An analytic network process-based approach for location problems: the case of a new waste incinerator plant in the Province of Torino (Italy)

Abstract

Starting from the topicality of the issues related to the location of undesirable facilities and on the basis of a brief review of the types of models that are currently being used in the Municipal Solid Waste Management context, the present paper proposes a multicriteria approach that is able to support decision makers in the choice of the best location for a waste incinerator plant in the Province of Torino (Italy). Three alternative sites have been compared through the use of the Analytic Network Process (ANP) method. The application allows the dependence relationships among the aspects and criteria to be assessed and the relative importance of all the elements that play an influence on the final choice to be elicitated.

The decision-making process was developed through the identification of 31 environmental and socio-economic indicators that were grouped into clusters and organized in four subnetworks according to the Benefits, Opportunities, Costs and Risks (BOCR) model in order to compare the three alternatives by means of a holistic approach and to better highlight the tradeoffs between the aspects involved in the decision.

The aim of this work is to analyse the contribution that the ANP technique offers in sustainability assessment of undesirable facilities, paying particular attention to the use of quantitative indicators in the evaluation process.

The strengths and weaknesses of the ANP approach, combined with the use of measurable and verifiable indicators, are also discussed and three different sensitivity analyses have been performed in order to test the robustness of the proposed model and the stability of the results, exploring also rank reversal thresholds.

The main findings of the present work have proved that the use of quantitative indicators as nodes of the ANP–BOCR structure significantly improves the internal coherence of the model and makes the decision process more traceable and reliable. Copyright © 2011 John Wiley & Sons, Ltd.

1. INTRODUCTION

The Municipal Solid Waste Management (MSWM) process has the aim of recycling most of the waste materials that are produced; the non-recyclable fractions of the wastes are dumped in landfills or treated in specific waste incinerator plants. These types of industrial plants belong to the group of undesirable facilities whose location presents two main problems: (i) social opposition and (ii) a huge number of social, economic and environmental data that have to be taken into account (Aragonés-Beltràn et al., 2010).

In this sense, MSWM is an intrinsically complex problem because it involves different interconnected elements and must achieve objectives that are often in conflict (Haastrup et al., 1998). The waste problem is getting more and more acute over the years and the selection for waste facilities becomes conflict-ridden. In this context, Decision Makers (DMs) have to be able to justify their choices concerning the location of disposal sites. MSWM problems involve a number of characteristics that require a formal multicriteria analysis (MCA; Bouyssou et al., 2006; Figueira et al., 2005; Roy and Bouyssou, 1995). Moreover, MCA can facilitate communication among DMs and stakeholders in order to reach a justifiable decision through a systematic, transparent and documented process. Considering these factors, the paper proposes the use of the Analytic Network Process (ANP) technique to evaluate different alternative locations for siting a waste incinerator plant in the Province of Torino (Italy).

After the Introduction Section, the paper is organized as follows: Section 2 offers a brief literature review regarding the application of Decision Support Systems (DSS) and MSWM-related problems and Section 3 presents the ANP method; the application of the ANP-based model to the study case is shown in Section 4, considering the structuring of the decision problem, the determination of the elements priorities and the final ranking of the alternatives; Section 4 presents also the results of the model, where the aggregation formulas that have been used and the sensitivity analyses that have been performed are illustrated. Finally, Section 5 discusses the main findings of the application with particular reference to the use of strategic criteria and summarizes the conclusions that have been drawn from the study, putting in evidence the opportunities for expanding the work.

2. MUNICIPAL SOLID WASTE MANAGEMENT (MSWM) AND DECISION SUPPORT SYSTEMS (DSS): LITERATURE REVIEW

Since the 1960s many works have been developed concerning the modelling of MSWM problems. The first applications referred to land use models and had the aim of optimizing collection routes and facilities for the selection of a site (Truitt et al., 1969).

In the late 1980s, more sophisticated models were set up; these models focused on the economic aspects of the problem and had the objective of minimizing the overall costs connected to MSWM (Gottinger, 1988). As far as the economic approach is concerned, mention can be made to specific models, based on the Cost Benefit Analysis (CBA), that have been implemented in order to assess different strategies for MSWM (Smith and Baetz, 1991).

During the 1990s, MSWM models started to consider the complexity that is intrinsic in decision problems and some MCA applications were proposed (Caruso et al., 1993). These models consider the full range of waste streams to be managed and view the available waste management practices as a menu of options from which to select the preferred solution on the basis of site-specific environmental and economic considerations. Many MCA models are available to address MSWM problems, including Analytic Hierarchy Process (AHP) (Dey and Ramcharan, 2008), PROMETHEE (Khalil et al., 2004; Queiruga et al., 2008), ELECTRE (Hokkanen and Salminen, 1997; Norese, 2006), ANP (Aragonés-Beltràn et al., 2010; Khan and Faisal, 2008; Tseng, 2010; Tuzkaya et al., 2007), GIS and fuzzy MCA (Chang et al., 2008).

Other models consider the whole life cycle of products, by means of Life Cycle Analysis (LCA), with the aim of making a comprehensive assessment of the MSWM strategies environmental impacts systems (Barton et al., 1996; McDougall et al., 2001).

Recently, MWSM problems have been studied taking into account the sustainable development approach; in this sense, for a waste management system to be sustainable, it has to be environmentally effective, economically affordable and socially acceptable. Mention should be made to the fact that the environmental effectiveness is a measure that includes a full range of variables (e.g. air pollution, energy production, recycling, etc.) and it has been operationalized in a variety of ways (Poloni-Staudinger, 2008). Generally speaking, it can be said that a project is environmental effective if it goes in the direction of minimizing the use of the non-renewable natural resources and of optimizing the use of renewable natural resources. In this sense, an environmental effective project will cause fewer negative intergenerational effects.

With particular reference to the social aspects of MSWM problems, the models have to be able to reflect the public's opinions concerning incinerators and other waste management plants. The point of view of the population involved in the problem usually presents the characteristics of social opposition that affects the construction of undesirable facilities. This phenomenon is also known as NIMBY (Not-In-My-Back-Yard), NOTE (Not-Over-There-Either), LULU (Locally-Unacceptable-Land-Use) and BANANA (Build-Absolutely-Nothing-Anywhere-Near-Anything). An analysis of the literature pertaining to this context has highlighted a lack of models that are able to consider the three dimensions of sustainability together and to reflect the intergenerational effects of the proposed MSWM strategies (Erkut et al., 2008: Morrissey and Browne, 2004).

3. THE ANALYTIC NETWORK PROCESS

3.1. State of the art

The ANP is a decision support tool, belonging to the MCA family, that has recently been gaining popularity. Developed by T.L. Saaty (Saaty, 2005; Saaty and Vargas, 2006) as the generalization to dependences and feedbacks of the more well-known AHP (Saaty, 1980, 2000), the ANP represents a theory of relative measurement on absolute scales of both tangible and intangible criteria based on both the judgement of experts and on existing measurements and statistics needed to make a decision. What makes the ANP different from the AHP is that the former incorporates the influences and interactions among the elements of the system (criteria and alternatives), as perceived by the DM, and groups them into clusters inside a network. Many decision problems cannot in fact be structured hierarchically since they involve the interaction and dependence of the higher level elements in the hierarchy on the lower level ones. Moreover, the feedback networks lead to the consideration that the importance of the alternatives determines the importance of the criteria, whereas the traditional hierarchy only leads to the consideration that the importance of the criteria determines the importance of the alternatives (Saaty, 2003). Thus, in order to deal with the complexity of real problems in a non-simplistic way, we have to use feedback networks to arrive at the kind of decisions needed to cope with the future. The ANP enables such inter-dependences to be surveyed and measured by generalizing the approach of the super-matrices introduced by the AHP and it is gaining merit as a useful tool to help technicians make their decision processes traceable and reliable.

A very large and consolidated amount of MCA literature exists in which it is possible to find a wide range of techniques (Figueira et al., 2005). As far as both the AHP and the ANP are concerned, the basic reference is the literature production of the American researcher T.L. Saaty, starting from 1980. With particular reference to ANP, the literature is more recent and some publications can be found in different fields. Mention can be made of some works in the sphere of waste management (Aragonés-Beltràn et al., 2010; Khan and Faisal, 2008; Promentilla et al., 2006; Tuzkaya et al., 2007), transport infrastructures (Tuzkaya and Onut, 2008), strategic policy planning (Ulutas, 2005), environmental impact assessment of territorial transformations (Bottero et al., 2008; Bottero and Mondini, 2008; Liu and Lai, 2009), market and logistics (Agarwal et al., 2006; Ayag and Özdemir, 2007; Burnaz and Topcu, 2006; Jharkharia and Shankar, 2007; Liang and Li, 2008; Razmi and Rafiei, 2009), economics and finance (Niemura and Saaty, 2004) and civil engineering (Neaupane and Piantanakulchai, 2006; Piantanakulchai, 2005).

3.2. Methodological background

From the methodological point of view, the ANP requires a network structure to represent the problem, as well as pairwise comparisons to establish the relationships within the structure. In order to develop an ANP model, it is necessary to carry out five fundamental steps.

The first step consists in developing the structure of the decision-making process. This involves defining its main objective and identifying groups or ‘clusters’ constituted by various elements (‘nodes’) that influence the decision, and alternatives or options from which to choose. After having chosen which structure is more suitable in the decisional context, whether the simple or the complex Benefits–Opportunities–Costs–Risks (BOCR) one (Saaty, 2005), the relationships between the different elements of the network must be identified. All the elements in the network can be related in different ways since the network can incorporate feedback and complex inter-relationships within and between clusters, thus providing a more accurate modelling of complex settings.

The second step consists of pairwise comparisons, in order to establish the relative importance of the different elements, with respect to a certain component of the network.

Comparative or relative judgements are made on pairs of elements to ensure accuracy. In paired comparisons, the smaller element is used as the unit, and the larger element becomes a multiple of that unit with respect to the common property or criterion for which the comparisons are made. It is important to highlight that there are two levels of pairwise comparisons in the ANP: the cluster level, which is more strategic, and the node level, which is more specialized. In pairwise comparisons, a ratio scale of 1–9, that is the Saaty's fundamental scale, is used to compare any two elements. The main eigenvector of each pairwise comparison matrix represents the synthesis of the numerical judgements established at each level of the network (Saaty, 1980).

The third step consists of the progressive formation of three supermatrices: the initial or unweighted one, the weighted one and, finally, the limit one. The unweighted supermatrix contains all the eigenvectors that are derived from the pairwise comparison matrixes of the model. The eigenvector obtained from the cluster-level comparison, with respect to the control criterion, is applied to the initial supermatrix as a cluster weight and the result is the weighted supermatrix. The supermatrix elements allow a resolution to be made of the interdependencies that exist between the elements of the system.

The fourth step concerns the elicitation of the final priorities. In this step, the weighted supermatrix is raised to a limiting power, as in Equation (1), in order to converge and to obtain, as stated in the Perron–Frobenius theorem, a long-term stable set of weights that represents the final priority vector.

• (1)

In the case of the complex network structure, it is necessary to synthesize the outcome of the alternative priorities for each of the BOCR subnetworks in order to obtain their overall synthesis through the application of different aggregation formulas (Saaty and Vargas, 2006).

The fifth and last step consists in carrying out the sensitivity analysis on the final outcome of the model in order to test its robustness.

4. THE DECISION PROBLEM AND THE ANP APPROACH

4.1. Research objectives and structure of the work

The decision problem presented in the paper concerns the choice of the most suitable location for a waste incinerator plant, which has to be constructed in the Province of Torino. The work conducted in this paper is based on a scientific study that was developed by the Provincial Administration (ATOR, 2008). In this study, three alternative locations are presented and all the available information concerning the territorial context of these locations have been organized according to a qualitative/quantitative approach based on indicators. The object of this paper is to experiment the use of the ANP technique for the synthesis of the available data in the decision problem. Particularly, the baseline information that is used as the input of the model has been directly derived from the aforementioned indicators system. In this sense, the pairwise comparison matrixes required in the analysis have been filled in taking into consideration the quantitative data given by the specific indicators. This choice allows the difficulties related to questioning experts and DMs for elicitating their preferences to be overcame (Gomez-Navarro et al., 2009). Taking into account technical elements that are measurable, and thus objectively comparable, is essential to build consensus around a decision, to reduce conflicts and consequently to pave the way to the location of undesirable facilities. This is why an approach based on indicators, most of which of quantitative nature and thus verifiable, will be adopted. Furthermore, it will be possible, by means of the ANP technique, to assess the influences among the elements of the decision problem. The integration of indicator systems and the ANP method is not new: applications are available in the infrastructure and logistic (Piantanakulchai, 2005) and environmental assessment (Bottero and Ferretti, 2010; Wolfslehner and Vacik, 2008).

A number of frameworks exists for the analysis of the decision-making process. In the present application, we will follow what is perhaps the most widely accepted generalization of the decision-making process, which was first introduced by Simon (1960, 1982) and then adapted to territorial planning (Geneletti and Abdullah, 2009; Joerin et al., 2009; Sharifi and Rodriguez, 2002). The aforementioned framework divides the decision-making process into the following four major phases: intelligence, design, selection and choice and detailed analysis and implementation. This model will be adopted in order to identify the most suitable site for the location of the new incinerator, as explained in the following subsections.

4.2. Description of the case study context

As already mentioned, the study case considered in this paper refers to the location of a waste incinerator plant that has to be constructed in the Northern part of the Province of Torino (Italy). At the moment, one incinerator plant is already under construction and it will service the southern part of the Province. However, according to the MSWM provincial plan (ATOR, 2008), the construction of a second incinerator is necessary. The plant will have a capacity of about 290 000 t/year and it will serve an area that includes 177 municipalities.

After a detailed study of the territory and the land use plans of the different municipalities in the area, three potential plant locations have been identified on the basis of a technical decision process conducted by the Provincial Authority. The three sites are located in the municipalities of Ivrea, Rivarolo Canavese and Settimo Torinese (Figure 1).

Figure 1. Representation of the three potential plant locations for the waste incinerator plant to be constructed in the Province of Torino.

4.3. The ANP BOCR model

4.3.1. Intelligence phase: determination of the network

The problem definition overlaps the decision-making intelligence phase, which refers to the structuring of the problem, the identification of the objectives and the selection of criteria or attributes to describe the degree of achievement of each objective.

The location of a new waste incinerator plant is a complex land use planning problem in which the presence of interrelated elements and conflicting aspects suggests the use of a MCA that is able to provide a rational base for the systematic analysis of the alternative options. Due to the aforementioned complexity of the problem under analysis, and with the aim of finding the most significant aspects involved in the decision and the most suitable location for the project, the ANP method has been developed according to the so-called BOCR model, which allows BOCR to be considered (Saaty and Ozdemir, 2008). Each decision in the BOCR approach can be characterized by the presence, in the short-medium term, of some favourable sure concerns (Benefits) and some unfavourable ones (Costs), and, in the long term, of some uncertain positive concerns that the decision might create (Opportunities) and some negative factors that it can entail (Risks). In the ANP–BOCR method, each of these four concerns utilizes a simple separate network structure for the decision.

In the present application, the Benefits and Opportunities, apart for the temporal dimension, have been identified as the aspects that have to be maximized in the selection of the most compatible site for the location of the new waste incinerator facility. The Costs and Risks, leaving aside the temporal dimension, have instead been identified with those factors that have to be minimized in the aforementioned decision context. Furthermore, the Benefits and Costs refer to endogenous aspects that describe the territorial system, whereas the Opportunities and Risks concern the exogenous factors that the construction of the plant could determine on the territorial system.

Starting from the overall objective of the analysis, which is the identification of the most suitable site for the location of a new waste incinerator plant, a comprehensive set of evaluation criteria that reflect all the concerns relevant to the decision problem has been identified (Table I). Taking into account the full range of aspects relevant to the decision problem enhances the quality of the final decision, allowing the totality of the effects of the transformation project to be considered and the negative externalities and the intergenerational effects to be minimized. It is necessary to put in evidence that the criteria considered in the present application reflect the requirements coming from the legislative framework in the context of Environmental Impact Assessment (first of all, the European Directive 11/97). Further information for the structure of the decision model has been derived from the specific legislation in the field of waste management both at the national and at the local level. In this sense, of particular interest is the Waste Management Plan of the Province of Torino (ATOR, 2008), which provides a list of aspects to be considered for the location of waste facilities.

Table I. List of the indicators for the ANP–BOCR model and the associated performances of the alternative sites

							Performances of the alternatives
BOCR	Control criteria	Clusters	Indicators	Abbreviations	Unit of measure	Assessment direction	Ivrea	Rivarolo canavese	Settimo torinese
Benefits	Environmental aspects	Noise and electromagnetic fields	Acoustic class	AC	Classes		III	III	VI
	Socio-economic aspects	Infrastructural aspects	Infrastructural systems inside the 2 km range area	ID	km^−1		6.1	3.6	6.2
Opportunities	Environmental aspects	Air	Emission reductions due to energy recovery	ER	Classes		Low	Low	Medium
		Soil and water	Industrial area requalification	IA	Expert judgement		Absence	Medium	High
			Aquifer transmissivity	AT	m^2/s		2.78 × 10^−3	6 × 10^−4	4.5 × 10^−2
	Socio-economic aspects	Urban planning	Coherence with the planning instruments in force	CP	Classes		Low	Medium	High
Costs	Environmental aspects	Soil and water	Geological risk	GR	Expert judgement		Yes	No	No
			Surface water quality index	SW	Classes		Medium	Medium/low	Low
			Vulnerability [% of areas with high vulnerability inside the 5 km buffer]	VA	%		91	31	32
		Air	NO2: number of cells exceeding the annual limit value (40 µg/m^3)	NO	Number		0	13	29
			PM10: number of cells exceeding the annual limit value (40 µg/m3)	PM	Number		0	4	12
		Noise and electromagnetic fields	Length of the electricity transmission network	LE	km		4.6	9.8	26.1
		Landscape and ecosystems	Biodiversity index [% of the territory inside the 2 km range area characterized by a high biodiversity index]	BI	%		39.6	35.7	3.8
			Natural value	NV	Classes		High	High	Low
			Landscape quality	LQ	Classes		High	Medium	Low
			Significance of the cultural and historical heritage	SC	Classes		High	Low	Medium
	Socio-economic aspects	Urban planning	Presence of constraints	PC	Presence/absence		Yes	No	No
		Infrastructural aspects	Actual traffic flows inside the 2 km range from the site	AF	Number of vehicles		12 200	16 600	27 315
			Number of accidents per 100 km	NA	Number		25.3	43.9	39.2
		Demographic system	Density of population	DP	People/km^2		931	28	449
			Number of resident inhabitants	NR	Number		40 596	20 410	82 517
			Rural real estate	RR	€		4 031 079	6 813 622	5 096 515
			Residential real estate	RE	€		108 546 700	497 44 860	87 335 748
Risks	Environmental aspects	Air	Dispersive capacity of the area: average of the annual average concentrations of NOx evaluated on the 50 cells with the highest average concentration (ìg/m^3)	DC	Number of cells		2.84	1.83	1.58
			CO2 emitted	CO	t/year		220.6	154.3	125.1
		Landscape and ecosystems	Landscape sensitivity	LS	Classes		High	Medium	Low
		Noise and electromagnetic fields	Presence of sensitive receptors within 500 m	PS	Number		6	6	2
	Socio-economic aspects	Infrastructural aspects	Distance from the nearest train station	DN	km		5.8	3.1	6.7
			km covered in 1 year for the collection of the wastes	KM	km		1 102 782	771 393	625 354
			Variation of the percentage of heavy lorries within a range of 2 km from the site	VH	%		0.8	0.6	0.4
		Demographic system	Number of buildings per each covered km	NB	Number/km		4.3	7.7	6.8

Since the complex decision problem under analysis involves both environmental and socio-economic aspects, the present paper proposes a complex BOCR structure where the two aforementioned aspects have been considered as a control criteria in each of the four subnetworks. Mention should be made to the fact that, taking into account the nature of the decision problem under examination, it has been generally agreed that the environmental aspects play a very important role in the evaluation process; in any case, for a comprehensive analysis of the problem, the model has to reflect also the social dimension and the economic aspects (Morrissey and Browne, 2004). In this work, it has been decided to analyse all the aforementioned aspects, paying particular attention to the implications that the project has from the environmental point of view. For this reason, the elements of the problem have been organized according to two control criteria, namely environmental aspects and socio-economic aspects. At the same time, this choice allows the internal structure of the four subnetworks to be balanced, having the same number of elements belonging to the environmental aspects control criterion and to the socio-economic aspects control criterion.

As far as the structuring of the network is concerned, mention should be made to the fact that, as stated by Miller (1956), it is important not to have too many elements to compare through the expression of relative judgements because the decision aid tool should not be interpreted as an algorithm that is able to automatically give the desired solution but it should instead support the DM who has to make a systematic analysis of the alternative solutions and who is solely responsible for the final choice. For the sake of coherence, particular care has been taken in the present application, to have subnetworks that are easily managed by the DMs. In this sense, for each of the control criteria in the four subnetworks, the number of the elements is limited (from a minimum of 4 elements for the environmental aspects of the Benefits subnetwork to a maximum of 13 elements for the environmental aspects of the Costs subnetwork). Moreover, the number of the elements belonging to the two control criteria is almost the same for each of the fours subnetworks (4 elements for both the two control criteria of the Benefits subnetwork, 6 and 4 elements for the environmental aspects and socio-economic aspects control criteria, respectively, of the Opportunities subnetwork, 13 and 10 elements for the environmental aspects and socio-economic aspects control criteria, respectively, of the Costs subnetwork and 7 elements for both the two control criteria of the Risks subnetwork). The design of the model described allows small and balanced networks to be considered. This is particularly important from a cognitive point of view because it has been generally agreed that the DM tends to give more importance to those aspects that are described by a higher number of elements in the model (Saaty, 1980; Simon, 1960).

As described above (see Section 4.1), the present paper proposes a set of indicators as nodes of the network, since they are supported by sure information.

A measurement scale and an assessment direction (

for Benefits and Opportunities and

with reference to Costs and Risks) must be established for each indicator.

Table I presents the set of indicators that have been identified for the analysis and provides the control category (BOCR) to which each indicator belongs, the control criteria, the measurement unit, the assessment direction and the evaluation of the performance for the three alternative sites. With specific reference to the analysis of the environmental aspects of the problem, these describe the main impacts that a waste incinerator plant is likely to have on the environmental system. In this sense, under the environmental aspects control criterion four different clusters have been identified, namely air pollution, soil and water, landscape and ecosystems, noise and electromagnetic fields. Table II provides a brief description of the aforementioned clusters.

Table II. Environmental aspects of the decision problem

Clusters	Description
Air	The criterion considers the existing air quality in the territorial system under examination and the possible variations deriving from the polluted emissions of the waste incinerator plant, with reference to both the construction phase and the operation one
Soil and water	The criterion takes into account the presence of possible constraints from the point of view of the characteristics of the soil (e.g. hydrogeological risk, agricultural land, etc.) that could prevent the construction of the plant; moreover, the criterion considers the existence of positive concerns that could orient the location of the waste incinerator (e.g. areas subjected to land reclamation). With reference to the water component, the criterion considers the quality of the surface water bodies and the vulnerability of the underground water; furthermore, the transmissivity of the aquifer that could positively affect the location of the plant from the point of view of the water supply is taken into account
Landscape and ecosystems	The criterion reflects the sensitivity of the landscape with respect to the introduction of the incinerator plant in the surrounding area, paying particular attention to the visual impacts of the project
Noise and electromagnetic fields	The criterion takes into account the protection of the environmental system from the negative impacts deriving from the acoustic emission generated by the incinerator. Moreover, the presence of electric lines that could negative interfere with the plant from the point of view of electromagnetic pollution is considered

It is important to underline that the analysis dimension concerns a 2-km range area from each site. In the context of the undesirable facilities location problems, the aforementioned assumption leads to identify more negative aspects (Costs and Risks) than positive ones (Benefits and Opportunities) since the former spread more easily on the territory. Mention should also be made to the fact that social opposition-related indicators have not been considered in the structuring of the network, since they would have been equal for the three alternative sites.

4.3.2. Design phase: determination of the elements and clusters priorities

The design phase involves data collection and processing and the elicitation of the preference of the DMs. Taking into consideration the structure of the model identified for the analysis of the decision problem (Table I), the application will only be illustrated with reference to the Costs subnetwork with the aim of simplifying the explanation. The application is similar for the other subnetworks of the model (Benefits, Opportunities and Risks).

The Costs subnetwork concerns the negative aspects of the territorial context, which have to be minimized in order to obtain the most suitable solution (Figure 2). Two control criteria have been identified, namely environmental aspects and socio-economic aspects. The environmental aspects have been further divided into four clusters (soil and water, landscape and ecosystems, noise and electromagnetic fields, air), whereas the socio-economic aspects have been organized in three clusters (urban planning, demographic system, infrastructural aspects).

Figure 2. The ANP–BOCR model for the case under examination. As an example, the figure shows in detail the structure of the subnetwork costs, according to the two control criteria that have been identified.

According to the ANP methodology, once the network has been identified, it is necessary to represent the influences among the elements. It has been chosen to approach this task according to the following strategies. To start with, all the elements in the four subnetworks are supposed to have an influence on the alternatives. Secondly, in order to take into account the complexity of the decision problem, the feedbacks of the alternatives on the elements of the model are considered. Further relationships have then been identified concerning the potential influences among the elements of each subnetwork. These influences reflect the natural dynamics of the environmental and territorial systems, where link and interaction pathways exist between individual elements, which can, positively or negatively, affect each other. For example, with reference to the Cost subnetwork (Figure 2), considering the environmental aspects, it has been decided to insert in the model the influence that the element ‘water quality’ (‘soil and water’ cluster) exerts on the elements ‘biodiversity index’ and ‘natural value’ (‘landscape and ecosystems’ cluster). This relationship is represented in the model with an arrow that goes from the ‘soil and water cluster’ to the ‘landscape and ecosystem’ cluster.

After the identification of the influences among the elements of the model, it is necessary to develop the pairwise comparisons in order to determine the priorities of the elements of the problem. In this case, the judgments that have to be used in the comparison matrixes have been formulated according to the information contained in the related indicators. The degree to which the objectives are met, as measured by the indicators, is the basis for comparing alternatives. The factor scores related to the performance of the three alternatives, with reference to each indicator, have been normalized using the mode ideal normalization, as represented in the following equation (2):

• (2)

The selected standardization function offers the advantage of maintaining the ratio between the normalized and the original scores. The scores obtained from the standardization have then been translated into a 1-to 9-point scale. In particular, the highest score that has been obtained through the standardization function (i.e. the score 1) is made to correspond to the highest score in the 1-to 9-point scale (i.e. the score 9); in a proportional way, the other normalized scores have been translated from the [0,1] domain into the [1,9] domain. The difference between the thus obtained scores represents the judgments that have to be used to fill in the related comparison matrix.

For example, with reference to the ‘residential real estate’ indicator, Table III shows the procedure followed for the translation of the indicator values into the 1-to 9-point scale scores, whereas Table IV represents the comparison matrix of the three alternatives with respect to the considered indicator.

Table III. Scores of the ‘residential real estate’ indicator for the three alternative plant locations

Alternatives	Indicator value	Normalized indicator score	Indicator score on the 1-to 9-point scale
Ivrea	108 546 700 €	1	9
Rivarolo	49 744 860 €	0.46	4.14
Settimo	87 335 748 €	0.80	7.20

Table IV. Pairwise comparison matrix of the alternatives with reference to the ‘residential real estate’ indicator

Residential real estate	Ivrea	Rivarolo	Settimo	Priorities
Ivrea	1	5	2	0.58
Rivarolo	1/5	1	1/3	0.11
Settimo	1/2	3	1	0.31

The procedure that has been followed for the standardization of the indicator values allows the feedbacks of the alternatives on the indicators to be assessed, as well as the interrelationships among the indicators: this is the crucial point of the ANP method. For example, with reference to the influences of the Ivrea site on the indicators of the ‘demographic system’ cluster, Tables V and VI represent the standardized scores of the considered indicators and the related pairwise comparison matrix, respectively.

Table V. Standardized scores of the indicators related to the ‘demographic system’ cluster for the Ivrea site

Elements	Indicator value	Indicator score on the 1-to 9-point scale
Density	931 ab/km^2	9
Number of inhabitants	40 596	4.41
Rural real estate	4 031 079 €	5.31
Residential real estate	108 546 700 €	9

Table VI. Pairwise comparison matrix of the indicators related to the ‘demographic system’ cluster with reference to the Ivrea site

Ivrea	Density	Number of inhabitants	Rural real estate	Residential real estate	Priorities
Density	1	5	4	1	0.41
Number of inhabitants	1/5	1	1/2	1/5	0.07
Rural real estate	1/4	2	1	1/4	0.12
Residential real estate	1	5	4	1	0.41

Once all the pairwise comparison matrixes have been filled in, the totality of the related priority vectors forms the unweighted supermatrix. In order to alleviate the mathematical burden, all the calculations have been implemented using the Superdecision software (www.superdecision.com). Table VII represents the unweighted supermatrix for the control criterion of the socio-economic aspects under the Costs subnetwork. The priorities of the elements that were previously compared (Tables IV and VI) are shown.

Table VII. Unweighted supermatrix for the socio-economic aspects under the Costs subnetwork

The priorities of the clusters have been derived from expert opinions of the Provincial Administration and have been used to weight the initial supermatrix: the result is the weighted supermatrix.

4.3.3. Choice phase: calculation of the final priorities

4.3.3.1. Priorities of the elements

During the choice phase, alternatives are ranked according to their performance. In this case, the limit supermatrixes are obtained for all the considered subnetworks by raising the weighted supermatrixes to successive powers. The results of the models are shown in Table VIII. For the abbreviations in this table refer to Table I.

Table VIII. Limit priorities for the elements of the model

BOCR	Control criteria	Clusters	Elements	Limit priorities
Benefits	Environmental aspects (0.25)	Alternatives	I	0.07
			R	0.07
			S	0.36
		Noise and electromagnetic fields	AC	0.50
	Socio-economic aspects (0.75)	Alternatives	I	0.16
			R	0.06
			S	0.28
		Infrastructural aspects	ID	0.50
Opportunities	Environmental aspects (0.83)	Alternatives	I	0.07
			R	0.07
			S	0.36
		Air	ER	0.42
		Soil and water	IA	0.04
			AT	0.04
	Socio-economic aspects (0.17)	Alternatives	I	0.03
			R	0.12
			S	0.35
		Urban planning	CP	0.50
Costs	Environmental aspects (0.75)	Alternatives	I	0.16
			R	0.12
			S	0.18
		Soil and water	GR	0.01
			SW	0.01
			VA	0.01
		Air	NO	0.17
			PM	0.09
		Noise and electromagnetic fields	LE	0.05
		Landscape and ecosystems	BI	0.09
			NV	0.04
			LQ	0.03
			SC	0.04
	Socio-economic aspects (0.25)	Alternatives	I	0.16
			R	0.15
			S	0.19
		Urban planning	PC	0.07
		Infrastructural aspects	AF	0.10
			NA	0.12
		Demographic system	DP	0.04
			NR	0.05
			RR	0.07
			RE	0.06
Risks	Environmental aspects (0.33)	Alternatives	I	0.30
			R	0.15
			S	0.05
		Air	DC	0.12
			CO	0.15
		Landscape and ecosystems	LS	0.08
		Noise and electromagnetic fields	PS	0.15
	Socio-economic aspects (0.67)	Alternatives	I	0.13
			R	0.19
			S	0.16
		Infrastructural aspects	DN	0.09
			KM	0.09
			VH	0.06
		Demographic system	NB	0.28

Mention should be made to the fact that each subnetwork has been further subdivided into two separated models, concerning the two control criteria that have been identified for the analysis. This leads to two different limit supermatrixes for the two models in each of the four subnetworks.

It is possible to note, from Table VIII, that the final priority vector contains the priorities for all the elements in the analysis. With reference to the more articulated subnetworks, the results of the elements of the ANP–BOCR model from the priority list highlight some interesting findings. Great importance has been given to the ‘air’ cluster inside the ‘environmental aspects’ control criterion in the evaluation model. With specific reference to the Opportunities and Costs subnetworks, the highest priorities refer to the ‘emission reductions due to energy recovery’ indicator (0.417) and to the ‘NO₂: number of cells exceeding the annual limit value (40 µg/m³)’ indicator (0.165), respectively. It is important to note that the most important elements are also the most interconnected since the supermatrix elements represent the influence of the elements in the network on other elements in that network (Saaty, 2005).

Finally, in order to obtain the ranking of the locations for the four subnetworks, it is necessary to synthesize the raw priorities of the alternatives obtained from the limit supermatrixes (Table VIII) by normalizing them by cluster and multiplying the values thus obtained for the weights of the control criteria. These weights have been derived from expert opinions. The synthesized priorities for the merits are given in Table IX.

Table IX. Priorities for the alternatives under BOCR

Alternatives	B	O	C	R
Ivrea	0.28	0.12	0.34	0.35
Rivarolo	0.13	0.16	0.27	0.38
Settimo	0.59	0.72	0.39	0.27

4.3.3.2. Aggregation

In the case of the BOCR network structure, it is necessary to synthesize the outcomes of the alternative priorities for c (Table IX) in order to obtain an overall synthesis. Different aggregation formulas are available and the formula one chooses depends on the use one wants to make of the results. If the purpose is to peak the best alternative, any of the five formulas will do (Saaty, 2003). Table X shows the final ranking of the alternative sites according to the different formulas that are available.

Table X. Final ranking of the alternatives according to the different formulas

	Aggregation of the priorities
Alternative sites	B + O–C–R	B + O + 1/C + 1/R	B + (1–C) + O + (1–R)	(B*O)/(C*R)	B^1/2*C^−1/2*O^1/2*R^−1/2
Ivrea	−0.407	0.271	0.227	0.062	0.062
Rivarolo Canavese	−0.433	0.267	0.213	0.044	0.044
Settimo Torinese	0.160	0.462	0.560	0.894	0.894

Table XI. Sensitivity analysis modifying influences among elements and alternatives in the unweighted supermatrix

	Iteration	Unweighted supermatrix values		Limit priorities
		AC vs S	S vs AC	I	R	S
Benefits: environmental aspects	1	0.71	1.00	0.07	0.07	0.36
	2	0.96	1.00	0.01	0.01	0.48
	3	0.36	1.00	0.16	0.16	0.18
	4	—	—	—	—	—
	5	—	—	—	—	—
		ID vs S	S vs ID	I	R	S
Benefits: socio-economic aspects	1	0.56	1.00	0.16	0.06	0.28
	2	0.84	1.00	0.06	0.02	0.42
	3	0.28	1.00	0.25	0.11	0.14
	4	—	—	—	—	—
	5	—	—	—	—	—
		ER vs S	S vs ER	I	R	S
Opportunities: environmental aspects	1	0.71	1.00	0.07	0.07	0.36
	2	1.00	1.00	0.01	0.01	0.48
	3	0.35	1.00	0.14	0.15	0.21
	4	—	—	—	—	—
	5	—	—	—	—	—
		CP vs S	S vs CP	I	R	S
Opportunities: socio-economic aspects	1	0.71	1.00	0.03	0.12	0.35
	2	1.00	1.00	0.00	0.01	0.49
	3	0.35	1.00	0.09	0.23	0.18
	4	—	—	—	—	—
	5	—	—	—	—	—
		NO vs S	S vs NO	I	R	S
Costs: environmental aspects	1	0.65	0.75	0.16	0.12	0.18
	2	0.65	1.00	0.16	0.12	0.18
	3	0.65	0.37	0.16	0.12	0.17
	4	0.97	0.75	0.15	0.09	0.22
	5	0.32	0.75	0.17	0.15	0.14
		NA vs S	S vs NA	I	R	S
Costs: socio-economic aspects	1	0.32	0.33	0.16	0.15	0.19
	2	0.32	0.49	0.16	0.15	0.19
	3	0.32	0.16	0.16	0.14	0.20
	4	0.48	0.33	0.16	0.13	0.21
	5	0.16	0.33	0.17	0.16	0.17
		CO vs I	I vs CO	I	R	S
Risks: environmental aspects	1	0.63	0.50	0.30	0.15	0.05
	2	0.63	0.75	0.30	0.15	0.05
	3	0.63	0.25	0.30	0.15	0.05
	4	0.94	0.50	0.34	0.12	0.04
	5	0.31	0.50	0.25	0.18	0.07
		NB vs R	R vs NB	I	R	S
Risks: socio-economic aspects	1	0.56	1.00	0.13	0.19	0.16
	2	0.84	1.00	0.11	0.28	0.09
	3	0.28	1.00	0.13	0.19	0.16
	4	—	—	—	—	—
	5	—	—	—	—	—

As is possible to notice from Table X, all the available formulas converge in considering Settimo Torinese the most compatible site; this is followed by Ivrea and finally by Rivarolo Canavese.

4.3.4. Detailed analysis and implementation: sensitivity analysis

After obtaining a ranking of the alternatives and despite the coherence obtained in the results, it was considered useful to perform a sensitivity analysis on the final outcome of the model in order to test its robustness. The sensitivity analysis is concerned with a ‘what if’ kind of question to see if the final answer is stable when the inputs, whether judgments or priorities, are changed. It is of special interest to see whether these changes modify the order of the alternatives.

In the present paper three different sensitivity analyses have been undertaken in order to study the robustness of the model with respect to the components and interdependencies of the network. In the first one, the stability of the solution has been studied with regard to the control criteria (BOCR) priorities. In the second one, the analysis has explored the modification of the influences of the alternatives on the criteria and of the criteria on the alternatives. Finally, in the third study, the paper attempts to verify the rank reversal of the alternatives (Saaty, 2006) by eliminating one alternative at a time from each subnetwork of the model and thus studying the resulting final ranking searching for potential changes.

In the first study, while measuring the sensitivity of the alternatives to the BOCR weights, an additive formulation is used, since the meaningful changes could not be obtained by a multiplicative formulation (Tuzkaya et al., 2007). The sensitivity analysis for the four subnetworks is represented in Figure 3, where the x-axis represents the changes in the weight of the control criteria, whereas the y-axis represents the changes in the weights of the alternatives.

Figure 3. Sensitivity analysis of each subnetwork using the additive (reciprocal) formula.

When the relationships between the Benefits dimension and the location alternatives are considered, it becomes clear that the share of Settimo Torinese increases and Rivarolo Canavese decreases, while the weight of the benefits increases (Figure 3(a)). This implies that Settimo Torinese has positive features and Rivarolo Canavese has negative features, in terms of benefits. The main reason for this is the higher number of infrastructures and acoustic class in Settimo Torinese and the lower level of the same indicators in Rivarolo Canavese. Although Ivrea is not sensitive to the changes in the weight of the benefits, mention should be made to the inversion in the ranking of the alternatives from Settimo Torinese–Rivarolo Canavese–Ivrea (for 0% benefit weight) to Settimo Torinese–Ivrea–Rivarolo Canavese (for 100% benefit weight), which depends on the weight the benefits are assigned.

With reference to the Costs dimension (Figure 3(b)), the sensitivity analysis shows that this is the most unstable subnetwork, since both the results and the rank of the alternatives are very sensitive to the changes in the weight of the costs. It can be noticed that the ranking of the alternatives changes from Settimo Torinese–Ivrea–Rivarolo Canavese (for 0% cost weight) to Settimo Torinese–Rivarolo Canavese–Ivrea (for 50% cost weight) to Rivarolo Canavese–Settimo Torinese–Ivrea (for 70% cost weight) to Rivarolo Canavese–Ivrea–Settimo Torinese (for 100% cost weight). On the basis of the costs factors, it may be asserted that Rivarolo Canavese and Ivrea have positive features and the increase in the weight of the costs affects them positively. On the other hand, Settimo Torinese has negative features in terms of costs.

Similar observations can be made for the other subnetworks. It is possible to see that the Ivrea and Rivarolo Canavese sites have almost equal performances in the Opportunities and Risks subnetworks (Figure 3(c and d)) and that Ivrea can be identified as being the least sensitive alternative to the changes in the weight of the control criteria.

With reference to Settimo Torinese, which is the winning alternative for all the aggregative formulas that have been considered, the sensitivity analysis highlights that the site has positive performances in terms of Benefits and Opportunities and negative performances in terms of Costs and Risks. Mention should be made to the fact that the Settimo Torinese site has also resulted to be the best solution in the study carried out by the Provincial Administration (ATOR, 2008).

In the second sensitivity analysis, the influences between the elements and the alternatives have been modified and the resulting final priority list of the alternatives has been analysed in order to see if some changes occurred.

Particularly, the priorities of the elements of the model resulting from the limit supermatrix have been considered and, for each control criterion of the four subnetworks, the highest priority alternative and the highest priority element have been taken into account.

In order to perform the analysis, the influences among the aforementioned elements have been changed in the unweighted supermatrix. The original values have been modified of ± 50% in a three-step process (Aragonés-Beltràn et al., 2010), resulting in several possible combinations which have generated new rankings of alternatives. Table XI represents the results of the analysis where, for each control criterion, the first iteration represents the original values and the corresponding final priorities of the alternatives. The following iterations consider instead the subsequent modifications of influences and the resulting new priorities.

Mention should be made to the fact that for some control criteria less iterations have been performed: this happens in those cases where there is only one element to be put in relationship with the highest priority alternative.

As it is possible to see, there are no relevant changes in the new ranking of the alternatives compared with the original one, except for the few cases which are highlighted in bold.

Finally, in order to test the robustness of the model with respect to the rank reversal of the alternatives (Saaty, 2006), the present paper proposes a third sensitivity analysis consisting in the elimination of one alternative at a time from the original model and in the evaluation of the new results. Table XII thus illustrates, for each subnetwork of the model, the original ranking of the alternatives and the results arising from the elimination of each alternative. From the observation of Table XII it is possible to conclude that the final result of the model is stable since the rank reversal of the alternatives occurs only once in the Costs subnetwork, as highlighted in bold in the table.

Table XII. Sensitivity analysis with respect to the rank reversal of the alternatives

	Benefits
Eliminated alternative	Environmental aspects		Socio-economic aspects
	S>R = I	S = 0.36 R = 0.07 I = 0.07	S>I>R	S = 0.28 I = 0.16 R = 0.06
S	I = R	I = 0.25 R = 0.25	I>R	I = 0.37 R = 0.13
R	S>I	S = 0.42 I = 0.08	S>I	S = 0.33 I = 0.17
I	S>R	S = 0.42 R = 0.08	S>R	S = 0.40 R = 0.10
	Opportunities
	Environmental aspects		Socio-economic aspects
	S>R = I	S = 0.36 R = 0.07 I = 0.07	S>R>I	S = 0.35 R = 0.12 I = 0.03
S	R>I	R = 0.26 I = 0.24	R>I	R = 0.42 I = 0.08
R	S>I	S = 0.42 I = 0.08	S>I	S = 0.45 I = 0.05
I	S>R	S = 0.42 R = 0.08	S>R	S = 0.40 R = 0.10
	Costs
	Environmental aspects		Socio-economic aspects
	S>I>R	S = 0.18 I = 0.16 R = 0.12	S>I>R	S = 0.19 I = 0.16 R = 0.15
S	I>R	I = 0.23 R = 0.22	R>I	R = 0.27 I = 0.23
R	S>I	S = 0.23 I = 0.22	S>I	S = 0.29 I = 0.21
I	S>R	S = 0.29 R = 0.21	S>R	S = 0.29 R = 0.21
	Risks
	Environmental aspects		Socio-economic aspects
	I>R>S	I = 0.30 R = 0.15 S = 0.05	R>S>I	R = 0.19 S = 0.16 I = 0.13
S	I>R	I = 0.35 R = 0.15	R>I	R = 0.30 I = 0.20
R	I>S	I = 0.42 S = 0.08	S>I	S = 0.31 I = 0.19
I	R>S	R = 0.38 S = 0.12	R>S	R = 0.29 S = 0.21

5. DISCUSSION OF THE RESULTS AND CONCLUSIONS

5.1. Strategic criteria and BOCR model

The ANP method can make use of strategic criteria that are organized in an additional top layer and that can be very useful for determining the weights of the BOCR merits. The idea of using strategic criteria comes from the necessity of linking the global objectives of the problem to the particular decisions. In this sense, the strategic criteria are objectives or criteria that the decision making entity needs to make sure that they are always served. According to this approach, it is possible to link the global invariant strategic criteria to the alternatives and to use that to evaluate the importance of the BOCR elements for the decision, instead of directly comparing the BOCR against each other with respect to the goal.

In real terms, using the strategic criteria in the model leads to have a multi-layered structure with BOCR merit nodes in the top-level network, control criteria in their attached subnetworks and bottom-level decision subnetworks containing the alternatives that are attached to the control criteria. The top-level model has also an attached Ratings component for evaluating the importance of the BOCR through the use of the strategic criteria (Saaty, 2003). The mechanism for using the strategic criteria in the model can be described as follows:

• (a)
  definition of the strategic criteria;
• (b)
  weighting of the strategic criteria with respect to the goal;
• (c)
  determination of the BOCR weights with respect to the strategic criteria;
• (d)
  the use of the BOCR weights in the aggregation formula for the overall synthesis of the alternative priorities.

Mention should be made to the fact that while the weighting of the strategic criteria with respect to the goal is made according to the pairwise comparison approach (Saaty, 1980), the determination of the BOCR weights may follow a different procedure. By means of the rating process, in fact, it is possible to rate how the highest priority alternative in the four BOCR subnetworks affects each strategic criterion. This process makes use of a matrix, where the columns represent the strategic criteria and the raws represent the BOCR elements. Holding in mind the best alternative under BOCR, it is necessary to rate across the raw how this alternative affect in a beneficial, opportune, costly and risky way, respectively, each strategic criterion. For example, considering the Opportunities subnetwork, it is necessary to assess how does the best alternative under Opportunities impact the strategic criteria in a positive way. If the rating categories are High, Medium and Low, selecting High on a strategic criterion means that the alternative is really good and positive for that strategic criterion. Turning, for example, to Costs, using the same High, Medium and Low categories, it is possible to assess how much the highest priority alternative costs for each of the control criteria. Selecting High on a strategic criterion means that the highest priority alternative costs a lot for that strategic objective.

A large amount of literature pertaining to the use of strategic criteria in ANP models exists (Saaty and Ozdemir, 2008; www.superdecision.com).

In the case under examination in this work, further improvements refer to the use of the strategic criteria in the evaluation model. Particularly, the strategic criteria should reflect the general objectives which have to be pursued in territorial transformation projects, namely ‘population well-being and quality of life’, ‘optimization in the use of natural resources’ and ‘equity in the reallocation of the economic benefits’. Figure 4 gives a representation of the ANP model considering the use of strategic criteria, whereas Table XIII shows how the BOCR merits have been rated on the set of the aforementioned strategic criteria. Mention should be made to the fact that in prioritizing the strategic criteria, the aspects related to ‘population well-being and quality of life’ were given the highest importance (0.54 in the final priority vector), followed by the aspects related to the ‘optimization in the use of natural resources’ (0.30) and finally by the aspects related to the ‘equity in the reallocation of the economic benefits’ (0.16). This leads to have for the BOCR merits the following priorities: 0.26 for the Benefits, 0.47 for the Opportunities, 0.19 for the Costs and 0.09 for the Risks. The aforementioned priorities can be used in order to synthesize the outcome of the alternatives for each of the BOCR structures and to obtain the overall synthesis. Particularly, the global formula bB + oO–cC–rR has been applied, where b, o, c and r represent the priorities that have been obtained by rating the BOCR with respect to the strategic criteria and B, O, C and R represent the ideal priorities of the alternatives in the BOCR subnetworks, respectively (Saaty, 2005). The results of the calculations made show that the site of Settimo Torinese has the highest priority (1.06), followed by the site of Ivrea (−0.12) and finally by the site of Rivarolo Canavese (−0.15). It is possible to notice that these results perfectly confirm the ranking that has been previously obtained by carrying on the aggregation without using the BOCR weights (see Section 4.3.3.2 and Table X).

Figure 4. Representation of the complete multilayered ANP model including the use of strategic criteria for the decision problem under examination.

Table XIII. Strategic criteria and rating scale for the ANP model

BOCR	Priorities	Equity in the reallocation of the economic benefits [0.16]	Population well-being and quality of life [0.54]	Optimization in the use of natural resources [0.30]
Benefits	0.26	Medium	High	Medium
Opportunities	0.47	Medium	High	High
Costs	0.18	High	Medium	Medium
Risks	0.09	Low	Low	Medium

5.2. Conclusive remarks

The paper illustrates the application of an evaluation model based on the integration of the ANP method with a system of indicators, to rank three sites for the location of a waste incinerator plant for the Province of Torino.

The present application has highlighted that a site is suitable for hosting a waste incinerator plant because of its position, which can maximize the positive factors that derive from the location and thus create added value. The negative drawbacks can in fact be minimized for all the alternative sites through the use of the best available technologies.

The results of the performed analysis show that the combination of the ANP–BOCR model with a system of environmental and socio-economic indicators used as criteria ordered in a domain, some of which have to be maximized and others to be minimized, is suitable to represent the real problems of a territorial system and offers an enrichment of a simply state-based view of a figurative understanding of a multidimensional problem. MCA in fact provide the means of performing complex trade-offs on multiple evaluation criteria, while taking the DM's preferences into account (Rossi and Tsoukias, 2009).

The main drawback in the practical application of the ANP is a consequence of the complexity of the decision-making problem that has to be analysed. To this end, the ANP prescribes a high number of comparisons that occasionally become too complex to understand for DMs who are not familiar with the method. Hence, a great deal of attention should be devoted to the elaboration of the questionnaires and the comparison process must be helped by a facilitator (Gomez-Navarro et al., 2009; Aragonés-Beltràn et al., 2010).

The main findings of the present work have proved that the use of measurable and verifiable indicators as nodes of the ANP–BOCR structure significantly improves the internal coherence of the model. Furthermore, this approach makes the decision process more traceable and reliable and thus helps to move forward shared and justified decisions.

The sensitivity analysis has resulted to be an explanatory process by which the DMs achieve a deeper understanding of the structure of the problem. It helps the analyst to learn how the various decision elements interact to determine the most preferred alternative and which elements are important sources of disagreement among DMs and the interest group. Thus, it can be stated that the ANP is not only an aid that can be used to select the best alternative but also helps DMs to understand why an alternative is preferred over the other options (Khan and Faisal, 2008).

However, there are still a number of opportunities for expanding the study and for validating the obtained results. First, it would be of scientific interest to weight the BOCR merits by means of the use of strategic criteria through multidisciplinary focus groups in order to move collaborative decision processes forward. Future research could also explore whether the assignation of weights to the BOCR merits would result in more stable and significant final results (Wijnmalen, 2007). Secondly, the model could be combined with a CBA method in order to develop an overall assessment of the transformation project impacts (Tsamboulas and Mikroudis, 2000), paying particular attention at the social discount rates in order to guarantee intergenerational equity.

Moreover, considering the actual international trends on decision analysis, MCA methods based on indirect preference information are of increasing interest because they require less cognitive effort by the DM: in this context, mention should be made to the theory of the Dominance-based Rough Sets Approach (DRSA) which offers the possibility of elucidating the DM's preferences from the analysis of exemplary decisions provided by the DM (Greco et al., 2001; Fortemps et al., 2008). The application of the DRSA technique to the case under examination could be of scientific interest in order to verify the reliability of the obtained results. Finally, given the spatial nature of the decisional problem under analysis, future improvements of the work will refer to the integration of the MCA tool with Geographic Information Systems in order to develop a Multicriteria Spatial Decision Support Systems (MCSDSS) that will enable multipurpose planning. In this sense, visualization techniques are of major importance in presenting and communicating the results to DMs and the interest group (Malczewski, 1999).

In conclusion, any integration of the ANP with other environmental support tools constitutes a very promising research line in the field of decision problems concerning territorial transformations (Gomez-Navarro et al., 2009).