Developing a Strategic Sustainability Assessment Methodology for Free Zones Using the Analytical Hierarchy Process Approach

Abstract

The application of sustainability within free zones can be considered a means to enhancing their competitiveness. Free zones with a high level of sustainability are more likely to attract investment and other kinds of support, while meeting global obligations with regard to sustainable development. Accordingly, adopting sustainability assessment tools is crucial for measuring the sustainability effectiveness of free zones. In this context, this study addresses the sustainability assessment of free zones using the analytical hierarchy process (AHP) decision tool. The first contribution of this article is proposing a novel model for the strategic sustainability assessment in free zones, which considers all dimensions of sustainability. The second contribution is deriving the weights and priorities of the related model using the AHP method. The results demonstrate that the economic dimension is considered the most regarded sustainability pillar, with a percentage of 41.81%, followed by the environmental pillar at 24.97%; then, the social pillar at 22.26%, and finally, the organizational pillar at 10.96%. Although this study addressed sustainability assessment indicators, it did not delve into the individual components deeply, which may open opportunities to direct future research toward developing other sustainability assessment models in the context of free zones.

1. Introduction

Recently, free zones have received significant attention across the world. In 2018, the number of free zones reached nearly 2300, accounting for approximately 42.6% of all special economic zones [1]. Such a significant increase comes with international competition between countries to attract foreign investments for achieving national objectives, such as enhancing economic development and raising local industrial capabilities. However, free zones face considerable contemporary challenges, including the emerging industrial revolution and sustainable development [1].

In 2019, global annual carbon dioxide emissions reached about 33 gigatons [2]. In addition, maritime transportation generates 940 million tons of carbon dioxide yearly, representing 2.5% of global GHG emissions annually, referring to IMO in 2019 [3]. As such, in the past few decades, the sustainability agenda has placed increasing pressure on stakeholders along the supply chain, including companies and free zones, to demonstrate their commitment towards sustainability. However, such pressures could be competitive opportunities for free zones toward attracting investments that have sustainability obligations. In this respect, sustainability can play a vital role in uplifting free zone competitiveness [4,5,6,7]. Referring to UNCTAD [8], a free zone with a high sustainability level will be more attractive to investors and more likely to receive various sorts of support from organizations, governments, or the public.

Globally, there has been significant growth in free zones. They are usually established by nations to promote trade, industry, and economic development. Notably, the increasing growth of free zones can result in both positive and negative economic, environmental, and social effects. Accordingly, tools and methods need to be developed to ascertain the performance of free zones from a sustainability perspective. Despite the recent growing academic interest in sustainability, there is a significant research gap in proposing novel models to assess sustainability in the context of free zones effectively. Practically, there is a lack of empirical research addressing the effectiveness of sustainability assessment methods in free zones. While there are many tools for sustainability assessment, there is limited research on how such methods work in practice and whether they are effective in identifying and addressing sustainability issues in free zones. Hence, there is a requirement to bridge such gaps in theory and practice by using multidisciplinary decision-making approaches. The approach considered in this study gathers experts’ opinions from academia, governments, and industry, including environmental, economic, social, and organizational fields. In this regard, the article develops a strategic sustainability assessment methodology for free zones using the analytical hierarchy process (AHP) decision tool.

The AHP method is considered an effective practical tool for multidimensional decision-making [9]. It is a commonly effective method to assess sustainability in general and can be utilized within the assessment of free zones [10,11,12,13]. Furthermore, the AHP can facilitate the analysis of concepts hierarchically and combine quantitative and qualitative judgments, with the objective of prioritizing objectives, and in this context, the various dimensions of sustainability.

This study is distinguished from its predecessors by two main contributions. First, proposing a novel and more comprehensive strategic assessment framework for free zones that aggregates sustainability indicators into four organized clusters: organizational, social, environmental, and economic indicators. Second, identifying the perceived most important sustainability indicators in accordance with multicriteria decision-making implemented using the AHP. In this context, this article aims to achieve three objectives. First, to examine the sustainability indicators of free zones from the literature that could be utilized in an assessment. Second, to suggest a novel model for sustainability assessment to free zones. Third, to apply the AHP approach to the proposed model to examine the sustainability indicators that are perceived to be more important and influential, and to identify the weights of such importance.

2. Literature Review

2.1. Free Zones Overview

The concept of free zones dates back more than 2000 years [14] to when ships transported goods for import and export with little to no intervention from local governments [1]. Although the concept “free zones” is not particularly new, its application in modern global trade is considered a recent proposal [14]. The concept of “freedom” indicates a set of administrative, business, and financial benefits that countries offer to free zones executively and do not apply to other types of zones existing in the same local economy [15,16]. In 2019, the United Nations Conference on Trade and Development (UNCTAD) published a detailed classification of economic zones in its annual report. In this official definition, the term “special economic zone” was used to refer to all economic zones, including free zones. Referring to UNCTAD [1], special economic zones (SEZs) are “geographically delimited areas within which governments facilitate industrial activity through fiscal and regulatory incentives and infrastructure support” [1]. In addition, UNCTAD defined free zones as “essentially separate customs territories, in addition to relief from duties and tariffs, most zones also offer fiscal incentives, business-friendly regulations with respect to land access, permits, and licenses, or employment rules, and administrative streamlining and facilitation” [1]. Furthermore, the World Free Zone Organization (WFZO) suggested another definition of free zones that included additional economic activities and services. Referring to WFZO, “a Free Zone is an area designated by one or more government(s) where economic activities, whether production or trade, physical or virtual with respect to goods, services or both, are permitted and relieved (totally or partially) from customs duties, taxes, fees or with specific regulatory requirements that would otherwise apply” [17].

In general, economic zones can manifest in a variety of forms. Referring to the World Bank [18], there are six basic types of economic zones as follows: First, freeports, which typically cover large areas. They usually include a broad range of activities, such as retail sales and tourism, and enjoy considerable incentives and benefits [18]. Secondly, free trade zones (FTZs). Most free trade zones were created in developing countries and were mostly used for export- and import-related activities [19]. A FTZs’ primary objective is to facilitate imports of foreign goods by reducing regular customs rules [20,21] Additionally, free trade zones are usually located inside or near ports, airports, border regions, or along main transit axes such as rail, road, and sea [22]. Thirdly, export processing zones or industrial estates are “fenced-in industrial estates specializing in manufacturing for export and offering their resident firms free-trade conditions and a liberal regulatory environment” [23]. Activities in export processing zones are generally focused on foreign markets [1]. Fourth, enterprise zones. They are economic zones that offer a combination of tax breaks and privileged financial support to help struggling urban and rural areas [18,24]. Fifth, single factory exporting processing zones. These types of zones provide a number of incentives to businesses regardless of where they are located [18]. Sixth, hybrid logistic zones (HLZs). To enhance competitiveness, the European Union suggests hybrid logistic zones. HLZs are defined as “a delimited, usually fenced-in area, where a grouping of activities and companies dealing with manufacturing, trade (mostly export), freight distribution, transportation, logistics, and supporting services, is promoted through granting of free-trade conditions, liberal regulatory environment and various fiscal and financial incentives” [24]. Seventh, special economic zones. Special economic zones or (SEZs) is a general terminology [18,22], including the most recent evolutions in traditional zones, including free trade zones, export processing zones, industrial parks, and free zones [1,22]. The SEZs include various economic activities, such as green and smart zones, logistics parks, petrochemical zones, and science and technology parks [18]. According to the United Nations Conference on Trade and Development (UNCTAD) [1], special economic zones can be described as “geographically delimited areas within which governments facilitate industrial activity through fiscal and regulatory incentives and infrastructure support”. The free zone is the most famous type of special economic zone [22]. In addition, special economic zones emanated their concept from the free zone by providing incentives and decreasing tariffs, taxes, and bureaucracy [1]. Recent SEZ models create new supportive zones to meet emerging industries’ growing needs, such as sustainable development, high technology, finance or tourism, and scientific marketing.

Usually, free zones compete to attract investments. Due to the recent steadily increasing environmental and social awareness, there has recently been a noticeable interest in sustainability in general, particularly in Free Zones. In addition, many obligations related to sustainability are being imposed on investments and all other stakeholders across the whole supply chain. In this regard, sustainability assessment could play a vital role in attracting investments. Free zones with a high level of sustainability may be more attractive to investors and are more likely to obtain other types of support from governments, organizations, or the public (UNCTAD). Therefore, it is necessary to understand and evaluate the various sustainability aspects that may influence the overall sustainability performance of free zones as detailed in the following sections and includes the motivation for sustainability, representative indicators, and evaluation tools.

2.2. Free Zones Sustainability

Free zones generally strive to attract investments and benefit from direct and indirect economic advantages. To draw investors, free zones attempt to boost operational effectiveness and offer better services and incentives. In this sense, assessing sustainability may be crucial for both investors and free zones. For investors, sustainability assessment provides a parameter that may help them choose the best free zone in line with their business objectives. Regarding the free zones, sustainability indicators may be a helpful tool for assessing performance, including determining the extent to which plans are being achieved, benchmarking performance with competitors, and evaluating regulatory compliance. By adopting sustainability, free zones boost their competitiveness capability [4,5,6,7]. High-sustainability free zones could be more attractive to investors and more likely to attract other sorts of support from governments, organizations, or society [8].

Sustainability is defined as “ensuring that development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [25]. From a practical point of view, sustainability may be characterized as the necessary strategies and actions that consider safeguarding and sustaining natural and human resources to meet the objectives of both free zones and stakeholders [26,27]. According to Dekker et al. [28], sustainability is the coordination of three goals: maximizing economic output, minimizing harmful environmental effects, and maximizing community social benefits. In this context, the organizational aspect was considered as the fourth pillar of sustainability [4,29,30]. Furthermore, the United Nations Conference on Sustainable Development (UNCSD) included the organizational aspect in its proposed framework as the fourth pillar of sustainability [31]. To be sustainable, free zones should strike a balance among the three sustainability pillars of environmental protection, social welfare, and economic development [32,33]. Focusing on one pillar without considering the other sustainability pillars does not achieve sustainable development [34,35]. While some scholars have suggested that certain sustainability pillars are more crucial than others [13], the majority have emphasized the need of maintaining a balance across all sustainability pillars [36]. Additionally, it has been noted that organizational sustainability is essential for striking a balance among different free zone sustainability aspects [35], enhancing performance, and boosting competitiveness [37,38].

2.3. Free Zone Sustainability Assessment Indicators

In general, sustainability in free zones seeks to minimize environmental impacts and maximize both social welfare and economic benefits [1]. This section details indicators that could be used as part of the sustainability assessment indicators, including economic, environmental, social, and organizational pillars.

2.3.1. Economic Sustainability Assessment Indicators

In the economic aspect of sustainability for free zones, there are a set of proposed indicators as follows: the capital investment indicator [12,13,31,39,40,41], foreign direct investment indicator [4,13,29,31,39,40], revenue increase indicator [4,13,29,33,35,40,42,43], capacity indicator [12,29,31,33,41,44], efficiency indicator [5,12,13,35,39,43,44,45,46], quality indicator [4,5,29,33,35,39,40,41,42,45,47], tax incentives indicator [29,48], and supportive infrastructure indicator [4,13,29,33,35,39,40,41,43,46,47].

2.3.2. Environmental Sustainability Assessment Indicators

Concerning the environmental pillar of sustainability for free zones, there are a set of proposed indicators as follows: greenhouse gas emissions [4,5,10,11,12,13,31,39,40,41,42,43,45,47,49,50,51,52]; water, air, land, and noise pollution [4,5,10,11,12,13,31,39,40,41,42,43,45,46,47,50,53,54]; waste management [4,5,10,11,12,29,39,40,41,42,45,46,47,48,54,55]; renewable energy [4,5,11,35,40,41,43,46,47]; and landscape and green materials [10,11,12,29,35,39,41,46,47,50,51,53,56].

2.3.3. Social Sustainability Assessment Indicators

Regarding the social aspect of sustainability for free zones, there are a set of proposed indicators as follows: health, security, safety, housing and work conditions indicators [4,5,10,13,29,33,35,39,40,41,42,45,48,50,51,53,55,57]; jobs provision and employee welfare indicators [4,5,11,13,29,31,33,39,40,41,42,47,51,52,53,55,57]; employee training and education indicators [5,11,13,29,33,35,39,40,41,42,43,44,45,47,51,52,57]; surrounding society development and work hour standardization indicators [29,52,53,57]; and the gender equality indicator [52,58].

2.3.4. Organizational Sustainability Assessment Indicators

As for the organizational pillar of sustainability for free zones, there are a set of proposed indicators as follows: strategy and policy indicators [4,31,35,41,44], the risk management indicator [4,12,13,29,35,41,42,45], public relations indicator [47], and innovation [52,59].

2.4. Free Zone Sustainability Assessment Tools

The literature is rich with articles covering the various aspects of sustainability in the context of free zones. While some research addressed the fundamental sustainability pillars (economic, environmental, and social), others were limited to addressing single dimensions. Furthermore, literature has used a variety of tools to assess sustainability. Roh et al. [33], Dinwoodie [35], Kannika et al. [39], Lu et al. [40], and Asgari et al. [45] used interviews to address sustainability assessment in free zones, including economic, environmental, and social pillars. Similarly, using the analytic hierarchy process approach, Sengar et al. [13], Laxe et al. [29], Ahmadi et al. [36], Olfat et al. [44], and Asgari et al. [45] assessed environmental, social, and economic sustainability pillars. In addition, using surveys and the importance–performance analysis (IPA) method, Oh et al. [5] assessed sustainability in seaports. Likewise, Moldavska and Welo [42] proposed a sustainability assessment tool for corporate sustainability assessment (CSA). In the same context, using the fuzzy analytic network process (FANP), Wicher et al. [50] suggested a framework to assess environmental, social, and economic sustainability. Besides, Olfat et al. [44] offered economic, environmental, and social indicators for sustainability assessment in airports. Buaban et al. [31] presented a sustainability indicator comprising environmental, economic, and social sustainability in border special economic zones utilizing simple linear regression (SLR) analysis. Finally, Laxe et al. [29] considered organizational sustainability as the fourth sustainability pillar in their study in Spain to propose a sustainability assessment indicator using the global synthetic index of sustainability (GSI). Moreover, Muangpan and Suthiwartnarueput [4] suggested a sustainability assessment indicator using exploratory factor analysis and one-way ANOVA.

Multicriteria decision-making (MCDM) is a commonly used method to enhance decision-making processes that include multiple criteria and stakeholders. MCDM offers a systematic and structured approach to assess and compare various alternatives based on a set of criteria and preferences [60]. In this context of sustainability, MCDM can enhance a wide range of processes, such as choosing the most sustainable alternatives, identifying tradeoffs between sustainability dimensions, incorporating stakeholder preferences, and assessing sustainability performance [61]. According to Roy [60], MCDM can be classified based on mathematical programming, outranking, and fuzzy sets. Such approaches differ in their assumptions, methods, and applicability based on the problem and context. Recent developments in MCDM have focused on improving its effectiveness and applicability in sustainability assessment, including the following. First, integrated sustainability assessment, in which integrating various aspects of sustainability, including environmental, economic, social, and organizational dimensions are critical for assessment. In this regard, MCDM can be applied to address the complexity and interconnectedness of sustainability issues and provide a more comprehensive overview of the impacts of decisions [62]. Second, multi-objective optimization, in which competing objectives are common in sustainability assessment problems, such as economic efficiency, environmental performance, and social responsibility. Multi-objective optimization methods have been applied within MCDM to facilitate such decision-making [63]. Third, participatory approaches, in which sustainability assessment usually includes the perspectives and options of various stakeholders. Participatory approaches were developed within MCDM to engage stakeholders in the decision-making process by identifying their preferences and weights [64]. Fourth, big data and artificial intelligence, in which recent data management and artificial intelligence (AI) developments have empowered the MCDM analysis capabilities to address large and complex sustainability assessment issues. In this regard, AI and big data can enhance the efficiency and accuracy of decision-making [65]. Finally, uncertainty and risk analysis, in which sustainability is often associated with uncertainties and risks that should be considered in the assessment process. In this regard, MCDM is an effective approach to addressing uncertainty in decision-making and contributes towards the improvement of decision robustness and reliability [66]. Overall, recent MCDM advancements have improved its effectiveness and applicability for sustainability assessment. By integrating multiple dimensions of sustainability, considering various objectives and stakeholders, leveraging big data and AI, and addressing uncertainty and risk, MCDM can support comprehensive and robust decision-making in sustainability assessment.

3. Materials and Methods

This study develops a strategic sustainability assessment methodology for Free Zones using the AHP decision tool based on input from relevant academics, professionals, and officials. This article is among the first studies to address sustainability assessment in free zones, encompassing economic, environmental, social, and organizational dimensions. As shown in Figure 1, the proposed framework began by collecting the relevant sustainability assessment indicators of free zones based on a systematic literature review. The second step was to develop a novel sustainability assessment model to be evaluated using the AHP decision tool, after which an AHP questionnaire was prepared to collect data provided with a precise rating scale. In the AHP questionnaire, fourteen experts from academia, government, and the free zones industry participated in completing pairwise comparison processes among sustainability assessment indicators. Then, pairwise comparisons were synthesized to construct the sustainability indicators’ priority. Afterward, a consistency test was carried out to validate the AHP comparison processes. After that, the sensitivity analysis was conducted. Based on the results of the consistency test, weights and ranks of the sustainability indicators were determined. Finally, the overall priority and ranking of sustainability assessment indicators were developed.

3.1. Model Development

To adequately develop the sustainability assessment model for free zones, this study systematically reviewed articles published in the Scopus database between 2015 and 2022. Accordingly, in the research, titles, abstracts, and keywords, the article used the following keywords: (sustainability) AND (assessment, ranking, or index) AND (free zones, free trade zones, ports, or export processing zones). After going through a filtering phase, the study selected 28 out of 130 studies, detailed in Section 2.3, which were used to construct the sustainability assessment indicators for the proposed model, as shown in Figure 2.

This study summarized and rearranged the sustainability indicators mentioned in the previous literature, as illustrated in

Table 1. Sustainability assessment indicator coding.

Sustainability (SUS)	Economic sustainability (ECO)	Indicator Name	Indicator Code
		1.1 Economic contribution	ECO1
		1.1.1 Capital investment	ECO1.1
		1.1.2 Foreign direct investment	ECO1.2
		1.1.3 Revenue increase	ECO1.3
		1.2 Economic performance	ECO2
		1.2.1 Capacity	ECO2.1
		1.2.2 Efficiency	ECO2.2
		1.2.3 Quality	ECO2.3
		1.3 Economic incentives	ECO3
		1.3.1 Tax incentives	ECO3.1
		1.3.2 Supportive infrastructure	ECO3.2
	Environmental sustainability (ENV)	2.1 Reducing pollution pressures	ENV1
		2.1.1 Greenhouse gas emissions	ENV1.1
		2.1.2 Air pollution—other pollutants	ENV1.2
		2.1.3 Water pollution	ENV1.3
		2.1.4 Noise pollution	ENV1.4
		2.1.5 Land pollution	ENV1.5
		2.2 Encouraging green responses	ENV2
		2.2.1 Green energy 2.2.2 Treated water	ENV2.1 ENV2.2
		2.2.3 Green materials	ENV2.3
		2.2.4 Waste management	ENV2.4
		2.2.5 Landscape	ENV2.5
	Social sustainability (SOC)	3.1 Work conditions	SOC1
		3.1.1 Healthcare	SOC1.1
		3.1.2 Housing	SOC1.2
		3.1.3 Safety	SOC1.3
		3.1.4 Security	SOC1.4
		3.1.5 Welfare	SOC1.5
		3.2 Skill enhancement	SOC2
		3.2.1 Education	SOC2.1
		3.2.2 Training	SOC2.2
		3.3 Social responsibility	SOC3
		3.3.1 Gender equality	SOC3.1
		3.3.2 Job provision	SOC3.2
		3.3.3 Surrounding society development	SOC3.3
		3.3.4 Work hour standardization	SOC3.4
	Organizational sustainability (ORG)	4.1 Strategy	ORG1
		4.2 Policy	ORG2
		4.3 Risk management	ORG3
		4.4 Public relations	ORG4
		4.5 Innovation	ORG5

. The proposed model is structured into four sustainability pillars: economic, environmental, social, and organizational. First, the economic pillar includes eight indicators grouped into three categories: (1) economic contribution, including capital investment, foreign direct investment, and revenue increase indicators; (2) economic performance, including capacity, efficiency, and quality indicators; (3) economic incentives, including tax incentives and supportive infrastructure indicators. Second, the environmental pillar consists of ten indicators grouped into two categories: (1) reducing pollution pressures, including greenhouse gas emissions, air pollution—other pollutants, water pollution, noise pollution, and land pollution indicators; (2) encouraging green responses, including green energy, treated water, green materials, waste management, and landscape indicators. Third, the social pillar, including eleven indicators grouped into three categories: (1) work conditions, including health care, housing, safety, security, and welfare indicators; (2) skill enhancement, including education and training; (3) social responsibility, including gender equality, job provision, surrounding society development, and work hour standardization. Fourth, the organizational pillar, including strategy, policy, risk management, public relations, and innovation indicators. In this regard, this article examines the sustainability indicators that are perceived to be more important and influential and identifies the weights of such importance.

3.2. Solution Methodology

The analytical hierarchy process (AHP) is one of the most effective methods for undertaking multidimensional decisions [9]. AHP is usually a common tool for assessing sustainability in free zones [10,11,12,13]. AHP can facilitate the analysis of concepts in a hierarchical form and integrate the assessment process by combining quantitative and qualitative aspects. The AHP method systematically gathers opinions through pairwise comparisons [67].

The AHP method could be characterized as a “compensatory decision method” since the effective alternatives of an objective(s) could be offset by its performance concerning the other goals [68]. The AHP can be used in a harmonious hierarchy to apply experience, data, intuition, and insight in a logical and comprehensive structure [67]. Additionally, AHP enables weights to be assigned derivatively by researchers and decision-makers. This feature guarantees logical weighing and prevents arbitrary weighting [67]. After that, weights are utilized in a trade-off analysis between the indicators. The basis of the AHP method is the ordinal pairwise comparison process of ranking attributes. Typically, the AHP aims to identify the most important individual indicators and determine the importance values of such indicators [9]. To indicate the degree of preference, the AHP uses a semantic scale with a range of 1 to 9. If the scale’s degree is one, the two separate indications are considered to be equally important. A preference for degree nine means that the given indicator is nine times more important than the other indicator. In the AHP, the actual number of comparison processes is defined by a combination of target indicators for comparison n × (n − 1)/2 [69].

$$\mathrm{Objective}=\left[\begin{array}{ccc}1\hfill & 1/2& \hfill 4\\ 2\hfill & 1& \hfill 6\\ 1/4\hfill & 1/6& \hfill 1\end{array}\right]$$

For instance, the sustainability index has a 3 × 3 reciprocal matrix from paired comparison. Suppose that sustainability has three pillars to be compared (environmental, economic, and social). Therefore, there are three comparison processes.

Table 2. Sustainability example using the AHP.

Objective	Environmental	Economic	Social
Environment	1	1/2	4
Economic	2	1	6
Social	1/4	1/6	1

represents the simple AHP hierarchy for the problem undertaken. Suppose that experts’ opinions were summarized as follows: (1) the economic pillar is twice as important as the environmental pillars; (2) the social pillar is four times less important than the environmental one; (3) the economic pillar is six times more important than the social pillar.

Reciprocal values of the upper diagonal are used to complete the lower part of the comparison matrix (Aii = 1 and Aij = 1/Aji). Although the AHP method requires many calculations, its resulting weights are less sensitive to judgment errors [67]. Additionally, redundancy allows for judgment error measures (inconsistency ratio). Referring to Saaty [9], weights are unaffected if the ratio of inconsistency is between 0.1 and 0.2. Expression 1 and Equation (2) represent the geometric mean for synthesizing individual judgments [70].

$$\mathrm{x}1,\mathrm{x}2,\mathrm{x}3={\left(\begin{array}{cc}{\displaystyle \prod }_{\mathrm{i}=1}^{\mathrm{n}}& \\ & \mathrm{xi}\end{array}\right)}^{1/\mathrm{n}}$$

(1)

Thus,

$$\mathrm{G}\left(\mathrm{x}1,\mathrm{x}2,\mathrm{x}3\right)={(\mathrm{x}1\ast \mathrm{x}2\ast \mathrm{x}3)}^{1/3}$$

(2)

where G = geometric mean, x = pairwise comparison scale by experts, and n = number of experts [70].

SuperDecisions is a decision-making software used to support the AHP method’s implementation. The software was established in 1996 by the AHP method’s founder, Thomas Saaty, and provided through the Creative Decisions Foundation for educational purposes. The foundation supports software development, research, and education in cutting-edge decision-making processes, including the AHP method, which enhances the resolution of social problems, conflict management, and resource allocation optimization in the private and public sectors.

Step 1: Developing a model for assessing sustainability in free zones from the literature review, and restructuring it into a hierarchy of goals, criteria, sub-criteria, and alternatives.

Step 2: Deriving priorities (weights). Weights are measured by collecting sustainability experts’ opinions from academia, governments, and the free zone industry based on the Saaty scale by performing pairwise comparisons. Saaty’s pairwise comparisons scale includes several intensity levels of importance [71]. An intensity score of 9 indicates extreme significance, where the preferred option is “more extremely” important than another. A score of 7 denotes very strong importance, where the selected alternative is “more strongly” favored over another. A score of 5 indicates strong importance, where the chosen option is “strongly” favored over another. A score of 3 represents moderate importance, where the preferred alternative is “slightly” favored over another. A score of 1 denotes equal significance for both options. The intermediate values of 2, 4, 6, and 8 represent a compromise between the abovementioned list of preferences.

By supposing that data collected from surveys is used to create the pairwise comparison matrix A. Suppose that a pairwise comparison matrix is A; “W” is calculated as the principal right eigenvector of matrix A. The eigenvector technique is used if ${\mathrm{a}}_{\mathrm{ik}}$ ${\mathrm{a}}_{\mathrm{kj}}$ = ${\mathrm{a}}_{\mathrm{ij}}$ is not verified for every k, j, and i [72]. In case the matrices are inconsistent, the pair comparisons matrix cannot be used as a normalizing column to obtain Wi. Referring to Jalaliyoon et al. [72], the eigenvector method can be used for reversed and positive matrices as follows:

$${\mathrm{e}}^{\mathrm{T}}=\left(1,1,\dots ,1\right)$$

(3)

$$\mathrm{W}=\underset{\mathrm{k}\to \infty }{\lim }\frac{{\mathrm{A}}^{\mathrm{k}}\ast \mathrm{e}}{{\mathrm{e}}^{\mathrm{T}}\ast {\mathrm{A}}^{\mathrm{k}}\ast \mathrm{e}}$$

(4)

Computation should be performed repeatedly to achieve convergence among expert responses, especially with inconsistent matrices. Afterward, the following formula is used to convert the raw data into understandable absolute values and normalized weights: $\mathrm{w}=\left({\mathrm{w}}_1,{\mathrm{w}}_2,\dots {\mathrm{w}}_{\mathrm{n}}\right)$ [69].

$$\mathrm{Aw}={\mathsf{\lambda }}_{\max }\ge \mathrm{n}$$

$${\mathsf{\lambda }}_{\max }=\frac{\sum \mathrm{ajwj}-\mathrm{n}}{\mathrm{w}1}$$

(5)

$$\mathrm{A}=\left\{{\mathrm{a}}_{\mathrm{ij}}\right\}\mathrm{with}{\mathrm{a}}_{\mathrm{ij}}=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${\mathrm{a}}_{\mathrm{ij}}$}\right.$$

(6)

where

A = the pair wise comparison;

W = the normalized weight vector;

${\mathsf{\lambda }}_{\max }$ = the maximum eigen value of matrix A;

${\mathrm{a}}_{\mathrm{ij}}$ = the numerical comparison between values of i and j [69].

In this study, there are three levels for assessing overall free zone sustainability. The first level includes four criteria, “sustainability pillars”. The second level comprises 13 sub-criteria. The third level contains 37 alternatives (sustainability indicators). To derive the global priorities: first, derive the priorities of criteria by comparing them in terms of sustainability. Second, derive the sub-criteria priorities by comparing them concerning the criteria. Third, derive the alternative priorities (sustainability indicators) by comparing them to sub-criteria.

Step 3: Checking the consistency of judgments. Use the consistency index (CI) to ensure a reasonable level of consistency in terms of proportionality and transitivity. The decision is valid if the CI is less than 0.1. Otherwise, repeat the pairwise comparison to identify the error until consistency is achieved. The consistency ratio (CR) is computed using the formula CR = CI/RI [69]. The consistency index (CI) is calculated using Equation (7). The value of RI is taken from a calculated “random consistency index” [73], that connects to the matrix’s dimension.

$$\mathrm{CI}=\frac{{\mathsf{\lambda }}_{\max }-\mathrm{n}}{\mathrm{n}-1}$$

(7)

Step 4: Synthesizing the sustainability model. Alternative priorities are combined as a weighted sum to determine the overall alternatives’ priorities. The best option is the one that generally has the highest priority.

Step 5: Conducting sensitivity analysis.

Step 6: Making a final decision. Priorities of the sustainability indicators are determined based on the synthesis results and sensitivity analysis.

4. Results

After proposing the sustainability assessment model, this article implemented the AHP method and conducted sensitivity analysis. This section presents the study results and the sensitivity analysis details.

4.1. AHP Application and Results

After collecting the sustainability assessment indicators for free zones, the next phase is to examine such indicators using the analytical hierarchy process (AHP). The AHP method is explained in detail in the previous sections; therefore, this study briefly presents the basic steps in this section. Step 1: based on the Saaty scale, fifteen related experts from industry, academia, and governments participated in performing pairwise comparisons among the proposed sustainability assessment indicators. Step 2: outcomes of the AHP pairwise comparisons were normalized via standard arithmetic operations to form a normalized matrix, as shown in

Table 3. Computed Eigenvalues.

	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6	Expert 7	Expert 8	Expert 9	Expert 10	Expert 11	Expert 12	Expert 13	Expert 14	Mean
ECO1.1	0.06457	0.01684	0.01437	0.00695	0.01681	0.0178	0.11085	0.0035	0.2013	0.12531	0.00129	0.02379	0.10215	0.016415	0.004223
ECO1.2	0.02712	0.00735	0.00905	0.02695	0.01993	0.01121	0.01656	0.00052	0.2013	0.02737	0.00129	0.01309	0.10215	0.015346	0.010907
ECO1.3	0.10249	0.03854	0.02281	0.00299	0.0709	0.07063	0.04947	0.00156	0.2013	0.01195	0.00129	0.04322	0.10215	0.011966	0.019239
ECO2.1	0.02158	0.00565	0.00493	0.00887	0.00565	0.01512	0.16264	0.02135	0.05161	0.00311	0.01235	0.01965	0.08734	0.007811	0.012063
ECO2.2	0.0863	0.0319	0.01128	0.04681	0.00712	0.06001	0.04415	0.00336	0.06503	0.01975	0.01235	0.05871	0.08734	0.015346	0.021815
ECO2.3	0.0863	0.03002	0.01291	0.10588	0.02689	0.09525	0.05991	0.03389	0.04097	0.03135	0.01235	0.13156	0.08734	0.015076	0.00962
ECO3.1	0.04854	0.00243	0.00367	0.04422	0.00365	0.00583	0.0067	0.00095	0.04628	0.44437	0.00846	0.00764	0.00448	0.003548	0.002928
ECO3.2	0.14563	0.01213	0.00367	0.30954	0.02557	0.02331	0.02681	0.00758	0.00514	0.05555	0.00846	0.03821	0.04032	0.02424	0.00883
ENV1.1	0.01464	0.01854	0.05262	0.05515	0.04886	0.16145	0.01438	0.0076	0.00568	0.00277	0.01251	0.09119	0.01676	0.004905	0.00883
ENV1.2	0.03936	0.02433	0.13087	0.01752	0.04886	0.04519	0.00545	0.00177	0.00124	0.0007	0.01251	0.10525	0.01676	0.022318	0.014703
ENV1.3	0.07742	0.02899	0.18082	0.0087	0.04886	0.10011	0.0158	0.02932	0.02506	0.01088	0.01251	0.11481	0.01676	0.02597	0.007645
ENV1.4	0.00609	0.00424	0.10728	0.0044	0.04886	0.01369	0.00226	0.019	0.00397	0.00688	0.01251	0.01735	0.01676	0.001758	0.008155
ENV1.5	0.01464	0.00766	0.12225	0.0044	0.04886	0.06625	0.0019	0.00995	0.00649	0.00432	0.01251	0.04035	0.01676	0.001758	0.004028
ENV2.1	0.01316	0.0878	0.00358	0.0102	0.01764	0.00977	0.10956	0.0155	0.00989	0.00338	0.10008	0.02772	0.01676	0.003886	0.010139
ENV2.2	0.01059	0.03905	0.0134	0.0038	0.0841	0.03842	0.03631	0.08703	0.00706	0.01994	0.10008	0.01158	0.01676	0.012943	0.003401
ENV2.3	0.00481	0.0878	0.00888	0.0017	0.0873	0.02452	0.01971	0.06821	0.01452	0.01525	0.10008	0.01465	0.01676	0.006219	0.004658
ENV2.4	0.02003	0.10249	0.03181	0.00148	0.03842	0.05178	0.06348	0.30773	0.00924	0.05469	0.10008	0.01558	0.01676	0.002467	0.002212
ENV2.5	0.00212	0.01791	0.01656	0.00086	0.01683	0.00442	0.00968	0.13032	0.00174	0.03446	0.10008	0.00426	0.01676	0.015346	0.043629
SOC1.1	0.01626	0.10445	0.0162	0.03508	0.00789	0.00917	0.02437	0.01089	0.00929	0.01349	0.0097	0.00708	0.01461	0.018995	0.019239
SOC1.2	0.01626	0.09497	0.01829	0.00707	0.03528	0.02374	0.00729	0.04856	0.00929	0.00085	0.02511	0.00981	0.01461	0.001758	0.010913
SOC1.3	0.03252	0.04028	0.02146	0.0992	0.03375	0.01568	0.00641	0.0406	0.00929	0.00295	0.01659	0.00771	0.01461	0.001758	0.009591
SOC1.4	0.0172	0.02506	0.0162	0.05294	0.04763	0.02647	0.01308	0.02636	0.00929	0.00289	0.028	0.02398	0.01461	0.023019	0.01515
SOC1.5	0.01314	0.02612	0.01073	0.01855	0.0341	0.05153	0.00396	0.01958	0.00929	0.00279	0.05451	0.00675	0.01461	0.000677	0.005435
SOC2.1	0.00596	0.0606	0.0174	0.04749	0.00881	0.02961	0.02152	0.01144	0.02322	0.07714	0.01153	0.05534	0.06493	0.023019	0.00505
SOC2.2	0.01788	0.01212	0.0174	0.04749	0.00881	0.0074	0.00307	0.00143	0.02322	0.00964	0.01153	0.05534	0.00812	0.000976	0.003016
SOC3.1	0.02131	0.04016	0.01391	0.00698	0.03482	0.00475	0.02691	0.00698	0.00152	0.01325	0.02678	0.00339	0.00674	0.017959	0.02057
SOC3.2	0.0465	0.01819	0.02826	0.01969	0.09826	0.01341	0.09358	0.0628	0.00528	0.00246	0.09293	0.0108	0.04312	0.017845	0.011315
SOC3.3	0.0155	0.00582	0.06451	0.00346	0.01729	0.00236	0.01337	0.00247	0.00076	0.00072	0.04237	0.03115	0.01846	0.001758	0.005309
SOC3.4	0.01208	0.00855	0.02488	0.00166	0.00826	0.00113	0.0308	0.01973	0.00174	0.0018	0.06016	0.01	0.00473	0.000677	0.005435
ORG1	0.04565	0.01929	0.07861	0.30931	0.01857	0.01027	0.06793	0.00885	0.02698	0.0035	0.08996	0.06295	0.01012	0.138113	0.14465
ORG2	0.00775	0.00405	0.04929	0.07636	0.01318	0.00639	0.0886	0.02149	0.02698	0.00445	0.13657	0.08839	0.00539	0.151281	0.086387
ORG3	0.00745	0.01929	0.05947	0.0351	0.01566	0.03452	0.05486	0.01022	0.02698	0.00798	0.02075	0.03945	0.00304	0.026363	0.037927
ORG4	0.00384	0.00373	0.02973	0.02084	0.00279	0.00603	0.0461	0.00527	0.0054	0.00198	0.00991	0.01953	0.00376	0.026363	0.077012
ORG5	0.01884	0.01923	0.02973	0.14766	0.00791	0.0181	0.25341	0.00135	0.02698	0.03325	0.06396	0.03421	0.02299	0.046038	0.048098
ECO1	0.17795	0.05861	0.03482	0.01515	0.10139	0.09214	0.08651	0.00533	0.53547	0.15621	0.00262	0.06052	0.29257	0.046038	0.0202
ECO2	0.17795	0.06314	0.02193	0.06636	0.03735	0.15755	0.13044	0.05583	0.13975	0.05144	0.02515	0.1586	0.25015	0.046038	0.076352
ECO3	0.17795	0.0136	0.00553	0.1453	0.02752	0.02694	0.01639	0.00813	0.04559	0.47435	0.01149	0.03464	0.04278	0.075641	0.057591
ENV1	0.13943	0.07827	0.44727	0.03703	0.23009	0.35758	0.01946	0.06445	0.03763	0.02424	0.04246	0.27874	0.07999	0.0064	0.011643
ENV2	0.04648	0.31307	0.05591	0.00741	0.23009	0.11919	0.11677	0.58006	0.03763	0.12118	0.33971	0.05575	0.07999	0.0019	0.002462
SOC1	0.08742	0.2718	0.06242	0.08741	0.14943	0.11705	0.02696	0.13911	0.04118	0.02179	0.0909	0.04181	0.06974	0.075641	0.028796
SOC2	0.02186	0.06795	0.02622	0.03901	0.0166	0.03422	0.01203	0.01227	0.04118	0.08235	0.01565	0.08362	0.06974	0.006021	0.007315
SOC3	0.08742	0.06795	0.09908	0.01306	0.14943	0.02002	0.08054	0.08764	0.00824	0.01729	0.15086	0.04181	0.06974	0.008788	0.015079
ECO	0.53386	0.13536	0.06228	0.22681	0.16626	0.27662	0.23334	0.06929	0.72081	0.682	0.03926	0.25375	0.5855	0.008788	0.023936
ENV	0.1859	0.39134	0.50317	0.04444	0.46018	0.47678	0.13623	0.64452	0.07527	0.14542	0.38217	0.33449	0.15998	0.006021	0.006308
SOC	0.19669	0.40771	0.18772	0.13948	0.31546	0.17129	0.11953	0.23902	0.09059	0.12142	0.25742	0.16724	0.20923	0.006021	0.010198
ORG	0.08354	0.06559	0.24683	0.58927	0.0581	0.07531	0.5109	0.04718	0.11333	0.05116	0.32115	0.24452	0.04529	0.008788	0.037997
SUS	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000	0	0.999999

. Step 3: after obtaining the normalized values, a consistency test was performed to ensure meeting the accepted consistency condition in the AHP, which should be less than 0.1. Step 4: priority ranking. After verifying the consistency conditions and conducting the sensitivity analysis, the sustainability assessment indicators were ranked in two ways—first, based on the structure of the proposed model, as shown in

Table 4. Ranks and priorities of sustainability indicators.

Criteria Level	Sustainability Indicators	Normalized Weights	Ranking of Level 1	Ranking of Level 2	Ranking of Level 3
Level 1	ECO	0.41809	1
	ENV	0.24969	2
	SOC	0.22259	3
	ORG	0.10962	4
Level 2	ECO1	0.13902		3
	ECO2	0.22069		1
	ECO3	0.05839		7
	ENV1	0.08323		5
	ENV2	0.16646		2
	SOC1	0.10983		4
	SOC2	0.04358		8
	SOC3	0.06918		6
	ORG1	0.02948		10
	ORG2	0.01823		12
	ORG3	0.02114		11
	ORG4	0.00712		13
	ORG5	0.03365		9
Level 3	ECO1.1	0.06245			4
	ECO1.2	0.03123			12
	ECO1.3	0.06245			4
	ECO2.1	0.03541			10
	ECO2.2	0.07082			2
	ECO2.3	0.14163			1
	ECO3.1	0.01639			19
	ECO3.2	0.04918			5
	ENV1.1	0.02482			16
	ENV1.2	0.01371			21
	ENV1.3	0.03673			8
	ENV1.4	0.00718			26
	ENV1.5	0.01104			23
	ENV2.1	0.02866			14
	ENV2.2	0.04773			6
	ENV2.3	0.02866			14
	ENV2.4	0.06678			3
	ENV2.5	0.01512			20
	SOC1.1	0.02647			15
	SOC1.2	0.01308			22
	SOC1.3	0.03543			9
	SOC1.4	0.03113			13
	SOC1.5	0.01723			18
	SOC2.1	0.03916			7
	SOC2.2	0.00979			24
	SOC3.1	0.00951			25
	SOC3.2	0.03291			11
	SOC3.3	0.01764			17
	SOC3.4	0.01764			17

, and second, according to the priorities in a descending order in

Table 5. Ranks and priorities of sustainability indicators—descending order.

Criteria Level		Sustainability Indicators	Normalized Weights	Ranking of Level 1	Ranking ofLevel 2	Ranking of Level 3
Level 1	Economic pillar	ECO	0.41809	1
	Environmental pillar	ENV	0.24969	2
	Social pillar	SOC	0.22259	3
	Organizational pillar	ORG	0.10962	4
Level 2	Economic performance	ECO2	0.22069		1
	Encouraging green responses	ENV2	0.16646		2
	Economic contribution	ECO1	0.13902		3
	Work conditions	SOC1	0.10983		4
	Reducing pollution pressures	ENV1	0.08323		5
	Social responsibility	SOC3	0.06918		6
	Economic incentives	ECO3	0.05839		7
	Skills enhancement	SOC2	0.04358		8
	Innovation	ORG5	0.03365		9
	Strategy	ORG1	0.02948		10
	Risk management	ORG3	0.02114		11
	Policy	ORG2	0.01823		12
	Public relations	ORG4	0.00712		13
Level 3	Quality	ECO2.3	0.14163			1
	Efficiency	ECO2.2	0.07082			2
	Waste management	ENV2.4	0.06678			3
	Capital investment	ECO1.1	0.06245			4
	Revenues increase	ECO1.3	0.06245			4
	Supportive infrastructure	ECO3.2	0.04918			5
	Treated water	ENV2.2	0.04773			6
	Education	SOC2.1	0.03916			7
	Water pollution	ENV1.3	0.03673			8
	Safety	SOC1.3	0.03543			9
	Capacity	ECO2.1	0.03541			10
	Job provision	SOC3.2	0.03291			11
	Foreign direct investment	ECO1.2	0.03123			12
	Security	SOC1.4	0.03113			13
	Green energy	ENV2.1	0.02866			14
	Green materials	ENV2.3	0.02866			14
	Healthcare	SOC1.1	0.02647			15
	Greenhouse gas emissions	ENV1.1	0.02482			16
	Surrounding society development	SOC3.3	0.01764			17
	Work hour standardization	SOC3.4	0.01764			17
	Welfare	SOC1.5	0.01723			18
	Tax incentives	ECO3.1	0.01639			19
	Landscape	ENV2.5	0.01512			20
	Air pollution—other pollutants	ENV1.2	0.01371			21
	Housing	SOC1.2	0.01308			22
	Land pollution	ENV1.5	0.01104			23
	Training	SOC2.2	0.00979			24
	Gender equality	SOC3.1	0.00951			25
	Noise pollution	ENV1.4	0.00718			26

.

The AHP analysis was conducted on three levels. First, Level 1, the criteria level, including the main pillars of sustainability in free zones: economic, environmental, social, and economic. In this level, the findings revealed that the economic pillar was ranked first at 41.81%, followed by the environmental pillar at 24.96%, then the social pillar at 22.26%, and finally, the organizational pillar at 10.96%.

Second, Level 2, the sub-criteria level, includes thirteen sustainability components. The results of the second level indicated that economic performance (ECO2) ranked first with a score of 0.22069, followed by encouraging green responses (ENV2) with a score of 0.16646, economic contribution (ECO1) with 0.13902, work conditions (SOC1) with a score of 0.10983, reducing pollution pressures (ENV1) with a score of 0.08323, social responsibility (SOC3) with a score of 0.06918, economic incentives (ECO3) with a score of 0.05839, skills enhancement (SOC2) with a score of 0.04358, innovation (ORG5) with a score of 0.03365, strategy (ORG1) with a score of 0.02948, risk management (ORG3) with a score of 0.02114, policy (ORG2) with a score of 0.01823, and public relations (ORG4) with a score of 0.00712, respectively.

The third, alternative level, includes twenty-nine sustainability indicators. The third level results indicate that the quality indicator (ECO2.3) ranked first with a score of 0.14163, followed by efficiency (ECO2.2) with a score of 0.07082, waste management (ENV2.4) with a score of 0.06678, capital investment (ECO1.1) and revenue increase (ECO1.3) with a score of 0.06245; supportive infrastructure (ECO3.2) with a score of 0.04918, treated water (ENV2.2) with 0.04773, education (SOC2.1) with a score of 0.03916, water pollution (ENV1.3) with a score of 0.03673, safety (SOC1.3) with a score of 0.03543, capacity (ECO2.1) with a score of 0.03541, job provision (SOC3.2) with a score of 0.03291, foreign direct investment (ECO1.2) with a score of 0.03123, security (SOC1.4) with a score of 0.03113, green energy (ENV2.1) and green materials (ENV2.3) with a score of 0.02866, healthcare (SOC1.1) with a score of 0.02647, greenhouse gas emissions (ENV1.1) with a score of 0.02482, surrounding society development (SOC3.3) and work hour standardization (SOC3.4) with a score of 0.01764, welfare (SOC1.5) with a score of 0.01723, tax incentives (ECO3.1) with a score of 0.01639, landscape (ENV2.5) with a score of 0.01512, air pollution—other pollutants (ENV1.2) with a score of 0.01371, housing (SOC1.2) with a score of 0.01308, land pollution (ENV1.5) with a score of 0.01104, training (SOC2.2) with a score of 0.00979, gender equality (SOC3.1) with a score of 0.00951, and noise pollution (ENV1.4) with a score of 0.00718.

4.2. Sensitivity Analysis

The aim of conducting a sensitivity analysis is to examine how the priorities of alternatives change as the priorities of criteria/sub-criteria vary. For example, if the economic sustainability pillar becomes more important, does the most important alternative change? To which and by how much? In this part, our study shows the results of conducting a sensitivity analysis on three levels. (1) Criteria with respect to sustainability sensitivity analysis. The first level of the sensitivity analysis examines the changes in sustainability criteria priorities according to changes in the sustainability weights. (2) Sub-criteria with respect to criteria sensitivity analysis. The second level of sensitivity analysis investigates the changes in sub-criteria priorities according to changes in the criteria weights. (3) Alternatives with respect to sub-criteria sensitivity analysis. The third level of sensitivity analysis examines the changes in alternatives priorities according to changes in the sub-criteria weights. Note that sustainability, criteria, sub-criteria, and alternatives represent Level 0, Level 1, Level 2, and Level 3, respectively, as illustrated in Table 5.

First, criteria with respect to sustainability sensitivity analysis: the analysis examines how the priorities of Level 1 (sustainability criteria) change according to changes in the sustainability (Level 0) weights. As explained in Section 6, the study results showed that the most important criterion is the economic pillar (ECO), followed by the environmental (ENV), social (SOC), and organizational (ORG) pillars, respectively. As shown in Figure 3, the sensitivity analysis shows that the quality (ECO2.3) indicator is the most important criterion when the priority of the economic criterion (ECO) is ≥0.25531, “the rank reverse point”, with respect to sustainability; otherwise, waste management (ENV2.4) is the most important. As noted in Figure 4, the sensitivity analysis shows that the quality (ECO2.3) indicator is the most important criterion when the priority of environmental criterion (ENV) is ≤0.41317, “the rank reverse point”, with respect to sustainability; otherwise, waste management (ENV2.4) is the most important.

As illustrated in Figure 5, the sensitivity analysis shows that the quality (ECO2.3) indicator is the most important criterion when the priority of social criterion (SOC) is ≤0.51052, “the rank reverse point”, with respect to sustainability; otherwise, education (SOC2.1) is the most important. As noted in Figure 6, the sensitivity analysis shows that the quality (ECO2.3) indicator is the most important criterion in all priority values of the organizational criterion (ORG).

Second, sub-criteria with respect to criteria sensitivity analysis: this analysis analyses how the sub-criteria of Level 2 (sustainability sub-criteria) change according to the changes in the criteria (Level 1) weights. As mentioned in Section 6, the study results showed that the most important sub-criterion is economic performance (ECO2), followed by encouraging green responses (ENV2), economic contribution (ECO1), work conditions (SOC1), reducing pollution pressures (ENV1), social responsibility (SOC3), economic incentives (ECO3), skill enhancement (SOC2), innovation (ORG5), strategy (ORG1), risk management (ORG3), policy (ORG2), and public relations (ORG4), respectively.

Economic criterion sensitivity analysis: As shown in Figure 7, the sensitivity analysis shows that the quality indicator (ECO2.3) is the most important when the priority of the sub-criterion economic contribution (ECO1) is ≤0.52894, “the rank reverse point”, with respect to the economic criterion (ECO); otherwise, capital investment (ECO1.1) is the most important. As noted in Figure 8, the sensitivity analysis shows that the quality indicator (ECO2.3) is the most important when the priority of the sub-criterion economic performance (ECO2) is ≥0.33161, “the rank reverse point”, with respect to the economic criterion (ECO); otherwise, capital investment (ECO1.1) is the most important.

As noted in Figure 9, the sensitivity analysis shows that the quality indicator (ECO2.3) is the most important when the priority of the sub-criterion economic incentives (ECO2) is ≤0.31845, “the rank reverse point”, with respect to the economic criterion (ECO); otherwise, supportive infrastructure (ECO3.1) is the most important. Environmental criterion sensitivity analysis: the sensitivity analysis shows that the quality indicator (ECO2.3) is the most important in all cases for the sub-criteria reducing pollution pressures (ENV1) and encouraging green responses (ENV2) with respect to the environmental criterion (ENV), as shown in Figure 10.

Social criterion sensitivity analysis: The sensitivity analysis shows that the quality indicator (ECO2.3) is the most important in all cases for the sub-criteria of work conditions (SOC1) and social responsibility (SOC3) with respect to the social criterion (SOC), as shown in Figure 11. The sensitivity analysis shows that the quality indicator (ECO2.3) is the most important when the priority of the sub-criterion skill enhancement (SOC2) is ≤0.70785, “the rank reverse point”, with respect to the social criterion (SOC); otherwise, education (SOC2.1) is the most important, as noted in Figure 12.

Organizational criterion sensitivity analysis: the sensitivity analysis shows that the quality indicator (ECO2.3) is the most important in all cases for the sub-criteria of strategy (ORG1), policy (ORG2), risk management (ORG3), public relations (ORG4), and innovation (ORG5) with respect to the organizational criterion (ORG).

Third, alternatives with respect to sub-criteria sensitivity analysis: this analysis examines how the sustainability alternatives of Level 3 (sustainability alternatives) change according to the changes in the sub-criteria (Level 2) weights. As explained in Section 6, the results showed that the most important alternative is quality (ECO2.3), followed by ECO2.3, ECO2.2, ENV2.4, ECO1.1 and ECO1.3, ECO3.2, ENV2.2, SOC2.1, ENV1.3, SOC1.3, ECO2.1, SOC3.2, ECO1.2, SOC1.4, ENV2.1 and ENV2.3, SOC1.1, ENV1.1, SOC3.3 and SOC3.4, SOC1.5, ECO3.1, ENV2.5, ENV1.2, SOC1.2, ENV1.5, SOC2.2, SOC3.1, and ENV1.4, respectively.

Sensitivity analysis of the economic contribution sub-criterion (ECO1) the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of capital investment (ECO1.1) is ≤0.90518, “rank reverse point”, with respect to the economic contribution sub-criterion (ECO1); otherwise, capital investment (ECO1.1) is the most important, as shown in Figure 13. Also, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of foreign direct investment (ECO1.2) is ≤0.90518, “the rank reverse point”, with respect to the economic contribution sub-criterion (ECO1); otherwise, foreign direct investment (ECO1.2) is the most important, as shown in Figure 14. Additionally, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of revenue increase (ECO1.3) is ≤ 0.90518, “the rank reverse point”, with respect to the economic contribution sub-criterion (ECO1); otherwise, revenue increase (ECO1.3) is the most important.

Sensitivity analysis of the economic performance sub-criterion (ECO2). The sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of capacity (ECO2.1) is ≤0.39475, “the rank reverse point”, with respect to economic performance sub-criterion (ECO2); otherwise, capacity (ECO2.1) is the most important, as shown in Figure 15. Also, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of efficiency (ECO2.2) is ≤0.44211, “the rank reverse point”, with respect to economic performance sub-criterion (ECO2); otherwise, efficiency (ECO2.2) is the most important, as shown in Figure 16. In addition, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of quality (ECO1.3) is ≥0.40002, “the rank reverse point”, with respect to the economic performance sub-criterion (ECO2); otherwise, efficiency (ECO2.2) is the most important. Economic incentives sub-criterion (ECO3): the sensitivity analysis shows that quality (ECO2.3) is the most important alternative for all priority values of supportive infrastructure (ECO3.1) and tax incentives (ECO3.1) with respect to the economic incentives sub-criterion (ECO3). Regarding the reducing pollution pressures sub-criterion (ENV1), the sensitivity analysis shows that quality (ECO2.3) is the most important alternative for priority values of greenhouse gas emissions (ENV1.1), air pollution—other pollutants (ENV1.2), water pollution (ENV1.3), noise pollution (ENV1.4), and land pollution (ENV1.5) with respect to the reducing pollution pressures sub-criterion (ENV1).

Sensitivity analysis of the encouraging green responses sub-criterion (ENV2): The sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of green energy (ENV2.1) is ≤0.75521, “the rank reverse point”, with respect to the encouraging green responses sub-criterion (ENV2); otherwise, green energy (ENV2.1) is the most important. Additionally, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of green materials (ENV2.2) is ≤0.75521, “the rank reverse point” with respect to the encouraging green responses sub-criterion (ENV2); otherwise, green materials (ENV2.2) is the most important. Also, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of green water (ENV2.3) is ≤0.75521, “the rank reverse point”, with respect to the encouraging green responses sub-criterion (ENV2); otherwise, green water (ENV2.3) is the most important. In addition, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of landscape (ENV2.4) is ≤0.75521, “the rank reverse point”, with respect to the encouraging green responses sub-criterion (ENV2); otherwise, the landscape (ENV2.4) indicator is the most important. Finally, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative when the priority of waste management (ENV2.5) is ≤0.75521, “the rank reverse point”, with respect to the encouraging green responses sub-criterion (ENV2); otherwise, the waste management (ENV2.5) indicator is the most important.

Sensitivity analysis of the work conditions sub-criterion (SOC1): the sensitivity analysis shows that quality (ECO2.3) is the most important alternative for all priority values of healthcare (SOC1.1), housing (SOC1.2), safety (SOC1.3), security (SOC1.4), and welfare (SOC1.5), with respect to the work conditions (SOC1) sub-criterion. Regarding the skill enhancement (SOC2) sub-criterion, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative for all priority values of education (SOC2.1) and training (SOC2.2) with respect to the skills enhancement (SOC2) sub-criterion. Concerning the social responsibility (SOC3) sub-criterion, the sensitivity analysis shows that quality (ECO2.3) is the most important alternative for all priority values of gender equality (SOC3.1), job provision (SOC3.2), surrounding society development (SOC3.3), and work hour standardization (SOC3.4) with respect to the social responsibility (SOC3) sub-criterion.

5. Discussion

This section analyzes the results obtained from implementing the sustainability assessment model using the AHP approach. In general, this study seeks to achieve two main objectives: to propose a sustainability assessment model for free zones and to examine the model using the AHP method. In the article, the AHP method was implemented to the proposed sustainability assessment model at three levels.

In the first level, the level of sustainability pillars, the results showed that the economic pillar (ECO) was ranked the first among the sustainability pillars with a value of 0.41809; then, the environmental pillar (ENV), the social pillar (SOC), and the organizational pillar (ORG), with values of 0.24969, 0.22259, and 0.10962, respectively. The results indicate that the economic pillar is the most important in the sustainability of free zones with 41.81%, while the organizational pillar is the least important, with a percentage of 10.96% of overall sustainability. Regardless of the variance in the importance level found in sustainability pillars, the results generally revealed considerable importance for all sustainability pillars, which is consistent with the concept of sustainability in terms of the need to balance objectives of maximizing economic and social benefits without harming the environment [28]. The findings of the article are consistent with Asgari et al.’s [45] results, ranking economic sustainability first, environmental sustainability second, and social sustainability third. Such an agreement could indicate that the Asgari et al. [45] conclusions validly confirm this article’s findings. Similarly, Laxe et al.’s [29] conclusions agree with this article ranking economic sustainability first; however, it significantly differed regarding the other sustainability pillars. While organizational sustainability was ranked fourth, it was ranked second by Laxe et al. [29]. In contrast, environmental and social sustainability ranked second and third, respectively; they were ranked third and fourth by Laxe et al. [29]. Notably, the findings of Sengar et al. [13] completely differ from the conclusions drawn from this study. While economic sustainability ranked first, it was ranked third by Sengar et al. [13]. In addition, environmental sustainability ranked second, whereas it was ranked first by Sengar et al. [13]. Furthermore, while social sustainability ranked third, it was ranked second by Sengar et al. [13].

In the second level, the components ranked by priority are as follows: ECO2 > ENV2 > ECO1 > SOC1 > ENV1 > SOC3 > ECO3 > SOC2 > ORG5 > ORG1 > ORG3 > ORG2 > ORG4. This result is consistent with the ranking of the first level, as the economic criteria and economic pillar have the highest ranking in both levels. In this level, the economic criteria received high ranks: ECO2 (economic performance) ranked first with a score of 0.22069, ECO1 (economic contribution) ranked third with a value of 0.13902, and ECO3 (economic incentives) ranked seventh with a score of 0.05839. Such a finding suggests that economic sustainability is paramount in free zones. The environmental criteria ranked as follows: ENV2 (encouraging green responses) ranked second with a score of 0.16646, while ENV1 (reducing pollution pressures) ranked fourth with a value of 0.08323. This result indicates that environmental sustainability is essential for sustainability in free zones. The social criteria ranked as follows: SOC1 (work conditions) ranked fourth with a score of 0.10983, SOC3 (social responsibility) ranked sixth with a score of 0.06918, and SOC2 (skill enhancement) ranked eighth with a score of 0.04358. Such a finding suggests that while social sustainability is important, it may not be as crucial as economic and environmental sustainability in free zones. The organizational criteria received the lowest ranks as follows: ORG5 (innovation) ranked ninth with a score of 0.03365, ORG1 (strategy) ranked tenth with a score of 0.02948, ORG3 (risk management) ranked eleventh with a score of 0.02114, ORG2 (policy) ranked twelfth with a score of 0.01823, and ORG4 (public relations) ranked thirteenth with a score of 0.00712. This result suggests that while organizational sustainability is vital, it may not be as crucial as economic, environmental, and social sustainability in free zones. Overall, the second level suggests that the sustainability of free zones is heavily influenced by economic and environmental factors, with social and organizational factors playing a relatively smaller role. This finding aligns with sustainability as the need to balance economic, social, and environmental objectives. In general, the second level provides further insight into the priorities of the sustainability components toward achieving sustainable development in free zones.

In the third level, indicators are ranked by priority as follows: ECO2.3 > ECO2.2 > ENV2.4 > ECO1.1 and ECO1.3 > ECO3.2 > ENV2.2 > SOC2.1 > ENV1.3 > SOC1.3 > ECO2.1 > SOC3.2 > ECO1.2 > SOC1.4 > ENV2.1 and ENV2.3 > SOC1.1 > ENV1.1 > SOC3.3 and SOC3.4 > SOC1.5 > ECO3.1 > ENV2.5 > ENV1.2 > SOC1.2 > ENV1.5 > SOC2.2 > SOC3.1 > ENV1.4. This result is consistent with results of Level 1 and Level 2, as the first ranking was the economic indicator. According to the outcome of Level 3, the economic indicators were assigned a high priority. Such a finding indicates that economic sustainability is crucial for the overall sustainability of free zones. Among the economic indicators, the quality indicator (ECO2.3) was ranked first, with a score of 0.14163, indicating that the quality of economic activities and products is considered essential for sustainability. Then, the efficiency indicator (ECO2.2) was ranked second, with a score of 0.07082, indicating that efficient use of resources and reduction of waste in economic activities is crucial for sustainability. After that, the capital investment (ECO1.1) and revenue increase (ECO1.3) indicators were ranked fourth, with a score of 0.06245, indicating that sustainable economic growth is essential for sustainability. Additionally, the supportive infrastructure indicator (ECO3.2) was ranked fifth, with a score of 0.04918, indicating that the availability of infrastructure to support economic activities is also essential for sustainability. Then, the capacity indicator (ECO2.1) was ranked tenth, with a score of 0.03541, indicating that the ability of an economy to produce and provide a high level of goods and services is essential for sustainability. After that, the foreign direct investment indicator (ECO1.2) was ranked twelfth, with a score of 0.03123, indicating that foreign investment can significantly contribute to economic sustainability. Finally, the tax incentives indicator (ECO3.1) was ranked nineteenth, with a score of 0.01639, indicating that while tax incentives can stimulate economic activity, they are, however, considered less important for sustainability compared to other economic indicators. Overall, the high ranking of economic indicators in the third level highlights the importance of a sustainable and robust economy for achieving overall sustainability. Second, the environmental sustainability indicators: In the third level of sustainability indicators, environmental indicators were also assigned significant priority. This result indicates that environmental sustainability is essential for free zones’ overall sustainability. Among the environmental indicators, the waste management indicator (ENV2.4) was ranked third, with a score of 0.06678, indicating that proper waste management and reduction of waste generation are crucial for sustainability. Then, the treated water indicator (ENV2.2) was ranked sixth, with a score of 0.04773, indicating that ensuring the availability of clean and treated water is essential for sustainability. After that, the water pollution indicator (ENV1.3) was ranked eighth, with a score of 0.03673, indicating that preventing and reducing water pollution is essential for sustainability. Additionally, the green energy (ENV2.1) and green materials (ENV2.3) indicators were ranked fourteenth, with a score of 0.02866, indicating that using renewable energy and sustainable materials is vital for sustainability. In addition, the greenhouse gas emissions indicator (ENV1.1) was ranked sixteenth, with a score of 0.02482, indicating that reducing greenhouse gas emissions is necessary for sustainability. Then, the landscape indicator (ENV2.5) was ranked twentieth, with a score of 0.01512, indicating that preserving natural landscapes and biodiversity is crucial for sustainability. Furthermore, the air pollution—other pollutants indicator (ENV1.2) was ranked twenty-first, with a score of 0.01371, indicating that reducing air pollution caused by other pollutants is important for sustainability. Also, the land pollution indicator (ENV1.5) was ranked twenty-third, with a score of 0.01104, indicating that preventing and reducing land pollution is also crucial for sustainability. Finally, the noise pollution indicator (ENV1.4) was ranked twenty-sixth, with a score of 0.00718, indicating that reducing noise pollution is considered the least important among the environmental and overall sustainability indicators. Overall, the high ranking of environmental indicators in the third level highlights the importance of protecting the environment and preserving natural resources for achieving overall sustainability. Third, social sustainability indicators: In the context of free zones, social sustainability is crucial for ensuring a safe and healthy working environment, promoting the well-being of workers and their families, and supporting the development of the local community. The social indicators in the third level provide insights into the performance of free zones in terms of education, safety, job provision, security, healthcare, surrounding society development, work hour standardization, welfare, housing, training, and gender equality. The education indicator (SOC2.1) ranked seventh as the highest-ranked social indicator, with a score of 0.03916, indicating the importance of the educational system in ensuring sustainability for free zones. Education is crucial for ensuring workers have the skills and knowledge to perform their jobs effectively and advance their careers within the free zone. Then, the safety indicator (SOC1.3) ranked ninth with a score of 0.03543, indicating that free zones should work to provide a safe working environment for their users. A high level for this indicator suggests that the free zone has effective safety regulations and procedures, which contributes to a safer and more sustainable environment. After that, the job provision indicator (SOC3.2) ranked eleventh, with a score of 0.03291, indicating that job creation and employment opportunities are essential for sustainability in free zones. Such a finding suggests that free zones should focus on creating job opportunities for individuals within the host country to ensure their economic stability and growth. Additionally, the security indicator (SOC1.4) ranked thirteenth, with a score of 0.03113, suggesting that security measures are also crucial for sustainability in free zones. This result indicates that free zones can boost sustainability by investing in security measures to ensure the safety and protection of investments and individuals within such zones. In addition, the healthcare indicator (SOC1.1) ranked fifteenth, with a score of 0.02647, indicating that healthcare is a relatively crucial social indicator for sustainability in free zones. After that, the surrounding society development (SOC3.3) and work hour standardization (SOC3.4) indicators ranked seventeenth with a score of 0.01764, indicating that such indicators have relatively vital priority in achieving sustainability in free zones. Then, the welfare indicator (SOC1.5) ranked eighteenth, with a score of 0.01723, indicating that the wellbeing of individuals within free zones may not be a top priority for sustainability in a direct way. However, welfare can be enhanced indirectly through other social indicators, such as education, safety, and job creation. The housing indicator ranked twenty-second, with a score of 0.01308, suggesting that providing housing is a relatively significant consideration for achieving sustainability in free zones. The training indicator ranked twenty-fourth, with a score of 0.00979, indicating that training opportunities for individuals within free zones may not take considerable priority compared to other social indicators. However, such a finding may have a relationship with the education indicator, which had a significantly high priority, as education may indirectly contribute to enhancing training opportunities. Additionally, it could be inferred that education has a higher impact on sustainability than training in the context of free zones. Finally, the gender equality indicator (SOC3.1) ranked twenty-fourth, with a score of 0.00951, indicating that gender equality may not be a significant consideration for sustainability in free zones. However, this does not mean free zones do not support gender equality; it may simply imply that other social indicators are considered more critical for sustainability in these zones. Overall, the findings indicate that sustainability efforts should prioritize economic and environmental indicators while considering social and organizational indicators to achieve sustainability in free zones.

6. Conclusions

Lately, the need to adopt sustainability in strategic objectives has increased, and it is considered one of the vital factors in enhancing the competitiveness of free zones, in which those with a high level of sustainability are more likely to attract investment and other kinds of support [1]. Accordingly, sustainability assessment is crucial for increasing sustainability effectiveness in free zones [74]. Although some studies have addressed the topic, there remains a need to further study sustainability in the context of free zones. As such, this study is considered among the first to address free zones sustainability assessment from a strategic perspective. In this regard, the main contributions of this study can be summarized as follows: first, a systematic literature review of sustainability assessment for free zones; second, the proposal of a strategic model for assessing sustainability of free zones; third, the determination of the proposed sustainability indicators’ weights using the AHP by taking the opinions of related experts from academia, governments, and industry. Finally, it provides insight into future research by identifying relevant challenges and gaps.

The results revealed that the economic aspect is the most important pillar of sustainability in the context of free zones with a percentage of 41.81%, followed by the environmental pillar at 24.97%, the social pillar at 22.26%, and finally, the organizational pillar at 10.96%. Although the results revealed a variance in the importance of sustainability pillars, from the highest (economic) to the lowest (organizational), there is significant importance for all sustainability pillars, which is consistent with the definition of sustainability in terms of the need to balance sustainability objectives that involve maximizing economic and social benefits without harming the environment.

There are some limitations to this study. For instance, although we included a diverse group of related experts from various fields and sectors, the scope was restricted to the Middle East. Hence, future research can involve more experts from various backgrounds, including diverse geographical regions, to inform the sustainability assessment model and the constituting prioritization approach. Furthermore, a larger quantity and variety of experts from academia, government and industry from various disciplines can be considered in future analysis. Future work can utilize various other decision-making tools and compare against the outcomes of the AHP presented in this study. Although the AHP method has some drawbacks, such as complexity [62], subjectivity [62,75], and sensitivity to small changes [62], it remains among the most effective tools for multicriteria decision making. It is suitable for sustainability assessment since there are various dimensions and objectives that need to be considered and prioritized. In this regard, AHP can aggregate complex decisions into smaller parts and prioritize them according to their relative importance, enabling decision-makers to prioritize the most critical sustainability factors and identify the most sustainable alternatives for free zone development.

Although this study addressed a novel model for sustainability assessment indicators, it did not delve into the components individually, which may open opportunities to direct future research toward developing more sustainability assessments in the context of free zones. Finally, this article is among the first studies to propose a strategic sustainability assessment model for free zones. Within this, our study suggested separating the organizational pillar from the social dimension in the proposed model. In addition, the model included vital indicators for enhancing sustainability assessment in free zones, such as strategies, policies, innovation, and public relations.