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

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<ul><li><p>RESEARCH AND ANALYSIS</p><p>Comparison of Life-CycleInventory DatabasesA Case Study Using Soybean Production</p><p>Shelie A. Miller and Thomas L. Theis</p><p>Summary</p><p>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.</p><p>Keywords</p><p>agricultureair emissionsboundary definitioneconomic input-outputenergy use in transportation (GREET)</p><p>modellife-cycle assessment</p><p>Address correspondence to:Shelie A. MillerInstitute for Environmental Science and</p><p>PolicyUniversity of Illinois at Chicago2121 West Taylor StreetChicago, IL 60612 USA</p><p> 2006 by the Massachusetts Institute ofTechnology and Yale University</p><p>Volume 10, Number 12</p><p> Journal of Industrial Ecology 133</p></li><li><p>RESEARCH AND ANALYS I S</p><p>Introduction</p><p>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.</p><p>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).</p><p>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</p><p>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.</p><p>Methods</p><p>Description of Models</p><p>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.</p><p>GREET Model</p><p>The GREET 1.5 model was developed byArgonne National Laboratory in the U.S. to</p><p>134 Journal of Industrial Ecology</p></li><li><p>RESEARCH AND ANALYS I S</p><p>Table 1 Comparison of three inventory models for soybean farming</p><p>EIO-LCA GREET SimaPro</p><p>Description Matrix-based model basedon the linkages ofeconomic sectors</p><p>Detailed process-basedmodel based on userassumptions</p><p>Modules of process-basedinventory data that areassembled by user</p><p>Scope 485 U.S. commoditysectors; includes airemissions, water, andenergy use</p><p>U.S. transportation, bothtraditional andalternative fuels,includes air emissionsand energy use; currentand future estimates</p><p>U.S. and European datafor large variety ofsectors that isexpandable; includesmost environmentalimpacts</p><p>Primary datasources</p><p>U.S. Department ofCommerce EconomicInput-Output Model,AIRS database</p><p>US EPA AP-42documents, publishedstudies, governmentdocuments</p><p>Franklin US LCI,BUWAL 250,IDEMAT 2001,ETH-ESU 96, industryand archival data,others</p><p>Advantages Quick, easy, publiclyavailable, uses monetaryunits, boundaryexpansion</p><p>Transparent, usermanipulation possible,flexible to userassumptions, relativelyquick, publicly available</p><p>Applicable to any process,transparent,multiple-user interface,large inventory,expandable, includesimpact assessmentcapabilities</p><p>Disadvantages Aggregation of data, onlyfor established sectors,no manipulationpossible, difficult totrack data sources,outdated data possible,does not include usephase</p><p>Limited to transportationand related sectors</p><p>Expensive, labor-intensive,inconsistencies possiblebetween differentmodules</p><p>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).</p><p>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,</p><p>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</p><p>Miller and Theis, Comparison of LCI Databases: Soybean Production 135</p></li><li><p>RESEARCH AND ANALYS I S</p><p>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.</p><p>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</p><p>Table 2 Default inputs for the greenhouse gases, regulated emissions, and energy use in transportation(GREET) model</p><p>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</p><p>Energy usage in farming (kJ/kg soybeans)Diesel 823Gasoline 368LPG 32.3Electricity 19.9Transportation to extraction facility 203(as diesel fuel)</p><p>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)</p><p>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.</p><p>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.</p><p>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 .</p><p>136 Journal of Industrial Ecology</p></li><li><p>RESEARCH AND ANALYS I S</p><p>EIO-LCA Model</p><p>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.</p><p>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</p><p>to...</p></li></ul>


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