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Water Resour Manage (2010) 24:4075–4091 DOI 10.1007/s11269-010-9648-2

A Multicriteria Group Decision Model to Support Watershed Committees in Brazil Vanessa B. S. Silva · Danielle C. Morais · Adiel T. Almeida

Received: 29 December 2009 / Accepted: 14 April 2010 / Published online: 7 May 2010 © Springer Science+Business Media B.V. 2010

Abstract The involvement of multiple decision makers in water resources management can be very complex, involving the possibilities of conflicts amongst the stakeholders and the influence of powerful members over the preference of others. The inherent characteristic of decisions also increases this complexity due to many alternatives being involved and there being multiple criteria. Some of these criteria conflict with each other and the consequences of which will have great impact on those involved and on third parties. Therefore, a group decision support system model based on multicriteria analysis can be a powerful tool to support this kind of management. This study presents a tool to support the committee responsible for the management of the watersheds in Brazil in order to promote decentralization and the participation of all involved in the water resources management. The tool provides a ranking of alternatives for the environmental recuperation of the watershed through the use of the multicriteria method PROMETHEE II. For each decision maker, the alternatives were ranked and then the individual rankings were combined into a global ranking which contained the preferences of the whole group. Keywords Multicriteria analysis · Group decision · PROMETHEE · Water resources management

V. B. S. Silva · D. C. Morais · A. T. Almeida Production Engineering Department, Federal University of Pernambuco, Cx. Postal 7462, Recife, Pernambuco 50630-970, Brazil V. B. S. Silva (B) Production Engineering Department, Federal University of Pernambuco, Av. Acadêmico Hélio Ramos, s/n—Cidade Universitária, UFPE, CTG, 5◦ Andar do Bloco Administrativo Recife, 50.740-530, Brazil e-mail: [email protected]

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1 Introduction In Brazil, decisions related to water resources management are made by specific committees established for each watershed. These committees inform the Brazilian Water Resources National Policy (Ministry of the Environment-MMA, 2006), the main objective of which is decentralization and to ensure the participation of all involved in the water resource management process—civil society, the public sector and water resources users. In general, simple majority voting is used to support the decisions. During the voting process, each decision maker uses his/her own decision criteria and some of them are influenced by others, which increases the possibilities of conflicts due to the lack of structuring and transparence. Multicriteria analysis is a technique to structure and analyze complex decisions, which involve multiple criteria, some of which conflict with each other, and to produce actions, the consequences of which have economic, social and environmental impacts. This kind of analysis guarantees a transparent, structured, rigorous and objective evaluation of options (Hajkowicz 2008). This technique has been applied to decisions on the management of natural resources over the last 20 or so years (Hajkowicz and Higgins 2008; Liu and Stewart 2004). As for water resources management, Hajkowicz and Collins (2007) identified eight areas of application: catchment management; ground water management; infrastructure selection; project appraisal; water allocation; water policy and the planning of supply; water quality management; and marine protected area management. Multicriteria analysis can provide solutions for complex water decision-making problems (Morais and Almeida 2006). For recent surveys among others, see Gonçalves and Pereira (2009), Opricovic (2009), Raju and Kumar (2006), and Raju et al. (2000). Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), developed by Brans and Vincke (1985), is a multicriteria decision making (MCDM) technique that provides a valued outranking relation. Most of its applications are in environmental management. Its use for water management is quite new in comparison with the other environmental issues that PROMETHEE applications have dealt with, but this has grown considerably since 1995—about 28 papers already published in the 100 most important scholarly journals (Behzadian et al. 2009). However, what has not been verified is the use of PROMETHEE to rank alternatives to mitigate the degradation effects in a watershed, based on economic, social and environmental aspects, using a group decision approach to support a governmental policy. In this study, a multicriteria decision making model is presented as a possible tool to support watershed committees in Brazil. The model was developed based on the PROMETHEE II method, since it is a relatively simple application method that allows decision makers to choose the type of preference function, and preference and indifference thresholds, thus ensuring better modeling that matches the decision makers’ preferences. A group decision approach was used since the decisions related to the watershed are taken by a committee. Liu and Stewart (2004) argue that this approach is the most appropriate for natural resources management. According to Morais and Almeida (2007), connecting the opinions of each member involved makes the decision process more transparent than when it is analyzed in a closed way. The authors argue that

Multicriteria Group Decision Model Supports Watershed Committees

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when people regard the decision process as having been transparent, the changes are better accepted and credibility is guaranteed. This paper is structured as follows: Section 2 presents the fundamentals of group decision and multicriteria analysis and presents the family of multicriteria methods PROMETHEE; Section 3 presents the Group Decision Support System Model; Section 4 provides the model application; Section 5 provides a sensitivity analysis of the results; and Section 6 presents some conclusions.

2 Approaches Used 2.1 Group Decision Group decision is usually understood as the reduction of different individual preferences to a single collective preference (Leyva-López and Fernandéz-González 2003). The decision is made based on a collective preference derived from the aggregation of individual preferences. The authors distinguish two main approaches to aggregating these preferences: input level aggregation and output level aggregation. For input level aggregation, the group is asked to agree on the alternatives, criteria, weights and remaining parameters, which are established in an open discussion that occurs at the beginning of the process. This approach is at its most appropriate when there is little divergence amongst the group members in their choosing of parameters. For output level aggregation, a group consensus is needed only for defining the actions. Each member constructs his own individual result, which will be aggregated with other results into a final collective one. During the aggregation, each member is considered as a separate criterion and each receives a weight corresponding to his/her importance in the group. The inherent characteristic of committees suggests that the most appropriate approach to aggregate preferences is output level aggregation, since the open discussion, required by input level aggregation, can be impaired by conflicts of interest and by social/intellectual differences among the members of committees. On the other hand, decision-making of the kind which usually involves a technical evaluation of alternatives according to different aspects, suggests that the most appropriate approach is input level aggregation. The next subsection presents some important concepts used in multicriteria analysis. 2.2 Multicriteria Analysis Multicriteria analysis has been used in group decision problems and it is particularly important in decision making that directly involves the population at large. This is the case of social decisions, such as environmental ones. It consists of untangling large problems into discrete components; evaluating these components; reintegrating them; and using the results to construct the decision (Farrell 1996). Multicriteria analysis seems to be an appropriate policy tool, since it makes it possible to take into account different evaluation criteria beyond financial criteria (Arrow and Raynaud 1986). Also, this tool enhances the transparency of the process, which is an essential feature in decisions involving public decisions (Munda 2008).

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Thus, multicriteria analysis is a powerful framework to support the activities of watershed committees, since it can provide consideration of many aspects, such as economic, social and environmental ones, which are essential in decision involving water resources management. Moreover, it enhances the transparency of the process and provides for effective participation of everyone involved in the decisions i.e. those from the public sector, the private sector and civil society. Vincke (1992) defines a multi-criteria decision problem as being a situation in which, having defined a set A of actions and a family F of criteria, the decision maker wishes: to determine a subset of actions considered to be the best with respect to F (choice problem); to divide A into subsets according to some norms (sorting problem); to rank the actions of A from the best to worst (ranking problem). Many methods have been drawn up to support the choosing, sorting and ranking of alternatives in decisions involving multicriteria problems. Roy (1985) classifies these into three approaches: (1) unique synthesis criterion; (2) outranking synthesis; and (3) interactive local judgment. The unique synthesis criterion approach consists of aggregating the different points of view into a unique function which will be optimized. The outranking synthesis approach consists of building a relation called an outranking relation, which represents the decision maker’s preferences, after which this relation is exploited in order to help the decision maker to solve his/her problems. The outranking methods seem to be the most successful because of their adaptability to real problems and the fact that they are more easily comprehended by decision makers (Al-Rashdan et al. 1999). The interactive local judgment approach proposes methods which alternate calculation steps, giving successive compromise solutions, and dialogue steps, meaning extra sources of information on the decision maker’s preferences. Moreover, the methods can be classified according to the meaning of criteria weights. When weighting leads to trade-offs amongst criteria, methods are compensatory, which allows for a disadvantage in some criteria to be compensated for by a large advantage in another criterion. When the weights mean relative importance coefficients, methods are non compensatory, and thus avoid trade-offs amongst criteria. For the case of social decisions, Munda (2008) includes debate about weak/strong sustainability to determine the best approach for aggregating intra-criteria information, such as, weights and number of criteria, and the evaluation of each alternative in relation to criteria. The concept of strong sustainability is related to the idea that natural resources cannot be compensated for by financial resources. Thus, the author concludes that the compensatory multicriteria aggregation approach does not allow implementation of strong sustainability. As far as water management is concerned, consideration of economic and social development is not enough. What must also be ensured is that the compromise solution will be the one in which overall performance is the best considering all dimensions of the problem. To put it simply, the strong sustainability concept must be considered, and, consequently, the multicriteria method must not be compensatory. 2.3 PROMETHEE The starting point of PROMETHEE technique is an evaluation matrix of alternatives based on an appropriate set of criteria. Then, a preference function is assigned


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for each criterion. This function describes how the decision maker’s preference changes with the difference in performance level for two alternatives in a specific criterion, g j(a) − g j(b ), where each term indicates the performance level for an alternative in the criterion j (Brans and Vincke 1985). The preference function provides the intensity of preference for one alternative over another considering a specific criterion. The preference intensity must be calculated for all criteria and for each pair of alternatives. The next step is to determine a preference index for each pair of alternatives using the preference intensity and the weights given to the criteria that represent their relative importance. The preference index provides the preference intensity for one alternative over another considering all criteria. It is defined as a weighted average of preferences of the individual criteria. The preference index defines a valued preference relation that can be used to rank the alternatives (Brans and Vincke 1985). P(a, b ) =

n 1 w j P j(a, b ) W j=1

where

W=

n

wj

(1)

(2)

j=1

where w j is the weight of criterion j. Two indices are calculated using the preference index: positive outranking flow, Q+ (a), and negative outranking flow, Q− (a), (Belton and Stewart 2002). The positive outranking flow expresses the extent to which an alternative outranks all the other ones; how much bigger the positive outranking flow is, better the alternative is. The negative outranking flow expresses the extent to which an alternative is outranked by all the other ones; how much smaller the negative outranking is, the better the alternative is. These parameters are used to explore the relation among alternatives. They are defined by the following expressions, respectively. Q+ (a) =

P(a, b ) n−1

(3)

P(b , a) n−1

(4)

a=b

Q− (a) =

a=b

where n in the number of alternatives. The following methods of PROMETHEE family are described in the literature (Brans and Vincke 1985; Brans et al. 1986): PROMETHEE I (partial ranking), PROMETHEE II (complete ranking), PROMETHEE III (ranking based on intervals), PROMETHEE IV (continuous case), PROMETHEE V (PROMETHEE II and integer linear programming), PROMETHEE VI (weights of criteria are intervals) and PROMETHEE GAIA (graphical representation of PROMETHEE). In PROMETHEE II a complete pre-order (complete ranking) of alternatives is obtained from the net flow that was calculated for each alternative. The net flow is obtained from the difference between the positive and negative flow (Brans et al. 1986). Q(a) = Q+ (a) − Q− (a)

(5)

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One of the advantages of PROMETHEE over other outranking methods, such as ELECTRE methods, is related to the fact that the decision makers find it easy to understand the concepts and parameters inherent in the method, which makes the preference modeling simpler and, consequently, increases the effectiveness of applying the methods. The ELECTRE family uses a concept of concordance and discordance to measure the relative advantage and disadvantage between pair or alternatives. Sometimes, these concepts leave the decision makers confused, making hard the implementation of the method. This aspect is especially important in the case of social decisions, in which decision makers usually have different skills. The PROMETHEE family methods allow each decision maker to select his/her own preference function. These methods are also flexible as to defining preference and indifference thresholds. Moreover they allow each decision maker to assign different relative importance to criteria. All these characteristics make the PROMETHEE methods very suitable for applying to group decision making. The number of practitioners who are applying the PROMETHEE method has been increasing year by year. Behzadian et al. (2009) created a reference bank which includes 217 papers that have been published in 100 high-ranking scholarly journals since 1985. All papers describe methodologies and applications in PROMETHEE categorized into nine main areas: environmental management; hydrology and water management; business and financial management; chemistry; logistics and transportation; manufacturing and assembly; energy management; social; and other topics, which include medicine, agriculture, education, design, government, and sports. It was verified that the application for environmental management is the most common use for PROMETHEE. An investigation revealed that PROMETHEE is the most appropriate multicriteria technique for ranking environmental projects (Al-Rashdan et al. 1999). Hajkowicz and Collins (2007) verified that around 12% of applications using multicriteria analysis for water resources planning and management used the PROMETHEE technique.

3 The Proposed Model In this section we describe a multicriteria group decision making model to rank alternatives for the environmental recuperation of watersheds. The method provides a ranking of alternatives based on economic, social and environmental aspects, and takes into account the points of view of the committee responsible for that watershed. The ranking is obtained through an outranking multicriteria method. The model is divided into four stages: (1) the problem characterization stage; (2) the evaluation stage, where a multicriteria decision aid method is used with each individual; (3) the aggregation stage, where the individual results are aggregated; and (4) the conflict resolution stage, which may return to the problem structuring stage, when necessary (Fig. 1). 3.1 Problem Characterization Stage The goals of this stage are to characterize a real situation in the watershed in order to formulate the set of alternatives; to identify the main group interest involved in

Multicriteria Group Decision Model Supports Watershed Committees Fig. 1 Flowchart of the model

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the process; and to determine the criteria. Also, in this stage, the preference function associated with each criterion must be defined. Alternatives The members of the watershed committees in Brazil are professionals who have multidisciplinary skills, and include chemical, electrical and civil engineers, and specialists in environmental management. Therefore, the group is indisputably able to propose alternatives to mitigate the problems identified in watershed. The main degradation sources and their effect must be presented to the committee and an open discussion must be established, conducted by the analyst, in order to formulate alternatives to mitigate the problems identified. Only a few of the most important issues should be considered since attempting to deal with a large number of issues is not likely to lead to a few being given the attention required that might well lead to breakthrough solutions being found (Al-Rashdan et al. 1999). The discussion must be focused on the information about the status of degradation in order to avoid the consideration of alternatives based on specific particular interests, in other words, the aim of the discussion is to find solutions to the causes which are actually degrading the watershed and not solutions which will benefit only a particular group. Thus, for each alternative proposed, the decision makers must present technical arguments which explain how the action will contribute to the mitigation of the degradation problem detected. On the other hand, the decision makers who disagree with an alternative must present arguments, which emphasize the negative aspects of the alternative against its positive aspects, which have just been presented. In the end, the alternatives formulated by the group must be accepted by each decision maker. In other words, the group must agree on alternatives, which is a characteristic of the input level aggregation approach for group decision. Decision Makers The members of the watershed committee have the right to express their preferences in relation to the watershed concerned. However, multicriteria evaluation may be based on the priorities and preference of only a few decision makers (Munda 2008). Therefore, the point of view and preference of only a few members of each group represented (civil society, public sector and water resources users) will be considered in this model. The proportion of these representatives is in accordance with the Brazilian Water Resources National Policy, which establishes that committees must comprise 40% of water resources users (industrial, agro-industrial, etc.); 40% government representatives and 20% from civil society. Criteria Watershed committees are entities which promote the participation of all interest groups with regard to the watershed under investigation. As a consequence, there are usually significant social and intellectual differences among the members, which can influence relationships in the committee. One possible effect is that a powerful decision maker may influence the behavior of others. Another consequence of this participative process is the fact that some members might not be able to express, for the committee, which evaluation criteria they consider important. In order to avoid these negative effects, defining evaluation criteria must be undertaken by the analyst. However, the analyst should take into consideration the issues addressed by all interest groups during committee meetings he or she attended. Moreover, consideration should be given to the degradation status, its scope (point source or diffuse) and the urgency of implementing actions.


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Therefore, the definition of criteria is a technical activity, performed by a technical team, which can be composed by the analyst and specialists. The family of criteria incorporates the preferences of interest groups and the economic, social and environmental aspects of the real situation of the watershed. The criteria will be used to measure the performance of alternatives. They must be divided into three dimensions: economic, social and environmental. Preference Functions The analyst must assign a preference function, P j(a, b ), to each criterion. He should select one of the six basic types (Brans et al. 1986). This function estimates how the decision maker’s preference changes with the difference in performance level for two alternatives within a specific criterion. The selection of the preference functions is made globally. In this case, the preference functions were the same for all decision makers. For subjective criteria, it must be considered that if the performance of one alternative is slightly higher than the performance of another, then the former is entirely preferable. In this case the Usual Criterion is the most appropriate preference function. Some functions require the definition of a preference and indifference threshold, p and q, respectively. The preference threshold is a value above which the decision maker considers an alternative preferable to another one. The indifference threshold is a value bellow which the decision maker considers an alternative indifferent to another one. The definition of these parameters should be in accordance with that determined by a competent organization.

3.2 Evaluation Stage Assessment of Weights of the Criteria An open discussion among decision makers must take place during a meeting in order to present the criteria, which are classified into three dimensions, economic, social and environmental, and their respective preference functions. Also, the scales, used to evaluate each criterion, are presented, emphasizing those used to evaluate the subjective criteria. From the decision makers’ point of view, this meeting is the most important task of the process, during which all questions about the multicriteria method and parameters are clarified and a shared understanding should be reached. In the end, a climate of trust between the analyst and decision makers should be established. During the meeting, the analyst should encourage the decision makers to assign the same weight to each dimension (economic, social and environmental), 0.333, and split this value among the criteria within it proportionally. Munda (2008) has suggested not assign different priorities to dimensions in order to reduce internal team conflicts. Each decision maker assigns the weights of the criteria according to his/her own preference. For this reason, the assessment of weights is carried out individually by each decision maker in order to avoid the influence of powerful members. At this point the model displays characteristics of the output level aggregation approach for group decision, in which each decision maker presents his/her individual preference in a private meeting between him/her and the analyst only. The analyst must be impartial; however, if any inconsistency in the assessment is verified, he/she must re-explain the process and parameters involved. The decision makers need to understand what each criterion means and how the criteria will be

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evaluated on their respective scales. It should be clear that the weight of the criterion is related to the relative importance of the criterion within the dimension. Each decision maker must assign a value between 0 and 100 to each criterion which represents the weight of the criterion. If a decision maker considers that some criteria are not relevant to him/her, he/she can assign a weight equal to zero to these criteria. Evaluation of Alternatives The alternatives must be evaluated based on estimates by a competent organization. This was justified by the fact that the evaluation of alternatives is usually a highly complex activity, requiring specific studies, which are very expensive and lengthy, and which would be impracticable in most social decisions. Therefore, the decision makers could not evaluate the alternatives, and consequently the values assigned by the competent organizations are considered. Individual Ranking The analyst will use the evaluation of a competent organization and the set of criteria weights assigned by each decision maker to calculate the individual results, through the use of PROMETHEE II method. For each individual result, the analyst calculates the intensity of preference for one alternative over another for each criterion and for each pair of alternatives; followed by the preference index for each pair of alternatives; and then, the positive and negative flows (Brans et al. 1986). Finally, the net flow is calculated using the positive and the negative flow for each alternative (Brans et al. 1986), which indicates the overall performance of each according to the decision maker’s preference. Based on the net flow information, the rankings of each decision maker are obtained, and the alternatives are ordered in decreasing order of their net flows. 3.3 Aggregation Stage This stage provides a new solution for the multicriteria problem using the PROMETHEE II method where the alternatives are the same and the criteria are the decision makers. In the PROMETHEE II method, a preference function should be assigned to each criterion. However, it does not seem realistic to assign different preference functions to these criteria, i.e. to each decision maker (Macharis et al. 1998). The respective net flows are computed on the basis of individual preferences and are therefore expressed on the same scale (Macharis et al. 1998). As all criterion values are expressed in the same units, these values can be directly aggregated. Consequently, the weighted sum of the individual net flows can be easily computed. So, the global net flow for the entire group, for a particular alternative is defined by the following expression. QG (ai ) =

R

ϕ r (ai )wr

(6)

r=1

where R are the decision makers, r = 1, 2, ..., R, and ϕ r (ai ) the net flow for alternative ai , for the decision maker r.


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This special treatment, one without a preference function, is called “0-option” (Macharis et al. 1998). As we can see in Eq. 6, the global net flow is weighted by the weights of the decision makers, wr , which are related to the importance of the decision makers in the decision process. The sum of these weights should be 1. In watershed committees, it is considered that all members have the same importance, so the same weight will be assigned to all decision makers. The methodology used to compute the global ranking is the same as that used to compute the individual rankings. In this case, an alternative a outranks an alternative b if the global net flow of a is bigger than the global net flow of b , to put it simply, QG (a) > QG (b ), and a is indifferent to b if its global net flows are equal, to put it simply, QG (a) = QG (b ). Based on the global net flow information, the global ranking is obtained, and the alternatives are ordered in decreasing order of their global net flows. 3.4 Conflict Resolution During the conflict resolution stage, the analyst will present to the group the final result and the evaluation of alternatives provided by the competent organization. If any decision maker does not agree with the final result, the group is asked to assign new weights to criteria. To support them, the analyst will perform a sensitivity analysis by changing the weights of the criteria assigned by a decision maker and observing the changes in his/her respective individual ranking. This analysis will demonstrate by how much each criterion weight can be changed without affecting the global ranking. A new ranking will be constructed using the new set of weights. If some decision makers do not want to change the weight assigned to the criteria, their current set of values will be considered. If decision makers remain dissatisfied, they should present arguments for performing a new evaluation of alternatives. If a solid argument does not exist, the current evaluation will be considered and other assignment of weights will be allowed. The analyst will be responsible for ending this stage according to his/her convenience.

4 Application of the Model The model was applied to the watershed of the Jaboatão River (Pernambuco, Brazil), which has a drainage area of 426.70 km2 , used for urban occupation, diverse industrial activities, agricultural activities, especially the cultivation of sugar-cane, and also has areas of Atlantic forest and mangroves. The watershed is located in a region with a high population density (about 80 inhabitants per square kilometer), covering part of the townships of Cabo de Santo Agostinho, Jaboatão dos Guararapes, Moreno, Recife, São Lourenço da Mata and Vitória de Santo Antão. 4.1 Characterization of Jaboatão River Watershed The characterization of the problems (causes and effects) of the watershed of the Jaboatão River was designed through interviews with some members of committee,

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Table 1 Characterization of the problem Source

Direct effects

Domestic sewage entering the watershed

Eutrophication and severe reductions in water quality, fish, and other animal populations Visual pollution

Disposal of solid residues (pieces of fishing line and netting, rope for tying up boats, plastic bags, drink containers, foam packaging for food and drinks) Irrigation using the main waste from sugar-cane Industrial emissions which are not treated Disposal of waste of pesticides and fertilizers used in farming practices

Eutrophication and severe reductions in water quality, fish, and other animal populations Severe reductions in water quality, fish, and other animal populations Reductions in water quality, fish, and other animal populations and visual pollution

who undertook an extensive field trip along the watershed in order to register its real situation. The interviews were essential since the documents about the field trips were unconcluded. Table 1 presents the main degradation sources (causes) verified and their direct effects. This table was presented to the committee in a meeting held at the committee room. In the end, the group formulated a set of five alternatives to mitigate the problems identified (Table 2). All members agreed on alternatives proposed. During the meeting, it was defined who members would represent the committee. By convention, the group is composed by five decision makers. The decision makers DM1 and DM2 (40% of public sector representatives) work at the Municipal Secretariat for Sanitation and Environment of Jaboatão dos Guararapes and at the Pernambuco Company of Sanitation. DM3 and DM4 (40% of representatives of users) work in local industries. As for the civil society representative (20%; DM5), she works at the Federal University of Pernambuco (UFPE). Representatives of the Regional Water Treatment and Supply Company participated in the meeting also in order to support the analyst in the formulation of the criteria that must be taken in account. The criteria suggested by them are presented in Table 3. Criteria C1 and C2 are quantitative and a monetary unit is

Table 2 Alternatives in order to mitigate the degradation process of the watershed ID

Description

A1

Secondary sewage treatment in Jaboatão dos Guararapes, requiring that industrial waste be pre-treated according to the standards laid down Educational campaign in the townships within the watershed (with the exception of Recife) Development of a plan of sustainable agriculture specific to the rural producers of Vitória de Santo Antão which focuses on soil and water conservation for the watershed of the Jaboatão River Recovery of native vegetation along the banks of the Jaboatão River Improving the collection of waste material along the river, such as providing periodic trash removal

A2 A3

A4 A5


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Table 3 Criteria Dimension

ID

Criteria

Description

Economic

C1

Financial investment

C2

Maintenance costs

C3

Impact on industrial and agricultural activities

Environmental

C4

Effectiveness

Social

C5

Quality of life

C6

Number of people affected

This is the monetary value for implementing action. The monetary unit is given in Brazilian currency (Reais). A smaller value is preferable to a higher value This is the monetary value to maintain the action in annual operation. The monetary unit is given in Brazilian currency (Reais). A smaller value is preferable to a higher value Corresponds to the negative impacts that the action will cause on industrial and/or agricultural activities from the operational (such as production process changes), economic (reduction of jobs) or legal (fines and fees) points of view. The measure is an ordinal scale (very low, low, regular, high, very high). A lower value is preferable to a smaller value Corresponds to the territorial scope of the environmental benefits in the area of the watershed and how fast these benefits will be achieved. The measure is an ordinal scale (very low, low, regular, high, very high). A higher value is preferable to a smaller value Corresponds to the positive impacts that the action will cause on human life, including human health, recreation, visual aesthetics. The measure is on an ordinal scale (very low, low, regular, high, very high). A higher value is preferable to a smaller value Corresponds to an estimate of the number of people, who will be affected by the benefits of the action. The measure is an ordinal scale (very low, low, regular, high, very high). A higher value is preferable to a smaller value

Table 4 Preference functions and the respective parameter p

Criteria

Function

Parameter p

C1 C2 C3 C4 C5 C6

V-shape criterion V-shape criterion Usual criterion Usual criterion Usual criterion Usual criterion

100,000 50,000 NA NA NA NA

Table 5 Criteria weights

Decision makers

C1

C2

C3

C4

C5

C6

DM1 DM2 DM3 DM4 DM5

0.19 0.15 0.07 0.11 0.07

0.15 0.15 0.11 0.07 0.07

0.11 0.07 0.15 0.11 0.07

0.19 0.15 0.15 0.19 0.26

0.19 0.22 0.22 0.15 0.30

0.19 0.22 0.22 0.15 0.19

Criteria

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V.B.S. Silva et al. Ranking

DM1

DM2

DM3

DM4

DM5

1 2 3 4 5

A1 A2 A5 A3 A4

A1 A2 A3 A5 A4

A1 A5 A2 A3 A4

A1 A5 A2 A3 A4

A1 A2 A5 A3 A4

used to evaluate the alternatives in relation to them. Criteria C3, C4, C5 and C6 are subjective in nature, so a verbal scale was used, which was converted into a numerical scale. For C1 and C2, the decision maker’s preference for one alternative in relation to another one was considered to increase linearly with the difference in performance between them. Based on the preference threshold considered, one alternative was found to be preferable to the others. Consequently, the V-shape criterion function suggested by PROMETHEE (Brans et al. 1986) was associated with criteria C1 and C2. Table 4 shows the functions chosen for each criterion and their respective parameters, which were determined by the Water Treatment and Supply Company. The other criteria are subjective type. 4.2 Evaluation Stage A second meeting was established to present the criteria and their respective preference functions to the group. All decision makers agreed on criteria and functions. After that, a face-to-face meeting was held with each decision maker individually in his/her own place of work. During this meeting they assessed the criteria weights, which reflected the level of importance of each criterion according to their preferences. The normalized values are presented in Table 5. The evaluation of the alternative in relation to the criteria was based on the estimates by the regional Water Treatment and Supply Company. The information collected from each decision maker (set of criteria weights), the evaluation provided by the Water Treatment and Supply Company and the remaining parameters considered by the analyst (preference functions and threshold parameters) were combined through the PROMETHEE II method to obtain the individual rankings (Table 6). After that, the results were aggregated to create the global result. The global ranking is shown in Table 7.

Table 7 Global ranking

Ranking

Alternative

Global net flow

1 2 3 4 5

A1 A2 A5 A3 A4

0.294 0.066 0.032 −0.128 −0.264


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5 Results of the Application Sewage treatment (A1) is placed first in all individual rankings, while recovery of native vegetation (A4) is the last. Plan of sustainable agriculture (A3) is fourth in four rankings. This same structure is repeated in the global result. Second place in the global ranking is occupied by educational campaign (A2), which appears three times in that position in the individual rankings, while collection of waste (A5) appears twice, and is ranked third in the global ranking. This brief analysis indicates that the global ranking is consistent with the individual ones, which means that the aggregation of individual results was satisfactory. Sewage treatment (A1) requires higher financial investment for its implementation, operation and maintenance; nevertheless, it was the alternative placed first, indicating that the non-financial aspects were incorporated by all stakeholders. Concomitantly, this alternative presents the best overall performance in relation to the environmental and social dimension according to expert evaluation. The second placed alternative, educational campaign (A2), also presents a good performance in relation to the environmental and social dimension. Also, it does not cause any impact on industrial or agricultural activities. Moreover it is the alternative which requires the least financial investment for its implementation. Thus, the second suggestion of the ranking seems to be the best alternative, should there be budget constraints. The third placed alternative, collection of waste (A5), requires the second largest investment for its implementation, operation and maintenance; however, it presents the second best overall performance in relation to the environmental and social aspects and it does not cause any impact on industrial or agricultural activities. The public sector will be responsible for financing action. Therefore, it can be supposed that the global ranking may not be in accordance with traditional Brazilian public sector values, since the most expensive alternative is at the top, while the expectation is the contrary. A sensitivity analysis was performed to evaluate the behavior of the results if the representatives of government had assigned a higher weight to the criterion which evaluates the investment necessary for each action. This specific analysis is not related with the conflict resolution state, since it was assumed that all decision makers agreed on final result and this stage was suppressed. An increase was provoked in the weights assigned by the public sector representatives (DM1 and DM2) to the financial investment criteria (C1), from 19% to 25% and from 15% to 25% for DM1 and DM2, respectively, while the weight of the other financial criteria (C2) remained the same. As expected, the individual rankings for DM1 and DM2 showed change in their top position: from the more expensive alternative, sewage treatment (A1), to the cheaper alternative, educational campaign (A2). However the global ranking was not changed, emphasizing the strength of the other representatives in constructing the final decision.

6 Conclusion This study presents a group decision support system model which can be used as a powerful tool to support committees responsible for the management of watersheds. The model is based on multicriteria analysis which allows the consideration

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of different aspects, in compliance with sustainability principles, thus making the decision process more transparent and, consequently, reducing the possibility of conflicts amongst the different interest groups. The group approach adopted by the model provides for effective participation of everyone involved in the decisions taken by committees. Also, the multicriteria analysis used, including the analysis of economic, social and environmental aspects, separated by criteria, guarantees a better modeling of the different points of view. Moreover, the model helps to reduce the influence of powerful members over other members, since some stages are held in individual meetings between the analyst and each decision maker. All these aspect are very important to assure effective decentralization and the participation of everyone involved in the water resource management process. Since the decisions will usually have great impact on the activities of the state, city and private sectors, the possibility of conflict is very high. Thus, the analyst must be as impartial as possible in order to establish a relationship of trust among the group. All stages of the model are concerned about promoting the impartiality of the analyst, including the formulation of alternatives, which is performed by the committee, the involvement of a competent organization to evaluate the alternatives, the stage to conflict resolution, in which it is possible to change some parameters of the model, and so on. Acknowledgements This work is part of a research program funded by the Brazilian Research Council (CNPq). The authors gratefully acknowledge the valuable suggestions made by anonymous reviewers to a previous version of this paper, which have contributed to make this a better final version.

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