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Journal of Applied Operational Research (2011) 3(2), 75–90 © Tadbir Operational Research Group Ltd. All rights reserved. www.tadbir.ca

ISSN 1735-8523 (Print), ISSN 1927-0089 (Online)

Assessing urban requalification scenarios by combining environmental indicators with the Analytic Network Process

Marta Bottero * and Valentina Ferretti

Politecnico di Torino, Turin, Italy

Abstract. Environmental Assessment of territorial transformation projects is an intrinsically complex multi-dimensional process, because it considers different elements, such as the physical-chemical, biological, cultural and socio-economic components. The use of decision support methods can therefore be beneficial for Decision Makers. When talking referring to Environmental Assessment and territorial transformations, a very consolidated approach that is used to report information concerning the various aspects of the development is the one that makes use of indicators. Of these methods, it is worth mentioning a recent approach presented by the Organization for Economic Co-operation and Development where environmental indicators have been organized according to the so-called Driving forces- Pressures-State-Impacts-Responses (DPSIR) framework. Unfortunately, this approach results in a linearity in the relationships between the actions of a project, the impacts on the environmental system and the interferences with human activities and thus fails to study the system complexity in depth. In order to overcome the limits of this approach, this paper proposes a combined decision support tool that employs the DPSIR environmental indicator framework to analyse the different environmental aspects of the problem and the Analytic Network Process (ANP) method to manage the interdependencies among the factors, which can be organized in categories of Benefits, Opportunities, Costs and Risks (BOCR structure). The paper illustrates the application of the combined DPSIR/ANP model according to the BOCR structure to assess three alternative projects for the requalification of a downgraded urban area in Northern Italy. The results show the most relevant environmental indicators which describe the transformation and the ranking of the three considered options.

Keywords: environmental indicators; multicriteria analysis; analytic network process; decision-making

* Received October 2010. Accepted January 2011

Introduction

Environmental Assessment (EA) can be defined as a systematic identification and evaluation of the potential effects of proposed projects, plans or programmes on the environmental system (Canter 1990; Glasson et al. 2005). EA is a multidimensional concept that includes physical-chemical, biological, cultural and socio-economic components. A useful support in the context of EA processes is provided by Multicriteria Analysis (MCA). Generally speaking, MCA are used to make a comparative assessment of alternative projects or heterogeneous measures and they allow several criteria to be taken into account simultaneously (Roy and Bouyssou 1995; Figueira et al. 2005). With specific reference to EA, MCA can be used to prioritize projects (or plans or programmes) on the basis of selected criteria /variables that reflect the environmental impacts of the foreseen transformation (Nijkamp et al. 1990). Referring

* Correspondence: Marta Bottero, Politecnico di Torino, Housing

and City Department, Viale Mattioli, 39 – 10125 Turin, Italy. E-mail: [email protected]

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to EA, mention can be made of the use of specific indicators that are able to give information about the environmental aspects of the problem (OECD 1993, 2004). The environmental indicators, within this approach, are organized according to the so called Driving forces-Pressures-State-Impacts-Responses (DPSIR) framework (EEA 1995), as illustrated in Table 1. Table 1. The DPSIR environmental indicator framework. DPSIR category Description

Driving forces

Indicators for Driving forces describe the social, demographic and economic developments in societies and the corresponding changes in lifestyles (e.g. population growth, Gross National Product). These primary driving forces provoke changes in the overall levels of production and consumption and exert pressures on the environment.

Pressures Pressure indicators describe developments in the release of substances (emissions), physical and biological agents, the use of resources and land by human activities (e.g. CO2-emissions per sector, use of rock, gravel and sand for construction, amount of land used for roads).

State State indicators give a description of the quantity and quality of physical phenomena (such as temperature), biological phenomena (such as fish stocks) and chemical phenomena (such as atmospheric CO2-concentrations) in a certain area.

Impacts Impact indicators are used to describe changes in the state of the environment due to pressures. These changes then have impacts on the functions of the environment, such as human and ecosystem health, resources availability, losses of manufactured capital and biodiversity.

Responses

Response indicators (such as the relative amount of cars with catalytic converters and recycling rates of domestic waste) refer to responses given by the society in order to prevent, compensate, ameliorate or adapt to changes in the environmental system. The Responses offer feedback on the driving forces, on the pressures or on the state or impacts directly, through adaptation or curative action.

Figure 1 shows how the DPSIR framework is used to assess the relationships between human activities, their environmental impacts and the societal responses to these impacts. The DPSIR approach, which proposes a linearity among the relationships concerning the actions of a project, the impacts on the environmental system and the interferences with human activities, fails to study the system complexity in depth because it does not allow interdependencies among the elements and feedbacks to be considered. In order to overcome the limits of this approach, the paper proposes the integration of the DPSIR framework with a Multicriteria Analysis, namely Analytic Network Process (ANP). The ANP method, which is based on the use of utility-ration functions, is particularly suitable for dealing with such problems because it allows qualitative and uncertain information to be considered and interconnections between criteria to be represented (Saaty 2005). Furthermore, within the ANP method, it is possible to simplify the problem structuring by classifying the issues at stake in categories of Benefits, Opportunities, Costs and Risks, through the creation of the ANP – BOCR model (Saaty and Ozdemir 2008).

Driving forces

Pressures

State

Impacts

Responses

e.g. quality

e.g. health ecosysteme.g. pollutants

e.g. policies and targets

e.g. causes

Eco-efficiency indicators, emission factors

Dispersion modelsDose-response indicators

Risk assessment, cost-benefi analysis

Effectivenenss ofresponses

Fig. 1. The DPSIR framework (source: elaboration from EEA, 2003)

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Starting from the analysis of the possibilities of integrating the DPSIR framework and the ANP technique in the field of Environmental Assessment (Wolfslehner and Vacik 2008; Bottero and Ferretti 2010), the objective of the paper is to study the problem in depth, trying to explore the positive and negative environmental aspects of territorial transformations by linking the DPSIR framework to the ANP - BOCR method. The paper shows an application of the combined DPSIR/ANP model, based on the BOCR structure (BOCR- DPSIR/ANP) to assess different scenarios for the requalification of a downgraded urban area in Torino (Italy).

After the introduction section, the paper is organized as follows: the second section briefly illustrates the ANP state of the art and methodological background, the third section describes the application of the BOCR-DPSIR /ANP combined model to the study case and the fourth section contains the discussion on the main findings and the conclusions derived from this research.

The Analytic Network Process

State of the art

The Analytic Network Process (ANP) is a Multicriteria decision support tool 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 Analytic Hierarchy Process (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 Decision Maker (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, while 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 reference to the ANP, the literature is more recent and some publications can be found in different fields. Mention can be made of some very recent researches in the sphere of waste management (Promentilla et al. 2006; Aragonés-Beltràn et al. 2010; Tuzkaya et al. 2007; Khan and Faisal 2008), transport infrastructure assessment (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; Razmi and Rafiei 2009), economics and finance (Niemura and Saaty 2004) and civil engineering (Piantanakulchai 2005; Neaupane and Piantanakulchai 2006).

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 chose. 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


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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, named 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 concerning 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. This operation allows to capture the transmission of influences along all the possible paths in the network. For example, in order to obtain the indirect influence through a third element the supermatrix has to be powered to the square.

kk

W∞→

lim (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; Saaty 2003; Wijnmalen 2007). 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 and to verify the stability of the results.

Case study

Presentation of the case study area and description of the alternatives

The study refers to the downgraded urban area of Basse di Stura (Figure 2), which is situated on the Northern side of the City of Torino, the capital of Piedmont. The area is crossed by several heavily trafficked urban axes and the Northern Torino motorway, an important artery that ensures connection with the national and international motorway network. Moreover, the area has two urban waste landfills, one of which has already been completed and reconverted into a green area and the another which is still in use. However, the real challenge for Basse di Stura remains the land reclamation of those areas, closest to the Stura river, that in the past were used as unauthorized waste disposal sites for waste from mining and industrial activities. Finally, Basse di Stura also requires attention from the social point of view, due to the presence of gypsy camps and to forms of illegal activities induced by the state of abandonment of most of the area (Turi 2008).

The presence of other particular factors, such as the final stretch of the Stura river, the numerous farmhouses, some of which are still operational and of undoubted historical value, and the numerous production and trade activities make Basse di Stura an extremely heterogeneous area.


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Agricultural land

Waste site

River basin

City of Turin

Land reclamation area

Motorway

Fig. 2. Area under analysis

Due to the above mentioned reasons and since it covers a total surface area of about 540 hectares, Basse di Stura represents both a criticality and a potentiality as, on one hand, it is characterized by environmental degradation and social conflicts and, on the other, it represents an enormous opportunity for the future development of the metropolitan area to the North of Torino, due to its strategic position in terms of traffic and accessibility.

Thanks to the organization of an international workshop (Turi 2008), the process of redesigning this articulated and complex environment has started and different Master Plans have been proposed in order to regenerate the quality of the environment and the quality of life in this part of the city. Since it represents a compromised area, but full of potentialities, it is of great interest to study and to measure the sustainability of different possible developments in the area. With this objective, the present study has considered three transformation scenarios, whose main characteristics are illustrated in Table 2.

Table 2. Description of the alternative scenarios.

Scenarios Description

“Do nothing” This scenario represents the situation without any project. In this way, the study area would still be characterized by heavy environmental pressures due to the presence of contaminated areas, infrastructures,unauthorized landfills, etc.

Land reclamation

This scenario represents an intermediate step between the “do nothing” scenario and the Master Plan. It consists of studies finalized to carry out the site characterization plan for the purpose of determining the extent of soil contamination from industrial wastes over an area of 150 hectares along the right bank of the Stura River. Land reclamation actions, such as the reclamation of the industrial landfills, some protectioninterventions for the artificial basins with a view to subsequent renaturalization, some maintenance works along the shores of Lake Bechis and some protection interventions for the upper plains will then be conducted to ensure a safer environment for those living in the area (Turi 2008).

Master Plan

This scenario leads to a radically new multifunctional design of the city. The Master Plan is based on three fundamental concepts: sustainable energy production, quality of life and the green heart of the Torino park system (Turi 2008). The main actions that would be accomplished according to this scenario are: the setting up of solar efficient energy plants on the landfill that would no longer be in use, the creation of a hydrogen district, the transformation of the current industrial nature of this area into a new generationpole (including service industries, research and training facilities, residences) and the creation of an urban “green heart”. This scenario would also ensure land reclamation of the contaminated areas.


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The BOCR model based on the DPSIR environmental indicator framework

The above mentioned decision environment represents a complex system where the presence of interrelated elements and conflicting aspects suggests the use of a Multicriteria Analysis (MCA) that is able to provide a rational base for the systematic analysis of the alternative scenarios. With the aim of finding the most significant aspects involved in the decision and the most suitable scenario for the transformation, the DPSIR framework has been used in combination with the ANP method. The DPSIR framework has been adopted in order to identify, through a holistic approach, a set of representative environmental indicators for the case study under examination. The ANP method has been developed according to the so-called BOCR model, which allows Benefits, Opportunities, Costs and Risks to be considered. Each decision within 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 separate simple network structure for the decision.

In order to link the DPSIR framework to the ANP-BOCR model, the environmental indicators have been classified in categories of Benefits, Opportunities, Costs and Risks. Table 3 presents the set of indicators that have been identified for the analysis and provides the BOCR control criteria for each indicator, the DPSIR category to which each indicator belongs, the source and the measurement unit.

It is important to underline that the DPSIR framework focuses more on the environmental aspects of the decision problem, while the social-economic elements are considered less. This is the reason for our attempt to consider the economic aspects related to the transformation within the analysis of the Costs and Risks categories by evaluating the economic dimension of the Responses indicators. Although the Responses represent positive countermeasures that are finalized to reduce the negative impacts, they are in fact characterized by economic costs in both the short and long term.

Table 3. List of the DPSIR indicators for the ANP-BOCR model.

BOCR control criteria

DPSIR category Indicators Source Unit of measure

Small/medium agricultural activities with low environmental impact

ARPA(a) number

Schools Agenda 21 number Population ARPA number

D

Land use ARPA % Soil consumption per inhabitant Agenda 21 m2/inhabitant P Contaminated sites already characterized ARPA % Urban biodiversity Agenda 21 number Presence of historical, cultural and natural heritage ARPA number of elements S

Presence of natural areas and stepping zones ARPA % Soil productivity Personal elaboration tons/year I

Soil permeability ARPA cm/sec Environmental communication Agenda 21 number of initiatives/year Days vehicles cannot circulate Agenda 21 number

Ben

efits

R

Proposal of plans/programmes of requalification/ reclamation/ management

Personal elaboration number

Services ARPA % Agricultural land ARPA m2 D

Energy production from renewable sources ARPA GWh Energy consumption from renewable sources Modified from ARPA % Km covered by green fuels Modified from ARPA % Local mobility and passenger transportation ECI(b) % for each type of transporta-

tion Green areas OECD(c) m2

P

Population structure Agenda 21 % according to age classes Public and private vehicle fuel type Agenda 21 % Availability of historical, cultural and natural heritage Cassatella and Peano expert judgment Number of slow food presidium throughout the territory Personal elaboration number People living within 300 meters of open public areas ECI %

Opp

ortu

nitie

s

S

State of conservation of the historical- architectural heritage Cassatella and Peano expert judgment


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Availability of local public open areas and services ECI % of population Local quality of the air ECI number of times the air qual-

ity limit values are exceeded Variation in the population’s well being EUSI(d) qualitative judgment Biodiversity variation Personal elaboration qualitative judgment

I

Changes in the real estate market EUSI % Monitoring activity of dangerous industries ARPA number Integrated Environmental Authorizations released ARPA number Start- up of small and middle size businesses Personal elaboration number Organizations with environmental certification ARPA number Products promoting sustainability ECI % of specialized shops

R

Differentiated waste disposal ARPA % Gypsy camps Personal elaboration number Consumption of fossil fuels by industrial activities ARPA GWh Non authorized dumping Personal elaboration number Energy production plants ARPA number

D

Dangerous industries ARPA number Emissions into water, air and soil Modified from ARPA number of times the limit val-

ues are exceeded Level of crime Agenda 21 n. of crimes Deteriorated landscapes Cassatella and Peano % Special and dangerous waste production ARPA tons/ year

P

Contaminated sites ARPA % Noise, atmospheric and soil pollution Modified from ARPA number of times the limit val-

ues are exceeded Citizen’s satisfaction with the local community ECI classes Perception of the crime level Agenda 21 % of population for each age

class

S

Environmental state of the river basin ARPA classes Presence of toxins in the agricultural products ARPA number I

Complaints due to noise pollution ARPA number Reclaimed farms Personal elaboration number New cycle tracks ARPA km

Cos

ts

R

Environmental monitoring stations Agenda 21 number Linear infrastructures for transport purposes ARPA km Vehicles ARPA Number D

Urbanization and infrastructures ARPA % Consumption of water, energy and natural resources Modified from ARPA qualitative judgment Soil consumption ARPA m2 Acoustic emissions Modified from ARPA dB Atmospheric emissions Modified from ARPA g/cm3

P

Urban waste production ARPA kg/inhabitant Air pollution level ARPA number of times the limit val-

ues are exceeded Noise level Modified from ARPA number of times the limit val-

ues are exceeded

S

Soil use destination ARPA % Territorial fragmentation ARPA % I

House price-to-income ratio Agenda 21 % Areas where land reclamation has been performed ARPA number Environmental reclaiming Modified from ARPA number Restoration and conversion of abandoned buildings Agenda 21 number

Ris

ks

R

Reconstruction of abandoned areas for new urban uses, in-cluding public green areas

Agenda 21 %

a) ARPA: Italian Regional Association for Environmental Protection (ARPA Piemonte 2009) b) ECI: European Common Indicators (Ambiente Italia Research Institute 2003) c) OECD: Organization for Economic Cooperation and Development (OECD 1993, 2004) d) EUSI: European System of Social Indicators (Berger-Schmitt and Noll 2000)

Example of a subnetwork analysis

According to the ANP methodology (Saaty 2005; Saaty and Vargas 2006), the first step of the analysis consists in structuring the decision problem and in recognizing the objective of the evaluation, that should later be divided into “clusters”, that are made up of various elements (“nodes”), and alternatives or options. The relationships between the different parts of the network should then be identified.


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As already seen in the pervious section, in this case an ANP-BOCR model has been developed in order to take into account the complexity of the decision problem. The decision problem has been divided into five clusters (Driving forces, Pressures, State, Impacts and Responses) that have been organized in four different subnetworks. The general objective of the evaluation is to identify the best scenario for the re-qualification of the Basse di Stura area.

The ANP application model has been developed through the use of the specific Superdecision software (www.superdecision.com). With the aim of simplifying the explanation, the model is only illustrated with reference to the Opportunities sub-network. However, the application is the same for the other sub-networks (Benefits, Costs and Risks) of the network under examination. The outcome of the alternatives priorities for each of the BOCR sub networks will be then synthesized by means of specific aggregation formulas (Saaty 2003, 2005) and the overall ranking will be obtained.

The final results of the analysis performed for the case under examination are given in the following sub-section. The Opportunities sub-network is represented in Figure 3 with reference to the structure of the assessment model that has been given in Table 3. Each category of the DPSIR framework has been assigned to a cluster and the related indicators have been considered as the different elements of the cluster. The relationships established among the elements reflect the flows of influences according to the DPSIR framework.

PRESSURES

Do nothing scenario (DN)

Land reclamation scenario (LR)

ALTERNATIVES

Master Plan scenario (MP) Services (SE)

Energy production from renewable sources (EP)

DRIVING FORCES

Energy consumption from renewable sources (EC)

Km covered by green fuels (KM)

Population structure (PS)

Local mobility and passenger transportation (LM)

Green areas (GA)

STATE

Public and private vehicle fuel type (VH)

Availability of historical, cultural and natural heritage (AH)

Number of slow food presidium (SF)

People living within 300 m from open public areas (PL)

State of conservation of the historical –architectural heritage (SC)

IMPACTS

Availability of open public areas and services (AO)

Local air quality (AQ)

Biodiversity variation (BV)

Variation in the well-being of the population (WB)

Changes in the real estate market (RE)

RESPONSESMonitoring activities of dangerous industries

(MD)

Integrated Environmental Authorizations (EA)

Start-up of new businesses (NB)

Organizations with environmental certification (OE)

Differentiated waste disposal (WD)

Products promoting sustainability (PS)

Agricultural land (AL)

Fig. 3. Opportunities subnetwork

Once the network has been identified and all the relationships among the elements have been established, it is

necessary to develop the pairwise comparisons by using a ratio scale of 1-9 to compare any two elements (Saaty, 1980). Pairwise comparisons are made to establish the relative importance of the different elements with respect to a certain component of the network, both at the cluster level and at the element level.With reference to the cluster level, considering for example the Responses cluster as the parent element, the questions that must be answered to compile the pair matrix are the following: From the point of view of the Responses category of indicators, which aspect leads to the biggest opportunities? And how much more?


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Alternatives 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Driving forces Alternatives 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Impacts Alternatives 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Pressures Alternatives 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Responses Alternatives 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 State

Driving forces 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Impacts Driving forces 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Pressures Driving forces 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Responses Driving forces 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 State

Impacts 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Pressures Impacts 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Responses Impacts 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 State

Pressures 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Responses Pressures 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 State

Responses 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 State The judgments have been established taking into consideration the structure of the cause-consequence chains

of the DPSIR framework (Figure 1). The established judgments were used to fill in the comparison matrix (Table 4). According to the ANP methodology, the final priority vectors that result from the comparison matrices at the cluster level determine the columns of the cluster matrix. Table 5 represents the cluster matrix for the Opportunities subnetwork and the column corresponding to the clusters previously compared (Table 4) is shown.

Table 4. Clusters pairwise comparison matrix from the Responses category of indicators (Opportunities sub-network).

Alternatives Driving forces Impacts Pressures Responses State Priorities Alternatives 1 7 4 6 3 5 0,43 Driving forces 1/7 1 1/5 1/2 1/4 1/3 0,04 Impacts 1/4 5 1 3 1/4 2 0,14 Pressures 1/6 2 1/3 1 1/2 1/3 0,06 Responses 1/3 4 4 2 1 3 0,23 State 1/5 3 1/2 3 1/3 1 0,10

Table 5. Cluster matrix (Opportunities sub-network).

Alternatives Driving forces Impacts Pressures Responses State Alternatives 0,00 0,13 0,19 0,19 0,43 0,73 Driving forces 0,55 0,00 0,00 0,00 0,04 0,00 Impacts 0,05 0,00 0,08 0,00 0,14 0,19 Pressures 0,25 0,87 0,00 0,08 0,06 0,00 Responses 0,03 0,00 0,73 0,00 0,23 0,00 State 0,12 0,00 0,00 0,73 0,10 0,08

Once the clusters comparison has been conducted, it is necessary to study the problem in depth through the

analysis of the elements (or nodes) of the model. The judgment attribution is performed filling in the pairwise comparison matrices that are made according to the influence and interdependence relations set in the network. With reference, for example, to the element “monitoring activities of dangerous industries” (cluster of Responses) and its relationships with the alternative scenarios, the questions that must be answered to compile the matrix are the following: Considering the Response indicator “monitoring activities of dangerous industries”, which alternative leads to the biggest opportunities? And how much more?


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Land reclamation scenario 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Do nothing Scenario Land reclamation scenario 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Master Plan scenario

Do nothing scenario 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Master Plan scenario The previously established judgments were used to fill in the specific comparison matrix (Table 6).

Table 6. Comparison of the alternatives from the monitoring activities of dangerous industries (Opportunities sub-network).

Monitoring activities Land reclamation scenario Do nothing scenario Master Plan scenario Priorities Land reclamation scenario 1 3 1/7 0,15 Do nothing scenario 1/3 1 1/9 0,07 Master Plan scenario 7 9 1 0,78

In order to show the feedbacks made possible by the ANP methodology, an example from an alternative scenario

point of view is also illustrated.Considering the “Master Plan scenario” and its relationships with the Impacts category of indicators cluster, the questions that must be answered to compile the matrix are the following: With reference to the Master Plan scenario, which element of the Impact category of indicators leads to the biggest opportunities? And how much more?

AO 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 AQ AO 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 WB AO 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 RE AO 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 BV AQ 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 WB AQ 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 RE AQ 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 BV WB 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Re WB 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 BV RE 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 BV

The abbreviations refer to Figure 3. The previously established judgments were used to fill in the specific

comparison matrix (Table 7). Once all the pairwise comparison matrixes have been compiled, the totality of the related priority vectors forms the unweighted supermatrix (Table 8). The priorities of the elements that have been previously compared (Tables 6 and 7) are shown.

Table 7. Comparison matrix of the Impacts indicators from the Master Plan scenario (Opportunities sub-network).

Master Plan scenario AO AQ WB RE BV Priorities AO 1 5 1/5 1/5 5 0,13 AQ 1/5 1 1/7 1/7 3 0,05 WB 5 7 1 1 9 0,40 RE 5 7 1 1 9 0,39 BV 1/5 1/3 1/9 1/9 1 0,03


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Table 8. Unweighted supermatrix for the Opportunities sub-network.

LR DN MP EP SE AL AO AQ WB RE BV GA EC KM LM PS MD EA NB OE PS WD VH AH SF PL SC

LR 0,00 0,00 0,00 0,15 0,09 0,00 0,09 0,15 0,13 0,13 0,15 0,00 0,09 0,09 0,09 0,09 0,15 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09

DN 0,00 0,00 0,00 0,07 0,09 0,00 0,09 0,07 0,08 0,08 0,07 0,00 0,09 0,09 0,09 0,09 0,07 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09 0,09

MP 0,00 0,00 0,00 0,79 0,82 0,00 0,82 0,79 0,79 0,79 0,79 0,00 0,82 0,82 0,82 0,82 0,78 0,82 0,82 0,82 0,82 0,82 0,82 0,82 0,82 0,82 0,82

EP 0,50 0,50 0,13 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

SE 0,50 0,50 0,87 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00

AL 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00

AO 0,07 0,14 0,13 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,58 0,00 0,08 0,65

AQ 0,07 0,14 0,05 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,88 0,88 0,00 0,74 0,00 0,25 0,83 0,00 0,00 0,00 0,00

WB 0,53 0,43 0,40 0,00 0,00 0,00 0,75 0,88 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,13 0,13 0,00 0,09 0,75 0,75 0,17 0,23 0,33 0,33 0,12

RE 0,20 0,14 0,39 0,00 0,00 0,00 0,25 0,13 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,13 0,00 0,59 0,23

BV 0,13 0,14 0,03 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,17 0,25 0,00 0,00 0,06 0,67 0,00 0,00

GA 0,00 0,00 0,00 0,00 0,28 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

EC 0,25 0,13 0,25 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,17 0,00 0,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

KM 0,25 0,13 0,04 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,83 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

LM 0,25 0,13 0,11 0,00 0,07 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

PS 0,25 0,63 0,59 0,00 0,65 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

MD 0,51 0,50 0,05 0,00 0,00 0,00 0,00 0,14 0,39 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

EA 0,23 0,10 0,03 0,00 0,00 0,00 0,00 0,43 0,06 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

NB 0,08 0,10 0,29 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

OE 0,11 0,10 0,31 0,00 0,00 0,00 0,00 0,43 0,10 0,00 0,25 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

PS 0,04 0,10 0,23 0,00 0,00 0,00 0,00 0,00 0,34 0,00 0,75 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

WD 0,03 0,10 0,09 0,00 0,00 0,00 0,00 0,00 0,11 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

VH 0,14 0,20 0,07 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

AH 0,14 0,20 0,22 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,17 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00

SF 0,43 0,20 0,03 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,00 0,00 0,00 0,00 0,00 0,00 0,00

PL 0,14 0,20 0,47 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,83 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

SC 0,14 0,20 0,22 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Alte

rnat

ives

Driv

ing

forc

esIm

pact

sPr

essu

reRe

spon

ses

Stat

e

Alternatives Driving forces Impacts Pressures Responses State

The eigenvector obtained from the cluster level comparison is applied to the initial supermatrix as a cluster

weight. The result is the weighted supermatrix (Saaty 2005). In the final step, the weighted supermatrix is made to converge to obtain a long-term stable set of weights. The supermatrix is raised to a limiting power to obtain the limit supermatrix, where all the columns are identical and each gives the global priority vector. Normalizing each element by cluster, it is possible to obtain the ranking of the alternatives: in the case of the Opportunities subnetwork, the preferred solution refers to the Master Plan scenario (81%), then to the Land reclamation scenario (10%) and in the end to the Do nothing scenario (9%).

Final results and sensitivity analysis

The development of the analysis for the four subnetworks leads to the identification of the final priorities of all the elements of the model. These priorities are shown in Table 9 where the white histogram bars represent the priorities of the alternative scenarios, while the coloured grey histogram bars represent the priorities of all the other elements that have an influence on the analysis. It is possible to note that the final priority vector contains the priorities for all the elements in the analysis. In order to obtain the ranking of the alternatives, it is necessary to synthesize the raw priorities obtained from the limit supermatrix by normalizing them. In the case of the BOCR network structure, it is necessary to synthesize the outcome of the alternative priorities for each of the four subnetworks in order to obtain an overall synthesis.


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Table 9. Final priorities of the elements of the analysis derived from the limit supermatrix. Do nothing 0,135Land Reclamation 0,122Master Plan 0,181Small/medium agricultural activities with low environmental impact 0,021Schools 0,031Population 0,137Land use 0,064Soil consumption for inhabitant 0,127Contaminated sites already characterized 0,045Urban biodiversity 0,008Presence of historical, cultural and natural heritage 0,033Presence of natural areas and stepping zones 0,032Soil productivity 0,018Soil permeability 0,015Environmental communication 0,008Days ot vehicles circulation’ stop 0,003Proposal of plans/ programs of requalification/ reclamation/ management 0,018Do nothing 0,027Land Reclamation 0,029Master Plan 0,249Services 0,135Agricultural land 0,001Energy production from renewable sources 0,034Energy consumption from renewable sources 0,049Km covered by typology of green fuels 0,006Local mobility and passenger transportation 0,018Green areas 0,033Population structure 0,120Typology of the circulating public and private vehicles by fuel type 0,112Availability of historical, cultural and natural heritage 0,014Number of slow food presidium on the territory 0,005People living within 300 meters of a public open area 0,043State of conservation of the historical- architectural heritage 0,007Availability of local public open areas and services 0,005Local quality of the air 0,026Variation of the population’s well being 0,021Biodiversity variation 0,003Changes in the real estate market 0,123Monitoring activity on dangerous industries 0,010Integrated Environmental Authorizations released 0,012Start- up of small and middle size businesses 0,002Organizations with environmental certification 0,013Products promoting sustainability 0,012Differentiated waste disposal 0,003Do nothing 0,224Land Reclamation 0,092Master Plan 0,042Gypsy camps 0,051Consumption of fossil fuels by industrial activity typology 0,020Non authorized dumping 0,029Energy production plants 0,025Dangerous industries 0,074Emissions into water, air and soil 0,115Level of crime 0,036Deteriorated landscapes 0,045Special and dangerous waste production 0,007Contaminated sites 0,060Noise, atmospheric and soil pollution 0,042Citizen’s satisfaction with the local community 0,033Perception of the crime level 0,011Environmental state of the river basin 0,009Presence of toxins in the agricultural products 0,004Complaints due to noise pollution 0,038Reclaimed farms 0,002New cycle tracks 0,001Environmental monitoring stations 0,040Do nothing 0,105Land Reclamation 0,088Master Plan 0,211Linear infrastructures for transport purposes 0,059Vehicles 0,023Urbanization and infrastructures 0,144Consumption of water, energy and natural resources 0,013Soil consumption 0,062Acoustic emissions 0,020Atmospheric emissions 0,021Urban waste production 0,035Air pollution level 0,033Noise level exceedances 0,032Soil use destination 0,076Territorial fragmentation 0,026House price to income ratio (Gentrificazione) 0,016Areas where land reclamation has been performed 0,003Environmental reclaiming 0,005Restoration and conversion of abandoned buildings 0,003Reconstruction of abandoned areas for new urban uses 0,024

Impacts

Responses

Alternative scenarios

Driving forces

RIS

KS

Driving forces

Pressures

State

Impacts

Responses

Pressures

State

Responses

OPP

OR

TUN

ITIE

S


BEN

EFIT

S


Driving forces

Pressures


Driving forces

CO

STS

Pressures

State

Impacts

Responses

State

Impacts


87

Different aggregation formulas are available; 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 10 shows the final ranking of the alternative scenarios according to the different formulas that are available. As it is possible to notice from Table 10, all the available formulas agree in considering the Master Plan scenario the most preferable one; this is followed by the Land Reclamation scenario and finally by the “Do nothing” scenario.

Table 10. Final ranking of the alternatives according to the different formulas available.

Alternative priorities Aggregation of the priorities Alternative scenarios

B O C R B+O-C-R B+O+1/C+1/R B+(1-C)+O+(1-R) B*O/C*R B1/2 * C-1/2 * O1/2 * R-1/2

Do nothing 0,309 0,089 0,626 0,259 -0,429 0,250 0,222 0,028 0,077

Reclamation 0,278 0,096 0,257 0,219 -0,026 0,297 0,320 0,077 0,027

Master Plan 0,413 0,815 0,117 0,522 0,545 0,453 0,458 0,895 0,895

Despite the coherence obtained in the results, it is 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. 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 4. With reference, for example, to the Opportunities subnetwork (Fig. 4b), it can be seen that the share of the three transformation scenarios increases with the increase in the Opportunities weight. This implies that the three transformation scenarios have positive features in terms of Opportunities. As far as the Opportunities subnetwork is concerned, the Master Plan scenario is the best alternative and there is a negligible difference between the “Do nothing” scenario and the Land Reclamation one. Similar observations can be made for the other subnetworks. Mention should be made to the fact that the Risks subnetwork (Fig. 4d) is the most unstable since it involves two inversions in the ranking of the alternatives. As far as the Risks subnet is concerned, the Master Plan scenario becomes the least preferable solution since it is characterized by uncertain aspects and by considerable urban transformations with larger environmental impacts and thus involving more environmental risks.

-0,8-0,6-0,4-0,2

00,20,40,60,8

0 0,2 0,4 0,6 0,8 1

Benefits

Master Plan

Reclamation

Do nothing

(a)

-0,8-0,6-0,4-0,2

00,20,40,60,8

1

0 0,2 0,4 0,6 0,8 1

Opportunities

Master Plan

Reclamation

Do nothing

(b)

-0,8-0,6-0,4-0,2

00,20,40,60,8

0 0,2 0,4 0,6 0,8 1

Costs

Master Plan

Reclamation

Do nothing

(c)

-0,8-0,6-0,4-0,2

00,20,40,60,8

0 0,2 0,4 0,6 0,8 1

Risks

Master Plan

Reclamation

Do nothing

(d)

Fig. 4. Sensitivity analysis of each subnetwork using the additive (negative) formula


88

Conclusions

From the analysis of the final results (Table 9), it is possible to highlight some interesting findings. Although the result can be taken for granted, the model confirms that the best scenario for the requalification of the Basse di Stura area is that of the Master Plan. As already mentioned, the evaluation model performed is based on the DSPIR framework, which is more oriented toward the analysis of the environmental aspects of a problem and tends to overlook the economic costs of the projects and the social relapses. These assumptions are perfectly reflected on the final ranking of the alternatives, where the preferred solution is the Master Plan scenario which aims at a profound environmental requalification of the area under examination. The results of the elements of the BOCR - ANP/DPSIR model from the priority list are more interesting. All the most important elements in the evaluation model belong to the Driving forces and Pressures categories of indicators. In particular, the highest priorities refer to the “population” indicator (0,137) in the Benefit subnetwork, to the “services” indicator (0,135) in the Opportunities subnetwork, to the “emissions into water, air and soil” indicator (0,115) in the Costs subnetwork and to the “urbanization and infrastructures” indicator (0,144) in the Risks subnetwork. The outcomes of the analysis reflect the internal dynamics structure of the DPSIR model according to which the Driving Forces influence all the other categories of the model.

The results of the performed analysis show that the combined BOCR - ANP/DPSIR model is efficient in representing the real problems of a territorial system and that it offers an enrichment of a simply state-based view of a figurative understanding of a multi-dimensional problem. In fact, the BOCR- ANP/DPISR model succeeds in representing the complexity of territorial transformations by integrating information about interdependencies through the ANP and it is able to resolve the limitations of the DPSIR framework, which can only suggest linear unidirectional causal chains, while oversimplifying the linkages and the structures of the real situations. Furthermore, the combined model allows the temporal dimension of the problem to be taken into account in the evaluation, as well as some economic considerations. Finally, although the DPSIR approach usually considers adverse aspects leading to environmental impacts, a first attempt has been made in the present paper to also try to identify the positive issues related to the territorial transformations. Apart from the aforementioned advantages, one of the most significant strengths of the ANP methodology is represented by the fact that the DM gains more awareness of the elements at stake while structuring the model and thus learns about the problems while solving them.

In addition, with reference to the ANP methodology, 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 preferred alternative and which elements are important sources of disagreement between the 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). 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 analyzed. 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 should be helped by a facilitator (Gomez- Navarro et al. 2009; Aragonés-Beltràn et al. 2010).

However, there are still a number of opportunities for expanding the study and for validating the results obtained herein. First, it would be of scientific interest to weight the BOCR control criteria by means of multidisciplinary focus groups in order to move collaborative decision processes forward. Future research could also explore and verify whether the assignation of weights to the BOCR control criteria would result in more stable and significant final results (Wijnmalen 2007). To this end, a set of strategic control criteria could be defined in order to combine the BOCR in a meaningful way (Tuzkaya et al. 2007). Furthermore, the model could be combined with a Cost Benefits Analysis method in order to develop an overall assessment of the transformation project impacts (Tsamboulas and Mikroudis 2000). Finally, in order to test the stability of the final results and the robustness of the model, an attempt to verify the rank reversal of the alternatives (Saaty 2006) could be done by eliminating the winning alternative from each subnetwork of the model and thus studying the resulting final ranking searching for potential changes.Given the spatial nature of the decisional problem under analysis, future improvements of the work will also refer to the integration of the MCDA tool with Geographic Information Systems in order to develop a Multicriteria Spatial Decision Support System (MCSDSS) that will enable multi-purpose planning (Malczewski 1999). In this sense, visualization techniques are of great importance to present and communicate the results to


89

DMs and the interest groups (Al-Kodmany 1999; Wu et al. 2010). In conclusion, any integration of the ANP with other environmental support tools constitutes a very promising research line in the field of Environmental Assessment (Gomez-Navarro et al. 2009).

Acknowledgments— The case study illustrated in the present paper is based on the results that were obtained at the Basse di Stura International Workshop held in Torino in 2008 in the context of the “Transmitting Sustainable Cities” Project, one of the collateral events of the XXIII UIA World Congress Torino 2008. The authors of the paper would like to thank the Fondazione dell’Ordine degli Architetti Pianificatori, Paesaggisti e Conservatori della Provincia di Torino (FOAT) for providing the study material concerning the area under examination.

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