Sustainable Airport Planning Using a Multi-Criteria Decision-Making Approach with Fuzzy Logic and GIS Integration

Abstract

Sustainable design in large-scale infrastructure projects, such as airports, is crucial for minimizing environmental impacts while ensuring long-term financial feasibility. This study focuses on selecting the most sustainable pavement solution for airport construction, using Tabuk Airport in Saudi Arabia as a case study. The purpose of this study is to evaluate four pavement alternatives using a multi-criteria decision-making approach to identify the optimal solution in terms of sustainability, cost-effectiveness, and feasibility. The alternatives were assessed based on nine key criteria, including environmental impact, durability, cost, and maintenance. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks the alternatives, while the Fuzzy Analytic Network Process (FANP) calculates the criteria weights, addressing uncertainties and interdependencies. Geographic Information System (GIS) is integrated to incorporate spatial factors affecting pavement sustainability. The results show that the alternative using recycled materials (A4) is the most suitable, offering the best balance of sustainability and cost. A4 achieved the highest ranking in the evaluation, making it the recommended choice for the upcoming Tabuk Airport project. This study demonstrates the effective application of decision-making tools, such as TOPSIS, FANP, and GIS, in guiding sustainable infrastructure development and providing a replicable framework for similar projects worldwide.

1. Introduction

The development of airport projects remains controversial due to their significant impact on surrounding communities, particularly regarding land use, noise levels, air quality, traffic congestion, and secondary developments [1]. Aviation has long been associated with significant environmental challenges, with both the construction and operation of airports contributing to ecological degradation and societal concerns [2]. The adverse effects of airports extend to air and noise pollution, habitat disruption, and resource depletion, all of which have measurable consequences for both the natural environment and human well-being [3]. Given these impacts, it is imperative to implement strategies aimed at reducing the environmental footprint of airports while simultaneously fostering socio-economic benefits through sustainable business practices [4]. The integration of sustainability principles into airport development and management is essential for the future of aviation infrastructure. This raises a critical question: Can aviation truly achieve sustainability, or is the concept of “sustainable aviation” an unattainable ideal [5]?

Airports are essential hubs for transportation and economic growth, yet their development and operation exert considerable pressure on natural resources, contribute to greenhouse gas emissions, and affect local communities [6]. As the global demand for air travel grows, integrating sustainability into airport planning becomes a necessity rather than an option. Sustainable airport development seeks to balance operational efficiency, environmental responsibility, and economic viability by adopting innovative construction materials, optimizing energy use, and minimizing ecological impact [7,8]. The concept of sustainability was first formulated in the Brundtland Report [9], which stated that the goal of sustainability is to “meet the needs of the present generation without compromising the ability of future generations to meet their own needs”. Sustainability has become a fundamental goal in modern development practices and plays a central role in guiding environmental management strategies. It emphasizes the need to balance economic growth, environmental protection, and social well-being to ensure that the needs of the present are met without compromising the ability of future generations to meet their own needs. In the context of civil and infrastructure engineering, sustainable development aims to reduce resource consumption, minimize environmental impacts, and promote long-term resilience in built environments. Terminology directly related to sustainable development or closely associated with it is becoming increasingly common. The use of sustainability-related terminology has been steadily increasing across various disciplines, reflecting a broader societal awareness of the critical importance of sustainability. This trend highlights not only a growing commitment to integrating sustainable practices but also the complexity associated with defining the concept itself. Numerous studies have shown that the meaning of “sustainability” can vary significantly depending on the context in which it is applied [10,11,12]. In environmental sciences, sustainability often refers to the responsible management of natural resources to preserve ecosystems for future generations. In engineering and infrastructure, it may emphasize the durability, efficiency, and minimal environmental impact of materials and designs. Meanwhile, in social and economic contexts, sustainability can focus on promoting equity, community resilience, and long-term economic stability. This contextual flexibility, while enriching the concept, also presents challenges in developing universal metrics and standards for assessing sustainability efforts. As awareness continues to expand globally, achieving a shared understanding remains a crucial task for researchers, policy-makers, and practitioners alike. It may refer to ecological sustainability, economic sustainability, social sustainability, or other forms. As mentioned in the Hajian and Kashani [13], the general definition of sustainability refers to meeting present requirements while also allowing future generations to meet their own requirements. Furthermore, sustainability is divided into three main divisions, which are illustrated in Figure 1.

Several studies have been conducted globally to assess the effectiveness of sustainable airport practices. A study conducted by Lim, Koh [14] analyzed the implementation of renewable energy sources at Changi Airport in Singapore, highlighting a reduction in energy consumption by 25% through solar panel installations and enhanced energy management systems. Similarly, the Amsterdam Schiphol Airport has integrated circular economy principles, using sustainable building materials and waste recycling programs, leading to a 50% reduction in landfill waste [15]. In the United States, a study by Kousoulidou and Lonza [16] assessed the carbon footprint of major airports, concluding that integrating biofuels and electric ground support equipment significantly decreased carbon emissions. The Denver International Airport has implemented extensive water conservation strategies, reducing water usage by 30% while maintaining operational efficiency [17].

Kingdom of Saudi Arabia (KSA), under its Vision 2030 initiative, has placed significant emphasis on sustainable infrastructure, including airport development [18]. KSA aims to modernize its transportation sector as well as reduce its carbon footprint, and enhance energy efficiency [19]. Airports in Saudi Arabia are currently under rapid development and face the challenge of maintaining environmental sustainability while meeting increasing passenger demands [20,21]. Integrating sustainable practices into airport infrastructure, such as utilizing eco-friendly materials and incorporating renewable energy sources, aligns with the nation’s broader sustainability goals [22]. The Saudi General Authority of Civil Aviation (GACA) has acknowledged the importance of sustainability in aviation infrastructure and has been promoting green airport initiatives, including improved waste management systems, energy-efficient designs, and the use of recycled construction materials [23]. Saudi Arabia’s airport infrastructure has undergone rapid expansion in recent years, with the country investing heavily in new airport projects and upgrades to existing facilities. However, the current state of aviation infrastructure poses several environmental and logistical challenges. Many airports rely on traditional construction materials, which contribute to high carbon emissions and long-term maintenance costs. Furthermore, the arid climate of KSA presents unique sustainability challenges, such as water scarcity and high energy consumption for cooling systems [24].

Despite these challenges, KSA has made strides in integrating sustainability into its airport planning. The King Abdulaziz International Airport in Jeddah and the King Khalid International Airport in Riyadh have implemented renewable energy projects, smart waste management systems, and water conservation techniques [25]. However, still, the need for a comprehensive, data-driven framework for sustainable airport planning remains critical. Addressing those challenges and integrating mitigation strategies can help in several ways, such as the adoption of eco-friendly construction materials, such as recycled concrete, and energy-efficient building designs, which can significantly reduce carbon emissions [26,27,28]. Moreover, incorporating sustainable water management systems can mitigate the effects of water scarcity in arid regions, ensuring that airport operations remain resilient to environmental stressors [23].

Addressing the complexities of sustainable airport planning requires advanced decision-making methodologies. The integration of fuzzy logic and multi-criteria decision-making (MCDM) techniques offers a sustainable approach to evaluating various sustainability factors [29]. Traditional decision-making methods often struggle with qualitative factors, such as environmental impact and community satisfaction [30]. Fuzzy logic enables decision-makers to incorporate subjective assessments into a structured evaluation framework, thereby improving the reliability of sustainability assessments [31]. MCDM techniques offer structured methodologies for evaluating sustainability alternatives. Also, MCDM allows for the ranking of multiple options based on criteria such as economic feasibility, environmental impact, and social acceptability [32,33]. Furthermore, combining fuzzy logic and MCDM for airport planning can make informed decisions that balance technical, environmental, and economic considerations, ultimately leading to more sustainable airport infrastructure.

The purpose of this paper is to discuss sustainability from the perspective of urban infrastructure, with a focus on conducting a sustainability assessment for the proposed international airport pavement in Tabuk City, Saudi Arabia. In this study, the term “sustainable pavement” refers to pavement systems that are designed, constructed, operated, and maintained to achieve optimal environmental, economic, and social performance throughout their life cycle. A sustainable pavement is a safe, efficient, economic, and environmentally friendly pavement that meets the needs of present-day users without compromising those of future generations. It ensures functional requirements such as durability, structural capacity, and safety while minimizing environmental impacts through the use of recycled or low-carbon materials, energy-efficient construction practices, and reduced greenhouse gas emissions [34,35]. Moreover, sustainable pavements typically exhibit excellent fatigue resistance, which enhances their energy-saving, emission-reduction effects, and economic benefits, making them significantly more advantageous than traditional asphalt pavements over the same analysis period [36]. Based on the discussion of the literature, this study aims to select an eco-friendly pavement for the international airport in Tabuk City. The Saudi Arabian authority encourages the use of eco-friendly solutions to reduce the ecological footprint and minimize the impacts on the environment and human health. Many studies were conducted that focused on and discussed the sustainable planning and development of infrastructure such as airports. However, very few studies assess airport sustainability pavement using a hybrid method that combines the Fuzzy Analytic Network Process (FANP) and the Order of Preference by Similarity to Ideal Solution (TOPSIS) method. By applying this hybrid methodology, the study aims to provide valuable insights into enhancing sustainability within airport infrastructure, particularly in selecting environmentally friendly pavements. It demonstrates the proactive approach of KSA in adopting eco-friendly solutions to reduce negative impacts on the environment and human health. This research contribution represents an important step toward integrating sustainability principles into the design and construction of infrastructure projects. This study makes several important contributions to the field of airport infrastructure sustainability. It proposes a novel hybrid framework that combines the FANP and the TOPSIS method, uniquely integrated with Geographic Information System (GIS) spatial analysis, to assess the sustainability of airport pavements. It demonstrates the application of this framework through a case study conducted at Tabuk Airport in Saudi Arabia, addressing the specific regional challenges related to climate, operational demands, and socio-economic conditions. A comprehensive set of evaluation criteria is developed, including lifecycle cost considerations and community engagement factors, enriching the existing body of research on MCDM in airport planning. The study also introduces a sensitivity analysis to examine the trade-offs between economic and environmental factors, ensuring decision-making resilience. Moreover, the methodology is designed to be adaptable not only to the regional context but also to diverse international settings, making it applicable to a wide range of climatic, operational, and socio-economic environments around the world. Sustainable airport infrastructure is becoming increasingly critical as the aviation sector faces growing environmental, economic, and social pressures. Traditional planning methods often fall short in addressing the complexity and multidimensional nature of sustainability challenges. There is a pressing need for integrated decision-making frameworks that can incorporate uncertainty, multiple criteria, and spatial variability. This research was motivated by the desire to fill this gap by developing a robust, flexible methodology capable of supporting sustainable airport development. By leveraging advanced MCDM techniques, fuzzy logic, and GIS spatial analysis, the study aims to offer practical solutions for both regional and international infrastructure planning. Despite the significant progress in sustainable airport practices, a gap exists in the literature regarding the use of hybrid methodologies that combine the FANP, the TOPSIS, and GIS for evaluating the sustainability of airport pavements. While FANP and TOPSIS have been applied individually in various contexts, studies that integrate these methods to address multiple sustainability criteria, such as environmental impact, economic feasibility, and social acceptability, in the context of airport infrastructure remain sparse. Moreover, few studies have considered the unique regional challenges, such as climate and socio-economic factors, particularly in countries like Saudi Arabia. This research seeks to fill this gap by using the FANP and TOPSIS methods, complemented by GIS spatial analysis, to assess the sustainability of airport pavements in Tabuk City, Saudi Arabia. The aim is to provide a comprehensive and adaptable methodology that can inform sustainable airport infrastructure decisions, while also contributing to the global body of knowledge on airport sustainability.

2. Research Methodology

2.1. Study Site Location and Spatial Data

For this study, a case study was developed to assess the geographical impact of airport site selection optimization in Tabuk City, located in the KSA. This region spans longitudes from 34.6° E to 39.9° E and latitudes from 24.6° N to 29.97° N. The arid region covers an area of 146,072 km² and is home to over 900,000 residents. Tabuk’s urban development is closely tied to the agricultural industry and the NEOM project, making it essential to plan the airport infrastructure carefully to accommodate the anticipated population growth and increasing transportation needs.

During the present study, Quantum Geographic Information System (QGIS) software (version 3.22.14, QGIS.ORG, Laax, Switzerland) will play a crucial role in spatial data analysis and decision-making. As a powerful open-source GIS, QGIS is widely recognized for its versatility in handling various types of spatial data, including both vector and raster formats. This capability allows for comprehensive integration and analysis of different data layers, which is essential for optimizing site selection in this study. QGIS offers a wide range of tools and features that facilitate complex spatial analysis, including data preprocessing, visualization, and modeling. The software supports multiple data sources, allowing researchers to overlay various layers of information, such as land use, topography, environmental constraints, and transportation networks. This ability to combine multiple datasets makes QGIS particularly valuable for evaluating site suitability across a broad spectrum of criteria. Here, QGIS will be used to visualize spatial patterns, calculate relationships between data layers, and create thematic maps that help identify the most appropriate sites. Its integration with fuzzy logic and Fuzzy Analytic Network Process (FANP) method enhances the decision-making process by allowing the analysis of uncertain and imprecise data. Furthermore, QGIS’s user-friendly interface and customizable features provide flexibility, enabling researchers to adapt the software to the specific requirements of the study, thereby improving the overall efficiency and accuracy of site selection. The study employed QGIS software to analyze spatial data and identify the most suitable location for a new airport in the Tabuk region.

2.2. Mathematical Methods

In this study, several mathematical methods have been incorporated to enhance accuracy and manage uncertainties. The first method utilized is the fuzzy set theory (FST), which effectively addresses uncertainties in decision-maker judgments. FST allows for a more nuanced representation of the decision-making process, recognizing that judgments are rarely binary or absolute. The second method employed is FANP, which facilitates the determination of aspect and indicator weights. FANP enables comprehensive analysis by assigning appropriate weights to all relevant factors, ensuring that every aspect is considered when evaluating alternatives. Moreover, TOPSIS is a well-established decision-making technique that compares alternatives against both positive and negative attributes. By evaluating each alternative based on its proximity to an ideal solution, TOPSIS ranks them according to how closely they align with the optimal state. Recent studies have increasingly applied MCDM methods in airport sustainability assessment [2,37,38]. However, few have integrated spatial GIS analysis with both fuzzy weighting methods and crisp ranking techniques. The framework developed in this study addresses this gap by coupling the FANP for weight elicitation under uncertainty with the TOPSIS for deterministic alternative ranking. Compared to other MCDM techniques, the combined use of FANP and TOPSIS presents several advantages. Fuzzy numbers are widely utilized to capture and model the linguistic uncertainty often associated with decision-maker judgments, and it has been demonstrated that FANP can provide more accurate and robust results compared to other fuzzy MCDM methods. TOPSIS offers clear interpretation through the calculation of closeness coefficients relative to the ideal solution and is particularly effective for handling crisp data, including GIS-based data layers. By first applying FANP to derive crisp, uncertainty-adjusted weights and subsequently employing TOPSIS to rank alternatives using deterministic data, the methodology achieves higher precision and reliability in sustainability assessments.

2.2.1. Fuzzy Linguistic Variables

According to Zadeh (1975) [39], a linguistic variable refers to words or sentences from a language (natural or artificial) rather than numbers. Linguistic variables can offer better characterization for complex phenomena that otherwise cannot be accurately described by using traditional quantitative terminology. Therefore, linguistic variables are considered very useful when dealing with circumstances that are described in qualitative expressions. In the context of performance assessment, using the linguistic variable method is a suitable way for decision-makers (DMs) to express their assessments. For example, when DMs want to assign rates for performance criteria, they can use the linguistic scale that suits the assessment situation, such as the widely used scale of five basic fuzzy subsets: Very High (VH), High (H), Moderate (M), Low (L), Very low (VL).

2.2.2. Arithmetical Operations Based on Support Values

Fuzzy number arithmetic is fundamentally based on the extension principle, which provides a systematic approach to generalizing conventional mathematical operations to fuzzy domains. This principle enables operations to be performed on discrete points of fuzzy input numbers to derive the corresponding membership functions for fuzzy output variables. In the proposed model, arithmetic operations are conducted using support values due to their computational efficiency and ease of implementation. This approach enhances the practicality and accessibility of the assessment model for users. By utilizing the support values method, arithmetic operations on two input triangular fuzzy numbers (TFNs) are employed to generate the resulting TFNs in a structured and methodical manner.

2.2.3. Defuzzification

Within fuzzy set theory, expert evaluations are often expressed using linguistic variables. To facilitate their application in rating, weighting, or grading processes, these qualitative expressions must be transformed into numerical values through a process known as defuzzification. Among various defuzzification techniques, the centroid method—also referred to as the center of gravity approach—is widely preferred due to its reliability, computational simplicity, and efficiency. This method ensures a balanced and representative numerical output, making it particularly suitable for practical implementations requiring precise decision-making. Because of these features, the centroid method, Equation (1), is used for defuzzification in the proposed model [40]. Equation (1) calculates a numerical (crisp) value “X” from fuzzy variables “x” with membership functions “µ_c(x)”. The symbol (.) indicates multiplication.

$$X={\displaystyle \frac{\int {\mu }_c\left(x\right)xdx}{\int {\mu }_c\left(x\right)dx}}$$

(1)

2.2.4. Fuzzy Analytic Network Process (FANP)

The Analytic Hierarchy Process (AHP) is a structured decision-making method that simplifies complex problems by breaking them down into a hierarchy of smaller, more manageable components. It uses pairwise comparisons and logical judgments to assign relative importance to each criterion. AHP is especially effective in civil and pavement engineering, where professionals must evaluate alternatives based on multiple, often conflicting factors such as cost, performance, durability, and environmental impact. For example, it can be used to compare pavement materials or design options by assessing both technical data and expert opinions.

To address more complex systems where elements are interrelated, an advanced version of AHP, known as the Analytic Network Process (ANP), was developed. Unlike AHP, ANP does not rely on strict hierarchical structure. Instead, it allows for feedback and interdependence among factors, making it more flexible and realistic in modeling civil engineering problems. In pavement applications, ANP can incorporate the dynamic interactions between traffic loads, climate effects, and material behavior, leading to more accurate and reliable decisions.

Both AHP and ANP offer valuable support in multi-criteria decision-making, helping engineers and planners optimize designs, evaluate risks, select sustainable materials, and prioritize infrastructure investments effectively. ANP boasts a non-linear network structure that is “looser”; ANP permits both intangible and tangible criteria to be included in the decision-making process; ANP provides a real-world view of the problems through the use of clusters; and ANP can include highly complex as well as interdependent relationships. However, ANP still has some disadvantages, such as ignoring the subjectivity of the comparisons. To overcome this and other problems, FANP has been proposed, which combines fuzzy set theory with the ANP method. The FANP computing procedure, based on Chang’s [41] extent analysis approach, involves four steps, as explained below.

In these steps, let X = (x1, x2, …, xn) be an object set and G = (g1, g2, …, gm) be a goal set. According to the method presented in [41] extent analysis, each object is taken, and an extent analysis for each goal, gi, is performed, respectively. Therefore, M extent analysis values for each object can be obtained with the following signs:

$$\begin{array}{cc}{M}_{gi}^1, M_{gi}^2, \dots , M_{gi}^n,& i=1, 2, \dots , n\end{array}$$

(2)

Here, all $M_{gi}^j\left(j=1, 2,\dots ,m\right)$ are TFNs.

The steps in [41,42] extent analysis can be written as follows:

Step 1: The value of fuzzy synthetic extent (S_i) with respect to the ith object is defined as

$$S_i={\sum }_{j=1}^m{M}_{gi}^j⨂{\left[{\sum }_{i=1}^n{\sum }_{j=1}^m{M}_{gi}^j\right]}^{-1}$$

(3)

To obtain ${\sum }_{j=1}^m{M}_{gi}^j$, perform the fuzzy addition operation of m extent analysis values for a particular matrix, such that

$${\sum }_{j=1}^m{M}_{gi}^j=\left({\sum }_{j=1}^m{l}_j,{\sum }_{j=1}^m{m}_j,{\sum }_{j=1}^m{u}_j\right)$$

(4)

To obtain ${\left[{\sum }_{i=1}^n{\sum }_{j=1}^n{M}_{gi}^j\right]}^{-1}$, perform the fuzzy addition operation of $M_{gi}^j\left(j=1, 2,\dots ,m\right)$ values, such that

$${\sum }_{i=1}^n{\sum }_{j=1}^m{M}_{gi}^j=\left({\sum }_{i=1}^n{l}_i,{\sum }_{i=1}^n{m}_i,{\sum }_{i=1}^n{u}_i\right)$$

(5)

Then, compute the inverse of the vector in Equation (5), such that

$${\left[{\sum }_{i=1}^n{\sum }_{j=1}^m{M}_{gi}^j\right]}^{-1}=\left({\displaystyle \frac{1}{{\sum }_{i=1}^n{u}_i}},{\displaystyle \frac{1}{{\sum }_{i=1}^n{m}_i}},{\displaystyle \frac{1}{{\sum }_{i=1}^n{l}_i}}\right)$$

(6)

Step 2: The degree of possibility of M₂ = (l₂, m₂, u₂) ≥ M₁ = (l₁, m₁, u₁) is defined as

$$V\left(M_2\ge M_1\right)=\mathrm{s}\mathrm{u}\mathrm{p}\left[\mathrm{m}\mathrm{i}\mathrm{n}\left({\mu }_{M_1}\left(x\right),{\mu }_{M_2}\left(y\right)\right)\right]$$

(7)

and can be equivalently expressed as Equation (8):

$$V\left(M_2\ge M_1\right)=\mathit{hgt}(M_1\cap M_2)=\left\{\begin{array}{c}1, if m_2\ge m_1\\ 0, if l_1\ge u_2\\ {\displaystyle \frac{l_1-u_2}{\left(m_2-u_2\right)-\left(m_1-l_1\right)}}, otherwise,\end{array}\right.$$

(8)

where d is the ordinate of the highest intersection point D between μ_M1 and μ_M2.

To compare M₁ and M₂, we need the values of both V (M₁ ≥ M₂) and V (M₂ ≥ M₁).

Step 3: The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers M_i (i = 1, 2… k) can be defined by

V(M ≥ M₁, M₂, …M_k) = V[(M ≥ M₁) and (M ≥ M₂) and…(M ≥ M_k)]
= min V(M ≥ M_i), i = 1, 2, …, k

(9)

Assume that

d′(A_i) = minV(S_i ≥ S_k)

(10)

For k = 1, 2, …, n; k ≠ i. The weight vector is then given by

W′ = (d′(A₁), d′(A₂), …d′(A_n))^T,

(11)

where A_i (i = 1, 2, …, n) are n elements.

Step 4: Via normalization, the normalized weight vectors are

W = (d(A₁), d(A₂), …d(A_n))^T

(12)

where W is a nonfuzzy number.

2.2.5. TOPSIS Method

In the preceding sections, we discussed the method of formulating importance weights in evaluation criteria using the fuzzy ANP approach. We explained how the fuzzy ANP helps in ranking alternatives. It is important to note that when dealing with criteria that have limitations in terms of quantity, it is necessary to follow each step of the fuzzy ANP for ranking the alternatives. However, in the context of this work, only the fuzzy ANP method has been used to calculate the relative weights in the evaluation criteria. This was done to ensure that the pairwise comparisons made by decision-makers stay below a certain threshold. Once the relative weights are determined, the TOPSIS method is used to obtain the final ranking results.

TOPSIS comprises the following six sequential steps:

Step 1: Construct the normalized decision matrix, where each value is transformed into a dimensionless form to ensure comparability across different criteria. The normalization process is defined as follows:

$$r_{ij}=x_{ij}\sqrt{{\sum }_{i-1}^m{x}_{ij}^2} \mathrm{i}=1, 2, \dots , \mathrm{m}\mathrm{and} \mathrm{j}=1, 2, \dots , \mathrm{n}.$$

Step 2: Develop the weighted normalized decision matrix by incorporating the assigned weights of the criteria. The weighted normalized value, denoted as ν_ij, is computed as follows:

$${\nu }_{ij}=r_{ij}\times w_j i=1, 2, \dots , \mathrm{m}\mathrm{and} j=1, 2, \dots , \mathrm{n}.$$

(13)

where $w_j$ represents the weight of the $j^{th}$ criterion, satisfying the condition ${\sum }_{j=1}^n{w}_j=1$.

Step 3: Identify the ideal best (A*) and ideal worst (A⁻) solutions by selecting the optimal and least favorable values across all criteria, respectively.

$${{\displaystyle A}}^{*}=\{(\underset{i}{\max }{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_b),(\underset{i}{\min }{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_c)\}=\left\{{{\displaystyle v}}_j^{*}\right|j=1,2,\dots ,m\}$$

(14)

$${{\displaystyle A}}^{-}=\{(\underset{i}{\min }{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_b),(\underset{i}{\max }{{\displaystyle v}}_{ij}|j\in {{\displaystyle C}}_c)\}=\left\{{{\displaystyle v}}_j^{-}\right|j=1,2,\dots ,m\}$$

(15)

Step 4: Compute the separation distances for each alternative using the Euclidean distance in an m-dimensional Euclidean distance. The distance of each alternative from both the ideal best and ideal worst solutions is calculated as follows:

$${{\displaystyle S}}_i^{*}=\sqrt{{\displaystyle \sum _{j=1}^m{({{\displaystyle v}}_{ij}-{{\displaystyle v}}_j^{*})}^2,j=1,2,\dots ,m}}$$

(16)

$${{\displaystyle S}}_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{({{\displaystyle v}}_{ij}-{{\displaystyle v}}_j^{-})}^2,j=1,2,\dots ,m}}$$

(17)

Step 5: Determine the relative closeness of each alternative to the ideal best solution. The closeness coefficient for an alternative A_i with respect to A* is given by

$${{\displaystyle RC}}_i^{*}={\displaystyle \frac{{{\displaystyle S}}_i^{-}}{{{\displaystyle S}}_i^{*}+{{\displaystyle S}}_i^{-}}},i=1,2,\dots ,m$$

(18)

2.3. Sustainability Indicators (SIs)

To evaluate and analyze the best alternatives, a questionnaire survey was conducted to collect data on the key social impacts identified. The survey is designed to target a diverse and representative group of participants, ensuring the collection of a wide range of perspectives and experiences. Participants include engineers involved in infrastructure and environmental projects, residents who are directly impacted by municipal initiatives, decision-makers and officials from Tabuk Municipality responsible for urban planning and service delivery, as well as academicians and researchers who contribute expert knowledge and analytical insights. This broad participation enhances the reliability and depth of the survey results, supporting more informed evaluations and decision-making processes. The data gathered is essential for quantifying and analyzing the significance of each SI, helping to guide subsequent analysis and decision-making. To further explore the relationships between the selected SIs, expert interviews are conducted. These interviews provide qualitative insights that deepen the understanding of the interactions between social factors.

Engaging with experts helps reveal hidden connections, dependencies, and complexities that are not always evident from quantitative data alone. Fuzzy Cognitive Mapping (FCM) is then employed to model and analyze the interconnections between the SIs. This method allows for the visualization of complex and dynamic relationships, considering uncertainties and interdependencies that exist in real-world social systems. FCM enables the identification of feedback loops, causal relationships, and emergent patterns, providing a comprehensive view of the system under study. GIS are integrated into the methodology to incorporate spatial data, such as demographic patterns, land use, and regional characteristics, further enhancing the analysis of social impacts. GIS enables researchers to assess how geographical factors influence the distribution and intensity of SIs, providing an additional layer of spatial understanding to the evaluation process. Finally, the developed FCM model is validated through a real-world case study, applying the methodology to assess sustainability in infrastructure projects. This practical application demonstrates the model’s effectiveness and relevance in addressing social impact issues, particularly in complex and dynamic environments. The results of the case study provide valuable evidence of the applicability and robustness of the FCM-GIS model in conducting sustainability assessments for infrastructure projects.

Our sustainable model for selecting the optimal concrete is structured into three hierarchical levels, each representing a critical phase of the decision-making process. Level 1 involves problem definition, where the objective selecting the most sustainable concrete is clearly stated. In this phase, all possible alternatives (Ai), representing different types of concrete mixtures, are identified alongside the criteria (Ci), which include factors such as environmental impact, cost, durability, lifecycle emissions, and resource efficiency. Defining these elements ensures that the assessment is comprehensive and aligned with sustainability goals.

Level 2 focuses on establishing the pairwise comparison matrix. Each criterion is compared against others to determine its relative importance using expert judgment or stakeholder input. Based on these comparisons, weights are calculated for each criterion. This stage is crucial because it ensures that the evaluation reflects real-world priorities, acknowledging that not all sustainability factors carry equal importance.

Level 3 entails the evaluation of alternatives. Using the previously determined weights, each alternative is assessed against the criteria. Mathematical tools such as FANP and TOPSIS are used to aggregate the evaluations and calculate an overall sustainability score for each alternative. The concrete type with the highest score is ultimately selected as the optimal sustainable choice.

The complete assessment process is organized into eight structured steps, leading systematically from initial problem framing to final decision-making. Figure 2 provides a visual summary of the Sustainability Assessment Model, illustrating how each phase and step logically connects to guide the user toward selecting the most sustainable concrete alternative.

Various definitions for sustainability assessment exist in the literature. Devuyst, Hens [43] defined sustainability assessment as “a tool that can help decision-makers and policy-makers decide what actions they should take and should not take in an attempt to make society more sustainable”. Different methodologies can be found for sustainability assessment, and the indicator approach is perhaps the most promising in terms of transparency, consistency over time, and usefulness in the decision-making process. In this study, to identify sustainability indicators that are utilized by industry, a list of sustainability indicators was derived from an extensive literature review of published materials in academic and practitioner journals. For sustainability indicators four aspects of sustainability can be divided according to their indicators. These four aspects are (1) economic aspect, (2) technical aspect, (3) environmental aspect, and (4) social aspect.

Table 1. Sustainability indicators for this study.

Aspect	Indicator	Measurement Unit	Description/Example of Measurement
Economic	C1: Capital Cost	Monetary unit (SAR)	Total initial investment required for project implementation, including materials, labor, and equipment.
	C2: Benefits	QT (Quantitative)	Net present value (NPV), return on investment (ROI), internal rate of return (IRR); measured using SAR or percentage over project lifetime.
	C3: Affordability	QT (Quantitative)	Cost-to-income ratio (%), payback period (years), or affordability index based on local standards.
Technical	C4: Performance	Young’s Modulus (kgf/cm2)	Material stiffness or mechanical strength, obtained via laboratory tests (e.g., ASTM standards).
	C5: Flexibility	QL	Ability of system/material to adapt to different conditions or demands; rated on a qualitative scale (e.g., Low–Medium–High).
Environmental	C6: GHG Emissions	T CO2 eq (tons of CO2 equivalent)	Total greenhouse gas emissions across project lifecycle (cradle-to-grave), including production, transport, installation, maintenance, and disposal. GHG emissions were evaluated based on secondary data from published literature and environmental impact assessments (EIA), and were estimated qualitatively rather than quantitatively using LCA databases. A full life cycle assessment (LCA) was not performed, and this approach is acknowledged as a limitation. Future studies should aim to conduct a detailed LCA for more precise quantification.
	C7: Ecological Impacts	Qualitative (QL)	Assessment of biodiversity disturbance, soil and water contamination; based on EIA or scoring matrix.
Social	C8: Safety and Security	QL	Evaluation of risk to workers and users; compliance with safety regulations and security protocols; assessed via incident rates, checklists.
	C9: Community Engagement	QL	Level of public participation, acceptance, and feedback; measured by surveys, number of meetings held, stakeholder satisfaction index.

charts these indicators.

2.4. Developing Alternatives

The objective of assigning sustainability indicator ratings to various alternatives is to evaluate each option using the indicators selected during the assessment phase. Quantitative indicators such as energy use, greenhouse gas emissions, or raw material consumption can be addressed using conventional engineering calculations. However, for qualitative indicators, such as esthetics, public perception, or ease of implementation, traditional methods may fall short. To address this, fuzzy logic is applied to translate qualitative judgments into numerical values. This transformation is achieved using linguistic variables and membership functions, which enable subjective criteria to be evaluated systematically. For instance, terms like “low”, “moderate”, and “high” are assigned values using predefined fuzzy scales. These linguistic variables and their associated membership functions are outlined in

Table 2. Linguistic scale for rating of project alternatives.

Linguistic Set	Fuzzy Number
Very poor (VP)	(0, 0, 0.25)
Poor (P)	(0, 0.25, 0.5)
Moderate (M)	(0.25, 0.5, 0.75)
Well (W)	(0.5, 0.75, 1.0)
Very well (VW)	(0.75, 1.0, 1.0)

, which serves as a guide for decision-makers to rate qualitative indicators consistently. In civil and pavement engineering, this methodology is particularly valuable when evaluating the sustainability of alternative materials or design solutions. For example, the long-term durability or environmental impact of a pavement design can be assessed more effectively by combining both quantitative data and qualitative insights through the fuzzy approach. This leads to more balanced, transparent, and informed infrastructure decisions. The proposed alternatives are summarized in

Table 3. Proposed project alternatives.

Alternative	Description	Key Features	Rationale for Selection
A1	Pavement with Natural Materials (Construction Strategy 1)	The sub-base layer uses locally sourced natural materials, and the pavement layer is designed for a lifespan of 20 years. It focuses on minimal environmental impact by using sustainable, abundant materials.	Sustainability: Utilizes natural, locally available materials to reduce the carbon footprint associated with transportation. Durability: The 20-year lifespan ensures long-term performance, reducing the need for frequent repairs or replacements, which contributes to cost-effectiveness and resource efficiency over time.
A2	Pavement with Natural Materials (Construction Strategy 2)	Similar to A1, but the pavement layer is designed for a shorter lifespan of 10 years. It still uses natural materials for the sub-base layer, but with a focus on more cost-effective construction and maintenance.	Cost-Efficiency: The 10-year lifespan reduces initial investment costs, making it a more budget-friendly option. Sustainability: Like A1, it uses locally sourced natural materials, though the shorter lifespan means more frequent maintenance and potential material replacements, which could affect long-term sustainability.
A3	Pavement with Recycled Materials (Construction Strategy 1)	Incorporates recycled materials (e.g., crushed concrete or asphalt) in the sub-base layer, with a pavement layer designed for 20 years. This approach helps divert waste from landfills while promoting the reuse of materials.	Environmental Impact: Reduces waste and the consumption of virgin materials, contributing to a circular economy. Durability and Sustainability: The 20-year design ensures long-term performance while using recycled materials, which can significantly reduce the environmental footprint of construction [44].
A4	Pavement with Recycled Materials (Construction Strategy 2)	Uses recycled materials in the sub-base layer, but with a pavement layer designed for a 10-year lifespan. This offers a shorter-term solution, balancing sustainability with budget considerations.	Cost-Effectiveness and Sustainability: Like A3, it reduces waste and uses recycled materials, but with a shorter lifespan, it is a more affordable option. This strategy can be particularly effective in regions where frequent repaving is acceptable or when short-term durability is prioritized.

. Proposed alternatives, which outline four pavement options based on construction strategies and material types. These include pavements utilizing natural or recycled materials in the sub-base layer, designed for either 10- or 20-year lifespans, highlighting key features and sustainability considerations for each alternative.

3. Results and Discussion

3.1. Outcomes of Spatial Analysis

The QGIS software used in this study for spatial analysis facilitated the integration and analysis of key geospatial datasets, such as land use, topography, transportation networks, population density, and environmental factors. Advanced QGIS tools, including overlay analysis, proximity assessments, and suitability mapping, enabled a systematic evaluation of potential sites based on predefined criteria. This comprehensive approach ensured that technical, social, and environmental considerations were effectively incorporated into the decision-making process. Furthermore, with QGIS’s robust analytical framework, this study provided a data-driven methodology to select the optimal location for the proposed airport infrastructure. Figure 3 illustrates the various spatial factors analyzed, including land use, elevation distribution, geological composition, infrastructure networks, hydrological features, and sustainable site selection criteria.

The buffer zone analysis (Figure 3a) helped determine suitable distances from urban areas, ensuring accessibility while minimizing potential environmental and social disruptions. This analysis was instrumental in delineating areas with minimal conflict between residential zones and airport operations. Furthermore, the elevation distribution map (Figure 3b) revealed that areas with relatively stable topography were preferable for construction, as excessive elevation variations could lead to higher earthwork costs and engineering challenges. Geological assessments (Figure 3c) provided insights into soil composition, identifying regions with stable substrata that can support large-scale infrastructure. The land cover classification (Figure 3d) was critical in evaluating the environmental impact, ensuring that the selected site avoided ecologically sensitive zones such as agricultural lands and natural reserves. Infrastructure proximity analysis (Figure 3e) assessed the accessibility of the proposed airport location to existing road networks and essential services, ensuring logistical efficiency.

Hydrological analysis (Figure 3f) examined major watercourses in Tabuk City, mitigating flood risks and ensuring sustainable water resource management. By synthesizing these geospatial insights, the study identified an optimal site that balances technical feasibility, environmental sustainability, and socio-economic considerations, which is illustrated in Figure 3g. This integrative approach underscores the value of GIS-based analysis in strategic infrastructure planning, reinforcing the importance of data-driven decision-making in sustainable airport development.

This study further evaluated land use dynamics to ensure that the chosen site aligns with long-term urban planning objectives. The analysis of historical satellite imagery and current zoning regulations helped identify areas undergoing significant transformation, allowing for better prediction of future land use conflicts. The integration of population density data provided a comprehensive understanding of potential airport users, guiding the placement of airport facilities to maximize accessibility while avoiding high-impact residential areas. A key aspect of this research was the multi-criteria decision analysis framework embedded within QGIS, which allowed for an objective ranking of potential sites. This framework accounted for environmental constraints, economic viability, and social acceptability, ensuring a balanced assessment. The results demonstrated that GIS-based decision-making enhances transparency and efficiency in infrastructure planning by systematically evaluating diverse spatial parameters. The methodological rigor employed in this study also ensures replicability in other regions considering airport development. By applying a standardized GIS-based approach, decision-makers in different geographical contexts can adapt the findings to their specific requirements. The study’s emphasis on sustainability aligns with global trends in airport site selection, where reducing environmental impact and optimizing resource allocation are paramount.

One of the most significant contributions of this research is its demonstration of how spatial data can bridge the gap between technical feasibility and social considerations. Traditional site selection methodologies often prioritize economic and engineering aspects while overlooking community concerns and environmental sustainability. By leveraging GIS capabilities, this study presents a holistic approach that integrates stakeholder interests, making the planning process more inclusive and equitable. The study also highlights the limitations of relying solely on conventional site selection criteria, which may not capture the dynamic nature of urban expansion and environmental changes. The ability of QGIS to incorporate real-time data allows for continuous updates to site suitability assessments, ensuring long-term relevance. This adaptability is crucial in regions experiencing rapid development, where static planning approaches may become obsolete within a few years. The findings of this study provide a foundation for policy-makers to make informed decisions regarding airport development, minimizing conflicts with existing land use and optimizing resource efficiency. By aligning spatial planning with sustainability principles, this research contributes to a broader discourse on responsible infrastructure development, emphasizing the need for integrated and forward-thinking solutions. The application of QGIS in this study exemplifies the transformative potential of GIS technology in infrastructure planning. The ability to analyze complex spatial relationships systematically ensures that decision-makers can identify the most suitable sites while accounting for environmental, social, and economic factors. This research serves as a model for future studies seeking to apply GIS methodologies to similar large-scale projects, reinforcing the critical role of spatial analysis in sustainable development.

3.2. Evaluating Qualitative Indicators of Each Alternative

The numerical rates for the qualitative indicators of the four alternatives, as outlined in

Table 4. Numerical rates for the qualitative indicators of each alternative.

	A1	A2	A3	A4
Affordability (C3)	0.25	0.4	0.6	0.8
Flexibility (C5)	0.28	0.63	0.28	0.63
Ecological impacts (C7)	0.0	0.2	0.5	0.7
Personal safety/security (C8)	0.4	0.5	0.7	0.8
Community engagement (C9)	0.25	0.25	0.5	0.5

, highlight key differences in affordability, flexibility, ecological impacts, personal safety/security, and community engagement. These indicators provide a multi-faceted evaluation of each alternative, enabling a comprehensive assessment of their relative merits and drawbacks. For affordability (C3), alternatives A3 and A4, which utilize recycled materials, exhibit higher scores (0.6 and 0.8, respectively) compared to A1 and A2, reflecting the cost-effectiveness of incorporating recycled materials. This aligns with the growing emphasis on sustainable and budget-conscious infrastructure development. In terms of flexibility (C5), A2 and A4 demonstrate superior ratings (0.63), suggesting their potential adaptability to changing conditions, such as maintenance needs or usage patterns. This makes them attractive for long-term sustainability planning.

Regarding ecological impacts (C7), alternatives with recycled materials (A3 and A4) outperform those with natural materials, achieving scores of 0.5 and 0.7, respectively. These results highlight the environmental benefits of using recycled materials, such as reduced resource depletion and waste. For personal safety/security (C8), A3 and A4 lead with higher scores (0.7 and 0.8), indicating a stronger emphasis on user safety. Finally, community engagement (C9) is equal for alternatives A1 and A2 (0.25) and for A3 and A4 (0.5), showing that incorporating recycled materials may foster greater community support. These results underscore the overall advantages of recycled materials, particularly in affordability, ecological impact, and community engagement.

Other characteristics of each alternative are given in the Supplementary Materials. To effectively integrate sustainability indicator values into the assessment process, it is necessary to normalize the quantitative indicators. Normalization adjusts the range of values so they can be compared on a common scale, especially when indicators differ in units or magnitudes. This step is crucial in decision-making models, as it prevents any single criterion from disproportionately influencing the results. Within pavement design or civil infrastructure projects, normalization supports fair evaluation of alternatives across economic, environmental, and social dimensions, contributing to more balanced and sustainable engineering decisions. The normalized values of the quantitative indicators, presented in

Table 5. Normalized indicator values.

Indicator	Alternatives
		A1	A2	A3	A4
Total Capital cost	C1	0.0	0.47	0.54	1.0
Benefits	C2	0.2	0.4	0.7	1.0
Affordability	C3	0.25	0.4	0.6	0.8
Performance	C4	1.0	1.0	0.0	0.0
Flexibility	C5	0.28	0.63	0.28	0.63
GHG	C6	0.0	0.89	0.11	1.0
Ecological Impacts	C7	0.0	0.2	0.5	0.7
Safety and Security	C8	0.4	0.5	0.7	0.8
Community engagement	C9	0.25	0.25	0.5	0.5

, offer a comprehensive assessment of the sustainability performance of each alternative (A1–A4). These values enable direct comparison across various criteria, with local weights for the sustainability aspects and indicators calculated in the Supplementary Materials. In terms of total capital cost (C1), A4 exhibits the highest value (1.0), indicating the highest cost, while A1 scores the lowest (0.0), representing minimal initial investment. This contrast highlights significant cost differences among the alternatives. Similarly, for benefits (C2), A4 provides the highest value (1.0), followed by A3 (0.7), A2 (0.4), and A1 (0.2), suggesting that alternatives incorporating recycled materials deliver greater overall benefits. Affordability (C3) results show A4 as the most affordable option (0.8), aligning with its high benefit score, whereas A1 ranks the lowest (0.25). However, when considering performance (C4), A1 and A2 achieve the highest scores (1.0), underscoring their reliability, while A3 and A4 score zero. For flexibility (C5), A2 and A4 perform equally well (0.63), reflecting their adaptability to varying conditions. Regarding GHG emissions (C6) and ecological impacts (C7), A4 again stands out with the highest values, emphasizing its environmental advantages. Community engagement (C9) is consistent across material types, with recycled material alternatives (A3 and A4) scoring higher (0.5) than natural material alternatives (A1 and A2, scoring 0.25). Overall, A4 emerges as the most sustainable alternative, excelling in affordability, benefits, environmental performance, and community engagement, while A1 is the least favorable due to its lower performance in these areas.

3.3. Evaluating Relative Closeness Values

Achieving environmental sustainability in aviation infrastructure increasingly relies on the use of recycled and eco-friendly materials. Airport pavements contribute to environmental pollution by emitting greenhouse gases, volatile organic compounds, odors, and dust during their production and maintenance. As a result, airport decision-makers face growing pressure to select pavement design and rehabilitation strategies that not only meet performance standards but also align with environmental, economic, and social priorities. Given the wide range of available materials, technologies, and design approaches, evaluating these alternatives can become complex and challenging. This complexity often makes it difficult to make consistent and well-balanced decisions across all aspects of airport infrastructure projects. To address this, various decision-support models and mathematical tools have been developed to analyze and compare competing options. Among the most widely applied methods in civil engineering is TOPSIS. This method helps decision-makers rank alternatives by measuring their relative closeness to an ideal solution. In the context of airport pavements, TOPSIS supports the evaluation of sustainable design choices by integrating technical, environmental, and economic criteria, making it a valuable tool for infrastructure planning and sustainability assessment. In general, TOPSIS is used to optimize complex multi-criteria decision analysis systems [45]. The TOPSIS method is based on the principle that the optimal point should be the closest to the positive ideal solution and the furthest from the negative ideal solution. In this section, the TOPSIS method is employed to determine the best alternative. The weights are determined, with the values of the indicators being multiplied by weights. Each alternative (A1, A2, A3, A4) is evaluated according to various criteria functions (C1, C2, C3, C4, C5, C6, C7, C8, C9), and the ranking is carried out by comparing the closest alternative to the ideal solution, as shown in

Table 6. Relative closeness values.

Alternative	A1	A2	A3	A4
E+	0.51	0.27	0.45	0.28
E−	0.25	0.43	0.20	0.51
E−/(E− + E+)	0.33	0.61	0.31	0.65

and Figure 4. Also, both the ideal and negative solutions are determined. Using the same method, the negative solutions are calculated. The separation from the ideal and negative solutions is calculated (Supplementary Materials), and the relative closeness to the ideal solution is also calculated. Based on the outcomes of the TOPSIS analysis, it can be seen that the relative closeness value of alternative A4 exhibited the highest value of 0.65, which indicates the best option for adoption in the construction of airport pavement in Tabuk City. A study conducted by AlKheder, AlKandari [4] found similar findings, where they performed FAHP and stated that recycled asphalt material is a superior choice in terms of maintenance, construction simplicity, sustainability, as well as cost saving.

4. Discussion and Limitations

In Saudi Arabia, there is a profound commitment from both the government and private sector to tackle climate change through sustainable development practices. This commitment is prominently reflected in the Saudi Vision 2030 plan, which prioritizes mitigating the environmental impacts of industrial activities and emphasizes sustainability as a cornerstone of national policy. Against this backdrop, the construction of the new airport in Tabuk presents a critical opportunity to apply innovative approaches that align with these sustainability goals. Our study thoroughly evaluated a range of alternatives for the new airport’s construction, focusing on minimizing environmental impacts associated with heavy construction and long-term industrial use. By employing a comprehensive methodological framework that includes FST, FANP, and TOPSIS, this study systematically assessed the sustainability of each alternative (Figure 5). These mathematical tools provided a nuanced analysis of the alternatives based on multiple criteria, ensuring a robust evaluation process. The integration of QGIS played a pivotal role in enhancing the analysis throughout the study. QGIS enabled the incorporation of spatial data into the evaluation process, allowing for a more comprehensive understanding of various factors such as land use patterns, proximity to environmentally sensitive areas, and resource distribution. This integration not only improved the accuracy and efficiency of the site selection process but also contributed to more informed and sustainable decision-making, ensuring that the selected sites align with the broader environmental and developmental objectives. The use of QGIS proved invaluable in visualizing complex spatial relationships and patterns, ultimately guiding the study towards optimal outcomes.

The outcomes demonstrate that utilizing recycled materials significantly enhances environmental sustainability while maintaining cost efficiency and long-term durability. The integration of QGIS further strengthened the analysis by providing spatial insights into key environmental and infrastructural factors, allowing for a more holistic approach to decision-making. Furthermore, the study contributes to the growing body of research on sustainable infrastructure development by illustrating how multi-criteria decision-making techniques can be applied in real-world scenarios. The combination of GIS and fuzzy-based decision frameworks allows for better-informed policy and planning decisions, particularly in regions where balancing sustainability and economic feasibility is crucial. Despite its contributions, this study has certain limitations. First, the assessment is limited to a specific case study in Tabuk, Saudi Arabia, which may not fully generalize to other regions with different climatic, economic, and infrastructural conditions. Second, while the FANP and TOPSIS methods provide robust decision-support capabilities, they still rely on expert judgment, which introduces a degree of subjectivity in the weighting process. Future studies could enhance the objectivity of the assessment by integrating machine learning algorithms to refine the criteria weighting process.

Future research should extend the application of the proposed framework to other regions within Saudi Arabia and internationally, particularly in areas experiencing rapid infrastructure expansion. Comparative studies across different climatic and socio-economic contexts would validate the model’s broader applicability. Real-time environmental monitoring and remote sensing data integration with GIS platforms could significantly enhance the accuracy and responsiveness of evaluations. Integrating machine learning algorithms to refine the criteria weighting process would help reduce subjectivity and improve decision robustness. Future work should emphasize stronger stakeholder engagement, including community input and regulatory feedback, to ensure that sustainability goals are achieved both technically and socially. Expanding the scope to assess lifecycle impacts during construction and operation phases would further support comprehensive sustainable development strategies aligned with national and international objectives.

5. Conclusions

This study successfully achieves its primary goal of developing and applying a novel, integrated decision-making framework to evaluate sustainable airport pavement alternatives. The proposed methodology brings together fuzzy set theory, FANP, TOPSIS, and GIS-based spatial analysis into a cohesive structure—an innovative hybrid model that enhances the accuracy, contextual relevance, and multidimensional analysis of infrastructure alternatives. This integration represents the main novelty of the research, offering a methodological advancement over conventional decision-making approaches in the field of pavement sustainability assessment.

By applying the framework to the case of the new Tabuk airport in Saudi Arabia, the study demonstrates the practical value of the approach in a real-world setting. Among the evaluated alternatives, alternative A4, which incorporates recycled construction materials, was identified as the most environmentally sustainable solution. This result underscores the framework’s ability to highlight resource-efficient and climate-conscious choices in infrastructure design. The methodology’s strength lies not only in its technical robustness but also in its sensitivity to site-specific factors, captured through GIS-based spatial analysis, which ensured that the evaluation was grounded in the environmental realities of the Tabuk region.

One of the study’s key contributions is the identification of “Performance” (criterion C4) as a critical driver of sustainability. While alternatives A2 and A3 also displayed favorable environmental and technical characteristics, A4 excelled in both short- and long-term indicators, particularly in reducing greenhouse gas emissions, conserving natural resources, and aligning with circular economy principles. This confirms the importance of adopting an integrated perspective when evaluating pavement solutions, going beyond initial costs and considering lifecycle impacts.

In terms of broader implications, the study contributes to Saudi Arabia’s environmental objectives and Vision 2030 by supporting sustainable infrastructure development. The recommendation to implement alternative A4 offers a solution that is not only ecologically sound but also economically viable over the long term. This option has the potential to reduce lifecycle costs, enhance operational efficiency, and serve as a model for sustainable airport construction in arid environments.

Future research should focus on real-world pilot implementations to validate the modeled outcomes and monitor long-term performance under operational conditions. The integration of real-time monitoring systems and predictive maintenance tools into the framework could provide dynamic feedback loops, further enhancing infrastructure resilience and sustainability.

To extend the applicability of this framework, future studies should apply it to other airports located in diverse climatic zones and socio-economic contexts. The flexibility of the proposed methodology allows it to be tailored to various geographic and environmental settings from desert regions to tropical and temperate zones. By incorporating local stakeholder input and aligning with region-specific environmental and policy priorities, the framework can serve as a valuable tool for promoting climate-resilient, sustainable airport infrastructure globally.