Rural Ecosystem Health Assessment and Spatial Divergence—A Case Study of Rural Areas around Qinling Mountain, Shaanxi Province, China

Abstract

The rapid progress of urbanization and rural revitalization in developing countries has led to dramatic changes to the rural ecological environment. Assessing the rural ecosystem health (REH) is a crucial foundation for promoting sustainable development in rural areas. This study, focusing on rural areas around the Qinling Mountains in Shaanxi Province, establishes an evaluation system based on appropriate evaluation indicators for assessing the composite ecosystem. This evaluation system comprises four rural ecosystem subsystems: resource, environment, society, and economy. By employing a comprehensive indicator evaluation model and remote sensing image data, this study examines the health status of rural ecosystems in the 40 counties and districts across the study area, as well as their spatial differentiation characteristics, using ArcGIS (10.8) spatial analysis. The REH scores of these areas range from 0.6856 to 0.8818, with a fluctuating downward trend from north to south. This suggests that the rural ecosystems around the Qinling Mountains in Shaanxi Province are relatively healthy, with the northern area being notably healthier than the southern area. Spatial Gini coefficient analysis reveals a much smaller coefficient for the overall ecosystem compared to the subsystems in the study area, indicating that the distribution of health levels is dispersed and not concentrated. After establishing REH grades and quantity metrics, the 40 counties and districts are categorized into 13 types, followed by an analysis of the influencing factors for each type. Recommendations and management strategies are then proposed to enhance the health of rural ecosystems.

1. Introduction

The primary objective of environmental management is to ensure the health of ecosystems [1,2]. Initially, most research on ecosystem health focused on the conceptual definition [3,4,5,6]. Ecosystem health is defined as the ecosystem’s capability to meet the reasonable needs of human society and its ability for self-repair and renewal [7,8], which is an essential component of ecosystem security. In the 1990s, Costanza et al. introduced three indicators, i.e., vitality, organizational strength, and resilience, to assess the performance and health of ecosystems [9]. Ecosystem health is linked to the ecological and economic domains and should be assessed by combining human values with biophysical processes [8,10], highlighting adaptation to the external environment and its connection with population health levels. In recent years, there has been a growing emphasis on the significance of ecosystem health among scholars. This has led to an increase in research dedicated to developing evaluation models and methods, as well as optimizing ecological indicators [11,12]. At present, international research primarily revolves around assessment, with rural ecosystem studies integrating economic aspects into the evaluation and forecasting of material and resource utilization efficiency. The main focus is on the ecological environment in rural areas [12,13,14,15]. Ecosystem health research has focused on evaluating various types of ecosystems, such as watersheds, wetlands, forests, cities, and agriculture [16,17,18,19,20]. The principal methods employed for studying the ecosystem health evaluation methods can be categorized into three types: (1) utilizing the vigor–organization–resilience (VOR) assessment framework and functional indicators to assess the health of natural ecosystems, emphasizing a single-dimensional evaluation while overlooking the holistic consideration of the social and economic system [16,17,18]; (2) examining the causal relationship between ecosystems and human activities to uncover the internal evolution mechanism, mainly using the pressure–state–response (PSR) model, driving–state–response (DSR) model, and driving–pressure–state–impact–response (DPSIR) model [19,20,21,22], which are methods that also concentrate on the single-dimensional evaluation of natural ecosystems, neglecting effective measures of ecosystem integrity; (3) shifting focus towards ecosystem integrity, there is a growing interest in assessing the social–economic–natural complex ecosystem, such as the REHI index method [23,24]. On the whole, there is a scarcity of studies on rural ecosystem health (REH). The evaluation system of REH has not been clearly defined, and the selection and quantification of evaluation indicators pose unresolved pressing issues.

A rural ecosystem refers to the ecological, economic, and technological composite system composed of the rural population, rural environment (light, heat, water, air, soil, etc.), rural organisms (plants, animals, microorganisms), rural buildings (houses), agricultural production tools, rural science and technology, and rural culture within the scope of rural areas. This system is an ecological unit with the basic functions of matter circulation and energy flow [25]. Compared to the general ecosystem, the rural ecosystem is a distinctive artificial and semi-artificial ecosystem, in which many environmental and biological compositions are created and altered by the residents themselves through “labor”. Therefore, the rural ecosystem exhibits structural complexity and functional diversity. It encompasses both a typical natural ecosystem and the pronounced synthetic characteristics of a social–economic system. This comprehensive expression is shaped by the flow of material, energy, and information between humans and external resources, the environment, society, and the economy. The resource and environment subsystems directly influence the input of natural capital, while the social and economic subsystems impact the external drivers of production relations and the accumulation of material wealth in rural populations (Figure 1). With the rapid advancement of urbanization and rural revitalization, the consumption income of rural residents has been increasing [26,27,28]. However, rural ecosystems have been affected to a certain degree. For example, resource wastage, depopulation, and environmental pollution are increasing the pressure on rural ecosystems and decreasing the functionality of ecosystems [29,30,31]. Currently, for many developing countries such as China, South Africa, and India, the most important settlements are still rural areas. Rural environmental health has always been a focal issue for the government, with significant efforts made to enhance and address it [32]. The health of rural ecosystems is vital for promoting sustainable rural development. Research on rural ecosystem health can aid in understanding the underlying causes of rural ecological challenges, guiding rural revitalization, reconstruction, and sustainable development.

In China, scholars have predominantly focused their research on urban areas, while rural research has mainly centered around topics like rural environmental assessment, spatial differentiation of rural settlements, rural reconstruction, and integrated rural development [33,34,35,36,37,38]. Studies have shown that out of 4398 ecosystem health research references, only 18 (0.4%) were dedicated to rural areas and the countryside, with various evaluation systems established from different perspectives [30,31,32]. However, the selected indicators are relatively limited in scope, the research content is somewhat homogeneous, and there is a lack of comprehensive studies that integrate ecosystems with human factors such as rural socioeconomics. The holistic examination of rural nature, society, and economy is often overlooked. Currently, there is still a lack of research on developing a mountain ecosystem index system that aligns with international standards. The rural areas around the Qinling Mountains in Shaanxi Province, China, are positioned in south-central Shaanxi Province and feature a complex topography and diverse climate types, making it the region with the most intricate natural geography in the entire province [39]. There are noticeable variations in rural ecology and landscape patterns among counties and districts. As the area is a typical mountainous rural ecosystem, its rural ecology and landscape patterns are greatly influenced by factors such as mountain resources, economy, and policies in the Qinling Mountain area. In the context of rural revitalization, the counties around the Qinling Mountains are undergoing further enhancements in terms of mountain ecological resource protection, comprehensive rural environment improvement, rural social development, and the enhancement of rural living conditions. The level of rural development shows a pattern of gradual decline from north to south [40]. Taking 40 representative counties and districts around the Qinling Mountains in Shaanxi Province as a case study, we established a rural ecosystem health evaluation system from the perspective of the natural–social–economic composite system to assess the rural ecosystems in the region. By classifying the REH into different types according to the health level of rural ecosystem subsystems, we examined the spatial differentiation patterns and identified issues and characteristics within rural ecosystems. The goal is to establish a standardized index system for assessing the health of mountainous rural ecosystems, so as to provide a benchmark for evaluating the mountain rural ecosystem health in Shaanxi Province and other areas in developing countries.

2. Materials and Methods

2.1. Overview of the Study Area

The Qinling Mountains, spanning across Shaanxi, Gansu, Qinghai, and Henan and extending eastwards to the Nanyang and Xiangyang basins, are a mid- to high-altitude mountain range across the center of China. This mountain range is connected to the Qilian Mountains and Kunlun Mountains in the west and Tongbai Mountains and Dabie Mountains in the east. It serves as a natural demarcation line for geography, geology, climate, wildlife, and flora areas in mainland China. In Shaanxi, the Qinling Mountains lean against the Guanzhong Plain to the north and the Hanzhong and Ankang basins to the south. They also act as the watershed dividing the Yellow River and Yangtze River systems, with the mountains rising steeply in the north and gradually sloping in the south. Based on the “Regulations on the Protection of Qinling Ecological Environment”, the scope of Qinling ecological environmental protection encompasses the Qinling Mountain region in Shaanxi Province, bordered by the provincial boundaries in the east and west and by the base of the Qinling Mountains in the north and south [41]. The Qinling Mountains include Shangluo City and parts of Xi’an, Baoji, Weinan, Hanzhong, and Ankang, and can be divided into the Guanzhong region and southern Shaanxi region. The southern Shaanxi mountainous area is vast, characterized by a mild climate, rich species, complex topography, relatively underdeveloped economic and social conditions, and a high poverty rate [42]. The Guanzhong Plain features a vast area, deep loess deposits, and a long history of development. In this study, the 2020 administrative division of Shaanxi Province, including counties, county-level cities, and municipal districts, was used as research units, with a total of 40 geographic units identified. The comprehensive development level is higher in Hanyin County among these 40 counties, due to its abundant natural resources, minimal ecological environmental pollution, good environmental quality, and relatively high level of rural economic development. On the other hand, Liuba County and Zhashui County have a poor comprehensive development level with limited resource conditions, a degraded ecological environment, an underdeveloped rural economy, an unreasonable industrial structure, inadequate infrastructure construction, and low living standards for rural residents (Figure 2).

2.2. Data Sources

The evaluation scale of ecosystem health typically considers a specific geographical scope or a unified administrative boundary as the spatial unit. The county-level administrative unit is the smallest administrative entity that offers comprehensive statistics and data summaries of various social and economic activities in China. Taking the county-level administrative scale as the spatial unit facilitates the collection of detailed data [23]. To ensure data availability and accuracy, 40 district and county administration and data statistics units were selected to gather spatial data from 40 districts and counties in 2020. These data primarily included the 1:50,000 topographic map, land use data, NDVI data, water system data, natural environment data, and social and economic data of Shaanxi Province in 2020. The specific data sources are shown in

Table 1. Selected data sources.

Data Type	Data Source	Clarification
Administrative divisions of Shaanxi Province (1:50,000)	Geospatial Data Cloud (https://www.gscloud.cn/, accessed on 5 April 2021)	Vector data
LUCC data (50 m × 50 m)	Cold and Arid Regions Science Data Center (https://www.westdc.westgis.ac.cn, accessed on 6 May 2021)	Used to calculate BI and ESV
NDVI data (30 m × 30 m)	Geospatial data cloud network (http://www.gscloud.cn, accessed on).	Calculated vegetation coverage
Water system data (1:40,000)	Chinese River System dataset [M],1988, Beijing	Used to calculate wetland area ratio
Natural environment data	Ecol Environ Status Bull (2020) (https://sthjt.shaanxi.gov.cn/, accessed on); Water Soil Conserv. Bull. (2020) (http://slt.shaanxi.gov.cn/, accessed on 6 July 2021)	Characterize the health status of rural resources and environment subsystem
Social and economic data	Statistical Yearbook for Cities and Counties (2020) (http://www.shaanxi.gov.cn/, accessed on 6 July 2021)	Characterize the health status of rural social and environmental subsystems

.

2.3. Selection of Indicators for Rural Ecosystem Health Assessment

The health of the rural ecosystem depends on the stability of the rural social, economic, and natural subsystems. The rural natural ecosystem comprises the rural resource subsystem and the environmental subsystem [23,43]. In this study, following the principles of integrity, regionalism, operability, accessibility, and quantification, an evaluation index system for rural ecosystem health in the Qinling Mountains was developed. Member experts from the Shaanxi Rural Ecology Association conducted two rounds of index screening and feedback, resulting in the establishment of a comprehensive evaluation index system. This system incorporates dimensions of rural resources, environment, economy, and society, emphasizing the significance of the sustainable development concept in assessing rural ecosystem health. A Sustainable Development Goal (SDG17) was integrated into the construction of the index system. A three-level evaluation index system was established, comprising 28 indicators such as per capita arable land area, per capita ecosystem service value, per capita water resources, forest coverage rate, and air quality compliance rate (

Table 2. County-based rural ecosystem health assessment framework, indicator weights, and reference values.

Subsystems	Indicators	Units	Reference Value	Weight	Indicator Character
Rural resources subsystem (RRS)	Arable land area per capita	hm2	0.09	0.0391	+
	Water resources per capita	m3	2077.75	0.0531	+
	Proportion of wetland area	%	1.676	0.0412	+
	Forest cover rate	%	18	0.0228	+
	Biological richness index	%	75	0.0291	+
	Ecosystem services value per hectare	CNY/hm2	380,427.8	0.0538	+
Rural environment subsystem (REnvS)	Up-to-standard rate of rural surface water quality	%	70	0.0819	+
	Up-to-standard rate of air quality per year	%	76.71	0.0204	+
	Rate of comprehensive utilization of solid waste	%	90	0.0279	+
	Up-to-standard rate of comprehensive improvement of rural environment	%	77.66	0.0270	+
	Farmland use of pesticides per hectare	t/hm2	0.01	0.0100	−
	Farmland chemical fertilizer application per hectare	t/hm2	250	0.0176	−
Rural social subsystem (RSS)	Natural population growth rate	%	3.81	0.0279	−
	Density of rural population	Person/km2	718	0.0584	−
	Per capita self-owned housing area in rural area	m3	41.8	0.0297	+
	Rural annual per capita household electricity consumption	kw·h/person	720	0.0372	+
	Per capita living consuming expenditures of rural residents	CNY	10,935	0.0253	+
	Per capita education funds expenditures	CNY	1387	0.0343	+
	Engel coefficient of rural residents	%	25.9	0.0261	−
	Number of doctors per 1000 persons	Person	11.23	0.0537	+
	Number of health care institutions beds per 1000 persons	–	6.86	0.0525	+
Rural economic subsystem (REcoS)	Per capita grain output of rural residents	kg	783.2	0.0383	+
	Per capita disposable income of rural residents	CNY	12,326	0.0365	+
	Proportion of non-farm payrolls among rural residents	%	51.67	0.0254	−
	Proportion of rural secondary and tertiary industry product	%	101.35	0.0304	−
	Output value of agriculture, forestry, animal husbandry, and fishing per hectare	10,000 CNY/hm2	1.7202	0.0503	+
	General power capacity of agriculture machinery per hectare	kw/hm2	1.1340	0.0500	+

Note: “+” and “−” indicate positive and negative correlations with ecosystem health.

). These indicators offer a holistic view of rural ecosystem health in the Qinling Mountain region.

(1)

Subsystem of rural resource ecosystem

Natural resources form the foundation for rural production and livelihoods. The rural resource subsystem, a crucial carrier of rural production and livelihood, serves as a key indicator for evaluating the ecological health of a specific region. It aligns with the Sustainable Development Goals’ aim of “zero hunger” and seeks to highlight the stable capacity of the rural ecosystem to sustainably meet the reasonable needs of human activities, as well as self-maintenance and renewal. This reliance is determined by the endowment capacity and potential conditions of rural natural resources, arable land resources, agricultural production capacity, water resources, and forest resources. This study has identified six key natural resource indicators for evaluation, including per capita arable land area, per capita water resources, forest coverage, wetland area ratio, biological abundance index, and ecosystem service value to characterize the health status of the rural resource subsystem. The per capita arable land area emphasizes the arable potential of land resources and the production capacity of agricultural products, directly influencing the production and supply capacity of agricultural materials in mountainous rural ecosystems. Per capita water resources, forest coverage, wetland area ratio, bio-abundance index, and ecosystem service value are sustainable indicators used to assess the self-maintenance and renewal function of rural ecosystems, reflecting the natural endowment of forest resources, water resources, and biological resources in rural areas. Per capita arable land area and water resources represent the scale of arable land and water resources in the region. Forest coverage rate, wetland area ratio, and biological abundance index reflect the quantity of forest, water resources, and biological resources within rural areas. These indicators serve as proxies of rural ecological resilience and regeneration capacity. The biological abundance index (BI) is then calculated based on the proportion of different land use types (Formula (1)). The ecosystem service value (ESV) assessment methodology is derived from the research by Costanza et al. [44], incorporating ecological value coefficients and per-hectare land values for different land use types, which have been derived through the modification and calculation of ecosystem biomass factors proposed by Xie Gao Di et al. [45,46] (Formula (2)).

$$BI=A_{bio}\times \left(\begin{array}{c}0.35\times A_1+0.21\times A_2+0.28\times A_3\\ +0.11\times A_4+0.04\times A_5+0.01\times A_6\end{array}\right)/A_{total}$$

(1)

where $BI$ is the biological abundance index and $A_{bio}$ is the normalization factor. $A_1~A_6$ denote the woodland, grassland, wetland, arable land, construction land, and barren land, respectively (Technical Standard for Ecosystem Condition Evaluation (HJ 192-2015)). $A_{total}$ indicates the total area of the study area in km², and $Amax$ indicates the maximum BI value before normalization. $A_{bio}=100/Amax$.

$${ESV}_k={\sum }_{k=1}^n{A}_k\times {VC}_k$$

(2)

$${VC}_k={\sum }_{g=1}^l{EC}_k\times E_a$$

$$E_a={\displaystyle \frac{1}{5}}{\sum }_{f=1}^m{G}_f\times H_f\times {\displaystyle \frac{B_f}{J_m}}$$

where $ESV$ is the integrated ecosystem service value at the county scale, ${ESV}_k$ is the ecosystem service value of the land use type k (CNY), $k$ indicates the land use type, $g$ indicates the ecosystem service type, $A_k$ is the area of land use type $k$, ${VC}_k$ is the ecosystem service value per unit area of land use type $k$, ${EC}_k$ is the value equivalent of ecosystem service of category $g$, $E_a$ is the value of ecosystem services, $m$ is the type of food crops grown in Shaanxi Province, $G_f$, $H_f$, and $B_f$ are the price, output, and planting area of food crop $f$, respectively, and $J_m$ is the total planting area of crops.

(2)

Subsystem of rural environment ecosystem

The rural environmental subsystem is a crucial integrated representation for maintaining a stable ecological environment, safeguarding agricultural production systems from symptoms of dysregulation, and addressing external stressors, in alignment with the Sustainable Development Goals of “clean water and sanitation, responsible consumption and production, climate action”. The rural ecological environment plays a pivotal role in rural development and economic growth. It is emphasized that the sustainable capacity of rural ecosystems to self-sustain and renew depends on factors such as pollution infestation and interference with agricultural production, especially in regions around the Qinling Mountains. Given its close ties to the ecological preservation of the Qinling Mountains, the quality of the rural ecological environment becomes paramount. This study selected six indicators to characterize the health status of the rural environmental subsystem, including the rate of rural surface water quality meeting standards, the proportion of days when air quality met standards, the comprehensive utilization rate of solid waste, and the rate of village environment comprehensive improvement meeting standards. These indicators reflect the status of rural environmental quality. The amount of pesticide and fertilizer applied per unit area of arable land represent human agricultural production activities and cleaner production technology, which are negative indicators.

(3)

Subsystem of rural social ecosystem

The rural social subsystem aims to enhance the well-being of rural residents by utilizing rural natural capital input, which is indicative of the production and living conditions of rural residents. This is closely linked to the level of rural economic wealth and the accumulation of artificial capital. In alignment with the Sustainable Development Goals of “no poverty, decent work and economic growth, industrial innovation and infrastructure, reducing inequality, sustainable cities and communities”, the emphasis lies on the quality of rural material infrastructure and the production and living conditions of residents. The health status of the rural social subsystem was characterized by eight key indicators that evaluate population dynamics, living conditions, healthcare, consumption, and education. The key metrics include the natural population growth rate, population density, housing space per capita, number of doctors and beds per 1000 people, electricity consumption per capita, rural per capita consumption expenditure, per capita education expenditure, and Engel’s coefficient. The natural population growth rate and population density indicate the growth rate and spatial distribution of the population in the region. Rural per capita consumption expenditure, per capita education expenditure, per capita housing area, number of doctors and beds per 1000 people, and electricity consumption per capita serve as comprehensive reflections of rural employment, elderly care, education, and medical social services, with higher values of these indicators signifying better rural social development. Conversely, the Engel coefficient of rural residents acts as a negative indicator, where lower values correspond to higher levels of rural social development.

(4)

Subsystem of rural economic ecosystem

The rural economic subsystem, which represents the accumulation of artificial material wealth resulting from rural natural capital investment, aligns with the Sustainable Development Goals of “no poverty, zero hunger, affordable clean energy, decent work and economic growth, industrial innovation and infrastructure”. It aims to highlight the capacity of rural ecosystems to fulfill reasonable human needs, contingent on the rural economic development level, agricultural production level, production activity intensity, industrial structure, and per capita economic level conditions. In combination with the current situation of rural economic development around the Qinling Mountains in Shaanxi Province, six indicators were selected to depict the health status of the rural economic subsystem. These indicators include the per capita grain output of farmers, per capita disposable income of farmers, per capita gross output value of agriculture, forestry, animal husbandry, and fisheries, per capita gross power of agricultural machinery, ratio of rural non-agricultural labor force population, and ratio of secondary and tertiary industries. Notably, rural agricultural productivity indicators such as the per capita grain output of farmers, the per capita gross output value of agriculture, forestry, animal husbandry, and fisheries, and per capita total power of agricultural machinery are all positive indicators. On the other hand, the per capita disposable income of farmers, the proportion of rural non-agricultural labor force population, and the proportion of secondary and tertiary industries reflect the typical per capita income level and agricultural economic structure. The former is a positive indicator, with a higher indicator representing a higher rural economic level, while the latter two are negative indicators, with a larger indicator indicating a weaker dominant position of the agricultural industry and greater disturbance of economic production activities to the rural ecosystem. Based on the above analyses, the index system for evaluating the rural ecosystem health around the Qinling Mountains in Shaanxi Province was established using the hierarchical analysis method, as shown in Table 2.

2.4. Determination of Indicator Weights

Due to the variations in the significance of indicators in the comprehensive assessment, this study employed the hierarchical analysis method [47] and the Delphi method [48] to assign weights to the indicators. The Delphi method, also known as the expert survey method, involves gathering opinions from experts in the relevant fields to determine the specific contributions of indicators to the REH evaluation. The appropriateness of indicator weights greatly influences the assessment results. Weight determination methods typically fall into two categories: subjective weighting methods and objective weighting methods. Objective weighting methods involve systematic calculations to determine the weight of each index, requiring substantial data and precision. However, this approach may lead to a lack of differentiation in the evaluation model, limiting its ability to comprehensively assess regional characteristics. Subjective weighting methods, on the one hand, align more closely with the actual context but may introduce biases due to differences in perceptions among subjects. Overall, it is difficult to achieve the desired effect by using either the objective or subjective weighting method alone due to their shortcomings. Based on the analytic hierarchy process (AHP), with 28 relevant experts and scholars in rural ecosystem health assessment, the weights of the indicators were iteratively adjusted through three rounds of consultation to ensure that the assessment results accurately reflect the actual conditions in the study area. This approach seeks to mitigate the limitations of both objective and subjective weighting methods to achieve a more robust assessment outcome.

On the basis of the AHP, 28 experts and scholars specializing in rural ecosystem health assessment were consulted. Through three rounds of consultation, the weights of each indicator were repeatedly adjusted to ensure that the evaluation results accurately reflected the actual situation in the study area. This approach aimed to minimize the limitations of objective and subjective weighting methods, acquiring more reliable evaluation results.

(1)

Indicator normalization

Since the natural disaster risk assessment system contains too many assessment indicators related to various types of disaster risk impact factors, there are significant differences in the order of magnitude, unit, and scale of each indicator, making direct comparisons challenging. To achieve equal calculation and comparison results of the impact indicators, it is necessary to normalize the detailed parameters of each indicator, mapping the final value of each impact indicator to a range of [0, 1] [49]. The formula is as follows:

$$\mathrm{Positive}\mathrm{indicators}: Y_{mn}=X_{mn}-X_{m min}/X_{m max}-X_{m min}$$

(3)

$$\mathrm{Negative} \mathrm{indicators}: Y_{mn}=X_{m max}-X_{mn}/X_{m max}-X_{m min}$$

(4)

where $Y_{mn}$ is the standardized value;${ X}_{mn}$ is the original value of the nth item and mth column indicators; $X_{m max}$ and $X_{m min}$ are the maximum and minimum values of the indicators for the 39 counties in the studied area, respectively.

(2)

AHP hierarchical analysis method

The AHP hierarchical analysis method is a highly holistic, simple, and practical decision-making method that simplifies complex decision processes by breaking them down into hierarchical structures. In this method, decision elements are decomposed into guidelines, programs, objectives, and other levels through hierarchical analysis, followed by systematic decision-making calculations. The strength of hierarchical analysis lies in its simplicity and systematic nature to decision analysis. However, it may lack strong subjectivity due to certain differences in knowledge and conceptual understanding among researchers. In this study, the AHP method was utilized within Decision-making based on Preference and Similarity (DPS, 19.05, China) to construct a preference matrix and analyze the selected indicators, thus obtaining the weights of each indicator after preliminary calculation.

(3)

Rural ecosystem health index

Calculating the REH index of county units in the study area required benchmark values of indicators in each county for reference. Due to the areas around the Qinling Mountains in Shaanxi Province being relatively more economically developed, in order to ensure the feasibility and validity of the evaluation, the benchmark values of the indicators were determined based on recognized values, regional averages, domain and local standards [50], national standards, and Shaanxi Province standards. The computational formula [51] is as follows:

$$\mathrm{Positive} \mathrm{Indicators}: \mathrm{When} x_m\ge y_m, S_m=1; \mathrm{When} x_m<y_m, S_m=x_m/y_m$$

(5)

$$\mathrm{Negative} \mathrm{indicators}: \mathrm{When} x_m\le y_m,{ S}_m=1; \mathrm{When} x_m>y_m,{ S}_m=y_m/x_m$$

(6)

where $S_m$ is the ecosystem health index for each county,${ x}_m$ is the actual value for each county, and $y_m$ is the calculated reference value of the index.

(4)

Rural ecosystem health evaluation model

Based on the index values of the REH assessment framework and their corresponding calculated index weights in each county unit around the Qinling Mountains in Shaanxi Province, the REH subsystem indices and the comprehensive REH index were obtained. Then, the REH assessment model for the counties around the studied area was constructed.

$$\mathrm{R}\mathrm{R}\mathrm{S}={\sum }_{\mathrm{i}=\mathrm{p}}^{\mathrm{q}}{\mathrm{w}}_{\mathrm{i}}\times {\mathrm{S}}_{\mathrm{i}}$$

$$\mathrm{R}\mathrm{E}\mathrm{n}\mathrm{v}\mathrm{S}={\sum }_{\mathrm{i}=\mathrm{p}}^{\mathrm{q}}{\mathrm{w}}_{\mathrm{i}}\times {\mathrm{S}}_{\mathrm{i}}$$

$$\mathrm{R}\mathrm{S}\mathrm{S}={\sum }_{\mathrm{i}=\mathrm{p}}^{\mathrm{q}}{\mathrm{w}}_{\mathrm{i}}\times {\mathrm{S}}_{\mathrm{i}}$$

$$\mathrm{R}\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{S}={\sum }_{\mathrm{i}=\mathrm{p}}^{\mathrm{q}}{\mathrm{w}}_{\mathrm{i}}\times {\mathrm{S}}_{\mathrm{i}}$$

(7)

$$\mathrm{R}\mathrm{E}\mathrm{H}=\mathrm{R}\mathrm{R}\mathrm{S}+\mathrm{R}\mathrm{E}\mathrm{n}\mathrm{v}\mathrm{S}+\mathrm{R}\mathrm{S}\mathrm{S}+\mathrm{R}\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{S}$$

(8)

where $\mathrm{R}\mathrm{R}\mathrm{S}$, $\mathrm{R}\mathrm{E}\mathrm{n}\mathrm{v}\mathrm{S}$, $\mathrm{R}\mathrm{S}\mathrm{S},$ and $\mathrm{R}\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{S}$ are the scores of each rural ecosystem subsystem;$\mathrm{R}\mathrm{E}\mathrm{H}$ is the score of the comprehensive rural ecosystem; ${\mathrm{w}}_{\mathrm{i}}$ is the indicator weight of each subsystem; ${\mathrm{S}}_{\mathrm{i}}$ is the ecosystem health index of each indicator; p and q are the serial numbers of each indicator in the nth subsystem.

(5)

Classification of rural ecosystem health

Based on the calculated scores of ecosystem health indices, the health levels of four subsystems for each county unit were categorized into four classes: healthy, sub-healthy, unhealthy, and diseased. The formula for calculating the scores of subsystem health is as follows:

$${\mathrm{B}}_{\mathrm{i}\mathrm{j}}=\sqrt{{\displaystyle \frac{{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{({\mathrm{X}}_{\mathrm{i}\mathrm{k}}^{\prime }-{\mathrm{X}}_{\mathrm{j}\mathrm{k}}^{\prime })}^2}{\mathrm{n}}}}$$

(9)

where ${\mathrm{B}}_{\mathrm{i}\mathrm{j}}$ is the coefficient of similarity between the subsystem scores of counties i and j;${\mathrm{X}}_{\mathrm{i}\mathrm{k}}^{\prime }$ is the standardized value of the kth subsystem score of county i;${\mathrm{X}}_{\mathrm{j}\mathrm{k}}^{\prime }$ is the standardized value of the kth subsystem score of county j; n is the number of counties around the Qinling Mountains in Shaanxi Province, and n = 39.

(6)

Identification of types of rural ecosystem health

In regional rural development, the classification of various rural ecosystem health types serves as a critical initial step and plays a pivotal role in fostering the sustainable and healthy development of rural ecosystems. By identifying diverse types and levels of rural ecosystem health strengths and aligning them with the local socioeconomic context, stakeholders can better understand and address rural development challenges in a timely manner. In this study, the classification and identification of REH types were determined based on the ranking of ecosystem health within each subsystem [52]. The division criteria designed for this study, as shown in

Table 3. Criteria for the classification type of rural ecosystem health.

Health Levels of Subsystems (H)	Health Type	Diseased Levels of Subsystems (D)	Disease Type
H ≥ 3	Comprehensive health	D ≥ 3	Comprehensive disease
H = 2	Compound health	D = 2	Compound disease
H = 1	Single health	D = 1	Single disease

, were developed by referring to the criteria used by other scholars to determine ecosystem health grades, while the classification methodology is illustrated in Figure 3.

(7)

Analysis of spatial distribution of rural ecosystem health in counties

To explore the spatial distribution of integrated rural ecosystems and the health status of each subsystem in 39 counties around the study area, the spatial distribution of each subsystem was evaluated using the spatial Gini coefficient, with the following formula:

$$\mathrm{N}\mathrm{p}={\sum }_{\mathrm{q}=1}^{\mathrm{m}}\left|\mathrm{G}\mathrm{p}\mathrm{q}-\mathrm{G}\mathrm{p}/\mathrm{m}\right|/\left[{\sum }_{\mathrm{q}=1}^{\mathrm{m}}\mathrm{G}\mathrm{p}\mathrm{q}+\left(\mathrm{m}-2\right)\mathrm{G}\mathrm{p}/\mathrm{m}\right]$$

(10)

where $\mathrm{N}\mathrm{p}$ denotes the spatial Gini coefficient, $\mathrm{G}\mathrm{p}\mathrm{q}$ denotes the health score of the rural ecosystem in county q, and $\mathrm{G}\mathrm{p}$ denotes the sum of the health scores of the system p. m = 39, which is the total number of units in the counties around the studied area, and p represents the type of rural ecosystem. A larger $\mathrm{N}\mathrm{p}$ value indicates a denser spatial distribution of rural ecosystem health in the study area, while smaller values indicate a more dispersed distribution of rural ecosystem health.

(8)

Analysis of factors affecting rural ecosystem health assessment

Grey correlation analysis is commonly used to explore intricate relationships among various factors and variables [53], helping to mitigate empirical bias and subjective arbitrariness in system analysis. In this study, grey correlation analysis was used to examine the correlation types between indicators and subsystems of rural ecosystems concerning the REH status in each county. Additionally, variable conversion and selective deletion were implemented to manage interrelated variables. The calculation steps are as follows:

(1)

Select $\mathrm{X}\mathrm{p}\left(\mathrm{k}\right)$ as the reference series, and $\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)$ as the comparison series, where q = 1, 2, 3 …k, i.e., the kth number in the series;

(2)

Dimensionless: ${\mathrm{X}}^{\prime }\mathrm{q}\left(\mathrm{k}\right)=\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)/\mathrm{X}\mathrm{q}\left(1\right)$ , where q = 0, 1, 2 …k;

(3)

Find the correlation coefficient:

$$\begin{array}{c}\hfill \mathsf{\epsilon }\left(\mathrm{K}\right)=\left(\min \mathrm{q}\min \mathrm{p}\left|\mathrm{X}\mathrm{p}\left(\mathrm{k}\right)-\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)\right|+\mathsf{\rho }\max \mathrm{q}\max \mathrm{p}\left|\mathrm{X}\mathrm{p}\left(\mathrm{k}\right)-\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)\right|\right)\\ \hfill /\left(\left|\mathrm{X}\mathrm{p}\left(\mathrm{k}\right)-\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)\right|+\mathsf{\rho }\max \mathrm{q}\max \mathrm{p}\left|\mathrm{X}\mathrm{p}\left(\mathrm{k}\right)-\mathrm{X}\mathrm{q}\left(\mathrm{k}\right)\right|\right)\hspace{1em}\end{array}$$

(11)

where $\mathsf{\rho }$ is the resolution factor, taking a value in the interval [0, 1], generally 0.5.

(4)

Calculation of correlation:

$$\mathsf{\epsilon }=1/\mathrm{n}{\sum }_{\mathrm{k}=1}^{\mathrm{n}}\mathsf{\epsilon }\left(\mathrm{k}\right)$$

(12)

where ɛ takes a value in the interval [0, 1], and the higher the value, the stronger the correlation.

3. Results

3.1. Analysis of Rural Ecosystem Health Score

Complying with the REH assessment framework, the health scores of each ecosystem subsystem and the comprehensive REH scores in the counties and districts around the Qinling Mountains in Shaanxi Province were calculated based on health indices and weights of the indicators. The results are shown in

Table 4. Ecosystem health scores of 39 county units around the Qinling Mountains in Shaanxi Province, China.

County	Score
	RRS	REnvS	RSS	REcoS	REH
Chang’an	0.1469	0.1754	0.3215	0.2012	0.8449
Huyi	0.1465	0.1809	0.2958	0.2224	0.8456
Lantian	0.2046	0.1823	0.2518	0.2163	0.8550
Zhouzhi	0.1546	0.1763	0.2465	0.2078	0.7852
Lintong	0.1693	0.1747	0.2952	0.2286	0.8679
Baqiao	0.1006	0.1754	0.3057	0.2286	0.8103
Weibin	0.1666	0.1849	0.3097	0.1520	0.8133
Chencang	0.1798	0.1778	0.2320	0.2123	0.8019
Qishan	0.1656	0.1849	0.2532	0.2202	0.8238
Mei	0.1950	0.1785	0.2701	0.2024	0.8460
Feng	0.2381	0.1849	0.2204	0.1240	0.7674
Taibai	0.2188	0.1849	0.2029	0.01238	0.7304
Tongguan	0.1504	0.1769	0.2734	0.2241	0.8249
Huayin	0.1666	0.1830	0.2688	0.2275	0.8459
Huazhou	0.1672	0.1789	0.2477	0.2258	0.8196
Linwei	0.1580	0.1737	0.3076	0.2281	0.8675
Chenggu	0.2202	0.1791	0.2368	0.1986	0.8347
Yang	0.2164	0.1785	0.2140	0.1838	0.7928
Xixiang	0.2030	0.1782	0.2100	0.1712	0.7624
Mian	0.2068	0.1781	0.2344	0.1909	0.8101
Ningqiang	0.1995	0.1783	0.2277	0.1577	0.7633
Lueyang	0.1802	0.1782	0.2039	0.1542	0.7166
Liuba	0.1511	0.1792	0.1895	0.1187	0.6377
Foping	0.1742	0.1779	0.2406	0.1238	0.7165
Hantai	0.1642	0.1795	0.3067	0.2131	0.8635
Hanyin	0.2094	0.1846	0.2529	0.2113	0.8586
Shiquan	0.1849	0.1965	0.2465	0.1892	0.8171
Ningshan	0.1440	0.1848	0.2307	0.1065	0.6661
Ziyang	0.1801	0.1849	0.2222	0.1881	0.7753
Xunyang	0.2115	0.1846	0.2549	0.1713	0.8223
Hanbin	0.1857	0.1867	0.2783	0.2045	0.8552
Langao	0.1882	0.1845	0.2184	0.1507	0.7418
Shangzhou	0.1511	0.1843	0.2338	0.1531	0.7224
Luonan	0.1953	0.1840	0.2259	0.1700	0.7756
Danfeng	0.1454	0.1842	0.2386	0.1412	0.7094
Shangnan	0.1692	0.1826	0.2375	0.1409	0.7303
Shanyang	0.1678	0.1830	0.2347	0.1405	0.7261
Zhen’an	0.1910	0.1851	0.2339	0.1419	0.7520
Zhashui	0.1538	0.1845	0.2075	0.1329	0.6788
Jintai	0.1666	0.1849	0.3097	0.1520	0.8133

. The overall distribution of REH scores around the studied area ranges from 0.6377 to 0.8679, with an average health score of 0.7865 and a standard deviation of 0.0594. Thirty-seven of the forty counties score between 0.7 and 0.9, indicating that the region’s ecosystems are relatively healthy. In accordance with the REH score, we plotted the health score curves for four subsystems and the comprehensive ecosystem (Figure 4) and analyzed the spatial distribution change characteristics. As shown in Figure 4, the REH scores around the Qinling Mountains in Shaanxi Province vary greatly, showcasing a fluctuating downward trend from Guanzhong to southern Shaanxi. The average REH score of Guanzhong is 0.8562, and the standard deviation is 0.0351. The average REH score of southern Shaanxi is 0.7562, and the standard deviation Is 0.0612, indicating an overall better rural ecosystem health in Guanzhong than that in southern Shaanxi.

The health scores of the rural resource ecosystem subsystem range from 0.1006 to 0.2381, with an average of 0.1775 and a standard deviation of 0.0272, indicating that this region generally exhibits good ecosystem health. The minimum value is observed in the Baqiao District of Xi’an City, while the maximum value is in the Feng County of Baoji City. An analysis of Figure 3 reveals significant fluctuations in the health scores of the resource ecosystem subsystems across counties, showing a fluctuating upward trend from north to south.

The health scores of the rural environmental ecosystem subsystem range from 0.1737 to 0.1849, with an average of 0.1811 and a standard deviation of 0.0037. The maximum value is observed in Taibai County, Baoji City, and the minimum value is seen in the Linwei District of Weinan City.

The average health score of the social ecosystem subsystem is 0.2498, with scores for each county unit ranging from 0.1895 and 0.3215, and the highest value is observed in Chang’an District of Xi’an City. The standard deviation is 0.0342. The health scores of the rural social ecosystem subsystem for each county unit exhibit a fluctuating downward trend from north to south.

The health scores of the rural economic ecosystem subsystem range from 0.1065 to 0.2286, with an average of 0.1788 and a standard deviation of 0.0372. The maximum value is in Baqiao District of Xi’an City, while the minimum value is in Ningxia County, Ankang City. The bar demonstrates noticeable fluctuations, showing a downward trend from north to south, as illustrated in Figure 4.

3.2. Spatial Distribution of Rural Ecosystem Health Subsystems around the Qinling Mountains in Shaanxi

As shown in

Table 5. Spatial Gini coefficients of rural ecosystems and subsystems around the Qinling Mountains in Shaanxi Province, China.

Type	RRS	REnvS	RSS	REcoS	REH
Di	6.9333	7.0592	9.6819	6.9998	30.6743
Gi	0.1310	0.0197	0.1105	0.1924	0.0670

Note: D_i denotes the Dagum Gini coefficient value for each subsystem; G_i denotes the Gini coefficient value for each subsystem.

and Figure 5, the spatial Gini coefficients of rural resources, environment, society, and economy subsystems are 0.1310, 0.0197, 0.1105, and 0.1924, respectively. The spatial Gini coefficient for the integrated rural ecosystems is 0.0670. These findings indicate a lack of strong spatial clustering of the health levels of rural ecosystems and their subsystems, highlighting significant spatial variations. The spatial distribution of the health levels within the rural environmental ecosystem appears to be the least concentrated among the subsystems, whereas the rural resource and economic ecosystem subsystems exhibit more concentrated distributions, suggesting better spatial agglomeration.

As shown in Figure 6a, the spatial distribution of health grades within the rural resource ecosystem subsystem is relatively concentrated. In southern Shaanxi, the health scores of the rural resource subsystem are significantly higher than those in Guanzhong, except for Ningshan County and Zhashui County in northern Ankang, Shangzhou District and Danfeng County in central Shangluo, and Liuba County in central Hanzhong, which exhibit lower natural resource scores. Generally, the remaining counties have higher health scores and better natural resource abundance. Counties with healthy grades are mainly found in Feng and Taibai counties in Baoji City, Lantian County in Xi’an City, Chenggu, Yang, Mian, and Xixiang counties in Hanzhong City, and Hanyin and Xunyang counties in Ankang City, all boasting health scores of 0.1994 or higher. On the other hand, counties with diseased grades are mainly located in Zhouzhi County, Huyi District, Chang’an District, and Baqiao District in Xi’an City, Tongguan County in Weinan City, Liuba County in Hanzhong City, Ningshan County in Ankang City, and Shangzhou District, Danfeng County, and Zhashui County in Shangluo City, all having health scores below 0.1546. Unhealthy and sub-healthy-grade counties are distributed across all regions, mainly in the northeastern part of the study area and its surroundings. Diseased-grade county units are mostly distributed in the central region close to Guan–zhong, and healthy-grade county units are primarily distributed in southern Shaanxi. This result aligns with the changes in health scores within the rural resource ecosystem subsystem, indicating a higher degree of variability in natural resources around the Qinling Mountains.

The health grade of the rural environmental ecosystem subsystem in southern Shaanxi significantly surpasses that in Guanzhong (Figure 6b). Counties with healthy grades are mainly distributed in Feng County, Taibai County, Weibin District, and Qishan County in the south of Baoji City, all county units in Ankang City, and Shangzhou District, Luonan County, and Danfeng County in the north of Shangluo. These counties account for 41% of the total number of counties in the region, with health scores ranging from 0.1830 to 0.1849. Counties with diseased grades are mainly located in the northern part of the Qinling Mountains, including Zhouzhi County, Chang’an County, Baqiao District, Lintong District in Xi’an City, and Linwei District in Weinan City, constituting 12.8% of the total counties in the region. All counties in Hanzhong within the study area fall into the unhealthy grade. Apart from Hanzhong, all counties in Shan Nan are categorized into the healthy and sub-healthy grades, with six out of seven falling into the healthy grade.

The health grades of the rural social ecosystem subsystems vary greatly, with Guazn–hong exhibiting higher health grades than southern Shaanxi (Figure 6c). Counties with healthy grades are mainly situated in the relatively more economically developed areas, including Weibin District of Baoji City, Hantai District of Hanzhong City, Huyi, Chang’an, Baqiao, and Lintong Districts of Xi’an City, and Linwei District of Weinan City. These areas are centrally located in the northern part of the region, proximate to Guanzhong, and constitute 18% of the total counties around the Qinling Mountains. The health scores in these areas range from 0.2783 to 0.3215. The counties in this region cover a large range of unhealthy and diseased grades, with generally lower health scores for the social subsystem in the southern part of Baoji City, most of Hanzhong City, all of Shangluo City, and the northern part of Ankang City. These areas are in proximity to the west-central and southern parts of the Qinling Mountains.

The spatial distribution of the health grade of the rural economic ecosystem subsystem demonstrates a significant pattern of “poor in the middle and good in the north and south” (Figure 6d). Counties with healthy grades include Huyi District, Baqiao District, Lantian County, Lintong District in Xi’an City, Linwei District, Huazhou District, Huayin City, Tongguan County in Weinan City, Chencang District in Baoji City, Qishan County in Baoji City, Hantai District in Hanzhong City, and Hanyin County in Ankang City, predominantly distributed in Guanzhong District. These counties account for 83% of the total number of healthy-grade counties, with health scores ranging from 0.2078 to 0.2286. This trend aligns with the spatial distribution of rural per capita disposable income. Counties with diseased grades mainly include Feng County and Taibai County in Baoji City, Liuba County and Foping County in Hanzhong City, Ningshan County in Ankang City, Zhen’an County and Zhashui County in Shangluo City, Shanyang County, Danfeng County, and Shannan County, accounting for 25.6% of the total counties. These areas are mainly situated in the northern part of southern Shaanxi and the southern part of Guan–zhong.

3.3. Classification of Rural Ecosystem Health Types around the Qinling Mountains in Shaanxi Province

(1)

Analysis of the development of rural ecosystems in healthy counties

According to the classification criteria presented in Table 3 and Figure 3, healthy rural ecosystems include 16 counties of five types, constituting 40% of the total number of counties (

Table 6. Statistics on rural ecosystem health types.

Types			Number			Counties
First Class	Second Class	Third Class	First Class	Second Class	Third Class
Health type	Comprehensive health	-	-	1	-	Hanyin
	Compound health	Resource–environment type	-	6	1	Xunyang
		Resource–economic type			1	Lantian
		Environmental–social type			2	Weibin, Jintai
		Environmental–economic type			1	Qishan
		Social–economic type			1	Hantai
	Resource health	–		2	-	Chenggu, Mian
	Environmental health	–		4	-	Shiquan, Ziyang, Hanbin, Luonan
	Social health	–		0	-
	Economic health	–		3	-	Chencang, Huayin, Huazhou
Disease type	Comprehensive disease	–	-	2	-	Liuba, Zhashui
	Compound disease	Resource–environment disease type	-	6	3	Chang’an, Zhouzhi, Baqiao
		Resource–economic disease type			2	Ningshan, Danfeng
		Economic–social disease type			1	Taibai
	Resource disease	-		3	-	Huyi, Tongguan, Shangzhou
	Environmental disease	-		2	-	Lintong, Linwei
	Social disease	-		4	-	Yang, Xixiang, Langao, Lueyang
	Economic disease	-		5	-	Foping, Shangnan, Shanyang, Feng, Zhen’an
Comprehensively unhealthy type		-	2	-	-	Mei, Ningqiang

). The comprehensive health type is exemplified by Hanyin County, characterized by abundant natural resources, minimal ecological pollution, favorable environmental quality, and a relatively high level of rural economic development. Given these attributes, it should be considered a priority for strategic development to sustain healthy and sustainable growth. The compound health type includes 10 counties of five types, mainly concentrated in Guanzhong and southern Shaanxi. These counties typically exhibit favorable environmental or economic conditions but face challenges concerning natural resources and social conditions. The single health type includes 19 counties of four types, primarily concentrated in most of southern Shaanxi and some areas of northern Shaanxi. These counties possess a moderate level of ecosystem health and boast distinctive strengths in terms of resources, environment, society, and economy, although their weaknesses and shortcomings are also significant.

(2)

Analysis of the development of rural ecosystems in diseased health counties

Diseased rural ecosystems are categorized into three groups, including 21 counties, which make up 53.8% of the total number of counties. The comprehensive disease type includes two counties, Liuba County and Zhashui County, where protection measures must be implemented across rural resources, environment, society, and economy. The compound disease type includes six counties, with Chang’an District, Zhouzhi County, and Baqiao District as the resource–environment disease type, Ningshan and Danfeng Counties as the resource–economy disease type, and Taibai County as the economy–social disease type (Figure 7). The compound disease type counties primarily exhibit resource–environment characteristics and are distributed in the Guanzhong region. Resources and the environment are the main factors constraining the ecological health of these areas. The single disease type is categorized into four groups, including 13 counties, accounting for 33% of the total number of counties. Huyi District, Tongguan County, and Shangzhou District are the resource–disease type, Lintong District and Linwei District are the environment disease type, Yang County, Xixiang County, and Langao County are the social disease type, and Foping County, Feng County, Shangnan County, Shanyang County, and Zhen’an County are the economy disease type. Through practical investigation, it is evident that these areas suffer from poor resource conditions, a degraded ecological environment, underdeveloped rural economies, unbalanced industrial structures, inadequate infrastructure, and low living standards of rural residents.

According to the principle of categorization, the sub-healthy type of rural ecosystems, positioned between the healthy type and the diseased type, is categorized as other types. The comprehensively unhealthy type includes two counties, Mei County and Ningqiang County, accounting for 5.1% of the total number of counties. The ecosystem health of this type is at a low to medium level in Shaanxi Province, necessitating comprehensive management. This area is pivotal for facilitating the transition from disease types to healthy and sub-healthy types.

4. Discussion

4.1. Perspective Selection for Rural Ecosystem Health Assessment

The evaluation of rural ecosystem health should be approached holistically. OSTROM et al. emphasized that an ecosystem comprises a complex interplay of human society, economic activities, and natural conditions, embodying the essence of sustainable development through the interconnectedness of the human system and ecosystem. This holistic view encompasses five essential components: humans, resources, environment, society, and economy [12,54,55,56]. The health of a rural ecosystem can be gauged by the stability of rural production, livelihoods, and ecological spaces, which are influenced by the interactions within the intricate rural social–economic–nature system. The level of stability dictates the trajectory of development, change, and sustainable growth of the rural ecosystem [57]. Therefore, assessing the health of a rural ecosystem necessitates a comprehensive understanding of its essence, considering it as the culmination of interactions among various subsystems and human activities. The evaluation framework should integrate dimensions of social and economic subsystems alongside natural ecological subsystems. This study elucidates the concept of rural ecosystem health by emphasizing ecosystem integrity, devises a scientific and quantitative evaluation system tailored for the health of mountain rural ecosystems, and expounds the spatiotemporal patterns and classifications. This approach is crucial for addressing challenges related to ecological conservation and fostering balanced economic development in rural mountainous areas.

4.2. Effectiveness and Scientificity of the Rural Ecosystem Health Evaluation Index System

The rural ecosystem health assessment index system established in this study has proven to be effective for the research area. Compared with the existing ecosystem health evaluation system, the established rural ecosystem health evaluation index system encompasses “resource–environment–social–economy” components. The selection of indicators involved a thorough process, with 28 evaluation indicators identified through a literature review and two rounds of expert screening conducted by members of the Shaanxi Provincial Rural Ecological Protection Association. These indicators have been widely used in previous ecosystem health assessment studies, encompassing factors such as water quality [58,59], forest coverage rate [60], resident Engel coefficient [61], and per capita food production [62], making them reliable measures for characterizing rural ecosystem health. Furthermore, the health status of the rural ecosystem around the Qinling Mountains in Baoji City was assessed and compared using the Ecological Environment Status Index (EI) and the pressure–state–response (PSR) model employed by our team for health assessment (

Table 7. Comparison of ecosystem health assessment results of the rural areas around the Qinling Mountains using different evaluation systems.

Time		2021	2022	2023
Evaluation model		PSR model	EI index method	Nature–economy–society evaluation model
Health score		0.6528	80.5700	0.7865
Health status		Health	Health	Health
Evaluation level	Comprehensively healthy	(0.8, 1.0]	(≥75)	(0.9, 1.0]
	Healthy	(0.6, 0.8]	(55, 75]	(07, 0.9]
	Sub-healthy	(0.4, 0.6]	(35, 55]	(0.5, 0.7]
	Diseased	(0.2, 0.4]	(20, 35]	(0.3, 0.5]
	Comprehensively unhealthy	(0, 0.2]	(0.4, 0.6]	(<20)

). The health score of the “nature–economy–society” assessment model was calculated as 0.7865, aligning closely with scores obtained by the other two methods, indicating a consistent assessment of health status. This verification underscores the practicality, effectiveness, and scientificity of the evaluation system.

4.3. Applicability of Rural Ecosystem Health Evaluation Index System

This study introduces the innovative integration of the sustainable SDG 17 reference standard into the rural ecosystem health evaluation index, aligning well with international reference standards and evaluations. Significant spatial differentiation variations in the health levels of rural ecosystems in the rural areas around the Qinling Mountains in Shaanxi Province were identified, with rural society and resource subsystems exerting the most significant impacts (

Table 8. Correlation coefficients of the primary factors affecting ecosystem health in counties around the Qinling Mountains in Shaanxi Province.

Impact Factor	Value of Ecosystem Services per Capita	Annual per Capita Rural Electricity Consumption	Rural per Capita Expenditure on Education	Rural per Capita Disposable Income
Pearson’s correlation	0.385	0.323	0.410	0.402
Significance	0.016	0.045	0.010	0.011

). The findings exhibit both similarities and differences in comparison to previous research on rural ecosystems in Jiangsu and Chongqing, China [23,63]: (1) The similarity lies in the core influences of rural social and economic development on rural ecosystem health, which is basically consistent with the actual situation of rural development around the Qinling Mountains in Shaanxi Province. In recent years, Shaanxi Province has seen substantial improvements in rural social and economic development through initiatives promoting rural revitalization and urban–rural integration. (2) Compared with rural areas in Jiangsu, China, mountainous villages around the Qinling Mountains in Shaanxi face more prominent constraints due to transportation challenges, the flow of external production factors, and geographical locations. Consequently, economic growth and material wealth accumulation can be achieved through sustained investment in natural resources, leading to the enhancement of rural social subsystem development. The analysis indicates that the index system established in this study holds broad applicability for evaluating the health of mountainous rural ecosystems in China and other developing countries.

Rural ecosystem health is an important research topic within the field. Preliminary results have been obtained in the areas of REH evaluation, spatial distribution, and type classification in this exploratory study. Subsequent research will further enhance the examination of the dynamic evolution of REH in the vicinity of the Qinling Mountains. This will aid in gaining a more thorough comprehension of the development process of REH and uncovering its underlying principles, thereby providing a more comprehensive reference for the construction of rural areas in developing countries.

5. Conclusions

The health scores of rural ecosystems around the Qinling Mountains in Shaanxi Province were generally healthy, showing a fluctuating downward trend from Guanzhong to southern Shaanxi.

The spatial distribution of REH grades in counties around the Qinling Mountains in Shaanxi was dispersed and not concentrated. Moreover, the spatial distribution of individual subsystems appeared more concentrated, exhibiting better clustering and noticeable cluster distribution patterns.

Identification of the REH type was conducted in the 40 counties around the Qinling Mountains in Shaanxi. Healthy-type counties were mainly distributed in Guanzhong and southern Shaanxi, while diseased-type counties were predominantly distributed in the east-central areas of Guanzhong and southern Shaanxi.

The level of rural socioeconomic development is the key factor influencing the ecosystem health of counties around the Qinling Mountains in Shaanxi, China.