Prioritisation of Investments in Sewage Projects: A Multicriteria Model

Asfor, Jose Carlos; Freitas, Neurisangelo Cavalcante de; Pinheiro, Placido Rogério

doi:10.3390/w17213065

Open AccessArticle

Prioritisation of Investments in Sewage Projects: A Multicriteria Model

by

Jose Carlos Asfor

¹

,

Neurisangelo Cavalcante de Freitas

¹

and

Placido Rogério Pinheiro

^2,*

¹

Ceará State Sanitation Company, Fortaleza 60422-901, Brazil

²

Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza 60811-905, Brazil

^*

Author to whom correspondence should be addressed.

Water 2025, 17(21), 3065; https://doi.org/10.3390/w17213065 (registering DOI)

Submission received: 14 August 2025 / Revised: 17 October 2025 / Accepted: 21 October 2025 / Published: 26 October 2025

(This article belongs to the Special Issue Urban Water Pollution Control: Theory and Technology)

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Abstract

The sanitation sector faces significant challenges in achieving the universalisation goals established by the new 2020 regulatory framework. Prioritising these investments is essential due to the limited financial resources available, especially in sanitation projects. This article proposes a prioritisation model based on the Measuring Attractiveness by a Category-Based Evaluation Technique (MACBETH) method, aiming to order the execution of sewage projects by municipality when considering the perspectives of water and sewage concessionaires. The methodology involves brainstorming and Web-Delphi steps to identify criteria and subcriteria, as well as the use of the M-MACBETH software version 2.5.0 to define weights and value judgements. The research, conducted as a case study, employs a qualitative, quantitative, exploratory, and descriptive approach, emphasising interdisciplinary collaboration and model validation with experts. The conclusion highlights that the proposed model can be replicated in various contexts, enabling dealers to make more informed and effective decisions. Suggestions for future research include adapting the model to other areas of sanitation and integrating advanced technologies, such as artificial intelligence, for dynamic data analysis and management.

Keywords:

prioritisation model; sewage projects; multicriteria analysis; project prioritisation; MACBETH

1. Introduction

Law No. 14,026 of 15 July 2020, known as the legal framework for sanitation, determined several obligations related to the provision of basic public sanitation services. To this end, universalisation targets were defined to guarantee that 99% (ninety-nine per cent) of the population would have access to drinking water and 90% (ninety per cent) would have access to sewage collection and treatment by 31 December 2033.

Furthermore, the standard sets quantitative targets for non-intermittent supply, loss reduction, and improvement of the treatment process.

Given the challenging scenario for the sanitation sector in Brazil, which requires significant investments to achieve the universalisation goals set by the new regulatory framework in 2020, prioritising investments is a critical factor for sanitation companies due to the limited resources available to finance the sector [1,2].

According to the National Association and Union of Private Concessionaires of Public Water and Sewage Services [1,3,4], it is necessary to implement more than 300,000 kilometres of network to universalise sewage collection in Brazil by 2033, as foreseen in the new legal framework for sanitation.

In 2021, according to data from the National Sanitation Information System (SNIS) [5], only 84.2% of the Brazilian population had access to drinking water (177 million inhabitants), while only 55.8% had access to a sewage system (117.3 million).

According to a study conducted by the Brazilian Association of Private Concessionaires of Public Water and Sewage Services [1] and Klynveld Peat Marwick Goerdele (KPMG), universalising sanitation in Brazil by 2033 will necessitate investments of approximately R$498 billion in sanitation infrastructure expansion: R$144 billion in water distribution and R$354 billion in sewage collection and treatment. These values are extravagant when compared to the investments made in water supply and sewerage systems in 2020 (R$11.91 billion) and 2021 (R$15.11 billion). Thus, a gap is evident between the technical necessity to increase service supply and the availability and utilisation of the financial resources required for the sector’s universalisation [2].

Considering this, it is of paramount importance to prioritise substantial investments to achieve the population service goals established by the Brazilian policymaker, which go far beyond the availability of resources and the speed of project execution observed in previous years [6,7]. Furthermore, in Brazil, there are sanitation companies that have not yet incorporated an adequate model for prioritising sewage projects into their strategic planning [8].

These gaps lead to poor decision-making, as the lack of adequate criteria makes project assessments random. Consequently, the population no longer receives the benefit of adequate sanitation in a shorter period, and public authorities remain obliged to carry out improvements and enhancements to the service provision, as per Article 23, Section V, of Law No. 13,460 of 26 June 2017.

Therefore, it is necessary to rethink portfolio management, focusing on complying with the sector’s new regulatory framework and universalising the provision of services, based on an appropriate multicriteria methodology. To this end, the possibilities of reconciling the provision of services with the demand for resources and compliance with the obligations established by the policymaker must be assessed. Furthermore, it is necessary to balance public health and environmental aspects, which justifies the essential nature of prioritising strategic projects and the multicriteria methodology in the basic sanitation sector.

Proper planning of sanitation projects is a fundamental premise for improving the lives of the population. Projects of this nature bring direct benefits across several interfaces: social, environmental, technical and public health.

Given the need to invest in a prioritisation process that considers the importance, relevance, and contribution of projects to achieving a sanitation company’s strategic objectives. Furthermore, multicriteria analysis, particularly when applied with the MACBETH method, enables the evaluation of sewage system projects that incorporate contributions from various stakeholder profiles. Each profile plays a crucial role in generating and evaluating alternatives, as well as in defining and applying relevant criteria. The decision-maker diagnoses the current situation, prioritises the multiple available alternatives, and defines the overall contribution of each alternative to management. Understanding and integrating these roles is fundamental to the success of the decision-making process.

In this sense, this research aims to present a multiple-criteria model that enables the prioritisation of investments by ordering the execution of sewage projects by municipality from the concessionaires’ perspective based on the Measuring Attractiveness by a Category-Based Evaluation Technique (MACBETH) method.

To this end, the use of brainstorming and Web-Delphi methodologies stands out, which allows the identification of the Fundamental Points of View considered fundamental for the analysis, in dialogue with experts; the M-MACBETH software, which enables the definition of value judgements and weights for each Fundamental Point of View and Elementary Point of View; and the multicriteria model. Furthermore, this is a bibliographical and documentary research study, conducted in the case study format, employing both qualitative and quantitative approaches, and utilising exploratory and descriptive methods.

The contribution of the prioritisation model, derived from the judgement matrix, involves a more effective direction of investments and the execution of projects capable of addressing the challenges and goals set in the strategic plan of a water and sewage company. Based on the experience of the Water and Sewage Company of the State of Ceará (CAGECE). The article is divided into three parts: sewage projects and multicriteria methodology; structuring and evaluation of the M-MACBETH model; and, finally, the application of the model, based on the value functions.

The remainder of the paper is structured as follows. Section 2 presents a Sanitation project with the main concepts, definitions, and approaches. Moreover, Section 3 focuses on Multicriteria Methodology, including its characterisation, as well as the structuring and application phases necessary for the classification and prioritisation of monitored points. Additionally, Section 4 presents the evaluation model’s structure, which includes the definition of criteria and parameters for classifying and prioritising monitored points. Furthermore, Section 6 presents the results and discussions of this application using Sensitivity Analysis, followed by a conclusion. Finally, Section 7 presents the conclusion and Future Works of this research.

2. Sanitation Projects

Within the scope of sewage systems, portfolio management must consider several structures that play specific roles in the sewage collection, treatment, and disposal process, such as collection networks, pumping stations, sewage treatment plants (STPs), and final disposal systems [9].

When considering this reality, this research employed the portfolio definition presented by the Project Management Institute [10], which comprises a set of projects, programmes, sub-portfolios, and operations managed collectively to achieve strategic objectives.

In turn, a project is a temporary endeavour undertaken to create a unique product, service, or result. It is temporary, indicating a beginning and an end for the entire project work or just one phase. Projects can be independent or part of a programme or portfolio.

In this context and based on a multicriteria classification methodology to support constructivist decision-making (MCDA-C), this study adopts an approach that prioritises investments by ordering the execution of sewage projects by municipality from the concessionaires’ perspective.

Multicriteria methods can be applied to different decision-making contexts to help decision-makers better understand the situation through judgment and evaluation [11].

According to Figure 1, the three main phases of the MCDA-C process are structuring, evaluation, and recommendation [12].

The structuring stage is crucial to ensure that the model adequately reflects the complexities and specificities inherent in decision-making in the sanitation area.

To this end, the multicriteria decision support (MDS) approach aims to provide managers with tools that allow them to progress in solving decision problems and to consider several objectives, which often have contradictory points of view, as is the case, for example, of the dichotomy between reducing costs and increasing quality [13,14].

At this stage, the primary assessment elements (EPAs) are the initial concerns that emerge from decision-makers when confronted with the analysed context [15].

Moreover, defining EPAs is an initial step in developing the cognitive map, which involves objectives, decision-makers’ values, targets, actions, options, and alternatives.

Defining these elements is crucial for obtaining a comprehensive cognitive map. The cognitive map is a kind of flowchart that links different terms in a cause-and-effect or means-end relationship. Thus, it ultimately connects even the most basic concerns in a mesh that naturally funnels towards high-level strategic concerns [16].

The fundamental points of view are the aspects considered by the decision-maker as the axes for evaluating the problem because of their essential nature.

In this research, elementary points of view (EPVs) were considered as means to achieve an end, enabling the reach of fundamental points of view [17]. Furthermore, ref. [18] states that “a descriptor can be defined as a set of levels, associated with a Point of View (PV), which will describe, in an exhaustive, homogeneous, and unambiguous way, the possible impacts of potential actions.”

In each descriptor, in addition to the potential actions, anchoring or reference levels must be established (“Good” level and “Neutral” level).

These levels define the threshold ranges within which the impacted shares are considered within the market acceptance range. In this context, actions that exceed the “Good” level are considered excellent, while those placed below the “Neutral” level are referred to as having compromising performance [19].

In turn, ref. [12] explains that the evaluation phase seeks to understand the differences in attractiveness between the levels of performance indicators, expresses how these indicators compensate each other and then diagnoses the status quo of the alternatives in question. Finally, the recommendations stage supports the decision-maker in identifying ways to improve the current state of their object of study.

Thus, it enables the identification of the strategic-level consequences that these improvements will have [12,20].

The proposed prioritisation method was applied to the municipalities that make up the so-called “Serra”—i.e., a group of hills of sedimentary nature—da Ibiapaba (UN-BSI), in the State of Ceará (Figure 2), which are in alphabetical order: Carnaubal, Chaval, Croatá, Graça, Guaraciaba do Norte, Ibiapina, Mucambo, Pacujá, Pires Ferreira, Reriutaba, São Benedito, Tianguá, Ubajara, Varjota, and Viçosa do Ceará. The Ibiapaba Mountains, with their natural wealth, tourism potential, and social importance, stand out as a priority region for investments in basic sanitation. The adoption of integrated policies that combine economic development, environmental preservation, and the improvement of the population’s quality of life is essential to ensuring a sustainable future for the region. Adequate sanitation not only meets a basic need but also strengthens the Ibiapaba Mountains’ strategic position as a model of sustainable development in the Brazilian Northeast. It is a natural watershed, home to essential springs that supply the Ceará and Piauí river basins, with a climate distinct from the surrounding backwoods. The region boasts lush vegetation and rich biodiversity, factors that directly depend on the quality and availability of water. In the context of sanitation, the presence of strategic water resources in the mountain range necessitates adopting sustainable practices to preserve water quality and prevent contamination, which could compromise both the environment and human and agricultural supplies. The Ibiapaba Mountains are one of the most popular tourist destinations in the Northeast region. They are renowned for attractions such as Ubajara National Park, featuring caves and waterfalls, as well as the pleasant climate of mountain towns like Viçosa do Ceará and Ipu, which represent significant tourist potential for the state. Given this context, we selected this region for the model’s application.

The results prioritising investments in sewage systems were obtained using the M-MACBETH tool. These results are based on evaluations conducted by nineteen sanitation specialists from CAGECE, including sanitation engineers who design and manage water and sewage systems and were consulted. Additionally, environmental sanitation technologists who plan and implement projects, along with technicians and professionals involved in the operation and maintenance of water, sewage, and solid waste networks, were surveyed through questionnaires.

In this scenario, ref. [9] reports that it is not easy for any manager to choose a strategy to implement when there are many options, mainly because each one can have different consequences. Therefore, the use of tools that assist the decision-maker can be beneficial. To this end, the structuring, evaluation and recommendation phases of the proposed model described above were applied.

This model has the potential to optimise the application of resources according to the investment prioritisation obtained for the municipalities of Serra da Ibiapaba, in Ceará.

Thus, the relevance of the proposed method stands out in the challenging context of the national dispute over investments in environmental sanitation, given the significant impact of this sector on public health and environmental preservation.

3. Multiple-Criteria Methodology

The multicriteria methodology, also known as Multiple-Criteria Decision Analysis (MCDA), is a sophisticated approach that facilitates decision-making in complex contexts. Ref. [21] highlights MCDA as a tool for analysing situations permeated by the interaction of several variables. Bana and Costa are pioneers in this field and emphasise the importance of engagement and mutual understanding among stakeholders [22,23].

The objective of MCDA methodologies is to help decision-makers organise and synthesise information to make decisions with greater certainty and clarity. This includes the ability to explain and manage the subjectivity involved, while minimising the chance that the decision will be optimal for one evaluated criterion and unacceptable for another [21].

According to [24,25], the multicriteria decision-making support methodology addresses the structuring of complex decision-making contexts. It aims to support the decision-maker (problem owner) in selecting the best alternative solution based on criteria informed by the decision-maker’s values and perceptions. In this sense, MCDA is fully aligned with resolving infrastructure problems and primarily involves environmental issues.

The reason for this is that environmental science decision-making problems are multidimensional and necessitate the involvement of numerous stakeholders [26]. In most cases, the decision-maker cannot make rational decisions due to the difficulty of grouping and analysing all relevant data.

Phases of the Proposed Model, According to the M-MACBETH Method

According to [27], selecting an applicable multicriteria decision-making method requires adapting it to the specific characteristics of the problem at hand. To accomplish this, it is necessary to consider the nature of the decision, the features of the alternatives, as well as the quality and structure of the information that is accessible. The method should be consistent with the decision maker’s level of familiarity with the topic, reasoning approach, and the precision of the data. Divergent outcomes may result from selecting from various decision-making methodologies. Nevertheless, these discrepancies are more a result of methodological diversity than they are genuine contradictions. The adoption of the Macbeth method in this study was substantiated by its alignment with the decision maker’s reasoning process. Specifically, the evaluator’s intuitive and trustworthy evaluation was facilitated by the clarity and relevance of the pairwise comparisons.

Furthermore, established criteria are available to help determine whether the chosen method is suitable for the decision context. In addition to this point, the necessity of assessing the data’s approval, the properties of the data employed by the method, and the extent to which the results facilitate the decision-making process is emphasised. In contrast, the research problem is characterised by a multicriteria compensatory method that employs quantitative measures to aggregate performance across all criteria. This approach resolves the trade-off between the criteria, resulting in an overall score that is subsequently employed to rank alternatives, such as those in the MACBETH method. Secondary issues, such as the existence of instruments like M-MACBETH software, were also observed, as they facilitated a more comprehensive integration with the problem being addressed. Nevertheless, ref. [28] has observed that the multicriteria decision support landscape is characterised by a diverse array of methods that apply to a wide range of scenarios.

According to Figure 3, the classes of problems addressed in Multiple-Criteria Decision-Making are presented in [29]. Furthermore, ref. [24] defines four decision support problems:

(a): Pγ (ordering), according to which the decision process aims to recommend an ordering of alternatives.
(b): Pα (selection), which aims to indicate the choice of an alternative.
(c): Pδ (description or cognition), which designates the clarification of the decision through a description in an appropriate language.
(d): Pβ (class allocation), by which the purpose of the selection process is to suggest the screening of alternatives into pre-established categories (classes), which may or may not be ordered.

Figure 3. Decision support issues. Source: Prepared by the authors.

In the proposed model, we can state that the decision support problem is of the “ordering” type.

According to [23], structuring is the great differentiator and the most important part of the methodology. It demonstrates the context in which the problem is inserted, based on the decision-maker’s perception, and reveals the criteria and expansion of knowledge under analysis.

In this regard, and in view of the goals established by the new legal framework for sanitation, it is important to consider project portfolio management. This approach defines the appropriate criteria for prioritising and selecting projects to optimise resource allocation and achieve the desired results [30].

Project management is the application of knowledge, skills, tools and techniques to defined activities to meet previously defined requirements.

Furthermore, this guides the efforts expended on project work, allowing project teams to achieve the desired results using a wide range of approaches, such as predictive, hybrid, and adaptive [10].

According to [31], a committee must carry out the selection of projects through a formal prioritisation process, which allows for the visualisation of the list of projects to be worked on in the following years.

This formal selection process is crucial for ensuring transparency and objectivity in project selection, thereby strengthening governance and accountability in portfolio management. In this context, ref. [32] developed a model for project portfolio selection, which is presented in Table 1 below, in five stages:

In a complementary vision, ref. [33] suggests that projects should be prioritised based on their importance for achieving the organisation’s strategic objectives, compared to other projects. This comparative approach is crucial to ensuring that resources are directed to projects that offer the most significant strategic value to the organisation.

Furthermore, it is essential to evaluate projects that are already underway, as their continuity must adhere to the same principles. Also, this ensures that they remain aligned with the organisation’s strategic priorities and that resources are used efficiently and effectively.

The indicators or criteria used in this research were initially based on the internal work of an Internal Committee of CAGECE that listed 18 prioritisation criteria in a consolidated manner, through which, after documentary research and bibliographic surveys that motivated the adaptation of some upper criteria/subcriteria, following the new regulatory framework for sanitation (Law No. 14,026, of 15 July 2020), taking into account micro-regionalisation and universalisation goals for the provision of services by the concessionaire, and with a focus on the sewage aspect.

As an example of bibliographic surveys, we can mention the list of indicators for evaluating sanitation projects proposed by [34], which is based on bibliographic research that included approximately fifty international and national publications.

The summary of these indicators is presented in Table 2, categorised into four dimensions: environmental, social and health, technical, and economic-financial.

Subsequently, using the brainstorming technique, the most relevant Fundamental Points of View and Elementary Points of View were identified through meetings and face-to-face conversations with five high-ranking specialists from CAGECE, aiming to prioritise investments in sewage system projects.

For this research, the following Fundamental Points of View were selected: economic-financial, environmental, technical-regulatory, institutional/legal, social, and health, with a total of 11 sub-criteria. Within the economic-financial dimension, the feasibility analysis of a project includes a detailed analysis of the relationship between its implementation costs and the expected benefits.

Additionally, this enables the determination of whether the benefits justify the costs and whether the project will make a positive contribution to society and the environment [35].

To evaluate these projects, various metrics are used, including EBITDA Margin, Net Present Value, Internal Rate of Return, and Return on Investment, among others.

In the environmental dimension, one of the indicators used to assess the environmental impact of municipalities is the influent load they generate, expressed in kg BOD/day [14,36].

Biochemical Oxygen Demand (BOD) is a parameter that measures the amount of oxygen required for the biological decomposition of organic matter present in water. A high BOD load indicates a large amount of organic matter, which can lead to a decrease in dissolved oxygen levels in water bodies, thus affecting aquatic life and contributing to the eutrophication process [37].

The indicator above considers both the influent load of Sewage Treatment Plants (STP) and the influent load of Individual Solutions [1,3]. By this logic, municipalities that generate a higher organic load and have limited sewage coverage should be given priority consideration for efficient treatment systems.

Next, the technical-regulatory dimension includes Reference Standard No. 8/2024, approved by the National Water Agency (the federal regulator, abbreviated as ANA for “Agência Nacional das Águas”), which sets out progressive targets for universalising water supply and sanitation, access indicators, and an evaluation system.

Among the topics covered in this standard is the universalisation of the provision of sanitation services. Its article 10 details that, for monitoring and evaluating the achievement of universalisation goals, the coverage and service of 99% (ninety-nine percent) of households with drinking water, and the coverage and service of 90% (ninety percent) of households with sewage collection and treatment until 31 December 2033, in each municipality, according to the standard’s indicators, are considered. All these changes contribute to an efficient interaction between basic sanitation and human health.

In the institutional/legal dimension, the existing legal/regulatory notes in the municipality are evaluated in the context of the dialogue between financial impact and criticality. Finally, in the social and health dimension, the Municipal Human Development Index (MHDI) combines the three dimensions of the Global HDI, making necessary adaptations to the Brazilian context and utilising available national indicators.

In this way, although both focus on the same phenomena, the indicators considered in the HDI are more appropriate for assessing the development of Brazilian municipalities and metropolitan regions [8].

Additionally, in this dimension, it is essential to note the Social Vulnerability Index (IVS), which complements the HDI by providing indicators organised into three key dimensions: urban infrastructure, human capital, and income and work.

This index enables a unique mapping of exclusion and social vulnerability within the context of Brazilian municipalities [38]. From this perspective, the IVS is a crucial tool for identifying the most vulnerable areas that require increased attention in terms of sanitation investments.

The importance of the subject is directly related to diseases associated with inadequate environmental sanitation (DRSAI), which result from sanitation deficiencies originating from orofecal routes and/or transmitted by insect vectors.

These diseases are related to inadequate water supply, poor sanitation, solid waste contamination, and/or substandard housing conditions. Like dengue fever and Zika, they are linked precisely to poor hygiene and can be controlled by sanitising the environment in which people live.

According to [39], confirm the direct relationship between the deficit in basic sanitation services and the incidence of DRSAI. This relationship is attributed to the population’s direct exposure to pathogenic agents present in water and soil contaminated by untreated sewage, as well as through contact with poorly managed solid waste.

Data from the Instituto Trata Brasil indicate that in 2024, the country registered a total of 70,000 hospitalisations for DRSAI among children aged 0 to 4 years, which accounted for 20.0% of the total hospitalisations for these diseases. Among the senior citizens over 60 years of age, 80,900 hospitalisations were recorded, accounting for 23.5% of the total hospitalisations for DRSAI [40]. When they do not lead to the death of an individual, they can weaken their health, lead to their absence from work and cause disturbing repercussions on the individual’s economic and social conditions in the long term.

Given the prioritisation challenges faced, and the dimensions detailed above, it is now necessary to analyse the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) approach. This method enables the numerical representation of decision-makers’ judgements about the overall attractiveness of actions, integrating information with the Fundamental Points of View within a comprehensive evaluation model.

Also, this is an interactive approach that helps to construct cardinal measures of judgements about the degree (of attractiveness) to which the elements of a finite group of potential actions (“A”) feature some criteria (“P”) [41].

According to [22,42], MACBETH is characterised as:

(a): humanistic, in the sense that it should be used to help decision-makers ponder, communicate, and discuss their value systems and preferences.
(b): interactive, because it is a process of reflection and learning that can best be spread through socio-technical facilitation underpinned by simple question and answer protocols.
(c): constructive, because it is based on the idea that full convictions about the type of decision to be made do not (pre-)exist in the mind of the decision-maker, nor in the mind of each of the members of a group decision, who can be helped to form such convictions and build robust (shared) preferences concerning the different possible options for solving the problem.

Moreover, this multicriteria decision-making (MCDM) approach has garnered attention in various fields due to its ability to address complex decision-making problems. Indeed, the MACBETH method is a valuable tool for solving multicriteria decision-making problems, as it “[…] includes several participatory processes, namely Web-Delphi processes and a decision-checking process” [43].

From a practical perspective, it is observed that the interaction between the actors involved benefits significantly from an efficient and user-friendly decision support system, such as the M-MACBETH software.

According to [44], this method, based on qualitative judgments from interested parties, enables the construction of quantitative value models, thereby supporting the interactive learning process regarding the problem under examination. Its methodology can be divided into three main application phases: model structuring, model evaluation and results analysis.

During the structuring phase, it is necessary to identify and evaluate the options, their performances and the values of interest in the form of a tree, usually called the “Value Tree”: an organised structure of the several concerns in question. When assessing the model, MACBETH involves a series of pairwise comparisons, evaluated by stakeholders who must specify the difference in attractiveness between all alternatives from the Fundamental Points of View.

According to [22,42], it is emphasised that the transition from ordinal information to cardinal information constitutes a considerable leap in terms of information richness. From then on, the results analysis phase begins. Once the model is structured and completed, the MACBETH method provides apparent results in the form of a ranking.

It allows the identification of the attractiveness of the criteria and the alternatives to the problem. To this end, the structuring, assessment, and recommendation phases of the MCDA-C decision-making process will now follow the roadmap detailed in Table 3:

Therefore, by incorporating participatory processes that enhance robustness and inclusion in decision-making, MACBETH becomes relevant for investments in sewage systems, which involve diverse stakeholder perspectives and complex uncertainties. Next, we proceed to detail each phase of the proposed model, as outlined in Table 3.

4. Research Methodology

The data and results obtained represent a fundamental aspect of understanding the scope of this study. To this end, a practical application of the model is carried out using M-MACBETH.

4.1. Structuring Phase

As explained by [12], in the model structuring phase, it is necessary to define the contextualisation, the social actors, the label, which expresses the objective of the model; the assessment elements (Fundamental Points of View (PVFs) and Elementary Points of View (PVEs)); the value tree and the descriptors. Furthermore, this makes it possible to identify, organise, and measure critical aspects that, in the decision-maker’s view, best express their values and preferences. After establishing the actors involved in the decision-making process, it is necessary to define the alternatives or potential actions to be assessed [11].

The contextualisation of the research refers to the scenario of national competition for financial resources among all sanitation companies in Brazil in 2024, with a focus on the Ceará Water and Sewage Company (CAGECE) and the objective of meeting the universalisation metrics of the new legal framework for the sector, which are serving the population with 90% of sanitation and 99% of water supply. Given this, project prioritisation stands out as a key strategy to optimise and enhance the application of resources.

Next, the headline has been defined to express the objective of the model: “Prioritisation of investments in sewage system projects”. In this line, the leading actors in the process were identified, namely

(a): Decision maker: A person responsible for decision making;
(b): Facilitator: A consultant who will support the decision-making process;
(c): Intervenors: Those who can influence the decision maker, but do not have decision-making power;
(d): Affected: People who will suffer the consequences of the decisions taken [45].

Given this classification and the context of the organisation, Table 4 identifies each of these actors in this research.

In the next step, we identify the assessment elements. To identify them, it is important to follow specific steps during the process [17]:

(a): identification of primary assessment elements (EPAs);
(b): construction of cognitive maps; and
(c): identification of Fundamental Points of View (PVFs).

In the proposed model, the steps of identifying EPAs, preparing the cognitive map and identifying PVFs occur using a survey of the prioritisation of Fundamental Points of View listed by the Company’s Internal Committee. To this end, the following areas were involved: projects, market, planning, concession and regulation, assets, and operational development.

The Value Tree, as explained by [17,46], proposes decomposing a more complex criterion into Elementary Points of View (PVEs) that can be measured more easily. In the present research, it is formed by:

(a): A strategic objective (blue colour);
(b): Five PVFs or criteria (green colour); and
(c): Eleven PVEs or subcriteria (orange colour).

Figure 4 shows the MACBETH Tree, which was built for the multicriteria model in question:

Figure 4 lists the five PVFs and the 11 PVEs that will be analysed in the decision-making process. These PVFs can be used as criteria in multicriteria methods [47].

Regarding the descriptors, each possible state was associated with an impact level Nj, where j corresponds to the decreasing order of the decision maker’s preference, that is:

(a): N5—Impact level with the greatest attractiveness (upper limit);
(b): N4—Impact level with immediately lower attractiveness;
(c): N3—Impact level with intermediate attractiveness;
(d): N2—Impact level with immediately lower than intermediate attractiveness;
(e): N1—Impact level with the lowest level of attractiveness (lower limit) [19].

In this research, two reference levels, high level (A) and low level (B), were defined for the criticality/importance of the subcriterion, which facilitates the understanding of the descriptors and allows identification during the decision-making process by the specialist, based on the following indications:

(a): With a very high level of criticality/importance (located above level A);
(b): On the other hand, with a medium level of criticality/importance (located between levels A and B);
(c): Also, with a very low level of criticality/importance (situated below level B), according to the perception of the decision-makers.

A PVF is presented that relates to the feasibility of investment projects. In this specific case, it concerns the concession of sewage services in a municipality. It considers the net present value (NPV) and the internal rate of return (IRR) of that concession as references. It also considers the project’s operational profitability, excluding the effects of financial expenses, taxes, depreciation, and amortisation. After breaking down the PVF, the following PVEs are obtained: NPV/IRR and the municipality’s EBITDA margin, with their respective reference levels. These levels were defined based on the experience of experts in the project solicitation process. As an application of a descriptor, Table 5 is presented, containing the PVF descriptor 1—Economic-financial.

4.2. Assessment Phase

The assessment phase is carried out through open interviews. In this context, the decision-maker’s judgments and preferences are used to transform ordinal scales, as defined in the structuring phase, into cardinal value functions. From then on, the integration of evaluation criteria is promoted. At this stage, the decision-maker diagnoses the current situation, prioritises the multiple available alternatives and defines the overall contribution of each alternative to management [48].

The value function can be obtained through the semantic judgment method, which involves translating pairwise comparisons of the difference in attractiveness between potential actions. These comparisons are made by interviewees, who qualitatively express the intensity of preference for one activity over another, using a semantic ordinal scale [17]. From this perspective, the Measuring Attractiveness by a Categorical Base Evaluation Technique (MACBETH) method was used to obtain this function. To complete the paired comparison matrices, the semantic categories described in Table 6 are utilised.

Subsequently, it is necessary to transform the ordinal scales into cardinal interval scales. This transformation process is essential since the descriptors are initially defined in the form of ordinal scales. Thus, for each reference level of the descriptor, a comparison is made between the other reference levels to assess attractiveness. That is, for a given reference level, the decision maker must assess the attractiveness of another reference level [48]

Through MACBETH linear programming, the value functions (FV) are determined. However, there must be consistency between the properties; otherwise, there will be incompatibility in the system of linear equations. In this case, the software itself indicates the occurrence and demands adjustments [49].

For application, Table 7 presents the FV of PVE 1.2—EBITDA margin of the municipality, constructed using MACBETH.

5. Results

Also, according to [49], to equalise the model, the scales of the descriptors’ value functions are transformed so that the good level is anchored on the 100 scale and the neutral level on the 0 (zero) scale. The author clarifies that descriptors above the good level have scales greater than 100, while descriptors with a level below neutral have negative scales. He refers to the numerical values resulting from this transformation as transformed functions or anchoring functions. It should be noted that the transformation of the value function into the anchoring function occurs through mathematical procedures, specifically a linear transformation performed using the M-MACBETH software [49].

Table 8 displays the M-MACBETH software screen, which includes the anchoring functions of PVE 1.2—EBITDA Margin for the municipality. This data is important for the competent manager to make project prioritisation decisions.

Table 9 presents the replacement rates of the model’s criteria and subcriteria.

In MCDA, replacement rates reflect, according to a decision maker’s judgment, the performance loss that a potential action must suffer in one PVF to compensate for the performance gain in another PVF [29]. According to [17], substitution rates, also known as weights, are parameters that decision-makers consider appropriate for aggregating local performances (in the criteria) into an overall performance in a compensatory manner. Table 9 was developed from information provided by M-Macbeth. The case study presented reflects a decision analysis process applied to scenarios where a project prioritisation strategy is required to optimise resources and improve the efficiency of investment allocation in sewage systems, as shown in Figure 5, extracted from the software. This process enables the assessment of different municipalities and their respective needs for sewage infrastructure, with a focus on universalising service provision.

Application of the Model

To demonstrate the practical application of the approach proposed in this study, we employed multicriteria analysis techniques, emphasising economic-financial, environmental, technical/regulatory, institutional/legal, and social and health criteria. The UN-BSI, which includes the municipalities of Serra da Ibiapaba, was chosen as a case study. In the second phase, the municipalities that are part of this Company unit (options) are selected to assess investment prioritisation and ordering using the multicriteria model, as shown in Table 10, which shows the configuration of the options in MACBETH.

Based on the PVFs and PVEs previously defined by the decision-makers, their substitution rates (weights) reflect the decision-makers’ preferences. A simple additive aggregation model is used to evaluate each option. According to [50], the additive aggregation model can be classified as a single-criterion synthesis method. In this case, the value function for each criterion v_j(a) is considered to obtain the global value function v(a), which will allow the choice of the alternative that will present the highest global value v(a):

v (a) = \sum_{j = 1}^{n} k_{j} v_{j}

where

v(a) is the overall score of the option;
k_j is the weight of criterion j;
v_j is the partial score of the option.

According to [50], the additive aggregation procedure serves as the basis for several methods, which are distinguished by multiple aspects. These, for the most part, are related to the preference modelling process, that is, elicitation from the decision maker. The MACBETH approach uses the additive value aggregation model as a reference, ensuring coherence in the assistance provided during the multicriteria (“global”) preference construction process. The choice of an additive model is due to its simplicity and recognition, as well as its clear and easy-to-interpret technical parameters. Also, preventing financial gains from outweighing poor health or environmental outcomes requires incorporating non-financial factors into the decision-making framework. This involves using methods such as multi-objective optimisation, monetising externalities, and prioritising long-term value over short-term financial gains. Furthermore, this model enables the precise evaluation of the relative importance of criteria and provides a measure of the overall attractiveness of each option (municipality) for subsequent selection.

By applying the proposed method, the investment prioritisation results detailed in Table 11 and Table 12 were obtained using the additive value model developed in the municipalities that make up the Serra da Ibiapaba. This data was obtained using the M-MACBETH tool, based on assessments conducted by 19 experts who completed questionnaires and provided information to define this ordering.

The following order was obtained, based on the highest overall scores, of municipalities prioritising investments: Guaraciaba do Norte, São Benedito, Tianguá, Viçosa do Ceará, Ubajara, Barroquinha, Carnaubal, Croatá, Varjota, Chaval, Reriutaba, Graça, Mucambo, Ibiapina, Pires Ferreira, and Pacujá.

This ordering is a descending list of global benefit values, based on the importance/criticality perceived by the evaluators, and reflects the municipalities that should receive priority investments. With a specific analysis of the municipality of Guaraciaba do Norte, the following points are observed:

(a): For the criteria with the highest weight—NPV/IRR, EBITDA Margin, Generated Load, Service Level, and Service Escalation—the municipality of Guaraciaba obtained the highest partial scores.
(b): For the Sustainable Development and Notes criteria, the municipality received the lowest partial scores; however, these criteria have less significant weights compared to the others.
(c): The above situations (weight and partial scores) result in a better overall score for this municipality.

From the perspective of a state-owned sanitation concessionaire, the disparity between municipalities’ social needs (high vulnerability) and investment capacity (low financial viability) represents one of the most significant challenges to universalising water and sewage services. This structural inequality imposes on the concessionaire the duty to plan and execute investments that reconcile technical, economic, and social criteria, ensuring that service also advances in less financially attractive areas. A critical analysis of compensation and investment mechanisms in municipalities with lower revenue collection capacity is essential to ensure the service delivery model remains sustainable and equitable. Many municipalities in the interior face severe fiscal constraints, making them dependent on state and federal transfers for any investment in basic infrastructure. At the same time, they face high social demands, with low sanitation coverage rates and high vulnerability indicators. In this context, the concessionaire’s role is to propose and support prioritisation criteria and compensatory policies that consider not only the local environmental or economic impact, but also the historical social deficit and the strategic importance of these locations in reducing regional inequalities.

Furthermore, even when funds are transferred, factors such as low municipal self-management capacity and budgetary rigidity can limit the effective allocation of funds to priority areas, compromising the expected social outcome. Therefore, the concessionaire seeks to work in an integrated manner with municipal and state entities to promote balanced investment models, based on multicriteria criteria and territorial social justice, to direct efforts and resources to the neediest locations.

Therefore, defining technical and socioeconomic criteria to prioritise investments in municipalities with greater social deprivation is an essential part of the concessionaire’s planning strategy, reinforcing its institutional commitment to regional balance, financial sustainability, and the universalisation of sanitation services.

6. Sensitivity Analysis

The sensitivity analysis of a criterion weight allows us to examine the extent to which the model’s recommendations change when the weight of a given criterion varies, while maintaining the proportional relationships among the remaining weights [23]. This procedure helps assess how modifying any weight (within the permissible interval) would impact the overall results of the model. For the present model, this analysis considered the five municipalities with the highest global scores (Guaraciaba do Norte, São Benedito, Tianguá, Viçosa do Ceará, and Ubajara) and the criteria with the greatest weights (Service Level, Service Escalation, DRSAI, Generated Load, and NPV/IRR).

Each line in the graphs depicts the variation in the global score of the corresponding option as the criterion weight changes from 0% to 100%. Figure 6 and Figure 7 present the sensitivity analysis for the Service Level criterion. The red vertical line represents the current weight of this criterion, which is 12.42% in this case.

The main conclusions from this criterion are as follows:

(a): Since the lines for Guaraciaba, Ubajara, and Viçosa do not intersect, one option is always more attractive than the others, regardless of the criterion’s weight. Specifically, Guaraciaba is always more attractive than Ubajara and Viçosa.
(b): Similarly, São Benedito is always more attractive than Tianguá, since their lines do not intersect.
(c): In the case of Ubajara and Tianguá, the lines intersect. When the weight of the Service Level criterion increases (from 12.42% to 20.4%), both scores increase, but in different proportions. From 20.4% onwards, Ubajara’s global score grows at a significantly higher rate compared to Tianguá.

A similar situation is observed between São Benedito and Viçosa do Ceará: as the weight of this criterion increases (from 12.42% to 26.8%), both scores rise, but Viçosa’s increases more substantially, surpassing São Benedito beyond this threshold.

Following the same logic, the sensitivity analysis for the remaining criteria yielded the following insights:

-

Service Escalation:

Guaraciaba remains superior to São Benedito and Ubajara regardless of weight.
Tianguá is always more attractive than Viçosa.
An increase to 14.4% favours Ubajara slightly over Viçosa.
At 28.3%, Ubajara surpasses Tianguá more significantly.

-

DRSAI:

Guaraciaba consistently remains the most attractive alternative.
No curve intersections are observed, reinforcing the robustness of this criterion.

-

Total Generated Load:

São Benedito is always more attractive than Tianguá.
Guaraciaba consistently remains superior to Ubajara and Viçosa.
When the weight exceeds 11.7%, São Benedito surpasses Guaraciaba.
Tianguá becomes more attractive than Guaraciaba when the weight surpasses 16.7%.

-

NPV/IRR:

Guaraciaba remains the most advantageous option, regardless of weight.
No curve intersections were observed, suggesting strong stability of the model in this economic-financial dimension.

When comparing where the lines intersect, for example, in relation to the municipalities of Tianguá and Guaraciaba, it is seen that when the weight of this criterion increases (from 11.64% to 16.70%), both Tianguá’s overall score increases (on the vertical axis) and Guaraciaba’s does as well, but at different rates. Therefore, it is evident that the weight of this criterion, initially at 16.70%, causes Tianguá’s overall score to increase at a rate greater than that of Guaraciaba’s.

7. Conclusions

An appropriate multicriteria model is needed to prioritise investments in sewage projects, focusing on concessionaires, to support decision-making in a sector vital for public health, environmental preservation, and socioeconomic development. It is essential to prioritise and select sewage projects based on their importance/criticality for meeting the goals of universal sanitation, as well as the impact that the absence or deficiency of sewage services can have on public health and the environment. Furthermore, this enables the definition of an investment prioritisation strategy for executing works in the municipalities served, thereby generating the most significant possible benefit, in accordance with the preferences and objectives of the decision-maker.

To this end, structuring and evaluating the model was a crucial step in ensuring its robustness and adherence to the needs of the dealerships. During this phase, it was necessary to define criteria and subcriteria that reflected strategic priorities, such as economic viability (NPV/IRR), environmental impacts, municipal sustainability, level and scheduling of care, institutional relationships, and social and health aspects. Collaboration with experts and careful validation of parameters strengthened the technical foundation of the model. It is noted that using the MACBETH method to define weights and value functions, by transforming ordinal scales into cardinal interval scales based on decision-makers’ judgments, ultimately provides a hierarchy of investment alternatives.

The practical application of the model, focusing on the Ibiapaba Mountains region, demonstrated its effectiveness in identifying priority municipalities for investments in sewage systems, based on the criticality and potential impact of interventions. Guaraciaba do Norte stood out as the highest priority municipality, reflecting the alignment between local needs and the concessionaire’s strategic objectives. The results also demonstrated the model’s robustness in handling variability in criterion weights, thereby ensuring greater reliability in decision-making.

The exclusive focus on sewage overlooks other key aspects of the basic sanitation sector, including water supply, solid waste management, and urban stormwater drainage. Although this approach allowed for a more in-depth analysis, it limits the model’s application to the universe under evaluation. Furthermore, the model’s scope was developed based on the judgments and preferences of decision-makers working specifically as sanitation service providers.

The geographic limitation of the Ibiapaba Mountains region limits the direct generalisation of the results to other regions with distinct socioeconomic and environmental characteristics. In this regard, expanding the model’s scope to include criteria related to social inclusion is a possibility, considering cultural specificities and the needs of vulnerable populations. Assessing the model’s scalability and flexibility could be a meaningful exercise. Furthermore, other aspects of basic sanitation, such as water supply, solid waste management, and urban drainage, promote an integrated approach that reflects the sector’s set of challenges. For the model to be applicable in government planning, it should integrate participatory governance mechanisms involving various social actors, such as regulatory agencies, the federal government, state governments, city governments, social movements, universities, and municipal councils.

Based on the results obtained, future research could

(a): Expand the scope of the model, adapting the methodology to other aspects of basic sanitation, such as water supply, solid waste management, and urban drainage, with an integrated approach that reflects the sector’s set of challenges.
(b): Incorporate advanced technologies, integrating the model with artificial intelligence and machine learning systems for dynamic data analysis and real-time prioritisation.
(c): Adapt the model to public policies so that it can be applicable in government planning. It is recommended that future research integrate participatory governance mechanisms, involving different social actors, including regulatory agencies, the federal government, state governments, city halls, social movements, universities, and municipal councils.
(d): Explore other multicriteria methods that also allow for an optimised construction of weights in the evaluation criteria by using quantitative data and observing the variation between the alternatives in each criterion.

Therefore, future research is recommended to develop and refine the proposed model to a broader perspective, with a focus on public policies.

Author Contributions

Conceptualization, J.C.A. and N.C.d.F.; Methodology, J.C.A. and P.R.P.; Software, N.C.d.F. and P.R.P.; Validation, P.R.P.; Formal analysis, J.C.A., N.C.d.F. and P.R.P.; Investigation, N.C.d.F. and P.R.P.; Data curation, N.C.d.F.; Writing—original draft, J.C.A.; Supervision, P.R.P. All authors have read and agreed to the published version of the manuscript.

Funding

The work of Plácido Rogério Pinheiro was supported by the National Council for Technological and Scientific Development (CNPq) under Grant 306435/2023-3.

Data Availability Statement

The synthetic data will be made available upon request.

Acknowledgments

Plácido Rogério Pinheiro is grateful to the National Council for Scientific and Technological Development (CNPq) for its contribution to the development of this project. The authors thank the Ceará State Sanitation Company for its support of this research.

Conflicts of Interest

Authors Jose Carlos Asfor and Neurisangelo Cavalcante de Freitas are employed by the company, Ceará State Sanitation Company. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. MCDA-C process phases. Source: Prepared by the authors.

Figure 2. Location Map Indicating the Municipalities where the Method was Applied.

Figure 4. MACBETH Tree. Source: Prepared by the authors.

Figure 5. Weighted criteria and subcriteria. Source: prepared by the author (2024).

Figure 6. Sensitivity analysis of the Service Level criterion—Intersection Barroquinha—Carnaubal. Source: Prepared by the authors.

Figure 7. Sensitivity analysis of the Service Level criterion—Intersection Ubajara–Tianguá. Source: Prepared by the authors.

Table 1. Model for project portfolio selection.

Pre-selection	Assessment of the alignment of projects with the organisational strategy and identification of mandatory projects.
Individual assessment of projects	Calculation of the values of each established Fundamental Point of View, based on the outlines of the pre-selected projects;
Selection	In-depth assessment of the characteristics of each project, focusing on eliminating those projects that do not meet minimum values according to pre-established Fundamental Points of View;
Optimal portfolio selection	Utilisation of multicriteria methods and integer linear programming to capture the interactions between different projects, including resource constraints and interdependencies.
Final portfolio adjustment	Adjustments to the final portfolio assessment aim to achieve a balance that satisfies stakeholders and reduces portfolio implementation risks.