Waste Management 31 (2011) 1907–1915

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Waste Management

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Municipal solid waste generation in municipalities: Quantifying impactsof household structure, commercial waste and domestic fuel

S. Lebersorger, P. Beigl ⇑Institute of Waste Management, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences, Vienna, Muthgasse 107, A-1190 Wien, Austria

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 August 2010Accepted 24 May 2011Available online 19 June 2011

Keywords:Municipal solid wasteInfluencing factorRegressionQuantificationDomestic fuelCommercial waste

0956-053X/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.wasman.2011.05.016

⇑ Corresponding author. Tel.: +43 1 3189900 310; fE-mail address: peter.beigl@boku.ac.at (P. Beigl).

Waste management planning requires reliable data concerning waste generation, influencing factors onwaste generation and forecasts of waste quantities based on facts. This paper aims at identifying andquantifying differences between different municipalities’ municipal solid waste (MSW) collection quan-tities based on data from waste management and on socio-economic indicators. A large set of 116 indi-cators from 542 municipalities in the Province of Styria was investigated. The resulting regression modelincluded municipal tax revenue per capita, household size and the percentage of buildings with solid fuelheating systems. The model explains 74.3% of the MSW variation and the model assumptions are met.Other factors such as tourism, home composting or age distribution of the population did not signifi-cantly improve the model. According to the model, 21% of MSW collected in Styria was commercial wasteand 18% of the generated MSW was burned in domestic heating systems. While the percentage of com-mercial waste is consistent with literature data, practically no literature data are available for the quan-tity of MSW burned, which seems to be overestimated by the model. The resulting regression model wasused as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).

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1. Introduction

Waste management planning requires reliable data concerningwaste generation, influencing factors on waste generation and fore-casts of waste quantities based on facts. Information about relevantinfluencing factors is essential for estimating the consequences ofchanges in general conditions (e.g. economic system, demography,domestic heating systems), policy measures (Mazzanti and Zoboli,2008) or waste management measures (e.g. increasing the rate ofhome composting) on future waste quantities. Thus, forecast modelsshould include multiple factors and predictions according to socialand economic changes (Chung, 2010; Purcell and Magette, 2009).

Until now, there has been a great deal of studies aimed at deter-mining influencing factors and at quantifying their impact onmunicipal solid waste (MSW) generation (Beigl et al., 2008). Beiglet al. (2008) reviewed 45 waste generation modeling approachespublished before 2006. The highly heterogeneous models were clas-sified according to the following criteria: (i) the regional scale, (ii)the modeled waste streams, (iii) the hypothesized independent vari-ables, and (iv) the modeling method. Beigl et al. (2008) revealed sev-eral shortcomings of published models and proposed proceduralpractice guidelines. Only a few of the previously published modeling

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approaches provide implications which can be directly imple-mented by local waste management authorities.

Studies at the household level (Bandara et al., 2007; Benítez et al.,2008; Dennison et al. 1996a,b; Eder et al., 1983) or of settlementareas (Emery et al., 2003) are able to discover valuable quantitativeand qualitative information at the expense of costs and limited sam-ple size. Costs are partly due to extensive sorting analyses and ques-tionnaire surveys (Eder et al., 1983; Dennison et al., 1996a,b;Bandara et al., 2007). Studies at the national level (Mazzanti andZoboli, 2008; Bogner et al., 1993; Daskalopoulos et al., 1998) or theanalysis of time series of a single region (Chung, 2010) reveal generalcorrelations, such as the linkage between MSW and gross domesticproduct (Bogner et al., 1993; Daskalopoulos et al., 1998) and couldbe interesting at a high political level. However, they cannot be usedfor precise planning at lower regional levels because no informationis provided about the spatial distribution which is consideredequally essential as the gross quantity of waste concerning wastemanagement planning (Purcell and Magette, 2009).

One of the advantages of studies at the municipality, commu-nity or county level is that existing statistical data can be used(e.g. Bach et al., 2004; Hockett et al.,1995; Salhofer and Graggaber,1999; Miller et al., 2009; Purcell and Magette, 2009; Jenkins, 1993;Beigl et al., 2008). Collection quantities of MSW at the municipalitylevel also include different amounts of commercial waste, in addi-tion to residential waste, and are therefore able to provide moreinformation than studies at the household level. Some studies

1908 S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915

found that household-related variables, such as householdbehavior and characteristics (Mazzanti and Zoboli, 2008), per capi-ta income or percentage of urban population (Hockett et al., 1995),add some useful hints, but seem to have no great influence onwaste generation at the municipality or national level.

Due to the heterogeneity of the models (see Beigl et al., 2008),their results can hardly be compared. Different definitions are usedfor the dependent variable, such as the collected amount of wastepaper (Bach et al., 2004), per capita MSW quantities per day (Hock-ett et al., 1995) or per year (Salhofer and Graggaber, 1999), orannual MSW tonnages (Miller et al., 2009). Also, the number andtypes of investigated independent variables varies considerably.Most models consider demographic (e.g. household size (Benítezet al., 2008; Dennison et al. 1996a; Jenkins, 1993; Miller et al.,2009; Salhofer and Graggaber; 1999), population density (Jenkins,1993; Mazzanti and Zoboli, 2008; Miller et al., 2009; Salhofer andGraggaber; 1999), age (Dennison et al. 1996a; Jenkins, 1993; Milleret al., 2009), education (Benítez et al., 2008; Dennison et al. 1996a;Miller et al., 2009), overnight stays (Bach et al., 2004; Salhofer andGraggaber, 1999)) and socioeconomic factors (e.g. household or percapita income (Benítez et al., 2008; Hockett et al., 1995; Jenkins,1993; Miller et al., 2009), retail sales (Hockett et al., 1995; Milleret al., 2009), employees (Bach et al., 2004; Salhofer and Graggaber,1999), purchasing power (Bach et al., 2004)), and some alsoconsider climatic aspects (e.g. season (Eder et al., 1983), tempera-ture and precipitation (Jenkins, 1993)).

Only a few studies included waste management-related indica-tors, such as density of collection sites (Bach et al., 2004), wastedisposal fees (Hockett et al., 1995), user fees charged for residentialsolid waste service (Jenkins, 1993), transportation and disposalcost per ton of MSW (Miller et al., 2009) or the implementationof waste-related directives (Mazzanti and Zoboli, 2008). The typeof domestic heating system was considered by Salhofer andGraggaber (1999) at the municipality level and by Dennison et al.(1996a) and Eder et al. (1983) at the household level.

Previous regression models usually did not quantify the effectsof significant influencing factors. There are a few exceptions, suchas Miller et al. (2009) which quantified the impact of changes inretail sales expenditures on the tonnage of MSW recycled. Esti-mates about the percentage of commercial waste in MSW areavailable from an empirical survey conducted by Salhofer (2000)in Vienna. Purcell and Magette (2009) assessed the quantity of bio-degradable municipal waste from commercial establishments andbusinesses by using waste generation rates from the literature.

This paper aims at identifying and quantifying differencesbetween municipalities’ waste collection quantities based on datafrom waste management and socio-economic indicators. A largeset of 116 indicators from 542 municipalities in the Province ofStyria was investigated. The resulting regression model is used asbasis for a waste prognosis model, which is presented in a con-nected paper (Beigl and Lebersorger, in preparation). The overallaim was to develop a model for forecasting MSW at the municipal-ity level, which uses existing data, is as simple as possible, is basedon reliable evidence and is applicable by local authorities. The pro-cedure followed the procedural guidelines suggested by Beigl et al.(2008).

2. Materials and methods

2.1. Model region: the Province of Styria (Austria)

The Province of Styria has a population of 1.2 million inhabitants,an area of 16,392 km2 and a population density of 73 persons persquare kilometer, and it is the second largest of Austria’s nine prov-inces. The province has four regions: the largest of them is Upper

Styria, the northern, generally mountainous and densely wooded re-gion with extensive industrial sites. Western Styria is a hilly regionwith predominantly industrial zones. Eastern Styria is an agricultur-ally dominated region which is economically weak because it isadjacent to the former ‘‘iron curtain’’. The central region aroundthe capital Graz is the area of the province with the most rapid eco-nomic growth. The province is divided into 542 municipalities in 17districts. Seventy-five percent of the municipalities have less than2000 inhabitants, 18% have between 2000 and 4000 inhabitantsand 7% have more than 4000 inhabitants. With regard to waste man-agement, the municipalities have constituted 17 waste manage-ment associations which, with a few exceptions, correspond to thedistricts. Municipalities have a legal obligation to collect MSW.Thirty-five municipalities collect residual waste by themselves,and the others contract private waste collection firms or operatewith public–private partnership models (Amt der SteiermärkischenLandesregierung, 2010).

Collection schemes for separate collection of waste materials arehighly developed. Starting with pilot projects from 1987 to 1989,Styria became the first province in Austria to regulate the separatecollection of biogenic municipal waste (bio-waste) by law in 1990.While some municipalities promote using bio-waste containers, inother municipalities individual backyard or community compostingis prevailing. In 2008, bio-waste containers were available to114,615 households (27% of a total of 431,341 households in Styria).174,757 households (41% of households) were treating bio-wastewith individual backyard composting and 11,626 households (3%of households) participated in community composting (Amt derSteiermärkischen Landesregierung, s.a.a). The separate recovery ofrecyclables has been promoted since 1990. To date, waste paper(kerbside collection and/or bring-scheme), glass (bring-scheme),metals (bring-scheme), plastic packaging (kerbside collection and/or bring-scheme) and wood (recycling centers) are collected sepa-rately. Based on pilot projects, the separate collection of waste elec-trical and electronic equipment had already started in 1995 and wasadapted to the requirements of the WEEE ordinance in 2005.

2.2. Waste data and definition of dependent variable

Data basis was the documentation of waste management-relateddata in Styria (Amt der Steiermärkischen Landesregierung, s.a.b),which differentiates up to 49 different waste types. Data on MSWcollection quantities are reported by the municipalities to the pro-vincial government on an annual basis and were available for theyears 1991–2008. Because of data inconsistencies, data for the years1991–1994 were not analyzed. MSW comprises waste from house-holds, which municipalities are legally obliged to manage, and fromsmall businesses, services and institutions that produce waste com-parable to household waste and are required for using the municipalcollection scheme. Waste from industry and commerce were notconsidered, since they are not included in municipal waste statisticsand data are also not available at neither the municipality nor pro-vincial levels.

The dependent variable is the per capita municipal solid wastecollection quantity (MSW), which includes the following separatelycollected waste streams: residual waste (31% by mass of MSW in2007), bulky waste (15%), biogenic waste from collection in bio-waste containers (9%), waste paper and cardboard (23%), light-weight packaging (7%), metal packaging (2%), waste glass (9%), otherrecyclables (2%), waste electrical and electronic equipment (2%) andhazardous household waste (1%). Waste streams such as construc-tion and demolition waste; separately recovered waste streamsfrom gardens, parks and graveyards; litter; tyres and wheel rims;end-of-life vehicles and ferrous scrap were considered inappropri-ate for modeling and therefore excluded. These waste streams showunsteady distributions and data inconsistencies (missing data,

S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915 1909

implausible quantities, fluctuating quantities), and their generationis determined by factors other than the generation of householdwaste. In 2007, these excluded waste streams accounted for approx-imately 11% by mass (65,000 Mg) of the total MSW (570,000 Mg)collected in Styria. Per capita quantities were given priority overtotal quantities, in order to smooth out the dominant influence ofpopulation (cf. Bogner et al., 1993; Chung, 2010; Hockett et al.,1995; Miller et al., 2009).

The original data were controlled, checked for plausibility and,if necessary, corrected. Some data, which were only available inunits of volume or numbers of units, were converted into mass,for example biogenic waste (conversion factor 150 kg m�3), orsome waste electrical and electronic equipment (monitors: 25 kgper unit, gas discharge lamps: 0.33 kg per unit, refrigerators andfreezers: 30 kg per unit). In order to identify outliers, trends ofthe absolute and per capita MSW quantities between 1995 and2007 were determined for each municipality and residuals wereanalyzed. Values with a deviation of more than 30% from the lineartrend line were replaced by the trend value. This procedure wasnecessary for 41 values (0.6% of all 6508 values) between 1995and 2007, which means that data consistency was good. Fig. 1illustrates the distribution of MSW for selected years. The wastequantity has been on a constant upward trend, the median increas-ing from 171 kg cap�1 yr�1 in 1995 to 279 kg cap�1 yr�1 in 2007. Asthe data are not normally distributed, MSW was transformed toMSWt = log10(MSW) before inclusion in regression models.

2.3. Independent variables

The database comprised 109 indicators for demographic and eco-nomic influencing factors and for regional structure, as well as sevenindicators relating to waste management measures (e.g. home com-posting, number of bio-waste containers, collection systems forlightweight packaging and paper, pay-as-you-throw systems),which were predominately available in absolute numbers. Addition-ally, 45 indicators were deduced from these original data by convert-ing them into comparative data (e.g. relating them to residentialpopulation) or by summarizing into larger intervals (e.g. agegroups). Out of these, 30 indicators which were assumed to havean influence on MSW were preselected and analyzed. Table 1 showsthe most important of these. Different groups of indicators can beidentified, referring to waste generated by private households (e.g.POP, HHS, AGE, IMG), commercial waste (e.g. ICOM, TAX, ONS, TER),methods of waste disposal (illegal use of waste as domestic fuel

Fig. 1. Per capita quantities of MSW in the municipalities of Styria in selected years(n = 542).

(HEAT) as well as desired methods (HOME)) and general indicatorsdescribing regional structure (PSA, DENS, MFD, FARM). Unlike wastequantity data, continuous time-series were available for only somesocio-economic data (POP, AGE, TAX, ONS). Only for the year 2001were values available for almost all indicators.

Data related to individual consumption, such as purchasingpower or household expenditures, were not available at the munic-ipality level and therefore could not be considered. However, themunicipal tax revenue per capita (TAX) was available as an indica-tor for the wealth of a municipality. Indicators which are primarilyused to explain differences of waste generation at the householdlevel, such as education or attitudes, were considered insignificantto modeling MSW at the municipality level (cf. Mazzanti andZoboli, 2008; Hockett et al., 1995). Others might hypothesize thatthese individual indicators may have a significant influence onsmall municipalities. However, this hypothesis was not tested inour study, since reliable indicators at the municipality level weremissing. Source separation rates as possible indicators for wasterelated behavior could explain different quantities of separatelycollected waste streams, but not total MSW generation, unless itis implied that source separation behavior correlates with wasteminimization behavior. No evidence for this hypothesis could befound. Besides, the quantities of separately collected waste streamsdepend not only on individual behavior, but also on a variety ofother factors such as the collection scheme, the influence of com-mercial waste or the overall quantity generated. Thus, includingsource separation rates in the model would have increased uncer-tainties and sources of error, and was therefore not included.

2.4. Method

The analysis, which comprised an explorative data analysis anda multiple regression analysis, was conducted at the municipalitylevel and used data from the year 2001 due to data availabilityand quality. The sample size was 542 municipalities. For the anal-ysis, the program packages Microsoft Excel 2003 and SPSS 15.0were used.

The purpose of the explorative analysis was the basic character-ization of the data. Measures of central tendency and dispersion,extreme values, outliers and bivariate correlations among the inde-pendent variables, and between the independent variables and thedependent variable MSW (see Table 1) were determined. As mostof the data were not normally distributed, Spearman’s rank corre-lation coefficients were used. The results served as a basis forreducing the number of potential input variables for the regressionmodel. Criteria for the pre-selection of significant indicators werefactual considerations (representation of the different groups ofinfluencing factors), methodological considerations (high correla-tion with MSW, no or only low correlation among the pre-selectedindicators), but also data availability. The pre-selected variableswere HHS, TAX, RCOM, HEAT and ONS, which, however, were partlycorrelated (see Table 3). But at that intermediate step, factual con-siderations were given priority.

Regression models were used to find the combination of inde-pendent variables which explain MSW best, which means an opti-mum between model fit (high coefficient of determination, lowstandard error) and complexity (low number of parameters). Vari-ables which did not show normal distribution were transformedbefore their inclusion in the model. Starting with only one inde-pendent variable, multiple regression models with up to four inde-pendent variables were investigated progressively. The modelswere recalculated after the exclusion of outliers, and by weighingthe datasets by residential population (log10(POP)), which wasbased on the hypotheses that waste quantities from small munic-ipalities are more unsteady than those from large municipalities.Outliers were identified by means of leverage values, standardized

Table 1Selected and analyzed socio-economic and waste management indicators, their data sources, statistical measures of central tendency and correlation with MSW (Spearmancorrelation coefficient and significance) for data from the year 2001.

Variable name Variable label Availability (years) Sourcea Mean Minimum Maximum Normality Correlationwith MSW

POP Residential population (No. of inhabitants) 1991, 2001–2007 C, PR 2183 128 226,244 No 0.372**

SCR Secondary residences (No. per POP) 1991, 2001 C 0.062 0.000 0.485 No 0.397**

HHS Household size (Persons per household) 1991, 2001 C 2.96 1.99 4.45 Yes �0.749**

HH1 Percentage of single-households 1991, 2001 C 0.216 0.062 0.431 Yesb 0.701**

AGE0005 Percentage of population aged 0–5 years 1991, 2001–2007 C, PR 0.050 0.022 0.093 Yesb �0.201**

AGE0515 Percentage of population aged 5–15 years 1991, 2001–2007 C, PR 0.125 0.075 0.174 Yes �0.362**

AGE1560 Percentage of population aged 15–60 years 1991, 2001–2007 C, PR 0.612 0.524 0.680 Yes �0.048 nsAGE60 Percentage of population aged 60 years or more 1991, 2001–2007 C, PR 0.214 0.146 0.374 Yesb 0.230**

IMG Percentage of immigrants (per POP) 1991, 2001–2007 C, PR 0.024 0.000 0.153 No 0.565**

ICOM Index of commutersc 1991, 2001 C 62.0 12.6 347.1 No 0.492**

RCOM Relative commuter balanced 1991, 2001 C �0.17 �0.42 1.09 No 0.520**

TAX Municipal tax revenue per capita (EUR cap�1 yr�1) 1994–2006 TAS 668 456 1475 No 0.570**

EMP Employees per POP 1991, 2001 WS 0.230 0.014 1.479 No 0.560**

ONSe Overnight stays (No. per inhabitant) 1999–2007 TS 19.59 0.20 305.76 No 0.015 nsPRI Percentage of employees in primary sector 1991, 2001 WC 0.103 0.003 0.388 No �0.604**

SEC Percentage of employees in secondary sector 1991, 2001 WC 0.345 0.143 0.564 Yes �0.325**

TER Percentage of employees in tertiary sector 1991, 2001 WC 0.548 0.296 0.822 No 0.619**

PSA Permanent settlement area (104 m2) 2005 PC 910 101 9430 No �0.119**

DENS Population density (inhabitants per PSA) 2005 PC 2.28 0.36 28.66 No 0.533**

MFD Percentage of residential buildings with three or moreapartments

2001 BC 0.047 0.000 0.445 No 0.702**

FARM Percentage of residential buildings affiliated to a farm 2001 BC 0.218 0.005 0.600 No �0.767**

HEAT Percentage of buildings with solid fuel heating systems 2001 BC 0.314 0.053 0.670 Yesb �0.774**

HOME Home composting (percentage of households) 1995–2007 WI 0.526 0.000 1.000c No �0.150**

a Sources: C – census, PR – population register, TAS – tax statistics, WC – workplace census, TS – tourism statistics, BC – building census (Statistik Austria, s,a.); WI – wasteinquiry (Amt der Steiermärkischen Landesregierung, s.a.b), PC – provincial census (Mayer, 2008, personal communication).

b Histogram indicates approximate normality, but no normality according to Kolmogorov–Smirnov test.c Quotient of persons employed in municipality and resident employees � 100.d Difference of in-commuters and out-commuters, related to residential population.e The number of overnight stays (ONS) is only reported for 61% (332) of 542 municipalities.

** Significant at the 0.001 level.

1910 S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915

residuals and studentized deleted residuals. Indications for poten-tially influential cases were leverage values which exceeded thevalue of 2⁄p/n (p: number of estimated coefficients; n: number ofdatasets) and residuals greater than 2 (Grüner, 2007). For checkingpurposes, stepwise regression models were also calculated, usingall of the variables listed in Table 1 as input variables first, andsubsequently different subsets of variables.

The assumptions of linear regression – linearity of the relation-ship between dependent and independent variables, homoscedas-ticity, multicollinearity and normality of the error distributionwere tested for each regression model. Linearity was examinedon the basis of a scatterplot between the standardized predictedvalues and the standardized residuals. The assumption of homo-scedasticity (constant variation of the errors) was assessed bymeans of the Glejser test [Glejser (1969) cited in Grüner (2007)],a regression of the absolute residuals towards the absolute pre-dicted values of the dependent variable. The assumption that theindependent variables are not highly correlated (no multicollinear-ity) was examined by means of the variance inflation factor (VIF),tolerance, eigenvalue and condition index measurements. Theforecasting ability of the model was analyzed by means of 5-foldcross validation in which the sample was partitioned randomlyinto five complementary subsets. A single subsample was eachretained as the validation data for testing the model and theremaining four subsamples were used as training data.

3. Results and discussion

3.1. Significant indicators

From the various tested models, the model with the indepen-dent variables household size (HHS), municipal tax revenue per

capita (TAX) and percentage of buildings with solid fuel heatingsystem (HEAT) was selected as the best model (Eq. (1)). Thirty-three of 542 municipalities were excluded from the regressionanalyses: For two municipalities thereof, the transformation pro-duced extreme values of the variable TAXt. One municipality ex-ceeded the critical leverage value, and 30 municipalities showedan absolute value of the standardized residual higher than 2.

log10ðMSWÞ ¼ 2:845� 0:099 � HHS� 0:506 � HEAT � 0:072

� TAXt ð1Þ

The model explains 74.3% of the variation of MSW betweenmunicipalities (Table 2), which is rather high compared to otherregression models. Except for models with a small sample sizeand a large number of independent variables (e.g. Miller et al.,2009; Jenkins, 1993), reported R2 are usually in the range of about50% (Hockett et al., 1995; Bach et al., 2004; Benítez et al., 2008).The retransformed relative standard error of the model is 17%. Thestandardized coefficients (Table 2) indicate that HEAT has the high-est relative impact, followed by HHS and TAX. It has to be noted thatdue to the transformation of TAX, the sign of TAXt changed, whichmeans that MSW increases with increasing per capita municipaltax revenue (TAX), with decreasing household size (HHS) and witha decreasing percentage of buildings with solid fuel heating system(HEAT). The model assumptions are met. The dependent variableand the independent variable show normal distribution and a linearrelationship. Homoscedasticity is given since there is no correlationbetween the unstandardized residuals and the unstandardized pre-dicted values. The standardized residuals are normally distributed.

Because of the high correlation between HHS and HEAT (Table3), problems with multicollinearity could have been expected.However, the results of multicollinearity measurements do notclearly confirm multicollinearity. The variance inflation factors of

Table 2Final regression model for MSW, 2001, coefficients and standard error (n = 509).

Dependent variable R2 Standard error Independent variable Coefficient Standardized coefficients t Value (significance)

MSWt 0.743 0.069 Constant b0 2.845 115.583 (0.000)HHS b1 �0.099 �0.328 �8.581 (0.000)HEAT b2 �0.506 �0.454 �11.885 (0.000)TAXt b3 �0.072 �0.188 �6.854 (0.000)

MSWt = log10(MSW); TAXt = log10((1474.81 � TAX)/(TAX � 456.269)).

Table 3Spearman correlation coefficients among the independent variables and between theindependent variables and other indicators for the year 2001 (Only correlationcoefficients > 0.5, significant at the 0.01 level, two-sided, and significant correlationcoefficients with the population size are shown.).

Variable HHS TAX Correlated variables (variable labels see Table 1)

HHS �0.58 HH1 (�0.89), AGE015 (0.53), IMG (�0.60), ICOM(�0.53), RCOM (�0.55), EMP (�0.63), PRI (0.76),TER (�0.62), DENS (�0.67), MFD (�0.80), FARM(0.86), POP (�0.45)

HEAT 0.80 �0.55 HH1 (�0.72), IMG (�0.61), ICOM (�0.51), RCOM(�0.52), EMP (�0.60), PRI (0.73), TER (�0.67),DENS (�0.64), MFD (�0.73), FARM (0.89), POP(�0.43)

TAX �0.58 HH1 (0.61), IMG (0.50), ICOM (0.83), RCOM (0.84),EMP (0.80), MFD (0.53), FARM (�0.56), POP(+0.32)

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HHS and HEAT are 3.0, which is smaller than the recommendedlimit value of 5 and therefore within acceptable limits accordingto Grüner (2007). Condition index and variance proportions arejust within the critical limits. The condition index is 27.4, whichindicates a possible multicollinearity problem because it is largerthan 15 but smaller than 30 (Grüner, 2007). The variance propor-tion for HHS is 0.99 and 0.49 for HEAT, which is strictly speakingwithin the limits, while multicollinearity must be considered aproblem if the proportion of variance in two or more variablesexceeds 0.5 (Grüner, 2007). Partial correlations reveal that thecorrelation between HHS and MSWt is still significant at the0.001 level if controlled for HEAT (Pearson correlation coefficient�0.342), as well as the correlation between HEAT and MSWt if con-trolled for HHS (Pearson correlation coefficient �0.453). Thismeans that a significant proportion of the variance of MSWt cannotbe explained by either of the variables. Therefore HHS and HEAT,which are related to principally different impacts, are both in-cluded in the model.

Household size (HHS) is an indicator for waste originating fromprivate households. A large number of models reported in the liter-ature also identified a significant negative impact of household sizeon per capita quantities of MSW (e.g. Dennison et al., 1996b;Benítez et al., 2008; Beigl et al., 2008; Bandara et al., 2007; Jenkins,1993). Household size correlates with the presence of children(AGE015) and serves as an indication for urban or rural structure.Municipalities with high average household size show a high posi-tive correlation with the percentage of employees in the primarysector (PRI) and the percentage of residential buildings affiliatedto a farm (FARM), as well as a medium negative correlation withpopulation density (DENS) and a low to medium negative correla-tion with residential population (POP) (Table 3).

The percentage of buildings with solid fuel heating systems(HEAT) is used as an indicator for the amount of waste which isburned in domestic heating systems, and therefore reduces thequantity of MSW. In Table 3 it can be seen that HEAT, like house-hold size (HHS), shows a high correlation with indicators for ruralstructure. Although the heating system is often mentioned as animportant influence (Boer et al., 2010; Dennison et al., 1996a,b;

Salhofer and Graggaber, 1999; Eder et al., 1983) it is not usuallyincluded in regression models, except for the model of Salhoferand Graggaber (1999) The findings about the influence of the heat-ing system are contradictory. While Salhofer and Graggaber (1999)found a significant negative impact on MSW and Eder et al. (1983)identified a significant negative impact on rural municipalities,results of Dennison et al. (1996a,b) support the hypothesis thatthe use of solid fuel increases MSW quantity due to ash. Resultsfrom a composition analysis of residual waste in Styria (TBU,2004), which was conducted during the heating season as well asduring the non-heating season, refute this hypothesis by indicatingno difference in the percentage of fines between rural and urbanareas. The fraction below 40 mm amounted to 27.7% by mass ofresidual waste in urban areas, and 31.0% in rural areas. Inert finesaccounted for 1.0% of residual waste in urban areas and 1.9% in rur-al areas (TBU, 2004). The verification of the use of waste as domes-tic fuel through the analysis of separately collected paper,cardboard and lightweight packaging can only provide an approx-imation, because the separately collected quantity is also influ-enced by factors other than HEAT, such as the collection system,commercial waste, as well as the generated amount; and no dataabout the composition of residual waste at the municipality levelare available. The amount of separately collected waste paper,cardboard and lightweight packaging shows a significant mediumcorrelation with HEAT (�0.64, Spearman Rho).

The municipal tax revenue per capita (TAX) is an indicator of theeconomic activity of a municipality. It includes dues levied bymunicipalities (property tax, local tax, other charges and taxes,dues for the use of municipal facilities) and profit-shares of collec-tive federal dues (Mayer, 2007). As TAX substantially depends onthe economic structure of a municipality, it is used as an indicationfor waste from small commerce, services and institutions in themodel. TAX is highly correlated with other economic indicators,such as the index of commuters (ICOM), relative commuter balance(RCOM) and employees per population (POP) (Table 3). TAX was gi-ven priority over other economic indicators because of better dataavailability and also slightly better model results than with alter-native variables RCOMt (transformed) or EMPt (transformed), whichproduced slightly lower R2 (0.738 with RCOMt, 0.733 with EMPt)and higher standard errors (0.071 and 0.072, respectively). Mone-tary economic indicators such as GDP (Beigl et al., 2008; Bogneret al., 1993), household income (Hockett et al. 1995; Benítezet al., 2008; Jenkins 1993) or household expenditures (Mazzantiand Zoboli, 2008; Daskalopoulos et al., 1998) were included in alot of precious models and proved to be significant. However, anindicator comparable to TAX at the municipality level has not beenreported to date.

3.3. Excluded and non-significant indicators

Tourism was not included in the model because data aboutovernight stays were only available for 332 municipalities, andthe inclusion of the log-transformed variable overnight stays perinhabitant (ONSt) in the model would not produce significantlybetter results. The inclusion of ONSt produced a slightly higher R2

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Fig. 2. Model validation: predicted versus observed quantities of MSW (inversetransformed) for municipalities included in the model and for excluded outliers.

1.9

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test set 1test set 2test set 3test set 4test set 5Linear (test set 5)Linear (test set 4)Linear (test set 2)Linear (test set 3)Linear (test set 1)

Fig. 3. Cross-validation: predicted versus observed quantities of MSW, predictionsfor test sets calculated by means of coefficients obtained by the four other sets;MSWt = log10(MSW).

1912 S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915

(0.779) than the selected model (Table 2); the standard error was0.069. The influence of ONSt is barely significant (0.049), and thestandardized coefficient (0.056) indicates a low relative impor-tance of ONSt. ONS neither showed a significant bivariate correla-tion with MSW (Table 1) nor medium or high correlations withother indicators. No indication could be detected that the non-inclusion of ONS had biased the results of the selected modelbecause the unstandardized residuals showed no correlation withONS. The number of overnight stays proved to be a significantfactor in the regression models of Bach et al. (2004) and Salhoferand Graggaber (1999), but no information about the relativeimportance of this factor was provided.

The effect of individual composting (HOME) on MSW quantitieswas also investigated, but in the end it was not included in themodel. A stepwise regression conducted with all 542 municipali-ties resulted that the inclusion of the transformed variable HOMEt

(percentage of households home composting), which occurred inthe 4th step after HEAT, HHS and TAXt, only marginally increasedR2 from 0.664 to 0.667. The effect of HOMEt was significant butlow, with a standardized coefficient of 0.064. It has to be noted thatthe validity of this result is poor due to poor data quality – the per-centages of households with home composting are usually esti-mates from the municipalities – and the fact that our MSWdefinition only comprised part of biogenic waste, namely the quan-tity from collection in bio-waste containers, but no separately col-lected garden and park waste (see Section 2.2).

None of the different age groups (AGE0005, AGE0515, AGE1560,AGE60) proved significant in any stepwise model. Only if importantfactors such as HEAT were excluded from stepwise regression, a sin-gle significant impact of age group showed up. That means, if at all,there is only a low-ranking influence of age pattern of the popula-tion, which concurs with the findings of Mazzanti and Zoboli(2008). Other regression models found a significant age influencesuch as a positive influence of the percentage of the population aged18–49 on the quantity of residential waste discarded per capita(Jenkins, 1993), or a positive influence of the percentage of the pop-ulation aged 15–59 years on total MSW generation in medium-in-come European cities (Beigl et al., 2008).

An alternative calculation of the selected regression modelwhich weighed the datasets by residential population (log10(POP))resulted in a slightly higher R2 (0.766) but increased the standarderror significantly from 0.069 to 0.121. Also, with regard to modelsimplicity, the model without dataset weighing was preferred.

3.4. Model fit

Fig. 2 illustrates the correlation between predicted and (inversetransformed) observed per capita MSW quantities, as well as theregression line for the 509 municipalities which were included inthe calculation of the model (‘‘inclusion in model’’ group). No prom-inent outliers can be detected in this group. The absolute value of thestandardized residual exceeds 2.0 for 23 of the 509 municipalities,with the maximum being 2.47. The predicted MSW quantities werealso calculated for the 33 excluded municipalities and are depictedin Fig. 2, except for the 2 municipalities with extreme values of TAXt

and predicted MSW (83 and 1672 kg cap�1 yr�1). The model overes-timates MSW for some municipalities with low per capita MSWquantities and underestimates it for some municipalities with high-er MSW quantities. Among those municipalities with a poor modelfit, small municipalities are overrepresented. 57.6% of the 33 ex-cluded municipalities have less than 1000 inhabitants, comparedto only 32.8% of the other 509 municipalities. On the one hand, thepoor model fit for those municipalities can be explained by the factthat the unsteadiness of waste data has a higher impact on smallmunicipalities and a prognosis is therefore much more difficult.On the other hand, the municipalities with a poor model fit show

unusual combinations of the indicators used in the model, and in-clude some municipalities which are outliers in terms of severalother independent variables. Municipalities for which the modeloverestimates MSW (below the regression line in Fig. 2) show signif-icantly lower mean values for HHS and HEAT, but a significantlyhigher mean for TAX, than municipalities with a satisfying modelfit and comparable observed MSW quantities, whereas the oppositeapplies to municipalities for which the model underestimates MSW.

Fig. 3 shows the results from the 5-fold cross validation. Thesample was split into five equal subsamples (n = 102 respectively101) at random. For each subsample (test set) the predicted MSWt

values were determined by using model coefficients obtained by aregression model with the four other subsamples, and were plottedagainst the observed MSWt values in Fig. 3. The five regressionlines, which are nearly congruent, indicate a good match. The coef-ficients of determination are 0.715 for test set 1, 0.703 for test set2, 0.761 for test set 3, 0.764 for test set 4 and 0.789 for test set 5.For the five training-set models, R2 range between 0.741 and0.706 and show a deviation between 0.4% and 1.7% from the coef-ficient of determination for the total model, which indicates a goodmodel quality.

3.5. Quantification of the effects

Based on the model for very heterogeneous municipalities, iso-lated effects, such as tonnage of commercial waste or tonnage ofwaste burned in domestic heating systems, were estimated. In

S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915 1913

the following, these effects will first be derived from the model.Then the resulting estimation formulas are compared with the ob-served data. For that purpose, the logarithmic relationship of Eq.(1) with the coefficients shown in Table 2, was transformed intolinear form (Eq. (2)).

MSW ¼ 10b0 � 10b1�HHS � 10b2�HEAT � 10b3�TAXt ð2Þ

The estimation of the amount of commercial waste (COMM)collected in the municipal collection system is based on the factthat municipal tax revenues per capita (TAX) highly correlate withthe number of employees (EMP) (see Table 3). Municipalities at theminimum bound, with taxes around 500 Euro, have nearly nocommercial waste impact, while municipalities with TAX of morethan 1200 Euros usually have more employees than inhabitants.Taking the minimum bound (TAX = 500) as a reference, the massof commercial waste is calculated as the difference of MSW calcu-lated with the actual value of TAX (TAX = a) and MSW which resultsfrom the assumption that TAX is 500 (Eq. (3)). Considering thetransformation of TAXt (see Table 2) and inserting the values ofb3 and TAX = 500, this results in Eq. (4).

COMM¼ ½MSWðTAX ¼ aÞ�MSWðTAX ¼ 500Þ�=½MSWðTAX ¼ aÞ�¼ ½10b0 �10b1�HHS �10b2�HEAT � ð10b3�TAXtðTAX¼aÞ �10b3�TAXtðTAX¼500ÞÞ�=ð10b0 �10b1�HHS �10b2�HEAT �10b3�TAXtðTAX¼aÞÞ

¼ 1�10b3�TAXtðTAX¼500Þ=10b3�TAXtðTAX¼aÞ ð3Þ

COMM ¼ 1� 100:072�flog 10½ð1474:81�TAXÞ�ðTAX�456:269Þ��1:3481g ð4Þ

Fig. 4 illustrates the impact of commercial waste if the other twoinfluencing factors are kept constant. For that purpose, only munic-ipalities with small household size and low percentage of buildingswith solid fuel heating are shown. Obviously, the differences of MSWbetween the municipalities can be explained by the impact of thenumber of employees. As a result of Eq. (4), the percentage of com-mercial waste (COMM) in the individual municipalities variesbetween 0.5% and 34%, except for one outlier with 51% of commer-cial waste, which is Bad Radkersburg (municipality 61513 inFig. 4), a spa and touristic center with extremely high tax revenuesper capita. Overall, commercial waste accounts for 20.9% of MSWin the Province of Styria, which is within the range reported by thesparse literature on that topic. According to Salhofer (2000), the per-centage of commercial waste in MSW is estimated at 20–50%. For thecity of Vienna, a detailed study (Salhofer, 2000) found that the com-mercially and industrially generated volume in the municipal

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Fig. 4. Commercial waste (estimated with Eq. (4)) in MSW of municipalities with similaheating systems (HEAT) < 0.2, and classified according to employees per residential pop

collection scheme amounted to about 26%. In comparison, for Graz,a city with about 226,000 inhabitants and metropolitan structurecomparable to Vienna (municipality 60101 in Fig. 4), the modelestimated a percentage of 34% of commercial waste.

Similarly, the estimated mass of MSW burned in domestic heat-ing systems (BURN), was deduced on the basis of Eq. (5). The massof burned waste is the difference between a municipality withoutsolid fuel heating [MSW(HEAT = 0)] and the collected MSW [MSW(HEAT = h)]. Eq. (6) shows the final calculation formula for BURNas a function of HEAT.

BURN ¼ ½MSWðHEAT ¼ 0Þ �MSWðHEAT ¼ hÞ�=½MSWðHEAT ¼ hÞ�¼ ½10b0 � 10b1�HHS � 10b3�TAX � ð10b2HEATðHEAT¼0Þ � 10b2�HEATðHEAT¼hÞÞ�=ð10b0 � 10b1�HHS � 10b3�TAX � 10b2�HEATðHEAT¼hÞÞ ð5Þ

BURN ¼ 100�506�HEATðHEAT¼hÞ � 1 ð6Þ

The fit of the estimation formula is illustrated in Fig. 5, whichshows municipalities with a household size between 3 and 3.5and TAX between 600 and 800 Euros. Municipalities with the lowestpercentages of buildings with solid fuel heating have the highestamount of MSW collected, and vice versa for the group with highestvalues of HEAT. The total height of the columns in Fig. 5 symbolizesthe potential of MSW in the case of no domestic burning.

Summing up the quantities of BURN resulting from Eq. (6) foreach municipality and relating the sum to the quantity of MSW col-lected in Styria, it results that 18% of MSW generated in Styria,respectively 73 kg cap�1 yr�1, is burned in domestic heating sys-tems, and respectively 82% is collected by the municipalities. Inthe individual municipalities, the percentage of burned wasteranges between 5% and 54%. These quantities appear very high.For a comparison, only data from Eder et al. (1983) are available.Based on waste composition analyses in Germany and the resultsof a questionnaire survey, Eder et al. (1983) estimated the quantityof burned waste at 13 kg cap�1 yr�1 if 25% of households burnedwaste, and 23 kg cap�1 yr�1 if 35% of households burned waste. Re-lated to the amount of household waste, the burned quantity rangedfrom about 4% to 9% of household waste. The results of Eder et al.(1983) likely underestimate the quantity because of consideringonly selected waste types such as paper, cardboard and the mixedfraction with particle sizes from 8 to 40 mm. Furthermore, partici-pants in a questionnaire survey presumably tend to underreportburning MSW (cf. Pieters, 1989). Nevertheless, the quantity ob-tained from the model appears to overestimate the real quantity of

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r characteristics. Household size (HHS) < 2.3, percentage of buildings with solid fuelulation (EMP).

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Fig. 5. Burned waste (estimated with Eq. (6)) and collected MSW of municipalities with similar characteristics. Household size (HHS) between 3.0 and 3.5, municipal taxrevenue per capita (TAX) between 600 and 800 EUR cap�1 yr�1, and three different ranges of percentage of households with solid fuel heating (HEAT).

1914 S. Lebersorger, P. Beigl / Waste Management 31 (2011) 1907–1915

waste burned in domestic heating systems. It has to be taken into ac-count that estimations for BURN from the model may be biasedbecause of potential multicollinearity between HHS and HEAT.

4. Conclusions

The regression model identified household size, municipal taxrevenues per capita and the percentage of buildings with solid fuelheating as the most important influencing factors on MSW at themunicipality level. The model explains 74.3% of the MSW variation,and the model assumptions are met, with the reservation that mul-ticollinearity is just below the upper bound of tolerance. Other fac-tors, such as tourism, home composting or age distribution of thepopulation, turned out to be secondary. They did not significantlyimprove the model and were therefore not included. Also the sizeof the residential population was not a significant factor.

Based on the model, the percentage of commercial waste inMSW can be estimated. Twenty-one percent of MSW in Styria iscommercial waste, which is consistent with literature data. Thisindicates that the model provides a good estimation on an aggre-gated level. The model implicates the simplified assumption, thatthe percentage of small commerce, services and institutions usingthe municipal collection scheme is equal for all municipalities,which is not the case in reality in Austria. Ideally, empirical datashould be collected in order to refine the model.

The model provides a pioneer basis for the estimation of thequantity of MSW burned in domestic heating systems. Virtuallyno reference values have been reported in the literature to date.From the model, it resulted that 18% of MSW generated in Styriais burned in domestic heating systems. This estimate is consideredless accurate than the estimate for commercial waste and seems torepresent an upper bound of the real rate of MSW burned. Furtherresearch is necessary, also with regard to possible overlappingeffects with household size in the model.

The model is used as basis for the prognosis of MSW quantities(Beigl and Lebersorger, in preparation) and will be verified withdata for other regions. Furthermore, the procedure shall also bereproduced for other waste streams such as bio-waste or wastepaper.

Acknowledgements

The authors wish to thank the Styrian Provincial Government,Fachabteilung (Specialized Division) 19D – Waste and MaterialFlow Management (www.abfallwirtschaft.steiermark.at) for fund-ing the study as well as for provision of waste-related data. Valu-able support was given by Mr. Martin Mayer from Fachabteilung(Specialized Division) 1C – Regional Statistics.

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