Resident Participation in Environmental Governance of Sustainable Tourism in Rural Destination

Abstract

The rapid development of rural tourism has placed significant pressure on the rural environment, and relying solely on the government and market forces is insufficient for effective governance. It is urgent to integrate endogenous rural forces into environmental governance. The development of social behavior theory offers new insights into exploring sustainable approaches for resident participation in environmental governance in rural tourism areas. This paper, based on the Stimulus-Organism-Response (SOR) theoretical framework from social behavior theory, outlines the entire process of rural tourism residents transitioning from individual stimuli to psychological responses and ultimately to participation in environmental governance. This study combines the Motivation-Opportunity-Ability (MOA) model to analyze the stimulus factors affecting local residents and jointly constructs a path mechanism model for resident participation in environmental governance in rural tourism areas. A total of 462 valid questionnaires were collected through a survey, and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used for empirical testing to determine the path coefficients between variables. On this basis, a system dynamics model was constructed to simulate the dynamic evolution of the relationships between variables. This study found the following: (1) In the process of rural tourism, residents’ participation in environmental governance relies on motivational factors at the stimulus level to play a core leading role; opportunity factors act as catalysts, and ability factors serve as auxiliaries; participation willingness at the organism level plays a crucial role. (2) Material pursuit and formal institutions are the strongest single sustainable factors for residents’ participation in environmental governance in rural tourism areas; combinations of variables such as local attachment, governance knowledge, and governance identity have significant effects. It is recommended that future rural tourism leverage government guidance, coordinate interests, and adhere to a collaborative development approach to ensure the sustainable development of the rural tourism environment.

1. Introduction

Environmental governance is a crucial issue for global sustainable development. Compared to non-tourism destinations, tourism destinations face additional challenges in environmental governance due to the large number of tourists, the seasonality of tourist flows, and geographical conditions [1,2]. Particularly in popular rural tourism destinations, the influx of tourists during peak seasons generates a significant amount of waste. Coupled with the relatively weak environmental infrastructure, these areas face severe environmental governance challenges [3]. For instance, in Dazhai Village, Guangxi, from June to August each year, a large number of tourists visit for sightseeing and vacation, with peak periods seeing over 15,000 visitors daily. However, due to cost constraints, only one cleaner is responsible for maintaining the village’s environment on a daily basis. Local residents are the most direct participants, beneficiaries, and supervisors of environmental governance in rural tourism areas [4]. However, the enthusiasm of residents for participating in environmental governance is low, with a common phenomenon being “the government acts, villagers watch” [5]. Against this backdrop, exploring sustainable ways for residents to engage in environmental governance in rural tourism areas is particularly important [6].

Existing literature has explored the sustainable paths for residents’ participation in environmental governance in rural tourism areas from various perspectives. Some scholars argue that the public nature of environmental governance in rural tourism areas makes government intervention indispensable [7]. National governments can impose environmental regulations to constrain or penalize local residents for pollution [8]. For instance, in 2021, China released the “Five-Year Action Plan for Improving and Enhancing Rural Living Environments (2021–2025)”, encouraging local legislation to improve village cleanliness and rural waste management systems. However, due to the profit-driven nature of residents and the imperfect regulatory systems in rural tourism areas [9], these environmental policies are difficult to fully implement [10]. Some scholars also argue from a market perspective that residents in rural tourism areas should bear the cost of environmental governance [11]. However, the environmental awareness of most rural residents is low, and they believe the government should be responsible for environmental governance, resulting in a low willingness to pay [12]. Although public–private partnership (PPP) models can partially address the funding shortages in rural environmental governance [13], the dispersed geographical distribution of rural tourism areas, wide range of pollution sources, and high governance costs hinder the formation of scale effects [14].

The development of social behavior theory provides new insights for exploring sustainable ways for residents to participate in environmental governance in rural tourism areas. The SOR theoretical framework, proposed by environmental psychologists Mehrabian and Russell, investigates how various internal and external stimuli influence individuals’ cognitive or psychological responses and subsequent behaviors [15,16,17]. Recently, the SOR framework has been applied in rural tourism studies to analyze residents’ participation in rural public activities from the perspectives of soft benefits, hard benefits, and altruism, exploring how to enhance residents’ participation in rural governance amidst the rapid development of rural tourism [18,19]. The MOA model, proposed by Macinnis and Jaworski in 1989, interprets individual behavior from the aspects of motivation, opportunity, and ability. The model posits that individuals’ behaviors are easily triggered by the combined effects of intrinsic motivation (“whether they want to do it”), external opportunities (“whether they are allowed to do it”), and personal capabilities (“whether they can do it”) [20]. Scholars in tourism-related disciplines have also attempted to use this model to explain individuals’ behaviors in the tourism environment [21,22].

Although the SOR framework and MOA model have been widely applied in tourism research, contributing positively to discussions on sustainable development in tourist destinations, they have yet to be fully utilized in studies on residents’ participation in environmental governance in rural tourism areas. In particular, there is a lack of research combining these two frameworks to explain how individual residents, influenced by various stimuli, generate organismic responses that lead to participatory behavior. This gap hinders the comprehensive analysis and deep understanding of the pathways and interrelationships among factors influencing residents’ participation in environmental governance in rural tourism areas, making it difficult to propose realistic development recommendations for multi-factor synergistic development and policy innovation. While a few studies have applied both frameworks to the governance of public affairs in rural tourism areas, these studies remain focused on the “net effects” of individual factors. Given that rural tourism areas are complex systems characterized by multidimensional interactions between humans and nature, the interplay and dynamic relationships among factors can have a significant and complex impact on local residents’ participation in environmental governance. This highlights a lack of research on sustainable development pathways from a dynamic perspective. Therefore, it is crucial to explore the pathways and sustainable development directions for residents’ participation in environmental governance in rural tourism areas based on the SOR framework and MOA model.

Given the limitations of existing research, this study focuses on two questions regarding residents’ participation in environmental governance in rural tourism areas: (1) What are the path mechanisms for residents’ participation in environmental governance under the SOR and MOA theoretical frameworks? (2) What are the sustainable paths for residents’ participation in environmental governance from a dynamic perspective? To effectively address these research questions, this study follows three main steps: First, a rural tourism case area is selected, and a survey is conducted among local residents. Second, the SOR-MOA theoretical model is used to analyze the path mechanisms of residents’ participation in environmental governance in rural tourism areas, and PLS-SEM is used to analyze the path coefficients. Finally, system dynamics methods are applied to explore sustainable paths for residents’ participation in environmental governance in rural tourism areas.

This study’s marginal contributions are mainly in the following two aspects: (1) The current literature lacks research on residents’ participation in environmental governance in rural tourism areas from the perspective of social behavior theory. This study uses the SOR-MOA model to construct a path mechanism model for residents’ participation in environmental governance in rural tourism areas, discussing the path mechanisms from the perspective of social behavior theory. This approach addresses the issues of impracticality and high economic costs of resident participation in environmental governance from government and market perspectives, providing new insights for promoting sustainable development in rural tourism areas. (2) This study explores sustainable paths for residents’ participation in environmental governance in rural tourism areas using system dynamics methods. Existing research on the paths of residents’ participation in environmental governance in rural tourism areas has analyzed the “net effect” of different factors from a static perspective [23,24] but has not addressed the dynamic factors influencing sustainable paths, which is detrimental to the efficient and sustainable development of rural tourism areas. This study establishes a system dynamics model to reveal the dynamic causal feedback loops of various variables in the process of residents’ participation in environmental governance in rural tourism areas. It explores the sustainable paths of single and combined variables, responding to the academic call for enhancing the motivational mechanisms of environmental governance in rural tourism areas [25], and provides a more scientific reference for practical activities exploring residents’ participation in environmental governance in rural tourism areas.

The following sections of the paper are organized as follows: Section 2 presents the research hypotheses, Section 3 describes the methodology, Section 4 details the research process and results, and Section 5 provides the conclusions and implications.

2. Hypotheses Development

2.1. Participation Willingness and Participatory Behavior

Participation willingness refers to the subjective psychological tendency of rural tourism residents to hold either positive or negative attitudes toward engaging in environmental governance. It reflects the degree to which residents are willing to participate in such activities. In traditional technology acceptance models, participation willingness significantly influences participatory behavior [26]. For instance, Ajzen’s Theory of Planned Behavior posits that individual behavior is directly driven by intentions, which are, in turn, influenced by attitudes, subjective norms, and perceived behavioral control. As an antecedent to behavior, willingness acts as a predictor and psychological preparation for the likelihood of the behavior occurring. Therefore, individual participation willingness is a key indicator predicting actual participatory behavior. This conclusion has been confirmed in studies on community participation among rural tourism residents, where residents, after engaging in rural tourism services, realize that participating in village environmental governance also protects their own interests, thereby enhancing their participation willingness and subsequently leading to public governance behavior. When rural tourism residents hold a positive attitude towards environmental governance, they are theoretically more likely to participate; conversely, their likelihood of participation decreases with a negative attitude. Therefore, this paper proposes the following hypothesis:

H1: 

Residents’ participation willingness in environmental governance positively influences their participatory behavior.

2.2. Hypotheses Based on the MOA Model

The MOA framework proposed by Macinnis et al. [20] posits that motivation, opportunity, and ability are the three fundamental prerequisites that determine individual participation behavior and can be used to explain the manifestation of such behaviors. The World Tourism Organization’s guidelines on destination management highlight that stakeholder motivation is a critical governance factor in promoting the sustainable development of tourism destinations [27]. Motivation (M) refers to the intrinsic drive that stimulates an individual to exhibit a certain behavior, encompassing their willingness, interest, and desire to perform the behavior. It is a crucial factor influencing individual behavioral decisions [20]. Maslow’s hierarchy of needs theory indicates that individuals are motivated by physiological needs, safety needs, social needs, esteem needs, and self-actualization needs [28]. Material pursuit refers to the extent to which individuals prioritize material wealth and economic benefits. According to rational choice theory, individuals tend to weigh the pros and cons when making behavioral decisions, aiming to maximize their own benefits. In rural tourism areas, residents develop material pursuit motivations driven by physiological needs (such as food and shelter) and safety needs (economic security and job security). Hometown affection refers to an individual’s emotional bond and sense of belonging to their place of residence. According to place attachment theory, when an individual has a strong emotional attachment to a particular place, their behavior is often motivated by the desire to maintain and protect that place. Residents of rural tourism areas develop a sense of hometown affection driven by social needs, such as family companionship and community ties, making them more likely to engage in community affairs to safeguard the environment and social well-being of their hometown. Given the fulfillment of the aforementioned basic needs, residents pursuing higher levels of self-actualization (such as a personal vision for a better hometown) become increasingly aware of the importance and necessity of participating in village environmental governance, thereby enhancing their willingness to do so. Therefore, this paper proposes the following hypotheses:

H2: 

Material pursuit factors positively influence residents’ participation willingness in environmental governance;

H3: 

Hometown affection positively influences residents’ participation willingness in environmental governance.

In the MOA model, opportunity refers to the perceived favorable elements in the external environment at a specific time and place that facilitate specific behaviors characterized by objectivity, subjectivity, and favorability [20]. Opportunity is closely linked to the external environment while also emphasizing individual subjective perception of the external environment. The World Tourism Organization’s 2019 Guidelines on Strengthening Destination Management Organizations states that strategic leadership involves harnessing the efforts and energy of stakeholders to achieve a collective vision, developing strategies to achieve this vision, advocating the advantages and principles of effective tourism management, and promoting public–private partnerships [27]. In this paper, this is reflected in the regulations and policies established by the government and villages that encourage residents to participate in the environmental governance activities of rural tourism areas. These opportunity factors primarily include formal government institutions and informal village norms. According to social cognitive theory, external environmental stimuli can promote individual learning, cognition, and the implementation of positive psychological perceptions and subsequent behaviors [29]. Due to the support of environmental policies provided by governments at various levels and strong internal promotion within villages, residents’ awareness of environmental governance is enhanced. Additionally, the reduction in the costs of environmental governance and increased incentives in rural tourism areas also boost residents’ participation willingness in environmental governance. Existing tourism-related research also indicates that government policy support and internal village reward and punishment systems have a significant positive impact on residents’ participation willingness in environmental governance [30]. Based on this, the following hypotheses are proposed:

H4: 

Formal institutional factors positively influence residents’ participation willingness in environmental governance;

H5: 

Informal institutional factors positively influence residents’ participation willingness in environmental governance.

In the MOA model, ability generally refers to the subjective conditions necessary for an individual to complete a specific behavior [20]. An individual’s ability to participate in behavior includes favorable subjective factors such as basic knowledge, relevant skills, and psychological identification. The World Tourism Organization’s 2019 Guidelines on Strengthening Destination Management Organizations also emphasize the effective execution of destination responsibilities by stakeholders [27]. In this paper, it refers to the ability of residents in rural tourism areas to assume environmental governance responsibilities, primarily involving two essential prerequisites: psychological identification with governance and the acquisition of governance knowledge. On the one hand, villagers’ observation and interaction with external tourists enrich their knowledge and subtly influence their mindset, gradually transforming traditional “indifferent” bystanders of village environmental governance into active participants, thus enhancing their participation willingness [31]. On the other hand, social identity theory suggests that individuals, through social categorization, identify with their group and, out of a need to maintain a positive social identity, develop in-group preferences [32]. As direct beneficiaries of rural tourism, villagers enhance their sense of identification and belonging to village culture through continuous systematic and standardized services, thereby increasing their willingness to maintain the village environment. Based on this, the following research hypotheses are proposed:

H6: 

Governance knowledge factors positively influence residents’ participation willingness in environmental governance;

H7: 

Governance identification factors positively influence residents’ participation willingness in environmental governance.

The SOR-MOA theoretical model can reflect changes in the psychological willingness of rural tourism residents to participate in environmental governance when stimulated by external opportunities, internal motivation, and personal abilities, leading to different participatory behaviors. Based on the SOR-MOA theoretical model and previous research hypotheses, this paper constructs a pathway mechanism model for rural tourism residents’ participation in environmental governance, as shown in Figure 1.

3. Methodology

3.1. Overview of Case Study Sites

This study considers the geographical and cultural differences between northern and southern China. To ensure the representativeness of data collection and the generalizability of the research conclusions, Dazhai Village in Guangxi and Zhonghaoyu Village in Shandong were selected as case study sites for the questionnaire survey.

Dazhai Village is located in the northern mountainous area of Longji Town, Longsheng County, Guilin City, Guangxi Zhuang Autonomous Region. It is a village with a long history and great charm. Dazhai Village, nestled in the northern part of Longji Town, boasts a unique geographical location and breathtaking natural scenery. The village is surrounded by lush mountains and green valleys, with magnificent terraced fields. The terraced fields cover an area of 66 square kilometers and are renowned as the “Original Home of the World’s Terraces”. Dazhai Village is the heart of the Red Yao culture. For generations, the Red Yao people have cultivated rice paddies on the slopes, creating a distinctive terraced landscape and a rich ethnic culture. The Red Yao traditional clothing, known for its vibrant colors and intricate patterns, is recognized as a national intangible cultural heritage. Dazhai Village was listed as one of the first key villages for rural tourism in China and was named one of the “Best Tourism Villages” by the United Nations World Tourism Organization in 2022.

Zhonghaoyu Village is located in Chishang Town, Boshan District, Zibo City, Shandong Province. It is an ancient village with beautiful natural scenery and a deep historical and cultural heritage. Situated in a mountainous area with a forest coverage rate of over 96%, Zhonghaoyu Village has leveraged its exceptional natural conditions to develop rural tourism, seamlessly blending natural scenery with cultural landscapes to create a unique tourist attraction. Zhonghaoyu Village has received numerous honors and titles, including being named one of the first key villages for rural tourism in China and a model village for rural governance.

3.2. Questionnaire Design

Besides demographic information and participation behavior of residents, the research variables were measured using a 5-point Likert scale (1 representing very dissatisfied/disagree, 5 representing very satisfied/agree). The specific measurement methods for each variable are as follows: The measurement design for the “Participation willingness” in environmental governance by rural tourism residents was based on Li’s study [33]. The “motivation” for participating in environmental governance includes the pursuit of material wealth and local attachment, and the measurement variables were mainly drawn from Yang’s research, including six items [34]. The measurement of “opportunity” (formal and informal institutions) was primarily based on Borongan’s research [35]. The measurement of “ability” (governance knowledge and governance identification) was mainly drawn from Yang’s research [36]. The measurement of rural tourism residents’ environmental governance behavior was mainly based on Shi’s research [37]. The measurement items are shown in

Table 1. Measurement items.

	Variables	Items
Motivation	Material Pursuit	Participating in village environmental governance can increase tourism income. Participating in village environmental governance can improve the quality of life. Participating in village environmental governance can enhance the collective income of the village.
	Hometown Affection	Participating in village environmental governance can leave a good impression on tourists. Participating in village environmental governance can make the village look cleaner and more beautiful. Participating in village environmental governance can promote better development of the village’s tourism industry.
Opportunity	Formal Institutions	Government fiscal policies greatly influence your participation in village environmental governance. Government environmental publicity greatly influences your participation in village environmental governance. Other government policies greatly influence your participation in village environmental governance.
	Informal Institutions	Village rules and regulations greatly influence your participation in village environmental governance. Neighbors and fellow villagers greatly influence your participation in village environmental governance. Village penalties greatly influence your participation in village environmental governance.
Ability	Governance Knowledge	You are familiar with the basic knowledge of village environmental governance. You have mastered the basic methods of village environmental governance. You are well aware of the environmental governance situation in your village.
	Governance Identification	You can bear the costs of village environmental governance. You understand the significance of village environmental governance for tourism development. You have sufficient time and energy to participate in village environmental governance.
Participation willingness		You are willing to participate in activities related to village environmental governance. You are willing to spend time and money on village environmental governance. You are willing to collaborate with other villagers for environmental governance. You are willing to encourage others to participate in activities related to village environmental governance.
Participation Behavior		You participate in the formulation of implementation plans or regulations for village environmental governance. You have provided feedback on village environmental governance issues to the village committee. You actively participate in village environmental governance activities.

.

3.3. Data Collection

To ensure the representativeness of the survey subjects, this study conducted a preliminary survey with 80 villagers from the two case study sites, resulting in 68 valid samples. After the preliminary survey, some ambiguous items and language errors in the initial questionnaire were revised based on feedback. The Cronbach’s alpha coefficients were all greater than the threshold value of 0.7, indicating that the scale has acceptable reliability. Thus, the final questionnaire was established.

The research team conducted field surveys in Dazhai Village, Guilin City, Guangxi Zhuang Autonomous Region, from 22 to 29 April 2024 and in Zhonghaoyu Village, Zibo City, Shandong Province, from 15 to 21 August 2024. This study employed a combination of cluster sampling and stratified sampling methods. Since both Dazhai Village and Zhonghaoyu Village are located in mountainous areas with dispersed households, cluster sampling, which is suitable for geographically dispersed samples, was chosen. Stratified sampling, which divides the population into different subgroups and then randomly samples from each, was also used to ensure the representativeness of villagers from different age groups in both case study sites. To ensure the validity of data collection, the questionnaire included conditional screening and attention-check questions. A total of 500 questionnaires were distributed during the field surveys, yielding 462 valid responses, with an effective response rate of 92.40%. The demographic characteristics of the respondents are detailed in

Table 2. Demographics of respondents (n = 462).

Variable	Category	n	%
Sex	Male	239	51.73
	Female	223	48.27
Age	18~29	90	19.48
	30~39	94	20.35
	40~49	96	20.78
	50~59	95	20.56
	>60	87	18.83
Education Level	Primary school and below	84	18.18
	Middle school	91	19.70
	High school, Technical secondary school	118	25.54
	Junior college	104	22.51
	Bachelor’s degree and above	65	14.07
Monthly Income (RMB)	Below 2000	21	4.55
	2000~4000	59	12.77
	4001~6000	90	19.48
	6001~8000	120	25.97
	8001~10,000	115	24.89
	Over 10,000	57	12.34

.

3.4. Data Analysis Methods

This study comprehensively uses PLS-SEM and system dynamics for data analysis. PLS-SEM is employed to validate the measurement model and structural model, while system dynamics is used to explore the sustainable development effects of influencing factors. PLS-SEM focuses on the net effects of independent variables on dependent variables, treating independent variables as competing to explain changes in dependent variables. It assumes the constancy, consistency, additivity, and symmetry of relationships. In contrast, system dynamics assumes complex causal relationships between variables and focuses on their asymmetric relationships. Therefore, system dynamics can serve as a complement to PLS-SEM and has been applied in various disciplinary fields [38,39]. The combined use of these two methods can further extend the existing causal theoretical framework based on additivity and symmetry, allowing for a re-examination of previous empirical findings and contradictory research conclusions.

3.4.1. PLS-SEM

Structural equation modeling is divided into covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM), with the latter aimed at predicting key target variables, while the former focuses on theory testing, theory confirmation, and comparative analysis of alternative theories. Compared to CB-SEM, PLS-SEM has the advantages of analyzing small sample data, handling non-normally distributed data, and managing complex structural models with multiple constructs and relationships. Therefore, PLS-SEM has been widely used in disciplines such as organizational management, management information systems, and public administration in recent years. Given the complexity of the research model and the data characteristics, this study uses PLS-SEM for data analysis.

3.4.2. System Dynamics

System dynamics can explore how complex social problems caused by multiple causations occur from a systems perspective. In dealing with complex causal issues, system dynamics has the following advantages: ① Through model construction, it can reflect the complex and dynamic relationships between multiple factors, especially nonlinear relationships. ② It can make quantitative long-term predictions of the development of factors and variables in the model, providing a reliable basis for decision-making. To better reveal the sustainable pathways for rural tourism residents’ participation in environmental governance, this study uses system dynamics.

4. Research Process and Results

4.1. Process and Results of PLS-SEM Analysis

This study used Smart PLS 3.0 software for Partial Least Squares Structural Equation Modeling (PLS-SEM) to test all hypotheses. The analysis results are as follows.

4.1.1. Measurement Model Assessment

The Smart PLS 3.0 software’s Algorithm program was used to measure and calculate the model’s reliability and validity indicators (

Table 3. Measurement model assessment.

Variable	Loadings	α	CR	AVE
Material Pursuit	0.916 0.845 0.938	0.882	0.928	0.811
Hometown Affection	0.830 0.874 0.826	0.797	0.881	0.711
Formal Institutions	0.939 0.903 0.917	0.909	0.943	0.846
Informal Institutions	0.935 0.913 0.849	0.885	0.927	0.809
Governance Knowledge	0.921 0.874 0.897	0.879	0.925	0.806
Governance Identification	0.856 0.896 0.897	0.859	0.914	0.780
Participation willingness	0.738 0.777 0.767 0.761	0.758	0.846	0.579
Participation Behavior	0.847 0.769 0.802	0.731	0.848	0.650

). All variable loadings in the model exceeded the standard value of 0.5, ranging from 0.738 to 0.939. Both Cronbach’s α values and Composite Reliability (CR) values were above the standard value of 0.7, with Cronbach’s α values between 0.731 and 0.909 and CR values between 0.846 and 0.943, indicating good reliability of the constructed model. In terms of convergent validity, all Average Variance Extracted (AVE) values were above 0.5, meeting the required standards. The test results in

Table 4. Discriminant validity of the measurement model.

	1	2	3	4	5	6
Material Pursuit	0.901
Hometown Affection	0.333	0.843
Formal Institutions	0.456	0.459	0.92
Informal Institutions	0.259	0.299	0.306	0.9
Governance Knowledge	0.523	0.489	0.496	0.282	0.898
Governance Identification	0.356	0.323	0.391	0.28	0.422	0.883

show that all the square roots of the AVE are higher than their corresponding correlation coefficients, indicating that the model constructed in this paper has good discriminant validity.

4.1.2. Structural Model Assessment

The SmartPLS 3.0 software’s Bootstrapping resampling procedure was used to test the path influence relationships between variables, with the number of samples set to 2000. The analysis results show that, firstly, from the perspective of intrinsic factors influencing residents’ participation in environmental governance in rural tourism areas, participation willingness positively influences participation behavior (β = 0.683, p < 0.001), thus supporting H1. Secondly, from the perspective of stimulus factors, material pursuit, and hometown affection positively influence participation willingness (β = 0.314, p < 0.001; β = 0.091, p < 0.001), supporting H2 and H3. Similarly, formal institutions, informal institutions, governance knowledge, and governance identification also positively influence participation willingness (β = 0.297, p < 0.001; β = 0.084, p < 0.001; β = 0.177, p < 0.001; β = 0.271, p < 0.001), thus supporting H4, H5, H6, and H7.

4.2. Process and Results of System Dynamics Analysis

This study employs system dynamics to explore the sustainable pathways for rural tourism residents’ participation in environmental governance. Firstly, residents and the environment in rural tourism areas are viewed as a system. The S in the SOR framework is defined as residents who have not participated in environmental governance under the influence of “stimulus” factors, O is defined as residents willing to participate in village environmental governance influenced by “organism” perception factors, and R is defined as residents who “respond” by participating in environmental governance. Finally, the Vensim PLE software was used to establish a system dynamics model of residents’ participation in environmental governance in rural tourism areas, simulating the impact of various factors on participation.

4.2.1. Model Assumptions

To constrain the scope of the system dynamics simulation model for rural tourism residents’ participation in environmental governance, the following assumptions are made: ① Under the baseline scenario, social, economic, and demographic factors will change according to the government’s medium- and long-term plans. ② Within the research timeframe, key factors affecting village tourism development, such as government policies, economic investment, and technological innovation, remain stable. ③ The number of residents in the rural tourism area is 1000, and initially, the vast majority of these residents are not involved in the environmental governance of the rural tourism area.

4.2.2. Causal Relationships

The system dynamics model constructed in this study includes the following causal relationship chains: ① Material pursuit and hometown affection → Participation willingness → Environmental governance behavior. ② Formal institutions and informal institutions → Participation willingness → Environmental governance behavior. ③ Governance knowledge and Governance Identification → Participation willingness → Environmental governance behavior. ④ Participation willingness → Environmental governance behavior.

4.2.3. System Stock-Flow Diagram

Causal relationships present the logical connections and feedback loops within the system. To conduct a quantitative simulation of the entire system’s operation, it is necessary to draw a system stock-flow diagram based on these causal relationships. This study uses Vensim PLE 6.3 software to draw the system stock-flow diagram (Figure 2). The model includes two rate variables: stimulus and response. State variables include S: residents not participating in environmental governance, O: residents willing to participate in environmental governance, and R: residents participating in environmental governance. Auxiliary variables include motivation, opportunity, ability, and participation willingness. Other relevant constants include material pursuit, hometown affection, formal institutions, informal institutions, governance knowledge, governance identification, and total population N.

4.2.4. Parameter Settings

Based on the questionnaire data, numerical values for relevant constants were set. Due to differences in the units and magnitudes of these constants, normalization and efficacy coefficient methods were used during equation formulation to ensure the accuracy of subsequent simulation results, with values ranging from 0 to 1, where higher values represent greater levels. The relationships between constants and auxiliary variables were set according to the existing literature [40], and the quantitative relationships of multiple independent variables influencing dependent variables were determined based on the path coefficients output from the PLS-SEM analysis. The parameter settings for rate variables were also referenced from existing literature [41]. The specific equations are as follows:

(1)

N = O + R + S;

(2)

O = INTEG (+stimulus − response, 10);

(3)

R = INTEG (response, 10);

(4)

S = INTEG (−stimulus, 980);

(5)

Hometown affection = 0.426;

(6)

Response = Participation willingness × O × 0.150;

(7)

Motivation = Material pursuit × 0.339 + Hometown affection × 0.091 + Opportunity × 0.006 + Ability × 0.004;

(8)

Participation willingness = Motivation + Opportunity + Ability;

(9)

Opportunity = Formal institutions × 0.284 + Informal institutions × 0.083;

(10)

Formal institutions = 0.441;

(11)

Governance knowledge = 0.392;

(12)

Governance Identification = 0.390;

(13)

Material pursuit = 0.389;

(14)

Ability = Governance knowledge × 0.184 + Governance Identification × 0.267;

(15)

Informal institutions = 0.372;

(16)

Stimulus = (Motivation + Opportunity + Ability) × O × S/N × 2 × 0.350.

4.2.5. Model Validity Test

Based on the principles of importance and observability, this study takes the model’s S: residents not participating in rural tourism environmental governance, O: residents willing to participate in environmental governance, and R: residents participating in environmental governance as observation targets. The validity of the system model is tested by comparing the simulation results with real-world logic. The simulation process is set for 50 months with a time step of 1 month. The simulation results of the model observation variables under the given initial values and coefficient conditions are shown in Figure 3.

① As shown in Figure 3, in the early stages of the simulation, the majority of the village population consists of residents not participating in environmental governance. This is because, prior to rural tourism development, the village environment was less affected, and due to the time and financial costs of participating in environmental governance, most residents did not participate. ② From the early to middle stages of the simulation, S begins to decline rapidly, while O and R increase rapidly, with O being greater than R. This is because, with the development of rural tourism, the village environment is increasingly affected, and the number of residents willing to participate in environmental governance increases due to their hometown affection and the influence of formal and informal government institutions. Thus, the number of residents not participating in environmental governance decreases. However, due to the financial cost of participation, the number of residents willing to participate is greater than the number of residents actually participating. ③ From the middle to late stages of the simulation, the decline in S slows down, the number of O changes from increasing to decreasing, and the number of R rises rapidly. This is because the total population of the village is limited in the early to middle stages. The decline in S slows down, and the number of O decreases due to the material wealth accumulated by residents from rural tourism development, reducing the limitation of governance costs. As a result, the number of O decreases, and the number of R increases rapidly.

During the field research, interviews with village cadres from the two case study sites revealed that the results of the three main observed variables in the aforementioned simulation model align with the actual changes observed in the case study sites, and the variations in the number of participants are largely consistent. Therefore, this study concludes that the structure of the model can accurately depict the process by which residents in rural tourism areas participate in environmental governance.

4.2.6. Model Sensitivity Analysis

System dynamics can use the sensitivity analysis function in the model to test the effects of different measures during the simulation period by adjusting the parameters of various variables and proposing sustainable development paths. This study uses this function to analyze the sustainable paths of residents’ participation in environmental governance in rural tourism areas. The specific steps are as follows: ① Select the number of “R: residents participating in rural tourism environmental governance” as the observation variable for sensitivity analysis because the scale of residents participating in village environmental governance is the most intuitive measure of participation effect. ② Use the six variables obtained from the previous path mechanism model of residents’ participation in environmental governance in rural tourism areas (material pursuit, hometown affection, formal institutions, informal institutions, governance knowledge, and governance identification) as the simulation adjustment variables. ③ Analyze the sustainable paths of residents’ participation in environmental governance in rural tourism areas by adjusting the parameters of the simulation variables.

(1)

Simulation Results of Adjusting Single Variable Parameters

By increasing the parameters of the six simulation variables by 100%, the system simulation results in Figure 4 show that as the parameters change, the impact of each factor on the system is enhanced to varying degrees. Among them, material pursuit > formal institutions > governance identification > governance knowledge > hometown affection > informal institutions > maintaining the status quo. With the increase in simulation time, the effect gap between the development paths first widens and then narrows. Material pursuit and formal institutions have the greatest impact on the sustainable development of residents’ participation in environmental governance in rural tourism areas. Material pursuit is an internal variable of individual residents and the core motivation for participating in village environmental governance. Formal institutions are external variables of individual residents, and the greater the government policy support, the higher the enthusiasm of residents in rural tourism areas for participating in environmental governance. Therefore, maintaining the vitality of rural tourism and using formal government systems to mobilize residents’ enthusiasm for environmental governance are sustainable paths for achieving residents’ participation in environmental governance in rural tourism areas.

(2)

Simulation Results of Adjusting Combination Variable Parameters

In the operation of real systems, a change in the parameters of a single variable may not have a significant effect, but when combined with other variables, it can have a significant impact. To explore more efficient sustainable paths for residents’ participation in environmental governance in rural tourism areas, according to the simulation results of adjusting single variable parameters, this study divides the top three influencing factors into a strong influence group (material pursuit, formal institutions, and governance identification) and the bottom three influencing factors into a weak influence group (governance knowledge, hometown affection, and informal institutions). Accordingly, the combinations of variables are divided into weak–weak combinations (governance knowledge, hometown affection, and informal institutions), strong–strong combinations (material pursuit, formal institutions, and governance identification), and more complex strong–weak combinations, such as (material pursuit and governance knowledge) and (formal institutions and hometown affection). Due to space constraints and the complexity of variable combinations, this study lists three representative combinations among the above combinations: a three-variable combination in the weak–weak combination (governance knowledge, hometown affection, and informal institutions), a two-variable combination in the strong–strong combination (formal institutions and governance identification), and a two-variable combination in the strong–weak combination (governance identification and informal institutions) for simulation analysis. The steps are as follows: ① Keep all parameters of the system simulation model unchanged to obtain the simulation results under the current development measures. ② Increase the parameters of governance knowledge, hometown affection, and informal institutions variables by 100% simultaneously to obtain the simulation results of the three-variable combination in the weak–weak combination. ③ Obtain the simulation results of the strong–strong combination (formal institutions and governance identification) and the strong–weak combination (governance identification and informal institutions) through similar operations, as shown in

Table 5. Composite variable simulation results.

	1	10	20	30	40	50
weak–weak	10.961	57.608	392.040	743.075	896.784	956.462
strong–weak	10.958	57.295	389.824	741.260	895.798	955.980
strong–strong	11.109	79.087	517.941	831.451	940.856	976.510

Note: Because the simulation values of weak–weak and strong–weak combinations are very close, and the image format is not clear, a table format is used to present them.

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The simulation results show that all combination schemes have significant impacts. Among them, the strong–strong combination scheme has the fastest growth rate, and the weak–weak combination scheme grows faster than the strong–weak combination scheme. This indicates that the combination of strong influence variables has the greatest impact on the system’s development. Increasing the number of variables in the combination scheme, even with weak influence variables, can have a substantial impact. Comparing the simulation results of the weak–weak combination (governance knowledge, hometown affection, and informal institutions) and the strong–weak combination (governance identification and informal institutions) with the single variable simulation results, it is found that the combination schemes promote residents’ participation in environmental governance better than the single variable schemes. In summary, the combination schemes of variables produce more significant effects than single variable schemes, and the more variables in the combination, the more pronounced the impact on residents’ participation in environmental governance. The simulation results of the weak–weak combination show that the strong–strong and strong–weak combinations also achieve more significant effects by increasing the number of combined variables, thus achieving the goal of efficient and sustainable development.

The previous simulation results of adjusting single variable parameters show that individual variables such as governance knowledge, hometown affection, and informal institutions have weaker effects on promoting residents’ participation in environmental governance. However, Table 5 shows that when governance knowledge, hometown affection, and informal institutions are increased simultaneously, the system operation produces a more significant impact through the synergistic influence of these three weak variables. Therefore, in addition to maintaining the vitality of the tourism industry and using formal government systems to fully mobilize residents’ enthusiasm, rural tourism areas should also adopt the concept of synergistic development to leverage the advantages of the combined effects of multiple system variables.

5. Conclusions and Prospects

5.1. Conclusions

Through a survey of a typical rural tourism case, this study used PLS-SEM and system dynamics methods to explore sustainable paths for residents’ participation in environmental governance in rural tourism areas. The specific conclusions are as follows.

First, the factors influencing residents’ willingness to participate in environmental governance in rural tourism areas mainly include motivational factors (material pursuits, hometown affection), opportunity factors (formal institutions and informal institutions), and capability factors (governance knowledge and governance identification). (1) Motivation is the dominant factor and the starting point for residents’ decisions to participate in environmental governance. Material pursuit is the most significant motivator for residents’ willingness to engage in environmental governance in rural tourism areas (β = 0.314, p < 0.001), and hometown affection also significantly influences this willingness. Motivational factors positively impact residents’ willingness to participate in environmental governance, thereby indirectly promoting their engagement in environmental governance activities. (2) Opportunity is a catalytic factor, representing the favorable external conditions that encourage residents to participate in environmental governance. Formal institutions and informal institutions are the primary external opportunity factors affecting residents’ participation in environmental governance in rural tourism areas (β = 0.297, p < 0.001; β = 0.084, p < 0.001). These external opportunities can enhance residents’ cognitive learning and positive perception of environmental governance, increasing their willingness to participate and promoting their engagement in environmental governance activities. (3) Capability is a supporting factor, serving as the advanced condition for residents’ participation in various environmental governance activities. Governance knowledge and governance identification are the primary capability factors for residents’ involvement in village environmental governance (β = 0.177, p< 0.001; β = 0.271, p < 0.001). Capability factors positively influence residents’ willingness to engage in environmental governance, thereby indirectly promoting their participation in environmental governance activities.

Second, in the process of residents’ participation in environmental governance in rural tourism areas, material pursuit and formal institutions are the strongest long-term driving external and internal variables. The synergistic effect of multiple weak variables is stronger than that of a single strong variable. This indicates that the development of synergistic elements is a sustainable path for residents’ participation in environmental governance in rural tourism areas.

5.2. Practical Implications

Based on the research conclusions, this study offers two practical suggestions for residents’ participation in environmental governance in rural tourism areas:

(1) Promoting resident participation in environmental governance through interest alignment. Currently, environmental governance in rural tourism areas faces a “dilemma” [42]; some villages fall into the trap of “paternalistic” governance due to government-led environmental efforts, while others suffer from high costs and low efficiency due to a lack of public participation. The government should unite the stakeholders of rural tourism to establish an environmental governance mechanism centered on residents, with participation from diverse entities such as tourism enterprises and external tourism operators. Field research revealed that Zhonghaoyu Village, in its rural tourism development, adheres to the “Haoyu Model”, which involves the entire village as shareholders and promotes shared prosperity. Villagers are responsible for daily tasks such as environmental sanitation, waste sorting, and sewage treatment, with the “Three Guarantees in Front of the Door” system ensuring that environmental governance responsibilities are assigned to each household. The local government provides policy support and financial assistance to help the village improve infrastructure and enhance tourism quality. Tourism companies contribute by investing in environmental facilities, promoting eco-friendly practices, and encouraging tourists to participate in environmental activities, thus advancing environmental governance in rural tourism areas and improving efficiency and effectiveness. The villagers, government, and tourism companies form a symbiotic force in environmental governance, preserving the ecological environment of Zhonghaoyu Village and ensuring the sustainable development of rural tourism.

(2) Use government guidance as an opportunity to ensure residents’ participation in environmental governance. Increase publicity on the urgency of environmental governance to enhance residents’ psychological perception of environmental crises and reduce the public’s “free rider” and “government dependency” mentality. On the one hand, government departments at all levels and social organizations can use new media methods, such as short videos and live lectures, to deepen residents’ understanding of the ecological value of rural tourism areas. On the other hand, by strengthening the guidance of typical cases in environmental governance, the public can be made aware of the tourism benefits of improving the rural tourism environment, emphasizing that residents’ participation can have a significant positive impact on environmental issues, thus attracting more residents to participate in environmental governance, especially providing more guidance to the elderly and low-education groups.

5.3. Limitations and Prospects

This study has some limitations. First, due to research level and paper length, only three combinations of variables were analyzed in the simulation process, and other combinations of variables have not yet been analyzed. It is suggested that the impact of multivariate coordination on residents’ participation in environmental governance in rural tourism destinations should be further studied. Secondly, in the context of the rapid development of online self-media and digital intelligence technology, residents’ participation in environmental governance in rural tourism areas will also be dynamically influenced by media and technology, which is worth exploring in future research.