Ecological Enhancement Through Smart Green Village Development: Strategic Options, Key Influencing Factors, and Simulation Evidence from Hunan Province, China

Abstract

Against the dual backdrop of the “digital countryside” initiative and the development of ecological civilization, the construction of smart green villages has increasingly emerged as a vital pathway for improving rural ecological environment. This study utilizes a three-dimensional framework—including strategic choice, influencing factors, and simulation practice—to construct an evolutionary game model involving the government, enterprises, and villagers. A systematic simulation is conducted based on a field case from Village P in the hilly region of Hunan Province, China. The results of the study reveal the following: (1) Under the combination of high financial incentives, low technical support, and high villager participation, the ecosystem achieves the most stable and positive evolution. Moreover, collaborative governance outperforms unilateral control. (2) Financial support, technological provision, and environmental awareness constitute the three core variables driving the evolution of ecological governance. (3) Cognitive feedback mechanisms significantly influence the dynamic trajectory of green behaviors in enterprises. (4) The simulation results indicate a risk of “sub-stable” divergence in the collaborative mechanism. Furthermore, the stability of the governance system heavily relies on the alignment between policy configuration and information structure. This study offers theoretical support and empirical validation for the institutional design of and strategic interventions for smart green villages, serving as a valuable reference for local-level implementation.

1. Introduction

As global digitalization accelerates, the development of smart green villages has emerged as a crucial strategy for addressing rural ecological challenges [1]. In 2016, the United Nations released Transforming Our World: the 2030 Agenda for Sustainable Development (hereafter “the Agenda”), explicitly proposing that rural ecological environments should be improved through the synergy of advanced technologies and ecological governance to achieve rural revitalization and environmentally sustainable development [2]. Building on this, the Netherlands has significantly reduced the use of chemical fertilizers and pesticides by implementing precision agriculture technologies, thereby mitigating the ecological impacts of agricultural production. Similarly, Germany has developed advanced resource recycling practices through the implementation of the “eco-village” model, offering valuable lessons for global ecological governance [3]. As the world’s largest developing country, China has actively implemented the Agenda by releasing policy documents such as the Outline of Digital Rural Development Strategy and Digital Rural Construction Guidelines 2.0 [4]. These initiatives propose the “smart green countryside” as a new governance paradigm and promote a comprehensive system characterized by efficient resource allocation, synergistic technological evolution, and self-reinforcing ecological cycles [5]. At present, driven by both top-down policies and bottom-up local practices, various regions in China have begun to explore smart approaches to rural ecological governance. For example, Village P in City C of Hunan Province—representative of the hilly regions in this province—has implemented intelligent irrigation systems, environmental monitoring, and green agricultural technologies. These are coupled with financial incentives and environmental awareness mechanisms to foster a multi-stakeholder ecological governance model involving the government, enterprises, and villagers [6]. This village has achieved significant improvements in ecological indicators, technological coverage, and public participation, offering a valuable case for exploring sustainable governance strategies in areas characterized by complex terrain.

Despite significant practical progress, the construction of “smart green villages” continues to face two main dilemmas. Firstly, the three key stakeholders—the government, enterprises, and villagers—exhibit strategic biases in areas such as financial investment, green technology adoption, and participation feedback [7]. This leads to difficulty in forming a stable, synergistic feedback loop, which, in turn, undermines the efficiency of governance and ecological improvements [8]. Secondly, existing research has generally focused on isolated incentives, such as financial support [9] or technological innovation [10], so in-depth analyses of the key factors influencing the effectiveness of governance and their relationships are lacking. As a result, the unclear connections between these factors hinder the stability and sustainability of ecological improvements in rural environments.

This study focuses on improvements in the ecological environment as a result of the construction of “smart green villages,” using Village P in Hunan Province as an empirical case. A “government–enterprise–villagers” tripartite evolution game model is constructed to explore the strategies selected by and roles of each stakeholder in the process of ecological environment improvement, as well as to identify the core factors influencing the evolution of strategies. Simulation experiments and sensitivity analyses are further conducted to evaluate the impacts of different strategy combinations on ecological improvements under various key influencing factors. Finally, a balanced approach to integrating “stability” and “sustainability” in strategy selection is explored, with the aim of achieving sustainable ecological improvements and supporting the green transformation of rural areas.

The contributions of this study are as follows: first, it combines the construction of smart green villages with evolutionary game theory to develop a strategy evolution model under three-party synergistic governance; second, it incorporates field research data to enhance the realism of the model and reveal the mechanisms of behavioral synergy in the context of technological empowerment; and third, using a case that is representative of hilly areas, it provides a theoretical foundation for optimizing ecological governance strategies and institutional design in areas characterized by complex terrain, offering policy recommendations and enriching the research paradigm of ecological governance in smart villages.

2. Literature Review

2.1. The Main Actors in Rural Ecological Governance

Rural ecological governance constitutes a vital pillar of China’s rural revitalization strategy, which is increasingly being shaped by a multi-actor framework involving the coordinated participation of governments, villagers, and enterprises [11]. The government—as the primary provider of institutional frameworks and policy enforcement—is responsible for formulating environmental regulations, allocating fiscal resources, and overseeing implementation. Studies have shown that incentive-compatible policy systems established by governments can effectively guide local resource allocation and facilitate the implementation of projects [12,13]. Villagers, as both direct stakeholders and beneficiaries of the rural ecological environment, play a pivotal role in shaping the effectiveness of governance. Existing research has highlighted that villagers’ environmental awareness, degree of participation, and trust in public policies are key determinants of governance performance [14]. However, in practice, villagers often face challenges such as low motivation and unclear division of responsibilities, underscoring the need for institutional arrangements that enhance their willingness to participate and capacity for collective action [15]. Enterprises, meanwhile, play a vital role in promoting green technologies, cultivating ecological industries, and introducing market-based mechanisms [16]. By supplying clean production equipment and developing circular agricultural models, enterprises contribute to the integration of rural ecological and economic systems, thereby strengthening both the technological and market foundations of environmental governance.

2.2. Influencing Factors in Rural Ecological Governance

Rural ecological environmental management performance is influenced by a combination of factors, with financial inputs, technological incentives, and villagers’ willingness to participate serving as the three primary drivers. First, financial inputs provide material support for policy implementation [17] and play a crucial role in the development of infrastructure, ecological restoration projects, and ongoing maintenance. Research has indicated that the availability of financial resources significantly impacts the speed and coverage of environmental initiatives in rural areas, particularly in less-developed regions where financial subsidies and ecological compensation are critical for initiating governance. Second, technological incentives have gained increasing importance in enhancing the effectiveness of governance [18]. The adoption of green agricultural technologies, clean energy equipment, and ecological restoration methods not only improves resource utilization efficiency but also lowers the marginal cost of governance, facilitating a shift from reliance on administrative directives to a more scientific and technological approach to environmental protection. The capacity to innovate and advance technology has become a key indicator of the modernization of rural ecological governance. Finally, the active participation of villagers forms the behavioral foundation of governance practices [19]. While institutional design and technical support provide the structure and tools for governance, its success ultimately depends on villagers’ cognitive awareness, participation attitudes, and behaviors. Strengthening villagers’ environmental awareness and establishing a trust-based shared governance mechanism are vital for enhancing the resilience and sustainability of governance.

2.3. The Role of Digital Technology in Enhancing Rural Ecological Governance

In the context of digital transformation, digital technology has become an increasingly important tool for enabling rural ecological environmental governance. Its impact is primarily reflected in three key areas: improving governance efficiency, promoting the green transformation of agriculture, and enhancing ecological monitoring and early warning capabilities. First, digital technology facilitates the real-time collection and processing of environmental data through the Internet of Things, big data, and cloud computing [20]. By leveraging intelligent decision support systems, the government can precisely identify pollution sources, optimize resource allocation, and improve the scientific accuracy and timeliness of governance. Second, regarding the green transformation of agriculture, precision agriculture technologies aid in the detailed management of production activities [21]. The integration of remote sensing techniques, sensors, and intelligent control systems enables precise irrigation, compliance with environmental regulations, and the efficient use of green inputs, thus reducing resource consumption and guiding agriculture towards low-carbon and sustainable development. Finally, digital technology enhances ecological monitoring and early warning capabilities [22]. The use of remote sensing imagery, drones, and sensor networks enables the comprehensive monitoring of air, water quality, and soil conditions, allowing for the timely identification of ecological risks and improving both the responsiveness and foresight of governance actions.

2.4. Application of Evolutionary Game Theory in Rural Ecological Governance

Evolutionary game theory offers a dynamic analytical framework for multi-agent interactions in the context of rural ecological governance [23] and has been widely applied to model the strategic decision-making processes and evolutionary trajectories of governments, enterprises, and farmers. In prior studies [24], evolutionary games have been employed to model the formation of public goods provision and environmental cooperation mechanisms, with a particular emphasis on the role of community institutional evolution in facilitating collective action [25]. Some scholars have also examined the development of synergistic governance mechanisms, arguing that incentive design and feedback systems play crucial roles in shaping the behavioral evolution of farmers [26]. Considering the government–enterprise relationship, game-theoretic models can reveal the strategic interdependence among policy incentives, technology adoption, and regulatory intensity. Due to information asymmetries and externalities [27], achieving Pareto optimality through a single policy instrument is often infeasible, necessitating the optimization of incentive compatibility via policy combinations. At the farmer level, evolutionary games can effectively explain the free-rider problem and the strategic stability of environmental behavior, highlighting that enhancing farmers’ perceived benefits and strengthening social norms are key pathways to improving participation rates [28].

2.5. Critical Reflection on the Literature

A review of the above literature reveals that the existing studies have addressed the functional roles of multiple stakeholders, the driving mechanisms of financial and technological factors, and the logic behind applying evolutionary game theory to assess the evolution of strategic choices and behaviors. These findings enhance our understanding of the interactions among governments, enterprises, and villagers, providing a foundation for modeling and analyzing the coupling effects among policy incentives, technology adoption, and participatory behaviors. However, two key gaps remain in the existing literature: (1) A lack of embedded mechanisms for green technologies and digital platforms—the existing research has predominantly focused on the direct application of technologies or their one-way empowerment effects, without systematically analyzing their roles in facilitating tripartite strategic interactions and enhancing synergistic efficiency. This limits their ability to interpret behavioral adjustment pathways and performance improvement processes under technological enablement. (2) Insufficient exploration of dynamic multi-actor co-governance mechanisms—most existing evolutionary game models focus on bilateral relationships or staged evolution, without revealing how tripartite strategies evolve into stable equilibrium structures under varying conditions. Moreover, there is a lack of systematic depictions of the synergistic effects arising from institutional configurations and technological interventions. In order to address these gaps, this study takes smart green village construction as an entry point and aims to bridge the divide between technology-centered game modeling and tool-based synergy analyses. Through field research, the strategic space is identified and a tripartite strategy framework encompassing governments, enterprises, and villagers is constructed. Building on this foundation, we develop an evolutionary game model involving all three actors, enabling the integration of simulation experiments with contextual analyses to uncover the dynamic mechanisms and equilibrium pathways of collaborative governance under technological empowerment. Ultimately, this study offers theoretical insights and practical guidance for sustainable ecological improvement.

3. Research Design and Methodology

3.1. Research Design

To systematically present the logical structure and modeling process of this study, Figure 1 illustrates the flow of model construction and analysis. The study is divided into four stages, forming a closed-loop research framework: “identification—modeling—analysis—verification.” The first stage involves identifying the subject strategies, in which the three key actors—government, enterprises, and villagers—are defined, along with their respective strategy combinations, using case study and interview data to establish the foundation for model development [29]. The second stage is model construction, which includes clarifying the strategy space, setting relevant parameters, constructing the benefit function, and deriving dynamic equations to form the computational framework for game-theoretic analysis [30]. The third stage is factor identification, in which sensitivity analysis of the core model variables with respect to actual data is conducted to identify key factors affecting the effectiveness of ecological governance [31]. The fourth stage is simulation analysis, involving equilibrium point analysis, scenario simulations, and sensitivity testing based on the evolutionary game model, in order to assess the environmental performance of selected strategy combinations under varying conditions [32].

3.2. Case Study and Identification of Actor Strategies

To identify the key behavioral mechanisms and stakeholder interactions in the context of smart green village construction, this study adopts a case study approach [33], selecting Village P in City C, Hunan Province, as a representative case. Located in a hilly region and long dependent on traditional agriculture, this village faces acute tensions between agricultural practices and ecological sustainability, highlighting an urgent need for green transformation and technological innovation for the improvement of environmental quality. In recent years, supported by government policies, the village has actively undergone smart green village construction. In particular, it has implemented intelligent irrigation systems, environmental monitoring platforms, and digital agricultural management tools, significantly improving agricultural productivity and resource use efficiency. Villagers have also adopted organic fertilizers, precision farming techniques, and waste separation practices, thereby reducing their environmental impacts and contributing to the sustainable enhancement of the local ecosystem.

The research employed semi-structured in-depth interviews and on-site observations, focusing on three key actors: government, enterprises, and villagers [34]. The interviews were designed around three core topics: first, policy and financial mechanisms; second, technology application and service pathways; and third, behavioral responses and willingness to participate in environmental greening. Combining local policy documents and village-level data, a case description database was created to inform the parameter settings for subsequent modeling [35,36]. Specifically, the range of strategy choices for each category of core actors was determined as outlined in the following subsections.

3.2.1. Government Strategy

The government’s role in smart green village construction is primarily expressed through its policy choices, which can be categorized into two strategic options: “high financial input” and “low financial input” [37]. Under the high-input strategy, the government advances ecological improvement through substantial financial support and policy incentives. As noted by the head of the village committee, “The government’s high investment provided us with sufficient funds to improve infrastructure and environmental protection facilities.” Villagers echoed this view: “Government subsidies have helped us transition to green agriculture, reduce the use of chemical fertilizers and pesticides, and improve soil quality.” Enterprise representatives added that “High government input has stimulated our technological innovation and promoted the use of smart agricultural equipment.” In contrast, under the low-input strategy, governmental support and incentives are relatively limited, resulting in constraints on project implementation [38]. According to the village committee head, “Although some subsidies exist, the funding is insufficient to support large-scale environmental infrastructure projects.” Villagers also noted that “Compared to the high-input policy, the low-input approach has a more limited effect.” Enterprise actors further remarked that “The low-input policy constrains our investment in green technologies, and slows down market-driven green transformation.” These findings suggest that a high-input strategy fosters cross-actor motivation and supports a virtuous cycle of “incentive–response–coordination.” In contrast, a low-input approach often results in fragmented efforts, undermining the long-term sustainability and systemic stability of ecological governance [39].

3.2.2. Enterprise Strategies

Enterprise strategies can be categorized into “high R&D investment” and “low R&D investment” approaches. Under the high-R&D-investment strategy, enterprises support smart green village development by increasing investments into green technologies [40]. As one enterprise representative stated, “With government financial incentives, we have increased R&D in smart agricultural equipment and green technologies—especially in precision irrigation and eco-agriculture—which has improved production efficiency and reduced resource waste.” Another executive added that “High R&D investment has enhanced our market competitiveness while enabling us to provide efficient and eco-friendly solutions to villages, thereby promoting green agricultural transformation.” This strategy has significantly promoted both technological innovation and ecological improvement, fostering a positive cycle involving villager engagement and government support [41]. In contrast, under the low-R&D-investment strategy, technological innovation progresses more slowly. As one enterprise representative explained, “Due to financial and market uncertainties, we rely more on traditional technologies and lack innovation, making it difficult to enhance ecological efficiency.” Another noted that “Low R&D investment slows down our ability to diffuse and innovate green technologies, hindering long-term ecological improvement.” These findings suggest that a high-R&D strategy can generate technological spillovers and cognitive incentives, while a low-investment approach is prone to structural bottlenecks such as “technological lag–synergy failure” [42].

3.2.3. Villager Strategies

Villager strategies are categorized as either “high participation” or “low participation.” Under the high-participation strategy, villagers respond actively to governmental policy incentives and engage in diverse activities aimed at improving the ecological environment [43]. As villager Li noted, “With government subsidies and technical support, I actively participated in the green agriculture project and used organic fertilizers, which significantly improved soil quality.” Another villager, Auntie Wang, shared that “We’ve established an environmental protection group in the village. We clean up waste together, and seeing our improved surroundings makes us more willing to participate.” These cases suggest that active villager participation not only improves the ecological environment, but also strengthens community cohesion and deepens collective environmental actions [44]. Under the low-participation strategy, villager involvement in ecological governance remains limited. Despite the presence of policy incentives and technical support, the outcomes are minimal. Villager Zhang stated that “We know about the government subsidies and training, but I don’t find the new technologies or environmental measures very effective, so I don’t participate actively.” Another villager, Auntie Li, noted that “Even with subsidies, some villagers are unwilling to join environmental projects, as they believe the measures have little relevance to their daily lives.” These findings indicate that insufficient incentives and limited awareness-raising dampen villager enthusiasm, preventing the full realization of ecological improvement outcomes [45].

3.3. Model Construction

A three-party evolutionary game approach was employed to construct the model, which dynamically simulates strategic interactions among governments, villagers, and enterprises in the context of ecological governance within smart green village construction. Unlike traditional static game models, which assume complete rationality and fixed strategies, evolutionary game theory is grounded in the assumption of bounded rationality. It emphasizes that agents adjust their strategies over time in response to external feedback, thereby more accurately reflecting the behavioral dynamics characterizing complex social systems. In this study, the modeling process consisted of the following key steps:

Step 1: Parameter Setting and Assumptions—Define the strategic space and underlying assumptions for governments, enterprises, and villagers.

Step 2: Payoff Function and Replicator Dynamics—Calculate the payoff functions for each actor based on their choice of strategy. Then, derive the replicator dynamic equations to capture how the strategies evolve over time and identify potential equilibrium states.

3.3.1. Parameter Settings and Fundamental Assumptions

To clarify the evolutionary dynamics among the three core actors—namely, government, villagers, and enterprises—in the context of smart green village development, a set of basic assumptions is proposed based on their strategic choices. The corresponding parameters and their definitions are presented in

Table 1. Model parameters and their definitions.

ID	CODE	MEANING	ID	CODE	MEANING
1	HD	High-investment policy	13	x	Probability of government adopting a high-investment strategy
2	DD	Low-investment policy	14	y	Probability of villager participation
3	CZ	Financial support	15	z	Probability of enterprises choosing high R&D investment strategy
4	GCY	Strong strategic motivation	16	JSCX	Technological innovation
5	DCY	Weak strategic motivation	17	SSYH	Tax incentives
6	YFCB	R&D costs	18	CPSJ	Product upgrading
7	JS	Technical support	19	SZSY	Digital literacy
8	C0	Cooperation effect coefficient	20	MR	Market returns
9	JJXY	Economic benefits	21	TIS	Technological innovation success rate
10	SHXY	Social reputation	22	HBYS	Environmental awareness
11	PSR	Policy sensitivity index	23	ZFJL	Government reward–punishment coefficient (>1 = incentive; <1 = penalty)
12	CEI	Synergy index	24	JTXD	Collective action awareness

.

Hypothesis 1.

The Relationship Between Government Strategic Choices and the Responses of Villagers and Enterprises.

Under a high-input policy, the government increases financial support (CZ) to accelerate the construction of smart green villages. The effectiveness of such a policy critically depends on the active participation of villagers (GCY). When villagers respond positively and engage actively, they can effectively absorb and utilize government resources, enhance ecological conditions, and drive rural economic sustainability. In such cases, governance performance (ZFJL) is expected to exceed 1 significantly [46]. High-input policies are typically accompanied by increased subsidies and technical support (JS) [47], which encourage villagers to enhance their environmental awareness (HBYS), engage in ecological protection, and improve their quality of life. These effects collectively form the cooperative influence factor (C0) [48]. Simultaneously, if enterprises pursue a high-R&D-investment strategy, they will facilitate green technology innovation (JSCX) and product upgrading (CPSJ) [49], thereby enhancing both economic returns (JJXY) and social reputation (SHXY) [50]. However, if villagers adopt a low-participation strategy (DCY), even high government input may fail to deliver results. Inefficient resource utilization would lead to reduced policy effectiveness, declining governance efficiency, and weakened policy sensitivity (PSR). In the strategic game framework, the probability of the government selecting a high-input policy is denoted as x, while the probability of choosing a low-input policy is (1 − x) [51].

Hypothesis 2.

The Influence of Villager Participation on Government and Enterprise Strategies.

As direct participants in smart green village construction, villager strategy choices significantly influence the behavioral responses of governments and enterprises [52]. Under the high-participation strategy (GCY), villagers actively engage in ecological protection and public works. This not only enhances environmental governance performance (HBYS) [53] and improves their quality of life, but also increases policy responsiveness, enabling greater access to financial subsidies and technical support (CZ, JS). This positive behavioral mechanism motivates the government to increase its investment willingness (ZFJL) and fosters the formation of a virtuous cycle of “participation–incentive–co-construction” (C0) [54]. Simultaneously, extensive villager participation (YFCB) encourages enterprises to increase their R&D investment, accelerate green technology innovation (JSCX) and product development (CPSJ), and thereby improve their market competitiveness and economic returns (JJXY). However, if villagers adopt a low participation strategy (DCY) and fail to respond positively to government policies, the inputs from both the government and enterprises will decline. As a result, the ecological improvement effect will be limited, adversely affecting the overall construction outcomes [55]. In this scenario, the government’s reward–punishment coefficient (ZFJL) becomes a key variable, while the absence of a strong sense of collective action (JTXD) reduces the effectiveness of policy implementation and weakens social benefits. In the proposed framework, the probability of villagers selecting active participation is denoted as y, while the probability of low participation is (1 − y).

Hypothesis 3.

Feedback Mechanism of Enterprise R&D Investment on Government and Villager Strategies.

Enterprises play a central role as providers of technical support and product services in the construction of smart green villages [56]. When enterprises adopt a high-R&D-investment strategy—thus incurring greater R&D costs (YFCB) and promoting new technologies and product development—they significantly enhance the efficiency and overall quality of construction, thereby improving the technical support capacity for project implementation. Technological innovation (JSCX) and product upgrading (CPSJ) not only strengthen an enterprise’s market competitiveness, but also directly influences their willingness to invest further—as reflected in the dynamic feedback loops of market returns (MR) and technological innovation success rates (TIS) [57]. Moreover, enterprise R&D strategies are shaped by external conditions—particularly governmental high-investment policies (HD) and financial incentives (CZ)—which stimulate enterprise investment behaviors, enhance social reputation (SHXY), and elicit positive responses from villagers [58]. Conversely, if enterprises pursue a low-R&D strategy and fail to achieve technological breakthroughs, this may result in stagnant innovation and reduced market returns and hinder the overall ecological governance process. Persistent R&D failures (i.e., low TIS) can erode an enterprise’s willingness to invest, potentially resulting in a prolonged low R&D equilibrium. Once a technological breakthrough occurs, it can trigger positive responses from both the government and villagers, enhance the synergy effect index (CEI), and activate a reinforcing feedback mechanism, thus establishing a virtuous government–enterprise–villager interaction cycle. In the proposed framework, the probability of an enterprise selecting a high-R&D-investment strategy is denoted as z, while that of selecting a low-R&D strategy is (1 − z) [59].

Hypothesis 4.

Synergies and Conflicts of Interest Among the Government, Enterprises, and Villagers.

In smart green village construction, the strategic decisions of the government, enterprises, and villagers are interdependent, potentially generating either synergies or conflicts of interest. Ideally, when the government adopts a high-investment policy (HD), villagers engage actively (GCY), and enterprises increase R&D investment (YFCB), the three actors form a synergistic alliance. This facilitates the effective application of advanced technologies (JSCX), product optimization (CPSJ), and ecological governance practices [60]. In this scenario, financial support (CZ), policy incentives (ZFJL), and environmental awareness among villagers (HBYS) jointly enhance the synergy effect index (CEI), significantly promoting ecological benefits, social returns, and the optimal allocation of resources. However, in practice, the interests of the three actors often diverge. While the government seeks to improve governance performance and public satisfaction through high investment [61], enterprises may opt for low R&D investment (DYF) to avoid the risks of technological failure (TIS) and uncertain market returns (MR) [62]. Villagers may similarly adopt a low-participation strategy (DCY) driven by short-term economic considerations (JJXY) or limited environmental awareness (HBYS). Under such conditions, policy responsiveness is weakened and the tripartite collaboration mechanism becomes fragile, ultimately hindering the sustainable advancement of smart green village initiatives [63]. Based on the aforementioned assumptions, the payoff matrix for the tripartite evolutionary game in this study is presented in

Table 2. Payoff matrix for the tripartite evolutionary game.

			Enterprise Strategy
		Villager Participation Strategy	High-R&D Strategy	Low-R&D Strategy
Government Strategy	High-investment policy	High participation	+ZFJL + CZ + PSR + CEI/+ HBYS + CZ + JS + JJXY + C0/ + JSCX + CPSJ + MR +SHXY + TIS − YFCB	+ZFJL + CZ − PSR/ + HBYS + CZ + JS + JJXY + C0/ + JJXY − JSCX − MR − SHXY
		Low participation	+CZ − C0 − HBYS − PSR/ + CZ − HBYS − JJXY/ + JSCX + CPSJ − MR − TIS + SHXY − YFCB	+CZ − PSR − HBYS − C0 − CEI/ + CZ − HBYS − JS − JJXY/ − MR − JSCX − TIS − SHXY
	Low-investment policy	High participation	+ZFJL + PSR + CEI/ + HBYS + JS + JJXY + C0/ + MR + TIS + SHXY − YFCB	+ZFJL + PSR + CEI/ + HBYS + JJXY − CZ − JS − C0/ − JSCX − MR − TIS − SHXY
		Low participation	+CZ − ZFJL − PSR − CEI/ − HBYS − JS − JJXY/ + JSCX − MR − TIS − SHXY − YFCB	+CZ − ZFJL − PSR − CEI/ + CZ − HBYS − JS − JJXY − C0/ − JSCX − MR − TIS − SHXY

.

3.3.2. Payoff Functions and Replicator Dynamics Equations

A rational payoff function was constructed and replicator dynamic equations for the three participating agents were derived, serving as a fundamental basis for analyzing strategy evolution paths, identifying conditions for system-wide collaboration, and evaluating equilibrium stability.

First, for the government agent, the expected payoffs for selecting a “high-input policy” and a “low-input policy” are denoted as $E_A$ and $E_B$, with the average expected payoff represented as $E_{AB}$. Other variable definitions follow Table 1. Accordingly, the average expected return across strategies is given by

$$E_{AB}=x{E}_A+(1-x)E_B$$

The replicator dynamic equation is defined as

$$f\left(x\right)=x(E_A-E_{AB})$$

where the expected payoffs are

$$\begin{gathered} E_A=x\cdot (GCY\cdot CZ)+(1-x)\cdot (1-y)\cdot (1-z)\cdot (1-GCY\cdot CZ); \\ {E}_B=(1-x)\cdot (1-GCY\cdot CZ) \\ {E}_{AB}=x\cdot [x\cdot (GCY\cdot CZ)+(1-x)\cdot (1-y)\cdot (1-z)\cdot (1-GCY\cdot CZ\left)\right]+(1-x)\cdot (1-GCY\cdot CZ) \end{gathered}$$

In evolutionary game theory, replicator dynamic equations are employed to describe how boundedly rational agents adjust their strategies by learning from and imitating more successful ones. These equations capture how the proportion of individuals adopting a certain strategy affects the likelihood of continued adoption, thereby influencing the equilibrium state of the game.

Accordingly, the replicator dynamic equation for the government’s strategy selection is expressed as

$$f\left(x\right)=(1-x)\cdot (1-y)\cdot (1-z)\cdot (1-GCY\cdot CZ)-(1-x)\cdot (1-GCY\cdot CZ)$$

Second, for the villager agent, the expected payoffs for selecting the “active-participation strategy” and “low-participation strategy” are denoted as $E_1$ and $E_2$, respectively, while the average expected payoff is represented as $E_{12}$. The expected payoffs are defined as follows:

$$\begin{gathered} E_1=y\cdot (x\cdot JS·HBYS)+(1-y)\cdot (1-x)\cdot (1-JS) \\ {E}_2=(1-y)\cdot (x\cdot JS·HBYS)+y\cdot (1-x)\cdot (1-JS) \\ {E}_{12}=y\cdot (x\cdot JS·HBYS)+(1-y)\cdot (1-x)\cdot (1-JS) \end{gathered}$$

Thus, the replicator dynamic equation for the villager agent’s strategy is

$$f\left(y\right)=y(E_1-E_{12})$$

Upon simplification, the equation becomes

$$\begin{array}{l}f\left(y\right)=y\cdot \left[y\cdot \left(x\cdot JS·HBYS\right)+\left(1-y\right)\cdot \left(1-x\right)\cdot \left(1-JS·HBYS\right)\right]\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}-\left[y\cdot \left(x\cdot JS\right)+\left(1-y\right)\cdot \left(1-x\right)\cdot \left(1-JS\right)\right]\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=y\cdot (y-1)\cdot (x\cdot JS-(1-x)\cdot (1-JS\left)\right)\end{array}$$

Third, for the enterprise agent, the expected payoffs for choosing the “high-R&D strategy” and “low-R&D strategy” are denoted as $E_3$ and $E_4$, respectively, with the average expected payoff represented by $E_{34}$. These are defined as follows:

$$\begin{gathered} E_3=z\cdot (x\cdot JJXY·MR)+(1-z)\cdot (1-x)\cdot (1-JJXY·JS) \\ {E}_4=(1-z)\cdot (x\cdot JJXY)+z\cdot (1-x)\cdot (1-JJXY·JS) \\ {E}_{34}=z\cdot \left[z\cdot \left(x\cdot JJXY·MR\right)+\left(1-z\right)\cdot \left(1-x\right)\cdot \left(1-JJXY·JS\right)\right]+\left(1-z\right)\cdot [\left(1-z\right)\cdot \left(x\cdot JJXY·MR\right)+z\cdot \left(1-x\right)\cdot \\ \left(1-JJXY·JS\right)]=z\cdot (x\cdot JJXY·MR)+(1-z)\cdot (1-x)\cdot (1-JJXY·JS). \end{gathered}$$

.

Therefore, the replicator dynamic equation for the enterprise agent’s strategy selection is

$$\begin{array}{l}f\left(z\right)=z\left(E_3-E_{34}\right)\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=z\cdot \left[z\cdot \left(x\cdot JJXY·MR\right)+\left(1-z\right)\cdot \left(1-x\right)\cdot \left(1-JJXY\right)\right]\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}-\left[z\cdot \left(x\cdot JJXY·MR\right)+\left(1-z\right)\cdot \left(1-x\right)\cdot \left(1-JJXY·JS\right)\right]\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=z\cdot (z-1)\cdot (x\cdot JJXY·MR-(1-x)\cdot (1-JJXY·JS\left)\right)\end{array}$$

4. Simulation and Evolution Results

4.1. Dual-Path Identification of Key Drivers: Modeling and Field Perspectives

This study adopts a dual analytical framework of “theory and practice” to identify key factors driving ecological improvement in the construction of smart green villages. The theoretical dimension builds on the previously constructed evolutionary game model, while the practical dimension draws from field research conducted in Village P, Hunan Province. Together, these dimensions provide conceptual and empirical support for the subsequent simulation analysis, enabling coherent interpretation of the strategy evolution paths through the integration of model-based reasoning and real-world insights.

4.1.1. Theoretical Drivers Identified from the Evolutionary Game Model

Based on analysis of the evolutionary game model, three core factors were identified as being critical for ecological improvement in smart green villages: financial support, technical support, and environmental awareness.

Financial support (CZ) serves as the foundational resource for effective policy implementation. Sufficient funding enhances the development of infrastructure, expands green project coverage, and lowers participation costs for both enterprises and villagers through subsidies or ecological compensation, thereby strengthening the effectiveness of the policy. Conversely, inadequate financial support constrains policy execution and undermines systemic synergy [64]. Technical support (JS) is a core driver of green transformation. For enterprises, access to effective technologies reduces R&D barriers, increases innovation success rates, and stimulates green product development. For villagers, technological applications improve agricultural practices and reduce pollutant emissions. Government provision of such support enhances both the efficiency and adaptability of the governance system. Environmental awareness (HBYS), in contrast, represents an endogenous driver of villagers’ participation in ecological governance [65]. A high level of awareness encourages proactive behaviors such as waste sorting and ecological restoration, while also fostering community-level self-organization. More importantly, public awareness feedback can shape governmental policy orientation and corporate behavioral expectations, thereby contributing to a broader green synergy. Conversely, low awareness weakens policy transmission, dampens market responsiveness, and limits the effectiveness of governance.

4.1.2. Empirical Drivers Identified from Field Study in Hunan

Through field research conducted in Village P, City C, Hunan Province, this study identified key drivers through which green village initiatives contribute to ecological improvement. The development of green villages plays a vital role in enhancing rural ecological conditions. Empirical observations have led to the determination of three primary mechanisms through which green villages drive ecological improvements. First, enhanced public environmental awareness forms the foundation of green village development. Environmental education programs, community-based campaigns, and awareness initiatives enable villagers to systematically acquire ecological knowledge, enhance their environmental consciousness, and engage in practices such as waste sorting and resource conservation. These initiatives have not only sparked environmental interest among younger generations, but also cultivated a cohort of residents embracing sustainable development principles [66]. As Ms. Zhang, a local villager, noted: “Since the environmental protection course in our village, my children have started reminding us to save water and electricity. Everyone seems more environmentally aware now.” Second, effective community organization and resource management play a vital role. Villages have established environmental protection groups and volunteer teams, enabling cross-sector collaboration with local governments, NGOs, and social enterprises. Initiatives such as land-use optimization, improved agricultural planning, and refined waste and sewage treatment systems have contributed to better resource efficiency and environmental quality. According to Mr. Li, leader of the village’s environmental team: “Through collaboration with the government, we’ve set up a waste-sorting station, and villagers are now actively participating. The environmental improvements are clear.” Finally, economic transformation is key to sustainable green development. Eco-tourism initiatives integrate natural and cultural assets, increasing villagers’ income and reinforcing environmental incentives. The promotion of organic and circular agriculture practices reduces chemical inputs and facilitates the transition to green farming [67]. Additionally, digital platforms have expanded market access for green agricultural products, generating both economic and environmental benefits. As Mr. Wang, a local farmer, commented: “Switching to organic vegetables and selling through e-commerce increased our income and reduced fertilizer use—it’s a win-win.” Together, these three drivers offer multidimensional and long-term momentum for the advancement of green villages.

4.2. Simulation Analysis

4.2.1. Equilibrium Points and Stability Analysis

An Evolutionarily Stable Strategy (ESS) is a foundational concept in evolutionary game theory that refers to a strategy profile that remains stable within a population as any additional agent following an alternative strategy cannot outperform the majority.

In the proposed tripartite game model, setting all three replicator dynamic equations to zero—that is, $f\left(x\right)=f\left(y\right)=f\left(z\right)=0$—yields eight equilibrium points, corresponding to all possible combinations of pure strategy profiles among government, villager, and enterprise agents. To analyze the local stability of these equilibria, we constructed the Jacobian matrix $I$ based on the partial derivatives of each replicator dynamic function with respect to the strategic variables $x$, $y$, and $z$:

$$I=\left[\begin{array}{ccc}{I}_1& I_2& I_3\\ I_4& I_5& I_6\\ I_7& I_8& I_9\end{array}\right]=\left[\begin{array}{ccc}{\displaystyle \frac{\partial f\left(x\right)}{\partial x}}& {\displaystyle \frac{\partial f\left(x\right)}{\partial y}}& {\displaystyle \frac{\partial f\left(x\right)}{\partial z}}\\ {\displaystyle \frac{\partial f\left(y\right)}{\partial x}}& {\displaystyle \frac{\partial f\left(y\right)}{\partial y}}& {\displaystyle \frac{\partial f\left(y\right)}{\partial z}}\\ {\displaystyle \frac{\partial f\left(z\right)}{\partial x}}& {\displaystyle \frac{\partial f\left(z\right)}{\partial y}}& {\displaystyle \frac{\partial f\left(z\right)}{\partial z}}\end{array}\right]$$

Using definitions for variables consistent with Table 1, the specific derivatives can be expressed as follows:

$${\displaystyle \frac{\partial f\left(x\right)}{\partial x}}=-(1-y)\cdot (1-z)\cdot (1-GCY\cdot CZ·HBYS)+(1-y)\cdot (1-GCY\cdot JS·HBYS)$$

$${\displaystyle \frac{\partial f\left(x\right)}{\partial y}}=-(1-x)\cdot (1-z)\cdot (1-GCY\cdot CZ·HBYS)$$

$${\displaystyle \frac{\partial f\left(x\right)}{\partial z}}=-(1-x)\cdot (1-y)\cdot (1-GCY\cdot CZ·HBYS)$$

$${\displaystyle \frac{\partial f\left(y\right)}{\partial x}}=\mathrm{y}\cdot \left(\mathrm{y}-1\right)\cdot \left(\mathrm{J}\mathrm{S}-\left(1-\mathrm{J}\mathrm{S}·HBYS\right)\right)$$

$${\displaystyle \frac{\partial f\left(y\right)}{\partial y}}=\left(2\mathrm{y}-1\right)\cdot \left(\mathrm{x}\cdot \mathrm{J}\mathrm{S}-\left(1-\mathrm{x}\right)\cdot \left(1-\mathrm{J}\mathrm{S}·HBYS\right)\right)$$

$${\displaystyle \frac{\partial f\left(y\right)}{\partial z}}=0$$

$${\displaystyle \frac{\partial f\left(z\right)}{\partial x}}=\mathrm{z}\cdot \left(\mathrm{z}-1\right)\cdot \left(\mathrm{J}\mathrm{J}\mathrm{X}\mathrm{Y}·MR-\left(1-\mathrm{J}\mathrm{J}\mathrm{X}\mathrm{Y}·JS\right)\right)$$

$${\displaystyle \frac{\partial f\left(z\right)}{\partial y}}=0$$

$${\displaystyle \frac{\partial f\left(z\right)}{\partial z}}=\mathrm{z}\cdot (2\mathrm{z}-1)\cdot (\mathrm{x}\cdot \mathrm{J}\mathrm{J}\mathrm{X}\mathrm{Y}·MR-(1-\mathrm{x})\cdot (1-\mathrm{J}\mathrm{J}\mathrm{X}\mathrm{Y}·JS))$$

According to the application of Lyapunov’s indirect method [68], an equilibrium point is considered asymptotically stable if all the eigenvalues of the Jacobian matrix have negative real parts; this implies that the corresponding strategic configuration constitutes an evolutionarily stable strategy (ESS). If at least one eigenvalue has a positive real part, the corresponding equilibrium point is unstable and cannot be classified as an ESS. If the eigenvalues include both negative and zero real parts, the point is considered a saddle or critical point and its local stability cannot be definitively determined via linear approximation alone. Accordingly, by substituting the eight equilibrium points into the Jacobian matrix, the corresponding eigenvalue signs, numerical values, and local stability conclusions were obtained. The results are presented in

Table 3. Stability conditions at different equilibrium points.

Equilibrium	Eigenvalue			Stability Conclusion
	eigenvalue1	eigenvalue2	eigenvalue3
E1	0	0	0	Not sure
E2	0	0	1	Not sure
E3	0	1	0	Not sure
E4	0	1	1	Not sure
E5	1	0	0	Unstable
E6	1	0	1	Unstable
E7	1	1	0	ESS
E8	1	1	1	Not sure

, from which it can be seen that the strategy combination at point E7 (1, 1, 0) constitutes an asymptotically stable equilibrium, indicating the existence of an ESS in the three-party evolutionary game. The corresponding phase diagram depicting the dynamic evolution around this point is illustrated in Figure 2.

Figure 2 illustrates the strategic evolution trajectories of the government, villager, and enterprise agents during the construction of smart green villages, converging toward the stable equilibrium point (1, 1, 0). The blue arrows indicate the government’s strategy dynamics, highlighting how increased financial subsidies, policy incentives, and infrastructure investments stimulate villager engagement and accelerate the overall development process. The green arrows represent the evolution of villagers’ participation, which transitions from low to high as improvements in living standards become evident, thereby reinforcing the effectiveness of policy and ecological enhancement. The orange arrows capture the strategic adjustments of enterprises. As indicated in Table 3, under conditions of high government input and strong villager participation, firms adopt a low-R&D-investment strategy, rationally leveraging existing technologies while avoiding excess innovation costs. Finally, the purple dotted line traces the trajectory of joint strategy convergence, ultimately stabilizing at the point (1, 1, 0). This equilibrium reflects a synergistic configuration: high government support, active villager engagement, and cost-efficient enterprise participation together form a sustainable governance mechanism that fosters long-term ecological improvement.

4.2.2. Simulation Outcomes Under Varying Strategy Combinations

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Scenario 1: High Financial Incentives, Low Technological Input, and Active Participation

As shown in Figure 3, the green, blue, and red lines in the figure represent high fiscal incentives, low technological investment, and active participation, respectively. The simulation results under the strategy combination of high financial incentives, low technological inputs, and active villager participation indicate that fiscal support plays a central role in driving ecological improvement. Specifically, government financial input (CZ) is set at 0.7, reflecting strong fiscal commitment, while the incentive coefficient (ZFJL) is 1.2, effectively encouraging both enterprises and villagers to engage. The participation probability of villagers (y) reaches 0.6, suggesting widespread involvement in environmental actions driven by subsidies and support mechanisms. Despite the low level of technological input (JS = 0.4), which limits the intensity of technological innovation (JSCX = 0.5) and product upgrading (CPSJ = 0.3), the villagers’ environmentally responsible behaviors, which are reinforced by government support, contribute to noticeable improvements in ecological indicators. As a result, both economic returns (JJXY) and social reputation (SHXY) increase moderately, showcasing the benefits of collective environmental action [69]. However, the simulation also revealed key limitations. Due to the constrained technological base, the depth and sustainability of ecological improvement remain sub-optimal. Growth in market returns (MR) and the synergy effect index (CEI) are relatively slow, indicating restricted capacity for system-wide innovation and transformation. Thus, while high fiscal investment coupled with community engagement can initiate positive environmental change, sustained progress requires a gradual increase in technological inputs to enhance the capacity for innovation and long-term ecological resilience.

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Scenario 2: High Financial Incentives, High Technological Support, and Active Participation

As shown in Figure 4, the yellow, blue, and red lines in the figure represent high fiscal incentives, high-tech investment, and active participation, respectively. Under the strategy configuration of high financial incentives, high technological support, and active villager participation, the simulation revealed significant improvements in ecological governance outcomes. In this scenario, government financial support (CZ) is set at 0.7, technical support (JS) at 0.8, and the participation level of villagers (y) reaches 0.7. The government’s incentive coefficient (ZFJL) remains at 1.2, while technological innovation (JSCX) and product upgrading (CPSJ) are 0.8 and 0.7, respectively. Environmental awareness (HBYS) is also relatively strong, set at 0.7. The simulation curves demonstrate a pronounced upward trend, indicating that the synergistic combination of strong fiscal commitment, advanced technological input, and broad public engagement leads to substantial ecological improvements. In particular, the effective coupling of financial incentives and technological empowerment accelerates the implementation of green infrastructure and environmental projects [70]. Furthermore, the positive feedback loop—in which technological success reinforces government confidence and villager trust—enhances coordination among stakeholders and strengthens institutional resilience. This scenario illustrates that maximizing both financial and technical capacity, alongside fostering grassroots participation, constitutes a highly effective pathway for smart green village construction and long-term ecological sustainability.

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Scenario 3: Low Financial Incentives, High Technological Support, and Active Participation

As shown in Figure 5, the yellow, blue, and red lines in the figure represent low fiscal incentives, high-tech investment, and active participation, respectively. In the scenario characterized by low financial incentives, high technological support, and active villager participation, the simulation reveals a mixed outcome. Here, government financial input (CZ) is set at 0.3, technical support (JS) at 0.8, the probability of villager participation (y) at 0.7, the success rate of technological innovation (TIS) at 0.6, and the reward–penalty coefficient (ZFJL) at 0.9. The results suggest that, while strong technical support and high levels of villager engagement initially facilitate improvements in ecological indicators, the system ultimately fails to reach a stable equilibrium. The lack of adequate financial support constrains the depth, coverage, and sustainability of ecological improvement efforts [71]. Although technological innovation and active participation yield short-term gains, the absence of sufficient fiscal backing undermines long-term environmental outcomes and strategic cohesion. This finding underscores the indispensable role of financial support in enabling a green transformation. Adequate funding not only ensures the implementation of technological measures, but also provides sustained incentives for grassroots participation. In this scenario, low financial input weakened the overall system synergy, reduced the effectiveness of both technical support and civic engagement, and prevented the achievement of optimal ecological efficiency.

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Scenario 4: Low Financial Incentives, Low Technological Support, and Active Participation

As shown in Figure 6.,the yellow, blue, and red lines in the figure represent low fiscal incentives, low technological investment, and active participation, respectively. Under the strategy configuration of low financial incentives, low technological support, and active villager participation, the simulation results indicate limited and unsustainable ecological improvements. In this scenario, government financial input (CZ) is 0.3, technical support (JS) is 0.4, villager participation probability (y) is 0.7, environmental awareness (HBYS) is 0.6, the government’s reward–penalty coefficient (ZFJL) is 0.8, and the success rate of technological innovation (TIS) is 0.5. While high levels of civic engagement initially stimulate positive environmental changes, the system struggles to maintain momentum over time [72]. The trajectory reveals an early improvement in ecological indicators, followed by a deceleration and eventual failure to converge to a stable equilibrium. The limited fiscal and technical inputs erode the long-term motivation of villagers, weakening their continued participation and reducing the overall resilience of the governance system. This scenario highlights a critical insight: even with strong grassroots participation, insufficient financial and technological support impedes the realization of sustained ecological gains. In the absence of adequate enabling conditions, the system lacks the necessary structural reinforcement to transition toward long-term ecological balance and smart village sustainability.

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Scenario 5: High Financial Incentives, Low Technological Support, and Active Participation

As shown in Figure 7, the yellow, blue, and red lines respectively represent high financial incentives, low technological support, and active villager participation. In the simulation scenario featuring high financial incentives, low technological support, and active villager participation, the model is configured with financial support (CZ) set at 0.3, technical support (JS) at 0.4, villager participation probability (y) at 0.8, environmental awareness (HBYS) at 0.6, the government reward–penalty coefficient (ZFJL) at 0.8, and a technological innovation success rate (TIS) of 0.5. Despite the villagers’ high willingness to participate and relatively strong environmental awareness, the simulation results reveal that the ecological improvement remains limited and does not converge toward a stable optimal equilibrium. The lack of robust technological support weakens the efficiency and scalability of green initiatives, while the limited financial input constrains the implementation of infrastructural and institutional reforms. This scenario underscores a critical constraint: relying solely on grassroots engagement without adequate technical capacity and systemic support is insufficient for long-term ecological optimization [73]. Although villager participation remains a vital endogenous force, the absence of complementary external resources—particularly technological enablers—prevents the full realization of smart green village development. To ensure sustainability, ecological governance requires not only civic engagement but also strong, coordinated support from governmental and enterprise actors.

4.2.3. Sensitivity Analysis

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Sensitivity of Financial Support on Ecological Outcomes

The red, green, blue, pink, and purple lines in the diagram represent the values of CZ ranging from 0.15 to 0.55, increasing by 0.1 sequentially. As shown in Figure 8, the results demonstrate a clear pattern: as financial support increases, the convergence rate of ecological improvements accelerates markedly, improving the overall environmental performance accordingly. At lower levels of financial support (e.g., the red curve), the system shows slow initial improvements and even temporary negative fluctuations, indicating possible resource bottlenecks and governance implementation constraints under insufficient fiscal input [74]. In contrast, with a gradual increase in financial support (represented by the green, blue, and purple curves), the trajectory exhibits more rapid gains and earlier stabilization, underscoring the positive catalytic role that financial input plays in ecological governance. This trend is particularly evident in the steep upward slopes on the left side of the curves, revealing a significant positive marginal effect of financial support on ecological outcomes in the early stages.

However, an important turning point emerges: once financial support surpasses a certain threshold (e.g., in the pink and upper blue curves), the improvement curves converge closely, stabilizing around a value of 1, and the incremental difference between varying levels of support becomes negligible. This implies a diminishing marginal return on fiscal input: after a certain point, additional funding no longer yields significant environmental gains. The sensitivity of the system to financial inputs becomes saturated and the limiting factors shift to other dimensions, such as villager participation and enterprise-led technological innovation. Thus, optimal ecological outcomes depend on balanced, multi-dimensional input rather than unilateral financial investment.

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Sensitivity of Technological Support on Ecosystem Outcomes

In this simulation, the green, blue, purple, pink, and black lines in the diagram represent the values of JS ranging from 0.1 to 0.5, increasing by 0.1 sequentially. As shown in Figure 9, the results show that, although the evolutionary trajectories of ecosystem improvement under different JS levels varied slightly during the initial stages, all exhibited a consistent upward trend and ultimately converged to a stable equilibrium. As the level of technical support increases, all path curves rapidly ascend from an initial value of approximately 0.5, with significantly accelerated convergence rates. This indicates that technical inputs exert a direct and sustained influence on both the evolutionary speed and the quality of convergence within the ecological governance system. Particularly under high-technology support scenarios (e.g., black asterisks and pink pentagons in the graph), the system reached a stable optimum in a notably short timeframe, reflecting enhanced system responsiveness and adaptive regulation capacity. In contrast, scenarios characterized by low technical support (e.g., green triangle curves) demonstrate visible—albeit slower—improvement trajectories. This implies that insufficient technological input delays the realization of policy effectiveness, weakening the system’s ability to absorb and operationalize incentives efficiently. From a theoretical perspective, this finding aligns with S. Shan’s assertion that “green technology provides systemic support for sustainability transitions” [75], as well as S.A.R. Khan’s emphasis on “technology supply by firms as a critical exogenous driver” [76]. Overall, JS not only plays a pivotal structural role in ecological evolution, but also acts as an early-stage amplifier of synergy between policy incentives and villager engagement. Thus, it emerges as a core enabling force for enhancing both the effectiveness and quality of smart ecological governance.

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Sensitivity of Environmental Awareness on Ecological Outcomes

As shown in Figure 10, the red, green, black, blue, and pink lines in the diagram represent the values of HBYS ranging from 0.3 to 0.7, increasing by 0.1 sequentially. The simulation results indicated that the system state consistently evolved toward a stable value of 1 across different HBYS levels, demonstrating that environmental awareness exerts a sustained positive influence on the ecological governance system. However, notable differences were observed in the rise rate and convergence time during the early stage of evolution. Higher environmental awareness markedly enhanced system responsiveness and stability. In particular, when the time was less than 0.02, scenarios with high environmental awareness—indicated by pink pentagrams and black asterisks—converged rapidly, highlighting the “accelerator effect” of individual cognitive levels in early evolution. This mechanism implies that environmental awareness, as an endogenous variable, not only affects villagers’ initial behavioral decisions within the evolutionary game system but also amplifies synergistic effects via behavioral externalities, thereby enhancing the governance system’s stability. Unlike external incentives such as financial aid or technological inputs, the marginal effect of environmental awareness does not decline with intensity; its influence relies more on cognitive feedback, community interactions, and internalization of norms, resulting in stronger momentum, shorter lag, and greater early-stage stability.

The simulation results aligned closely with field observations from Village P, where villagers’ voluntary participation in waste sorting, river restoration, and green agricultural practices has led to tangible improvements in ecological quality. This outcome highlights a positive feedback mechanism linking environmental awareness, behavioral engagement, and systemic evolution. Such alignment underscores that environmental awareness serves as a foundational driver of sustainable ecosystem transformation within smart green villages. It not only initiates behavioral participation, but also sustains system-wide ecological improvement over time. Consequently, environmental awareness is pivotal for transitioning from externally stimulated governance models toward endogenous, self-reinforcing ecological governance systems.

4.2.4. Evolutionary Dynamics of System Equilibrium Points

In Figure 11, the different colored lines represent the evolving trends of the equilibrium state in the evolutionary game, rather than the specific strategy choices of each stakeholder. Each line illustrates the evolution of the three main stakeholders under different strategy combinations, reflecting the gradual approach of the system from the initial state toward the equilibrium state. The use of different colors is solely for distinguishing between evolution paths, which aids in illustrating the interactions and feedback mechanisms among the government, enterprises, and villagers. Over time, the evolution paths of the lines show a trend of convergence from the initial state toward the final stable point, highlighting the stability and convergence rate of the system under different strategies. In the tripartite evolutionary game model for smart green village development, the evolutionarily stable strategy (ESS) is characterized by the government adopting a high-input policy (G1), villagers engaging in active participation (V1), and enterprises opting for a low-R&D strategy (E0). As illustrated in Figure 11, this equilibrium state demonstrated strong convergence over 100 iterative cycles, indicating that all three actors achieved optimal coordination within the dynamic game framework. The system thereby stabilizes around a high-efficiency ecological governance trajectory, reflecting the self-reinforcing nature of collaborative mechanisms under differentiated strategic roles.

Firstly, as the core actor responsible for supply and resource allocation in the system, the government’s high-input strategy (G1) serves as the primary driver of smart green village development. Through increased financial investments and enhanced policy incentives, the government significantly strengthens the provision of green infrastructure and ecological governance capacity and provides an enabling environment for technological advancement. This strategy not only enhances villagers’ motivation through clear policy signals but also creates stable market and institutional expectations for enterprises, thereby forming a positive feedback loop of “public input–behavioral incentives–governance performance.” Secondly, villagers’ active participation (V1) in response to government input enhances the depth of governance practices and strengthens community cohesion. By engaging in waste sorting, ecological restoration, and the adoption of clean energy, villagers contribute to the efficiency of policy implementation, foster self-organized governance, and reinforce an environmental synergy mechanism of “perception–action–feedback.” This collective behavioral feedback offers a dynamic demand basis for government policy refinement and enterprise product iteration. In contrast, the low-R&D strategy (E0) adopted by enterprises in this context is not indicative of passivity, instead reflecting a rational cost–benefit response. Given strong governmental support and high community engagement, existing green technologies sufficiently meet current needs. Enterprises thus prioritize technology promotion and application to reduce operational risks and improve market efficiency. While this approach may dampen innovation momentum in the short-term, it enhances the system’s elasticity and resource efficiency, aligning with the pragmatic governance logic under resource constraints.

Therefore, under the evolutionarily stable strategy (G1–V1–E0), the three actors establish a dynamic equilibrium characterized by mutual reinforcement and functional complementarity. Government leadership, villager coordination, and enterprise adaptation collectively form the behavioral foundation for stability in ecological governance. This equilibrium reflects a pragmatic governance trajectory of “multi-party coordination–moderate role differentiation–structural optimization” in the context of sustainable rural development. The results not only validate the internal logic of the evolutionary model, but also align closely with empirical observations from the case study in Village P. This alignment demonstrates the feasibility of achieving stable collaboration and sustainable system evolution through differentiated yet synergistic strategies adopted by diverse actors.

5. Discussion

This study investigated the synergistic evolution mechanism associated with ecological environmental governance in the context of smart green villages. Using Village P in Hunan Province as a case study, a tripartite evolutionary game model involving the government, enterprises, and villagers was constructed to simulate and examine the impacts of various strategy combinations and influencing factors. The study’s results revealed the following key findings.

In terms of strategy selection, collaborative governance proves more effective than unilateral dominance [77]. When implementing a strategy combining high financial incentives, low technical support, and strong villager participation, the ecosystem demonstrated the fastest and most stable trajectory of positive evolution. This synergistic approach exhibits greater systemic stability and sustainability compared to strategies that are solely dependent on financial subsidies or corporate leadership. This finding aligns with Leventon’s observation that multiple synergistic pathways are more suitable for rural green governance [78]. However, this study goes further by expanding upon Shvets’ framework, which argues that single, policy-led strategies fail to establish a long-term equilibrium. It does so by revealing the nonlinear interaction mechanisms embedded within strategy portfolios through dynamic simulation [79]. Specifically, this study’s unique contribution lies in the optimization of strategy portfolios through precisely aligning fiscal and technological pathways, as well as enhancing the marginal effect of villagers’ participation under resource-constrained conditions.

With regard to influencing factors, the study identified a triadic coupling system comprising finance, technology, and cognition. Model-based simulations further revealed that the ecosystem’s stable evolution is primarily driven by three categories of factors: financial support (external incentive) [80], technological provision (capacity support) [81], and villagers’ environmental awareness (behavioral foundation) [82]. While financial investment can boost early-stage system performance, incentive failure may arise in the absence of technological support and villager participation [83]. In contrast, sustained improvements in environmental awareness [84] play an irreplaceable guiding role in ensuring the system’s long-term homeostasis. These findings are broadly consistent with the “financial–technological dual-wheel drive model” proposed by Fan and colleagues [85]. However, this study extends upon that framework by proposing a “financial–technological–cognitive” triadic coupling model. Of particular note is that villagers’ environmental awareness serves as a cognitive feedback channel influencing the willingness of enterprises to invest in green initiatives—an aspect largely overlooked or oversimplified in existing research [86]. This study underscores that cognitive variables should be considered to occupy a central position within the ecosystem response mechanism.

In simulation practice, intelligent modeling serves as a powerful tool for localized policy optimization. By conducting scenario-based modeling and simulation in Village P, Hunan Province, this study first demonstrated the strong adaptability and practical utility of simulation models in the context of rural ecological governance. Second, the simulation effectively captured the nonlinear effects of different policy combinations on the system’s convergence trajectories. Unlike most green rural studies, which have emphasized qualitative policy analysis [87], this study established a closed-loop analytical process—from theoretical construction and empirical validation to situational simulation—thereby responding to Hofer et al.’s call for “institutional modelling embedded in practical scenarios” [88]. This methodological approach not only fulfills that principle, but also enhances the generalizability and policy spillover potential of the findings.

In summary, this study innovatively constructed an evolutionary model that captures the interactions among the government, enterprises, and villagers, while integrating field research data to enhance the empirical foundation for parameter setting—thereby significantly improving the model’s explanatory power and applicability. At the theoretical level, the study incorporated the adoption of green technology and environmental awareness into an evolutionary game framework, expanded the theoretical boundary of the “cognition–feedback–equilibrium” mechanism in the context of digital village governance, and unveiled the bidirectional coupling between behavioral expectations and institutional adaptation. Overall, this study enables a deeper understanding of the multi-synergistic governance mechanisms underpinning smart green village development and offers actionable pathway recommendations and theoretical support for the optimization of dynamic interventions and institutional designs, thus delivering strong practical relevance and policy spillover potential.

6. Conclusions and Research Limitations

6.1. Key Findings and Policy Implications

In the context of smart green village development, achieving synergistic evolution among governments, enterprises, and villagers in ecological governance is essential for promoting sustainable rural development. In this study, we developed a tripartite evolutionary game model and integrated it with data derived from a case study of Village P in Hunan Province’s hilly region in order to systematically examine the logic behind each of the actor’s strategy choices, the key influencing mechanisms, and the conditions for stable evolutionary outcomes. The main findings are as follows.

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The combination of high government investment, active participation by villagers, and a low-R&D strategy by enterprises forms a stable evolutionary path for the system. This finding aligns with HU’s study, which emphasized the positive impact of multi-stakeholder synergy on green village governance [89], while contrasting with Swart’s “single incentive dominance” model [90]. The results further demonstrated that the stability of collaborative strategies is influenced by the relationship between behavioral feedback and institutional adaptation, underscoring the dynamic equilibrium between policy incentives and social feedback.

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Financial support, technology supply, and environmental awareness were found to serve as the core drivers of strategy evolution for the three key actors. The interactions between these factors can significantly enhance system resilience and increase participation retention. This finding aligns with Cattino’s conclusion that financial support can boost willingness to adopt new strategies by lowering the participation threshold [91]. However, this study further highlighted environmental awareness as an endogenous driver of villagers’ behavior, exerting a reverse influence on the decision-making expectations of both firms and the government. This contrasts with Du’s study [92], which emphasized the direct impact of technology supply on strategy selection.

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The simulation results indicated that the stability of a strategy combination is highly dependent on its contextual configuration, with a risk of entering a “sub-stability zone” in the synergy mechanism. In situations of insufficient financial input, technological failures, or cognitive deficits, the three-party strategies may fail to converge to a high level of synergy. This finding aligns with Chien’s study [93], which also found that strategy synergy is often hindered by resource shortages or asymmetric information. However, this study emphasized the importance of policymakers considering the dynamic adaptation and feedback resilience of institutional incentives in order to avoid the pitfalls of path lock-in or strategy divergence. This research addresses a gap in the existing literature by discussing the risks associated with “sub-stability zones.”

Based on these findings, the following policy recommendations are proposed: For the government, it is essential to improve the timing and coordination of financial incentives. By leveraging digital platforms to track changes in enterprise and villager strategies, the government can achieve precise alignment between resource allocation and strategic evolution, enhancing the agility and continuity of policy implementation. For enterprises, green behaviors should not rely solely on government incentives. Companies must also respond to villagers’ preferences and public feedback, integrating the local deployment of green technologies with participatory innovation. This approach can be expected to improve the diffusion efficiency and social acceptance of environmentally friendly technologies. For villagers and grassroots organizations, efforts should focus on building mechanisms for community consensus. Through environmental education, digital governance tools, and the cultivation of trust-based relationships, communities can strengthen collective participation and stabilize green behaviors. These actions serve to amplify the guiding role of public governance in the process of synergistic ecological evolution.

6.2. Research Contributions

The contributions of this study are reflected in three key aspects. First, this study applied evolutionary game theory to examine the improvement in the ecological environment through the construction of smart green villages, developing a three-party evolutionary game model involving government, enterprise, and villager agents. Unlike traditional research, which has generally focused on single incentives, this study delved into strategy selection and the mutual influences within the multi-party synergistic mechanism. In this way, it revealed how the three parties can achieve a dynamic equilibrium through feedback mechanisms and strategy adjustments, providing a theoretical foundation for sustainable improvement of the green ecological environment. Second, the realism and applicability of the proposed theoretical model were enhanced through the incorporation of field research data obtained from Village P in Hunan Province. These case data allowed for validation of the model, as well as offering practical insights into the dynamic adaptability of incentive mechanisms and the implementation of behavioral synergy mechanisms in policy design. Finally, the study analyzed the strategic interactions among the three considered parties based on evolutionary game theory, addressing ecological governance challenges in Village P. It examined how different strategy combinations impact governance outcomes and demonstrated how sustainable ecological governance can be achieved through multi-party synergy under complex geographic conditions, using both simulation experiments and sensitivity analyses. The recommendations provided are both relevant and actionable. This study offers new perspectives on ecological governance for areas characterized by complex terrains, as well as theoretical support for policy implementation in similar regions.

6.3. Limitations

This study involved the construction of an evolutionary game model and key mechanisms were identified using qualitative data; however, the data may have limitations due to constraints relating to manpower and time. Additionally, a series of assumptions were made during model development, which may have resulted in discrepancies between the model’s outcomes and real-world conditions, failing to fully capture the complexity of behavioral patterns and strategic choices. Furthermore, this study focused on the hilly area of Village P in Hunan Province. While this provided region-specific practical guidance, the generalizability of the results needs further verification, particularly under varying geographic and social conditions. Moreover, the presented model does not fully account for the impacts of differences in network structures, such as social capital and information flow channels.

6.4. Directions for Future Research

Future research can advance both the theory and practice of smart green rural governance in several ways. First, research with larger sample sizes should be conducted to validate and extend the findings of this study through cross-regional comparisons; particularly in different geographic contexts (e.g., plains, mountainous areas, coastal regions) and administrative levels (e.g., county, municipal, and township). These studies would help to provide tailored recommendations for policy formulation across various regions. Second, longitudinal studies should be performed to explore the long-term impacts of collaborative governance strategies on sustainability and well-being, using extended data collection processes to refine our understanding of the strategy evolution process. Finally, future research should delve deeper into the heterogeneity of subjects and differences in network structures, examining how to foster synergy and collaboration among various stakeholders within complex networks. Additionally, the exploration of innovative methods and technologies to improve the efficiency and effectiveness of collaborative governance is crucial. These future studies are expected to provide valuable insights for policymakers, practitioners, and researchers, further advancing the sustainable development of smart green villages.