Decision support system for the optimal location of electrical and electronic waste treatment plants: A case study in Greece

Waste Management 30 (2010) 870–879

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Waste Management journal homepage: www.elsevier.com/locate/wasman

Decision support system for the optimal location of electrical and electronic waste treatment plants: A case study in Greece Ch. Achillas *, Ch. Vlachokostas, N. Moussiopoulos, G. Banias Laboratory of Heat Transfer and Environmental Engineering, Aristotle University, Thessaloniki, Box 483, 54124 Thessaloniki, Greece

a r t i c l e

i n f o

Article history: Accepted 30 November 2009 Available online 23 December 2009

a b s t r a c t Environmentally sound end-of-life management of Electrical and Electronic Equipment has been realised as a top priority issue internationally, both due to the waste stream’s continuously increasing quantities, as well as its content in valuable and also hazardous materials. In an effort to manage Waste Electrical and Electronic Equipment (WEEE), adequate infrastructure in treatment and recycling facilities is considered a prerequisite. A critical number of such plants are mandatory to be installed in order: (i) to accommodate legislative needs, (ii) decrease transportation cost, and (iii) expand reverse logistics network and cover more areas. However, WEEE recycling infrastructures require high expenditures and therefore the decision maker need to be most precautious. In this context, special care should be given on the viability of infrastructure which is heavily dependent on facilities’ location. To this end, a methodology aiming towards optimal location of Units of Treatment and Recycling is developed, taking into consideration economical together with social criteria, in an effort to interlace local acceptance and financial viability. For the decision support system’s needs, ELECTRE III is adopted as a multicriteria analysis technique. The methodology’s applicability is demonstrated with a real-world case study in Greece. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Businesses, governments, customers and the public are becoming increasingly interested in the alternative management of industrial products in a global scale, when those reach the end of their useful life. Especially in the case of Electrical and Electronic Equipment (EEE), due to the fact that such products contain high-value materials (e.g. Iakovou et al., 2009; Nnorom and Osibanjo, 2009; Kahhat et al., 2008; Kumar and Putnam, 2008; Zuidwijk and Krikke, 2008; Truttmann and Rechberger, 2006; Kang and Schoenung, 2005), as well as toxic ones (e.g. Dimitrakakis et al., 2009; DEFRA, 2006; IPTS, 2006; Poulios et al., 2006; Hicks et al., 2005; Petreas et al., 2005; AEA Technology, 2004; Five Winds International, 2001), their environmentally sound end-of-life management has become an issue of critical importance. Moreover, Waste Electrical and Electronic Equipment (WEEE) represent a constantly increasing current of the total volume of municipal solid waste, which is expected to continue escalating in the near future (Karagiannidis et al., 2007; Lehtinen and Poikela, 2006; Menegaki et al., 2006; Hischier et al., 2005; Widmer et al., 2005; Goosey, 2004; EEA, 2003). Sending EEE products to landfills should not be considered at any cost as an end-of-life option for manufacturers in modern soci* Corresponding author. Tel.: +30 2310 996092; fax: +30 2310 996012. E-mail address: [email protected] (Ch. Achillas). 0956-053X/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2009.11.029

eties. Apart from discarding illegally WEEE to open dumpsites or disposing them to sanitary landfills, there are other alternatives available. Equipment that is no longer required by its original user could in many cases be reused by other potential users. Even in the cases when no further use for the EEE product is possible or viable, their materials could be exploited in other products. In that sense, the components of a product that need to be disposed are limited to those which are not suitable either for reuse or recycling. Still, it is crucial that their disposal takes place in a sustainable way. In the framework of sustainable development, there have been international initiatives, agreements and cooperation, in parallel with the development of legislation that enforces certain obligations to manufacturers, based on the principles of the ‘‘Extended Producer Responsibility”. Towards the same direction, the EU has put forward the Integrated Product Policy (IPP), an instrument that focuses on the reduction of the environmental impacts of products throughout their life cycle, taking also into consideration social issues (European Commission, 2001). Along the lines of IPP, in a changing global market where consumers are becoming more environmentally concerned, it has become a great challenge for the private industry to design and produce goods that have minimum environmental impact, which are also cost competitive (Kollikkathara et al., 2009; Moussiopoulos et al., 2006). From a legislative angle, the EU has imposed strict limits regarding both the use of hazardous substances in EEE, as well as WEEE management, with the enforcement of the European Directives 2002/95/EC

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(European Commission, 2002a) and 2002/96/EC (European Commission, 2002b) respectively, highlighting the problem in the most urgent way. In order to efficiently manage EEE products at the end of their useful life, adequate infrastructure is a prerequisite. As a minimum, infrastructure includes WEEE collection points, either run by manufacturers, municipalities, retailers, third party logistics, etc., as well as WEEE treatment and recycling facilities. Taking into account the investment costs for the development of a reverse supply chain network, one of the most crucial aspects for any WEEE collective take-back and recycling scheme relates to the required Units of Treatment and Recycling (UTR). A critical number of UTR need to be developed in order (i) to decrease transportation – and consequently management – cost and (ii) to expand reverse logistics network in order to cover more areas. However, their development requires high expenditure for their set up. In the material to follow, a multicriteria methodology aiming towards optimal location of UTR is developed and demonstrated, combining economical and social criteria, in an effort to interlace local acceptance and financial viability. In contrast to the majority of the currently employed methodologies, the presented approach creates a tractable interface between mathematical modelling and policy making since the decision support system enables the evaluation of sites for the location of WEEE treatment plants in terms of their combined impact on environmental and economic aspects of alternative scenarios. Moreover, the importance of the work presented here lies in supporting public authorities’ planning schemes towards assessing the impact of policy interventions at country level with the adoption of possible UTR location scenarios. In the bibliography, a lot of effort has been directed towards identifying the interactions between operational research and environmental management (e.g. Daniel et al., 1997; Bloemhof-Ruwaard et al., 1995). More specifically, modelling of waste management is also not a new idea. A comprehensive summary of the models developed are analytically presented in the thorough review of Morrissey and Browne (2004). Waste management modelling efforts have been mostly focused on areas such as routing and scheduling (e.g. multi-objective models developed for optimal routing of hazardous waste in the work of List and Turnquist (1998)), as well as location problems. As regards the latter, scientific work mainly attempts to minimise the distance in order to provide optimal sitting. Most waste management models consider economic and environmental aspects, but very few consider social aspects (Morrissey and Browne, 2004). In particular, there are other criteria often with a higher priority than costs (e.g. Not In My Back Yard Syndrome) and consequently those need to simultaneously be taken into consideration. Nearly thirty years ago, Ross and Soland (1980) argued that practical problems involving the location of public facilities are multicriteria problems and ought to be modelled as such. However, the scientific work is relatively restricted to municipal solid waste management (e.g. Guo and Huang, 2009; Erkut et al., 2008; Vego et al., 2008; Simões Gomes et al., 2008; Lahdelma et al., 2002; Vaillancourt and Waaub, 2002; Haastrup et al., 1998; Hokkanen and Salminen, 1997; Karagiannidis and Moussiopoulos, 1997), neglecting somehow the alternative management of other special waste streams such as WEEE, with only a few exceptions (e.g. Rousis et al., 2008; Queiruga et al., 2008). To our knowledge, this is the first attempt to seek WEEE optimal UTR location solutions in Greece.

2. Decision support system: basic structure and components The development of a UTR requires consideration of a critical number of mutually conflicting criteria in order to select the optimal location among alternative scenarios. Most importantly, the

871

decision maker needs to consider: (i) development and operation costs, (ii) existence of all necessary basic infrastructure (road network, available workforce, etc.), (iii) distance from existing UTR, (iv) target population, and (v) social acceptance. The proposed methodology follows the path of multicriteria analysis, since these mathematical models are able to take into account conflicting criteria in the decision making process (e.g. Erkut et al., 2008; Rousis et al., 2008; Steuer and Na, 2003; Hokkanen and Salminen, 1997). In the literature, applications of multicriteria methods gain wide acceptance in the last few years over quantitative models, as the former embody many variables, quantitative as well as qualitative in their analysis (Queiruga et al., 2008). The special characteristics of several scenarios are simultaneously assessed and the alternatives according to different criteria are classified in order to export the optimal solution. In the presented methodology the decision process towards optimal location of UTR requires the adoption of a number of logical steps, as those presented in the flowchart of Fig. 1. The methodology is described in its generic form for the potential development of n UTR. Initially, the decision maker needs to undertake a thorough survey on available sites (alternatives) suitable for the development of UTR in the area under examination. This is followed by the clarification of the decision criteria for selecting the optimal location, together with their relative significance (weighting factors). This step allows the incorporation of specific strategic goals according to the stakeholders’ philosophy to the decision process. The presented multicriteria approach requires clear definition of the parameters for the valuation of all available alternatives and their detailed comparison. It should be emphasised that the exact number of criteria for the decision making process depends on the decision maker (Munier, 2004). As soon as the criteria and their weight are clearly defined, data collection follows. This quantification of alternative locations’ values for the selected criteria is considered the most time and capital demanding task in the decision making process. As a next step, in order to facilitate monitoring and direct comparison between individual criteria, the quantified values of all criteria j for all alternative locations A are normalised in a scale 0–10 with the utilisation of the normalisation index Nj (A), as follows:

Nj ðAÞ ¼

g j ðAÞ  g min j g max  g min j j

 10

where gj is the value of criterion j for alternative A, g min is the minj is the maximum value of criterion j. imum value of criterion j, g max j Multicriteria evaluation of sites for the location of UTR consists a problem which is formulated by using a set of alternatives (A1, A2, A3. . .) and a set of criteria (C1, C2, C3. . .). As already mentioned, the evaluation of criterion j for alternative A is described as gj(A). The approach adopted in the framework of this analysis uses a ranking scheme, following ELECTRE III principles (Roy, 1978). ELECTRE III approach has a long history of successful practical applications in various thematic areas such as environment, energy, construction, etc. and especially in examining environmental issues (e.g. Xiaoting and Triantaphyllou, 2008; Rogers and Bruen, 1998; Hokkanen and Salminen, 1997; Karagiannidis and Moussiopoulos, 1997). More specifically, ELECTRE III is found to be the most commonly used method for waste management decisions in the literature (Morrissey and Browne, 2004). Salminen et al. (1998) compared three multicriteria methods in the context of environmental problems and concluded that ELECTRE III was the most suitable. As already discussed, in the decision process for the location of a UTR it is most important to consider environmental aspects affecting it together with economical and social criteria. ELECTRE III has the ability to incorporate a large number of evaluation criteria for selecting the UTR location, coupled with

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Ch. Achillas et al. / Waste Management 30 (2010) 870–879

Fig. 1. Decision support system for the optimal location of WEEE treatment plants.

the possibility of a large number of different decision makers. It should be emphasised that data uncertainty is likely to drive decision makers to misleading conclusions. ELECTRE III requires the determination of three thresholds, namely threshold of negligence, threshold of preference and the veto threshold in the effort to better adapt to such uncertainties (Roy and Bouyssou, 1993). With the use of those thresholds, the technique does not address only the two ends of the problem, but also intermediate levels in between. Last but not least, with the ELECTRE III, the decision maker is able to take into account either quantitative (e.g. distances, price of

sites, etc.) or qualitative criteria (e.g. aesthetics, landscape degradation, etc.), since the technique shows a very good fit of data in such applications. ELECTRE III is based on binary outranking relations in two major concepts; ‘‘Concordance” (cj) when alternative A1 outranks alternative A2 if a sufficient majority of criteria are in favour of alternative A1 and ‘‘Non-Discordance” (dj) when the concordance condition holds, none of the criteria in the minority should be opposed too strongly to the outranking of A2 by A1. The assertion that A1 outranks A2 is characterised by a credibility index which permits

Ch. Achillas et al. / Waste Management 30 (2010) 870–879

knowing the true degree of this assertion (Roussat et al., 2009). To compare a pair of alternatives (A1, A2) for each criterion, the assertion ‘‘A1 outranks A2” is evaluated with the help of pseudo-criteria. The pseudo-criterion is built with two thresholds, namely indifference (qj) and preference (pj), for which the following apply:

873

 When gj(A1)  gj(A2) 6 qj, then no difference between alternatives A1 and A2 for the specific criterion j under study is identified. In this case cj(A1, A2) = 0.  When gj(A1)  gj(A2) > pj, then A1 is strictly preferred to A2 for criterion j. In this case cj(A1, A2) = 1.

In practice, environmental managers often need to investigate the simultaneous development of a number of more than one UTR. In this case, the presented approach supports the re-calculation of the criteria values for the remaining potential locations, closing the loop depicted in Fig. 1. It needs to be emphasised that the looping procedure provides the decision maker with a safer approach due to alterations to specific parameters of the remaining alternatives compared to the initial optimal solution. In this context, values for are all criteria should be re-calculated, e.g. destinations from existing UTR are altered after the selection of the initial optimal solution.

For a criterion j and a pair of alternatives (A1, A2), the concordance index is defined as follows:

3. Optimal location of WEEE treatment plants in Greece

8 g ðA1 Þ  g j ðA2 Þ  qj () cj ðA1 ; A2 Þ ¼ 0 > > < j g ðA Þg j ðA2 Þqj qj < g j ðA1 Þ  g j ðA2 Þ < pj () cj ðA1 ; A2 Þ ¼ j 1 pj q j > > : g j ðA1 Þ  g j ðA2 Þ  pj () cj ðA1 ; A2 Þ ¼ 1 A global concordance index C A1 A2 for each pair of alternatives (A1, A2), is computed with the concordance index cj(A1, A2) of each criterion j:

Pn C A1 A2 ¼

j¼1 wj

 cj ðA1 ; A2 Þ Pn ; j¼1 wj

where wj is the weight of criterion j. As already mentioned, a discordance index dj(A1, A2) is also taken into consideration for all pairs of alternatives and each criterion j. Discordance index (dj) is evaluated with the help of pseudo-criteria with a veto threshold (vj), which represents the maximum difference gj(A1)  gj(A2) acceptable to not reject the assertion ‘‘A1 outranks A2”, as follows:  When gj(A1)  gj(A2) 6 pj, then there is no discordance and therefore dj(A1, A2) = 0.  When gj(A1)  gj(A2) > vj, then dj(A1, A2) = 1.  Discordance index (dj) can be represented as follows:

8 g ðA2 Þ  g j ðA1 Þ  pj () dj ðA1 ; A2 Þ ¼ 0 > > < j g ðA Þg j ðA1 Þpj pj < g j ðA2 Þ  g j ðA1 Þ < v j () dj ðA1 ; A2 Þ ¼ j 2 v j p j > > : g j ðA2 Þ  g j ðA1 Þ P v j () dj ðA1 ; A2 Þ ¼ 1 The index of credibility dA1 A2 of the assertion ‘‘A1 outranks A2” is defined as follows:

dA1 A2 ¼ C A1 A2

Y 1  dj ðA1 ; A2 Þ ; 1  C A1 A2 j2F

  with F ¼ j 2 F; dj ðA1 ; A2 Þ > C A1 A2 In the case that a veto threshold is exceeded for at least one of the selected criteria, the index of credibility is null. In other words, the assertion ‘‘A1 outranks A2” is rejected. As regards the ranking procedure of all available location alternatives Aj, two complete pre-orders are constructed through a descending and an ascending distillation procedure. In a nutshell, descending distillation refers to the ranking from the best available alternative to the worst, while ascending distillation refers to the ranking from the worst available alternative to the best. All the above input data is imported in the multicriteria analysis mathematical modelling approach for the optimal site location of the first UTR (n = 1). As a last step of the developed methodology, sensitivity analysis is available, since parameter values in real life applications originate from estimations which are sometimes more or less reliable (weighting factors, thresholds, criteria qualitative values, etc.).

In this section, the applicability of the proposed methodology is demonstrated with the implementation in a real life case for the development of two UTR in Greece. It should be noted that for the study needs a number of existing WEEE treatment facilities located in the municipalities of Ag. Theodoroi, Aspropirgos, Larisa and Polikastro (Fig. 2) are taken into consideration (Recycling of Appliances, 2009a). Mean annual WEEE production in Greece for the period 2003–2006 was approximately 170,000– 175,000 t, representing about 3.8% of total domestic solid waste (Karagiannidis et al., 2005). However, only a small percentage of those massive quantities ended up in the existing UTR, since it has been estimated that for the same time period 90% of WEEE was either recycled with other materials without any proper pre-treatment (‘‘grey recycling”) or mixed with other municipal solid waste (Papaoikonomou et al., 2009), a rather common ‘‘practise” in a European level where WEEE represent at least 1% of total waste quantities ending up in landfills (Jantz and Bilitewski, 2009). Initially, all capitals cities of the Hellenic Prefectures were reckoned as alternative locations for the development of the two UTR and specific sites are appointed for each alternative. Taking into account minimum requirements of the optimal site location (veto thresholds) resulted to the overall 22 alternative locations as those presented in Fig. 2. The veto thresholds taken into consideration referred to land connection – all islands were not considered as alternatives for reasons of accessibility – and minimum distance of existing infrastructure in UTR to overcome a minimum of 100 km, both in order to avoid competition and meet the needs of population of wider areas. After the determination of all alternative sites, the criteria to be taken into account were decided upon. In any study concerning site locations, the most important question is defining the criteria to be considered. In this light, a critical number of relevant stakeholders – all experts on the thematic area under study and thus very well informed on the current status of WEEE alternative management in Greece, the special characteristics of the problem, as well as the country’s specific geographical aspects – were personally interviewed in order to decide which criteria to use in our case study of providing the optimal locations for the development of two WEEE treatment plants in Greece (Achillas, 2009). Those criteria included: i. Local population (c1) as an indicator for both WEEE production – since no data regarding WEEE quantities were available for all cities separately – and opportunities for the promotion of UTR’s end-products to the secondary market (recyclers, second hand electronics, etc.). ii. Population served (c2) as an indicator for WEEE quantities ending up in the UTR. iii. Distance from existing UTR (c3) as an indicator for competitiveness and viability of the proposed investment. As

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Fig. 2. Alternative locations for the development of UTR in Greece.

iv.

v.

vi.

vii.

viii.

ix.

discussed, a minimum of 100 km was set as a veto threshold for the specific criterion. Land value (c4) as an indicator for investment cost, since all other cost elements do not differentiate significantly in a national level. Unemployed population (c5) as an indicator for both available workforce and social acceptance for the development of an industrial facility. Land connection (c6) as an indicator for accessibility of the facility. As already discussed, this specific criterion was set as a veto threshold. Financial status of local population (c7) as another indicator for social acceptance of the facility, expressed in terms of Gross Domestic Product per capita. Distance from the capital of the region (c8) as an indicator of available infrastructure. Proximity to regions’ capitals is considered a powerful incentive to invest capital, both for reasons of political will, bureaucracy and due to adequate infrastructure (roads, railways, etc.). Distance from nearest port (c9) as an indicator for available infrastructure.

Towards optimal site location, the values of criteria ‘‘Local population” (c1), ‘‘Population served” (c2) and ‘‘Unemployed population” (c5) need to be maximised. On the contrary, the values of criteria ‘‘Distance from existing UTR” (c3), ‘‘Land value” (c4), ‘‘Financial status of local population” (c7), ‘‘Distance from the capital of

the region” (c8) and ‘‘Distance from nearest port” (c9) should be minimised. The individual performances of the alternative locations are calculated for the selected criteria and depicted in Table 1. As discussed in the methodological section, the values of Table 1 are normalised in a common scale from 0 to 10 using the normalisation index Nj(A) (Table 2). As regards c1, c5 and c7, data originates from the National Statistical Service of Greece (NSSG) and refer to estimates for the year 2007 (NSSG, 2009). The values for c2 refer to the population potentially served by the development of a UTR in each alternative location, based on the criterion of minimum distances. Similarly, minimum distances are taken into account for the calculation of criteria c3, c8 and c9. Land value (c4) is based on data sourcing from the Hellenic Ministry of Economy and Finance (2009). The values of c2 and c3 are dynamic and therefore subject to change in a second run for the development of an additional UTR on top of the initial optimal solution. In other words, the development of a UTR and its introduction in the Hellenic WEEE take-back scheme will alter the corresponding distances and hence the relative performances of the remaining alternative sites in those two criteria. For example, the development of a UTR in the municipality of Alexandroupoli (Fig. 2) would invalidate any subsequent future development of a second UTR in Xanthi or Komotini, as those two alternative locations would not satisfy the veto threshold of 100 km minimum distance from an existing facility. In addition, the ‘‘population served” criterion of the alternative UTR location in Kavala would be considerably reduced after the development

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Ch. Achillas et al. / Waste Management 30 (2010) 870–879 Table 1 Performances of the alternative locations for the selected criteria. Alternative location

Local population (residents)

Population served (residents)

Distance from existing UTR (km)

Land value (€/ha)

Unemployed population (number)

Land connection (yes/no)

Financial status of local population (GDP/capita)

Distance from the capital of the region (km)

Distance from nearest port (km)

Messologhi Arta Patra Grevena Drama Alexandroupoli Karpenissi Pirgos Igoumenitsa Ioannina Kavala Kastoria Kozani Sparti Lefkada Kalamata Xanthi Preveza Komotini Lamia Florina Amfissa

218,154 71,219 338,648 31,520 100,620 148,928 19,620 180,097 42,685 177,212 140,238 53,741 154,349 93,155 22,291 164,581 106,144 57,404 111,275 166,756 54,254 37,799

1,188,001 733,985 1,130,597 718,856 796,222 506,585 475,749 1,092,443 496,211 527,731 607,205 790,075 790,075 526,505 733,985 437,833 607,205 714,365 506,585 224,175 639,161 548,221

179 276 147 165 146 314 222 247 288 201 162 198 124 153 304 185 214 310 259 222 196 156

4600 5900 20,000 3000 5000 2500 2000 8200 3500 15,000 4000 4000 3500 6400 14,000 4000 5500 2000 3000 3500 2500 2600

10,671 3186 20,439 1668 7022 4972 857 12,005 2005 7848 6607 5319 8555 2792 752 7070 5669 3001 4310 6921 2709 1707

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

9806 11,342 12,964 9510 10,692 11,314 11,081 10,023 10,347 12,806 11,255 9626 13,003 9372 11,188 10,704 10,780 10,994 10,213 11,616 11,039 11,892

60 75 0 165 36 153 288 97 86 0 0 198 124 275 133 214 52 104 98 214 196 203

60 48 0 165 36 0 157 47 0 86 0 198 124 43 0 0 52 0 57 17 196 29

Table 2 Normalised performances of the alternative locations for the selected criteria. Alternative location

Local population (residents)

Population served (residents)

Distance from existing UTR (km)

Land value (€/m2)

Unemployed population (number)

Land connection (yes/no)

Financial status of local population (GDP/capita)

Distance from the capital of the region (km)

Distance from nearest port (km)

Messologhi Arta Patra Grevena Drama Alexandroupoli Karpenissi Pirgos Igoumenitsa Ioannina Kavala Kastoria Kozani Sparti Lefkada Kalamata Xanthi Preveza Komotini Lamia Florina Amfissa

6.2 1.6 10.0 0.4 2.5 4.1 0.0 5.0 0.7 4.9 3.8 1.1 4.2 2.3 0.1 4.5 2.7 1.2 2.9 4.6 1.1 0.6

10.0 5.3 9.4 5.1 5.9 2.9 2.6 9.0 2.8 3.1 4.0 5.9 5.9 3.1 5.3 2.2 4.0 5.1 2.9 0.0 4.3 3.4

2.9 8.0 1.2 2.2 1.2 10.0 5.2 6.5 8.6 4.1 2.0 3.9 0.0 1.5 9.5 3.2 4.7 9.8 7.1 5.2 3.8 1.7

1.4 2.2 10.0 0.6 1.7 0.3 0.0 3.4 0.8 7.2 1.1 1.1 0.8 2.4 6.7 1.1 1.9 0.0 0.6 0.8 0.3 0.3

5.0 1.2 10.0 0.5 3.2 2.1 0.1 5.7 0.6 3.6 3.0 2.3 4.0 1.0 0.0 3.2 2.5 1.1 1.8 3.1 1.0 0.5

10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0

1.2 5.4 9.9 0.4 3.6 5.3 4.7 1.8 2.7 9.5 5.2 0.7 10.0 0.0 5.0 3.7 3.9 4.5 2.3 6.2 4.6 6.9

2.1 2.6 0.0 5.7 1.3 5.3 10.0 3.4 3.0 0.0 0.0 6.9 4.3 9.5 4.6 7.4 1.8 3.6 3.4 7.4 6.8 7.0

3.0 2.4 0.0 8.3 1.8 0.0 7.9 2.4 0.0 4.3 0.0 10.0 6.3 2.2 0.0 0.0 2.6 0.0 2.9 0.9 9.9 1.5

of the first UTR in Alexandroupoli, since thereafter all WEEE sourcing from areas in the proximity of the latter would end up in Alexandroupoli. On the contrary, the values of all other criteria are independent of existing infrastructure and remain stable in further runs of the model. The values of c2 and c3 are dynamic and therefore subject to change in a second run for the development of an additional UTR on top of the initial optimal solution. In other words, the development of a UTR and its introduction in the Hellenic WEEE take-back scheme will alter the corresponding distances and hence the relative performances of the remaining alternative sites in those two criteria. For example, the development of a UTR in the municipality of Alexandroupoli (Fig. 2) would invalidate any subsequent future development of a second UTR in Xanthi or Komotini, as those two alternative

locations would not satisfy the veto threshold of 100 km minimum distance from an existing facility. In addition, the ‘‘population served” criterion of the alternative UTR location in Kavala would be considerably reduced after the development of the first UTR in Alexandroupoli, since thereafter all WEEE sourcing from areas in the proximity of the latter would end up in Alexandroupoli. On the contrary, the values of all other criteria are independent of existing infrastructure and remain stable in further runs of the model. For the case under consideration, weighting factors, negligence, preference and veto thresholds are presented in Table 3. Their values are calculated as averages of the corresponding views of various stakeholders involved in the reverse supply chain of EEE products (manufacturers, recyclers, environmental managers, third party logistics, take-back scheme regulators), analytically

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Table 3 Weighting factors and thresholds for the development of a UTR in Greece.

Weighting factor (%) Threshold of negligence Threshold of preference Veto threshold

Local population (residents)

Population served (residents)

Distance from existing UTR (km)

Land value (€/m2)

Unemployed population (number)

Land connection (yes/no)

Financial status of local population (GDP/capita)

Distance from the capital of the region (km)

Distance from nearest port (km)

20

30

12

2

2

0

1

30

3

30,000

100,000

50

0.2

4000

–

1000

0

0

80,000

250,000

100

0.5

10,000

–

2000

25

25

–

–

100

–

–

No

–

–

–

presented elsewhere (Achillas, 2009). In order to overcome subjectivity issues, the sensitivity analysis that follows, as well as the ease to re-calculate optimal solution with modified parameters, provides the decision maker with an easy-to-use tool.

4. Results and discussion Following the definition of all alternative sites, the calculation of the normalised values of the selected criteria, weighting factors and thresholds, the mathematical model for the evaluation of the optimal location of WEEE treatment plants in Greece is resolved. Model runs are realised with the use of LAMSADE software (LAMSADE, 2009). Fig. 3a presents both ascending and descending distillation procedure for the optimal location of the first UTR. Both distillations show the area of Patra as the optimal location, which is also reflected in the final distribution of the alternative sites presented in Fig. 4a. The optimal site (Patra) can be interpreted as a result of the specific site’s excellent performances in the ‘‘Local population” (c1) and ‘‘Population served” (c2) criteria, as currently there is no existing UTR in the western part of the country. This is further reinforced by the fact that western Greece is not easily accessed from the east due to Pindos mountain range. As shown in Fig. 4a, next best option for the development of the first UTR is the site located in Messologhi, which is very close to the one in Patras – especially after the construction of the ‘‘Rio-Antirrio” bridge – and therefore could serve almost the same geographical areas, followed by the sites in Pirgos, Kavala and Ioannina.

Fig. 3b illustrates ascending and descending distillations for the optimal location of the second UTR – after the one developed in Patra, while Fig. 4b presents the corresponding sites’ ranking. It should be noted that despite the fact that with the initial run, Messologhi was the second optimal site for the location of the first UTR, the second application of the methodological framework defines Kavala as the optimal location, satisfying the demand arising from the regions of north-eastern Greece. In this case, Messologhi is not even regarded as an alternative in the second application since the site does not fulfil the veto threshold of minimum 100 km from the existing UTR in Patra. Sensitivity analysis is an advantage of the presented methodological approach on the grounds that real life applications input data originate from estimations which, although assumed constant, are sometimes more or sometimes less reliable. General sources of individual uncertainties could come from data series uncertainties, uncertainty about the future, synergies and idiosyncrasies in the interpretation of ambiguous or incomplete information. In any case it should be underlined that the simultaneous consequences of potential variations of parameter values, decision variables and constraints could be studied by new runs model, since the low computational time gives the opportunity for fast reformed optimal solutions. On this basis, ELECTRE III is preferable, since it is considered to better adapt to uncertainties (Roy and Bouyssou, 1993). In this light, for sensitivity analysis purposes, the problem under study is resettled with modified parameters (weighting factors and thresholds). Table 4 presents the ranking of the five optimal locations for developing a UTR. Together with the base-case sce-

Fig. 3. Ascending and descending distillations of the optimal location for the development of (a) first and (b) second UTR in Greece.

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Fig. 4. Sites’ ranking for the development of the (a) first and (b) second UTR in Greece.

nario, for which parameters are analytically described in Table 3, twelve more parameter-based scenarios with differentiating preference and indifference thresholds by 50% (increasing and decreasing) were examined. For all scenarios, Patra appears the optimal solution for the initial run of the model, which provides the decision maker with additional confidence that the UTR needs to be developed in this specific location. Accordingly, for the next optimal solutions (second–fifth), the ranking of the sites is not significantly affected with the modification of preference and indifference thresholds, which demonstrates the robustness of the presented methodology. Similarly, sensitivity analysis is conducted for the optimal location of the second UTR. Kavala is the optimal site for all scenarios examined, with the exception of increased thresholds by more than 30%. In this case, the site in Ioannina appears as good as the one in Kavala. Apart from the aforementioned twelve threshold-based scenarios examined, sensitivity analysis is also conducted with the modification of criteria weighting factors by 50% (increasing and decreasing) taking all thresholds constant those were assessed in the base-case scenario (Table 3). Once again, for all weighting factors-based scenarios, Patra appears the optimal location. Only in the extreme scenario where the weighting factors of all criteria,

apart from the criterion ‘‘Population served”, are halved, while at the same time all thresholds of the latter criterion are reduced by at least 80%, the optimal solution changes. In this extreme case, Messologhi is considered optimal location for the development of the first UTR. As regards the location of the second UTR, Kavala seems to be more ‘‘sensitive” to modifications of the criteria weighting factors. Increase 20–30% in the weighting of the ‘‘Local population” (c1) criterion, together with a simultaneous decrease of the ‘‘Population served” (c2) criterion, alters optimal site solution, which in this case is located in Ioannina. Similarly, halving all weighting factors – with the exception of increase in the ‘‘Population served” (c2) criterion – shifts optimal solution from Kavala and locates the second UTR in Drama. As mentioned, decision making on the optimal location for the development of a UTR in Greece constitutes an issue of critical importance, both for the viability of the investment itself and the efficiency of the WEEE take-back scheme. Taking into account special characteristics of the Hellenic WEEE reverse supply chain network (Moussiopoulos et al., 2010) and the existing infrastructure in UTR, the average WEEE transportation cost in a national level is in the order of 72 €/t (Achillas, 2009). In the case that a UTR is developed in the optimal location as defined with the implementa-

Table 4 Thresholds’ sensitivity analysis for the development of the first UTR. Scenario

First

Second

Third

Base-case scenario +5% +10% +20% +30% +40% +50% 5% 10% 20% 30% 40% 50%

Patra Patra Patra Patra Patra Patra Patra Patra Patra Patra Patra Patra Patra

Messologhi Messologhi Messologhi Messologhi, Messologhi, Messologhi, Messologhi, Messologhi Messologhi Messologhi Messologhi Messologhi Messologhi

Pirgos, Kavala Pirgos, Kavala Kavala

Fourth

Ioannina, Drama Ioannina, Drama Pirgos, Pirgos, Pirgos, Pirgos, Pirgos,

Kavala Kavala Kavala Kavala Kavala, Kavala, Kavala, Kavala, Kavala, Kavala,

Pirgos, Pirgos, Pirgos, Pirgos, Pirgos, Pirgos,

Fifth

Drama Drama Drama Drama Drama Drama

Ioannina, Ioannina, Ioannina, Ioannina, Ioannina,

Drama Drama Drama Drama Drama

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tion of the presented methodology (Patra), transportation costs are expected to be reduced to 67 €/t. Furthermore, development of a second UTR in Kavala would further squeeze transportation costs to 62 €/t. Therefore, with the development of additional UTR and their introduction in the WEEE reverse logistics scheme, transportation costs are expected to be significantly reduced. Since WEEE production in Greece is reported to have overcome 47,000 t for 2008 (Recycling of Appliances, 2009b), reduction of 10 €/t in transportation costs is translated into annual cost savings in the order of 235,000 €. Indicatively, investment costs – taking into consideration land purchase, building infrastructure and mechanical equipment – for the development of a WEEE treatment facility with a capacity of about 300,000 appliances per year is in the order of 5 M€ (EcoRec, 2003). On this basis, the investment is conservatively calculated (WEEE quantities are expected to increase in the near future and therefore annual cost savings will become even greater) to pay-off within a period of about 20 years, which is adequately acceptable. This indicates that the financing of such investments, apart from environmentally appropriate, proves to be also economically viable, especially when also considering externalities (e.g. CO2 emissions reduction from transportation). At this point, it should be emphasised once again that despite the fact that transportation costs equally decrease in the cases of UTR development in Patra and Kavala (5 €/t), the two sites could not be interpreted as equally optimal locations, considering that the optimal location is not based only on strict economic criteria but is the outcome of multicriteria analysis.

5. Conclusions The development of additional infrastructure in WEEE treatment and recycling facilities presents a critical decision for the efficiency of an integrated waste management scheme. In this context, the location where UTR should be located need to be thoroughly examined as this would be crucial, mostly in terms of reverse logistics transportation costs. Reduced transportation costs would further result in proportional reduced recycling fees that customers would have to pay for purchased goods and would thus create a more competitive EEE market in a national level. The multicriteria analysis techniques adopted in the decision-making framework for the development of additional UTR need to be emphasised, in the effort to take into account apart from economical issues, environmental, as well as social concerns. The basic aim of this study was not to develop a quantitative model based only on criteria easily quantified (e.g. costs), but also on qualitative issues which represent pivotal questions to effectively locate recycling plans according to stakeholders consulted in the framework of this analysis. In this light, social acceptance, a question of critical importance for investments worldwide, is achieved. This paper presents a decision support system developed to support optimal location of electrical and electronic waste treatment plants. The presented methodological framework provides decision makers with an easy-to-use tool that could be employed either by private investors or public WEEE take-back scheme regulators. The methodology is successfully implemented for the case of Greece. However, the procedure could be easily adopted – with slight modifications and adjustments to the special requirements of the problem under consideration – in order to solve similar problems in countries other than the one examined in the present analysis. Necessary adjustments mainly have to do with the system regulator’s specific objectives and strategic goals, as well as the geographical characteristics of the area under study. The methodological approach is also not limited only to support the specific decision; it can be also used in the effort to locate optimal sites for the development of collection points, sorting centres, etc. In

these cases, different criteria may be decided to be utilised, but the overall methodology remains practically unaltered. Similarly, the proposed decision support system can be also used – once again with the required modifications and adjustments – in order to assist environmental managers and decision makers in their judgements towards the development of other waste streams’ (e.g. construction and demolition wastes) treatment facilities, in an effort to promote their alternative management. However, such decision making processes are not included in the present paper and remain future challenges for the authors.

Acknowledgements We would like to thank the anonymous reviewers for their valuable comments, which greatly improved the quality of the manuscript.

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