fuel Standard (LCFS) Program of the California Air Resources Board (CARB) employs a similar singleshock analysis to estimate iLUC for specific biofuels. CARB uses the GTAP model [2] to make these estimates, rather than FASOM/FAPRI models used by the US EPA. Since CARB has not published any estimates of the overall iLUC obtained from their modeling runs, we ran the GTAP model (version ARB2011F released October, 6, 2011) [4] with arbitrary scenarios to investigate whether or not the overall and the singleshock analyses are additive, in order to detect nonlinearity. Our results using GTAP reveal approximately a 20% difference between the overall analysis (each biofuel volume shock applied simultaneously) and the sum of the single-shock analyses. Thus it is probable that the California LCFS Program also ignores the effects of interactions between different biofuels. Unfortunately, previous iLUC studies for specific biofuels based on global agricultural economic models used in the RFS2 and the LCFS programs (e.g., Searchinger et al. [3], Tyner et al. [4], etc.) never address interactions between different biofuels. These interactions would be reflected in differences between the sum of the iLUC effects of the individual biofuels and the iLUC value resulting from the overall analysis. Not including these interactions will give unreliable iLUC estimates unless the interactions between different biofuels are essentially zero. Based on our analysis as reported here, these interactions are not essentially zero. Thus the key assumption behind the regulatory approach used by both US EPA and CARB, the assumption of linearity, is invalid. It is not that the results from iLUC calculations are imprecise or uncertain. Precision is not the issue. The modeling approach used by both CARB and EPA to predict iLUC values rests on a faulty assumption; thus the flaw is a mathematical and a systemic one. The iLUC results obtain by CARB and EPA are not simply imprecise or uncertain. They are simply wrong.
3.
Discussion: what to do?
During the review process for this paper, one of the reviewers asked: “what to do?” which we take to mean “if this approach to calculating iLUC is fundamentally flawed, what should we do instead?” The Editor invited us to suggest what we think the EPA and other regulatory bodies might do if the current linear approach to regulating this highly nonlinear system is inappropriate. We appreciate the invitation and will try to respond honestly, directly and clearlydbut we hope, without giving offense. We think there are two basic ways to deal with the issue of potentially increased greenhouse gas generation due to land use change induced by biofuels. The first approach involves rejecting iLUC as a regulatory tool and instead regulating biofuels to incentivize positive outcomes rather than trying to reduce negative outcomes through iLUC. The second approach retains iLUC but focuses on improving iLUC to be more empirically rigorous as well as more fair and honest.
b i o m a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 4 0 8 e4 1 2
3.1. First approach: regulate biofuels for direct environmental improvement We think much of the driving force behind iLUC is a simple desire on the part of some to limit corn-based ethanol (and the underlying corn production system) as an alternative to petroleum fuels, by whatever means necessary. But corn-based ethanol is here to stay, and at very large scale. We frankly think the time and effort of these detractors would be better spent in helping improve the environmental performance of the corn production system. In fact, continued use of iLUC as an arbitrary, deeply-flawed regulatory tool to “punish” corn ethanol actually undermines the cause of genuine environmental improvements for first generation biofuels. iLUC forces us enter into largely unproductive debates instead of actually improving the environment. Instead of using iLUC to regulate against corn ethanol, we could instead regulate for improvements we wish to see occur. For example, it is well known that increased use of cover crops, double crops, riparian buffers, and so forth would do much to improve the environmental performance of corn production.
3.2.
Second approach: improve regulation based on iLUC
Regulators deeply invested in the iLUC approach will probably not abandon their efforts to apply it to biofuel regulation. So we have two suggestions to improve biofuel regulations based on iLUC. The first suggestion is to make iLUC regulation more empirically rigorous by accounting for the interactions between different biofuels. The second suggestion deals with improving the fairness of the regulatory system by treating petroleum fuels as we do biofuels.
3.2.1. First suggestion: improve the empirical rigor of regulations using iLUC We have shown that the system is nonlinear; and that the single shock values do not include the effects of interactions between biofuels. However, the overall analysis (DL in Equation (1)) does include these interactions. We suggest that the overall DL be allocated to the individual biofuels by economic parameters or physical properties (e.g., market prices, cropland requirements, energy contents, etc.) as seen in Equation (7). Li ¼ DL$ai
(7)
where Li is the iLUC associated with the ith biofuel and ai is the allocation factor for the ith biofuel. (The sum of the individual
411
ai is one.) Using the cropland requirements for each biofuel production as allocation factors, we estimated GHG emissions associated with the iLUC for corn ethanol, soybean diesel, and sugarcane ethanol as illustrated in Fig. 1. Most background data used in this calculation (e.g., biofuel yield, co-products, etc.) are obtained from the Greenhouse gases, regulated emissions, and energy use in transportation model [21]. Other allocation approaches could be used but the objective would be to allocate based on DL.
3.2.2. Second suggestion: make the iLUC regulation system more fair and honest We hope that our desire to be clear and honest in our response to the Editor's invitation does not conflict with our wish not to offend. But here is the truth: the current iLUC regulatory system is both unfair and dishonest. It is unfair because it does not treat petroleum fuels and biofuels equally. It is dishonest because it continues to ignore this unfairness, even after the unfairness has been pointed out, repeatedly and over many years. Current iLUC regulation unfairly uses different system boundaries and makes different comparisons for biofuels versus petroleum fuels. For example, petroleum fuel GHGs are based on the average barrel but they are compared with biofuels on the marginal barrel. A more fair comparison would be to compare the marginal barrel of “new” petroleum fuels (e.g., from deep water drilling, hydraulic fracturing or bitumen (“tar sands”)) to the marginal barrel of biofuels. Conventional, on-shore petroleum production is declining, and biofuels should be evaluated based on new sources of petroleum, rather than old, declining sources. These new sources of petroleum are, without any doubt, more polluting, more expensive and more GHG intensive than conventional petroleum. They are the proper comparison with biofuels, not conventional oil. The fact that different system boundaries are used for petroleum fuels versus biofuels is also deeply unfair. Indirect GHG effects are assessed against biofuels, but not against petroleum fuels. We cite here just three examples of indirect effects of petroleum fuels; there are undoubtedly others. First, as petroleum prices rise, other energy sources will be used, when possible, in place of oil, for example, burning coal instead of oil to produce electricity or making synthetic liquid fuels based on coal, with its much higher GHG emissions per megajoule (MJ). Thus, biofuels should be “credited” with their market-mediated reduction in petroleum prices and the resulting reduced use of coal with its attendant high GHG emissions.
Fig. 1 e Single-shock analysis versus allocation [based on the U.S. EPA calculations].
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Second, continued use of fossil fuels drives global warming, especially in the Arctic. As the Arctic tundra and shallow polar seas warm, they may release gigatons of methane from methane clathrates. Evidence of very large methane releases through warming tundra has recently emerged. Substitution of fossil fuels with biofuels could reduce net GHG emissions and thereby reduce warming and the resulting methane release. Biofuels should receive a GHG “credit” for this effect in a truly fair and comprehensive analysis. Third, petroleum exploration in ever more remote areas, such as the Peruvian Amazon, will require road building, which will increase human settlement in lands adjacent to these roads and subsequently more land clearing and agriculture, with its attendant GHG releases. Biofuels will reduce the need for additional petroleum exploration and thereby reduce these indirect GHG releases from increased petroleum exploration. It is time for the US EPA, CARB and European regulatory organizations that claim to account for indirect as well as direct GHG emissions of biofuels to play fair. If they continue to use iLUC as a regulatory tool, then they must account for indirect GHG releases from petroleum exploration and use, for example, by the three pathways outlined above. This is a minimal requirement for fair treatment of biofuels versus petroleum fuels.
Acknowledgments This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC0207ER64494) and DOE OBP Office of Energy Efficiency and Renewable Energy DE-AC05-76RL01830).
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