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RESEARCH AND ANALYSIS

Comparison of Life-CycleInventory DatabasesA Case Study Using Soybean Production

Shelie A. Miller and Thomas L. Theis

Summary

Three established life-cycle inventories of agricultural oper-ations were used to generate air emissions data for soy-bean production: the greenhouse gases, regulated emissions,and energy use in transportation (GREET) model; the eco-nomic input-output life-cycle assessment (EIO-LCA) model;and SimaPro software equipped with the Franklin database.EIO-LCA and GREET baseline data were compared to eval-uate differences in boundary definitions that apply specificallyto U.S. soybean agriculture and processing, which resulted inseveral major findings. The EIO model estimated for emis-sions of particulate matter less than 10 micrograms (PM10)resulting from wind erosion that were not included in GREET,but neglected indirect nitrous oxide (N2O) and nitrogen ox-ides (NOx) emissions from fertilizer application. EIO also as-sumed significantly lower process energy requirements andlower volatile organic compounds (VOC) for soybean crush-ing and oil extraction. The GREET and SimaPro models werecompared using identical boundary and assumption data, toreveal major discrepancies in fundamental assumptions of en-ergy inventories. Key emission factors varied by several ordersof magnitude for basic energy generation and combustionprocesses, potentially impacting results for any inventory anal-ysis that contains significant energy consumption. The Franklindatabase assumed VOC and sulfur oxides (SOx) emissionsmore than an order of magnitude higher than GREET forall categories investigated, with significantly lower N2O andmethane (CH4) emission factors.

Keywords

agricultureair emissionsboundary definitioneconomic input-outputenergy use in transportation (GREET)

modellife-cycle assessment

Address correspondence to:Shelie A. MillerInstitute for Environmental Science and

PolicyUniversity of Illinois at Chicago2121 West Taylor StreetChicago, IL 60612 USA

2006 by the Massachusetts Institute ofTechnology and Yale University

Volume 10, Number 12

http://mitpress.mit.edu/jie Journal of Industrial Ecology 133

RESEARCH AND ANALYS I S

Introduction

The life-cycle inventory (LCI) process has of-ten been criticized for extensive data require-ments, uncertainties in reported data, and thesusceptibility of results to differing assumptionsand boundary definitions (Arnold 1993; Ayres1995; Beattie 1995; Ehrenfeld 1997; Graedelet al. 1995; Owens 1997; Thomas et al. 2003;White and Shapiro 1993). Due to these diffi-culties, previously constructed studies or mod-els are often used to obtain inventory data. Al-though numerous methods and modeling toolsexist to expedite the LCI process, results may varygreatly between models, and it is often difficult todetermine the source of inconsistencies. Differ-ing inventory estimates result from inconsistentboundary definitions, disagreements in source as-sumptions regarding material and energy use, orfundamental differences in the assumed emissionsassociated with upstream processes. Overall dis-crepancies are generally a combination of thesefactors, making the origin of disagreements diffi-cult to identify.

In this study, two types of comparisons weremade between three widely used models. First,models using different approaches (input-outputand process-based) were compared to identifyboundary and source assumption differences. Sec-ond, two process-based models with identical as-sumptions regarding energy andmaterial use werecompared to illuminate fundamental differencesbetween key datasets. Although many tools canbenefit from this type of analysis, the threemodelschosen represent the most common types of LCImodels currently available. Peereboom and col-leagues (1998) conducted similar comparisons onsix European datasets analyzing PVC (polyvinylchloride) production and found differences rang-ing from 10 to 1100%. Other studies have alsoevaluated inventory models and found similar re-sults, highlighting the need for greater data agree-ment and transparency of results (Joshi 1999;Lenzen 2000; Lenzen and Treloar 2002; Norrisand Yost 2001).

The use of biobased material, or biocommodi-ties, as a substitute for petroleum-based prod-ucts and subsequent environmental comparisonsare important contemporary research topics. Bio-commodities are products made predominantly

from biomass and are often proposed as substi-tutes for nonrenewable products currently on themarket (Lynd et al. 1999). The most prominentbiocommodities are biofuels such as biodiesel orethanol; however, there are numerous biobasedproducts1 that have the potential to become im-portant substitutes, including plastics, polymers,inks, solvents, packaging, and lubricants (Dale1999; Hartmann 1998; Kosbar and Japp 2001;Lynd et al. 1999; National Academy of Sci-ences 2000; U.S. Department of Energy and U.S.Department of Agriculture 2002; Warwel et al.2001). This study focuses specifically on soybeanproduction. Soybeans have many potential uses,are abundantly grown, are inexpensive, and havereadily accessible inventory data.

Methods

Description of Models

The three models analyzed in this study are:greenhouse gases, regulated emissions and en-ergy use in transportation (GREET); economicinput-output life cycle assessment (EIO-LCA);and SimaPro. A summary evaluating the at-tributes of each model can be found in table 1.Each model compiles a different set of inventorymetrics: EIO-LCA quantifies economic contri-butions, energy and water consumption, and airemissions; GREET focuses specifically on energyconsumption and air emissions; SimaPro gener-ates output data that depend upon the organiza-tion of process modules. Metrics vary dependingupon information contained within the modules.Because all three models consistently report airemission data, these are the basis of comparisonfor this study. Although an analysis of air emis-sion data alone does not constitute a completeLCI, it is an important component, and generatessufficient comparative information about the dif-ferences in these types of modeling tools. A morecomplete inventory, which includes supplemen-tal aqueous and solid emissions as well as energyconsumption, is currently being conducted by theauthors.

GREET Model

The GREET 1.5 model was developed byArgonne National Laboratory in the U.S. to

134 Journal of Industrial Ecology


Table 1 Comparison of three inventory models for soybean farming

EIO-LCA GREET SimaPro

Description Matrix-based model basedon the linkages ofeconomic sectors

Detailed process-basedmodel based on userassumptions

Modules of process-basedinventory data that areassembled by user

Scope 485 U.S. commoditysectors; includes airemissions, water, andenergy use

U.S. transportation, bothtraditional andalternative fuels,includes air emissionsand energy use; currentand future estimates

U.S. and European datafor large variety ofsectors that isexpandable; includesmost environmentalimpacts

Primary datasources

U.S. Department ofCommerce EconomicInput-Output Model,AIRS database

US EPA AP-42documents, publishedstudies, governmentdocuments

Franklin US LCI,BUWAL 250,IDEMAT 2001,ETH-ESU 96, industryand archival data,others

Advantages Quick, easy, publiclyavailable, uses monetaryunits, boundaryexpansion

Transparent, usermanipulation possible,flexible to userassumptions, relativelyquick, publicly available

Applicable to any process,transparent,multiple-user interface,large inventory,expandable, includesimpact assessmentcapabilities

Disadvantages Aggregation of data, onlyfor established sectors,no manipulationpossible, difficult totrack data sources,outdated data possible,does not include usephase

Limited to transportationand related sectors

Expensive, labor-intensive,inconsistencies possiblebetween differentmodules

Note:AIRS=Aerometric Information Retrieval System; AP-42 documents=U.S. Environmental Protection AgencysCompilation of Air Pollutant Emission Factors; BUWAL = Bundesamt fur Umwelt, Wald und Landschaft [Federal agencyfor the environment, forests, and land] (Switzerland); IDEMAT = Inventory Data of Materials (Technical Universityof Delft, The Netherlands); ETH-ESU = Eidgenossische Technische Hochschule, Gruppe Energie-Stoffe-Umwelt [theEnergy-Materials-Environment Group at the Swiss Federal Institute of Technology in Zurich] (Switzerland).

assess the environmental impacts of using tradi-tional and alternative fuel sources in transporta-tion (Wang 1999; Wang 2000). GREET usesan Excel-based spreadsheet to determine energyconsumption and material releases of criteria airpollutants and greenhouse gases (volatile organiccompounds [VOCs], carbon monoxide [CO], ni-trogen oxides [NOx ], particulate matter less than10 micrograms (m) [PM10], sulfur oxides [SOx ],methane [CH4], nitrous oxide [N2O], and carbondioxide [CO2]) for both current and future sce-narios. By relying on detailed user assumptions,

emission factors obtained through the U.S. En-vironmental Protection Agencys Compilation ofAir Pollutant Emission Factors, also known as AP-42 documents (USEPA 1995), government doc-uments, and previous studies, GREET calculatesair emissions and energy use for each stage offuel cycles from production to use (Ahmed 1994;Baker and Johnson 1981; Davis and McFarlin1997; Delucchi 1993; Delucchi and Lipman1997; Mudahar andHignett 1987; Shapouri et al.1995; Sheehan et al. 1998). Because the inven-tory spreadsheet is entirely transparent, users can

Miller and Theis, Comparison of LCI Databases: Soybean Production 135


manipulate any of the inputs and view all calcu-lation cells to determine how a value was derived.In addition to detailed petroleum refining and en-ergy generation data, GREET contains inventorydata for corn and soybean agriculture, transporta-tion, and processing into ethanol and biodiesel,respectively.

Of the three models examined, GREET hasthe most clearly defined boundary definitions andbaseline assumptions. GREET includes materialand energy flows within the agricultural systemand the manufacturing stages of the materialsused within the system (i.e., fertilizers and chem-icals). No capital goods are included, and themodel focuses solely on air emissions and en-ergy use. Air emissions data pertain primarily toemissions from the combustion of fossil fuels, al-though estimates of N2O and NO emissions re-sulting from residual fertilizer in water runoff areincluded. For soybeans, the emission rates of N2Oand NO are 1.3% and 0.65%, respectively, of thetotal amount of applied nitrogen fertilizer. VOCemissions resulting from hexane extraction arealso incorporated. Particulate emissions due to

Table 2 Default inputs for the greenhouse gases, regulated emissions, and energy use in transportation(GREET) model

Chemicals (g/kg soybeans)Nitrogen fertilizer 4.36Phosphate fertilizer 13.68Potash fertilizer 23.28Herbicide 1.75 (36.2% atrazine, 63.8% metolachor)Insecticide 0.018

Energy usage in farming (kJ/kg soybeans)Diesel 823Gasoline 368LPG 32.3Electricity 19.9Transportation to extraction facility 203(as diesel fuel)

Inputs for soybean oil extractionSoybeans 5.7 (kg/kg oil)N-Hexanea 0.013 (kg/kg oil)Natural gas for steam generation 6,730 (kJ/kg oil)Natural gas 6,500 (kJ/kg oil)Electricity 1,423 (kJ/kg oil)

Note: LPG = liquefied propane gas. N-Hexane = solvent used to extract soybean oil from meal. One kilogram (kg,SI) 2.204 pounds (lbs); one gram (g) = 103 kilograms (kg, SI) 0.035 ounces (oz); one kilojoule (kJ) = 103 joules(J, SI) 0.239 kilocalories (kcal) 0.948 British Thermal Units (BTU).a GREET assumes hexane upstream production is similar to LPG from crude. GREET default settings show a value of476 kJ LPG/kg oil.

wind erosion of agricultural fields are includedfor corn farming, although not for soybean farm-ing. The default input parameters for the GREETmodel are detailed in table 2, although these val-ues may be manipulated at the users discretion.The electricity generation mix is assumed to be53.8% coal, 18.0% nuclear, 14.9% natural gas,1.0% oil, and 12.3% others (predominantly re-newable energy in the form of hydropower). Airemissions fromnonfossil electricity sources are as-sumed to be zero, although precursors of electric-ity generation (i.e., uranium mining for nuclearpower) are included. Efficiencies and emissionsare calculated using industry standard values, na-tional averages, and the AP-42 documents.

A recently released beta version ofGREET1.6uses Monte Carlo analysis to incorporate vari-ability into life-cycle assessment (Wang 2005).The values reported in this study are taken fromGREET 1.5; the updated version, however, onlymakes minor changes in the emission factors rel-evant to this study. Both versions are availableat .



EIO-LCA Model

Economic input-output life cycle assessmentplaces LCI in the context of complex interac-tions within an economy. Originally developedby Leontief (1986), EIO can model the interde-pendence of sectors within an economy, trackingthe exchange of goods and services. It tracks theinterrelations of 485 economic sectors, based onthe 1992 U.S. Department of Commerce com-modity input-outputmatrix of theU.S. economy.When economic activity is stimulated within asingle sector, the model quantifies the economicimpact of all relevant sectors that contribute tothe operation of that sector. EIO-LCA attributesinventory data to each economic sector usingdata collected from a variety of sources, includ-ing the Aerometric Information Retrieval Sys-tem (AIRS) database, the Toxic Release Inven-tory, emissions factors from AP-42 documents,commodity purchasing obtained from the input-output workfiles, and census data. EIO-LCA as-signs emissions to each commodity sector andthen determines the aggregate emissions of aproduct by examining the sectors that contributeto that process (Hendrickson et al. 1998). Anelectronic version of the EIO-LCA model and amore detailed description of the process, devel-oped by the Green Design Initiative at CarnegieMellon University, is available on the Internet at (Carnegie Mellon University2003). In addition to linking environmental im-pacts to economic drivers, the EIO-LCA modelreduces uncertainties concerning boundary defi-nitions, because the system has been expanded toinclude an entire economy. In this way, capitalgoods are included, although they generally donot contribute significantly to the inventory of aproduct or process.

The primary disadvantage of using EIO-LCAdata is that assumptions for the model are notreadily transparent. They are drawn from nu-merous databases, and although specific assump-tion information can be gathered by examinationof source data, it can be difficult and time-consuming. Material outputs from EIO are de-scribed as mass emission/$ activity, which mustbe converted to mass emission/mass functionalunit (in this case, soybean oil) for purposes ofcomparison with other models, which may lead

to additional uncertainty if there are significantprice fluctuations or market instability. If a prod-uct is new, or if the sector has changed dramati-cally, the information obtained through EIOmaynot be reliable. This model also pertains specifi-cally to the U.S. economy. Although this is nota liability in the case study of U.S. soybean pro-duction, it may need to be taken into accountin analyses outside the U.S., or those includingsignificant international trade. It should also benoted that the EIO model uses the 1992 matrix,thus commodities undergoing analysis need to beconverted into 1992 dollars. The EIO sector OilBearing Crops was used for values pertaining tosoybean agriculture, based on the assumption thatsoybeans are the predominant oil-bearing cropwithin the United States and that other com-mon oil crops such as corn and cotton are con-tained within other sectors. Soybean processingdata were obtained from the EIO sector SoybeanOil Mills.

SimaPro

SimaPro software (Version 5.1) was devel-oped by PRe Consultants (2001). It is an ex-pandable and transparent software program thatintegrates inventory data for a broad spectrumof industrial and economic sectors. Process-basedinventories of many common systems are com-piled into modules of information to be assem-bled by the user into a complete inventory. Inthis way, a user does not need to determine emis-sions data for basic inputs, such as electricity useor transportation, but may use available infor-mation to simplify an analysis. Users may utilizetheir own data to build newmodules, or to updateand supplement the softwares libraries. It con-tains both European and U.S. databases, includ-ing BUWAL 250, IDEMAT 2001, ETH-ESU 96,and the Franklin Database, and is expandablewith new libraries (Frischknecht and Jungbluth2001; Goedkoop 2003; Norris 2003; Spriensma2003). It also provides impact assessment tools,including Eco-Indicator 99, to be used to corre-late inventory data with environmental impacts.The SimaPro software contains data on mostbasic processes, with significantly more Europeanthan U.S. data. Data completeness varies fromprocess to process, because the files are obtained



from many data sources, which seldom have thesame level of detail. Like the GREET 1.6 model,a newer version of the software, SimaPro 6, in-corporates Monte Carlo Analysis to quantify un-certainty in inventory results (PRe Consultants2005).

Because the SimaPro model is not equippedwith soybean farming or processing modules, de-fault GREET values, as shown in table 2, wereused to create soybean-specific data. Because theresults of both SimaPro and GREET are basedon similar input data, inconsistencies in resultsare indicative of discrepancies in the emissionsof fundamental processes assumed by the twodatabases, such as petroleum refining, transporta-tion, chemical production or electricity genera-tion. All data used in this analysis were obtainedfrom the Franklin U.S. LCI database. The elec-tricity generation mix from the Franklin databaseis 67.0% coal, 19.1% natural gas, 6.3% nuclear,4.0% hydropower, and 3.6% refined and distillatefuel oils (2.4% and 1.2%, respectively).

Soybean Agriculture and Processing

The United States produces approximately75 million metric tons of soybeans annually,representing 45% of global soybean production.Soybeans are produced throughout the UnitedStates, with intensive agriculture throughoutthe Midwest, especially Illinois, Iowa, Min-nesota, and Indiana. Average yields throughoutthis region are 4146 bushels/acre or 2,8003,100 kilograms/hectare (Ash and Dohlman2002). Soybeans are generally rotated with cornor other crops to maintain soil nutrient balances.Because soybeans are legumes, and thus nitrogen-fixers, they have lower fertilizer requirementsthan most other crops, with approximately 20%of soybean acreage receiving fertilizer annually(USDA 2003). Insecticide use for soybeans isalso relatively low. Due to the prevalence ofgenetic modifications to U.S. soybeans, mostare glyphosate-resistant (Round-Up Ready);therefore the primary herbicides used on soybeanfields contain glyphosate (Carpenter et al. 2002).

Once soybeans are harvested, they are trans-ported to processing plants to be separated intooil and meal. Approximately 74% of soybeans byweight is soy meal, 18% is oil, and hulls make up

the remaining 8%. In many cases, the hulls areground into the meal, except for better quality,high-protein meal, where the soybean hulls areseparated and can be used for other purposes. Thesoybeans are dried and cleaned and then crackedinto suitable pieces for dehulling and flaking. Thebeans are then coarsely ground and pressed be-fore being subjected to hexane extraction, whichdraws the oil from the meal. The hexane is re-covered and recycled through the process. Onceseparated, the oil is usually passed through a clayfilter to refine, bleach, and deodorize the crudeoil. The soybean meal can be processed into nu-merous forms depending upon the end use (White1995). This process is described in figure 1.

In todays market, soy meal is more valuablethan soybean oil, due to its abundant use in thefeed industry. In recent years, increased demandfor soy meal and reduced desirability of hydro-genated soybean oil in foodstuffs has created asurplus of oil, which can be used for nonediblecommodity goods, including biodiesel fuel, lubri-cants, inks, and solvents. Because these productsare derived from soybean oil, the functional unitfor this analysis is the kg of soybean oil, which caneasily be converted to the appropriate functionalunit for the end biocommodity. Reported emis-sions for soybean farming have been convertedto soybean oil, but not allocated. If allocation isconducted on a mass basis, the reported valuesshould be multiplied by 0.18.

Results and Discussion

Inventory data for soybean farming and pro-cessing were compiled from the three models, us-ing default assumptions. For the purposes of thisstudy, only criterion air pollutants and green-house gases are analyzed, because these metricsare common to all three models.

Boundary and Assumption Comparison

To make the data comparable, the invento-ries were normalized to mass of emissions perkg of soybean oil. The EIO-LCA model gener-ates data in the form of emissions/$ of activity(in 1992 dollars), which were converted to amass basis. A bushel of soybeans was assumedto cost $5.60, and soybean oil was calculated at$0.27/lb,2 as determined by pricing data supplied



Ground Beans Soybean

Grinding and Flaking

Oil Extraction

Oil Degumming

Oil Recovery

Meal ProcessingSoy Meal

Soybean Oil +

Hexane

Crude Oil

Hexane

Oil

Figure 1 Process flow diagram for soybean crushing process. Adapted from Sheehan and colleagues(1998).

by the United States Department of Agriculture(USDA) (Ash et al. 2002; Good 2001). Also, toconvert from soybeans to soy oil, an allocationfactor of 18% was assumed. The hulls were as-sumed to be incorporated into the meal, and notseparated.

The results of baseline model comparisons forsoybean farming are normalized to kg of oil pro-duced, allocating emissions on a mass basis tothe co-products. The most apparent difference inthese data is the EIO output for PM10 emissions,resulting from a difference in source data for themodels, with EIO and GREET containing emis-sion values of 40.84 g PM10/kg and 0.41 g PM10/kgsoybean oil, respectively. The National Emis-sions Trends (NET) network contained withinthe AIRS database on which EIO emission infor-mation is based (US EPA 1996) reports wind ero-sion from fields as the largest contributor of PM10emissions in agriculture. The GREET model in-cludes only PM10 emissions associated with fuelcombustion, and does not incorporate wind ero-sion. If the effects of wind erosion are subtractedfrom the EIO estimates, values similar to the

GREETestimates are obtained. Similarly, this pa-rameter, or some fraction thereof, could be addedto the GREET calculations, depending on thescope of the assessment.

As shown in figure 2, which neglects partic-ulate matter emissions from wind erosion, EIOand GREET estimates of CH4, N2O, and SOxemissions vary by more than a factor of 2. ForN2O values, this inconsistency is attributed to as-sumptions about fertilizer application and nitro-gen transport from the fields. The GREET modelincorporates the denitrification/nitrification re-actions of NO3 in agricultural runoff into N2Oand NO emissions (assuming an aggregate 1.3%of applied fertilizer ultimately transforms intoN2O and 0.65% into NO). Unfortunately, thedata sources for the NET database are not readilyavailable, so it is uncertain whether this transfor-mation is taken into account in their estimates.It is a logical assumption that this informationis not included within the EIO data, becausethe differential between the models is similar tothe GREET value for nitrogen transformation.This difference is also partially responsible for



Soybean Farming Air Emissions Neglecting Wind Erosion

0.01.02.03.04.05.06.07.08.09.0

CO PM10 CH4 N2O NOx VOC SOx CO2**

Emis

sion

s (g/

kg oi

l)

EIOGREETSimaPro

Figure 2 Agricultural air emissions from EIO, GREET, and SimaPro, neglecting particulate emissions due towind erosion. All emissions are given in grams per kilogram (g/kg) of oil, except for CO2 , which is in kg/kgoil. CO = carbon monoxide; PM10 = particulate matter less than 10 m in size; CH4 = methane; N2O =nitrous oxide; NOx = nitrogen oxides; VOC = volatile organic compounds; SOx = sulfur oxides; CO2 =carbon dioxide.

the higher value of NOx emissions in theGREETmodel.

In the case of CH4 and SOx emissions, theprimary discrepancy in the data appears to bedifferences in the quantity of emissions assumedto be released through electricity generation.Although the majority of air emission values forelectricity generation are within the same rangefor EIO and GREET, air emissions of SOx andCH4 are quite dissimilar. GREET generates val-ues of 510 and 280milligrams emissions permega-joule (mg emissions/MJ),3 whereas EIO gives val-ues of 1,150 and 1,130 mg emissions/MJ for SOxand CH4, respectively, assuming a price of 5.12cents per kilowatt-hour for industrial electric-ity consumption4 (Energy Information Admin-istration 2003). Because the EIO calculations arenot readily transparent, the source of disagree-ment pertaining to electricity generation emis-sions cannot be verified; however, it appears thatEIO assumes greater fugitive methane emissions

during natural gas firing or during mining oper-ations. This is likely due to the variability asso-ciated with methane release and whether flaringis employed. The explanation for the SOx differ-ential may be assumptions about sulfur emissionsgenerated at coal-fired plants before Title IV ofthe 1990 Clean Air Act Amendments was en-acted. If electricity consumption during farmingis factored out of EIO, the levels of SOx becomealmost identical, and the CH4 values are alsosimilar.

Initially, energy consumption patterns in themodels are within a factor of 2, with total energyconsumption estimated at 3.1 MJ/kg soybeans forGREET and 2.2MJ/kg soybeans for EIO. This dif-ferential is a result of higher process energy allo-cated to upstream processes by GREET. GREETassumes upstream energy consumption of fertil-izers and pesticides to be 0.89 and 0.64 MJ/kgsoybeans, respectively. EIO assumes values of0.23 and 0.12 MJ/kg soybeans. Although sectors



such as transportation are responsible for someof this difference, the differences in assumed en-ergy consumption during chemical processes areresponsible for the majority of the energy discrep-ancy. Although total assumed electricity con-sumption associated with soybean agriculture issimilar (0.067 kWh/kg soybeans for bothmodels),the allocation of electricity to various processes isquite different. EIO assumes that electricity usedduring agricultural operations is 0.030 kWh/kgsoybeans, and the remaining 0.036 kWh/kg soy-beans is used for upstream operations. GREET as-sumes electricity usage of 0.006 kWh/kg soybeansfor farming operations and 0.61 kWh/kg soybeansin upstream processes. It therefore appears thatthe agreement of total electricity consumptionvalues is coincidental. Due to the nature of EIO,the lower upstreamelectricity consumption is sur-prising due to the expanded system boundaries; itwould be expected that EIO would include elec-tricity consumption from all stimulated sectorsand would therefore be greater.

Air Emissions from Soybean Oil Extraction

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

COPM

10 CH4

N 2O

NOx

VOC

SOx

CO2**

Emis

sion

s (g/

kg oi

l)

EIOGREETSimaPro

Figure 3 Air emissions from soybean oil processing from the EIO, GREET, and SimaPro models. Allemissions are given in grams per kilogram (g/kg) of oil, except for CO2 , which is in kg/kg oil. CO = carbonmonoxide; PM10 = particulate matter less than 10 m in size; CH4 = methane; N2O = nitrous oxide;NOx = nitrogen oxides; VOC = volatile organic compounds; SOx = sulfur oxides; CO2 = carbon dioxide.

Air emissions generated from soybean oil millsare shown in figure 3. Values from the EIOmodelfor soybean oil processing are consistently lowerthan GREET values, particularly in the case ofVOCemissions. The overall lower values are a re-sult of lower process energy assumptions, whereasthe VOC discrepancy originates from differencesin assumptions pertaining to hexane loss. Duringthe solvent extraction process, which separatesthe oil from the meal, large quantities of hex-ane are consumed. Although most is recycled,a fraction is volatilized and lost to the environ-ment. The GREET assumptions regarding hex-ane emissions derive from the study by Sheehanand colleagues (Sheehan et al. 1998), which as-sumes a loss of 2.4 g solvent/kg flaked beans,using a recovery rate of 99.8%. Other studieshave shown a range of hexane emissions from1.4 to 2.8 g solvent/kg flaked beans (Woerfel1995). The VOC emissions in this process areextremely sensitive to this assumption; advancesin technology that either reduce the amount of



hexane consumed or increase the recovery ratewill greatly impact the amount of VOCs emitted.For instance, if hexane emissions are assumed tobe 1.4 g VOC/kg flaked beans, VOC emissions as-sociated with the GREET model would decreaseby almost 50%. The sensitivity of the analysis tothis factor is important in the case of soybean bio-commodities, because products such as soy-basedinks, solvents, and lubricants are viewed as fa-vorable alternatives to the higher-vapor-pressurepetroleum counterparts currently in use. Becauseit is difficult to determine the exact assumptionsfor the EIO sector of Soybean Oil Mills used togenerate the EIO data, the true source of the dif-ferential cannot be determined, although loweroverall process energy assumptions appear to bea logical conclusion.

Due to lack of consensus and uncertain in-ventory results, updated mills and data may havesignificant impacts on this stage of the process.Neither the GREET nor EIO model data shouldbe used as stand-alone data. Supplemental pro-cess data should be used to obtain the appropriateinventory values.

Table 3 Comparison of GREET and Franklin emission factors for supply chain production of basic energysectors (shown as mg/MJ)

VOC CO NOx PM10 SOx CH4 N2O CO2

ElectricityGREET 0.06 0.20 1.43 0.10 1.75 0.96 0.00 666.09Franklin 0.47 0.34 2.41 0.13 5.19 1.46 0.01 665.63

DieselGREET 8.39 22.70 29.63 2.73 16.36 98.44 0.19 14,104.71Franklin 167.58 21.23 28.28 5.54 86.14 13.53 0.01 8,780.71

GasolineGREET 16.44 25.73 36.74 3.49 21.19 106.96 0.25 18,476.89Franklin 161.76 20.48 27.29 5.37 83.15 13.04 0.01 8,481.67

Natural gasGREET 2.93 19.41 29.54 0.79 3.19 187.08 0.12 6,423.19Franklin 246.30 106.89 55.77 1.76 915.50 176.59 0.01 7,309.38

LPGGREET 6.71 20.18 24.12 1.78 9.91 100.48 0.14 10,224.92Franklin 159.00 20.52 27.18 5.13 82.58 12.82 0.01 8,431.30

RFOGREET 6.66 18.16 18.65 1.49 8.82 91.49 0.11 8,292.97Franklin 167.58 21.30 28.41 5.57 86.48 13.57 0.01 8,825.01

Note:VOC= volatile organic compounds; CO= carbon monoxide; NOx = nitrogen oxides; PM10 = particulate matterless than 10 m in size; SOx = sulfur oxides; CH4 = methane; N2O = nitrous oxide; CO2 = carbon dioxide; LPG =liquefied propane gas; RFO = residual fuel oil. One milligram (mg, SI) = 103 grams (g) 3.53 105 ounces (oz);one megajoule (MJ) = 106 joules (J, SI) 239 kilocalories (kcal) 948 British Thermal Units (BTU).

Fundamental Assumptions Comparison

In the previous section, the EIO and GREETmodels were used to isolate differences in overallsystem assumptions, such as boundary definitionsand material and energy flows. The other majorsource of model discrepancy is differences in de-tailed assumptions pertaining to individual pro-cesses common tomany inventories. In this study,differences between the GREET and Franklindatasets were isolated using the same materialand energy flow data. The default GREET val-ues shown in table 2 were inserted into theSimaPro model, using the Franklin database forupstream processes. Ideally, similar parameter in-puts would yield similar output values. As seenin figure 2, substantial differences are observed atthe agricultural stage for CH4, VOC, and SOxemissions. These inconsistencies result from dif-ferent sets of emission factors associated with theupstream processing and use phases of combus-tion fuels.

Table 3 demonstrates the disparities in emis-sion factors from fundamental supply chain



Table 4 Comparison of GREET and Franklin emission factors for combustion during basic energyprocesses (shown as mg/MJ)

VOC CO NOx PM10 SOx CH4 N2O CO2

Natural gas industrialboilersGREET 2.56 38.96 88.05 3.51 0.29 1.04 1.04 56,738.95Franklin 4.41 27.86 143.92 4.36 34.82 1.62 54,781.53

RFO industrial boilersGREET 0.86 15.35 168.90 5.83 122.88 3.07 0.34 78,360.47Franklin 0.86 15.39 169.25 43.08 735.48 3.08 80,010.22

LPG industrial boilersGREET 1.79 17.44 102.36 3.07 0.00 1.02 4.61 67,816.05Franklin 1.33 17.44 102.58 3.08 0.09 69,752.50

Coal-fired industrialboilersGREET 1.42 11.91 270.15 12.00 568.91 0.71 0.28 92,108.51Franklin 0.36 4.15 29.50 3.33 88.51 0.43 0.21 12,894.44

Diesel industrialboilersGREET 0.67 16.78 80.28 3.35 11.95 0.17 0.37 76,206.09Franklin 0.67 16.83 80.78 5.05 97.61 0.18 75,394.25

Diesel trucksGREET 85.30 473.91 284.34 41.25 11.95 4.18 1.90 75,212.78Franklin 126.89 703.46 706.82 100.30 121.84 76,740.57

Gasoline trucksGREET 199.04 1137.38 189.56 7.40 9.16 32.05 1.90 69,306.96Franklin 76.47 1417.43 217.46 161.51 16.19 68,655.86

Note: Dash indicates no reported emissions. VOC = volatile organic compounds; CO = carbon monoxide; NOx =nitrogen oxides; PM10 = particulate matter less than 10 m in size; SOx = sulfur oxides; CH4 = methane; N2O =nitrous oxide; CO2 = carbon dioxide; LPG = liquefied propane gas; RFO = residual fuel oil.

processes for GREET and SimaPro. Table 4 re-ports emission factors for the combustion phase.As seen in table 3, only 20 of the 48 upstreamemission factors are within a factor of 2, predom-inantly those pertaining to CO and NOx emis-sions. Emission factors associated with electricityproduction are also consistently similar for thetwo models, unlike the EIO and GREET com-parison, which demonstrated significant disagree-ment for CH4 and SOx emissions during electric-ity generation. The Franklin database assumesconsistently higher emission factors for SOx ,PM10, and VOC emissions than the GREET val-ues, whereas GREET demonstrates significantlyhigher emission factors for CH4 and N2O. Itis difficult to determine the source of discrep-ancies in these factors. GREET uses data ob-tained from the US Environmental ProtectionAgencys (US EPA) AP-42 documents, which

rely on calculated emission factors, whereas theFranklin database uses aggregated data obtainedfrom industry sources. Both datasets have beenpeer-reviewed for appropriateness.

It appears that the Franklin model may ne-glect CH4 emissions during petroleum extrac-tion. GREET assumes an emission factor of 86mgCH4/MJ during extraction and 13 mg CH4/MJduring processing. The Franklin emission fac-tor for methane is 13 mg CH4/MJ, which ap-pears to be solely for processing. The sources ofthe other differentials are not apparent. In ad-dition, high variability is associated with emis-sions from energy sectors. The recently releasedGREET 1.6 beta version and SimaPro 6 versionaddress variability by incorporating Monte Carloanalysis to report ranges of values. This type ofdata enhances assessments, in order to show therange of possibilities given different conditions.



A study examining the use of uncertainty data inLCA is currently underway.

Assumptions regarding combustion emissionsfor basic energy processes are found in table 4.For these processes, 28 of the 56 categories varyby more than a factor of 2. Similar to upstreamprocess emissions, sulfur oxide emissions are con-sistently higher in the Franklin database. Nitrousoxide emissions are not reported for any of theFranklin combustion processes except coal-firedindustrial boilers. Emissions from coal-fired boil-ers and mobile equipment yield the most dissimi-lar factors, whereas the two models are most con-sistent in reporting emissions factors for diesel,liquefied petroleum gas (LPG), and residual fueloil (RFO) boilers. Carbon dioxide emission fac-tors are consistent throughout, with the excep-tion of coal-fired industrial boilers, where datafrom the Franklin database are obviously toolow.

To ascertain whether emission factors werethe primary contributor to discrepancies betweenGREET and SimaPro values, the Franklin datacontained within SimaPro were amended toreflect GREET emission factors. This resolvedthe major disparities between the models outputvalues, demonstrating that the emission factorsare primarily responsible for differences inmodel outputs, not material and energy flows inupstream processes.

Figure 3 shows the default results from thesoybean oil processing stage for the models. Thedifferences in results from both SimaPro andGREET are similar to those from soybean farm-ing, resulting from differences in emission factors.Using similar emission factors, the twomodels arein good agreement.

GREET, with default values obtained fromAP-42 documentation, predicts significantlyhigher greenhouse gas emission values thanSimaPro for any analysis in which combus-tion processes represent a significant fraction.SimaPro generates consistently higher SOx andVOC estimates.

Inventory Assessment and ModelSensitivity

One of the important steps in life-cycleassessment is to evaluate which process stage is

responsible for the majority of the environmen-tal impacts. In discussions of biocommodities,agriculture is often cited as energy- and material-intensive. This assertion is confirmed by thefindings of this study, with soybean oil extractionproducing a smaller fraction of the aggregateemissions, with the exception of VOC emissions.

Because LCI data can vary widely accordingto user assumptions, the sensitivity of modelsto changes in assumptions should be analyzed.EIO data cannot be manipulated by the user, andtherefore cannot undergo as rigorous an analysisas the GREET model. The GREET database wastherefore examined further to test the sensitivityof the model.

Figure 4 shows the contribution of each sectorto the total quantity of emissions, as estimatedby the GREET model. It can be seen that ni-trogen fertilizer production contributes negligibleemissions when compared to the use of the farm-ing equipment in all cases except SOx and CH4,whereas emissions from runoff are predominantlyresponsible for the evolution ofN2O and for someportion of NOx . This also implies that changes inassumptions for the use of farming equipment willhave a much greater impact on VOC, CO, NOx ,and PM10 than changes in fertilizer assumptions.Emissions are sensitive to changes in each sectorcommensurate with the percentage of the emis-sions generated by that sector. For instance, N2Oemissions are almost directly related to changesin the amount of nitrogen in runoff. Halving as-sumptions pertaining to farming equipment op-eration will reduce SOx emission values by 15%,but will reduce VOC emissions by nearly 40%.Future work will focus on significant changes inagriculture, such as a shift to sustainable prac-tices, but this is too extensive a topic to discussin this article.

Figure 4 also shows that to improve the en-vironmental impacts of current soybean farm-ing practices, reduction in farming equipmentuse would have the greatest impact on improv-ing the majority of air emissions. Reduction offertilizer inputs would have the greatest impacton improving N2O and SOx emissions. Changesin agricultural practices, such as an increase inconservation tillage farming, would allow net de-creases in farming equipment use, and its subse-quent emissions.



0%10%20%30%40%50%60%70%80%90%

100%

VOC

CO

NOx

PM10

SOx

CH4

N 2O

CO2

Allocation of Emissions

RunoffTransportationHerbicidePotashPhosphorusNitrogenFarming Equip

Figure 4 Allocation of emissions for various farming sectors contained within the GREET model. VOC =volatile organic compounds; CO = carbon monoxide; NOx = nitrogen oxides; PM10 = particulate matterless than 10 m in size; SOx = sulfur oxides; CH4 = methane; N2O = nitrous oxide; CO2 = carbon dioxide.

Conclusions

Three different techniques were used to gener-ate LCI data for the production of soybeans. Thisstudy shows that widely accepted models containdissimilar data, reinforcing the need for detailedboundary and source assumptions to help identifythe sources of discrepancies. Initial air emissionscomparisons between GREET and EIO resultedin five of eight categories having values differingby a factor greater than 2. The largest disparity,PM10 emissions, was a result of whether the mod-els included wind erosion of fields in the analysis.Differences in N2O emissions were traced backto air emissions due to chemical transformationof nitrogen in agricultural runoff. Discrepanciesin CH4 and SOx between the EIO and GREETmodels were shown to be a result of differencesin electricity use assumptions during farming, aswell as differences in assumptions about emissionsduring electricity generation.

Large dissimilarities were found to exist inbasic emission factors assumed for the GREET

and SimaPro models, in some cases exceedinga factor of 300. The SimaPro data, which weretaken from the Franklin database, exhibited sig-nificantly larger emission factors for SOx , PM10,and VOC, whereas GREET consistently calcu-lated greater CH4 and N2O emissions. These as-sumptions ultimately impact the output values.

While initial analysis of the three modelsrevealed significant differences, the major dis-crepancies in source assumptions were identified.This article has sought to evaluate the differ-ent assumptions associated with each in orderto demonstrate how these will impact the finalresults. Using this knowledge, it may be possibleto use one of the models, but incorporate aspectsor assumptions from other models that may seemrelevant.

Acknowledgments

The authors would like to thank Dr. MichaelWang of Argonne National Laboratories forhis valuable expertise and advisement, and Dr.



Thomas Seager of the University of New Hamp-shire for his insightful comments. This researchwas funded by the National Science Founda-tions Integrative Graduate Education Researchand Training (IGERT) (Grant DGE-9720779)and PREMISE (Grant DMI-0225912) programsand Alcoa, Inc.

Notes

1. Editors Note: For articles on the industrial ecologyof biobased products, see the special issue of theJournal of Industrial Ecology on biobased products(volume 7, issue 34).

2. One pound (lb) 0.4536 kilograms (kg, SI).3. One milligram (mg, SI) = 103 grams (g) 3.53

105 ounces (oz); one megajoule (MJ) = 106 joules(J, SI) 239 kilocalories (kcal) 948 British Ther-mal Units (BTU).

4. One kilowatt-hour (kWh) 3.6 106 joules (J,SI) 3.412 103 British Thermal Units (BTU).

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About the Authors

Shelie A. Miller is a graduate student at the Insti-tute for Environmental Science and Policy at the Uni-versity of Illinois at Chicago in Chicago, Illinois, USA,where Thomas L. Theis is the Institutes director.


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