Critical Success Factors for Green Port Transformation Using Digital Technology

Abstract

Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience.

1. Introduction

Ports occupy a central position in international trade [1,2,3]. As a key hub for maritime logistics, the intermodal transport process is crucial for coordinating all participants in the transport chain [4,5,6,7]. This process aims to refine the transportation of traffic and commodities, thereby diminishing expenses and enhancing operational efficacy [8]. Globalization and digital technology have expanded port services to encompass digital international logistics [2,9]. Port digitization marks a shift from physical to virtual operations, expediting logistics through data exchange, reducing cargo turnaround times, increasing productivity, and better meeting the needs of customers and vessels [10,11].

Digital technologies are crucial in driving the green transformation of ports, helping reduce energy consumption and emissions while enhancing resource efficiency for a more sustainable port environment [12,13]. One notable example of real-world port digitalization is the Port of Gothenburg, which has implemented a wide range of digital initiatives, including a platform launched in 2021 to digitally link and coordinate operations among shipping companies, freight forwarders, and rail operators. These efforts have optimized port operations and contributed significantly to its sustainable development [14]. Stringent environmental regulations are driving the digital transformation of the maritime sector, particularly the optimization of vessel scheduling through real-time berth information, which reduces waiting times and carbon emissions [15,16,17]. At the same time, changing customer demands are pushing technological advancements. The widespread adoption of digital technologies has profoundly changed the maritime sector and is critical for creating environmentally friendly, sustainable ports [2]. In building green ports, the deep integration of digital technologies—especially in data collection and real-time monitoring—has improved work efficiency while significantly reducing environmental burden. By integrating digital technologies with environmental protection concepts, ports gradually transform into smart ports, promoting green transformation and sustainable development. However, there is a notable lack of scholarly inquiry into the critical success factors necessary for deploying digital technologies to facilitate the greening of port operations.

Existing studies suggest that with the rise of digital technology, global investment in the construction of digital equipment in ports has reached USD 10 billion [18]. The study by [15] revealed that despite substantial investments in digital equipment construction at ports, operational costs were only reduced by 25% to 55%, falling short of the expected savings. At the same time, productivity actually declined by 7% to 15%, primarily due to the adaptation period required for the equipment, the complexity of integrating old and new technologies, and inadequate personnel training. Furthermore, the literature on digitization and digital technologies within the maritime domain is relatively sparse, and a comprehensive synthesis of the maritime sector with digital technologies is lacking [19]. Analysis of surveys conducted between 2003 and 2017 reveals that only 33% of the documented research pertained to the advancement of information systems, suggesting that port digitization remains nascent, with substantial capital investment as the principal impediment [18]. Therefore, this study proposes the following research questions (RQ):

RQ1. What are the key determinants of the successful application of digital technologies in facilitating a green transition in ports?

RQ2. What is the nature and extent of the linkages between these key determinants?

In answering these RQs, we conducted a comprehensive examination of the Korean maritime sector to identify the critical success factors for deploying digital technologies in promoting the green transformation of ports. This study focuses on three key dimensions of the green transformation of ports from a strategic perspective: integrating digital technologies with infrastructure, optimizing digital management and operations, and enhancing environmental and safety management in a digitally driven environment. Previous studies have meticulously investigated the pivotal success factors associated with deploying digital technologies to advance the greening of port infrastructures. Combining the expert evaluation method with the fuzzy DEMATEL method, this study quantitatively and qualitatively analyzed 12 related factors and constructed a comprehensive analysis framework. This framework integrates sustainable supply chain management and system dynamics theories to explore these factors and their interactions.

This research examines the economic and strategic significance of digital technologies in driving the green transformation of ports, highlighting their impact on environmental, social, and political dimensions. The study offers key insights and practical recommendations for port operators and governments, addressing green transformation challenges and leveraging digital opportunities.

The subsequent sections of this investigation are as follows: Section 2 constitutes a comprehensive literature review; Section 3 describes the fuzzy DEMATEL method; Section 4 presents the results; and Section 5 provides a discussion, conclusions, research limitations, and suggestions for future research.

2. Literature Review

2.1. Research on the Digitalization and Green Transformation of Ports

With the rapid development of the global economy and continuous technological progress, the digitalization and green transformation of ports have become the core driving force for the maritime industry to move toward a higher level. Academic research in this field focuses on three key areas: the development process and stages of port digitalization, the implementation and impact of digital port technology, and the performance evaluation and decision support of digital ports. Examining the progression and phases of port digitalization elucidates the transformation of ports from paperless to automated to fully digital applications [20]. This evolution takes place in three distinct stages. The first stage was the information digitization phase in the 1980s, characterized by electronic data interchange (EDI) technology [21]. Next is technological digitization from 1990 to 2010, encompassing the Truck Appointment System (TAS) and Automatic Identification System (AIS) for vessels and environmental awareness and positioning identification technologies. This period is foundational for the subsequent automation of terminal operations. Third is the digital application stage from 2010 to now, emphasizing information flow and data communication within port areas and promoting automation through sensor and machine communication technology [21]. This digitalization process improves the performance and safety of the port and reduces operating costs. However, the success of digital transformation also relies on specialized software and professionals with expertise in port operations and information systems, serving as a bridge to promote cross-departmental collaboration and staff training [16]. These factors work together to enable the digital transformation of ports to proceed smoothly, laying a solid foundation for future development [22,23].

The second major area of research on the digitalization and green transformation of ports focuses on the implementation and impact of digital port technologies. This investigation explores the potential to significantly improve the operational efficiency, safety, and sustainability of ports through the extensive application of cutting-edge technologies, such as the Internet of Things (IoT), automation systems, electronic data interchange (EDI), and the Automatic Identification System (AIS). Ref. [24] argues that the IoT is the fundamental component in developing smart ports, emphasizing the critical role of cutting-edge technological innovations. Incorporating smart technologies has significantly bolstered port operations, delivering improvements in efficiency, shortening delays, and heightening transparency and security.

Digital technologies address labor shortages, maintain operational stability, and protect employee well-being [18]. Smart ports advance by embracing environmental and sustainability principles, striving to establish a greener operational framework. These ports enhance efficiency through improved information flows, optimizing transportation routes and alleviating congestion in port areas [25]. This strategy improves efficiency and reduces waiting times and emissions, aligning with environmental objectives. Ref. [16] proposed four alternative approaches for analyzing smart port development, focusing on critical aspects. Ref. [26] identified the determinants that influence the design of smart ports in the domains of automation, environmental considerations, and intelligence. In a separate study, ref. [26] utilized the analytical hierarchy process (AHP) and the decision and evaluation of multiple attributes technique (DEMATEL) to systematically assess the key indicators of smart port service quality and their interdependencies. Their research underscores the importance of accuracy, security, and efficiency in cargo handling, electronic document exchange, and the optimization of container logistics.

The third research area of port digitalization and green transformation focuses on performance evaluation and decision support for digital ports. Scholars are committed to analyzing key performance indicators (KPIs) and exploring how to improve port operational efficiency through effective evaluation tools and models. To quantitatively assess the comprehensive performance of smart ports, ref. [27] introduced the Smart Port Index (SPI), a comprehensive framework that includes four fundamental performance metrics: operations, environmental footprint, energy efficacy, and safety and security. Ref. [23] conducted a unique study identifying the critical success factors for port digitalization. They categorized these factors into three primary dimensions: integrating innovative digital technologies, deploying novel digital solutions, and executing effective digital business management. They drew inspiration from Wiesböck’s foundational information technology (IT) framework. Consequently, ref. [28] implemented an innovative hybrid assessment methodology to evaluate the performance of six Chinese ports, considering a wide range of factors, including service quality, technology, sustainability, clustering, hub functionalities, and governance and policy considerations. Ref. [29] created a game theory model to determine the most effective strategy for adopting smart technologies in ports and examined the impact of network externalities on port pricing and adoption decisions.

Scholars frequently employ the expert evaluation method in the port industry to rank and evaluate the relative significance of various service quality items by assigning specific weight values. Ref. [30] implemented the analytic hierarchy process (AHP) to evaluate the competitiveness of ten ports in South Korea and China. Ref. [31] implemented the AHP to determine that port efficiency is the most significant factor influencing shippers’ decisions. In the context of the fourth industrial revolution, ref. [32] examined the strategic requirements of the shipping and port logistics industry and employed AHP to evaluate the significance of various strategies. Ref. [33] identified six critical factors for successfully implementing blockchain in the maritime industry through the study of AHP.

Moreover, ref. [34] implemented AHP to assess various proposals and identify an appropriate location from three logistics areas to establish a smart port. For example, to evaluate the relative importance of the three elements of smart ports—intelligence, automation, and environment—[26] implemented AHP. Ref. [2] also implemented AHP to ascertain the critical success factors of smart ports, which include tangibility, reliability, assurance, responsiveness, and empathy. Furthermore, scholars implemented the fuzzy Delphi method and fuzzy AHP in shipping research. Ref. [35] examined the primary determinants influencing adopting big data in the maritime industry. In addition, ref. [36] investigated the fundamental components of sustainable port operations, while ref. [37] employed the Delphi method to identify the primary variables that influence container carriers using coastal shipping.

Further, ref. [18] used expert interviews and literature reviews to evaluate smart ports’ development status and challenges on domestic and international scales. They implemented the fuzzy Delphi method and fuzzy AHP to identify the critical dimensions and factors of the target company. The study determined that the “digital solutions” dimension was of the greatest importance, with the following factors ranking among the top five: “carbon emission management”, “port security enhancement”, “technical standardization”, “digital asset management”, and “supply chain optimization”. To establish causal relationships between risks and port management technology, ref. [38] implemented DEMATEL, and ref. [39] implemented it to investigate the development of intelligent ports while [40] employed the gray system theory and DEMATEL to examine the impediments to developing smart ports in China. Scholars frequently use the DEMATEL technique to address multi-criteria evaluation issues involving multiple criteria’s interdependence. DEMATEL can quantify the relationships between criteria to identify the causal relationships between key factors, thereby providing robust support for decision-making.

Ref. [27] point out that although the maritime logistics industry has embarked on digital transformation, many areas still have not fully enjoyed the dividends of digitalization. There are various reasons behind this phenomenon. For example, information security issues pose a significant obstacle, and the existing laws and regulations are not yet perfect, resulting in an insecure online environment severely restricting the widespread use of digitalization at the enterprise level. Despite the potential for digital technology to enhance operational efficiency in shipping and port operations, integrating new and legacy technologies is a protracted endeavor, necessitating substantial investment in transforming conventional terminals. In addition, it takes time for terminal workers to become familiar with new technologies, accompanied by high training costs. It also requires more technical personnel with professional skills [18]. At the systemic level, while individual ports diligently construct their proprietary digital platforms, standardization remains a critical area for enhancement.

Consequently, given the intricate interplay among diverse factors, this research employs the fuzzy DEMATEL method to delineate the pivotal success factors for promoting port green transformation through digital technology. The advantage of the fuzzy DEMATEL method is that it can effectively deal with data ambiguity and uncertainty while comprehensively evaluating the interactions between multiple factors. Compared with AHP+DEMATEL, fuzzy DEMATEL is more suitable for analyzing port green transformation because it can more accurately capture the complex relationships and key success factors under the influence of digital technology. This study marks the pioneering use of the fuzzy DEMATEL method for analyzing the critical success factors in promoting the green transformation of ports via digital technology.

2.2. Theory Development and Critical Success Factors

Synthesizing principles from sustainable supply chain management and system dynamics theories provide the theoretical foundation for this research. The objective is to examine the key success factors associated with using digital technology to drive the green transformation of ports. As a forward-looking management philosophy, the sustainable supply chain management theory reveals the deep logic behind the green transformation of ports from an ecological integration perspective. It focuses on the linear process of the supply chain and emphasizes the harmonious coexistence of three factors: ecology, economy, and society. Empowered by digital technology, this theory explores the central role of technological innovation in advancing green supply chain upgrades [41].

In the integration of digital technologies and infrastructure, the harmonious blend of intelligent vessel scheduling and traffic optimization, cutting-edge logistics equipment, IoT technology, and sophisticated energy management systems collectively drives the green transformation of ports. This synergy enhances operational efficiency, reduces environmental impact through optimized vessel movements and reduced emissions, streamlines cargo handling with automated equipment, leverages real-time data for informed decision-making, and ensures sustainable energy use, all contributing to a more eco-friendly and efficient port ecosystem [42].

System dynamics theory, with its unique dynamic simulation capabilities, provides an in-depth systematic analysis tool for the green transformation of ports. With the help of digital technologies, the theory reveals how complex feedback loops and structural changes can effectively promote the green transformation of port systems [43]. In the analysis process, system dynamics theory emphasizes the direct impact of digital technologies on energy efficiency, pollution control, and resource recycling and explores in depth the dynamic changes and cumulative effects of these impacts over time [44]. Specifically, in digital management and operational optimization, the system dynamics theory underscores the necessity of key factors such as automated customs clearance management, digital twin technology, intelligent risk monitoring, predictive maintenance, and equipment management. Concurrently, with optimizing environment and safety management in a digitally driven environment, the theory highlights the importance of environmental and waste management systems, blockchain and port safety management, digital carbon emission monitoring, and green supply chain information. These elements collectively constitute a robust theoretical and practical framework essential for fostering the green transformation of ports.

3. Research Methodology

We began by conducting a comprehensive literature search and expert evaluation. Through Google Scholar, Web of Science, and SCOPUS, we identified 12 success factors for greening ports with digital technologies. We then interviewed three maritime industry veterans with more than two decades of experience to verify these success factors. We conducted a comprehensive literature review to investigate the impact of international rules, regulations, standards, and guidelines on using port digital technology to facilitate a green transition in collaboration with subject matter experts. Through these assessments, we identified the major success factors of digital technologies for greening ports (

Table 1. Critical success factors.

Code	Critical Success Factor	Definition	Sustainable Supply Chain Management Theory	System Dynamics Theory	Source
C1	Intelligent vessel scheduling and traffic optimization	Shipping can use artificial intelligence (AI) technology and big data analysis to optimize the scheduling of ships’ arrivals, departures, and routes, thereby reducing fuel consumption and waiting times in ports and reducing emissions.	√	√	[1,2,6,14,16,18,23,25,26,27,28,30,31,32,33,34,37,38,39,40,41,42,45,46,47,48]
C2	Intelligent logistics equipment	Automated cranes and driverless freight vehicles are examples of intelligent equipment that can enhance port operational efficiency, decrease energy consumption, and decrease carbon emissions.	√	√
C3	Internet of Things (IoT) technology	Ports can optimize operations and energy consumption while improving environmental performance by deploying IoT devices to monitor equipment, energy use, and emissions in real time. To address vulnerabilities introduced by IoT systems, integrating robust cybersecurity measures ensures the protection of critical data and systems, maintaining secure and efficient port operations in the digital age.	√	√
C4	Intelligent energy management	Implementing a platform for intelligent energy management can optimize and monitor energy consumption in the port, encourage the utilization of renewable energy sources (e.g., solar and wind power), and mitigate fossil fuel dependence.	√	√
C5	Automated customs clearance management	Employing artificial intelligence and blockchain technology can enhance the efficiency of customs clearance, decrease the duration of cargo detention, and decrease energy consumption and carbon emissions throughout the logistics supply chain.	√	√
C6	Digital twin technology	This technology can optimize operations and resource allocation, predict equipment failures, energy consumption, and logistics bottlenecks, and achieve the objective of green operations by utilizing digital twin technology to simulate port operation scenarios.	√	√
C7	Intelligent risk monitoring	Intelligent risk monitoring can identify potential risks in the logistics and customs clearance processes in advance through big data analysis to minimize human intervention and resource waste.		√
C8	Predictive maintenance and equipment management	Utilize sensor technology and big data analysis to conduct predictive maintenance on port equipment, thereby reducing the frequency of equipment failures, extending the lifespan of the equipment, and reducing the waste of resources and carbon emissions that result from maintenance.	√	√
C9	Environmental and waste management systems	Digitally managing the waste treatment processes can help track and optimize the treatment of solid waste and wastewater generated by the port, promoting a circular economy and reducing environmental pollution.	√	√
C10	Blockchain and port safety management	Utilizing blockchain technology can improve the efficiency of port operations, reduce the carbon footprint, and reduce the amount of paperwork and human error to ensure the transparency and security of port logistics, cargo tracking, and transaction data.	√	√
C11	Digital carbon emission monitoring	Establishing a digital carbon emission monitoring system can enable port operators to make timely adjustments to reduce their carbon footprint and track real-time greenhouse gas emissions from port operations.	√	√
C12	Green supply chain information	Utilizing digital technology to optimize all facets of the supply chain and logistics can reduce unnecessary transportation, storage, and resource waste and achieve green supply chain management.	√	√

Note: The symbol "√" in the table indicates that the corresponding critical success factor aligns with and is supported by the respective theory (e.g., Sustainable Supply Chain Management Theory or System Dynamics Theory). It signifies that the factor is either grounded in or validated by the theoretical framework, demonstrating its relevance and applicability within the context of the study.

). These critical factors bolster system dynamics and sustainable supply chain management theories. System dynamics theory prioritizes feedback mechanisms and system dynamics, while sustainable supply chain management theory prioritizes sustainability and efficiency. These factors are significant to both theories due to their shared interests.

In the second phase of the study, we contacted more than 100 maritime-related institutions in South Korea and selected 36 potential partners. After introducing the content of the study by email, we received positive responses from 27 institutions. However, due to time constraints and the busy work schedules of many professionals in this highly specialized field, 11 experts were unable to participate. In the end, 16 experts participated in the survey, each with many years of experience in maritime and port management. This group of experts was carefully selected to ensure diversity in perspectives, including managers from port operations and logistics departments, professional managers specializing in shipping operations and supply chain management, and professors with expertise in maritime logistics, port sustainability, and digital transformation. These diverse professional backgrounds ensured a comprehensive understanding of the research topic and provided robust insights into the study.

Table 2. Expert demographics.

Profile	Number of Respondents	Percentage (%)
Position
Department manager and above	3	18.8%
Professional manager	6	37.5%
Professor	3	18.8%
Government managers	4	25.0%
Years of employment with the organization
6–10	5	31.3%
11–15	7	43.8%
≥16	4	25.0%
Type of enterprise
Shipping companies	6	37.5%
Port operators	4	25.0%
Marine insurance companies	3	18.8%
Maritime research institutes	3	18.8%
Number of employees
<50	2	12.5%
51–150	2	12.5%
151–300	6	37.5%
>301	6	37.5%
N = 16

lists their basic information, including their qualifications, professional roles, and contributions to the research.

This study collected expert opinions through email, interviews, and questionnaires. Initially, we used the matrix-filling method; however, feedback from the experts revealed that matching factor codes and names was overly complex and prone to misinterpretation. To address this, we revised the methodology and adopted a detailed questionnaire format, similar to a Likert scale [49], to simplify the process and improve clarity.

The interviews were conducted in a semi-structured format to ensure consistency while allowing flexibility for experts to elaborate on specific areas of their expertise. These interviews were carried out either in person or via virtual platforms, depending on the availability and location of the respondents. Each session lasted approximately 25 to 55 min, during which the concepts and factors were explained in detail to ensure a shared understanding. Experts were encouraged to ask clarifying questions and provide additional feedback on the questionnaire design.

In addition to the interviews, a comprehensive email correspondence was maintained to clarify any uncertainties regarding the survey. This step was particularly valuable for respondents who could not participate in the interviews due to time constraints. The final questionnaire, refined based on the feedback from initial interactions, was distributed to all 16 participants. Their responses were collected and analyzed using the DEMATEL framework.

Table 3. Original measurement table of one expert.

CSF	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
C1	0	2	3	4	4	3	1	2	1	2	3	4
C2	2	0	1	3	3	2	1	2	1	2	3	4
C3	3	1	0	2	2	1	1	1	3	2	3	4
C4	4	3	2	0	2	2	1	2	1	2	3	4
C5	4	3	2	2	0	2	1	2	1	2	3	4
C6	3	2	1	2	2	0	1	2	1	2	3	4
C7	2	1	1	2	1	2	0	1	1	3	2	3
C8	2	2	1	2	2	2	1	0	1	2	3	4
C9	1	2	1	2	2	2	1	2	0	2	3	4
C10	1	1	2	1	1	3	1	3	1	0	2	3
C11	2	2	3	2	2	3	2	3	2	0	0	2
C12	3	4	4	3	3	4	3	4	3	2	1	0

Note: for code definitions, refer to Table 1.

presents an example of the data provided by one of the participating experts.

The fuzzy DEMATEL method was chosen for the third phase of this study because it is uniquely suited to analyzing complex systems with interdependent factors. Unlike traditional models, it effectively captures both direct and indirect relationships between key success factors while accommodating uncertainty and ambiguity in expert evaluations. By integrating fuzzy set theory, the method ensures flexibility, reliability, and interpretability in processing nuanced expert judgments, making it ideal for identifying the causal and resultant factors driving the green transformation of ports through digital technology. Although we can employ a variety of quantitative models, including AHP, SEM, EFA, ELECTRE, TOPSIS, and ANP, to assess these factors (

Table 4. Comparing DEMATEL with other methodologies.

Method	Description	Features	Comparison with DEMATEL
AHP (Analytic Hierarchy Process)	Assesses the temporal and spatial distribution of objectives by comparing different elements but may not fully capture the interdependencies between factors	Assess the temporal and spatial distribution of objectives Easy to use	Complex interdependencies between factors may be overlooked
SEM (Structural Equation Modeling)	Examines the structural relationships between different elements but requires large data sets for comprehensive analysis	Investigate structural relationships Requires large amounts of data	High demand for data may not be suitable for situations where data is scarce
EFA (Exploratory Factor Analysis)	Simplifies the complexity of factors and applies to multiple variables	Simplify complexity Applicable to multiple variables	May not be able to capture all relevant relationships
ELECTRE (Elimination and Choice Expressing Reality)	Prioritizes multiple attribute factors and applies to complex decision-making processes in a constrained procedure	Prioritize multiple-attribute factors Applicable to complex decisions	May not be suitable for all types of decision-making problems
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)	Defines ideal and anti-ideal solutions and explores alternative solutions	Develop solution definitions Explore alternatives	May not be suitable for all types of decision-making problems
ANP (Analytic Network Process)	Analyzes the interdependencies between factors but is unpopular due to its complexity	Analyze the interdependencies between factors High complexity	Complexity may lead to increased difficulty of application

), DEMATEL is particularly noteworthy for its suitability for the analysis of complex systems, particularly in cases where information is unclear. DEMATEL is more effective than regression analysis and structural equation modeling when addressing complex systems due to its ability to minimize the necessity for parameter estimation and extensive data statistics. The method analyzes the cause-and-effect relationships of complex systems by combining expert experience with graph theory and matrices [50]. Additionally, it establishes a hierarchical framework to facilitate comprehension of the issue and to recognize critical components and strategies [45]. DEMATEL employs matrix operations to analyze the correlation between elements, relying on expert knowledge. It is an exceptional structural modeling technique that generates a system model of the system through numerical evaluation.

Despite the fact that existing methods are highly effective and compatible when addressing complex systems, they concentrate on individual components. Methods that can accommodate random dependencies are necessary for the comprehensive examination of cause-and-effect interactions. Traditional models find it challenging to quantify these relationships due to the ambiguity of experts’ descriptions of the relationships between factors. Therefore, in order to resolve this issue, we implement fuzzy set theory within the DEMATEL method to circumvent the constraints of conventional DEMATEL in managing fuzzy information [51].

Fuzzy set theory is capable of effectively managing imprecise and ambiguous information regarding probability. The fuzzy DEMATEL model is capable of identifying causal relationships in complex decision-making environments, maintaining interpretability and flexibility, and providing intuitive evaluation and decision support for decision-makers [45,52]. The trend of utilizing the fuzzy DEMATEL method in maritime industry research is illustrated in

Table 5. Recent studies in the shipping industry using the fuzzy DEMATEL method.

Author	Industry	Method	Number of Factors	Number of Experts
Wan et al., 2021 [45]	Shipping	fuzzy DEMATEL	10	6
Kuzu, 2021 [46]	Shipping	fuzzy DEMATEL	18	5
Kuzu et al., 2023 [47]	Shipping	fuzzy DEMATEL	18	8
Soner, 2021 [53]	Shipping	fuzzy DEMATEL	18	3

.

4. Result

The Direct Influence Matrix represents the immediate relationships between critical success factors in green port transformation. Each element in this matrix quantifies the direct impact one factor has on another. This foundational matrix provides the basis for analyzing the interdependencies among factors, allowing us to observe which elements have a prominent direct influence on the system’s structure (

Table 6. Direct Influence Matrix.

CSF	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
C1	0.0000	0.4854	0.7188	0.9521	0.9375	0.7188	0.2667	0.4854	0.2667	0.5133	0.7466	0.9375
C2	0.4854	0.0000	0.2521	0.7042	0.7042	0.4854	0.2521	0.4563	0.2521	0.4992	0.7320	0.9229
C3	0.7188	0.2521	0.0000	0.4708	0.4708	0.2521	0.2521	0.2521	0.2521	0.4846	0.7180	0.9229
C4	0.9521	0.7042	0.4708	0.0000	0.4708	0.4708	0.2521	0.4563	0.2521	0.4846	0.7180	0.9229
C5	0.9375	0.7042	0.4708	0.4708	0.0000	0.4708	0.2521	0.4563	0.2521	0.4846	0.7180	0.9229
C6	0.7188	0.4854	0.2521	0.4708	0.4708	0.0000	0.2521	0.4563	0.2521	0.4846	0.7180	0.9229
C7	0.2667	0.2521	0.2521	0.2521	0.2521	0.2521	0.0000	0.2521	0.2521	0.2805	0.5138	0.7188
C8	0.4854	0.4708	0.2521	0.4708	0.4708	0.4708	0.2521	0.0000	0.2521	0.4846	0.7180	0.9229
C9	0.2667	0.4563	0.2521	0.4708	0.4708	0.4708	0.2521	0.0479	0.0000	0.4992	0.7320	0.9229
C10	0.2667	0.2521	0.2521	0.2521	0.2521	0.2521	0.2521	0.2667	0.0479	0.0000	0.4992	0.7188
C11	0.5000	0.4854	0.7042	0.5000	0.4854	0.7042	0.4854	0.6896	0.4854	0.0336	0.0000	0.4708
C12	0.7333	0.9375	0.9375	0.7333	0.7188	0.9375	0.7333	0.9375	0.7333	0.4706	0.2378	0.0000

).

The Normalized Influence Matrix refines the Direct Influence Matrix by standardizing its values to fall within a comparable range. This matrix ensures that the influence levels of each factor are proportionately adjusted, facilitating clearer comparisons of influence strengths and maintaining consistency across the model. It aids in emphasizing the most impactful factors while ensuring that all interrelations are considered within a balanced scale (

Table 7. Normalized Influence Matrix.

CSF	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
C1	0.0000	0.0599	0.0886	0.1174	0.1156	0.0886	0.0329	0.0599	0.0329	0.0633	0.0921	0.1156
C2	0.0599	0.0000	0.0311	0.0868	0.0868	0.0599	0.0311	0.0563	0.0311	0.0616	0.0903	0.1138
C3	0.0886	0.0311	0.0000	0.0581	0.0581	0.0311	0.0311	0.0311	0.0311	0.0598	0.0885	0.1138
C4	0.1174	0.0868	0.0581	0.0000	0.0581	0.0581	0.0311	0.0563	0.0311	0.0598	0.0885	0.1138
C5	0.1156	0.0868	0.0581	0.0581	0.0000	0.0581	0.0311	0.0563	0.0311	0.0598	0.0885	0.1138
C6	0.0886	0.0599	0.0311	0.0581	0.0581	0.0000	0.0311	0.0563	0.0311	0.0598	0.0885	0.1138
C7	0.0329	0.0311	0.0311	0.0311	0.0311	0.0311	0.0000	0.0311	0.0311	0.0346	0.0634	0.0886
C8	0.0599	0.0581	0.0311	0.0581	0.0581	0.0581	0.0311	0.0000	0.0311	0.0598	0.0885	0.1138
C9	0.0329	0.0563	0.0311	0.0581	0.0581	0.0581	0.0311	0.0059	0.0000	0.0616	0.0903	0.1138
C10	0.0329	0.0311	0.0311	0.0311	0.0311	0.0311	0.0311	0.0329	0.0059	0.0000	0.0616	0.0886
C11	0.0616	0.0599	0.0868	0.0616	0.0599	0.0868	0.0599	0.0850	0.0599	0.0041	0.0000	0.0581
C12	0.0904	0.1156	0.1156	0.0904	0.0886	0.1156	0.0904	0.1156	0.0904	0.0580	0.0293	0.0000

).

The Total Influence Matrix reflects both the direct and indirect influences of each factor on the others. By aggregating these influence types, this matrix captures the overall effect each factor exerts within the system. This comprehensive view highlights the cumulative significance of factors and enables us to identify those with the most substantial influence, directly or indirectly, on green port transformation through digital technology (

Table 8. Total Influence Matrix.

CSF	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
C1	0.2128	0.2427	0.2510	0.2968	0.2935	0.2675	0.1527	0.2248	0.1476	0.2094	0.2988	0.3858
C2	0.2310	0.1556	0.1716	0.2382	0.2368	0.2123	0.1317	0.1945	0.1273	0.1816	0.2597	0.3354
C3	0.2358	0.1678	0.1279	0.1963	0.1949	0.1698	0.1213	0.1560	0.1172	0.1658	0.2377	0.3089
C4	0.2924	0.2452	0.2066	0.1721	0.2254	0.2223	0.1386	0.2041	0.1339	0.1896	0.2717	0.3528
C5	0.2903	0.2448	0.2062	0.2264	0.1700	0.2219	0.1383	0.2037	0.1337	0.1892	0.2712	0.3522
C6	0.2475	0.2048	0.1667	0.2078	0.2064	0.1504	0.1280	0.1890	0.1237	0.1750	0.2509	0.3259
C7	0.1387	0.1265	0.1188	0.1285	0.1275	0.1278	0.0645	0.1183	0.0920	0.1099	0.1683	0.2254
C8	0.2145	0.1966	0.1599	0.1997	0.1983	0.1980	0.1238	0.1296	0.1197	0.1693	0.2427	0.3154
C9	0.1778	0.1840	0.1496	0.1872	0.1860	0.1864	0.1170	0.1265	0.0829	0.1614	0.2308	0.2979
C10	0.1339	0.1216	0.1146	0.1235	0.1226	0.1228	0.0915	0.1164	0.0653	0.0723	0.1605	0.2175
C11	0.2218	0.1998	0.2099	0.2068	0.2039	0.2247	0.1498	0.2079	0.1467	0.1244	0.1702	0.2772
C12	0.3121	0.3062	0.2847	0.2920	0.2887	0.3061	0.2135	0.2847	0.2080	0.2232	0.2699	0.3147

).

Table 9. Factor analysis results: impact, centrality, and weighted rankings.

Factor	Influence	Affected	Centrality	Cause	Weight	Sort	Factor Attribute
C1	2.9833	2.7086	5.6919	0.2747	0.1	2	Cause
C2	2.4759	2.3958	4.8716	0.0801	0.086	6	Cause
C3	2.1994	2.1675	4.3668	0.0319	0.077	9	Cause
C4	2.6546	2.4750	5.1296	0.1796	0.09	4	Cause
C5	2.6478	2.4542	5.1021	0.1936	0.09	4	Cause
C6	2.3761	2.4101	4.7862	−0.0340	0.084	7	Result
C7	1.5462	1.5705	3.1167	−0.0243	0.055	12	Result
C8	2.2675	2.1556	4.4230	0.1119	0.078	8	Cause
C9	2.0874	1.4980	3.5853	0.5894	0.063	10	Cause
C10	1.4623	1.9711	3.4335	−0.5088	0.061	11	Result
C11	2.3432	2.8321	5.1753	−0.4890	0.091	3	Result
C12	3.3037	3.7090	7.0127	−0.4053	0.124	1	Result

depicts the factor analysis results, listing the multiple attributes of the 12 factors (C1 to C12), including their influence on and by other factors and results, their centrality, reasonableness, weight, and their ranking and attribute classification (cause or result factor) in the analysis.

We additionally performed an instrumental variable (IV) analysis utilizing a tool variable matrix derived through Principal Component Analysis (PCA). This approach allowed us to address potential endogeneity issues and ensure the robustness of the results. The detailed results of the IV analysis are presented in Appendix A. The IV test, conducted using a PCA tool variable matrix, confirms the robustness of our findings. The weight rankings of the factors derived from the IV analysis align closely with the original results, as shown in

Table A1. PCA-IV Comprehensive Influence Matrix.

CSF	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12
C1	0.1248	0.6437	0.8893	1.1568	1.1398	0.8987	0.3647	0.6330	0.3617	0.6527	0.9461	1.1941
C2	0.6368	0.0909	0.3610	0.8666	0.8658	0.6258	0.3376	0.5854	0.3349	0.6218	0.9078	1.1492
C3	0.8803	0.3587	0.0749	0.6013	0.6005	0.3600	0.3316	0.3519	0.3292	0.5974	0.8802	1.1335
C4	1.1542	0.8707	0.6075	0.1005	0.6186	0.6167	0.3414	0.5909	0.3387	0.6114	0.9004	1.1595
C5	1.1379	0.8705	0.6073	0.6192	0.0993	0.6164	0.3413	0.5906	0.3386	0.6112	0.9001	1.1591
C6	0.8870	0.6214	0.3582	0.6081	0.6073	0.0878	0.3354	0.5822	0.3329	0.6028	0.8880	1.1436
C7	0.3564	0.3345	0.3302	0.3355	0.3349	0.3354	0.0377	0.3299	0.3146	0.3538	0.6289	0.8740
C8	0.6268	0.6016	0.3542	0.6032	0.6024	0.6025	0.3331	0.0757	0.3306	0.5995	0.8833	1.1374
C9	0.3794	0.5793	0.3482	0.5957	0.5950	0.5957	0.3292	0.1238	0.0484	0.6099	0.8908	1.1271
C10	0.3537	0.3317	0.3278	0.3326	0.3320	0.3324	0.3143	0.3440	0.0879	0.0416	0.6091	0.8692
C11	0.6464	0.6181	0.8497	0.6374	0.6207	0.8586	0.5888	0.8335	0.5872	0.1077	0.0997	0.6492
C12	0.9402	1.1471	1.1338	0.9283	0.9114	1.1467	0.8818	1.1339	0.8787	0.6176	0.4050	0.1854

. For instance, C12, C1, and C11 consistently rank as the top three critical factors across both methods. This alignment indicates that our original factor analysis is statistically reliable and supports the validity of the results. The consistency further reinforces the theoretical framework of system dynamics and sustainable supply chain management, demonstrating the pivotal role of digital technologies in driving the green transformation of ports.

Figure 1 depicts the cause–effect relationship graphically, with the degree of centrality as the horizontal axis and the degree of rationality as the vertical axis.

The integration of digital technology has emerged as a vital and pivotal element in driving the green transformation of ports. Throughout this transformative journey, three key factors stand out for their pivotal roles, shaping the direction of the transformation and ensuring its efficacy [18,34]. First, C12, representing green supply chain information, emerges as the leading factor with a weight of 0.124, topping the list of all factors. This finding underscores the importance of achieving ultimate environmental and social benefits in the port’s green transformation. C12 encompasses critical outcomes such as pollution reduction and energy efficiency enhancement, which enhance the port’s operational efficiency and economic gains and confer positive environmental and social benefits to the surrounding environment and community. By leveraging digital technology, ports can precisely monitor and manage energy use, waste disposal, and carbon emissions, thereby mitigating negative environmental impacts at their source. This results-oriented transformation strategy aids ports in realizing environmental and social sustainability while pursuing economic benefits.

Second, C1, Intelligent Vessel Scheduling and Traffic Optimization, is the cause factor with a weight of 0.1, ranking second. This finding indicates that investment in green technology and enhancing operational processes are the primary catalysts for green transformation. The high impact, affected, centrality, and cause degree of C1 highlight it as the critical link between digital technology and the goals of green transformation. Integrating advanced green technologies, including adopting renewable energy sources, intelligent energy management frameworks, and automated cargo handling systems, allows ports to significantly improve energy efficiency while concurrently reducing energy consumption and greenhouse gas emissions. Additionally, refining operational processes, such as optimizing logistics routes, minimizing empty loads, and enhancing cargo handling efficiency, can reduce the port’s overall environmental footprint. The significance of C1 lies in its role as technical support for green transformation and as a cornerstone for long-term sustainability [18].

Third, C11, Digital Carbon Emission Monitoring, is a result factor with a weight of 0.091, placing it third. It focuses on the specific outcomes of port operations, such as reducing waste generation and improving resource recycling. These specific results are the tangible manifestation of a green transformation and are crucial indicators for assessing the effectiveness of the transformation. Through digital technology, ports can achieve sophisticated waste disposal management and reduce the discharge of solid waste and hazardous substances [25]. Simultaneously, the advancement of resource recycling diminishes resource consumption and lessens the port’s environmental impact. The importance of C11 lies in its emphasis that a green transformation is not merely a technological shift but a comprehensive revolution in the port’s entire operational system and management model.

However, to pursue a greener future for the port, including other key factors is essential to drive the port’s green transformation. Therefore, although the weightings are not as high as C1, C12, and C11, they are also important. C4, with a weight of 0.09 and ranking fourth, emerges as a foundational element in optimizing port operations through intelligent energy management. This factor enhances efficiency and equips staff with the skills to navigate the green transition effectively [21]. Similarly, C5, with a weight of 0.09 and tied for fourth, is pivotal in fostering a collaborative environment through automated customs clearance management. This role is crucial for facilitating seamless information exchange and collaboration, helping to reduce the port’s environmental impact and further its sustainability goals [23].

C2, ranked sixth with a weight of 0.086, is fundamental in establishing a robust legal and incentive framework that encourages adopting intelligent logistics equipment, making it feasible and incentivized, thus setting the stage for transformation [23]. C8, in eighth place with a weight of 0.078, is vital for understanding and anticipating equipment needs, ensuring that port services align with environmental objectives, and improving customer satisfaction [18]. C3 ranked ninth with a weight of 0.077, propelling technological advancement by leveraging the Internet of Things (IoT) technology to automate cargo handling, reduce emissions, and improve operational efficiency, significantly contributing to the port’s sustainability journey [23]. C6, seventh with a weight of 0.084, is essential for identifying and mitigating operational risks through digital twin technology, ensuring that the port’s operations are sustainable and resilient against potential challenges.

C9, ranked tenth and weighing 0.063, supports the transformation by utilizing environmental and waste management systems to enforce and implement green policies with precision and effectiveness [16]. C7, twelfth with a weight of 0.055, is indispensable for fostering a culture of sustainability among employees through intelligent risk monitoring, ensuring they are motivated and well-prepared to contribute to the port’s green objectives [16]. Finally, C10, ranked eleventh with a weight of 0.061, is crucial for engaging customers in green initiatives through blockchain and port safety management, reinforcing the port’s commitment to sustainability, and building a positive brand image. These factors collectively create a dynamic and adaptive ecosystem, driving the green transformation of ports with the strength of digital technology. This approach ensures streamlined operations, aligned with environmental goals promoting the sustainable growth and advancement of port infrastructure.

5. Discussion

This study investigates the critical success factors (CSFs) for green port transformation driven by digital technology, offering an integrated framework that combines sustainable supply chain management and system dynamics theories. Through the fuzzy DEMATEL methodology, it identifies and evaluates 12 key factors, emphasizing their complex interdependencies and the pivotal roles they play in achieving green transformation. These findings not only extend existing research [1,2,14,16,40,48] but also provide a comprehensive foundation for both theoretical advancements and practical applications in port management.

The findings highlight the centrality of C12, C1, and C11. These factors represent the core mechanisms through which digital technology enhances port sustainability. For instance, C12, with the highest weight and centrality, underscores the systemic integration of green principles into digital supply chain practices, addressing both operational efficiency and environmental sustainability [14]. C1, as a causative factor, emphasizes the strategic importance of optimizing vessel operations to reduce emissions and enhance resource utilization [16,18]. Conversely, C11, as a resultant factor, captures the tangible benefits of technology adoption, such as improved resource recycling and reduced waste [25].

This research also demonstrates how mid-tier factors like C4 and C5 contribute to the broader ecosystem. These factors complement the leading CSFs by enabling operational automation, enhancing energy efficiency, and fostering cross-organizational collaboration. For example, C5 facilitates seamless data exchange and streamlines customs processes, reducing delays and emissions across the supply chain [23]. Similarly, C4 integrates renewable energy sources into port operations, aligning with sustainability objectives and ensuring long-term resilience [21].

Compared to previous studies, this research provides a more nuanced understanding of the interplay between causative and resultant factors. While prior analyses often treat these factors in isolation [27,35], the use of fuzzy DEMATEL in this study highlights their interdependencies, offering a more holistic view of how digital technologies drive green transformation. For instance, the Total Influence Matrix reveals that mid-tier factors, such as C3 and C8, play critical intermediary roles, bridging operational improvements with strategic sustainability goals [1,26,45,48].

Moreover, this study contributes to theoretical integration in international port management. By combining sustainable supply chain management and system dynamics theories, it develops a framework that not only addresses traditional management elements like training and awareness but also incorporates emerging challenges such as digital risk management, blockchain transparency, and regulatory compliance [40,44]. This approach enhances the theoretical discourse on digital ports while providing actionable insights for stakeholders.

Practical implications are equally significant. Port operators must prioritize the deployment of IoT-enabled monitoring systems and predictive maintenance technologies to enhance real-time operational efficiency [19]. Government departments should focus on fostering international cooperation and implementing regulatory frameworks that support the adoption of digital twin technologies for proactive planning and risk mitigation [18]. Shipping companies, in turn, need to invest in blockchain-based safety management systems and intelligent logistics equipment to align with global sustainability standards [20,35].

6. Conclusions

The findings of this study underscore the transformative potential of digital technologies in driving the green transition of port operations, highlighting their capacity to reshape the maritime industry toward greater sustainability. While the integration of digital tools has become indispensable for achieving operational efficiency, resource optimization, and environmental stewardship, this study emphasizes that their successful deployment depends on a strategic alignment of technological innovation, policy support, and systemic management practices.

The green transformation of ports, facilitated by intelligent systems such as green supply chain information platforms, intelligent vessel scheduling, and digital carbon emission monitoring, is not merely a technological endeavor but a multifaceted evolution encompassing economic, environmental, and social dimensions. By establishing digital ecosystems that optimize operations, monitor emissions in real time, and enhance risk management, ports can elevate their role as nodes of sustainability within the global supply chain. However, achieving this transformation requires addressing persistent challenges, such as balancing technological investments with equitable access, fostering international collaboration for standardization, and ensuring that digital advancements translate into measurable environmental and societal benefits.

This research highlights the importance of a forward-looking, systemic perspective that integrates digital innovation with sustainable supply chain and system dynamics theories. It calls for a shift from isolated technology adoption to a more cohesive strategy that involves stakeholders across governments, port operators, and shipping companies. Such an approach can accelerate the greening of ports while positioning them as leaders in environmental responsibility and resilience.

As ports continue to face the dual pressures of globalization and climate change, this study serves as a strategic roadmap for leveraging digital technologies to achieve sustainable transformation. By navigating the complexities of technological, policy, and operational interplay, the maritime industry can pioneer a future where efficiency and sustainability are no longer trade-offs but synergistic goals.

This study has certain limitations in its exploration of the role of digital technologies in advancing the greening of ports. First, applying the fuzzy DEMATEL method, which amalgamates qualitative and quantitative analysis, relies on the subjective judgments of experts, which can introduce bias and uncertainty into the outcomes. Second, the study’s geographical focus on specific ports and regions within South Korea may restrict the broader applicability of the findings, as economic, political, and cultural nuances across different regions can significantly influence the determinants of successful green transformation. Third, while the study has pinpointed the key success factors for digital technology-driven green port transformation, future research should broaden its scope. Integrating a wider array of management theories and strategic analysis frameworks would be beneficial, such as incorporating PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) analysis to holistically evaluate the multifaceted role of digital technologies in port greening. Additionally, the GROW (Goal, Reality, Options, Will) model could facilitate the intricate interplay between digital technology and environmental, economic, and social factors within ports. Using big data analysis and artificial intelligence technology would also enable a deeper dive into a more extensive data repository, enhancing the accuracy and reliability of the research. These methodological advancements and expansions will enrich the academic discourse on port greening and offer more nuanced and comprehensive guidance for the sustainable development of ports worldwide.