Prioritizing Criteria for Establishing a Green Shipping Corridor Between the Ports of Sines and Luanda Using Fuzzy AHP

Abstract

As port authorities and cargo operators seek strategies to reduce carbon emissions while ensuring operational efficiency, some are turning to the concept of green corridors. These solutions aim to establish formalized partnerships among ports, carriers, shippers, and countries. During the process, the stakeholders must consider four priority areas (alternative fuels, bunkering infrastructure, vessel decarbonization pathways, and cargo demand dynamics) from seven angles (environmental, economic, infrastructure, regulatory, operational, technological, and social). This study explores the prioritization of these criteria for establishing a green maritime corridor between two major ports in Portugal and Angola, which would be a significant step toward promoting sustainable global trade. Utilizing the fuzzy AHP, this research analyzes all these factors and their associated sub-criteria derived from a comprehensive literature review and consultations with stakeholders from the Ports of Sines and Luanda. The findings show the dominance of environmental compatibility and economic viability, while social acceptance shows the lowest score. This framework guides the decision-making process for developing a sustainable shipping corridor. The results offer valuable insights for policymakers which can guide them in fostering resilient maritime transport routes, accelerating the adoption of decarbonization strategies and playing a critical role in achieving the IMO’s zero-emission targets by 2050.

1. Introduction

Maritime transportation plays a crucial role in global trade, facilitating the distribution of over 80% of internationally traded goods by volume [1]. However, this sector faces a significant challenge regarding environmental sustainability due to its substantial contribution to greenhouse gas (GHG) emissions [2]. Operating mainly through fossil fuels, the shipping sector today contributes 2–3% of the world’s CO₂ emissions; if proper management is not implemented, that percentage might increase to 17% by 2050 [3]. The International Maritime Organization (IMO) serves as a specialized agency of the United Nations, ensuring the safety and security of maritime operations while addressing the prevention of pollution caused by vessels in marine and atmospheric environments. The initiatives undertaken by the IMO are aligned with the sustainable development goals established by the United Nations. Recognizing the urgency of addressing emissions from shipping, the IMO and the European Commission reached an agreement in 2018. This landmark agreement outlines ambitious targets to diminish the total annual GHG emissions from international shipping by a minimum of 50% by 2050, with 2008 as the reference year [4]. This requires implementing innovative strategies, substantial investment, and infrastructure development; all are relevant for facilitating this transformation and collectively decreasing emissions within the shipping industry [5,6,7].

As part of this holistic approach, “green shipping corridors” accelerate decarbonization and are a mid-term strategy to mitigate GHG emissions from maritime transportation by focusing on specific routes and fostering strong partnerships among essential stakeholders. These corridors, conceived in the Clydebank Declaration at COP26, are designated shipping routes where the technological, economic, and regulatory potential for zero-emission shipping is activated through collaborative efforts between public and private entities. They can help overcome energy and technology barriers on a manageable scale [8,9,10,11]. The IMO defines three types of green shipping corridors: single point, point-to-point, and network. This paper focuses on prioritizing the criteria for establishing a point-to-point green shipping corridor between the Port of Sines (Portugal) and the Port of Luanda (Angola). Both are major ports in their respective country, and are committed to promoting sustainable global trade [12,13]. Their strategic locations empower them to play key roles in the movement of goods across borders, significantly boosting economic development and trade connectivity. The Port of Sines serves as a vital hub with broad international connections, and the Port of Luanda is emerging as a crucial hub in international shipping networks. The establishment of a green shipping corridor between these key ports will further enhance their significance on the global stage. This corridor will not only strengthen the maritime connectivity between Europe and Africa but will also promote environmental sustainability by implementing eco-friendly practices in transportation.

Developing a framework for the green shipping corridor between the Ports of Sines and Luanda requires the thorough consideration of various essential criteria. These criteria encompass a broad spectrum of considerations, each playing a pivotal role in shaping the corridor’s viability and long-term sustainability. Given the complexity inherent in assessing and prioritizing these criteria, a systematic approach is imperative. This requirement is addressed in this paper by using the fuzzy analytic hierarchy process (AHP). The AHP is a widely utilized method for modeling subjective decision-making processes that consider multiple attributes, particularly in the assessment of green initiatives related to seaports. It provides a robust framework for addressing intricate decision-making scenarios and achieving efficient prioritization [14,15]. The AHP is widely recognized for its strengths in multi-criteria decision-making (MCDM). Its application often requires additional justification due to the existence of alternative methods [16,17,18]. The key advantage of the AHP is its flexibility regarding sample size, as it does not require a large sample to yield reliable results [15]. Additionally, the AHP ensures consistency in yielding expert judgments by incorporating consistency checks that help reduce its bias, even with subjective inputs, which is crucial for aligning diverse perspectives [16]. Furthermore, the AHP’s straightforward implementation saves time by enabling decision-makers to break down complex problems into manageable components and efficiently perform pairwise comparisons. The original AHP approach employs a crisp scale that summarizes the decision-maker preferences as a single crisp number and documents them in comparison matrices. However, due to the inherent uncertainties in perception, human judgments and preferences cannot be precisely represented by crisp numbers [18]. To tackle this issue of vagueness and uncertainty, and to better capture human judgments on ratio scales, fuzzy set theory can be integrated into the conventional AHP by incorporating fuzzy numbers into the weighting scale [19]. This study makes a valuable contribution by utilizing the fuzzy AHP method to prioritize the essential criteria for establishing a green shipping corridor. The research analyzes all these factors, including their associated sub-criteria derived from a comprehensive literature review and consultations with stakeholders from the Ports of Sines and Luanda. The fuzzy AHP method plays a crucial role in this process. It helps in identifying four priority areas (alternative fuels, bunkering infrastructure, vessel decarbonization pathway, and cargo demand dynamics) from seven angles (environmental, economic, infrastructure, regulatory, operational, technological, and social). Additionally, a sensitivity analysis was conducted. By employing this method, the aim is to provide decision-makers with a structured and systematic approach, ensuring that efforts are directed towards the most impactful and sustainable outcomes.

2. Conceptual Development and Literature Review

A green shipping corridor is a route between two or more ports with zero-emission shipping solutions. It is not a strict set of rules. Instead, it is a strategically designed ecosystem of factors related to a specific economic zone. The key performance indicators (KPIs) and goals of a specific solution vary from project to project [20,21,22]. The concept of the green corridor dates back to 2007 when the European Commission unveiled its Freight Transport Logistics Action Plan (FTLAP) [23,24]. This document introduced a range of policy initiatives and a set of short- to medium-term actions aimed at fostering integrated, efficient, and environmentally friendly freight transportation between major hubs and over extensive distances [25]. The FTLAP provided a broad outline of green corridors without a precise definition. To address this gap, the Swedish Logistics Forum endeavored to clarify the concept by offering a more structured definition, emphasizing sustainability, integrated logistics, harmonized regulations, strategic transshipment points, supportive infrastructure, and innovation platforms [26]. In 2010, the European Commission initiated a new three-year project named “SuperGreen” under the 7th Framework Programme [27]. This project aimed to advance sustainable freight transport networks by assessing various green corridors across Europe’s key transportation routes and representative regions, alongside green technologies [28]. The 2011 white paper on transport [29] stands as the paramount document in the European Union’s (EU) transport policy, outlining the European Commission’s vision for future transportation and the corresponding strategy for the following decade. This system aims to increase mobility and employment, eliminate major barriers in key areas, and reduce fuel consumption. Emphasis, in this approach, was placed on green transport corridors, characterized by a reduced environmental and climate impact while simultaneously enhancing safety and efficiency through the application of sustainable logistics solutions, intermodality, information and communication technology (ICT) infrastructure, and transparent legal regulations [23].

Theoretically, green transport corridors are aligned with sustainability, multi-modality, network, and supply chain principles [20,21]. The concept itself addresses environmental concerns such as air, soil, and water pollution, as well as noise pollution, which combine both environmental and social challenges, impacting human health and well-being [22]. Customers opting to utilize a corridor expect environmentally friendly transportation and economic benefits. Hence, economies of scale regarding bundled cargo represent another aspect of a green corridor, and this aspect is complemented by reliable timetables and tailored schedules for trains, ferries, and other line traffic [23]. Moreover, the concept addresses social dimensions like transport security and congestion reduction [22]. Furthermore, green transport corridors also include aspects such as entrepreneurial expansion and cluster development, which both play a crucial role in most projects involving these corridors. Additionally, a key feature common to all green transport corridor initiatives is an emphasis on guaranteeing equitable and non-discriminatory access to corridors and their trans-shipment facilities [20].

The implementation tools of green corridors include the concept of core network corridors, which aim to enhance connectivity and sustainability through coordinated planning and implementation [26]. The European-funded project the East-West Transport Corridor, under the Baltic Sea Region Programme 2007–2013, introduced a “Green Corridor Manual” which has been pivotal [30]. The document outlines KPIs as well as their measures and potential impacts while proposing a governance model for the gradual implementation of a green transport corridor [31].

The IMO defines three types of green shipping corridors: single point, point-to-point, and network. Single-point corridors establish zero-emission shipping routes to a particular location, such as a port hub that allows round-trip bunkering [20]. Point-to-point corridors are single-route green corridors between two ports. Network green corridors establish routes among three or more ports where vessels can sail on alternative fuels. There are currently a minimum of 44 green shipping corridor initiatives worldwide (refer to Figure 1), involving 171 stakeholders from a diverse array of industry and non-profit organizations, governments, ports, and public–private partnerships. These initiatives are at different stages of development, with some still in the early phases and others progressing through their planning process [9]. A significant development in this area is the collaboration between the Maritime and Port Authority of Singapore (MPA) and the Port of Rotterdam to develop the longest green and digital shipping corridor in the world, supporting low and zero-carbon shipping on the Asian-European shipping routes [32].

Recent research has made significant contributions to the development and implementation of sustainable transport corridors, with a subset of studies specifically focusing on green shipping corridors. For instance, Fozza and Recagno [38] directed their attention to sustainable technologies and innovation in green corridors. They meticulously selected representative transport corridors within Europe and identified performance indicators that are crucial for evaluating corridor services. Through a survey, they identified suitable sustainable technologies for greening these corridors, characterizing them based on specific parameters. For the corridors that their study evaluated, they assessed the impact of these technologies on the ecological aspect of the corridors and the impact on the corridors’ environmental aspect, and developed a tool for comparing different technology scenarios. Meanwhile, Batarliene and Jarašuniene [39] have study the utilization of advanced technologies (ATs) within green transport corridors. Their study emphasized the importance of understanding how the acceptability and adaptation of these technologies vary across different countries. They highlighted the role of ATs in optimizing transport activities and improving mobility conditions, focusing on enhancing the efficiency of road transport and port interaction in freight transportation along green corridors. Their insights shed light on potential improvements that could be made through the application of ATs. Clausen et al. [27] have also delved into green corridors using ICT applications to enhance environmental sustainability in the transport of European goods. They evaluated how ICTs, such as routing planners and tracking applications, could contribute to this objective. By enabling efficient resource utilization and supply chain management, these ICTs were found to reduce CO₂ emissions and improve transport efficiency. The study involved collecting various logistics ICTs, testing them in logistics nodes and corridors, and assessing their potential ecological and economic benefits.

In alignment with the broader European green initiatives, Beškovnik and Twrdy [40] introduced a comprehensive green logistics plan for Southeast Europe, focusing on enhancing multi-modal transportation and creating eco-friendly transport routes. They emphasized the importance of intermodal infrastructure development and stakeholder collaboration in realizing sustainable transport solutions. Additionally, Hunke and Prause [31] addressed the management of green corridor performance within the context of harmonized transnational transport systems. Their research highlights the essential role of stakeholder governance models and instruments in ensuring the efficient operation of green transport corridors.

Junior et al. [41] proposed a methodology to assess the efficiency of green transport corridors using a logistics composite index based on KPIs. By integrating various transport modes such as highways, railways, and waterways, the study aimed to facilitate sustainable freight transportation in Brazil. Employing a dual-step approach involving a network equilibrium model and data envelopment analysis, the research revealed that corridors incorporating planned railways exhibited the most favorable indexes, particularly in the northeastern and southern regions. Prause and Schröder [42] also contributed to this discourse by concentrating on developing KPIs that are crucial for successful green transport corridor implementation. Their balanced scorecard approach integrates cooperative and long-term perspectives, offering a comprehensive control system to effectively guide corridor management. Furthermore, Kabashkin and Sansyzbayeva [43] introduced a methodological framework for sustainable transport corridor modeling using Petri nets. Their approach integrates environmental, social, and economic indicators, providing planners and policymakers with a flexible simulation tool to analyze corridor sustainability and identify optimization opportunities. Péra et al. [25] modeled optimal transportation corridors for Brazilian soybean exports to China to minimize costs and CO₂ emissions. They evaluated corridors from the Brazilian center-west to export ports and China, highlighting the importance of strategic investments in port infrastructure to building sustainable transport routes. Their findings suggest that reducing CO₂ emissions also lowers transport costs, particularly for Capesize ships, and they advocate for port infrastructure investments to enhance vessel-berthing capacity, thereby promoting greener logistics.

Carballo-Penela et al. [44] dedicated research to the carbon footprints of ports, focusing on the Port of Gijón in Spain. By estimating the carbon footprint using financial accounts, the study assessed GHG emissions from the supply chain of products, identifying areas for improvement. The findings emphasized the significance of addressing indirect emissions in moving towards carbon-neutral ports and establishing sustainable sea motorways within green corridors. Wang et al. [1] proposed alternative fuel production sites, transportation links, and bunkering ports to support a large fraction of the global marine fuel demand, highlighting potential future green shipping corridors worldwide.

Schröder and Prause [21] explored the risks of handling and transporting dangerous goods in green transport corridors, proposing a comprehensive risk management concept. This concept aimed to address economic, ecological, and social risks, while Hunke and Prause [23] investigated hub development along green transport corridors in the Baltic Sea Region, providing insights into the infrastructure and logistical aspects that are crucial for establishing efficient and sustainable maritime transportation networks. Song et al. [45] authored a review paper aiming to broaden the policy discourse and deliberation on the practical significance of green shipping corridors. Their comprehensive analysis not only underscores the challenges associated with implementing these corridors from diverse stakeholders’ perspectives but also outlines global research agendas and forward-looking pathways for achieving decarbonization within the maritime industry under the green shipping corridors paradigm. Table 1 provides a comprehensive overview of key studies on green transport corridors, summarising each study’s primary focus and contributions to sustainable transportation advancements.

3. Study Area

Portugal and Angola share a rich history of interaction, trade, and cultural exchange [12]. For example, during the period from 2019 to 2023, the trade of tradable goods (excluding services) between the two countries amounted to an average of EUR 1149.3 million in exports and EUR 488.1 million in imports, resulting in a positive trade balance of around EUR 661.3 million. Specifically in the agricultural and agri-food, sea, and forest sectors, Portugal exported an average of EUR 292.7 million annually to Angola, making Angola the ninth largest recipient among all countries. On the other hand, the imports from Angola totaled EUR 18.9 million, ranking Angola in the 47th position, resulting in a positive trade balance of EUR 273.8 million for Portugal. These sectors represented approximately 25.5% of the total export of tradeable goods and 3.9% of the corresponding imports. Focusing on the agricultural and agri-food sectors alone, the Portuguese exports to Angola averaged EUR 239.3 million annually, positioning Angola as the seventh largest recipient globally. The imports in this sector averaged EUR 9.6 million, placing Angola in the 53rd position, leading to a positive trade balance of EUR 229.7 million. These values represent approximately 81.7% of the exports and 50.7% of the imports within the agro-forest and sea sectors [13].

Recent developments include the signing of several contracts between Portugal and Angola, notably a cooperation protocol on marine affairs [12]. This protocol signifies a strategic collaboration to enhance maritime connectivity and cooperation between the two nations. A notable initiative deriving from this collaboration is the partnership between two major ports in the partner countries, the Port of Sines in Portugal and the Port of Luanda in Angola, both founding members of the APLOP—Association of Portuguese-Speaking Seaports. This partnership includes an agreement to exchange information related to maritime port operations and provide mutual support in security, environmental audits, and international certification processes [12,13].

3.1. Port of Sines

The evaluation of this green corridor is particularly important given the fact that the Port of Sines positions itself as Europe’s Atlantic gateway. Renowned as the largest artificial and deep-water port in the country, the Port of Sines boasts exceptional geophysical characteristics, acting as the primary gateway for crucial energy supplies like natural gas, coal, oil, and derivatives [46]. Strategically positioned on the southeastern edge of Europe, the Port of Sines intersects major international maritime routes, extending its influence far beyond Portugal’s borders (see Figure 2). Additionally, as a major hub for the Ibero-Atlantic front, its direct access to a broad hinterland encompassing southern and midland Portugal, as well as extending into Spain, establishes it as a key player in both regional and international trade dynamics [47]. The Port of Sines, situated 58 nautical miles south of Lisbon at coordinates 37° 57′ N, 8° 53′ W, holds a significant position in Portugal’s maritime landscape [48].

The jurisdiction of this port covers 6.31 km² of land and 147.5 km² of maritime area [50]. The port is home to several specialized terminals catering to a diverse range of maritime needs. The Liquid Bulk Terminal is the largest in Portugal, featuring a design that accommodates multiple clients and products. It boasts six jetties and natural beds that extend down to 28 m zero hydrography (ZH), accommodating vessels of up to 350,000 tons deadweight (DWT). The Petrochemical Terminal, managed by Repsol Polímeros under a private use concession, supports petrochemical product handling via a dedicated pipeline that connects vessels to the petrochemical complex in the Sines Industrial and Logistics Area. This terminal includes two jetties with beds of 12 m ZH, accommodating vessels with up to 20,000 m³ of cargo. The Sines Multipurpose Terminal is designed for dry bulk, general cargo, and ro-ro operations, with a quay measuring 645 m at the extrados and 296 m at the intrados, and draughts of up to 18 m ZH, supporting ships up to 190,000 tons DWT. The Natural Gas Terminal, managed by REN Atlântico, is crucial for Portugal’s natural gas supply. The Port of Sines has a single jetty that offers LNG tanker reception up to 225,000 m³. The Container Terminal supports Post-Panamax ships with an annual TEU capacity of 2,300,000. The Fishing Harbor and Leisure Port are additional facilities. Figure 3 illustrates the layout of the Port of Sines, highlighting the key terminals and their locations.

Beyond its primary function of energy handling, the port plays a multifaceted role in managing general and containerized cargo, offering significant growth opportunities [47]. The latest available data underscore its robust performance, with 1927 ship calls and a total cargo movement of 44,812 units, including an impressive 1,662,600 units of container movement [51,52]. Remarkably, it stands as the top-earning port in Portugal, with an annual income reaching approximately EUR 12 million [46]. The port accommodates vessels of various sizes without constraints, positioning it as a leading port for cargo handling in a competitive shipping market that is increasingly dominated by larger container ships [47]. Its modern facilities and advanced berthing infrastructure further enhance its capabilities, solidifying its position as one of the few deep-water European ports capable of handling substantial cargo volumes [53]. Regarding connectivity, the Port of Sines annually handles traffic with approximately 200 ports across 80 countries worldwide [54].

3.2. Port of Luanda

The Port of Luanda is a crucial maritime hub in Africa that is located in the urban district of Luanda and is strategically situated in the bay between the Atlantic Ocean and the Island of Luanda. As Angola’s largest port, it spans a concession area of over 600 hectares and boasts a substantial cargo-handling capacity of seven million tons [55]. The port leverages exceptional features that enhance its maritime access and ensure the safety and efficiency of vessel operations. Its location in a naturally formed bay, shielded from the open sea by the Island of Nossa Senhora do Cabo, provides natural protection against currents and waves. These favorable conditions create an ideal environment for maneuvering and docking incoming vessels [56]. The maximum draft at the port is 13.2 m at high tide, with quay depths ranging from 10.5 to 12.5 m, except at the Cabotage Terminal, where the draft varies between 3.5 m and 5.5 m [56]. Additionally, the port encompasses 19 warehouses, with a total covered area of 55,500 m² and a stacking area of 729,219 m² [57].

The port’s structural configuration has evolved with the expansion of ship berthing docks and progressive changes in its management models. This evolution has witnessed the port’s transition from an “Operator” port model to a “Tool Port” type and, finally, to a “Landlord Port” model, marking significant improvements in its organizational framework [58]. Currently, it serves as the primary hub for supplying goods to the northern region of the country, encompassing provinces such as Luanda, Bengo, Uíge, Zaire, Kwanza Norte and Sul, Malange, and Lunda Norte. Furthermore, its strategic position extends its reach to neighboring countries through various transportation corridors, enhancing trade connectivity across borders. With a vast hinterland spanning 358,813 km², the port acts as a crucial lifeline for several industries, notably the oil sector—including the prominent Luanda refinery and entities like Cimangola (Angolan cement factory)—and the metalworking and civil construction sectors. Additionally, the port’s connectivity extends beyond provincial boundaries, integrating with road, air, and rail networks like the Luanda Railway and facilitating trade across the region [59].

The Port of Luanda has specialized terminals for different types of cargo and services. The General Cargo Terminal by Multiterminais handles liquid bulk and cereals. The Container Terminal by SOGESTER offers multiple access points. Unicargas’ Multipurpose Terminal handles general cargo and containers. Sonangol’s Oil Support Terminal serves the oil sector. DP World’s Multi-Use Terminal handles general cargo and containers. The Passenger Terminal focuses on passenger services. The Cabotage Terminal supports coastal shipping activities [60]. These integrated terminals collectively ensure that the Port of Luanda operates smoothly and efficiently, meeting the diverse needs of its users. Figure 4 provides the layout of the Port of Luanda, showing the locations of the different specialized terminals.

4. Materials and Methods

To effectively prioritize the main criteria and sub-criteria for establishing a green shipping corridor between the Ports of Sines and Luanda, a systematic approach employing the fuzzy AHP was adopted. This methodology, ensuring a comprehensive and transparent decision-making process, is outlined in detail below. A schematic diagram further illustrating the steps involved is provided in Figure 5.

4.1. Identification of Criteria and Sub-Criteria

This study commenced with an extensive review of the existing literature, aiming to identify the primary criteria for developing a sustainable maritime corridor. Through this initial exploration, a set of criteria emerged, which were further refined and validated through consultations with industry experts and stakeholders. This collaborative effort ensured that all essential elements required for the success of the green corridor project were considered. The identified criteria and their respective sub-criteria, along with brief explanations, are provided below for clarity:

C1. Environmental Compatibility: evaluates the degree to which human activities and developments align with environmental sustainability goals.

C1.1. Air Pollution: refers to emissions of pollutants such as sulfur oxides (SO_x), nitrogen oxides (NO_x), particulate matter (PM), volatile organic compounds (VOCs), and carbon dioxide (CO₂) from shipping activities.

C1.2. Water Pollution: involves contamination of marine environments due to sewage discharge, oil spills, chemical pollutants, and waste from ships.

C1.3. Noise Pollution: indicates excessive or disruptive levels of sound generated by maritime activities such as ship engines, vessel traffic, and port operations, which can disturb marine life, affect underwater ecosystems, and affect coastal communities.

C1.4. Biodiversity Conservation: focuses on preserving marine ecosystems and endangered species within and around the corridor, including minimizing habitat destruction and protecting vulnerable species.

C2. Economic Viability: evaluates the financial feasibility and profitability of the corridor.

C2.1. Initial Investment: refers to the upfront costs required to implement green initiatives and infrastructure upgrades for the corridor.

C2.2. Long-term Operational Costs: considers ongoing expenses associated with maintaining and operating the green corridor over its lifespan.

C2.3. Trade Volume and Economic Benefits: evaluates the potential increase in trade volume and the resulting economic benefits, such as revenue generation, to determine the corridor’s economic impact.

C2.4. Job Creation: focuses on the creation of employment opportunities stimulated by the corridor, contributing to regional development and economic growth.

C3. Infrastructure Readiness: focuses on the readiness and adequacy of physical infrastructure to support corridor operations.

C3.1. Port Capacity and Performance: assesses the ability of ports to efficiently handle increased cargo volumes and vessel traffic.

C3.2. Availability of Green Infrastructure: considers the presence of eco-friendly infrastructure and services at ports, such as shore power and waste management facilities.

C3.3. Transportation Connectivity: examines the connectivity of transportation networks, including links to hinterland regions and intermodal transportation facilities such as rail and road networks.

C4. Regulatory and Policy Compliance: assesses alignment with international and regional regulations for maritime activities and environmental protection, along with adherence to government policies promoting sustainability and green initiatives.

C4.1. Compliance with IMO Regulations: ensures compliance with international maritime regulations on safety, pollution prevention, and vessel operation set by the International Maritime Organization.

C4.2. Adherence to EU Environmental Standards: ensures compliance with environmental directives and standards set by the European Union, ensuring alignment with EU regulations.

C4.3. Government Policies and Incentives: considers supportive policies and financial incentives provided by governments to promote green initiatives and sustainable practices within the corridor.

C5. Social Acceptance: assesses the degree of acceptance and support for the corridor among stakeholders, including local communities, indigenous groups, and the public.

C5.1. Stakeholder Engagement: involves collaboration with local communities, indigenous groups, and other stakeholders affected by the corridor, ensuring their involvement and support.

C5.2. Public Awareness and Acceptance: includes education and awareness programs to inform the public about the benefits and impacts of the corridor, as well as addressing concerns and perceptions regarding its social implications.

C6. Operational Efficiency: focuses on optimizing the efficiency and effectiveness of corridor operations.

C6.1. Reliability and Efficiency: focuses on minimizing transit times, reducing waiting times at ports, and optimizing operational efficiency to ensure smooth and efficient operations within the corridor.

C6.2. Safety and Security: involves the implementation of safety protocols and measures to protect against maritime security threats, ensuring the safety and security of vessels, cargo, and personnel.

C7. Technological Advancements: evaluates the integration of innovative technologies to improve environmental performance, operational efficiency, and sustainability within the corridor.

C7.1. Adoption of Green Technologies: refers to the integration of eco-friendly vessel designs, renewable energy sources, and other green technologies to minimize environmental impact.

C7.2. Digitalization: involves the implementation of digital solutions for route optimization, fleet management, and real-time monitoring of emissions, improving operational efficiency and environmental performance.

4.2. Development of Hierarchical Structure

In this step, the identified factors are organized into a hierarchical structure to facilitate the prioritization process. This study adopts a structured approach with three levels. At the top level, the overarching objective is defined, which is to establish a sustainable maritime corridor between the Ports of Sines and Luanda. The second level comprises the main criteria that contribute to achieving the objective. These criteria represent broad categories of factors that will influence the development of the green corridor. Subsequently, at the third level, specific sub-criteria are delineated, providing detailed insights into each main criterion. This hierarchical structure ensures clarity and organization in the prioritization process, enabling a systematic evaluation of factors at different levels. It allows for an effective assessment of the relative importance of each criterion and sub-criterion to achieving the overarching objective of establishing a sustainable maritime corridor. Figure 6 provides a visual representation of the hierarchical structure developed for this study.

4.3. Pair-Wise Comparison of Criteria

After establishing the hierarchy, 14 experts from the Ports of Sines and Luanda (seven professionals from each country) with top-management positions (including the government, the port authority, and major consignees of the port’s terminals) and relevant subject matter expertise, each possessing over 20 years of extensive experience, were selected to perform pair-wise comparisons. These comparisons involved evaluating each criterion at the same level against every criterion from the preceding (upper) level. The experts’ linguistic preferences were then translated into triangular fuzzy numbers, as shown in Table 2.

4.4. Constructing Pair-Wise Comparison Matrix

The fuzzy pair-wise comparison matrix $\tilde{A}$ is constructed by comparing criteria in pairs based on their relative importance. Each element in the matrix $\tilde{A}$ is a triangular fuzzy number that consists of a lower bound, middle value, and upper bound, denoted as ($l_{ij},m_{ij},u_{ij})$. The matrix $\tilde{A}$ is structured as follows:

(1)$\tilde{A}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \dots & a_{1n}\\ a_{21}& a_{22}& \dots & a_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]=\left[\begin{array}{cccc}(\mathrm{1,1},1)& (l_{12},m_{12},u_{12})& \dots & (l_{1n},m_{1n},u_{1n})\\ (l_{21},m_{21},u_{21})& (\mathrm{1,1},1)& \dots & (l_{2n},m_{2n},u_{2n})\\ ⋮& ⋮& \ddots & ⋮\\ (l_{n1},m_{n1},u_{12})& (l_{n2},m_{n2},u_{n2})& \cdots & (\mathrm{1,1},1)\end{array}\right]$

where:

• $\tilde{A}$ is the fuzzy comparison matrix;

• $a_{ij}=\left(l_{ij},m_{ij},u_{ij}\right)$ represents the fuzzy comparison value between criterion $i$ and criterion $j$;

• Diagonal elements $a_{ii}=\left(\mathrm{1,1},1\right)$ indicate that each criterion is equally important when compared to itself.

4.5. Consistency Check

Inconsistencies in judgments can lead to unreliable results and undermine the subsequent analysis’s validity. Therefore, the consistency check serves as a critical step to verify the soundness of the decision model and enhance confidence in the obtained results [61]. The Gogus and Boucher method [62] was applied in this study to determine the consistency ratio for fuzzy pair-wise comparison matrices. This method comprises several steps which are outlined below.

First, two matrices are formed from the original fuzzy pair-wise comparisons matrix: the matrix of mean values $\left(A^m\right)$ and the matrix of geometric means of the lower and upper bounds $\left(A^g\right)$. Next, the weight vectors for the matrices $A^m$ and $A^g$ are computed. For $A^m$, the weight vector ${(w}^m)$ is calculated using Equation (2):

(2)$w_i^m={\displaystyle \frac{1}{n}}\sum _{j=1}^n{\displaystyle \frac{a_{ijm}}{\sum _{i=1}^n{a}_{ijm}}}$

where:

• $w_i^m$ is the weight vector for matrix $A^m$;

• $a_{ijm}$ represents the mean fuzzy comparison value between criterion $i$ and criterion $j$;

• $n$ is the total number of criteria.

Similarly, for $A^g$, the weight vector $\left(w^g\right)$ is derived using Equation (3):

(3)$w_i^g={\displaystyle \frac{1}{n}}\sum _{j=1}^n{\displaystyle \frac{\sqrt{a_{iju}.a_{ijl}}}{\sum _{i=1}^n\sqrt{a_{iju}.a_{ijl}}}}$

where:

• $w_i^g$ is the weight vector for the geometric means matrix $A^g$;

• $a_{iju}$ is the upper bound of the fuzzy number between criterion $i$ and criterion $j$;

• $a_{ijl}$ is the lower bound of the fuzzy number between criterion $i$ and criterion $j$;

• $n$ is the total number of criteria;

• $\sqrt{a_{iju}.a_{ijl}}$ represents the geometric mean of the upper and lower bounds.

Following this, the largest eigenvalues for matrices $A^m$ and $A^g$ are determined using Equations (4) and (5):

(4)${\lambda }_{max}^m={\displaystyle \frac{1}{n}}\sum _{i=1}^n\sum _{j=1}^n{a}_{ijm}\left({\displaystyle \frac{w_j^m}{w_i^m}}\right)$

(5)${\lambda }_{max}^g={\displaystyle \frac{1}{n}}\sum _{i=1}^n\sum _{j=1}^n\sqrt{a_{iju}.a_{ijl}}\left({\displaystyle \frac{w_j^g}{w_i^g}}\right)$

where:

• ${\lambda }_{max}^m$ and ${\lambda }_{max}^g$ are the largest eigenvalues for the matrices $A^m$ and $A^g$, respectively;

• $a_{ijm}$ is the mean fuzzy comparison value between criteria $i$ and $j$ in matrix $A^m$;

• $\sqrt{a_{iju}.a_{ijl}}$ is the geometric mean of the upper and lower bounds of the fuzzy number between criteria $i$ and $j$ in matrix $A^g$;

• $w_j^m$ and $w_j^g$ are the weight vector elements for criterion $j$ in matrices $A^m$ and $A^g$, respectively;

• $w_i^m$ and $w_i^g$ are the weight vector elements for criterion $i$ in matrices $A^m$ and $A^g$, respectively;

• $n$ is the number of criteria used in the comparison.

Subsequently, the consistency indices $\left(CI\right)$ for both matrices are calculated as follows:

(6)${CI}^m={\displaystyle \frac{({\lambda }_{max}^m-n)}{(n-1)}}$

(7)${CI}^g={\displaystyle \frac{({\lambda }_{max}^g-n)}{(n-1)}}$

where:

• ${CI}^m$ and ${CI}^g$ are the consistency indices for the matrices $A^m$ and $A^g$, respectively;

• ${\lambda }_{max}^m$ and ${\lambda }_{max}^g$ are the largest eigenvalues for matrices $A^m$ and $A^g$;

• $n$ is the number of criteria.

Finally, the consistency ratios for the matrices $A^m$ and $A^g$ are computed using the following equations:

(8)${CR}^m={\displaystyle \frac{{CI}^m}{{RI}^m}}$

(9)${CR}^g={\displaystyle \frac{{CI}^g}{{RI}^g}}$

where:

• ${CR}^m$ and ${CR}^g$ are the consistency ratios for matrices $A^m$ and $A^g$, respectively;

• ${RI}^m$ and ${RI}^g$ are the random index values for matrices $A^m$ and $A^g$, and depend on the size of the matrix.

The RI values for the matrix of mean values (${RI}^m$) and the matrix of geometric means (${RI}^g$) are listed in Table 3:

According to Saaty [63], a $CR$ value below 0.1 indicates an acceptable level of consistency in the pair-wise comparison matrix. However, a $CR$ value exceeding 0.1 suggests potential inconsistencies in the judgments in the matrix. Therefore, if both ${CR}^m$ and ${CR}^g$ exceed 0.1, it indicates significant inconsistencies in the decision-maker’s evaluations. In such cases, a comprehensive review and refinement of all judgments are recommended, focusing particularly on areas where larger discrepancies are observed. Alternatively, if only ${CR}^m$ (or ${CR}^g$) exceeds 0.1 while the other remains within the acceptable range, it indicates inconsistencies primarily in the mean (or extreme) values of the judgments. In this scenario, the decision-maker should reassess these aspects while maintaining the extreme values (or mean values) as unchanged.

4.6. Weighting Criteria and Sub-Criteria

The extent analysis method developed by Chang [64], a widely recognized technique in fuzzy AHP, was employed to calculate the weights of criteria and sub-criteria in this research. The detailed steps involved in this process are described below.

4.6.1. Calculating the Fuzzy Synthetic Extent Value

In this step, the sum of the fuzzy numbers in each row of the comparison matrix $\tilde{A}$ is initially calculated. This requires adding up the lower, middle, and upper values of the triangular fuzzy numbers for each row. The sum for the i-th row is given by:

(10)$\sum _{j=1}^n{\tilde{a}}_{ij}=\left(\sum _{j=1}^n{l}_{ij},\sum _{j=1}^n{m}_{ij},\sum _{j=1}^n{u}_{ij}\right)$

where:

• ${\sum }_{j=1}^n{l}_{ij}$ is the total of the lower bounds for row $i$;

• ${\sum }_{j=1}^n{m}_{ij}$ is the total of the middle values for row $i$;

• ${\sum }_{j=1}^n{u}_{ij}$ is the total of the upper bounds for row $i$.

To calculate the fuzzy synthetic extent for each criterion, the row sums are normalized by dividing each row sum by the total sum of all elements in the comparison matrix. Let k range from 1 to n in order to sum all rows, and let i represent the current row being normalized. The fuzzy synthetic extent $\left(S_i\right)$ for the i-th criterion is computed as:

(11)$S_i={\displaystyle \frac{{\sum }_{j=1}^m{\tilde{a}}_{ij}}{{\sum }_{k=1}^n{\sum }_{j=1}^n{\tilde{a}}_{kj}}}$

In terms of triangular fuzzy numbers, $S_i$ can be expressed as:

(12)$S_i=\left({\displaystyle \frac{{\sum }_{j=1}^n{l}_{ij}}{{\sum }_{k=1}^n{\sum }_{j=1}^n{u}_{kj}}}, {\displaystyle \frac{{\sum }_{j=1}^n{m}_{ij}}{{\sum }_{k=1}^n{\sum }_{j=1}^n{m}_{kj}}}\right.,\left.{\displaystyle \frac{{\sum }_{j=1}^n{u}_{ij}}{{\sum }_{k=1}^n{\sum }_{j=1}^n{l}_{kj}}}\right)$

where:

• ${\sum }_{k=1}^n{\sum }_{j=1}^n{u}_{kj}$, ${\sum }_{k=1}^n{\sum }_{j=1}^n{m}_{kj}$, and ${\sum }_{k=1}^n{\sum }_{j=1}^n{l}_{kj}$ represent the total sums of the upper, middle, and lower values of all elements in the matrix, respectively.

4.6.2. Determining the Degree of Possibility

For each pair of fuzzy synthetic extents $(S_i$ and $S_j),$ the degree of possibility $\left(V\left({\tilde{S}}_i\ge {\tilde{S}}_j\right)\right)$ is defined as:

(13)$V\left({\tilde{S}}_i\ge {\tilde{S}}_j\right)=\left\{\begin{array}{ccc}1& if m_i\ge m_j& \\ 0& if l_j\ge u_i& i,j=1,\dots ,n; j\ne i\\ {\displaystyle \frac{l_j-u_i}{\left(m_i-u_i\right)-(m_j-l_j)}}& otherwise& \end{array}\right.$

where:

• ${\tilde{S}}_i$ =($l_i,m_i,u_i)$ and ${\tilde{S}}_j$ =($l_j,m_j,u_j)$ are the fuzzy synthetic extents for criteria i and j;

• $l_i,m_i,u_i$ are the lower, middle, and upper values of the fuzzy number for criterion i;

• $l_j,m_j,u_j$ are the lower, middle, and upper values of the fuzzy number for criterion j.

4.6.3. Determining the Minimum Degree of Possibility

This step involves finding the smallest degree of possibility for each criterion, which represents how strongly one criterion is preferred over all others. For each criterion i, the minimum degree of possibility is defined as:

(14)$d_i=min V\left({\tilde{S}}_i\ge {\tilde{S}}_j\right)$

where $d_i$ is the minimum degree of possibility that fuzzy synthetic extent ${\tilde{S}}_i$ is greater than or equal to all other extents ${\tilde{S}}_j$.

4.6.4. Calculating the Normalized Weight Vector

The initial priority vector $\left(W^{\prime }\right)$ is formed from the minimum degrees of possibility:

(15)$W^{\prime }=\left[d_1,d_2,\dots ,d_n\right]$

This vector is then normalized to ensure that the weights sum to 1. The normalized weight vector is computed as:

(16)$W=\left[{\displaystyle \frac{d_1}{{\sum }_{i=1}^n{d}_i}},{\displaystyle \frac{d_2}{{\sum }_{i=1}^n{d}_i}},\dots ,{\displaystyle \frac{d_n}{{\sum }_{i=1}^n{d}_i}}\right]$

where:

• $W$ is the normalized weight vector, with each element representing the relative weight of the corresponding criterion;

• ${\sum }_{i=1}^n{d}_i$ is the total sum of all minimum degrees of possibility;

• Each element ${\displaystyle \frac{d_n}{{\sum }_{i=1}^n{d}_i}}$ represents the normalized weight of the i-th criterion, ensuring that the total weight vector sums to 1.

4.7. Sensitivity Analysis

Sensitivity analysis is a critical component in optimization and decision-making, providing insights into how variations in input parameters can influence the outcomes of analytical models. This analysis enables the exploration of “what if” scenarios, enhancing the credibility of the model by quantifying the resilience of the optimal solution amid changing conditions.

The application of sensitivity analysis in fuzzy AHP is particularly valuable for several reasons:

• Facilitating Decision-Maker Engagement: in many public decision-making scenarios, decision-makers may hesitate to express explicit preferences due to political constraints or concerns over losing control of the process. Sensitivity analysis allows analysts to present critical information without imposing a need for decision-makers to quantify their preferences, thus fostering a more collaborative environment;

• Accommodating Multiple Decision-Makers: when multiple decision-makers are involved, disagreements may arise regarding the relative importance of objectives. Sensitivity analysis offers a mechanism for generating insights without necessitating a consensus among the decision-makers, thus supporting a more flexible decision-making process;

• Integrating Quantifiable and Qualitative Objectives: the AHP’s capacity to combine measurable objectives, such as minimizing costs, with qualitative aspects, such as enhancing public perception, necessitates sensitivity analysis. The final rankings are influenced by the decision-makers’ subjective evaluations of these objectives, and in environments characterized by subjectivity and uncertainty, sensitivity analysis becomes essential;

• Managing Inconsistencies: the AHP method allows for some level of inconsistency in the comparisons of objectives, acknowledging the reality of human judgment. However, this introduces the need for sensitivity analysis to assess how these inconsistencies affect the final decision outcomes.

The sensitivity analysis in this study employs a specific formula [65,66] to quantify the impacts of changes in criteria weights. When the weight of a criterion, denoted as $w_p$, is adjusted by an amount ${∆}_p$, the new weight for any other criterion $j$ is calculated using Equation (17):

(17)$w_j^{\prime }={\displaystyle \frac{1-w_p-{∆}_p}{1-w_p}}\times w_j j=\mathrm{1,2},\cdots ,k j\ne p$

where $w_j^{\prime }$ represents the new weight of criterion $j$, while $w_j$ denotes its original weight. This formula illustrates how a change in the weight of one criterion affects the weights of the remaining criteria. A 10% change was applied here to the weight of the criterion $w_p$, facilitating an assessment of how this adjustment impacts the overall decision-making model.

5. Results

In this section, the results from the fuzzy AHP analysis are provided, highlighting the most important criteria and sub-criteria that decision-makers should consider when establishing the green corridor. Supporting data related to the results presented in this section can be found in Tables S1 to S16.

5.1. Prioritization of Main Criteria

Table 4 presents the prioritization results for the main criteria based on their normalized weights. Environmental compatibility emerged as the top priority with a weight of 0.250, followed closely by economic viability at 0.243 and infrastructure readiness at 0.203. Regulatory and policy compliance, operational efficiency, technological advancements, and social acceptance were also considered, in descending order of significance.

The calculated consistency ratios (${CR}^m$ = 0.0165, ${CR}^g$ = 0.0482) are below the typical threshold of 0.1, indicating an acceptable level of consistency in the judgments of the main criteria.

5.2. Prioritization of Sub-Criteria

The main criteria were further dissected into sub-criteria to provide a granular view of their respective contributions and priorities.

Table 5 illustrates the local weights and rankings of the sub-criteria within environmental compatibility, highlighting water pollution as the most critical factor, with a weight of 0.437, followed by biodiversity conservation at 0.402. Air pollution and noise pollution, while important, were assigned with lower weights of 0.112 and 0.048, respectively.

Table 6 displays the results for the economic viability sub-criteria, where long-term operational costs lead with a weight of 0.410. The trade volume and economic benefits criterion is closely behind, with a weight of 0.308, followed by initial investment at 0.218. Job creation is deemed the least significant, with a weight of 0.062.

The results for the sub-criteria under infrastructure readiness are presented in Table 7. Transportation connectivity received the highest ranking, with a local weight of 0.456, followed by port capacity and performance at 0.414. Availability of green infrastructure had a lesser impact, with a weight of 0.129.

Table 8 outlines the sub-criteria under regulatory and policy compliance, detailing their local weights and ranks. Government policies and incentives ranked the highest with a weight of 0.511, followed by compliance with IMO regulations at 0.278 and adherence to EU environmental standards at 0.210.

Table 9 shows the prioritization results for the social acceptance sub-criteria. Stakeholder engagement holds the highest weight of 0.731, securing the top position, while public awareness and acceptance comes next with a weight of 0.269.

In the operational efficiency category, the safety and security criterion is given the highest weight (0.558), highlighting its paramount importance. Following closely behind are reliability and efficiency, which ranks second with a weight of 0.442 (refer to Table 10).

Finally, Table 11 highlights the prioritization of the sub-criteria within the technological advancements category. Adoption of green technologies emerges as the top priority, with a significant local weight of 0.614. Digitalization follows with a weight of 0.386, ranking second.

The consistency ratios (${CR}^m$ and ${CR}^g$) for the environmental compatibility, economic viability, infrastructure readiness, and regulatory and policy compliance categories were all below 0.1, indicating a high level of consistency in the prioritization process. In the case of the social acceptance, operational efficiency, and technological advancements categories, the consistency ratio was not computed. According to the Gogus and Boucher method, for a 2×2 matrix comparison with only two sub-criteria, the random index (RI) is zero. As a result, calculating both consistency ratios (${CR}^m$ and ${CR}^g$) involves division by zero, rendering them mathematically undefined and therefore not applicable. Nevertheless, it is worth noting that 2×2 matrix comparisons inherently exhibit consistency within the AHP method. This inherent consistency arises because such comparisons involve a single pair-wise judgment. This simplicity ensures that there are no extra pair-wise comparisons, reducing the risk of variability or inconsistency in assessments. Therefore, despite the limitation in calculating the inconsistency ratio due to the constraints of the Gogus and Boucher method, the judgments made in these comparisons remain inherently reliable and stable.

Table 12 summarizes the global weights and ranks assigned to each sub-criterion, revealing their relative importance in establishing a green corridor between the ports of Sines and Luanda. Water pollution is identified as the most critical sub-criterion, with the highest global weight of 0.109, earning it the top rank. Close behind, biodiversity conservation and long-term operational costs both have significant global weights of 0.100, tying for the second rank. Transportation connectivity follows with a weight of 0.093, securing the third rank. The efficiency and performance of port capacity and performance are crucial, with a global weight of 0.084, placing it fourth. Trade volume and economic benefits ranks fifth with a weight of 0.075, highlighting its importance in economic evaluations. Government policies and incentives comes next with a global weight of 0.060, earning it the sixth rank and emphasizing the role of supportive policies. Initial investment is a key consideration, with a weight of 0.053, placing it seventh. Safety and security also holds importance, with a weight of 0.045, ranking eighth. The adoption of green technologies has a weight of 0.039, positioning it 9th, while reliability and efficiency follows closely with a weight of 0.035, placing it 10th. The weights of several sub-criteria are notably close: compliance with IMO regulations ranks 11th with a global weight of 0.032, and stakeholder engagement follows closely with a weight of 0.031, ranking 12th. Air pollution is ranked 13th with a global weight of 0.028, while availability of green infrastructure holds a weight of 0.026, placing it 14th. Adherence to EU environmental standards ranks 15th with a weight of 0.025, and digitalization has a weight of 0.024, placing it 16th. In the lower ranks, job creation holds a global weight of 0.015, ranking 17th. Public awareness and acceptance and noise pollution both have a global weight of 0.012, tying for the final rank.

5.3. Sensitivity Analysis Results

This section presents the results of the sensitivity analysis for the main criteria and sub-criteria. The sensitivity test examines how a 10% increase and decrease in the weight of each criterion or sub-criterion affects its relative importance and rank. The analysis provides insight into how robust the prioritization of criteria and sub-criteria is and how shifts in emphasis might alter the ranking.

5.3.1. Main Criteria Sensitivity Analysis

The sensitivity analysis for the main criteria shows the effects of a 10% increase or decrease in each criterion’s weight. The results, summarized in Table 13, demonstrate the adjusted weights and the corresponding new ranks for each criterion.

Environmental compatibility (C1), originally ranked first with a weight of 0.250, is sensitive to changes in weight. When increased by 10%, it maintains its position as the most important criterion. However, when decreased by 10%, it drops to the second rank, being overtaken by economic viability (C2). This demonstrates the strong competition between environmental and economic factors, where a slight reduction in emphasis on environmental sustainability allows economic considerations to take precedence.

Economic viability (C2), with an initial weight of 0.243 and ranked second, becomes the most important criterion when its weight is increased by 10%, surpassing Environmental sustainability. This suggests that the economic feasibility of the green corridor is highly valued and can easily take priority over environmental concerns if given slightly more emphasis. However, when the weight of economic viability is reduced by 10%, its rank remains stable at second place, indicating that, while important, it does not lose significant ground when de-emphasized.

Infrastructure readiness (C3), with an original weight of 0.203, shows stability in its ranking as the third most important criterion, even after a 10% increase or decrease in its weight. This suggests that the readiness of infrastructure is perceived as a crucial but consistent factor, and moderate adjustments in its weight do not significantly alter its importance in the overall decision-making process.

Similarly, regulatory and policy compliance (C4) maintains its fourth-place ranking, demonstrating that, while regulatory factors are essential, they are not as sensitive to weight changes as economic and environmental factors. This indicates that regulatory compliance, though necessary for ensuring legal and environmental standards, does not dramatically affect decision-making unless substantial changes in weight are applied.

Social acceptance (C5), operational efficiency (C6), and technological advancements (C7) consistently maintain their original ranks of seventh, fifth, and sixth, respectively, despite the weight adjustments. This indicates that these criteria, while important, are perceived as secondary to the key drivers of environmental sustainability and economic viability. Their rankings suggest that decision-makers place more emphasis on the broader, systemic factors of the project, with social and technological considerations being less sensitive to minor changes in weighting.

In summary, the sensitivity analysis of the main criteria shows that environmental sustainability (C1) and economic viability (C2) are the most sensitive to weight changes, directly competing for the top spot in terms of their effects on decision-making. Other criteria, such as infrastructure readiness (C3) and regulatory and policy compliance (C4), demonstrate greater stability, with minimal shifts in rank. This provides valuable insights for decision-makers regarding the key factors driving the establishment of a green corridor between the two ports.

5.3.2. Sub-Criteria Sensitivity Analysis

The sensitivity analysis of the sub-criteria, summarized in Table 14, explores how weight changes affect the importance of individual factors within each main criterion. Each sub-criterion was increased and decreased by 10% to assess the impact on rankings within its respective categories.

In the environmental compatibility (C1) category, water pollution (C1.2) stands out as the top priority with an initial weight of 0.437. Increasing its weight by 10% strengthens its dominance, reaffirming the need for robust water pollution control measures. A 10% reduction in its weight, however, causes it to drop to second place, illustrating its sensitivity and competition with biodiversity conservation (C1.4), which rises in priority. In contrast, the rankings of air pollution (C1.1) and noise pollution (C1.3) remain stable, reflecting a secondary focus on these issues compared to water quality and biodiversity.

Within the economic viability (C2) category, long-term operational costs (C2.2) is the most influential sub-criterion, with an initial weight of 0.410. Even with a 10% weight adjustment, it retains its first-place ranking, underscoring the focus on long-term financial sustainability. Trade volume and economic benefits (C2.3) consistently holds second place, emphasizing its significant role in evaluating economic impacts. Meanwhile, initial investment (C2.1) and job creation (C2.4) remain third and fourth, respectively, suggesting that these factors, while important, are secondary to operational costs and trade.

In the infrastructure readiness (C3) category, transportation connectivity (C3.3) is ranked as the top sub-criterion with an initial weight of 0.456. A 10% weight increase solidifies its importance, while a 10% reduction pushes it to second place, indicating some sensitivity to weight adjustments. Port capacity and performance (C3.1), initially in second place, temporarily rises to first with a 10% weight increase, showcasing its importance. Green infrastructure (C3.2) remains stable in third place, suggesting it is less critical than connectivity and port capacity but still relevant.

For the regulatory and policy compliance (C4) category, government policies and incentives (C4.3) emerges as the most important sub-criterion, with an initial weight of 0.511. It retains its top spot even with the weight adjustments, indicating the vital role of government support. Compliance with IMO regulations (C4.1) stays in second place throughout, signaling its consistent significance. Adherence to EU environmental standards (C4.2) holds third place without change, suggesting that it plays a necessary but less critical role compared to the other sub-criteria.

In the social acceptance (C5) category, stakeholder engagement (C5.1) dominates with an initial weight of 0.731. Even with a 10% increase or decrease, it remains the top priority, highlighting its central role in securing the project’s success. Public awareness and acceptance (C5.2), with a weight of 0.269, consistently ranks second, reflecting its supportive role but secondary importance compared to direct stakeholder engagement.

In the operational efficiency (C6) category, safety and security (C6.2) holds the most weight, starting at 0.558, and remains the top priority despite the weight adjustments, underscoring the critical importance of safety in operations. Reliability and efficiency (C6.1) ranks second and retains its position even with a 10% increase or decrease, indicating its steady, but secondary, role in operational decision-making.

Lastly, in the technological advancements (C7) category, adoption of green technologies (C7.1) is the highest priority, with an initial weight of 0.614. It maintains its top rank regardless of the weight adjustments, underscoring the critical importance of green technologies to sustainability. Digitalization (C7.2), with a weight of 0.386, consistently ranks second, highlighting its complementary role in enhancing project outcomes without surpassing the importance of green technologies.

6. Discussion

The prioritization results provide valuable guidance for the development of the green corridor between the Ports of Sines and Luanda, highlighting key areas that require focused attention and strategic action. The top priority given to environmental compatibility, particularly water pollution and biodiversity conservation, stresses the importance of placing environmental stewardship at the heart of the corridor’s development. This finding is consistent with broader green shipping corridor initiatives, where environmental goals are prioritized to ensure long-term sustainability [1,44]. In this context, the corridor must consider the potential environmental impact in adopting technologies and policies, ensuring compliance with global environmental standards while fostering sustainable development in the region. Economic viability emerged in second place, emphasizing the necessity of balancing long-term operational costs with an enhanced trade volume and economic benefits. The significance of economic viability is closely tied to the challenges faced in establishing sustainable transport solutions. Studies by Notteboom and Rodrigue [67] highlight the necessity for cost-effective strategies in the maritime sector that enhance operational efficiency and support long-term resilience. Furthermore, the Review of Maritime Transport 2023 published by UNCTAD [68] emphasizes the need to balance environmental goals with economic needs while underscoring that the cost of inaction far outweighs the necessary investments to realize a decarbonized shipping industry. Strategic investments that align initial expenditures with long-term economic benefits, thereby attracting investors and stakeholders committed to fostering sustainable economic growth, are crucial for ensuring the corridor’s viability.

In third place, the importance of infrastructure readiness, particularly transportation connectivity and port capacity and performance, underscores the necessity for substantial investment in infrastructure development. Enhancing transportation links and improving port performance are critical to efficient operations and the seamless movement of goods. As emphasized by the World Economic Forum [69], effective investments in infrastructure are crucial for achieving sustainable mobility and facilitating efficient transport systems.

Government policies and incentives appear in the fourth place and play a significant role, highlighting the importance of leveraging regulatory frameworks and financial incentives to support sustainable practices. Collaboration with regulatory bodies to navigate legal requirements will enhance the credibility and global appeal of the corridor.

Furthermore, the emphasis on safety and security within the operational efficiency category implies that high standards must be maintained to protect both human and environmental health. Implementing stringent safety protocols and security measures will be crucial to conducting reliable operations. Ensuring operational reliability and efficiency through best practices and continuous improvement can enhance the corridor’s reputation and performance. These measures will contribute to the overall robustness and resilience of the corridor.

The above criteria are followed by technological advancements that play a pivotal role in enhancing operational effectiveness and sustainability within the corridor. This is especially relevant in the case of Ports of Sines and Luanda since they are equipped with technological platforms for information management named Single Logistics Window (JUL) and Single Port Window (JUP), respectively. The adoption of green technologies, such as energy-efficient vessel designs and renewable energy systems, will be necessary to reduce emissions and meet stringent environmental targets. The integration of digital technologies for real-time monitoring and emissions tracking will further improve the operational efficiency and ensure the corridor’s alignment with future sustainability trends.

Lastly, social acceptance, although it ranked the lowest, is a pivotal consideration. Proactive stakeholder engagement and public awareness initiatives are necessary to foster support and reduce resistance. Involving local communities, industry stakeholders, and other interested parties in the planning and implementation phases can build trust and ensure that the project reflects the interests and concerns of all stakeholders. This will be important for maintaining long-term support and ensuring the corridor’s success.

Point-to-point green corridors between two ports should consider the specifics of each seaport. The Port of Sines and the Port of Luanda, both founding members of the APLOP (Association of Portuguese Language Ports), reveal distinct advantages and challenges that reflect their unique contexts in the Community of Portuguese Language Countries (CPLP). The Port of Sines benefits from modern, advanced infrastructure, including large, deep-water terminals and state-of-the-art green technologies such as shore power systems, and a unique strategic position, with unique connectivity to the European market, enhancing its role as a hub for international trade.

On the other hand, the Port of Luanda has unique climate conditions and holds a strategic location on the West African coast, making it a key player in regional trade and a potential global market provider for renewable energy, including LNG. Their comparison suggests that tailored investments are required for each port to maximize their contributions to the green corridor. While the Port of Sines needs to focus on managing operational costs and navigating complex regulatory frameworks, the Port of Luanda requires infrastructural upgrades and modernization to align with international green corridor standards.

This study’s findings indicate that applying the fuzzy analytic hierarchy process (fuzzy AHP) provides substantial benefits in establishing a sustainable green corridor between the Ports of Sines and Luanda. Specifically, the fuzzy AHP facilitates the integration of expert judgments, allowing for a nuanced understanding of the relative importance of various factors involved in decision-making. This structured approach supports a balanced assessment that considers sustainable development’s critical environmental, economic, and social dimensions. However, this method also presents notable drawbacks. Its reliance on subjective assessments can introduce biases that may have skewed the prioritization outcomes. Additionally, the complexity involved in managing numerous pairwise comparisons can lead to inconsistencies in evaluations, complicating the decision-making process. To enhance the applicability of the fuzzy AHP model in other port contexts, it is essential to update the evaluation criteria based on local conditions and stakeholder feedback. This involves conducting localized expert consultations to accurately capture specific challenges and opportunities, integrating real-time data and feedback mechanisms, and ensuring that the assessment aligns with the unique regulatory and economic landscapes that are relevant to each operational environment. By addressing these factors, the fuzzy AHP model can be effectively tailored to diverse maritime settings, thus supporting the successful implementation of sustainable initiatives.

The analysis carried out by the authors has some limitations, which opens up opportunities for further research. The first limitation is the subjectivity in expert judgment. The fuzzy AHP method heavily depends on expert opinions, leading to a certain level of subjectivity. These assessments could impact the precision of the evaluations. Second is geographical and contextual limitations. The results include exclusively the Sines–Luanda maritime corridor. Using these findings on other routes may not be precise because of regional economic, environmental, and political variations. Third, there is a lack of prior research studies on green shipping corridors.

Future studies could focus on digitalization as an enabler of the use of digital-enabled optimization for efficient green shipping corridors and the expansion of a geographic area network of green corridors, starting with the APLOP economic space ports using JUL or JUP and exploring the application of alternative weighting methods such as the analytic network process (ANP) and data envelopment analysis (DEA) to compare the results with those obtained through the fuzzy AHP, further validating the findings and offering a broader perspective on decision-making processes. Comparative analyses with other global green shipping corridors could also provide insights into the best practices to optimize the Sines–Luanda initiative. Looking ahead, future research can further enhance the understanding and implementation of such corridors by investigating technological innovations, such as alternative fuels like green ammonia or hydrogen, to explore their potential in decarbonizing maritime transport. Longitudinal studies assessing the environmental and economic impacts of green policies over time would be valuable for tracking emission reductions and operational efficiency. Finally, additional research on economic policies and financial incentives, particularly in the context of international collaboration and regulatory alignment, would further bolster the project’s long-term sustainability and global relevance.

7. Conclusions

Green shipping corridors are essential for decarbonizing the maritime sector, offering advantages to a wide range of stakeholders, including port authorities, ship and port operators, consumers, and local communities. The members of the partnerships made to carry out this solution must be able to make efficient and effective decisions. As a part of the decision support process, this research identifies and prioritizes the key criteria for establishing a green maritime shipping corridor between two strategic partners that impact trade between Europe and Africa, the Ports of Sines (Portugal) and Luanda (Angola), using the fuzzy AHP methodology. The findings show the dominance of environmental compatibility and economic viability over infrastructure readiness, government policies and incentives, operational efficiency, technological advancements, and social acceptance. This study highlighted that environmental compatibility is the top priority, with water pollution control and biodiversity conservation being critical factors to address. This emphasizes the importance of environmental stewardship in the corridor’s development. Economic viability was also found to be essential in managing long-term operational costs and enhancing trade benefits to ensure the corridor’s financial sustainability. Furthermore, infrastructure readiness, including transportation connectivity and port capacity, is relevant to ensuring smooth operations and long-term success. Accordingly, the successful development of the green shipping corridor depends on effectively addressing these top-ranked criteria, while also considering other influential factors. Additionally, research aimed at increasing stakeholder engagement and public awareness is crucial to achieving broad social acceptance, influencing both policymaking and public support. The potential environmental benefits of this project are significant, encompassing enhanced biodiversity and a cleaner marine ecosystem, alongside economic advantages such as lower operational costs and reduced anchorage durations. The realization of these benefits is contingent upon the implementation of advanced technology and automation. Innovations in technology will enable more precise arrival predictions, boost operational efficiency, and enhance productivity through ‘Just-in-Time’ arrivals. Consequently, this will contribute to a diminished carbon footprint for transported goods and assist in achieving national emission-reduction objectives. Most shipping industry players are hopeful about the potential of green corridors. While many questions remain about how to implement them, the potential they hold is promising. This paper provides an overview of the four priority areas for stakeholders to consider regarding green shipping corridors: alternative fuels [1,3,10], bunkering infrastructure [20], vessel decarbonization pathways [20,45], and cargo demand dynamics [7,23,44]. Stakeholders should consider the feasibility of these areas from seven angles: environmental, economic, infrastructure, regulatory, operational, technological, and social. By analyzing the feasibility of the four priority areas across these seven dimensions, potential risks and challenges in implementing green corridors will emerge. By prioritizing the criteria, it is made possible to implement mitigation actions to address them and find solutions to move green corridor projects forward.

Author Contributions

Conceptualization, A.A.B., S.A.A.-B., V.C. and J.L.C.; methodology, S.A.A.-B.; software, S.A.A.-B.; validation, V.C., A.A.B. and J.L.C.; formal analysis, S.A.A.-B.; investigation, A.A.B., S.A.A.-B., V.C. and J.L.C.; resources, A.A.B., V.C. and J.L.C.; data curation, S.A.A.-B.; writing—original draft preparation, A.A.B. and S.A.A.-B.; writing—review and editing, M.F., V.C. and S.A.A.-B.; visualization, S.A.A.-B.; supervision, V.C.; project administration, V.C.; funding acquisition, M.F., V.C. and S.A.A.-B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting this study are available upon request from the corresponding authors.

Acknowledgments

We extend our sincere gratitude to the experts from the Ports of Sines and Luanda for their participation in the survey. Their valuable contributions and professional insights were instrumental in enriching the findings of this research. We also thank the Foundation for Science and Technology (FCT), Ministry of Science, Technology and Higher Education (MCTES), for the financial support to CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020) and the Research Unit on Governance, Competitiveness and Public Policies (UIDB/04058/2020) + (UIDP/04058/2020).

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Green corridors map (adapted from [32,33,34,35,36,37]).

Figure 2. Strategic location of the Port of Sines (adapted from [49]).

Figure 3. Layout of the Port of Sines.

Figure 4. Layout of the Port of Luanda.

Figure 5. Schematic diagram of the research methodology.

Figure 6. Hierarchical structure developed for this study.

Table 1

Summary of studies on green transport corridors.

Authors	Focus	Contribution
Wang et al. [1]Leet [10]	- Ammonia-based green shipping corridors	- Demonstrated feasibility of using green ammonia for environmentally friendly maritime transportation.
Jesus et al. [3]	- LNG-based green shipping corridors	- Evaluates LNG use in the frame of the economic framework for green shipping corridors.
Schröder and Prause [21]	- Transportation of dangerous goods	- Addressed comprehensive risk management concepts to ensure safe and environmentally responsible transport of dangerous goods.
Péra et al. [25]	- Modeling optimal transportation corridors for Brazilian soybean exports to China	- Highlighted strategic investments in port infrastructure to minimize costs and CO2 emissions in Brazilian soybean exports.
Clausen et al. [27]	- Information and Communication Technologies (ICTs)	- Emphasized ICTs’ role in reducing CO2 emissions and improving resource utilization in freight transport corridors.
Hunke and Prause [31]	- Governance challenges in corridor management	- Emphasized stakeholder involvement and governance model alignment for efficient corridor performance.
Fozza and Recagno [38]	- Sustainable technologies and innovations	- Groundwork for enhancing the environmental sustainability of transport corridors through technology evaluation and selection.
Batarliene and Jarašuniene [39]	- Advanced Technologies (AT) in green corridors	- Highlighted AT’s role in optimizing transport activities and improving mobility conditions, with a focus on road and port interaction.
Beškovnik and Twrdy [40]	- Macro green logistics strategy	- Advocated for intermodal infrastructure development and stakeholder collaboration to realize sustainable transport solutions.
Junior et al. [41]	- Efficiency evaluation using Logistics Composite Index	- Provided a quantitative method to identify efficient corridors and inform infrastructure investment decisions.
Prause and Schröder [42]	- Key Performance Indicators (KPIs) for green corridors	- Developed a balanced scorecard approach for effective corridor management with cooperative and long-term perspectives.
Kabashkin and Sansyzbayeva [43]	- Methodological framework for corridor modeling	- Introduced a flexible simulation tool integrating environmental, social, and economic indicators for corridor sustainability.
Carballo-Penela et al. [44]	- Carbon footprint assessment of ports	- Offered practical approaches to evaluate the environmental impact of maritime transportation through port carbon footprint.
Song et al. [45]	- Green shipping corridors, as proposed at the 26th Congress of the Parties	- Reviewed current developments in green shipping corridors. - Identified key challenges in green shipping corridor implementation. - Set out global research agendas and future directions for decarbonizing the maritime industry within the context of green shipping corridors.

Table 2

Linguistic and triangular fuzzy AHP scales used in this study.

Linguistic Terms	Triangular Fuzzy Numbers
Equally important	(1,1,1)
Moderately important	(2,3,4)
Important	(4,5,6)
Very important	(6,7,8)
Extremely important	(8,9,10)
Intermediate	(1,2,3), (3,4,5), (5,6,7), (7,8,9)

Table 3

Random indices.

Size (n)	RIm	RIg
1	0	0
2	0	0
3	0.4890	0.1796
4	0.7937	0.2627
5	1.0720	0.3597
6	1.1996	0.3818
7	1.2874	0.4090
8	1.3410	0.4164
9	1.3793	0.4348
10	1.4095	0.4455
11	1.4181	0.4536
12	1.4462	0.4776
13	1.4555	0.4691
14	1.4913	0.4804
15	1.4986	0.4880

Table 4

Ranking of main criteria.

Criteria	Weight	Rank
Environmental Compatibility	0.250	1
Economic Viability	0.243	2
Infrastructure Readiness	0.203	3
Regulatory and Policy Compliance	0.117	4
Social Acceptance	0.043	7
Operational Efficiency	0.080	5
Technological Advancements	0.063	6
Consistency ratio: ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0165, ${\mathit{C}\mathit{R}}^{\mathit{g}}$ = 0.0482

Table 5

Ranking of environmental compatibility sub-criteria.

Sub-Criteria	Weight	Rank
Air Pollution	0.112	3
Water Pollution	0.437	1
Noise Pollution	0.048	4
Biodiversity Conservation	0.402	2
Consistency ratio: ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0121, ${\mathit{C}\mathit{R}}^{\mathit{g}}$ = 0.0342

Table 6

Ranking of economic viability sub-criteria.

Sub-Criteria	Weight	Rank
Initial Investment	0.218	3
Long-term Operational Costs	0.410	1
Trade Volume and Economic Benefits	0.308	2
Job Creation	0.062	4
Consistency ratio: ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0090, ${\mathit{C}\mathit{R}}^{\mathit{g}}$ = 0.0261

Table 7

Ranking of infrastructure readiness sub-criteria.

Sub-Criteria	Weight	Rank
Port Capacity and Performance	0.414	2
Availability of Green Infrastructure	0.129	3
Transportation Connectivity	0.456	1
Consistency ratio: ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0155, ${\mathit{C}\mathit{R}}^{\mathit{g}}$ = 0.0413

Table 8

Ranking of regulatory and policy compliance sub-criteria.

Sub-Criteria	Weight	Rank
Compliance with IMO Regulations	0.278	2
Adherence to EU Environmental Standards	0.210	3
Government Policies and Incentives	0.511	1
Consistancy ratio: ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0045, ${\mathit{C}\mathit{R}}^{\mathit{m}}$ = 0.0126

Table 9

Ranking of social acceptance sub-criteria.

Sub-Criteria	Weight	Rank
Stakeholder Engagement	0.731	1
Public Awareness and Acceptance	0.269	2
Consistency ratio: Not applicable

Table 10

Ranking of operational efficiency sub-criteria.

Sub-Criteria	Weight	Rank
Reliability and Efficiency	0.442	2
Safety and Security	0.558	1
Consistency ratio: Not applicable

Table 11

Ranking of technological advancements sub-criteria.

Sub-Criteria	Weight	Rank
Adoption of Green Technologies	0.614	1
Digitalization	0.386	2
Consistency ratio: Not applicable

Table 12

Global weights and rankings of sub-criteria.

Main Criteria	Sub-Criteria	Global Weight	Rank
Environmental Compatibility	Air Pollution	0.028	13
	Water Pollution	0.109	1
	Noise Pollution	0.012	18
	Biodiversity Conservation	0.100	2
Economic Viability	Initial Investment	0.053	7
	Long-term Operational Costs	0.100	2
	Trade Volume and Economic Benefits	0.075	5
	Job Creation	0.015	17
Infrastructure Readiness	Port Capacity and Performance	0.084	4
	Availability of Green Infrastructure	0.026	14
	Transportation Connectivity	0.093	3
Regulatory and Policy Compliance	Compliance with IMO Regulations	0.032	11
	Adherence to EU Environmental Standards	0.025	15
	Government Policies and Incentives	0.060	6
Social Acceptance	Stakeholder Engagement	0.031	12
	Public Awareness and Acceptance	0.012	18
Operational Efficiency	Reliability and Efficiency	0.035	10
	Safety and Security	0.045	8
Technological Advancements	Adoption of Green Technologies	0.039	9
	Digitalization	0.024	16

Table 13

Sensitivity analysis of main criteria and their impacts on rankings.

Criteria	Change Type	Original Weight	Original Rank	New Weights	New Rank
C1	Increase by 10.0%	0.250	1	[C1: 0.275, C2: 0.235, C3: 0.196, C4: 0.113, C5: 0.042, C6: 0.077, C7: 0.061]	1
	Decrease by 10.0%			[C1: 0.225, C2: 0.251, C3: 0.210, C4: 0.121, C5: 0.044, C6: 0.082, C7: 0.065]	2
C2	Increase by 10.0%	0.243	2	[C1: 0.242, C2: 0.267, C3: 0.197, C4: 0.113, C5: 0.041, C6: 0.077, C7: 0.061]	1
	Decrease by 10.0%			[C1: 0.256, C2: 0.219, C3: 0.213, C4: 0.122, C5: 0.045, C6: 0.084, C7: 0.066]	2
C3	Increase by 10.0%	0.203	3	[C1: 0.244, C2: 0.237, C3: 0.223, C4: 0.114, C5: 0.042, C6: 0.078, C7: 0.061]	3
	Decrease by 10.0%			[C1: 0.258, C2: 0.251, C3: 0.183, C4: 0.121, C5: 0.044, C6: 0.083, C7: 0.065]	3
C4	Increase by 10.0%	0.117	4	[C1: 0.247, C2: 0.240, C3: 0.200, C4: 0.129, C5: 0.042, C6: 0.079, C7: 0.062]	4
	Decrease by 10.0%			[C1: 0.252, C2: 0.243, C3: 0.203, C4: 0.105, C5: 0.043, C6: 0.080, C7: 0.063]	4
C5	Increase by 10.0%	0.043	7	[C1: 0.249, C2: 0.242, C3: 0.202, C4: 0.116, C5: 0.047, C6: 0.080, C7: 0.063]	7
	Decrease by 10.0%			[C1: 0.250, C2: 0.243, C3: 0.203, C4: 0.117, C5: 0.039, C6: 0.080, C7: 0.063]	7
C6	Increase by 10.0%	0.080	5	[C1: 0.248, C2: 0.241, C3: 0.201, C4: 0.116, C5: 0.043, C6: 0.088, C7: 0.062]	5
	Decrease by 10.0%			[C1: 0.252, C2: 0.245, C3: 0.205, C4: 0.118, C5: 0.043, C6: 0.072, C7: 0.064]	5
C7	Increase by 10.0%	0.063	6	[C1: 0.248, C2: 0.241, C3: 0.202, C4: 0.116, C5: 0.043, C6: 0.079, C7: 0.069]	6
	Decrease by 10.0%			[C1: 0.265, C2: 0.258, C3: 0.215, C4: 0.124, C5: 0.046, C6: 0.085, C7: 0.057]	6

Table 14

Sensitivity analysis of sub-criteria and their impacts on rankings.

Criteria	Sub-Criteria	Change Type	Original Weight	Original Rank	New Weights	New Rank
C1	C1.1	Increase by 10.0%	0.112	3	[C1.1: 0.123, C1.2:0.431, C1.3: 0.047, C1.4: 0.397]	3
		Decrease by 10.0%			[C1.1: 0.101, C1.2: 0.442, C1.3: 0.049, C1.4: 0.408]	3
	C1.2	Increase by 10.0%	0.437	1	[C1.1: 0.103, C1.2: 0.481, C1.3: 0.044, C1.4: 0.370]	1
		Decrease by 10.0%			[C1.1: 0.121, C1.2: 0.393, C1.3: 0.052, C1.4: 0.441]	2
	C1.3	Increase by 10.0%	0.048	4	[C1.1: 0.111, C1.2: 0.435, C1.3: 0.053, C1.4: 0.400]	4
		Decrease by 10.0%			[C1.1: 0.113, C1.2: 0.441, C1.3: 0.043, C1.4: 0.405]	4
	C1.4	Increase by 10.0%	0.402	2	[C1.1: 0.104, C1.2: 0.408, C1.3: 0.045, C1.4: 0.442]	1
		Decrease by 10.0%			[C1.1: 0.120, C1.2: 0.469, C1.3: 0.052, C1.4: 0.362]	2
C2	C2.1	Increase by 10.0%	0.218	3	[C2.1: 0.240, C2.2: 0.398, C2.3: 0.299, C2.4: 0.060]	3
		Decrease by 10.0%			[C2.1: 0.196, C2.2: 0.421, C2.3: 0.316, C2.4: 0.064]	3
	C2.2	Increase by 10.0%	0.410	1	[C2.1: 0.203, C2.2: 0.451, C2.3: 0.286, C2.4: 0.058]	1
		Decrease by 10.0%			[C2.1: 0.233, C2.2: 0.369, C2.3: 0.329, C2.4: 0.066]	1
	C2.3	Increase by 10.0%	0.308	2	[C2.1: 0.208, C2.2: 0.392, C2.3: 0.339, C2.4: 0.059]	2
		Decrease by 10.0%			[C2.1: 0.228, C2.2: 0.428, C2.3: 0.277, C2.4: 0.065]	2
	C2.4	Increase by 10.0%	0.062	4	[C2.1: 0.216, C2.2: 0.407, C2.3: 0.306, C2.4: 0.068]	4
		Decrease by 10.0%			[C2.1: 0.218, C2.2: 0.410, C2.3: 0.308, C2.4: 0.055]	4
C3	C3.1	Increase by 10.0%	0.414	2	[C3.1: 0.455, C3.2: 0.119, C3.3: 0.423]	1
		Decrease by 10.0%			[C3.1: 0.379, C3.2: 0.138, C3.3: 0.487]	2
	C3.2	Increase by 10.0%	0.129	3	[C3.1: 0.409, C3.2: 0.143, C3.3: 0.449]	3
		Decrease by 10.0%			[C3.1: 0.414, C3.2: 0.130, C3.3: 0.456]	3
	C3.3	Increase by 10.0%	0.456	1	[C3.1: 0.379, C3.2: 0.118, C3.3: 0.502]	1
		Decrease by 10.0%			[C3.1: 0.415, C3.2: 0.129, C3.3: 0.410]	2
C4	C4.1	Increase by 10.0%	0.278	2	[C4.1: 0.306, C4.2: 0.202, C4.3: 0.491]	2
		Decrease by 10.0%			[C4.1: 0.250, C4.2: 0.218, C4.3: 0.530]	2
	C4.2	Increase by 10.0%	0.210	3	[C4.1: 0.270, C4.2: 0.231, C4.3: 0.497]	3
		Decrease by 10.0%			[C4.1: 0.286, C4.2: 0.189, C4.3: 0.525]	3
	C4.3	Increase by 10.0%	0.511	1	[C4.1: 0.249, C4.2: 0.188, C4.3: 0.562]	1
		Decrease by 10.0%			[C4.1: 0.319, C4.2: 0.241, C4.3: 0.460]	1
C5	C5.1	Increase by 10.0%	0.731	1	[C5.1: 0.804, C5.2: 0.196]	1
		Decrease by 10.0%			[C5.1: 0.658, C5.2: 0.340]	1
	C5.2	Increase by 10.0%	0.269	2	[C5.1: 0.704, C5.2: 0.296]	2
		Decrease by 10.0%			[C5.1: 0.758, C5.2: 0.242]	2
C6	C6.1	Increase by 10.0%	0.442	2	[C6.1: 0.486, C6.2: 0.514]	2
		Decrease by 10.0%			[C6.1: 0.398, C6.2: 0.598]	2
	C6.2	Increase by 10.0%	0.558	1	[C6.1: 0.386, C6.2: 0.614]	1
		Decrease by 10.0%			[C6.1: 0.498, C6.2: 0.502]	1
C7	C7.1	Increase by 10.0%	0.614	1	[C7.1: 0.675, C7.2: 0.325]	1
		Decrease by 10.0%			[C7.1: 0.553, C7.2: 0.438]	1
	C7.2	Increase by 10.0%	0.386	2	[C7.1: 0.575, C7.2: 0.425]	2
		Decrease by 10.0%			[C7.1: 0.662, C7.2: 0.347]	2

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16219563/s1, Table S1: Integrated Fuzzy Comparison Matrix for Main Criteria; Table S2: Analysis of Fuzzy Preference Rankings for Main Criteria; Table S3: Integrated Fuzzy Comparison Matrix for Environmental Compatibility Sub-criteria; Table S4: Analysis of Fuzzy Preference Rankings for Environmental Compatibility Sub-criteria; Table S5: Integrated Fuzzy Comparison Matrix for Economic Viability Sub-criteria; Table S6: Analysis of Fuzzy Preference Rankings for Economic Viability Sub-criteria; Table S7: Integrated Fuzzy Comparison Matrix for Infrastructure Readiness Sub-criteria; Table S8: Analysis of Fuzzy Preference Rankings for Infrastructure Readiness Sub-criteria; Table S9: Integrated Fuzzy Comparison Matrix for Regulatory & Policy Compliance Sub-criteria; Table S10: Analysis of Fuzzy Preference Rankings for Regulatory & Policy Compliance Sub-criteria; Table S11: Integrated Fuzzy Comparison Matrix for Social Acceptance Sub-criteria; Table S12: Analysis of Fuzzy Preference Rankings for Social Acceptance Sub-criteria, Table S13: Integrated Fuzzy Comparison Matrix for Operational Efficiency Sub-criteria; Table S14: Analysis of Fuzzy Preference Rankings for Operational Efficiency Sub-criteria; Table S15: Integrated Fuzzy Comparison Matrix for Technological Advancements Sub-criteria; Table S16: Analysis of Fuzzy Preference Rankings for Technological Advancements Sub-criteria.

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