Market Entry Strategies in Sustainable Innovation: A Comparative Study of Profit-Oriented and Environmental, Social, and Governance-Driven Digital Platforms in the Recycling Market

Abstract

With the rise of sustainable innovation, digital platforms have emerged as key facilitators in the second-hand e-commerce recycling market. This study investigates the entry strategies of profit-oriented and ESG-driven platforms, focusing on their sustainable innovation efforts, entry conditions, and cooperation strategies with recyclers in both monopolistic and duopolistic recycling markets. By developing and analyzing a two-stage Stackelberg model, we derive several key managerial and theoretical insights. First, in a monopolistic market, we delineate the sustainable innovation cost thresholds necessary for platform entry. While both platform types contribute to enhancing recycling volume, consumer surplus, and social welfare, their entry conditions remain mutually exclusive, reflecting their fundamentally different sustainability approaches. A profit-oriented platform can only enter when sustainable innovation costs are low, whereas an ESG-driven platform requires higher levels of innovation investment to balance consumer surplus, environmental benefits, and profitability. Second, in a duopolistic market, we explore three distinct cooperation strategies for profit-oriented and ESG-driven platforms: (1) exclusive cooperation with a low-expertise recycler, (2) exclusive cooperation with a high-expertise recycler, and (3) simultaneous cooperation with both recyclers. We further identify the optimal cooperation strategy based on entry feasibility, profit maximization, and the conditions necessary to achieve a “win-win” outcome. This study sheds light on how profit-oriented and ESG-driven digital platforms strategically enter the recycling market through sustainable innovation, offering insights into their cooperation strategies, market competition, and broader implications for sustainable platform ecosystems.

1. Introduction

The rapid depletion of natural resources and the intensification of environmental pollution have exacerbated global challenges such as climate change, ecological degradation, and waste accumulation (Singh and Singh, 2016 [1]). Against this backdrop, Guide et al. (2000) [2] and Dowlatshahi (2000) [3] emphasize that recycling and reuse constitute widely accepted sustainable approaches that aim to improve resource efficiency and mitigate environmental impacts within the circular economy framework. As a fundamental pillar of the circular economy, the recycling market plays a crucial role in minimizing resource waste, extending product lifecycles, and reducing carbon emissions. In recent years, digital platforms have increasingly expanded their presence across various industries. In China, platforms, as asset-light companies, have actively sought partnerships with recyclers to enter the second-hand recycling market since 2014. The “platform + recycler” collaboration model has rapidly gained traction in the recycling industry, significantly reshaping market dynamics. According to iiMedia Research, the transaction volume of China’s second-hand e-commerce sector surged from CNY 45.9 billion in 2015 to CNY 3745.5 billion in 2020, reflecting a remarkable compound annual growth rate of 141.2%. Simultaneously, the market penetration rate of second-hand e-commerce increased from 2.2% to 35.6%, underscoring the expanding influence of digital platforms in the circular economy [4].

As highlighted by Servaes and Tamayo (2013) [5], growing consumer awareness of environmental issues has become a critical driver of corporate sustainability efforts. In this context, digital platforms are increasingly prioritizing sustainable innovation to attract users and strengthen partnerships with recyclers. A notable example is Xianyu’s “Old Clothes Recycling” initiative in collaboration with Ouyan. Xianyu, a comprehensive product recycling platform launched by Alibaba, integrated the “Ant Forest” program into its old clothes recycling efforts in 2018. This initiative encouraged greater recycling participation by allowing users to earn “Ant Forest energy” when recycling clothing through the platform. Once a certain threshold was reached, Xianyu planted a tree in environmentally degraded areas on behalf of the user. The program achieved remarkable success. In 2019 alone, Xianyu recycled 30,000 t of old clothes and planted 458,000 Saxaul trees [6]. Following this success, Xianyu expanded the “Ant Forest” initiative to include other product categories, reinforcing its commitment to circular economy principles. By 2023, Xianyu’s recycling efforts had resulted in a total carbon reduction of 3.142 mt [7]. Additionally, the China Circular Economy Association recognized Xianyu as one of the “Top Ten Technological Innovations Empowering Green Transformation with Digital Technology”, making it the only idle goods transaction platform in China to receive this distinction. Another example is JD.com’s collaboration with Atrenew Inc., a specialist in 3C electronic product recycling. In 2022, JD.com launched the “One Yuan Green Action for Electronic Product Exchange” initiative. Under this program, the company donates one yuan to afforestation projects for every completed transaction involving used electronic products.

The collaboration between digital platforms and recyclers has undergone significant advancements in practice. However, despite the increasing influence of platforms in the recycling sector, existing research has primarily focused on traditional stakeholders—such as remanufacturers, retailers, and recyclers—while largely neglecting the role of platforms in shaping recycling ecosystems. Notably, a platform may enter the recycling industry with distinct strategic objectives: (A) a profit-oriented platform, which prioritizes profit maximization as its primary goal (e.g., JD.com), or (B) an ESG-driven platform, which integrates consumer surplus and environmental benefits into its operational strategy (e.g., Xianyu). This study seeks to examine how a platform with different strategic objectives establishes a cooperative relationship with a recycler and under what conditions such collaboration can yield a “win-win” outcome for both parties. To enhance the practical relevance of our analysis, we further extend our model to a duopolistic market structure, capturing a more competitive and realistic industry setting. More formally, this research aims to address the following key questions:

1.

What are the entry conditions under which profit-oriented and ESG-driven platforms adopt sustainable innovation in monopolistic and duopolistic recycling markets, respectively?

2.

How do entry conditions and investment strategies differ between profit-oriented and ESG-driven platforms?

3.

In a duopolistic recycling market characterized by professional heterogeneity between two recyclers, which recycler should the platform select as its cooperation partner?

To address our research questions, we develop a two-stage Stackelberg model that investigates the entry strategies of a profit-oriented and an ESG-driven platform in the recycling market, focusing on their cooperation with recyclers through sustainable innovation in both monopolistic and duopolistic market structures. This paper presents several key findings.

In a monopolistic market, we establish the lower and upper bounds of sustainable innovation costs required for platform entry. Within these entry boundaries, both platform types can successfully enter the market and contribute to increased recycling volume, consumer surplus, and social welfare. However, their entry conditions do not overlap, underscoring their fundamentally different approaches to sustainability. A profit-oriented platform can only enter when sustainable innovation costs are low, ensuring profit generation with limited sustainable innovation efforts. In contrast, an ESG-driven platform requires higher innovation cost levels to balance environmental and social objectives alongside profitability. In a duopolistic market, we analyze three cooperation strategies for the platform: (1) exclusive cooperation with a low-expertise recycler (Recycler A), (2) exclusive cooperation with a high-expertise recycler (Recycler B), and (3) simultaneous cooperation with both recyclers. For a profit-oriented platform, entry feasibility depends on the average residual value of used items. If the average residual value is low, the platform should cooperate with both recyclers; otherwise, it should cooperate with Recycler A. The profit-maximizing cooperation strategy is influenced by innovation costs: exclusive cooperation with Recycler B is preferable when innovation costs are low, whereas collaboration with both recyclers becomes optimal when costs are high. A “win-win” scenario arises only when the platform collaborates with both recyclers, as exclusive cooperation negatively impacts the non-cooperating recycler’s profitability. For an ESG-driven platform, cooperation with Recycler A fails to meet the platform’s entry objectives, as its sustainability commitments require greater innovation efforts, leading to higher costs. To offset these costs, the platform needs a more profitable partnership, but a low-expertise recycler cannot generate sufficient returns. Therefore, the platform can successfully enter the market only by cooperating with Recycler B or both recyclers. Similar to profit-oriented platforms, a “win-win” outcome is only achievable when the ESG-driven platform partners with both recyclers, ensuring higher recycling volumes, greater consumer surplus, and enhanced social welfare.

This paper is structured as follows. Section 2 reviews the relevant literature. Section 3 outlines the model framework and key assumptions. In Section 4, we analyze the entry strategies of profit-oriented and ESG-driven platforms that adopt sustainable innovation in a monopolistic recycling market and discuss the implications of platform entry under this market structure. Section 5 extends the analysis to a duopolistic recycling market, examining how a profit-oriented platform and an ESG-driven platform enter the market and strategically select recyclers for cooperation. Finally, Section 6 summarizes the key findings and discusses future research directions.

2. Literature Review

Our study builds upon and contributes to three distinct research streams: recycling business, platform entry, and sustainable investment. While these domains have received considerable attention, our research provides a fresh perspective by examining the collaboration between the platform and the recycler through sustainable innovation. In the following sections, we delve into each of these areas, emphasizing the unique insights and contributions our work brings to the field.

2.1. Recycling Business

Research on the reverse supply chain has achieved significant maturity, as evidenced by the comprehensive reviews conducted by Atasu et al. (2008) [8] and Atasu and VanWassenhove (2012) [9]. However, there are two critical areas that still warrant further exploration based on existing studies.

First, the majority of the literature has predominantly focused on traditional players in the reverse supply chain and has provided profound insights into their roles and dynamics, such as producers, remanufacturers, and retailers (e.g., refs. [2,10,11,12,13]). However, empirical research by Cui and Sosic (2019) [14] has highlighted a significant gap in the recycling of global Municipal Solid Waste (MSW), with producers and remanufacturers taking responsibility for only a small portion of recyclable items. Consequently, this research shifts attention to the role of recyclers and recycling platforms. Recyclers, undeniably, play a crucial role in the recycling business. Feng et al. (2017) [15] explore a two-tier reverse supply chain involving a recycler and a recycling dealer, investigating dual-channel recycling strategies. Matsui (2022) [16] analyzes the optimal timing for recycling firms to announce acquisition prices. Esenduran et al. (2020) [17] consider competition between two recycling supply chains, finding that competition drives recyclers to adopt more stringent processing standards. In recent years, the role of circular reuse platforms in the recycling sector has gained increasing prominence, attracting scholarly attention. For instance, Dhanorkar and Muthulingam (2020) [18] argues that increased access to the Internet, such as through platforms, can amplify the effectiveness of e-waste legislation, further reducing MSW per capita. Dhanorkar et al. (2021) [19] evaluate the significance of expert services provided by platforms in online material and waste exchange. Xu et al. (2023) [20] examine the combination of remanufacturing and blockchain in the context of platforms with the potential to scale the market. Qiu et al. (2023) [21] investigate the vertical integration of a platform with an online secondhand platform. Bai et al. (2023) [22] discuss how firms should adjust their trade-in strategies in response to the emergence of third-party resale platforms. However, research on how platforms enter the recycling market and their impact on the recycling industry remains limited.

Second, customers may be motivated to recycle due to monetary incentives and sustainability concerns. Many recycling-focused studies have overlooked customer recycling motivations, assuming that recycling quantities are solely linked to production quantities and the firm’s recycling efforts (e.g., refs. [23,24]). Alternatively, some studies have only considered the influence of acquisition prices on customer recycling intentions, such as those by Kleber et al. (2020) [25], Dong et al. (2023) [26], and Esenduran et al. (2020) [17]. Additionally, aleizadeh and Sadeghi (2019) [27] consider offering other rewards within the reverse supply chain to incentivize customer participation. However, research on how sustainable incentives can drive customer participation in recycling activities also deserves attention.

Addressing these two potentially investigable dimensions, our work delves into the interaction between a platform and a recycler and its impact on the recycling market, while also exploring how sustainable innovation enhances customer recycling motivation. Therefore, our work draws inspiration from and contributes to the two following literature streams as well: platform entry and sustainable investment.

2.2. Platform Entry

In recent years, platforms have increasingly penetrated various sectors. The impact of their entry on incumbents has consistently intrigued scholars. Sharma and Mehra (2021) [28] examine the impact of platform entry into the market when the platform’s ad revenue depends on the accessed product’s quality. Gal-Or and Shi (2022) [29] and Deng et al. (2023) [30] delve into the effects of platform entry on incumbent competitive dynamics and their responsive strategies. He et al. (2020) [31] employ data from a Chinese e-commerce platform to scrutinize the entry strategy determination through seller competitiveness and exclusivity considerations. Dhanorkar (2019) [32] highlights the role of online matching platforms such as Craigslist, FreeCycle, and Gumtree in promoting product reuse by reducing reliance on traditional recycling and disposal methods. Guan et al. (2023) [33] explore how manufacturers respond to the entry of third-party platforms and how they can build their own platforms in response. Additionally, empirical studies by Burtch et al. (2018) [34], Foerderer et al. (2018) [35], and Liu et al. (2023) [36] have explored the impact of platform entry into new industries. However, the literature also indicates that the entry of platforms into new industries does not always guarantee positive outcomes [37,38]. Given the considerable amount of research centered on platform entry into emerging industries, our aim is to offer a distinctive perspective by investigating platform entry into the recycling market. By delving into this specific context, we seek to contribute to the current research on platform entry strategies, shedding light on the dynamics, challenges, and potential outcomes associated with their engagement in the recycling business.

2.3. Sustainable Investment

This study is also closely related to the literature on sustainable investments. Agrawal et al. (2019) [39], Van Wassenhove (2019) [40], Atasu et al. (2020) [41], and Dai and Tang (2022) [42] have provided comprehensive reviews on environmental investments and sustainable development. Building on this foundation, many studies have shown that sustainable investment plays a critical role in shaping firms’ strategic and operational decisions. For example, Dye and Hsieh (2024) [43] develop a dynamic model linking green innovation, pricing, and advertising, revealing how sustainability efforts influence goodwill and long-term profits. Yu et al. (2022) [44] examine firms’ sustainability strategies under various environmental policies, showing that investment in sustainability is driven by profitability and policy alignment. Additionally, recent studies have examined the environmental and strategic implications of remanufacturing in various supply chain settings. Yang et al. (2020) [45] analyze a closed-loop supply chain under cap-and-trade regulation and demonstrate that remanufacturing not only enhances carbon emission reductions and firm profitability but also affects the manufacturer’s choice of collection mode depending on emission intensity and cost factors. Orsdemir et al. (2014) [46] model the competition between an original equipment manufacturer (OEM) and an independent remanufacturer, showing that the OEM’s strategic use of product quality and core quantity varies with its market position, and that encouraging independent remanufacturing may not always yield environmental or consumer welfare benefits. Chen and Chen (2019) [47] study the interactions among OEMs, remanufacturers, and refurbishers in China, revealing that remanufacturing faces higher entry barriers but offers superior environmental and social benefits compared to refurbishing, while OEMs inherently resist third-party recovery efforts. Building on the insights from the existing literature, this study investigates how investment strategies in sustainable innovation can serve as a mechanism to facilitate effective collaboration between platforms and recyclers. Moreover, we extend the sustainable investment literature by incorporating the carbon offset objectives of recycling platforms, thereby capturing the environmental dimension of platform-driven sustainability initiatives.

In summary, despite the growing body of literature on reverse supply chains, platform economies, and sustainable investments, several important gaps remain unaddressed. First, existing research has primarily centered on traditional actors—such as producers, remanufacturers, and retailers—while the strategic role of recyclers and their interaction with digital platforms remains underexplored. Although recent studies have begun to acknowledge the presence of circular reuse platforms, limited attention has been given to how platforms strategically enter the recycling market and shape industry outcomes through partnerships with recyclers. Second, while the literature on platform entry has examined its effects in various industries, little is known about platform entry dynamics in the recycling context, particularly under the dual considerations of profit orientation and environmental responsibility. The implications of platform–recycler collaboration under different market structures, such as monopoly and duopoly, also remain largely uninvestigated. Third, although sustainable investment has received increasing scholarly attention, most prior research focuses on carbon emissions and environmental benefits at the operational level. Few studies have considered how platforms engage in sustainable innovation as a strategic lever for entry, cooperation, and customer engagement in recycling activities. Addressing these research gaps, our study investigates the entry conditions and investment strategies of profit-oriented and ESG-driven platforms in recycling markets, with a particular focus on their collaboration with heterogeneous recyclers and the design of sustainable incentives to enhance customer participation. By integrating perspectives from reverse supply chain management, platform economics, and sustainable innovation, this research aims to offer a more comprehensive understanding of platform-led sustainability transitions in the recycling sector.

3. Model Setup and Preliminaries

We consider a recycling market consisting of a monopolistic recycler, a platform, and customers who need to dispose of used items. Prior to the emergence of the platform, the recycler directly collected used items from customers at a unit acquisition price p, processed them, and resold the output either as a commodity or raw material in the reselling market, generating revenue based on the average residual value r per unit of used items, as illustrated in Figure 1. The unit average residual value represents the net value after accounting for the unit cost incurred by the recycler for processing each unit of recyclables, including collection and refurbishment fees. When the platform emerges, if the recycler cooperates with the platform, he will offer the acquisition price p for the customer through the platform, and the customer’s used items are collected directly by the recycler, as shown in Figure 2. Furthermore, the recycler is required to pay a commission fee $\delta {\pi }_r$ to the platform, and the platform will make an innovation effort s which can increase customers’ utility.

Given the heterogeneous nature of customers’ perceptions regarding the residual value of preowned items, we assume that customer valuation for a used item is represented by v, where $v\sim U[0,1]$ (e.g., Bohlmann et al., 2002 [48], Dou et al., 2017 [49], Chen et al., 2024 [50]). The average residual value will not exceed the maximum perceived value of customers, so we constrain r within the range $[0,1]$. To model customers’ additional environmental utility, we employ a horizontally differentiated utility function, following the framework of Han et al. (2022) [51]. In this model, the utility derived from environmental benefits is assumed to be proportional to the platform’s innovation effort s. Accordingly, a customer with valuation v derives a net utility of $u(p,s)=p-v+\lambda s$ when selling a used item, where $\lambda$ represents the relative importance customers place on monetary rewards and innovation efforts. To facilitate clearer managerial insights, we further assume that customers value monetary rewards and innovation efforts equally, setting $\lambda =1$. Without loss of generality, we normalize the potential size of the recycling market to 1, assuming that each customer possesses a single unit of a preowned item.

The recycler is a profit-maximizer, and their profit function can be expressed as

$$\underset{p}{max}{\pi }_r=\underset{p}{max}(1-\delta )D(r-p),$$

(1)

where D is the total recycling volume and $\delta =0$ if the recycler does not cooperate with the platform. Additionally, we use the subscript ° to denote the scenario in which the recycler does not engage in cooperation with the platform.

As mentioned previously, as an asset-light platform without the facilities or capabilities to recycle and process used items, the platform must operate its recycling business in partnership with the recycler. The platform invests in sustainability and undertakes an innovation effort s, representing the total carbon emissions reduced through sustainable innovation. First, we consider the optimization problem for the profit-oriented platform:

$$\begin{array}{cc}\hfill \underset{s}{max}{\pi }_p=& \underset{s}{max}\{\delta min[p+s,1](r-p)-k{s}^2\}\hfill \\ \hfill s.t.& \left\{\begin{array}{c}{\pi }_r\ge {\pi }_r^{\circ }\hfill \\ {\pi }_p\ge 0\hfill \\ p>0\hfill \end{array}\right.,\hfill \end{array}$$

(2)

where $k>\underline{k}$ represents the cost parameter of sustainable innovation, and $\underline{k}=\delta /2$ denotes the lower bound of the sustainable innovation cost, preventing unrealistic scenarios in which excessively low costs would allow for infinite investment. Equation (2) indicates that such cooperation is feasible only under specific conditions. From the recycler’s perspective, partnering with the platform is advantageous only if the resulting profit exceeds the profit earned in the absence of collaboration. Similarly, for the platform, cooperation is viable only if its revenue remains non-negative, ensuring its long-term sustainability. Furthermore, we assume that the recycler cannot act as a free rider in the partnership. Specifically, the recycler must offer a positive acquisition price rather than setting it to zero, as the latter would result in a scenario where customers can only donate their used items without receiving any financial incentives.

An ESG-driven platform incorporates both environmental benefits and consumer surplus into its development objectives. Thus, in this section, we introduce the concept of augmented consumer surplus ($ACS$). Specifically, following the study by Anand and Giraud-Carrier (2020) [52], which defines $ACS$ as

$$ACS=CS+E\left(C\right),$$

(3)

where $CS$ represents consumer surplus, defined as the benefit derived from the difference between the acquisition price and consumers’ perceived value. Additionally, $E\left(C\right)$ denotes environmental benefits, which is commonly quantified in monetary terms (see Atasu and VanWassenhove, 2012 [9]). The function $E\left(C\right)$ is assumed to be increasing and convex in total carbon offset C. Specifically, we define

$$E\left(C\right)=b{C}^2,$$

(4)

where $b>0$, the environmental benefit factor, measures the improvement society gains from pollution reduction, and C denotes the total carbon offset generated by the recycling business. Furthermore, the total carbon offset is given by

$$C=eD+s,$$

(5)

where e represents the carbon offset rate per unit of recycled items. Thus, the optimization problem for the ESG-driven platform is given by

$$\begin{array}{cc}\hfill \underset{s}{max}({\pi }_p+ACS)=& \underset{s}{max}\{\delta D(r-p)-k{s}^2+{\int }_0^{D}u(p,s)dv+b{(eD+s)}^2\}\hfill \\ \hfill s.t.& \left\{\begin{array}{c}{\pi }_r\ge {\pi }_r^{\circ }\hfill \\ {\pi }_p\ge 0\hfill \\ p>0\hfill \end{array}\right.\hfill \end{array}$$

(6)

The sequence of events and decisions associated with the different parties is depicted in Figure 3. Before the strategic interactions begin, the platform negotiates a commission rate $\delta$ with the recycler, which is treated as an exogenous variable. At the beginning of the game, the platform determines the innovation effort s to maximize its objective. Given the innovation effort s, the recycler decides whether to cooperate with the platform and then makes the acquisition price decision p. Finally, customers decide whether to sell their used products. To enhance clarity and facilitate comprehension,

Table 1. Summary of notation.

Notation	Description
p	The acquisition price
r	The average residual value per unit of used items
$\delta$	Commission rate
v	Customer valuation for a used item
s	Platform’s sustainable innovation efforts
D	Total recycling volume
u	The customer’s net utility from recycling a unit of used items
${\pi }_r$ (${\pi }_p$)	The profit of the recycler (the platform)
k	The cost of sustainable innovation
$ACS$	Augmented consumer surplus
$CS$	Consumer surplus
E	Environmental benefits
C	The total carbon offset generated by the recycling business
b	The environmental benefit factor
e	The carbon offset rate per unit of recycled items
$\theta$	The customer’s hassle level of low-expertise recycler
$p_A$ ($p_B$)	The acquisition price of Recycler A (Recycler B)
${\pi }_A$ (${\pi }_B$)	The profit of the Recycler A (Recycler B)
$u_A$ ($u_B$)	The customer’s net utility from recycling through Recycler A (Recycler B)

provides a notation summary of the key variables and parameters employed throughout the analysis.

4. Benchmark Cases: Monopolistic Recycling Market

In this section, we first examine a scenario in which a monopolistic recycler operates in the recycling market. Drawing inspiration from the successful real-world case of Xianyu, we consider a setting where a platform can introduce sustainable initiatives to attract customers, facilitate cooperation with the recycler, and enter the recycling market. We begin by analyzing the benchmark scenario, in which the platform has not yet entered the market, denoted by the superscript “∘”. We then explore two distinct cases: a profit-oriented platform, represented by the superscript “$PM$”, and an ESG-driven platform, denoted by the superscript “$EM$”. For both platform types, we examine the optimal sustainable innovation decisions and assess the entry conditions required for successful market participation.

4.1. No Platform

We begin by analyzing the benchmark scenario in which the platform has not yet entered the market. In this setting, the recycler independently collects used goods from customers. Before the platform’s involvement, the recycler maximizes profits by solving the optimization problem in Equation (1), leading to the following lemma.

Lemma 1.

In a monopolistic recycling market, prior to the emergence of the second-hand platform, the recycler sets the optimal acquisition price as $p^{\circ }={\displaystyle \frac{r}{2}}$. Under this scenario, the corresponding profit of the recycler, augmented consumer surplus, and social welfare are given by $p^{\circ }={\displaystyle \frac{r}{2}}$, ${\pi }_r^{\circ }={\displaystyle \frac{r^2}{4}}$, $AC{S}^{\circ }={\displaystyle \frac{r^2}{8}}(1+2b{e}^2)$, $W^{\circ }={\displaystyle \frac{r^2}{8}}\left(3+2b{e}^2\right)$.

4.2. Profit-Oriented Platform Entry

With the emergence of the platform, sustainable innovation is made to encourage greater customer participation in recycling and facilitate cooperation with the recycler. We define a “win-win” situation as one in which all parties—the platform, recycler, and customers—benefit, while overall recycling volume increases and environmental benefits are enhanced. This section examines the entry conditions and win-win feasibility for a profit-oriented platform.

We analyze the model using backward induction. First, we determine the number of customers participating in recycling. Customers are assumed to sell their used products if their net utility is positive. Thus, for any acquisition price p, customers with valuations $v\sim [0,min\{p+s,1\left\}\right]$ participate in recycling. Accordingly, the demand function is given by $D=min\{p+s,1\}$. Next, the monopolistic recycler sets the acquisition price to maximize profit. By solving Equation (1), the recycler derives the optimal response function for the acquisition price, $p\left(s\right)$, with respect to the platform’s innovation effort s. Finally, the profit-oriented platform sets the optimal innovation effort s by optimizing Equation (2). We then derive the equilibrium outcomes for both the platform and the recycler, along with the entry conditions and criteria for achieving a win-win outcome, as summarized in Proposition 1.

Proposition 1.

Considering the entry of a profit-oriented platform into the monopolistic recycling market, cooperation between the platform and the recycler will be established if and only if $0<\delta <{\displaystyle \frac{3}{4}}$ and $\underline{k}<k\le {\displaystyle \frac{1+\sqrt{1-\delta }}{4}}$. Under these conditions, the cooperation constitutes a win-win situation. The recycler’s optimal acquisition price is $p^{PM}={\displaystyle \frac{2kr}{\delta -4k}}+r$, and the platform’s optimal innovation effort is $s^{PM}={\displaystyle \frac{\delta r}{4k-\delta }}$. The corresponding market demand, recycler’s profit, platform’s profit, augmented customer surplus, and social welfare are as follows: $D^{PM}={\displaystyle \frac{2kr}{4k-\delta }}$, ${\pi }_r^{PM}={\displaystyle \frac{(1-\delta )4k^2{r}^2}{{(4k-\delta )}^2}}$, ${\pi }_p^{PM}={\displaystyle \frac{\delta k{r}^2}{4k-\delta }}$, $AC{S}^{PM}={\displaystyle \frac{2k^2{r}^2+b{r}^2{(\delta +2ek)}^2}{{(4k-\delta )}^2}}$, $W^{PM}={\displaystyle \frac{r^2\left(b{\delta }^2+k^2\left(4b{e}^2+6\right)+\delta k(4be-\delta )\right)}{{(4k-\delta )}^2}}$.

The feasible region derived in Proposition 1 is illustrated in Figure 4. Specifically, when the commission rate is moderate ($0<\delta <{\displaystyle \frac{3}{4}}$) and the cost of sustainable innovation is below the threshold ($k\le {\displaystyle \frac{1+\sqrt{1-\delta }}{4}}$), corresponding to the area below the green line in Figure 4, the profit-oriented platform can successfully establish cooperation with the monopolistic recycler and thereby enter the recycling market. In this scenario, the platform’s investment in sustainability initiatives incentivizes greater customer participation in recycling, resulting in an overall increase in recycling volume. Consequently, augmented consumer surplus improves, environmental benefits increase, and social welfare is enhanced, ultimately fostering a “win-win” outcome where all parties—the platform, recycler, and customers—benefit from the cooperation.

4.3. ESG-Driven Platform Entry

In this section, we examine the sustainable innovation strategy of an ESG-driven platform. Unlike a purely profit-oriented platform, an ESG-driven platform integrates consumer surplus and environmental considerations into its decision-making process, aligning its objectives with long-term sustainability and stakeholder welfare.

Based on Equation (6), let $f\left(s\right)={\pi }_p^{EM}+ACS$, then we obtain:

$$f^{\prime }\left(s\right)=\underset{\mathrm{profit}\mathrm{effect}}{\underbrace{{\displaystyle \frac{\delta (r+s)}{2}}}}\underset{\mathrm{cost}\mathrm{effect}}{\underbrace{-2ks}}\underset{\mathrm{CS}\mathrm{effect}}{\underbrace{+{\displaystyle \frac{r+s}{4}}}}\underset{\mathrm{Environmental}\mathrm{effect}}{\underbrace{+{\displaystyle \frac{1}{2}}be(e+2)r+{\displaystyle \frac{1}{2}}b{(e+2)}^{2}s}}$$

(7)

The impact of innovation effort s on the ESG-driven platform’s objective can be decomposed into three components: the profit effect, the cost effect, and the augmented consumer surplus effect, which encompasses both the consumer surplus and environmental factors. Given that both the consumer surplus and environmental benefits are positive, the ESG-driven platform is expected to invest more in innovation effort compared to the profit-oriented platform. As a result, the recycler’s profit increases, allowing the platform to extract a higher commission fee. Furthermore, greater customer participation enhances both consumer surplus and environmental benefits. However, a higher innovation effort also implies greater innovation costs for the platform.

By solving the optimization problem defined in Equation (6), the ESG-driven platform determines the optimal innovation effort s that maximizes its objective, resulting in the following proposition:

Proposition 2.

Considering the entry of an ESG-driven platform into the monopolistic recycling market, cooperation between the platform and the recycler will be established if and only if $0<\delta <{\displaystyle \frac{3}{4}}$ and $max[{\displaystyle \frac{2b(e+1)(e+2)+2\delta +1}{4}},k_1^{EM}]<k<k_2^{EM}$. Under these conditions, the cooperation constitutes a win-win situation. The recycler’s optimal acquisition price is $p^{EM}={\displaystyle \frac{1}{2}}\left({\displaystyle \frac{r\left(2be\right(e+2)+2\delta +1)}{2b{(e+2)}^2+2\delta -8k+1}}+r\right)$, and the platform’s optimal innovation effort is $s^{EM}={\displaystyle \frac{r\left(2be\right(e+2)+2\delta +1)}{8k-\left(2b{(e+2)}^2+2\delta +1\right)}}$. The corresponding market demand, recycler’s profit, platform’s profit, augmented customer surplus, and social welfare are as follows: $D^{EM}={\displaystyle \frac{2r\left(b\right(e+2)-2k)}{2b{(e+2)}^2+2\delta -8k+1}}$, ${\pi }_r^{EM}={\displaystyle \frac{(1-\delta )4r^2{(b(e+2)-2k)}^2}{{\left(2b{(e+2)}^2+2\delta -8k+1\right)}^2}}$, ${\pi }_p^{EM}={\displaystyle \frac{r^2\left(4\delta {(b(e+2)-2k)}^2-k{(2be(e+2)+2\delta +1)}^2\right)}{{\left(2b{(e+2)}^2+2\delta -8k+1\right)}^2}}$, $AC{S}^{EM}={\displaystyle \frac{r^2\left(2b^2{(e+2)}^2+b\left({(2\delta +1)}^2+16e^2{k}^2+16k(\delta e-1)\right)+8k^2\right)}{{\left(2b{(e+2)}^2+2\delta -8k+1\right)}^2}}$, and $W^{EM}={\displaystyle \frac{r^2\left((2\delta +1)(b-k)-2b{e}^{2}k\right)}{2b{(e+2)}^2+2\delta -8k+1}}+{\displaystyle \frac{4(1-\delta )r^2{(b(e+2)-2k)}^2}{{\left(2b{(e+2)}^2+2\delta -8k+1\right)}^2}}$.

The feasible range identified in Proposition 2 is illustrated in Figure 5. Specifically, Proposition 2 shows that when the commission rate remains low (i.e., $0<\delta <{\displaystyle \frac{3}{4}}$) and the cost of sustainable innovation falls within a moderate range—bounded above by the green line and below by the red line in Figure 5 (i.e., $max\left[{\displaystyle \frac{2b(e+1)(e+2)+2\delta +1}{4}},k_1^{EM}\right]<k<k_2^{EM}$)—the ESG-driven platform can successfully establish cooperation with the monopolistic recycler. Once this cooperation is formed, the platform achieves a win-win outcome. Furthermore, the sustainable innovation effort s, determined by the platform, increases with the carbon offset rate e and the environmental benefit factor b. Simultaneously, the acquisition price p, set by the recycler, also rises in response to these factors. Additionally, as e and b increase, market demand D, recycler’s profit ${\pi }_R$, augmented consumer surplus $ACS$, and social welfare W all improve, reinforcing the positive impact of environmental considerations on the recycling market.

4.4. Comparison

From Propositions 1 and 2, we observe the existence of innovation cost boundaries that are critical for platform entry. By comparing the entry boundaries of profit-oriented and ESG-driven platforms, we find that platforms with different strategic objectives should adopt sustainable innovation programs with distinct cost structures. This insight highlights the fundamental differences in sustainability-driven innovation between a profit-maximizing platform and an ESG-driven platform. We formally present this finding in Proposition 3.

Proposition 3.

The maximum threshold of sustainable innovation cost that allows a profit-oriented platform to enter the market is strictly lower than the minimum threshold required for an ESG-driven platform:

$${\displaystyle \frac{1+\sqrt{1-\delta }}{4}}<max\left[{\displaystyle \frac{2b(e+1)(e+2)+2\delta +1}{4}},k_1^{EM}\right].$$

Proposition 3 establishes that the entry conditions for profit-oriented and ESG-driven platforms are mutually exclusive. In other words, achieving a “win-win” outcome in the recycling market requires a profit-oriented platform to focus on low-cost sustainable innovation (i.e., $k<{\displaystyle \frac{1+\sqrt{1-\delta }}{4}}$), whereas an ESG-driven platform must prioritize high-cost sustainable innovation (i.e., $k>max\left[{\displaystyle \frac{2b(e+1)(e+2)+2\delta +1}{4}},k_1^{EM}\right]$). This distinction highlights their inherently different strategic approaches to sustainability and market participation.

On the one hand, a profit-oriented platform would undertake only limited sustainable innovation, which would be insufficient to significantly enhance customer participation in recycling when the cost of sustainable innovation is high. Without a significant increase in recycling volume, the cooperation fails to generate sufficient profitability for the recycler, thereby preventing the platform from successfully entering the market. As a result, a profit-oriented platform can enter the recycling market only if it focuses on low-cost, sustainable innovation. On the other hand, an ESG-driven platform can enter the recycling market only if it commits to relatively high levels of sustainable innovation. This is because an ESG-driven platform is willing to invest more in sustainability efforts than a profit-oriented platform. In this case, as shown in Equation (7), the innovation effort has a positive effect on consumer surplus and environmental benefits but a negative effect on profitability. Thus, high innovation costs actually play a balancing role between profitability and the platform’s $ACS$ considerations.

This proposition offers valuable insights for platform strategists and sustainability-focused decision-makers. The mutually exclusive entry conditions suggest that profit-oriented and ESG-driven platforms will adopt fundamentally different sustainable innovation strategies. As illustrated by the real-world case mentioned in the introduction, Xianyu, aligned with ESG goals, integrates high-cost environmental initiatives such as the “Ant Forest” program and has been recognized as the only idle goods transaction platform to receive the “Top Ten Technological Innovations Empowering Green Transformation with Digital Technology” award. In contrast, JD.com, through its partnership with Aihuishou in the “One Yuan Green Action”, promotes low-cost, large-scale participation to boost operational efficiency. These examples demonstrate that although the two platform types follow distinct strategic paths, both can successfully enter the recycling market. For managers and policymakers, this distinction underscores the importance of aligning sustainability initiatives with platform identity and recognizing the signaling value of different types of sustainable innovation when designing incentives or partnerships.

5. Extension Cases: Duopolistic Recycling Market

In this section, we examine a duopolistic recycling market in which two competing recyclers, Recycler A and Recycler B, possess distinct recycling expertise. We separately analyze the cases of a profit-oriented platform and an ESG-driven platform, each collaborating with the recycler through sustainable innovation to enter the recycling market. Unlike the monopolistic case, where the platform exclusively partners with a single recycler, the duopolistic setting allows the platform to collaborate with only one of the two recyclers or establish partnerships with both for market entry.

To characterize the heterogeneity in recycling expertise between the two recyclers, we use ${\theta }_i$ ($i\in \{A,B\}$) to represent the hassle level associated with the expertise level of Recycler i, which influences customer perception and participation in recycling. Specifically, a lower level of recycling expertise negatively affects customer utility, as customers perceive higher expertise as more beneficial for the recycling process. In other words, a higher hassle level $\theta$ implies that customers require a stronger incentive to participate in recycling due to lower perceived expertise. For example, some recyclers offer door-to-door collection services or implement professional quality inspection processes, which significantly enhance recycling efficiency and improve the customer experience. In contrast, less professional recyclers may lack standardized procedures, provide non-transparent pricing, or fail to ensure a reliable recycling process, leading to a greater inconvenience for customers. We assume that Recycler A has lower expertise, while Recycler B has higher expertise. Thus, the hassle levels of the two recyclers should satisfy ${\theta }_A>{\theta }_B$. Without loss of generality, we normalize Recycler B’s expertise as ${\theta }_B=1$ and define Recycler A’s expertise as ${\theta }_A=\theta \in (1,2]$. This implies that $\theta$ not only represents the hassle level of Recycler A but also quantifies the expertise gap between the two recyclers. Recycler A’s lower expertise discourages customer participation, and as $\theta$ approaches 1, the expertise levels of the two recyclers become more similar, thereby intensifying competition between them. Conversely, as $\theta$ increases, the expertise gap between the two recyclers widens. We exclude trivial cases where $\theta \to \infty$, as this would resemble a monopolistic scenario in which Recycler B holds an overwhelming advantage, rendering Recycler A nonviable. Accordingly, the utility functions for customers choosing each recycler are defined as

$$u_A(p_A,s)=p_A-\theta v+s$$

(8)

for Recycler A, and

$$u_B(p_B,s)=p_B-v+s$$

(9)

for Recycler B, where $p_A$ and $p_B$ denote the acquisition prices set by Recycler A and Recycler B, respectively.

Building upon the previous model structure, we analyze the platform’s optimal decisions regarding sustainable innovation, assess the entry conditions, and explore the criteria for achieving a “win-win” situation in a duopolistic recycling market. We first examine the benchmark scenario, in which the platform has not yet entered the market, as illustrated in Figure 6a and denoted by the superscript †”. Subsequently, we investigate how both the profit-oriented platform and the ESG-driven platform collaborate with recyclers under competitive conditions. To clearly distinguish these scenarios, we use the superscripts “P” and “E” to continue representing the profit-oriented and ESG-driven platforms, respectively. In addition, the superscripts A”, B”, and T” are introduced to indicate cases where the platform collaborates with Recycler A, Recycler B, or both recyclers, respectively, as illustrated in Figure 6b–d.

5.1. No Cooperation

Before the platform enters the recycling market, the two recyclers compete in the market. Based on Equations (8) and (9), customers will choose Recycler A if their utility satisfies $u_A(p_A,s)\ge 0$ and $u_A(p_A,s)\ge u_B(p_B,s)$, while they will choose Recycler B if their utility satisfies $u_B(p_B,s)\ge 0$ and $u_A(p_A,s)<u_B(p_B,s)$. Consequently, customers with valuations in the range $v\in [0,{\displaystyle \frac{p_A-p_B}{\theta -1}}]$ will opt for Recycler A, whereas those with valuations in $v\in ({\displaystyle \frac{p_A-p_B}{\theta -1}},p_B]$ will choose Recycler B. Therefore, these two recyclers’ payoff functions can be given by

$$\underset{p_A}{max}{\pi }_A=\underset{p_A}{max}{\displaystyle \frac{\left(p_A-p_B\right)}{\theta -1}}\left(r-p_A\right)$$

(10)

$$\underset{p_B}{max}{\pi }_B=\underset{p_B}{max}\left(p_B-{\displaystyle \frac{p_A-p_B}{\theta -1}}\right)\left(r-p_B\right)$$

(11)

Using backward induction, we derive the equilibrium acquisition prices for the two recyclers in the absence of the platform, which are formally summarized in the following lemma.

Lemma 2.

In a duopolistic recycling market, before the platform emerges, the two competing recyclers set their optimal acquisition prices as follows: $p_A^{†}={\displaystyle \frac{3\theta r}{4\theta -1}}$, $p_B^{†}={\displaystyle \frac{(2\theta +1)r}{4\theta -1}}$. The corresponding market demand, profits, augmented customer surplus, and social welfare are given by $D_A^{†}={\displaystyle \frac{r}{4\theta -1}}$, $D_B^{†}={\displaystyle \frac{2\theta r}{4\theta -1}}$, ${\pi }_A^{†}={\displaystyle \frac{(\theta -1)r^2}{{(4\theta -1)}^2}}$, ${\pi }_B^{†}={\displaystyle \frac{4(\theta -1)\theta r^2}{{(4\theta -1)}^2}}$, $AC{S}^{†}={\displaystyle \frac{r^2\left(2b{(2e\theta +e)}^2+\theta (4\theta +5)\right)}{2{(4\theta -1)}^2}}$, $W^{†}={\displaystyle \frac{r^2\left(2b{(2e\theta +e)}^2+\theta (12\theta -1)-2\right)}{2{(4\theta -1)}^2}}$.

The equilibrium outcomes provide a baseline for understanding how recyclers compete for customers based on their expertise levels. Building on these results, we now examine how hassle level $\theta$ influences key decisions and overall market performance. Specifically, the following corollary identifies how changes in $\theta$ affect acquisition prices, market demand, profits, augmented customer surplus, and social welfare.

Corollary 1.

As θ increases, its impact on key decisions and market performance can be characterized as follows: ${\displaystyle \frac{\partial p_A^{†}}{\partial \theta }}<0$, ${\displaystyle \frac{\partial p_B^{†}}{\partial \theta }}<0$, ${\displaystyle \frac{\partial D_A^{†}}{\partial \theta }}<0$,${\displaystyle \frac{\partial D_B^{†}}{\partial \theta }}<0$. ${\displaystyle \frac{\partial {\pi }_A^{†}}{\partial \theta }}>0$ if $1<\theta <{\displaystyle \frac{7}{4}}$, and ${\displaystyle \frac{\partial {\pi }_A^{†}}{\partial \theta }}\le 0$, otherwise. ${\displaystyle \frac{\partial {\pi }_B^{†}}{\partial \theta }}>0$, ${\displaystyle \frac{\partial AC{S}^{†}}{\partial \theta }}<0$, ${\displaystyle \frac{\partial W^{†}}{\partial \theta }}<0$.

As demonstrated in Corollary 1, the acquisition prices and market demand for both Recycler A and Recycler B decrease as the hassle level $\theta$ increases. This is because a higher $\theta$ reflects a lower level of operational expertise for Recycler A, resulting in greater inconvenience for customers participating in the recycling process. To stimulate participation and secure sufficient recycling volume, Recycler A is compelled to lower its acquisition price $p_A$. In response, and in order to maintain its market share, Recycler B must also reduce its acquisition price $p_B$. Notably, due to its competitive advantage, Recycler B’s profit increases monotonically as Recycler A becomes less competitive. In contrast, the profit dynamics of Recycler A exhibit a non-monotonic pattern. Specifically, Corollary 1 reveals that when the hassle level $\theta$ is relatively low (i.e., $1<\theta <{\displaystyle \frac{7}{4}}$), the two recyclers exhibit a higher degree of homogeneity in recycling expertise, thereby intensifying market competition. As $\theta$ increases, the divergence in operational efficiency between Recycler A and Recycler B becomes more pronounced, leading to greater market differentiation and a weakening of competitive intensity. In response to this disadvantage, Recycler A reduces its acquisition price to attract more price-sensitive consumers, who typically exhibit lower valuations for used products. Although this pricing strategy diminishes Recycler A’s recycling volume, the increased profit margin per unit compensates for the volume loss, resulting in an initial improvement in overall profitability. However, when $\theta$ reaches a sufficiently high level (i.e., ${\displaystyle \frac{7}{4}}\le \theta <2$), Recycler A’s competitive position deteriorates to the extent that even enhanced margins cannot offset the decline in market participation, ultimately leading to a reduction in its profit. Thus, Recycler A’s profit follows a non-monotonic pattern—first increasing due to improved margins, and later decreasing as its relative disadvantage in the market becomes more severe.

Moreover, both augmented customer surplus and social welfare decline as Recycler A’s expertise deteriorates, i.e., with an increase in the hassle level $\theta$. This occurs for two main reasons. On the one hand, when $\theta$ is low, weakened competition results in a clearly segmented customer base for both recyclers, leading to lower acquisition prices and reduced recycling volume, which directly diminishes customer surplus and environmental benefits. On the other hand, when $\theta$ is high, Recycler A’s lower expertise increases the hassle disutility for customers participating in recycling, further reducing overall welfare.

5.2. Profit-Oriented Platform Entry

In this section, we examine the sustainable innovation strategies and entry conditions of a profit-oriented platform in a duopolistic recycling market, where the platform can adopt different modes of cooperation with recyclers. Specifically, we examine three cases, Case $PA$, Case $PB$, and Case $PT$, which correspond to the profit-oriented platform exclusively collaborating with Recycler A, Recycler B, or both recyclers simultaneously, respectively.

Taking Case $PT$ as an example, we define the profit function of the profit-oriented platform as follows:

$$\begin{array}{cc}\hfill \underset{s}{max}{\pi }_p^{PT}=& \underset{s}{max}\{\delta [D_A(r-p_A)+D_B(r-p_B)]-k{s}^2\}\hfill \\ \hfill s.t.& \left\{\begin{array}{c}{\pi }_A^{PT}\ge {\pi }_A^{†}\hfill \\ {\pi }_B^{PT}\ge {\pi }_B^{†}\hfill \\ {\pi }_p^{PT}\ge 0\hfill \\ p_A^{PT}>0\hfill \\ p_B^{PT}>0\hfill \end{array}\right.\hfill \end{array}$$

(12)

As discussed in the benchmark case, if the platform collaborates with both recyclers, it must ensure that the profits of Recycler A and Recycler B exceed their respective profits under a purely competitive recycling market without platform intervention. However, if the platform collaborates exclusively with a single recycler, it is not required to improve the non-cooperating recycler’s profit. Specifically, in Case $PA$, the constraint ${\pi }_B^{PA}\ge {\pi }_B^{†}$ can be relaxed, as Recycler B is not involved in the cooperation. Similarly, in Case $PB$, the constraint ${\pi }_A^{PB}\ge {\pi }_A^{†}$ can be relaxed, since Recycler A does not participate in the platform collaboration. In addition, the profit-oriented platform must guarantee that its own revenue remains non-negative, ensuring the financial viability of its operations. Lastly, both Recycler A and Recycler B must offer positive acquisition prices to ensure that customers receive financial incentives for recycling, thereby preventing donation scenarios.

To further examine the interaction between the platform and the two recyclers in a duopolistic recycling market, we exclude scenarios in which the platform partners with one recycler to enter the market while simultaneously driving the other recycler out. Such a scenario would produce outcomes similar to the monopoly case. Moreover, in practice, governments closely monitor antitrust cases, particularly those related to platform market entry. Therefore, for the first two cases, where the platform exclusively collaborates with one recycler, we establish Lemma 3 to outline the conditions under which the excluded recycler would be forced out of the market and thus exclude these cases from further analysis.

Lemma 3.

In a duopolistic recycling market, a profit-oriented platform will drive a recycler out of the market under the following conditions:

• Recycler B is eliminated if the platform exclusively collaborates with Recycler A, provided that the innovation cost satisfies $k\le {\displaystyle \frac{2\delta \theta -\delta }{8{\theta }^2-10\theta +2}}$;
• Recycler A is eliminated if the platform exclusively collaborates with Recycler B, provided that the innovation cost satisfies $k\le {\displaystyle \frac{\delta \theta (2\theta -1)}{4{\theta }^2-5\theta +1}}$.

Under the conditions presented in Lemma 3, the profit-oriented platform should exercise caution and avoid exclusive cooperation with a single recycler. Therefore, in the subsequent analysis, we focus solely on scenarios where both recyclers coexist in the recycling market. Next, we examine how the profit-oriented platform cooperates with recyclers in each case. For clarity, we present only the key equilibrium results in the main text, including the optimal acquisition prices set by the two recyclers, the optimal innovation effort determined by the profit-oriented platform, and the entry conditions. A detailed discussion of recycler profits and recycling volumes is provided in the Appendix A. The equilibrium outcomes for these three cases are formally summarized in Proposition 4.

Proposition 4.

In a duopolistic recycling market, the entry conditions, the optimal acquisition prices set by recyclers, and the sustainable innovation effort decisions of the profit-oriented platform under each feasible cooperation case are presented as follows:

• For Case $PA$: Cooperation with Recycler A is feasible if $0<\delta <1-{\displaystyle \frac{1}{{(4\theta -1)}^2}}$ and ${\displaystyle \frac{2\delta \theta -\delta }{8{\theta }^2-10\theta +2}}<k<{\displaystyle \frac{\left(\sqrt{1-\delta }+1\right)\theta (2\theta -1)}{4{\theta }^2-5\theta +1}}$. In this case, the platform’s optimal innovation effort is $s^{PA}={\displaystyle \frac{\delta (\theta -1)(2\theta -1)r}{(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)-\delta }}$. The optimal acquisition prices set by recyclers are $p_A^{PA}={\displaystyle \frac{r\left(\delta {(1-2\theta )}^2-3(\theta -1)\theta (4\theta -1)k\right)}{\delta -(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)}}$, and $p_B^{PA}={\displaystyle \frac{r\left(\delta \theta \right(1-2\theta )+(\theta -1\left)\right(4\theta -1\left)\right(2\theta +1\left)k\right)}{(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)-\delta }}$.
• For Case $PB$: Cooperation with Recycler B is feasible if $0<\delta <{\displaystyle \frac{1}{2}}+{\displaystyle \frac{1}{2{(4\theta -1)}^2}}$, and ${\displaystyle \frac{\delta \theta (2\theta -1)}{4{\theta }^2-5\theta +1}}<k<k_1^{PB}$. In this case, the platform’s optimal innovation effort is $s^{PB}={\displaystyle \frac{\delta (\theta -1)\left(\right(4\theta -3)\theta +1)r}{(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)-\delta }}$. The optimal acquisition prices set by recyclers are $p_A^{PB}={\displaystyle \frac{r\left(\delta \theta \right((7-2\theta )\theta -5)+\delta -3(\theta -1\left)\theta \right(4\theta -1\left)k\right)}{\delta -(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)}}$, and $p_B^{PB}={\displaystyle \frac{r\left(\delta \theta \right(2-3\theta )+(\theta -1\left)\right(4\theta -1\left)\right(2\theta +1\left)k\right)}{(\theta -1)\left({(4\theta -1)}^{2}k-4\delta \theta \right)-\delta }}$.
• For Case $PT$: Cooperation with two recyclers is feasible if $0<\delta <min\left({\displaystyle \frac{3\theta (5\theta -2)}{{(4\theta -1)}^2}},1-{\displaystyle \frac{{(2\theta r+r)}^2}{{(4\theta -1)}^2}}\right)$ and $max\left({\displaystyle \frac{\delta (\theta -1)(4\theta +1)}{(4\theta -1)\left(2\theta \right(2-r)-r-1)}},{\displaystyle \frac{\delta (\theta -1)(4\theta +1)}{3\theta (4\theta -1)}}\right)<k<{\displaystyle \frac{4{\theta }^2-3\left(\sqrt{1-\delta }+1\right)\theta -1}{{(4\theta -1)}^2}}$. In this case, the platform’s optimal innovation effort is $s^{PT}={\displaystyle \frac{\delta \left(\theta \right(3-4\theta )+1)r}{\delta (\theta -1)(4\theta +1)-{(4\theta -1)}^{2}k}}$. The optimal acquisition prices set by recyclers are $p_A^{PT}={\displaystyle \frac{r\left(\delta \theta \right(3-4\theta )+\delta +3\theta (4\theta -1\left)k\right)}{\delta \theta (3-4\theta )+\delta +{(4\theta -1)}^{2}k}}$, and $p_B^{PT}={\displaystyle \frac{r\left(\delta \theta (3-4\theta )+\delta +\left(8{\theta }^2+2\theta -1\right)k\right)}{\delta \theta (3-4\theta )+\delta +{(4\theta -1)}^{2}k}}$.

Proposition 4 establishes that, regardless of whether the platform adopts an exclusive cooperation strategy with a single recycler or collaborates with both recyclers simultaneously, successful market entry is contingent upon meeting specific economic conditions. Specifically, there exists a threshold for the commission rate, as well as lower and upper bounds for the sustainable innovation cost, such that when the commission rate remains low and the innovation cost falls within a feasible interval, the platform can profitably enter the recycling market. What’s more, the entry conditions in the duopoly case indicate that a profit-oriented platform can extract a higher commission rate when cooperating with Recycler A, whereas it can only extract a lower commission rate when cooperating with Recycler B, i.e., $1-{\displaystyle \frac{1}{{(4\theta -1)}^2}}>{\displaystyle \frac{1}{2}}+{\displaystyle \frac{1}{2{(4\theta -1)}^2}}$. This outcome is intuitive, as Recycler B possesses higher expertise, making it more difficult to secure higher revenue for the recycler. Consequently, the platform can only impose a lower commission rate in this case. Furthermore, whether the platform can extract a higher commission rate when cooperating with both recyclers depends on the profitability of the overall recycling process, specifically, the average residual value of used items (r).

However, while cooperation with Recycler A makes it easier to reach the required revenue level, the partnership yields limited profitability for both parties, directly affecting the affordable innovation cost. In contrast, cooperation with Recycler B generates higher overall profit, enabling a higher innovation cost. However, for the platform, achieving the required revenue level becomes more challenging due to Recycler B’s higher profitable ability.

Additionally, the feasibility of cooperating with both recyclers depends on the profitability of the overall recycling process. Thus, we next analyze how the platform selects its cooperation partner to ensure entry feasibility. Furthermore, we provide insights into how the platform chooses its cooperation partner with respect to profit maximization and achieving a “win-win” situation.

Corollary 2.

Aiming to achieve different cooperation goals, the cooperation strategies of a profit-oriented platform are summarized as follows:

1.

Entry Feasibility: To minimize the innovation cost required for market entry, the platform should

• If $0<r<{\displaystyle \frac{2\theta \left(\theta \right(4\theta -11)+5)+1}{1-4{\theta }^2}}$, cooperating with both recyclers requires the lowest innovation cost.
• If ${\displaystyle \frac{2\theta \left(\theta \right(4\theta -11)+5)+1}{1-4{\theta }^2}}\le r<1$, cooperating exclusively with Recycler A requires the lowest innovation cost.

2.

Profit Maximization: Given that the platform meets the entry conditions

• If $k<k_2^{PB}$, cooperating with Recycler B yields the highest profit.
• If $k\ge k_2^{PB}$, cooperating with both recyclers maximizes the platform’s profit.

3.

“Win-Win” Situation: A “win-win” situation, where all parties benefit, can only be achieved when the platform cooperates with both recyclers simultaneously.

From Corollary 2, we first derive insights into the entry feasibility of a profit-oriented platform in a duopolistic recycling market. The platform’s choice of cooperation partner depends on the average residual value of used items, r. If the average residual value is low ( i.e., $0<r<{\displaystyle \frac{2\theta \left(\theta \right(4\theta -11)+5)+1}{1-4{\theta }^2}}$), the unit profit margin per recycled item is also low. Consequently, both recyclers lack incentives to set a higher acquisition price, leading to reduced customer participation. In this scenario, the platform benefits from collaborating with both recyclers, as sustainable innovation implemented on the platform has a greater marginal impact on attracting customers, thereby increasing recycling volume. Conversely, if the average residual value is high ( i.e., ${\displaystyle \frac{2\theta \left(\theta \right(4\theta -11)+5)+1}{1-4{\theta }^2}}\le r<1$), the unit marginal profit is also high. As a result, Recycler B leverages its higher recycling expertise to attract customers, while Recycler A, with lower expertise, captures only a small share of the market profit. In this case, it is easier to enhance Recycler A’s profitability and establish a successful partnership with Recycler A alone. However, due to its higher expertise, Recycler B inherently holds a competitive advantage in the recycling market. As a result, the platform must meet more stringent conditions to establish cooperation with Recycler B. This analysis suggests that platform operators should assess the average residual value of used items as a strategic market signal when selecting recycler partners. In low-value markets, joint cooperation is more effective for stimulating customer participation, whereas in high-value contexts, collaboration with a single, weaker recycler may suffice for market entry with lower innovation investment. Strategically allocating innovation resources based on item value distribution can thus improve entry efficiency.

Second, given the overlapping range of innovation costs that allow the profit-oriented platform the flexibility to choose which recycler to collaborate with, we observe that cooperating with the low-expertise recycler (Recycler A) consistently yields the lowest profit. However, the platform’s choice between cooperating exclusively with Recycler B or engaging with both recyclers depends on the innovation cost, k. When the innovation cost is relatively low (i.e., $k<k_2^{PB}$), partnering with Recycler B and further enhancing its competitive advantage enables the platform to achieve higher profitability. In contrast, when the innovation cost is high (i.e., $k\ge k_2^{PB}$), the platform’s ability to invest in innovation efforts becomes constrained. Under these conditions, cooperating with both recyclers while maintaining a certain level of market competition allows the platform to maximize its profit potential. This finding highlights the importance of cost-sensitive partner selection strategies. Profit-oriented platforms should favor exclusive partnerships with high-expertise recyclers when innovation costs are manageable, as such collaborations yield stronger financial returns. However, when cost pressures limit innovation intensity, engaging both recyclers ensures competitive balance and broader market coverage, ultimately protecting profitability under constrained conditions.

Lastly, achieving a “win-win” situation is only possible when the platform collaborates with both recyclers simultaneously. This is because, in scenarios where the platform partners with only one recycler, the non-cooperating recycler experiences a decline in profitability, preventing an outcome in which all parties benefit.

5.3. ESG-Driven Platform Entry

In this section, we consider an ESG-driven platform in a duopolistic recycling market, which incorporates not only its profit but also consumer surplus and environmental benefits into its sustainability objectives. Similarly, the ESG-driven platform can engage in three different cooperation modes, Case $EA$, Case $EB$, and Case $ET$, which correspond to the platform exclusively collaborating with Recycler A, Recycler B, or both recyclers simultaneously, respectively. As an example, we define the objective function of the ESG-driven platform for Case $ET$ as follows:

$$\begin{array}{cc}\hfill \underset{s}{max}({\pi }_p^{ET}+ACS)=& \underset{s}{max}\{\delta [D_A(r-p_A)+D_B(r-p_B)]-k{s}^2+{\int }_0^{D_A}{u}_A(p_A,s)dv+\hfill \\ \hfill & {\int }_0^{D_B}{u}_B(p_B,s)dv+b{[e(D_A+D_B)+s]}^2\}\hfill \\ \hfill s.t.& \left\{\begin{array}{c}{\pi }_A^{ET}\ge {\pi }_A^{†}\hfill \\ {\pi }_B^{ET}\ge {\pi }_B^{†}\hfill \\ {\pi }_p^{ET}\ge 0\hfill \\ p_A^{ET}>0\hfill \\ p_B^{ET}>0\hfill \end{array}\right.\hfill \end{array}$$

(13)

First, we outline the conditions under which the platform, when exclusively cooperating with one recycler, causes the market exit of the non-cooperating recycler, as stated in Lemma 4.

Lemma 4.

In a duopolistic recycling market, an ESG-driven platform will drive a recycler out of the market under the following conditions:

• Recycler B is eliminated if the platform exclusively collaborates with Recycler A, provided that the innovation cost satisfies $k\le k_1^{EA}$;
• Recycler A is eliminated if the platform exclusively collaborates with Recycler B, provided that the innovation cost satisfies $k\le k_1^{EB}$.

In the subsequent analysis, we continue to focus on scenarios where both recyclers remain active in the recycling market. We present the key equilibrium results, including the optimal acquisition prices set by the recyclers, the optimal innovation effort chosen by the ESG-driven platform, and the entry conditions, which are formally summarized in Proposition 5.

Proposition 5.

In a duopolistic recycling market, the entry conditions, the optimal acquisition prices set by recyclers, and the sustainable innovation effort decisions of the ESG-driven platform under each feasible cooperation case are presented as follows:

• For Case $EA$: Cooperation with Recycler A is not feasible.
• For Case $EB$: Cooperation with Recycler B is feasible if $0<\delta <{\displaystyle \frac{1}{2}}+{\displaystyle \frac{1}{2{(4\theta -1)}^2}}$, and $k_2^{EB}<k<k_3^{EB}$. In this case, the platform’s optimal innovation effort is $s^{EB}={\displaystyle \frac{2(\theta -1)r\left(\theta \left(2be(\theta (e+4\theta -5)+1)+{(1-2\theta )}^2\right)+\delta (\theta -1)((4\theta -3)\theta +1)\right)}{2{\left(4{\theta }^2-5\theta +1\right)}^{2}k-2b{(\theta (e+4\theta -5)+1)}^2-{(1-2\theta )}^2\left(2\delta (\theta -1)+{(1-2\theta )}^2\right)}}$. The optimal acquisition prices set by recyclers are $p_A^{EB}={\displaystyle \frac{3\theta r}{4\theta -1}}+{\displaystyle \frac{(2\theta -1)}{4\theta -1}}{\displaystyle \frac{2(\theta -1)r\left(\theta \left(2be(\theta (e+4\theta -5)+1)+{(1-2\theta )}^2\right)+\delta (\theta -1)((4\theta -3)\theta +1)\right)}{2{\left(4{\theta }^2-5\theta +1\right)}^{2}k-2b{(\theta (e+4\theta -5)+1)}^2-{(1-2\theta )}^2\left(2\delta (\theta -1)+{(1-2\theta )}^2\right)}}$, and $p_B^{EB}={\displaystyle \frac{(2\theta +1)r}{4\theta -1}}-{\displaystyle \frac{1}{4\theta -1}}{\displaystyle \frac{2(\theta -1)r\left(\theta \left(2be(\theta (e+4\theta -5)+1)+{(1-2\theta )}^2\right)+\delta (\theta -1)((4\theta -3)\theta +1)\right)}{2{\left(4{\theta }^2-5\theta +1\right)}^{2}k-2b{(\theta (e+4\theta -5)+1)}^2-{(1-2\theta )}^2\left(2\delta (\theta -1)+{(1-2\theta )}^2\right)}}$.
• For Case $ET$: Cooperation with two recyclers is feasible if $0<\delta <min\left[{\displaystyle \frac{3\theta (5\theta -2)}{{(1-4\theta )}^2}},1-{\displaystyle \frac{{(2\theta r+r)}^2}{{(1-4\theta )}^2}}\right]$ and $max[k_1^{ET},k_2^{ET},k_3^{ET}]<k<k_4^{ET}$. In this case, the platform’s optimal innovation effort is
  $s^{ET}={\displaystyle \frac{r\left(2be\right(2\theta +1\left)\right(2(e+2)\theta +e-1)+2\delta (\theta -1\left)\right(4\theta +1)+\theta (4\theta +5\left)\right)}{(6\delta -5)\theta +2\delta +2{(4\theta -1)}^{2}k-2b{(2(e+2)\theta +e-1)}^2-4(2\delta +1){\theta }^2}}$. The optimal acquisition prices set by recyclers are $p_A^{ET}={\displaystyle \frac{3\theta r}{4\theta -1}}-{\displaystyle \frac{\theta -1}{4\theta -1}}{\displaystyle \frac{r\left(2be\right(2\theta +1\left)\right(2(e+2)\theta +e-1)+2\delta (\theta -1\left)\right(4\theta +1)+\theta (4\theta +5\left)\right)}{(6\delta -5)\theta +2\delta +2{(4\theta -1)}^{2}k-2b{(2(e+2)\theta +e-1)}^2-4(2\delta +1){\theta }^2}}$, and
  $p_B^{ET}={\displaystyle \frac{(2\theta +1)r}{4\theta -1}}-{\displaystyle \frac{2(\theta -1)}{4\theta -1}}{\displaystyle \frac{r\left(2be\right(2\theta +1\left)\right(2(e+2)\theta +e-1)+2\delta (\theta -1\left)\right(4\theta +1)+\theta (4\theta +5\left)\right)}{(6\delta -5)\theta +2\delta +2{(4\theta -1)}^{2}k-2b{(2(e+2)\theta +e-1)}^2-4(2\delta +1){\theta }^2}}$.

After deriving the equilibrium decisions of the recyclers and the ESG-driven platform, we further analyze how the platform selects its cooperation partner when aiming for entry feasibility and a “win-win” situation. Since the ESG-driven platform does not prioritize profit generation, we omit profit maximization as a cooperation objective.

Corollary 3.

Aiming to achieve different cooperation goals, the cooperation strategies of an ESG-driven platform are summarized as follows:

1.

Entry Feasibility: An ESG-driven platform cannot successfully enter the recycling market by collaborating exclusively with a low-expertise recycler. However, entry is feasible through collaboration with a high-expertise recycler or by partnering with both recyclers simultaneously.

2.

“Win-Win” Situation: A mutually beneficial (“win-win”) outcome can only be achieved when the platform collaborates with both recyclers simultaneously.

From Proposition 5 and Corollary 3, we observe that cooperation cannot be established between the ESG-driven platform and Recycler A alone. This is primarily due to the platform’s inherent commitment to making greater sustainable efforts, which often requires sacrificing its own profit to enhance consumer surplus and environmental benefits. In such a scenario, the platform’s financial viability is compromised, as the low-expertise recycler’s weak competitiveness makes it challenging for the platform to generate a positive profit. In the other two cases, the ESG-driven platform can successfully cooperate either with Recycler B or simultaneously with both recyclers to enter the duopolistic recycling market. Finally, a “win-win” situation, in which both the platform and recyclers achieve mutually beneficial outcomes, is only realized when the ESG-driven platform collaborates with both recyclers. Even if an ESG-driven platform enters the recycling market and partners with a single recycler, the non-cooperating recycler will be worse off compared to the pre-entry scenario. These findings highlight that ESG-driven platforms, due to their stronger sustainability commitments, face stricter cooperation constraints and must be selective in choosing partners who possess sufficient operational expertise. Collaborating with low-capability recyclers not only undermines the platform’s financial viability but also limits the realization of environmental and consumer welfare goals. Therefore, for ESG-oriented platforms to achieve both mission alignment and operational feasibility, forming inclusive partnerships—particularly with a mix of high- and low-expertise recyclers—is essential. Moreover, policymakers and platform operators should recognize that achieving system-wide sustainability and mutual benefit often requires multi-party collaboration, rather than isolated bilateral arrangements.

6. Discussion

Driven by the increasing involvement of digital platforms, the second-hand e-commerce market has undergone significant transformation, as demonstrated by real-world cases. This study examines the entry strategies of both a profit-oriented platform and an ESG-driven platform in the recycling market, with a particular focus on their cooperation with recyclers through sustainable innovation. We develop a theoretical model to analyze the entry conditions, the feasibility of achieving a “win-win” outcome, and the cooperation strategies of these two platform types within both monopolistic and duopolistic recycling markets. By distinguishing between profit-oriented and ESG-driven platforms, we offer a comprehensive framework for understanding how platforms with different sustainable innovation objectives strategically enter the recycling market.

In a monopolistic market structure, we identify the entry conditions for both platform types, defining the lower and upper bounds of sustainable innovation costs. Our findings indicate that both profit-oriented and ESG-driven platforms can successfully enter the recycling market within these sustainable innovation cost boundaries. Their involvement increases customer participation, enhances recycling volume, and improves consumer surplus and social welfare, ultimately creating a “win-win” situation. A key distinction between these platforms lies in their entry conditions, which do not overlap, underscoring their fundamentally different approaches to sustainability. A profit-oriented platform can only enter the market if the cost of sustainable innovation is low, as high costs discourage investment in sustainability efforts, making cooperation unprofitable for the recycler. Conversely, an ESG-driven platform requires a higher level of sustainable innovation cost, as sustainable innovation plays a crucial role in balancing consumer surplus, environmental benefits, and platform profitability. If the innovation cost is too low, the sustainability objectives will outweigh profit considerations, leading to excessive investment in sustainability efforts, which may undermine the platform’s profitability and potentially result in operational losses. These results suggest that platform managers should align their sustainability investment levels with their core strategic orientation. Profit-driven platforms should prioritize low-cost, efficiency-focused innovations (e.g., JD.com’s One Yuan Green Action), while ESG-driven platforms must carefully balance environmental goals with financial viability (e.g., Xianyu’s Ant Forest initiative). Policymakers can use observed investment patterns as signals to identify platform orientation and design differentiated incentives accordingly.

We extend our analysis to a duopolistic recycling market, where a platform can strategically choose to cooperate with Recycler A, Recycler B, or both recyclers simultaneously, depending on its cooperation objectives. For a profit-oriented platform, we establish the entry conditions and provide insights into its optimal cooperation strategy with recyclers. For entry feasibility, when the average residual value of used items is low, the unit profit margin per recycled item is also low, leading to low acquisition prices set by recyclers. In this case, the platform benefits most from cooperating with both recyclers, as sustainable innovation has a greater impact on attracting customers and increasing recycling volume through customer participation. Conversely, if the average residual value is high, the low-expertise recycler captures only a small market share, making it easier for the platform to enhance Recycler A’s profitability and establish cooperation. For profit maximization, within the range of innovation costs that allow flexibility in selecting a cooperation partner, partnering with Recycler A consistently yields the lowest profit. The platform achieves maximum profitability either by exclusively cooperating with Recycler B or by collaborating with both recyclers. If the innovation cost is low, exclusive cooperation with Recycler B maximizes profitability by enhancing its competitive advantage. Conversely, if the innovation cost is high, the scope of sustainable efforts becomes constrained, making joint cooperation with both recyclers more beneficial while maintaining market competition. For achieving a “win-win” outcome, a mutually beneficial outcome can only be achieved when the platform cooperates with both recyclers, as exclusive cooperation with a single recycler reduces the non-cooperating recycler’s profitability. Platform operators should dynamically adjust their partnership strategies based on market characteristics such as residual value and recycler heterogeneity. When the market environment limits profitability, inclusive cooperation with multiple recyclers may offer both higher customer engagement and broader ecosystem value. Strategic collaboration should also consider long-term partner viability, not just short-term margin maximization.

We further analyze the entry conditions for an ESG-driven platform under the same three cooperation scenarios. For entry feasibility, an ESG-driven platform cannot enter the market when collaborating with a low-expertise recycler, as its commitment to consumer surplus and environmental benefits necessitates greater innovation efforts, increasing innovation costs, and reducing financial viability. In contrast, the ESG-driven platform can successfully enter the market by partnering with a high-expertise recycler or by collaborating with both recyclers simultaneously. For achieving a “win-win” outcome, similar to the profit-oriented platform, a “win-win” situation is only realized when the ESG-driven platform cooperates with both recyclers, ensuring mutually beneficial outcomes for all parties involved. For ESG-oriented platforms, achieving financial sustainability while upholding social and environmental goals requires selecting capable partners or building cooperative ecosystems. Single-party partnerships with weaker recyclers may compromise mission fulfillment and entry feasibility. A dual-recycler cooperation strategy can help ESG-driven platforms distribute innovation efforts more effectively and preserve long-term impact.

This study highlights the strategic interplay between platforms, recyclers, and consumers in the recycling market, emphasizing the critical role of sustainable innovation in market entry and cooperation strategies. We investigate and compare the innovation decisions, entry conditions, and cooperation strategies of profit-oriented and ESG-driven platforms within monopolistic and duopolistic recycling markets. Through this analysis, we derive managerial insights that contribute to a deeper understanding of platform-recycler interactions and the strategic implications of sustainable innovation in the recycling industry.

Despite these insights, several open questions remain. First, the proposed model simplifies complex real-world operations and may not fully capture the intricacies of multi-tier or networked supply chains. In practice, recycling systems often involve multiple layers of intermediaries, a dynamic flow of materials, and coordination challenges across different stakeholders. Additionally, capacity constraints—both on the recycler side and in terms of platform capabilities—are not explicitly modeled, which could significantly affect cooperation outcomes and investment incentives. Second, the model does not consider policy interventions such as government subsidies, carbon taxes, or extended producer responsibility (EPR) regulations. These instruments are increasingly influential in shaping firm behavior and market entry decisions in sustainability-focused industries. Incorporating such policy tools would allow future studies to assess how regulatory mechanisms interact with platform strategies and influence the feasibility of sustainable innovation. Third, the current analysis is purely theoretical and lacks empirical validation. While the model is motivated by real-world practices, its assumptions and outcomes have not been tested against data from actual platform-recycler collaborations. Future research could use field data, case studies, or experimental methods to empirically examine the effectiveness of platform entry strategies and cooperation mechanisms, thereby enhancing the robustness and applicability of the theoretical insights. By addressing these limitations, future research can offer a more comprehensive and context-sensitive understanding of platform-driven sustainability transitions in recycling markets.