Sustainable Governance of Digital Platform Ecosystem: A Life Cycle Perspective Through Multiple Governance Parties

Abstract

Today, the digital economy has pursued the required social and economic development, and enterprises can achieve green transition and long-term sustainability by relying on the digital platform ecosystem. However, the development of the digital economy and the rapid growth of platform governance have also generated some problems. This study’s purpose was to explore the issue of green governance of the digital platform ecosystem and to find effective methods of such governance under different life cycle stages based on multiple parties. This study used the tripartite evolutionary game method and analyzed the changes and effects of platform green governance under different development stages from the perspectives of government, platforms, and enterprises. The results indicate that in the early stages, digital platforms primarily relied on the government’s obligation to regulate. With the digital platform ecosystem’s evolution, the platform organization green governance and settled enterprises’ green transition effects gradually appeared, establishing the three common constraints of governance mode. Finally, when the digital platform ecosystem is in its self-renewal period, the government effect is reduced, with the platform itself and settled enterprises taking primary roles. The goal of this study is to enrich the theoretical basis of platform green governance and enlighten the management of different governance subjects.

1. Introduction

A worrying increase in global energy consumption, resource depletion, and stringent environmental regulations has been brought on by the present comprehensive economic development model of high input, high consumption, and high pollution, as well as the acceleration of global urbanization and industrialization [1,2,3]. As a result, academics and decision-makers are increasingly concentrating on striking a balance between environmental preservation and economic growth [4,5]. Enterprises are essential pillars of economic growth and play a major role in resource use and climate change [6]. Enhancing enterprises’ green transition performance is investigated as a necessary and practical means of achieving economic and environmental balance [7,8].

In today’s rapid development of the digital economy, very few companies can make the green transition by themselves. Conceptualizing the company as an autonomous, independent entity fighting for competitive advantage does not fully explain today’s reality [9]. In the technology-driven digital world, many of the largest and most successful companies now operate as “digital platforms”, and attempt to rely on the platform to carry out green governance. Such organizations have subverted many industries, including retail and manufacturing, and are actively entering new areas such as financial services [10,11]. In the past few decades, digital platforms have redefined business models, promoted the market allocation of green elements, lowered the threshold of green transformation for SMEs through standardized services, and created huge value for society [12,13].

To realize the full potential of the green governance function on digital platforms, a new trend in strategic development is the construction of a digital platform-based ecosystem [14]. Ecosystem theory regards enterprises as part of a highly interdependent and interconnected network of collaborative and competitive entities. Moore [15] believes that members of the ecosystem have common goals and visions and develop together in a dynamic business environment. The digital platform and the stakeholders interacting with it constitute the platform ecosystem [16]. Previous studies have explained this ecosystem based on different theories. The social network theory perspective goes back to Rochet and Tirole (2003), who examined digital platforms in the presence of network externalities [17]. They described how the value for one side of the market increases as the number of actors on the other side increases in the platform. From the resource-based view (RBV), the digital platform ecosystem is viewed as a collection of technical resources and software-based platforms; that is, extensible codebases that provide core functionality, supplemented by modular services [18,19]. Based on this, we consider the digital platform ecosystem as a socio-technical system encompassing both technological elements (e.g., software, hardware, and architecture) and social elements (e.g., activities, actors, processes, rules, and standards).

A key element of this perspective is how the platform ecosystem is best governed [20], particularly considering the multiple, differing interests that must be balanced among the set of (previously unrelated) actors [21]. The digital platform ecosystem includes the government, enterprises, scientific research institutions, financial institutions, intermediaries, and other stakeholders. In the digital platform ecosystem, digital platforms utilize digital technologies to develop and control green resources beyond the scope of the company, create value by facilitating multi-party connectivity, and affect enterprises through network effects [16]. Different organizations continue to use digital technology to reconstruct production relations, build an ecological governance chain through data integration, rule co-construction, and value redistribution, and help enterprises improve green transformation performance.

However, the existing research on the green transition of enterprises focuses on government environmental regulations, which attempt to find effective ways of promoting the green production of platforms and enterprises through mandatory government measures, including pollutant emissions requirements, green credit policies, and subsidies [22,23,24,25]. Transaction costs, the impacts of crowding out, and the hazards of rent-seeking restrict the role of environmental policy in directing green transition of enterprises [26]. These studies, however, tend to regard platform governance tools as influenced by external factors. They ignore the internal self-restraint of uneven platforms. More notably, the existing literature generally lacks any micro perspective, such as public evaluation, the intention of enterprise green transformation, and other factors. In contrast, the literature pays excessive attention to the macro-level relationships between GF and its returns, environmental quality, CO₂ emissions, and green total factor production [27,28,29,30,31,32,33,34]. Therefore, previous studies have not explored the green governance of the digital platform ecosystem from a multi-agent perspective.

In reality, the green transition for enterprises actually entails teamwork to support green technology innovation, operational efficiency enhancement, pollution prevention, and social responsibility initiatives [35]. Internal growth inside an organization can drive this effort; for example, it might increase R&D spending to speed the creation of green goods [36], and go through a clean technology revolution to produce in an eco-friendly manner. Beyond relying on internal forces, platform ecosystems have distinctive advantages in external avenues [37]. The digital platform ecosystem builds a systematic support framework for enterprises’ green transformation by integrating technology, data, and resources and collaborating with multiple entities [38]. Its core value is to reconstruct the traditional linear value chain into a dynamic and cyclic green ecological network, and to provide enterprises and governments with scientific basis such as carbon emission monitoring and energy efficiency assessment through real-time data collection and analysis. While previous studies mostly considered platform green governance’s impact from the perspective of external factors, this study fills the research gap by focusing on the platform’s common internal and external factors, taking into account external government constraints, platform autonomy, and the impact of enterprise behaviors on the platform. We study the influences on platform green governance from a multiple-parties perspective.

However, the digital platform ecosystem’s evolutionary ecosystem is not static [39,40]. As a complex socio-technical ecosystem, the digital platform-based ecosystem also has an unexpected evolutionary trajectory, which is still an elusive topic in the early literature [41]. Life cycle theory proposes several stages in the evolutionary process, tracking phenomena from birth and growth to maturity and decline [42]. Researchers and managers are paying increasing attention to understanding how platform ecosystems are formed and managed, and how they evolve over time [43]. Teece [44] uses a four-stage model of birth, expansion, leadership, and self-renewal to analyze the needs of each stage of the platform life cycle and its dependence on high-level dynamic capability categories. Muzellec et al. [45] divided the development of multi-sided platforms into embryonic, emerging, growth, and maturity stages. Isckia et al. [46] designed a life cycle for platform-based ecosystems including the birth, expansion, maturity, leadership, and renewal stages. Therefore, different digital platform development stages should have different green governance methods.

Although these studies detail the evolution of successful platform ecosystems, they are still mostly descriptive [47]. Previous research has recognized that digital economy architecture evolves over time, accompanied by an increasing number of platform ecosystem participants and co-evolutionary dynamics [48]. However, this body of work still lacks a process-oriented perspective on how green governance mechanisms within digital platforms evolve and develop [49]. We therefore propose that the evolution of platform ecosystems represents a process of gradual maturation and renewal [50]. To effectively study green governance requires a life cycle perspective. Multi-agent green governance will exhibit distinct characteristics at different stages of the platform’s life cycle.

In summary, this study’s purpose is to explore the differences in multi-agent governance under different life cycles of the digital platform ecosystem’s development. This study adopts a four-stage model of birth, expansion, leadership, and self-renewal to analyze the governance needs for each stage of the digital platform. The hypothesis is that there are three main agents in the digital platform ecosystem: government, platform, and enterprise. The government will implement valid or invalid regulations. The platform will choose green governance and green negligence. The enterprise will choose green transition or green negligence. Through the evolutionary game method, we study the development of different subjects (government, platform, and settled enterprises) in the digital platform ecosystem, including co-governance effects at different stages. The study’s objectives are to identify effective methods of platform green governance in different life cycle stages, and to enlighten methods of digital platform green development and enterprises green transformation. Thus, in the remainder of this article, we first propose model assumptions and then construct a tripartite evolutionary game model, which presents the results by constructing contributions from different platform ecosystem perspectives and influence factors. Finally, we discuss the propositions generated from this analysis to propose suggestions for digital platform ecosystem development and enterprise green transition.

2. Evolutionary Game Model

2.1. Model Variables and Assumptions

To better explore the dynamic adjustment of regulatory strategies alongside changes in the digital platform ecosystem’s life cycle, we adopted the tripartite evolutionary game method. There are three reasons for this choice [51]: (1) Evolutionary game theory can simulate the nonlinear trajectories of key variables—such as government regulatory intensity, platform governance costs, and enterprises’ willingness to participate—through replicator dynamic equations. (2) Platform green governance involves bounded rationality by governments, platforms, and enterprises. Unlike traditional game theory, which assumes fully rational actors, evolutionary game theory accommodates strategy adjustments through trial-and-error learning, better reflecting real-world decision-making scenarios. (3) Evolutionary game theory can construct a tripartite income matrix and reveal governance’s dependence on subject strategy. Based on the previous discussion and some assumptions, this study’s object is the digital platform ecosystem, but because its green governance is affected by multiple agents, a tripartite evolutionary game model including the government, platform organizations, and enterprises is proposed.

2.1.1. Assumption 1

Digital platforms have constraints related to local government laws and regulations. Under such effective governance, if the platform organization does not conduct green governance, it will face punishment; however, if green governance is maintained, the platform organization will also receive subsidies or awards, although there is a related government expense for platform regulation. For example, the Regulations on the Administration of Green Packaging for E-commerce in Zhejiang Province require the platform to establish a traceability system for the whole life cycle of packaging. The platform meeting these standards can obtain annual operating subsidies and enjoy priority quotas for logistics carbon emission rights trading. Enterprises that fail to achieve the packaging target will be charged for ecological compensation at 3% of the average daily turnover. However, platform green production can enhance the government’s reputation. If the government does not take any platform regulation measures, it will also face public dissatisfaction and complaints.

$E_1$ is the government’s social reputation brought about by the platform green governance; $\alpha$ is the social sensitivity of the green production problems; $C_1$ is the government governance costs for the platform; $\theta$ is the current level of green governance on the platform; $s$ is the platform green autonomous subsidies; $l$ is the punishment for platform green negligence; $m$ is the benefit for the government from platform green governance; $n$ is the loss for the government from the platform green governance negligence; $r$ is the benefits brought to the government by enterprises’ cooperation with platform green governance; and $p$ is the loss caused by consumer complaints faced by the government’s failure to regulate.

2.1.2. Assumption 2

For the platform organization, in addition to daily operating costs, there is an expense for green governance; if the green governance effect is good, a good reputation will develop among settled enterprises. Improving its own reputation will bring simultaneous benefits to the government. For example, to reduce carbon emissions in its logistics operations, Alibaba’s Cainiao platform must invest in green governance initiatives in addition to covering standard operating costs, such as warehousing and distribution. These green investments include developing an electronic waybill system, promoting intelligent packaging algorithms, sourcing biodegradable packaging materials, and establishing carbon emission monitoring platforms. Such measures require additional investment in technology research and development funds and environmentally friendly material procurement. This includes using algorithmic optimization to reduce package transportation miles and circular delivery boxes instead of disposable cartons. Such a logistics enterprise wins praise from consumers for “low-carbon distribution” because of their participation in the green alliance. This industry demonstration effect then attracts participation from additional environmental protection brands. However, if a platform ignores green governance, this can cause damage to the platform’s reputation and will also bring losses to the government.

$E_2$ is the basic operating income of the platform organization; $C_2$ is the basic operating cost of the platform organization; $C_3$ is the additional cost for platform green governance; $h$ is the reputation improvement resulting from the platform’s green governance; and $k{v}^2$ is the complaint loss faced by a green governance negligence platform ($k$ is the loss coefficient).

2.1.3. Assumption 3

For settled enterprises, if they can actively participate in platform green governance, it promotes platform regulation, which is related to settled enterprises’ awareness of green transition. Meanwhile, if a platform ignores green governance, it will cause some losses to settled enterprises. For example, Meituan’s “Qingshan Plan” developed a green points system, requiring merchants to accumulate points through energy conservation, emission reduction, use of environmentally friendly packaging, and other behaviors. This links to platform ranking and traffic allocation. To obtain a greater number of points and actively transform the supply chain, the system deeply links firms’ green transformation consciousness to the platform governance mechanism, forming a two-way constraint and incentive mechanism.

$C_4$ is the cost of settled enterprises relying on the platform for green transformation; $\beta$ is the enthusiasm of settled enterprises to cooperate with platform green governance; $E_3$ is the welfare brought by platform green governance to settled enterprises; $w$ is the hindrance of settled enterprises green transition for platform green governance negligence; and $u$ is the level of improving settled enterprises’ awareness of green transition.

2.2. Game Strategy

Governments, platform organizations, and settled enterprises belong to an evolutionary game group with bounded rationality. Based on the above assumptions, if the government can actively manage the platform, it will pay a certain governance cost. However, a good platform can bring a good social reputation to the government, and in turn, the government can also earn profits. If the platform organization can achieve green governance, it can obtain relevant government subsidies and become favored by settled enterprises. If settled enterprises cooperate in platform governance, they can make the green transition easier. However, if the platform does not act with green governance, it will be punished by the government. Simultaneously, the platform’s reputation will be affected.

Based on the above analysis, beginning with the different behavioral strategies of the government, platform, and settled enterprises, this study constructs the payment matrix of the tripartite evolutionary game, as presented in Table 1 and Table 2.

Assume the probability that the government chooses the “valid governance” strategy is $x$ and the probability of choosing the “invalid governance” strategy is (1 − $x$); suppose the probability that the platform chooses the “green governance” strategy is $y$ and the probability of choosing the “green negligence” strategy is (1 − $y$); and suppose the probability that enterprises choose the “green transition” strategy is $z$ and the probability of choosing the “green negligence” strategy is (1 − $z$), of which 0 <$x$ < 1, 0 <$y$ < 1, and 0 < $z$ < 1. The values of $x$, $y$, and $z$ change in the government, platform, and settled enterprise’s constant imitating and learning processes, but the initial value is established.

3. Modern Analysis

3.1. Analysis of the Government

The government’s expected benefit when it chooses the “valid formal governance” strategy is calculated as:

Based on Table 1, it can calculate that the expected returns of the government choosing “Valid government regulation” and “Invalid government regulation” are, respectively, $G_1$ and $G_2$,

$G_1=yz{a}_1+y(1-z)a_2+(1-y)z{a}_3+(1-y)(1-z)a_4$

$G_2=yz{a}_5+y(1-z)a_6+(1-y)z{a}_7+(1-y)(1-z)a_8$

The government’s average expected benefit is $\overline{G}$

$\overline{G}=x{G}_1+(1-x)G_2$

While the government’s dynamic replicator equation is:

$F(x)={\displaystyle \frac{dx}{dt}}=x(G_1-\overline{G})=x(1-x)\left[c_1-\alpha {\mathrm{E}}_1-\theta l-pz+(s+\theta l)y\right]$

The first derivative of $x$ and the set $G(y)$ are, respectively:

${\displaystyle \frac{d(F(x))}{dx}}=(1-2x)\left[c_1-\alpha {\mathrm{E}}_1-\theta l-pz+(s+\theta l)y\right]$

$G(y)=c_1-\alpha {\mathrm{E}}_1-\theta l-pz+(s+\theta l)y$

Based on the stability theorem of differential equations, the probability that the government chooses “valid government regulation” as a stable state must be satisfied:

$F(x)=0$ and $d(F(x))/d(x)<0$, as $\partial G(y)/\partial y>0$, $G(y)$ is the increasing function of $y$.

Thus, when $y={\displaystyle \frac{c_1-\alpha {\mathrm{E}}_1-\theta l-pz}{-s-\theta l}}=y^{*}$, $G(y)=0$, then ${\displaystyle \frac{d(F(x))}{dx}}\equiv 0$. Government governance cannot determine a stabilization strategy; when $y<y^{*}$ or $G(y)<0$, then ${\left.d(F(x))/dx\right|}_{x=0}<0$, $x=0$ is the government’s evolutionarily stable strategy (ESS); conversely, $x=1$ is the government’s ESS. The strategy evolution phase diagram of government is shown in Figure 1.

3.2. Analysis of the Platform Organization

The platform organization choosing “green governance” and “green negligence” are, respectively, $P_1$ and $P_2$,

$P_1=xz{b}_1+x(1-z)b_2+(1-x)z{b}_5+(1-x)(1-z)b_6$

$P_2=xz{b}_3+x(1-z)b_4+(1-x)z{b}_7+(1-x)(1-z)b_8$

The platform organization’s average expected benefit is $\overline{P}$

$\overline{P}=y{P}_1+(1-y)P_2$

While the platform organization’s dynamic replicator equation is:

$F(y)={\displaystyle \frac{dy}{dt}}=y(P_1-\overline{P})=y(1-y)\left[k{v}^{2}z+(s+\theta l)x-c_3+h\right]$

The first derivative of $y$ and the set $J(z)$ are, respectively:

${\displaystyle \frac{d(F(y))}{dy}}=(1-2y)\left[k{v}^{2}z+(s+\theta l)x-c_3+h\right]$

$J(z)=k{v}^{2}z+(s+\theta l)x-c_3+h$

Based on the stability theorem of differential equations, the probability that the platform chooses “green governance” as a stable state must be satisfied:

$F(y)=0$ and $d(F(y))/d(y)<0$, as $\partial J(z)/\partial z<0$, $G(y)$ is the minus function of $z$.

Thus, when $z={\displaystyle \frac{(s+\theta l)x-c_3+h}{-k{v}^{2}z}}=z^{*}$, $J(z)=0$, then ${\displaystyle \frac{d(F(y))}{dy}}\equiv 0$. Government regulation cannot determine a stabilization strategy; when $z<z^{*}$ or $J(z)<0$, then ${\left.d(F(y))/dy\right|}_{y=0}<0$, $y=0$ is the platform’s ESS; conversely, $y=1$ is the platform’s ESS. The strategy evolution phase diagram of the platform is shown in Figure 2.

3.3. Analysis of the Settled Enterprise

The platform organization choosing “green transition” and “green negligence” are, respectively, $C_1$ and $C_2$:

$C_1=xy{c}_1+x(1-y)c_3+(1-x)y{c}_5+(1-x)(1-y)c_7$

$C_2=xy{c}_2+x(1-y)c_4+(1-x)y{c}_6+(1-x)(1-y)c_8$

The settled enterprise’s average expected benefit is $\overline{C}$.

$\overline{C}=z{C}_1+(1-z)C_2$

While the settled enterprise’s dynamic replicator equation is:

$F(z)={\displaystyle \frac{dz}{dt}}=z(C_1-\overline{C})=z(1-z)\left[u+\beta w-(c_4+w)\beta x+rxy\right]$

The first derivative of $z$ and the set $H(x)$ are, respectively:

${\displaystyle \frac{d(F(z))}{dz}}=(1-2y)\left[u+\beta w-(c_4+w)\beta x+rxy\right]$

$H(x)=u+\beta w-(c_4+w)\beta x+rxy$

Based on the stability theorem of differential equations, the probability that the platform chooses “green transition” is in a stable state must be satisfied:

$F(z)=0$ and $d(F(z))/d(z)<0$, as $\partial H(x)/\partial x>0$, $H(x)$ is the increasing function of $x$.

Thus, when $x={\displaystyle \frac{u+\beta w}{(c_4+w)\beta -ry}}=x^{*}$, $H(x)=0$, then ${\displaystyle \frac{d(F(z))}{dz}}\equiv 0$. Government regulation cannot determine a stabilization strategy; when $x<x^{*}$ or $H(x)<0$, then ${\left.d(F(z))/dz\right|}_{z=0}<0$, $z=0$ is the settled enterprise’s ESS; conversely, $z=1$ is the settled enterprise’s ESS. The strategy evolution phase diagram of settled enterprises is shown in Figure 3.

4. Evolution Strategy Analysis

According to the replicating dynamic equations, eight stable points can be obtained: $\left(0,0,0\right)$, $\left(0,1,0\right)$, $\left(0,1,1\right)$, $\left(0,0,1\right)$, $\left(0,0,1\right)$, $\left(1,0,0\right)$, $\left(1,1,0\right)$, $\left(1,0,1\right)$, and $\left(1,1,1\right)$. The asymptotic stability of the eight stable points depends on the positive and negative properties of the system’s Jacobian matrix; that is, the system satisfies the ESS only if all the eigenvalues of the Jacobian matrix are negative. The Jacobian matrix is:

$J=\left[\begin{array}{ccc}{\displaystyle \frac{\partial F\left(x\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(x\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(x\right)}{\partial z}}\\ {\displaystyle \frac{\partial F\left(y\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(y\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(y\right)}{\partial z}}\\ {\displaystyle \frac{\partial F\left(z\right)}{\partial x}}& {\displaystyle \frac{\partial F\left(z\right)}{\partial y}}& {\displaystyle \frac{\partial F\left(z\right)}{\partial z}}\end{array}\right]=\left[\begin{array}{ccc}{J}_{11}& J_{12}& J_{13}\\ J_{21}& J_{22}& J_{23}\\ J_{31}& J_{32}& J_{33}\end{array}\right]$

Including:

$\begin{array}{l}{J}_{11}=(2x-1)(C_1-\alpha E_1-\theta l-pz+sy+\theta ly)\\ J_{12}=x(x-1)(s+\theta l)\\ J_{13}=-px(x-1)\\ J_{21}=-y(y-1)(x+\theta l)\\ J_{22}=-(2y-1)(k{v}^{2}z-C_3+h+sx+\theta lx)\\ J_{23}=-k{v}^{2}y(y-1)\\ J_{31}=z(z-1)\left[\beta (C_4+w-ry\right]\\ J_{32}=-rxz(z-1)\\ J_{33}=-(2z-1)\left[u+\beta (w-C_{4}x-wx)+rxy\right]\end{array}$

By bringing eight equilibrium points into the Jacobian matrix, the eigenvalues of all the equilibrium points can be calculated, and whether they are the ESS of the system can be determined according to the positive and negative eigenvalues. According to the actual situation, the reputation improvement of platform green governance should be greater than platform green governance’s additional cost; the infringement of platform green negligence to settled enterprises is greater than zero, and the promotion of settled enterprises’ green transition is greater than zero. Table 3 presents the analysis results.

5. Numerical Analysis of the Tripartite Game in Different Life Cycle Stages

Based on extant research, this study argues that the digital platform life cycle can be divided into four stages: birth, expansion, leadership, and self-renewal. Government subsidies are gradually increasing in these four development processes.

To more intuitively illustrate the evolutionary process and patterns of ecological cooperation among the various parties—and to verify the effectiveness of the evolutionary stability analysis—this study uses the Matlab 2018b software for numerical simulations. Drawing on previous research and aligning with real-world conditions, we aim to generalize our conclusions and adjust the simulation data accordingly. The specific values used do not reflect actual quantities but rather represent the relative magnitudes of each parameter. To meet the requirements for meaningful outcomes and to ensure rational and ideal simulation results [52], the initial parameters were set as follows:

Following Kell (1993) [53], we set $E_1$ = 40, and consumer complaints will cause the loss of the government’s reputation; otherwise, they will enhance the government’s reputation [54]. We set $p$ = 2. $r$ = 2. The government’s platform governance will generate some costs, which will also increase with the continuous expansion of the platform’s scale [55]; therefore, we set $\theta$ to 0.5, the initial value of $C_1$ is 9, and $l$ = 6. In addition. Good governance’s impact will improve the platform’s reputation [56,57,58]; therefore, we set the initial value of $h$ as 1, $s$ as 5, and $C_3$ as 10. On the basis of the survey conducted by Barnes (2008) [59], we set $\beta$ = 0.5, $C_4$ with an initial value of 10, $\alpha$ = 0.5, $k$ = 0.5, and $v$ = 2, The initial value of $u$ is 3.

On this basis, with the gradual standardization of platform development, the cost of platform green governance will be reduced, and settled enterprises’ awareness of green transition will also be continuously enhanced. Consequently, the opportunity cost of settled enterprises green transition and the effect of platform green governance on platform reputation will also continuously increase. Based on the above phenomena, this study conducts a numerical simulation analysis of the evolution of the digital platform’s three parties in different stages.

5.1. Birth Stage

The parameters were set as follows:

$\begin{array}{l}\alpha =0.5, E_1=40, C_1=9, s=5, \theta =0.5, l=6, p=2,\\ C_3=10, h=1, k=0.5, v=2, u=3, \beta =0.5, C_4=10, r=2\end{array}$

In the early stage of platform ecosystem formation, the government, platform organizations, and settled enterprises are all in the stage of continuous exploration. The government invests relatively little in platform governance, and the level of subsidies is relatively low. Platform green governance has just started, and governance has not yet formed a scale effect, which leads to a high governance cost and the platform reputation’s small influence. As settled enterprises are new to the platform ecosystem, their attention to environmental issues is not high, and efforts to green the transition are not enough. Figure 4 shows government regulation can rapidly even out and implement positive regulation strategies, while platform organizations cannot achieve good green governance, and settled enterprises’ green transition remains weak. This corresponds to the equilibrium point (1,0,0), but this point is not the ESS of the system.

To further verify the validity of the evolutionary stability analysis, the results of a 100-time evolution through simulation of the birth stage in the digital platform ecosystem are shown in Figure 5. The three-dimensional evolution results show that in the birth stage, the stable point of the model’s layout is (1,0,0); that is, at this stage, only the government tends to valid government regulation, while the platform and settled enterprises tend to green negligence, which is consistent with the conclusion in Figure 4.

5.2. Expansion Stage

The parameters were set as follows:

$\begin{array}{l}\alpha =0.5, E_1=40, C_1=11, s=6, \theta =0.5, l=6, p=2,\\ C_3=10, h=3, k=0.5, v=2, u=3, \beta =0.5, C_4=8, r=2\end{array}$

In the expansion stage of platform ecosystems, platform organizations are in the stage of explosive growth, and the government gradually attaches importance to investment in platform regulation. Platform green governance has not adapted to the rapid enhancement of platform organizations, resulting in high governance costs. More settled enterprises have come into contact with platform organizations; however, they remain in the trial and exploration stage with enthusiasm for green transition still not high. Figure 6 shows governance is still dominated by government regulation, and the government can rapidly turn to the governance strategy. Although the green transition of settled enterprises is still not strong, the platform green governance starts gradually and shows an upward trend. This corresponds to the ESS point (1,1,0).

On this basis, the results of a 100-time evolution through simulation of the expansion stage in the digital platform ecosystem are shown in Figure 7. The three-dimensional evolution results show that in the expansion period, the model’s layout stability point is (1,1,0); that is, at this stage, the government still tends to valid government regulation, while the platform tends to green governance, and settled enterprises still tend to green negligence, which is consistent with the conclusion in Figure 6.

5.3. Leadership Stage

The parameters were set as follows:

$\begin{array}{l}\alpha =0.5, E_1=40, C_1=12, s=7, \theta =0.5, l=6, p=2,\\ C_3=8, h=4, k=0.5, v=2, u=7, \beta =0.5, C_4=8, r=2\end{array}$

In the platform ecosystem’s leadership stage, the platform organization grows further, but the development rate is relatively slow. At this time, the government continues to increase investment in platform regulation, which has gradually adapted to the platform ecosystem’s development mode, and the cost of autonomy has decreased. Settled enterprises have an improved cooperation with the platform organization, a preliminary awareness of green transition has been formed, and they are gradually willing to participate in the platform green governance. Figure 8 shows the government, platform, and settled enterprises gradually tend to balance and jointly manage the platform ecosystem. This corresponds to the equilibrium point (1,1,1).

On this basis, the results of a 100-time evolution through simulation of the leadership stage in the digital platform ecosystem are shown in Figure 9. The three-dimensional evolution results show that in the leadership stage, the stable point of the model layout is (1,1,1); that is, in this stage, the government still tends to valid government regulation, while the platform tends to green governance, and settled enterprises tend to green transition, which is consistent with the conclusion in Figure 8.

5.4. Self-Renewal Stage

The parameters were set as follows:

$\alpha =0.5, E_1=40, C_1=15, s=5, \theta =0.5, l=6, p=2, C_3=6, h=8, k=0.5, v=2, u=10, \beta =0.5, C_4=8, r=2$

In the self-renewal stage of the platform ecosystem, platform development is relatively mature, and the government continues to invest in regulating the platform. Platform green governance gradually normalized, leading to a decline in governance costs. Settled enterprises’ awareness of green transition has greatly improved, and they can actively participate in platform green governance. Figure 10 shows both platforms and settled enterprises can rapidly reach a balance and participate in green activities, while government decision-making gradually tends to be “ungovernance”, which means that government regulation at this stage is not the dominant regulatory mode, but relies more on platform green governance and settled enterprises’ green transition in the platform ecosystems. This corresponds to the ESS point (0,1,1).

On this basis, the results of a 100-time evolution through the simulation of the self-renewal stage in the digital platform ecosystem are shown in Figure 11. The three-dimensional evolution results show that in the maturity stage, the model’s layout stability point is (0,1,1); that is, in this stage, the platform tends to green governance, settled enterprises tend to green transition, and the government tends to not govern, which is consistent with the conclusion in Figure 10.

6. Influencing Factors Analysis of Digital Platform Governance

Based on the analysis of the evolution trend of the government, platform, and settled enterprises at each stage, some factors will still affect the overall evolution trend at each stage of the development of the platform ecosystem. Therefore, considering the self-renewal stage parameter as an example, we further select three factors from the government’s social reputation, platform green negligence degree, and the level of improving settled enterprises’ green transition consciousness to explore the influence of different factors on the evolution trend.

6.1. Impact on Government Reputation

With the development of democratic politics and the expansion of citizenship in the digital economy, civil society has proposed higher requirements for the reputation of the government. The reputation of the government represents whether the political power of the government can be generally recognized by society, which means whether the government can be generally recognized by the public when holding state power [60]. A good reputation for the government can effectively enable the government to manage social public affairs and provide public goods effectively [61], thus improving the overall welfare level of the public. Figure 12 depicts the effect of government reputation on model evolution trends.

As shown in Figure 12, with the continuous improvement of government reputation E1, the probability of government regulation increases, the probability of platform green governance first increases and then decreases, and the probability of enterprise green transition decreases. This is probably because the improvement in the reputation of the government indicates that the public has a stronger sense of trust in the government. To continue to maintain a good reputation, the government will strengthen the supervision of environmental issues like digital platform green governance. It is also because the public has more trust in the government, making the platforms and settled enterprises more dependent on the supervision of the government while the degree of their green governance and transition is weakened.

6.2. Influence of Platform Green Negligence

In the era of the digital economy, relying on the development of information technology such as big data, cloud computing, and artificial intelligence and the popularization of the Internet, several digital platforms have built new business models, risen rapidly, and already have a high market share and number of users [62,63]. This provides more possibilities for the green governance of the platform, but it is also easy to ignore environmental issues in order to seek the most economic interests. Figure 13 depicts the effect of platform green negligence on evolution trends.

As shown in Figure 13, as the degree of platform green negligence continues to increase, the probability of government supervision also increases, while the degree of enterprise green transition decreases. This is probably because if the degree of platform green negligence increases, the willingness of green governance of platform giants decreases after they gain economic benefits. At this time, the government should assume the responsibility of supervision. If the platform green negligence situation has been formed, it will make enterprises think that green transition will have little effect; thus, it will decrease their enthusiasm for green transition in the platform.

6.3. Impact on the Green Transition Consciousness of Settled Enterprises

The enterprises on the platform are the direct beneficiaries of the digital platform. The digital platform can provide more green elements and information to the enterprises on it, and help them find suitable partners, and so on. Therefore, the development of the digital platform is also an important guarantee for its sustainable growth. The green transition consciousness of the enterprises on the platform is an important force in promoting the development of the digital platform [64].

As shown in Figure 14, with the continuous improvement of the awareness of the green transition among settled enterprises, the probability of government supervision gradually decreases, and the probability of platform green governance increases. This is probably because the improvement of the awareness of green transition of the settled enterprises to some extent is beneficial to the platform supervision, and the burden of government governance is shared to some extent. Therefore, the probability of government governance decreases with the improvement of enterprises’ green transition awareness, while the increase of enterprises’ awareness of green transition also puts pressure on digital platforms, forcing them to focus on their governance, so the probability of platform green governance increases.

7. Discussion

First, digital platform ecosystems often rely on mandatory government regulation in the early developmental stages. Digital platform ecosystems have evolved from scratch. At the beginning of the platform ecosystem, the number of internal platform organizations and settled enterprises was small, and the operation of the platform ecosystem was still in the exploratory stage [65,66]. Most platform organizations and settled enterprises took a wait-and-see attitude and did not act. At this time, government departments continued to formulate and improve relevant regulations and impose mandatory constraints for the platform ecosystem’s green operation [62]. It can be seen from the research that in the formative period, the green effects of platform organizations and settled enterprises are not obvious, and only government supervision is effective. On the one hand, in the early stages, the green governance mechanism of platform organization has not been formed, and settled enterprises have also come into contact with this emerging model; the green transition awareness is not perfect. On the other hand, government regulation of the platform and settled enterprises is stricter. Therefore, in a platform’s early stage, the government plays a greater role in guiding, restraining, and helping the platform ecosystem to follow good governance.

Second, with greater ecosystem development, governments, platform organizations, and settled enterprises all come to play an important role in platform regulation. As it has entered a period of expansion and leadership, these two stages are processes in which the scale of the platform organization continues to steadily expand. The research shows that in these stages, the green effect of platform organizations and settled enterprises gradually emerges. Specifically, in the expansion period, the green governance effect of the platform organization gradually increases, and in the leadership period, the green transition effect of settled enterprises becomes more obvious, and even tends to overtake that of the platform. This may be because, with the ecosystem’s continuous maturation, the platform’s own governance and development mechanisms are also constantly rebuilt and improved, which produces a set of effective methods to deal with the green production problems encountered in development [67]. At the same time, platform-settled enterprises’ awareness of green transition has also steadily increased. Through continuous experimentation, effective and reasonable safeguards are formed, and the platform and enterprise’s non-green behaviors have become restrained [68]. At this time, the government, platform organization, and settled enterprises impose joint green production.

Third, at the last stage, the common governance of platform organizations and settled enterprises prevails, and compulsory government regulation is weakened. In the self-renewal stage, owing to technological or market changes, platforms encounter the need for fundamental renewal [44]. At this time, the platform ecosystem’s green autonomy and enterprise green transition awareness play major roles, while the government’s supervision effect gradually weakens. This shows that with further platform development, if the platform wants to thrive and increase green development, it must rely on its strong green governance mechanism, cooperating with enterprise green transition. At this stage, the government’s compulsory supervision is gradually withdrawn as a primary constraint.

According to the different governance characteristics of the development stage of digital platforms, we have integrated the following Figure 15.

8. Case Study

To further verify our conclusions, we select the development of China’s Tianjin Teda intelligent environmental protection platform as a case study. Specifically, in the birth stage, because the platform was new, the relevant technology and management mode are imperfect, and mandatory government supervision was mainly relied on to promote green governance. In the early stage of platform construction, the Bureau of Ecological Environment of Tianjin Economic Development Zone used administrative means to force enterprises in the region to access the platform and collect key information such as real-time pollution source emission factors. Through the government’s coercive power, the basic framework of the environmental monitoring network was rapidly constructed, laying the foundation for subsequent governance.

During the expansion stage, platform functions became increasingly diversified, the value of data began to emerge, and the effectiveness of platform-led governance and enterprise green transformation gradually became evident. The platform established a comprehensive monitoring network supported by big data, including integrated data centers and collaborative supervision hubs. At this stage, the platform guided enterprises in their green transformation through a series of rules, standards, and regulatory frameworks. For example, enterprises that meet environmental protection standards will be given preferential display and policy preferences on the platform. The enterprises that do not meet standards receive precise supervision and guidance using platform data. To achieve a superior development environment, enterprises also begin to actively cooperate with the platform, increase investment in environmental protection, improve production processes, and thus achieve green transformation.

During the leadership period, Teda’s influence expanded rapidly, and more enterprises and third-party institutions participated in environmental governance. The platform utilized its technical advantages, discovered environmental risks in advance through intelligent research, judgment, and early warning systems, and managed collaborative governance. By this time, enterprises have fully realized the importance of green development, and implemented both self-restraint and green innovation. For example, some enterprises used the data services provided by the platform to develop more environmentally friendly products and production processes. The green governance vitality of the entire platform ecosystem is thus fully engaged.

At the self-renewal stage, Teda’s intelligent environmental protection platform formed an autonomous system. A positive interactive mechanism was established between the platform and enterprises. The firms consciously obey environmental protection rules and implement green production. Through quantitative diagnosis, evaluation, scientific analysis, and guidance, the platform continuously optimizes governance strategies and maintains a positive ecological environment. At this stage, the government shifts from enforcing mandatory measures to adopting a more supervisory and guiding role. Both the platform and participating enterprises demonstrate strong autonomy, effectively advancing sustainable green governance and fostering a positive interaction between environmental and economic development.

9. Implications

9.1. Theoretical Implications

First, this study expands the research on the life cycle of the platform ecosystem. Previous studies on platform governance rarely consider the platform lifecycle and ignore the particularity of different stages of platform development [39], which will inevitably deviate from the effectiveness of platform green governance. This study combined the life cycle theory with the platform governance theory and divided the platform life cycle into start-up, explosion, leadership, and maturity periods. Additionally, the platform green governance mechanism under different life cycles was studied in detail to further improve the research on the platform life cycle.

Second, this study enriches the research on the relationship among three governance tools in the digital platform ecosystem. Previous studies on enterprise green transition have somewhat considered it from the perspective of the government, through legislation, subsidies, fines, and other mandatory ways to govern the platform [69,70]; however, from the governance effect perspective, they cannot fundamentally address the problem of green development. This study considers the comprehensive impact of three governance subjects—including the government, platform, and enterprise—on platform green governance. In particular, drawing on previous research that focuses on external governance elements such as government regulation, this paper explores the impact of internal elements—namely, the platform and its associated enterprises—on green governance. Our findings show that as the digital platform ecosystem evolves, the role of government regulation shifts from dominant to supportive, while the governance capacity of platforms and enterprises strengthens temporally. This trend aligns more closely with the practical development trajectory of digital platform ecosystems.

Third, this study reveals the trade-offs between government intervention and platform autonomy. The results show that different governance entities have different effects at different stages during digital platform ecosystem development. Considering the internal and external factors that impact the green governance of digital platform ecosystems, government regulation initially plays a favorable role. As time passes, the role of the platform autonomy mechanism gradually emerges. Therefore, for new platforms, the government must promote development through mandatory measures. For growing platforms, a combination of government oversight and internal platform governance is required. As platforms mature, the government should gradually delegate authority, allowing platform autonomy to drive more effective governance outcomes.

9.2. Practical Implications

This study also provides practical implications for the government, platform organizations, and enterprise managers. First, the government should strengthen the supervision and governance of the platform. Research shows that government regulation plays an important role in the early stages of platform development. The government should leverage digital technology innovation to establish a comprehensive policy framework for green transformation. This includes developing AI algorithms for optimizing energy consumption, blockchain systems for carbon tracking, and liquid-cooled data center technologies to create a low-carbon technological support system spanning the entire industrial chain. Dynamic monitoring platforms based on satellite remote sensing and the Internet of Things should be deployed to quantify, in real-time, the carbon reduction benefits of digital services that replace physical activities, thereby enabling intelligent environmental impact assessments. Simultaneously, the government should lead the creation of cross-border digital carbon accounting standards and develop systems for transnational environmental data sharing, using digital tools to support global green governance. A mix of policy instruments—such as tax incentives to encourage green technology adoption, carbon credits to guide low-carbon consumption, and dedicated funding for key technological research—can align digital innovation with sustainability goals and systematically address the environmental externalities of platform ecology.

Second, platform green autonomy has become an important part of the supervision. The platform can conduct in-depth analysis of the production process data of established firms, accurately identify the links between high energy consumption and high pollution, and provide optimization suggestions to help them achieve green production. In addition, integrating Internet of Things technology can achieve real-time monitoring and intelligent regulation of the platform’s operating facilities. This includes, for example, intelligent regulation of a data center’s refrigeration system, dynamic adjustment of energy consumption according to equipment operation, reducing the platform’s energy consumption, and, overall, effectively promoting the green transformation of the ecosystem.

Third, enterprises can rely on digital platforms to better realize their own green transformation. Enterprises can embed in the construction of a digital economy network framework, and create a good digital ecology. Enterprises should actively adopt digital technologies to optimize production processes and support green transformation. Digital tools such as 3D modeling, simulation, and other green design technologies enable companies to account for environmental and resource impacts during the product design stage, facilitating lightweight, low-carbon, and circular product development while reducing energy consumption and emissions at the source. During production, intelligent technologies—such as advanced scheduling and process control—can improve production efficiency and reduce energy waste. Additionally, green supply chain management technologies, including digital platforms for information sharing, allow enterprises to manage the entire supply chain—from raw material procurement to product delivery—in a transparent and efficient manner. These tools support real-time tracking of environmental impacts and help build low-carbon, sustainable supply chains. By fully leveraging digital innovation, enterprises can construct integrated green manufacturing systems, strengthen their environmental competitiveness, and contribute to the broader green transformation of the digital platform ecosystem.

10. Limitations and Future Research

Our study has several limitations that should be addressed in future research. First, this study only considers the platforms and enterprises in the digital economy network and can adopt more cooperative thinking for heterogeneous subjects in the future, such as universities and research institutions. Second, this study did not distinguish different industries when classifying the platform life cycle, and discussion is scarce regarding cross-industry applicability. Future research should explore whether governance strategies differ for other platforms, such as e-commerce platforms, social media networks, and fintech ecosystems. Finally, more factors should be measured more scientifically, and future studies should test their models in real-world platform settings or use qualitative case studies.

11. Conclusions

Based on the digital economy, this study analyzes the effective green mechanism of platform governance in different life stages under the joint action of government, platform, and enterprise. This study finds that government supervision, platform green autonomy, and enterprise green transition are effective methods of platform governance. In the early stages of platform development, the government can achieve good green governance effects through coercive means. With the development and maturity of the platform, platform green autonomy, as an effective means of autonomy, can achieve a benign state of self-organization. This study constructs the stage analysis framework of platform governance under the digital economy and provides a useful direction for government supervision and enterprise green transition. At the theoretical level, this study innovatively integrates life cycle theory with tripartite evolutionary game theory to construct a dynamic analytical framework for the green governance of digital platforms. It identifies the behavioral rules of governments, platforms, and enterprises, addressing the limitations of traditional static governance models that overlook stage-specific differences in platform development. It provides an innovative methodology for studying the digital and green economies together. This study also provides practical operational guidelines for phased regulation of governments, platforms, and enterprises. It provides a replicable method of reducing the cost of green transformation and realizing the transformation of the governance model from “external constraint” to “endogenous drive”.

Author Contributions

Methodology, Y.H. and L.X.; Software, L.X.; Writing—original draft, Y.H.; Funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Phase diagram of government strategy evolution. Note: $y^{*}={\displaystyle \frac{c_1-\alpha {\mathrm{E}}_1-\theta l-pz}{-s-\theta l}}$.

Figure 2 Phase diagram of platform strategy evolution. Note: $z^{*}={\displaystyle \frac{(s+\theta l)x-c_3+h}{-k{v}^{2}z}}$.

Figure 3 Phase diagram of settled enterprise strategy evolution. Note: $x={\displaystyle \frac{u+\beta w}{(c_4+w)\beta -ry}}=x^{*}$.

Figure 4 Dynamic evolution process in the birth stage.

Figure 5 Array Evolution 100 results of the birth stage.

Figure 6 Dynamic evolution process in the expansion stage.

Figure 7 Array Evolution 100 results of the expansion stage.

Figure 8 Dynamic evolution process in the leadership stage.

Figure 9 Array Evolution 100 results of the leadership stage.

Figure 10 Dynamic evolution process in the self-renewal stage.

Figure 11 Array Evolution 100 results of the self-renewal stage.

Figure 12 Impact on government reputation.

Figure 13 Impact on platform green negligence.

Figure 14 Impact on settled enterprises’ awareness of green transition.

Figure 15 Governance strategies for each lifecycle of the platform.

Table 1

Payoff matrix for the game’s three sides.

Strategies		Settled Enterprise
		Green Transition	Green Negligence
Valid government regulation	Platform green governance	$\left(a_1,b_{1,}{c}_1\right)$	$\left(a_2,b_{2,}{c}_2\right)$
	Platform green negligence	$\left(a_3,b_{3,}{c}_3\right)$	$\left(a_4,b_{4,}{c}_4\right)$
Invalid government regulation	Platform green governance	$\left(a_5,b_{5,}{c}_5\right)$	$\left(a_6,b_{6,}{c}_6\right)$
	Platform green negligence	$\left(a_7,b_{7,}{c}_7\right)$	$\left(a_8,b_{8,}{c}_8\right)$

Table 2

Benefits of the game’s three sides.

Game Strategies	Government	Platform Organizations	Settled Enterprise
$\left(a_1,b_1,c_1\right)$	$\alpha E_1+m-c_1-s$	$E_2-C_2-C_3+s+h$	$E_3+u-\beta C_4+r$
$\left(a_2,b_2,c_2\right)$	$\alpha E_1+m-c_1-s$	$E_2-C_2-C_3+s+h$	$E_3$
$\left(a_3,b_3,c_3\right)$	$\alpha E_1-c_1+\theta l-n$	$E_2-C_2-\theta l-k{v}^2$	$u-\beta C_4-w$
$\left(a_4,b_4,c_4\right)$	$\alpha E_1-c_1+\theta l-n$	$E_2-C_2-\theta l$	$-w$
$\left(a_5,b_5,c_5\right)$	$m-p$	$E_2-C_2-C_3+h$	$E_3+u-\beta C_4$
$\left(a_6,b_6,c_6\right)$	$m$	$E_2-C_2-C_3+h$	$E_3$
$\left(a_7,b_7,c_7\right)$	$-n-p$	$E_2-C_2-k{v}^2$	$u-\beta C_4-w$
$\left(a_8,b_8,c_8\right)$	$-n$	$E_2-C_2$	$-w$

Table 3

Eigenvalues and stability conditions of the equilibrium point.

Equilibrium Points	Eigenvalues			ESS Conditions
	${\mathit{\lambda }}_1$	${\mathit{\lambda }}_2$	${\mathit{\lambda }}_3$
$\left(0,0,0\right)$	$u+\beta w$	$h-C_3$	$\alpha E_1-C_1+\theta l$	unstable equilibrium point
$\left(0,1,0\right)$	$u+\beta w$	$C_3-h$	$\alpha E_1-s-C_1$	unstable equilibrium point
$\left(0,0,1\right)$	$-u-\beta w$	$k{v}^2-C_3+h$	$p-C_1+\alpha E_1+\theta l$	unstable equilibrium point
$\left(0,1,1\right)$	$-u-\beta w$	$-k{v}^2+C_3-h$	$p-C_1-s+\alpha E_1$	$\begin{array}{l}u+\beta w>0 \\ \mathrm{and} C_3<k{v}^2+h \\ \mathrm{and} \alpha E_1+p<C_1+s\end{array}$
$\left(1,0,0\right)$	$u-\beta C_4$	$C_1-\alpha E_1-\theta l$	$h-C_3+s+\theta l$	unstable equilibrium point
$\left(1,1,0\right)$	$C_1+s-\alpha E_1$	$r+u-\beta C_4$	$C3-h-s-\theta l$	$\begin{array}{l}{C}_1+s<\alpha E_1 \\ \mathrm{and} r+u<\beta C_4 \\ \mathrm{and} C_3<h+s+\theta l\end{array}$
$\left(1,0,1\right)$	$\beta C_4-u$	$C_1-p-\alpha E_1-\theta l$	$k{v}^2-C_3+h+s+\theta l$	unstable equilibrium point
$\left(1,1,1\right)$	$\beta C_4-u-r$	$C_1-p+s-\alpha E_1$	$-k{v}^2+C_3-h-s-\theta l$	$\begin{array}{l}\beta C_4<u+r \\ \mathrm{and} s+C_1<\beta +\alpha E_1 \\ \mathrm{and} k{v}^2+h+s+\theta l>C_2\end{array}$

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