ecological –economic modelling for strategic regional waste

E C O L O G I C A L E C O N O M I C S 5 9 ( 2 0 0 6 ) 1 1 5 – 1 3 0

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ANALYSIS

Ecological–economic modelling for strategic regional wastemanagement systems

S.E. Shmeleva,⁎, J.R. Powellb

aEERU, The Open University, UKbCCRU, University of Gloucestershire, UK

A R T I C L E I N F O

⁎ Corresponding author. Tel.: +44 7729733366E-mail addr ess: s.shme [email protected] (S.E

0921-8009/$ - see front matter © 2005 Elsevidoi:10.1016/ j.ecolecon.2005. 09.030

A B S T R A C T

Article history:Received 17 September 2004Received in revised form23 September 2005Accepted 27 September 2005Available online 5 December 2005

This paper summarises some recent work exploring the development of a multi-criteriaoptimisation tool for achieving sustainable solutions formunicipal solidwastemanagementsystems (MSWMS). The aim of the project was to provide a newmethodological backgroundfor the regional solid waste management modelling taking into account spatial andtemporal patterns of waste generation and processing, environmental as well as economicimpacts of the system development with a particular emphasis on public health andbiodiversity.

The research has focused on integrating three different approaches to the spatial-temporalanalysis of the MSWMS, namely a life cycle inventory analysis, which helps to identifyemission patterns within the MSWMS, a multi-criteria optimisation approach, which helpsto find compromise solutions among environmentally and economically preferred options,and a geographic information systems approach, which provides a tool for identifying wastemanagement facilities, transportation environmental and social impacts, aswell as analysis ofenvironmental impacts on valuable ecosystems. A Russian methodology for calculatingenvironmental damage was used to weight the importance of different sub-territoriescovered by the system as well as simplifying the analysis of emissions from the wastetreatment plants. The approach provides a new perspective for the analysis of municipalsolid waste management systems at the regional scale. The principal novelty of theproposed complex MSW strategic management model is an integration of the different typesof data–geographical, environmental and economic–using relational database technology.Simulations using the dataset for Gloucestershire were performed on a simplified version ofthe model. Simulations were undertaken to explore the potential effects on wastemanagement infrastructure of introducing the EU Landfill Directive.Understanding the strengths and weaknesses inherent in the methods utilised hassuggested that a relatively affordable and easy to use tool can be developed for strategicanalysis of themunicipal solid wastemanagement system in a region, giving useful supportto the decision-maker regarding the potential development paths and trade-offs betweeneconomic and environmental performance of a proposed waste management system.

© 2005 Elsevier B.V. All rights reserved.

Keywords:Ecological–economic modellingWaste managementIntegrated approachUK

.. Shmelev) .

er B.V. All rights reserved.

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http://dx.doi.org/10.1016/j.ecolecon.2005.09.030

116 E C O L O G I C A L E C O N O M I C S 5 9 ( 2 0 0 6 ) 1 1 5 – 1 3 0

1. Introduction

Strategic decision-making for dealing with municipal solidwaste is a problem currently exercising the minds of manylocal governments (Gloucestershire County Council, 2002)throughout the European Union (EU). This paper is devotedto ecological–economic modelling of the strategic develop-ments in Municipal Solid Waste (MSW) Management Systemsat the regional level.

The waste management problem in the EU is characterisedby increasing per capita production of waste materials, theneed for high levels of investment in physical infrastructure(incinerators, landfills, recycling facilities), institutional bar-riers (such as the long-term nature of contracts), a wide rangeof stakeholders and a dynamic policy arena (e.g. the WasteElectrical and Electronic Equipment and Landfill Directives aretwo instruments aimed at reducing the amounts of biode-gradable and electronic waste being landfilled). The wastestream itself varies in composition over time and space withseasonal and longer term changes in the quantities andamounts of various materials and the market for ‘recycled’materials is characterised by uncertain demand and fluctuat-ing prices.

Strategic decision-making for waste is a complex pro-blem that appears to offer scope to mathematical model-ling procedures in order to find “optimal” solutions.Although standard modelling approaches are limited asthe ideal solution looks very different depending whereyou are situated: from the household or local governmentpoint of view the best solution would be to eliminate thewaste and remove the need for any waste service provisionin the first place, while the view from the waste industrywould be one of maximising the number of waste streamsand quantities of waste over time to ensure survival of theindustry. This paper considers whether ecological economicmodelling approach has anything new to offer thepolicymaker.

2. Description of the problem

Ecological–economic modelling is an aid to strategic deci-sion-making for waste management where there is nearlyalways strong local opposition to the siting of waste facil-ities, where alternative waste management approachesplace heavy demands on the environment, where futureEU policy threatens to put the onus on producer responsi-bility and thus remove significant quantities of high valuematerials from the waste stream, and solutions are drivenas much by local politics as by economic factors. Standardeconomic modelling approaches seeking the optimum orleast cost solution fail as they cannot incorporate thewide range of factors that need to be included in a decisionthat must be based on achieving the Best Practicable Envir-onmental Option (BPEO). Decision-makers do need assis-tance in making strategic choices that cause social andenvironmental impacts, and tie up large amounts ofmoney and land for significant periods of time. Theapproach presented here is a first step in developing an

ecological economic modelling approach that attempts tointegrate life cycle inventory analysis, environmentalimpact assessment and economic appraisal within a geo-graphic information system (GIS) framework. The aim is notto provide an “optimum” solution but to highlight to deci-sion-makers the trade-offs inherent through investing indifferent mixes of waste management technology at arange of scales from the local to the regional. In otherwords, it can reveal, for a particular area or region, howwaste management should be ‘integrated’ in order toachieve the BPEO solution.

The waste management problem has a complex naturewith a range of important dimensions such as multiplicity ofthe types of waste generated in the system, complex spatialpattern of waste arisings, the necessity to transport wastelong distances for processing, a variety of emissions fromwaste collection, transporting and treatment to the environ-ment, and the almost unpredictable and localised character ofimpacts of these emissions on humans and ecosystems. Andalthough there have been attempts to analyse regional wastemanagement systems taking into account environmentalimpacts of processes under study, most of them have notformed a holistic method for analysing all spatial, temporalas well as qualitative aspects of the problem. Therefore, theaim of the paper is to provide a new methodological back-ground developing regional municipal solid waste manage-ment modelling, taking into account spatio-temporal pat-terns of waste generation and processing, environmental aswell as economic impacts of the system development with aparticular emphasis on public health and biodiversity.

This paper takes the first steps to develop a model formunicipal solid waste management system at the regionallevel. The paper analyses the post-consumption stages ofthe waste life cycle, namely collection, sorting, treatmentand final disposal. The municipal solid waste managementsystem under study is illustrated by Fig. 1, which shows themain material flows within the system. The figure revealsthat the whole life cycle of materials entering and leavingthe waste management system consists of several stages—raw materials extraction, processing, sale, consumption,finally becoming waste when they are discarded by consu-mers. These materials in the waste stream then undergo col-lection, sorting (removal of recyclable materials) andtreatment (which can be thermal or biological), with the finalstage being disposal in the landfill. The shaded areas in thediagram are the stages of the life cycle explicitly takenaccount of in this paper.

3. Previous and current approaches to wastemanagement modelling

There have been many attempts to analyse municipal solidwaste management systems over the past decade. Economicas well as environmental and social aspects of their perfor-mance have been taken into account. Despite the largeamount of research done, the application of the major meth-ods employed does not provide a holistic picture of munici-pal solid waste management systems that can examineenvironmental impacts and the economic costs of siting,

Raw materials extraction

Processing

Sale

Consumption

Waste collection

Deep separation

Without separation

Organic/ non-organic

Landfill

Incineration

CompostingComplex recycling

Paper, glass, metals, organic etc.

Impacts of transport

Recovered energyRecovered materialStages of the waste

Non-organic waste

Organic waste

Unseparated waste

Fully separated waste

Impacts of processes

management process

Waste treatment

Sorting

System boundary

Fig. 1 –The municipal solid waste management system: material flows.

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technological processes involved, transportation, impactsand their spatio-temporal distribution, or identify the partiesaffected. The main spheres of research in the field ofMSWMS in 1990s have been: analysis of waste generationdeterminants (Hockett et al., 1995; Daskalopoulos et al.,1998; Chen and Chang, 2000, siting of waste managementfacilities (Huang et al., 1995; Chang and Wang, 1996; Fre-driksson, 2000), the choice of the waste treatment method(Huhtala, 1997; Dalemo et al., 1998; Highfill and McAsey,2001), environmental impacts of different waste manage-ment technologies (Nixon et al., 1997; Slater and Frederick-son, 2001; Powell, 1996), economic mechanism of managingMSWS (Morris and Holthausen, 1994; Jenkins et al., 2000;Palmer et al. 1997; Fullerton and Wu, 1998; Hong, 1999),transportation of waste (Bhat, 1996; Kulcar, 1996), macroeco-nomics of recycling (Nakamura, 1999; Ferrer and Ayres, 2000;Masui et al. 2000) and complex planning (Huang et al., 1997;Chang and Wang, 1997; Chang et al., 1997; Haastrup et al.,1998). In the majority of this research, the focus has been onsingle aspects of the problem, for example, Chang and Wang

(1997) looked at management costs, air pollution and therecycling goals, but missed out water and soil pollution,noise, road congestion, employment and health impacts;Haastrup et al. (1998) concentrated on costs, air, water andsoil pollution, road congestion, technological reliability, butdid not cover noise, employment, health impacts and recy-cling goals.

A substantial amount of research on local aspects of muni-cipal solid waste management modelling has been carried outusing LCI methodology based on the recent models developedby White et al. (1999) and the Environment Agency's WISARDmodel. Powell et al. (1996), for example, compared environ-mental and social impacts of a kerbside collection scheme forrecyclable household waste with a bring scheme, using lifecycle assessments and economic valuation for assigning rela-tive weights to these impacts, while Powell et al. (1998)explored alternative approaches to waste management forsix district councils in Gloucestershire. Powell (2000) investi-gated the potential for using LCI analysis in local authoritywaste management decision-making.


Many aspects of the waste management systems perfor-mance were not integrated in a holistic model taking intoaccount spatial distribution of environmental as well aseconomic impacts, and analysing transportation, technolo-gical and siting issues simultaneously. Unfortunately,almost all of these models are of minimal use by the deci-sion-makers as they miss some of the key institutionaldimensions of waste management as identified by Vigileos(2002), in particular the unequal social impacts of wastemanagement, the nature of contracts drawn up betweenthe industry and local authorities which are long-term,and sometimes require delivery of guaranteed amounts ofwaste, political pressures to recycle, barriers imposed bygovernment regulations and the lack of communicationsbetween different participants in the waste managementsector.

Although there are examples of environmental–economicanalysis of municipal solid waste management systems onthe regional level by Haastrup et al. (1998), Chang and Lin(1997) and Chang and Wang (1996), many applications donot incorporate an integrated analysis of environmentalimpacts from all stages of the life cycle of municipal solidwaste, spatial ecological–economic modelling of the distri-bution of impacts, non-substitutable treatment of environ-mental and economic characteristics of the development ofthe system, including non-monetary valuation of environ-mental damage. What is missing is a technique for solvingregional waste problems which inevitably have a large num-ber of possible solutions due to variable population densi-ties, incomes, multiple (actual and potential) locations forwaste management infrastructure, protected landscapeareas and high value ecological sites. There is thus anurgent need for improved methods for identifying BPEO1

solutions to waste management problems at the regionallevel. The range of potential development paths for a solidwaste management system, for example, could include alarge centralised regional facility, or a set of small localisedones, depending on the physical conditions, and this re-presents a situation of choice between multidimensionalscenarios.

4. A comparison of approaches for analysingthe municipal solid waste management problem

Among the methods used for analysis of MSWMS during thepast 10 years several should be mentioned here: input–out-put approach (Nakamura, 1999; Ferrer and Ayres, 2000), mul-tiple regression analysis (Hockett et al., 1995; Daskalopouloset al., 1998), life cycle analysis (Powell et al., 1998; Song and

1 The standard notion of BPEO-Best Practicable EnvironmentalOption is defined as follows “A BPEO is the outcome of asystematic and consultative decision making procedure, whichemphasises the protection and conservation of the environmentacross land, air and water. The BPEO procedure establishes, for agiven set of objectives, the option that provides the most benefitsof least damage to the environment as a whole, at acceptablecost, in the long term as well as the short term”, in the 12th Reportof the Royal Commission on Environmental Pollution, 1988.

Hyun, 1999; Craighill and Powell, 1996; Powell, 2000), opera-tions research methods (Chang and Wang, 1997; Chang etal., 1997), multi-criteria assessment (Hokkanen and Salmi-nen, 1997; Rogers and Bruen, 1998; Salminen et al., 1998) andexpert systems (Barlishen and Baetz, 1996; Haastrup et al.,1998). All of these methods have particular uses in specificareas and Table 1 below identifies their strengths andweaknesses.

The life cycle inventory approach (see Fig. 2) providesinformation on the spectrum and quantities of emissionsfrom a given technological process and when it comes tocomparing different scenarios sophisticated methods ofmulti-criteria assessment (Munda and Romo, 2001) could beapplied. However, LCI methodology does not include any geo-graphical or time dimension nor provide any estimates of theeffect of the emissions inventoried. When used in isolation, itcannot identify the best solution (i.e. BPEO) of the waste man-agement problem.

MCDA, optimisation, Delphi on the other hand allow forcomparison between alternatives that need to be integratedwith an approach that can analyse the waste managementsystem itself.

Geographic Information technology is a powerful tool foranalysing and exhibiting spatial data. However, rating andscoring of several scenarios (which is done often in geo-spatial environmental impact assessment, EIA) is notenough for performing an integrated analysis of the devel-opment of the municipal solid waste management system.It is necessary to perform a significant amount of simula-tion experiments, changing different spatial siting patterns,processing capacities, waste collection and sorting schemesto arrive at the decision space from which a selection canbe made. All of these approaches need to be underpinnedby some impact assessment methodology. The one selectedhere is the Russian methodology for environmental damagecalculation that was developed by Balatsky et al. (Vremen-naja tipovaja metodika, 1983; Vremennaja metodika, 1999)and allows for taking into account the spatial dimension ofenvironmental impacts in the form of coefficients of envir-onmental value of the territories or regions (Vremennajametodika, 1999). At the same time, it lowers the dimensionof the analysed vector of environmental characteristics ofthe given waste management system, which can be in turndivided into negative effects of recycling, incineration andother waste treatment options, as well as negative effectson air, water and soil. Such lowering of the dimensionsimplifies the decision-making significantly and allows fordealing with only two dimensions of the waste manage-ment planning problem—environmental and economic.

The Russian methodology for estimating environmentaldamage uses coefficients of environmental harm, attributedto each type of emission into water and air. These coeffi-cients are developed from laboratory based biologicalresearch on animals (i.e. standard toxicological studies)and extrapolation of these effects on humans. This informa-tion is then integrated with another set of coefficients—coefficients of environmental value of the territories orregions that are based on the ecosystem value of themajor biomes, soils, water reserves, located in the territoryof the given region.

Table 1 – Current tools for municipal solid waste management decision-making

Method Strengths Weaknesses

LCI—life cycle inventory • Reflects a wide spectrum of emissions • Only an inventory of emissions• Allows integration of environmental data with economic • No information on impacts to the recipientsdata • No time or space related dimensions• Flexible, allows easy comparison of different scenarios • Unable to make local/regional/global trade-offs

MCDA—multi-criteriadecision analysis

• Allow comparison of multi-attribute or multi-objective • Problems with weight estimationscenarios• Flexibility in the choice of criteria

• Limitations by comparing only a relative smallnumber of alternatives, which could not represent

• Allows integration of quantitative and qualitative data the efficient set of solutions

Optimisation • Gives the best solution from the feasible set • The opportunity to solve large scale non-linear• Permits solving of multi-objective problems byemploying goal programming, compromise programmingtechniques, etc.

mixed integer problems limited by the existingalgorithms• Certain assumptions about the relationships in

• Allows the user to identify the efficient frontier of thesolution space for subsequent decision-making

the model have to be made

GIS—geo-informationsystems

• Reflects spatial patterns of the geographical distribution • Does not have a time dimensionof actors, flows and sensitive areas• Allows the user to perform geographic analysis based on

• Requires integration with other techniques forperforming comparative analysis of scenarios

intersection, overlapping of different objects, etc. • The amount of output information is too high fordecision-making

Environmental damagecalculation methodology,Russia (1983, 1999)

• Allows for integration of many types of emissions into asingle measure of environmental damage

•No common and recognisedmeasurement unit ofenvironmental damage

• Explicitly takes into account geographical peculiarities ofthe given territories

• No account taken of the receptors of pollutingemissions

Delphi method • Allows the user to analyse complex situations with • Subjectivism of estimatesuncertain information and/or lack of time/resources fordecision-making using experts

• Possibilities of unequal understanding theproblem in question by the experts

Environmental impactassessment (EIA)

• Allows detailed examination of all the impacts fromspecific sites and technologies

• Very expensive in terms of time, resources, datademands

• Can combine economic, environmental and social • Necessary to combine with dispersion modellinginformation • Very superficial types of studies

• Focus is on the impacts and not the waste systemitself

Pollution dispersion models • Show detailed spatial distribution of emissions given therelief, climate and the characteristics of the source of

• Substantial computational power is needed (esp.for multiple sources)

emissions • A very expensive tool• Difficult to analyse the impacts on the finalrecipients


In summary, we can say that LCI is good at modelling thewaste system but is only a first stage in identifying environ-mental impacts as it concentrates on emissions to water,land and air, but does not provide any indication of theimpact or significance of emissions locally. It needs to beintegrated with other techniques such as EIA which can pro-vide the impact analysis needed based on the siting of infra-structure, or movement of waste and with some optimisationprocedure that can begin to deal with the issues of trading offeconomic costs and benefits against the social and environ-mental impacts of alternative waste management systems.Thus, it is clear that what is required is a combination ofseveral methods in order to perform the complex analysisof the potential development of the municipal solid wastesystem.

Several studies have already tried to combine some ofthese methods. During the 1990s, for example, GIS and EIA

were combined into a geo-spatial EIA (Patil et al., 2002;Antunes et al., 2001), GIS and MCDA were combined by Daiet al. (2001), LCI and MCDA were integrated by Powell et al.(1996), and Munda and Romo (2001) and Powell et al. (1999)integrated a simple multi-criteria approach to examine envir-onmental impacts from alternative waste management sce-narios for the city of Bristol.

In summary, these studies are still limited and cannot beused to solve regional waste problems because they have notelaborated all the complex of factors influencing waste man-agement processes at the regional level—namely spatial dis-tribution of waste arisings, impacts of transportation andprocessing of waste as well as multidimensional characterof these emissions, time dimension of waste generation,building new or expanding existing facilities and spatial dis-tribution of impacts of waste treatment processes onhumans and valuable ecosystems. This paper reports on

Administrative borders

Waste treatment facilities

Administrative borders of wards

Incineration with energy recovery

Civic amenity sites

Landfills

Materials recycling facilities

Scrapyards

Transfer stations

Fig. 2 –Location of the waste treatment plants in Gloucestershire.


research (Shmelev, 2003; Shmelev and Powell, 2004) that hasfocused on the integration of three different approaches tothe spatio-temporal analysis of the MSWM problem, namelya life cycle inventory approach (LCI module), which helps toidentify emission patterns within the MSWMS, a multi-cri-teria optimisation approach (MO module), which helps tofind compromise solutions among environmentally, eco-nomically and socially preferred options, and a geographicinformation systems approach (GIS module), which providesa base for siting waste management facilities, transpor-tation, social impacts, as well as locating environmentalimpacts on valuable ecosystems. A Russian approach tocalculating environmental damage was utilised to weightthe importance of different sub-territories covered by thesystem. It is hoped that this approach will provide a newperspective for the analysis of municipal solid waste man-agement systems.

5. Development of the integrated methodology

Based on an understanding of the weaknesses of the methodsidentified in Table 1 above, it was decided that by combining

different methods a more useful tool might be developed forthe development of strategic municipal solid waste manage-ment plans. The aim therefore was to develop an integratedtechnique that would give useful support to the decision-maker regarding the potential development paths and trade-offs between economic and environmental performance ofalternative waste systems.

Research carried out in Russia (St. Petersburg and theregion) and the UK (Gloucestershire) has concentrated on acomplex analysis of the MSWMS, taking ecological, economicas well as social aspects of the management of municipalsolid waste into account.

Due to software limitations, it was decided to limit theanalysis of the municipal solid waste management systemto examination of five major components: i.e. economiccosts of running the system, public health, the state ofthe flora and fauna, saving of material resources and land-scape quality. Four out of the five factors chosen to char-acterise the waste management system relate to the maingoals of the EU Landfill Directive (European Council, 1999)(reduction of adverse effects of the landfill of waste on theenvironment, in particular on surface water, groundwater,soil, air and human health) and also correspond to the

2 The approach used here was one developed by the Russianenvironmental economic school, which is explained in Vremen-naja metodika (1999) and Vremennaja tipovaja metodika (1983)The main idea of calculating environmental damage according tothe Russian methodology consists of integrating the amounts oemitted pollutants into a single index of environmental damageThe actual emissions of polluting substances are multiplied bycoefficients of environmental harm, which are in the inverserelation to the MAC (maximum allowable concentrations) opollutants in question. MAC are based on the results osubstantial medical and environmental research (i.e. toxicologicadata). The list of coefficients can be found in Appendix A.


most relevant subject themes of the UN Sustainable Devel-opment Indicators: social (health), environmental (atmo-sphere, biodiversity) and economic (consumption andproduction patterns) (United Nations, 2001). The fifth com-ponent, ‘landscape quality’, was selected to reflect theimportant role that landscape plays in local communitiesas stated in the European Landscape Convention, whichidentifies landscape as “a key element of individual andsocial well-being”. Under Chapter II of the Convention,signatories agree to “integrate landscape into…regionaland town planning policies…as well as in any otherpolicies with possible direct or indirect impact on land-scape” (Council of Europe, 2000). These five componentsoffered a relatively simple and straightforward means ofanalysing economic–environmental trade-offs. The mone-tary costs of operating a waste management system areof critical concern to local authorities, materials savings(i.e. recycling) are of national concern, and for strategicdecision-making purposes both these elements need tobe directly compared to the impacts on environmentaland social factors (i.e. human health, environmental‘health’ and landscape quality). The data on the selectedcomponents were also available and relatively easy toobtain.

Life cycle analysis using the Proctor and Gamble (2001)model was integrated with a GIS and an optimisation techni-que. The LCA model allowed the researchers to examine awide range of emissions from alternative waste managementscenarios; the GIS allowed actual and proposed waste man-agement sites, along with ecologically sensitivity of the land-scape to bemapped; the single criteria optimisation techniquepermits the possibility of deriving a unique solution of theproblem.

The most difficult choice was that of the optimisationprocedure. There are well known, fast and reliable methodsfor linear problems, whereas it gets more complex whenthe situation requires mixed integer programming. Thereare ways for reducing the multidimensional problems tosingle-criterion ones, however, and taking account of sev-eral objectives simultaneously. The type of optimisationproblem employed here (linear mixed integer programmingproblem) is complex and demands significant computa-tional power and efficient algorithms, especially for thereal scale modelling. Constraints on resources and compu-tational power led to a focus on a two-dimensional pro-blem by examining single-criteria overall system costminimisation with simultaneous calculation of an addi-tional parameter (such as the environmental damagecaused by the system performance). Although limited thistwo-dimensional solution space still provides a usefulstarting point for understanding how useful such an inte-grated methodology might be.

The large sets of heterogeneous data used in the model(geographical, economic, environmental and social) are inte-grated using relational database technology. The databasesystem consists of several interrelated tables representingdifferent aspects of the problem under study (e.g. differenttypes of waste analysed, spatially distributed waste genera-tion centres, a range of waste treatment facilities, multitudeof emission types, etc.).

6. Description of the modules within theintegrated method

6.1. The GIS module

The key elements of the GIS module are the digitised maps ofthe county of Gloucestershire, UK, obtained from a range ofdifferent sources. The maps are overlaid and allow graphicalanalysis of the location of the physical waste infrastructure,and transport routes in relation to the environmentally sensi-tive areas and the centres of population density. The censusward was taken as aminimal geographical unit for populationdata.

6.2. The impact assessment module

It should be noted that the methods of the analysis and com-parison of the emission inventory results within life cycleanalysis is an area open to debate. In some cases, the list ofthe emissions analysed numbers several hundreds items. Inorder to deal with this vast amount of information in thecurrent research, the methodology expressed in the Vremen-naja metodika (1999) and Vremennaja tipovaja metodika(1983) was taken as an instrument for comparing scenarioswith heterogeneous outputs. The list of substances taken intoaccount in the analysis can be seen in Appendix A. The toxi-city coefficients database for all the pollutants allows conver-sion of the wide spectrum of the different substances into aunified index of the environmental damage, which lessensthe dimension of the problem substantially and simplifiesthe solution procedure.2

The method is used here to provide the spatial dimen-sion of environmental damage around waste treatmentinfrastructure sites in the form of the coefficients of theimportance (significance) of the territories around thewaste treatment plants. Such coefficients were derived byperforming a series of operations on the GIS maps. Thedispersion of pollutants from the various waste treatmentfacilities was approximated by a 5-km radius circle aroundeach of the sites. The coefficients of significance werederived based on the weighting of sensitive areas by agroup of experts based at the University of Gloucestershireusing a Delphi approach. Standard national designations ofecological and landscape importance were utilised by theexperts: Sites of Special Scientific Interest (SSSI), NationalNature Reserves (NNR), Special Areas of Conservation (SAC),Specially Protected Areas (SPA), RAMSAR sites and an indi-

.

f.

ffl

Table 2 – Gloucestershire real problem dimensions

Set Definition Quantity of elements

J Waste generation points 145H Waste types 9I Waste treatment centres 86K Waste treatment technologies 6T Periods of system functioning 20

Which amounts to 13,560,480 variables in the mixed integer pro-grammingmodel, including integer variables: 92,880, real variables:13,467,600 and number of constraints: 13,591,278 (without trivialconstraints, stating non-negativity of decision variables—123,678).


cator of population density. The average number of peopleliving within 5-km circle around the waste treatment plantswas calculated using the average population density ofneighbouring wards covered by the circle. The expertswere asked to rate their perceptions of the relative sensitiv-ity of the designated areas to the potential emissions fromthe waste management facilities on a 1–10 scale. This infor-mation was then integrated into a Randomised Preferencesmethod (Hovanov, 1996), which took the relational dataderived from the experts to derive weighting factors. A Del-phi approach was utilised because of the complexity anduncertainty over the impacts of the regional waste manage-ment system on the different aspects of the human andnatural environments. The Delphi approach provided aquicker and cheaper alternative to more narrowly definedpollution dispersion modelling approaches. The Delphiapproach has the added advantage of enabling localisedpriorities to be integrated into the significance measures,but the methodology employed needs to be transparent inorder to understand the trade-offs generated. Thus, such anapproach will provide variations in significance measures indifferent regions, related to population geography and pro-tected areas.

Then overall indices of the importance of the territoriesaround the waste treatment plants were obtained. First, allthe 5-km radius circles around the waste treatment plantswere overlayed onto the maps of the different types ofsensitive areas (including centres of population density).Then the percentage of the intersection of each circle byeach type of the sensitive area was multiplied by the impor-tance factor for a given sensitive territory and it was sum-mated over all six types of areas analysed according to theformula:

Ik ¼XJ

j¼1

FjTSðCk \ EjÞSðCkÞ

� �

where Ik is the importance score of the circle around kthwaste treatment plant; Fj is an importance factor for theenvironmental sensitive territory type j, j=1…J; S denotesarea; C is an area of the circle around a waste treatmentplant; Ej is a joint object consisting of the parts of environ-mentally sensitive areas falling within a given circle Ck.

The borders of each of the geographical objects are storedin the digital database with additional information such asthe name of the object, areas and geographical coordinates.The centre points of the census wards are used to define thewaste generation places, and transport routes are consideredhere as the links connecting the centroids of the wards andthe waste treatment plants.

6.3. The LCI module

In the framework of the analysis carried out here, the life cycleanalysis is bounded on the one side by the post-consumptiongeneration of waste and on the other side by final disposal. Itincludes the analysis of the municipal solid waste streamcomprising eight components—paper, glass, ferrous andnon-ferrous metals, plastics (film), plastics (rigid), textile,organic and “other”.

For each of the types of waste mentioned three basictreatment technologies are analysed: recycling, incinerationwith energy recovery and landfilling. The emissions to air,water and soil are analysed. In order to get the integral indexof environmental damage, the amounts of the pollutingemissions are multiplied by the respected coefficients ofenvironmental harm, according to Vremennaja metodika(1999) and Vremennaja tipovaja metodika (1983) as describedabove.

6.4. Optimisation module

This module integrates the information on plant locationsand distances between the centres of population density andwaste treatment plants from the GISmodule and the informa-tion on the amounts of emissions from each type of waste,collected, sorted and treated by each of the technologies fromthe LCImodule. It is here that the choice of collection systems,sorting and treatment technologies, as well as geographicaldistribution of the waste management facilities are optimisedover the time period of interest according to the total systemcost minimisation criteria. The problem that is being analysedhere belongs to the class of linear mixed integer programmingproblems. LINGO optimisation software uses branch andbound methods to solve problems of this type. The informa-tion on the problem dimensions for the Gloucestershire casestudy is laid out in Table 2.

The initial problem is set in a single-criteria cost mini-misation framework. The reason for this is that all theimprovements in the environmental performance of thewaste management systems are bound by the budgets ofthe related administrative units, and cost minimisation isstill the dominant criteria for waste management systemdevelopment. The environmental damage is calculatedhere as a by-product of the minimum costs scenario accord-ing to the formula:

ED ¼XKk¼1

IkTXKk¼1

gITElk;

where ED is a total systems environmental damage, Ik is animportance score of the territory around kth waste treat-ment plant, Ekl is the amount of emissions of the lth type(l=1…L) (from the LCI module) and γl is an environmentaldamage coefficient for the emission type l.

The final two-dimensional solution space in the form pre-sented in Fig. 7 is obtained by performing a sensitivity

Table 3 – Composition of municipal solid waste inGloucestershire, 1998/1999

Material Gloucestershireaverage, %

Nationalaverage, %

Fines 2.3 7Ferrous 4.0 6Glass 3.4 9Green 11.3 21Putrescibles 34.4Misc. com. 5.8 8Misc. non-com. 0.5 2.20Non-ferrous 1.0 2Paper and card 20.8 32Plastic film 4.9 5Rigid plastics 7.6 6Textiles 3.9 2

Gloucestershire figures do not include the recycled waste.


analysis, which changes the waste treatment capacities, inrelation to landfill space. It allows the decision-maker to ana-lyse the given waste management system in terms of thetrade-offs between environmental and economic objectives.3

The simplified version of the model utilised here based onthe work of Baetz and Neebe (1994) was built using the LINGO7.0 optimisation software. The model only permitted exami-nation of a reduced set of problem dimensions: three wastetreatment technologies—recycling, incineration and landfill-ing were considered over 10 time periods with no considera-tion of space dimension. The problem's dimensions weredetermined by the constraints in the number of boolean andcontinuous variables in the Demo version of LINGO 7.0. Due tothe resource constraints (software limitations), the model hasnot been realised to its full potential.

Two versions of the model were developed, which useOpen Data Base Connectivity (ODBC) technology for transfer-ring data from one software package to the other. The useful-ness of the developed model is that it allows the user tochange the initial data outside the model and then to “plug-in” the new datasets for subsequent solving. It is useful in thecase of sensitivity analysis involving large number of para-meter changes.

It should be stressed here that solving this problem in realdimensions with standard tools would require handling vec-tors of model variables with 15,000,000 components. This willdefinitely require usingmore powerful databasemanagementsystems (e.g. SQL Server) and the problem could be solvedfaster if its special structure could be taken into account.The problems that can be analysed include the choice ofwaste processing technology (e.g. among landfilling, compost-ing, waste-to-energy incineration, recycling), waste manage-ment facility plant siting and optimisation of the wholeMSWMS performance using different goal functions.

The next section examines a simple application of themodel to waste management in Gloucestershire.

7. Case study of Gloucestershire

Gloucestershire lies in the west of England (South WestRegion), has a total area of 2,618,000 km2 and a population of574,000 (2001). Gloucestershire comprises six local authorities:Cheltenham Borough, Cotswolds District, Gloucester City, For-est of Dean District, Stroud District and Tewksbury Borough.Average number of people in the households is 2.41. Theaverage disposable income per person per year is £10,073(1999, data for the South West Region), and annual wastearisings range from a low of 280 to a high of 432 kg of muni-cipal solid waste per person per year is produced in Glouces-tershire. The annual recycling rate in 1998/1999 ranged from

3 Description of the software used—LINGO, ACCESS, MAPINFO,Procter and Gamble LCI MODEL.The integration of the hetero-geneous software was necessary for building the workinginteractive modelling system. In the current research, theoptimisation software package LINGO 7.0 (demo version), GISpackage MapInfo Professional 6.0, Database management systemMS Access 2000 and spreadsheet MS Excel 2000 were used, alongwith the Procter and Gamble LCI model.

6% in Gloucester to 19% in Cotswolds District. The dominantmunicipal solid waste treatment method is landfilling (82% inthe South West Region of the UK).

The average composition of municipal solid waste in Glou-cestershire is presented in Table 3.

Fig. 2 illustrates location of waste facilities and Table 3compares waste composition to the UK national average.

Simulations using the dataset for Gloucestershire wereperformed on the simplified version of the model. 8140simulations were undertaken (see Fig. 7), where the wastetreatment capacities for recycling, incineration and landfill-ing were changed. This could illustrate, for example, thepossible consequences of introducing the EU Landfill Direc-tive in the county, which may result in fewer landfills andincreased recycling capacity, with consequent impacts ontransport routes and costs across the county. The objectiveof the directive is to prevent or reduce as far as possiblenegative effects on the environment from the landfilling ofwaste, by introducing stringent technical requirements forwaste and landfills, and reducing the quantity of biodegrad-able material going to landfill. The scenarios examined hereshow the potential effects of reductions in available landfillspace as a result of the Directive and explore the impactsof increased tipping fees and recycling subsidies on theenvironmental and economic performance of the system.Fig. 7 illustrates the combinations of minimal costs andcorresponding environmental damages for the wholerange of scenarios examined. All the combinations ofpotential environmental damage and economic costs aregiven here under equal economic conditions. Only thelandfill and waste treatment capacities were changed inthis analysis.

8. The description of the results of thesimulations experiments

The results of a series of simulation experiments are depictedin Figs. 3–7. The study of the developed model of the regionalwaste management system was conducted along the follow-ing main lines: it was decided to study the sensitivity of themodel first to the changes in technological parameters of the

Total costs and environmental damage

0

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Index ofenvironmentaldamage

Fig. 3 –Scenario 3. RE=200, W=200, LL=5000.


available capacity of the existing landfill under different com-binations with other technological parameters being fixed,and later to the changes in price parameters—the cost ofrecycling of a ton of waste and costs of collection and trans-porting waste to the landfill. The combinations of the systemparameters used in sensitivity analysis are shown in Table 4.ED—environmental damage denotes the index of environ-mental damage and C—costs denotes total managementcosts in British pounds.

In the first scenario, the recycling and incineration capa-cities were limited by 200,000 t/year, there is no opportunity toopen an additional landfill. With parameter L decreasing atfirst against a background of considerable growth in costs, theslow growth in environmental damage takes place, caused byintensive use of incineration as an alternative for the decreas-ing landfill capacity; then with decreasing Lb5500 the substi-

Total costs and enviro

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tutional transition to recycling part of the waste takes place,causing considerable decrease in environmental damage by afactor of 1.08.

In scenario 2, there is an option of opening a small addi-tional landfill with the capacity of 1,000,000m3. The first localminimum of environmental damage is found at L=5500. Sucha sharp decrease in environmental damage is caused by thegrowth in recycling, instead of harmful landfilling in thelandfill; the following growth in damage is caused by openingof an additional landfill in the 9th period; and the rapiddecrease in environmental damage starting at L=4500 canbe explained by the ever increasing rate of recycling. Thecosts at the same time are starting to grow at a naturallyfaster rate.

In scenario 3 (Fig. 3), the rapid decrease in environmen-tal damage as L approaches the value of 5500 is caused by

nmental damage

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600, W=200, LL=0.

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1451351251151059585756555

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Fig. 5 –Scenario 6. RE=600, W=200, L=5000, LL=0, illustrating changes in A.


the growth of the share of recycling; the rapid growth inenvironmental damage after Lb4500 is caused by the open-ing of a new landfill for 5,000,000 m3 in the 6th periodwith simultaneous decrease in the share of the recyclingand incineration. After that, the growth in the share ofwaste being incinerated is growing. The following localminima can be explained by the shifts of the momentof opening an additional landfill to 5th, 4th period and soon.

The tendency for the environmental damage and mini-mal management costs to change in scenario 4 (Fig. 4) couldbe divided into three different stages—10,000NLN5500,5500NLN100, Lb100. In the first stage, the gradual growthof the share of the waste being incinerated takes place,which causes the slow growth in environmental damageand costs; decreasing environmental damage and costsgrowing at the faster rate in the second stage are causedby the growth in the share of waste undergoing complexrecycling at Lb100; when the landfilling capacity even for

Total costs and environme

230

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Fig. 6 –Scenario 7. RE=600, W=200, L=5

placing the incineration residue becomes critical, the shifttowards recycling at a larger scale takes place.

In the 5th scenario, everything develops similarly to the4th; however, due to the larger planned incineration capacityand smaller recycling capacity, the shift to the second stage ofintensive recycling takes place later, at about L=750, and tothe third–earlier–around L=200.

The sensitivity of the solution to the problem to thechanges in price parameters is illustrated in Figs. 5 and 6.Analysing the changes in environmental damage, causedby the decreasing price of complex recycling of a ton ofwaste (parameter A, recycling costs, Fig. 5), we come tothe conclusion about the lack of changes in environmen-tal damage with parameter A being reduced from 145 to110. Then the sharp decrease in environmental damage—more than by a factor of 1.7 with the following decreasein A to 80, and again, at the interval [55…80] environ-mental damage is at the lower than in the first case, butstable level.

ntal damage

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000, LL=0, illustrating changes in B.

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Total management costs, thous. GBP

Ind

icat

or

of

po

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tial

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age

Fig. 7 –Two-dimensional solution space.


Changes in the parameter B—costs of collection andtransportation of waste to the place of their landfilling inthe Landfill 1 could suggest the optimal level for the trans-port costs set up in the interests of the environmental pro-tection (under conditions of legal waste discharges by thecompanies and municipalities). The results of the simula-tions experiments that could be seen in Fig. 6 show that,with the transport costs being increased up to a certain level(in our case B=120) and given the laws are observed, trans-porting waste to the landfill may become less desirable thanrecycling.

The main result of the work—two-dimensional solutionspace, which is a integration of the results of sets of simula-tion experiments 1 to 5 (Table 4), shows that, by increasingtotal system management costs by a factor of 1.82, it is possi-ble to diminish the total environmental damage by a factor of2.99.

Table 4 – Parameters changed in sensitivity analysis

Set ofsimulationexperiments

LL RE W L Changedparameter

Theinterval ofchange

1 0 200 200 L 10:10,0002 1000 200 200 L 10:10,0003 5000 200 200 L 10:10,0004 0 600 200 L 10:10,0005 0 400 400 L 10:10,0006 0 600 200 5000 A 55:1457 0 600 200 5000 B 10:300

Key: L—available capacity of the existing landfill, thousands m3 peryear; LL—available capacity of the additional landfill, thousandsm3 per year; RE—recycling capacity, thousand of tons per year; W—incinerating capacity, thousand of tons per year.

The shape of the thick curve representing the set ofnon-dominated solutions (solutions that are equal or notworse off than all the others) depicts the peculiarities ofthe complex problem of the development of a waste man-agement system giving the decision-maker the range ofoptions he or she could choose from and thereby helpinghim trade-off economic versus environmental aspects ofthe development of the system in question. We are deci-sively not proposing the decision-maker “the best solu-tion” or BPEO, but providing him or her a freedom ofinformed choice; however, hard it may be to make one.The latter appears in the realm of pure political decision-making.

9. Discussion

The results presented here illustrate an application of a sim-plified ecological–economic model of a municipal solid wastemanagement system. Full development of the model wouldallow solution of more complex problems involving real deci-sions of siting, choice of treatment technology, collection andsorting method. Certain weaknesses remain in the approachtaken here, primarily software limitations and probably lackof pollution dispersion modelling.

The main strength of the model is that it allows the deci-sion-maker to analyse the ecological–economic trade-offs inthe development of the municipal solid waste managementsystem. It examines possible strategies of the development ofthe system, taking into account different siting options,choice of waste treatment technologies, performs preliminaryinvestment planning and explicitly takes account of spatialdimension of environmental impacts on public health andvaluable ecosystems.


In the life cycle analysis performed here, the boundariesare defined by the post-consumption solid waste generationthrough to the moment of it's final disposal. If theboundaries were altered to include elements related tothe production of the waste processing equipment, trans-portation fuel life cycle, analysis of materials and pro-ducts the solid waste was derived from, the results couldchange significantly.

The model presented in this paper could be developedfurther to take into account the real dimensions of the pro-blem, such as transportation of waste, improved pollutiondispersion models and introduce hyperbolic discounting(Daly and Farley, 2004). If we take into account the origins of

Air Particulates 0Air CO 0Air CO2 0Air CH4 0Air NOx 0Air N2O 0Air SOx 0Air HCl 0Air HF 5Air H2S 0Air HC 0Air Chlor. HC 0Air Dioxins/furans 0Air NH3 0Air As 0Air Cd 0Air Cr 0Air Cu 0Air Pb 0Air Hg 3Air Ni 0Air Zn 0Air Landfill gas (250 nm3/t) generation (t/t) 0Water BOD 0Water COD 0Water Sus. sol. 0Water TOC 0Water AOX 0Water Chlor. HCs 0Water Dioxins/furans 0Water Phenol 0Water NH4 4Water Tot. metals 0Water As 0Water Cd 0Water Cr 0Water Cu 0Water Fe 0Water Pb 0Water Hg 0Water Ni 0Water Zn 0Water Cl 0Water F 9Water NO3 0Water S- 0

waste and work on material flows accounting of productsentering the system in the first place, then with programmingimprovement a full scale decision support tool for strategicregional wastemanagement could be created. The next step isto apply more powerful software, possibly integrate pollutiondispersion models for all sources of pollution and analysemore rigorously the chains of impacts. It could be valuableto integrate the analysis of environmental impacts of trans-portation, take into account noise and congestion impacts.Models of this type could then be expanded and applied atthe regional level in the EU, to provide improved informationon the tradeoffs to be made what are inherently difficultpolitical problems.

Appendix A. The list of emission coefficients

Sector of the ecosystem Emission type Recycling Incineration Landfilling Damage coefficients

.00327 0.00002 0 2.7

.00228 0.0004 3.125E−06 0.41.1293 0.2209825 0.40 0.098215 0.7

.00231 0.0016 0 16.5

.000053 0 0 30

.003947 0.0003 0 20

.0000033 0.0001 1.625E−05 20E−09 0 3.25E−06 500.000012 0 0.00005 500.001692 0.0001 0.0005 20

0.0001 8.75E−06 505E−13 0 50,000

.0000004 0 0 28.50.0000025 0 5000.0000005 1.4E−09 5000.0000063 1.65E−10 16700.0000063 0 5000.0000063 1.275E−09 5000

E−09 0.0000005 1.025E−11 50000.0000025 0 5000.0000063 1.875E−08 5000 250 0

.00239 0 0.0004751 5

.02084 0 0.0004751 20 0.000015 0.15

.000004 0 0.0000003 50

.0000025 0 0.0000003 10000 1.545E−07 00 4.8E−14 00 5.7E−08 0

.47E−07 0 0.0000315 10 1.442E−05 00 2.1E−09 900 2.1E−09 2500 9E−09 5500 8.1E−09 5500 1.425E−05 10 9.45E−09 110 9E−11 15,0000 2.55E−08 900 1.02E−07 90

.000011 0 0.0000885 550

.7E−07 0 5.85E−08 5500 0 0.2

.000006 0 0 550


Appendix B. Types of environmentally sensitiveareas taken into account by the model

AONB (Areas of Outstanding Natural Beauty)—the areas pro-tected by the Government of the UK since 1949 “NationalParks and Access to the Countryside Act”. The main goal ofthe designating AONB is preservation of the natural beauty ofthe landscapes, and the recreational use is not a major goalhere and is permitted to the extent that such use is in accor-dance with the preservation of the natural beauty and theneeds of the agriculture, forestry and other spheres of theregional development as well as economic and social interestof the local communities. Such areas number 41 in 2002—theycover approximately 15% of the territory of England andWales.

SSSI (Sites of Special Scientific Interest)—the land desig-nated as such according to the 1981 “Wildlife and CountrysideAct” (UK) (as amended).

NNR (National Nature Reserves)—lands designated accord-ing to the “National Parks and the Access to the CountrysideAct” of 1949 (UK).

SAC (Special Areas of Conservation)—lands, which statusis drawn in the EC Directive 92/43/ÅÅÑ on Conservation of thenatural environments, wild fauna and flora”. The dataacquired have a status “candidate”.

SPA (Special Protection Areas)—lands, classified accordingto the EC Directive 79/409 on the preservation of the wildbirds. The data acquired have the status “classified”.

RAMSAR (unique wetland complexes)—the land, whichhas a status of the Wetlands of International Importanceaccording to Ramsar convention. The Convention on Wet-lands, signed in Ramsar, Iran, in 1971, is an intergovernmentaltreaty, which provides the framework for national action andinternational cooperation for the conservation andwise use ofwetlands and their resources. There are presently 138 Con-tracting Parties to the Convention, with 1364.30 wetland sites,totaling 119.6 million hectares, designated for inclusion in theRamsar List of Wetlands of International Importance.

Appendix C. Data requirements

The dynamic spatial ecological–economic model of theMSWMS built here links different types of data: GIS datasets, environmental impact information, economic informa-tion, specific waste related information, time information.

The required GIS data sets include:

• County, district and ward boundaries;• General purpose layers: rivers and waterways, motorways,urbanised areas;

• Population density within wards;• Areas of ecological significance (Sites of Special ScientificInterest (SSI), National Nature Reserves (NNR), Special Areasof Conservation (SAC), Special Protection Areas (SPA);

• Sites of existing and proposed waste management facilities;• Distances between the points in question (between wastetreatment plants and centroids of the chosen populationareas), other characteristics of transport routes.

The environmental impact information needed will con-sist of:

• Emission coefficients of waste treatment by different tech-nologies (recycling, RDF, landfilling, etc.), taking intoaccount the analysed types of waste (paper, glass, etc.) andthe list of substances of interest;

• Emission coefficients of using different types of fuel fortransporting waste;

• Coefficients of environmental harm from different sub-stances emitted into air and water according to the Russianenvironmental damage estimation methodology;

• Expert weights of relative importance of the environmen-tally sensitive areas examined with respect to placing wastetreatment plants near them.

Economic information comprises:

• Costs for processing different types of waste by differenttechnologies;

• Investment costs for building new waste processing plants;• Transportation costs;• Prices of recycled materials and energy derived from waste.

Specific waste related information:

• Types of waste under consideration;• Respective technologies used for processing each of thetypes of waste;

• Waste composition in the districts;• Sorting and collection information.

Time related information:

• Timescale of the model (number of periods under consid-eration, length of periods;

• Impacts which could differ over time (e.g. gaseous emis-sions from landfills).Time factor in economic decisions (dis-count factor).

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White, R.P., Franke, M., Hinde, P., 1999. Integrated Solid WasteManagement: A Lifecycle Inventory. Kluwer AcademicPublishers.

http://www.un.org/esa/sustdev/natinfo/indicators/isdms2001/table_4.htm

http://www.un.org/esa/sustdev/natinfo/indicators/isdms2001/table_4.htm

ecological –economic modelling for strategic regional waste

Business

find compromise solutions

vremennaja tipovaja metodika

municipal solid waste

waste treatment plants

special scientific interest

waste treatment capacities

calculating environmental damage

eu landfill directive