In the context of DSS frameworks for sustainability, several MCDM methods are widely adopted due to their unique features and suitability for complex evaluation tasks. This subsection describes the core approaches, such as AHP, ANP, PROMETHEE, TOPSIS, and VIKOR, each offering different advantages depending on the structure and requirements of the decision-making problem.

Analytical hierarchy process method.

The AHP and Fuzzy AHP (FAHP) are quantitative methods used to sort decision alternatives and select an option that satisfies given criteria based on the pairwise comparison principle [21], [36]. This group of methods is the best choice for meeting the decision-maker's criteria by comparing pairs of options and a specific calculation mechanism [51]. By employing the relative scale measurement, a set of pairwise comparison matrices for each of the lower levels, with one matrix for each element in the level, is generated. Pairwise comparisons are made regarding which element is preferred over the other [21]. The model of the AHP method is presented in Figure 3a.

Figure 3.

The decision-making model of the pairwise comparison and hierarchy approach: AHP method (a) and ANP method (b)

Analytical network process method.

The ANP method is a network analysis method that considers, within the system, the hierarchy and the interaction between the criteria [25], [40], [51][53]. In practice, ANP is a combination of two parts: one is a network of standards and criteria, and the other is a network of influences between factors and criteria clusters [25]. The model of the ANP method is presented in Figure 3b [52].

Figure 4.

The model of the PROMETHEE method [57]

Technique for order preference by similarity to ideal solution method.

TOPSIS, which is one of the methods of multi-criteria decision making, is founded on the fundamental premise that the best solution has the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution [23], [24], [28], [29], [43], [55], [59]–[61]. Therefore, alternatives are evaluated using a global index based on their distance from the optimal solutions [61]. The model of the TOPSIS method is shown in Figure 5.

Figure 5.

The model of the TOPSIS method [52], [60]

Visekriterijumska optimizacija i kompromisno resenje_method.

The VIKOR method (translated from Serbian: Multi-Criteria Optimisation and Compromise Solution method) evaluates the solutions based on their distance to ideal and anti-real points [62]. For individual decision-making variants, it assumes determining [27], [43], [60], [63] the weighted average distance from the ideal solution, the maximum weighted distance from this point, and the so-called comprehensive criteria. The model of the VIKOR method is shown in Figure 6.

Figure 6.

The model of the VIKOR method [52], [60]

The primary MCDM methods are broadly classified as follows: VIKOR and TOPSIS are reference point approaches [64], [65], while PROMETHEE is a higher ranking approach [66]. ANP and AHP are pairwise comparison and hierarchical approaches [51]. Table 2 presents the strengths and weaknesses of the MCDM methods in the context of sustainability.

Analysis shows that these methods are suitable for designing and developing DSSs in a sustainable context. AHP and ANP are suitable for hierarchical sustainable decision problems, with ANP being more applicable for complex systems with interdependencies [67].

PROMETHEE focuses on outranking sustainable alternatives based on clear preferences, while TOPSIS and VIKOR aim to identify the best sustainable alternative using different approaches to evaluate distances from the ideal and anti-ideal solutions [68].

The inherent characteristics of each method provide distinct advantages and limitations when applied to sustainability challenges. For example, AHP, despite its simplicity and ability to handle both quantitative and qualitative data, suffers from limitations when handling large numbers of criteria [28], [41], [42] due to the exponential increase in pairwise comparisons required. The assumption of criterion independence can also be a drawback in highly interconnected sustainable systems. ANP, an extension of AHP, addresses these interdependencies, providing a more precise analysis and a more explicit method for assessing criterion weights [25]. However, the increased complexity of this method requires significant expertise and can be computationally intensive [40]. In addition, Taherdoost and Madanchian (2023) presented a comprehensive review of the Analytic Network Process (ANP), highlighting its strength in handling complex, interrelated decision-making problems across various domains, while also discussing its methodological advantages and practical limitations [69].

PROMETHEE is recognised for its simple ranking and the use of preference functions that reflect the decision maker's specific bias, making it transparent and intuitive for higher rankings [56]. However, a notable challenge is that the method lacks an inherent weighting mechanism, often requiring external sources, which can lead to subjectivity. The method also requires careful definition of preference functions and can be sensitive to changes in weights or criteria scores [70].

TOPSIS and VIKOR are both reference point methods that reduce subjectivity by evaluating alternatives based on their proximity to ideal and anti-ideal solutions [71]. TOPSIS provides a direct ranking, while VIKOR focuses on the compromise solution by considering both utility and regret. While generally easy to apply, common weaknesses of both are their reliance on external weighting criteria and their sensitivity to data normalisation and input variations. Both can struggle with computational complexity for very large problems and may assume linear relationships that do not always hold in real-world sustainability problems. VIKOR's assumption of an ideal solution may not always be feasible.

Different MCDM methods could be combined to exploit the effectiveness of both methods fully, for example, AHP with TOPSIS [28], [29], TOPSIS and VIKOR [27], and AHP with VIKOR [41]. These integrations improve the accuracy of decisions, reduce the number of calculations and make them more objective and less dependent on expert judgement. Furthermore, the integrated methods improve the efficiency of the DSSs concerning uncertain criteria [18], [22], [23], [29].

These MCDM methods are combined with fuzzy logic when users seek to develop a tool for evaluating intricate objects encompassing both quantitative and qualitative criteria. It is important to note that DSS frameworks, designed for specific audiences, should only be applied within those target groups. In DSS frameworks utilising expert opinions for assessment, the evaluation quality is highly dependent on the expertise of the involved stakeholders. Fuzzy logic presents an alternative solution, aiding decision-makers in selecting the best option with minimal reliance on experts.

One disadvantage of these integrated methods is their complex structure. Moreover, they require users to be familiar with a wide range of computational methods. It is a challenge for users to assess sustainability. As a result, these methods are not generally explored and utilised, even though they can produce high-quality evaluation outcomes. The choice of either individual and specific method, or integrated methods, depends on the problem that needs to be decided and the advantages that the methods bring to the decision-makers.

Table 3 summarises the applications of these methodologies in various case studies.

The versatility of MCDM methods is evident in their diverse applications in a variety of sustainability contexts. In the following subsections, specific implementations of each method are explored, highlighting real-world decision-making situations where each technique has been used effectively.

Beyond the widely used methods, various other approaches have been developed for specific situations. Buchholz et al. (2009) assessed the potential of Multi-Criteria Analysis (MCA) to facilitate the design and implementation of sustainable bioenergy projects [45]. These MCA tools use four methods: AHP, Delta, PROMETHEE II, and Novel Approach to Imprecise Assessment and Decision Environments (NAIADE), for sustainability assessment. Research results show that different tools can give different results. Shaw et al. (2012) presented an integrated approach to select the appropriate supplier in the supply chain, addressing carbon emissions, using fuzzy AHP and multi-objective linear programming [30]. In this model, fuzzy AHP was first used to calculate the criteria weights, and then fuzzy linear programming was used to find the optimal solution to the problem. Khalili-Damghani & Sadi-Nezhad (2013) developed a DSS using TOPSIS-based fuzzy goal programming (FGP) to solve sustainable Multi-Objective Project Selection problems with Multi-Period Planning Horizon (MOPS-MPPH) [44]. Mattiussi et al. (2014) presented a framework for an energy supply DSS based on the AHP method for sustainable plant design and production, utilising an innovative use of multi-objective and multi-attribute decision-making (MODM, MADM) modelling together with impact assessment of the emission outputs [42]. Other methodologies were explored in the period 20152021 by Aydin et al. [48], Afsordegan et al. [29], Luthra et al. [41], Rahmanpour & Osanloo [49]; Balaman et al. [47]; Piwowarski et al. [43], and Alavi et al. [50].

Pavlovskaia asserted that sustainability criteria and their content should be linked to the concept of sustainable development and sustainability [75]. Pavlovskaia also described sustainability criteria as requirements for a product's sustainable quality and its sustainable production process, which must be satisfied to achieve sustainable status or certification [76]. Zink posited that these criteria are applied to assess opportunities and risks arising from economic, environmental, and social sustainability facets [77]. Meanwhile, Koplin et al. (2007) emphasised that environmental sustainability criteria establish requirements for suppliers, aiming to reduce natural resource inputs and mitigate environmental risks through enhanced supplier efficiency [78].

Sustainability criteria play a crucial role in effectively incorporating a sustainability perspective [79]. These criteria support long-term sustainability assurance, investment protection, and measurement of decision-makers' expectations, creating a standard framework for sustainability to guide development for businesses and for a country. The present review indicates that the selected sustainability criteria in the case studies are based on four pillars:

• Technology as considered in [19], [24], [28], and [29].

• Economy reflected in [19], [21], [23], [24], [28], [29], [41].

• Environment addressed in the same papers as Economy.

• Society evaluated in the same papers as Economy and Environment.

In 29 reviewed papers, 14 studies considered the hierarchy of sustainability assessment criteria, including criteria and sub-criteria. These studies are identified as follows: [19]–[24], [27]–[29], [35], [39]–[42]. For example, Ahmad & Tahar (2014) used four criteria (technical, economic, social, and environmental) and 12 sub-criteria to select renewable energy sources [19]. Three criteria (technical, economic and environmental) with 24 sub-criteria were considered in Mattiussi et al. (2014) paper [42]. Besides, Luthra et al. (2016) employed 22 sub-criteria and divided them into three criteria economic, environmental and social [41]. In these studies, 7/9 cases used economic, environmental, and social pillars as criteria. The remaining 2/9 cases employed other criteria, such as productivity, stability, efficiency, durability, compatibility, and equity, to assess sustainability in agriculture [39]. In addition, four out of nine cases considered technology as an additional sustainability criterion. The criteria were also divided into qualitative and quantitative ones. For example, social acceptability was used as a qualitative criterion [29], whilst transportation cost and CO₂ emission were quantitative. Besides that, the criteria were used at different scales in different countries or enterprises. For example, Piwowarski et al. (2018) used 14 SDGs to evaluate the level of sustainable development of EU countries [43], while Shaw et al. used specific criteria for supplier selection [30]. The number of criteria was different in studies. García-Melón et al. (2010) used 13 criteria [25], while there were 16 criteria in Vinodh & Girubha (2012) [38]. The weighting of criteria in the DSSs can vary depending on the specific case studies and relevant context.

The aggregated criteria and sub-criteria used to assess and select options at the company level are shown in Figure 7.

Figure 7.

Main sustainable criteria and sub-criteria from the literature

The scales of the DSSs in the sustainability context could be divided into two main categories, see Figure 8: the enterprise level and the regional & national levels [80].

At the enterprise level, DSSs have been employed in numerous research projects for a broad range of applications. Four studies employed DSSs to help enterprises in choosing the best supplier [23], [30], [41], [50]. DSS was employed in two studies to select the design of the energy systems [45], [46]. In the other research, DSSs were used to select design options [49], identify development strategies [18], and evaluate sustainability projects [40]. Two studies utilised DSSs to define the significant criteria and essential factors for sustainable development [18], [38]. Furthermore, DSSs have been employed to select plans for utilising green materials [27], identify key development strategy goals [22], and manage cage aquaculture by assisting with site classification, selection, holding capacity determination, and economic appraisal [35].

At the regional and national levels, DSSs were used in four studies to select the best options for energy and renewable energy development [19], [24], [42], [47]. DSSs were considered in one study as a tool to help managers evaluate alternatives in urban planning and city development planning, such as assessing sustainable residential development in terms of culture and life [26]. There were three cases employing DSSs to evaluate the sustainability of cities as a primary planning future development policy [20] and rank cities according to the criteria of sustainability [37]. Kumar et al. (2015) considered the DSS as a tool to help Indian managers develop a strategy for a sustainable transport system [21]. Talukder & Hipel (2018) employed the DSS to select sustainable agricultural development plans for localities [39]. Another study used DSS to help managers evaluate the effectiveness of tourism planning & development [25]. Additionally, two other studies utilised DSS to assess the sustainability ratings of different regions and cities and identify areas that need special attention to achieve common sustainable development goals [36], [37]. Lastly, one study used the DSS to evaluate countries' sustainability ratings against each other [43], which was the basis for assessing the effectiveness of countries' sustainability policy implementation.

Figure 8.

Divisions of the DSS applications by scale

Beyond the enterprise and regional/national scales, MCDM methods especially AHP, TOPSIS, VIKOR, and their fuzzy or hybrid forms have been extensively applied across a multitude of sustainability-related fields, providing a structured framework for evaluating complex problems involving multiple, often conflicting, criteria.

In municipal solid waste management, MCDM has been used to compare landfill, incineration, and recycling technologies using criteria such as cost, greenhouse gas emissions, and local feasibility [81]–[84]. For example, MCDM is applied to evaluate and select sustainable urban solid waste treatment technologies, based on 12 criteria in three groups: economic, social and environmental [82]. The use of MCDM allows for a systematic comparison between technologies, thereby determining the optimal solution – in this case, waste-to-energy incineration technology – suitable for the practical context of Ho Chi Minh City.

In sustainable agriculture, MCDA assists in crop selection, irrigation strategies, and soil nutrient management [85][87]. According to Heiba et al. (2023) MCDM method is a useful tool to solve complex problems involving many factors acting simultaneously, such as in agricultural land potential assessment [87]. By integrating climate, soil, topography and social factors, MCDM – combined with AHP – helps determine the suitability of land for farming, supporting strategic decision making on efficient land use and sustainable agricultural development.

In sustainable transportation, MCDA supports the assessment of public transport systems, infrastructure upgrades, and emission reduction strategies [88], [89]. Khakbazan et al. (2025) applied the AHP method to evaluate and rank cities in Iran for metro system development, based on various criteria such as population, air pollution, and traffic congestion [88]. The use of AHP within the MCDM framework provides a structured and data-driven method to support metro investment decisions, while providing a basis for sustainable urban policy. Tirkolaee and Aydin (2022) proposed a fuzzy bi-level decision support system integrating supply chain and transportation network design for perishable products, using multi-objective MILP models and fuzzy weighted goal programming to address uncertainty, perishability, and sustainability in large-scale logistics systems [90].

For renewable energy planning, MCDM methods are used to compare solar, wind and biomass options [91]–[93]. MCDM allows for the assessment of investment costs, energy potential, environmental impacts and policy incentives. MCDM is also effective in climate change adaptation. Teng et al. (2025) used AHP within the MCDM framework to evaluate and rank coastal adaptation strategies, considering criteria such as environmental impact, cost-effectiveness, social acceptability, and long-term sustainability [94].

In water resource management, MCDA has been applied to water allocation strategies, wastewater treatment options, and investment priorities. For example, Fathi et al. (2025) applied the TOPSIS method within the MCDM framework to evaluate and select the optimal water resources management strategy for the Dehgolan Delta, Iran, based on 12 important indicators and scenarios simulated by the WEAP model [95]. Fetanat and Tayebi (2022) developed a novel picture fuzzy set-based decision support system to prioritise petroleum-refinery effluent treatment technologies, integrating sustainability and circularity criteria to support a circular water economy transition in the oil and gas sector [96].

For low-carbon technology evaluation, MCDM was used to balance cost, efficiency, and environmental impact. For example, Esangbedo & Tang (2023) applied the grey-MEREC-MAIRCA hybrid MCDM method to evaluate the carbon emission reduction strategies of six Chinese enterprises, thereby identifying direct emission control and energy saving efficiency as the top priorities [97]. In the context of a circular economy, MCDM supports decisions regarding material selection, recycling technology, and reverse logistics [98], [99]. Filho et al. highlighted the integration of MCDM methods with artificial intelligence (AI) is opening up a new approach to reverse solid waste (RSCSW) supply chain management according to the circular economy model, helping to automate decision making, reduce costs and increase waste traceability [107]. Finally, the emerging combination of MCDM and AI enhances the ability to automate decisions and data processing [100].

DSSs offer significant advantages in navigating the complexities of sustainability decision-making. Even when some sustainability criteria are qualitative and inherently difficult to quantify, DSSs can effectively assist decision-makers in making optimal selections.

The comprehensive benefits derived from applying DSSs, as detailed in the literature, are summarised in Table 4. Five studies applied judgment integration support tools to assess qualitative and uncertainty criteria [18], [23], [29], [40]. Furthermore, to enhance objectivity or address situations with limited expert availability, fuzzy methods have been widely adopted in seven studies for ranking or assessing sustainability [18], [22]–[24], [28]–[30]. Besides that, six cases used integrated methods in the DSSs to make the decision more accurately [28]–[30], [41], [43],[49]. Half of these cases used the AHP method to rank the criteria [28], [29], [41], while the other method was employed to rank alternatives.