Comparison of Life-Cycle Inventory Databases a Case Study Using Soybean Production

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Life-Cycle analysis



    Comparison of Life-CycleInventory DatabasesA Case Study Using Soybean Production

    Shelie A. Miller and Thomas L. Theis


    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.


    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 Journal of Industrial Ecology 133



    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.


    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


    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


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