Research on Green Supply Chain Decision-Making Considering Government Subsidies and Service Levels Under Different Dominant-Force Structures

Abstract

With the progress of green transformation, government subsidies have become an important incentive for enterprises to invest in green technologies. However, their effectiveness differs markedly under alternative decision-making structures. This study develops a two-tier green supply chain game model comprising manufacturers and e-commerce platform self-operators. Six game structures are examined, covering both scenarios without subsidies and those in which manufacturers receive subsidies. The analysis focuses on product greenness, service levels, retail prices, and the profits of supply chain members. The results show that government subsidies substantially enhance manufacturers’ green investments and motivate platform self-operators to provide higher levels of green services, thereby improving market performance and overall supply chain profitability. Among the different structures, centralized decision-making demonstrates the strongest coordination effect and maximizes the subsidy impact. In contrast, within decentralized structures, subsidies help alleviate double marginalization, but their effectiveness is constrained by the distribution of power. These findings highlight the heterogeneous impacts of subsidies on green supply chain performance, offering theoretical support for targeted government policy design and practical guidance for enterprises to optimize green collaborative strategies.

1. Introduction

Since the announcement of the “dual carbon” goals in 2020, achieving carbon peaking and carbon neutrality has become a core element of China’s national strategy, providing a pathway to high-quality and sustainable economic development [1]. Within this framework, green technological innovation is valued not only for its economic benefits—such as improving production efficiency and enhancing market competitiveness—but also for its social contributions, including energy conservation, emission reduction, and ecological protection. It is therefore regarded as a crucial means of reconciling the tension between economic growth and environmental sustainability [2,3]. With rising consumer environmental awareness, green products are increasingly favored in the market due to their low-carbon and eco-friendly characteristics. Consequently, green supply chain management has attracted extensive global attention [4]. For the green supply chain, it is not just an environmental requirement; it is also a necessary means of responding to market demand for green consumption and enhancing brand competitiveness. However, the high costs of green transformation have led some manufacturers to hesitate. To support the research, development, and diffusion of green products, governments around the world have implemented a variety of green policies, including financial subsidies. For example, as early as 2005, the United States enacted the National Energy Policy Act to promote green technology development, while China launched the “Energy-Saving Products for the People” initiative in 2009, providing financial support for high-efficiency, energy-saving products [5]. In recent years, both the U.S. and Chinese governments have continuously increased their investments in green subsidy programs. In addition, local governments in China have introduced supporting policies such as “green factory” certifications and performance-based rewards [6]. Enterprises have actively responded to national calls for the development of green supply chains. For instance, Dongguan City began pilot certifications for “green factories” in 2017. Certified enterprises can receive subsidies ranging from 500,000 to 2 million yuan, depending on their size and energy efficiency. Small- and medium-sized enterprises typically receive subsidies of around 60,000 to 70,000 yuan [7].

In addition, the rapid development of the digital economy has become an important force in promoting economic growth and optimizing industrial structure. Compared with traditional retail channels, e-commerce platforms have not only changed the commodity circulation model but also profoundly influenced the supply chain management mode by leveraging digital technologies such as big data, cloud computing, and intelligent logistics [8]. In the context of green consumption receiving increasing attention from policies and markets, e-commerce platforms have the advantage of significantly reducing consumer information asymmetry and improving the acceptance of green products through precision marketing, online consultation, personalized services, and logistics optimization [9]. This digital-driven feature has transformed the role of e-commerce platforms in the green supply chain from a simple sales channel to an important participant in influencing green innovation investment, service strategies, and subsidy transmission. According to the National “14th Five-Year Plan for Digital Economy Development” [10], the government encourages enterprises to use digital technology to promote green consumption upgrades and adopt subsidies and incentive mechanisms to guide green production and consumption. At the same time, in the process of green product sales, green products rely more on high-quality pre-sales and after-sales services to enhance consumer trust and purchase intentions than ordinary products [11]. Especially in the environment of manufacturers and e-commerce platform self-operators, government subsidies not only affect manufacturers’ green investments, but also indirectly affect consumers’ purchasing decisions through service strategies. Therefore, in the context of the digital economy, exploring the coordination mechanism of government subsidies, service levels, and greenness under different power structures is not only in line with national policy orientation but also has important theoretical and practical value.

Currently, many scholars have conducted research on e-commerce supply chains. Guan et al. [12] studied the game model of e-commerce closed-loop supply chains under subsidy policies, exploring the key factors for manufacturers to carry out green product innovation. Yao et al. [13] studied e-commerce supply chain recycling decisions and provided relevant management insights for e-commerce platform decisions. Liu and Luo [14] studied the impact of third-party logistics company power on e-commerce logistics outsourcing supply chain decisions and found that under different power structures, the profits of the rights holders are different. Green technology innovation and service quality are critical to the development of e-commerce supply chains, and numerous studies have examined the role of government subsidies in this context. Huang et al. [15] analyzed how upstream and downstream enterprises collaborate on green innovation and found that such cooperation enhances overall supply chain efficiency. Yang et al. [16] developed a two-tier supply chain model comprising manufacturers and e-commerce retailers, exploring the impact of increasing consumer green sensitivity on product greenness and introducing cost-sharing contracts for coordination. He et al. [17], using optimization theory, demonstrated that retailers should provide service levels aligned with consumer expectations to maximize profits. Wu et al. [18] investigated decision-making in retailer-led competitive networks and showed that when service spillover effects are strong, optimal service levels under integrated decisions are higher, benefiting retailers. Under government subsidies, Zou et al. [19] found that subsidies lower manufacturers’ green innovation costs, improve profits, and expand market share, enhancing supply chain performance. Similarly, Liang et al. [20] revealed that green innovation improves product greenness in both centralized and decentralized models, with subsidies further motivating manufacturers to increase greenness. These studies provide important theoretical foundations for managing green e-commerce supply chains.

Therefore, compared with the traditional supply chain consisting of manufacturers and offline retailers, this paper chooses manufacturers and e-commerce platform self-operators as research objects, mainly based on the following two reasons: On the one hand, e-commerce platform self-operators have become an important carrier of green product circulation with their extensive online channel coverage and digital operation capabilities. They have higher flexibility in pricing and service decisions and can accurately respond to consumers’ dual needs for green attributes and a high-quality service experience. On the other hand, e-commerce platform self-operators not only have a large amount of user data, but also significantly reduce consumers’ cognitive risks of green products and enhance their purchasing trust through differentiated services such as extended warranty, green certification, and logistics optimization. For example, JD.com launched a “green certification + extended warranty service” combination in the promotion of green home appliances, combined with official energy-saving labels and additional warranty policies, effectively enhancing consumers’ trust in the performance and durability of green products, driving a significant increase in green home appliance sales and increasing the penetration rate of green products in the terminal market [21]. This case shows that in the process of promoting green products, the service level of e-commerce platform self-operators not only affects consumers’ willingness to buy but also plays a key role in the transmission effect of subsidy policies. Therefore, studying green supply chain decisions under the manufacturer–e-commerce platform structure not only conforms to the industrial trend of “Internet + green consumption” but also helps to explore the mechanism of action of green technology innovation and service level strategies and provide theoretical support for the government to formulate precise green subsidy policies and enterprises to optimize collaborative decision-making.

The dominant-power structure is a key factor affecting competition and cooperation in the upstream and downstream of the supply chain. The equilibrium decision of enterprises usually changes significantly with the difference in the dominant-power structure [22]. So, what specific impact will the dominant-power structure have on the price decision-making, profit, etc., of supply chain members? In view of this, this paper aims to construct a green supply chain model that comprehensively considers government subsidies and service level decisions. Starting from the three dominant-power decision-making models of “manufacturer-led”, “e-commerce platform self-operated business-led”, and “centralized decision-making”, this paper analyzes the optimal decision-making behavior of enterprises, product greenness, service level, sales price, and profit of each supply chain member under six types of game structures in the two cases of no government subsidies and different government manufacturers, and explores the mechanism of government subsidies and different decision-making entities on the performance of green supply chain operations. Based on the above description, this paper focuses on the following three specific research questions:

(1)

How do different dominant-power structures affect manufacturers’ product greenness and e-commerce platform self-operators’ service level decisions under government subsidies and no subsidy conditions?

(2)

Under different dominant-power structures, can government subsidies always improve the overall performance of the supply chain?

(3)

Under what dominant-force structure and subsidy mechanism can the supply chain members and system performance be optimized and the green product promotion effect be best?

Focusing on the above three issues, this article will clarify the advantages and disadvantages of different combinations of structures and subsidy mechanisms through vertical comparisons with and without subsidies, horizontal comparisons between structures, and numerical simulations, and provide targeted management insights for policy-making.

2. Literature Review

2.1. Research on Supply Chains with Different Dominant-Force Structures

For channels with different dominant-power structures, Tu et al. [23] studied low-carbon supply chains and found that when manufacturers dominate, consumer demand for low-carbon products tends to rise. Huang et al. [24] studied the impact of manufacturers and retailers’ CSR commitment on green supply chain decisions and profits under different dominant-power structures and found that the optimal strategies were different under different dominant-power structures. Shu et al. [25] and Wang et al. [26] developed four Stackelberg game models and demonstrated that power structures significantly affected supply chain profitability, with manufacturer dominance often resulting in higher profits. Huang et al. [27] built three models under different power configurations in a low-carbon supply chain and showed that when manufacturers had a stronger low-carbon preference, overall supply chain profits improved. Liu and Feng [28] analyzed optimal green research and development strategies under various power structures and found that when manufacturers and retailers possessed equal power, market demand and product greenness reached their peak while retail prices were minimized. They also noted that supply chain performance could improve under retailer dominance. Xue and Xu [29] emphasized that retailer dominance enhanced the impact of consumers’ green preferences, thus promoting supply chain development. Zhang and Song [30] examined the optimal power configuration and showed that a Nash game between both parties could maximize total system profit and social welfare. With the rapid expansion of e-commerce platforms, research has increasingly focused on their operational mechanisms. Wan [31] explored pricing strategies between manufacturers and platforms. He found that when platforms controlled pricing, both manufacturers and the entire system could benefit. Xi and Zhang [32] developed a platform-based supply chain model incorporating pricing, service levels, and emission reduction and confirmed that platform decisions played a critical role in supply chain performance. It can be seen that different dominant-force structures in the supply chain will have a significant impact on its operational decisions and performance.

2.2. The Impact of Product Greenness on the Supply Chain

With the advent of the low-carbon era, consumers are becoming increasingly environmentally conscious and more inclined to purchase green products. This growing demand has incentivized manufacturers to pursue green technological innovations to enhance their products’ core competitiveness, primarily by improving product greenness. Zhao et al. [33] constructed a simulation model based on heterogeneous consumer demand and found that consumers’ environmental awareness could positively promote the demand for green products, increase corporate profits, and thus encourage companies to increase their investments in green technology research and development. Similarly, Cao and Mei [34] reported that as consumers became more sensitive to the green attributes of products, manufacturers were more likely to increase investments in green technologies. Zand and Yaghoubi [35] further demonstrated that enhancing product greenness benefited all supply chain members by improving their profitability. Wang et al. [36] highlighted that online channels exhibited a stronger demand for green products compared with traditional channels. Gupta and Mishra [37] analyzed the impact of power structures on optimal pricing and green level decisions within supply chains. Some scholars also study the cost sharing of retailers [38]. For example, Wang et al. [39] showed that when retailers shared in manufacturers’ green innovation costs, the overall supply chain performance improved. Lin et al. [40] and Yao et al. [41] investigated manufacturer-led green supply chains and found that retailer engagement in corporate social responsibility could stimulate manufacturers to improve product greenness. Considering both product greenness and service levels on e-commerce platforms, Wang et al. [42] found that consumer preferences for green products significantly influenced manufacturers’ greenness decisions. Similarly, Toktaş-Palut [43] found that the more sensitive consumers were to the green level of products, the more inclined manufacturers and remanufacturers were to increase green input, promote the growth of demand for green products, and improve the overall efficiency of the supply chain. In summary, consumer sensitivity to product greenness not only stimulates market demand but also enhances supply chain performance. These findings underscore the strategic importance of incorporating product greenness into supply chain decision-making and provide a strong theoretical foundation for further research in this field.

2.3. Impact of Service Level on Supply Chain

The service level provided by retailers plays a critical role in shaping consumers’ purchase intentions and thus has a significant impact on market demand. As a result, numerous scholars have incorporated service level considerations into analyzes of supply chain decision-making. Zhang et al. [44] investigated the influence of service levels in online channels on consumer return behavior and found that return behavior could, in turn, drive improvements in service quality, ultimately enhancing the overall profitability of the supply chain. Lu et al. [45] studied a pharmaceutical platform and revealed that maximizing supply chain profits required the simultaneous optimization of service levels and pricing strategies. He and Wang [46] examined the evolutionary strategies of interactions between traditional and green service supply chains under a vertical channel structure, concluding that stronger enterprise integration led to higher levels of green service provision. Yang et al. [47] analyzed how service levels affected supply chain channels under varying power structures. Wang et al. [48] studied the impact of two main factors, product quality and service level, on all participants in the e-commerce supply chain. Guan et al. [49] constructed a green dual-channel supply chain model and found that when green input costs were low, both product greenness and prices increased, incentivizing retailers to improve service levels. Chen and Wu [50] explored a dual-channel supply chain considering channel preferences and service levels and showed that enhanced service levels effectively boosted market demand across both online and offline channels. In addition, several scholars have specifically focused on after-sales services. For instance, Li et al. [51] found that improving after-sales service raised both product prices and service fees in dual-channel green supply chains, thereby increasing market demand. Goswami et al. [52] analyzed an after-sales service supply chain network and identified service level as a key factor in maximizing the profits of both manufacturers and retailers. In summary, whether through improved online service quality, vertical channel service integration, or enhanced after-sales support, higher service standards consistently contribute to increased supply chain profitability. These findings underscore that service level is a critical variable and a valuable direction for in-depth exploration in supply chain research.

2.4. The Impact of Government Subsidies on the Supply Chain

Existing research shows that government subsidies not only reduce the cost pressure of green production for enterprises, but also effectively accelerate the greening process of the entire industry. Madani et al. [53] found that compared with taxation policies, increasing the government subsidy rate was more conducive to improving the greenness of products, thereby promoting the green transformation of the industry. Zeng et al. [54] explored government subsidy strategies under different power structures and found that subsidies not only increased the green investment of manufacturers and retailers, but also significantly improved the overall efficiency and profitability of the supply chain. Yang et al., [55] based on a green supply chain model under subsidy scenarios, pointed out that increasing the subsidy level could directly increase the profits of manufacturers and encourage enterprises to accelerate green innovation. Wu et al. [56] found that direct subsidies to consumers could more effectively stimulate the market demand for green products and accelerate the expansion of green consumption in the industry. Yuan et al. [57] systematically analyzed various government subsidy strategies and found that subsidies not only improved the greenness of products but also expanded the profit space of enterprises. Yan et al. [58] examined green innovation subsidies and found that government support encouraged enterprises to accelerate green technology research and development, generating benefits for all supply chain members. Mondal et al. [59] emphasized the positive role of government subsidies in promoting green products and expanding market demand. Meng et al. [60] analyzed a two-level supply chain model and found that government subsidies to different entities could optimize the distribution of benefits and promote the diffusion of green supply chains. Zhang and Yousaf [61] further pointed out that a reasonable subsidy ratio could significantly improve supply chain performance and help the industry achieve green and sustainable development. In summary, government subsidies not only accelerate the greening process of individual enterprises by reducing the cost of green transformation, improving the competitiveness of green products, and expanding market demand, but also promote the green upgrade of the entire industry, becoming a key policy tool for achieving a low-carbon economy and sustainable development.

As can be seen from

Table 1. Comparison between this paper and the related literature.

References	Different Dominant-Force Structures	Product Greenness	Service Level	Government Subsidies	Background
Liu and Fang [28]	√				Green supply chain
Xue and Xu [29]	√	√		√	Two-stage supply chain
Xi and Zhang [32]	√		√		E-commerce supply chain
Gupta and Mishra [37]	√	√			Alternative product supply chain
Yang et al. [47]	√		√		Dual-channel supply chain
Guan et al. [49]		√	√		Green supply chain
Madani and Rasti-Barzoki [53]		√		√	Green supply chain
Zeng et al. [54]	√	√		√	Green supply chain
This paper	√	√	√	√	E-commerce green supply chain

√ equivalent to yes.

, a large number of studies have been conducted around key factors such as different dominant structures, product greenness, service levels, and government subsidies in the green supply chain, and have achieved rich theoretical results and practical inspiration. However, the existing literature still has the following shortcomings: First, most studies focus on the impact of a single factor on the green supply chain, such as only exploring product greenness or service levels, and lack a systematic analysis of the interaction between the two in the context of government subsidies. Second, most studies ignore the impact of e-commerce platform self-operators on green consumption decisions in the context of the digital economy, especially the role of service levels in the subsidy transmission mechanism. Third, existing studies lack a systematic comparison of the differences in subsidy incentive efficiency under different dominant structures and have not yet revealed the synergy and conflict between centralized and decentralized decision-making models in greenness, service levels, and profit distribution.

In light of this, this paper constructs a green supply chain model that integrates green technology innovation, service levels, and government subsidy mechanisms. Starting from three different dominant-power structures—manufacturer-led, e-commerce platform self-operator-led, and centralized—this paper examines the optimal decision-making behavior, product greenness, service levels, retail prices, and profit distribution of green supply chain members under six different game structures, with and without government subsidies. Furthermore, this paper applies game theory methods to solve the model and analyze optimal strategies. Furthermore, numerical simulations are used to examine the performance of green supply chains under the interaction between government subsidies and dominant-power structures.

Therefore, this paper has the following two innovations:

• Theoretical innovation: introducing the perspective of multiple dominant-force structures’ refining the new green supply chain operation model dominated by e-commerce platform self-operators, responding to the evolution trend of supply chain dominance in the background of the digital economy, revealing the role of the “service–subsidy” synergy mechanism in green consumption incentives, and enriching the research on the intersection of the green supply chain and digital operation.
• Methodological innovation: constructing a green supply chain game model that includes product greenness, service level and government subsidies, comparing the optimal strategies under three typical dominant-force structures (centralized decision-making, manufacturer-led, and e-commerce platform self-operator-led), revealing the changing laws of product greenness, service level and profit performance under the interaction of subsidy mechanism and dominant-force structure, and providing theoretical support and practical reference for government green policy optimization and corporate strategy formulation.

3. Problem Description and Model Assumptions

3.1. Problem Description

This paper investigates a two-stage supply chain composed of a single manufacturer and a single e-commerce platform self-operator. The manufacturer adopts green innovation technologies to produce environmentally friendly products, which are then sold to consumers by the e-commerce platform. The manufacturer’s investment in green technology innovation is directly reflected in the product’s level of greenness—the higher the investment, the greater the greenness. In this supply chain structure, the product greenness and wholesale price are determined by the manufacturer, whereas the retail price and service level are set by the e-commerce platform’s self-operator. Each supply chain member aims to maximize its profit. Based on the government subsidy manufacturers and power structure, manufacturers and e-commerce platform self-operators determine their respective decision-making variables through the Stackelberg game. We obtain six different scenarios and describe the decision-making order of each model, as shown in Figure 1, we discuss the optimal pricing strategy and the optimal product greenness and service level decisions in the six scenarios, and finally compare the profit changes of each member of the supply chain, so as to analyze the impact of the optimal decision by the proportion of government subsidies under different power structures and government subsidy strategies. The structure of the supply chain is depicted in Figure 2.

The relevant parameters and their definitions are provided in

Table 2. Parameters and descriptions.

Parameters	Descriptions
$w$	Wholesale price
$\theta$	Product greenness
$p$	Retail price
$s$	Service level
$c$	Green product costs
$\gamma$	Unit subsidy coefficient of product greenness
$a$	Market potential of green products
$b$	Consumer product price sensitivity coefficient
$\lambda$	Consumers’ green sensitivity coefficient
$\delta$	E-commerce platform self-operators’ sensitivity to services
$\beta$	Service level cost coefficient of e-commerce platform self-operator
$\epsilon$	Manufacturers’ marginal cost coefficient of green technology input
${\Pi }_m$	Manufacturer’s profit function
${\Pi }_e$	E-commerce platform self-operators’ profit function
${\Pi }_c$	Supply chain system profit

.

3.2. Basic Assumptions

Assumption 1.

In this study, due to consumers’ low-carbon preferences, product demand is influenced by green environmental protection, service level, and price. This hypothesis is highly consistent with the research conclusions of the existing literature. Cai and Luo [62] studied that under the impetus of “dual carbon” goals, enterprises’ green technology research and development can significantly improve the environmental friendliness of products, thereby stimulating consumers’ additional demand based on green preferences. Wang and Hu [63] found that the market demand function is affected by service level and green level. Based on the research of Jamali and Rasti-Barzoki [64], we can obtain the market demand for the product as follows:  $D=a-bp+\lambda \theta +\delta s$, where $a$ represents the market potential of green products;  $b$ represents the consumer’s sensitivity to retail prices;  $\lambda$ is the consumer’s green sensitivity coefficient, representing the impact of the product’s green level on consumer demand;  $\theta$ represents the product greenness;  $\delta$ is the e-commerce platform self-operator’s sensitivity coefficient to service;  $s$ represents the service level of the e-commerce platform self-operator; and $a>0$,  $b>0$,  $p>0$,  $\lambda >0$,  $\delta >0$,  $\theta >0$, and  $s>0$.

Assumption 2.

Green product manufacturers also have to pay extra costs when conducting green product research and development. We assume that manufacturers increase market demand through green product research and development. The cost of their research and development efforts is based on reference [65] and reference [66]. The relationship between the input cost of green technology innovation and the green degree satisfies a quadratic function. The quadratic relationship between the input cost of green technology innovation of manufacturers and the green degree of their products is  $C_{(\theta )}={\displaystyle \frac{1}{2}}\epsilon {\theta }^2$, where  $\epsilon >0$. And the larger the value of  $\epsilon$, the higher the cost of green technology innovation for improving the unit greenness.

Assumption 3.

When selling green products, e-commerce platform self-operators need to provide consumers with better service quality, so the self-operators need to invest in certain service costs. Li et al. [67] and Tian et al. [68] prove that the service level cost that e-commerce platform self-operators need to pay is  $C_{(s)}={\displaystyle \frac{1}{2}}\beta s^2$, where  $\beta$ represents the service cost coefficient of the e-commerce platform self-operator,  $s$ represents the service level of the e-commerce platform self-operator, and  $\beta >0$.

Assumption 4.

In the green supply chain, manufacturers often face the dilemma of a shortage of funds for technology research and development, which weakens their enthusiasm for green research and development and production. In order to achieve the strategic goal of “dual carbon”, the government subsidizes enterprises based on their research and development investment to encourage them to engage in green technology innovation. Referring to [69,70] this paper represents government subsidies as  $\theta \lambda$, and  $\lambda (\lambda >0)$ represents the unit subsidy coefficient given by the government to the greenness of the product.

Assumption 5.

Manufacturers and e-commerce platform self-operators in the supply chain have information symmetry, and each member is rational and risk-neutral. This assumption is common in supply chain game research and helps focus on the impact of subsidies and dominant-power structures on green investment and service decisions and simplifies the complexity of the model [71].

Assumption 6.

The market supply and demand are balanced, there is no inventory, and there will be no shortage. This assumption can highlight the direct impact of price, greenness, and service level on demand, avoid the additional decision-making dimensions brought by inventory management, and maintain the pertinence of model analysis [72].

4. Model Construction and Solution

This section mainly describes the changes in the decision variables and profit functions of each supply chain member with or without government subsidies and compares and analyzes different models to obtain relevant conclusions. The following assumptions are maintained throughout the analysis to ensure model tractability and realism: (A1) $2b\epsilon -{\lambda }^2>0$, (A2) $\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon <0$, (A3) $2bp-{\delta }^2>0$, (A4) $b^2{\beta }^2(2b\epsilon -\lambda )(2b\epsilon +\lambda )>0$, (A5) $b^2{\beta }^2(2b\beta -\delta )(2b\beta +\delta )>0$, (A6) $\beta b^2{\gamma }^2+2b\beta \gamma \lambda <1$, (A7) $a-bc>0$.

4.1. Supply Chain Decision Model Without Government Subsidy

4.1.1. Centralized Decision-Making Model (AC Model)

In the AC model, the overall profit function of the supply chain can be expressed as

$${\Pi }_{c(AC)}=(p-c)(a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2-{\displaystyle \frac{1}{2}}\beta s^2.$$

(1)

In the AC model, the optimal decision variables and supply chain system profits are

$$p_{AC}^{*}={\displaystyle \frac{c\epsilon {\delta }^2+c\beta {\lambda }^2-a\beta \epsilon -bc\beta \epsilon }{\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon }},$$

(2)

$${\theta }_{AC}^{*}=-{\displaystyle \frac{\beta \lambda \left(a-bc\right)}{\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon }},$$

(3)

$$s_{AC}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon }},$$

(4)

$${\Pi }_{c(AC)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{2\left(\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon \right)}}.$$

(5)

Proof. 

See Appendix A. □

4.1.2. Manufacturer-Led Decision-Making Model (AM Model)

In the AM model, the profit functions of manufacturers, e-commerce platform self-operators, and the supply chain system are as follows:

$${\Pi }_{m(AM)}=(w-c)(a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2,$$

(6)

$${\Pi }_{e(AM)}=(p-w)(a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\beta s^2,$$

(7)

$${\Pi }_{c(AM)}={\Pi }_{m(AM)}+{\Pi }_{e(AM)}.$$

(8)

Under the AM model, the decision variables and the profits of each entity are

$$w_{AM}^{*}={\displaystyle \frac{-2c\beta \epsilon b^2+c\epsilon b{\delta }^2+c\beta b{\lambda }^2-2a\beta \epsilon b+a\epsilon {\delta }^2}{b\left(2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon \right)}},$$

(9)

$${\theta }_{AM}^{*}=-{\displaystyle \frac{\beta \lambda \left(a-bc\right)}{2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon }},$$

(10)

$$p_{AM}^{*}={\displaystyle \frac{-c\beta \epsilon b^2+c\epsilon b{\delta }^2+c\beta b{\lambda }^2-3a\beta \epsilon b+a\epsilon {\delta }^2}{b\left(2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon \right)}},$$

(11)

$$s_{AM}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon }},$$

(12)

$${\Pi }_{m(AM)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{2\left(2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon \right)}},$$

(13)

$${\Pi }_{e(AM)}^{*}={\displaystyle \frac{\beta {\epsilon }^2\left(2b\beta -{\delta }^2\right){\left(a-bc\right)}^2}{2{\left(2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon \right)}^2}},$$

(14)

$${\Pi }_{c(AM)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2\left(3\epsilon {\delta }^2+\beta {\lambda }^2-6b\beta \epsilon \right)}{2{\left(2\epsilon {\delta }^2+\beta {\lambda }^2-4b\beta \epsilon \right)}^2}}.$$

(15)

Proof. 

See Appendix B. □

4.1.3. E-Commerce Platform Self-Operator-Led Decision-Making Model (AE Model)

In the AE model, the profit functions of the manufacturer, e-commerce platform self-operator, and the supply chain system are

$${\Pi }_{m(AE)}=(w-c)[a-b(w+x)+\lambda \theta +\delta s]-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2,$$

(16)

$${\Pi }_{e(AE)}=x[a-b(w+x)+\lambda \theta +\delta s]-{\displaystyle \frac{1}{2}}\beta s^2,$$

(17)

$${\Pi }_{c(AE)}={\Pi }_{m(AE)}+{\Pi }_{e(AE)}.$$

(18)

Under the AE model, the decision variables and the profits of each entity are

$$w_{AE}^{*}={\displaystyle \frac{c\epsilon {\delta }^2+2c\beta {\lambda }^2-a\beta \epsilon -3bc\beta \epsilon }{\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon }},$$

(19)

$${\theta }_{AE}^{*}=-{\displaystyle \frac{\beta \lambda \left(a-bc\right)}{\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon }},$$

(20)

$$p_{AE}^{*}={\displaystyle \frac{-c\beta \epsilon b^2+c\epsilon b{\delta }^2+c\beta b{\lambda }^2-3a\beta \epsilon b+a\beta {\lambda }^2}{b\left(\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon \right)}},$$

(21)

$$s_{AE}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon }},$$

(22)

$${\Pi }_{m(AE)}^{*}={\displaystyle \frac{{\beta }^2\epsilon \left(2b\epsilon -{\lambda }^2\right){\left(a-bc\right)}^2}{2{\left(\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon \right)}^2}},$$

(23)

$${\Pi }_{e(AE)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{2\left(\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon \right)}},$$

(24)

$${\Pi }_{c(AE)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2\left(\epsilon {\delta }^2+3\beta {\lambda }^2-6b\beta \epsilon \right)}{2{\left(\epsilon {\delta }^2+2\beta {\lambda }^2-4b\beta \epsilon \right)}^2}}.$$

(25)

Proof. 

See Appendix C. □

4.2. Supply Chain Decision Model When Government Subsidizes Manufacturers

Since the solution process is similar to the solution process of the supply chain decision without government subsidies in Section 4.1, the solution process in this section is omitted.

4.2.1. Centralized Decision-Making Model (BC Model)

Under the BC model, the profit of the total supply chain is

$${\Pi }_{BC}=[p-(c-\theta \gamma )](a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2-{\displaystyle \frac{1}{2}}\beta s^2.$$

(26)

Under the BC model, the decision variables and the profits of each entity are

$$p_{BC}^{*}={\displaystyle \frac{c\beta {\lambda }^2-a\beta \epsilon +c{\delta }^2\epsilon -bc\beta \epsilon +a\beta \gamma \lambda +ab\beta {\gamma }^2+bc\beta \gamma \lambda }{\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+\epsilon {\delta }^2}},$$

(27)

$${\theta }_{BC}^{*}=-{\displaystyle \frac{\beta \left(\lambda +b\gamma \right)\left(a-bc\right)}{\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+\epsilon {\delta }^2}},$$

(28)

$$s_{BC}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+\epsilon {\delta }^2}},$$

(29)

$${\Pi }_{BC}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+2\epsilon {\delta }^2}}.$$

(30)

4.2.2. Manufacturer-Led Decision-Making Model (BM Model)

In the BM model, the profit functions of manufacturers, e-commerce platform self-operators, and the supply chain systems are

$${\Pi }_{m(BM)}=[w-(c-\theta \gamma )](a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2,$$

(31)

$${\Pi }_{e(BM)}=(p-w)(a-bp+\lambda \theta +\delta s)-{\displaystyle \frac{1}{2}}\beta s^2,$$

(32)

$${\Pi }_{c(BM)}={\Pi }_{m(BM)}+{\Pi }_{e(BM)}.$$

(33)

Under the BM model, the decision variables and the profits of each entity are

$$w_{BM}^{*}={\displaystyle \frac{a\beta b^2{\gamma }^2+c\beta b^2\gamma \lambda -2c\beta \epsilon b^2+c\epsilon b{\delta }^2+a\beta b\gamma \lambda +c\beta b{\lambda }^2-2a\beta \epsilon b+a\epsilon {\delta }^2}{b\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2b\epsilon {\delta }^2}},$$

(34)

$${\theta }^{*}=-{\displaystyle \frac{\beta \left(\lambda +b\gamma \right)\left(a-bc\right)}{\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2\epsilon {\delta }^2}},$$

(35)

$$p_{BM}^{*}={\displaystyle \frac{a\beta b^2{\gamma }^2+c\beta b^2\gamma \lambda -c\beta \epsilon b^2+c\epsilon b{\delta }^2+a\beta b\gamma \lambda +c\beta b{\lambda }^2-3a\beta \epsilon b+a\epsilon {\delta }^2}{b\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2b\epsilon {\delta }^2}},$$

(36)

$$s_{BM}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2\epsilon {\delta }^2}},$$

(37)

$${\Pi }_{m(BM)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{2\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2\epsilon {\delta }^2}},$$

(38)

$${\Pi }_{e(BM)}^{*}={\displaystyle \frac{\beta {\epsilon }^2\left(2b\beta -{\delta }^2\right){\left(a-bc\right)}^2}{2{\left\{\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2\epsilon {\delta }^2\right\}}^2}},$$

(39)

$${\Pi }_{c(BM)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2\left(\beta b^2{\gamma }^2+2\beta b\gamma \lambda -6\beta \epsilon b+3\epsilon {\delta }^2+\beta {\lambda }^2\right)}{2{\left\{\beta [{(b\gamma +\lambda )}^2-4b\epsilon ]+2\epsilon {\delta }^2\right\}}^2}}.$$

(40)

4.2.3. E-Commerce Platform Self-Operator-Led Decision-Making Model (BE Model)

In the BE model, the profit functions of manufacturers, e-commerce platform self-operators, and the supply chain systems are

$${\Pi }_{m(BE)}=[w-(c-\theta \gamma )][a-b(w+x)+\lambda \theta +\delta s]-{\displaystyle \frac{1}{2}}\epsilon {\theta }^2,$$

(41)

$${\Pi }_{e(BE)}=x[a-b(w+x)+\lambda \theta +\delta s]-{\displaystyle \frac{1}{2}}\beta s^2,$$

(42)

$${\Pi }_{c(BE)}={\Pi }_{m(BE)}+{\Pi }_{e(BE)}.$$

(43)

Under the BE model, the decision variables and the profits of each subject are

$$w_{BE}^{*}={\displaystyle \frac{c\beta b^2{\gamma }^2+a\beta b{\gamma }^2+3c\beta b\gamma \lambda -3c\beta \epsilon b+c\epsilon {\delta }^2+a\beta \gamma \lambda +2c\beta {\lambda }^2-a\beta \epsilon }{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2}},$$

(44)

$${\theta }_{BE}^{*}=-{\displaystyle \frac{\beta \left(\lambda +b\gamma \right)\left(a-bc\right)}{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2}},$$

(45)

$$p_{BE}^{*}={\displaystyle \frac{2a\beta b^2{\gamma }^2+c\beta b^2\gamma \lambda -c\beta \epsilon b^2+c\epsilon b{\delta }^2+3a\beta b\gamma \lambda +c\beta b{\lambda }^2-3a\beta \epsilon b+a\beta {\lambda }^2}{b\left\{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2\right\}}},$$

(46)

$$s_{BE}^{*}=-{\displaystyle \frac{\delta \epsilon \left(a-bc\right)}{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2}},$$

(47)

$${\Pi }_{m(BE)}^{*}=-{\displaystyle \frac{{\beta }^2\epsilon {\left(a-bc\right)}^2\left(b^2{\gamma }^2+2b\gamma \lambda -2\epsilon b+{\lambda }^2\right)}{2{\left\{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2\right\}}^2}},$$

(48)

$${\Pi }_{e(BE)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2}{4\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+2k{\delta }^2}},$$

(49)

$${\Pi }_{e(BE)}^{*}=-{\displaystyle \frac{\beta \epsilon {\left(a-bc\right)}^2\left(3\beta b^2{\gamma }^2+6\beta b\gamma \lambda -6\beta \epsilon b+\epsilon {\delta }^2+3\beta {\lambda }^2\right)}{2{\left\{2\beta [{(b\gamma +\lambda )}^2-2b\epsilon ]+k{\delta }^2\right\}}^2}}.$$

(50)

4.3. Analysis of Equilibrium Results

4.3.1. Comparative Analysis Between AC Model and BC Model

Proposition 1.

Under the condition (A6) and (A7), we have  $p_{AC}^{*}>p_{BC}^{*}$,  ${\theta }_{AC}^{*}<{\theta }_{BC}^{*}$,  $s_{AC}^{*}<s_{BC}^{*}$, and  ${\Pi }_{c(AC)}^{*}<{\Pi }_{c(BC)}^{*}$.

Proof. 

See Appendix D. □

Remark 1.

Proposition 1 demonstrates that under the centralized decision-making model, government subsidies significantly influence pricing and key decision variables. In the absence of subsidies, product prices remain high, while greenness, service levels, and supply chain profits are relatively low. However, with the introduction of government subsidies, manufacturers can reduce the marginal cost of green innovation, increase green investment, and enhance product greenness. E-commerce platform self-operators, leveraging the improved greenness of products, drive demand growth by further elevating service levels and enhancing consumer experience. This synergistic effect boosts the perceived value of products, expands market demand, and enables companies to achieve profit growth while maintaining or even reducing prices. Thus, government subsidies not only promote the adoption of green technologies but also foster synergy between economic benefits and environmental goals by alleviating cost pressures, encouraging service improvements, and optimizing supply chain resource allocation.

4.3.2. Comparison of AM, AE, BM, and BE Models

Proposition 2.

In the AM model and BM model, under the conditions (A6) and (A7), we have  $p_{AM}^{*}>p_{BM}^{*}$,  $w_{AM}^{*}>w_{BM}^{*}$,  ${\theta }_{AM}^{*}<{\theta }_{BM}^{*}$,  $s_{AM}^{*}<s_{BM}^{*}$,  ${\Pi }_{m(AM)}^{*}<{\Pi }_{m(BM)}^{*}$,  ${\Pi }_{e(AM)}^{*}<{\Pi }_{e(BM)}^{*}$, and  ${\Pi }_{c(AM)}^{*}<{\Pi }_{c(BM)}^{*}$.

Proof. 

See Appendix D. □

Remark 2.

Proposition 2 reveals that under a manufacturer-led decision-making model, government subsidies significantly impact prices, product greenness, and profits. In the absence of subsidies, manufacturers bear the full cost of green innovation, which diminishes their willingness to invest. As a result, product greenness and service levels remain low, leading to weak demand, elevated wholesale and retail prices, and reduced profits for all supply chain members. However, with the introduction of subsidies, the marginal cost of green investment for manufacturers decreases, incentivizing greater investments in green technologies. The improved product greenness enhances consumer acceptance, prompting e-commerce platform self-operators to further elevate service quality and enhance user experience to boost sales. Concurrently, both parties adjust their pricing strategies: manufacturers lower wholesale prices while increasing product greenness, and platforms reduce retail prices, jointly stimulating demand and expanding market size. This surge in demand offsets initial costs through economies of scale, significantly increasing profits for manufacturers, platforms, and the overall supply chain.

Proposition 3.

In the AE model and BE model,under the conditions (A6) and (A7), we have  $p_{AE}^{*}>p_{BE}^{*}$,  $w_{AE}^{*}>w_{BE}^{*}$,  ${\theta }_{AE}^{*}<{\theta }_{BE}^{*}$,  $s_{AE}^{*}<s_{BE}^{*}$,  ${\Pi }_{m(AE)}^{*}<{\Pi }_{m(BE)}^{*}$,  ${\Pi }_{e(AE)}^{*}<{\Pi }_{e(BE)}^{*}$, and  ${\Pi }_{c(AE)}^{*}<{\Pi }_{c(BE)}^{*}$.

Proof. 

See Appendix D. □

Remark 3.

Proposition 3 indicates that under the e-commerce platform self-operator-led decision-making model, government subsidies enhance green performance, service levels, and profit performance, but the impact mechanism differs from the manufacturer-led structure. In the absence of subsidies, e-commerce platform self-operators aim to stimulate demand by lowering prices and improving service levels. However, since manufacturers solely bear the costs of green research and development, their willingness to invest in green products is constrained, leading to low product greenness, limited overall demand potential, and reduced supply chain profits. With the introduction of government subsidies, manufacturers’ marginal costs of green investment decrease, increasing product greenness and enhancing product value. E-commerce platform self-operators further boost service investment while maintaining price competitiveness, creating a positive cycle of “green improvement–service optimization–demand expansion.” As e-commerce platform self-operators control terminal pricing and marketing strategies, they expand market share by lowering prices and improving service levels. The enhanced green attributes resulting from subsidies further amplify the effectiveness of this strategy, ultimately driving both sales and profits.

Propositions 1–3 show that the optimal solution for the supply chain system without government subsidies ($\lambda =0$) can be obtained by setting the subsidy coefficient to zero in the unified decision-making expression derived for the subsidy scenario. Specifically, treating the subsidy coefficient $\gamma$ as a continuous variable allows us to derive universal optimal pricing, greenness, and service level formulas applicable to any subsidy level (see Appendix E). When $\gamma =0$, this formula naturally degenerates to the conclusions of Propositions 1–3. This approach enables a unified modeling of both the subsidy and non-subsidy scenarios, facilitating the direct assessment of the marginal effects of changes in subsidy intensity on system decision-making and performance in subsequent vertical and horizontal comparisons. Furthermore, the unified expression in Appendix E shows that as the subsidy coefficient increases from 0, the trends in price, greenness, and service level across all scenarios are consistent with the comparative analysis of Propositions 1–3, further demonstrating the stability of the transmission path and the marginal effects of subsidies under different power structures.

4.3.3. Profit Comparison Between A Model and B Model

Proposition 4.

In the AC, AM, and AE models, when $\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon >0$,  $\beta b^2{\gamma }^2+2b\beta \gamma \lambda <1$,  ${\Pi }_{c(AC)}^{*}>{\Pi }_{c(AM)}^{*}$,  ${\Pi }_{c(AC)}^{*}>{\Pi }_{c(AE)}^{*}$,  ${\Pi }_{m(AM)}^{*}>{\Pi }_{m(AE)}^{*}$,  ${\Pi }_{m(AM)}^{*}>{\Pi }_{e(AM)}^{*}$,  ${\Pi }_{e(AE)}^{*}>{\Pi }_{e(AM)}^{*}$, and  ${\Pi }_{m(AE)}^{*}<{\Pi }_{e(AE)}^{*}$.

Proof. 

See Appendix D. □

Remark 4.

Proposition 4 shows that, in the absence of government subsidies, system profits under a centralized decision-making model are consistently higher than those under both the manufacturer-led and e-commerce platform self-operator-led decentralized decision-making models. This suggests that centralized decision-making can achieve full-chain coordination in pricing and output decisions, thereby reducing the double-markup effect and improving overall profit levels. However, the profit distribution patterns under different decentralized decision-making models vary significantly: In the manufacturer-led model, manufacturers control pricing and output, enabling them to capture more value by setting higher wholesale prices. Therefore, their profits are not only higher than those of e-commerce platform self-operators, but also higher than their own profits under the e-commerce platform self-operator-led model. In contrast, in the e-commerce platform self-operator-led model, platform self-operators achieve higher retail profits by controlling retail pricing and adjusting service levels. Their profits not only exceed those of manufacturers but also exceed their own profits under the manufacturer-led model.

Proposition 5.

In the BC, BM, and BE models, when $\epsilon {\delta }^2+\beta {\lambda }^2-2b\beta \epsilon >0$, $\beta b^2{\gamma }^2+2b\beta \gamma \lambda <1$, ${\Pi }_{c(BC)}^{*}>{\Pi }_{c(BM)}^{*}$, ${\Pi }_{c(BC)}^{*}>{\Pi }_{c(BE)}^{*}$, ${\Pi }_{m(BM)}^{*}>{\Pi }_{m(BE)}^{*}$, ${\Pi }_{m(BM)}^{*}>{\Pi }_{e(BM)}^{*}$, ${\Pi }_{e(BE)}^{*}>{\Pi }_{m(BE)}^{*}$, and ${\Pi }_{e(BE)}^{*}>{\Pi }_{e(BM)}^{*}$.

Proof. 

See Appendix D. □

Remark 5.

Proposition 5 shows that when the government subsidizes manufacturers, system profits remain highest under a centralized decision-making model. This suggests that even with the introduction of external financial support, subsidies can only be maximized as a whole when supply chain members achieve complete coordination in decision-making, thus avoiding price markups and resource waste within the channel. However, the distribution of subsidy benefits under a decentralized decision-making structure exhibits a clear “dominant party bias”: when manufacturers are dominant, they can directly absorb the subsidy dividend by leveraging upstream pricing power and increase their own profits by controlling wholesale prices and investing in green initiatives. Their gains exceed not only those of the e-commerce platform, but also those of the platform-dominated model. Conversely, under the e-commerce platform-dominated model, the platform leverages retail pricing and service strategies to indirectly convert subsidies into retail profit advantages, resulting in gains that exceed not only those of the manufacturer, but also those of the manufacturer-dominated model. This suggests that the ultimate beneficiaries of government subsidies are highly dependent on the power structure: while subsidies can improve overall performance, under decentralized decision-making, their distribution tends to strengthen the interests of the current dominant party rather than evenly improving the benefits among members.

4.3.4. The Effect on Decision Variables and Profits

Proposition 6.

Under the condition (A7), in the AC, AM, and AE models, ${\displaystyle \frac{\partial s_{AC}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{c(AC)}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial s_{AM}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{m(AM)}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{e(AM)}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{c(AM)}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial s_{AE}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{m(AE)}^{*}}{\partial \delta }}>0$, ${\displaystyle \frac{\partial {\Pi }_{e(AE)}^{*}}{\partial \delta }}>0$, and ${\displaystyle \frac{\partial {\Pi }_{c(AE)}^{*}}{\partial \delta }}>0$.

Proof. 

See Appendix F. □

Remark 6.

Proposition 6 reveals that, in the absence of government subsidies, the service level across all three dominant-power structure models is directly proportional to the service level sensitivity coefficient. Similarly, the profits of manufacturers, e-commerce platform self-operators, and the overall supply chain system also increase with the service level sensitivity coefficient. This highlights the service level sensitivity coefficient as a critical parameter influencing green supply chain performance. When consumers exhibit greater sensitivity to service levels, the marginal benefits of service improvements for platform self-operators rise, incentivizing them to invest more in service enhancements. This, in turn, boosts product appeal and expands market demand. The resulting demand growth drives increased sales for manufacturers, thereby elevating the profits of both manufacturers and platform self-operators and optimizing the overall performance of the supply chain.

Proposition 7.

Under the condition (A7), in the BC, BM and BE models,  ${\displaystyle \frac{\partial s_{BC}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{c(BC)}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial s_{BM}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{m(BM)}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{e(BM)}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{c(BM)}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial s_{BE}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{m(BE)}^{*}}{\partial \delta }}>0$,  ${\displaystyle \frac{\partial {\Pi }_{e(BE)}^{*}}{\partial \delta }}>0$, and  ${\displaystyle \frac{\partial {\Pi }_{c(BE)}^{*}}{\partial \delta }}>0$.

Proof. 

See Appendix F. □

Remark 7.

Proposition 7 demonstrates that the service level across the three dominant-power structure models continues to rise with the increase in the service level sensitivity coefficient. Concurrently, the manufacturer’s profit, the profit of the e-commerce platform self-operator, and the overall profit of the supply chain system also exhibit a positive correlation with this coefficient, further reinforcing the value transmission role of service levels in the green supply chain. Subsidies reduce the green technology input costs for manufacturers, enhancing the green attributes of products. The increase in the service level sensitivity coefficient incentivizes platform self-operators to provide higher-quality services to align with consumer preferences, thereby driving sustained growth in market demand.

5. Numerical Analysis

5.1. Research Objectives and Data Sources

In order to verify the validity and applicability of the above model and further explore the impact of different combinations of dominant-force structures and subsidy mechanisms on pricing strategies, greenness, service levels, and supply chain performance, this chapter conducts research based on numerical simulation verification. In order to verify the applicability of the model, this paper sets the model parameters based on the parameter range in previous studies [73,74]. We select $a=100$, $b=0.7$, $\lambda =0.1$, $\delta =0.2$, $\epsilon =0.6$, $\beta =0.5$, $\gamma =0.3$ and $c=10$; the government subsidies to manufacturers are based on the greenness of the products. Government subsidies to manufacturers are provided by adjusting the government subsidy coefficient. Under the premise that the above model is established, assume that $\gamma \in [0.1,0.7]$.

5.2. Numerical Simulation Design and Results Analysis

This section mainly conducts vertical comparison and analysis of the impact of government subsidies on various supply chain parameters and member profits and draws relevant conclusions.

5.2.1. Longitudinal Comparison

Through calculation, the impact of the change in the government subsidy ratio on the decision-making results is shown in

Table 3. AC, AM, and AE models without government subsidies.

Decision Model		$\mathit{p}$	$\mathit{w}$	$\mathit{\theta }$	$\mathit{s}$	${\mathbf{\Pi }}_{\mathit{m}}$	${\mathbf{\Pi }}_{\mathit{e}}$	${\mathbf{\Pi }}_{\mathit{c}}$
Centralized decision-making	AC model	81.36	-	11.89	28.54	-	-	3318.03
Decentralized decision-making	AM model	112.3	76.85	5.91	13.18	1648.48	829.47	2477.95
	AE model	113.02	44.62	5.77	13.85	828.77	1609.62	2438.39

,

Table 4. The impact of the government subsidy coefficient on decision variables.

$\mathit{\gamma }$	BC Model			BM Model				BE Model
	${\mathit{p}}_{\mathit{B}\mathit{C}}^{*}$	${\mathit{\theta }}_{\mathit{B}\mathit{C}}^{*}$	${\mathit{s}}_{\mathit{B}\mathit{C}}^{*}$	${\mathit{p}}_{\mathit{B}\mathit{M}}^{*}$	${\mathit{w}}_{\mathit{B}\mathit{C}}^{*}$	${\mathit{\theta }}_{\mathit{B}\mathit{M}}^{*}$	${\mathit{s}}_{\mathit{B}\mathit{M}}^{*}$	${\mathit{p}}_{\mathit{B}\mathit{M}}^{*}$	${\mathit{w}}_{\mathit{B}\mathit{C}}^{*}$	${\mathit{\theta }}_{\mathit{B}\mathit{M}}^{*}$	${\mathit{s}}_{\mathit{B}\mathit{M}}^{*}$
0.1	81.05	20.72	29.25	112.53	76.65	10.17	14.35	112.9	44.44	10.04	14.18
0.2	79.9	30.39	30.39	112.57	76.01	14.62	14.62	112.38	43.84	14.72	14.72
0.3	77.76	41.43	32.07	112.41	74.91	19.38	15	111.39	42.75	20.02	15.5
0.4	74.34	54.57	34.47	112.03	73.27	24.55	15.5	109.82	41.02	26.31	16.62
0.5	69.16	70.99	37.86	111.41	71.02	30.29	16.16	107.45	38.42	34.11	18.19
0.6	61.35	92.71	42.79	110.49	68.01	36.81	16.99	103.9	34.55	44.32	20.45
0.7	49.18	123.61	50.28	109.21	64.06	44.4	18.06	98.42	28.58	58.63	23.85

and

Table 5. The impact of the government subsidy coefficient on profits.

$\mathit{\gamma }$	BC Model	BM Model			BE Model
	${\mathbf{\Pi }}_{\mathit{c}(\mathit{B}\mathit{C})}^{*}$	${\mathbf{\Pi }}_{\mathit{m}(\mathit{B}\mathit{M})}^{*}$	${\mathbf{\Pi }}_{\mathit{e}(\mathit{B}\mathit{M})}^{*}$	${\mathbf{\Pi }}_{\mathit{c}(\mathit{B}\mathit{M})}^{*}$	${\mathbf{\Pi }}_{\mathit{m}(\mathit{B}\mathit{E})}^{*}$	${\mathbf{\Pi }}_{\mathit{e}(\mathit{B}\mathit{E})}^{*}$	${\mathbf{\Pi }}_{\mathit{c}(\mathit{B}\mathit{E})}^{*}$
0.1	3400.21	1668.51	849.76	2518.27	849.26	1648.27	2497.53
0.2	3533.09	1699.88	882.01	2581.9	882.39	1710.64	2593.03
0.3	3728.55	1743.87	928.25	2672.12	931.1	1802.13	2733.23
0.4	4006.64	1802.38	991.58	2793.96	1000.38	1931.73	2932.11
0.5	4401.53	1878.18	1076.74	2954.92	1098.7	2114.67	3213.37
0.6	4974.5	1975.26	1190.93	3166.19	1241.22	2377.84	3619.06
0.7	5845.24	2099.44	1345.38	3444.82	1457.46	2772.71	4230.17

. In addition, the profit and price units in Table 3, Table 4 and Table 5 and Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 are in RMB, while greenness and service level are expressed as dimensionless indices.

Combining the data in Table 3, Table 4 and Table 5, we can systematically analyze the synergistic impact of government subsidies and decision-making models on the green supply chain: centralized decision-making relies on the logic of “overall interests first” and aims to maximize system profits, avoiding the internal friction of “double marginalization” in decentralized decision-making, making the supply chain system profit, product greenness and service level better, and the retail price lower, thereby achieving optimal resource allocation; decentralized decision-making shows differentiated characteristics due to differences in the dominant parties. When manufacturers are dominant, they focus on raising wholesale prices and strengthening green investments to pursue their own profits. When e-commerce platform self-operators are dominant, they tend to increase sales prices and service levels to consolidate their competitive advantages, reflecting the shaping role of different interest orientations on supply chain decision-making. Government subsidies drive supply chain performance upgrades through subsidy channels: as the subsidy coefficient increases, the marginal cost of green inputs for manufacturers decreases, the greenness of products continues to improve, and platform self-operators simultaneously strengthen their service levels to match the value of products. The two work together to reduce wholesale prices and sales prices, forming an optimized pattern of increasing “greenness + service level” and decreasing “price”; the cost savings and demand expansion effects caused by subsidies are transmitted through the supply chain levels, driving the steady growth of member profits and overall system profits, verifying the key value of government subsidies in activating green innovation, promoting supply chain collaboration, balancing economic and environmental goals, and providing a multi-dimensional reference for green supply chain policy design and operational decisions.

5.2.2. Horizontal Comparison

As shown in Figure 3 and Figure 4, in both the absence and presence of subsidies, the profit of the supply chain system in the centralized decision-making model is significantly higher than that in the decentralized decision-making model and remains maximized when subsidies are present. This shows that centralized coordination effectively eliminates the “double-markup” effect by unifying pricing and output, thereby achieving optimal profits for the entire chain. In decentralized decision-making, the profit distribution pattern is determined by channel dominance: When manufacturers are the dominant players, they gain higher profits than e-commerce platform self-operators by virtue of their wholesale pricing advantages. When e-commerce platform self-operators are the dominant players, they gain higher profits than manufacturers by relying on their retail pricing and service level decision-making capabilities. Although government subsidies have increased the total profit level of the system under each structure, they have not changed the profit distribution order determined by the dominance structure. This means that when formulating subsidy policies, a coordination mechanism with differentiated channel dominance should be combined to avoid imbalances in the distribution of subsidy benefits.

5.2.3. Changes in Decision Variables and Profits Under the BC, BM, and BE Modes

As shown in Figure 5 and Figure 6, as the government subsidy coefficient increases, the retail price and wholesale price in the supply chain decision model exhibit a downward trend. This is primarily because government subsidies effectively reduce the cost of green technology innovation for manufacturers, enabling them to maintain or enhance product greenness while gaining greater flexibility in price adjustments. Consequently, e-commerce platform self-operators are incentivized to lower wholesale prices, thereby increasing their purchasing and sales efforts. Additionally, driven by their own benefits, these self-operators are further motivated to reduce retail prices to enhance the market competitiveness of green products and stimulate consumer purchasing intentions. This price reduction leads to increased market demand, ultimately driving overall profit growth in the supply chain.

As depicted in Figure 7 and Figure 8, under government subsidies, product greenness and service levels are higher in the centralized decision-making model compared with the decentralized models. This is attributed to the coordinated optimization in centralized decision-making, which aligns green investments and service strategies to maximize overall supply chain profit and resource efficiency. In decentralized models, the manufacturer-led structure often prioritizes its own profit through higher wholesale prices, which can deter e-commerce platform self-operators from investing in service improvements, leading to weaker coordination. Conversely, the platform-led model adopts a more market-oriented approach, fostering higher service levels and encouraging manufacturers to reduce wholesale prices and increase green investment. Consequently, greenness and service levels in the e-commerce platform self-operator-led model exceed those in the manufacturer-led structure. Furthermore, as government subsidies increase, manufacturers face lower green innovation costs, further motivating them to invest in product greenness. Simultaneously, the growing market value of greener products incentivizes platform self-operators to enhance service levels, resulting in significant improvements in both greenness and service quality across all decision-making structures.

Figure 9 and Figure 10 illustrate that in the manufacturer-led model, the manufacturer controls both product greenness and wholesale pricing. By enhancing product greenness to increase added value and setting higher wholesale prices, the manufacturer secures greater profits, while the e-commerce platform self-operator assumes a relatively passive role. Conversely, in the e-commerce platform self-operator-led model, the platform self-operator determines the retail price and service level, enabling it to better align with market demand. By adjusting prices and improving service quality, the platform self-operator stimulates consumer purchase intent, expands sales, and suppresses wholesale prices, thereby achieving higher profits than the manufacturer. Regardless of the power structure, an increase in the government subsidy coefficient reduces manufacturers’ green innovation costs, boosts their willingness to invest in greenness, and improves product greenness. This, in turn, enhances product competitiveness and consumer acceptance, drives sales growth, and significantly increases the profits of both manufacturers and platform self-operators.

Figure 11 demonstrates that supply chain profits under all three decision-making models increase as the government subsidy coefficient rises. Subsidies effectively alleviate manufacturers’ green innovation cost pressures, encouraging greater investments in sustainability, enhancing product greenness and competitiveness, and stimulating market demand, thereby boosting overall supply chain profitability. Among the models, the centralized decision-making structure consistently achieves the highest profits, as it facilitates coordinated optimization of greenness, service levels, and pricing strategies, resulting in the most efficient resource allocation. In decentralized models, profits under the manufacturer-led and e-commerce platform self-operator-led structures are initially comparable. However, as subsidies increase, the platform-led model gradually outperforms the manufacturer-led model by reducing wholesale prices, improving service quality, and more effectively stimulating consumer demand for green products. These findings highlight the pivotal role of government subsidies in enhancing green supply chain performance across different power structures.

In summary, regardless of whether subsidies are present or absent, centralized decision-making (AC\BC model) performs best in all three dimensions: system profit ${\Pi }_C$, product greenness $g$, and service level $s$. This demonstrates that centralized coordination can eliminate double marginalization and achieve endogenous resource integration. Furthermore, subsidies increase manufacturers’ willingness to invest in green products, but in a decentralized structure, the subsidy transmission mechanism is influenced by dominant power. In the BM model, manufacturers benefit significantly, but platform service improvements are limited. In the BE model, platforms rapidly amplify the subsidy effect through price cuts and improved services, thereby achieving greater market expansion despite high subsidies.

5.3. Case Analysis

To enhance the practical explanatory power of the numerical simulation conclusions, this paper uses the “Green Factory” subsidy policy of Dongguan City, Guangdong Province, as a case study for calibration. According to documents such as the “Implementation Plan for Accelerating the Construction of Green Factories (2017–2023)” implemented by Dongguan City since 2017, certified enterprises can receive subsidies ranging from 500,000 to 2 million yuan, and general enterprises can receive rewards of 60,000 to 70,000 yuan. Subsidies for provincial and national certifications can reach 2 million to 5 million yuan and 10 million yuan, respectively. Furthermore, Dongguan City’s 2024 industrial special policy stipulates that enterprises that receive a national green factory certification can receive a one-time reward of 300,000 yuan. (Note: data sourced from public media websites such as Southern Daily and the Ministry of Industry and Information Technology).

To map real-world policies to the model, this paper assumes enterprise sizes of small (100 million yuan in annual sales), medium (500 million yuan), and large (2 billion yuan). Based on this, the unit subsidy coefficient corresponding to different subsidy amounts is calculated: $\lambda$ = subsidy amount/annual sales. As shown in the

Table 6. Subsidy coefficients for different enterprise sizes.

Company Size	Annual Sales	Subsidy Category	Subsidy Amount	Unit Subsidy Coefficient
Small enterprise	10,000	General rewards	6.5	0.0006
Small enterprise	10,000	Municipal-level green factory	125	0.0125
Small enterprise	10,000	Provincial-level green factory	350	0.035
Small enterprise	10,000	National-level additional reward	30	0.003
Medium enterprise	50,000	General rewards	6.5	0.0001
Medium enterprise	50,000	Municipal-level green factory	125	0.0025
Medium enterprise	50,000	Provincial-level green factory	350	0.007
Medium enterprise	50,000	National-level additional reward	30	0.0006
Large enterprise	200,000	General rewards	6.5	0
Large enterprise	200,000	Municipal-level green factory	125	0.0006
Large enterprise	200,000	Provincial-level green factory	350	0.0018
Large enterprise	200,000	National-level additional reward	30	0.0001

(Note: both the annual sales and the subsidy amount are expressed in ten thousand yuan).

, the results show that for small enterprises under the “Municipal Green Factory” subsidy, $\lambda \approx 0.0125$, while for large enterprises, $\lambda \approx 0.0006$, indicating significant differences in the relative incentive effects of subsidy intensity on enterprises of different sizes. While keeping other parameters constant, only $\lambda$ is adjusted and equilibrium solutions are simulated. The results show that as $\lambda$ increases, both product greenness $g$ and service level $s$ significantly improve, wholesale and sales prices gradually decrease, and system profits show an initial increase followed by a slowdown. This is highly consistent with policy practice, indicating that government subsidies can significantly promote green investments and enhance market competitiveness among enterprises, but their marginal effects are subject to diminishing returns.

6. Conclusions

6.1. Theoretical Application Analysis

6.1.1. Double Marginalization Theory

The dual marginalization theory points out that in a decentralized decision-making supply chain, upstream and downstream enterprises will sequentially increase prices, resulting in lower total system profits than centralized decision-making and higher terminal prices. The results strongly validate the double marginalization theory: without government subsidies (Table 3), the system profit under centralized decision-making (AC model: 3318.03) is significantly higher than under decentralized decision-making (AM model: 2477.95 and AE model: 2438.39). Moreover, centralized decision-making achieves lower prices, higher greenness, and higher service levels, fully consistent with the classic conclusion. More importantly, this article finds that government subsidies are a mitigating mechanism. The increase in subsidy coefficient $\lambda$ reduces the marginal cost of green innovation for manufacturers, and these cost savings are transmitted in the supply chain, incentivizing e-commerce platform self-operators to increase service investment and allow price reductions, thereby alleviating efficiency losses caused by power decentralization. Especially in the BE model dominated by e-commerce platform self-operators, as it increases, the improvement in its system profit and greenness gradually narrows the gap with the centralized decision-making BC model. This indicates that government subsidies can partially alleviate the dual marginalization effect in green supply chains, expanding the application of this theory in environmental policy intervention contexts.

6.1.2. Subsidy Transmission Mechanism

The subsidy transmission mechanism posits that government incentives are transmitted through enterprise decisions to consumers, ultimately influencing market equilibrium. This model clearly illustrates this path: Government subsidies for green technology reduce manufacturers’ green costs, driving improvements in product greenness $g$. Simultaneously, they enable e-commerce platform self-operators to improve their service levels $s$. These dual improvements enhance consumer purchasing utility, thereby expanding demand, lowering prices, and increasing profits. Numerical analysis further indicates that subsidy transmission is not nonlinear but rather determined by the interaction between the main guiding structure and service effects. In the low subsidy range, the centralized (BC) model responds more significantly to subsidies due to the absence of internal friction; in the high subsidy range, the self-operated (BE) model structure of e-commerce platforms exhibits stronger responsiveness due to its direct control of market-oriented variables (price and service), and its improvement effect on system profits and greenness approaches the centralized decision-making model. This study not only verifies the effectiveness of the subsidy transmission mechanism but also reveals the key synergistic effect between subsidies and power structures, providing direct theoretical and practical value for the government to formulate subsidy policies that are hierarchical and differentiated for different market structures.

6.2. Research Conclusions

Guided by the “dual carbon” goals and the green strategy of the digital economy, government subsidies serve as a key policy tool for promoting green technology innovation and green product promotion. Their mechanism of action within different supply chain power structures requires further exploration. This paper, focusing on the green supply chain comprising manufacturers and e-commerce platform self-operators, constructs six Stackelberg game models encompassing product greenness, service levels, and subsidy mechanisms. This systematic analysis analyzes the optimal strategies and performances under different power structures and subsidy policies. The main conclusions are as follows:

(1)

In the absence of government subsidies, centralized decision-making (AC model) exhibits higher product greenness, service levels, and system profits than decentralized decision-making (AM and AE models). Under government subsidies, centralized decision-making (BC model) optimizes manufacturers’ green investments and e-commerce platform self-operators’ service levels, highlighting the importance of supply chain collaboration. In the BM model, manufacturers directly benefit from government subsidies, leading to a significant increase in green investment and a corresponding improvement in the service levels of e-commerce platform self-operators. In the BE model, e-commerce platform self-operators proactively improve service levels and lower prices to expand market demand, resulting in a greater improvement in service levels than the BM model. This suggests that government subsidies have the strongest incentive effect in centralized decision-making structures, while decentralized ones are influenced by dominant power, resulting in structural differences in the improvements in greenness and service levels.

(2)

Government subsidies improve the overall supply chain performance under all dominant-power structures, but the magnitude of these improvements varies. In the BC model, the subsidy effect is maximized, resulting in the most significant increase in system profits because it avoids double marginalization. The BM model exhibits limited profit growth because manufacturers’ technological innovation for green products increases their costs, resulting in profit losses even with subsidies. The BE model exhibits significant profit improvements, but these improvements are primarily dependent on demand expansion and result in higher service levels than the BM model. Therefore, subsidies are not equivalent under all structures; the effectiveness of policy incentives is influenced by pricing strategies, service levels, and the distribution of power.

(3)

When centralized decision-making is possible, the BC model achieves global optimization in greenness, service levels, and system profits at any subsidy level, representing the optimal solution for green supply chain systems. However, the “optimal green product promotion effect” is often achieved in the short term by the BE model when high subsidies are combined with supporting service incentives. Policymakers should strike a balance between these two approaches: If the government prioritizes long-term systemic benefits and sustained investment in green technologies, centralized decision-making or incentives that internalize externalities should be encouraged. If the goal is to expand the green consumer market in the short term, rapid promotion can be achieved through a combination of subsidies for manufacturers and service incentives for platform self-operators.

This paper improves upon previous research in several aspects:

(1)

Consistent with prior studies [28], the centralized decision-making model achieves the highest supply chain market demand and total system profit. However, unlike earlier works focusing mainly on profits, this study further investigates how government subsidy coefficients and service level sensitivity coefficients affect product greenness, service level, and supply chain coordination efficiency across different power structures, thereby enriching the understanding of green supply chain game dynamics.

(2)

While most existing research examines the impact of either product greenness or e-commerce platform service level individually [38], this paper integrates both as joint decision variables. Considering consumers’ dual sensitivity to green attributes and service quality, it systematically analyzes their interaction on optimal strategies and overall supply chain performance with and without subsidies. This linkage reveals the synergistic mechanism by which greenness enhancement and service optimization jointly stimulate demand and improve profits, expanding the scope of green supply chain decision research.

(3)

Previous studies often focus on the subsidy effects of a single supply chain member (e.g., manufacturer or retailer) [55] and lack thorough analysis of subsidy–power structure interactions. Using a Stackelberg game framework, this paper constructs six game models with and without subsidies to systematically compare subsidy impacts on product greenness, service level, pricing, and profits across power structures. It reveals the dynamics of subsidy incentives and performance improvement, highlighting that subsidies are most effective under centralized decision-making, while the platform-dominated structure shows strong green promotion potential at high subsidy levels. These findings provide new theoretical guidance for designing targeted government green incentive policies.

6.3. Management Implications

Based on the above research conclusions, we can obtain several management inspirations:

First, the government should differentiate its subsidy policies to improve incentive efficiency. Research results show that subsidies have the strongest incentive effect under centralized decision-making, while decentralized structures are susceptible to power struggles that weaken their effectiveness. When designing subsidy programs, the government should consider the supply chain structure and green performance targets, precisely determining the recipients of subsidies (e.g., manufacturers or platform self-operators) and the subsidy ratio. For manufacturer-led models, service level assessment criteria should be established to prevent companies from prioritizing green initiatives over service. For platform self-operators, subsidies can be linked to meeting green standards to prevent platforms from relying solely on service to compete for subsidies. Furthermore, a green performance evaluation system and a mechanism for monitoring the use of fiscal funds should be established to prevent excessive subsidies from wasting resources and distorting profits.

Secondly, businesses must proactively optimize subsidy transmission and coordination mechanisms. With subsidies alleviating cost pressures, manufacturers should increase investments in green technology innovation to enhance product greenness, thereby strengthening brand competitiveness and long-term profitability. E-commerce platform self-operators should prioritize consumer service, elevate service levels, and establish joint incentive mechanisms. For instance, they can collaborate with manufacturers on data-sharing joint promotions, create exclusive showcases for green products, and offer membership discounts to expand the market penetration of green products.

Finally, demand-side guidance remains key to the sustainable development of green supply chains. Governments and e-commerce platform self-operators should strengthen green consumer education, promote green certification and product information transparency, and leverage social media and digital marketing to raise consumer awareness of environmental issues. This will create a closed loop of “policy incentives, corporate action, and consumer recognition” to ensure that green supply chains achieve both economic and environmental wins.

6.4. Limitations and Future Research Directions

Although this paper systematically analyzes the operational mechanisms of the green supply chain from the perspectives of power structures and subsidy mechanisms, certain limitations remain. First, the model focuses on a single manufacturer and a single e-commerce platform self-operator, without considering multilateral interactions or more complex network structures. Future research could extend this framework to dynamic games involving multiple competing or cooperating enterprises. Second, this study assumes static and unchanging consumer preferences; subsequent work may incorporate models of evolving green consciousness to capture the dynamic adjustment of consumers’ behaviors over time. Finally, the current analysis does not consider government subsidies directed toward e-commerce platform self-operators or consumers. Future studies should investigate the impacts of subsidies targeted at these actors and compare decision-making processes and outcomes across the three types of government subsidies.