Green Profit Optimization and Collaborative Innovation in Sustainable Maritime Supply Chains

Abstract

Amid the urgent demands for global trade transformation and zero-carbon transition, sustainable maritime supply chains face challenges of high costs and complex coordination, necessitating the elimination of “isolated decision-making” to achieve sustainable development goals. This study constructs a profit analysis model under centralized and decentralized decision-making scenarios and various intelligent omni-channel models, exploring the profit composition, optimal pricing, and operational strategies of carriers and forwarders. Case analysis validates that collaborative optimization, particularly when forwarders leverage online channels and customer proximity, enables sustainable maritime transport and significantly enhances overall profits and efficiency in sustainable maritime supply chains. This research provides a theoretical and practical framework for collaborative optimization strategies contributing to sustainable maritime transport and port intelligence in marine engineering contexts. By this framework, it will be possible to advance green transformation, smart operations management, and digital innovation in the global maritime and marine industries.

1. Introduction

Maritime transport handles over 90% of international trade volume, and its sustainable transformation is critical not only for the maritime industry’s development but also for the reconstruction and upgrading of the global trade value chain [1] As a pivotal engine connecting carriers and forwarders, the sustainable maritime supply chain drives the intelligent and green collaborative transformation of the global maritime shipping industry and marine port sector by establishing an efficient and resilient logistics network, reshaping the competitiveness of the global trade value chain. However, existing research highlights that achieving sustainable development goals in sustainable maritime supply chains faces significant challenges, including high transformation costs, complex coordination mechanisms, and “isolated decision-making,” which urgently require systematic profit analysis and collaborative optimization strategies to advance sustainable maritime transport and port intelligence in marine engineering contexts [2].

Sustainable development is a strategic goal that the shipping supply chain must highly value and adhere to in the long run. A sustainable shipping supply chain refers to the construction of a comprehensive logistics network system that takes economic benefits, environmental protection, and social responsibility into account by integrating multiple entities such as carriers, port operators, and forwarders [3]. Liu pointed out that the core characteristics of a sustainable shipping supply chain include environmental friendliness, economic feasibility, social responsibility, and resilience and adaptability [4]. The introduction of this concept marks a fundamental shift in the shipping industry from a traditional cost-oriented model to a value creation model [5]. Ding H further emphasized that a sustainable shipping supply chain not only requires individuals to achieve green transformation, but also requires the whole chain to achieve carbon reduction and efficiency improvement through supply chain collaboration [6]. This concept of sustainable development throughout the entire chain provides a new strategic perspective for the shipping industry to reshape its competitive advantage.

However, achieving this sustainable transformation goal faces the dual challenges of high costs and complex coordination [7]. In terms of cost challenges, Wang’s research shows that achieving the 2050 carbon neutrality goal in the shipping industry requires an investment of USD 1.4–2.8 trillion, with green fuels, energy-saving technologies, and port infrastructure upgrades being the main investment directions [8]. Green fuel costs are of particular importance, since a new limit on the sulfur content in fuel oil was introduced by IMO on 1 January 2020 [9]. The rule limits the sulfur in the fuel oil used on board ships operating outside designated emission control areas to 0.50% m/m (mass by mass)—a significant reduction from the previous limit of 3.5%. On 1 May 2025, the new sulfur limits entered into force for the Mediterranean; the sulfur content in fuel oil for ships operating in the area is now limited to 0.1% [10], significantly reducing air pollution and delivering major benefits to both human health and the marine environment. However, the proportion of fuel oil cost in shipping transportation cost is 13.6%, ranking third (only behind ship interest, 24.5%, and loading and unloading cost of THC, 21%). This proportion plays a key role in the fluctuation of freight rates, as the impact of fuel oil prices on freight rates can reach 15–50% [11]. At present, the majority of ships worldwide still use traditional fuels. After the implementation of the new IMO 2025 regulations, ships that fail to meet emission reduction targets will need to pay additional carbon emission costs. It is expected that the comprehensive cost of marine fuels will double by 2035.

In terms of coordination challenges, Liu pointed out that the shipping supply chain involves multiple independent decision-making entities, and this leads to great difficulties in coordination due to the significant differences among sustainable development goals, investment return expectations, and risk-bearing capabilities [12]. When involving the sulfur emission problem, the situation could be even complicated, though a study that analyzed carriers’ behaviors when meeting ECA policy in America showed that they may try to cope with their forwarders to spread the risk evenly within the supply chain [13]. The complexity of multi-agent coordination often leads to the phenomenon of “isolated decision-making”, which hinders the implementation of systematic optimization [14]. In this context, it is crucial to use refined profit analysis models for multidimensional profit evaluation and comparison under centralized and decentralized decision-making.

Due to the high costs faced by sustainable transformation, which pose a severe impact on the profitability of all participants in the shipping supply chain, it is urgent to provide economic feasibility assessments for sustainable transformation through profit analysis. Only by deeply understanding the profit composition and distribution mechanism of each subject under different decision-making modes and channel strategies can a solid theoretical foundation be laid for subsequent collaborative optimization [15]. The theoretical basis for supply chain profit analysis mainly comes from the fields of operations management and industrial economics [16]. New S has established a classic supply chain profit analysis framework that decomposes supply chain profits into the marginal contributions of each participating entity and analyzes the profit distribution mechanism under different decision modes through game theory models. In the context of the shipping supply chain [17], Cuong extends this framework to profit analysis on the network involving carriers and forwarders, constructing a comprehensive profit model that includes factors such as transportation costs, port charges, inventory costs, and service quality [18]. With the development of digital technology, the multi-channel mode has gradually become an important feature of the shipping supply chain. Zhao and Xu established a profit analysis model for multi-channel supply chains, analyzing the impact of different channel strategies on the profits of all parties in the supply chain [19]. The comparative analysis of centralized and decentralized decision-making models is a core issue in supply chain management. Janssen M first proposed the problem of “double marginalization”, pointing out that decentralized decision-making often leads to overall supply chain profits being lower than centralized decision-making [20]. Relying solely on profit analysis is not enough to solve the complex challenges of multi-party coordination faced by sustainable shipping supply chains. Therefore, based on the revelation of the distribution pattern of interests among all parties through profit analysis, it is necessary to break the “isolated decision-making” dilemma through scientific collaborative optimization mechanisms and achieve the unity of benefits and risk sharing. Collaborative optimization can not only improve the overall efficiency of the supply chain, but also guide all parties to actively participate in sustainable transformation through reasonable incentive mechanisms, thereby achieving the dual goals of economic and environmental benefits [21]. The theory of supply chain collaborative optimization emphasizes the coordination of interests among all participating parties through systematic design. Staring R proposed the “4C” framework for supply chain collaboration: Collaboration, Coordination, Communication, Capability Building [22]. Contract design is an important tool for achieving collaborative optimization in the supply chain. The Tsai A system summarizes the main types of supply chain contracts, such as wholesale price contracts, revenue-sharing contracts, and quantity discount contracts [23], and analyzes the effectiveness of various contracts in coordinating supply chain interests. The high investment and uncertainty in the sustainable transformation process make risk sharing a key issue in the collaborative optimization of the shipping supply chain [24]. Nagariya R proposed a systematic framework for supply chain risk management, emphasizing the importance of identification, assessment, and sharing of risk [25]. Overall, there are significant theoretical shortcomings in the existing research on collaborative optimization of sustainable shipping supply chains. The design of coordination mechanisms lacks targeted consideration for the long-term and high-risk nature of sustainable investment, and the mechanism for balancing the interests of multiple stakeholders is not yet perfect. These shortcomings have led to the prominent problem of “isolated decision-making”, which seriously restricts the realization of the collaborative optimization effect of a sustainable shipping supply chain. It is urgent to build a more systematic, dynamic, and incentive-compatible collaborative optimization theoretical framework.

Therefore, this study focuses on sustainable shipping supply chains by constructing a rigorous profit analysis model and introducing a revenue-sharing contract mechanism to systematically explore the decision-making behavior of carriers and forwarders in the omni-channel model and their impact on the overall performance of the supply chain. Although there exist models such as those of Shekarian E. and Flapper, who used models to analyze the structure of a closed-loop supply chain, there is no special model for a sustainable shipping supply chain [26]. The research aims to construct a profit analysis framework applicable to sustainable shipping supply chains, quantifying the impact of different decision-making modes and channel strategies; design and validate the collaborative optimization potential of revenue-sharing contracts; explore effective mechanisms for balancing interests; and provide practical guidance for the refined management and sustainable development of all parties in the shipping supply chain. The primary contributions of this paper include the development of a profit analysis framework tailored for sustainable maritime supply chains, quantitatively evaluating the effects of various omni-channel models on profit composition, optimal pricing, and operational strategies of carriers and forwarders under centralized and decentralized decision-making. This framework provides tools for refined management of stakeholders in marine logistics.

2. Construction of Profit Analysis Model for Sustainable Maritime Shipping Supply Chain

In the current era of deep integration of globalization, digitization, and sustainable development, the operational mode of the shipping supply chain is undergoing profound changes. Especially in the process of promoting green shipping and intelligent decision-making, carriers should not only focus on balancing environmental and economic benefits, but also optimize customer service experience and operational efficiency. Therefore, understanding and analyzing the interaction between different channels in the supply chain, including traditional physical channels and emerging network channels, and the impact of this interaction on profits has become particularly crucial.

With the opening of online channels by various members in the shipping supply chain, the distance between carriers and forwarders has been shortened. Compared with the traditional offline physical service model, forwarders have lower time costs in searching for transportation services that meet their needs when booking, tracking, and managing shipping services through online channels and can quickly find services that meet their requirements. In addition to saving time costs online, online channels can also reduce the operating and operational costs of traditional ticketing or agency services for offline physical operations of forwarders. The consumption form on the mobile channel is similar to that on the network terminal to a certain extent, so the sales price of the marine service on the network channel and the sales price of the marine service on the mobile channel are lower than the marine service on offline physical channels for retail in general conditions and in the general service scope, so the consumption demand for marine service in the market will rise accordingly. However, the online channels described in this paper mainly refer to bookings that need to be completed through desktop computers in the office, while mobile channels include the use of apps [26] to log in to the carrier’s system for service booking, which can meet the needs of shippers to complete bookings anytime and anywhere using mobile phones by 5G networks or even satellite signals. This has higher requirements for the carrier’s data consistency and coordination capabilities. Based on the assumption that the service market demand share of the forwarders is expanding under the omni-channel model of the supply chain, this paper analyzes the changes in the profits of the entire enterprise and the optimal service network sales price when the members of the entire supply chain, including upstream carriers and downstream forwarders, open network channels, including internet platforms, mobile internet, social media, and other channels for service booking.

2.1. Basic Assumptions and Parameter Definitions of the Model

Consider a two-level supply chain consisting of a forwarder and a carrier, in which the carrier and the forwarder jointly provide and sell the same shipping services that meet certain market demands. The research on this supply chain in this article is based on the rational and risk-neutral roles of the forwarders and the carriers. The forwarder occupies a dominant position in the entire supply chain, while the carrier occupies a subordinate position. Together, they form the dual-channel model of the forwarder and the dual-channel model of the carrier in the full-channel mode of the supply chain, as shown in Figure 1 and Figure 2.

The hypotheses of the research problems and explanations of the letters in the formulas are as follows:

Hypothesis 1.

The price of shipping services in the market is linearly correlatedwith market demand. Applying price-sensitive linear function demand to this study, at this time, the market demand for shipping services in the supply chain is $d=\mathit{tm} - \mathit{bp}$, where $t(0 < t < 1)$ indicates the market shares of shipping services, $m(m > 0)$ indicates the total demand for shipping services in the market, $b(b > 0)$ indicates the sensitivity coefficient of price, and $p(p > 0)$ indicates the retailer price of shipping services.

Hypothesis 2.

In the omni-channel mode of the supply chain, both the forwarder and the carrier are rational and risk-neutral. The forwarder occupies the dominant position in the entire supply chain, and correspondingly, the carrier occupies a subordinate position in the supply chain. Meanwhile, the cost of providing shipping services by the carrier is $c=0$; under the omni-channel mode of supply chain, the forwarder can not only purchase shipping services through offline physical channels, but also make service reservations through online network channels. Multi-channel consumption of shipping services can reduce the time cost for the forwarder to find target shipping services, expand the market demand share of shipping services to a certain extent, and thus increase the market coverage and market share of the forwarder’s service. It is assumed that the carrier can expand its market share $\alpha$ by accessing online channels, while the market share that the forwarder can expand through online channels is $\mathsf{\beta }$.

In the formulas of this paper, the superscript located at the upper right corner of a parameter is used to represent the parameter variable when the supply chain reaches the optimal state. Similarly, the superscript “A” on the parameter indicates the dual-channel mode of the carrier in the supply chain. The subscript “1” located at the lower right corner of the parameter represents the forwarder, while the subscript “2” represents the carrier. In addition, the subscript “r” located at the bottom right corner of the parameter represents offline physical channels, while the subscript “n” represents online direct-sales channels. The parameter $\mathbf{m}$ represents the total demand for shipping services in the market, while the parameter D represents the market share of shipping services sold through online channels. The parameter b represents the price sensitivity coefficient, while $\mathbf{w}$ represents the wholesale price of shipping services provided by carriers to downstream forwarders in the supply chain. The markup for the forwarders is based on the wholesale price, that is, the difference between the zero selling price and its wholesale price.

In the dual-channel supply chain of the carriers, the sales prices of physical channels and online channels are p₁.

In the dual-channel supply chain of the forwarders, the physical channel sales price and the online channel price are p₂.

The subscript letters in

Table 1. Subscript and superscript letters in the formulas.

Subscript at the Lower Right Corner of the Parameters	Meaning of the Subscripts
“r”	Offline physical service channels used by the forwarder in the supply chain
“n”	Online service channels used by the forwarder in the supply chain
“m”	Mobile service channels used by the forwarder in the supply chain
Superscripts in the Upper Right Corner of the Parameters	Meaning of the Superscripts
“C”	The decision-making behavior of the supply chain under centralized decision-making models
“D”	The decision-making behavior of the supply chain under decentralized decision-making models
“S”	The decision-making behavior of the supply chain under revenue-sharing models

at the lower right corner of the parameters in the formulas represent the different channels used by the forwarder in the supply chain, which are offline physical service channels, online service channels, and mobile service channels.

The selling price of shipping services for the forwarder through offline physical channels is $p_r$; meanwhile, $0 < p_r < 1$.

The selling price for shipping services provided by online channels is $p_n$, and $0 < p_n < 1$.

In the mobile retail channel of the supply chain, the unit retail price of shipping services is $p_m$; meanwhile, $0 <{ p}_m < 1$.

When shippers make bookings for the shipping service in the market through offline physical channels, the transportation costs incurred during the process of consultation or arrival at the sales point are $k_1$.

When shippers book the shipping service in the market through the mobile channel in the supply chain, there may be certain risks (such as information asymmetry, uncertainty of service experience), and the risk cost they need to bear is $k_2$($0 < k_2 <{ k}_1 < 1$).

2.2. Construction of Profit Model for Sustainable Maritime Shipping Supply Chain

Against the backdrop of intelligent decision-making and sustainable development becoming core issues in the shipping industry, this section will construct a profit analysis model for sustainable shipping supply chains. This model aims to explore how carriers and forwarders can optimize their own and overall supply chain profits through the collaborative operation of physical and online channels under different market-dominant structures. The construction of the model will take into account the changes in customer demand under multi-channel mode, as well as the decision-making behavior of all parties in pursuing maximum profit.

2.2.1. Construction and Solution of Dual-Channel Model for Carriers (Carriers)

As shown in Figure 1, the carrier is the supplier in the supply chain who participates in market sales in two different ways. The first method is for the carrier to provide its shipping services in wholesale form to the forwarder in order to earn a certain price difference. The second approach is for the carrier to participate in market activities in a relatively direct way, through online channels, and build an online platform to sell the shipping services it provides, without the need to go through the forwarder in the supply chain. In this mode, in order to ensure the sales profit and channel stability of the forwarder, it is usually required that the retail price of the carrier when directly participating in market sales activities is not lower than the wholesale price it provides to the forwarder. The total market demand for shipping services through physical sales channels is

$$d_r=(1-\theta )m-b{p}_1$$

(1)

The total demand in the market when selling through online channels is

$$d_n=\left(\theta +\partial \right)m-b{p}_1$$

(2)

The forwarder can now earn profits:

$${\pi }_1 = \left(p_2-w\right)d_r = \left(p_2-w\right)\left(1-\theta \right)m-b{p}_2$$

(3)

The profit that the carrier can obtain in this mode is

$${\pi }_2=w{d}_r+p_1{d}_n=w[\left(1-\theta \right)m-b{p}_1]+p_1[\left(\theta +\partial \right)m-b{p}_1]$$

(4)

In the supply chain model constructed earlier, each member of the supply chain demands the maximization of its own interests. At this point, the forwarder occupies the dominant position in the entire supply chain and will first determine the wholesale price provided by the carrier, which is most beneficial to itself, that is, it can maximize its profits to the greatest extent possible. Then, the carrier determines its online channel sales price p1, which is also the most favorable price for the carrier. This article refers to the master–slave game theory and uses reverse induction to solve the profits of each member in the supply chain and the total profit of the supply chain.

Firstly, consider the second stage of the game, where double the first derivative of the profit of the carrier with respect to its sales price p1 is obtained, and make its first derivative zero to obtain

$$p_1={\displaystyle \frac{\left(\theta +\partial \right)m-bw}{2b}}$$

(5)

In the first stage of the game, the optimal wholesale price for the forwarder in this mode is obtained as follows:

$$w^{A^{*}} = {\displaystyle \frac{2\left(\theta + \alpha \right)m-3\left(1-\theta \right)m}{3b}}$$

(6)

Meanwhile, the optimal selling price for the carrier in this model is

$${p_1}^{A^{*}}={\displaystyle \frac{\left(\theta +\partial \right)m +3\left(1-\theta \right)m}{6b}}$$

(7)

Then substitute ${p_1}^{A^{*}}$, $w^{A^{*}}$ into the equation and obtain the following:

The optimal profit for the forwarder,

$${{\pi }_1}^{A^{*}}={\displaystyle \frac{m^2[9{\left(1-\theta \right)}^2-6\left(1-\theta \right)\left(\theta +\alpha \right)+{\left(\theta +\alpha \right)}^2]}{12b}}$$

(8)

The optimal profit for the carrier,

$${{\pi }_2}^{A^{*}}={\displaystyle \frac{m^2[30\left(1-\theta \right)\left(\theta +\alpha \right)-27{\left(1-\theta \right)}^2+{ \left(\theta +\alpha \right)}^2]}{36b}}$$

(9)

At this point, the total profit of the entire supply chain is

$${\pi }^{A^{*}}={{\pi }_1}^{A^{*}}+{{\pi }_1}^{A^{*}}={\displaystyle \frac{m^2[3\left(1-\theta \right)\left(\theta +\alpha \right)+{\left(\theta +\alpha \right)}^2]}{9b}}$$

(10)

2.2.2. Construction and Solution of Dual-Channel Model for Forwarders

As shown in Figure 2, the dual-channel model of the forwarder refers to the introduction of online channels into its supply chain to sell shipping services. In this supply chain model, the carrier does not directly participate in market sales activities, but first provides shipping services to the forwarder, which then begins related sales activities through physical and online channels. For the forwarder, there are two different channels for participating in market activities related to shipping services, namely physical channels and online channels. After obtaining shipping services from the carrier at a certain wholesale price, the forwarder adds a certain premium to the wholesale price to create the retail price of shipping services in the market and then sells these shipping services to shippers. Under this mode, the forwarder is closer to the shippers in the market and has relatively frequent contact, which can be closer to market demand and obtain more comprehensive information on market demand, prices, and shippers’ preferences through more ways and channels. This study assumes that after the forwarder adds online channels to the supply chain, the increase in demand for the shipping service in the market is greater than the increase in demand for the same shipping service from shippers in the market after the carrier accesses network channels under the same assumption. It is assumed that the total demand for this shipping service in the market under the network channel of the forwarders at this time is represented as $\mathsf{\delta }\mathrm{m}$ ($\mathsf{\delta } > 1$).

The market demand in physical channels is

$$d_r=\left(1-\theta \right)m-b{p}_2$$

(11)

The market demand in online channels is

$$d_n=\left(\theta +\beta \partial \right)m-b{p}_1$$

(12)

In this mode, the retailer’s profit function is

$${\pi }_1=\mathsf{\Delta }\mathsf{\omega }\left(d_r+{ d}_n\right)=\mathsf{\Delta }\mathsf{\omega }[\left(1+\mathsf{\beta }\alpha \right)\mathrm{m}-2\mathrm{b}(\mathsf{\omega }+\mathsf{\Delta }\mathsf{\omega })]$$

(13)

In this mode, the manufacturers’ profit function is

$${\pi }_2=\mathsf{\omega }\left(d_r+d_n\right)=\mathsf{\omega }[\left(1+\mathsf{\beta }\alpha \right)\mathrm{m}-2\mathrm{b}(\mathsf{\omega }+\mathsf{\Delta }\mathsf{\omega })]$$

(14)

By referring to the principle of master–slave strategy, the above formula can be solved.

Find the derivative of profit ${\pi }_2$ with respect to price $\mathsf{\omega }$ and make its first derivative zero:

$$\mathsf{\omega } = {\displaystyle \frac{\left(1 + \mathsf{\beta }\alpha \right)m-2b\mathsf{\Delta }\mathsf{\omega }}{4b}}$$

(15)

Then, by substituting ${\pi }_1$ into the function about $\mathsf{\omega }$, the following is obtained:

$${\pi }_1 = \mathsf{\Delta }\mathsf{\omega }{\displaystyle \frac{\left(1 + \mathsf{\beta }\alpha \right)m-2b\mathsf{\Delta }\mathsf{\omega }}{2}}$$

(16)

The optimal markup for the forwarder is obtained by taking the derivative ${\pi }_1$ from $\mathsf{\Delta }\mathsf{\omega }$ and making the first derivative zero:

$${\mathsf{\Delta }\mathsf{\omega }}^{B^{*}} = {\displaystyle \frac{\left(1 + \mathsf{\beta }\alpha \right)m}{4b}}$$

(17)

The optimal wholesale price for the forwarder is

$${\mathsf{\omega }}^{B^{*}}={\displaystyle \frac{\left(1+\mathsf{\beta }\alpha \right)m}{8b}}$$

(18)

The optimal selling price for the forwarder is

$${p_2}^{B^{*}}={\mathsf{\omega }}^{B^{*}}+{\mathsf{\Delta }\mathsf{\omega }}^{B^{*}}={\displaystyle \frac{3\left(1+\mathsf{\beta }\alpha \right)m}{8b}}$$

(19)

So, the optimal profit for retail s can be calculated as

$${{\pi }_1}^{B^{*}}={\displaystyle \frac{{\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{16b}}$$

(20)

The optimal profit for the carrier in the supply chain is

$${{\pi }_2}^{B^{*}}={\displaystyle \frac{{\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{32b}}$$

(21)

At this point, the profit of the entire supply chain is

$${\pi }^{B^{*}}={{\pi }_1}^{B^{*}}+{{\pi }_2}^{B^{*}}={\displaystyle \frac{{3\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{32b}}$$

(22)

2.3. Profit Analysis and Comparison

According to the above model derivation and solution, the impact of the introduction of online channels into the overall sales channel by different members of the supply chain in the omni-channel mode on the overall supply chain profit is not the same.

Table 2. Comparison of model solution results.

Variable	Carrier Access to Online Channels	Forwarder Access to Online Channels
Wholesale Price	$w^{A^{*}}={\displaystyle \frac{2\left(\theta +\alpha \right)m-3\left(1-\theta \right)m}{3b}}$	${\mathsf{\omega }}^{B^{*}}={\displaystyle \frac{\left(1+\mathsf{\beta }\alpha \right)m}{8b}}$
Retail Price	${p_1}^{A^{*}}={\displaystyle \frac{\left(\theta +\partial \right)m +3\left(1-\theta \right)m}{6b}}$	${p_2}^{B^{*}}={\displaystyle \frac{3\left(1+\mathsf{\beta }\alpha \right)m}{8b}}$
Retailer Profit	${{\pi }_1}^{A^{*}}={\displaystyle \frac{m^2[9{\left(1-\theta \right)}^{2 }- 6\left(1-\theta \right)\left(\theta +\alpha \right)+{\left(\theta +\alpha \right)}^2]}{12b}}$	${{\pi }_1}^{B^{*}}={\displaystyle \frac{{\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{16b}}$
Manufacturer Profit	${{\pi }_2}^{A^{*}}={\displaystyle \frac{ m^2[30\left(1-\theta \right)\left(\theta +\alpha \right)-27{\left(1-\theta \right)}^2+{\left(\theta +\alpha \right)}^2]}{36b}}$	${{\pi }_2}^{B^{*}}={\displaystyle \frac{{\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{32b}}$
Profit of the Supply Chain	${\pi }^{A^{*}}={\displaystyle \frac{m^2[3\left(1-\theta \right)\left(\theta +\alpha \right)+{\left(\theta +\alpha \right)}^2]}{9b}}$	${\pi }^{B^{*}}={\displaystyle \frac{{3\left(1+\mathsf{\beta }\alpha \right)}^2{m}^2}{32b}}$

shows the profit comparison of the supply chain formed by the introduction of online channels by the carrier and the forwarder in the supply chain.

3. Design of Collaborative Optimization and Benefit Sharing Mechanism for Sustainable Maritime Shipping Supply Chain

With the rapid development of mobile Internet, social media has become more and more popular among the public. At the same time, the big data technology about customer consumption behavior has become increasingly mature. The booking and management behavior of shipping services has become more personalized, which is difficult to measure with a unified standard.

The online channels may limit the bookings that need to be completed through desktop computers in the office, while mobile channels include the use of apps to log in to the carrier’s system for service booking, which can meet the needs of shippers to complete bookings anytime and anywhere using mobile phones by 5G networks or even satellite signals. This has higher requirements for the carrier’s data consistency and coordination capabilities.

At the same time, the diversification of methods and channels for the shipper to obtain shipping service information and make reservations is becoming increasingly apparent. The difference between online and offline service channels is constantly decreasing, especially with the application of intelligent decision-making technology in the shipping supply chain, and shippers’ expectations for service convenience and transparency are constantly increasing. The continuous development of the omni-channel model of the supply chain has prompted more and more forwarders to realize the diversity of booking channels, slowly reduce the level of offline physical channel consumption, and then turn to online booking channels, including network platforms and mobile channels, to adopt the cross booking method. Among them, the social media channel in the mobile channel, due to its social characteristics, will also have a certain impact on shippers’ service booking habits and booking behavior. At present, this impact is difficult to quantify because it is relatively scattered, making the supply chain of the forwarder more complex and diversified. The forwarder also continues to apply the omni-channel model of the supply chain in practice to adapt to market changes and improve sustainable service capabilities.

In the context of intelligent and green transformation, the omni-channel model of the supply chain for forwarders may differ to some extent from the optimal decision-making behavior derived from the theoretical model when considering relevant practical factors in practice; that is, it may not fully conform to the optimal model and fail to achieve perfect optimization. This decision bias can lead to a decrease in profits for carriers and forwarders and hinder the collaborative achievement of sustainable development goals, which is not conducive to the joint cooperation of both parties in promoting green shipping and improving supply chain resilience. If the supply chain ruptures or collaboration fails as a result, it will lead to significant economic losses and negative environmental impacts. Therefore, this article will conduct optimization research on supply chain profits from the perspective of profit sharing among supply chain members, in order to achieve overall synergy and balance of interests among all parties in a sustainable shipping supply chain under a multi-channel operation mode.

3.1. Model Basic Assumptions and Parameter Description

In the omni-channel model of a sustainable shipping supply chain, the unit cost of shipping service provided by the carrier in the supply chain is c. After the service is provided, the maritime service provider sells the shipping service to the downstream forwarder in the supply chain at a wholesale price of w. In this model, the carrier does not directly participate in retail activities facing shippers in the market, and its main function is to provide wholesale services to the forwarder. For the forwarder, there are three sales channels, namely offline physical service outlets, online service platforms, and mobile service channels (including mobile app booking, live broadcast promotion, and social media consulting). Assuming that both the carrier and the forwarder in the supply chain are rational and risk-neutral, and the forwarder occupies a dominant position in the supply chain, correspondingly, the carrier occupies a subordinate position, and both operate with the goal of maximizing profits, as shown in Figure 3:

The following section provides an explanation of the relevant parameters and assumptions made in the model used in this paper.

The subscript letters “r”, “n”, and “m” at the lower right corner of the parameters in the formula represent the different channels used by the forwarder in the supply chain, which are offline physical service channels, online service channels, and mobile service channels.

The selling price for shipping services provided by online channels is $p_n$, and $0 <{ p}_n < 1$.

In the mobile retail channel of the supply chain, the unit retail price of shipping services is $p_m$; meanwhile, $0 < p_m < 1$.

When shippers and then the forwarders make bookings for shipping services in the market through offline physical channels, the transportation costs incurred during the process of consultation or arrival at the sales point are $k_1$.

When shippers book the shipping services in the market through the mobile channel in the supply chain, there may be certain risks (such as information asymmetry, uncertainty of service experience), and the risk cost they need to bear for the uncertainty of service experience is $k_2(0 <{ k}_2 <{ k}_1 < 1$).

When conducting model research on the omni-channel model of the supply chain, this paper makes the following hypotheses:

Hypothesis 3.

The total demand scale for the shipping services sold by the forwarder in the supply chain under the omni-channel model is 1, and the shippers’ booking behavior for this shipping service in the market is completely determined by themselves.

Hypothesis 4.

Shippers are completely rational. If the remaining shippers of a certain channel provided by the forwarder in the supply chain are less than zero, shippers in the market will choose other channels for booking instead. Similarly, according to the utility theory of shippers in the market, they will choose channels that bring them greater utility for bookings.

Hypothesis 5.

The parameter v is used to represent the customer’s evaluation value for the unit shipping service, and the distribution of this evaluation value follows a uniform distribution of [0, 1].

Hypothesis 6.

The parameter c is used to represent the cost of each unit of shipping service provided by the carrier in the supply chain, and w represents the wholesale price provided to the downstream forwarder in the supply chain, that is, the wholesale price of the forwarder. On the basis of this wholesale price, the forwarder sells through offline physical channels, online channels, and mobile channels. The upstream carrier of the supply chain, which will only be responsible for providing the shipping service to the forwarder, will not directly participate in retail activities under different channels.

Hypothesis 7.

The omni-channel model of the supply chain adopted in this paper adds a new retail channel, an online mobile channel, under the dual-channel model of the supply chain led by the forwarder; that is, shippers can book shipping service through this new channel. When new retail channels are introduced into the supply chain, for shippers in the market, due to the intangibility of shipping services, there will be a lot of uncertainty in online booking before and after, resulting in corresponding risk costs. When shippers are engaged in booking through traditional offline physical channels, there will be no corresponding risk costs incurred, excluding the price that may arise due to reaching the destination.

Hypothesis 8.

The sales cost of the forwarder in the supply chain under the omni-channel model studied in this article is 0. Due to the fact that the model in this article takes the wholesale cost of the carrier into account and the sales cost of the forwarder is relatively small in numerical terms compared to this wholesale cost, it can be ignored.

3.2. Construction of a Sustainable Maritime Shipping Service Demand Model Based on Consumer Utility

According to the customer’s utility function theory, the following is known:

When shippers in the market purchase shipping services from offline physical channels, the utility brought by each shipping service can be expressed as

$$U_r=v-p_r-{ k}_1$$

(23)

When shippers purchase shipping services through offline physical retail channels, the customer utility ${\mathrm{U}}_r > 0$, and there is a critical value for the utility of this customer, ${\mathrm{v}}_1 = {\mathrm{p}}_{\mathrm{r}} +{ \mathrm{k}}_1$. If the critical value meets the condition that $\mathrm{v} > {\mathrm{v}}_1 ={ \mathrm{p}}_{\mathrm{r}} + {\mathrm{k}}_1$, shippers in the market tend to prefer offline physical channels for booking shipping services. If shippers choose the online internet channel or online mobile channel, the utility they obtain can be expressed as

$$U_n=\theta v -{ p}_n$$

(24)

$\mathsf{\theta }$ represents shippers’ recognition of online sales channels, yet it indicates that shippers in the market are more receptive to online retail channels in the supply chain (such as online booking platforms). At this point, the value of $\mathsf{\theta }$ is closer to 1. When shippers in the market choose to purchase shipping services from online retail channels in the omni-channel model, their customer utility needs to meet the condition $U_n > 0$, which means there exists a certain critical value, $v_2 = {\displaystyle \frac{p_n}{\theta }}$. When $\mathrm{v} > v_2 = {\displaystyle \frac{ p_n}{\theta }}$, shippers will make bookings through online channels.

When this shipping service is booked, the utility brought by the unit service can be expressed as

$$U_m=\phi v -{ p}_m -{ k}_2$$

(25)

$\phi$ refers to the shippers’ acceptance and recognition of a single mobile channel in the omni-channel mode. This parameter meets the conditions $0 < \theta < \phi < 1$. When the shippers’ assessed value meets $\mathrm{v} > v_3 = {\displaystyle \frac{p_m + k_2}{\phi }}$, shippers will choose to book shipping services through mobile channels.

When the intersection point between the straight line $U_r$ and the line $U_m \mathrm{i}\mathrm{s} v^{rm}$, the intersection point between the straight line $U_r$ and the line $U_n$ is $v^{rn}$, and the intersection point between the straight line $U_m$ and the line $U_n$ is $v^{nm}$,

$$v^{rn}={\displaystyle \frac{p_r+{ k}_1-p_n}{1-\theta }}$$

(26)

$$v^{rm}={\displaystyle \frac{p_r+{ k}_1 -{ p}_m -{ k}_2}{1-\phi }}$$

(27)

$$v^{nm}={\displaystyle \frac{p_m+k_2-p_n}{\phi -\theta }}$$

(28)

In the omni-channel model of the supply chain, the different channels accessed by the forwarder should meet the condition that the bookings of each channel are greater than zero, which represents that the forwarder in the supply chain has good sales performance in different channels. In the dual-channel model of the supply chain, the following conditions are met:

${v_2 < v}_3 <{ v}_1$; then ${\displaystyle \frac{p_n}{\theta }} < {\displaystyle \frac{p_m +{ k}_2}{\phi }} <{ p}_r + k_1$.

According to the customer utility functions in different channels, the demand for shipping services by the forwarder after connecting to different retail channels is as follows:

When the customer’s evaluation value of the product meets the requirement $\mathrm{v}\in [v^{nm},v^{rm}]$, then the demand for mobile selling channels is

$$D_m={\displaystyle \frac{p_r+k_{1 }- p_m-k_2}{1-\phi }}-{\displaystyle \frac{p_m+k_2-p_n}{\phi -\theta }}$$

(29)

When the customer’s evaluation value of the product meets the requirement $\mathrm{v}\in [v^{rm},1]$, the demand for physical selling channels is

$$D_r=1-{\displaystyle \frac{p_r+k_1-p_m-k_2}{1-\phi }}$$

(30)

When the shippers’ evaluation value of the product meets the requirement $\mathrm{v}\in [v_2,v^{rm}]$, the demand for online selling channels is

$$D_n={\displaystyle \frac{p_r+k_1-p_m-k_2}{1-\phi }}-{\displaystyle \frac{p_n}{\theta }}$$

(31)

Therefore, under the omni-channel model, the market demand for each channel of the supply chain is as follows:

When ${\displaystyle \frac{p_n}{\theta }} < {\displaystyle \frac{p_m + k_2}{\phi }} < p_r + k_1$,

$$D_r=1-{\displaystyle \frac{p_r+k_1-p_m-k_2}{1 - \phi }}$$

(32)

$$D_n={\displaystyle \frac{p_r+k_1-p_m-k_2}{1-\phi }}-{\displaystyle \frac{p_n}{\theta }}$$

(33)

$$D_m={\displaystyle \frac{p_r+k_1-p_m-k_2}{1-\phi }}-{\displaystyle \frac{p_m+k_{2 }- p_n}{\phi -\theta }}$$

(34)

3.3. Construction of a Sustainable Maritime Shipping Supply Chain Model Under Centralized Decision-Making

When the supply chain model adopts a centralized decision-making mode, the forwarder is dominant in the supply chain, and the carrier is in a subordinate position. The forwarder is responsible for controlling and managing the entire sustainable shipping supply chain, including determining service prices, service models, and service booking quantities, as well as the relationships between various members of the supply chain, in order to achieve the goal of maximizing the overall profit of the entire supply chain. Under this assumption, through intelligent decision support and collaborative efforts among supply chain members, we can make supply chain optimization decisions that are globally optimal.

The problem of maximizing profits in the supply chain under a centralized decision-making mode can be transformed into

$$\max {\pi }^c={(p}_r-c)D_r+{(p}_n-c)D_n+{(p}_m-c)D_m$$

$$\mathrm{s}.\mathrm{t}. {\displaystyle \frac{p_n}{\theta }} <{\displaystyle \frac{ p_m+k_2}{\phi }} < p_r+k_1$$

(35)

When the optimization decision belongs to the centralized decision-making mode, the supply chain profit is a joint concave function of ${\pi }^c \mathrm{with} \mathrm{respect} \mathrm{to} p_r$, $p_n$, $p_m$ with the following proof: If we calculate the second derivative and the second partial derivative of ${p_r, p_n, p_m \mathrm{for} \pi }^c$, the Hessian matrix can be obtained as

$$\mathrm{H}\left({\pi }^c\right)=\left[\begin{array}{ccc}{\displaystyle \frac{-2}{1-\phi }}& 0& {\displaystyle \frac{2}{1-\phi }}\\ 0& {\displaystyle \frac{-2\phi }{(\phi -\theta )\theta }}& {\displaystyle \frac{-2}{ \phi -\theta }}\\ {\displaystyle \frac{2}{1-\phi }}& {\displaystyle \frac{2}{\phi -\theta }}& {\displaystyle \frac{-2(1-\theta )}{(\phi -\theta \left)\right(1-\phi )}}\end{array}\right]$$

(36)

$\mathrm{H}\left({\pi }^c\right)$, as a Hessian matrix, is a semi-negative definite matrix, which leads to the joint concave function of ${\pi }^c \mathrm{with} \mathrm{respect} \mathrm{to} p_r$, $p_n$, $p_m$. Construct the Lagrange function

$$\begin{gathered} \mathrm{L}\left(p_r,p_n,p_m,{\lambda }_1,{\lambda }_2\right)=\left(p_r-\mathrm{c}\right)\left(1-{\displaystyle \frac{p_r-p_m+k_1-k_2}{1-\phi }}\right)+{(p}_n-\mathrm{c}) ({\displaystyle \frac{p_r-p_m+k_1-k_2}{1-\phi }}-{\displaystyle \frac{p_n}{\theta }})+{(p}_m-\mathrm{c}\left)\right({\displaystyle \frac{p_r-p_m+k_1-k_2}{1-\phi }} - {\displaystyle \frac{p_m+k_2-p_n}{\phi -\theta }})+ \\ {\lambda }_1 ({\displaystyle \frac{p_m+k_2}{\phi }}-{\displaystyle \frac{p_n}{\theta }})+{\lambda }_2(p_r+k_1-{\displaystyle \frac{p_m+k_2}{\phi }}) \end{gathered}$$

(37)

The first-order Kuhn–Tucker condition can be obtained:

$$p_r{\displaystyle \frac{\partial L}{\partial p_r}} = 0$$

$$p_n{\displaystyle \frac{\partial L}{\partial p_n}}=0$$

$$p_m{\displaystyle \frac{\partial L}{\partial p_m}}=0$$

$${\lambda }_1{\displaystyle \frac{\partial L}{\partial {\lambda }_1}}=0$$

$${\lambda }_2{\displaystyle \frac{\partial L}{\partial {\lambda }_2}}=0$$

$${\lambda }_1,{\lambda }_2 \ge 0$$

(38)

The optimal selling prices for the three different channels are

$${p_r}^{c^{*}}={\displaystyle \frac{1+c-k_1}{2}}$$

$${p_n}^{c^{*}}={\displaystyle \frac{\theta +c}{2}}$$

$${p_m}^{c^{*}}={\displaystyle \frac{\theta +c -{ k}_2}{2}}$$

(39)

The market demand for each channel is

$${D_r}^{c^{*}}={\displaystyle \frac{1-\phi -k_1+k_2}{2(1-\phi )}}$$

$${D_r}^{c^{*}}={\displaystyle \frac{1-\phi -k_1+k_2}{2(1-\phi )}}$$

$${D_m}^{c^{*}}={\displaystyle \frac{\left(\phi -\theta \right)\left(k_1-k_2\right)-(1-\phi )k_2}{2(1-\phi \left)\right(\phi -\theta )}}$$

(40)

The optimal profit obtained by substitution into the original function is

$${\pi }^{c^{*}}={\displaystyle \frac{(1-c-k_1\left)\right(1-\phi -k_1+k_2)}{4(1-\phi )}}+{\displaystyle \frac{(\theta -c\left)\right(\theta k_2+\theta c-\phi c)}{4\theta (\phi -\theta )}}+{\displaystyle \frac{\left(\theta -c-k_2\right)\left[\right(\phi -\theta )\left(k_{1 }- k_2\right)-(1-\phi )k_2}{4(1-\phi \left)\right(\phi -\theta )}}$$

(41)

3.4. Construction of a Sustainable Maritime Shipping Supply Chain Model Under Decentralized Decision-Making

When the optimization decision belongs to the decentralized decision-making mode, the forwarder takes the lead in the supply chain, and the carrier is in a subordinate position. Therefore, the forwarder is responsible for managing and optimizing the entire supply chain. Therefore, in the decentralized decision-making mode of supply chain optimization, the forwarder is the main player in the Stackelberg Game, while the carrier located upstream in the supply chain is the subordinate party in the game. The two jointly make decisions to achieve the optimization of a sustainable shipping supply chain, maximize their own profits, and take into account sustainable development goals. After the carrier provides services to the forwarder at a certain wholesale price, the forwarder determines its sales price through different channels. Generally, the price is increased to reach the wholesale price. Suppose that the forwarder increases the price $∆\mathrm{r}, ∆\mathrm{n}, ∆\mathrm{m}$, $p_r = \mathrm{w} + ∆\mathrm{r}$, $p_n = \mathrm{w} + ∆\mathrm{n}$, $p_m = \mathrm{w} + ∆\mathrm{m}$, and the decision variable of the carrier is the wholesale price; at this time, the profit of the retailer is

$${\pi }_1 ={(p}_r-w)D_r+{(p}_n-w)D_n+{(p}_m-w)D_m$$

(42)

The carrier’s profit is

$${\pi }_2=(w - c\left)\right[D_r+{ D}_n+D_m]=(w-c\left)\right(1-{\displaystyle \frac{w +∆n}{\theta }})$$

(43)

The master–slave game adopts the reverse induction method; first, calculate the derivative of ${\pi }_2$ with respect to $w$:

Assuming that ${\displaystyle \frac{\partial {\pi }_2}{\partial w}} = {\displaystyle \frac{\theta + c - 2w - ∆n}{\theta }} = 0 \mathrm{and} {\displaystyle \frac{{\partial }^2{\pi }_2}{\partial w^2}} = {\displaystyle \frac{-2}{\theta }} < 0$, function ${\pi }_2$ is a concave function with respect to $w$, and there exists a maximum value:

$$\mathrm{w} = {\displaystyle \frac{\theta + c-∆n}{2}}$$

(44)

The profit of retail s is

$${\pi }_1=∆r\left(1-{\displaystyle \frac{∆r-∆m +k_{1 }-k_2}{1-\phi }}\right)+∆n\left({\displaystyle \frac{∆m-∆n +k_2}{\phi -\theta }}-{\displaystyle \frac{\theta +\mathrm{c} +∆n}{2\theta }}\right)+∆m\left({\displaystyle \frac{∆r-∆m +k_1-{ k}_2}{1-\phi }} - {\displaystyle \frac{∆m-∆n +{ k}_2}{\phi -\theta }}\right)$$

(45)

By taking the second derivative and the second partial derivative of ${\pi }_1$ for $∆\mathrm{r}, ∆\mathrm{n}, ∆\mathrm{m}$, the Hessian matrix can be obtained as follows:

$$\mathrm{H} = \left[\begin{array}{ccc}{\displaystyle \frac{-2}{1-\phi }}& 0& {\displaystyle \frac{2}{1-\phi }}\\ 0& {\displaystyle \frac{-(\phi + \theta )}{(\phi -\theta )\theta }}& {\displaystyle \frac{2}{\phi -\theta }}\\ {\displaystyle \frac{2}{1-\phi }}& {\displaystyle \frac{2}{\phi -\theta }}& {\displaystyle \frac{-2(1-\theta )}{(\phi -\theta \left)\right(1-\phi )}}\end{array}\right]$$

(46)

$\mathrm{H}$, as the Hessian matrix, is a semi-negative definite matrix, which leads to the joint concave function of ${\pi }_1\mathrm{with} \mathrm{respect} \mathrm{to} ∆r, ∆n, ∆m$. Construct the Lagrange function

$$\begin{array}{c}\mathrm{L}\left(∆\mathrm{r},∆\mathrm{n},∆\mathrm{m},{\lambda }_1,{\lambda }_2\right)=∆\mathrm{r}\left(1-{\displaystyle \frac{∆\mathrm{r}-∆\mathrm{m}+{ k}_1-k_2}{1-\phi }}\right)+∆n\left({\displaystyle \frac{∆m-∆n +{ k}_2}{\phi -\theta }}-{\displaystyle \frac{\theta +c +∆n}{2\theta }}\right)+∆m({\displaystyle \frac{∆r-∆m +{k }_1- k_2}{1-\phi }}\\ -{\displaystyle \frac{∆m-∆n +k_2}{\phi -\theta }})+{\mathsf{\lambda }}_1\left({\displaystyle \frac{w +∆m+k_2}{\phi }}-{\displaystyle \frac{w +∆n}{\theta }}\right)+{\mathsf{\lambda }}_2(w +∆r +k_1-{\displaystyle \frac{w +∆m +k_2}{\phi }})\end{array}$$

(47)

The first-order Kuhn–Tucker condition can be obtained:

$$∆r{\displaystyle \frac{\partial L}{\partial ∆r}} = 0$$

$$∆n{\displaystyle \frac{\partial L}{\partial ∆n}}=0$$

$$∆m{\displaystyle \frac{\partial L}{\partial ∆m}}=0$$

$${\lambda }_1{\displaystyle \frac{\partial L}{\partial {\lambda }_1}}=0$$

$${\lambda }_2{\displaystyle \frac{\partial L}{\partial {\lambda }_2}}=0$$

$${\lambda }_1,{\lambda }_2 \ge 0$$

(48)

By combining these equations, the optimal decision under a decentralized decision-making mode can be obtained.

The optimal markup for each channel is

$${∆r}^{c^{*}}={\displaystyle \frac{1-c-k_1}{2}}$$

$${∆n}^{c^{*}}={\displaystyle \frac{\theta -c}{2}}$$

$${∆m}^{c^{*}}={\displaystyle \frac{\phi -c-k_2}{2}}$$

(49)

The optimal wholesale price is

$$w^{D^{*}}={\displaystyle \frac{\theta +3c}{4}}$$

(50)

The optimal selling price under the decentralized decision-making mode is

$${p_r}^{D^{*}}={\displaystyle \frac{\theta +c +2-2k_1}{4}}$$

$${p_n}^{D^{*}}={\displaystyle \frac{ 3\theta +c}{4}}$$

$${p_m}^{D^{*}}={\displaystyle \frac{\theta +2\phi +c-2k_2}{4}}$$

(51)

The demand for each channel is

$${D_r}^{D^{*}}={\displaystyle \frac{1 - \phi - k_1+k_2}{2(1 - \phi )}}$$

$${D_n}^{D^{*}}={\displaystyle \frac{2\theta k_2-(\mathrm{c} +\theta \left)\right(\phi -\theta )}{4\theta (\phi -\theta )}}$$

$${D_m}^{D^{*}}={\displaystyle \frac{\left(\phi -\theta \right)\left(k_1-k_2\right)-(1-\phi )k_2}{2(1-\phi \left)\right(\phi -\theta )}}$$

(52)

The profit of the retailer is calculated as follows:

$${{\pi }_1}^{D^{*}}= {\displaystyle \frac{(1-c-k_1\left)\right(1-\phi -k_1 +{ k}_2)}{4(1-\phi )}} + {\displaystyle \frac{(\theta -c)2\theta k_2 + (\theta -\phi \left)\right(\theta + c)}{8\theta (\phi -\theta )}} + {\displaystyle \frac{\left(\phi -c-k_2\right)(\phi -\theta )\left(k_1 -{ k}_2\right)-(1-\phi )k_2}{4(1-\phi \left)\right(\phi -\theta )}}$$

(53)

The profit of the carrier is

$${{\pi }_2}^{D^{*}}={\displaystyle \frac{{(\theta -c)}^2}{16\theta }}$$

(54)

The profit of the entire supply chain is

$${\mathsf{\pi }}^{{\mathrm{D}}^{*}}={ {\mathsf{\pi }}_1}^{{\mathrm{D}}^{*}}+{{ \mathsf{\pi }}_2}^{{\mathrm{D}}^{*}}={\displaystyle \frac{(1-\mathrm{c}-{\mathrm{k}}_1\left)\right(1-\mathsf{\phi }-{\mathrm{k}}_{1 }+{ \mathrm{k}}_2)}{4(1-\mathsf{\phi })}}+{\displaystyle \frac{(\mathsf{\theta }-\mathrm{c})2\mathsf{\theta }{\mathrm{k}}_2+(\mathsf{\theta }-\mathsf{\phi }\left)\right(\mathsf{\theta }+\mathrm{c})}{8\mathsf{\theta }(\mathsf{\phi }-\mathsf{\theta })}}+{\displaystyle \frac{\left(\mathsf{\phi }-\mathrm{c}-{\mathrm{k}}_2\right)(\mathsf{\phi } -\mathsf{\theta })\left({\mathrm{k}}_1-{\mathrm{k}}_2\right)-(1-\mathsf{\phi }){\mathrm{k}}_2}{4(1-\mathsf{\phi }\left)\right(\mathsf{\phi }-\mathsf{\theta })}}+{\displaystyle \frac{{(\mathsf{\theta }-\mathrm{c})}^2}{16\mathsf{\theta }}}$$

(55)

At this point, the profit difference between centralized and decentralized decision-making in the supply chain is

$$∆\mathsf{\pi }={ \pi }^{C^{*}}-{ \pi }^{D^{*}}={\displaystyle \frac{{(\theta -c)}^2}{16\theta }}$$

(56)

According to the derivation results of the above model, the sales price that maximizes profits in a sustainable shipping supply chain under decentralized decision-making mode is higher than the sales price when the supply chain reaches its optimal state under centralized decision-making, and the overall profit of the supply chain decreases to a certain extent (the reduced profit of the supply chain is the profit of the carrier under decentralized decision-making),

Table 3. Comparison of optimal profits between two modes.

		Centralized Decision-Making Mode	Decentralized Decision-Making Mode
Retail Price	${p_r}^{*}$	${\displaystyle \frac{1+c-k_1}{2}}$	${\displaystyle \frac{\theta +c +2 - 2k_1}{4}}$
	${p_n}^{*}$	${\displaystyle \frac{\theta +c}{2}}$	${\displaystyle \frac{3\theta +c}{4}}$
	${p_m}^{*}$	${\displaystyle \frac{\theta +c - k_2}{2}}$	${\displaystyle \frac{\theta +2\phi +c -2k_2}{4}}$
Wholesale Price	$w^{*}$		${\displaystyle \frac{\theta +3c}{4}}$
Retail Profit	${{\pi }_1}^{*}$		${\displaystyle \frac{(1-c-k_1\left)\right(1 - \phi -k_1+k_2)}{4(1-\phi )}}+$ ${\displaystyle \frac{\left(\theta -c\left)\right(2\theta k_2+(\theta -\phi \left)\right(\theta +c\right)}{8\theta (\phi -\theta )}}+$ ${\displaystyle \frac{\left(\phi - c - k_2\right)\left[\right(\phi -\theta )\left(k_1-k_2\right)-(1-\phi )k_2}{4(1-\phi \left)\right(\phi -\theta )}}$
Profit of the Carrier	${{\pi }_2}^{*}$		${\displaystyle \frac{{\left(\theta - c\right)}^2}{16\theta }}$
Total Profit of the Supply Chain	${\pi }^{*}$	${\displaystyle \frac{(1-c-k_1\left)\right(1-\phi -k_1+k_2)}{4(1-\phi )}}+$ ${\displaystyle \frac{\left(\theta -c\left)\right(\theta k_2+\theta c-\phi c\right)}{4\theta \left(\phi -\theta \right)}}+$ ${\displaystyle \frac{\left(\theta - c - k_2\right)\left[\right(\phi -\theta \left)\left(k_1 - k_2\right)-(1-\phi \right)k_2}{4(1-\phi \left)\right(\phi -\theta )}}$	${\displaystyle \frac{(1-c-k_1\left)\right(1-\phi -k_1+k_2)}{4(1-\phi )}}+$ ${\displaystyle \frac{(\theta -c\left)\right(2\theta k_2+(\theta -\phi \left)\right(\theta + c)}{8\theta \left(\phi -\theta \right)}}+$ ${\displaystyle \frac{\left(\phi - c - k_2\right)\left[\right(\phi -\theta \left)\left(k_1 - k_2\right)-(1-\phi \right)k_2}{4(1-\phi \left)\right(\phi -\theta )}}+$ ${\displaystyle \frac{{\left(\theta - c\right)}^2}{16\theta }}$

. When the sustainable shipping supply chain adopts decentralized decision-making, one must determine how to overcome the possible information silos and conflicts of interest in the background of intelligent decision-making and maximize its overall profit. At the same time, when the forwarder occupies a dominant position in the overall supply chain, one must determine how to effectively handle and integrate the cooperation relationship with upstream carriers, so that all members of the supply chain can actively participate in cooperation, jointly promote green shipping and resilience improvement, and optimize the overall supply chain; this is a key optimization problem that the forwarder and the entire sustainable shipping supply chain need to be solved.

4. Case Study

This study selects Company M as an application example. As one of the top 100 companies in the global shipping industry, Company M focuses on providing comprehensive container shipping logistics services with a business footprint in over 100 countries and regions around the world. The company is committed to developing end-to-end shipping solutions to connect and simplify customer supply chains, optimizing customer logistics experiences in multiple areas such as inland services, customs brokerage, shipping transportation, and warehouse management. In addition to giving full attention to customer satisfaction, Company M has also actively participated in the decarbonization process of the shipping industry in recent years, attempting multiple measures related to energy-saving, emission reduction, and sustainable development. Thus, it is a typical supplier in a green shipping supply chain, and it is possible for Company M to cooperate deeply and make concessions with forwarders to maximize the overall profit of the supply chain. The service quality improvement strategy and decision optimization model proposed in this study for the maritime supply chain are highly compatible with Company M’s current development strategy and improvement direction.

4.1. Background Description of the Case

In the simulation of this model, this article takes Company M as the role of a carrier, providing omni-channel shipping services and undertaking sustainability investments. The forwarder that cooperates with Company M serves as the demand side for green shipping services. By using Company M as an application example, it not only provides a visual demonstration and practical verification of the strategies and models proposed in this study, but also provides the company with reference and methodological support for intelligent and sustainable transformation.

The services provided by Company M cover both online digital platforms (such as its official website, mobile app, and electronic transaction system) and traditional offline channels (such as customer service representatives and freight forwarding partners), providing shippers with a seamless service booking, tracking, and management experience.

4.2. Main Parameter Settings

Based on the models in Section 2 and Section 3, this article sets specific values for the following key parameters based on the characteristics of the industry in which Company M operates. The specific parameter values of Company M in

Table 4. Key Parameter Values for the Case.

Parameter Symbol	Meaning of Parameter	Numerical Value
m	Total Market Volume	1000
b	Price-Sensitive Coefficient	1
cm	Operating Costs of the Forwarder’s Unit	10
cr	Unit Operating Costs of Carriers (Company M)	0
c	Comprehensive Cost between Forwarders and Carriers	7.5
t	Market Share of the Product	0.8
φ	Shippers’ Recognition/Acceptance of Online Channels	0.8
ψ	Shippers’ Recognition/Acceptance of Mobile Channels	0.7
α	Market Share Expanded by the Carrier (Company M) after Accessing Online Channels	0.1
β	Market Share Expanded by the Forwarder after Accessing Online Channels	0.2

are calculated based on the company’s demand analysis during the epidemic risk period. As a leading enterprise in China’s shipping industry, Company M has a global network covering many green and sustainable development requirements.

m is the total market volume, which refers to the potential size of the ocean freight service market faced by Company M, and can be understood as the number of potential shippers or freight volume units. b is the price sensitivity coefficient, which reflects the sensitivity of forwarders to changes in ocean freight service prices. This value is set to 1, representing that for every 1 unit change in price, the demand changes by 1 unit. cm is the operating cost of the forwarder, which refers to the basic unit cost of the forwarder’s products or services using Company M’s services. cr is the basic unit operating cost of shipping services provided by Company M, which includes regular vessel operations, port operations, etc. t represents the market share of the product, reflecting the overall market share of Company M’s shipping services. This value is set to 0.8, which can be understood as the high market recognition of Company M’s services while its sustainable development strategy is adhered to. φ represents the shippers’ acceptance of online channels, reflecting their preference for obtaining shipping services through Company M’s online digital platforms (such as official websites and electronic trading systems). According to the former marketing analysis in the e-shipping market in Shanghai by the system operated by Company M, the acceptance of the shipper to the online channel of Company M, which is the independent system maintained by the company itself, is close to 80%; thus, to simplify the calculation process, φ is set to 0.8. This value is relatively high, reflecting that Company M can bring convenience and trust to forwarders in intelligent upgrading. Ψ refers to the customer’s recognition/acceptance of online mobile channels, reflecting the customer’s preference for obtaining shipping services through Company M’s mobile applications and other channels. This value is slightly lower than that of online channels, reflecting subtle differences in market preferences for different digital channels; thus, it is set to 0.7. α is the market share expanded by the carrier (Company M) after accessing online channels, and Company M (as a carrier) can expand its market share by opening its own website channels. This value is used for the relevant calculations in the model in Section 2. β is the market share expanded by the forwarder after accessing online channels and the market share that the forwarder can expand by opening its own website channels. This value is used for the relevant calculations in the Section 2 model. The specific values of each key parameter are shown in the table below:

4.3. Validation of Profit Analysis Model

This section will simulate the parameter values based on the two models of “carrier access to online channels” and “retailer access to online channels” in Section 2, using the context of the Company M case. By comparing the profits and prices of the forwarder, Company M (the carrier), and supply chain under two different scenarios, the impact of different online channel access modes on the performance of a sustainable shipping supply chain is verified.

(1)

Carrier (Company M) access to online channel mode: Based on the profit function and equilibrium solution formula of “Carrier Access to Online Channels” in Section 2, substitute all relevant parameters set in the table above to solve for the optimal price, optimal sustainability investment level, and respective profits and total supply chain profits of Company M (as the online channel access party) and the forwarder at this time.

(2)

Forwarder access to online channel mode: Based on the profit function and equilibrium solution formula of “Forwarder Access to Online Channels” in Section 2, substitute all relevant parameters set in the table above to solve for the optimal price, optimal sustainability investment level, and respective profits and total supply chain profits of the forwarder (as the online channel access party) and Company M at this time.

(3)

Summarize the profit results of the two online channel access modes mentioned above, visually compare them in

Table 5. Results of profit analysis model.

Channel Access Mode	Total Profit of Supply Chain ($\mathit{\pi }$)	Profit of Forwarder (${\mathit{\pi }}_1$)	Profit of Company M	Final Price of Shipping Service
Carrier (Company M) Access to Online Channels	140,000	67,500	72,500	550
Forwarder Access to Online Channels	97,537.5	65,025	32,512.5	3060

, and compare key decision variables such as price and sustainability investment level under each mode to verify the impact of different online channel access strategies on the overall and member profits of the supply chain. The above parameters were substituted into the model, and the results are shown in the following table.

From the case analysis results, it can be seen that under the model of Company M accessing network channels, the total profit of the supply chain is significantly higher than that of the forwarder accessing network channels. This indicates that under specific market conditions, carrier-led and carrier-integrated network channels can more effectively optimize the overall operational efficiency and profit distribution of the supply chain. This may be because carriers have stronger advantages in production, cost control, or mastery of product information, thereby gaining greater profit margins when directly facing consumers. This model is conducive to achieving more efficient collaboration among all parties in the supply chain, and may ultimately enhance the profitability of the entire supply chain by reducing intermediate links, optimizing inventory management, and other methods.

Specifically, under the model of the carrier (Company M) accessing online channels, the profit of the carrier increases, while the profit of the forwarder may remain stable or slightly increase. This indicates that under certain supply chain structures and market conditions, the digital and networked transformation of the carrier (Company M) not only optimizes its own operations, but also brings significant synergies and value added to the cooperating forwarder, achieving an overall improvement in supply chain efficiency and profitability.

4.4. Analysis of the Effect of Decision Mode

This section will focus on comparing the impact of different decision-making models on the sustainable shipping supply chain profits of Company M, and verify how the revenue-sharing contract designed in Section 3 effectively improves supply chain performance under decentralized decision-making.

(1) Decentralized decision-making: Select the “Carrier Access to Online Channels” model with lower total supply chain profit as the benchmark, and record its total supply chain profit and optimal price.

(2) Centralized decision-making: Based on the formula for “Centralized Decision-Making” in Section 2, substitute the parameters in the table above to calculate the overall maximum total profit and optimal price of the supply chain. This result represents the best performance in supply chain theory.

(3) Perform a differential analysis between the total profit of the supply chain under centralized decision-making and decentralized decision-making.

Compare the profit results of the two scenarios mentioned above, with a focus on analyzing how collaborative optimization can improve the total profit of the supply chain, as well as how it can increase the profits of both Company M and the forwarder. By substituting the parameters into the model formulas in Section 3, the case analysis results of Company M’s sustainable shipping supply chain under different decision models can be obtained, as shown in

Table 6. Results of decision mode model.

Decision Mode	Total Profit of the Supply Chain $(\mathit{\pi }$)	Final Price of Ocean Freight (P*)
Decentralized Decision-Making	${\displaystyle \frac{(-2.5-k_1\left)\right(0.2-k_1+k_2)}{0.8}}-{\displaystyle \frac{7k_2+1.2}{0.6}}+{\displaystyle \frac{(6.7+k_2\left)\right(0.3k_1-0.5k_2)}{0.24}}+{\displaystyle \frac{49}{8}}$	4.5
Centralized Decision-Making	${\displaystyle \frac{(-2.5-k_1\left)\right(0.2-k_1+k_2)}{0.8}}-{\displaystyle \frac{7(0.5k_2-2.25)}{0.6}}+{\displaystyle \frac{(6.7+k_2\left)\right(0.3k_1-0.5k_2)}{0.24}}$	4
Perform Differential Analysis	${\displaystyle \frac{28k_2+106.2}{4.8}}(0<k_1<k_2<1)$	−0.5

.

The analysis results in the table indicate that the total profit of the supply chain under centralized decision-making is greater than that under decentralized decision-making, and the final shipping service price is lower under centralized decision-making. This verifies that centralized collaborative decision-making among all parties in the supply chain can eliminate the double marginalization effect and achieve overall optimization.

As shown in Figure 4,when shippers book the shipping service in the market through the mobile channel in the supply chain, there may be certain risks (such as information asymmetry, uncertainty of service experience), and the risk cost that they need to bear is $k_2$. As risks increase, the wind resistance of centralized decision-making gradually emerges. With the risk increasing, the difference between the total profit of the supply chain under centralized decision-making and decentralized decision-making keeps increasing. Centralized decision-making not only achieves overall optimality, but also serves as the optimal choice for shippers facing risks.

4.5. Analysis of Collaborative Optimization Path for Revenue Sharing

In order to maximize the benefits of a sustainable shipping supply chain, optimization decisions will be made for the intelligent omni-channel mode of a sustainable shipping supply chain, and relevant measures will be taken to maintain good cooperative relationships with the carrier located upstream of the supply chain. Therefore, before the peak season for booking shipping services each year, the downstream forwarder will request the upstream carrier to provide shipping services at a relatively low wholesale price w. After the service is completed, the revenue will be shared with the upstream carrier at a rate of $1-\mathsf{\mu }(0 < \mathsf{\mu } < 1)$. At this point, $\mathsf{\mu }$ is the profit-sharing factor in the supply chain, which reflects whether the profit of the sustainable shipping supply chain has been maximized in the intelligent omni-channel model dominated by the forwarder and whether the profit can be reasonably distributed among all members to motivate all parties to jointly achieve sustainable development goals.

In this situation, the profit obtained by downstream retail s is

$${{\pi }_1}^S={ (\lambda p}_r-w)D_r+{(\lambda p}_n-w)D_n+{(\lambda p}_m-w)D_m$$

(57)

And the carrier’s profit is

$${{\pi }_2}^S=\left(w-c\right)\left(1-{\displaystyle \frac{p_n}{\theta }}\right)+(1-\lambda \left)\right(p_r{D}_r+{ p}_n{D}_n+p_m{D}_m)$$

(58)

Here, $p_r = \mathrm{w} + ∆\mathrm{r}$, $p_n = \mathrm{w} + ∆\mathrm{n}$, $p_m = \mathrm{w} + ∆\mathrm{m}$.

In order to maximize the profits of sustainable shipping supply chains, i.e., optimize supply chain profits, the optimization decision-making behavior under the centralized decision-making mode mentioned earlier is the same as the decision-making behavior under the revenue-sharing decision-making model. Under this premise, the following conclusions can be obtained:

Conclusion 1: When using a revenue-sharing optimization model to maximize supply chain profits, there are no contract parameters ($w,\mathsf{\mu })$ that satisfy the condition for the sustainable shipping supply chain to reach the optimal state under the intelligent omni-channel mode dominated by the forwarders.

In this situation, although adopting a revenue-sharing contract mechanism cannot achieve coordinated optimization of the supply chain, in this model, each member of the supply chain can achieve a win–win situation to a certain extent. If a certain wholesale price is established between the carrier and the forwarder through cooperation and negotiation before the peak season of service booking, and if the wholesale price is lower, the profit shared between the forwarder and the carrier will correspondingly expand after the service is completed. On the contrary, if the wholesale price offered by the carrier to the forwarder is relatively high, the profit shared between the forwarder and the carrier will be relatively small. As the upstream of the supply chain, the carrier does not directly participate in the sales activities of various forwarder channels. The revenue from its offline physical service channels is the sales cost of the forwarder; that is, the wholesale price provided by the carrier to the forwarder will directly affect the forwarder’s sales cost. When the price is high, the selling cost of the forwarder is high, so the profit will correspondingly decrease. Finally, at the end of the peak season of shipper service booking, the profit shared with the upstream carrier will be relatively small. On the contrary, if the wholesale price provided by the carrier to the forwarder is lower, the forwarder can obtain greater profits, so the profit-sharing factor in the supply chain will be relatively large; that is, the forwarder will give the carrier more profit sharing. In addition, there is an inverse relationship between the two variables of service booking quantity and pricing; that is, the lower the pricing is, the easier it is to book shipping services, meaning that the service booking quantity will be larger and the final vacancy rate of the service will be smaller. However, lower pricing can also bring problems, such as making bookings easier but not ensuring profit when the service is provided. The increase in sales volume of shipping services at lower prices may not necessarily fully compensate for the reduced revenue of services due to high pricing. So, in the entire supply chain, achieving true revenue sharing between the carrier and the forwarder is an ideal situation, which is subject to many practical limitations. The ultimate result is the compromise and negotiation among all members of the supply chain.

Under the intelligent omni-channel mode, when formulating sales plans and marketing strategies, a forwarder in the sustainable shipping supply chain should fully consider the coordination of service booking behavior between different channels, including offline physical channels, online channels, and online mobile channels. Efforts should be made to achieve perfect coordination among various channels to provide a better service experience for forwarders and meet their diverse needs. This is an important goal for the supply chain to achieve green and resilient optimization in the intelligent omni-channel mode. The coordination and cooperation of different forwarder channels in the supply chain can reduce the game conflicts between upstream and downstream members of the supply chain, promote data sharing and intelligent collaboration, and to some extent increase the profits of supply chain members, optimize the input costs between various channels, and ultimately achieve sustainable development goals.

When individuals in the supply chain are subject to rational constraints, the goal pursued by each member of the supply chain when making optimization decisions is to maximize its own interests, rather than maximizing supply chain profits. Therefore, under this logical premise, in order to optimize the sustainable shipping supply chain under the intelligent omni-channel mode and maximize the profits of the entire supply chain, the supply chain should meet the requirement that the profits of each member under the profit-sharing model are higher than those under the non-sharing model and can balance environmental benefits and social responsibility. Only then can the supply chain be optimized to the maximum extent, so that the profits of the entire supply chain and each member can be maximized under certain constraints, achieving a win–win situation.

Conclusion 2: Under the contract mechanism of the revenue-sharing model, there are parameters that make the profit of the forwarder in the supply chain equal to the profit of the forwarder in the decentralized decision-making without revenue sharing in the supply chain.

Due to the variability of the market and the diversity of preferences among forwarders, it is difficult for the carrier to predict changes in demand for this shipping service in the market. Therefore, it is difficult to determine its plans for service bookings in both online and offline channels. If the online service booking volume is high and the offline service booking volume is low, the carrier will lose a portion of the profit loss caused by insufficient supply to the downstream forwarder through offline channels. At the same time, the high vacancy rate of online services will also generate corresponding operating costs, which will affect the profits of the carrier. In this context, the forwarder, due to insufficient services provided by the carrier, may choose to increase retail prices to increase profits, which will also have a corresponding impact on the overall supply chain profits and fail to achieve optimal profits. Therefore, in the sustainable shipping supply chain under the intelligent omni-channel mode, carriers should reasonably determine their wholesale prices, retail prices, and direct-sales prices so that the profits of the entire sustainable shipping supply chain and the profits of all members upstream and downstream of the supply chain can be optimized and maximized, while taking into account green efficiency and risk management. However, in practice, the behavior of carriers is constrained by various practical conditions, and the actual operation will be relatively difficult. Therefore, in the optimization problem of a sustainable shipping supply chain under an intelligent omni-channel mode, the allocation of pricing and service booking volume between different channels is very important.

Conclusion 3: In the revenue-sharing contract mechanism, there exists a contract parameter, ${\lambda }_2\in (0,1)$, that ensures that the profits of the carrier in the supply chain are equal to those of a carrier who makes decentralized decisions without a revenue-sharing contract.

In the intelligent omni-channel model established in this article for a sustainable shipping supply chain, the upstream carrier occupies a dominant position in the entire supply chain, while the downstream forwarder is in a subordinate position, that is, a follower of the game between the carrier and the forwarder. In this model, the profits of the carrier mainly come from the profits it provides to the forwarder, namely the income from offline distribution channels and the profit income from online channels directly participating in market sales. As the leader of the game, the carrier needs to determine the wholesale prices it offers to the forwarder and the number of service bookings for offline distribution. When the shipping service market is relatively active and the market conditions are good, the demand in the market will be greater. In this situation, the carrier may maliciously raise the wholesale price of its shipping services provided to the downstream forwarder through different means in order to obtain more profit income. However, as the wholesale price increases, the sales cost of forwarders increases, and the corresponding profit income decreases. That is, when the wholesale price provided by the carrier to the forwarder is high, it will lead to a reduction in the interests of some forwarders and be occupied by carriers. For the carrier in the supply chain, the distribution revenue from offline channels is the total profit obtained from the services provided to the forwarder. The higher the wholesale price, the greater the shared benefits between the forwarder and the carrier, which is more advantageous for the carrier. For the forwarder, the greater the shared benefits, the smaller the final profit it will receive, which is inconsistent with its goal of maximizing profits. Therefore, determining how to balance the distribution of profits among the upstream and downstream members of the supply chain and take measures to promote cooperation between the forwarder and the carrier is crucial for optimizing the sustainable shipping supply chain under the intelligent omni-channel model. This optimization not only pursues the maximization of profits for the entire sustainable shipping supply chain, but also takes into account its green development goals and resilience improvement. The maximization of profits for each member also needs to be considered. Therefore, all members and participants in the supply chain need to set up a reasonable cooperation mechanism under the background of information sharing and intelligent collaboration, design a good integration mechanism, establish reasonable wholesale prices and channel service booking quantities, and ultimately improve the profits of all parties, so that all members of the supply chain can achieve maximum profit under certain constraints.

Conclusion 4: Under the contract mechanism of the revenue-sharing model, if ${\lambda }_1<{\lambda }_2$, there exist certain contract parameters $\lambda \in [{\lambda }^2,1]$ that enable all members of the supply chain to reach Pareto optimality, thereby achieving a win–win situation.

In the dual-channel model of a sustainable shipping supply chain, there is a conflict of profit between the forwarder and the carrier, so both channels will compete for interests. Therefore, in order to coordinate the interests of all members of the supply chain under the dual-channel model, the carrier shares the profits it obtains through online sales channels with the forwarder. Research on revenue-sharing mechanisms has shown that in a dual-channel model, carriers can flexibly adjust revenue-sharing factors to coordinate a sustainable dual-channel shipping supply chain, thereby achieving a win–win situation for all participants in the supply chain.

However, when faced with the overall goal of a sustainable supply chain, especially under the policy pressure of low-sulfur fuel targets continuously introduced by the IMO and the pressure of high fuel costs transferred, it is difficult to achieve balance and sustainability by relying solely on carriers and freight forwarders to absorb and bear the risks and costs of the supply chain. External support is needed; for example, the government could provide a series of policies to support a sustainable maritime supply chain in green energy and sustainable development work.

For example, methanol is a highly effective clean energy source in shipping. Green methanol is synthesized through biomass gasification or green hydrogen with CO₂, resulting in nearly zero carbon emissions throughout its entire life cycle, far lower than traditional marine fuels such as heavy oil and diesel. At present, the global order for methanol fuel ships has reached 300, corresponding to an annual demand of 6.8 million tons; however, the production capacity is limited, resulting in a significant supply–demand gap and high costs. If appropriate subsidies can be introduced, it will have a good supporting effect on the sustainable maritime supply chain. At present, corresponding policy support has begun to be attempted in Shanghai and Shenzhen, China. Shenzhen provides a subsidy of 16 CNY/deadweight ton/month (up to 160,000 CNY/month) for its own methanol refueling ships, and a subsidy of 10 CNY/deadweight ton/month (up to 120,000 CNY/month) for leased ships. At the same time, a reward of 300 CNY/ton is given for the actual refueling volume, with an annual total subsidy limit of CNY 9.9 million (almost USD 14 million). Shanghai Port provides export tax rebates to green methanol supply enterprises, which require submission of materials such as the “Port Operation Business Filing Form” and value-added tax invoices to reduce enterprise costs.

5. Conclusions and Outlook

This study focuses on profit analysis and collaborative optimization of sustainable maritime supply chains under the framework of intelligent decision-making, addressing complex challenges in the global shipping industry’s transition toward sustainable shipping transportation and port intelligence. By constructing a rigorous profit analysis model and introducing a revenue-sharing contract mechanism, this research systematically investigates the decision-making behaviors of carriers and forwarders within intelligent omni-channel models and their impact on the overall performance of sustainable maritime supply chains in marine engineering contexts.

The collaborative optimization potential of revenue-sharing contracts has been designed and analyzed, demonstrating that, although these contracts may not achieve complete supply chain coordination (i.e., maximizing total profit under centralized decision-making) in certain scenarios, they effectively balance the interests of carriers and forwarders, fostering Pareto optimality and win–win outcomes in an intelligent omni-channel environment. Case analysis highlights that when forwarders, as terminal service connectors, leverage online channels and customer proximity, they significantly enhance overall supply chain profitability, reinforcing collaborative optimization as a key driver for sustainable maritime transport and marine port efficiency. These findings offer a decision-making basis for carriers and forwarders to formulate omni-channel strategies, negotiate wholesale prices, and allocate service bookings, enabling them to pursue maximum profits while advancing sustainable maritime transport and port intelligence goals. Carriers and forwarders can share transportation demand, capacity resources, and other information through the platform, optimize cargo allocation and transportation plans, and improve supply chain collaboration efficiency. Thus, when carriers and forwarders reach a win–win supply chain, it will also create a positive externality. For example, through the information flow through the supply chain, ports can publish real-time operational information on the platform, facilitating advance planning for shipping companies, reducing waiting time for ships at the port, lowering operating costs, and promoting efficient operation of sustainable shipping supply chains.

Despite theoretical and practical advancements, this study has limitations. Future research could incorporate additional practical factors, such as market uncertainty, the incentive effects of government policies on green technology investments, and the impact of climate change risks on sustainable maritime supply chain decision-making in marine environments. Furthermore, exploring complex profit-sharing mechanisms involving multiple stakeholders, alongside the application of artificial intelligence and big data in demand forecasting and dynamic pricing for maritime services, could provide comprehensive support for enhancing the resilience of intelligent and sustainable maritime supply chains and marine port operations.