1. Introduction
As key centers of maritime transport, ports handle around 80% of global trade by volume and over 70% by value, highlighting their critical role in international trade [1,2,3]. However, air pollution, energy consumption, and noise generation during port operations have significant environmental impacts and can also threaten human health [4,5]. To address these challenges, the concept of “sustainable ports” has emerged as a strategic solution. Sustainable ports are characterized by the integration of safe, energy-efficient, and environmentally responsible practices into their operations. This stage aims to achieve a balance between economic growth, environmental preservation, and social responsibility [6,7].
Building on this foundation, the green ports concept further emphasizes mitigating the environmental impacts of port activities through innovative strategies. The environmental governance framework employs systematic policy instruments and technological innovations to establish comprehensive control mechanisms addressing atmospheric emissions, water quality standards, pollutant dispersion, energy mix optimization, ecosystem preservation, and resource utilization efficiency. Within the social dimension, emphasis is placed on modernizing port governance capabilities, fostering harmonious port-community relationships, and enhancing workforce welfare systems. The economic aspect focuses on optimizing financial resource allocation, improving port operational efficiency, and advancing green infrastructure modernization [8,9,10,11,12]. Since its formal introduction at the 2009 United Nations Climate Change Conference, the green ports initiative has gained significant global traction. Prominent ports such as Rotterdam, Singapore, and Los Angeles have successfully implemented green strategies, resulting in improved energy efficiency and controlled pollutant emissions [13,14,15,16]. Empirical research has demonstrated that the adoption of green port strategies not only enhances local environmental conditions but also promotes the utilization of clean energy and supports the restoration of sustainable port ecosystems [9].
From a definitional perspective, a green port is defined as a port where production and operational activities pursue economic and social benefits while prioritizing the protection of the ecological environment and the efficient use of resources and energy [17,18,19]. This approach reflects a commitment to understanding and addressing the environmental impacts of port development [20]. Both eco-ports and low-carbon ports fall under the green ports category, with low-carbon ports focusing more on reducing ecological impacts through port resource and energy management [21]. Eco-ports place greater emphasis on controlling pollutant emissions through various measures and strategies, alongside enhanced environmental monitoring and pollutant management. In light of the above, the green ports referenced in this paper include both eco-ports and low-carbon ports [18]. To provide a more comprehensive overview of the current state of research in this field, the scope of green ports research in this paper encompasses aspects such as efficiency, energy consumption, pollutant emissions, and the ecological environment, in addition to other related aspects of sustainable port operations.
Scholars in the field of green ports have conducted extensive evaluations to assess the environmental benefits and developmental stages of ports [22,23,24]. The findings from these studies provide valuable insights into the effectiveness of green ports in environmental protection, reducing pollutant emission, and promoting resource recycling. These evaluations help identify areas for improvement, optimize operational processes, and adjust energy structures. Furthermore, the results provide a scientific foundation for adjusting green port transformation strategies [25].
However, many evaluation methods are tailored to specific contexts and lack a comprehensive global perspective on their practical applicability. The wide array and complexity of methodologies and indicators present significant challenges for scholars in developing new indicator systems. Building on previous studies, this literature review is the first to comprehensively examine the applicability of evaluation methods across various contexts. Additionally, it categorizes and statistically analyzes key indicators to help researchers in clarifying their hierarchy, offering a valuable reference for green port evaluation.
2. Literature Review
2.1. Sources and Selection
To provide a comprehensive overview of the existing literature on green port evaluation, this study selects the Web of Science (WoS) Core Collection, a globally recognized repository of high-impact scholarly literature. The selection of WoS is justified by its extensive coverage of international peer-reviewed journals and conference proceedings. Notably, the database’s emphasis on a globally representative scope ensures the inclusion of diverse geographical contexts across developed and developing port regions.
The temporal scope of this review spans from 2009 to 2024, anchored by the formal introduction of the “green port” concept in 2009. This period captures the evolution of evaluation frameworks, from early theoretical models to contemporary multicriteria decision-making approaches. To ensure methodological transparency and reliability, rigorous selection criteria were applied (Table 1).
We conducted the literature search using several thematic keyword groups. These included: (1) “ecological”, “green”, “low-carbon”, and “sustainable” for environmental focus; (2) “port”, “harbor”, and “terminal” for the specific domain of interest; and (3) “evaluation”, “index”, and “indicator system” for assessment-related concepts. These keyword groups were combined to ensure a comprehensive and relevant search of the literature. A total of 923 articles are identified using the advanced search function with the time range of 2009–2024 in the WoS database. By perusing the abstracts and conducting an advanced search for keywords, the process of removing duplicate literature is initiated. As a result, 121 articles were retained. The results were subjected to a further screening process based on the criteria set out in the literature selection table of this review. This process involved the removal of non-compliant articles, leaving 87 articles from WoS. Additionally, the retained literature was perused individually to identify and exclude sources not aligned with the objectives of the review, leaving 74 articles. Then, the references were examined and consulted to supplement the relevant literature that had not been identified through the initial search. To ensure comprehensive coverage in terms of both time and geographical scope, we conducted supplementary searches and supplemented our collection with seven additional papers. In total, 82 articles were selected. The screening process is illustrated in Figure 1.
2.2. Analysis of Literature
2.2.1. Geospatial Analysis
In order to better understand the distribution of research in the field by region, we analyzed the screened literature by geographical region. We marked the research locations of the authors in the literature according to the location of their affiliation, and analyzed the research density in different regions. Figure 2 presents the distribution of research studies by country and continent, highlighting significant regional disparities. The bar chart reveals that China leads with 38 studies, followed by the United Kingdom (10) and Spain (7), while many other countries contribute only one to three studies. European countries collectively exhibit a strong research presence, the United Kingdom maintains a significant position in global academia and Spain is endowed with exceptional coastal and port resources [26].
In contrast, North America, South America, Oceania, and Africa show relatively limited research output, reflecting a potential imbalance in global academic engagement in this field. The pie chart further illustrates the continental distribution, where Asia accounts for the largest share (47.9%), followed by Europe (36.8%). North America and South America each contribute 6%, whereas Oceania and Africa have the lowest representation at 1.7% each.
One of the key reasons for this dominance of Asia and Europe is the high number of ports in these regions and their well-developed port infrastructure. According to Lloyd’s List (2023) [27], the top 15 global container ports in terms of throughput are all located in Asia and Europe, with China alone hosting nine of them. A solid foundation of port development provides the necessary conditions for advancing research on green ports. Moreover, substantial funding and policy incentives in these regions further support research activities. Additionally, China’s particularly high research output could be driven by national strategic initiatives, government-led funding, and a strong academic–industrial collaboration framework [28].
Interestingly, despite North America being home to ports with high levels of green development [29], research on green port evaluation accounts for only 6% of the total studies. This discrepancy may stem from the fact that green port evaluation represents just one research cluster within the broader field of green port studies [30,31], making its overall share relatively limited. In North America, green port evaluation has not emerged as a mainstream academic focus. The reason is that limited transparency in sustainability reporting—evidenced by only 29% of port management authorities publishing formal stand-alone sustainability reports [32]—contributes to reduced scholarly attention and comparability challenges in environmental performance assessments.
In contrast, regions such as Africa and South America may face challenges such as limited research funding, fewer specialized institutions, and weaker international collaboration networks [33,34]. This uneven distribution highlights the need for global efforts to strengthen research in underrepresented regions, promoting a more balanced and inclusive academic landscape. Collaboration among scholars and institutions from regions with varying development levels has already contributed to this field [33,35,36], and may become a growing trend in the future.
In summary, these findings highlight significant regional disparities in research priorities within the green port evaluation domain, emphasizing the need to consider geographical variations when analyzing the global landscape of green port evaluation studies.
2.2.2. Temporal Analysis
A temporal analysis was conducted to map the distribution of publication years, as depicted in Figure 3. Overall, the publications have shown a steady upward trend, which reflects the growing academic interest in the field, aligning with the broader development trajectory of green ports. During this period of 2009–2015, the number of publications remained minimal, with no relevant literature identified in 2009 and 2010. This scarcity of research can be attributed to the early stage of green port development, where the concept was still emerging, and a comprehensive evaluation framework had yet to be established. Additionally, the lack of sufficient empirical data hindered systematic assessment efforts.
A significant shift occurred in 2016, with research output increasing substantially. Publications from 2016 onward account for approximately 90% of the total studies, indicating a surge in academic interest. The increasing number of publications on green port evaluation aligns closely with the global emphasis on sustainability, particularly following the introduction of the United Nations Sustainable Development Goals (SDGs) in 2015 [37]. Among these, SDG 13 (climate action), SDG 11 (Sustainable Cities and Communities), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 7 (Affordable and Clean Energy) have played a crucial role in shaping research priorities. Green port evaluation prioritizes the mitigation of GHG and exhaust emissions, demonstrating strong congruence with SDG 13’s climate action agenda through measurable targets and innovative sustainability practices [38]. As ports are integral to urban infrastructure and industrial development, SDG 11 has driven research on port-city environmental impacts, climate resilience, and sustainable governance, making these factors essential components of green port evaluations. Similarly, SDG 9 has influenced studies on green ports, promoting innovation in evaluation frameworks [39]. The role of SDG 7 is evident in the increasing focus on port electrification, renewable energy integration, and energy efficiency metrics within green port assessments.
The peak in publications was observed in 2020–2021, followed by stable high output. It can be attributed to multiple factors. Firstly, policy interventions, such as the IMO 2020 sulfur emission regulations [40], likely played a significant role in driving research interest, as regulatory changes often catalyze academic discussions and evaluation frameworks. Additionally, by 2020, green port initiatives had been developing for nearly a decade, leading to the accumulation of substantial empirical data—a time horizon commonly used in longitudinal studies. The subsequent decline in 2022–2023 can be partially explained by the global impact of the COVID-19 pandemic, which shifted port development priorities toward public health and safety, temporarily diverting focus from green port evaluation. Given the inherent lag between real-world developments and academic research outputs, this shift is reflected in the publication trends. However, the decline was not substantial, and the recovery observed in 2024 suggests that research in green port evaluation remains resilient. This indicates that despite external disruptions, sustainability remains a fundamental aspect of port development, reinforcing the long-term significance of green port assessment in both academic and policy domains.
2.2.3. Keyword Analysis
Keyword clustering analysis was conducted on the selected literature, with a frequency greater than four selected to generate Figure 4. The network visualization of the keyword co-occurrence map reveals three key themes:
Red Cluster: Focused on management and indicator selection, with the core keywords including “sustainability”, “management”, “indicators”, and “system”. This suggests that research is concentrated on sustainable port management, particularly the green port evaluation indicator systems.
Green Cluster: Focused on environment efficiency and performance, with the keywords “environmental efficiency”, “performance”, “DEA”, and “container terminals”. This highlights the environment as a key topic in green port evaluation, especially for container ports. The DEA approach is widely utilized in this field.
Blue Cluster: Concentrated on performance evaluation criteria, featuring “ports”, “emissions”, and “energy efficiency”. This highlights another key research focus in green port evaluation, which is the assessment of emissions and energy efficiency in port operations.
In conclusion, the topic network reveals the following: Green port evaluation is closely related to port management development. The establishment of a well-structured evaluation indicator system is important for green port evaluation. The efficiency evaluation of the environment and energy are key research areas.
The overlay visualization illustrates the relationship between research hotspots and time, with the color transitioning from blue to light green to yellow. The development is divided into two stages based on the temporal sequence of research themes. In the early development stage, “environment management” was the core concept. This research focused on qualitative analysis of green port management. Studies during this stage were relatively foundational. In the later stage, the research shifted its focus to energy, emission control, and technical efficiency, exploring the areas of port environmental performance and energy efficiency assessment. Greater attention was given to analyzing the environmental impacts of port operations, including carbon emissions and pollution control, using DEA and improved models. Research related to China has been growing, which aligns with the trends shown in Figure 2.
The two figures illustrate the effectiveness of the methods used in this paper for reviewing and screening the literature.
3. Evaluation Methods and Subjects
As discussed in Section 2.2, it can be seen that the development of comprehensive evaluation methods in green ports research has made great progress in recent years. We conducted a critical analysis of their applicability across diverse research subjects, aiming to provide methodological references for scholars.
3.1. Common Methods
As illustrated in Figure 5, the most prevalent evaluation methods in the literature are data envelopment analysis (DEA) and Analytic Hierarchy Process (AHP). Among these, improved DEA approaches are employed more frequently than traditional DEA approaches. This is because improved DEA approaches enhance the ability to process diverse inputs and outputs, adapt to multi-dimensional complexity, and account for dynamic efficiency variations. The AHP approach is frequently combined with other methods, as it complements both quantitative and qualitative techniques in complex multi-indicator evaluations. Thereby improving accuracy and interpretability for more comprehensive evaluations. The FCE approach is also utilized, primarily in combination with other methods, offering advantages in addressing uncertainty and ambiguity. Methods such as Delphi, importance–performance analysis (IPA), Analytic Network Process (ANP), and Balanced Scorecard Method (BSM) are versatile in their application, demonstrating robust efficacy both as standalone frameworks and within integrated methodological architectures. The entropy weight method (EMW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Decision-making Trial and Evaluation Laboratory (MEMATEL) are typically integrated into the multicriteria decision-making (MCDM) framework to enhance evaluation comprehensiveness. System dynamics (SD) and Self-Diagnostic Method (SDM) are more commonly applied independently due to their theoretical maturity, explanatory power, and suitability for specific evaluation types.
In summary, there is an observable trend toward diversification in the selection of evaluation methods within the field of green port evaluation. This is evidenced by the frequent combined use of methods, highlighting the need for multi-dimensional evaluation to address the complexity of green ports.
3.2. Evaluation Subjects
We categorized the evaluation subjects into three areas based on their characteristics: port efficiency, port expressiveness, and resources and energy. These aspects encompass the core components of green ports and align with the key research hotspots in green port evaluation discussed in Section 2.2.3.
3.2.1. Efficiency Evaluation
The evaluation of port efficiency encompasses three key areas: environmental efficiency, green efficiency, and eco-efficiency. Environmental efficiency concerns the maximum input–output ratio or the ratio of added value to the environmental impact that a port can achieve with limited environmental resources while considering a range of comprehensive environmental factors [41,42]. Eco-efficiency is based on a comprehensive assessment of port operational indicators, incorporating the impact of port emissions on the surrounding environment and society [42,43]. Green efficiency evaluates port operations by balancing both economic and environmental benefits [44]. These three dimensions highlight distinct indicators specific to the level of green port development.
Port efficiency evaluations are based on quantitative analyses of data and are often evaluated using both parametric and non-parametric approaches. The stochastic frontier analysis (SFA) is one of the parametric approaches. Quintano [45] evaluated the eco-efficiency of several European ports using the SFA. Durán [46] employed SFA to assess the efficiency of Mexican and Chilean ports. The parametric approach, based on explicit economic theory and mathematical models, helps elucidate the causes of changes in port efficiency.
However, the current research on green port efficiency tends to favor non-parametric methods, as highlighted in Table 2. The non-parametric approach does not depend on a specific economic model and instead evaluates efficiency purely through data, offering greater flexibility in accommodating diverse data types and characteristics. Lin [47] used an inverse DEA approach to evaluate container performance sustainability. The traditional DEA approach allows each DMU to select weights to maximize its efficiency score. However, differences between ports can introduce bias, prompting the development of improved DEA models. Wang [44] used a cross-efficiency model to comprehensively evaluate the green efficiency of 18 Chinese ports. This approach reduces the self-assessment bias of traditional DEA models, enhancing the objectivity of the results.
Traditional DEA models typically only consider desirable outputs and ignore undesirable outputs such as pollutant emissions, which can result in overestimating the efficiency of assessment units. To address this limitation, many scholars have incorporated specific environmental variables as undesirable outputs. Sun [48] evaluated the environmental efficiency of several Chinese ports by considering NOx emissions as an undesirable output. The results of DEA are often influenced by external environmental and stochastic variations. To mitigate these effects, two-stage or three-stage DEA models are employed. Cui [49] chose CO2 emissions as an undesirable output and constructed a RAM-Tobit-RAM model to evaluate environmental efficiency. Wang [50] evaluated the environmental efficiency of Chinese ports by considering PM emission as an undesirable output. In the context of green efficiency assessments, models that incorporate undesirable outputs are particularly effective for emissions-related efficiency evaluations. Research in this area has increased significantly in recent years [41,51,52,53,62].
In summary, the selection of undesirable outputs is closely related to the evaluation subjects. DEA models considering undesirable outputs allow for a flexible reflection of decision-makers’ attention to environmental factors, enabling a comprehensive evaluation of port environmental management and its contribution to overall efficiency. Furthermore, green port efficiency evaluations exhibit clear time correlations, and the Malmquist productivity index can be introduced to identify efficiency changes from a dynamic perspective. Chang [54] used a DEA-Malmquist model to dynamically estimate productivity changes in ports over the period 2004–2014. Na [55] employed an input–output SBM to assess the environmental efficiencies of Chinese container ports over the period 2005–2014. The SBM model addresses the limitations of traditional efficiency measurement methods, particularly in handling slack variables, thereby improving the accuracy of efficiency evaluations. However, the SBM model is still subject to the issue of redundant variables, which can affect the accuracy and robustness of the evaluation. Wang [56] built a Super-SBM model by introducing super-efficiency as an additional constraint, which was combined with the Global Malmquist–Luenberger index to assess the spatial–temporal evolution of port cluster eco-efficiency. This improved method can handle interdependent slack variables [58,63], and the Global Malmquist–Luenberger index demonstrates superior consistency and stability by unifying production frontiers across multiple time points. However, it is important to note that the DEA approach is susceptible to data quality. Inaccurate or poor-quality data may distort the model results. Jiang [57] introduced uncertainty parameters to deal with data uncertainty and noise, improving the robustness of the model.
Compared to parametric methods, non-parametric methods, especially DEA models, have been continuously improved and applied to the environmental efficiency evaluation related to pollutant emissions. In the future, when evaluating port efficiency, researchers should emphasize data quality and its application, and consider integrating SFA and DEA methods to develop more sophisticated hybrid models [59,60,61]. There is growing interest in using multi-dimensional and multi-stage DEA models to assess efficiency across different time and spatial periods, offering a more objective and comprehensive evaluation framework.
Additionally, our analysis reveals that efficiency evaluations, predominantly quantitative in nature, require robust data infrastructure, leading to a concentration of representative studies in Asia and Europe. In contrast, green port development in Africa and Latin America remains nascent, with research constrained by data. North American studies are comparatively limited due to lower data transparency and accessibility. As green port initiatives advance, burgeoning data availability and methodological advancements are expected to catalyze increased scholarly outputs in these regions.
3.2.2. Expressiveness Evaluation
Green port evaluation can also be conducted by assessing capacity and performance. Some researchers have systematically evaluated the sustainability performance of ports, employing diverse methodologies such as the Performance Index [64] and DEA [65]. And, AHP was applied to evaluate the port’s capacity for green development [66,67,68]. In this study, green port expressiveness refers to the port’s ability to integrate and advance the surrounding environment, ecology, and economy while accommodating human activities. This concept encompasses not only ecological and environmental carrying capacities but also green port competitiveness. Research on the evaluation of green port expressiveness has undergone gradual and diversified development (Table 3).
The ecological carrying capacity of ports focuses on the health and functionality of ecosystems, including the interactions and impacts of socio-economic and resource environments. Guo [69] constructed an ecological carrying capacity model for ports based on ecological footprint theory and proceeded to evaluate it for a port. However, the model lacks temporal dynamic constraints, making it difficult to capture long-term evolutionary characteristics. Zhang [70] used the system dynamics theory, combined with the state space evaluation method, to establish a composite evaluation model of the ecological carrying capacity of the port and carried out dynamic evaluation.
Environmental carrying capacity assessments typically consider pollution of the atmosphere, water bodies, and soil from port activities, as well as impacts on biodiversity [76]. The AHP combined with the expert scoring method was used to determine weights of resource and environmental carrying capacities [71], and AHP was also combined with the information entropy theory to establish an evaluation mechanism of shoreline resources [72]. Li [73] quantitatively measured and dynamically analyzed the environmental carrying capacity of 20 seaports in China using DEA-Malmquist index analysis and cluster analysis.
Unlike environmental and ecological carrying capacity, green port competitiveness focuses on environmental protection and ecological health, which takes into account more comprehensive evaluation indicators. Gao [74] used Fuzzy-AHP to evaluate the port competitiveness of Quanzhou Port and proposed the corresponding improvement suggestions for green port transformation. Li [75] used a combination of the SBM and EW-TOPSIS methods to quantitatively assess the green competitiveness of 25 major coastal ports in China.
From a methodological perspective, the AHP approach is highly applicable in evaluating green port expressiveness due to its clear structure and operational efficiency. There has been a notable shift from traditional single-method models to integrated multi-method approaches. Additionally, the dynamic evaluation of the temporal dimension has become a key research focus. Future studies could explore the integration of dynamic simulation and forecasting techniques to enhance the evaluation of green port expressiveness.
3.2.3. Energy Evaluation
Using renewable energy can improve the energy efficiency and environmental performance of ports [77]. The results of energy-related evaluations in ports provide a comprehensive understanding of energy efficiency, helping to identify energy-saving opportunities. This, in turn, forms the basis for decisions regarding natural resource management, energy optimization, equipment renewal, and other related issues [62,78,79].
Port-integrated energy systems (PIES) can effectively help green ports to achieve energy saving targets. Establishing an effective evaluation method has become a research hotspot in recent years [80]. He [81] proposed a PIES evaluation index using AHP-Fuzzy for comprehensive evaluation. Attanasio [82] used the Delphi method to study the future trends of energy management in ports, analyzing the key performance indicators for monitoring consumption and emissions. PIES evaluation relies on qualitative analyses, supplemented by objective weighting methods. The port microgrid system is an important application within PIES management. Xu [83] developed a port microgrid operational efficiency evaluation model based on the improved CRITIC-TOPSIS method to provide data support for decision-makers. This approach enhances objectivity by analyzing data and attributes to determine the weights of indicators.
Wind power projects are among the most commonly implemented options by ports to integrate PIES solutions into their operations [84]. Some scholars conducted comprehensive evaluations at the early stages of project planning. Zhao [85] used AHP-EWM to calculate the weights and comprehensively evaluate the reasonableness of the design of port self-sufficient wind power systems. To maximize the benefits of the wind power project for PIES, the location of the wind farm needs to be assessed prior to construction. Huang [86] proposed an AHP-Fuzzy approach to evaluate siting options for port wind farms and wind turbine groups. Nevertheless, there is currently a lack of studies on the evaluation of onshore wind power siting in ports. Ocean energy is another critical component of PIES for green ports, helping reduce the costs associated with transporting and storing energy. For example, wave energy has been adopted to power facilities at the Peniche port in Portugal. Cascajo [87] studied the feasibility of wave energy utilization in ports using the Delphi method to establish a comprehensive evaluation index system. Despite the substantial reserves of ocean energy, there is a lack of studies evaluating its viability for use in green ports, presenting a promising avenue for future research.
As shown in Table 4, the number of studies in this field has increased in recent years, highlighting the urgent need for optimizing the energy structure for green port development. The current research focuses more on the energy supply side, with limited attention to demand-side evaluations [88]. Most evaluation methods rely on subjective approaches such as AHP, Fuzzy, and Delphi, as PIES development in green ports is still in its early stages, and data availability remains limited. As PIES continues to develop and more data are accumulated, it will be possible to incorporate objective methods, such as the entropy weight method, for more comprehensive evaluations. Future studies should emphasize the integration of generation, grid, load, and storage for more comprehensive insights [89,90].
4. Green Port Evaluation Indicator System
The green port evaluation is a systematic project [91], and the establishment of a scientific and comprehensive evaluation index system is a prerequisite for a comprehensive evaluation. The selection of system indicators should be based on a clear understanding of the connotations and characteristics of green ports, guided by the principles of comprehensiveness, systematization, operability, and dynamism [92,93]. To ensure the scientific rigor and effectiveness of the green port evaluation indicator system, scholars have developed various theoretical frameworks, while port authorities have progressively refined the index system through practical experience.
4.1. Evaluation Indicator System Investigation
In this paper, representative indicator systems from the literature are selected for analysis (Table A1). A literature review was conducted to cluster the indicators and analyze their frequencies (Figure 6). Due to variations in evaluation methods across the literature, the frequency statistics reflect only the instances where relevant indicators are explicitly mentioned.
As green ports evolve from the broader concept of sustainable ports, their evaluation metrics are structured around three interconnected dimensions: environment, society, and economy. Overall, environmental indicators appear more frequently and cover more elements, indicating that greater emphasis is placed on the environmental aspects of green port evaluation compared to the economic and social dimensions [94]. Economic and social indicators feature fewer high-frequency terms compared to environmental indicators, but key elements such as “Institutions and policies”, “Emergency response”, “Finance and investment”. This reflects the significant influence of policies and funding on green port development, as well as the essential role of an effective emergency response plan in ensuring the success of green ports [95].
The environmental indicators encompass a wide range of elements, including “Air”, “Water”, “Waste”, “Energy”, and other aspects, reflecting the comprehensive requirements for environmental sustainability in this field. The frequent inclusion of “three wastes” (air emissions, waste water, and solid waste) management highlights its central role as a primary determinant of the green ports environment. Additionally, energy-related indicators such as “Energy consumption”, “Clean energy”, and “Energy-saving technologies” are frequently observed, underscoring the significance of energy efficiency in green port development. As environmental standards rise, energy consumption becomes a key issue in green port construction. Energy-efficient, low-carbon port energy systems can significantly reduce pollution and carbon emissions, contributing to sustainable port operations [96,97]. Economic indicators are quantifiable, with a strong emphasis on factors such as costs, inputs, and profits. Most of these indicators are related to production and operation. Throughput and regional GDP-related indicators are important economic indicators, which can reflect the economic volume and scale of the ports [98]. The social indicators are mainly qualitative and can be classified into three main categories: “Employees”, “Communities”, and “Management”. These indicators focus on the interests of relevant port stakeholders and the management and development of port operations. In the future, further quantification of green port performance in promoting community well-being and social equity may be beneficial.
In terms of data attributes, high-frequency indicators combine qualitative and quantitative measures. Quantitative indicators provide clear, objective metrics for evaluating the environmental, economic, and resource utilization aspects of green ports, offering numerical data that enable precise assessment [99,100]. For example, indicators such as “energy consumption” and “pollution emissions” can provide quantitative data in support of environmental and economic benefits. However, it is often difficult for quantitative indicators to cover a number of complex social and ecological factors, particularly soft factors such as policy influence and community satisfaction [101]. Qualitative evaluation complements quantitative analysis, facilitating reflection on the influence of management strategies, environmental protection policies, and social satisfaction in green port development. This approach introduces a subjective dimension and a more comprehensive perspective to the evaluation system.
In conclusion, the comprehensive green port evaluation system comprises at least three major categories of indicators: environment, economy, and society. In the field of green port evaluation, the conclusions of this paper can be used to determine more detailed and specific indicators by combining the analysis of the evaluation targets and their development levels.
4.2. Green Port Certification System
Several international port-related organizations have established various green port certification systems. Among these, the most widely recognized are the European EcoPorts environmental management standards, the North American Green Marine Environmental Program (GMEP), and the Asia-Pacific Green ports Award System (GPAS). Initiated by the European Sea Ports Organization in 1997, it currently has 133 members [102]. GMEP, one of the most influential environmental programs for ports and shipping in North America, has had over 170 participants since its inception in 2007, including ports, shipowners, and terminal facilities [103]. Initiated in 2016 by the Asia-Pacific Seaport Network, GPAS is the only green port certification system in the Asia-Pacific region, with 69 ports and terminals recognized to date [104]. Figure 7 shows the geographic distribution of membership by organization.
From a spatial perspective, green port certification shows a clear regional distribution, with Europe, North America, and Asia as the primary regions, corresponding to areas with the highest levels of green port development globally. Reflecting the sequence of green port development, certification in European ports preceded that in North America and Asia. Europe was the first to initiate the green ports concept and its implementation, positioning itself at the forefront of environmental protection and green port development, with the eco-ports standard serving as a model for other regions [105]. The launch of the GMEP reflects North America’s responsiveness to the global imperative for environmental protection, particularly given the port industry’s growing recognition of the significance of green transformation [106]. The establishment of the GPAS in Asia reflects a growing awareness among Asian countries of the importance of addressing port environmental protection alongside rapid economic growth.
Figure 8 shows the certification processes and key metrics of the three certification organizations. In terms of process, all three certification systems follow a structured SDM approach. In this process, the applicant port conducts a self-evaluation based on the indicators specified by each certification system. Following this, the certification committee organizes external experts or institutions to conduct the official certification. The SDM approach enables ports to ascertain their own strengths and weaknesses, thereby encouraging proactive improvement on the part of port managers [107]. On the basis of SDM, investigations by external experts are introduced to ensure the objectivity and credibility of the evaluation results.
In terms of indicators, eco-ports guide ports towards EU standards, providing detailed assessment checklists and specific recommendations. GMEP focuses on fostering continuous improvement, incorporating stricter performance indicators to ensure progress [108]. GPAS, recognizing the varying development levels of participating ports, adopts a more flexible assessment system that encourages gradual improvement.
The preceding analysis demonstrates that the development of the green port certification system has followed a pattern of incremental progress, resulting in a situation of multi-regional synergistic development. These three certification systems, all of which have been developed at the local level, are appropriate to the level of development of the evaluation target. They are representative of the international port field and promote the development of green ports.
5. Conclusions
This review provides a comprehensive overview of research and applications related to green port evaluation. It begins by clarifying the definition and scope of green ports, followed by a visualization of the research hotspots and emerging trends in the literature, offering valuable insights for future studies. The review categorizes green port evaluation subjects into three areas: port efficiency, expressiveness, and energy, with an analysis of suitable evaluation methods for each. It also examines the hierarchical structure, high-frequency indicators, and key indices of green port evaluation systems. Additionally, the study discusses the processes and key indicators used by international green port certification organizations.
Based on this review, the following conclusions can be drawn:
Evaluation Subjects
The literature reveals that the evaluation of green ports encompasses a broad range of subjects, including efficiency, capacity, and energy. Future research could refine these categories and address the dynamic nature of green port development. For instance, energy evaluations could be more detailed, distinguishing between energy consumption in port production units (e.g., ships, onshore power supply, and warehousing equipment). Such detail would enable decision-makers to identify issues more effectively and implement targeted improvements. Furthermore, as green port development goals, such as carbon neutrality and resource reuse, are dynamic and long-term, future research should integrate these goals into evaluation frameworks.
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Evaluation Methods
The existing literature highlights the use of subjective and non-parametric methods such as AHP, fuzzy logic, and DEA. While these methods offer certain advantages, their application in different contexts warrants deeper analysis. The advent of multi-dimensional data and intricate systems in green ports has rendered a unidimensional evaluation method inadequate for capturing the non-linear interrelationship between energy, resources, and efficiency. Future research could explore the integration of technologies such as machine learning and big data analysis, to handle high-dimensional and complex data. These technologies could facilitate more accurate energy efficiency predictions and evaluations.
Additionally, traditional models such as DEA and AHP, often rely on deterministic assumptions, which may fail to capture uncertainties, especially in data-scarce or highly heterogeneous environments. Future research could incorporate probabilistic models, such as Bayesian Networks, to quantify and address uncertainties. These models would combine historical data with expert judgment, providing a more robust approach to evaluating complex environmental factors and dynamic changes.
The multidisciplinary nature of green port development calls for the integration of ecological, economic, and systems engineering models. In the future, combining these approaches could create a cross-disciplinary and more comprehensive evaluation framework. For instance, the Ecosystem Services Theory could be integrated with economic models to assess the environmental, economic, and social benefits of green ports, offering a more holistic evaluation.
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Indicator Systems
The green port evaluation indicator system is typically divided into three main categories: environmental, social, and economic indicators, each with a clear hierarchical structure. However, the choice of specific indicators should be aligned with the characteristics and goals of the evaluation object. The granularity of indicator selection depends on the scope and stage of development of the evaluated port. Maintaining consistency in the indicator hierarchy is essential to ensure accurate and credible evaluation results.
Additionally, indicator systems must be adaptable to changes over time. Given that different indicators are interrelated, updates to the system should account for these interactions. For example, clean energy indicators may directly influence carbon emissions, and environmental regulations could impact economic costs. Future research could explore the interactions between different indicators using system dynamics models, based on specific data sets, to enhance evaluation robustness.
In conclusion, this study offers a solid theoretical foundation for improving green port evaluation systems, with implications for both academic research and practical application. The proposed future research directions aim to enhance the effectiveness and adaptability of green port evaluation frameworks, fostering the continued development of sustainable, environmentally friendly ports. It should be noted that the generalizability of findings may be constrained by the coverage of the analyzed databases, warranting future expansions to capture diverse ports comprehensively.
Conceptualization, H.F. and X.P.; methodology, H.F.; software, H.F.; formal analysis, H.F.; writing—original draft preparation, H.F.; writing—review and editing, H.S. and X.P.; visualization, H.F.; supervision, H.S. and X.P.; project administration, X.P.; funding acquisition, X.P. All authors have read and agreed to the published version of the manuscript.
Not applicable.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| WoS | Web of Science |
| DEA | data envelopment analysis |
| DMUs | Decision-Making Units |
| AHP | Analytic Hierarchy Process |
| FCE | Fuzzy Comprehensive Evaluation |
| DPSIR | Driver–Pressure–State–Impact–Response Model |
| ANP | Analytic Network Process |
| TOPSIS | Technique for Order Preference by Similarity to Ideal Solution |
| EWM | entropy weight method |
| SDM | Self-Diagnostic Method |
| CRITIC | Criteria Importance Through Intercriteria Method |
| SD | system dynamics |
| BSM | Balanced Scorecard Model |
| MEMATEL | Decision-making Trial and Evaluation Laboratory |
| IPA | importance–performance analysis |
| MCDM | multicriteria decision making |
| SFA | stochastic frontier analysis |
| SBM | Slack-Based Measure |
| RAM | Range-Adjusted Measure |
| CRM | Comprehensive Reallocation Model |
| CPM | Comprehensive Production Model |
| CEM | Comprehensive Emission Model |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 5. Statistics on green port evaluation methods. Note: “Improved DEA”: the data envelopment analysis models developed on the traditional framework, such as data envelopment analysis with consideration of undesirable outputs, multi-stage data envelopment analysis, and dynamic data envelopment analysis models. “DEA”: traditional data envelopment analysis models, including the model assuming constant returns to scale and the model assuming variable returns to scale. “Integrated”: the method being used in conjunction with other approaches. “Standalone”: the method used independently for evaluation.
Literature selection criteria.
| Criteria | Interpretations |
|---|---|
| Types | From high-quality journals, published monographs, and academic conferences (excluding Graduation thesis) |
| Geographical Coverage | Ensuring comprehensive geographical representation |
| Relevance | Related to the comprehensive evaluation with a focus on green ports (excluding port logistics, shipping, ships, etc.) |
| Citation | Priority is given to more cited and influential literature |
| Authority | From leading scholars and research organizations |
Summary of previous papers about green port efficiency evaluation.
| Subjects | Methods | Undesirable Outputs | References |
|---|---|---|---|
| Environmental efficiency of 24 ports in Europe | DEA | Greenhouse gas emissions | [ |
| Green efficiency of 18 ports in Asia | Cross-efficiency DEA | NOX and SOX | [ |
| Eco-efficiency in 24 ports in Europe | SFA | CO2 | [ |
| Sustainable efficiency of 17 ports in Latin American | SFA | CO2 | [ |
| Sustainability efficiency of 15 container ports in Asia | Inverse DEA | CO2 and NOX | [ |
| Efficiency of 17 listed port companies in Asia | Non-radial DEA | NOX | [ |
| Environmental efficiency of 10 ports in Asia | RAM-Tobit-RAM | CO2 | [ |
| Environmental efficiency measurement of 11 ports in Asia | CRM-CPM-CEM | PM | [ |
| Environmental efficiency of 10 ports in Asia and Europe | SBM-DEA | CO2 | [ |
| Environmental efficiency of 2 ports in Europe | Two-stage DEA | Energy consumption and emissions | [ |
| Sustainable efficiency of container terminals in Asia | SBM-DEA | CO2 | [ |
| Technical efficiency of 14 ports in Latin American | DEA-Malmquist | / | [ |
| Technical environmental efficiency of 8 ports in Asia | Input–output | CO2 | [ |
| Eco-efficiency of 18 ports in Asia | Super-SBM and GML | NOX | [ |
| Sustainability efficiency of 23 seaports in Asia | Uncertain DEA | Air and water pollutants | [ |
| Environmental efficiency of 26 ports in Asia | SBM-DEA | CO2 | [ |
| Eco-efficiency of 24 ports in Europe | SBM-DEA and SFA | Emissions | [ |
| Port efficiency of 2 ports in Asia | DEA-SFA-DEA | NOX | [ |
| Green efficiency of 15 seaports in Asia | Super-SBM-DEA, SFA, Super-SBM-DEA | CO2 | [ |
Summary of the literature on green port expressiveness evaluation.
| Subjects | Methods | References |
|---|---|---|
| Ecological carrying capacity | Ecological footprint theory | [ |
| System dynamics theory | [ | |
| Environmental carrying capacity | AHP and expert scoring method | [ |
| AHP and information entropy theory | [ | |
| DEA | [ | |
| AHP | [ | |
| Green port competitiveness | Fuzzy-AHP | [ |
| SBM and EWM-TOPSIS | [ |
Summary of the literature on green port energy evaluation.
| Years | Subjects | Methods | References |
|---|---|---|---|
| 2024 | PIES | AHP-Fuzzy | [ |
| 2023 | Port Energy Management | Delphi method | [ |
| 2023 | Port microgrid operational efficiency | CRITIC-TOPSIS | [ |
| 2023 | Port self-sufficient wind power energy systems | AHP-EWM | [ |
| 2024 | Port wind farm site selection | AHP-Fuzzy | [ |
| 2022 | Feasibility of wave energy utilization in ports | Delphi method | [ |
Appendix A
Green port evaluation-related indicator systems and references.
| Years | Indicator Systems | Methods | References |
|---|---|---|---|
| 2009 | Assessment indices of port environment resources, Planning objectives of port environment resources | AHP | [ |
| 2010 | Green port evaluation indicator system | Delphi and AHP | [ |
| 2010 | Assessment indices of port environment resources, Planning objectives of port environment resources | Expert scoring, AHP, and Gray clustering | [ |
| 2011 | Green port evaluation indicator system for planning, construction and operation periods | Staged analysis | [ |
| 2013 | Low-carbon evaluation indicators for container ports | AHP, Fuzzy, | [ |
| 2013 | Green and low carbon port evaluation indicator system | Expert scoring and AHP | [ |
| 2014 | Evaluation indicator system for eco-port cluster | ANP | [ |
| 2014 | Environmental performance indicators in port | Expert scoring and | [ |
| 2015 | Port low carbon green development evaluation indicator system | Cloud model | [ |
| 2015 | Eco-port evaluation indicator system | R-cluster, coefficient of variation, and expert experience | [ |
| 2015 | The expert-based port sustainability indicators | Social construction of technology and rough sets theory | [ |
| 2016 | Economic and environmental indicators of sustainability port system | Synthetic indices and cluster analysis | [ |
| 2017 | Green port competitiveness evaluation indicator system | ANP | [ |
| 2017 | Evaluation indicator system for low carbon green development of oil terminals | DPSIR model and AHP | [ |
| 2017 | Proposed reference indicators of environmental and economic performance for ports | Semi-structured | [ |
| 2017 | Port performance indicators | Delphi, AHP, | [ |
| 2018 | Means of importance and performance ratings on the sustainability assessment criteria | Importance–performance analysis and expert interviews. | [ |
| 2018 | Evaluation model for quantitative measurement of green ports | DPSIR model and AHP | [ |
| 2019 | Eco-port evaluation indicator system | DPSIR model and set pair analysis | [ |
| 2019 | Weights for criteria and indicators in the green fishery harbor evaluation structure | Modified Delphi | [ |
| 2019 | Environmental performance indicators for green ports | Entropy | [ |
| 2019 | Literature classification of aspects of sustainability using clustering of sustainability analytical indicators | Cluster | [ |
| 2020 | Port eco-efficiency performance indicators | Comprehensive | [ |
| 2020 | A green port indicator system tailor-made for Zhuhai Port | Fuzzy and importance–performance analysis | [ |
| 2020 | Container terminal performance evaluation method framework | ANP | [ |
| 2021 | Creation of composite index of port region sustainability | Entropy and preference ranking organization method | [ |
| 2021 | Environmental performance indicators from the Global Reporting Initiative | Benchmarking | [ |
| 2023 | Sustainability performance criteria of the marine seaports | Delphi and WASPAS technique | [ |
| 2023 | Environmental, social, and governance framework for assessing sustainability | CRITIC | [ |
| 2024 | Port low carbon evaluation indicator system | Pressure-State- | [ |
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; Shi, Hongda 2 ; Pan, Xinying 2 1 Department of Ocean Engineering, College of Engineering, Ocean University of China, Qingdao 266404, China
2 Department of Ocean Engineering, College of Engineering, Ocean University of China, Qingdao 266404, China; Shandong Provincial Key Laboratory of Ocean Engineering, Ocean University of China, Qingdao 266100, China