Evaluating the Sustainable Development of Rural Communities: A Case Study of the Mountainous Areas of Southwest China

Abstract

Rural areas are complex multi-level regional systems comprising multiple elements such as natural resources, human resources, social systems, and economic elements. Drawing on the socio-ecological system framework, we develop a new evaluation system to better understand rural sustainable development and the interactions between economic, social, and natural factors. Applying this system to the case of Guizhou Province reveals the following: First, the overall level of sustainable development of rural communities is low. Furthermore, the development gap between communities is significant, mainly driven by differences in the resource system and economic outcomes. Second, the overall coupling and coordination level among the rural sustainable development subsystems is low, and they are all in the grinding and less coordinated stage. Compared with communities with lower sustainable development, those with higher sustainable development levels exhibit higher coupling and coupling coordination. Third, the obstacles to sustainable development in rural communities are mainly concentrated in the resource systems and economic outcomes, including construction land, housing, government funding, asset growth, income growth, profitability, and bonus sharing.

1. Introduction

Challenges related to ecological fragility, sustainable environmental development, and reconstruction and repair of damaged ecosystems have gradually become one of the core issues in adapting to global environmental change and promoting sustainable development [1,2,3]. Persistently occurring new ecological stresses and climate extremes can cause individual ecosystems to collapse. Moreover, through pressure shifts and interactions between destabilizing factors, they can cause the collapse of nearby ecosystems, thereby accelerating the collapse of entire ecosystems [4]. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), between 3.3 and 3.6 billion people live in “highly fragile” environments, with rural and agricultural areas being the most widely affected [5]. Undoubtedly, this poses a great challenge to sustainable rural development. On the one hand, weather disasters (e.g., floods and droughts) and ecosystem changes (e.g., biodiversity reduction) can increase the risks to the livelihoods of rural residents by decreasing productive assets, where residents are primarily reliant on low-quality land resources and unskilled labor to maintain basic subsistence [6,7]. Simultaneously, the subsequent catastrophes caused by natural disasters frequently have a disastrous effect on public welfare and economic development [8]. In fact, smallholder farming cannot substantially increase residents’ economic incomes, hindering their ability to address natural hazards and adapt to climate change [9,10]. On the other hand, with increasing global integration, rural areas also face the problem of overexploitation of external forces; examples include the overexploitation of rural natural resources by urbanization and industrial development, which has resulted in an imbalance between the supply and demand for the ecosystem services provided by rural areas [11,12,13].

Because rural development is complicated and multifaceted, researchers are increasingly focusing on establishing appropriate study methods and instruments. The indicator evaluation method can be an appropriate and effective research tool to not only identify the root causes of the current crisis but also provide reasonable guidance for management decisions and development strategies. Many studies evaluate rural sustainable development based on the United Nations’ Sustainable Development Goals (SDGs). The SDGs encompass all aspects of society, economy, and the environment relevant to rural areas [14]. This includes eradicating poverty due to the lack of resources, income, social public services, food security, and sustainable agriculture, among other factors. Indeed, some studies have developed a rural sustainable development indicator system by drawing on the framework of the SDGs to comprehensively analyze the local features of the rural system and rural revitalization plans [15,16]. However, some researchers also argue that the SDGs are a global framework and that the assessment of rural sustainable development should consider local variances, which can help in tailoring regional rural sustainable development strategies. In the Chinese context, some researchers contend that sustainable rural development should be assessed from a multifunctional perspective [17]. Overall, studies observe that the rural space has multifunctionality in production, life, and ecology, and use it as a basis for establishing an assessment framework [18,19]. Still, some limitations exist in extant frameworks. Compared with past development models, the new development paradigm requires rural community development to be internally controlled and led. Specifically, local residents should be the primary leaders, participants, and beneficiaries of community development [20]. As the main carrier of information, energy, and material exchange between ecosystems and socioeconomic systems, community members’ behaviors and public awareness directly affect the community’s resilience and action in response to climate change and environmental crises [21]. Therefore, local resident action and interaction between socioeconomic activities and natural ecosystems should be included in the rural sustainable development evaluation system.

Currently, the socio-ecological system (SES) framework is viewed more as a broader instrument for evaluating the sustainability of SES than as a theoretical framework for developing collective action theory [22,23]. The advantages of the SES framework are that it assesses sustainability through the variable interaction analysis of human societies and ecological systems [24,25,26]. The SES framework is extensively considered as a valuable tool for analyzing complex rural issues [27,28]. Overall, these studies have sought to construct the SES frameworks tailored to rural territorial characteristics to better understand rural evolution and the interaction of economic, social, and natural factors [29,30]. In fact, integrating societal elements into the interdisciplinary field of sustainability science is a crucial aspect of contemporary applied modeling projects. Therefore, we regard rural communities as a specific SES. Theoretically, we explain not only the interaction between socioeconomics and natural resources but also the behavior of community actors and relevant governance rules. Practically, combining the indicator assessment with the coupling and coordination and diagnostic models, this study diagnoses and measures the attributes, performances, and properties of the sustainable development of rural communities from multiple perspectives.

The remainder of this article is structured as follows: Section 2 introduces the socio-ecological framework, indicator system, research area, and data collection. Section 3 presents research methodology and quantitative models. Section 4 presents the assessment results. Section 5 undertakes the discussion. Finally, Section 6 presents the conclusions of this study.

2. Indicator System and Materials

2.1. The Applicability of the SES Framework to Rural Sustainable Development

The SES framework, developed by Ostrom (2007), effectively integrates natural and economic elements and analyzes the interactions between actors, the social environment, and the natural ecosystem from a systemic analytical perspective [31]. The SES framework comprises four core subsystems: resource system (RS), resource unit (RU), governance system (GS), and actor (A). Together, they influence the interactions (I) and outcomes (O) of individuals and groups in a given context. Meanwhile, these variables are also influenced by external variables such as the economic and social-political context (S), and relevant ecosystem factors (ECO). Given the research needs, each of the variables and subsystems can be vertically expanded into Level 2 and 3 variables, offering an analytical framework for SES diagnosis. Moreover, the SES framework applies to the analysis of both public resources (e.g., rivers and forests) [32,33] and governance of general public affairs (e.g., farmers’ cooperatives and community public affairs [34].

A rural community is a typical SES, which is composed of elements such as natural resources (land, water, and landscape) and man-made resources (building structures, rural infrastructure, and irrigation), resource users and stakeholders (villagers, government officials, and concerned environmentalists), and governance structures (village decision-making bodies and collaborative governance) [35]. From a public resource perspective, rural community resources and assets are public goods that are non-exclusive and non-competitive, such as forests, mines, rivers, and ponds. In the context of China’s collective ownership system, the ownership, use, and benefit ownership of rural resources and assets are jointly owned by collective members. Community members have the right to use land resources, such as arable land, forest land, and grassland, through contractual management. These lands are recovered for redistribution when the contractual management contract expires. Further, government officials, such as the neighborhood committee, represent the collective members to unify the management of uncontracted arable land, forestry, collective construction land, collective accumulation funds, and government assistance funds through collective ownership. From the perspective of public affairs, rural community sustainable development requires the participation of multiple independent decision-makers (government, enterprises, farmers, cooperatives, consumers, etc.). The SES framework was originally applied to areas such as self-organization governance, multi-center governance, and adaptive collaborative management [36] Clearly, the SES framework provides a new window for the in-depth analysis of rural community development issues (Figure 1).

2.2. Indicators of Rural Sustainable Development with the SES Framework

According to the SES framework, sustainable rural development is determined by “five dimensions: actors, the governance system, the resource system, the social outcomes, and the economic outcomes.”

Resource system (RS). The rural community is a complex system comprising multiple elements, such as natural endowment, geographic conditions, economic base, human resources, and cultural practices [37]. First, land resources are crucial for the development of mountainous rural communities. In particular, land resource accessibility per individual is necessary to promote human wellbeing and environmental sustainability [38]. More research emphasizes that land consolidation can improve land resource utilization efficiency, which can create the conditions for the development of other resources, industrialization, and population agglomeration [39,40]. Second, rural residences are progressively becoming empty because of the rural population’s slow migration to cities [41]. Another problem is the low utilization of rural collective operating construction land [42]. Thus, these lands and properties can become the key resources for rural communities to be reintegrated, developed, and utilized. Furthermore, ecological resources, such as lakes, wetlands, and woodlands, can serve as an important part of rural landscapes due to their multiple values, such as cultural and ecological values [43,44]. Finally, public investment promotes rural social and economic development. Public and social funds provide the funding for rural communities to achieve infrastructure investment or to improve residents’ lives [45]. In summary, this study selected indicators such as cultivated land, woodland, water resources, operating construction land, houses, and government funds to evaluate the resource system.

Actors (A). Rural community actors mainly include community members, entrepreneurs, managers, government officials, and social organizations, among others. It is leaders such as entrepreneurs and managers who are the key to achieving autonomous governance in any type of resource system. When a virtuous or trustworthy leader participates in collective action, collective communication and organizational costs are reduced, and other members of the community are more willing to participate [46]. They can both act as the main investors in the community’s development by providing the essential political, economic, and public resources, as well as providing positive and effective inspiration through their authority and charisma [47,48]. Furthermore, local action groups are often also important actors in rural communities. They serve as a platform for the development of long-term cooperative partnerships with partners and public actors by organizing and mobilizing institutions, resources, and labor both internally and externally [49]. Lastly, the rural community also requires the involvement of the government. For example, understanding policies is important to make sure community actions and mobilization initiatives are aligned with government policies [50]. Additionally, informed by relevant studies [51], this study selected indicators such as the professionalism of community leaders, the educational level of community leaders, the community action group, and the degree of participation by the government sector to evaluate the actor system.

Governance system (GS). Public governance mainly involves the distribution of interests and allocation of power among members, community committees, the government, and external markets. Its development process involves coordinating the resource elements and conflicting interests among different subjects through certain forms or rules to enhance the continuity and effectiveness of collective action [52]. In particular, the construction of a property ownership system is considered to be an effective tool for solving the problem of distributing power and interests in the community [53]. The property ownership system of “collective ownership of land” in rural communities in China combines multiple attributes, such as collective and associational property ownership. It essentially grants community members basic ownership, like land and residential plots [54]. It also provides a legal basis for residents to enjoy public benefits, such as community healthcare and old-age care [55,56]. Thus, this property ownership system also has the function of maintaining public stability and providing public security [57,58]. In recent years, China has been promoting the property ownership system’s reform in rural communities to give more property ownership to farmers and provide more reasonable institutional safeguards for community governance [59,60]. Additionally, drawing on other relevant studies [61,62,63,64], we selected indicators such as community leadership appraisals, community leadership incentives, and benefit-sharing mechanisms for members to evaluate the governance system.

Social outcomes (O1). The development of public infrastructure and services and the improvement of democratic institutions are important indicators of positive socioeconomic development in rural areas. Public infrastructure and services can promote the development of the rural economy, improve the lives of the inhabitants, and enhance their well-being. Meanwhile, democratic institutions enable the elimination of the “top-down” decision-making process and help in better honoring the desires of community members [65]. More places have adopted the empowerment approach to realize the control and direction of collective citizens over the power and interests of the community and enhance the level of member participation in decision-making, management, and supervision [66]. Therefore, we chose the indicators of public utilities development and democratic participation to better assess community social outcomes.

Economic outcomes (O2). The economic diversification and sustainability of rural communities are one of the important elements of sustainable rural development. With the integration of urban and rural market economies, rural communities need market participants, such as investors and consumers, to realize the value of internal rural resources, thus providing a continuous material base for community development [67]. In particular, the management of village collective assets and the establishment of village-level enterprises are important means of supporting the economic vitality of rural communities [68]. This can not only generate asset returns but also provide employment opportunities. Brockington et al. (2018) argue that, compared to other indicators, the use of assets as an indicator is more effective in reflecting the economic development potential of rural communities [69]. Furthermore, rural communities may establish an efficient interface as an association of interests between smallholder farmers and the large market [70]. Under China’s goal of common prosperity [71], the country emphasizes the need for rural communities to not only maximize socioeconomic benefits but also ensure the ownership and interests of each community member through the equitable distribution of benefits and equalization of public welfare. Therefore, to better assess community economic outcomes, we chose the indicators of assets, income, profitability, and gainsharing.

To examine the sustainable development of rural communities in the mountainous areas of southwest China in 2020, this study constructs a three-level hierarchical structure model, with the uppermost level being the target level to establish the indicators of the level of sustainable development of rural communities, followed by the guideline level containing five secondary indicators and the sub-criteria level containing 20 tertiary indicators (

Table 1. Evaluation system for sustainable development of rural communities in the mountainous areas of southwest China in 2020 with the SES framework.

First Layer	Second Layer	Third Layer	Index Content	Attribute	Entropy
Sustainable development of rural communities	Resource system (RS)C1	Cultivated land C11	Area of cultivated land owned by each member, including paddy land, watered land, dry land, etc.	+	0.9
		Woodland C12	Area of woodland owned by each member, including forests, mountains, wasteland, orchards, etc.	+	1.66
		Water resource C13	Area of water resources owned by each member, including lakes, rivers, etc.	+	7.85
		Operating construction land C14	Mainly a community-owned collective operating on construction land	+	7.6
		House C15	Unused community primary schools, unused residences, etc.	+	11.88
		Government funds C16	Total funds spent by the government on community development this year	+	8.98
		Social funds C17	Total funds spent by enterprises, individuals, and social organizations on community development in the year	+	6.98
	Actors (A) C2	Professionalism of community leaders C21	Number of professional trainings and learning received by community leaders during the year	+	2.14
		Educational level of community leaders C22	Proportion of community leaders and managers with a high school diploma or higher level of education	+	0.62
		Community action group C23	Whether community action groups are formed (1 = yes, 0 = no)	+	0.64
		Level of government sector participation C24	Proportion of political party members in the community	+	1.07
	Governance system (GS)C3	Community leadership appraisals C31	Whether the effectiveness of community development is included in the assessment of leaders and managers (1 = yes, 0 = no)	+	3.06
		Community leadership incentives C32	Whether leadership compensation incentives are in place (1 = yes, 0 = no)	+	5.82
		Benefit-sharing mechanism for members C33	Whether a mechanism for distributing the benefits of membership is present (1 = yes, 0 = no)	+	2.77
	Social outcomes (O1)C4	Public utilities development C41	Expenditure on public utilities such as environmental protection, infrastructure, and education during the year	+	6.92
		Democratic participation C42	Number of meetings related to community development attended by members during the year	+	2.05
	Economic outcomes (O2)C5	Capital growth C51	Total community assets for the year	+	7.73
		Income growth C52	Combined value of operating and investment incomes for the year	+	5.84
		Profitability C53	Operating income is the amount of income after deducting all operating expenses for the year.	+	6.18
		Bonus sharing C54	A bonus for each member for the three-year cumulative value	+	9.31

). Meanwhile, the entropy method was also employed to calculate the weights of the indicators.

2.3. Research Area

Guizhou Province is located in the mountainous region of southwestern China, between longitude 103°36′–109°35′ and latitude 24°37′–29°13′ (Figure 2). It has a total area of 176,167 km², accounting for 1.8% of China’s land area. It is located on the Yunnan-Guizhou Plateau, where the terrain is low in the east and high in the west. Almost 95% of the region is hilly and mountainous, with a typical development of karst geomorphology. Besides geographic constraints, intensive population pressures and socioeconomic activities are key factors causing ecological deterioration in the region [72,73].

Therefore, Guizhou is also a typical “poverty trap” region, with a relatively lagging level of socioeconomic development. In 2020, Guizhou’s provincial GDP reached 1.782656 trillion yuan, representing a year-on-year growth rate of 4.5%. The added value of the primary, secondary, and tertiary industries was 253.988 billion yuan, 621.162 billion yuan, and 907.507 billion yuan, respectively. In the same year, the per capita disposable income of urban residents reached 36,096.19 yuan, while that of rural residents reached 11,642.35 yuan. It used to be the province with the highest concentration, largest area, and deepest degree of poverty [74], with the per capita disposable income of Guizhou’s rural residents in 2020 equivalent to 67.96% of the national average. The majority of rural households in Guizhou rely on traditional agricultural production activities, like raising cattle and cultivating crops, for their main means of income.

Guizhou’s arable land resources are characterized by a poor natural endowment with a generally low land quality. The province has 3.4485 million hectares of arable land, of which 2.5453 million hectares (74.53%) are dryland. More than 61.01% of the provincial territory has slopes exceeding 25 degrees, and 16.84% of the arable land is located in steeply sloped areas. Additionally, arable land with rocky desertification accounts for 6.01% of the province’s total cultivated land. The province is also home to a diverse population, including a significant proportion of Miao, Dong, and other ethnic minority groups. Their traditional livelihoods have long been adapted to the mountainous environment, but face pressures from land scarcity and environmental degradation. However, because of factors like soil erosion, low soil fertility, fragmentation of arable land, and outdated production technologies, incomes are low. Further, most residents choose to go out to work to sustain their families, which in turn also causes several public problems, such as loss of labor force and abandonment of land [75]. Yet, in recent years, Guizhou has accomplished the arduous task of eradicating absolute poverty under poverty alleviation policies such as industrial relocation and ecological poverty alleviation policies. Nevertheless, there is still a significant risk that Guizhou will relapse into poverty, particularly given the volatility of the macroeconomic environment, climate change, and other factors, as well as the region’s rural communities’ still relatively weak ability to develop sustainably.

2.4. Data Collection

This study collected micro-level field research data using rural communities in Guizhou Province as the primary survey units. During the preparatory phase, the research team conducted a comprehensive review of existing literature and, taking into account the developmental characteristics of mountainous rural communities, developed the Basic Statistical Form for Rural Community Development. This form was designed to collect objective data on the development status of rural communities in Guizhou Province in 2020. Completed by community staff, it includes detailed information on population size, geographic location, natural resources (e.g., total land area, arable land, forested land, and water bodies), and community assets (e.g., construction land and idle properties). In addition, the study recorded socioeconomic indicators such as asset investment, community economic income, the educational level and professional capacity of community leaders, and the state of public infrastructure development. The overall research process was conducted in two phases (Figure 3).

The first phase consisted of field research conducted in June 2021 across rural communities in Panzhou City, Zhenning County, Zhenfeng County, and other municipalities in Guizhou Province. Through interviews and statistical surveys, we systematically assessed community fundamentals, land resource utilization, and socioeconomic development levels. After gaining a comprehensive understanding of the development status of rural communities in Guizhou, we revised and refined the evaluation indicator system. For example, regarding the indicator measuring community economic benefit distribution, some rural communities allocate benefits not annually but biennially or triennially. Therefore, we adopted the three-year cumulative per capita dividend distribution as the evaluation metric.

The second phase, conducted from July to August 2021, involved large-scale field research across Guizhou’s rural communities using a combination of fixed-point random sampling and snowball sampling. Fixed-point random sampling ensured scientific rigor in sample selection, while snowball sampling improved participant diversity and inclusiveness. Due to the challenges associated with collecting micro-level data in rural communities, the investigation was completed with support from local agricultural and rural affairs departments. Within each prefecture-level city and autonomous prefecture in Guizhou (excluding the provincial capital, Guiyang), we selected one to two representative counties. In each selected county or district, we further selected two to three representative townships and randomly sampled three to four communities for on-site investigation. Through in-depth and semi-structured interviews, as well as the completion of the questionnaire, the Basic Statistical Form for Rural Community Development, we obtained primary data on community conditions. With assistance from township officials, we also obtained contact information for leaders of other communities within the same township, who subsequently completed the form via telephone or email. In order to ensure data accuracy, any unreturned forms underwent secondary verification through phone or email during data processing. Questionnaires were deemed invalid if community staff were unable to provide complete information. In total, 240 questionnaires were distributed to rural communities in Guizhou Province, a mountainous region in southwest China, and 227 valid questionnaires were returned, yielding a response rate of 94.58%. The 227 sampled communities included those with high, moderate, and low levels of socioeconomic development, effectively representing the regional distribution of the surveyed areas.

3. Research Methodology and Quantitative Models

3.1. Method for Calculating Indicator Weightings

The determination of indicator weights is a critical step in comprehensive evaluation. In this study, we employ the entropy method, an objective weighting technique, to calculate indicator weights. Based on information entropy theory, the entropy method is widely used in multi-indicator evaluation systems. Its core principle is to determine the weight of each indicator by measuring the degree of variation among samples. A higher information entropy value indicates lower variability of an indicator across samples, thereby reducing its discriminatory power in the comprehensive assessment. Conversely, greater variability reflects a stronger influence of the indicator on the evaluation results. Since the entropy method relies solely on the inherent variability contained in the data, the resulting weights are free from subjective interference, rendering the evaluation outcomes more objective, accurate, and scientifically robust.

The entropy value method can be used to determine the weights of various indicators. The mathematical method of calculating the entropy value to judge the randomness of an event and the degree of dispersion of a certain indicator, one may argue that the greater the degree of dispersion, the greater the impact of the indicator on the comprehensive evaluation [76]. The detailed procedures of the Entropy Method are described as follows [77]:

Step 1: Normalize the original indicators selected. The specific formula is as follows: the positive indicator is $Z_{ij}={\displaystyle \frac{x_{ij}-min\left\{x_{ij}\right\}}{max\left\{x_{ij}\right\}-min\left\{x_{ij}\right\}}}$; the reverse indicator is $Z_{it}={\displaystyle \frac{max\left\{x_{ij}\right\}-x_{ij}}{max\left\{x_{ij}\right\}-min\left\{x_{ij}\right\}}}$, where x_it is the observation data of the ith evaluation object of the jth indicator; Z_ij is the corresponding standardized data.

Step 2: Calculate the proportion $P_{ij}={\displaystyle \frac{Z_{ij}}{\sum _{i=1}^m{Z}_{ij}}}$ of the ith evaluation object of the jth indicator to the jth indicator, where $j=\mathrm{1,2},\dots n$, and $i=\mathrm{1,2},\dots m$.

Step 3: Calculate the entropy of the jth indicator using the formula $e_j=-k\sum _{j=1}^n{P}_{ij}In\left(P_{ij}\right)$, where $k={\displaystyle \frac{1}{In\left(i\right)}}$, $0\le e_{ij}\le 1$, and In is a natural logarithm.

Step 4: The utility value of the jth indicator can be calculated as $d_j=1-e_j$.

Step 5: Calculate the weight of each indicator using the formula $w_j={\displaystyle \frac{d_j}{\sum _{j=1}^n{d}_j}}$.

Step 6: The comprehensive indicator and the indicator of each subsystem of the community can be calculated using the formula $y_i=\sum _{j=1}^n{z}_{ij}\ast w_j$.

3.2. Coupling Coordination Degree Model

The evaluation index system of the sustainable development level of rural communities comprises five subsystems: resource system, actors, governance system, social outcomes, and economic outcomes. These subsystems are intricately connected. The coupling and coordination relationship between subsystems can directly affect the overall development level of rural communities. Hence, this study adopts the coupling coordination degree model to evaluate the degree of coupling and coordination between these internal subsystems. The model is elaborated below [78,79,80]:

$$C=5\times {\left\{{\displaystyle \frac{C_1\times C_2\times C_3\times C_4\times C_5}{{\left(C_1+C_2+C_3+C_4+C_5\right)}^5}}\right\}}^{{\displaystyle \frac{1}{5}}}$$

C₁ to C₅ indicate the five subsystems of the resource system, actors, governance system, social outcomes, and economic outcomes, respectively. C is the degree of coupling, the range of values $C=[0, 1]$. If $C=0$, the five subsystems do not have any coupling relationship; if $C=1$, the coupling relationship between them is good. Based on the degree of coupling, the community can be categorized into four categories: 0 < C ≤ 0.30 for the low-level coupling stage, 0.30 < C ≤ 0.50 for the antagonistic stage, 0.50 < C ≤ 0.80 for the grinding stage, and 0.80 < C ≤ 1 for the high-level coupling stage.

The coupling degree function is used to describe the degree of correlation between the systems. However, it is not possible to determine whether the systems are mutually reinforcing at a higher level or closely related at a lower level. Therefore, the following coupled coordination degree model is introduced:

$$D=\sqrt{C\times T}$$

$$T=f_1\times C_1+f_2\times C_2+f_3\times C_3+f_4\times C_4+f_5\times C_5$$

where D represents the degree of coupling coordination, C is the degree of coupling, T is the comprehensive coordination index of the five subsystems, and f₁ to f₅ denote the weights of actors, governance system, resource system, social outcomes, and economic outcomes, respectively. And the degree of coupling coordination D is divided into five stages: extremely uncoordinated (0 ≤ D < 0.2), less coordinated (0.2 ≤ D < 0.4), basically coordinated (0.4 ≤ D < 0.6), relatively coordinated (0.6 ≤ D < 0.8), and very coordinated (0.8 ≤ D < 1).

3.3. Obstacle Degree Model

The development level of each dimension or indicator of the evaluation system can directly affect the sustainable development level of rural communities. Therefore, this study further introduces the obstacle degree model to identify the constraints among the various factors [81,82,83]. The steps are as follows:

(1)

Determine the factor contribution degree. The factor contribution degree is a measure of the degree of influence of a specific indicator on the overall goal of sustainable development of rural communities. It is expressed by the weight of the indicator (w_ij), which is the weight of the indicators at all levels in the evaluation index system for the sustainable development of rural communities, calculated in the previous section.

(2)

Calculate the degree of deviation of indicators. Indicator deviation refers to the distance between each indicator and the ideal value. It is expressed by the difference between the standardized value of the single indicator and 100%, which can be calculated by $I_{ij}=1-Z_{ij}$, where I_ij is the degree of deviation of the jth indicator of the ith region, and Z_ij is the standardized value of each indicator.

(3)

Finally, the degree of obstacle imposed by a single indicator on the overall level of sustainable development of rural communities is computed. The larger the degree of the obstacle, the more it impedes development. The obstacle degree is computed as follows:

$$O_{ij}=I_{ij}\times {\displaystyle \frac{W_{ij}}{{\displaystyle \sum _{j=1}^j{W}_{ij}}\times I_{ij}}}$$

4. Results

4.1. Level of Sustainable Development of Rural Communities in Guizhou Province, a Mountainous Area in Southwest China, in 2020

Table 2. Level of sustainable development of rural communities in Guizhou Province.

Indicators	Average Value	Maximum	Minimum	Development Gap
C11	0.2914	0.9018	0.0000	0.9018
C12	0.3905	1.6604	0.0000	1.6604
C13	0.3690	7.8460	0.0000	7.8460
C14	0.3920	7.6041	0.0000	7.6041
C15	0.2246	11.8814	0.0000	11.8814
C16	0.1456	8.9755	0.0000	8.9755
C17	0.2080	6.9781	0.0000	6.9781
C21	0.3168	2.1443	0.0000	2.1443
C22	0.1891	0.6227	0.0000	0.6227
C23	0.5447	0.6440	0.0000	0.6440
C24	0.2610	1.0722	0.0000	1.0722
C31	1.5504	3.0604	0.0000	3.0604
C32	1.5382	5.8195	0.0000	5.8195
C33	1.4648	2.7709	0.0000	2.7709
C41	0.3679	6.9170	0.0000	6.9170
C42	0.3850	2.0458	0.0000	2.0458
C51	0.1470	7.7313	0.0000	7.7313
C52	0.1988	5.8353	0.0000	5.8353
C53	0.3942	6.1771	0.0000	6.1771
C54	0.2707	9.3122	0.0000	9.3122
C1	2.0212	18.4629	0.0010	18.4619
C2	1.3115	3.6125	0.1672	3.4453
C3	4.5534	11.6508	0.0000	11.6508
C4	0.7529	8.0380	0.0000	8.0380
C5	1.0106	21.4064	0.0000	21.4064
Overall value	9.6497	41.4657	0.5114	40.9543

shows the degree of sustainable development achieved by Guizhou’s rural communities and the numerous indicators and subsystems. First, the average, maximum, and minimum values for sustainable development are 9.6497, 41.4657 and 0.5114, respectively. The development imbalance is rather noticeable. Second, the average scores of the five subsystems in descending order are C3 (4.5534), C1 (2.0212), C2 (1.3115), C5 (1.0106) and C4 (0.7529). Clearly, the governance subsystem performs better. Evidently, the actor’s system has a relatively modest development gap, whereas the economic growth subsystem has the highest development gap. The highest value for C2 is 3.6125, with a difference of 3.4454 from the lowest value. The corresponding values for C5 are 21.4064 and 21.4064. Finally, among the three-level indicators, C31, C32 and C33 are some indicators that perform better. Meanwhile, the development gaps of C15, C54 and C16 are relatively high at 11.8814, 9.3122 and 8.9755, respectively.

To explore the proportion of communities at different levels of development, the communities are divided into five categories based on the overall value (

Table 3. Judgment table for types of rural communities in Guizhou Province.

Overall Value	Categories
0 ≤ T < 10	Weakest Communities
10 ≤ T < 20	Weak Communities
20 ≤ T < 30	Intermediate Communities
30 ≤ T < 40	Better Communities
40 ≤ T	Excellent Communities

). Regarding the distribution (Figure 4), 101 (44.50% of all) communities have an above-average development level. Only one (0.44% of all communities) excellent community is observed with a value of 41.4657, while 126 were the weakest communities, accounting for 55.51%. Thus, the overall level of sustainable development of rural communities in Guizhou is low.

4.2. Coupling Coordination Degree Between Subsystems of Rural Communities in Guizhou Province, a Mountainous Area in Southwest China, in 2020

Next, the degree of coupling and coupling coordination between subsystems is examined (Figure 5). The coupling degree of subsystems is 0.65 during the grinding period. Meanwhile, the coupling coordination degree is 0.25, and it is the less coordinated degree. Thus, the overall level of coupling between subsystems of sustainable development of rural communities in Guizhou is low, and the internal coordination is imbalanced. The weakest communities’ subsystem coupling and coupling coordination degrees are 0.19 and 0.07, respectively. Thus, they exhibit a low coupling, an extremely uncoordinated stage. Next, the weak communities’ subsystem coupling and coupling coordination degrees are 0.41 and 0.24, respectively. This demonstrates that they are in the antagonistic, less coordinated stage. The intermediate communities’ subsystem coupling and coupling coordination degrees are 0.51 and 0.34, respectively, and thus, in the grinding, there is a less coordinated stage. Next, the better communities’ subsystem coupling and coupling coordination degrees are 0.50 and 0.41, respectively, and thus, in the grinding, basically, the coordination stage. Finally, the excellent communities’ subsystem coupling and coupling coordination degrees are 0.69 and 0.54, respectively, and thus, in the grinding, basically, a coordination stage. Clearly, the level of sustainable development of rural communities is consistent with the trend of changes in the coupling and coordination degree of subsystems. As the coupling and coordination degrees between subsystems rise, the level of sustainable development of rural communities increases.

4.3. The Obstacle Factors of Sustainable Development of Rural Communities in Guizhou Province, a Mountainous Area in Southwest China, in 2020

Obstacle factors are critical factors that constrain the level of sustainable development of rural communities in mountainous areas. Thus, they should be the main targets for the implementation of optimization strategies. This study focuses on high and medium obstacle factors [84]. Specifically, factors larger than 5% are classified as major obstacle factors and described in detail (

Table 4. The obstacle factors of sustainable development of rural communities in Guizhou Province.

	Weakest Communities	Weak Communities	Intermediate Communities	Excellent Communities	All Communities
C11	0.65	0.70	0.69	1.08	0.68
C12	1.38	1.43	1.68	1.84	1.41
C13	8.06	8.53	8.13	13.4	8.30
C14	7.76	8.22	8.51	12.61	8.02
C15	12.73	13.28	12.43	13.41	12.94
C16	9.88	9.81	9.34	9.50	9.83
C17	7.41	7.69	7.22	7.96	7.52
C21	2.03	2.02	2.05	2.04	2.03
C22	0.49	0.46	0.45	0.38	0.48
C23	0.05	0.15	0.33	0.00	0.10
C24	0.90	0.90	0.84	1.02	0.90
C31	1.67	1.49	1.56	0.00	1.59
C32	4.87	4.16	6.09	6.63	4.65
C33	1.58	0.95	2.90	0.00	1.38
C41	7.15	7.39	7.23	7.36	7.25
C42	1.90	1.74	1.95	2.01	1.84
C51	8.34	8.59	8.08	8.8	8.43
C52	6.28	6.28	5.79	6.44	6.26
C53	6.53	6.26	6.04	6.81	6.40
C54	9.94	10.16	9.74	10.50	10.02

). For all communities, 10 major obstacle factors are identified, including five in the resource system, one in social outcomes, and four in the economic outcomes. C15, C54 and C16 obstacle indices rank in the top three. The distribution of the main obstacles is similar in some aspects and different in others among communities with different development levels. Specifically, similarity is manifested in the similar number of major obstacle factors, with 10 major obstacle factors in the weakest and weakest communities, 11 in medium, better, and excellent communities. Differences are mainly manifested in the different ordering of the main obstacle factors in different types of villages. For instance, the top three obstacle factors in the weakest, weaker, and medium communities are C15, C54 and C16. Those in better communities are C54, C14 and C15, and excellent communities are C14, C13 and C15. Regarding the frequency of the main obstacle factors in different types of communities, C13, C14, C15, C16, C17, C41, C51, C52, C53 and C54 appeared five times; C32 appeared three times, and the distribution of obstacle factors is similar to the overall situation.

5. Discussion

Under the SES framework, variables such as resource systems, actor systems, governance systems, economic outcomes, and social outcomes dominate the development trend of sustainable development in mountainous rural areas. This study shows that the level of sustainable development of rural communities in China’s mountainous regions is still low, and the development gap between communities is increasing. This finding is consistent with Yang et al. (2020) [85], who found that Guizhou’s rural community development is still in its early stages compared to other Chinese regions. Regardless of whether it is economic development or ecological protection, remote mountainous areas face significant challenges [86,87]. Meanwhile, as the sustainable development of rural communities improves, the degree of coupling and coordination among subsystems such as resource systems, actor systems, governance systems, economic outcomes, and social outcomes will rise, releasing stronger linkage effects. This result supports Liu et al. (2019) view that sustainable rural development is closely related to the degree of matching between various development factors [88]. The process of sustainable development of rural communities is a multi-level, multi-structural integrated system involving multiple factors, such as economic, social, cultural, educational, ecological, and technological. Only the coordinated development and effective interaction of these factors can produce a positive synergistic effect [89]. Traditional sustainable development evaluations have always been analyzed using easily quantifiable social indicator tools, thus neglecting the inclusive citizen participation process in natural resource-dependent rural communities [90,91]. Whether it is from the perspective of sustainable resource use or public participation in governance, the sustainability assessment criteria based on the socio-ecological framework are useful to identify the key elements of sustainable development in rural communities. These elements include the public resource pool composed of natural and physical resources, the cooperation and governance structure among managers and action groups that lead community development, and the mutual promotion of social welfare and economic benefits.

This study highlights the profound complexity and difficulty of achieving coordinated development across the internal systems necessary for sustainable development in mountainous rural communities. The intrinsic fragility of mountain ecosystems implies that once damaged, they rarely recover to their original state, thereby constraining both resource use and socioeconomic development in those regions. When the scale of local socioeconomic activity surpasses the ecological carrying capacity, environmental degradation intensifies, and the risk of natural disasters rises. The resultant secondary disasters often inflict severe damage on community development and social welfare [92]. Furthermore, adverse natural and economic conditions frequently trigger an exodus of rural residents, primarily male, relatively well-educated, younger laborers, leading to population loss. This demographic shift tends to leave behind an unbalanced social structure dominated by the elderly, women, and children [93]. This population loss further exacerbates social governance challenges by weakening public leadership at the village level, damaging rural social capital, and diminishing villagers’ sense of belonging [94].

This study reveals that economic and social outcomes are key factors constraining the sustainable development of rural communities in mountainous areas. In mountainous areas, the natural environment is only the first cause of delayed rural development; cultural values and socioeconomic development patterns that emanate from the natural environment may be crucial variables leading to the rural areas’ ongoing decline in the region. In fact, the low economic efficiency of the agricultural and rural sectors is the root cause of rural decay, while problems such as population loss and land degradation are only the result of economic degradation [95]. Moreover, the reasons for the lagging rural economic development may stem from two aspects. On the one hand, rural industries in mountainous areas are mainly based on traditional agriculture. However, agricultural growth can only play a limited role in contributing to the community’s economic efficiency and income growth. The growth momentum of rural non-agricultural industries in mountainous areas is insufficient, and the scale is relatively small. Hence, most rural communities also cannot achieve economic prosperity through the pull effects of industries and services [96]. On the other hand, the rural markets in the region have unbalanced development. With the rapid penetration of the market economy into the countryside, the rural society has become part of the market economy [97]. The unsound development of rural markets and the delayed market reform process in mountainous areas may cause problems such as delayed and misleading price and risk signals [98]. Rural enterprises, farmers, and other market players may be unable to accurately judge the market demand and adjust their production plans promptly, which can cause economic losses.

The shortage of material and financial capital imposes obvious constraints on the sustainable development of rural communities in mountainous areas. Factors such as operating construction land, houses, government funds, and social funds in the resource system are more constraining. This result is similar to related studies, which note that traditional factors of production, such as labor and natural resources, are still the main sources of livelihood for rural residents and village development in underdeveloped areas of China. Meanwhile, resources such as material capital and financial capital are relatively scarce [99,100]. The Chinese government has, in recent years, increased ecological compensation and the scale of capital investment in mountainous areas such as Guizhou. However, these subsidies and support are still at a low level and cannot fully compensate for the opportunity cost of the population, especially for low-income groups who choose to accept ecological compensation and give up livelihoods such as logging, grazing, and farming, which means a loss of income. Tao et al. (2021) also noted that although Guizhou and other regions have extensive abandoned lands, the use and development of housing and other assets are highly susceptible to geographic location, socioeconomic factors, etc. [101]. The further the assets are from the city and other economic centers, the lower the value of the development of these assets. Obviously, for underdeveloped areas, physical capital and finance are both scarce and key elements of rural community development. On the one hand, they can promote the transformation of the community’s economy from traditional agriculture to diversified industries, such as the planting of modern orchard bases, high-value-added processing industries, and services [102]. On the other hand, physical and financial assets can be improved by rural residents taking more precautionary measures to adapt to climate change, such as purchasing better agricultural equipment or agricultural insurance [103]. Therefore, considering the extremely limited per capita resources, rural areas should fully integrate resources such as land, residential land, housing, and capital on a community basis, and attract more enterprises and cooperatives to move into the countryside to undertake economic activities through the unified planning of rural communities and infrastructure development.

6. Conclusions

Rural areas are a complex multi-level regional system, comprising multiple elements such as natural resources, human resources, social systems, and economic elements. This study proposes an evaluation system that can be used while formulating sustainable development strategies for rural communities in such areas. This framework is in the form of an evaluation index system comprising 20 secondary indicators, which belong to five primary ones: resource system, actors, governance system, social outcomes, and economic outcomes. Next, the comprehensive evaluation, coupling coordination degree, and obstacle degree models are used to diagnose the conditions and dilemmas of the sustainable development of rural communities in mountainous areas. The conclusions are summarized below.

First, rural communities in mountainous areas have a low sustainable development level overall. Furthermore, the development gap between communities is significant, mainly driven by differences in the resource systems and economic outcomes. Second, the overall coupling and coordination level among the rural sustainable development subsystems is low, and remains in the grinding and less coordinated stage. Compared with communities with lower sustainable development, those with higher sustainable development levels exhibit better coupling and coupling coordination. Third, the obstacles to sustainable development in rural communities are mainly concentrated in the resource systems and economic outcomes, including operating construction land, housing, government funding, capital growth, income growth, profitability, and bonus sharing. In the future pursuit of sustainable development in rural communities, it is essential to continue addressing the challenges identified above. First, systematic surveys should be conducted to identify and catalog idle properties within rural communities. For residents who have permanently relocated, voluntary and compensated mechanisms for the permanent withdrawal of land-use and homestead rights should be explored. For long-term vacant properties where full withdrawal is not feasible, the transfer of homestead rights may be facilitated through the trading of homestead reconstruction rights. These approaches should be integrated with utilization models tailored to each community’s geographic conditions and industrial endowments. Second, government investment in rural community sustainability should be increased, including fiscal allocations at all administrative levels, agricultural infrastructure funding, comprehensive agricultural development funds, and targeted agricultural loans. By utilizing fiscal policy tools and signaling effects, additional social capital can be attracted to support rural community development. Finally, strengthening rural industrial operations is critical to ensuring sustained income growth for community members. Rural industries can be advanced through tourism development and digital economy initiatives, both of which can continuously enhance member earnings. Agricultural tourism and agritourism integration have become important drivers of sustainable agricultural and rural development, generating ecological, social, and economic benefits [104,105]. At the same time, in the context of worldwide digitalization and intelligent development, the digital economy can contribute to greener agricultural growth [106]. Digitalization can optimize the allocation of agricultural production factors and reduce pollution emissions. For example, digital platforms for information dissemination can help communicate fertilizer-reduction policies to farmers and strengthen their environmental awareness [107]. Moreover, in developing countries, farmers often exhibit low levels of digital literacy due to infrastructure constraints and limited educational resources. Therefore, government agencies, industry associations, standardization bodies, schools, social education, and training programs should also be encouraged to play active roles in cultivating farmers’ digital literacy.

The findings of the comprehensive evaluation and identification of obstacle factors are reliable and can provide an effective decision-making basis for the sustainable development of rural communities. However, this study has some limitations in data availability and sample size, resulting in some key factors not being included in the evaluation system. The data and information used in this study mainly came from field research. However, the sample range was insufficient because of limited resources and time. Although the sample collection was performed strictly in compliance with scientific sampling procedures and is representative to a certain extent, this study did not investigate all mountainous areas. Hence, the samples in this study did not accurately reflect the circumstances of all communities. Furthermore, the data utilized in this study are cross-sectional in nature. Thus, we are unable to dynamically track and analyze rural communities. For a more thorough and scientific analysis of the sustainable development of rural communities, scholars can analyze panel data and obtain dynamic information through long-term tracking of the sample area through fixed observation points. Next, an evaluation indicator system can be used to monitor, evaluate, and compare communities in different regions, as well as identify and summarize real-world experiences and challenges in the process of the sustainable development of rural communities in the mountainous areas.