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Models for waste life cycle assessment: Review of technical assumptions

Emmanuel C. Gentil a, Anders Damgaard a, Michael Hauschild b, Göran Finnveden c, Ola Eriksson d,Susan Thorneloe e, Pervin Ozge Kaplan e, Morton Barlaz f, Olivier Muller g, Yasuhiro Matsui h, Ryota Ii i,Thomas H. Christensen a,*

a Department of Environmental Engineering, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmarkb DTU Management, Innovation and Sustainability Group, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmarkc Environmental Strategies Research – fms, Royal Institute of Technology, (KTH) 100 44 Stockholm, Swedend Department of Technology and Built Environment, University of Gävle, S-801 76 Gävle, Swedene US EPA, Office of Research and Development, National Risk Management Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park NC 27711, USAf Department of Civil, Construction, and Environmental Engineering, NC State University, Raleigh, NC 27695-7908, USAg PricewaterhouseCoopers, 63, rue de Villiers, 92208 Neuilly-sur-Seine, Franceh Graduate School of Environmental Science, Okayama University, Okayama, Japani Pacific Consultants Co. Ltd., 1-7-5, Sekito, Tama-shi, Tokyo, Japan

a r t i c l e i n f o

Article history:Received 25 February 2010Accepted 3 June 2010Available online 5 July 2010

a b s t r a c t

A number of waste life cycle assessment (LCA) models have been gradually developed since the early1990s, in a number of countries, usually independently from each other. Large discrepancies in resultshave been observed among different waste LCA models, although it has also been shown that results fromdifferent LCA studies can be consistent. This paper is an attempt to identify, review and analyse method-ologies and technical assumptions used in various parts of selected waste LCA models. Several criteriawere identified, which could have significant impacts on the results, such as the functional unit, systemboundaries, waste composition and energy modelling. The modelling assumptions of waste managementprocesses, ranging from collection, transportation, intermediate facilities, recycling, thermal treatment,biological treatment, and landfilling, are obviously critical when comparing waste LCA models.

This review infers that some of the differences in waste LCA models are inherent to the time they weredeveloped. It is expected that models developed later, benefit from past modelling assumptions andknowledge and issues. Models developed in different countries furthermore rely on geographic specific-ities that have an impact on the results of waste LCA models. The review concludes that more effortshould be employed to harmonise and validate non-geographic assumptions to strengthen waste LCAmodelling.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Since the early 1990s, waste LCA tools have been developed tomodel the environmental performance of waste management sys-tems (Morrissey and Browne, 2004; Björklund et al., 2010). Thesemodels have been developed by a range of environmental protec-tion agencies, universities or consultancies, mainly in Europe andNorth America. However, due to the complex nature of waste man-agement modelling and the range of country-specific data, thesemodels have been developed in relative isolation and consequentlysuffer a lack of harmonisation.

LCA can be applied to waste management systems either byusing dedicated waste LCA tools or by using product LCA tools. In

this paper, the focus is on waste LCA tools for the assessment ofan integrated waste management system. It can however be notedthat essentially the same generic LCA methodology can be used ineither case (Finnveden, 1999a; Clift et al., 2000).

Winkler (2004) and Winkler and Bilitewski (2007) comparedLCA models for waste management, including a quantitativeassessment of six models (ARES, EPIC/CSR, IWM2, MSW-DST, OR-WARE and UMBERTO). The assessment was made by computingthe same waste management scenario (the city of Dresden in Ger-many) in all six waste LCA models. Discrepancies of up to 1400% forsome results, which lead to contradictory results among models,were identified. The work of Winkler and Bilitewski (2007) isimportant because the authors were the first to highlight andquantify significant differences among different waste LCA models.Similarly, Rimaityté et al. (2007) compared the incineration out-puts of the LCA–IWM model with measured emissions data andobserved large differences between the model and the measureddata. Since modelling assumptions, and possibly calculation errors

0956-053X/$ - see front matter � 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.wasman.2010.06.004

* Corresponding author. Address: Department of Environmental Engineering,Building 115, Technical University of Denmark, DK-2800 Kongens Lyngby,Denmark. Tel.: +45 45251603; fax: +45 45932850.

E-mail address: [email protected] (T.H. Christensen).

Waste Management 30 (2010) 2636–2648

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in the models, are leading to different results, it is important toidentify key criteria that could potentially have significant conse-quences on the results of waste LCA models.

LCA models are frequently used to compare waste treatmentalternatives (Finnveden and Ekvall, 1998; Björklund and Finnve-den, 2005; Villanueva and Wenzel, 2007). These studies have ingeneral concluded that the relative order of results is consistentamong different studies. In contrast, Winkler and Bilitewski sug-gested that large differences among models resulted in changesin the relative order of treatment and disposal alternatives.

The objective of this paper is to review and analyse a number ofdifferent LCA models, developed throughout the world, for wasteand recyclables management. The review is based on available lit-erature, consultation with LCA model developers and use of the dif-ferent models, where possible. The paper focuses on methodology,input parameters and modelling assumptions. For purpose of sim-plification, this paper excludes the life cycle impact assessment(LCIA) phase and does not provide a comparison of inventoriesamong models but rather focuses on the technical assumptionsleading to the results.

2. Waste management and LCA models

Waste LCA, as opposed to product LCA, is a system LCA thataims at assessing the environmental performance of a number ofinterconnected waste management technologies based on a spe-cific waste composition from the point of generation of the wasteto its final disposal. Waste management is defined by all the activ-ities including collection, transport, handling, treatment, materialand energy recovery and disposal of waste, as indicated in Fig. 1.

Fig. 1 illustrates a generic waste management system with thelinkages between the waste and the wider environment. In a waste

LCA, various elements contained in the waste (elemental wastecomposition) are often mathematically linked to the emissionsoriginating from the waste handling, treatments or disposal. Froma generic perspective, it is expected that a waste LCA model has theability to model the following aspects:

� Environmental performance for the management of a variablefractional waste composition. Models should respond to achange in fractional waste composition, such as varying contentof e.g. paper and plastic.� Emissions related to the elemental composition of the waste.

Models should respond to elemental waste specific emissions,such as, for example, mercury content in newspaper.� Emissions independent of the waste composition. Models

should respond to waste management processes’ operating spe-cific emissions, such as the amount of dioxin emitted.� Emission offsets with other systems. Models should include

substitution with energy systems and manufacturing of primaryresources, e.g. such as aluminium.� Flexible system boundaries. Models should be able to include

country-specific energy mix in the calculation.� Determination of life cycle inventory (LCI) of an integrated

waste management system. Models should include the assess-ment of an integrated and interconnected system composedof number of transportation and waste management processes,ranging from collection to final disposal.

3. Choice of waste LCA tools to be reviewed

Almost 50 LCA models are currently available in Europe (EPLCA,2008), and more on a worldwide basis, with different applicability,functionality, licensing restrictions and costs. In order to undertake

SocietalOUTPUTS

WasteQuantityFractionsElementsproperties

CollectionBinsBags

Bottle banks

TransportTrucksShipTrain

Individual vehicles

Biological TreatmentCompost

Anaerobic DigestionMBT

Thermal TreatmentIncineration

PyrolysisGasification

Material RecoveryOpen-loopClose-loop

LandfillOpen dumpBioreactor

InertHome Composting

Resources and Energy InputsConstruction – Maintenance – Decommission – Ancillary Materials - Energy

Decommission – Ancillary materials – Process related emissions – Waste related emissionsDirect Environmental Emissions

BiosphereForestry

Soil

Energy SystemElectricity

HeatFuel

Industrial SystemReprocessing

Carbon sink

Intermediate FacilitiesAutomatic

Manual

ExportCo-treatment

SYST

EM

EXC

HA

NG

E

IndirectEnvironmental benefits

Fig. 1. Generic integrated waste management system. The outer dotted line represents society at large (earth system and technosphere). The inner dotted line represents thewaste management systems represented by a number of waste management technologies (light shaded grey). The dark shaded grey represents the inputs and the outputs ofthe whole waste management system. The box indicating the system exchange shows the relationships of materials and energy flows between the waste industry and widersociety, through substitution.

E.C. Gentil et al. / Waste Management 30 (2010) 2636–2648 2637


a robust review of LCA tools, we evaluated models that fit into thepre-defined selection criteria as listed above.

Based on these selection criteria, the following models havebeen considered for the review:

� EASEWASTE, Denmark (Kirkeby et al., 2006);� EPIC/CSR, Canada (Haight, 1999, 2004);� IWM2, UK (McDougall et al., 2001);� LCA–IWM, EU (Den Boer et al., 2005a,b, 2007);� MSW-DST, USA (Weitz et al., 1999; Solano et al., 2002a,b; Thor-

neloe et al., 2007);� ORWARE, Sweden (Dalemo et al., 1997; Eriksson et al., 2002);� SSWMSS, Japan (Tanaka et al., 2004; Tanaka, 2008);� WISARD, UK (Ecobilan, 1997); and� WRATE, UK (Thomas and McDougall, 2003; Gentil et al., 2005;

Coleman, 2006).

Other tools, such as ARES (Schwing, 1999), HOLIWAST(HOLIWAST, 2006), LCA-LAND (Nielsen et al., 1998; Nielsen andHauschild, 1998), MIMES (Sundberg et al., 1994), MSWI (Ciroth,1998) and WAMPS (Moora et al., 2006) were not included becausethe availability of the information was either too scarce, the toolonly considered a specific waste management technology, or themodel was not an LCA tool. A summary of the development phasesof the different models are presented in Fig. 2.

4. Evaluation approach

A set of evaluation criteria has been used to assess the differentLCA models. The main comparison criteria include the functionalunit definition, system boundaries, waste composition, energymix and waste management processes. These criteria are consid-ered of key relevance in waste LCA models (Björklund andBjuggren, 1998; Björklund, 1998; Eriksson, 2003). Other evaluationcriteria, not covered in this paper, are also of importance, such asopen-loop recycling, multi-loop recycling, cut-off criteria, sensitiv-ity, uncertainty and Monte Carlo analyses. Finally, another veryimportant criterion is the inclusion of comprehensive metadata,critical for understanding the assumptions made for defining thewaste management processes; however, this is also excluded fromthis paper.

4.1. Functional unit

According to ISO 14040 (2006), the functional unit (FU) is thequantified performance of a product or a system for use as a refer-ence unit. The FU is defined in a similar fashion for all the models

reviewed. The FU generally include all the waste (with a specificcomposition) managed (in tonnes) in a waste management systemover a defined time period (e.g. usually 1 year), for a specific region.The FU can also be defined by normalising the data to one tonne ofwaste, but this can be adapted in all the models. However, the usershould be aware of these differences when interpreting the LCI andLCIA results. As long as the functional unit is defined consistentlyacross the models, this should not have any influence on the modelcomparison.

4.2. System boundaries

System boundaries are considered to be essential criteria forwaste LCA models, since their definition could drastically influencemodel results (Wenzel and Villanueva, 2006a). In the inventoryanalysis there are three groups of system boundaries (Guinée,2002). Time consideration can also be included as a system bound-ary. These types of system boundaries include:

� The technical system and the environment;� Time horizon boundaries;� The technical system and other technical systems (upstream

and downstream boundaries, such as the energy system); and� Significant and insignificant contributions (boundary conditions

for cut-off criteria).

4.2.1. Technical system and the environmentThe boundary conditions defined for a waste management sys-

tem and the environment influence directly the outcome of an LCAstudy. Waste entering the waste management system as an input,excludes the imbedded inputs because of the technical impossibil-ity to account for all the products’ life cycles ending up as a waste(‘‘Zero’’ burden approach). The level of inclusion and quantity ofthe inputs and the outputs entering and leaving waste manage-ment systems are different among the different models and canbe different among waste management activities. This is due tothe different complexity and exhaustiveness of the models wheredevelopers had to choose the type and quantity of inputs and out-puts based on best available knowledge at the time ofdevelopment.

The geographical boundaries and therefore the geographicalscope are also critical for the definition of the boundaries betweenthe technical system and the environment in waste LCA modelling.The reviewed models contain data that are country-specific andtherefore a particular attention should be taken when using a mod-el developed in one country and used in another. For instance, theenvironmental performance of electricity production could be

Model Country '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 Source

MIMES-waste SW Sundberg, 1994ORWARE SW Dalemo et al. , 1997, Eriksson et al. , 2002LCA-LAND DK Nielsen et al. , 1998a,bMSWI GER Ciroth, 1998ARES GER Schwing, 1999EPIC/CSR CA Haight, 1999, 2004MSW-DST USA Weitz et al. ,1999, Thorneloe et al ., 2007WISARD UK, FR, NZ Ecobilan, 1999IWM2 UK Mc Dougall, 2000SSWMSS JP Tanaka et al ., 2004, Tanaka, 2008LCA IWM EU Den Boer et al. , 2005a,b, 2007WAMPS SW Moora, et al. , 2006HOLIWAST EU HOLIWAST, 2006WRATE UK Gentil et al , 2005, Coleman, 2006EASEWASTE DK Kirkeby et al. , 2006

Fig. 2. Timeline of selected waste LCA models. The grey area indicates the launch time of the models. The solid line represents the active development phase and launch ofsubsequent versions of the same model, while the dotted line indicates the research leading to the development phase or the subsequent research not necessarily leading toan active development (use of the model as a research tool). This timeline has been developed based on available literature and discussions with authors and developers.

2638 E.C. Gentil et al. / Waste Management 30 (2010) 2636–2648


generated with two different coal inventories, leading to differ-ences in the waste LCA results.

4.2.2. Time horizon boundariesThe time horizon boundary is also an important assessment cri-

terion when comparing different models. The time horizon bound-ary is mainly relevant for the modelling of landfills, and to a lesserextent, land application of biotreated materials, since the emis-sions from other waste treatment technologies are immediate.For example, Hyks et al. (2009) found that heavy metals wereleaching out very slowly (over 10,000 years). The choice of timehorizon could significantly influence the outcome of the LCA study,if emissions are calculated over a short time horizon (less than orequal to 100 years) or over long time horizon (several thousandyears). Differences in time horizon boundaries are summarised inTable 1.

In one model (ORWARE) two time horizons are considered:emissions of the first 100 years, based mainly on monitoring andthe remaining emissions that will potentially be emitted in the fu-ture. In other types of models, a 100-year time horizon has beenchosen (EPIC/CSR, LCA–IWM and MSW-DST), although MSW-DSTalso allows a 20 and 500 year time horizon for landfill leachate.In some models (WISARD and WRATE), it is assumed that thelong-term impacts should consider ‘‘infinity’’ to encompass morethan 90% of the emissions. In WRATE, 20,000 years have been con-sidered as ‘‘infinite’’ for the modelling of leachate emissions, whichis suggested to correspond to about 95% of the potential emissions(Hall et al., 2005).

One model (EASEWASTE) allows the user to define the timehorizon which provides the greatest flexibility. The model also in-cludes results for longer term emissions using the concept of‘‘stored toxicity’’ for heavy metals. This parameter indicates the to-tal heavy metal leaching potential based on the composition andquantity of waste but, as default, only the first 100 years of emis-sions are calculated for the leachate emissions. The remaining, orstored toxicity potential, will eventually be released to the envi-ronment but remains in the landfill for an undetermined duration(Hauschild et al., 2008). IWM2 provides a different approach wherethe time horizon is not defined, instead the typical amount of land-fill gas and leachate generated produced per tonne of waste land-filled is defined.

The time horizon boundaries are also defined by the life timeof a waste management process. This is relevant for those mod-els including the environmental emissions of construction,maintenance and decommissioning, such as WISARD andWRATE. For example, it is expected that the annual environ-mental impacts of the construction, maintenance and demolitionof a waste facility will be lower if the life span of that processis greater. Finally, the time horizon boundary for sequestratedbiogenic carbon, when considered by the model, is based overa 100-year perspective of storage, except for WRATE(20,000 years).

4.2.3. Upstream and downstream boundariesUpstream boundaries are the environmental aspects associated

with the extraction, manufacturing, distribution and use of a givenproduct prior to its end of life. When modelling waste LCA systems,a ‘‘zero burden’’ assumption is taken, indicating that no imbeddedimpact is included in the waste modelled. The imbedded impactsare all the impacts generated from the production of a product be-fore it becomes a waste. All the models reviewed included the zeroburden assumption.

Some models, such as EASEWASTE, MSW-DST, WISARD andWRATE include the LCI of some upstream ancillary materials usedfor the operations of the plants (i.e. lime for air pollution controlduring waste combustion). The level of inclusion of upstreammaterials and the details of their respective LCI are different amongthe different models because of the different LCI databases usedand the specific technical requirements modelled.

One of the criteria of importance concerning system boundaries,that could significantly affect LCA results, is the definition of theboundaries of the materials and the energy recovered followingwaste management treatment, otherwise called downstream sys-tem boundaries. In the ORWARE model, the downstream bound-aries are part of the background system or the compensatorysystem. These downstream boundaries refer to the system ex-change in Fig. 1.

In IWM2, the downstream boundary for material recovery is de-fined when the material is leaving the material recovery facility(MRF). Subsequent stages (transport of separated materials leavingthe MRF and preparation and reprocessing of the secondary mate-rials) are excluded from the waste management system and there-fore outside the system boundaries.

In other models, the boundaries are extended to the transportand reprocessing stage of the materials separated by the wastemanagement system, and include avoided impacts from virgin pro-duction. The substitutional LCI value of downstream materials andenergy production is different among the different models becauseof their different geographical development and different choicesof LCI databases.

4.2.4. Boundary conditions for cut-off criteriaThe boundary conditions between significant parts and insignif-

icant parts, within a waste management system, are defined by theconcept of cut-off rules. According to ISO 14040 (2006), cut-offrules should be clearly understood and described. It is defined bythe amount of material or energy flow or the level of environmen-tal significance associated with unit process or system to be ex-cluded from a study. A cut-off rule on time horizon is also usedin LCA after a certain time period where cumulative impacts areconsidered to be insignificant (Finnveden, 1999b). In contrast, Fris-chknecht et al. (2004) have considered that no strict quantitativecut-off rules should be applied and would instead rely on expertjudgement to define the boundaries conditions between the signif-icant and insignificant parts of the system. Cut-off rules applied towaste management were specifically discussed by the Interna-tional Expert Group on life cycle assessment in waste management(Thomas and McDougall, 2003), however no recommendation wassuggested. Cut-off rules are not well documented in the models re-viewed. None of the models, however, support user-defined cut-offrules definition, as the cut-off rules are imbedded in the datasets.

A special case concerns the operational inputs of ancillary mate-rials, the construction, maintenance and decommissioning of theprocess. When one considers these technological boundaries, somedifferences are observed among the different models, as indicatedin Table 2. It is important to note that the more comprehensive theinformation on the technology (usually in the later models), themore realistic, but also the higher the environmental burdenscompared to other models including a more limited set of data.

Table 1Assumptions on leachate and landfill gas emissions.

Model Leachates Landfill gas

EASEWASTE Var. (100 years) Var. (100 years)EPIC/CSR 100 years 100 yearsIWM2 150 l/t waste 250 Nm3/t wasteLCA–IWM 100 years 156 Nm3/t wasteMSW-DST 20, 100 and 500 years 100 yearsORWARE 100 years, remaining time 100 yearsSSWMSS – 100 yearsWISARD 500 years 100 yearsWRATE 20,000 years 150 years



This includes the comprehensiveness of both the input and outputdata. This aspect needs to be carefully assessed, as it could explainthe differences observed when comparing models.

Table 2 indicates that only two models (WISARD and WRATE)include the environmental contribution of construction, mainte-nance and decommissioning of waste management processes,while EASEWASTE includes the LCI of soil transport to landfillsand MSW-DST includes the emissions of excavating and transport-ing daily cover material to the landfill. This could be explained bythe fact that it is generally considered that the environmental loadof construction, maintenance and decommissioning of a process isinsignificant compared to the direct emissions and the avoided im-pacts of a process. However, it has been argued by Frischknechtet al. (2007) that capital environmental costs could have a signifi-cant contribution in LCA studies, including waste managementprocesses, especially when environmental regulations are alwaysrequiring lower emissions during operations, which tends to in-crease the relative contribution of these capital goods.

4.3. Waste composition and properties

Knowledge on waste composition is the cornerstone of solidwaste LCA modelling because material fractions defining the wastehave different chemical and physical properties and can be direc-ted to different processes in the waste management system. Thewaste composition and chemical composition of each fractioncan furthermore vary from one region to another and from 1 yearto another, which can have a significant impact on the results.

Models have different assumptions on the definition of thewaste composition or waste characterisation, due to unavailabilityof international standards for characterising waste. However, theassumptions are similar for models originating from the samecountry.

EASEWASTE, MSW-DST and WRATE have the highest number offractions defined. All the primary waste fractions (wood, paper,etc. . .) are included in all the models, but the secondary fractionalcomposition (newsprint, cardboard, etc. . .) is different among themodels. Further, the elemental fractions (zinc, cadmium, etc. . .)may vary due to the type of elements analysed and the differentcompositional analyses methods among the countries of originfor the models.

Other waste properties (calorific value, ash content, moisturecontent, etc.. . .) are also determining factors for the LCA calculationand results. The main parameters used for the modelling are pre-sented in Table 3.

One of the major differences among the different models is thecalculation of carbon balance. For instance, IWM2 does not distin-guish between fossil and non-fossil carbon. In contrast, EPIC/CSRdoes not account for the emissions of CO2 from biological sources(in reality, biological processes do release CO2 and therefore shouldbe counted in the life cycle inventory but are assigned a globalwarming impact factor of zero). This will have major influenceon the global warming potential of the waste management system.The most detailed description of carbon modelling is found in OR-WARE where the biogenic (non-fossil and organic) carbon is de-fined in five different types (starch, fat, cellulose, protein andsugar). The distribution of biogenic carbon affects the degradationof organic material in anaerobic digestion, composting and landfill.The calculation of the ratio between biogenic carbon and fossil car-bon is also determinant for all the combustion technologies anddepends on how the model was developed. This ratio depends onthe assumptions made for defining the type of carbon for eachwaste fraction and the inclusion of ancillary fuel (i.e. fuel oil, bio-mass,. . .) during the combustion process.

The elemental (chemical) waste composition varies among thedifferent models. First, the list of chemical components of thewaste differs among the studied models. For instance, EASEWASTEand ORWARE include phosphorus (P), while WRATE excludes thiselement, although essential to the calculation of the eutrophicationpotential. Conversely, WRATE models silver (Ag) but not EASE-WASTE or ORWARE. The second aspect of elemental waste compo-sition is the actual relative proportion of elements defined for eachwaste fraction. This aspect is more critical for the models usingstoichiometric elemental mass balance, since the elemental com-position of the waste will have a direct impact on the direct emis-sions (waste dependent emissions). This is of particular importancefor heavy metals. For example, in EASEWASTE, the chromium (Cr)content of leather shoes is 0.46% TS, while it is 0.09% TS in LCA–IWM. In EASEWASTE and MSW-DST the elemental composition istaken into account for the combustion processes (mostly for heavymetals and carbon type), and a user can enter values for each wastetype. WRATE includes waste specific emissions for all its processes,including landfills.

While the fractional composition of waste can and should bedetermined by the end-user (even though default data exist), theelemental composition, more complex to determine, is usuallyembedded in the model, where the end-user has little or no possi-bility to change the data. The elemental composition is unlikely tochange significantly within a given model. Differences in elementalwaste composition have been observed among the models and in

Table 2Technological boundaries of waste LCA models.

EASEWASTE EPIC/CSR IWM2 LCA–IWM MSW-DST ORWARE SSWMSS WISARD WRATE

InputsMSW Y Y Y Y Y Y Y Y YFuel Y Y Y Y Y Y Y Y YMaterials Y N N Y N Y Y Y YWater Y Y N N N Y Y Y YEnergy Y Y Y Y Y Y Y Y YConstruction Ya N N N Ya N Y Y YMaintenance N N N N N N N Y YDecommissioning N N N N N N N Y Y

OutputsEnergy Y Y Y Y Y Y Y Y YProducts Y Y Y Y Y Y Y Y YConstruction N N N N N N Y Y YMaintenance N N N N N N N Y YDecommissioning N N N N N N N Y YDirect emissions Y Y Y Y Y Y Y Y Y

a Emissions data only for daily cover (extraction and transport).



order to harmonise the data, it is essential, as concluded by Burnley(2007), to develop a standardised waste characterisation method-ology: this would reduce significantly the relative spread of resultsand, consequently, minimise the error propagation at variousstages of the model. This is particularly relevant for the calculationof human toxicity and eco-toxicity impacts.

4.4. Energy aspects

The choice of electricity mix plays an important role in thewaste LCA results (Curran et al., 2005; Finnveden et al., 2005).The mix is dependent on the country and is evolving every year.The calculation of the LCI of the energy mix is outside the scopeof this paper. However, it is important to understand the differentmethodological approaches undertaken by the different models.

The differences among the models are based on several levels.First, the type of energy carriers differs among the models due tothe national specificity of energy sources. Second, the LCI for a gi-ven energy carrier is different due to the geographical specificity.For instance, the LCI of coal is different depending on where it ismined. The third level is the calculation of the energy mix. In OR-WARE and WRATE, each energy source is displayed and their ratiocan be user-modified. In the WRATE model, the power generationefficiency and the low, medium and high voltage transmissionlosses (based on the average technology type within a country)can also be defined by the user. Default energy data have beenforecasted for each year until 2020 in the WRATE model, for anumber of countries. In contrast, in EASEWASTE, MSW-DST andWISARD, the energy mix has been compiled and includes energyproduction efficiency, transmission losses and all the sources of en-ergy carriers in each dataset.

For EASEWASTE, ORWARE, WISARD and WRATE models, thereis a distinction between heat production and electricity productionfrom waste treatment technologies. This is reflected in differentoffsets from the energy from waste plants. For EASEWASTE andORWARE, it is also possible to use different mixes or fuels for heatand electricity simultaneously. For instance, wind-power used forinternal electricity use in an incineration plant and hydro powerin an anaerobic digester, while the generated electricity substituteshard coal. In contrast, in MSW-DST, WISARD and WRATE, the de-fault regional/national fuel mixes or user-defined fuel mix can beused for all the MSW processes, and the energy generated can off-set a different default or user-defined energy mix.

The type of energy sources used by the different models usuallyrepresents the energy mix for the countries where the model was

originally designed. Further, for electricity, the mix is calculated ona national or trans-national level, while heat mix is calculated on alocal or regional level. One of the key factors for the use of the wasteLCA applications is the ability for the user to choose country-specificenergy mix and the possibility for the user to modify it if needed.

The main assumptions for the modelling of energy mix are therelative contribution of each energy source to the mix, the type ofenergy carriers included in the models, the transmission losses andthe generation efficiency of power plants. All the models reviewedcan accommodate for the use of a mix or a single energy carrier, ex-cept LCA–IWM, since the user can only define the energy mix LCI ofa specific country but not specific energy sources (e.g. coal, oil,etc.). Similarly, general users of SSWMSS cannot edit the definitionof the energy mix.

4.5. Waste management processes

Modern waste management systems include an increasingnumber of technologies that should be represented by waste LCAsoftware for the appropriate modelling of waste and resource man-agement. Defining a waste management process in a LCA contextrequires a time consuming and labour intensive collection of data.This is the reason why a certain number of default data areincluded in waste LCA models. Seven main categories are usuallyincluded in the models; collection, transportation, intermediatefacilities, recycling, thermal treatment, biological treatment andlandfill. Further, a number of more specific processes (gasification,pyrolysis, bioethanol production, etc.. . .) representing existing ormodelled processes are included in the most recent models.

When comparing different waste LCA processes among differ-ent models, one has to make the distinction among the assump-tions (e.g. type of energy substituted, material substitution ratio),the type of technologies used in the comparison (e.g. moving grateor fluidised bed incinerators) and the external inventories used asmodel inputs (the LCI of virgin and recycled material may be differ-ent in the USA relative to the UK). This is illustrated in Fig. 3, wheredifferences among models are expected for each stage, leading toexpected variation in results among the models. Only the technicalassumptions are addressed in this paper. Further, when comparingseveral models, a distinction should be made between the func-tionality of the model and database availability.

4.5.1. Collection and transportThe first step of waste collection is characterised by the collection

containers (bins and bags). LCA–IWM includes basic information on

Table 3Main waste properties of selected waste LCA.

EASEWASTE EPIC/CSR IWM2 LCA–IWM MSW-DST ORWARE SSWMSS WISARD WRATE

Waste streams Y N N N Y Y Y N 11Waste fractions 48 7 9 11 48 22 13b 34 67Elemental composition 30 N N 18 17 39 8 26 26Total solids Y N N Y Y Y N N YCalorific value (LHV) Y N Y Y Ya Y N Y YAsh content Y N Y N Y Y Y N YMoisture content Y N Y Y Y Y Y Y YVolatile solids Y N N N Y Y Y N YTotal carbon Y N Y Y Y Y Y N YCarbon biological (% TS) Y N N Y Y Y Nc Y YCarbon fossil (% TS) Y N N Y Y Y Y Y YFibres (% TS) N N N N Y N N N NProteins N N N N N Y N N NCOD (% TS) N N N N N Y N N NFat (% TS) N N N N N Y N N NMethane potential Y N N Y Y Y N Y Y

a MSW-DST uses the Higher Heating Value (HHV).b Number of default fractions with composition information in the database.c Biodegradable carbon.



the manufacturing of bins. The user can choose among five differenttypes of container, but no inventory is assigned to the construction ofthe containers (similar to EASEWASTE and MSW-DST). The life timeof the waste containers can also be defined in LCA–IWM. WISARDand WRATE have the most comprehensive LCA information of wastecontainers (34 types in WRATE), which enables waste managers toundertake comparative analysis of different container types. It in-cludes the manufacturing inputs and outputs, the life span of thecontainers and maintenance requirement, such as the quantity ofdetergent for washing bins and the end of life. In IWM2, the hotwater used for washing the bins is considered to be a significantparameter in the LCI of collection system. No collection containerinventory data has been included in the other applications reviewedprobably because it is assumed that the contribution of collectioncontainers to the overall waste management system LCA isinsignificant.

Two groups of transport models have been identified in the re-view called mechanistic and deterministic modelling. Mechanistictransport modelling is based on a large number of user-defined in-put parameters used to calculate the total distance and fuel con-sumption of the vehicles in the system. Emissions are thendetermined from these calculated values (LCA–IWM, MSW-DST,ORWARE and SSWMSS). For instance, MSW-DST calculates thenumber of trucks required to collect a specified amount of wasteat a specified frequency and then estimates the distance travelledbased on a number of input parameters that characterise the route(e.g. time from garage to start of route, time between dwellings,etc.. . .). In contrast, deterministic transport modelling uses onlythe total distance and fuel consumption as a user-defined inputparameter and emissions are determined from these input values(EASEWASTE, IWM2, WISARD and WRATE). This modelling distinc-tion makes comparison among models more difficult.

Transportation is usually distinguished between waste collec-tion (vehicle going from the households to the central waste man-agement facility, including or not a transfer station) and theintermodal transport among waste management facilities. Defin-ing fuel consumption for waste collection and transportation is akey feature when including transportation in the model. Fuel con-sumption depends on the collection route, type of dwellings, trans-port mode (road, train, and water). Fuel consumption for transportalso depends on load size (weight and volume), distance and typeof road transport (rural, motorway, and urban). Emissions factorsare mainly dependent on the efficiency of the engine, the type offuel used and the transport pattern. For collection and intermodaltransportation, EASEWASTE requires only a fuel consumptionparameter to be entered by the user, calculated in l/t (collection)and l/km/t (intermodal). In comparison, the LCA–IWM model in-cludes 243 input parameters, with some default data. MSW-DSTand ORWARE also have a similarly high number of input parame-ters including distance, maximum load, normal load, staff andaverage velocity (for cost calculation), and specification if emptyor full on return transport (for allocation). Other relevant data suchas emission factors, fuel consumption can be changed by the end-user if necessary. The issue of such approach is the requirement for

the user to key in many parameters, which must be balancedagainst the flexibility to more closely represent site-specific sce-narios and potential changes to a collection system (e.g. new trucksor fuels).

The fuel consumption and direct emissions are highly depen-dent on the drive pattern. In WISARD and WRATE, three types oftransport patterns can be entered by the user (rural, urban andmotorway), which reflects the default consumption and emissionsfactors of the vehicles. In EASEWASTE, it is possible to create a newvehicle with a consumption pattern reflecting the drive pattern(high population density area versus low density area). Furthermodel specificity from EASEWASTE and MSW-DST can be identi-fied, where a distinction is made on the origin of the waste col-lected (single family, multifamily and commercial origins) whichinfluence the type of vehicles and collection route used and theirassociated fuel consumption and emissions. Finally, WISARD andWRATE include environmental aspects of manufacturing, mainte-nance and decommissioning for the collection containers and vehi-cles, while it is excluded in other models.

4.5.2. Intermediate facilitiesIntermediate facilities are defined differently in different mod-

els but are typically facilities where waste is stored temporarily,such as transfer stations, material recovery facilities and recyclingbanks. The environmental performance of transfer stations ismainly due to the use of various compactors, conveyors and rollingstock. The environmental performance of material recycling facili-ties is determined by the electrical energy required to operate var-ious pieces of equipment (e.g. eddy current separators, shredders),the use of fuel for rolling stock within the facility and the propor-tion of rejected waste material (contaminated recyclables andnon-recyclable materials that should be disposed to landfill orincinerated). EASEWASTE, MSW-DST, SSWMSS WISARD andWRATE include separation efficiency factors that take into accountthe amount and composition of rejected material. In the EPIC/CSRtool, the MRF or the transfer station module includes electricityand diesel consumption (user-entered parameters and default val-ues) and the proportion of rejects for the MRF. However, the com-position of the rejects is undetermined. The module is thereforenot sensitive to a variable waste composition. It is expected thatthe environmental performance of a MRF is linked to the intensityof material segregation, independent of the waste quantity (thehigher the mechanised separation, the higher the energy inputs,and therefore the higher the environmental impact expected).MSW-DST includes a modular approach to a MRF design i.e. the en-ergy requirement of a trommel, conveyer, magnet, baler, and bagopener are considered separately and combined in a larger module.In EASEWASTE, the MRF module is designed with a known numberof solid outputs, with a known energy consumption. Changing thenumber of outputs will require a change by the expert user of theenergy consumption of the module, to reflect the added machinery.In WRATE, the default MRF module calculates the energy con-sumption of the process based on the quantity of waste input,however it is not sensitive to an increase in fraction separation(i.e. the user decides on the number of separated fractions for agiven MRF, without change to the energy consumption of thatMRF). However it is possible to create a new user-defined processreflecting a change in the number of fractions separated.

ORWARE have no default models for separation. Facilities thatdo not change the chemical properties of the waste, like a MRF, amill or fuel preparation can, however, be constructed in the model.

4.5.3. Material recyclingThe modelling of material reprocessing is undertaken by sub-

tracting the LCI for the remanufacturing of secondary materialsfrom the LCI for the production of goods from virgin sources.

Waste ManagementProcess

Inventories used

Technology Type

Technical Assumption

stuptuOstupnI

Fig. 3. Aspects of a waste management process to be considered in waste LCAmodel comparison and potential sources of differences among models.



EASEWASTE, LCA–IWM, MSW-DST, SSWMSS, WISARD and WRATEinclude respectively 11, 6, 6, 7, 24 and 26 recycling treatment facil-ities in their default database. LCA–IWM provides additional infor-mation on waste from electrical and electronics equipment (WEEE)recycling.

A major difference among the models is the assumptions madeconcerning the substitution ratio between virgin and recycledmaterials. This is considered to be essential for the reprocessingmodelling as indicated by Wenzel and Villanueva (2006b). EASE-WASTE, MSW-DST and ORWARE provide a transparent user inputsubstitution ratio, while the recycling processes of WISARD andWRATE have a default substitution ratio (1:1) but this can be mod-ified by the expert user in the allocation formula of the process.Substitution ratio is often highly dependent on the type of recy-cling technology (newer processes will tend to have substitutionratio closer to 1:1). Furthermore, the ratio is also inherent of thetype of material recycled (the substitution ratio is different for pa-per and glass). WISARD provides information on the substitutedamount for each of the material processes, which is fixed for thestandard user. EPIC/CSR and LCA–IWM consider implicitly a 1:1 ra-tio. IWM2 has excluded the environmental aspects of materialrecovery from its boundaries. It is important to be aware of this ap-proach when using such a tool, as this will lead to significant differ-ences when comparing with other models.

Another key aspect of the material recovery performance is themodelling of the transport of the secondary material following sep-aration (MRF) prior to the actual reprocessing (distance but alsotype of transport such as roads, rail, river). Transport distancesprior reprocessing can be significant, such as secondary paper ex-ported to China from Europe. Some models like EASEWASTE, OR-WARE, WISARD, WRATE and can implement this requirementwith relative ease.

4.5.4. Thermal treatmentThermal treatment in MSW management includes waste inciner-

ation, pyrolysis and gasification. All of the models reviewed includewaste incineration. Recently emerged technologies including pyro-lysis and gasification are included in ORWARE and WRATE. In thissection, only waste incineration is analysed.

The essential technical assumptions to be compared in theincineration process models of the different tools are the calcula-tions of the calorific value and moisture content, the type of energyproduced and its efficiency and the type of energy sources substi-tuted. The transfer coefficients are also of paramount importance.Finally the determination of the solid output (bottom ash, fly ashetc.) quantity and composition is essential. The key aspects of ther-mal treatment parameters are summarised in Table 4.

Most of the models have created generic incineration plant butdifferent assumptions have been taken for the LCA modelling. InMSW-DST, the fly ash is collected, mixed with the bottom ash,and sent to a separate ash landfill. In Europe fly ash and bottomash are most often managed separately. This is reflected in themodels’ design.

One of the fundamental factors for determining the environ-mental performance of an incinerator is the energy (electricityand heat) recovery efficiency. The higher the energy recovery, thehigher the offset of other energy sources and therefore the higherenvironmental savings. All reviewed models include the energyconsumption and energy production. The produced energy canbe offset against marginal energy mix for some models, such asEASEWASTE, IWM2, LCA–IWM, MSW-DST, ORWARE, WISARDand WRATE, except EPIC/CSR. The type of energy offset (heat, elec-tricity) and the type of energy source used and offset are determi-nant for the results and a key parameter when comparing wasteLCA models.

The calculation of the lower heating value (LHV) plays a criticalrole in many incineration equations (quantity of energy produced,quantity of additional fuel used, quantity of flue gas produced,. . .).The calorific value is directly related to the composition of thewaste entering the incinerator. The calculation of the LHV is under-taken differently among the different models. In LCA–IWM, thisparameter is calculated based on the carbon, hydrogen, nitrogen,sulphur, oxygen and water content of the waste, according to theCerbe and Hoffmann (1994) equation. In SSWMSS, the parameteris calculated based on the carbon, hydrogen, sulphur, oxygen andwater content of the waste according to the Steuer (1926) equa-tion. In EASEWASTE, the calorific value was determined by analys-ing the LHV of 48 waste fractions (Riber et al., 2009); the combinedLHV is then calculated, based on the fractional composition ofthe waste. In IWM2, LCA–IWM, MSW-DST, ORWARE, WISARDand WRATE, the same approach has been employed, however thefractional calorific values are based on literature, rather thanmeasurement.

Another important factor for understanding the environmentalperformance of incineration is the emission abatement efficiency,which is related to the composition of waste and the quantity ofancillary materials (lime for acid gas neutralisation, ammonia forNOx reduction, activated carbon for mercury abatement). Abate-ment efficiency also depends on the technology, which needs tobe described in details to ensure transparency and enable modelcomparison.

According to Harrison et al. (2000) flue gas production pertonne varies considerably from component to component. In theMSW-DST model, the flue gas production per tonne of waste com-ponent is based on a stoichiometric combustion equation for theMSW components and relies on ultimate analysis studies that pro-vide the carbon, nitrogen, hydrogen, oxygen, sulphur and chlorinecontents of the waste constituents. In the MSW-DST, some emis-sions are estimated on the basis on stoichiometry alone while oth-ers are estimated on the basis of stoichiometry coupled withemitted concentrations of individual pollutants (e.g. SOx). TheMSW-DST offers significant flexibility as a user can input alternatestack gas concentrations where justified by specific data. In WI-SARD, air emissions are based on user-defined site-specific concen-trations multiplied by the volume of flux gases, based on the LHVof the waste.

IWM2 uses a similar modelling approach to the MSW-DST forair emissions, corresponding to metal based emissions (stoichiom-etric approach involving a combustion equation) and non-metalsbased emissions. The non-metal based emissions are calculatedby multiplying the emissions standard concentration (defined inthe legislation of the studied country) by the volume of flue gasgenerated per tonne of material combusted. For non-metals emis-sions, EASEWASTE includes process specific emissions, propor-tional to the quantity of waste input, relying on measured data,as well as literature data (Riber et al., 2008). The EPIC/CSR modelallows the user to define the direct emissions from the incinerator,which is interesting because the model could be applied to a spe-cific plant with known emission data. However, the model is notwaste composition sensitive (e.g. a change of waste compositiondoes not affect the emissions). SSWMSS defines direct non-metalsemissions by the air pollution control equipment.

The environmental performance of incinerators also depends onthe modelling assumptions made for the calculations of the quan-tity of fly ash and bottom ash produced, their chemical composi-tion and the specific transfer coefficients of the pollutants amongthe bottom ash, fly ash, wastewater and direct emissions. Thishas been described by Riber et al. (2008) and implemented in theEASEWASTE model. Transfer coefficients have also been used inthe LCA–IWM model, although these transfer coefficients are sig-nificantly different from the EASEWASTE coefficients, which could



lead to significant differences in the LCA results. This is particularlyrelevant for zinc (Zn), mercury (Hg) and cadmium (Cd). The OR-WARE model also uses transfer coefficients.

The calculation of the environmental impacts of the differentashes (transport and landfilling) plays an important role on theevaluation of the environmental performance of incinerators (Ast-rup et al., 2006). The modelling assumptions for the determinationof incinerator ashes composition and their leaching properties inlandfill are different for the different models reviewed. This canbe explained by the release dates of the applications, where theunderstanding of these parameters was not as clear in the earliermodels. EASEWASTE and ORWARE include a more sophisticatedmodelling of the fate of fly ashes, where a detailed elemental massbalance is undertaken among the incoming waste composition andquantity, the ashes composition and the transfer coefficients. Incontrast, WISARD and WRATE generate a generic ash compositionbased on a default waste composition, which means that a varia-tion of waste composition will not change the ash compositionand its behaviour in a landfill. For LCA–IWM, MSW-DST andWRATE, the quantity of ash is calculated based on the inert ashcontent of each waste fraction, while the chemical composition isfixed. The ash stabilisation process prior to landfill is excluded inall the models, except in EASEWASTE and ORWARE.

It should be noted that the air pollution control equipmentneeds to be replaced from time to time, during the life time ofthe incinerator. This constitutes a source of solid waste (potentiallyhazardous) that will require disposal to landfill. Disposal of air pol-lution control equipment has been excluded from all models, ex-cept for WRATE.

A specific issue to waste incineration is the modelling of bio-genic and fossil CO2 emissions. Earlier models did not distinguish

the origin of the CO2 emitted (IWM2), and thus overestimatingthe impacts from CO2 emissions (all CO2 emissions are included).In the other models, a distinction is made between the two typesof carbon but the assessment of the ratio between biogenic andfossil CO2 emissions differs among the different models mainlydue to the different assumptions in the waste composition.

4.5.5. Biological treatmentBiological treatment of waste (composting and anaerobic diges-

tion), is becoming more widespread for the management of thebiodegradable component of municipal waste (i.e. food and greenwaste). All the different waste LCA applications reviewed have abiotreatment module, although MSW-DST does not include anaer-obic digestion because of the low occurrence of this technology inthe USA at the time the DST was developed.

The different system boundaries assumptions among the differ-ent models can lead to different results as described by Hansenet al. (2006). Some models calculate the composition of the bio-treated material based on the waste composition (EASEWASTE,LCA–IWM and ORWARE). For EPIC/CSR, it is estimated that the pro-duced compost would offset 10% the total CO2 emissions, due tothe avoided emissions of GHG from fertilizer and peat production.No other emission is modelled from compost in EPIC/CSR. ForMSW-DST, complete decomposition is modelled where no CO2 isfurther emitted because it is argued that decomposition of com-post will continue after it is applied to land (Komilis and Ham,2000). In contrast, in EASEWASTE and WRATE, a separate landuse module for the modelling of biotreated materials is includedin the model. For IWM2, MSW-DST, WISARD and WRATE, typicalcompositions of compost are pre-defined in the models, with avarying degree of detail. Models that are waste composition

Table 4Main incineration parameters.

EASEWASTE EPIC/CSR IWM2 LCA–IWM MSW-DST12l ORWARE SSWMSS WISARD WRATE

Process related emissions Y N Y Y Y Y Y Y YInput related emissions Y Ya Y Y Y Y Yi Y YElectricity recovery efficiency Y Y N N Y Y Y Y YSteam recovery efficiency Y Y N N N N N Y YUser-defined energy efficiency Y Y N N Y Y N Y NDistrict heating offset Y Y N Y Nb Y N Y YMarginal energy Input Y N N N Y Y N N NMarginal energy output Y N N N Y Y N N YAverage energy mix input Y Y Y Y Y Y Y Y YAverage energy mix output Y Y Y Y Y Y Y Y NAncillary materials Y Y ? Y Yc Y Y Y YElemental mass balance Y Y N Y Y Y N N YBiological and fossil carbon Y N N Y Y Y Y Y YTransfer coefficient Y N N Y N Y N Yh NFly ash Y Yd Ye Y Y Y Y Y YBottom ash Y Yd Ye Y Y Y Y Y YTransport of ashes Y Y Y N Y Y Y Y YDisposal modelling of ashes Y N N Y Y Y N Y Yf

Recycling of ashes Y N N N N N Yj Y YUser-defined ash quantity Y Y N N N N N Y Yf

Waste related ash composition Y N N N Y Y N N NWaste related calorific value Y N Y Ng Y Y Yk Y Y

a Input related emissions only for CO2.b District heating is not widely available in the US, so this feature was not implemented in the model.c While the ancillary material is included in the calculations, it is not known whether the LCI of these materials have been included.d The ash composition is calculated based on the ash content of the waste fractions. By default 10% of the total ash is fly ash. About 5% of ash is added to take into account

ash production from ancillary materials.e In IWM2, the quantity of fly ash is determined by the ash content in each waste fraction. The composition of the ash is fixed.f In WISARD and WRATE, expert users can add a certain quantity of ash by creating a new user-defined process. The modelling of ash in the landfill has been done based on

default composition and modelled using LandSim.g The calorific value is defined by the Cerbe and Hoffmann (1994) equation.h WISARD calculates the carbon balance before calculations during the consistency tests and informs the user if carbon is unbalanced.i Input related emissions only for CO2 and CH4.j Recycled as molten slag.k The calorific value is defined by the Steuer (1926) equation.l Details described in Harrison et al. (2000).



sensitive (EASEWASTE and ORWARE) tend to be more realistic andmore precise than other models.

The key parameters used in the calculation of the compositionof the biotreated materials include specific waste properties, suchas the determination of C, N, P, K, used in the carbon managementand the fertilizer substitution calculations. The concentration ofheavy metals in the incoming waste is also used in the calculationof the biotreated material composition. For each of the elementsused in the calculation, transfer coefficients need to be defined toassess the elemental distribution in each environmental compart-ment (e.g. N loss in air emissions or P leaching in the groundwater).Only EASEWASTE and ORWARE provide this functionality. In theEASEWASTE model, it is assumed that all the heavy metals con-tained in the waste are transferred to the compost composition(it is assumed that no leaching during the treatment is occurring).The volatile solids (VS) and their degradation for each waste frac-tion are used in the EASEWASTE model. These parameters are in-cluded for the calculation of carbon emitted to air as CO2 or CH4,depending on the type of process and type of management. Mate-rial substitution (NPK fertilizer, peat, wood chips, straw) is alsocalculated (EASEWASTE, ORWARE, WISARD and WRATE) but spe-cifically excluded from the modelling in MSW-DST because thewaste treated in MSW-DST did not produce a material of sufficientquality to offset a fertilizer. In addition, ORWARE uses transfercoefficients to model the degradation of organic persistent pollu-tants (CHX, AOX, PAH, phenols, PCB and dioxins). The anaerobicdigestion module in ORWARE is based on the calculation of the dif-ferent carbon degradation, associated with hydraulic retentiontime factor.

The modelling of biotreatment processes is also differentiatedby the assumptions made concerning the biogenic carbon seques-tration. EASEWASTE calculates the quantity of biogenic carbonsequestered, estimated to be between 10% and 15%, dependingon the soil type, over a 100 year period. EPIC/CSR includes carbonsequestration but only for paper recycling (e.g. increased forestsequestration due to reduced demand on virgin paper) and landfill,but exclude sequestration from biotreatment processes. Carbonsequestration is not included in IWM2. In LCA–IWM, sequestrationis excluded from landfill but included in the soil application fromcompost (8.2%, over a 100 year period). It is possible to modelthe carbon sequestration in ORWARE, which is set at different val-ues for sequestration to soil (from land spreading), where 20% ofthe biogenic carbon is assumed bound to the soil and 3–10% ofthe biogenic carbon is assumed sequestered in the landfill. Thesevalues are currently been updated. In WRATE, sequestration is as-sumed to be 2% for soil application. These different assumptionswill generate differences in the LCA results and need to be ad-dressed when comparing waste LCA models.

The retention time parameter (processing time for biotreat-ment) is correlated to the level of degradation of the organic waste(the longer the retention time, the higher the emissions at thetreatment plant). In WRATE, the user can modify the retentiontime of a process for biotreatment process. In WISARD, a carbondegradation coefficient is defined for composting. Biogasification(anaerobic digestion) is calculated based on production of biogasper waste fraction. In EASEWASTE, a degradation factor is user-en-tered, based on the degradability of the 48 waste fractions (% deg-radation of VS). This approach enables the model to be sensitive towaste composition. A mass balance is used to estimate metals inthe compost product based on the metals content of the wastematerial, although no transfer coefficient is defined for the heavymetals distribution to the different environmental compartments.The carbon emissions are based on the quantity of degraded car-bon (calculated or user-entered). Methane emissions are calcu-lated as the percentage of methane produced from the degradedcarbon.

Energy consumption (mainly for composting processes) and en-ergy production (for anaerobic digestion processes) assumptionsare also critical when comparing different models but also whencomparing different processes within the same model. Theassumptions will have a major influence on the overall environ-mental performance.

One of the aspects that could lead to difference among the mod-els is the level of inclusion of the substances emitted by a wastemanagement process (this is true for all waste management mod-ules). For example, some models (EASEWASTE and ORWARE) in-clude N2O and CH4 emissions from composting facilities whileothers consider these substances to be insignificant and thereforeexcluded from the calculation.

Finally, construction, maintenance and decommission are mod-elled in WRATE and in WISARD but not in the other models.

EASEWASTE and ORWARE have the most advanced approachfor the modelling of organic waste, although Hansen et al. (2006)have reported that more complex models have been developedbut these are more specialised for the agricultural sector. EASE-WASTE and ORWARE include a specific module defining the agri-cultural profile where the biotreated material is spread on land.The agricultural module provides a number of soil and crop types,the definition of the nitrogen distribution and the carbon bindingproperties (for carbon sequestration). ORWARE includes energyconsumption and emissions from spreading of organic fertilisers.In ORWARE the utilisation of the biogas is a separate submodelwith various choices for the energy recovery (engine, boiler, bus-ses, cars and trucks).

4.5.6. LandfillThe environmental aspects associated with landfills are proba-

bly the most researched waste management process, despite thefact that landfill modelling remains the most challenging due tothe uncertainties associated with emissions over very long timehorizons. Mainly three approaches have been used in the model-ling of landfill in a LCA context. For WRATE, it is assumed that95% of all emissions to the environment are modelled in order tobe consistent with the emissions of other waste management pro-cesses (Hall et al., 2005). Alternatively, other models consider atime limitation for the release of emissions to 100 years as default(EASEWASTE, MSW-DST and WISARD), but can be modified. This isusually called the surveyable time (Finnveden et al., 1995), wheremost of the emissions of the easily released substances are as-sumed to have occurred. Finally, some models are including short(0–100 years) and long-term (100 years to infinity) emissions thatcannot be adjusted (ORWARE).

In WRATE, the landfill leachate time horizon has been calculatedover a period of 20,000 years to include most of the emissions. Whilethe level of uncertainty is rather high due to possible changes in thestructure of the landfill and climatic changes over this period, mostof the inputs to the LandSim model (Gronow and Harris, 1996) havebeen defined as probability density functions, describing the rangeand type of uncertainty associated with input parameters andenabling a probabilistic approach of leachate emissions during thisperiod, assuming that landfill structure and liner failure rate areknown and environmental parameters are constant (Hall et al.,2005).

MSW-DST provides three time horizons for the modelling oflandfill emissions, which can be selected by the user, a short-termtime frame (20 years) corresponding to the landfill’s period of ac-tive decomposition, and intermediate-term time frame (100 years)and a long-term 500 years).

EASEWASTE, IWM2, MSW-DST, ORWARE and WISARD, includeall the landfill modelling calculations within the LCA models them-selves. Whereas in WRATE, a significant amount of the landfillmodelling has been undertaken through the use of GasSim for



landfill gas modelling (Attenborough et al., 2002) and LandSim foremissions to groundwater (Gronow and Harris, 1996). The advan-tage of the modelling approach used in WRATE is to enable high le-vel of modelling complexity without compromising the modellingspeed of the LCA software. The disadvantage of this approach is therequirement to run the third party landfill model and transfer thedata to the LCA model (this can only be performed by reassemblingthe software).

Despite this methodological approach, WRATE can model thelandfill emissions of all specific fractions of the municipal wastethrough the use of a loading factor attributed to each waste frac-tion determined through the external modelling (Hall et al.,2006). This loading factor is the environmental ‘‘load’’ carried withthe waste fractions that will be emitted from the landfill when thewaste is placed in the cell. This approach enables the model to cor-relate the leachate composition and quantity to a varying wastecomposition.

4.5.6.1. Landfill gas. In MSW-DST, the landfill gas collection effi-ciency and extent of methane oxidation can be varied annually.In LCA–IWM, the gas collection efficiency default is 30% duringthe operation phase and 70% after landfill closure. Due to the mod-elling approach used for the landfills in WRATE, the gas collectionis based on the optimum utilisation of the landfill gas over the150 years of landfill gas production. For instance, the model calcu-lates the quantity of gas generated every year and determinesautomatically whether the quantity and quality of the gas is suffi-cient for use in a landfill gas engine or whether it should be flared(Hall et al., 2005). It is therefore not possible for the user to model alandfill with flaring only or with no gas collection system. In addi-tion, it is not possible to add a new gas treatment technology forthe management of landfill gas (use of biocover for the oxidationof the fugitive methane emissions). The landfill model developedin WRATE does not allow the user to modify the landfill technology(developing a bioreactor model for example). WRATE includes ageneric landfill model where the user-entered data is limited tothe waste input (quantity and composition), the total capacity,the annual capacity and the type of landfill technology. It is as-sumed that 10% of the fugitive emissions are oxidised throughthe cap in WRATE and 18.5% in WISARD.

In EASEWASTE, MSW-DST and WISARD, the amount of methanegenerated in the landfill is directly related to the methane potentialof the waste landfilled, while the composition of the gas (trace gascomponents) is set at typical values within each period. In EASE-WASTE and MSW-DST, the user can specify the manner in whichthe gas is managed (vent, flare or energy recovery).

4.5.6.2. Leachate. In WRATE and WISARD, most of the leachate issent to a leachate treatment plant, located on the landfill site withcontaminant-specific removal factors (Hall et al., 2005). Similarly,EASEWASTE includes removal efficiency of the leachate collectedand treated in the leachate treatment plant. The removal efficiencyvalues are likely to be different between each model. Leachateemitted to groundwater due to liner failure, and other diffuseemissions are also modelled in WRATE. The removal efficiency ofthe leachate treatment plant cannot be modified in WRATE or WI-SARD, but the leachate quantity and composition are calculatedbased on the incoming waste composition. Rain fall is not consid-ered as a key factor for leachate production since the modelledlandfills are capped following operations. Precipitation is not usereditable in WRATE.

In the MSW-DST, the leachate collection efficiency and thequantity of leachate generated as a fraction of precipitation areuser input with defaults provided. The model also includespollutant specific removal efficiencies during leachate treatment.

However, leachate composition is not directly related to the wastecomposition by a mass balance.

In the EPIC/CSR model, it is possible for the user to modify theleachate collection efficiency system. The default values are 0%for unlined landfills with no collection system, 30% for unlinedlandfill with a leachate collection system and 90% for a lined sys-tem and a collection system (Haight, 2004). Further, the user candefine the leachate treatment plant removal efficiency of the leach-ate volume (but not the composition). In LCA–IWM, the leachate isbased on a function of annual rainfall, which is user-defined.

In EASEWASTE, the amount of leachate generated is set as typ-ical values (mm/year) representing the hydrological conditions(precipitation, evapotranspiration, runoff, etc.) at the site and thecomposition of leachate (main constituents as well as trace compo-nents) is set as typical values within each period. This means thatthe leachate composition is not directly related to the waste com-position on a mass balance basis. Similarly, in LCA–IWM, the leach-ate composition, comprising of 24 elements, changes four timesdepending on the age of the landfill.

In ORWARE, the leachate load/quantity and quality is related tothe waste input. There are partitioning coefficients in the model for45 substances, as described by Björklund (1998).

4.5.6.3. Carbon sequestration in landfill. Landfill can act as a sink forcarbon, where substances buried in landfills are very unlikely to bereleased in the environment within geological timescales.

The carbon sequestration is calculated in EASEWASTE as the dif-ference between the total amount of biogenic carbon entering thelandfill site and the biogenic carbon emitted over a 100 years hori-zon. According to Manfredi and Christensen (2009), the calculatedamount of sequestrated carbon is about 50% of the total incomingcarbon. A distinction is made between biogenic and fossil carbonsequestration in term of the contribution to global warming poten-tial (Christensen et al., 2009). Biogenic carbon is attributed a ben-eficial impact, while the sequestration of fossil carbon has noimpact, nor benefits on climate change. About 50% carbon seques-tration is also assumed in WRATE but no specific LCIA characterisa-tion factor has been included for carbon sequestration. Othersubstances are also assumed to be sequestrated in the landfilldue to various vitrification and fossilisation processes in WRATE.

In the EPIC/CSR model, a sequestration factor is applied for thepaper based waste only. This sequestrated carbon is removed fromthe carbon cycle and therefore subtracted from the inventories(Haight, 2004).

LCA–IWM have specifically excluded carbon sequestration po-tential from the landfill model but included carbon sequestrationin the compost module through the fixation of carbon in the com-post. In WISARD, carbon sequestration is currently not calculated.This may be reviewed depending on discussions at national level.

5. Conclusion

Eight waste LCA models were identified and reviewed to comparethe functional unit, system boundaries, energy modelling, and pro-cess models including collection, transport, separation, materialreprocessing, thermal and biological treatment, and landfilling.

This review has enabled us to understand more precisely thepotential differences in results when models are compared. Oneof the key aspects that could affect comparability of models isthe assumption of the time horizons for landfill emissions. How-ever, the choice of input data and parameters, assumptions madefor defining the waste management processes and the choice ofoutput parameters all have differences that will lead to differencesin the results when models are compared.

The observed differences in modelling assumptions often arelinked to the date of development and the current level of knowledge



at that time. Further, the models have been optimised in theirrespective countries of development and therefore tend to be mostsuitable for studies in the country where they have been designed.The user should pay particular attention when using waste LCA pro-cesses from another country than the country of the study, sincesome country-specific data might be used in the LCI. In the light ofthis, it is expected that differences among the models will arise. Tocompare the validity and robustness of waste LCA models, it is nec-essary to identify, analyse, challenge and harmonise the technicalassumptions included in these models, by using a rigorous frame-work for computer model validation. It is important to keep in mindthe sources of differences among the models, such as input data,technical assumption, technology type, inventories used and outputdata. Despite inherent differences among models, LCA modelling hassignificantly increased our knowledge of waste management systemperformance, allowing us to quantify environmental loads and ben-efits and optimise systems.

Further research would be needed to quantify and prioritise thesensitivity of key parameters among the models in order to at-tempt to harmonise and validate them internationally.

6. Disclaimer

Extensive consultation was undertaken with the developers ofthe models presented in this paper. Efforts have been taken to en-sure factual accuracy for the description of these tools. The infor-mation from the WRATE model is based on the main author’spast involvement in developing the model. Unfortunately, thedevelopers or their agents did not review the factual accuracy ofthe information following requests.

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