A System Dynamics Framework for Port Resilience Enhancement Along Maritime Silk Road: Insights from ESG Governance

Abstract

Port resilience performance (PRP) is a critical factor in advancing the sustainable development of the 21st Century Maritime Silk Road (MSR). The Environmental, Social, and Governance (ESG) framework, widely recognized as a cornerstone of global sustainability efforts, offers a robust foundation for enhancing PRP. This study employs a system dynamics (SD) approach to explore the impact of ESG on PRP along the MSR. By developing an ESG evaluation index system and a resilience assessment framework, the research examines the mechanisms and evolutionary patterns through which ESG influences port resilience. Simulations are conducted for four strategic ports: Chattogram Port, Singapore Port, Gwadar Port, and Djibouti Port. The findings reveal that ESG initiatives significantly enhance PRP, with Singapore Port exhibiting the most stable and rapid resilience improvement. In contrast, the other ports demonstrate varying levels of adaptation and enhancement. Among the intervention strategies, prioritizing social dimension (S) improvements proves most effective for achieving rapid short-term resilience gains. This study offers both theoretical insights and practical strategies for strengthening port resilience and fostering sustainable development along the MSR.

1. Introduction

Over the past decade since the initiation of the 21st Century Maritime Silk Road (MSR) initiative, an increasing number of countries have joined and engaged in comprehensive cooperation, yielding substantial development achievements [1,2]. However, while benefiting from the collaborative growth momentum, the MSR has also faced numerous challenges due to adverse factors such as global public health crises and geopolitical instability [3,4]. As critical hubs in MSR development, ports play an irreplaceable role in facilitating connectivity among participating nations. In the face of various disruptions and challenges, port resilience performance (PRP) is essential for ensuring the smooth operation of maritime trade corridors and further promoting economic and trade cooperation among MSR countries [5,6].

Resilience refers to the ability of an object to return to its original state after experiencing external forces [7]. With ongoing research, this concept has gradually expanded across multiple disciplines, including engineering, management, and economics [8,9,10]. Many scholars have deconstructed resilience into several specific indicators, with the widely accepted “4R” framework—robustness, resourcefulness, redundancy, and rapidity—providing theoretical support for evaluating the resilience of infrastructure and social systems [11,12]. As a critical node in the global supply chain and logistics system, ports often face severe operational challenges under external shocks such as natural disasters, extreme weather events, cyberattacks, economic downturns, and geopolitical risks. Consequently, PRP has garnered increasing attention in recent years. Although there is no universally accepted definition, PRP is generally understood as a port’s ability to maintain functionality, adapt quickly to disruptions, and restore normal operations in the face of external disturbances [13,14]. To measure PRP and facilitate port sustainability, Kim et al. [15] proposed a multi-layer resilience index structure comprising nine key factors: robustness, redundancy, visibility, flexibility, collaboration, agility, information sharing, responsiveness, and recoverability. Similarly, Wang et al. [16] analyzed multiple characteristics influencing PRP, identifying robustness, redundancy, visibility, flexibility, agility, and recoverability as primary evaluation indicators, supported by case studies. For PRP quantification, González-Solano et al. [17] integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL) with Interpretative Structural Modeling (ISM) to formulate key strategies for port planning and operations under a resilience-oriented approach. Gu et al. [18] employed Hierarchical Holographic Modeling (HHM) with a fuzzy cognitive map (FCM) to analyze PR factors from the perspective of cargo transportation during global public health crises. Additionally, various methodologies have been applied for PRP quantification, including system dynamics (SD) simulation [19,20], conceptual frameworks [21], and Bayesian networks [16,22]. Among them, SD demonstrates unique methodological advantages. By utilizing causal loop diagrams and stock-flow diagrams, SD can effectively capture the nonlinear interactions among various resilience factors in port systems, overcoming the limitations of static methods in simulating dynamic feedback mechanisms. Furthermore, SD enables scenario-based simulations to quantify the evolution of port resilience under different shock scenarios, making it particularly suitable for addressing time-varying challenges in complex adaptive systems like ports.

Environmental, Social, and Governance (ESG) is an emerging concept that integrates environmental protection, social responsibility, and governance enhancement to achieve sustainable development. By strengthening ecological conservation, fulfilling social obligations, and improving governance efficiency, ESG plays a crucial role in ensuring the stable and sustainable development of society and the economy [23,24]. Since its introduction in 2004, ESG has initially gained widespread attention from corporations, becoming a powerful driver for long-term business growth. As more countries recognize the practical benefits of ESG implementation, it has evolved into a globally acknowledged tool for promoting sustainable development across various sectors [25,26,27]. In terms of research methodologies, Lee et al. [28] examined ESG-driven growth strategies in the maritime and port industries using the multiple case study theory and analyzed key ESG initiatives through the quadruple helix model. Zhou and Yuen [29] applied a Bayesian network model to identify 45 sustainability risks associated with the ESG transition in the shipping industry. Similarly, Jia et al. [30] employed structural equation modeling (SEM) to explore the relationships among ownership structure, sustainability performance, and financial performance in the shipping sector. Additionally, some scholars have utilized system dynamics models to quantitatively assess ESG impacts by capturing feedback loops among ESG indicators [31]. ESG principles have also played an increasingly significant role in port development. Researchers have conducted extensive studies on how ESG contributes to enhancing port service capacity [32], reducing carbon emissions [33], and promoting sustainable development [34,35].

For ports along the MSR, effectively implementing sustainable development principles within the ESG framework is crucial. Strengthening ports’ adaptive capacity and recovery capability in response to climate change, social risks, and governance challenges contributes to enhancing overall PRP along the MSR. Currently, research on the impact of ESG principles on PRP remains in its early stages. The existing literature primarily focuses on how ESG promotes port sustainability [33,34]. However, there is a lack of systematic analysis on how ESG enhances ports’ adaptability and recovery capacity in complex external environments. Moreover, previous studies have not incorporated system-based approaches to examine the interactions and long-term effects of different ESG factors on PRP. To address this research gap, this study employs a SD approach to develop a simulation model for analyzing PRP evolution under the ESG framework along the MSR. The study explores how ESG factors influence PRP dynamics through environmental, social, and governance mechanisms. The objective is to identify key drivers and mechanisms of ESG-driven PRP enhancement, providing scientific insights and policy recommendations for sustainable port development and governance along the MSR.

The rest of this paper is organized as follows. Section 2 presents the research problem analysis and the methodology used in this study. Section 3 develops the SD model for simulating PRP under the ESG framework along the MSR. Section 4 discusses the methodology and simulation results. Section 5 is the conclusion of this study.

2. Methodology

2.1. Problem Description

As key hubs in the global shipping network, ports play a crucial role in ensuring the smooth operation of the maritime industry [36]. With the increasing frequency of extreme weather events and public safety incidents, enhancing PRP has become essential to support the increasingly complex MSR trade network and its sustainable development goals. The ESG framework encourages companies to improve sustainability, reduce emissions, enhance efficiency, and maintain a safe working environment. Initially, ESG principles were primarily applied to corporate investment decisions, business strategies, and operational practices. However, due to its effectiveness in promoting sustainable development, ESG has gradually been extended to various sectors, including government policymaking, public services, and industrial development. Consequently, ESG has had a profound impact on port development [32]. PRP encapsulates a port’s capacity to withstand, adapt to, and recover from multifaceted disruptions, ranging from security breaches and environmental catastrophes to economic volatility. The ESG framework, by systematically addressing challenges in environmental (E), social (S), and governance (G) dimensions, inherently influences PRP and, in turn, affects the long-term development of ports, the relevant concepts are shown in Figure 1. The relationship between ESG and PRP can be expressed through Equation (1). Here β represents the quantified PRP value,${\beta }_0$ denotes the baseline PRP level, $\mathsf{∆}\beta$ signifies the resilience variation induced by ESG initiatives, and $\delta$ represents external disturbances or disruptions:

$$\beta ={\beta }_0+\mathsf{∆}\beta +\delta .$$

(1)

2.2. Resilience Measurement

When measuring the PRPs along the MSR, employing SD for quantitative analysis proves highly effective. SD is a robust simulation methodology that enables the modeling and analysis of complex systems through integrated analytical and inferential techniques [37]. The theory posits that the components within a system, through their complex interactions, collectively determine the system’s external performance. The elements within the system interact through complex linear and nonlinear relationships, and their joint influence via positive and negative feedback mechanisms leads to the system’s dynamic and complex behavior [38,39]. PRP along the MSR is a systemic and comprehensive behavior emerging from complex interactions among multiple systems, with nonlinear dynamics shaping the changes in resilience. Using SD to analyze the impact of ESG initiatives on port resilience allows for a comprehensive understanding of the complex causal relationships between resilience influencing factors. This method can capture the dynamic changes in the system’s elements, providing insights into the resulting effects. Additionally, it offers reliable theoretical and data support for the formulation of policies aimed at enhancing port resilience.

In resilience measurement, most studies focus on analyzing the characteristics of resilience and quantifying it through multi-indicator weighted analysis methods [40,41]. In SD, the factors influencing resilience exist in the form of various variables, which are related to each other through dynamic equations that reflect their interdependencies. Figure 2 presents a basic SD model structure, illustrating the relationship between the stocks, flows, and target variables of a system under external inputs [42,43]. The quantitative relationship between stocks and flows can be described using differential equations, as shown in Equation (2). In SD, the type of equation for variable C can vary, including table functions, IF THEN ELSE functions, and commonly used addition, multiplication, and exponential functions. The change in the target variable is mainly caused by the influence of external factors, and its calculation can be expressed by Equation (3). The random disturbance $\delta$ can be modeled by a normal distribution with specific parameters, as represented in Equation (4). In this model, the quantified value of the target variable T can be calculated using Equation (5).

$$S=S(t_0)+{\int }_{t_0}^t\left[I\left(∆t\right)-O\left(∆t\right)\right]dt$$

(2)

$$∆\beta \left(T\right)=f(S,C)$$

(3)

$$\delta ={\rm N}(\mu ,{\sigma }^2)$$

(4)

$$\beta \left(T\right)=\beta \left(T_0\right)+∆\beta \left(T\right)+\delta$$

(5)

2.3. Resilience Analysis

By integrating the PRP evaluation index system under the influence of ESG initiatives, a SD simulation model for ports along the MSR is constructed. This model enables multidimensional quantitative assessment of port resilience through dynamic simulation. Firstly, the temporal changes in system variables can be analyzed. For example, the dynamic changes in resilience indicators of MSR ports under the influence of multiple factors, and the dynamic impact analysis of each ESG framework dimension, can be performed. This trend analysis can be represented by Equation (6), where x(t) represents the dynamic changes in the variable of interest and Ρ represents the other system variables related to the variable. Additionally, the sensitivity analysis of the constructed SD model can be conducted. The basic principle of this analysis is to alter input parameters of the model to observe their influence on the system’s output. This process can be expressed by Equation (7).

$$x\left(t\right)=g(t,\mathsf{{\rm P}})$$

(6)

$$S_{x,{{\rm P}}_i}={\displaystyle \frac{\mathit{\partial }x}{\mathit{\partial }{{\rm P}}_i}}\times {\displaystyle \frac{{{\rm P}}_i}{x}}$$

(7)

3. Model

3.1. Indicator Establishment

In recent years, the principles of green development, environmental protection, and sustainability have gained widespread acceptance and been actively implemented in the construction and development of ports. With regard to port ESG evaluation, numerous scholars have conducted relevant research. However, a consensus-based indicator system has yet to be established. In this study, the port ESG index system is constructed in accordance with the principles of comprehensiveness, applicability, availability, and comparability. The principle of comprehensiveness means that the index system should cover every aspect of Environmental, Social, and Governance to avoid missing key factors. The principle of availability means that the selected indicators should have quantifiable characteristics, reliable data sources or be easy to obtain through expert knowledge. The principle of applicability means that it should not only reflect the basic characteristics of ESG development, but also better demonstrate the fit between port resilience and sustainable development. The principle of comparability means that the selected indicators can be applied to effectively measure ports between different countries and regions. Based on these principles, and with reference to international frameworks and agreements such as the United Nations Sustainable Development Goals (SDGs), the International Maritime Organization (IMO) environmental regulations, and the Global Reporting Initiative (GRI) standards, this study integrates ESG indicator classifications from the relevant literature [34,35] to establish a comprehensive ESG indicator system for ports along the MSR, as shown in

Table 1. Port ESG Evaluation Indicator System.

Category	First-Level Indicator	Second-Level Indicator
Environmental	Environmental quality	Wastewater treatment (WT)
		Solid waste treatment (SWT)
		Air pollution treatment (APT)
	Climate change	Carbon emission intensity (CEI)
		Use of renewable energy (URE)
	Ecosystem	Ecological restoration investment (ERI)
		Ecological restoration effect (ERE)
Social	Employee welfare and development	Occupational health and safety (OHS)
		Smaller income gap (SIG)
	Social responsibility	Job creation (JC)
		GDP contribution rate (GCR)
		Promotion of public facilities construction (PPFC)
	Customer service	Port service level (PSL)
		Port digital innovation (PDI)
		Customer satisfaction (CS)
Governance	Governance capability	Management structure (MS)
		Protection of stakeholders’ rights and interests (PSRI)
		Response time to major incidents (RTMI)
		Response measures to major incidents (RMMI)
	Information disclosure	Policy transparency (PT)
		Disclosure frequency and quality (DFQ)

.

Ports serve as critical gateways for international trade and constitute a foundational and strategic component of national economic development. However, ports are very vulnerable to extreme events from different aspects, such as maritime accidents, local unsafe incidents, large-scale public health incidents, etc., which will lead to the interruption of normal port operations. Resilience refers to the ability of a system to return to its original state when faced with external shocks. Enhancing port resilience ensures rapid absorption of risks and disturbances during emergencies, thereby maintaining operational continuity to a significant degree. This capability is instrumental in safeguarding the overall reliability of the MSR transportation network. Based on a full analysis of the risk events that ports are susceptible to, combined with the relevant resilience theory [12,16], the performance characteristics of port resilience are mainly in the following four aspects.

Reliability (REL): When the port faces shocks and disruptions, it can quickly adapt to changes and continue normal operations, effectively avoiding a total collapse.

Robustness (ROB): After an emergency occurs in the port waters, the losses in the port area caused by the emergency are effectively reduced, and the basic stability of the port functions is guaranteed.

Redundancy (RED): When an emergency causes the destruction of some functions of the port, the backup facilities can be put into operation quickly to ensure that the system can still maintain the basic functional level in an emergency and avoid global paralysis due to single point failure.

Recoverability (REC): After an emergency occurs, the port can restore the system function to a certain level as soon as possible through the repair mechanism to reduce the long-term impact of the incident on the port operation.

3.2. Model Construction

The goal of this section is to develop a SD simulation model of port resilience along the MSR under the influence of the ESG initiative. In the previous section, the port ESG index system and resilience index system have been constructed. Combined with the analysis in Section 2.1, the ESG initiative promotes the sustainable development of ports to a certain extent and has a positive role in improving the level of port resilience. Specifically, ESG variables affect the various dimensions of resilience through different paths, thereby affecting the overall resilience level of ports.

Among them, the environmental dimension mainly improves the port environmental quality and ecosystem stability through factors such as carbon emission intensity, solid waste treatment, ecological restoration investment, and renewable energy utilization, thereby improving the robustness and reliability of ports in the face of external disturbances such as natural disasters and climate change. The social dimension enhances the recoverability of ports by promoting the construction of public facilities, ensuring the health and development of employees, narrowing the income gap, and improving customer service levels. The governance dimension mainly improves the robustness, reliability, and redundancy of ports through the construction of governance capabilities such as information disclosure, management structure, response measures, and policy transparency.

Ultimately, the various resilience dimensions in the model (including robustness, reliability, recoverability, and redundancy) work together to dynamically evolve the overall level of “port resilience”. When port resilience improves, it manifests itself as an enhancement of comprehensive capabilities in terms of economy, service, and social responsibility, which in turn can promote the port to further optimize its ESG performance, forming a positive feedback mechanism. On this basis, combined with logical analysis, the interaction relationship between the resilience evaluation indicators of ports along the MSR under the influence of the ESG initiative was constructed, as shown in Figure 3.

3.3. Data

This study employs a 9-point Likert scale to quantify ESG-related explanatory variables. In the process of questionnaire design, each model variable requiring initial data was clearly defined using concise language to ensure participant comprehension and response accuracy. Respondents scored each indicator based on their professional experience. The goal of this study is to analyze the impact of the ESG concept on PRP. Therefore, the target population of the questionnaire survey includes relevant practitioners such as shipping companies, ship operations, maritime authorities, port companies, and research scholars. They all have a certain understanding of the ESG concept or have a preliminary understanding of the ESG concept after being introduced by the research team. In accordance with the principle of comprehensiveness, the relevant potential interviewees are comprehensively screened, and after obtaining their consent through telephone inquiries, the interviewees answered the questionnaire according to the questions designed by the researchers based on their experience in the industry. A total of 230 relevant practitioners were randomly selected in this study, of which 224 agreed to participate in the questionnaire survey, and all participants were required to complete the questionnaire within five days. This study successfully collected 220 valid responses, with an effective response rate of 98.2%. This high response rate reflects the non-response bias of this study and the relevance of the research questions within the shipping management industry. The statistical characteristics of the questionnaire survey are shown in

Table 2. Statistical characteristics of the data (N = 220).

Characteristic	Category	Number of Respondents	Percentage (%)
Gender	Male	121	55.0
	Female	99	45.0
Age	<30	28	12.7
	30–35	68	30.9
	36–40	52	23.6
	41–45	41	18.6
	46–50	25	11.4
	>50	6	2.7
Education Level	Doctor degree	13	5.9
	Master degree	55	25.0
	Bachelor degree	145	65.9
	High school/Technical secondary school	7	3.2
Position	Group leader/professor/captain	24	10.9
	Department leader/associate professor/first mate	36	16.4
	Business backbone/lecturer/second mate	135	61.4
	Other practitioners	25	11.4
Working time	<3 year	14	6.4
	3–6 year	106	48.2
	7–10 year	78	35.5
	>10 year	22	10.0
Industry	Universities/research institutions	40	18.2
	Government departments	35	15.9
	Shipping companies	145	65.9

.

The MSR starts from the coast of China, extends westward to the Indian Ocean, and extends to the coast of Africa and the Mediterranean, involving many countries and regions. Here, based on the port-related information released on the official website of the Belt and Road Initiative (https://www.yidaiyilu.gov.cn/) and the port selection information in the relevant literature [44], considering the comprehensive selection of ports with different levels of development, four important ports along the MSR, Chittagong Port (Bangladesh), Port of Singapore (Singapore), Gwadar Port (Pakistan), and Port of Djibouti (Djibouti), are selected as the application research objects of the model. In the process of obtaining the initial values of the relevant indicators for these four ports, the aforementioned questionnaire survey method served as the primary source of data collection. Additionally, other methods were employed to supplement the data. Information about the ports, such as their basic details, current development status, and future development strategies, was gathered from the official websites of the ports and related platforms. Furthermore, extensive research on these ports was compiled from relevant studies to provide more detailed information, in order to enhance and validate the quantified results of the indicators. Based on the data collection methods described above, the initial data for these four ports were obtained, as illustrated in Figure 4.

4. Result and Discussion

4.1. Simulation Results of Port Resilience Evolution

Here, SD simulations were conducted for four strategically significant ports along the MSR, utilizing the collected initial data. Set the model simulation duration to 500 to ensure that the resilience of the four ports has fully stabilized under the impact of ESG during this period. The results are shown in Figure 5, and further specific analysis is carried out.

The initial resilience of Chittagong Port is 2.207. It grows rapidly from T = 0 to T = 45, then grows slowly and enters a stable period after T = 105. The final quantitative value fluctuates smoothly around 9.763. The initial resilience value of Port of Singapore is 5.857, and it rises to the maximum value at T = 45 and fluctuates smoothly, indicating that the resilience of Port of Singapore quickly reaches its peak under the influence of ESG and remains highly stable. The initial resilience of Gwadar Port is 2.157. The growth rate slows down significantly after T = 60 and tends to stabilize. It finally reaches the maximum value at T = 90 and fluctuates smoothly. The initial resilience of Port of Djibouti is 2.482, reaching 8.342 at T = 45, and then the growth rate slows down. It finally reaches the maximum value of 8.764 at T = 90 and fluctuates smoothly. From the quantitative results of resilience among different ports, at the initial time point, Port of Singapore has the highest resilience value, followed by Port of Djibouti, and Chittagong Port and Gwadar Port have the lowest initial resilience values. Around T = 120, the resilience of all ports tends to fluctuate stably.

Overall, the resilience of Port of Singapore under the influence of ESG shows extremely high stability and quickly reaches the highest resilience value in the early stage, indicating that it has leading advantages in environmental, social, and governance. The port resilience of Chittagong Port and Gwadar Port shows a similar gradual improvement trend, indicating that ESG policies have a continuous positive impact on them, but their adaptation speed is relatively slow. Port of Djibouti shows a significant dramatic short-term resilience gain, indicating that the impact of ESG policies has a significant improvement effect on it in the short term, indicating that it has good policy adaptability.

4.2. Simulation Results of 4R Indicators in Port Resilience Evolution

Under the influence of ESG policies, the 4R indicators of PRP along the MSR are shown in Figure 6. For Chittagong Port, REL and ROB exhibit consistent growth patterns, both starting at 4.7 at T = 0, reaching 10.103 at T = 45, and stabilizing at 10.385 after T = 60. This suggests that ESG policies significantly enhance the port’s reliability and robustness, but the rate of improvement is limited, leading to early stabilization. RED starts at 4.9, slightly higher than REL and ROB, but by T = 45, it only increases to 9.130, stabilizing at 9.412 after T = 60. This indicates that the port’s redundancy improves at a slower pace under ESG influence. REC starts at 4.6, the lowest among all indicators, but reaches 15.083 at T = 103, making it the highest indicator for Chittagong Port. This result highlights that the port’s recovery capability strengthens significantly under ESG influence, showing a continuous and rapid upward trend.

The initial values of REL and RED for Port of Singapore are both 8.0. They reach 10.003, 9.576 at T = 9, respectively, before stabilizing. This indicates that Port of Singapore inherently possesses strong reliability and redundancy, allowing it to rapidly achieve and maintain a high level of performance under ESG policies. The ROB index starts at 8.7, quickly rising to 10.703 at T = 9, and then stabilizing. This suggests that Port of Singapore also has a strong adaptive capacity in terms of robustness. The REC index has the highest initial value among the four indicators at 8.8, reaching 15.083 at T = 37, and then stabilizing. This demonstrates that Port of Singapore not only has a strong recovery capability but also achieves a high level quickly under ESG influence.

The initial REL value of Gwadar Port is 5.1, reaching 10.062 at T = 45 and stabilizing at 11.128 after T = 57. Compared to other ports, Gwadar Port shows a significant improvement in reliability, with its final level slightly higher than that of Chittagong Port. The ROB index starts at 4.5, the lowest among all indicators, eventually increasing to 10.513, indicating that there is considerable room for improvement in robustness under ESG influence. The RED index starts at 4.0, the lowest among all ports, and only increases to 8.560, suggesting that Gwadar Port’s redundancy has limited improvement under ESG influence, which could become a potential bottleneck for future development. The REC index increases from an initial value of 5.1 to 15.049, demonstrating strong recovery capacity enhancement. This indicates that Gwadar Port possesses a high level of long-term adaptability under ESG influence.

The initial REL and ROB values of Port of Djibouti are 5.0 and 5.5, respectively. These values eventually increase to 10.291 and 10.791, showing a strong upward trend and stabilizing after T = 37. The RED index starts at 4.5 and grows to 8.922. Although this final value is slightly higher than that of other ports, it remains significantly lower than its recovery capacity, indicating that Port of Djibouti has substantial room for improvement in redundancy. The REC index increases from 5.0 to 15.102, demonstrating strong recovery capacity growth, similar to Gwadar Port, ultimately reaching a high level of resilience.

Overall, Port of Singapore stands out across all indicators, particularly in REL, ROB, and RED, demonstrating its leading adaptability to ESG principles. The rapid increase in its REC value further indicates exceptional recovery capability. Chittagong Port shows a balanced and synchronized growth in REL, ROB, and RED, suggesting a stable resilience development pattern. The continuous growth of REC highlights that recovery capacity will be its key competitive advantage in the future. Although Gwadar Port has relatively lower values in REL, ROB, and RED, its REC value grows significantly, indicating strong recovery capability and substantial future development potential. The evolution trend of Port of Djibouti’s REC is similar to that of Gwadar Port, but its RED value is slightly higher, suggesting that it possesses strong recovery capacity and a certain level of redundancy under the influence of ESG policies.

4.3. Analysis of Port Resilience Evolution Under Different ESG

To systematically evaluate how distinct ESG dimensions influence PRP, this study formulates targeted ESG intervention strategies, as detailed in

Table 3. Intervention Strategy Design.

Intervention Strategies	E	S	G
Intervention Strategy A	Increase by 10%	-	-
Intervention Strategy B	-	Increase by 10%	-
Intervention Strategy C	-	-	Increase by 10%

. For each intervention strategy, the impact is independently simulated on the selected target ports using the system dynamics model. The changes in port resilience under different intervention strategies are reflected by characteristic indicators T, Mean, and Max, where T represents the time taken for the port resilience to reach its maximum value from the initial value. Mean refers to the average value of port resilience during the process of reaching the maximum value from the initial value. Max represents the maximum value of port resilience. These indicators are used to reflect the changes in resilience under the influence of different intervention strategies, as presented in

Table 4. Characteristics of PRP changes under different intervention strategies.

Intervention Strategies	A			B			C
Index	T	Mean	Max	T	Mean	Max	T	Mean	Max
Chittagong Port	103	9.082	11.290	93	8.975	11.299	103	9.146	11.305
Port of Singapore	37	10.394	11.362	34	10.368	11.358	37	10.346	11.288
Gwadar Port	86	8.737	11.288	78	8.581	11.303	86	8.825	11.326
Port of Djibouti	86	9.230	11.305	78	9.119	11.270	86	9.323	11.286

.

The simulation results reveal significant variations in PRP responses to Intervention Strategy A, highlighting distinct port-specific characteristics. Among the four ports, the enhancement of the environmental dimension (E) has the most significant impact on the resilience of Port of Singapore, achieving the highest resilience quantification value of 11.362, which outperforms the other intervention strategies. Following that, Chittagong Port also saw a substantial improvement, reaching a resilience quantification value of 11.305. This indicates that optimizing the environmental dimension can significantly enhance the final resilience level, especially in specific ports.

Compared to Intervention Strategies A and C, Intervention Strategy B has a significantly more pronounced effect in accelerating resilience enhancement. The improvement in the social dimension (S) reduced the time to reach the maximum resilience value for Chittagong Port, with its T value decreasing from 103 to 93, shortening the time to reach the maximum value by 10 units. Similarly, for Gwadar Port and Port of Djibouti, the T value decreased from 86 to 78, reducing the time to reach the maximum value by 8 units. This indicates that improvements in the social dimension play a crucial role in accelerating the resilience enhancement of ports.

In contrast, improvements in the governance dimension (G) have the most significant effect on enhancing both overall resilience levels and potential. Under Intervention Strategy C, Gwadar Port saw its maximum resilience value increase from 11.288 to 11.326, while its Mean increased from 8.737 to 8.825. Port of Djibouti saw its resilience average rise from 9.230 to 9.323. This indicates that governance optimization is the core driving factor for achieving long-term resilience improvement.

There are differences in how ports respond to ESG intervention strategies. Chittagong Port and Gwadar Port are most sensitive to improvements in the governance dimension (G), with governance optimization contributing the most to the overall resilience and potential enhancement. Port of Singapore reacts most strongly to improvements in the social dimension (S), with its T value significantly decreasing to 34 under Intervention Strategy B. Port of Djibouti, on the other hand, shows a balanced response to improvements in both the environmental (E) and governance (G) dimensions, but its response to changes in the social dimension (S) is weaker. Overall, to achieve rapid short-term resilience enhancement, the social dimension (S) should be prioritized. However, for long-term resilience improvement, the optimization of the governance dimension (G) is crucial. Additionally, specific ports should optimize the environmental dimension (E) based on their characteristics to fully unlock their resilience potential.

In general, the established MSR port resilience evaluation SD model can sensitively reflect the impact of different intervention strategies on changes in port resilience, indicating that the model has high sensitivity. In addition, the model predicts the evolution trend of port resilience under different strategies for specific ports, and is in line with scientific logic, indicating that it has good explanatory and predictive capabilities. The results of port resilience changes presented by the model under different ESG intervention strategies are highly reliable and can provide scientific decision-making support for policymakers and port managers.

4.4. PRP Model

The Environmental, Social, and Governance (ESG) concept enhances the adaptability and resilience of ports when facing extreme events and complex environments through improvements in environmental protection, social responsibility, and governance capacity. System dynamics (SD) as a method for simulating behaviors of complex systems, analyzes the overall changes in the system by modeling various feedback patterns. In this study, based on SD analysis methods, a simulation model was established to analyze the ports resilience performance (PRP) along the 21st Century Maritime Silk Road (MSR). Some conclusions are listed as follows:

(1)

Compared to traditional static statistical analysis or qualitative case studies, this study employs SD modeling to capture the dynamic impact mechanism of ESG policies on port resilience. In terms of dynamic feedback analysis, this study constructs a causal feedback mechanism of ESG policies affecting port resilience, enabling the simulation of the different effects of ESG in the short and long term. By simulating various ESG intervention strategies, the model quantifies the distinct impacts of the environmental, social, and governance dimensions on port resilience, providing data-driven support for policy formulation. The findings indicate that the social dimension (S) plays the most significant role in enhancing short-term resilience, while the governance dimension (G) is the key factor for long-term resilience improvement. This conclusion offers policymakers targeted ESG optimization pathways.

(2)

The ESG concept shows a significant positive growth in PRP’s improvement, with notable differences in resilience levels among the ports studied. Port of Singapore has a leading advantage in environmental governance, social responsibility, and governance efficiency, maintaining the highest resilience levels from the initial stage. While Chittagong Port and Gwadar Port show relatively slower resilience improvement, they gradually achieve steady enhancement under the continuous influence of ESG policies. Port of Djibouti, on the other hand, demonstrates a strong short-term policy adaptation capacity.

(3)

ESG policies have significantly improved the reliability (REL), robustness (ROB), redundancy (RED), and recovery (REC) capacity of ports by optimizing environmental quality, promoting social equity, and enhancing governance transparency. Among these improvements, the enhancement of reliability and recovery capacity stands out, which is particularly crucial for ensuring the long-term stability of the MSR transportation network.

5. Conclusions

This study adopts a System Dynamics (SD) model and integrates the Environmental, Social, and Governance (ESG) framework to explore the impact of ESG policies on the port resilience performance (PRP) evolution of ports along the 21st Century Maritime Silk Road (MSR). By constructing a comprehensive feedback mechanism model, the study examines the dynamic effects of different ESG dimensions on PRP. The main contributions of this study are as follows:

(1)

The findings indicate that the ESG framework provides an effective pathway for enhancing PRP. Countries along the MSR should focus on integrating ESG principles into all aspects of port management while developing differentiated policies tailored to the specific characteristics of each port. The social dimension (S) plays a significant role in enhancing short-term resilience, while the governance dimension (G) is a key factor for improving long-term resilience. In the development of ports along the MSR, policymakers should focus on the synergistic effects of different dimensions to achieve rapid improvements in PRP and long-term sustainable development.

(2)

Compared to traditional static statistical analysis and qualitative case studies, this approach allows for a more comprehensive simulation and prediction of both the long-term and short-term effects of policy implementation on PRP, thus providing data-driven policy support for port managers. The SD-based model can offer more scientific decision-making support for port management under ESG policies, promoting the sustainable development of ports along the MSR. The conclusions of this study contribute to the practical application of ESG in port management and provide practical guidance for the sustainable development of ports along the MSR.

(3)

This study’s findings hold significant implications for multiple stakeholders. For the academic community, it enriches research on port resilience by introducing a dynamic modeling framework centered on ESG. For practitioners, especially port operators and managers, the study offers actionable strategies to strengthen resilience through targeted ESG initiatives. For policymakers, the results underscore the importance of integrating ESG principles into national and regional port development strategies.

However, this study also has certain limitations. For example, the model is built based on generalized ESG policies, which may not fully reflect the policy environments of specific countries or ports. In addition, when selecting ports for case analysis, this study relied on expert knowledge to select several specific ports, which may have a certain impact on the universality of the research. Future research could further deepen the application scenarios of ESG in port development, explore broader possibilities for sustainable development, and contribute to the construction of the MSR.