modelling municipal solid waste generation a review

Waste Management 28 (2008) 200–214www.elsevier.com/locate/wasman

Review

Modelling municipal solid waste generation: A review

Peter Beigl, Sandra Lebersorger, Stefan Salhofer ¤

Institute of Waste Management, Department of Water, Atmosphere and Environment, BOKU – University of Natural Resources and Applied LifeSciences, Muthgasse 107, 1190 Vienna, Austria

Accepted 27 December 2006Available online 1 March 2007

Abstract

The objective of this paper is to review previously published models of municipal solid waste generation and to propose an implemen-tation guideline which will provide a compromise between information gain and cost-eYcient model development. The 45 modellingapproaches identiWed in a systematic literature review aim at explaining or estimating the present or future waste generation using eco-nomic, socio-demographic or management-orientated data. A classiWcation was developed in order to categorise these highly heteroge-neous models according to the following criteria – the regional scale, the modelled waste streams, the hypothesised independent variablesand the modelling method. A procedural practice guideline was derived from a discussion of the underlying models in order to proposebeneWcial design options concerning regional sampling (i.e., number and size of observed areas), waste stream deWnition and investigation,selection of independent variables and model validation procedures. The practical application of the Wndings was demonstrated with twocase studies performed on diVerent regional scales, i.e., on a household and on a city level. The Wndings of this review are Wnally summa-rised in the form of a relevance tree for methodology selection.© 2007 Elsevier Ltd. All rights reserved.

1. Introduction

Waste management for municipal waste is considered apublic service, providing citizens with a system of disposingof their waste in an environmentally sound and economi-cally feasible way. The amount and composition of wastegenerated comprise the basic information needed for theplanning, operation and optimisation of waste manage-ment systems. The demand for reliable data concerningwaste arising (waste generation) is implicitly included in themajority of national waste management laws. More explic-itly, waste legislation requires assessment of the currentwaste arising and forecasts, such as in Ireland (Dennisonet al., 1996a) and in Germany, where the competent publicauthorities (cities or counties (“Kreise”)) are required toassure “guaranteed disposal” for a period of 10 years inadvance (cf. Sircar et al., 2003).

* Corresponding author. Tel.: +43 1 3189900 319; fax: +43 1 3189900350.

E-mail address: [email protected] (S. Salhofer).

0956-053X/$ - see front matter © 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.wasman.2006.12.011

This entails a demand for reliable information on wastequantity and composition, which is diYcult to achieve on adisaggregated level. Other than in more centralised infra-structures like electricity supply (where the consumption ofeach single end-user can be measured), waste generationcan not be measured directly. Typically, in waste disposalsystems there are several parallel disposal channels (e.g.,public curbside collection; civic amenity sites for greenwaste, bulky waste, etc.; private collectors of, e.g., clothingtextiles; take back by retailers). The waste arising on a sin-gle household basis is measured only in rare situations, e.g.,in areas where Pay-As-You-Throw systems have beeninstalled. Thus waste generation cannot be measured on adetailed basis, which would allow further evaluation of dis-posal habits, changes and trends. In this case modelling is ofparticular importance.

Models and data from models are used in the planningof waste management systems, including:

– the development of waste-management strategies (Dask-alopoulos et al., 1998);

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P. Beigl et al. / Waste Management 28 (2008) 200–214 201

– the planning of waste collection services (Grossmanet al., 1974) and infrastructures (Dennison et al., 1996a)or treatment facilities and capacities (e.g., the capacityevaluation of MSW incinerators by Chang and Lin,1997); and

– land demand for facilities, especially in the context oflandWlling waste (Leao et al., 2001).

For the operation of waste management systems, wastegeneration related planning data have an essential inXuenceon:

– personnel and truck utilisation (Matsuto and Tanaka,1993), as well as operational costs (Grossman et al.,1974) with respect to collection and transportation; and

– monitoring of systems (e.g., assessing eVects of wasteprevention action, recycling activities, etc. (cf. OECD,2004)).

They serve as a basis for further improvements and opti-misation in terms of sustainability (environmental, eco-nomic and societal) targets.

The objective of this paper is to review previously pub-lished models of municipal solid waste (MSW) generationand to propose an implementation guideline which willprovide a compromise between information gain and cost-eYcient model development. Models that focus on the esti-mation, explanation or prediction of the whole, or parts ofthe MSW stream were reviewed. These streams can be deW-ned either according to composition of MSW (regardless ofwhere collected) or to means of collection (separate or com-mingled). Numerous, mainly statistically based modellingapproaches have been published in the literature since 1974,with more than 50 papers addressing the broader Weld ofthis topic published through the end of 2005. In this paper,the classiWcation of these models is described in Section 2,in order to structure the highly heterogeneous approaches.Focussing on crucial design options within the modellingprocedure, Section 3 discusses the beneWts and shortcom-ings of the models. Derived recommendations are summa-rised in a comprehensive guideline. These Wndingsconcerning relevant requirements for waste generationmodelling are then demonstrated with two case studies in amore detailed way focussing on model applications atdiVerent regional scales in Section 4. The conclusions of thisreview are given in Section 5, including a relevance tree formethodology selection.

2. ClassiWcation of waste generation models

To date, policies promoting greater sustainability inwaste management have not been followed by equal eVortsto boost adequate knowledge about waste generation.Climbing up the waste management hierarchy from land-Wlling, energy recovery, and material recycling up to wasteminimisation will lead to increasing data complexity, thusrequiring more detailed information on waste generation

and composition (ParWtt and Flowerdew, 1997). In spite ofthe fact that decision-support orientated waste manage-ment models, such as cost beneWt analyses, life cycle analy-ses and multicriteria decision analyses, have beenestablished over the last decades (Morrissey and Browne,2004), waste generation models, which deal with the under-lying, indispensable planning fundamentals, are laggingbehind and are far from reaching general modelling stan-dards. Due to the multitude of possible research designoptions, a high heterogeneity of models – from purelyapplication-oriented up to highly sophisticated tools – isavailable.

A systematic review of 45 waste generation modellingapproaches revealed four characteristic classiWcation crite-ria: regional scale, type of modelled waste streams, type ofindependent variables and modelling method. Brief descrip-tions in the following chapters aim at presenting represen-tative, as well as notably dissenting, design options. Anoverview of the reviewed studies according to these criteria,as well as other main characteristics, is shown in Table 1.

2.1. Regional scale

The regional scale refers to the size of the smallest identi-Wable sample unit observed in each study. The deWnition ofeach category is based on existing administrative units,except for settlement areas, where the socio-economichomogeneity of each area was considered by the studyauthors. Closely related to this criterion, the data sourcesused for waste-related data and for independent factors aredescribed in the following sections. Table 2 mentions theranges of size and number of observed regional units, aswell as all databases used concerning the independent vari-ables.

2.1.1. HouseholdsHousehold studies enable relationships between waste

quantity and a broad set of individual characteristics orhabits of either the household itself or the household’s rep-resentative to be analysed. Representativeness is strived forthrough appropriate sample sizes (ranging from 40 to 857),which are stratiWed by income (Abu Qdais et al., 1997) orage and education level (Lebersorger, 1998; Pladerer, 1999)or are based on a random sampling contingent on the elec-toral register (Dennison et al., 1996a). Waste generatedwithin the investigated period (due to the high eVortsinvolved limited to 3 weeks and 6 mo) is collected, sepa-rated by 6 up to 36 fractions and documented; these tasksare partly carried out by the participants themselves.Household characteristics are mainly gained by personalinterviews and surveys, as census data are not available onindividual level due to data protection issues.

2.1.2. Settlement areasPositive experience with regard to the relationship

between settlement structure and waste generation charac-teristics (cf. Christiani, 1997) corroborate the selection of

202 P. Beigl et al. / Waste Management 28 (2008) 200–214

homogeneous settlement areas as the sample unit. Homoge-neity of settlement density and dwelling types in a givenarea is assumed to implicitly control variables such asincome, employment status and household size. Theselected areas often correspond to the smallest administra-tive units, e.g., census blocks with several hundred inhabit-ants (Grossman et al., 1974; Lebersorger, 2004) orenumeration districts with approximately 400–600 house-holds (ParWtt and Flowerdew, 1997). An exception with

much larger areas, housing approximately 80,000 inhabit-ants per area, was described by Emery et al. (2003). House-hold waste analyses typically cover the documentation ofcollected waste quantities and sorting analyses of samplesfrom selected collection rounds or containers. Data sourcesfor independent variables cover census data from statisticaloYces, market-research based geo-demographic classiWca-tion packages (e.g., ACORN (cf. ParWtt and Flowerdew,1997)) and questionnaire surveys.

Table 1Characteristics of the reviewed models

n.c. – no comment.HH – Households; DI – Districts; SA – Settlement area; CO – Country. HWF – Household waste fractions; CS – Collection streams; MS – Material streams.C – Consumption-related; D – Disposal-related; P – Production and trade-related variables; GC – Group comparison; CA – Correlation analysis;MR – Multiple regression analysis; SR – Single regression analysis; IOA – Input–output analysis; TSA – Time-series analysis; SD – System dynamics.

a Sorting campaigns in 37 regions.

Reference Regions Time series length Waste streams Independent variables type Modelling method

Type Units Datasets Interval Type Number

Abu Qdais et al. (1997) HH 40 21 d HWF 6 C GC, CABach et al. (2003) DI 1071 – – CS 5 C, D MRBach et al. (2004) DI 649 – – CS 1 C, D MRBecker (1999) SA 6 – – CS 2 C GCBeigl et al. (2004) DI 55 622 y CS, HWF 6 C, D MR, SRBeigl et al. (2005) DI 27 2 8 y CS 1 C SRBogner and Matthews (2003) CO 31 4–7 y CS 1 C SRBogner et al. (1993) CO 13 – – CS 1 P, C SRBrahms and Schwitters (1985) CO 1 – – MS 20 P, C IOAChang and Lin (1997) DI 12 60 m CS 1 D TSAChen and Chang (2000) DI 1 14 y CS 1 – TSAChristiani (1997) SA 33 – – HWF 29 C, D GC, SRChristiansen and Fischer (1999) CO 14 614 y MS, CS 3 P, C TSADaskalopoulos et al. (1998) CO 2 24 y MS 6 C SRDennison et al. (1996a,b) HH 857 – – HWF 36 C, D GC, CADyson and Chang (2005) DI 4 3 10 y CS 1 C SDEder (1983) DI 260 11 6 w HWF 14 C, D GC, SREmery et al. (2003) SA 3 3 w HWF 30 C, D GCEuropean Commission (2002) SA n.c.a n.c. n.c. HWF n.c. C, D GCFranklin Associates (1999) CO 1 39 y MS 10 P, C IOAGay et al. (1993) DI 1 – – MS 5 P, C IOAGrossman et al. (1974) SA 103 – – CS 1 C MRHekkert et al. (2000) CO 1 – – MS 62 P IOAHockett et al. (1995) DI 100 – – CS 1 P, C, D MRJenkins (1993) DI 9 6108 m CS 2 P, C, D MRJoosten et al. (2000) CO 1 – – MS 15 P IOAKaravezyris et al. (2002) DI 1 n.c. n.c. CS 3 D SDKatsamaki et al. (1998) DI 1 260 d CS 1 C TSALeao et al. (2001) DI 1 n.c. n.c. CS 1 C TSALebersorger (1998) HH 50 626 w CS 7 C GC, SRLebersorger (2004) SA 6 – – HWF 10 C, D GCMartens and Thomas (1996) DI 44 4 y CS 2 C, D GC, SRMatsuto and Tanaka (1993) DI 1 365 d CS 2 C TSAMcBean and Fortin (1993) DI n.c. 14 y CS 2 C GC, SRNavarro-Esbrí et al. (2002) DI 3 6730 m/d CS 1 C TSAOECD (2004) CO 16 5 5 y CS 1 C TSAParWtt and Flowerdew (1997) SA 31 – – HWF 11 C GCParWtt et al. (2001) DI 375 – – CS 3 D GCPatel et al. (1998) CO 1 – – MS n.c. P IOAPladerer (1999) HH 50 626 w CS 7 C GC, SRRhyner and Green (1988) DI 1 4 y CS 1 C GCRuVord (1984) HH n.c. – – HWF n.c. C, D GCSalhofer and Graggaber (1999) DI 118 – – CS 1 C, D MRSkovgaard et al. (2005) CO 629 618 y MS, CS 4 P, C TSAThogersen (1996) CO 18 2 5 y CS 1 P SR


2.1.3. DistrictsBoth the competence of regional planning and the ready

availability of data justify the fact that the majority of themodels selected districts as the smallest regional unit (cf.Hockett et al., 1995). The term ‘district’ is here deWned asadministrative unit, which may correspond to municipal-ity, county, city district or city. This research designenables the achieving of full coverage of federal states(ParWtt et al., 2001; Hockett et al., 1995; Salhofer andGraggaber, 1999) or cities (Chang and Lin, 1997). If mod-elling is not limited to only one major region (Gay et al.,1993; Karavezyris et al., 2002), analysed samples cover upto several hundreds of essentially small and medium-sizedmunicipalities (Bach et al., 2004). Several studies have doc-umented the use of time series on an annual (Beigl et al.,2004; Chen and Chang, 2000), monthly (Chang and Lin,1997; Jenkins, 1993) or daily (Navarro-Esbrí et al., 2002;Matsuto and Tanaka, 1993) basis. While waste quantitystatistics, and in some cases also sporadically conductedsorting analyses, are used as waste data, census and eco-nomic data in addition to waste management-relatedinformation (Chang and Lin, 1997; Martens and Thomas,1996) and expert interviews (cf. Karavezyris and Marzi,1999; Karavezyris, 2001) are used for modelling of theindependent variables.

2.1.4. CountriesModels on this highest aggregation level can be classi-

Wed into three types: input–output, cross-sectional andtime-series analyses. While the Wrst type aims at estimatingwaste streams, such as plastics (Patel et al., 1998; Joosten

et al., 2000), paper and wood (Hekkert et al., 2000) or allmain fractions of the MSW (Franklin Associates, 1999;Brahms and Schwitters, 1985) in a single country, the othertwo regression-based methods focus on comparisonsbetween countries and/or in time by means of aggregatedvariables, such as the gross domestic product (GDP) (Tho-gersen, 1996; Mertins et al., 1999), private consumptionexpenditures for all (OECD, 2004), deWned product items(Daskalopoulos et al., 1998; Christiansen and Fischer,1999; Skovgaard et al., 2005) or various other indicators asshown in a cross-sectional comparison of 13 OECD-coun-tries by Bogner et al. (1993). The usual data sourcesinclude nationally aggregated waste quantities on anannual basis, census-related and economic data from sta-tistical oYces, and data from industry and trade associa-tions.

2.2. Modelled MSW waste streams

The waste streams modelled in the reviewed studies canbe classiWed into three concepts (Fig. 1):

– Material streams (Type A): This most comprehensivedeWnition, addressing all wastes originating from theWnal consumer, is only achieved by means of input–out-put analyses. Due to its nature, this method is not aimedat considering the collection procedure applied. Wastequantity records, if surveyed, are not essential for themodel results and may be used only for validation (seeChapter 2.4). In some studies (Daskalopoulos et al., 1998;Christiansen and Fischer, 1999; Skovgaard et al., 2005),

Table 2Characteristics of waste generation models by regional scale

Regional units observed Households Settlement areas Districts Country

Typical range ofresidents by unit

1–7 1200–10,000 60,000–3.5 Mio. 10–270 Mio.

Number of regionalunits

40–857 3–103 1–1071 1–31

Data sources fordependentvariables

Full sortinganalysis

Representative sorting analysis Waste quantity statistics Waste quantitystatistics

Self-documentedwaste quantity

Waste quantity statistics Representative sorting analysis

Data sources forindependentvariables

Householdinterview

Census Census Census

Householdquestionnairesurvey

Household questionnaire survey Branch-speciWc statistics Branch-speciWcstatistics

Waste-management relateddocumentations ofinfrastructure and activities

Waste-management relateddocumentations of infrastructure andactivities

Household budgetsurvey

Waste-management relateddocumentations of infrastructure andactivities

Macroeconomicaggregate

Product-relatedliterature andstatistics


results from other input–output analyses are used asdependent variables.

– Collection streams (Type B): Predominantly, oYcialwaste statistics are used in modelling of the total MSWcollected (e.g., Leao et al., 2001; Beigl et al., 2004; Chenand Chang, 2000; Thogersen, 1996; ParWtt et al., 2001;Hockett et al., 1995; Bogner and Matthews, 2003) or sin-gle collection streams, such as residual waste (Becker,1999; Chang and Lin, 1997; Grossman et al., 1974; Jen-kins, 1993; Martens and Thomas, 1996; Dyson andChang, 2005), the sum of all recyclables (ParWtt et al.,2001) or single recyclable materials, such as paper andcardboard, glass, plastics or metals (Bach et al., 2003,2004; Lebersorger, 1998; Pladerer, 1999). Beside theoYcially reported waste streams, signiWcant quantitativeinterchanges to other disposal options, such as privateWring (Salhofer and Graggaber, 1999; Dennison et al.,1996a), illegal disposal (Karavezyris et al., 2002) orinformal collection (cf. Fehr et al., 2000), are addressedby a few models.

– Fractions of household waste (Type C): Models basedon sorting analyses of commingled or residual wastes,respectively, from curbside collection enable the analy-sis of its composition, taking into account a range of 6(Abu Qdais et al., 1997) to 36 (Dennison et al., 1996a)categories.

2.3. Independent variables

Salhofer (2001) has classiWed models for the analysis ofwaste generation into two categories: input–output mod-els based on the Xow of material to waste generators(input) or from waste generators (output) and factor mod-

els that use factors describing the processes of waste gen-eration. While the Wrst classiWcation focuses on the purelydescriptive characterisation of waste streams over thestages in product life cycle (from production, over trade toconsumption), the second classiWcation aims at unveilinghypothesised causal relationships between factors for theprediction of waste generation. Sircar et al. (2003) haveproposed horizontal and vertical factors for the predic-tion of municipal waste quantities. Horizontal factorsdescribe the processes of interchanges between diVerentwaste types. As an example, shifts between residual waste,bulky waste, recyclables and illegally disposed waste aremainly caused by diVerent modes of separate collectionand do not aVect the total waste quantity. Vertical factorsare due to changes of the total sum of all waste streamsdepending on demographic, economic, technical andsocial developments.

Many independent variables have been hypothesisedand tested in order to explain the quantity of total or par-tial streams of MSW. These have partly been summarisedin previous reviews by Salhofer (2001), Beigl et al. (2003),Hockett et al. (1995) and Jenkins (1993). According to theabove mentioned categories, grouping is based on thefocussed stages in product life cycle: production and trade-related, consumption-related, and disposal-relatedvariables.

Data concerning production and trade contain direct orindirect information about the quantity of product andwaste streams over successive processing stages, at least onthe level of product groups. As mass-related data are rarelyavailable (Joosten et al., 1999), monetary data are predomi-nantly converted into physical data by surveying, assumingor statistically estimating (e.g., waste generated per GDP

Fig. 1. Concepts of waste stream modelling (Bars of sub-streams are labelled schematically).

Concepts of waste stream modelling

Organicmaterial

Commingledwaste

Organicmaterial

Organicmaterial

Paper

Paper

Glass

Glass

Paper

Plastics

Plastics

GlassMetals

Plastics

Metals

Othermaterials

Other collection streams

Othermaterials

Material streams Collection streams

Municipalsolidwaste

collectedon behalf

of the munici-pality

Material-relatedwaste

streams

Sourceseparatedwastestreams

Commingled (residual)waste

Materialfractions withincommingled (residual) waste

Fractions of household waste

A C

Illegal disposalInformal collectionOther disposal options

B


unit as done by Thogersen (1996), Bogner et al. (1993) andMertins et al. (1999)) the price per product unit. Gay et al.(1993) used conversion factors that were based on surveysof at least three major waste generators in each sector cate-gory of the standard-industrial-code (SIC). Joosten et al.(1999) proposed diVerent options due to data availability;the mean prices per material or product unit for each indus-try is given priority over average retail prices based onnational statistics and data from market inquiries, if avail-able. In order to consider the diVerences in the use phase,product-related surveys of the ‘residence time’ (i.e., theduration of the use phase) enable the assessment of wastegenerated (Patel et al., 1998).

Evaluations of consumption-related variables reXect therelationship between living conditions and waste generationpatterns. Further to the well documented impacts of residen-tial population and sporadically dwelling tourists on totalMSW quantity (Hockett et al., 1995; Salhofer and Grag-gaber, 1999; Bach et al., 2004), most of these variables serveas proxies for the general level of aZuence. This is especiallytrue for the variables related to income (Hockett et al., 1995;Jenkins, 1993; RuVord, 1984), tenure (i.e., tenancy) of prop-erty (RuVord, 1984; Dennison et al., 1996a), rental rate ofproperty (Abu Qdais et al., 1997; Grossman et al., 1974) andthe private consumption expenditures by product groups(OECD, 2004; Christiansen and Fischer, 1999; Daskalopou-los et al., 1998) for which most, but not all (cf. Jenkins, 1993),of the evaluations proved the expected positive relationship.Other signiWcant aZuence-related proxies are represented bydwelling type (Emery et al., 2003; ParWtt and Flowerdew,1997; Dennison et al., 1996a), employment status (Dennisonet al., 1996a; RuVord, 1984; Bach et al., 2004), and popula-tion density and urbanisation (Martens and Thomas, 1996;Jenkins, 1993; Eder, 1983), as well as development andhealth indicators, such as life expectancy and infant mortal-ity (Bogner et al., 1993; Beigl et al., 2004, 2005). Apart fromthe mentioned aZuence-related indicators, individual char-acteristics of households – namely the household size (Denn-ison et al., 1996b; Jenkins, 1993; RuVord, 1984), the agestructure (Jenkins, 1993; Sircar et al., 2003; RuVord, 1984;Beigl et al., 2004), the life-cycle stage of the household(Lebersorger, 1998; RuVord, 1984) or consumption habits(Dennison et al., 1996a; Lebersorger, 1998) observed bymeans of household interviews – proved to be signiWcant.

The third group of signiWcant variables contains dis-posal-related factors which may aVect horizontal shiftsbetween waste types. The employment by sectors, as well asbranch-speciWc sales data, were successfully used as proxyfor the percentage of commercial waste (Bach et al., 2004;Martens and Thomas, 1996; Hockett et al., 1995; Gay et al.,1993). SigniWcant impacts on the quantity of source-sepa-rated recyclables are the home heating arrangement (Denn-ison et al., 1996a; Salhofer and Graggaber, 1999), fosteredrecycling activities (cf. Haase, 2000), container size (Mar-tens and Thomas, 1996; Eder, 1983), density of collectionsites (Bach et al., 2004; ParWtt et al., 2001) and user fees(Jenkins, 1993; Hockett et al., 1995).

2.4. Modelling methods

The review revealed how a wide range of modellingtechniques of diVerent levels of complexity have beenapplied to date. Seven groups of applied methods could beidentiWed as enumerated in Table 1. DiVerences betweenthe methodological characteristics can best be describedby addressing the number of independent variables, themethod of model validation and the applicability for pre-dictions.

Methods enabling the consideration of only one inde-pendent variable (i.e., bivariate analysis) cover the timeseries analyses, correlation and regression analyses andgroup comparison. A common feature of these methodsis that the model validation is based on real waste data.Some of these approaches can be extended to multivari-ate models using up to Wve parameters. ParWtt et al.(2001) used Wve collection-infrastructure-related vari-ables as cluster criteria for a successive group compari-son. Skovgaard et al. (2005) applied a three-parametrictime series model. A method without the use of any inde-pendent variable (except the time series data with at leastthree values) was proposed by a projection with a greyfuzzy dynamic model proposed by Chen and Chang(2000). In addition to time series approaches, quantita-tive predictions can also be applied by means of singleregression analysis as shown by a prediction model formain material fractions of MSW by Daskalopoulos et al.(1998).

Multivariate methods, such as multiple regression anal-yses, system dynamics and input–output analyses, are farmore complex due to the manifold interactions betweenthe selected parameters. Therefore, model validation isoften very diYcult or impossible to achieve. While in thecase of regression models, analyses (cf. Grossman et al.,1974; Hockett et al., 1995) have to prove that each inde-pendent variable meets the stringent requirements (i.e.,independence of explanatory variables, and constant vari-ance and normality of errors) to not violate the fundamen-tal regression assumptions; comparable validationprocedures (e.g., to prevent intercorrelations) have notbeen applied for the other two methods. Regarding input–output analyses, Joosten et al. (2000) and Hekkert et al.(2000) highlight the problem that comparisons of theresults obtained with the model with externally observedwaste quantities on the highest aggregation levels are ques-tionable due to the presence of diVerent aggregations orlow consistency within the studies, or may even prove to beimpossible because “studies on Wnal consumption arealmost completely lacking” e.g., for plastic materials.Brahms and Schwitters (1985) compared their input–out-put analysis for main MSW fractions with a nationwidesorting analysis in Germany on the highest aggregationlevel proving low estimation errors for the packagingmaterials metals, paper/cardboard and plastics (<4%), butconsiderable errors for packaging glass (39%), textiles(16%) and organic waste (36%).


3. Discussion of design options and guideline development

Models contributing to the improved estimation of pres-ent and future waste quantities and characteristics areaimed at enabling the best possible waste managementplanning decisions within the given constraints. Thus, theadjustment of research design is mostly induced – eitherexplicitly or implicitly – by Wnding an appropriate trade-oVbetween information gain and cost-eYciency (cf. Gay et al.,1993; Chen and Chang, 2000). In order to enable the identi-Wcation of beneWcial modelling procedures in terms of thesetwo often contradictory goals, crucial design options con-cerning

– regional sampling,– waste stream deWnition and investigation,– selection of independent variables to be hypothesised,

and– model validation

are discussed below by presenting beneWts and short-comings of the reviewed literature and summarised in aproposed procedural guideline.

3.1. Regional sampling

Both the size and the number of regions to be observedrepresent crucial design parameters. The selection of exces-sively large, too few or too many observed regional unitsmay challenge the usefulness, as well as the cost-eYciency,of an investigation for planning issues.

With regard to the size of observed units, a consoli-dated waste strategy should be accompanied by theappropriate regional discretisation of a model. ParWtt andFlowerdew (1997) illustrate the close relationship betweenthe focus in waste management hierarchy and the typicaldata requirements. While the focus on material recyclingrequires the “local authority monitoring of recyclingschemes” in order to design material recovery facilities,the focus on the more sustainable waste minimisation andmaterial reuse strategies should be funded on household-waste audits and surveys to enable the identiWcation of“waste-creating activities”. Based on this argumentationconcerning the political appropriateness of the size unit(cf. Hockett et al., 1995), two-thirds of the reviewed mod-els (i.e., 31 out of a total of 45) focus on the scale of dis-tricts or smaller regional units. In contrast, the decisionmakers’ beneWt of some models, which are based on datarelated to countries or large regions, is often questionable.The modelling of waste potentials for the USA and forwhole Europe (Daskalopoulos et al., 1998) or even theestimation of recycled, legally and illegally disposed wastequantities on the level of city with more than 3 millioninhabitants (Karavezyris et al., 2002) can not provide rele-vant information about the regional variation, and thuscan not serve as a basis for waste management planningon a regional level.

As the sample size of inquiries, such as questionnairesurveys, interviews and accompanying sorting analyses isregarded as one of the main cost drivers (cf. ScharV, 1991),cost-saving methods with a small number of observations(depending on number of observed units and time serieslength as shown in Table 1) were proposed. In the follow-ing, the most extreme cases, these are modelling methodswhich focus on only one region (i.e., input–output analysesand time-series analyses) are discussed. These models haveto cope with the problem that hypotheses about the poten-tial impacts on waste generation can be proved only in spe-cial cases, if no other sources (e.g., accompanying cross-sectional analyses (e.g., McBean and Fortin, 1993)) areused. The successful identiWcation of seasonal impacts(Matsuto and Tanaka, 1993; Navarro-Esbrí et al., 2002) orweekly collection service patterns (Katsamaki et al., 1998)by applying time-series analyses of daily data over up to 2years is indisputable. More questionable is the deduction ofhypothesised causal impacts in the long term. The identiW-cation of the most signiWcant variable is often assumed todepend on the best relationship between the time seriesrelated to waste generation and that related to a factor,although this fact may not have been proven by cross-sec-tional analyses (e.g., OECD, 2004; Skovgaard et al., 2005).A further potential limitation to the gaining of informationis constituted by the missing balance between the samplesize and the complexity of a model, as discussed in Section3.4.

Cost-ineYcient sampling occurs, if the size of the sampleis too high in relation to the needed level of accuracy with-out leading to a signiWcant information gain. Dennisonet al. (1996a) conducted 857 sorting analyses on householdlevel, although the calculated required sample size for a95% conWdence interval was 384 sorting analyses. AbuQdais et al. (1997) could have avoided 24% of the 840 sam-ples evaluated by selecting the usually applied 95% conW-dence interval instead of a 99% conWdence interval.Furthermore, the ambitious inquiry of waste collectiondata of Bach et al. (2003) from 1071 municipalities seems tofar succeed the statistical requirements of a regressionmodel.

3.2. Waste stream deWnition and investigation

The number and type (cf. Section 2.2) of scoped wastestreams, as well as the level of accuracy of discrimination,exert an essential impact on the eVorts to collect waste-related data and the information content of the planningfundamentals.

Depending on the type of waste streams deWned (collec-tion streams or household waste fractions), experiencegained in the reviewed studies suggests beneWcial ranges ofthe number of observed waste streams in order to identifyan appropriate balance between information gain andeVorts. The majority of models based on collection-streamdata apply total MSW generation as only one dependentvariable. A common variety with two considered collection


streams (i.e., dependent variables) is the deWnition of a recy-cling and a commingled stream (Becker, 1999; Martens andThomas, 1996; ParWtt et al., 2001; Karavezyris et al., 2002).Further often expensive disaggregation into material-related recycling streams (e.g., waste paper and waste glass)does not aVord any useful information due to the impossi-bility of identifying whether varying collection quantities(e.g., in time or between regional units) are subject to adiVerent level of aZuence or to diVerent recycling quotas.For models based on sorting analyses of household waste,the results showed that 6 up to 14 main categories are suY-cient for evaluations. It is not advantageous to considermore categories or sub-categories of fractions as needed forthe study objective, as done by Emery et al. (2003) with 30sorting categories. If input–output analyses are applied, norecommendations can be given as the number of consideredmaterial and product streams depend on the amount ofdetail provided by data sources.

InsuYcient deWnition and standardisation of MSW is awell known problem, and experiences concerning the diVer-ences between MSW and household waste have beenreported (Fischer and Crowe, 2000). Frequently, the inves-tigated waste streams are not transparently deWned, so it ishardly comprehensible which (collection or material-related) streams are covered and how much informationexists concerning the quantity and quality of the excludedstreams. Related limitations were openly reported in only afew cases. One of these was the criticism made by ParWttand Flowerdew (1997) of the United Kingdom’s NationalHousehold Waste Analysis Programme, who stated thatthe deWned term “household waste” covered only the wastefrom curbside collection, while 33% of the household waste,mainly from civic amenity sites, had not been taken intoconsideration due to the inappropriate sampling procedure.Without any quantitative information as to the separatelycollected materials in three settlement areas, Emery et al.(2003) traced the lowest amount of newspapers in house-hold waste in the highest-income settlement area back tothe fact of high recycling rates because “newspapers pur-chased by more aZuent households tend to be larger”. Inboth cases, the inclusion of collection data other than thatpertaining to curbside collection sites would close this gapbetween the curbside collected stream and the complete col-lection stream.

Distortions of MSW streams related to other sources(e.g., commercial waste, tourism) or waste-related activities(private burning of waste, illegal disposal) remain sub-merged, but can be successfully estimated using appropriateproxy variables as described in Section 2.3 (cf. Hockettet al., 1995).

3.3. Selecting independent variables to be hypothesised

Still in the conception stage of model development, it canoften be pre-estimated whether a draft model with a deWnedset of hypothesised variables will be able to satisfy the basicinformation needs of waste management planners: timeli-

ness of data, applicability for predictions and suYcient dataquality. Appropriately quick reactions on new waste genera-tion trends require the provision of models based on timelydatabases. Here the delay between reference year of the lastobserved waste-related data and the publication year wastaken as benchmark. In most models, a delay of up to 3years was reported. It is notable that models based on exten-sive databases, namely input–output analyses and selectedmultiple regression models with a high number of hypothes-ised factors, are far from serving relevant up-to-date infor-mation. A comparison of the reference year of coredatabases and the Wrst publication date of each study provesdelays of 7 (Brahms and Schwitters, 1985), 9 (Patel et al.,1998), 10 (Joosten et al., 2000; Hekkert et al., 2000) up to 12years (Bach et al., 2003). Thus this diVerence points out therelevance of the up-to-date nature of existing primary datain order to support the strategic decisions based thereon.The necessity of up-to-date data can be supported with theconsiderable changes based on time-series data of Europeancountries from the years 1995–2003; the growth of MSWgeneration within 8 years ranges up to 50% (e.g., Ireland,Malta), while source-separated waste streams nearly triple insome cases (e.g., paper and cardboard collected in France,Italy and Ireland or organic waste collected in France)(European Communities, 2005).

The main objective of several models is to provide a pre-diction tool. The reader should be enabled to make inter-temporal forecasts or inter-regional predictions. Unfortu-nately, the majority of these models are often unusable dueto the lack of underlying data for the model parameters.For example, forecasted values for variables, such as prod-uct-related fractions of total consumer expenditures on anational level (Daskalopoulos et al., 1998) or actual indicesof purchasing power per capita on a municipal level (Bachet al., 2004), are very probably not available for the wastemanagement planners. A useful solution to this problem isproposed by Skovgaard et al. (2005), who provide forecastsfor all necessary predictors for MSW forecasts to potentialusers. Further improvement can be gained by the use ofparameters which are both easily comparable and predict-able, such as socio-economic variables (cf. Section 4.2).

The more independent variables are hypothesised, col-lected and evaluated, the more diYcult it is to guarantee alevel of data quality. The implementation of data-intensiveapproaches can be signiWcantly limited or aggravated byproblems of data availability and comparability. Further tothe above mentioned problems of data obsolescence, incon-sistent deWnitions and a lack of data are cost-relevant drivers,especially for input–output analyses based on up to thou-sands of independent variables. With reference to indirectwaste analyses using market-research data, Fehringer et al.(2004) pointed out the cost-eVectiveness of this method,whilst stating, however, that “the greater the lack of data, themore time and eVort has to be put in to achieve reasonableresults”. Assumptions and estimations usually have to bemade to allocate mass Xows within product groups, to trans-form prices into physical units (cf. Gay et al., 1993) and to


assume residence times per product (cf. Patel et al., 1998). Apossible countermeasure would be to check data availabilityand comparability during a preliminary examination prior tothe Wnal deWnition of research design.

3.4. Model validation

The accuracy of model results (i.e., the main model char-acteristics and selection criterion for practitioners of pre-diction tools according to Armstrong, 2001) can be limitedor distorted for two reasons: insuYcient validation ofmodel and model parameters and lack of balance betweenoversimpliWcation and overWtting.

3.4.1. Validation with waste dataIn the present paper, model validity is deWned as internal

validity or the capability of the model to explain the depen-dent variable ‘waste quantity’. It has an indirect, althoughnot constraining, impact on the more relevant externalvalidity, i.e., the ability to generalise the results obtained todiVerent temporal or spatial settings.

While validity was tested for group comparisons, correla-tion and regression models by means of common statisticaltests, comprehensive validity tests were not conducted for allreviewed system dynamics models and input–output models.The reviewed system dynamics models enable simulations ofinterconnected variables, whose hypothesised relationships(e.g., between household size and waste generation) are deter-ministically deWned, but not stochastically tested. Input–out-put analyses are also deterministic models, which aretypically not based on stochastically assumed variables.

3.4.2. Balancing model complexityWaste generation models that are either exceedingly sim-

ple or too complex can provide inappropriate results. Thelevel of complexity necessary depends on both the number

of applied parameters and their functional form. An unfa-vourable ratio between the identiWed degrees of freedom tothe sample size may cause overWtting or oversimpliWcationof the model (Tabachnik and Fidell, 1989).

Models with an excessively high number of partly non-linear parameters tend to unduly Wt to the data in the sam-ple. The impact of overWtting of intercorrelated variables tovariations due to measurement errors maximises the per-centage of variance explained (e.g., correlation coeYcient),but limits the ability of the results to be generalised. Withthe exception of model-speciWc tests (e.g., collinearity testsand tests of residues), the fundamental rule that the samplesize should exceed more than twofold the number ofparameters (Backhaus et al., 2003) may be of help in orien-tation. Indeed, the application of this condition strengthensthe suspicion that Jenkins’ (1993) 27-parametric forecastingmodel for the separate estimation of both residential andcommercial waste based on only nine areas may be aVectedby this phenomenon. Table 3 shows how the sample size ofmultiple regression models typically exceeds by 30-fold upto 90-fold the number of parameters. Furthermore, theseemingly arbitrary selection of non-linear regression func-tions of higher order, as presented by Daskalopoulos et al.(1998), Bogner et al. (1993) and Bach et al. (2004), shouldbe transparently justiWed with objective relationshipsbetween the variables to prove that they are not basedsolely on the maximisation of R2. An additional problemmay be represented by the increased diYculty of interpret-ing models with more than ten parameters, likely implyinga decreased practicability for waste managers.

On the contrary, the potential information gain couldeasily be increased in the case of an oversimpliWed modeldesign. Extensive survey-based databases, e.g., from Denni-son et al. (1996a), could be used in multivariate proceduresinstead of single correlation analysis, as proposed by Leber-sorger (2004), in order to ascertain existing relationships

Table 3Models based on multivariate regression equations

n.c. – no comment.a Time series from 55 cities.b Includes waste from the residential and commercial sector.c Time series from 9 communities.d 13 regressors and 14 regional dummy variables.

Reference Dependent variable (in kg/cap/yr,if not otherwise stated)

Sample size (n) IdentiWedparameters

Explainedvariance (R2)

Bach et al. (2003) Residual waste 1071 14 0.50Waste glass 507 7 0.53Light weight packaging – collected in bringsystem 71 3 0.400Light weight packaging – curbside collection 216 7 0.388Waste metals 156 7 0.538

Bach et al. (2004) Waste paper 649 8 0.487Beigl et al. (2004) MSW 550a 6 0.65Grossman et al. (1974) MSW (Gallons/week) 103 3 0.36Hockett et al. (1995) MSW 100 2 0.497Jenkins (1993) Residual wasteb (Pounds/cap/month) 600c 27d 0.921

Residual wasteb (Pounds/cap/year) 49c 27d 0.998Salhofer and Graggaber (1999) MSW 118 4 n.c.


between variables. Time-series based forecasting modelstaking into account no (Chen and Chang, 2000) or only one(Chang and Lin, 1997) exogenous impact could easily beimproved by the inclusion of one or more aZuence-relatedcensus variable (e.g., based on parallel cross-sectional anal-yses) in order to increase long-term predictability.

3.5. Guideline development

Fig. 2 shows the guideline developed on the basis of theaforementioned Wndings for the evaluation of the researchdesigns. This can be applied both in checking a waste gener-ation model prior to implementation or for evaluating thereliability of existing models.

4. Case studies

The selected regional scale of a waste generation modelproduces the highest impact on type of information gained

and eVorts required. The following two case studies demon-strate the beneWts and limits of two approaches on both ahousehold and on a city level. In both cases, the applicationof the developed guideline will be discussed.

4.1. Case study 1: modelling on a household level

Case study 1 (cf. Lebersorger, 2004) was aimed at identi-fying inXuencing factors on waste generation from privatehouseholds and at identifying indicators capable of fore-casting the amount of residual waste from a multifamilydwelling. Prior research had shown highly divergent per-capita quantities of residual waste from multi-family dwell-ings, for which no feasible explanation could be aVorded,even when taking into account the possibility of a diVerentrecycling performance or the production of waste fromsources other than households, i.e., garden or commercialwaste. Thus, a multi-family dwelling was hypothesized asbeing occupied by households living under similar circum-

Fig. 2. Guideline for the evaluation of research design.


stances with regard to income, family-situation, social stra-tum etc., thereby generating similar needs and resulting insimilar quantities of residual waste.

For the survey, six multi-family dwellings were selected,each with more than 600 inhabitants and more than 320households. As dependent variables, the quantities andcomposition of residual waste were determined at the levelof a multi-family dwelling by means of composition analy-ses and weighing the contents of the waste containers. Asindependent variables, data on socio-demographic charac-teristics and household activities were investigated bymeans of a questionnaire survey applied to 334 households,thus including at least 50 households from each multi-fam-ily-dwelling.

The determination of waste-related data at the level of amulti-family dwelling was preferred over that of householdlevel due to practical and methodical considerations (cf.Lebersorger et al., 2003):

– availability of aggregated information concerning alarge number of households at reasonable costs andeVort.

– similar circumstances for all households in a given multi-family dwelling with regard to the recycling system, costfor housing, construction issues, neighbourhood etc.

– little inXuence of the questionnaire survey on the actualbehaviour of the household. In multi-family dwellings itis not usually possible to determine waste quantities on ahousehold level without involving the household itself.The awareness of participating in a scientiWc investiga-tion and the subjective feeling of “being controlled” willlikely aVect the households’ actual behaviour (Haw-thorne or guinea-pig eVect).

These advantages were considered to outweigh the dis-advantage of not being able to attribute waste data to anindividual household.

The data were analysed in two steps by means of contin-gency analyses and multiway frequency analyses.

1. Correlation between the quantity of a deWned wastecomponent (data at the level of a multi-family dwelling)and relevant household activities (data at household-level): for example the inXuence of the frequency of foodpreparation, frequency of wasted food, consumption offresh food, consumption of pre-packaged food etc. onthe quantity of wasted food.

2. Correlation between household consumption activitiesand socio-demographic indicators (household type, age,income, life-cycle stage, educational level of the respon-dent), at household level.

Age and household type had an eVect on most of thehousehold activities, both the main eVects of either of themand the interaction of the two variables “age” and “house-hold type”. Due to the considerable eVect produced by theinteraction “age” and “household type”, the composite var-

iable life-cycle stage (cf. Tabachnik and Fidell, 1989), whichclassiWes households according to number and age of adultsand children, was considered an appropriate indicator. Theresults obtained illustrated how the presence of a high num-ber of elderly couples and singles was indicative of lowwaste quantities from a multi-family dwelling, whereashouseholds with infants and schoolchildren were likely togenerate the highest waste quantities.

However, population statistics do not generally providesuch composite data and interaction eVects thus cannot beconsidered. Furthermore, interaction eVects, particularlythree-way or higher eVects, are very diYcult to interpret. Interms of practical applicability, the eVects should be simpli-Wed.

In order to verify the results of the case study, detailedinformation concerning the socio-demographic characteris-tics and waste quantities from 10 multifamily dwellings,available from a former investigation (cf. Grassinger et al.,2000), were used. Fig. 3 shows the correlation between thequantities of residual waste of each multi-family dwellingand the household type and age (Kendall’s tau-b ¡0.293;pD0.000***). The higher the percentage share of house-holds with children and the younger the residents, thehigher was the waste quantity of the multifamily dwelling,which corroborates the results found in the Viennese casestudy.

It can be concluded that an investigation performed onboth a multifamily-dwelling and household level may be ofuse when applied to clarify a speciWc research issue or toobtain detailed basic information. However, it is not appli-cable on a larger scale due to several limitations. The sam-ple-size is restricted by the eVort required to determine thewaste quantities from a multifamily-dwelling (separateweighing, visual inspections required in order to checkpotential inXuences on waste quantities such as gardenwaste or commercial waste), as well as the eVort needed tosurvey the residents.

4.2. Case study 2: modelling on a city level

The objective of case study 2 (cf. Beigl et al., 2004) wasthe development of a long-term forecasting tool for the esti-mating of MSW generation in European cities. It wasaimed at Wnding a suitable compromise between an appro-priate level of accuracy and validity, and a comparable easeof applicability by municipal oYcers as targeted users. Thecentral hypothesis postulated a relationship between cen-sus-based, aZuence-related indicators and both the quan-tity of per-capita MSW generation.

An investigation carried out in association with sixregional partners assessed the collection and inspection ofwaste-related data, as well as demographic and socio-eco-nomic indicators, in all major European cities with morethan 500,000 inhabitants. Both regional data at city levelsprovided from local city representatives and national datafrom international organisations, such as the UnitedNations or OECD, were used. Based on the availability and


quality of data, 55 cities (from a total of 65) from the EU-25 countries were included in the study; these cities pre-sented an average time-series length of 10 years in the sam-ple providing both total MSW quantity and results for 14national and city-related socio-economic indicators.

The selection of this research design based on cross-sec-tional time-series data on a city level was derived from thefollowing practical and methodological considerations:

– waste generation on a city level serves as primary plan-ning information,

– data quality on a city level was assumed to have highestdata quality for small regions (cf. Petersen et al., 1999),

– knowledge of the time-shifted long-term developmentsof waste generation and potential impacts was hypothes-ised by the authors to be generalisable for cities with asimilar welfare level,

– availability of time series enabled testing of the Wnalmodel under real conditions.

Data analyses were carried out in two steps:

– Attribution of datasets to welfare-related groups usinghierarchical cluster analyses: Each single dataset from atotal of 550, representing the total MSW generation of aspeciWc city in 1 year, was attributed to groups in orderto fade out high welfare diVerences. Additionally, a pros-perity-related factor was modelled by means of principalcomponent analysis.

– Regression between total MSW generation and indica-tors: For each of the three groups, regression equationswere estimated using combined forward and backwardregression. The most signiWcant, not inter-correlatedvariables were identiWed. Tests of collinearity, autocorre-lation and residual analyses agreed with the regressionassumptions.

Both welfare-related and demographic indicators wereidentiWed as the most signiWcant parameters to explain the

Fig. 3. Ten multifamily-dwellings ranked by quantity of residual waste and distribution of their residents by household type (left Wgure) and age (rightWgure). Mfd: Multifamily dwelling; with children >10 y/<10 y: household with children older/younger than 10 years.

Mfd by quantity of residual waste (kg/cap/y)

196

169

163

160

130

129

90

87

86

63

% o

f hou

seho

lds

100

90

80

70

60

50

40

30

20

10

0

household type

with children >10 y

with children <10 y

couple

single

Mfd by quantity of residual waste (kg/cap/y)

196

169

163

160

130

129

90

87

86

63

% o

f hou

seho

lds

100

90

80

70

60

50

40

30

20

10

0

Age

elder (over 50)

middle-age (35-49)

young (up to 34)

Table 4Prosperity-related regression models for total MSW generation in European cities

a Except the logarithmic relationship of the infant mortality rate.b National indicators.

Prosperity level

Medium High Very high

IdentiWed linearamodel parameters on the dependent variable MSW generation (kg/cap/yr) (ranked in order of signiWcance)

Constant ¡360.657 276.529 359.536GDP per capita (US-$ PPP, 1995 prices) 0.0156b 0.014b

Infant mortality rate (Deaths per 1000) ¡375.581b ¡126.485 ¡197.057Urban population aged 15–59 years (%) 8.928Household size (Pers/hh) ¡123.895Life expectancy at birth (Years) 11.702

CoeYcient of determination (R2) 0.600 0.523 0.506

Limits and threshold values between groups (Approximate values resulting from hierarchical cluster analyses)GDP per capita (US-$ PPP, 1995 prices) 3000 13,800 20,200 40,000Infant mortality rate (Deaths per 1000) 20 8.1 6.3 3.5


variation of total MSW generation. While the gross domes-tic product and infant mortality rate were identiWed as sig-niWcant parameters for high-income cities, the agestructure, household size and both health indicators (i.e.,infant mortality rate and life expectancy at birth) provedcapable of explaining variations observed in medium-income cities (Table 4).

In order to verify the expected forecasting accuracy, thedynamic modiWcation of the model was tested by means ofex-post forecasting. Thus the development of the actualMSW quantity was compared with the amount estimatedusing the model parameter, and starting from a deWnedbase year. The median relative error of the growth rate inMSW quantity of all cities’ time series (ranging between alength of 5 and 21 years) as key indicator for the accuracyamounted for 0.6%.

Based on a comparison of the procedure and the resultswith other forecasting tools by Beigl et al. (2004, 2005), itcan be surmised that both the regional variation betweencities on similar welfare-level and the temporal variationcan be explained more suitably than with pure cross-sec-tional analyses (Bach et al., 2004), which do not allow forvalidation tests, and single time series analyses (e.g., Skovg-aard et al., 2005), which may be increasingly aVected bymeasurement errors, especially in short time series. Anadditional beneWt is the comparably ready availability ofthe applied demographic forecast data for the independentvariables (cf. Lindh, 2003).

5. Conclusions

Assessments of impacts on current and future wastestreams are essential and indispensable fundamentals inwaste management planning. A literature review of previ-ously published approaches revealed a high heterogeneityof applied models, in spite of the fact that issues to besolved were remarkably similar. These models can best bedescribed by four speciWc criteria: the focussed regional

scale, ranging from household up to country perspective;the type of modelled waste streams; the hypothesised inde-pendent variables and the modelling method.

A procedural guideline was developed in order to iden-tify crucial design options with signiWcant impacts on infor-mation gain and cost-eYciency of waste generation models.Based on a discussion of previous studies, beneWcial choicesconcerning regional sampling (i.e., number and size ofobserved areas), waste stream deWnition and investigation,selection of independent variables and model validationprocedures were proposed. The implementation of thederived Wndings was practically demonstrated in two casestudies with diVerent settings: a survey-based analysis ofhousehold waste generation at multi-family dwellings and acensus-data-based development of a forecasting tool forcities. The comparison of both approaches corroborates thehypothesis that, due to the presence of various planningissues, the use of only one ‘optimum’ procedure is not suY-cient for diVerent study objectives and border conditions.The setting of minimum requirements and criteria for mod-elling procedures should balance information gain andimplementation costs.

Beside these general checklist-like recommendations, theadaptation of overall model design to the planning problemplays a fundamental role. The discussion revealed severalshortcomings concerning the choice of methods to be used.Fig. 4 shows a proposed relevance tree for appropriatemethodology selection. The main selection criterion is thetype of waste streams to be investigated. In the majority ofcases, correlation and regression analyses, as well as groupcomparisons, are the most beneWcial modelling methods,both to test the relationship between the level of aZuenceand the generation of total MSW or a material-related frac-tion, and to identify signiWcant eVects of waste managementactivities on recycling quotas. The application of time seriesanalyses and input–output analyses is advantageous forspecial information needs (e.g., assessment of seasonaleVects for short-term forecasts) or for appropriate data

Fig. 4. Relevance tree for methodology selection.

Modeledwaste stream

Models based on sorting analyses

Separatecollectionstreams

Analysis of impacts on material-relatedgeneration (i .e. the sum of fraction of

commingled waste and separated collected quantity)

Analysis of impacts on recyclingquotas by fraction

Assessment of seasonalimpacts?

Availability of timely secondary

data on the focused regional scale?

Significant interchanges with

other disposaloptions expected ?

consumption-related variables

consumption and disposal-relatedvariables

Time series analysesbased on monthly or daily records

Correlation and regressionmethods

to test mainly affluence-related impacts by analysing

Group comparisons and regressionto test effects of waste management

activities on recycling quotas

Input -output analyses

Material streams (regardless of

collection )

Total MSW Yes

No

Yes

No

Commingled waste(percentage of total MSW)

Other collection streams

Yes

No


availability. Sorting analyses are indispensable, if impactson the quantity of separately collected waste streams (e.g.,of recyclables) are to be quantiWed.

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modelling municipal solid waste generation a review

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