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Environmental Sustainability of Wood-derived Ethanol: A Life Cycle Evaluation of Resource Intensity and Emissions in Maine, USA

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Environmental Sustainability of Wood-derived Ethanol: A Life Cycle Evaluation of Resource Intensity and Emissions in Maine, USA
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  Environmental sustainability of wood-derived ethanol: a life cycle evaluationof resource intensity and emissions in Maine, USA Binod Neupane a , * , Anthony Halog b , Robert J. Lilieholm a a School of Forest Resources, University of Maine, Orono, ME 04469, USA b School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, Queensland 4072, Australia a r t i c l e i n f o  Article history: Received 23 January 2012Received in revised form12 October 2012Accepted 26 November 2012Available online 5 December 2012 Keywords: BioethanolEcosystem goods and servicesEnvironmental sustainabilityLife cycle assessmentNear-neutral hemicellulose extractionprocessWoodchips a b s t r a c t The existing methods of process-based life cycle assessment (LCA) fail to account for the role of ecosystem goods and services derived from natural capital. This study presents an in-depth analysis of resource consumption and atmospheric emissions across a wood-derived bioethanol supply chain. Theanalysis is based on energy consumption, Industrial Cumulative Exergy Consumption (ICEC), andEcological Cumulative Exergy Consumption (ECEC) of resources used in the production of one ton of ethanol from woodchips using the near-neutral hemicellulose extraction technology. We found thatwhen compared with fossil-based fuels and corn ethanol, wood-based cellulosic ethanol derived underthe near-neutral hemicellulose extraction process demonstrated superior environmental performance.Renewable resources    mostly sunlight and detrital matters  e  are the dominant contributors to ICECanalysis, whereas non-renewable resources such as crushed stone, crude oil, ores and mineralscontribute more to total ECEC. Lime manufacturing, inorganic chemicals production for green liquorpreparation, and anthraquinone production have the highest resource consumption. The woodchipwashing process and transportation stages consume relatively fewer resources. A performance metricanalysis suggests that even though cellulosic ethanol uses a renewable feedstock, its environmentalsustainability performance is reduced due to the large consumption of non-renewable resources duringthe ethanol production stage.   2012 Elsevier Ltd. All rights reserved. 1. Introduction Concerns over climate change, energy security and economicdevelopmentaregrowingacrosstheglobe,andareincreasinglythefocus of research, policy, and public discussions. One of the mostsigni fi cant contributing factors driving global climate change is theemission of greenhouse gases (GHG) (IPCC, 1997; Danielsen et al., 2009; NRC, 2010). Anthropogenic GHG are primarily generated from the combustion of coal and petroleum products. Though GHGemission portfolios of countries consist of many sources, fordeveloped economies such as the U.S., the transportation sector isa major source. For instance, GHG emissions from the trans-portation sector contribute about one-third of total GHG emissionsin the U.S. (USEPA, 2009).These considerations underscore the urgent need to developalternatives to fossil fuels that reduce GHG emissions and supportdomestic economic development. Among the various options of alternative energy, biofuels produced from forest biomass havegarnered much interest and support owing to their renewabilityand domestic srcin e especially in heavily forested regions of theU.S. such as Maine. As a result, increased effort is being made toimprove the economic viability and technological advancement of processes that convert forest biomass to biofuels (Solomon et al.,2007; Kemppainen and Shonnard, 2005; Neupane et al., 2011). In the U.S., signi fi cant policies and funding have focused on fosteringthe bioenergy sector. At the local and regional levels, a growingnumber of industries are transforming their processes and infra-structures to meet national-level goals.There is an increasing interest on cellulosic ethanol producedfromwoodybiomassasitisarenewableresourcewhensustainablyharvested (Lippke et al., 2004). Moreover, cellulosic ethanol hasrelatively low GHG emissions, and the potential for fewer adverseenvironmental impacts when compared to many biofuel alterna-tives (Wang, 2005; Baral et al., 2012). Biofuels metrics such as GHG emissions bene fi ts alone, however, may be poor guides for thedevelopment of biofuels policies. Indeed, resource consumptionand energyintensityof cellulosic ethanol production areimportantconsiderations in biofuels policy-making as evidenced by the *  Corresponding author. Tel.:  þ 1 207 581 2841. E-mail address:  binod.neupane@maine.edu (B. Neupane). Contents lists available at SciVerse ScienceDirect  Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro 0959-6526/$  e  see front matter    2012 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.jclepro.2012.11.039  Journal of Cleaner Production 44 (2013) 77 e 84  inclusion of sustainability criteria in biofuels policy under therecently amended Renewable Fuel Standard (RFS2), which setstargets for reducing ecosystem impacts.Assessingecosystemimpactsandnaturalresourceconsumptionassociated with cellulosic ethanol production requires carefulscienti fi c measurement. Yet the role of natural capital such asecosystem goods and services in biofuels production is oftenignored in process-based life cycle studies. When fully considered,theimpactof cellulosicethanolproductionon ecologicalgoodsandservices may entail a substantial shift in consumption of resourcessuch as minerals, ores, soil, and water. Thus, a comprehensiveunderstanding of the energy and environmental implications of ethanol production is critically important before embarking onlarge-scale commercial production. Furthermore, quantifying theaggregate impacts on ecosystem goods and services helps inaccurately assessing the sustainability and resource competitive-ness of alternative fuels (Baral et al., 2012; Zhang et al., 2010a,b). The concept of ecosystem services has captured the attention of many researchers. The Economics of Ecosystems and Biodiversity(TEEB) de fi nes ecosystem services as  “ the direct and indirect contributions of ecosystems to human well-being  ”  (Sukhdev, 2008).The Millennium Ecosystem Assessment (2005) has raised globalawareness of the importance of ecosystem goods and services.Ecosystems generate a wide array of goods and services acrosspeople and societies. Most often these services are highly interre-lated, making categorization and aggregation challenging (Zhanget al., 2010a). Furthermore, ecosystem goods and services arestrongly intertwined with the long-term health and viability of human societies. Understanding the role of these linkages andservices within the environmental system is important in deter-mining the overall environmental sustainability of a productionsystem.Ecology, economics, and life cycle-based studies have largelyfailed to account for the critical role of ecosystem services (Boydand Banzhaf, 2007). Efforts have been made to quantify andunderstand the implications of ecosystem services on humanwell-being, but such efforts are progressing slowly (Naidoo et al., 2008;Daily et al., 2009). Different approaches have been proposed toaccount for ecosystem services in a production system. Jeswaniet al. (2010) described options for advancing the current practiceof process-based LCA by integrating different concepts such asenvironmental input e output analysis, multi-criteria decisionanalysis, and material  fl ow analysis, etc. Integration of environ-mental input e output analysis into process-based LCA to forma hybrid LCA is one important approach in assessing, in addition toGHG emissions, different environmental indicators such as ther-modynamicenergy, exergy, and emergyof the system(Zhanget al.,2010b).Although the scienti fi c understanding of ecosystem productionfunctions is improving, much remains unknown, and the infor-mation that is available is oftentimes omitted from decisions andpolicy-making. In the context of life cycle assessment, the role of natural capital iscriticalinattemptstoassess the overallimpactsof a product or production system across its life cycle (Cascio et al.,1996). Baral and Bakshi (2009) have applied the concept of ther- modynamics aggregation  e  which includes cumulative energy,Industrial Cumulative Exergy Consumption (ICEC), and EcologicalCumulative Exergy Consumption (ECEC) e to provide insights intothe ability of various thermodynamic-based methods to compareand aggregate resource use over the life cycle of alternative trans-portation fuels. ICEC, which is related to exergy, is the maximumuseful energy that can be obtained as the system achieves equi-librium with the reference environment (Dincer, 2000). Exergy isabetterindicatorasitaccountsforthequalityof theenergy.Exergyconsiders only that part of a resource that can be converted touseful work. ECEC, which is related to emergy, is equivalent tocumulative exergy consumption when ecosystems are alsoconsideredinthecalculationandrepresentallresourcesintermsof a common unit  e  usually solar equivalent joules (sej). For moreinformation on ICEC and ECEC, see Baral and Bakshi (2009), Zhang et al. (2010a), and Ukidwe and Bakshi (2007). Ecologically-based LCA (Eco-LCA) is an emerging LCA approachthat seeks to account for the role of natural resources such as landand ecosystem goods and services (Zhang et al., 2010b). Thisapproach accounts for many provisioning and supporting services,as well as some regulating services by representing their  fl ows inphysical units. The physical  fl ows information for material andenergy for each process are presented in the economy level basedon economic input e output analysis. The tool is based on the 1997U.S. Economic Input e Output (EIO) model. The EIO model contains491sectors of the North American Industry Classi fi cation System(NAICS) standard, which is used by federal agencies in classifyingbusinesses for the purpose of collecting, analyzing, and publishingstatistical data (Zhang et al., 2010a; Baral et al., 2012). Realizing the need to account for the role of ecosystem goodsand services in addition to GHG emissions, we integrated the Eco-LCA approach and a process-based LCA to understand the overallenvironmental sustainability of cellulosic ethanol production usingthe near-neutral hemicellulose extraction technology. Near-neutralhemicellulose extraction is a new technology developed at theUniversity of Maine that involves extraction of wood hemicelluloseusing green liquor prior to conventional Kraft pulping. The inte-gration of this extraction technology in existing pulp and papermills has the potential to bring additional revenues from hemi-cellulosic ethanol without altering the quantity and quality of thewood pulp (Mao et al., 2008; Halog and Mao, 2011). Given its extensive forest coverage, Maine has signi fi cant potential toproducecellulosicbiofuelfromwoodybiomass.Currently,thereare11 major pulp and paper mills in the state, about half of which useKraft pulping processes (Dickerson and Rubin, 2010). Under exist-ing technology, the only product produced from these mills is pulpand paper. As suggested by Halog and Mao (2011) and Dickerson and Rubin (2010), many of these mills could be upgraded into anintegrated biore fi nery system by adding the near-neutral hemi-cellulose extraction process.This study focuses on three key aspects of environmentalsustainability related to wood-based cellulosic ethanol producedusing the near-neutral hemicellulose extraction process: (1)resource intensity in terms of ICEC and ECEC; (2) performancemetrics; and (3) emissions. By addressing these topics, we provideimportant insights on biofuels return-on-energy-invested, renew-ability, and sustainability indices in order to inform decisionmakers and future biofuels policies. 2. Methodology  A hybrid LCA approach is used in this study to account for thenatural resources consumption and environmental emissions inone ton of ethanol produced from hardwood chips. The hybridmodel consists of an economic input e output life cycle inventorymodel derived from the Eco-LCA model, and a process-basedinventory for direct ecosystem inputs. Fig. 1 shows the systemboundary and how Eco-LCA and process-based LCA can becombined. As shown in the Figure, system inputs are derived fromboth the economy and ecosystem levels. Inputs at the economylevel are purchased from economic sectors. The Eco-LCA approachwas used to quantify the resources consumed at the ecosystemlevel.The process-based LCA approach was used to account forresources consumed directly from nature that are not captured in B. Neupane et al. / Journal of Cleaner Production 44 (2013) 77  e 84 78  Eco-LCA in the woodchip production stage (e.g., sunlight, detritalmatter, soil, etc.). The process-level approach also includes down-streamsideemissions.Theseprocess-basedLCAresultswereaddedto the results obtained from Eco-LCA.  2.1. Unit process description 2.1.1. Woodchip production Forests cover roughly 90% of Maine, and over 95% of theseforests are established via natural as opposed to arti fi cial regen-eration. As natural forest types are dominant in the state, thisstudy is limited to this forest type. Importantly, there is no appli-cation of fertilizers or other industrial economic inputs such aspesticides and herbicides. Instead, this production stage receivesonly diesel to harvest and convert trees into woodchips. The studyconsiders hardwood species, particularly maple (  Acer   spp.), birch( Betula  spp.),and beech ( Fagus spp.), as primary inputs tothenear-neutralhemicelluloseextractionprocessinaproposedbiore fi nery.Forest productivity, woodchip moisture content, and other rele-vant assumptions are presented in the Data Collection sectionbelow.Mao et al. (2008) estimated that the near-neutral hemi-cellulose extraction process integrated into a biore fi nery systemproduces 39.5 tons of ethanol as co-product per kiloton of pulpproduced. There are other co-products and by-products in theproduction system. In this integrated biore fi nery system, woodpulp is the major product, augmented by acetic acid (co-product),ethanol (co-product) and furfural (by-product). Mass-based allo-cation is used in this studyas it is a widely used allocation methodin LCA studies (Luo et al., 2009; Neupane et al., 2011). Further- more, Baral and Bakshi (2009) observed that mass-based alloca-tion is more suited for exergy and emergy analyses than market-based allocations. The converted woodchips are transported tothebiore fi nery facility by truck. The transportation distance isassumed to be 60 miles, which is considered the limit foreconomically feasible feedstocks in Maine (Dickerson and Rubin,2008).  2.1.2. Ethanol production Atthebiore fi nerygate,woodchipsarescreenedandheatedwithanthraquinone and green liquor. Green liquor (composed of NaOH,Na 2 S,Na 2 CO 3 andNa 2 SO 4 )isusedtodissolvetheorganiccontentinthe woodchips. Causticizing is done to convert sodium carbonate(Na 2 CO 3 ) into sodium hydroxide (NaOH), and to remove variousimpurities introduced from the recovery boiler furnace and limekiln. Sulfuric acid (H 2 SO 4 ) is used to hydrolyze the hemicellulose tocontrolpHvalue.Approximately2.2tonsofsulfuricacidisaddedinthe acid hydrolysis unit. Use of green liquor and sulfuric acid arecategorized under inorganic chemicals, whereas organic chemicalsrepresent anthraquinone application in this study. Liming is doneto raise the pH of the solution and precipitate sulfate ions.Approximately 1.34 tons of lime material is consumed to produceone ton of ethanol.Throughout the entire process, approximately 4800 tons of steam is consumed in the proposed Kraft pulping system (Maoet al., 2008). We assume that the needed energy for the bio-ethanol production system is produced from a hog fuel boilerfueled by wood wastes and residues, augmented by a recoveryboiler. Mao et al. (2008) found that the near-neutral hemicelluloseextraction system may require some external energy from theelectrical power grid, or may produce an excess of energy thatcould be sold back to the grid. In this analysis, however, it wasassumed that the energy produced from the system is suf  fi cient tosupply all internal processing needs and no grid-based energy isbought or sold. For the production of one ton of ethanol, approxi-mately 13,290 tons of water are used in the washing process.  2.1.3. Ethanol distribution Currently,ethanolisblendedwithgasolineatvariouslevels,e.g.,E10 and E85. Since commercial production of cellulosic ethanol inMaine has not yet begun, we assumed that the biore fi nery willdeliverethanol to existing gasoline collection facilities in Maine forblending purposes. There are three major gasoline collectionfacilities in Maine e in Portland, Bangor and Searsport. We assumethat the biore fi nery will distribute ethanol to one of these existing Fig. 1.  System diagram of wood-based cellulosic ethanol production. B. Neupane et al. / Journal of Cleaner Production 44 (2013) 77  e 84  79  facilities. The distance between the biore fi nery and the collectionfacility is estimated to be 100 miles. The study considers tankertruck transportation as it is unclear which transportationmethod(s)willbeusedbyfuturebiore fi neries.Theblendedethanolis distributed to retail fueling stations for  fi nal use.  2.1.4. Transportation The transportation stage includes upstream pipeline trans-portation of diesel from Portland to Bangor, Maine. It also includeswoodchip transportation and downstream ethanol distribution.Results from all of these activities are summarized under thetransportation stage of the life cycle. Process-based life cycleinventory results were added separately where Eco-LCA did notcover associated emissions.  2.2. Data Collection Table 1 shows the inputs purchased from the economy level,with the various economic sectors represented by NAICS codes. Allinputs are represented by producer-level prices in the IO-basedmodel. Another important consideration is that since the IO tableis based on the 1997 U.S. economy, dollar values are adjusted to1997-year equivalents.The Eco-LCA tool does not consider information on directecological goods and services required by the system (Baral andBakshi, 2009). Therefore, to calculate direct ecological goods andservices, process-level information was collected from a variety of sources. A number of direct ecological goods and services are usedin woodchip production, including solar energy, soil resources,nitrogenmineralization,nitrogendeposition,rainfall,wind,detritalmatter, etc. This study considers only the major components of direct ecological goods and services important to the forestproduction system  e  e.g., sunlight, soil, wind, rainfall and detritalmatter. Table 2 provides the direct ecological goods and servicesconsumed in terms of ICEC and ECEC. Since Eco-LCA does notcapture the energy and exergy of fuel used in the transportation of ethanol, process-level information is added separately. Emissionsdata for process-based LCI are collected from the U.S. Life CycleInventory (USLCI) database (USLCI accessed 2012). 3. Results  3.1. Industrial Cumulative Exergy Consumption (ICEC) Fig. 2 presents the contribution of resources in terms of ICECanalysis. ICEC analysis allows us to aggregate and represent theinputs on a common basis of available useful energy. The cumula-tive exergy consumption in the industrial links of ethanol produc-tion chains is presented through this approach of exergy analysis.As shown in Fig. 2, sunlight is the dominant contributor to ICEC.Since sunlight dominates the other resources, its value  e  alongwith detrital matter e is presented by data labels on the top of thedata bars for clarity. In calculating ICEC, solar exergy derived fromthe large amountof sunlight that fuels forest growth contributes toICEC. This solar exergy transferred to other economic sectors alsocontributes to the dominant role of sunlight. The contributions of minerals and fossil fuels are found to be relatively low. In terms of life cycle stages, the woodchip production stage contributes themost since it captures more resources such as sunlight and detritalmatter. Renewable resources such as sunlight, detrital matter andwood are dominant in total ICEC analysis because exergy analysisdoes not account for the substitutability and quality differencesbetween resources. Furthermore, it also ignores ecological linkswithin the ethanol production chain. As a result, ICEC analysisassumes that all ecosystem products and services are identical, andhave a constant transformity 1 value. Since ICEC analysis ignoresexergy consumption in the ecological stages of the productionchain and, consequently, cannot distinguish quality differencebetween ecological products and services, a different approach  e i.e., the Ecological Cumulative Exergy Consumption (ECEC)  e  isused to address this shortcoming.  3.2. Ecological Cumulative Exergy Consumption (ECEC) ECEC analysis accounts for the exergy consumed by ecosystemgoods and services that contribute to the product supply chain.Including ECEC overcomes the limitations of ICEC analysis dis-cussedabove. When the contributions of resources arerepresentedin terms of ECEC or emergy as shown in Fig. 3, it appears that non-renewable resources contribute more to total ECEC, unlike the casefor ICEC. This is because non-renewable resources require moreecosystem work, which causes their transformities to be relativelylarge. In terms of ECEC, the contribution of sunlight is very low,while non-renewable resources such as crushed stone, crude oil,ores and minerals are relatively large contributors. In terms of lifecycle stages, inorganic chemicals for green liquor preparation andanthraquinone production have the highest resource consumption.Lime manufacturing and woodchip production also have largecontributions to resource consumption. These organic and inor-ganicchemicalproductionsystems usesigni fi cant amountsof non-renewable resources.  3.3. Environmental emissions Fig. 4 shows life cycle environmental emissions, includingemissions to air, water and land. As shown in the Figure, carbondioxide, carbon monoxide, particulate matter, and volatile organiccompounds emissions are higher as compared to other emissions.Thetotalglobalwarmingpotentialfromthesystemwasfoundtobe6087 kgCO 2  equivalent using ReCiPe midpoint characterizationfactors for GWP100 year time period (Goedkoop et al., 2008). Interms of life cycle stages, emissions were found to be high inwoodchip production, inorganic chemical manufacturing, limemanufacturing and transportation, as these stages require theburning of more fossil fuels. About 5300 kg of CO 2 , 30 kg of CH 4 ,238 kg of CO, 24 kg of VOC, and 30 kg of PM10 are emittedthroughout the ethanol supply chain. Ammonia, NO, styrene, lead,andhydro fl uorocarbonarerelativelylowwithrespecttoemissions.Thetransportationstageincludespetroleumre fi neriesandpipelineand truck transportation of fuel and woodchips. Process-basedinventory results were added separately for pipeline and trucktransportation.  3.4. Performance metrics Several performance metrics were evaluated using the resultsobtained from the ICEC and ECEC analyses in order to betterunderstand the overall environmental impacts of woodchip-derived cellulosic ethanol using the near-neutral extractionprocess. Table 3 provides a list of metrics analyzed in the study,along with their de fi nitions and results.  3.4.1. Yield ratio The yield ratio in Table 3 indicates the proportion of the totalECEC required that is derived from the economy. In other words, itis the ratio of total ECEC requirements to indirect ECEC require-ments (Ukidwe, 2005). The yield ratio becomes higher if a product 1 Transformity is de fi ned as the emergy per unit of available energy. B. Neupane et al. / Journal of Cleaner Production 44 (2013) 77  e 84 80  system obtains a large portion of its ECEC requirements directlyfrom ecosystems. As a result, products that use mostly serviceindustries,whichareembeddedintheeconomicnetworkandhaverelatively lower direct reliance on ecological resources, have lowyield ratios. As shown inTable 3, cellulosic ethanol has a yield ratioof one. This positive yield ratio is because cellulosic ethanolproduction derives a signi fi cant fraction of its energy requirementsdirectlyfromtheenvironment.However,thisratioisconstrainedtounity due to the greater dependency of cellulosic ethanol on non-renewable resources than renewable resources. As such, itsigni fi es that the ethanol production system also relies oneconomic activities. In a similar study, Baral and Bakshi (2009)calculated a yield ratio of 1.26 for corn ethanol and 7.69 for gaso-line.Whencomparedwiththeyieldratioof cornethanol,cellulosicethanol produced under the technology considered here performsmarginally better (i.e.,1.08 versus 1.26).  3.4.2. Environmental loading ratio The environmental loading ratio is the ratio of total ECECrequirements from non-renewable resources to those fromrenewable resources. It indicates the relative reliance of a processor product on non-renewable resources (Ukidwe, 2005). Theenvironmental loading ratio is higher for a product that consumesmore non-renewable resources. When the environmental loadingratio of a product is below unity, it relies more on renewableresources. As shown in Table 3, the environmental loading ratioof cellulosic ethanol is about two, meaning that it fails therenewabilitythresholdofone.Thisratioisgreaterthanonebecauseethanol has relatively higher reliance on non-renewable resourcessuch as metallic and non-metallic minerals, and fossil energysources. Baral and Bakshi (2009) found the environmental loadingratio of corn ethanol and gasoline to be about three and eight,respectively.Thus, comparedtocorn ethanol,cellulosicethanol hasa much lower environmental loading ratio, whereas the gasolinehas the highest.  3.4.3. Yield-to-loading ratio This ratio indicates when a product or process relies onecosystems, but has lower reliance on non-renewable resourcesand is thus more environmentally sustainable. Economic sectorsthat use non-metallic minerals have low yield-to-loading ratios. Asshown in Table 3, the yield-to-loading ratio of cellulosic ethanol is0.45. Baral and Bakshi (2009) found yield-to-loading ratios of cornethanol and gasoline to be 0.41 and 0.0095, respectively. Here, theratio obtained for cellulosic ethanol is marginally better than thatfor corn-based ethanol. One reason for the marginal difference isthe low ethanol production rate from the near-neutral extractionprocess.In emergy analysis, the yield-to-loading ratio is oftentimesconsidered the  “ index of sustainability ”  (Ukidwe, 2005). The termas used in this study, however, only re fl ects the resourceconsumption side of sustainability, and thus should be interpretedcautiously. For a complete sustainability assessment of the cellu-losic ethanol supply chain, other dimensions such as social andeconomic factors should be considered Halog and Manik (2011).  3.4.4. ECEC/ICEC ratio This ratio estimates the degree to which ICEC analysis is limitedto calculate the cumulative exergy consumption by ignoring itsecological linkages. As such, it indicates the  “ degree of non-renew-ability ”  of a product system (Berthiaume et al., 2001), althougha more rigorous analysis is required to propose any correlation(Ukidwe, 2005). The ECEC/ICEC ratio is higher for a product thatconsumes more non-renewable resources and stems from the factthat the transformity values of non-renewable resources are higherthanthoseofrenewableresources.TheECEC/ICECratiointhisstudywas found to be 98 (Table 3), indicating that cellulosic ethanol isdependent on non-renewable resources. Baral and Bakshi (2009)reported ECEC/ICEC ratios for gasoline and corn ethanol to be 450and 75,500, respectively. Comparing the ratios, it can be concludedthatthewood-basedcellulosicethanolanalyzedhereissigni fi cantlyless dependent on non-renewable resources as compared to cornethanol and gasoline.  Table 1 Inputs purchased from the economy to produce one ton of ethanol.Inputs NAICS codes Quantity Unit Cost ($) per unit Yr. Cost ($) 1997 cost ($)Wood a 113,300 110 tons 8.00 2010 880 646.3Diesel b 324,110 164 gal 0.603 1997 98.89 98.89Process Water c 221,300 57,600 gal 0.0018 2010 103.68 75.02Sulfuric Acid c 325,180 2.2 tons 0.11 2010 242 179Lime c 327,410 1.34 tons 0.15 2010 201 149Anthraquinone c 325,190 0.1 tons 3800 2012 380 267Green Liquor d 325,180 1.5 tons 500 2012 750 527 Transportation services from the economy Pipeline transportation (Diesel) e 486,000 164 gal 0.053 2011 8.69 6.29 Ethanol transportation to blending station Diesel 324,110 1.4 gal 0.603 1997 0.84 0.84 a Average pulpwood-quality price in Maine was obtained from the Maine Forest Service (http://www.maine.gov/doc/mfs/pubs/pdf/stumpage/10stump.pdf ). b Obtained from Neupane et al. (2011). c Obtained from Mao et al. (2008). Producer level prices were obtained from experts in chemical engineering at the University of Maine. d Green liquor is mostly composed of Na 2 CO 3  (about 65%) and Na 2 S (about 30%). Price was derived from the volume composition and unit price of these two chemicals.About 75% of green liquor is assumed to be produced from internal chemical processes in the mill. The remaining 25% is purchased from the economy. e Portland, Maine, to Bangor, Maine, petroleum products pipeline transportation cost obtained from: http://www.exxonmobil.com/USA-English/EMPCo/tariff_nemw.aspx  Table 2 Direct inputs from nature to produce one ton of ethanol.Inputs ICEC(j) ECEC(sej) a Sunlight b 2.73267E þ 11 2.73267E þ 11Soil c 1.11E þ 08 8.17825E þ 12Wind c 1.66E þ 09 4.16159E þ 12Rainfall d 3.90E þ 08 1.19362E þ 13Detrital matter e 1.58E þ 12 1.30947E þ 15 ECEC of fuel consumed in ethanol transportation Diesel 1.58E þ 12 1.30947E þ 15 a Transformity values were obtained from Odum (1996). b Solar insolation was annual average for Maine. Data: 3.82 kWh/m 2 /day;albedo  ¼  0.23 (NASA, 2012). Calculations are based on the area of six acres (i.e.,24,281 m 2 ) of forestland required to produce one ton of ethanol under consideredtechnology. Moisture content of wood was assumed to be 46%. c Obtained from Felix (2006). d Data: Annual rainfall of Maine ¼ 1074 mm per year; density of water 1000 kg/m 3 . e Obtained from Baral and Bakshi (2009). B. Neupane et al. / Journal of Cleaner Production 44 (2013) 77  e 84  81
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