Channel Selection Strategies of Chinese E-Commerce Supply Chains Under Green Governmental Subsidies

Abstract

In the era of the green digital economy, network platforms as a new form of economic format have gained significant attention from e-commerce companies. This paper intends to address the channel selection strategy for e-commerce enterprises and the coordination of the whole supply chain under the government’s green subsidy policy. Game theory is used to analyze the strategy of channel selection based on logistics distribution, e-commerce platform, consumer preference, and green governmental subsidy. The findings are as follows: (1) Self-established logistics cost and platform commission rates are important factors affecting channel selection. With the increase in consumers’ preference for a green economy, consumers are more inclined to choose platform channels. (2) Green governmental subsidies represent an advantageous strategy for the whole supply chain, and under its influence, the Pareto improvement of the supply chain can be realized. (3) Cooperation with other companies using the platform franchise system can maximize the benefits of the supply chain, which also can improve consumer satisfaction and increase the profits of e-commerce enterprises at the same time. In conclusion, a platform franchise contract is proposed to coordinate the supply chain and realize the rapid development of the green economy.

1. Introduction

With the development of e-commerce enterprises, live streaming and take-out delivery services have entered people’s daily lives. Network platforms as a new form of economy format have attracted wide attention [1]. The latest data from the National Bureau of Statistics show that, as of 2024, China’s online retail transaction volume was 9.26 trillion CNY, up 9.5 percent year on year. With the continuous emergence of platform technology and model innovation, the consumption of key platform channel network services has increased by 20.2%. Thus, as choosing the right supply chain channel is crucial in the competitive environment of business, many e-commerce enterprises are considering introducing the platform channel. At the same time, a smart economy alongside a green supply chain has become the theme of social development. Promoting a green and low-carbon economy is the key to achieving high-quality development [2]. The economic priority is to build a green and low-carbon society. Based on this, the Chinese government has introduced relevant green subsidy policies to promote the development of green industries. The green transformation of enterprises requires high costs; due to this, many enterprises are hesitating, despite government subsidies being able to fully alleviate the cost pressure of the green reform of enterprises to promote the development of green industry [3]. On the other hand, with the improvement of consumers’ awareness of environmental protection, they pay more attention to the environmental pollution of online shopping products; consumers prefer to buy green products as an effective way to save energy and reduce emissions. In this instance, green platform channels have attracted the attention of e-commerce enterprises [4] and become a powerful tool to improve the revenue of enterprises. What is more, the introduction of platform channels not only opens up the sales market for enterprises but also provides transaction communication channels for consumers and enterprises, and it improves the overall benefits of the product supply chain [5]. The platform channel has attracted more and more consumers [6]. The operation of the platform is undertaken using the principal-agent mechanism to improve sales services for e-commerce, and charges commissions from them, which may vary according to different enterprises and commodities [7]. The pricing of goods sold through platform channels is controlled by the platform, and enterprises have the right of direct pricing of goods. Moreover, the pricing of the platform is reflected in various platform marketing strategy activities. At present, the popular 10 billion active subsidy, the Double Eleven subsidy, and various festival activity vouchers are all different platform pricing strategies [8]. Compared with traditional multi-level distribution channels, the powerful data analysis and intelligent cloud computing technology of platform channels can allow the faster and more accurate gathering of market sales information [1]. In addition, the platform’s shared information service can provide data support for manufacturing and e-commerce enterprises [9]. Thus, for e-commerce enterprises, the introduction of platform channels lies in the mastery of consumer market information [10]. The introduction of marketplace channels by e-commerce companies provides merchants and consumers with a platform for direct transactions, but at the same time it also intensifies product competition in the market. A series of problems arising from the selection of marketplace channels can be described as follows:

(1) Distribution selection. With the development of the e-commerce economy, consumers are becoming more and more sensitive to logistics [11]. For example, distribution in the catering industry requires timeliness. In order to preserve the taste of food, constant temperature distribution is required and the delivery time to customers needs to be as short as possible. KFC and Pizza Hut and other large chain enterprises have chosen to build their own logistics distribution system, with ‘express delivery’, while M Cake is outsourced to urban express logistics. How should enterprises choose their mode of logistics distribution? Should they build their own distribution system or outsource it to third-party logistics?

(2) Platform selection and information sharing. Network broadcasting has become a new sales model; businesses have also identified the business opportunities it brings, namely opening up online sales. Do enterprises therefore need to introduce platform channels? Open information channels can improve distribution efficiency, allowing distribution staff to realize the transfer order, and multiple orders can thus be filled. Whether the investment costs for the open sharing of information can be compensated for by increased benefits is still up for debate. For example, Pizza Hut has a self-established application APP for express delivery, and it has also joined the Meituan delivery platform, and consumers can choose to buy its products from both channels. M Cake only has platform channels and has not opened a self-channel, and consumers can only buy products through the platform.

(3) Green governmental subsidy effect. The government provides green subsidies to the platform to improve the economy by reducing the pressure of selling green products, associated, for example with their high cost, so as to guide and encourage retailers to actively sell green products. However, the question remains: how should the relationships within the entire supply chain be coordinated to ensure the policy’s effectiveness?

As described above, logistics distribution and platform operations constitute fundamental factors influencing the e-commerce supply chain channel selection. Current research lacks comprehensive strategies to address this complex issue and fails to take consumers’ preferences into consideration, which represents a significant research gap in the field of e-commerce supply chain management. Therefore, under the government’s green subsidy policy, this paper constructs an e-commerce supply chain composed of an e-commerce self-operated channel and a platform channel. Considering consumers’ green preferences, we analyze the supply chain channel selection strategy from the aspects of logistics distribution, platform selection, and coordination management. The following questions are addressed: (1) Should companies choose self-established distribution logistics or third-party distribution logistics? (2) What are the selection conditions of each channel? What channel strategy should enterprises adopt? (3) Should the supply chain be managed cooperatively under government subsidies and achieve green and low-carbon goals?

The contents of this study are organized as follows: the relevant literature is outlined in Section 2. In Section 3, we propose five models of logistics selection, platform channel selection and government coordination, and derive equilibrium solutions for each scenario. Section 4 analyzes strategies related to logistics and platform channel and green channel information sharing. In Section 5, a franchise contract is used to coordinate the whole supply chain under the government green subsidy. Section 6 presents a numerical analysis to visually illustrate the main findings. Section 7 summarizes the paper and proposes potential directions for future research.

2. Literature Review

The literature related to this study covers four aspects: government green subsidy research, e-commerce platform business model selection, supply chain coordination, and systems approaches in sustainable supply chains.

2.1. Government Subsidies

In terms of government subsidies for supply chains, green and low-carbon policies have been a hot topic recently. For example, Liao [12] examined the impact of government policy intervention of green tax and green subsidy on corporate earnings. The results show that compared with tax relief policies, green subsidy policies can better stimulate producers’ enthusiasm for green production with higher profits. Duan et al. [13] discussed the impact of green and low-carbon strategies from the perspective of supply chain competition and analyzed the relationship between green performance and the revenue of manufactured products. Additionally, the behavior of supply chain members is also discussed in the research and the game theory is introduced. Zheng, J. et al. [14] studied the coordination of green supply chain members’ behaviors under government subsidy policies. Based on the comparison between cooperative and non-cooperative games, the Nash equilibrium game was used to coordinate the supply chain and obtain better results. Zhao et al. [15] investigated the range and degree of government subsidies and took the cost of green production as the measurement standard. When the cost is high, the government should adopt a broad subsidy policy, that is, reduce the access standard of subsidies and the subsidy amount at the same time. On the other hand, when the cost is low, the government should adopt a subsidy strategy that involves raising access standards and subsidy quotas. Ma et al. [16] used carbon trading and carbon tax policies to coordinate energy conservation and emission reduction in green supply chain contracts, and effectively alleviated double margins by using two-part contracts. Xiao et al. [17] found that green government subsidies can enhance the green degree of products, reduce offline retail prices, increase market demand, and boost profits, collectively promoting the green economy.

2.2. Channel Selection

In terms of channel selection for the supply chain, dual online and offline channels are widely discussed. Lu et al. [18] compared online and offline sales channels and found that, when a competitive relationship between the two models exists, online channels have more advantages in meeting information demands and can fully satisfy the personalized needs of consumers. Pu et al. [19] studied the marketing model of manufacturers and demonstrated that their profits were greatly improved by the single marketing offline model compared to the mixed customization model with supplier participation. However, another import factor that influences channel selection is price. In this field, Ali et al. [20] developed a pricing model for platform sales of homogeneous goods of multiple retailers and found that malicious bidding can occur in platform channels in non-cooperative competition models. Haiju et al. [21] investigated the pricing strategy of a dual-channel green supply chain, taking equity concerns into account, and found that government subsidies can improve manufacturers’ profits and consumers’ satisfaction. Similarly, Wiredu et al. [22] discussed the influence of consumer reference quality on the selection of cooperation strategy in dual-channel green supply chains and proposed a cooperation model with vertical cooperation and horizontal alliances. What is more, service price has also received researchers’ attention. Feng et al. [23] studied the value of platform-type enterprises, based on service value, co-created value with the ecosystem, and guided its realization mechanism with the supply chain. Mondal and Giri [24] considered the impact of the joint decision of price and service on the selection of a dual-channel supply chain model and pointed out that the higher the service sensitivity of consumers, the higher the logistics distribution price of online sales. Wang et al. [25] used dynamic game theory to study the differences in the product quality of different channels, to consider consumer benefits and service level, and to enhance channel competitiveness with high quality and service.

2.3. Supply Chain Channel Coordination

Since the introduction of platform channels, platform operators and e-commerce businesses have formed direct competition in the terminal consumer market, and this channel rush has been a focus of academic circles [26], with many scholars conducting in-depth research on multi-channel coordination. Zhang and Zhang [27] point out that two-part pricing contracts can incentivize retailers and facilitate supply chain coordination. Al-Awamleh et al. [28] found that strategic innovation compensation alone could not achieve the overall optimization of the supply chain, so they designed a two-part pricing contract to coordinate the dual-channel supply chain. In contrast, Luo et al. [29] studied the compensation strategy to coordinate a dual-channel supply chain in an e-commerce environment and ensured a win–win situation for supply chain participants within a certain range. Kumar et al. [30] studied chain–chain competition under manufacturers’ diseconomies of scale and found that two-part price-setting contracts are the dominant equilibrium contract used by manufacturers and retailers to achieve Pareto improvement. Masruroh et al. [31] designed a revenue-sharing contract with fixed compensation to realize the coordination of a dual-channel supply chain, which is characterized by diseconomies of scale in production. Based on a supply chain consisting of two competing manufacturers and one retailer, Wang and Thelkar [32] investigated the impact of retailers’ extended warranty services and market competition on manufacturers’ selection of two-part pricing contracts and found that a balanced two-part pricing contract could coordinate the supply chain and achieve a Pareto improvement for retailers’ earnings. In addition to the two-part pricing contract, other contracts can also alleviate conflicts in multiple channels, such as quantity discount contracts, revenue sharing contracts, and cost-sharing contracts. Ngoh and Mellema [33] pointed out that price discount contracts have the advantages of easy execution and can effectively coordinate channel conflicts. Consistent pricing schemes allow retailers to achieve higher revenues and thus reduce channel conflicts. Some scholars use benefit-sharing contracts to realize multi-channel coordination. Hebaz et al. [34] used a benefit–cost sharing contract to realize the Pareto improvement of a dual-channel supply chain within a closed-loop supply chain with a dual-channel cost recovery difference. Jiang et al. [35] realized the coordination of dual channels by designing a revenue-sharing contract with a lower wholesale price and allowing retailers to compensate manufacturers with a certain proportion of the revenue of traditional retail channels. Li and Lv [36] both considered the application of revenue-sharing contracts in supply chain coordination. Some scholars also discussed supply chain coordination contracts in more complex situations. For example, Ju et al. [37] designed a dual-channel drug supply chain under the influence of government policies, using a contract coordination mechanism involving price limits, fixed payments, and government subsidies.

2.4. Systems Approaches for Sustainable Supply Chains

In recent years, sustainable supply chain systems have been applied in the e-commerce sector, among which sustainable logistics and supply chain management are regarded as key factors [38]. Sustainable logistics distribution systems are crucial methods for effectively reducing logistics costs in e-commerce [39]. Jayarathna and Dawes [40] found that collaborating with third-party logistics companies to improve vehicle load rates and reduce empty trips can effectively lower energy consumption and emissions. Schachenhofer et al. [41] compared the advantages and disadvantages of sustainable collaborative logistics systems versus self-operated logistics systems, finding that collaborative logistics systems are more cost-effective for bulk and large-volume goods. Additionally, Li [42] applied deep reinforcement learning algorithms to solve optimal transportation scheduling problems under carbon emission constraints in demand-uncertain logistics systems. Their research highlighted that mastering logistics distribution information and rationally planning distribution schemes are essential for improving transportation efficiency. In today’s digital economy, smart technologies such as Cainiao’s electronic waybills, packing algorithms, and intelligent route planning are enabling sustainable logistics development [43]. These technologies facilitate full lifecycle energy conservation and emission reduction in operations, reducing resource consumption and environmental pollution while enhancing information transmission efficiency and total factor productivity. This promotes the sustainable transformation and upgrading of the logistics industry, bringing about changes in quality, efficiency, and speed. Sustainable logistics systems not only improve the quality and efficiency of logistics services but also reduce the environmental impact of transportation and lower costs, and increase corporate profits. Moreover, sustainable logistics enhances a company’s social image and competitiveness, earning trust and support from the government and the public [44]. Based on these insights, this study recognizes the pivotal role of information systems in fostering sustainable logistics and e-commerce development. To address this critical need, we propose an information sharing system in channel decision-making to enhance the sustainability of logistics operations.

The effective implementation of sustainable supply chains requires not only support from government departments but also enhanced supply chain management. Yu et al. [45] found that partnerships, information sharing, and supply chain management are all critical factors influencing supply chain system management, primarily involving joint decision-making and supply chain performance evaluation. Of these, resources, processes, and technology are key elements affecting corporate strategic decisions. Ji et al. [46] elaborated on the composition of sustainable supply chain management from a systemic perspective, noting that the production system, consumption system, social system, and environmental system are four aspects. Durmaz [47] emphasized that in order to meet the ever-changing demands of customers, enterprises must coordinate and collaborate with each other to promote overall benefits for the supply chain. Sahoo et al. [48] highlighted the importance of strengthening supply chain management to enhance competitive advantages and broaden cooperative channels, engaging in closer and more detailed collaboration with supply chain members. Cano [49] suggested that consumer demand for sustainable products stimulates upstream and downstream enterprises in the supply chain to engage in sustainable activities. By improving supply chain management, companies can enhance communication with consumers, cater to their preferences, and meet their personalized needs, thereby gaining a more accurate understanding of customer requirements and providing more tailored sustainable products and services. Furthermore, Singh et al. [50] found that under the contextual factors of high-level internal supply chain management integration and supplier integration, sustainable innovation can promote the improvement of corporate financial performance. The sustainable supply chain systems, based on a unified environmental goal, encourages sharing resources and risks, which can help enterprises acquire more available resources, thereby enhancing the overall profitability of the supply chain.

In the above discussions about channel selection and supply chain coordination, existing research on supply chain platform channels is limited to the sales volume, while platform distribution and consumer preference are not involved. In addition, the platform operation model has not been analyzed under the conditions of government subsidies and the current landscape lacks an integrated system capable of effectively coordinating supply chain operations. To fill this gap, this work studies channel selection strategies by considering the platform channel under sustainable governmental subsidies. Furthermore, we propose an innovative franchise system architecture grounded in coordinated supply chain principles, specifically designed to achieve green and sustainable development objectives.

3. Model Description and Analysis

In this research, we built an online sales system composed of e-commerce self-operated channels and platform channels. This study focuses on the strategy of logistics service, channel selection, and the coordinated management of green governmental subsidies. Logistics services can be divided into self-established logistics service modes and third-party logistics service modes; of these, the level of the self-established logistics service is higher than that of third-party logistics, but its operation cost is also higher. The purchase channels are also divided into e-commerce self-operated channels and platform channels, and the revenue for goods purchased through self-operated channels is directly transferred to the e-commerce account, while for purchases made through the platform channel, revenue is first collected by the platform and then transferred to the e-commerce account, after the platform deducts a certain percentage of commission. Notations used in this paper are summarized in

Table 1. Definition of notations.

Notation	Definition
$\lambda$	Commission
$\delta$	Investment coefficients
$L$	Logistics service level
$\theta$	Consumer green preference
$\mu$	Logistics sensitivity
$v$	Value of assessment
$U_i$	Utility of channel $i$
$s^j$	Investment of model $j$
$p_i^j$	Price model $j$ in channel $i$
$Q_i^j$	Demand model $j$ in channel $i$
${\prod }_i^j$	Revenue model $j$ in channel $i$
$M$	Franchise fee
Superscript $j$	$TP$—Third-party distribution mode in self-operated channel; $SL$—self-established distribution mode in self-operated channel; $IT$—Third-party logistics distribution mode in green platform channel; $IS$—Sharing distribution mode in platform channel; $IG$—Sharing distribution mode under green subsidy; $IGH$—Channel coordination model under government green subsidy; $IB$—Platform franchise system under platform channel
Subscript $i$	$e$—self-operated channel; $o$—platform channel

.

(1) To emphasize the coexistence of competition and cooperation between the self-operated channel and the platform channel, this paper assumes that both channels sell identical goods with the same marginal cost. Without a loss of generality, all marginal costs are set to zero.

(2) Assuming that consumers are equally sensitive to logistics services provided by different entities in the market, if an e-commerce company establishes its own logistics system, consumers’ sensitivity to the self-established logistics services of the e-commerce company is the same as their sensitivity to the logistics services provided by third-party logistics providers.

(3) To obtain the demand, we use the consumer utility function, assuming that consumers have heterogeneous valuations $v$ for the product, where $v$ is uniformly distributed over the interval [0, 1]. This article further assumes that improving logistics service levels can enhance the utility consumers derive from purchasing the product [51].

By analyzing the consumer utility function, we establish the relationship between logistics service levels, consumer valuations, and demand, thereby deriving the demand function under different logistics service conditions. The sequence of the game is as follows: e-commerce enterprises first decide whether to introduce platform channels. If they choose not to introduce platform channels, they evaluate two types of logistics decisions (self-established logistics or third-party logistics) [52]; if they introduce platform channels, the impact of platform commission and consumer preference on the overall income is considered to maximize the revenue of enterprises. Subsequently, a franchise system is employed to coordinate the supply chain, leveraging the government’s green subsidies for the platform to enhance overall supply chain efficiency. The decision-making model is shown in Figure 1. According to the channel structure in different situations, the revenue function of the commodity is obtained, and by using game theory and backward induction, the equilibrium solution of the game in each case is obtained.

3.1. Logistics Distribution Model

3.1.1. Third-Party Distribution Mode in Self-Operated Channel (TP Mode)

In the traditional e-commerce logistics model, goods are sold by e-commerce enterprises, and the products are distributed through third-party logistics after the order is accepted. The utility of the consumer purchasing the product is $v-p^{TP}+\mu L^{TP}$. When consumer utility is higher than zero, $v-p^{TP}+\mu L^{TP}>0$, the consumer chooses to buy the product, as shown in Figure 2a. In this mode, the market demand of the product is $Q^{TP}=1-p^{TP}+\mu L^{TP}$, and the revenue of e-commerce enterprises is:

$${\prod }^{TP}=\left(p^{TP}-f\right)Q^{TP}$$

(1)

In order to maximize profit, take the partial derivation for ${\prod }^{TP}$ at p and make it equal to 0, ${\displaystyle \frac{\partial {\prod }^{TP}}{\partial p^{TP}}}=0$; thus, we obtain $p^{TP}={\displaystyle \frac{1+f+\mu L^{TP}}{2}}$ and $Q^{TP}={\displaystyle \frac{1-f+\mu L^{TP}}{2}}$. To ensure $Q^{TP}>0$, results need to satisfy the condition $\mu >{\displaystyle \frac{f-1}{L}}$. Then, by substituting the derived optimal value into (1), we can obtain the maximum profit: ${\prod }^{TP*}={\displaystyle \frac{{(1-f+\mu L^{TP})}^2}{4}}$.

3.1.2. Self-Established Distribution Mode in Self-Operated Channel (SL Mode)

In this model, sales and distribution are completed by the e-commerce enterprises themselves, and the enterprises build their own logistics for distribution, as shown in Figure 2b. The utility of consumers purchasing goods is $v-p^{SL}+\mu L^{SL}$. When the utility of consumers is higher than zero, they choose to buy products. The market demand of products in this mode is $Q^{SL}=1-p^{SL}+\mu L^{SL}$. The income of e-commerce enterprises is

$${\prod }^{SL}=p^{SL}\left(1-p^{SL}+\mu s^{SL}\right)-\delta L^{S{L}^2}$$

(2)

In the self-established logistics model, product prices and logistics service levels are decided by e-commerce enterprises. The goal of e-commerce is to maximize revenue and find the Hessian matrix of Equation (2); when $\mu <2\sqrt{\delta }$, the Hessian matrix is negative, so we obtain $p^{SL}={\displaystyle \frac{2\delta }{4\delta -{\mu }^2}}$, $L^{SL}={\displaystyle \frac{\mu }{4\delta -{\mu }^2}}$, and $Q^{SL}={\displaystyle \frac{2\delta }{4\delta -{\mu }^2}}$, and finally substitute it into Equation (2) to obtain the maximum profit ${\prod }^{SL*}={\displaystyle \frac{\delta }{4\delta -{\mu }^2}}$.

3.2. Platform Channel Model

3.2.1. Third-Party Logistics Distribution Mode in Green Platform Channel (IT Mode)

After the introduction of green platform channels, consumers choose their own purchase channels, and purchase orders are uniformly delivered by e-commerce enterprises and distributed by third-party logistics. The utility of consumers buying self-operated channel goods is $v-p_e^{IT}+\mu L^{TP}$, and the utility of consumers choosing green platform channel based on their green preference is $\theta v-p_o^{IT}+\mu L^{TP}$. Decisions are made based on utility maximization. When $v-p_e^{IT}+\mu L^{TP}>\max \left\{\theta v-p_o^{IT}+\mu L^{TP},0\right\}$, consumers buy products through self-operated channels; when $\theta v-p_e^{IT}+\mu L^{TP}>\max \left\{v-p_o^{IT}+\mu L^{TP},0\right\}$, consumers choose platform channels to buy products, as shown in Figure 3.

In this model, the commodity demand of each channel is as follows: $Q_e^{IT}=1-{\displaystyle \frac{p_e^{IT}-p_o^{IT}}{1-\theta }}$ and $Q_o^{IT}={\displaystyle \frac{p_e^{IT}-p_o^{IT}}{1-\theta }}-{\displaystyle \frac{p_o^{IT}-\mu L^{TP}}{\theta }}$. The case of positive channel sales is studied and discussed as $Q_e^{IT}\ge 0$ and $Q_o^{IT}\ge 0$, so $p_e^{IT}+\theta -1<p_o^{IT}<\mu L^{IT}\left(1-\theta \right)+\theta p_e^{IT}$. The channel income of e-commerce enterprises and platforms are as follows:

$${\prod }_e^{IT}=\left(p_e^{IT}-f\right)\left(1-{\displaystyle \frac{p_e^{IT}-p_o^{IT}}{1-\theta }}\right)+p_o^{IT}\left(1-\lambda \right)\left({\displaystyle \frac{p_e^{IT}-p_o^{IT}}{1-\theta }}-{\displaystyle \frac{p_o^{IT}-\mu L^{TP}}{\theta }}\right)$$

(3)

$${\prod }_o^{IT}=\lambda \left(p_o^{IT}-f\right)\left({\displaystyle \frac{p_e^{IT}-p_o^{IT}}{1-\theta }}-{\displaystyle \frac{p_o^{IT}-\mu L^{TP}}{\theta }}\right)$$

(4)

The total revenue of the supply chain is as follows:

$${\prod }^{IT}={\prod }_e^{IT}+{\prod }_o^{IT}$$

(5)

When the platform channel is introduced and both parties utilize third-party logistics, the consumer preference coefficient is the median number $\theta =0.5$ [53,54]. Consumers pay more attention to the green environmental protection of their distribution logistics model. In this scenario, the sales model is structured as the platform provides sales channels, and the e-commerce company makes the sales decisions. The relationship between the e-commerce company and the platform is a leader–follower relationship. The sequence of the game between the two sides is as follows: the e-commerce enterprises, as the leader, decide the commodity prices of their own channels, while the platforms, as the followers, make decisions on the commodity prices of their platform channels after the e-commerce decisions. The Stackelberg game is used to solve this problem through backward induction [7,55].

The equilibrium results are as follows:

$p_e^{IT}={\displaystyle \frac{2\left(1+\mu L^{TP}\right)+5f}{\lambda }}-\mu L^{TP}$, $p_o^{IT}={\displaystyle \frac{1+\lambda f+\mu L^{TP}}{2\lambda }}+{\displaystyle \frac{5f}{4\lambda }}$; $Q_e^{IT}=2\mu L^{TP}+1-{\displaystyle \frac{3+3\mu L^{TP}}{\lambda }}+{\displaystyle \frac{\left(2\lambda -15\right)f}{2\lambda }}$; $Q_o^{IT}={\displaystyle \frac{2\left(\mu L^{TP}+1\right)+\left(5-2\lambda \right)f}{\lambda }}$, of which $\mu >{\displaystyle \frac{1+f\left(2\lambda -3\right)}{\left(1-2\lambda \right)L}}$.

The profits of each channel are as follows:

$$\begin{gathered} \begin{array}{l}{\prod }_e^{IT*}=\left({\displaystyle \frac{2\left(1+\mu L^{TP}\right)+\left(5-\lambda \right)f}{\lambda }}-\mu L^{TP}\right)\left(2\mu L^{TP}+1-{\displaystyle \frac{3+3\mu L^{TP}}{\lambda }}+{\displaystyle \frac{\left(2\lambda -15\right)f}{2\lambda }}\right)+\\ \left(1-\lambda \right)\left({\displaystyle \frac{1+\lambda f+\mu L^{TP}}{2\lambda }}+{\displaystyle \frac{5f}{4\lambda }}\right)\left({\displaystyle \frac{2\left(\mu L^{TP}+1\right)+\left(5-2\lambda \right)f}{\lambda }}\right)\end{array} \\ {\prod }_o^{IT*}={\displaystyle \frac{1}{4}}\left(2+\left(5+2\lambda \right)f+2\mu L^{TP}\right)\left(2\left(\mu L^{TP}+1\right)+\left(5+2\lambda \right)f\right) \end{gathered}$$

3.2.2. Sharing Distribution Mode in Platform Channel (IS Mode)

In this model, the platform integrates distribution and sales, which uses the platform’s self-established logistics to distribute goods. Consumers can choose e-commerce self-operated channels or platform channels and use the platform’s self-established logistics system for distribution. By sharing information about integrated distribution, consumers can view all aspects of the purchase and distribution process in real time and track each process of the order distribution. The platform also publicly discloses order information and shares the order progress, so that couriers can transfer orders. The service utility is related to the platform input $s^{IS}$, and the decision-making model is shown in Figure 4. The utility of goods obtained by consumers who choose e-commerce self-operated channels is $v-p_e^{IS}+\mu s^{IS}$, and the utility of goods obtained by platform channels is $\theta v-p_o^{IS}+\mu s^{IS}$. When $v-p_e^{IS}+\mu s^{IS}>\max \left\{\theta v-p_o^{IS}+\mu s^{IS},0\right\}$, consumers choose self-operated channels to buy goods. When $\theta v-p_o^{IS}+\mu s^{IS}>\max \left\{v-p_e^{IS}+\mu s^{IS},0\right\}$, consumers choose the platform channel to purchase goods.

The demands of self-operated channels and platform channels are $Q_e^{IS}=1-{\displaystyle \frac{p_e^{IS}-p_o^{IS}}{1-\theta }}$ and $Q_o^{IS}={\displaystyle \frac{p_e^{IS}-p_o^{IS}}{1-\theta }}-{\displaystyle \frac{p_o^{IS}-\mu s^{IS}}{\theta }}$ to ensure that sales volume is positive. In addition, the condition of $p_e^{IS}+\theta -1<p_o^{IS}<\mu s^{IS}\left(1-\theta \right)+\theta p_e^{IS}$ should be satisfied; then, the income of e-commerce self-operated channels is as follows:

$${\prod }_e^{IS}=\left(1-\lambda \right)p_e^{IS}\left(1-{\displaystyle \frac{p_e^{IS}-p_o^{IS}}{1-\theta }}\right)$$

(6)

In the platform sales and distribution integration model, the self-established logistics investment is $s^{IS}$, the revenue is as follows:

$${\prod }_o^{IS}=\lambda p_e^{IS}+p_o^{IS}\left({\displaystyle \frac{p_e^{IS}-p_o^{IS}}{1-\theta }}-{\displaystyle \frac{p_o^{IS}-\mu s^{IS}}{\theta }}\right)-{\displaystyle \frac{\delta {\left(s^{IS}\right)}^2}{2}}$$

(7)

In the sharing e-commerce system, the platform utilizes green and low-carbon shared logistics for unified distribution, which generate scale benefits and reduce the level of distribution. Simultaneously, considering the impact of consumers’ green preferences on the selection of purchase channels, the decision-making analysis is conducted by comparing the self-established logistics distribution through the self-operated channels of e-commerce. The equilibrium results are as follows:

$$\begin{gathered} {\prod }_e^{IS*}={\displaystyle \frac{\left(1-\lambda \right)\left(1-\theta \right)}{4}}\left(\left(1+{\displaystyle \frac{\lambda }{2}}\right)\theta +2\mu s^{IS}+1\right) \\ \begin{array}{l}{\prod }_o^{IS*}=\lambda \left({\displaystyle \frac{\left(1-\theta \right)}{2}}\left(\left(1+{\displaystyle \frac{\lambda }{2}}\right)\theta +2\mu s^{IS}+1\right)\right)\\ +\left(1-\theta \right)\left(\left(1+{\displaystyle \frac{\lambda }{2}}\right)\theta -{\displaystyle \frac{1}{2}}\left(1+\lambda \right)+{\displaystyle \frac{\mu s^{IS}(1-2\theta )}{\theta }}\right)\left(\left(1+{\displaystyle \frac{\lambda }{2}}\right)\theta +2\mu s^{IS}\right)-{\displaystyle \frac{\delta {\left(s^{IS}\right)}^2}{2}}\end{array} \end{gathered}$$

3.3. Government Coordination Model

3.3.1. Sharing Distribution Mode Under Green Subsidies (IG Model)

The government gives green subsidies to the platform, and all goods are distributed by the platform’s self-established logistics system, as shown in Figure 5.

The utility of goods when choosing self-operated channels is $v-p_e^{IG}$, and the utility of goods when choosing government green platform channels is $\theta v-p_o^{IG}+\mu s^{IG}$. When $v-p_e^{IG}>\max \left\{\theta v-p_o^{IBG}+\mu s^{IG},0\right\}$, the self-operated channel is selected; when $\theta v-p_o^{IG}+\mu s^{IG}>\max \left\{v-p_e^{IG},0\right\}$, the green platform channel is selected. The demand for each channel is $Q_e^{IG}=1-{\displaystyle \frac{p_e^{IG}-p_o^{IG}+\mu s^{IG}}{1-\theta }}$ and $Q_o^{IG}={\displaystyle \frac{p_e^{IG}-p_o^{IG}+\mu s^{IG}}{1-\theta }}-{\displaystyle \frac{p_o^{IG}-\mu s^{IG}}{\theta }}$, while to ensure a positive sales volume, $p_e^{IG}-\mu s^{IG}+\theta -1<p_o^{IG}<\theta p_e^{IG}-\theta \mu s^{IG}$ should be satisfied.

The decision models are as follows:

$${\prod }_e^{IG}=\left(\left(1-\lambda \right)p_e^{IG}\right)\left(1-{\displaystyle \frac{p_e^{IG}-p_o^{IG}+\mu s^{IG}}{1-\theta }}\right)$$

(8)

$${\prod }_o^{IG}=p_o^{IG}\left({\displaystyle \frac{p_e^{IG}-p_o^{IG}+\mu s^{IG}}{1-\theta }}-{\displaystyle \frac{p_o^{IG}-\mu s^{IG}}{\theta }}\right)+\lambda p_e^{IG}\left(1-{\displaystyle \frac{p_e^{IG}-p_o^{IG}+\mu s^{IG}}{1-\theta }}\right)+{\displaystyle \frac{\delta {\left(s^{IG}\right)}^2}{2}}$$

(9)

In this model, platform channels provide sales and distribution services, but sales strategies and product-related services are still decided by e-commerce enterprises. The game sequence is as follows: e-commerce determines the product price of the channel, and platforms determine the commission rate. The optimal solution can be obtained through the backward induction method:

$$\begin{array}{c}\hfill p_e^{IG}={\displaystyle \frac{\delta {\left(1-\theta \right)}^2}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}, \\ \hfill p_o^{IG}={\displaystyle \frac{\delta {\left(1-\theta \right)}^2(\theta \left(1+\lambda \right)+\frac{\lambda {\mu }^2}{\left(\theta -1\right)\delta })}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}+{\mu }^3\left({\displaystyle \frac{2\theta -1}{\delta \left(\theta -1\right)}}\right), \\ s_e^{IG}={\displaystyle \frac{\lambda \mu \delta {\left(1-\theta \right)}^2}{\delta {\left(\theta -1\right)}^2\left(\theta \left(1+\lambda \right)-1\right)-\delta \left(\theta -1\right){\mu }^2\lambda }}+{\displaystyle \frac{{\mu }^2\left(2\theta -1\right)}{\delta \left(\theta -1\right)}},\end{array}$$

By substituting the value into (8) and (9), the profits of each channel can be obtained as follows:

$$\begin{gathered} {\prod }_e^{IG*}=\left({\displaystyle \frac{\left(1-\lambda \right)\delta {\left(1-\theta \right)}^2}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}\right)\left(1-{\displaystyle \frac{1-\theta \left(1+\lambda \right)}{1-\theta }}\left({\displaystyle \frac{\delta {\left(1-\theta \right)}^2}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}\right)\right) \\ \begin{array}{l}{\prod }_o^{IG*}={\displaystyle \frac{\delta \left(1-\theta \left(1+\lambda \right)\right)\left(1-\theta \right)}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}\left({\displaystyle \frac{\delta {\left(1-\theta \right)}^2(\theta \left(1+\lambda \right)+\frac{\lambda {\mu }^2}{\left(\theta -1\right)\delta })}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}+{\mu }^3\left({\displaystyle \frac{2\theta -1}{\delta \left(\theta -1\right)}}\right)\right)+\\ {\displaystyle \frac{\lambda \delta {\left(1-\theta \right)}^2}{\left(1-\theta \right)\left(1-\theta \left(1+\lambda \right)\right)-{\mu }^2\lambda }}\left(1-{\displaystyle \frac{1-\theta \left(1+\lambda \right)}{1-\theta }}\right)+{\displaystyle \frac{\delta }{2}}{\left({\displaystyle \frac{\lambda \mu \delta {\left(1-\theta \right)}^2}{\delta {\left(\theta -1\right)}^2\left(\theta \left(1+\lambda \right)-1\right)-\delta \left(\theta -1\right){\mu }^2\lambda }}+{\displaystyle \frac{{\mu }^2\left(2\theta -1\right)}{\delta \left(\theta -1\right)}}\right)}^2\end{array} \end{gathered}$$

3.3.2. Channel Coordination Model Under Green Government Subsidies (IGH Model)

On the basis of the government subsidies, the overall collaborative management of supply chain sales channels is implemented. Through centralized decision-making, the overall maximum profit of the supply chain is obtained. The decision-making model is as follows:

$${\prod }^{IGH}=p_e^{IGH}\left(1-{\displaystyle \frac{p_e^{IGH}-p_o^{IGH}}{1-\theta }}\right)+p_o^{IGH}\left({\displaystyle \frac{p_e^{IGH}-p_o^{IGH}}{1-\theta }}-{\displaystyle \frac{p_o^{IGH}-\mu s^{IGH}}{\theta }}\right)+{\displaystyle \frac{\delta {\left(s^{IGH}\right)}^2}{2}}$$

(10)

The equilibrium result is $p_e^{IGH}={\displaystyle \frac{2\left(\mu s^{IGH}+\theta \right)}{4-\theta }}+{\displaystyle \frac{1-\theta }{2}}$, $p_o^{IGH}={\displaystyle \frac{2\left(\mu s^{IGH}+\theta \right)}{4-\theta }}$,

The maximum profit is ${\prod }^{IGH*}={\displaystyle \frac{2\left(\mu s^{IGH}+\theta \right)}{4-\theta }}-{\displaystyle \frac{\theta \left(2+\mu s^{IGH}\right)-2\mu s^{IGH}}{\theta {\left(4-\theta \right)}^2}}+{\displaystyle \frac{1-\theta }{4}}+{\displaystyle \frac{\delta {\left(s^{IGH}\right)}^2}{2}}$.

4. Channel Selection Strategy Analysis

4.1. Logistics Analysis

The logistics strategy of e-commerce enterprises is discussed based on the cost coefficient of self-established logistics $\delta$ and the logistics sensitivity of consumers $\mu$. The cost coefficient of self-established logistics $\delta$ is when the cost structures the logistics infrastructure delivery system of logistics, and the logistics sensitivity of consumers $\mu$ affects the consumers’ choice of purchase channel. A higher level means that the seller should utilize higher-quality logistics to attract the consumer to buy their products [56].

Proposition 1.

When  $\delta \le {\delta }^{*}$, third-party logistics is used for distribution; when $\delta >{\delta }^{*}$, self-established logistics is used for distribution, where ${\delta }^{*}={\displaystyle \frac{{\mu }^2\left(1-f+\mu L^{TP}\right)}{4{\left(1-f+\mu L^{TP}\right)}^2-1}}$.

Proof. 

When ${\prod }^{TP}-{\prod }^{SL}=0$, ${\prod }^{TP}-{\prod }^{SL}={\displaystyle \frac{1}{4}}{\left(1-f+\mu L^{TP}\right)}^2-{\displaystyle \frac{\delta }{4\delta -{\mu }^2}}=0$.

We obtain ${\delta }^{*}={\displaystyle \frac{{\mu }^2\left(1-f+\mu L^{TP}\right)}{4{\left(1-f+\mu L^{TP}\right)}^2-1}}$.

□

As $\delta \le {\delta }^{*}$, ${\prod }^{TP}\ge {\prod }^{SL}$ the investment of self-established logistics is high. Outsourcing services to third-party companies can effectively reduce the operating costs of enterprises. When $\delta >{\delta }^{*}$, ${\prod }^{TP}<{\prod }^{SL}$, the coefficient of self-construction increases, the input of self-construction logistics decreases, and e-commerce enterprises receive higher profit margins. Self-construction logistics can improve the effectiveness of consumers to purchase products, so enterprises choose to distribute through self-established logistic systems. Many enterprises such as Pizza Hut choose self-established logistics to increase their income because of their low investment in this system, while M Cake chooses third-party logistics services because of the high cost of its self-established logistics as a result of scale economy.

The influence of consumer logistics sensitivity on the investment coefficient of self-established logistics is further discussed as below.

Corollary 1.

When  ${\displaystyle \frac{f-1}{L}}<\mu <\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$, the investment coefficient of self-established logistics increases as the logistics sensitivity of consumers increases; when $\mu >\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$, the investment coefficient of self-established logistics decreases as the logistics sensitivity of consumers decreases.

Proof. 

Partial derivation for ${\delta }^{*}$ at $\mu$; when ${\displaystyle \frac{\partial {\delta }^{*}}{\partial \mu }}<0$, the latter is obtained as $\mu >\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$, and it is can be seen from the calculation in Section 3.1 that $\mu >{\displaystyle \frac{f-1}{L}}$ needs to be satisfied using the intersection of the two conditions $\mu >\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$.

When ${\displaystyle \frac{f-1}{L}}<\mu <\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$, ${\displaystyle \frac{\partial {\delta }^{*}}{\partial \mu }}>0$.

The proof is same as above.

□

From the above sections, it can be concluded that when consumers’ logistics awareness increases to $\sqrt{{\displaystyle \frac{1-f}{3-4f}}}+f-1$, the investment coefficient of self-established logistics decreases as the sensitivity increases, leading to lower investment costs and higher profits. As consumers are sensitive to logistics, the self-established logistics mode better satisfies consumers’ demand for logistics distribution services, and consumers are willing to pay higher fees for logistics services. Therefore, this increases sales volumes for goods and enables enterprises to obtain higher profits and further improve their self-established logistics systems and form a virtuous cycle process.

4.2. Platform Selection Decision Analysis

The key to channel selection is the commission rate of the platform $\lambda$. By comparing the equilibrium results of the platform channel and the self-operated channel, the selection strategy can be obtained.

Proposition 2.

When the commission rate of the platform channel $\lambda \le {\displaystyle \frac{3{\left(\mu L^{TP}-f+1\right)}^2-2\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)}{4f\left(\mu L^{TP}-f+1\right)-\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)}}$, the platform channel is selected; otherwise, the self-operated channel is selected.

Proof. 

When comparing the income of platform channels and the self-operated channels of e-commerce businesses, the introduction conditions are as follows:

when ${\prod }_e^{IT}-{\prod }_e^{TP}\ge 0$, ${\lambda }^{*}\le {\displaystyle \frac{3{\left(\mu L^{TP}-f+1\right)}^2-2\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)}{4f\left(\mu L^{TP}-f+1\right)-\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)}}$.

□

When e-commerce enterprises introduce platform channels and both parties use third-party logistics, platform commission is the main factor in channel competition. When platform commission is lower than ${\lambda }^{*}$, platform channels have lower unit costs and higher total revenue than self-operated channels. Introducing platform channels can increase total sales profits, while high profits also result in more bargaining space for channels and improve their competitive advantages. When the platform commission is higher than ${\lambda }^{*}$, more profits from e-commerce enterprises will be shared with the platform, which results in a decline in the total profits of enterprises. Choosing self-operated channels can effectively reduce the enterprises’ expenses and increase their sales volumes, thus increasing the total profits.

The influence of consumers’ logistics sensitivity on channel selection is further analyzed below:

Corollary 2.

Platform channels are more attractive to consumers who are more sensitive to logistics services.

Proof. 

The optimal rate of the platform can be obtained by the derivation of logistics sensitivity.

$$\begin{array}{l}{\displaystyle \frac{\partial {\lambda }^{*}}{\partial \mu }}={\displaystyle \frac{\left(2\mu L^{TP}-f+1\right)\left(\left(4f\mu L^{TP}-f+1\right)-{\left(\mu L^{TP}\right)}^2+{\left(f+1\right)}^2\right)-\left(3{\left(\mu L^{TP}-f+1\right)}^2\right)}{{\left(4f\left(\mu L^{TP}-f+1\right)-\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)\right)}^2}}\\ -{\displaystyle \frac{\left(3{\left(\mu L^{TP}-f+1\right)}^2-\left(2{\left(\mu L^{TP}\right)}^2-{\left(f+1\right)}^2\right)\left(4f\mu L^{TP}-2\mu L^{TP}\right)\right)}{{\left(4f\left(\mu L^{TP}-f+1\right)-\left(\mu L^{TP}-f+1\right)\left(\mu L^{TP}+f+1\right)\right)}^2}}\end{array}$$

As seen in Section 3.2, $\mu >{\displaystyle \frac{1+f\left(2\lambda -3\right)}{\left(1-2\lambda \right)L^{TP}}}$, ${\displaystyle \frac{\partial {\lambda }^{*}}{\partial \mu }}>0$.

□

A platform obtains the advantages of large-scale logistics services by concentrating on a large number of orders, which improves the logistics distribution services for consumers. However, the more sensitive consumers are to logistics services, the higher the platform commission rate, indicating that under the same conditions, more consumers choose platform channels and, thus, a platform can increase its commission rate, thereby improving its bargaining power and ultimately enhancing channel revenue and efficiency. For example, both Pizza Hut and M Cake have opened platform channels; the main reason for this is that unlike in other industries, consumers are highly sensitive to food quality, and delivery is very important for order fulfillment. Therefore, e-commerce enterprises should combine their own industry and enterprise characteristics, comprehensively consider the third-party logistics costs and platform commission rates, and make their own decisions by comparing their optimal revenue with their actual revenue under the current commission rate. Thus, when the actual commission rate is lower than the optimal commission rate, enterprises should introduce a platform channel; otherwise, they should choose a self-operated channel.

4.3. Decision Analysis of Green Channel with Information Sharing

The decision analysis is carried out by comparing the benefits of self-established logistics distributions and green channel distributions with information sharing distributions.

Proposition 3.

When the platform commission rate $\lambda \le 1-\left({\displaystyle \frac{4\delta \left(1-\theta \right)}{\left(4\delta -{\mu }^2\right){\left(2\mu s+1\right)}^2}}\right)$, sharing the platform information can create more profits for the enterprise.

Proof. 

When ${\prod }_e^{IS}-{\prod }_e^{SL}=0$,

$${\lambda }^{\#}=1-\left({\displaystyle \frac{4\delta \left(1-\theta \right)}{\left(4\delta -{\mu }^2\right){\left(2\mu s+1\right)}^2}}\right).$$

Then, calculate ${\prod }_e^{IS}-{\prod }_e^{SL}\le 0$ and we obtain $\lambda \le 1-\left({\displaystyle \frac{4\delta \left(1-\theta \right)}{\left(4\delta -{\mu }^2\right){\left(2\mu s+1\right)}^2}}\right)$.

□

Compared with the income of self-established logistics and platform systems, the determining factor is the platform rate, and when the rate is less than ${\lambda }^{\#}$, the platform channel yields the greatest benefit to the enterprise.

The influence of the consumer’s green preference on channel selection is further analyzed and discussed below.

Corollary 3.

When  $\delta >{\displaystyle \frac{\mu }{2}}$, the green preference of consumers is higher, and they are more likely to choose platform channels. When  $\delta \le {\displaystyle \frac{\mu }{2}}$, the higher the consumer’s green preference, the more inclined they are to choose self-operated channels.

Proof. 

${\lambda }^{\#}$ is the critical point of the decision; take the derivative of ${\lambda }^{\#}$ at $\theta$ and let it equal 0. Thus, we obtain ${\displaystyle \frac{\partial {\lambda }^{\#}}{\partial \theta }}={\displaystyle \frac{4\delta }{\left(4\delta -{\mu }^2\right){\left(2\mu s+1\right)}^2}}=0$ under the condition of $\delta ={\displaystyle \frac{\mu }{2}}$. So, $\delta >{\displaystyle \frac{\mu }{2}}$, ${\displaystyle \frac{\partial {\lambda }^{\#}}{\partial \theta }}>0$ and $\delta \le {\displaystyle \frac{\mu }{2}}$, ${\displaystyle \frac{\partial {\lambda }^{\#}}{\partial \theta }}\le 0$.

□

When the investment coefficient is more than twice the logistics sensitivity, the platform channel becomes more attractive, and its income is greatly improved by considering the green preference of consumers. However, when the investment coefficient is less than twice the logistics sensitivity, the benefit of logistics improvement cannot offset the investment cost, and the green preference of consumers cannot meet the sales advantage of the channel. In such cases, adopting the self-operated model yields higher profits.

5. Coordination Model Analysis for Channels with Green Government Subsidies

5.1. Analysis of Channels with Green Subsidies (IG Model)

Proposition 4.

Green governmental subsidies can improve the income of the all-channel e-commerce supply chain.

Proof. 

By comparing the revenue of each channel member operating under a green subsidy, the equilibrium result of Equations (6)–(9) can be obtained:

$${\prod }_e^{IG*}-{\prod }_e^{IS*}>0, {\prod }_o^{IG*}-{\prod }_o^{IS*}>0.$$

□

The conclusion shows that government subsidies can increase the income of e-commerce enterprises and platforms and achieve the Pareto improvement of the supply chain. Green subsidies for platforms increase the demand for green commodities in a channel, and thus, more consumers with green preferences choose platform channels. What is more, subsidies reduce commodity prices and improve consumers’ purchasing utility. The self-established logistics service of platform channels improves its logistics service level and consumer benefits and then improves its sales revenue, which is essentially a “free-rider” effect. The income from subsidies can make up for the impact of the platform rate increase on the self-operating income of e-commerce enterprises, so that the total income of e-commerce enterprises will increase. The commodity income of platform channels increases significantly. This is crucial for the development of the green economy. On the one hand, green commodities have long faced challenges in the market due to their higher prices. Although some consumers prefer to purchase green products, the elevated costs often deter them from making a purchase. On the other hand, in order to qualify for government subsidies, self-operated channels generally utilize a platform’s logistics services to enhance their service quality and increase their revenue. Moreover, the increased volume of logistics orders handled by a platform leads to economies of scale in the green economy, which can reduce costs and improve service efficiency.

5.2. Coordinated Strategy with Green Government Subsidies (IGH Model)

On the basis of the coordination of government subsidies, supply chain sale channels are managed as a whole, and the maximum profit of a supply chain is obtained through centralized decision-making. This approach reduces repeat distribution and minimizes multi-level information transfer while enhancing resource utilization which supports the development of a green economy.

It can be concluded that the optimal subsidy value is $s^{IGH*}={\displaystyle \frac{\left(3\theta -10\right)\mu }{{\left(4-\theta \right)}^2\theta \delta }}$, the leverage utility of the subsidy can be realized when ${\displaystyle \frac{\partial {\prod }^{IGH*}}{\partial s^{IGH}}}=0$, and the overall benefit can be maximized with the minimum subsidy input. In addition, the government can improve business channel cooperation through subsidies. However, ultimately, platform information sharing and enterprise marketing strategies can achieve complementary effects to achieve a win–win situation.

Proposition 5.

Under the IGH model of governmental collaborative management, the total revenue of the supply chain can be improved.

Proof. 

By comparing the revenue of decentralized decision-making and governmental coordination benefits, we can obtain the following from Equations (8)–(10):

$${\prod }_e^{IG}+{\prod }_o^{IG}<{\prod }^{IGH}$$

□

Under this coordination mechanism, the objective is to maximize the overall revenue of the supply chain, without the influence of the double marginal effect. Meanwhile, with the aim of maximizing revenue, the incentive mechanism is adopted for the channel, which improves both service level and revenue.

5.3. Platform Franchise System in the Platform Channel (IB)

The platform franchise system is an important coordination method for profit maximization and supply chain redistribution. A franchise contract is divided into a fixed price and a unit price. The revenue function of e-commerce and platform is as follows:

$${\prod }_e^{IB}=\left(1-\lambda \right)p_e^{IB}\left(1-{\displaystyle \frac{p_e^{IB}-p_o^{IB}}{1-\theta }}\right)-M$$

(11)

$${\prod }_o^{IB}=\lambda p_e^{IB}\left(1-{\displaystyle \frac{p_e^{IB}-p_o^{IB}}{1-\theta }}\right)+p_o^{IB}\left({\displaystyle \frac{p_e^{IB}-p_o^{IB}}{1-\theta }}-{\displaystyle \frac{p_o^{IB}-\mu s^{IB}}{\theta }}\right)+M$$

(12)

To maximize channel revenue, the equilibrium solution is obtained as follows:

$$p_o^{IB}={\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}+{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2}}, p_e^{IB}={\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}$$

$s^{IB}={\displaystyle \frac{\mu }{\delta \theta }}\left({\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}+{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2}}\right)$; add in (11) and (12), and we obtain ${\prod }_e^{IB*}={\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(1-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)-M$

$$\begin{array}{l}{\prod }_o^{IB*}={\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(\lambda +{\displaystyle \frac{\left(1-\lambda \right){\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)\\ -{\left({\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\right)}^2\left({\displaystyle \frac{1}{\theta }}-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2}}\right)+M\end{array}.$$

(13)

By verifying whether the income under a specific decision can maximize the income of the centralized decision, we can compare the channel income with the entire revenue; thus,

${\prod }_e^{IB}+{\prod }_o^{IB}={\prod }^{IGH}$ is obtained.

Proposition 6.

The conditions for the coordination strategy of the franchise system are as follows:

$$\begin{array}{l}{\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(\lambda +{\displaystyle \frac{\left(1-\lambda \right){\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)\\ -{\left({\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\right)}^2\left({\displaystyle \frac{1}{\theta }}-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2}}\right)-{\displaystyle \frac{1}{4}}\left({\displaystyle \frac{2\delta \theta }{2\delta -r^2}}+1-\theta \right)+\delta {\left({\displaystyle \frac{r}{2\left(2\delta -r^2\right)}}\right)}^2\\ <M<{\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(1-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)-\left({\displaystyle \frac{{\left(1-\lambda \right)}^2\delta }{2\delta -r^2}}\right)\left({\displaystyle \frac{1}{2}}-{\displaystyle \frac{\delta }{2\delta -r^2}}+{\displaystyle \frac{r^2}{2\theta \left(2\delta -r^2\right)}}\right)\end{array}$$

Proof. 

In order for an e-commerce enterprise to join a platform channel, its profit needs to be ${\prod }_e^{IB}>{\prod }_e^{IG},{\prod }_o^{IB}>{\prod }_o^{IG}$, that is, ${\prod }_e^{IB}-{\prod }_e^{IG}>0,{\prod }_o^{IB}-{\prod }_o^{IG}>0$; when ${\prod }_e^{IB}-{\prod }_e^{IG}>0$, we obtain $M<{\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(1-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)-\left({\displaystyle \frac{{\left(1-\lambda \right)}^2\delta }{2\delta -r^2}}\right)\left({\displaystyle \frac{1}{2}}-{\displaystyle \frac{\delta }{2\delta -r^2}}+{\displaystyle \frac{r^2}{2\theta \left(2\delta -r^2\right)}}\right)$ and when ${\prod }_o^{IB}-{\prod }_o^{IG}>0$, we obtain

$$\begin{array}{l}M>{\displaystyle \frac{\left(1-{\lambda }^2\right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\left(\lambda +{\displaystyle \frac{\left(1-\lambda \right){\mu }^2}{\delta {\theta }^2\left(1-\theta \right)}}\right)\\ -{\left({\displaystyle \frac{\left(1+\lambda \right)\left(\delta {\theta }^2\left(\theta -1\right)-\mu \right)}{2\delta {\theta }^2\left(\theta +\lambda \right)}}\right)}^2\left({\displaystyle \frac{1}{\theta }}-{\displaystyle \frac{{\mu }^2}{\delta {\theta }^2}}\right)-{\displaystyle \frac{1}{4}}\left({\displaystyle \frac{2\delta \theta }{2\delta -r^2}}+1-\theta \right)+\delta {\left({\displaystyle \frac{r}{2\left(2\delta -r^2\right)}}\right)}^2\end{array}$$

□

It can be concluded that when the franchise fee is within the optimal range, the income of e-commerce enterprises and platforms can achieve the Pareto improvement, and the rational profit distribution within the franchise contract system can achieve a mutually beneficial win–win situation. Furthermore, under these conditions, green subsidies optimize the whole supply chain and revenue. In addition, the supply chain can be coordinated which ultimately improves the development of the economy.

6. Numerical Analysis

In this section, numerical analysis is used to show the influence of each factor on the decision-making strategy. Through this analysis, the mean values of the parameters are set as $f=5$ and $L=1$.

6.1. Logistics Decision Analysis

Self-established logistics can enhance the level of logistics services, and as consumers’ logistics sensitivity increases, the advantages of self-established logistics become more apparent. The relationship between the logistics sensitivity of consumers in the research industry and service investment coefficient is as follows: when $\delta <2\mu$, the revenue of the self-established logistics increases and investment is reduced. However, despite the reduction in investment, the revenue remains lower than when third-party logistics systems are used, as depicted in Figure 6. Compared with third-party logistics services, when $\delta =12$, the two logistics distribution modes have the same income. When $\delta >12$, the revenue of self-established logistics systems in e-commerce enterprises is higher than that of third-party logistics. This indicates that when the investment coefficient is greater than 12, a self-established logistics system is a better choice.

The relationship between logistics sensitivity and logistics service level is shown in Figure 7. When consumers are sensitive to logistics, $\mu <4$, the more sensitive consumers are, the higher their optimal service investment will be. When consumers are sensitive of logistics, $\mu >4$, the more sensitive consumers are, the lower their optimal service investment will be. In other words, when consumers’ sensitivity to logistics reaches a certain level, the effectiveness of service investment on logistics will increase. Less investment can be exchanged for more income, which produces a leverage effect. This analysis elucidates the phenomenon of why e-commerce supply chains are increasingly dependent on logistics. The managerial implication lies in determining whether to establish self-operated logistics, which should be strategically decided based on varying consumer sensitivity to logistics services.

6.2. Platform Decision Analysis

When platform channels are introduced, different commission rates will have an impact on the income of e-commerce enterprises. Figure 8 shows the income curve of e-commerce enterprises under different platform rates. With the increase in the platform rate, the revenue of e-commerce decreases. However, when a self-operated channel adopts the third-party logistics model, the revenue remains unaffected by changes in the platform commission rate. The equilibrium point occurs when the commission rate is higher than 0.728; at this point, the platform channel will be used while the self-operated channel is also used. This represents a critical decision-making point for enterprises, as the selection of distribution channels is fundamentally determined by whether the commission structure can satisfy their profit requirements.

With green governmental subsidies, the revenue of platform channels increases, while the revenue of self-operated channels also improves as a result of the “free-rider effect” through the platform’s unified distribution mode. As shown in Figure 9, the revenue from both channels demonstrates an increasing trend when platform commission rates are higher. Green subsidies effectively reduce product prices, encouraging consumers to choose platform channels for purchases, thereby significantly boosting the commodity income of platform channels. Regarding self-operated channels, when an enterprise uses a platform channel’s logistics services, this simultaneously enhances both service quality and consumer benefits, resulting in a Pareto improvement under this policy framework.

6.3. Analysis of Governmental Coordination Mode

In combination with green governmental subsidies, supply chains achieve coordinated optimization as unified entities. According to the analysis in Section 5.3, in situations of centralized decision-making, optimization is carried out with the goal of maximizing the revenue of supply chain channels. Under different platform commission rates, decision-making results are shown in Figure 10. The revenue in self-operated channels demonstrate a decreasing trend, whereas platform channels experience revenue growth as commission rates rise. However, under the centralized decision-making mechanism, the total revenue of the supply chain remains constant. In combination with green governmental subsidies, the contract coordination of franchise systems is designed to maximize overall utility.

In the platform franchise system, the franchise fee should be between 0.032 and 0.086; when the fee is lower than this, the rational distribution of profits among channels can be realized and the overall collaborative management of the supply chain is achieved. The method outlined in this paper utilizes a franchise contract strategy to maximize system profitability. Based on the values of the aforementioned parameters,

Table 2. Comparison of coordination results in the franchise contract.

Non-Contract	F-Contract $\mathit{M}=0.035$	F-Contract $\mathit{M}=0.052$	F-Contract $\mathit{M}=0.067$	F-Contract $\mathit{M}=0.082$
$p_o^{IG}=0.3552$	$p_o^{IB}=0.2811$	$p_o^{IB}=0.2811$	$p_o^{IB}=0.2811$	$p_o^{IB}=0.2811$
$p_e^{IG}=0.5120$	$p_e^{IB}=0.5120$	$p_e^{IB}=0.5120$	$p_e^{IB}=0.5120$	$p_e^{IB}=0.5120$
$s^{IG}=0.1561$	$s^{IB}=0.2166$	$s^{IB}=0.2166$	$s^{IB}=0.2166$	$s^{IB}=0.2166$
${\prod }_e^{IG}=0.0211$	${\prod }_e^{IB}=0.0848$	${\prod }_e^{IB}=0.0598$	${\prod }_e^{IB}=0.0473$	${\prod }_e^{IB}=0.0223$
${\prod }_o^{IG}=0.0398$	${\prod }_o^{IB}=0.0414$	${\prod }_o^{IB}=0.0664$	${\prod }_o^{IB}=0.0789$	${\prod }_o^{IB}=0.1039$
${\prod }_o^{IG}+{\prod }_o^{IG}=0.0609$	${\prod }^{IGH}=0.1263$	${\prod }^{IGH}=0.1263$	${\prod }^{IGH}=0.1263$	${\prod }^{IGH}=0.1263$

demonstrates the effectiveness of this contract.

Firstly, the revenue of both channels increases, achieving a win–win outcome. Additionally, the overall supply chain revenue increases from 0.0690 to 0.1263. Although the selling price of goods in the platform channel decreases, the selling price in self-operated channels remains unchanged. This suggests that consumer welfare is further enhanced through the platform channel. Consequently, members of the e-commerce supply chain can maximize overall benefits by adopting the franchise contract and increasing their individual profits while ensuring the stable development of the system.

7. Conclusions and Policy Suggestions

This paper constructs a dual-channel online sales system composed of e-commerce enterprises’ self-operated channels and green platform channels and investigates the impact of green governmental subsidies on e-commerce enterprises’ supply chain channel selection strategies. The results show the following: (1) The logistics selection depends on the relationship between self-established logistics system costs and consumers’ logistics sensitivities. The higher the logistics sensitivity of consumers, the more likely e-commerce enterprises are to choose self-established logistics systems. (2) For channel selection, consumers’ green logistics sensitivities and platform commission rates are two critical factors in decision-making. The equilibrium condition for these factors is obtained by using game theory. When consumers are sensitive to logistics, they are more inclined to adopt platform channels, and platform commission rates will increase with the increase in sensitivity. (3) A platform can improve its efficiency through information sharing, thereby achieving the goal of green and low-carbon distribution. Green preferences of consumers increase the sales volumes of platform channels and improve their channel competitiveness. (4) Green governmental subsidies can achieve the Pareto improvement in a supply chain. Centralized decision-making through the governmental coordination and contract coordination of platform franchise systems can improve the overall efficiency of a supply chain.

When making logistics decisions and channel decisions, e-commerce enterprises should thoroughly consider consumers’ sensitivity to green logistics. For consumers with high logistics sensitivities, they are more inclined to choose self-operated logistics distribution modes. Whether enterprise use self-operated logistics systems or self-operated platform logistics systems, their service quality and green low-carbon levels are higher than those of third-party logistics organizations. However, investment cost is an important factor that affects the selection of systems, and the optimal scheme is obtained through balancing the benefits of the logistics systems. When making decisions regarding channel selection, a platform’s information sharing becomes an important factor. In addition to a platform’s operational costs, the commission rate is also influenced by consumers’ green preferences. The channel selection strategy proposed in this paper can better adjust the supply and demand relationship of commodities and more accurately utilize market demand information. Through the establishment of a green governmental subsidy model, this study expands the research perspective on channel integration. The implementation of franchise contracts reveals three significant outcomes: the maximization of the supply chain’s overall profitability; the effective coordination of inter-channel competitive relationships; and the enhancement of resource utilization efficiency and overall supply chain benefits.

In the era of the rapid development of e-commerce, consumers’ demands for purchasing services have significantly increased. When making channel selection decisions, enterprises must fully consider consumer preferences and comprehensively evaluate factors such as logistics costs and e-commerce platform commissions. For e-commerce enterprises, the improvement of management suggestions lies in establishing self-operated logistics to enhance consumer satisfaction, utilizing shared platforms to improve delivery efficiency, achieving green logistics, and increasing corporate profits. Furthermore, centralized decision-making emerges as the optimal strategy for government administrators to effectively coordinate the entire supply chain and maximize revenue. Additionally, franchise contracts can foster collaboration between businesses and stakeholders by promoting Pareto improvement. Consequently, such strategic implementation ensures the practical realization and operational effectiveness of the proposed policies. This approach not only enhances consumer welfare but also promotes the efficient and green development of the entire supply chain. Platform channel construction under green subsidies can promote information sharing among supply chain members and achieve rapid responses. Adopting a platform franchise system to coordinate a supply chain can facilitate the rapid development of the green economy. There are some limitations to this study, as it focuses solely on a single online platform channel. Multi-platform channel competition and offline channels also affect the channel selection of e-commerce enterprises, and these aspects will be explored in future research.