Evaluating Supply Chain Finance Instruments for SMEs: A Stackelberg Approach to Sustainable Supply Chains Under Government Support

Abstract

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers.

1. Introduction

The closure of many small and medium-sized enterprise (SME) manufacturing units and stagnation among distributors has been a common phenomenon observed worldwide as an aftermath of the COVID-19 pandemic. This has had a deeper impact on emerging economies like India because of working capital and vulnerable supply chains. In India, catering suppliers to Southern Railways suffered bankruptcies, while Ford Motor [1] revised its payment structure to support its suppliers. Fabindia’s [2] home-furnishing suppliers and handicraft manufacturers for brand like Dior and Gucci also faced severe liquidity issues from delayed payments and supply chain constraints. This highlights the fragility of supply chain financing systems for Indian SMEs.

According to the Indian MSME (Micro, Small and Medium Enterprises) Annual Report 2022–2023 [3], the MSME sector in India comprises 63.052 million micro enterprises, 0.331 million small enterprises, and 5000 medium enterprises registered with the Ministry. This sector significantly contributes approximately 30.1% to the nation’s GDP (Gross Domestic Product) through its participation in both domestic and international trade. Despite its substantial economic role, the MSME sector encounters critical challenges in managing working capital, primarily due to inadequate financing mechanisms and prolonged credit cycles. This underscores the necessity for MSMEs to adopt effective financial strategies that address both short-term liquidity needs and long-term financial sustainability.

The pandemic has also resulted in the fostering of pro-environmental behaviors and heightened awareness of sustainable consumption practices among individuals. This is substantiated through an investigation conducted by Leal Filho et al. [4] on the impact of the COVID-19 pandemic on sustainable consumption patterns through an international survey across 31 countries. The study also highlights the importance of consumer education and technological innovation in fostering sustainable consumption. Companies are now recognizing that adopting green practices can lead to improved brand image, cost optimization, and a competitive advantage in the market. For example, Apple has designed a closed-loop supply chain where products are recycled and disassembled and raw materials are reused to create new products. This approach reduces waste and contributes to cost saving [5]. The growing adoption of pro-environmental behaviors and increased awareness of sustainable consumption practices among individuals presents an opportunity for Indian SMEs to transition towards green supply chains (GSCs).

The shift towards GSC raises numerous challenges for manufacturers and distributors. Among these, financial constraints stand out as a significant barrier. High initial investments, rising operational costs, limited financing options, uncertain returns, and regulatory compliance collectively hinder the widespread adoption of green practices [6]. This was also evident during the COVID-19 pandemic, when capital shortages interrupted the ability of participants to sustain green practices [7]. To overcome these financial challenges and promote the shift to GSC, coordinated effort from policymakers, financial institutions, and industry stakeholders is essential.

Optimal financing strategies play a critical role in enabling the adoption of green initiatives with regard to capital-constrained supply chains. Such financing solutions can help manufacturers of the SME sector in implementing sustainable practices [8]. Moreover, sustainable marketing efforts by the distributor, such as promoting eco-friendly products by educating consumers on the environmental benefits of sustainable choices, may complement these initiatives. The feasibility of GSC can be enhanced further by supportive government policies including incentives, subsidies and regulatory frameworks that would encourage businesses to invest in green practices [9]. This integration can create a synergistic effect that can drive the entire supply chain towards sustainability. As a result, this would foster economic benefits by opening new market opportunities and driving innovation. The development of optimal financing strategies is thus essential for addressing dual objectives of sustainability and profitability [10].

To promote green initiatives within organizations, financing decisions such as trade credit and reverse factoring play a crucial role in alleviating capital constraints. Trade credit, traditionally extended by suppliers to allow deferred payment from buyers, offers short-term financial relief but often limits sustainable investments due to its cost implications and associated risks [11]. Similarly, reverse factoring is a buyer-initiated financing arrangement wherein a financial institution pays the supplier early on behalf of the buyer at a discounted rate, and the buyer repays the financier at a later date, thereby improving supplier liquidity and reducing financing costs [12]. The procedural framework and chronological progression of trade credit and reverse factoring used in our research are illustrated in Figure 1 and Figure 2.

As shown in the Stackelberg timeline for trade credit (Figure 1), the manufacturer sets the base price first, anticipating the distributor’s decisions on demand and sustainable marketing. Payment is deferred, creating an opportunity cost for the manufacturer, while the distributor uses the freed-up capital to fund operations and green marketing, aided by supportive government policies. Similarly, in reverse factoring (Figure 2), the manufacturer first sets the base price, anticipating the distributor’s response. The distributor then decides on order quantity and sustainable marketing. After the order is placed, a financial institution pays the manufacturer early at a discounted rate. The distributor later repays the full invoice amount with interest after the sales cycle. These financing mechanisms help mitigate short-term financial pressures as well as creating a conducive environment for long-term planning and investment in green initiatives. Furthermore, such strategies enhance the creditworthiness of smaller firms, such as SMEs, enabling them to participate more actively in sustainable supply chains. Therefore, leveraging these financing decisions can significantly contribute to the broader objective of sustainable development and corporate social responsibility [12].

Therefore, it is practical to address the financial decision-making challenges faced by these capital-constrained SMEs, while simultaneously investigating the implementation of sustainable initiatives under the influence of supportive government policies. So far, the existing research has focused separately on financing modes, carbon emission reduction, green marketing and green investments within supply chain entities. In contrast, our research focuses on constructing a Stackelberg game model to assess the financing decisions of both manufacturers and distributors within a decentralized decision-making framework, while also integrating the impact of favorable government policies on the level of sustainable marketing efforts. Research gaps are explained in the literature review section.

To address the current void, this study aims to answer the following questions:

• How do different financing strategies (reverse factoring vs. trade credit) affect the base and sales prices in GSCs?
• What impact do government policies have on the demand quantities of green products in decentralized decision-making supply chains?
• What is the relationship between sustainable marketing sensitivity and consumer demand for green products? How do sustainable marketing efforts by distributors under favorable government policy influence pricing strategies and profit distribution among supply chain participants?

To address these research inquiries, a Stackelberg game model is employed, which is particularly well-suited for scenarios involving hierarchical decision-making processes. Here, we have considered the manufacturer as the leader and the distributor as the follower. The leader’s strategy is first determined, with the anticipation of the follower’s optimal response, which subsequently influences the follower’s decisions. By solving this game using the backward induction method, the equilibrium that maximizes the profit for both manufacturer and distributor within the supply chain is identified [13]. Our study further examines critical decision variables, including the optimal base price, sale price, sustainable marketing efforts, and demand quantities, in scenarios where capital-constrained participants utilize trade credit and reverse factoring as financing methods. The Stackelberg game model, recommended for analyzing such dynamic and strategic interactions within supply chains, allows for a detailed examination of these variables and their interdependencies.

This research paper is structured as follows: Section 2 presents a review of the relevant literature. In Section 3, the models and equilibrium framework of the GSC without financial constraints are introduced. Section 4 compares the optimal solutions and the utility outcomes for both manufacturer and distributor across different models of trade credit and reverse factoring. Section 5 provides the numerical analysis conducted using Python, Section 6 concludes the study and Section 7 provides suggestions for future research.

2. Theoretical Background and Literature Review

2.1. Adoption of Green Supply Chain (GSC) Practices

Srivastava [14] highlighted that GSC incorporates environmentally sustainable practices, enhancing resource efficiency, reducing emissions, and ensuring regulatory compliance. Unlike traditional supply chains focused on economic efficiency, green models align with corporate social responsibility (CSR) objectives, as noted by Ahi and Searcy [15], improving brand reputation and stakeholder engagement. However, SMEs face challenges such as financial constraints, limited expertise [16], and regulatory compliance burdens [17].

2.2. Stackelberg Game Model on Supply Chain Finance (SCF) Instruments

To address capital constraints in GSC, SCF instruments like trade credit and reverse factoring have emerged as pivotal solutions. Chanda and Kumar [18] have highlighted the role of trade credit in optimizing ordering policies for short life-cycle products, considering dynamic adoption patterns. Further, Cosci et al. [19] noted that trade credit, enabling deferred payment for buyers, significantly improves liquidity. Moreover, Daripa and Nilsen [20] observed the dominance of net terms contracts in SMEs, often extended without interests or discounts. Wuttke et al. [12] described reverse factoring as a process where financial institutions pay suppliers upon invoice approval, reducing payment risks and improving supplier cash flow. Game Theory, particularly Stackelberg models, has proven effective in analyzing strategic interactions and financing decisions, emphasizing leader–follower dynamics in decentralized supply chains [21,22]. The comparison of pertinent research for our study is depicted in

Table 1. Comparison of pertinent research with our study.

Author	Capital Constraint		Sustainability		GP	SCF Program						Decision Variables
	S	B	GPM	GM		BF	TC	F	RF	E	H	PM	DQ
S. Zhu et al. [23]	×	✓	×	×	×	✓	×	×	×	×	×	✓	✓
Jena et al. [2]	✓	×	×	×	×	×	✓	×	✓	×	✓	✓	✓
Zhu and Ou [24]	✓	×	×	×	×	×	✓	✓	✓	×	×	×	✓
Emtehani et al. [25]	✓	✓	×	×	×	×	✓	×	×	×	✓	✓	✓
Ma et al. [26]	✓	×	✓	×	✓	✓	×	×	×	×	✓	✓	✓
Zong and Huang [27]	✓	×	✓	×	✓	✓	✓	×	×	×	×	✓	✓
Yang et al. [28]	✓	✓	×	×	×	✓	✓	×	×	×	×	✓	✓
Yang and Liu [29]	×	✓	×	×	×	✓	✓	×	×	×	×	✓	✓
Bi and Yang [6]	×	✓	×	×	×	✓	✓	✓	×	×	×	✓	✓
Yang et al. [30]	×	✓	×	×	×	✓	×	×	×	✓	×	×	✓
Silaghi and Moraux [31]	✓	✓	×	×	✓	×	✓	×	×	×	×	✓	✓
Hovelaque et al. [32]	×	✓	×	×	×	✓	✓	×	×	×	×	✓	✓
Zhang et al. [33]	✓	✓	✓	✓	×	✓	✓	×	×	×	×	✓	✓
Jin and Zhang [34]	✓	✓	×	×	✓	✓	✓	×	×	×	✓	✓	✓
Ding and Song [35]	×	✓	×	×	×	✓	✓	×	×	×	×	✓	✓
G Li et al. [36]	×	✓	×	×	×	✓	✓	×	×	×	✓	✓	✓
H. Li et al. [37]	×	✓	×	×	×	×	✓	×	×	×	×	✓	✓
Shen et al. [38]	✓	✓	×	×	×	✓	✓	×	×	×	✓	✓	✓
Jin and Wang [39]	✓	✓	×	×	×	×	✓	✓	×	×	✓	✓	✓
Peng and Pang [40]	✓	✓	×	×	×	✓	✓	×	×	×	✓	✓	✓
Hua et al. [41]	×	✓	×	×	×	✓	✓	×	×	×	×	✓	✓
Yang et al. [42]	×	✓	×	×	×	×	✓	×	×	✓	×	✓	✓
This Study	✓	✓	✓	✓	✓	×	✓	×	✓	×	×	✓	✓

S: Supplier; B: buyer; GPM: green product manufacturing; GM: green marketing; GP: government policy; TC: trade credit; F: factoring; RF: reverse factoring; BF: bank financing; E: equity; H: hybrid financing; PM: profit margin; DQ: demand quantity.

.

The literature on Stackelberg game models in exploring SCF instruments has evolved in various dimensions, investigating supply chain coordination in a Cournot competition framework [42], the role of option contracts in improving supply chain efficiency [41], and the mutual aid mechanisms resulting in Pareto improvement [40]. In recent years, an increasing number of studies have focused on hybrid approaches, including the investigation of hybrid partial factoring [39], mixed financing strategies that combine trade credit and bank financing [32,34,35,36,38], and hybrid reverse factoring [2].

Many more dimensions have been explored, such as the impact of ‘Trade Credit Insurance’ on default risk for the manufacturer [37], the impact of crowdfunding and bank loan on dynamic pricing and ordering of perishable goods [30], and the design of TC contracts [31]. Further, areas such as the impact of monitoring costs of financing strategies [6] and impact of green marketing [33] have been explored. Apart from these dimensions, the existing literature also focuses on time-sensitive stochastic demand [28], the role of government subsidies [27], cap and trade mechanisms [26], challenges of financial constraints in a hybrid contract [25], dynamics of reverse factoring [24] and the role of blockchain technology in mitigating demand volatility [23]. A detailed explanation is given in the existing literature corpus (Table S1, Supplementary Materials).

2.3. Government Policy and Green Marketing in Supply Chain Finance (SCF)

Government interventions and incentives play a pivotal role in promoting GSC practices. Y. Li et al. [9] highlighted the importance of regulatory standards and financial support, while Hsu et al. [43] underscored environmental regulations as catalysts for organizational innovation and greener practices. They further emphasized that subsidies and tax incentives alleviate financial constraints associated with sustainability initiatives. Q. Zhu et al. [44] observed that while government policies encourage SMEs to adopt sustainable practices, limited resources and expertise hinder compliance. Govindan et al. [45] integrated government subsidies into supply chain models to overcome financial barriers. Recent studies, such as Reza-Gharehbagh et al. [46], analyzed policy impacts on GSCs using Stackelberg models, highlighting the benefits of non-profit-driven policies. Other works explored green loans [47], subsidies for green innovation [48], optimizing green investments in capital-constrained supply chains [49] and the impact of green marketing [33].

2.4. Research Gap and Contributions

Based on the literature reviewed above, the following research gaps are identified.

• RF could provide manufacturers with immediate access to liquidity, enabling investments in green technologies or compliance with environmental regulations. However, the lack of research on integrating RF with sustainability objectives represents a critical gap.
• Prior studies have so far mostly examined TC with bank financing or equity financing in a two-echelon supply chain without any government intervention for sustainability-related initiatives. In this study, we have explored how trade credit and reverse factoring interact with government policies within a leader–follower dynamic to influence the performance of GSCs.
• The absence of research on zero-interest and no-discount trade credit represents a significant gap in the field of GSCF. Exploring this topic can contribute to the development of practical strategies and policy recommendations for fostering GSCs globally.
• Research on supportive government policies for sustainable marketing is an important gap in GSC financing. This would provide actionable insights into how governments can enable sustainable marketing to complement financial mechanisms, fostering a demand-driven transition to greener supply chains.

Our work is one of the first studies to compare trade credit and reverse factoring within a decentralized green supply chain using a Stackelberg game model, while integrating the role of government policy and sustainable marketing, a combination largely absent in the existing literature.

3. Core Definitions, Fundamental Assumptions and Base Model

This research focuses on a two-level GSC, where the manufacturer acts as the leader, and is responsible for developing sustainable products and processes. The distributor serves as the follower, taking charge of sustainable marketing efforts to promote these eco-friendly products to consumers. The government’s role is to support this ecosystem by implementing policies that encourage sustainable marketing. The distributor’s sustainable marketing initiatives are expected to significantly influence market demand.

3.1. Core Definitions

Definitions for notations and variables are provided in

Table 2. Symbol definitions.

Symbol	Description	Condition
$q_d$	Market demand, corresponding to the distributor’s demand quantity	$q_d>0$
$k$	Estimated size of the total market demand	$k\gg 0$
$a$	Price sensitivity coefficient	$a>0$
$c_g$	Manufacturer’s production cost per unit of green product	$c_g<p_b$
$p_b$	Base price of the product	$p_b>c_g$
$p_s$	Sale price of the product	$p_s>p_b$
$\theta$	Sensitivity of market demand to sustainable marketing efforts	$\theta >0$
$s_e$	Level of sustainable marketing efforts	$s_e>0$
$c$	Cost parameter associated with sustainable marketing efforts, considered as a constant	$c>0$
$f\left(s_e\right)$	The cost of sustainable marketing [33], since it reflects the increasing and convex nature of costs as marketing efforts intensify	$f\left(s_e\right)={\displaystyle \frac{1}{2}}c{s_e}^2$
$Z_j^i$	The profit outcomes of GSC member enterprises, where N represents no financing demand, T indicates trade credit financing, and F denotes reverse factoring financing; here, m and d denote manufacturer and distributor, respectively	($i=N,T,F)$ $(j=m,d)$
λ	An exogenous factor representing favorable government policies promoting sustainable marketing	$0<\mathsf{\lambda }<1$
$\beta$	Policy incentive index	$\beta =(1-\mathsf{\lambda })$
$h\left(\beta \right)$	Modified cost of sustainable marketing, influenced by government policy	$h\left(\beta \right)={\displaystyle \frac{1}{2}}\beta c{s_e}^2$
$r_t$	Interest rate representing the manufacturer’s opportunity cost in trade credit financing
$r_d$	The discount rate applied by the finance provider in the reverse factoring arrangement when the manufacturer opts for early payment
$r_f$	Interest rate on invoice loan repayment by distributor in reverse factoring financing

.

3.2. Fundamental Assumptions

The model adheres to the following assumptions:

• To ensure predictable and reliable profit outcomes, we assume all parameters are practical [50], with a positive cost coefficient ‘c’ ensuring concavity [51], and that supply chain members follow financing norms and honor contracts to maintain trust and network stability [52].
• Participants in the supply chains are assumed to operate in a perfectly competitive market [53] with equal access to information and homogeneous products, enabling efficient price discovery. They are expected to share information and mitigate risks to optimize profits [11,53], with a zero risk-free interest rate assumed for analytical simplicity. Also, the manufacturer’s production capacity is assumed to fully meet the distributor’s market demand.
• We assume that k > a$c_g$, which sets a condition for the market demand to be sufficiently high relative to the price sensitivity and production cost, ensuring the feasibility and profitability of producing and selling the green product.
• Assuming a conducive government policy supporting sustainable marketing efforts, businesses are incentivized to adopt and promote sustainable practices.
• It is assumed that the discount rate in reverse factoring is kept to a minimum. This approach ensures that the distributor maintains a strong credit rating, which benefits the manufacturer by enabling them to obtain cost-effective financing solutions.

3.3. Base Model

To begin, we develop a benchmark model for distributors operating without capital constraints. This initial model serves as a baseline for understanding how environmentally conscious consumer behavior influences market dynamics. Next, we introduce financing models for trade credit and reverse factoring, examining decentralized decision-making frameworks. These models account for the effects of trade credit and reverse factoring interest rates on equilibrium outcomes. Additionally, the analysis incorporates the impact of supportive government policies on sustainable marketing efforts.

If the distributor has sufficient financial resources, they can independently fund daily operations and sustainable marketing initiatives, thereby eliminating the need for external financing. At this stage, we outline the profit functions for the manufacturer and distributor under the assumption of no capital constraints. In this two-stage GSC system, the manufacturer sets the base price of the green product, while the distributor determines both the sale price of the green products ($p_s)$ and the level of sustainable marketing efforts ($s_e).$

$\lambda$ is an exogenous factor representing favourable government policies that promote sustainable marketing, with a value ranging between 0 and 1. A decrease in $\lambda$ or an increase in $\beta$ signifies enhanced government support. For instance, if $\lambda$ is 0.9, this implies that $\beta$ is 0.1, meaning the distributor’s cost of sustainable marketing is reduced by a factor of 0.1, reflecting the significant impact of governmental incentives. This cost reduction encourages distributors to increase their investment in sustainable marketing strategies, thereby enhancing their market attractiveness. At this stage, the profit functions for both the manufacturer and the distributor can be expressed as follows:

$$Z_m^N\left(p_b\right)=\left(p_b-c_g\right)q_d=\left(p_b-c_g\right)(k-a{p}_s+\theta s_e)$$

(1)

where $q_d=(k-a{p}_s+\theta s_e)$, drawn from previous research [7].

$$Z_d^N\left(p_s,s_e\right)=\left(p_s-p_b\right)\left(k-a{p}_s+\theta s_e\right)-{\displaystyle \frac{1}{2}}c{s_e}^2+{\displaystyle \frac{1}{2}}\beta c{s_e}^2$$

(2)

where $\beta =(1-\lambda )$.

Proposition 1.

In the context of the Stackelberg game model without financial constraints, and under the condition that $\lambda >{\displaystyle \frac{{\theta }^2}{ac}}$, the optimal equilibrium outcomes for the base price, sale price, sustainable marketing effort levels, and demand quantities are derived as follows:

$${p_b}^{N^{\mathrm{*}}}={\displaystyle \frac{k+a{c}_g}{2a}}; {p_s}^{N^{\mathrm{*}}}={\displaystyle \frac{2\lambda kac+(\lambda ac-{\theta }^2)(k+a{c}_g)}{2a(2\lambda ac-{\theta }^2)}}; {s_e}^{N^{\mathrm{*}}}={\displaystyle \frac{\theta (k-a{c}_g)}{2(2\lambda ac-{\theta }^2)}}; \hspace{1em}{q_d}^{N^{\mathrm{*}}}={\displaystyle \frac{\lambda ac(k-a{c}_g)}{2(2\lambda ac-{\theta }^2)}}$$

Proposition 2.

In the framework of the Stackelberg game model without financial constraints, and given the condition $\lambda >{\displaystyle \frac{{\theta }^2}{ac}}$, the optimal profit outcomes for both the manufacturer and the distributor are derived as follows:

$$Z_m^{N^{*}}={\displaystyle \frac{\lambda c{(k-a{c}_g)}^2}{4(2\lambda ac-{\theta }^2) }};{ Z}_d^{N^{*}}={\displaystyle \frac{\lambda c{(k-a{c}_g)}^2}{8(2\lambda ac-{\theta }^2) }}$$

4. Green SCF Strategies in Decentralized Systems

The distributor’s efforts to implement sustainable marketing strategies require increased financial resources. If the distributor’s initial capital is allocated exclusively to sustainable marketing activities, this may lead to difficulties in securing adequate funds for regular operational expenses. Therefore, financing becomes crucial. Internal financing within the supply chain, often referred to as trade credit financing, involves upstream manufacturers offering payment deferral options to distributors for goods supplied. Alternatively, external financing options, such as reverse factoring, can also be utilized.

4.1. Utilizing Trade Credit for GSC Financing

The procedural framework and timeline of trade credit, as utilized in our study, are presented in Figure 1. In the Stackelberg timeline diagram for trade credit, the manufacturer sets the base price $\left(p_b\right)$ first, anticipating the distributor’s optimal response. The distributor then determines the demand quantity $\left(q_d\right)$ and sustainable marketing efforts $\left(s_e\right)$. Payment to the manufacturer is deferred, resulting in an opportunity cost. During this period, the distributor utilizes the liquidity gained to support operations and invest in sustainable marketing, especially when supported by favorable government policies. In the Trade Credit financing technique, the manufacturer’s profit is calculated as the revenue from product sales minus the cost of capital loss due to delayed payment, minus production costs. Conversely, the distributor’s profit includes the income from product sales, minus the purchase cost of goods and sustainable marketing expenses. Here, $r_t$ represents the interest rate, reflecting the opportunity cost for the manufacturer. Additionally, the distributor benefits from government support for sustainable marketing, which helps reduce overall sustainable marketing expenses. The corresponding profits for the manufacturer and distributor can be expressed as follows:

$$Z_m^T\left(p_b\right)=\left(p_b-c_g\right)q_d-p_b{q}_d{r}_t=\left(\left(1-r_t\right)p_b-c_g\right)\left(k-a{p}_s+{\theta s}_e\right)$$

(3)

$$Z_d^T\left(p_s,s_e\right)=\left(p_s-p_b\right)q_d-{\displaystyle \frac{1}{2}}c{s_e}^2+{\displaystyle \frac{1}{2}}\beta c{s_e}^2$$

where $\beta =(1-\lambda )$.

$$Z_d^T\left(p_s,s_e\right)=\left(p_s-p_b\right)\left(k-a{p}_s+\theta s_e\right)-{\displaystyle \frac{1}{2}}\lambda c{s_e}^2$$

(4)

In accordance with the solution approach outlined in Section 3.3 and the Stackelberg game framework applied to the previously discussed two-stage GSC, the following propositions can be established.

Proposition 3.

Within the framework of the trade credit financing strategy, the optimal equilibrium values for the base price, sale price, sustainable marketing efforts, and demand quantity are derived as follows:

$${{ p}_b}^{T^{\mathrm{*}}}={\displaystyle \frac{k\left(1-r_t\right)+a{c}_g}{2a(1-r_t)}}; {p_s}^{T^{\mathrm{*}}}={\displaystyle \frac{k(1-r_t)(3\lambda ac-{\theta }^2)+{(\lambda ac-{\theta }^2)ac}_g}{2a(1-r_t)(2\lambda ac-{\theta }^2)}}$$

$${s_e}^{T^{\mathrm{*}}}={\displaystyle \frac{\theta \left(k\right(1-r_t)-a{c}_g)}{2(1-r_t)(2\lambda ac-{\theta }^2)}}; {q_d}^{T^{\mathrm{*}}}={\displaystyle \frac{\lambda ac\left(k\right(1-r_t)-a{c}_g)}{2(1-r_t)(2\lambda ac-{\theta }^2)}}$$

Proposition 4.

Under the framework of trade credit financing, the optimal profits for both manufacturer and the distributor are determined as follows:

$$Z_m^{T^{\mathrm{*}}}={\displaystyle \frac{\lambda c{\left(k\right(1-r_t)-a{c}_g)}^2}{4(1-r_t)(2\lambda ac-{\theta }^2)}}; Z_d^{T^{\mathrm{*}}}={\displaystyle \frac{\lambda c{\left(k\right(1-r_t)-a{c}_g)}^2}{8{(1-r_t)}^2(2\lambda ac-{\theta }^2)}}$$

Proposition 5.

By conducting a comparative analysis of the results derived from Propositions 1 and 3, the following conclusions can be drawn:

${p_b}^{T^{\mathrm{*}}}>{p_b}^{N^{\mathrm{*}}}$	This suggests that the base price is higher when trade credit is extended, likely due to the associated costs of providing credit, compared to situations where there are no capital constraints.
${p_s}^{T^{\mathrm{*}}}>{p_s}^{N^{\mathrm{*}}}$	Similarly, we found that the sale price is higher under trade credit conditions, as distributors may raise prices to offset the increased base prices and the costs associated with financing or delayed payments.
${q_d}^{T^{\mathrm{*}}}<{q_d}^{N^{\mathrm{*}}}$	We found that distributors tend to order smaller quantities under trade credit conditions, likely due to higher base prices limiting their purchasing capacity or increased caution in over-ordering when relying on credit.
${s_e}^{T^{\mathrm{*}}}<{s_e}^{N^{\mathrm{*}}}$	The sustainable marketing effort decreases under trade credit conditions, suggesting that the distributor has fewer resources or incentives to invest in sustainability initiatives when constrained by credit terms.

4.2. Utilizing Reverse Factoring for GSC Financing

Figure 2 illustrates the procedural framework and sequential development of reverse factoring as applied in our research. In the Stackelberg timeline diagram for reverse factoring, the process unfolds in a sequential leader–follower structure that mirrors real-world supply chain dynamics. Initially, the manufacturer, acting as the leader, sets the base price ($p_b)$ of the green product while anticipating the distributor’s optimal response. Subsequently, the distributor determines the order quantity ($q_d$) and the level of sustainable marketing efforts ($s_e$) based on the base price and expected market demand. Once the order is placed, a financial institution steps in to pay the manufacturer early on the distributor’s behalf, applying a discount rate ($r_d$) to the invoice value. After completing the sale cycle, the distributor repays the finance provider the full invoice amount ($p_b{q}_d$) along with an agreed interest rate ($r_f$). This approach enables the distributor to leverage the finance provider’s discount rate to manage cash flow more effectively, while aligning pricing and marketing strategies with the manufacturer’s pricing decisions to optimize overall profitability within the supply chain. Further, the financial institution is modelled as a passive facilitator offering fixed discount and interest rates.

While employing the reverse factoring external financing strategy, the manufacturer’s profit is derived from the revenue generated through wholesale transactions, after accounting for discounting costs. Conversely, the distributor’s profit is calculated by subtracting the costs associated with product procurement, invoice loan interest repayment ($r_f),$ and sustainable marketing expenses, which are mitigated by supportive government policies, from the revenue obtained through product sales. This financing strategy enables the distributor to effectively manage cash flow while absorbing the expenses related to green product promotion. The profit equations for both the manufacturer and the distributor can be expressed as follows:

$$Z_m^F\left(p_b\right)=\left(p_b-c_g\right)q_d- r_d{p}_b{q}_d$$

(5)

$${ Z}_d^F\left(p_s,s_e\right)=\left(p_s-p_b\right)q_d-r_f{p}_b{q}_d-{\displaystyle \frac{1}{2}}c{s_e}^2+{\displaystyle \frac{1}{2}}\beta c{s_e}^2$$

where $\beta =(1-\lambda )$.

$$Z_d^F\left(p_s,s_e\right)=\left(p_s-p_b\right)q_d-r_f{p}_b{q}_d-{\displaystyle \frac{1}{2}}\lambda c{s_e}^2$$

(6)

The reverse recursion method was also employed to solve Equations (5) and (6), leading to the derivation of the following proposition.

Proposition 6.

In the context of an external financing strategy (reverse factoring), the optimal equilibrium outcomes for the base price, sale price, sustainable marketing effort level, and demand quantity are derived as follows:

$${p_b}^{F^{*}}={\displaystyle \frac{k(1-r_d)+a{c}_g(1+r_f)}{2a(1+r_f)(1-r_d)}};{{ p}_s}^{F^{*}} ={\displaystyle \frac{k(1-r_d)(3\lambda ac-{\theta }^2)+a{(1+r_f)(\lambda ac-{\theta }^2)c}_g}{2a(1-r_d)(2\lambda ac-{\theta }^2)}};$$

$${s_e}^{F^{*}}={\displaystyle \frac{\theta \left(k\right(1-r_d)-a{c}_g(1+r_f\left)\right)}{2(1-r_d)(2\lambda ac-{\theta }^2)}}; {q_d}^{F^{*}}={\displaystyle \frac{\lambda ac\left(k\right(1-r_d)-a{c}_g(1+r_f\left)\right)}{2(1-r_d)(2\lambda ac-{\theta }^2)}}$$

Proposition 7.

Under the reverse factoring financing strategy, the respective optimal profits of the manufacturer and the distributor are determined as follows:

$$Z_m^{F^{*}}={\displaystyle \frac{\lambda c{\left(k\right(1-r_d)-a{c}_g(1+r_f\left)\right)}^2}{4(1+r_f)(1-r_d)(2\lambda ac-{\theta }^2) }};{ Z}_d^{F^{*}}={\displaystyle \frac{\lambda c{\left(k\right(1-r_d)-a{c}_g(1+r_f\left)\right)}^2}{8{(1-r_d)}^2(2\lambda ac-{\theta }^2) }}$$

Proposition 8.

Based on the findings from Proposition 6, the optimal decision to utilize external financing through reverse factoring reveals a relationship between the base price, sale price, intensity of sustainable marketing efforts, demand quantity, and the discount rate associated with external financing, as follows:

$${\displaystyle \frac{\partial {p_b}^{F^{*}}}{\partial r_d}}>0,\hspace{1em}{\displaystyle \frac{\partial {p_s}^{F^{*}}}{\partial r_d}}>0,\hspace{1em}{\displaystyle \frac{\partial {s_e}^{F^{*}}}{\partial r_d}}<0,\hspace{1em}{\displaystyle \frac{\partial {q_d}^{F^{*}}}{\partial r_d}}<0$$

The given partial derivatives represent how the optimal decisions for various variables respond to changes in the interest rate, $r_d.$ Specifically, they indicate the following:

${\displaystyle \frac{\partial {p_b}^{F^{*}}}{\partial r_d}}>0$	The base price increases with higher discount rates, indicating that manufacturers raise prices to compensate for the additional financing costs associated with increased borrowing rates.
${\displaystyle \frac{\partial {p_s}^{F^{*}}}{\partial r_d}}>0$	The sale price increases with rising discount rates, indicating that distributors pass higher financing costs on to consumers, resulting in higher prices throughout the supply chain.
${\displaystyle \frac{\partial {s_e}^{F^{*}}}{\partial r_d}}<0$	We found that sustainable marketing efforts decrease as the discount rate rises, indicating that firms divert resources from sustainability initiatives to manage increased borrowing costs.
${\displaystyle \frac{\partial {q_d}^{F^{*}}}{\partial r_d}}<0$	The quantity demanded decreases as the discount rate rises, reflecting reduced demand and purchasing due to higher base and sale prices, as both firms and consumers adjust to the increased costs.

Proposition 9.

Regardless of the chosen financing strategy, a clear relationship emerges between the manufacturer’s base price, the distributor’s selling price, the intensity of sustainable marketing efforts, the distributor’s demand quantity, and the degree of consumer sensitivity to green products, as outlined below:

$${\displaystyle \frac{\partial {p_b}^{X^{*}}}{\partial \theta }}=0, {\displaystyle \frac{\partial {p_s}^{X^{*}}}{\partial \theta }}>0, {\displaystyle \frac{\partial {s_e}^{X^{*}}}{\partial \theta }}>0, {\displaystyle \frac{\partial {q_d}^{X^{*}}}{\partial \theta }}>0 (X=T, F).$$

The relationships between the variables and the sustainable marketing sensitivity parameter $\left(\theta \right)$ are interpreted as follows:

${\displaystyle \frac{\partial {p_b}^{X^{*}}}{\partial \theta }}=0$	The equation demonstrates that the manufacturer’s base price $\left(p_b\right)$ remains constant, regardless of fluctuations in sensitivity to sustainable marketing $\left(\theta \right)$. This implies that the manufacturer does not adjust its pricing strategy in response to changes in the market’s responsiveness to sustainable marketing efforts.
${\displaystyle \frac{\partial {p_s}^{X^{*}}}{\partial \theta }}>0$	The expression shows that the sale price ($p_s$) increases with rising sustainable marketing sensitivity $\left(\theta \right)$, indicating that distributors capitalize on heightened consumer demand for green initiatives by setting higher prices.
${\displaystyle \frac{\partial {s_e}^{X^{*}}}{\partial \theta }}>0$	This expression suggests that sustainable marketing efforts $\left(s_e\right)$ increase with rising sustainable marketing sensitivity $\left(\theta \right)$, promoting manufacturers and distributors to expand initiatives such as eco-friendly practices, packaging, and environmentally focused campaigns.
${\displaystyle \frac{\partial {q_d}^{X^{*}}}{\partial \theta }}>0$	The result suggests that the distributor’s demand quantity ($q_d$) increases with rising sustainable marketing sensitivity $\left(\theta \right),$ indicating a positive correlation between market responsiveness to sustainable marketing and demand for environmentally friendly products.

Proposition 10.

Irrespective of the financial strategies, the manufacturer’s base price, the distributor’s sale price, the intensity of sustainable marketing efforts, the distributor’s demand quantity, and the policy incentive coefficient (β) demonstrate the following relationship:

$${\displaystyle \frac{\partial {p_b}^{X^{*}}}{\partial \beta }}=0, {\displaystyle \frac{\partial {p_s}^{X^{*}}}{\partial \beta }}>0, {\displaystyle \frac{\partial {s_e}^{X^{*}}}{\partial \beta }}>0, {\displaystyle \frac{\partial {q_d}^{X^{*}}}{\partial \beta }}>0 (X=T, F).$$

The interpretations of the given partial derivatives of various functions with respect to $\beta$, where $\beta$ represents the policy incentive coefficient, are as follows:

${\displaystyle \frac{\partial {p_b}^{X^{*}}}{\partial \beta }}=0$	This implies that the base price ${(p_b}^{X^{*}})$ remains unaffected by changes in the policy incentive coefficient $\beta ,$ indicating that government incentive policies do not influence the base price.
${\displaystyle \frac{\partial {p_s}^{X^{*}}}{\partial \beta }}>0$	The sale price $({p_s}^{X^{*}})$ increases with the policy incentive coefficient $\left(\beta \right),$ suggesting that government incentives drive higher prices by boosting consumer demand and influencing market dynamics.
${\displaystyle \frac{\partial {s_e}^{X^{*}}}{\partial \beta }}>0$	The sustainable marketing efforts ${{(s}_e}^{X^{*}})$ increase with the policy incentive coefficient $(\beta ),$ indicating that government incentives promote greater investment in sustainable marketing through support mechanisms such as grants, awareness campaigns, or regulations.
${\displaystyle \frac{\partial {q_d}^{X^{*}}}{\partial \beta }}>0$	The quantity ordered $({q_d}^{X^{*}})$ increases with policy incentive coefficient $\left(\beta \right),$ indicating that government incentives drive higher demand, likely due to reduced prices, enhanced marketing efforts, or more favourable market conditions.

4.3. Selection of GSC Financial Strategy

The previous sections have shown that the optimal decision variables are influenced by factors such as discount rates, environmental sensitivity, and the government’s policy incentive coefficient. From the perspective of a distributor operating under capital constraints, this section explores the decision-making process involved in choosing between two financing strategies.

Proposition 11.

When the cost of capital resulting from delayed payments in trade credit equals or exceeds the combined external financing discount rates and the interest rate on the invoice value (i.e., $r_t\ge r_d+r_f$), the optimal decisions between the two financing strategies follow these relationships:

$${p_b}^{F^{*}}\le {p_b}^{T^{*}}, {p_s}^{F^{*}}\le {p_s}^{T^{*}}, {s_e}^{F^{*}}\ge {s_e}^{T^{*}}, {q_d}^{F^{*}}\ge {q_d}^{T^{*}}$$

${p_b}^{F^{*}}\le {p_b}^{T^{*}}$	We infer from this inequality that the base price under reverse factoring $({p_b}^{F^{*}})$ is less than or equal to the base price under trade credit $({p_b}^{T^{*}}).$ This may be due to the fact that reverse factoring reduces the manufacturer’s risk and cost of capital, leading them to offer a lower base price.
${p_s}^{F^{*}}\le {p_s}^{T^{*}}$	The sale price under reverse factoring $({p_s}^{F^{*}})$ is less than or equal to the sale price under trade credit $({p_s}^{T^{*}}),$ indicating that reverse factoring results in lower or comparable sale prices, primarily due to the reduced financing cost.
${s_e}^{F^{*}}\ge {s_e}^{T^{*}}$	This suggests that reverse factoring enhances financial performance and promotes greater investment in sustainable marketing. It enables companies to allocate more resources to green initiatives, improve corporate social responsibility, and better align with consumer demand for environmentally friendly products compared to trade credit.
${q_d}^{F^{*}}\ge {q_d}^{T^{*}}$	The optimal demand quantity under reverse factoring $({q_d}^{F^{*}})$ is greater than or equal to that under trade credit $({q_d}^{T^{*}}),$ indicating that reverse factoring increases demand by improving cash flow and reducing financing costs for distributors.

For the detailed proofs of Propositions 1–11, refer to Appendix A. The interpretation of the inequalities and their implications for the base prices, sale prices, and quantities ordered under reverse factoring (RF) and trade credit (TC) are as follows.

Corollary 1.

When the rate $r_t$ meets or exceeds the combined rates of $r_d and r_f$, both manufacturers and distributors who require financing are more inclined to choose reverse factoring as a method of external financing. This tendency is attributed to the fact that, under current conditions, the manufacturer’s profit under trade credit, $Z_m^{T^{*}},$ is lower than their profit under reverse factoring, $Z_m^{F^{*}}.$ Similarly, the distributor’s profit, $Z_d^{T^{*}},$ is also surpassed by the profit they would achieve through reverse factoring, $Z_d^{F^{*}},$ i.e.,

$$Z_m^{T^{*}}<Z_m^{F^{*}}, { Z}_d^{T^{*}}<Z_d^{F^{*}}$$

After incorporating the optimal trade credit decisions, and substituting the derived equilibrium outcomes into Equation (4), the distributor’s optimal profit is expressed as

$$Z_d^{T^{*}}={\displaystyle \frac{\lambda c{\left(k\left(1-r_t\right)-a{c}_g\right)}^2}{8{\left(1-r_t\right)}^2\left(2\lambda ac-{\theta }^2\right)}}$$

(7)

Similarly, the distributor’s optimal profit under reverse factoring can be determined as

$$Z_d^{F^{*}}={\displaystyle \frac{\lambda c{\left(k\left(1-r_d\right)-a{c}_g\left(1+r_f\right)\right)}^2}{8{\left(1-r_d\right)}^2\left(2\lambda ac-{\theta }^2\right)}}$$

(8)

$$\begin{gathered} { {\mathrm{In\; addition}, Z}_d^{T^{*}}-Z}_d^{F^{*}}={\displaystyle \frac{\lambda c}{8\left(2\lambda ac-{\theta }^2\right)}}\left[{\displaystyle \frac{{\left(k\left(1-r_d-r_f\right)-a{c}_g\right)}^2}{{\left(1-r_d-r_f\right)}^2}}-{\displaystyle \frac{{\left(k\left(1-r_d\right)-a{c}_g-a{c}_g{r}_f\right)}^2}{{\left(1-r_d\right)}^2}}\right]< \\ 0 \end{gathered}$$

(9)

Based on Proposition 11, it becomes clear that when distributors opt to use reverse factoring financing, they secure sufficient resources to more effectively implement sustainable marketing strategies. The resulting increase in market demand exceeds the reduction in sale prices, leading to the outcome where ${ { Z}_d^{T^{*}}< Z}_d^{F^{*}}$. A similar relationship holds for manufacturers, where ${ { Z}_m^{T^{*}}< Z}_m^{F^{*}}$. Consequently, both distributors and manufacturers are incentivized to adopt reverse factoring financing.

5. Numerical Illustration

In this section, numerical examples are used to analyze and illustrate the research findings presented earlier. The primary parameters are set as follows: $k=400; a=5; c_g=45; c=7; r_{t }=0.1; r_{f }=0.05 \mathrm{and} r_{d }\in \left[\mathrm{0,0.05}\right]$. These values were chosen based on a prior literature benchmark [50,53] to ensure consistency with established modeling practices in green supply chain finance. The value of $k=400$ was selected to represent a sufficiently large market size, enabling meaningful variation in demand response to sustainable marketing and pricing. The trade credit interest rate $r_{t }=0.1$ reflects a typical annualized opportunity cost observed in credit-constrained SME environments, as documented in [12,52]. Similarly, the discount and invoice interest rates used in the reverse factoring scenario $(r_{d }, r_f)$ were calibrated to reflect realistic short-term financing terms offered by financial institutions to distributors with strong creditworthiness. The simulation results are comprehensively shown in

Table 3. Equilibrium outcomes of financing models considering the effects of the interest rates and green sensitivities with a zero policy incentive coefficient ($\beta$ = 0).

Financial Strategy			Trade Credit (i = M)					Reverse Factoring (i = F)
		${\mathit{r}}_{\mathit{d}}$	0.01	0.02	0.03	0.04	0.05	0.01	0.02	0.03	0.04	0.05
	$\mathit{\theta }$
${p_b}^{i^{*}}$	1		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.29	61.53	61.77
	2		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.29	61.53	61.77
	3		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.29	61.53	61.77
${p_s}^{i^{*}}$	1		72.88	72.88	72.88	72.88	72.88	72.04	72.16	72.29	72.41	72.54
	2		73.22	73.22	73.22	73.22	73.22	72.42	72.53	72.65	72.77	72.89
	3		73.84	73.84	73.84	73.84	73.84	73.12	73.22	73.33	73.44	73.55
${s_e}^{i^{*}}$	1		1.04	1.04	1.04	1.04	1.04	1.16	1.15	1.13	1.11	1.09
	2		2.18	2.18	2.18	2.18	2.18	2.44	2.40	2.37	2.33	2.29
	3		3.54	3.54	3.54	3.54	3.54	3.96	3.90	3.84	3.78	3.72
${q_d}^{i^{*}}$	1		36.60	36.60	36.60	36.60	36.60	40.92	40.30	39.67	39.03	38.37
	2		38.26	38.26	38.26	38.26	38.26	42.78	42.14	41.48	40.80	40.12
	3		41.10	41.10	41.10	41.10	41.10	46.29	45.59	44.88	44.15	43.41

and

Table 4. Equilibrium outcomes of financing models considering the effects of the interest rates and green sensitivities with policy incentive coefficient $\beta$ = 0.2.

Financial Strategy			Trade Credit (i = M)					Reverse Factoring (i = F)
		${\mathit{r}}_{\mathit{d}}$	0.01	0.02	0.03	0.04	0.05	0.01	0.02	0.03	0.04	0.05
	$\mathit{\theta }$
${p_b}^{i^{*}}$	1		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.30	61.50	61.77
	2		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.30	61.50	61.77
	3		65.56	65.56	65.56	65.56	65.56	60.82	61.05	61.30	61.50	61.77
${p_s}^{i^{*}}$	1		72.91	72.91	72.91	72.91	72.91	72.07	72.19	72.30	72.40	72.57
	2		73.33	73.33	73.33	73.33	73.33	72.55	72.66	72.80	72.90	73.01
	3		74.16	74.16	74.16	74.16	74.16	73.47	73.57	73.70	73.80	73.88
${s_e}^{i^{*}}$	1		1.31	1.31	1.31	1.31	1.31	1.46	1.44	1.42	1.39	1.37
	2		2.77	2.77	2.77	2.77	2.77	3.10	3.05	3.00	2.95	2.90
	3		4.60	4.60	4.60	4.60	4.60	5.14	5.07	4.99	4.91	4.82
${q_d}^{i^{*}}$	1		36.73	36.73	36.73	36.73	36.73	41.07	40.45	39.8	39.2	38.51
	2		38.85	38.85	38.85	38.85	38.85	43.44	42.78	42.1	41.4	40.73
	3		42.98	42.98	42.98	42.98	42.98	48.06	47.34	46.60	45.8	45.07

and Figure 3, Figure 4, Figure 5 and Figure 6. The findings suggest that, regardless of green sensitivity or the chosen financing strategy, an increase in the discount rate leads to a rise in the manufacturer’s base price. Under a trade credit scheme, factors such as sale price, demand quantity, and sustainable marketing efforts are significantly affected by the cost of capital associated with deferred payments. Conversely, in the case of reverse factoring, a higher discount rate results in increased base prices for the manufacturer, which in turn drives up sale prices and reduce demand. An increase in market sensitivity to sustainable marketing efforts enables distributors to set higher prices, allocate more resources to sustainable marketing initiatives, and place larger orders. Despite these changes, the base price set by the manufacturer remains unaffected by fluctuations in sensitivity to sustainable marketing. This trend is consistent across both trade credit and reverse factoring scenarios, thereby confirming the validity of Proposition 9.

In the given figures, TC represents Trade Credit and RF denotes reverse factoring. Figure 3 illustrates that when the cost of capital associated with delayed payments in trade credit equals the sum of the reverse factoring discount rate and the interest rate, both the base price and the selling price are consistently higher under the trade credit scheme. However, adopting a reverse factoring strategy results in an increased intensity of sustainable marketing efforts by distributors, along with a rise in demand quantity.

We can infer from the above that manufacturers may offer lower prices when reverse factoring is employed, as they receive prompt payment from the financier. This improves their cash flow and reduces the need to include a risk premium in their pricing. Additionally, the higher base and sale prices under the trade credit strategy reflect the additional costs associated with delayed payments, which manufacturers pass on to distributors. We also infer that reverse factoring improves cash flow and financial stability, enabling distributors to allocate funds to green initiatives without immediate financial strains. Since reverse factoring ensures suppliers are paid promptly, it may result in better supplier relationships and more favorable purchasing terms. Thus, Propositions 8 and 9 are also validated.

Figure 4 illustrates that adopting a reverse factoring strategy consistently yields higher profits for GSC member enterprises compared to a trade credit approach. Furthermore, when the discount rate in reverse factoring is set to zero, both manufacturer and distributors achieve maximum profitability. Therefore, implementing a minimal discount rate in reverse factoring proves to be a mutually beneficial strategy for both parties, validating Corollary 1.

The results presented in Figure 5 demonstrate that the base price of the product remains constant, regardless of supportive government policies aimed at promoting sustainable marketing by distributors. This suggests that upstream production costs are not affected by these policy incentives. However, the policies have a significant impact on the distributor side, as they stimulate consumer demand, which, in turn, leads to a rise in sale price due to heightened competition for the available supply. Additionally, these government incentives encourage greater sustainable marketing efforts, prompting businesses to adopt and promote environmentally friendly practices. As a result, there is an observable increase in the quantity of the product ordered, reflecting both the rise in demand and enhanced sustainable marketing initiatives.

6. Discussion

6.1. Theoretical Implications

Our study offers four key theoretical contributions. First, it integrates trade credit, reverse factoring, sustainable marketing, and government policy within a Stackelberg game framework, bridging financial decision-making and environmental sustainability in capital constrained green supply chains. Second, it identifies reverse factoring as a transformative financing tool that eases manufacturers’ liquidity pressures while enabling distributors to enhance sustainable marketing efforts. Third, the model demonstrates how government incentives, such as subsidies and tax relief, reduce marketing costs and boost consumer demand for green products, providing a basis for assessing policy effectiveness. Lastly, it reveals that decentralized decision-making leads to inefficiencies (e.g., suboptimal pricing and reduced sustainability investment), highlighting the need for coordinated financial strategies to improve overall supply chain performance.

6.2. Practical Implications

The present study has several important practical implications offering substantial assistance to stakeholders in the green supply chain. First, reverse factoring proves more effective than trade credit by enhancing sustainability, profitability, and working capital flow, benefiting managers, distributors and manufacturers. Second, government incentives such as subsidies and tax relief reduce financial barriers for SMEs and support green marketing, aiding policymakers and supply chain managers. Third, the study shows that reverse factoring improves pricing strategies by lowering risk premiums and enabling more competitive sale prices. Fourth, improved liquidity from reverse factoring supports sustainable marketing investments like eco-friendly packaging and consumer education, benefiting marketing professionals. Finally, the model demonstrates how SMEs gain liquidity and competitiveness through green investments, highlighting its relevance for SME managers and financial institutions.

7. Conclusions and Future Research

Based on the research findings, our study offers important conclusions aligned with its three primary research questions. Regarding RQ1 on financing strategies, the analysis reveals that trade credit increases the manufacturer’s base price and limits the distributor’s ability to invest in sustainable marketing due to the burden of deferred payments. In contrast, reverse factoring significantly improves cash flow, stimulates demand, and enables greater investment in sustainability initiatives. Therefore, the study concludes that reverse factoring is more effective for promoting sustainability and reducing operational inefficiencies within GSC.

For RQ2 on government policies, the findings highlight that tax reliefs, grants, and subsidies help reduce the distributor’s sustainable marketing costs. Additionally, these incentives enhance consumer demand for green products, indirectly benefiting all supply chain participants. Based on this, the study recommends that policymakers should broaden the scope and scale of green incentives to sustain environmentally friendly practices and ensure their widespread adoption, especially among capital-constrained SMEs.

In the case of RQ3 on sustainable marketing, the model shows that increased sensitivity to sustainability in consumer behavior positively affects demand and profitability. Furthermore, reverse factoring improves the distributor’s ability to allocate financial resources towards sustainable initiatives. Hence, the study concludes that financing and sustainable marketing efforts should be integrated to achieve optimal economic and environmental performance within decentralized supply chains.

These conclusions provide a foundation for practical decision-making and also suggest meaningful avenues for future research in sustainable supply chain finance.

7.1. Limitations and Future Research

The study acknowledges several simplifying assumptions that may limit its real-world applicability. The assumption of strict contractual compliance may not reflect practical deviations, which can disrupt trust and coordination across the supply chain. By not incorporating production constraints and unexpected demand fluctuations, the model risks overlooking scenarios where distributor orders go unfulfilled. The assumption of a perfectly competitive market fails to capture information asymmetry and product differentiation, reducing its effectiveness in realistic settings.

Additionally, the model’s reliance on supportive government policies may not hold across regions with varying political or regulatory environments. For instance, in emerging economies like India (as reflected in our case motivation), government-backed digital finance platforms and policy incentives significantly enhance the adoption and performance of reverse factoring among SMEs. In contrast, in less developed markets or informal economies, limited digital infrastructure, weak contract enforcement, and high transaction costs may restrict its scalability and reliability.

Moreover, reverse factoring outcomes may differ by supply chain type. In vertically integrated or capital-intensive industries (e.g., automotives, electronics), reverse factoring tends to be more effective due to stable buyer–supplier relationships and predictable cash flows. However, in fragmented or seasonal supply chains (e.g., agricultural, textiles), the variability in demand and limited bargaining power of suppliers may reduce the efficiency of reverse factoring strategy. Lastly, using minimal discount rates and a zero risk-free interest rate may not align with actual financial market conditions, potentially affecting the accuracy of financing and investment projections. These limitations open avenues for future research to build more adaptive and context-sensitive models.

7.2. Future Research Direction

Building on the present Stackelberg-based analysis of trade credit and reverse factoring, future research could develop dynamic green-financing models that incorporate fluctuating market conditions, shifting policy incentives, and real-time tools such as dynamic discounting. Such extensions would capture demand elasticity and evolving sustainability preferences more realistically. Researchers might also explore hybrid financing structures that blend supplier credit, bank loans, equity, and other instruments. Identifying thresholds at which mixed financing outperforms singular approaches would help optimize financial decision-making in green supply-chain contexts. In addition, incorporating the financial institution as a strategic player could help explore contract design, credit risk assessment, and equilibrium pricing from a tripartite perspective.

The rapid rise of digital technologies merits attention as well. Blockchain and other digital platforms may enhance transparency, traceability, and operational efficiency, thereby accelerating the adoption of green-finance practices. Finally, cross-sectoral comparisons across industries such as agriculture, manufacturing, and retail can help validate the study’s findings and identify sector-specific drivers for advancing sustainable supply chain finance.

These directions aim to enhance the understanding and implementation of sustainable financing strategies, addressing gaps in the literature and fostering the development of resilient GSC.