Enhancing Urban Resilience: Integrating Actions for Resilience (A4R) and Multi-Criteria Decision Analysis (MCDA) for Sustainable Urban Development and Proactive Hazard Mitigation

Abstract

Hazards stemming from extreme natural events have exhibited heightened prominence in recent years. The natural hazard management process adopts a comprehensive approach that encompasses all stakeholders involved in the disaster management cycle. “Actions for Resilience” (A4R) represents a standardised concept derived from ISO/TR 22370:2020 that integrates principles from various scientific disciplines to enhance resilience in systems, whether they are socio-ecological systems, communities, or organisations. A4R emphasises proactive measures and interventions aimed at fostering resilience rather than merely reacting to crises or disruptions. It recognises that resilience is a multifaceted concept influenced by various factors, including social, economic, environmental, and institutional dimensions. Central to A4R is the understanding of complex system dynamics. Also, A4R involves rigorous risk assessment to identify potential threats and vulnerabilities within a system, as well as to build adaptive capacity within systems. A4R advocates for the development of resilience metrics and monitoring systems to assess the effectiveness of interventions and track changes in resilience over time. These metrics may include indicators related to social cohesion, ecosystem health, economic stability, and public infrastructure resilience. In this context, the study aims to apply the proposed hierarchy of factors and group decision-making using fuzzy numbers to identify strategic priorities for improving the urban resilience of the pilot area. The identified priority factors are then analysed across different scenarios, and corresponding actions are described in detail.

1. Introduction

Resilience refers to the ability of a system to absorb disturbances, adapt to change, and maintain its basic functions and structures. Resilience management has emerged as a critical aspect of urban governance, particularly in the context of urban resilience, urban security, urban performance, and the achievement of Sustainable Development Goals (SDGs) [1,2,3]. The increasing complexity of urban environments, characterised by rapid population growth, climate change, economic fluctuations, and social disparities, necessitates a strategic and systematic approach to resilience management [4]. Cities today accommodate over 56% of the global population, a figure projected to rise to nearly 70% by 2050 [5]. This demographic shift underscores the need for robust resilience mechanisms to ensure that urban areas remain adaptive, secure, and functionally efficient despite potential disruptions.

Urban resilience refers to a city’s ability to absorb, recover from, and adapt to various shocks and stresses, including natural disasters, economic crises, infrastructure failures, as well as climate events. Effective resilience management involves the implementation of integrated policies that enhance a city’s capacity to withstand and respond to these challenges. For instance, cities investing in resilient infrastructure have been shown to reduce economic losses from natural disasters by up to 30% [6]. Incorporating resilience principles into urban planning ensures that cities are not only capable of mitigating risks but also of fostering long-term sustainability.

Closely tied to resilience is urban security, which encompasses public safety, emergency preparedness, and crime prevention. The rise in urbanisation has also led to an increase in security threats, necessitating advanced risk assessment and emergency response strategies. Studies indicate that urban areas with well-implemented resilience frameworks experience up to 40% fewer fatalities and economic damages from catastrophic events compared to cities with weaker resilience structures [7]. A comprehensive approach to resilience management enhances the effectiveness of urban security measures by integrating real-time monitoring systems, smart surveillance, and community-based resilience initiatives.

Urban performance, defined by a city’s economic vitality, infrastructural efficiency, and quality of life, is significantly influenced by resilience management. The adoption of resilient urban policies correlates positively with higher economic productivity and improved public health outcomes. According to the UNDRR Making Cities Resilient (MCR) initiative, cities that prioritise resilience strategies report a 20–25% increase in economic stability, as they can better anticipate and mitigate risks that would otherwise lead to economic downturns [8]. Additionally, investments in resilient transportation networks and smart city technologies contribute to enhanced mobility, reduced congestion, and improved air quality, directly benefiting urban residents.

The role of resilience management extends to the fulfilment of the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 16 (Peace, Justice, and Strong Institutions). The integration of resilience-based strategies accelerates progress toward these goals by fostering inclusive, safe, and sustainable urban environments. Research from the United Nations Development Programme highlights that cities implementing resilience frameworks and climate resilient development demonstrate a 35% higher likelihood of meeting SDG targets compared to those without structured resilience strategies [9]. Resilience management is thus indispensable in ensuring that urban development aligns with broader sustainability objectives.

As cities continue to evolve, resilience management will remain a cornerstone of effective urban planning and governance. The adoption of data-driven, forward-looking resilience strategies will not only safeguard urban populations from emerging threats but also enhance overall economic performance and environmental sustainability. By embedding resilience into the core of urban policies, cities can create adaptive and secure environments that support sustainable growth and improve the quality of life for all residents.

2. Theoretical Background and Literature Review

Resilience represents the ability or capacity of a system to maintain its functions in an appropriate time frame and with minimal harmful effects during and after the occurrence of an adverse event [10,11,12,13,14]. Every system is vulnerable to some extent, and this property is essentially related to the occurrence of an adverse event, primarily its frequency and the severity of its possible consequences. Therefore, it is necessary to consider both the properties and effects of the adverse event and the quality of the safety system, thus considering the risk and options for risk control more comprehensively.

There are different approaches to the conceptual determination of resilience. Definitions include the system’s ability to adapt its functioning in response to changes and disturbances, even under continuous stress [12,13], its characterisation as a form of adaptive behaviour, and its ability to absorb negative effects from the environment through both proactive and reactive strategies [14,15]. In [13], the authors highlighted four characteristics of a resilient system: monitoring, predicting, responding, and learning. As a dynamic characteristic, resilience applies to any event, including those for which there is incomplete knowledge regarding their manifestation and consequences [16,17]. The consequences are related to the effectiveness of protective barriers and their capacities [18]. Resistant behaviour can be categorised according to the types of responses [11,12,19]: homeostatic responses, responses utilising existing knowledge, and responses that anticipate and monitor the outcomes.

In the context of disaster risk reduction, resilience refers to a community’s ability to respond and recover from the consequences of adverse events through adequate preparation and the use of available resources. The focus is on risk reduction and adaptation to change, which can be achieved through appropriate local actions aimed at building capacity, realistic assessment of potentially vulnerable elements, infrastructure improvements, and community engagement to prevent the worst-case scenarios.

Large urban agglomerations represent particularly critical areas due to the concentration of residents, complex infrastructure, numerous essential services, and intense economic activity. The impact of adverse events on these services and activities can be severe. It is increased by inequality, population growth, unplanned development, and climate change. Therefore, preventive strategies and actions aimed at improving resilience become increasingly important. These actions focus on developing strategies that improve resilience to various hazards, with a particular emphasis on natural disasters and socio-economic challenges in urban areas.

Measuring resilience is essential for evaluating the current state of an urban well-being system and identifying areas for improvement, as it incorporates public health, environmental quality, social cohesion, and economic stability within the urban context. The process involves the use of both qualitative and quantitative data, focusing on key dimensions such as infrastructure development, social aspects, management (planning), economic aspects, environmental conditions, and information management (learning).

Urban areas are exposed to various shocks, such as large fires, floods, pandemics, earthquakes, as well as numerous stresses, including long-term cold or hot waves, poverty, lack of housing, urban heat, urban heat islands, and traffic collapses. Communities need to develop resilience capacity to effectively absorb the consequences of adverse events, maintain stability through protective actions, make the necessary adjustments in response to identified changes in the environment, and develop more flexible systems [20,21]. Additionally, they have to develop capabilities to eliminate risk causes or distribute risks as evenly as possible among the population.

Urban resilience implies the continuous improvement of existing systems to respond adequately to future challenges arising from both sudden adverse events and chronic pressures [22,23]. Key elements of urban resilience, which require special attention, include the development of a robust infrastructure (including monitoring and early warning systems), effective planning and management supported by local participation and economic diversity, and the preparation of health and social services for potential large-scale crises [24,25]. Creating a healthier environment with enough green spaces, diversifying energy sources with an emphasis on clean energy, and sustainable land use can further improve urban resilience. The primary goal is to ensure the provision of physical infrastructure and basic services for the functioning of the community. This also necessitates improved decision-making and governance, the promotion of sustainable technological development, the improvement of the living environment, and the maintenance of community well-being.

To assess urban resilience, it is essential to monitor corresponding measurable attributes. These indicators help evaluate the current situation, identify potential issues, and propose adequate improvements. However, there is no universal method for measuring urban resilience; existing approaches rely on the use of available data, both quantitative and qualitative. The former show the current state through numerical values (e.g., estimated disaster losses, available infrastructure capacity, and emergency service coverage), while the latter typically involve expert evaluations (e.g., risk assessment, governance effectiveness, and policy applicability) [12,26].

Risk mapping in relation to natural disasters is particularly significant [27]. It is challenging to realistically describe urban resilience using individual indicators alone. Achieving resilient cities requires a systemic approach [28] and corresponding decision-making based on the available data [29], with thorough analysis of the temporal variability of relevant indicators. This is essential for overcoming numerous practical challenges, many of which stem from inconsistencies [30] that can be avoided with proper planning, enhanced preparedness, increased community participation, and improved sustainability of the entire system [31,32].

In [33], the authors point out that the dynamic nature of efforts to achieve urban resilience, driven by a changing environment, can result in different evolution trajectories. Particular attention can be directed to certain elements of urban systems, such as infrastructure [34] or ecological aspects [35], especially concerning potential threats and the selection of appropriate security policies. It is important to incorporate various extreme scenarios into analyses [36], particularly extreme climate events [37,38], that are becoming more frequent and have growing pressure on urban systems. The importance of resilience-building activities that are not directly linked to adverse events is emphasised in [39], where the authors consider resilience dividends.

Modern society is fundamentally characterised by the necessity of managing human activities in an efficient, competitive, and systematic manner. This management relies on the implementation of standards, which serve as a structured foundation for ensuring consistency and quality across various domains. As society has evolved, so have skills, knowledge, and the division of labour, leading to an increasingly complex economic environment that mandates rigorous quality assurance in both product manufacturing and service delivery. The evolution of standardisation demonstrates its integral role in fostering economic stability, ensuring product and service quality. A standard is a formally established document designed to harmonise various aspects of a product, including its shape, size, quality, and testing methodology. By providing a unified framework, standards ensure consistency, reliability, and interoperability across industries and markets. The necessity for standardisation has become increasingly evident in the context of globalisation, where technical discrepancies create significant barriers to international cooperation and the seamless exchange of goods, services, and information.

Analysing urban resilience requires the simultaneous monitoring of various indicators and the engagement of different stakeholders. To systematise the management process in the context of urban resilience, various frameworks have been proposed. The City Resilience Index describes resilience using four dimensions, within which the indicators are analysed in detail [40]. Developed by Arup, it supports the achievement of twelve goals related to leadership and strategy, economy and society, well-being, and infrastructure and environment. These four dimensions include fifty-two indicators that determine the success of achieving goals related to urban resilience. On the other hand, the Resilient Cities Index defines resilience through nineteen indicators classified into four pillars [41]: environment (six indicators), critical infrastructure (five indicators), economic (four indicators), and socio-institutional elements (four indicators). For detailed analysis, forty-one sub-indicators are used, comprising seventeen quantitative and twenty-four qualitative sub-indicators. The City Resilience Profiling Tool uses four sets to describe the following [42]: the basic characteristics of the urban area (City ID), the specifics of the analysed local community (Local Governments and Stakeholders), potential adverse events of different dynamics and estimated impact (Shocks, Stresses, and Challenges), and the elements that make up the urban system and enable its successful functioning (Urban Elements). Key analytical functions and sub-functions are defined for each set. A total of nineteen functions are identified: eight for Set 4, six for Set 1, three for Set 2, and two for Set 3. These analytical functions encompass a total of sixty sub-functions.

The City Resilience Index (CRI) and the Resilient Cities Index (RCI) are tools for measuring urban resilience that differ in both approach and focus, in addition to the specific factors they consider. The CRI emphasises resilience at the systemic level through a holistic, process-oriented approach. It is primarily focused on urban planning and strategic development and serves as a self-assessment tool. In contrast, the RCI is data-driven, developed for comparative analysis of cities based on measurable outcomes related to urban productivity, infrastructure resilience, climate change adaptation, and socio-economic conditions. While complementary in their objectives, the two indices reflect distinct paradigms: the CRI serves as a tool for transformative planning and inclusive governance, while the RCI serves as a metric of resilience readiness and operational strength within urban systems. Their concurrent use may provide a more comprehensive understanding of resilience by capturing both the structural foundations and the dynamic capacities required to address contemporary urban challenges.

The built environment is recognised not merely as a static assemblage of structures and utilities, but as an integrated system of interconnected assets that directly influences urban functionality, safety, equity, and sustainability [43,44,45]. From a systems perspective, the resilience of the built environment, as part of urban resilience, involves the robustness, redundancy, resourcefulness, and recoverability of structural and infrastructural systems [44,45]. It is not solely a technical issue but is deeply connected to institutional, socio-economic, and environmental systems. Infrastructure investment decisions, land use governance, and the integration of nature-based solutions all affect the physical and functional resilience of urban space. Moreover, as climate risks intensify, increasing emphasis is placed on green and blue infrastructure, passive cooling design, and permeable surfaces, as they are strategies that align structural resilience with environmental sustainability.

A systemic approach to analysing urban resilience requires the integration of diverse data sources. However, including numerous indicators can lead to the “inflation of data”. An increase in data volume does not necessarily correlate with better usage. On the contrary, issues often arise in data utilisation, processing, or validation. The credibility of data is of key importance for effective decision-making, particularly during critical situations when resources are limited and damages are significant. Therefore, selecting the appropriate amount and type of data is essential. Multi-criteria decision analysis (MCDA) methods can support the previously mentioned processes.

Specialised numerical modules can be developed for resilience analysis using MCDA methods such as AHP [46], MACBETH [47], or PROMETHEE [48]. Identifying weaknesses of local communities can form the basis for recommending potential improvements [49]. In [50], the authors assessed urban resilience using MCDA to identify risky regions, employing fuzzy AHP to rank indicators, and other methods (WSM, VIKOR, and TOPSIS) for regional comparison. When indicators are hierarchically organised, AHP-like methods are useful for setting priorities and determining weight coefficients.

The importance of standardisation in various activities prompted consideration of its potential application to enhance urban resilience. The requirements presented in the ISO/TR 22370:2020 standard [51] were identified and used as a foundation for determining strategic approaches to improving urban resilience. By using fuzzy logic, uncertainty is accounted for in the decision-making process, while group decision-making ensures the involvement of diverse stakeholders. Based on varying values of the optimism index, priorities for achieving a higher level of resilience were identified.

3. Materials and Methods

For this research, the requirements of the ISO/TR 22370:2020 are used as the research basis. The ISO/TR 22370:2020 standard provides a comprehensive framework for resilience management, addressing the need for systematic approaches that enhance adaptive capacities and ensure continuity in both public and private sector operations. As resilience management continues to gain prominence in global discourse, its integration into international standardisation efforts highlights the importance of structured methodologies for risk reduction, preparedness, and recovery.

The ISO/TR 22370:2020 standard serves as a vital reference point for organisations seeking to strengthen their resilience strategies by offering guidelines that align with international best practices. This standard highlights the necessity of proactive risk assessment, resource optimisation, and cross-sector collaboration to mitigate vulnerabilities and enhance response mechanisms. With the increasing frequency and severity of global crises, such as climate change-induced disasters, pandemics, and cyber threats, the ability to anticipate, absorb, and adapt to disruptions has become a defining factor in ensuring sustainable development and economic stability.

One of the key contributions of ISO/TR 22370:2020 is its emphasis on the interconnectedness of resilience across different domains, including urban infrastructure, supply chains, and information systems. The standard recognises that resilience is not merely about recovery from shocks but also about fostering long-term adaptability and innovation. By integrating resilience management principles into urban planning and corporate governance, stakeholders can create robust systems that are capable of withstanding and evolving in response to emerging challenges. Furthermore, the implementation of resilience strategies under this standard enhances an organisation’s credibility, regulatory compliance, and operational efficiency, ultimately fostering trust among stakeholders and international partners.

The importance of resilience management in alignment with ISO/TR 22370:2020 extends beyond risk mitigation to encompass broader socio-economic and environmental benefits. By promoting resilience as a core organisational value, institutions can contribute to the achievement of global objectives such as the United Nations Sustainable Development Goals (SDGs), particularly those related to sustainable cities and communities, climate action, and economic growth. The adoption of resilience frameworks not only ensures the security and stability of critical infrastructure but also strengthens social cohesion and institutional robustness.

As challenges facing modern societies become more complex and unpredictable, the role of resilience management as outlined in ISO/TR 22370:2020 is paramount. The standard provides a structured approach to enhancing resilience across various sectors, ensuring that organisations and communities remain agile in the face of adversity. Through continuous evaluation, innovation, and integration of resilience principles, the standard fosters a culture of preparedness that is essential for long-term sustainability and security. By embedding resilience management into strategic decision-making processes, institutions can not only safeguard their operations but also contribute to a more resilient and adaptive global landscape.

The concept of Actions for Resilience (A4R), as outlined in ISO/TR 22370, plays a fundamental role in strengthening the resilience of urban systems by addressing critical elements such as the built environment, supply chains and logistics, basic infrastructure, mobility, municipal public services, social inclusion and protection, economy, and ecology. As urban areas continue to expand and confront increasing risks related to climate change, natural disasters, pandemics, economic crises, and socio-political disruptions, the need for a systematic and adaptive approach to resilience becomes extremely important. A4R provides a structured framework that enhances cities’ capacity to absorb, recover from, and adapt to diverse shocks and stressors, ensuring sustainable urban development and improved quality of life for residents.

The built environment is a central component of urban resilience, as it encompasses residential, commercial, and industrial structures that define the physical and functional landscape of cities. The A4R approach emphasises risk-informed urban planning, sustainable construction practices, and adaptive architectural designs that minimise vulnerability to hazards such as earthquakes, floods, and extreme weather events. Integrating resilient materials, energy-efficient designs, and smart urban technologies ensures that buildings and infrastructure can withstand and recover from disruptions while maintaining their functionality.

Supply chains and logistics represent another critical dimension of urban resilience, as the efficiency and flexibility of supply networks directly impact economic stability and public well-being. A4R highlights the importance of diversification, redundancy, and technological integration in supply chain management to mitigate the risks associated with transportation bottlenecks, material shortages, and global market fluctuations. By incorporating predictive analytics, automation, and decentralised production systems, urban supply chains become more adaptable and capable of sustaining essential services during crises.

Basic infrastructure, including water, energy, sanitation systems, and transport infrastructure, serves as the foundation of urban functionality. The A4R framework advocates for the development of robust, decentralised, and climate-resilient infrastructure that can maintain operational continuity despite environmental and technological disruptions. Smart grid technologies, renewable energy integration, and water conservation strategies contribute to the long-term resilience of urban infrastructure, reducing dependency on centralised systems that may be vulnerable to large-scale failures.

Mobility and transportation systems are integral to urban resilience, as they facilitate the movement of people, goods, and services within cities. The A4R approach promotes the adoption of resilient, multimodal, and sustainable transportation networks that enhance connectivity and accessibility while reducing congestion and emissions. Investments in intelligent traffic management, public transit infrastructure, and active mobility options such as cycling and pedestrian pathways strengthen urban adaptability and ensure the continuity of transportation services during emergencies.

Municipal public services, including healthcare, education, emergency response, and waste management, are essential for maintaining urban resilience. A4R promotes the importance of efficient governance, cross-sector collaboration, and digital transformation in ensuring the continuity and accessibility of public services. Strengthening the capacity of emergency response teams, leveraging digital platforms for public health monitoring, and improving waste management systems are key measures that enhance urban resilience in times of crisis.

Social inclusion and protection are pivotal elements of resilience, as they determine the ability of vulnerable populations to cope with and recover from shocks. The A4R framework advocates for policies that promote equity, social cohesion, and community engagement, ensuring that marginalised groups have access to essential services and support systems. Inclusive urban planning, affordable housing initiatives, and targeted social protection programmes strengthen community resilience and contribute to a more just and sustainable urban environment.

The economy plays a vital role in urban resilience, as financial stability and economic diversification determine a city’s ability to recover from disruptions. A4R highlights the need for adaptive economic strategies that enhance local business resilience, support small and medium enterprises (SMEs), and promote innovation-driven growth. By fostering economic diversification, entrepreneurship, and digital transformation, cities can reduce their dependence on vulnerable industries and build a more resilient economic foundation.

Ecology and environmental sustainability are integral to the A4R concept, as they influence a city’s ability to adapt to climate change and ecological disruptions. The integration of green infrastructure, urban biodiversity conservation, and climate adaptation strategies ensures that cities remain liveable and resilient in the face of environmental challenges. Sustainable land use planning, ecosystem restoration, and nature-based solutions contribute to long-term urban resilience by reducing exposure to climate-related risks and enhancing environmental health.

The holistic approach of A4R within ISO/TR 22370 promotes the interconnected nature of urban systems and the necessity of integrated resilience strategies. By addressing key elements such as the built environment, supply chains, basic infrastructure, mobility, municipal public services, social inclusion, economy, and ecology, A4R provides a comprehensive framework for enhancing urban resilience. Through proactive planning, technological innovation, and inclusive governance, cities can foster a culture of resilience that ensures long-term sustainability, security, and improved quality of life for all urban residents. Despite its comprehensive approach, A4R primarily focuses on recognising resilience requirements and establishing strategic goals, while lacking an objective decision-making mechanism that integrates stakeholder engagement. This limitation can be addressed by integrating Multi-Criteria Decision Analysis (MCDA), a methodological approach that facilitates evidence-based, transparent, and participatory decision-making processes. MCDA enhances the A4R framework by enabling the systematic evaluation of multiple resilience strategies based on quantitative and qualitative criteria, ensuring that urban resilience measures align with the priorities of diverse stakeholders, including policymakers, urban planners, businesses, and local communities. By addressing key elements such as the built environment, supply chains, basic infrastructure, mobility, municipal public services, social inclusion, economy, and ecology, A4R—when coupled with MCDA—ensures a resilient, inclusive, and sustainable urban future.

In that sense, the created hierarchical decision-making model represents a unique combination of the requirements of ISO/TR 22370:2020 standard, observed from the standpoint of different stakeholders, involved as experts in the decision-making process, mirroring the main A4R focus areas of urban context. The proposed ISO/TR 22370:2020 hierarchical structure consists of three levels, as shown in Figure 1. Factors at the second level influence each criterion at the first level, while the alternatives describe the individual factors in more detail.

By assessing these determinants and alternatives, urban planners, decision-makers, and policy creators can develop comprehensive strategies that align with the principles outlined in ISO/TR 22370:2020, ultimately enhancing the resilience and sustainability of urban ecological systems.

Due to limited available resources, it is necessary to identify priorities in the strategic consideration of potential improvements in urban resilience. The previously presented hierarchical structure enables the application of methods based on the analytical hierarchy process (AHP). These methods are used to establish priorities or to compare alternatives by incorporating both objective and subjective inputs in decision-making. Additionally, they allow for the use of not only crisp values but also linguistic variables and fuzzy numbers, enhancing the realism of the decision-making process by accounting for uncertainty.

To address the inherent uncertainty in safety-related problems, the basic model using crisp numbers is extended through the use of fuzzy numbers. Triangular fuzzy numbers (a, b, c) are used during the analysis, with the following member function:

$$\mu \left(x\right)=\left\{\begin{array}{c}\left(x-a\right)/\left(b-a\right), x\in \left[a,b\right]\\ \left(c-x\right)/\left(c-b\right), x\in \left[b,c\right]\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}0, \mathrm{elsewhere}\end{array}\right.,$$

(1)

where a ≤ b ≤ c; b is the most likely (modal) value; and a and c represent corresponding supports. Crisp value is obtained for a = b = c. Standard arithmetic operations with fuzzy numbers are applied, as shown in [52].

The procedure consists of the following steps: 1. Identifying the goal; 2. Selecting the criteria; 3. Forming the decision-making hierarchy; 4. Selecting experts; 5. Performing pairwise comparisons of alternatives using fuzzy numbers; 6. Applying the aggregation principle to comparisons of individual experts at each level; 7. Applying the extent analysis to determine priority vectors; 8. Defuzzifying fuzzy weights and selecting priority elements; and 9. Analysing different scenarios.

The following fuzzy scale is used during selection: (1,1,3) for equal alternatives (I); (1,3,5) for weak preference (III); (3,5,7) for strong preference (V); (5,7,9) for confirmed preference (VII); and (7,9,9) for absolute dominance (IX).

The comparison of alternatives at each level is shown using a matrix

$$\tilde{A}={\left[{\tilde{a}}_{ij}\right]}_{i,j=\overline{1,n}},$$

(2)

the elements of which represent aggregate values obtained from individual expert evaluations

$${\tilde{a}}_{ij}={\displaystyle \prod _{k=1}^m{\left({{\tilde{a}}_{ij}}^{\left(k\right)}\right)}^{1/m}},$$

(3)

where m is the number of experts. During the analysis, it is assumed that all experts have equal influence on the final decision. The consistency of group decision-making is assessed, according to [53], by comparing the calculated centric consistency index (CCI) value with acceptable thresholds: less than 0.31 for three elements, less than 0.35 for four elements, and less than 0.37 for comparisons involving five or more elements.

To determine priorities, two vectors of weights are calculated. The first vector consists of the weights of the criteria, ${\tilde{W}}_K=\left({\tilde{\omega }}_{K1},{\tilde{\omega }}_{K2},{\tilde{\omega }}_{K3}\right)$, whose values are determined as

$${\tilde{\omega }}_{Kj}={\left({\displaystyle \prod _{l=1}^3{\tilde{a}}_{il}}\right)}^{1/3}\otimes {\left({\displaystyle \sum _{i=1}^3{\left({\displaystyle \prod _{l=1}^3{\tilde{a}}_{il}}\right)}^{1/3}}\right)}^{-1},$$

(4)

for j = 1, 2, 3; ${\tilde{a}}_{ij}$ are the elements of the aggregate matrix for the comparison criteria.

Factors are compared using fuzzy numbers by considering their importance regarding each criterion:

$${\tilde{f}}_{ij}={\left({\displaystyle \prod _{l=1}^8{\tilde{a}}_{il}}\right)}^{1/8}\otimes {\left({\displaystyle \sum _{i=1}^8{\left({\displaystyle \prod _{l=1}^8{\tilde{a}}_{il}}\right)}^{1/8}}\right)}^{-1},$$

(5)

where f_ij represents the influence of the i-th factor in relation to the j-th criterion. Their weights form a vector

$${\tilde{W}}_F=\tilde{F}\otimes {\tilde{W}}_K=\left({\tilde{\omega }}_{F1},\dots ,{\tilde{\omega }}_{F8}\right).$$

(6)

The ranking is performed after the defuzzification of the obtained fuzzy values. According to [54], the total integral value is determined as:

$$I_T^{\vartheta }\left(\tilde{a}\right)=\left(\vartheta \cdot c+b+\left(1-\vartheta \right)\cdot a\right)/2,$$

(7)

where ϑ is the optimism index (0≤ ϑ ≤1), describing the decision-maker’s attitude towards risk. A higher value corresponds to a more optimistic (lower-risk) perspective, while a lower value indicates a more pessimistic (higher-risk) perspective. Typical values are ϑ = 1 for optimistic, ϑ = 0.5 for moderate, and ϑ = 0 for a pessimistic perspective of a decision-maker. Normalised values

$${{\omega }_j}^{*}=I_{Tj}^{\vartheta }/{\displaystyle \sum _{k=1}^n{I}_{Tk}^{\vartheta }},$$

(8)

are used for ranking, where j = 1, …, n. The ranking establishes priorities for potentially improving urban resilience. Based on these priorities, various development scenarios are analysed in more detail.

4. Results and Discussion

The decision-making hierarchy, shown in Figure 1, represents the key elements that can influence the improvement of urban resilience. The procedure was applied to identify strategic priorities for enhancing resilience in the urban area described in [55]. The analysis is based on ISO 22370, the EU Action Plan [56], the Serbian law on disaster risk reduction and emergency management [57], and the Serbian rulebook [58].

Serbia presents a strategically relevant and contextually justified pilot area for the implementation of the Actions for Resilience (A4R) concept in line with ISO/TR 22370, due to a confluence of legal, institutional, and risk-exposure factors that align with both national and international resilience-building agendas. The Serbian Law on Disaster Risk Reduction and Emergency Management (Official Gazette RS, No. 87/2018) explicitly integrates the principles of disaster risk prevention, preparedness, and resilience into national governance structures. It mandates a systematic and multi-sectorial approach to risk assessment and mitigation and promotes resilience as a core objective of both local and national institutions. This provides a robust legal framework that supports the implementation of A4R measures within existing governance mechanisms, ensuring institutional alignment with ISO/TR 22370’s emphasis on strategic, coordinated, and evidence-based resilience actions.

Serbia is a signatory and active participant in the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030, which prioritises the strengthening of disaster resilience at all levels. Serbia’s national strategy reflects the Sendai priorities, particularly in relation to understanding risk, strengthening governance, investing in resilience, and enhancing disaster preparedness. The A4R framework under ISO/TR 22370 offers a structured and standardised methodology to operationalise these Sendai priorities at the local and sectorial levels. Further, Serbia is geographically and socio-politically situated in a multi-hazard environment, with frequent exposure to floods, droughts, seismic events, forest fires, and increasing climate-related risks. These conditions make it a highly relevant testing ground for resilience strategies that can be replicated or scaled across similar high-risk areas in the Western Balkans and broader Southeast European region. Moreover, Serbia has demonstrated institutional openness to international standards and capacity-building tools, including participation in EU Civil Protection Mechanism programmes and various UN and NATO resilience and disaster risk reduction initiatives.

Niš is the first city in Serbia to adopt a formal local security strategy, marking a pioneering step in institutionalising risk governance and urban resilience at the municipal level. This strategy was developed in coordination with local institutions, academic bodies, and civil protection services, thus establishing an integrated framework for addressing public safety, disaster risk reduction (DRR), and emergency preparedness. It reflects a forward-looking governance model that resonates strongly with the procedural and adaptive logic of ISO/TR 22370, which emphasises coordinated, cross-sectorial actions based on evidence, stakeholder engagement, and continual improvement.

Geographically and functionally, Niš holds a central role as a regional urban and administrative hub in Southeastern Serbia, with strategic infrastructure including transportation corridors, energy systems, healthcare facilities, and critical institutions. Its exposure to multiple natural and anthropogenic hazards, such as earthquakes, floods, extreme weather events, industrial accidents, and infrastructure stress, makes it a representative urban setting for testing comprehensive resilience-building measures. The city’s risk profile aligns with the multidimensional resilience approach required by ISO/TR 22370, which encompasses people, assets, systems, and services.

Importantly, the city’s governance has demonstrated a willingness to align with EU and UN frameworks, including the Sendai Framework for Disaster Risk Reduction, and has participated in cross-border cooperation initiatives focused on civil protection and resilience. These factors create an enabling environment for ISO/TR 22370 implementation that is both technically feasible and politically supported. From a scientific perspective, the city of Niš combines essential conditions for experimental generalisation of resilience strategies: a well-documented hazard and risk profile; existing institutional frameworks for risk governance; and proactive strategic planning instruments, such as the local security strategy, which uniquely integrates disaster risk reduction (DRR), civil protection, and urban safety planning.

A group of n = 7 experts (DM_i), each with equal influence on the final decision, participated in the group decision-making process. A comparison of criteria using triangular fuzzy numbers is shown in

Table 1. Comparison of criteria (CCI = 0.016).

Experts		K1	K2	K3
DM1	K1	(1,1,1)	I	III
	K2	I−1	(1,1,1)	III
	K3	III−1	III−1	(1,1,1)
DM2	K1	(1,1,1)	III−1	III−1
	K2	III	(1,1,1)	I
	K3	III	I−1	(1,1,1)
DM3	K1	(1,1,1)	I	III
	K2	I−1	(1,1,1)	III
	K3	III−1	III−1	(1,1,1)
DM4	K1	(1,1,1)	III−1	III−1
	K2	III	(1,1,1)	I−1
	K3	III	I	(1,1,1)
DM5	K1	(1,1,1)	III	I
	K2	III−1	(1,1,1)	III−1
	K3	I−1	III	(1,1,1)
DM6	K1	(1,1,1)	III	I
	K2	III−1	(1,1,1)	III−1
	K3	I−1	III	(1,1,1)
DM7	K1	(1,1,1)	III−1	III−1
	K2	III	(1,1,1)	I
	K3	III	I−1	(1,1,1)
Aggregated matrix	K1	(1,1,1)	(0.50,0.86,2.17)	(0.50,0.86,2.17)
	K2	(0.46,1.17,1.99)	(1,1,1)	(0.54,1.00,2.17)
	K3	(0.46,1.17,1.99)	(0.46,1.00,1.85)	(1,1,1)
Fuzzy weights		(0.13,0.30,0.90)	(0.13,0.35,0.88)	(0.12,0.35,0.83)
Crisp weights	ϑ = 0	0.311	0.347	0.342
	ϑ = 0.5	0.326	0.342	0.332
	ϑ = 1	0.333	0.340	0.327

.

Comparison values are based on the previously described comparison scale. The table presents the aggregate matrix determined using Equation (3). Based on this aggregate matrix, the fuzzy criteria weights were determined using Equation (4). The table also contains crisp weight values, obtained using Equation (7), for three specific values of the optimism index.

The aggregate comparison matrix for factors is shown in

Table 2. Comparison of factors (CCI = 0.010).

Factors	F1	F2	F3	F4
F1	(1,1,1)	(0.68,1.10,2.36)	(0.18,0.29,0.73)	(0.27,0.50,0.99)
F2	(0.42,0.91,1.47)	(1,1,1)	(0.33,0.32,0.76)	(0.28,0.50,1.03)
F3	(1.17,3.23,5.25)	(1.17,2.87,2.96)	(1,1,1)	(0.93,1.35,2.79)
F4	(1.01,1.99,3.65)	(0.98,2.02,3.60)	(0.36,0.74,1.08)	(1,1,1)
F5	(1.72,3.92,5.99)	(2.02,3.88,5.24)	(0.73,1.60,2.73)	(1.17,2.15,3.92)
F6	(1.00,2.02,3.60)	(0.73,1.60,2.73)	(0.35,0.64,1.17)	(0.32,0.53,0.92)
F7	(0.73,1.37,2.17)	(0.40,0.79,1.17)	(0.47,0.59,1.37)	(0.24,0.44,0.85)
F8	(0.79,1.47,2.86)	(0.50,1.17,1.58)	(0.56,0.73,1.81)	(0.26,0.49,0.92)
Local weight	(0.03,0.09,0.32)	(0.04,0.10,0.32)	(0.08,0.25,0.68)	(0.07,0.20,0.59)
Final weight	(0.01,0.09,0.83)	(0.02,0.10,0.84)	(0.03,0.25,1.78)	(0.03,0.20,1.53)
Factors	F5	F6	F7	F8
F1	(0.17,0.26,0.58)	(0.37,0.73,1.47)	(0.39,0.73,1.37)	(0.46,0.85,1.72)
F2	(0.19,0.26,0.50)	(0.38,0.74,1.47)	(0.73,1.00,2.19)	(0.54,0.85,2.02)
F3	(0.37,0.62,1.37)	(0.85,1.56,2.86)	(0.85,2.14,3.31)	(0.85,2.17,2.76)
F4	(0.26,0.47,0.85)	(0.79,1.90,3.08)	(1.17,2.27,4.21)	(1.09,1.75,3.02)
F5	(1,1,1)	(1.37,2.17,5.12)	(1.37,2.86,4.76)	(1.27,2.73,4.59)
F6	(0.21,0.50,0.85)	(1,1,1)	(0.58,1.17,2.33)	(0.54,1.00,2.17)
F7	(0.21,0.35,0.73)	(0.43,0.85,1.72)	(1,1,1)	(0.42,1.01,1.47)
F8	(0.22,0.31,0.62)	(0.46,1.00,1.85)	(0.68,1.00,2.36)	(1,1,1)
Local weight	(0.12,0.36,1.02)	(0.05,0.14,0.44)	(0.04,0.11,0.34)	(0.05,0.12,0.40)
Final weight	(0.05,0.36,2.65)	(0.02,0.14,1.15)	(0.02,0.11,0.88)	(0.02,0.12,1.04)

. The elements of this matrix were obtained using Equation (3), based on the individual comparison matrices provided by the experts. The table contains local weights, calculated relative to the criteria, as well as the final weights, determined according to the overall goal.

During the analysis, the crisp weights of criteria (

Table 3. Crisp weights and ranks of criteria for different values of the optimism index.

Criteria	ϑ = 0	ϑ = 0.1	ϑ = 0.2	ϑ = 0.3	ϑ = 0.4	ϑ = 0.5	ϑ = 0.6	ϑ = 0.7	ϑ = 0.8	ϑ = 0.9	ϑ = 1
K1	0.311 (3)	0.316 (3)	0.319 (3)	0.322 (3)	0.325 (3)	0.326 (3)	0.328 (3)	0.330 (3)	0.331 (2)	0.332 (2)	0.333 (2)
K2	0.347 (1)	0.345 (1)	0.344 (1)	0.343 (1)	0.342 (1)	0.342 (1)	0.341 (1)	0.341 (1)	0.340 (1)	0.340 (1)	0.340 (1)
K3	0.342 (2)	0.339 (2)	0.336 (2)	0.334 (2)	0.333 (2)	0.332 (2)	0.330 (2)	0.330 (2)	0.329 (3)	0.328 (3)	0.327 (3)

) and factors (

Table 4. Final crisp weights and ranks of factors for different values of the optimism index.

Factors	ϑ = 0	ϑ = 0.1	ϑ = 0.2	ϑ = 0.3	ϑ = 0.4	ϑ = 0.5	ϑ = 0.6	ϑ = 0.7	ϑ = 0.8	ϑ = 0.9	ϑ = 1
F1	0.068 (8)	0.072 (8)	0.073 (8)	0.074 (8)	0.075 (8)	0.075 (8)	0.076 (8)	0.076 (8)	0.076 (8)	0.076 (8)	0.076 (8)
F2	0.071 (7)	0.074 (7)	0.075 (7)	0.076 (7)	0.076 (7)	0.076 (7)	0.077 (7)	0.077 (7)	0.077 (7)	0.077 (7)	0.077 (7)
F3	0.181 (2)	0.175 (2)	0.173 (2)	0.171 (2)	0.170 (2)	0.170 (2)	0.169 (2)	0.169 (2)	0.169 (2)	0.169 (2)	0.168 (2)
F4	0.148 (3)	0.146 (3)	0.145 (3)	0.145 (3)	0.145 (3)	0.144 (3)	0.144 (3)	0.144 (3)	0.144 (3)	0.144 (3)	0.144 (3)
F5	0.257 (1)	0.253 (1)	0.252 (1)	0.251 (1)	0.250 (1)	0.250 (1)	0.250 (1)	0.249 (1)	0.249 (1)	0.249 (1)	0.249 (1)
F6	0.104 (4)	0.105 (4)	0.106 (4)	0.106 (4)	0.106 (4)	0.106 (4)	0.107 (4)	0.107 (4)	0.107 (4)	0.107 (4)	0.107 (4)
F7	0.081 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)	0.082 (6)
F8	0.091 (5)	0.093 (5)	0.094 (5)	0.095 (5)	0.095 (5)	0.096 (5)	0.096 (5)	0.096 (5)	0.096 (5)	0.096 (5)	0.096 (5)

) were calculated for different values of the optimism index, with an increment of 0.1. The crisp values were determined from the fuzzy weights of the criteria and factors using Equation (7), followed by normalisation using Equation (8).

Although there are slight changes in the factor weights for different values of the ϑ index, the ranking of the factors remained unchanged, even in cases where the weights were very close (for example, criteria F₁ and F₂). Basic infrastructure (F₃) and Municipal public services (F₅) were identified as the most important criteria for potential resilience improvement. These are discussed in more detail below in the context of A4R and specific scenarios.

Basic infrastructure constitutes one of the most critical determinants of urban performance. Its role is indispensable for ensuring the continuity of social, economic, and environmental functions. Within the framework of ISO 22370, which guides strategic resilience planning, three prospective scenarios: the current scenario, the trend scenario, and the resilient and sustainable development scenario, serve as reference models for developing Actions for Resilience (A4R). Each scenario requires a distinct strategic orientation, reflecting varying degrees of adaptation, transformation, and sustainability in the management of basic infrastructure.

In the current scenario (CS), the evolution of infrastructure remains largely aligned with existing operational patterns, exhibiting limited adaptation to emerging challenges. Urban systems in this scenario maintain a predominantly reactive posture toward risks, with resilience strategies often materialising only in response to disruptions rather than through proactive anticipation. Within this context, prospective actions for resilience must emphasise incremental adaptation measures, focusing on retrofitting the existing infrastructure to enhance its capacity to withstand known hazards. Strengthening risk monitoring systems and early warning mechanisms becomes essential, enabling the timely identification of vulnerabilities and facilitating a more responsive approach to impending threats. Emergency response protocols must be refined and specialised for different types of infrastructure failures, ensuring a coordinated and efficient recovery process. Additionally, fostering capacity-building initiatives for infrastructure managers, urban planners, and maintenance personnel is crucial for instilling a culture of resilience awareness, albeit within the constraints of prevailing resource limitations.

Transitioning to the trend scenario (TS), the trajectory of development is influenced by the continuation of current trends, including growing urbanisation, technological innovation, and climate change pressures. While adaptation efforts are more visible compared to the CS, responses often remain fragmented and insufficiently integrated across sectors. In this context, the actions for resilience must extend beyond mere risk mitigation toward systemic adaptation. A strategic shift toward integrated infrastructure management is imperative, promoting cross-sectoral collaboration and interoperability among different urban systems. Investment in smart infrastructure technologies, such as predictive maintenance systems, sensor networks, and decentralised energy solutions, must be prioritised to enhance the agility and responsiveness of basic services. Scenario-based planning and simulation exercises should be institutionalised, allowing cities to anticipate complex disruptions and design flexible response strategies.

Finally, within the resilient and sustainable development scenario (RS), infrastructure systems are envisioned as deeply integrated components of a regenerative and inclusive urban environment. Here, resilience is not merely a protective attribute but an intrinsic characteristic of urban life, embedded in the design, governance, and daily operation of infrastructure. In this advanced scenario, Actions for Resilience must embrace transformative approaches. Basic infrastructure should be redesigned based on nature-based solutions and circular economy principles, reducing environmental impacts while enhancing system flexibility and redundancy. Long-term adaptive governance frameworks must be institutionalised, promoting iterative learning, stakeholder inclusivity, and policy coherence across local, regional, and global levels. Innovation ecosystems should be nurtured to continuously explore emerging technologies such as renewable energy microgrids, modular transportation systems, and climate-resilient building materials. Moreover, embedding equity considerations in infrastructure planning ensures that resilience benefits are distributed fairly, thereby strengthening the social fabric of urban communities and reducing vulnerability disparities.

Across these three scenarios, the progressive deepening of resilience, from reactive adaptation to proactive transformation, highlights the critical need for differentiated, scenario-sensitive actions. It is through such strategic calibration that cities can not only secure the continuity of basic infrastructure services but also harness infrastructure as a catalyst for sustainable and inclusive urban development, as shown in

Table 5. Scenarios for basic infrastructure development.

Scenario	Prospective Actions for Resilience (A4R)	Description
CS	Incremental adaptation of existing infrastructure	Retrofit and reinforce critical infrastructure (e.g., bridges, water plants) to withstand known hazards.
	Strengthening risk monitoring and early warning systems	Deploy basic hazard detection and monitoring technologies to identify vulnerabilities early.
	Emergency response enhancement for infrastructure failures	Develop rapid-response protocols specific to critical infrastructure disruptions.
	Basic capacity building for infrastructure managers	Train public officials and engineers in fundamental resilience practices and emergency preparedness.
TS	Integrated management of infrastructure systems	Promote cross-sector collaboration for holistic, system-wide infrastructure resilience strategies.
	Investment in smart infrastructure technologies	Implement sensors, predictive analytics, and decentralised systems to enhance infrastructure adaptability.
	Scenario-based planning and simulation exercises	Conduct regular drills and simulations to anticipate complex disruptions and improve response strategies.
	Community engagement in infrastructure resilience	Involve citizens in resilience planning processes, fostering ownership and localised action capacities.
RS	Redesign of infrastructure using nature-based and circular economy principles	Build systems that are regenerative, flexible, and low impact, such as green stormwater management or modular energy grids.
	Institutionalisation of long-term adaptive governance frameworks	Establish iterative, inclusive, and flexible planning processes that evolve with changing risks.
	Development of innovation ecosystems for infrastructure resilience	Foster continuous innovation through partnerships with universities, tech start-ups, and public agencies.
	Embedding equity and inclusion into infrastructure development	Ensure that resilience strategies equitably benefit all urban populations, especially vulnerable groups.

.

From a municipal public services perspective, they form the everyday interface between urban governance and citizens’ quality of life. Their resilience is vital for maintaining urban stability, fostering social cohesion, and ensuring sustainable development. Also, according to ISO 22370, prospective planning for resilience must be framed within three scenarios. In each case, prospective actions for resilience must be adapted to the anticipated pressures and opportunities characterising urban trajectories.

In the CS, municipal public services are maintained through conventional, often fragmented approaches, where resilience efforts are predominantly reactive. Within this context, actions for resilience should primarily focus on stabilising essential service provision through targeted improvements. One concrete action is the development of basic digital redundancy systems for municipal administrative services, ensuring that citizen records and critical public databases are securely backed up and accessible in emergencies. Additionally, municipalities should initiate basic waste management contingency planning, designing backup collection and disposal routes in case of disruptions such as natural disasters or labour strikes. Another critical action involves upgrading local emergency response capacity through simple but effective measures like cross-training personnel to serve multiple emergency functions (e.g., firefighting and medical first response), thereby optimising resource use in times of crisis. These actions, though incremental, build a necessary baseline of resilience by reducing immediate vulnerabilities and enhancing service continuity.

The TS envisions a moderate evolution of municipal public services, influenced by gradual technological adoption, changing demographic patterns, and increasing environmental pressures. In this future, while some resilience strategies are implemented, they often lack systemic coherence. Here, actions for resilience must focus on integration, anticipation, and agility. One essential A4R in this scenario is the adoption of smart waste management technologies, including sensor-equipped bins and dynamic route optimisation for waste collection, which can drastically increase efficiency and allow real-time adaptation to service disruptions. Another prospective action is the implementation of decentralised water treatment systems at the neighbourhood level, which would enhance water security and reduce dependence on centralised, potentially vulnerable facilities. Furthermore, municipalities must invest in predictive maintenance systems for public transportation networks and GPS-navigated waste trucks, enabling authorities to pre-empt failures before they impact service delivery. Complementing technological advancements, community-based resilience programmes should be established to engage citizens in the co-production of services, particularly in areas like neighbourhood sanitation and public health surveillance. This collaborative model strengthens the resilience of municipal services by rooting them within the social fabric of the city.

In the RS, municipal public services are envisioned as dynamic, equitable, and regenerative systems, inherently capable of absorbing shocks and adapting to long-term stresses. In this aspirational scenario, actions for resilience must be transformative in scope and inclusive in governance. A vital action is the mainstreaming of nature-based solutions into public service design—for instance, integrating urban wetlands for natural stormwater management, thereby reducing the burden on engineered drainage systems. Another transformative action involves the full digitalisation and decentralisation of administrative services, allowing citizens to access critical municipal functions securely and efficiently from anywhere, even during systemic crises. Public transportation systems should be reimagined as multi-modal, climate-resilient mobility ecosystems, with infrastructure designed to withstand extreme weather events and rapidly adapt to shifts in user demand. Moreover, municipalities must institutionalise adaptive governance frameworks, which establish iterative planning cycles, scenario testing, and transparent public engagement processes to continuously refine resilience strategies based on evolving conditions. Critically, equity must be embedded at the core of resilience initiatives, ensuring that resilience dividends are distributed fairly and that marginalised groups are not disproportionately burdened by infrastructural or service transformations.

The trajectory of resilience in municipal public services illustrates a deepening sophistication and integration of prospective actions. From stabilising existing services to building collaborative networks and ultimately transforming governance and infrastructure paradigms, these A4R strategies provide a vital roadmap for municipalities seeking to future-proof their service delivery against the complex uncertainties of the twenty-first century, as shown in

Table 6. Scenarios for municipal public services development.

Scenario	Prospective Actions for Resilience (A4R)	Description
CS	Incremental adaptation of existing infrastructure	Retrofit and reinforce critical infrastructure (e.g., bridges, water plants) to withstand known hazards.
	Strengthening risk monitoring and early warning systems	Deploy basic hazard detection and monitoring technologies to identify vulnerabilities early.
	Emergency response enhancement for infrastructure failures	Develop rapid-response protocols specific to critical infrastructure disruptions.
TS	Adoption of smart waste management technologies	Deploy sensor-equipped waste bins and dynamic collection routes to improve efficiency and real-time responsiveness.
	Implementation of decentralised water treatment systems	Install neighbourhood-level water systems to increase water supply resilience and reduce centralisation risks.
	Predictive maintenance systems for public transport	Use IoT and AI-based tools to anticipate infrastructure failures and avoid service interruptions.
	Community-based resilience programmes	Engage local communities in service co-production (e.g., neighbourhood sanitation, early warning networks).
RS	Mainstreaming nature-based solutions into service design	Use green infrastructure (e.g., wetlands, permeable pavements) for functions like flood management.
	Full digitalisation and decentralisation of administrative services	Enable citizens to access all critical municipal functions remotely, ensuring resilience under any conditions.
	Climate-resilient multi-modal public transport ecosystems	Develop integrated, flexible, and weather-adapted transport systems with low-carbon footprints.
	Institutionalizing adaptive governance frameworks	Create flexible, inclusive planning systems that evolve based on new risks and community feedback.

.

An aggregated summary view depicting reactive, proactive, and transformative A4R is shown in

Table 7. Aggregated summary.

Scenario	Reactive (Stabilisation)	Proactive (Adaptation)	Transformative (Sustainability)
CS	- Infrastructure retrofits - Risk monitoring - Emergency response protocols - Basic redundancy systems - Waste contingency planning
TS		- Integrated infrastructure management - Smart waste management - Decentralised water treatment - Predictive maintenance (transport) - Community resilience programmes
RS			- Nature-based solutions (green infrastructure, wetlands) - Adaptive governance frameworks - Innovation ecosystems - Fully digital public services - Climate-resilient transport ecosystems - Equity-based infrastructure planning

.

5. Conclusions

This paper presented a study on the key advantages of integrating A4R with MCDA, which lie in its ability to systematically evaluate complex, multidimensional problems by incorporating resilience-oriented actions within a decision-making framework. MCDA allows for the consideration of multiple criteria simultaneously, providing decision-makers with a comprehensive assessment of trade-offs and priorities. By embedding resilience-focused strategies into this analysis, stakeholders can make more informed choices that account for both immediate needs and long-term sustainability. This synergy enhances adaptability and robustness in decision processes, ultimately fostering more resilient systems that can effectively respond to uncertainty and disruptions. For instance, the A4R framework, when combined with MCDA, facilitates a participatory and transparent decision-making process. Given that resilience-building often requires input from diverse stakeholders, MCDA serves as a valuable tool for incorporating different perspectives, ensuring that decisions are not only technically sound but also socially and politically viable. This participatory approach aligns well with the core principles of A4R, which emphasise proactive and context-specific actions to strengthen resilience in various sectors.

Future research on the Actions for Resilience (A4R) concept, particularly in conjunction with Multi-Criteria Decision Analysis (MCDA), presents promising avenues for both theoretical advancements and practical applications. The integration of A4R with MCDA offers a structured approach to enhancing resilience across diverse domains, including environmental management, organisational decision-making, and policy development. However, while the benefits of this coupling are evident, certain limitations must be acknowledged to ensure a balanced perspective. Despite the aforementioned strengths, certain limitations must be considered in future research. One of the main challenges lies in the complexity of quantifying resilience, which is inherently dynamic and context-dependent. While MCDA provides a structured way to evaluate multiple criteria, translating qualitative resilience aspects into measurable indicators can be difficult. This challenge necessitates the development of robust methodologies for capturing both quantitative and qualitative dimensions of resilience, which remains an area for further investigation.

Additionally, the effectiveness of coupling A4R with MCDA depends on the availability and quality of data. In many real-world scenarios, decision-makers may face data scarcity, uncertainty, or inconsistencies, which could impact the reliability of MCDA outcomes. Future research should explore methodologies for dealing with incomplete or uncertain data, such as incorporating expert judgement, scenario-based modelling, or fuzzy logic approaches to enhance decision robustness.

Another important consideration is the potential subjectivity in weighting criteria within MCDA. While the method allows for structured decision-making, the assignment of weights to different resilience factors can be influenced by stakeholder biases or contextual preferences. Future studies should focus on refining weighting mechanisms, possibly through the use of machine learning or hybrid decision-support systems, to enhance objectivity and reduce bias in resilience assessments. The proposed hierarchy can be expanded by including additional factors and alternatives to address, among other things, some specificities of the local regulation. In the presented analysis, all experts had equal influence, and their number was limited. Obtaining experts’ weights based on their expertise and previous experience, as well as applying different types of fuzzy numbers, could further enhance the overall decision-making process.

The integration of A4R with MCDA represents a valuable advancement in resilience research, offering a structured and participatory approach to decision-making in complex environments. While the framework enhances adaptability, stakeholder engagement, and comprehensive assessment of resilience-related actions, future research should address methodological challenges related to quantification, data limitations, and subjective weighting. By refining these aspects, the combined application of A4R and MCDA can further strengthen resilience-oriented decision-making across various disciplines, paving the way for more robust and adaptive systems in the face of uncertainty.