Coupling Coordination Evaluation of Ecological Security in Coal Resource-Exhausted Villages

Abstract

Although the exploitation of coal resources has driven regional economic growth, it has also inflicted considerable ecological damage. The sustainable development of ecological security in coal resource-exhausted villages is challenged by multiple pressures, states, and response requirements. Identifying potential risks and assessing the coupling coordination in these areas is a critical research topic for promoting their transformation and development. This study uses Jiawang District, a representative coal resource-exhausted village in China, as a case study to examine the evolution of ecological security at the rural scale from 2000 to 2021. It innovatively constructs a comprehensive evaluation model based on “resilience support—state characteristics—response mechanism” and integrates coupling coordination degree analysis with grey relational analysis to quantitatively reveal the spatio-temporal differentiation features and driving mechanisms of ecological security coupling coordination in coal resource-depleted rural areas. The findings indicate the following: (1) Between 2000 and 2021, the comprehensive ecological security index of coal resource-exhausted villages in Jiawang District exhibited a sustained upward trend; (2) The coupling coordination degree of six sampled villages across the district displayed a gradient distribution pattern characterized by “higher in the west and lower in the east, higher in the north and lower in the south”, with each unit achieving phased improvements in coordination levels; (3) Through grey relational analysis, key factors influencing the coupling coordination of coal resource-exhausted villages were identified across three dimensions—coupling coordination degree, the overall Jiawang region, and the rural scale. This study offers targeted policy recommendations for coal resource-exhausted villages at varying levels of coupling coordination.

1. Introduction

Coal is one of the most polluting fossil fuels globally and a primary environmental driver of morbidity and mortality worldwide [1]. When mineral resource extraction becomes the dominant economic activity in a region, it significantly impacts social and cultural structures, exacerbating local economic inequality [2], inducing conflicts between land use and resource environments, and profoundly reshaping the identities of local villagers [3]. This leads to an imbalance in the “socio-ecological” system and creates a vicious cycle [4]. In regions like Europe and North America, where coal mining has a long history, numerous “post-mining syndromes” have emerged [5]. For instance, in Germany’s Ruhr region, soil structure and water ecosystems have gradually deteriorated after mining, posing risks of ecological space degradation. Rose M. Mueller (2022) found that morbidity and mortality rates among villagers in surface coal production areas of Appalachia, United States, are higher than those in other rural areas [6]. Marika (2022) revealed that lead concentrations within a two-kilometer radius of mining areas are elevated, exposing rural residents to the risk of lead poisoning [7]. Furthermore, ecological damage intensifies social spatial alienation, such as large-scale unemployment caused by the closure of mining enterprises [8] and issues of justice in the forced relocation of villagers [9]. Consequently, the ecological security of resource-exhausted villages is influenced by multiple factors in both natural and social ecology. This study aims to address the following scientific questions: (1) How can a coupling coordination assessment model for the ecological security of resource-exhausted villages be constructed? (2) What are the key factors influencing the coupling coordination of ecological security subsystems in resource-exhausted villages? Finally, this study will provide targeted policy recommendations for resource-exhausted villages with varying degrees of ecological security coupling coordination.

In China, the National Development and Reform Commission (NDRC) identified 69 typical resource-exhausted cities in three batches in 2008, 2009, and 2011, involving a total population of 154 million. In 2013, the State Council issued the “National Sustainable Development Plan for Resource-Based Cities (2013–2020)” (https://www.gov.cn), providing crucial guidance for the sustainable development of various types of resource-based cities. In 2021, the State Council approved the “Development Plan for Revitalization of Special Types of Regions during the 14th Five-Year Plan Period”, further emphasizing the strategic priority of transforming, restoring ecosystems, and promoting sustainable development in resource-exhausted regions. Currently, academic research on coal resource-exhausted regions primarily focuses on social transformation and psychological issues [10], relocation and demographic characteristics [11], the lifecycle of mining areas [12], economic restructuring, and enhancing ecological resilience [13]. Among these, the ecological transformation of resource-exhausted regions has emerged as a research hotspot, encompassing ecological security assessment [14], ecosystem services [15], and optimization of ecological landscape patterns [16] in resource-exhausted regions. Although there is substantial research on ecological security in resource-exhausted regions, exploration of the dynamic evolution of ecological security at the rural scale and in specific case studies remains limited. Quantitative research on the ecological security evolution of coal resource-exhausted regions predominantly adopts a land use structure perspective, while analysis of the “human-land” feedback relationship and its evolution laws is relatively scarce.

Essentially, existing research on rural ecological security primarily focuses on three key areas: ecological security assessment, ecological security coupling coordination degree, and ecological security driving factors. In the realm of ecological security assessment, the main research methods include ecological index evaluation, ecological footprint analysis [17], ecological risk assessment [18], and ecosystem service assessment [19]. Among these, the ecological index evaluation method predominantly employs models such as PSR, DPSIR, SPSR, and their derivatives like EBM-DPSER. Most studies are concentrated at the scale of urban agglomerations, provinces, or cities, while research on specific types of rural areas remains relatively scarce, hindering the vertical transmission and effective implementation of policies. From the perspective of ecological security coupling coordination degree, previous studies have largely focused on the coupling coordination between urbanization and ecological security [20], food production and agricultural ecology [21], and ecological security and economic development [22]. However, research on the interaction and influence mechanisms among the subsystems within the ecological security system is still limited. Although some scholars have used the PSR model [23] to assess the coupling coordination degree of rural ecological security, most of these studies remain at a single dimensional level or only analyze trends over time, without comprehensively considering the “society-ecology” composite dimension [24]. Additionally, there is a lack of research that presents the overall development trend from both static and dynamic perspectives. Regarding the research on ecological security driving mechanisms, many studies combine methods such as Logistic regression, geographically weighted regression models, and geographical detectors [25], aiming to reveal the driving mechanisms of ecological security in specific regions and propose corresponding governance measures. However, current research on the driving mechanisms of ecological security in coal resource-exhausted villages areas is relatively scarce, lacking attention to the continuous changes and key influencing factors of rural-scale ecological security over long time series. Scientific analysis of the changing patterns and driving mechanisms of ecological security in coal resource-exhausted villages areas can help adopt targeted management and supervision measures that align with local ecological characteristics and laws, thereby avoiding indiscriminate policy application [26].

Jiawang District is situated in the high groundwater table and vulnerable area of the Yellow-Huaihai region in China. It is an important coal mining area in eastern China, with proven coal reserves exceeding 468 million tons, and represents a typical coal resource-exhausted region [27]. For an extended period, the dual impacts of urbanization and mining activities have led to characteristics such as complexity, dynamism, and unsustainability in resource-exhausted areas. The challenge of coordinating the conflicts between village social development and ecological protection following mineral resource exploitation remains an area requiring further in-depth research. Therefore, the primary objectives of this study are as follows: (1) to reveal the dynamic evolution and current issues of village ecological security in resource-exhausted areas through the resilience support-state characteristic-response mechanism framework; (2) to analyze the spatio-temporal coupling patterns and feedback relationships among the three subsystems—resilience support, state characteristics, and response mechanisms—in six villages from 2000 to 2021 using the coupling coordination degree model; an (3) to uncover the complex interaction mechanisms among the ecological security subsystems via systematic analysis methods, thereby providing a scientific basis and solutions for resource allocation, policy formulation, and management implementation in resource-exhausted rural areas.

The main contributions of this study are as follows: (1) This study introduces the innovative concept of “Coal Resource-Exhausted Villages (CREVs)” as a fundamental unit for assessing the evolution of village ecological security, offering a new perspective and methodological framework for the transformation and development of coal resource-exhausted regions. (2) By integrating the Social–Economic–Natural Complex Ecosystem Theory (SENCE) with the Pressure-State-Response (PSR) model, and grounded in China’s policies for the green transformation and development of coal resource-exhausted villages, this study constructs an ecological security evaluation model at the rural scale from three dimensions: resilience support, state characteristics, and response mechanisms (RS-SF-RM). (3) The grey relational analysis method is employed to investigate the impact factors of various index elements on the coupling of complex systems in coal resource-exhausted villages, addressing the challenge of quantifying small-sample and small-scale data in traditional rural ecological security research. This provides a scientific basis and policy recommendations for resource allocation, policy formulation, and management implementation in resource-exhausted villages.

Section 2 details the methodologies, current status of the study area, and data sources for evaluating ecological security and coupling coordination in coal resource-exhausted villages. Section 3 provides an in-depth analysis of the ecological security evolution in six coal resource-exhausted villages in Jiaowan District from three dimensions: resistance support, state characteristics, and response mechanisms. It also examines the coupling coordination among subsystems and identifies key factors influencing this coordination. Section 4 compares the findings of this study with those from the other relevant literature, highlighting both similarities and differences. Section 5 summarizes the research, draws conclusions, and proposes targeted solutions and policy recommendations.

2. Materials and Methods

2.1. Analysis Framework of Ecological Security in Coal Resource-Exhausted Villages

This study constructs an ecological security analysis framework for coal resource-exhausted villages (Figure 1). This framework integrates three core components: “dynamic assessment—coupling coordination—influencing factors”. First, the dynamic assessment of ecological security in coal resource-exhausted villages is conducted. This assessment model is based on the Pressure-State-Response (PSR) analysis framework [28], which aligns well with data from the “China Statistical Yearbook” [29]. Additionally, it incorporates the Social Economic Natural Complex Ecosystem (SENCE) element analysis model to represent the “society-ecology” system, thereby constructing the Resistance support-Status feature-Response mechanism (RS-SF-RM) ecological security assessment model. The SENCE theory emphasizes the integration of social, economic, and natural systems to achieve harmonious and sustainable development [14]. By recognizing the interdependence among these systems, this theoretical foundation provides a comprehensive understanding of ecological security and serves as the basis for coordinating ecological security strategies [30]. Second, the study employs the RS-SF-RM model to assess ecological security, revealing the spatio-temporal evolution characteristics of each subsystem. It also explores the coupling coordination relationships among the subsystems and analyzes the evolution characteristics and trends of the six ecological security subsystems in Jiawang District from 2000 to 2021. Finally, based on the results of the ecological security assessment and coupling coordination degree, the study identifies key factors influencing the coupling coordination at both the rural and regional scales, providing policy recommendations to enhance the ecological security of coal resource-exhausted villages.

2.1.1. Evaluation of Ecological Security in Coal Resource-Exhausted Villages

Evaluation Index System: Based on the PSR model and guided by the SENCE theory, we established an Ecological Safety Assessment (ESA) framework for coal resource-exhausted villages. To develop a comprehensive ESA index system, we identified 28 key determinants, which are organized into three subsystems: resilience support, state characteristics, and response mechanisms (Table 1).

Resilience support (RS) is primarily reflected in three key dimensions: social resilience, resource resilience, and environmental resilience. Social resilience encompasses factors such as population structure, age distribution, and medical security [31]. Additionally, the assessment of resource resilience indicates that per capita water and electricity consumption in rural areas can have a negative impact on overall resilience support. Resource resilience refers to the total amount of resources required to sustain normal production and living activities in village areas. It is measured using indicators such as per capita cultivated land area and per capita water and electricity consumption, which reflect the dependence and utilization efficiency of rural areas on natural resources [32]. Regarding environmental resilience, the villages land stress index describes the physical relationship between cultivated land and construction land areas, while the proportion of resource extraction quantifies the extent of human impact on the natural environment [33].

State features (SFs) reflect an ecosystem’s self-sustaining capacity [6]. According to the guidelines for socio-economic and ecological transformation in resource-exhausted regions (https://www.gov.cn), at the rural scale, state features primarily encompass economic conditions, resource conditions, and environmental conditions. Specifically, the economic condition must align with China’s “green and clean production” standards for new rural development (https://www.ndrc.gov.cn, accessed on 25 September 2023). It is quantified by per capita GDP, the ratio of secondary and tertiary industries, and the proportion of GDP derived from resource extraction [23,29,34]. The resource condition, a defining feature of resource-exhausted villages, includes natural resources, vegetation, and wildlife, adhering to China’s policies on resource utilization and conservation. Lastly, the environmental condition is assessed based on landscape structure and environmental quality, incorporating metrics such as the Shannon diversity index and sprawl index [35].

Response mechanism (RM) primarily manifests in two dimensions: environmental response and technological response. In this study, we focus on the “response” aspect to quantify differences in ecological security restoration levels. To ensure a scientific and quantitative evaluation of environmental protection policies, we utilized key indicators such as rural sewage treatment rates and the degree of ecological function recovery, which are commonly reported in government work reports [29]. For the technological response dimension, we selected the proportion of professional teachers per 10,000 people and the proportion of scientific and technological personnel per 10,000 people as metrics to assess rural areas’ investment in local talent development and high-quality growth [33,36]. This selection is grounded in the observed similarity between the spatial distribution of education levels and socio-economic conditions in rural areas [37].

Table 1. Evaluation index system of ecological security in coal resource-exhausted villages.

System	Subsystem	Indicator	Connotation	Weight	Reference
Resistance support (RS)	Social Resilience	P11 Population density (−)	Registered population/Administrative village area	0.0355	Rui Zhang [29]
		P12 Proportion of elderly population (−)	Number of elderly population/Total rural population	0.0288	Xiaozhou [23]
		P13 Number of medical and health beds (+)	Total number of medical beds at all levels in rural areas	0.0379	Meizhu Hou [38]
	Resource Resilience	P21 Arable land per capita (+)	Total cultivated land area/Total population	0.0479	Yuqiu Jia [39]
		P22 Per capita domestic water consumption (−)	Total domestic water consumption/Total population	0.0333	Xiaozhou [23]
		P23 Per capita domestic electricity consumption (−)	Total domestic electricity consumption/Total household population	0.0346	Xiaozhou [23]
	Environment Resilience	P31 Degree of land stress (−)	Reduction of arable land/Expansion of construction land	0.0371	Meizhu Hou [38]
		P32 Degree of control over environmental events (+)	Initiative and efficiency in environmental protection and ecological crisis response	0.0321	Xuebin Zhang [40]
		P33 Proportion of resource extraction (−)	Dominant resource exploitation area/Rural arable area	0.0303	Eelu Wang [33]
Status feature (SF)	Economic Status	S11 Per capital GDP (−)	Gross GDP/Total population	0.0341	Rui Zhang [29]
		S12 Ratio of secondary and tertiary industries (−)	Total amount of secondary industry/Total amount of tertiary industry	0.0366	Xiaozhou [23]
		S13 Mining industry as a percentage of GDP (−)	GDP of coal resources exploitation/Gross domestic product	0.0280	Eelu Wang [33]
	Resource Status	S21 Species richness index (+)	Sum of the number of different species in an ecosystem or community	0.0505	Changxue Wu [41]
		S22 Forest and grass coverage rate in built-up area (+)	Total area of woodland and grassland/Total land use	0.0494	Xiaozhou [23]
		S23 Proportion of main resources consumed (−)	Major resources extracted/Total proven resources	0.0376	Meizhu Hou [38]
	Environment Status	S31 Water environment quality (−)	Refers to the annual number of water pollution incidents	0.0282	Rui Zhang [29]
		S32 Air environmental quality (−)	Refers to the number of times the concentration of pollutants in the atmosphere exceeds the standard	0.0228	Rui Zhang [29]
		S33 Acoustic environmental quality (+)	Refers to the number of major noise polluters in rural areas	0.0212	Libang Ma [42]
		S34 Shannon’s diversity index (−)	A higher value indicates a higher species richness in that ecosystem or community	0.0557	Libang Ma [42]
		S35 CONTAG (+)	The higher the value, the better the connectivity between patches of the type	0.0368	Yuqiu Jia [39]
		S36 Landscape edge density (−)	Length of boundary around various landscape patches/total landscape area	0.0451	Libang Ma [42]
Response mechanism (RM)	Environment Response	R11 Domestic sewage treatment rate (+)	The amount of sewage treated annually/The total amount of domestic sewage in the year	0.0264	Rui Zhang [29]
		R12 Harmless treatment rate of household garbage (+)	Amount of waste/Total amount of domestic waste treated harmlessly	0.0294	Meizhu Hou [38]
		R13 Degree of ecological function restoration (+)	Refers to the number of returning or new species/Number of lost species	0.0390	Changxue Wu [41]
		R14 Landscape Shape Index (−)	Complexity of landscape structure: the higher the value, the higher the complexity	0.0443	Changxue Wu [41]
		R15 Perimeter-area fractal dimension (+)	The higher the value, the more complex the landscape structure and the wider the distribution	0.0367	Yuqiu Jia [39]
	Technology Response	R21 Proportion of professional teachers (+)	Number of professional teachers/Total population	0.0279	Ma Xiaobin [36]
		R22 Proportion of scientific and technological personnel (+)	Number of scientific and technical personnel/Total population	0.0323	Eelu Wang [33]

Table 1. Evaluation index system of ecological security in coal resource-exhausted villages.

System	Subsystem	Indicator	Connotation	Weight	Reference
Resistance support (RS)	Social Resilience	P11 Population density (−)	Registered population/Administrative village area	0.0355	Rui Zhang [29]
		P12 Proportion of elderly population (−)	Number of elderly population/Total rural population	0.0288	Xiaozhou [23]
		P13 Number of medical and health beds (+)	Total number of medical beds at all levels in rural areas	0.0379	Meizhu Hou [38]
	Resource Resilience	P21 Arable land per capita (+)	Total cultivated land area/Total population	0.0479	Yuqiu Jia [39]
		P22 Per capita domestic water consumption (−)	Total domestic water consumption/Total population	0.0333	Xiaozhou [23]
		P23 Per capita domestic electricity consumption (−)	Total domestic electricity consumption/Total household population	0.0346	Xiaozhou [23]
	Environment Resilience	P31 Degree of land stress (−)	Reduction of arable land/Expansion of construction land	0.0371	Meizhu Hou [38]
		P32 Degree of control over environmental events (+)	Initiative and efficiency in environmental protection and ecological crisis response	0.0321	Xuebin Zhang [40]
		P33 Proportion of resource extraction (−)	Dominant resource exploitation area/Rural arable area	0.0303	Eelu Wang [33]
Status feature (SF)	Economic Status	S11 Per capital GDP (−)	Gross GDP/Total population	0.0341	Rui Zhang [29]
		S12 Ratio of secondary and tertiary industries (−)	Total amount of secondary industry/Total amount of tertiary industry	0.0366	Xiaozhou [23]
		S13 Mining industry as a percentage of GDP (−)	GDP of coal resources exploitation/Gross domestic product	0.0280	Eelu Wang [33]
	Resource Status	S21 Species richness index (+)	Sum of the number of different species in an ecosystem or community	0.0505	Changxue Wu [41]
		S22 Forest and grass coverage rate in built-up area (+)	Total area of woodland and grassland/Total land use	0.0494	Xiaozhou [23]
		S23 Proportion of main resources consumed (−)	Major resources extracted/Total proven resources	0.0376	Meizhu Hou [38]
	Environment Status	S31 Water environment quality (−)	Refers to the annual number of water pollution incidents	0.0282	Rui Zhang [29]
		S32 Air environmental quality (−)	Refers to the number of times the concentration of pollutants in the atmosphere exceeds the standard	0.0228	Rui Zhang [29]
		S33 Acoustic environmental quality (+)	Refers to the number of major noise polluters in rural areas	0.0212	Libang Ma [42]
		S34 Shannon’s diversity index (−)	A higher value indicates a higher species richness in that ecosystem or community	0.0557	Libang Ma [42]
		S35 CONTAG (+)	The higher the value, the better the connectivity between patches of the type	0.0368	Yuqiu Jia [39]
		S36 Landscape edge density (−)	Length of boundary around various landscape patches/total landscape area	0.0451	Libang Ma [42]
Response mechanism (RM)	Environment Response	R11 Domestic sewage treatment rate (+)	The amount of sewage treated annually/The total amount of domestic sewage in the year	0.0264	Rui Zhang [29]
		R12 Harmless treatment rate of household garbage (+)	Amount of waste/Total amount of domestic waste treated harmlessly	0.0294	Meizhu Hou [38]
		R13 Degree of ecological function restoration (+)	Refers to the number of returning or new species/Number of lost species	0.0390	Changxue Wu [41]
		R14 Landscape Shape Index (−)	Complexity of landscape structure: the higher the value, the higher the complexity	0.0443	Changxue Wu [41]
		R15 Perimeter-area fractal dimension (+)	The higher the value, the more complex the landscape structure and the wider the distribution	0.0367	Yuqiu Jia [39]
	Technology Response	R21 Proportion of professional teachers (+)	Number of professional teachers/Total population	0.0279	Ma Xiaobin [36]
		R22 Proportion of scientific and technological personnel (+)	Number of scientific and technical personnel/Total population	0.0323	Eelu Wang [33]

Data standardization and comprehensive weight calculation: The entropy weight method has strong objectivity in determining the weights of indicators. It measures the randomness of events and the effective information volume of data through the information entropy value, which can largely avoid the interference of human factors [43]. Considering the uncertainty of the data in the ecological security evaluation index system of coal resource-exhausted villages, this study adopts the entropy weight method to determine the weights of each influencing factor from an objective perspective. The specific steps are as follows: Firstly, the data standardization method (Equations (1) and (2)) is used to eliminate the dimensional differences and normalize each piece of data to the [0, 1] interval; Secondly, the entropy weight method is applied to calculate the weight values of each index system (Equations (3)–(5)); Finally, the ecological security assessment (ESA) index is calculated through the weighted sum model (Equation (6)).

Positive indicators:

$$X_i^{\prime }={\displaystyle \frac{X_i-X_{min}}{X_{imax}-X_{imin}}}$$

(1)

Negative indicators:

$$X_i^{\prime }={\displaystyle \frac{X_{\max }-X_i}{X_{i\max }-X_{i\min }}}$$

(2)

In the formula, $X_i^{\prime }$ denotes the standardized value; $X_i$ represents the original value of the index; and $X_{imax}$ and $X_{imin}$ correspond to the maximum and minimum values of the indicator, respectively.

$$P_{ij}={\displaystyle \frac{X_{ij}}{{{\displaystyle \sum }}_{i=1}^m{X}_{ij}}}$$

(3)

$$e_j=-1/\ln (n){\displaystyle \sum }_{i=1}^n{p}_{ij}\ln p_{ij}$$

(4)

$$W_j={\displaystyle \frac{1-e_j}{{{\displaystyle \sum }}_{j=1}^n\left(-e_i\right)}}$$

(5)

Here, $P_{ij}$ represents the weight of the $j$ indicator for the evaluated object; $e_j$ is the entropy of the $j$ indicator; $n$ is the indicator number; $W_j$ is the weight of the indicator; and $X_{ij}$ is the score of the $j$ indicator for the evaluated object.

$${\mathrm{E}\mathrm{S}\mathrm{A}}_i=\sum _{j=1}^n{X}_{ij}^{\prime }·W_j$$

(6)

Among them, $W_j$ denotes the weight of the $j$ indicator, and $n$ represents the total number of indicators.

2.1.2. Coupling Coordination Degree Analysis

The coupling coordination degree evaluation model aims to quantify the level of coupling and coordination among systems [44]. Its calculation formulas are:

$$C=3\times {\{{\displaystyle \frac{F(x)\times F(y)\times F(z)}{[F(x)+F(y)+F(z){]}^3}}\}}^{{\displaystyle \frac{1}{3}}}$$

(7)

$$T=\alpha F(x)+\beta F(x)+\gamma F(x)$$

(8)

$$D=(C\times T{)}^{1/2}$$

(9)

In the formulas, C denotes the coupling degree of the resilience support-state feature–response mechanism system; D denotes the coupling coordination degree of this system; and T denotes the comprehensive development index of this system. α, β, and δ represent the weights of the resilience support subsystem, state feature subsystem, and response mechanism subsystem, respectively, satisfying α > 0, β > 0, δ > 0, and α + β + δ = 1. The values of the coupling degree and coupling coordination degree range from 0 to 1. A value closer to 1 indicates a higher level of coupling coordination between the systems, while a lower value suggests weaker coordination. Based on the existing literature, this study has established a grading system and discrimination criteria for the coupling coordination degree (

Table 2. Criteria for coupling coordination degree.

Coupling Coordination Degree	Coordination Level	Coupling Coordination Degree	Coordination Level
0.0 < D ≤ 0.1	Serious Disorder (I)	0.5 < D ≤ 0.6	Forced Coordination (VI)
0.1 < D ≤ 0.2	Severe Disorder (II)	0.6 < D ≤ 0.7	Primary Coordination (VII)
0.2 < D ≤ 0.3	Moderate Disorder (III)	0.7 < D ≤ 0.8	Intermediate Coordination (VIII)
0.3 < D ≤ 0.4	Mild Disorder (IV)	0.8 < D ≤ 0.9	Good Coordination (IX)
0.4 < D ≤ 0.5	Borderline Disorder (V)	0.9 < D ≤ 1.0	Quality Coordination (X)

).

2.1.3. Grey Relational Analysis

Grey relational analysis (GRA) quantifies the factors within a system and compares the statistical relationships among research sequences to evaluate the degree of correlation among multiple factors [45]. Within a system, if two factors exhibit consistent change trends, their correlation degree is higher; conversely, it is lower. The calculation steps include determining the analysis sequence, conducting dimensionless processing of the sequence, calculating the correlation coefficient, computing the correlation degree, and ranking the correlation degrees. The primary calculation formulas are as follows:

$$x_i(k)={\displaystyle \frac{X_i(k)}{X_i(l)}}$$

(10)

$${\xi }_i(k)={\displaystyle \frac{{\min }_i{\min }_k{\mathsf{\Delta }}_i(k)+\rho {\max }_i{\max }_k{\mathsf{\Delta }}_i(k)}{{\mathsf{\Delta }}_i(k)+\rho {\max }_i{\max }_k{\mathsf{\Delta }}_i(k)}}$$

(11)

$$R_i={\displaystyle \frac{{\sum }_{k=1}^n{\xi }_i(k)}{n}}$$

(12)

In the formulas, ${\xi }_i(k)$ denotes the correlation coefficient, and $R_i$ denotes the grey correlation degree. The larger the value of $R_i$, the greater the influence of the sub-data series on the parent data series. Consequently, by comparing these values, the ranking of influencing factors for the coupling coordination degree can be determined.

2.2. Study Area

Jiawang District (34°17′~34°32′ N, 117°17′~117°42′ E) is located in the northeast direction of Xuzhou City, Jiangsu Province, China, with a total area of 612 square kilometers (Figure 2). Jiawang District is located in the transition zone between mountains and hills and the Huang-Huai impact plain. The terrain is mainly hilly and plain, showing that the northern region is higher than the southern region and the western region is higher than the eastern region. Jiawang coal mining has a history of more than 130 years, and in its heyday, there were 252 mines of different sizes in the region [46]. In 2011, Jiawang was recognized by the state as the only resource-exhausted city in Jiangsu Province, and it has high influence and representation in Chinese and even international academic circles [47,48,49]. It has 10 villages, namely, Jiangzhuang, Qingshanquan, Pananhu, Laokuang, Dawu, Daquan, Gongyeyuan, Zizhuang, Tasan, and Biantang. Six of these villages (three-fifths of the total) are dominated by coal mining. However, under the dual impact of the depletion of coal resources and the “two-carbon” target, the “sequelae of coal mining”, such as a rising unemployment rate, population outflow, environmental deterioration, geological subsidence, and soil erosion, are particularly obvious in Qingshanquan, Pananhu, Laokuang, Dawu, Daquan, and Zizhuang, which are six coal resource-depleted villages [34].

2.3. Data Sources

The data years used in this study are 2000, 2004, 2008, 2012, 2016, and 2021. Recognizing the critical role of coal mining subsidence wetlands in the ecological security of resource-exhausted rural areas, land use types were reclassified into cultivated land, forest land, grassland, water bodies, construction land, subsidence wetlands, and unused land. All spatial data were transformed to the WGS84_Albers projection coordinate system [50]. Landscape pattern indices were calculated based on supervised land use classification from remote sensing data, which were reprocessed using ENVI5.3 software [51]. Statistical yearbooks, socio-economic data, mineral resource information, and geological safety records were provided by local government departments (

Table 3. Data sources in this study.

Type	Data Type	Resolution	Data Sources
DEM	Raster data	30 m	Geospatial data cloud Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) dataset (https://urs.earthdata.nasa.gov/)
Land use/cover data	Raster data	30 m	Resource and Environmental Science and Data Center of the Chinese Academy of Science (https://www.resdc.cn)
Population density data	Raster data	1 km	World Pop (https://hub.worldpop.org)
Socio-economic data	Vector data	County level	China County Statistical Yearbook, and statistical yearbook data of Jiawang (https://www.xz.gov.cn)
Administrative division boundary data	Vector data	County level	Resource and Environmental Science and Data Center of the Chinese Academy of Science (https://www.resdc.cn)

).

3. Results

3.1. Evolution Characteristics of Ecological Security in CREVs

This study selected six coal resource-exhausted villages in Jiawang District as samples. Based on the 28 indicators of the ecological security assessment system for coal resource-exhausted villages, the entropy weight TOPSIS model was employed to determine the weights of each indicator. Using Equations (1)–(6), we calculated the comprehensive evaluation results of ecological security in Jiawang District from 2000 to 2021 (Figure 3), as well as the evaluation results for the three subsystems—resilience support, state characteristics, and response mechanism—in the six coal resource-exhausted villages (Figure 4, Figure 5 and Figure 6).

As shown in Figure 3, the comprehensive index of “resilience support—state characteristics—response mechanism” for ecological security in the coal resource-exhausted villages of Jiawang District exhibited a continuous upward trend from 2000 to 2021. Notably, the state characteristics index showed the most significant increase, rising from 0.3092 in 2000 to 0.7058 in 2021. Particularly during the period from 2012 to 2021, the state characteristics index grew rapidly, with an increase of 27.45%. This growth can be attributed primarily to a series of comprehensive guiding policies issued by Jiangsu Province, Xuzhou City, and Jiawang District in 2012, such as the “Opinions on Supporting the Transformation and Development of Resource-Exhausted Cities in Jiawang District, Xuzhou City” and the “Jiawang District’s Transformation and Development Plan for Resource-Exhausted Cities (2012–2020)” (http://www.jiangsu.gov.cn). These policies encompassed measures like ecological environment improvement, development of alternative industries, management of subsidy funds, scientific and technological innovation, and talent introduction, which significantly enhanced the ecological security capacity of the coal resource-exhausted villages in Jiawang District. In contrast, the growth of the response mechanism was relatively slow during the period from 2004 to 2008, with only a 3.26% increase. The reasons for this slower growth were twofold: first, there was limited experience in transforming coal resource-exhausted villages in China at that time, and relevant policies and ecological compensation systems were not yet fully developed; second, the closure of coal mines led to mass unemployment, prompting local governments to prioritize economic development over environmental governance to alleviate social conflicts.

3.1.1. Resistance Support Assessment Results

As shown in Figure 4, during the period from 2000 to 2021, all coal resource-exhausted villages exhibited a continuous upward trend in their ecological security resilience support index, with the exception of ZZV, which experienced a slight decline. Notably, there were significant differences among the villages. The resilience support index of QSV consistently ranked first, primarily due to its abundant natural resources, such as mountains, forests, and rivers, and its minimal disturbance from human activities like resource exploitation. In contrast, DWV’s overall resilience support level was relatively low, standing at 0.1199 in 2000, which was 25.22% below the average resilience level of Jiawang District that year. As coal resources gradually depleted, small coal mines in DWV were shut down, leading to a rapid increase in its resilience support index from 2008 to 2012, with an increase of 12.69%, significantly outpacing the growth rates of LKV (4.54%), DQV (5.49%), and PAV (12.11%) during the same period. Additionally, LKV’s ecological security resilience level consistently ranked among the bottom two, with a relatively slow growth rate from 2004 to 2016.

3.1.2. Status Feature Assessment Results

As shown in Figure 5, the ecological security state characteristic index of coal resource-exhausted villages in Jiawang District exhibited a steady upward trend, with an overall growth rate of 29.66%. This indicates that the series of environmental protection and ecological governance measures implemented in Jiawang District, such as ecological wetland restoration and soil improvement in subsidence areas, have significantly contributed to the steady enhancement of the villages’ ecological security status. From 2000 to 2021, DQV’s ecological security state characteristic consistently ranked higher than those of other villages, with a growth rate of 33.96%. This can be attributed primarily to its high forest and grassland coverage, reasonable proportions of secondary and tertiary industries, and relatively balanced social–ecological development. During the period from 2012 to 2021, PAV’s ecological security state characteristic index increased rapidly, with an increase of 24.01%. By 2021, the overall ecological security state characteristic levels of ZZV, LKV, QSV, and PAV were relatively close. Despite the introduction of the “Ecological Civilization Construction” strategy, the coal resource-exhausted villages in Jiawang District continued to pursue green transformation paths such as “Ecology + Industry”, “Ecology + Technology”, and “Ecology + Tourism”, which has also contributed to the reduction in differences among the villages.

3.1.3. Response Mechanism Assessment Results

From 2000 to 2021, the ecological security response mechanism index in the rural areas of Jiawang District exhibited a fluctuating upward trend. This indicates that there were differences in the transformation and development positioning, as well as policy investments, for different coal resource-exhausted Villages during this period. The varying shutdown times of coal enterprises and differing ecological compensation policies across villages, combined with the fact that sustainable social–ecological development in resource-based villages remains in an exploratory stage, contributed to these differences. As shown in Figure 6, the changes in the ecological security response mechanism levels in Jiawang District’s villages can be categorized into two types: “fluctuating climbing” and “steady rising”. Specifically, LKV and DWV belong to the “fluctuating climbing” type, while DQV, PAV, QSV, and ZZV fall into the “steady rising” category. In 2004, LKV had the highest ecological security response mechanism index at 0.7134, followed by DWV at 0.5770 and DQV at 0.557. As the village with the highest levels of urbanization and industrialization in Jiawang District, LKV benefited from relatively stable local fiscal environmental protection funding during the early stages of green transformation and from the spatial spillover effects of resources such as technology and talent to neighboring villages.

3.2. Coupling Coordination Degree Spatio-Temporal Evolution of Ecological Security

Based on Equations (7)–(9), the coupling coordination degree among the three subsystems of “resilience support”, “state characteristics”, and “response mechanism” in the ecological security of coal resource-exhausted villages in Jiawang District from 2000 to 2021 was calculated. This study examined the temporal trends and spatial distribution of coupling coordination in these villages.

3.2.1. Coupled Coordination Time Series Analysis of CREVs

As shown in

Table 4. Coupling coordination degree and coordination level of rural ecological security subsystem in Jiawang District with coal resource-exhausted villages.

Year	2000	2004	2008	2012	2016	2021
Daquan	0.4598	0.5242	0.6100	0.6804	0.7148	0.7886
Laokuang	0.4217	0.4651	0.5315	0.5982	0.6599	0.7542
Dawu	0.4350	0.4500	0.5359	0.5799	0.6243	0.6599
Pananhu	0.3527	0.4082	0.5832	0.6746	0.7823	0.8135
Qingshanquan	0.4132	0.4273	0.5768	0.6402	0.7045	0.7211
Zizhuang	0.3924	0.4432	0.4979	0.5724	0.6025	0.6145
Jiawang	0.4125	0.4397	0.5559	0.6276	0.6814	0.7253

, the Coupling Coordination Degree (CCD) of ecological security among the “Resilience Support”, “State Characteristics”, and “Response Mechanism” subsystems in Coal Resource-Exhausted Villages in Jiawang District increased from 0.5631 in 2000 to 0.7182 in 2021. The coordination level evolved from “mild disorder” to “good coordination”, progressing through the stages of mild disorder, near disorder, barely coordinated, primary coordination, moderate coordination, and good coordination. Overall, this indicates a positive growth trend. However, from 2016 to 2021, DAV and ZZV remained at the primary coordination stage, while DQV and QSV maintained a moderate coordination state. This suggests that there is still room for improvement in the coordinated development of ecological security in these villages. Specifically, the CCD of the six villages in Jiawang District consistently exhibited a year-by-year increasing trend.

The coupling coordination degree of DQV increased from 0.4598 to 0.7886 between 2000 and 2021, reflecting a marked enhancement in the coordination among the three subsystems of its ecological security. This transition moved the system from an initial “mildly disordered” state to one of “good coordination”. The coupling coordination degree of LKV ranged from 0.4217 to 0.7542, with an average value of 0.5718. Despite starting at a relatively low level, its growth trend paralleled that of DQV, indicating progress in achieving coordinated development in ecological security. For DWV, the coupling coordination degree rose from 0.4350 to 0.6599. Although this increase was slightly less pronounced than that of DQV, it still demonstrated a positive shift toward better ecosystem coordination. PAV exhibited the most substantial improvement, with its coupling coordination degree increasing from 0.3527 to 0.8135 (Average: 0.6024). This significant rise suggests that effective ecological restoration measures implemented after resource depletion have led to notable advancements. Lastly, the coupling coordination degrees of QSV and ZZV increased from 0.4132 and 0.3924 to 0.7211 and 0.6145, respectively. While their growth rates were modest, they nonetheless indicated a gradual optimization trend.

According to the change in the coupling coordination degree of Jiawang coal resource-exhausted villages, they can be divided into three categories: high coupling level area, more balanced coupling level area, and lagging collaborative development area. Figure 7 shows that DQV and PAV, as regions with high coupling levels, both reach a high level of coupling coordination in 2021, showing strong synergistic effects of toughness support, state characteristics, and response mechanisms. Secondly, LKV and DWV belong to regions with relatively balanced coupling levels, and their coupling coordination degree has increased, but the overall level is still in the medium stage. In the future, it is necessary to continue to strengthen the integration between ecological restoration and resource allocation subsystems. Finally, QSV and ZZV are still in the lagging stage of coordinated development, and the growth rate is smaller than that of other villages. It is suggested to increase the support for ecological environment governance and green industry transformation.

3.2.2. Spatial Coupling Coordination Degree Analysis of CREVs

The spatial distribution characteristics of the “Resistance Support-Status Feature-Response Mechanism” (RS-SF-RM) coupling coordination in the ecological security of six coal resource-exhausted villages in Jiawang District were depicted using ArcGIS10.8 software (Figure 8). From 2000 to 2021, the coupling coordination of ecological security in these villages significantly improved. The improvement radiated outward from the villages along the Xuzhou-Jiaowan Expressway, with the radiation effect gradually weakening as the distance increased. Overall, the spatial distribution of the coupling coordination degree in Jiawang District exhibited a pattern of “higher in the west and lower in the east” and “higher in the north and lower in the south”. In 2008, the spatial differentiation of the coupling coordination of ecological security in the coal resource-exhausted villages was relatively pronounced. The coordination level was predominantly at the barely coordinated stage, with the coupling coordination degree decreasing from DQV towards the east and west. Due to its greater distance from the main urban area of Xuzhou and lower accessibility, ZZV’s ecological security system development lagged behind. By 2012, the spatial differentiation gap in coupling coordination among the villages gradually narrowed. All six coal resource-exhausted villages entered the barely coordinated and primary coordinated stages, with PAV showing a significant increase in coupling coordination. Overall, the trend of coupling coordination growth was evident, and the spatial agglomeration effect continuously strengthened, leading to a narrowing of the spatial differentiation gap. Notably, from 2004 to 2021, the central village for ecological security in Jiawang District shifted from DQV to PAV. This shift was primarily due to PAV becoming a national pilot area for coal trace land ecological restoration. The local government significantly increased investment in funds, technology, and talent. Additionally, the Pan’an Lake National Wetland Park provided an important ecological source, supporting the stable development of ecological security in PAV.

3.3. Influencing Factors of Coupling Coordination Degree of Ecological Security

Based on relevant research findings, the grey relational degree is categorized into four levels: low relational degree [0, 0.35), medium relational degree [0.35, 0.65), relatively high relational degree [0.65, 0.85), and high relational degree [0.85, 1.0]. Using Equations (10)–(12), the influencing factors of the coupling coordination degree of the ecological security system in the six coal resource-exhausted villages in Jiawang District from 2000 to 2021 were calculated. The results of these calculations are presented in

Table 5. Influencing factors and correlation degree of Coupling Coordination Degree in Jiawang.

Indicator		Coupling Degree Excellent Region			Coupling Degree Equilibrium Region			Coupling Degree Lag Region			Jiawang
		DQV	PAV	Avg	LKV	DWV	Avg	QSV	ZZV	Avg
Resistance support (RS)	P11	0.74	0.730	0.735	0.545	0.600	0.573	0.619	0.619	0.619	0.642
	P12	0.709	0.732	0.721	0.689	0.589	0.639	0.619	0.572	0.596	0.652
	P13	0.714	0.765	0.740	0.670	0.599	0.635	0.693	0.570	0.632	0.669
	P21	0.694	0.694	0.694	0.585	0.599	0.592	0.619	0.605	0.612	0.633
	P22	0.744	0.759	0.752	0.701	0.722	0.712	0.610	0.593	0.602	0.688
	P23	0.772	0.732	0.752	0.710	0.561	0.636	0.679	0.576	0.628	0.672
	P31	0.710	0.728	0.719	0.748	0.561	0.655	0.661	0.593	0.627	0.667
	P32	0.813	0.715	0.764	0.703	0.669	0.686	0.645	0.593	0.619	0.690
	P33	0.702	0.525	0.614	0.706	0.721	0.714	0.644	0.581	0.613	0.647
Status feature (SF)	S11	0.798	0.776	0.787	0.707	0.598	0.653	0.619	0.593	0.606	0.682
	S12	0.580	0.525	0.553	0.670	0.521	0.596	0.592	0.691	0.642	0.597
	S13	0.603	0.702	0.653	0.625	0.545	0.585	0.557	0.683	0.620	0.619
	S21	0.680	0.739	0.710	0.737	0.621	0.679	0.635	0.593	0.614	0.668
	S22	0.680	0.762	0.721	0.781	0.832	0.807	0.668	0.593	0.631	0.719
	S23	0.811	0.702	0.757	0.691	0.667	0.679	0.640	0.593	0.617	0.684
	S31	0.751	0.759	0.755	0.726	0.665	0.696	0.703	0.593	0.648	0.700
	S32	0.750	0.838	0.794	0.706	0.628	0.667	0.662	0.593	0.628	0.696
	S33	0.765	0.794	0.780	0.670	0.667	0.669	0.686	0.593	0.640	0.696
	S34	0.736	0.747	0.742	0.684	0.620	0.652	0.638	0.549	0.594	0.662
	S35	0.715	0.702	0.709	0.708	0.650	0.679	0.649	0.593	0.621	0.670
	S36	0.650	0.702	0.676	0.695	0.517	0.606	0.601	0.593	0.597	0.626
Response mechanism (RM)	R11	0.773	0.722	0.748	0.670	0.598	0.634	0.641	0.619	0.630	0.671
	R12	0.754	0.718	0.736	0.670	0.610	0.640	0.668	0.619	0.644	0.673
	R13	0.782	0.718	0.750	0.708	0.668	0.688	0.651	0.565	0.608	0.682
	R14	0.720	0.739	0.730	0.670	0.654	0.662	0.771	0.562	0.667	0.686
	R15	0.731	0.702	0.717	0.670	0.529	0.600	0.661	0.570	0.616	0.644
	R21	0.803	0.702	0.753	0.670	0.640	0.655	0.693	0.553	0.623	0.677
	R22	0.707	0.902	0.805	0.706	0.561	0.634	0.644	0.523	0.584	0.674

Note: Black bold in the table is the top five indicators with the highest correlation degree.

.

According to Table 5, the correlation degree of the 28 ecological security indicators in coal resource-exhausted villages with respect to the coupling coordination degree of resilience support, state characteristics, and response mechanisms falls within the range (0.65, 0.85), indicating that all indicators are at least in the medium-correlation interval or higher. Specifically, 21 indicators (75% of the total) fall within the higher-correlation interval, while 7 indicators (25%) are in the medium-correlation interval. These results suggest that ecological security exerts a substantial influence on the coupling coordination degree of the subsystems in coal resource-exhausted villages.

From the perspective of coupling coordination levels, regions with an excellent coupling degree primarily include DQV and PAV. The top three indicators in terms of grey correlation degree are the proportion of scientific and technological personnel (0.805), air environmental quality (0.794), and per capita GDP (0.787). Among these, two indicators fall under the state characteristic dimension, suggesting that the state characteristics of ecological security significantly influence the coupling coordination degree of subsystems in regions with an excellent coupling degree, highlighting areas requiring urgent development and improvement. Regions with an equilibrium coupling degree mainly consist of LKV and DWV. The top three indicators in terms of the grey correlation degree are the forest and grass coverage rate in built-up areas (0.807), the proportion of resource exploitation area (0.714), and the per capita domestic water consumption (0.712). Unlike the key influencing factors in regions with an excellent coupling degree, resilience support plays the most critical role in determining the coupling coordination degree of ecological security subsystems in equilibrium coupling regions. Lagging coupling regions primarily include QSV and ZZV. The top three factors in terms of the grey correlation degree are the landscape shape index (0.667), water environmental quality (0.648), and harmless treatment rate of domestic waste (0.644). Two of these indicators belong to the response mechanism dimension, indicating that the response mechanism is the primary factor affecting the coupling coordination degree of subsystems in lagging coupling regions. In particular, enhancing the technological response to coal resource depletion is essential for these regions.

From the overall perspective of Jiawang District, the top three indicators in terms of the grey relational degree are S22 (0.719), S31 (0.7), and S32 (0.696). Notably, all three indicators belong to the state characteristic dimension, suggesting that the coupling coordination degree of the ecological security subsystem in Jiawang District is most strongly influenced by its state characteristics, particularly the resource and environmental states. These two factors collectively account for a significant proportion of the overall impact of state characteristics on the district.

From the rural perspective, the influencing factors of the coupling coordination level of the DQV ecological security subsystem are ranked by correlation as follows: P32 (0.813) > S23 (0.811) > R21 (0.803). This suggests that factors such as the environmental incident control, major resource consumption proportion, and proportion of scientific and technological personnel play a critical role in maintaining the internal stability of the DQV ecological security system. For the PAV ecological security subsystem, the main influencing factors are ranked as R22 (0.902) > S32 (0.838) > S33 (0.794). Unlike DQV, the proportion of scientific and technological personnel exhibits the highest correlation with the coupling coordination degree of PAV ecological security. Regarding LKV, the key factors affecting its coupling coordination, ranked by importance, are S22 (0.781) > P31 (0.748) > S21 (0.737). To enhance the coupling coordination level of LKV ecological security, local governments should prioritize aspects such as forest and grass coverage in built-up areas, land stress, and biological richness/poverty. For DWV ecological security, the “RS-SF-RM” coupling coordination level is influenced by the following factors, ranked by correlation: S22 (0.832) > P22 (0.722) > P33 (0.721). Similar to LKV, forest and grass coverage is also a significant factor for DWV, likely due to similarities in land use structure and the development degree of ecological security coupling coordination between these two coal resource-exhausted villages. For QSV, the key factors influencing its coupling coordination, ranked by importance, are R14 (0.771) > S31 (0.703) > P13 (0.693). Results indicate that factors such as the landscape shape index, water environment quality, and medical bed availability significantly contribute to the coordinated development of QSV’s ecological security subsystem. For ZZV, the primary factors influencing the coupling coordination among its three subsystems, ranked by correlation, are S12 (0.691) > S13 (0.683) > P11 (0.619). Unlike other coal resource-exhausted villages in Jiawang District, socio-economic factors such as the secondary-to-tertiary industry ratio, resource extraction’s share of GDP, and population density play a pivotal role in enhancing ZZV’s ecological security coupling coordination. To some extent, these factors also constrain the “RS-SF-RM” coupling coordination level of ZZV ecological security.

4. Discussion

4.1. Compared with the Results of Previous Studies

The findings on the coupling coordination of ecological security in Jiawang exhibit both similarities and differences compared to studies by domestic and international scholars. First, regarding the balanced development of transformation across different villages, a notable time lag effect is observed in the ecological security transformation of Jiawang’s rural areas. For instance, Pan’an Lake, as a national pilot project for coal mining subsidence area transformation [34,52], received substantial government policy and financial support during its transformation process, resulting in relatively high overall quality and efficiency. Despite this, ZZV still exhibits a relatively low level of coupling coordination. This finding aligns with Anmol Arora’s (2022) study on the fairness of transformation in Germany’s Ruhr region [53], where southern Ruhr received more support than northern Ruhr during the transition period, ranging from financial assistance to educational infrastructure. Second, concerning the dominant factors influencing the coordination of the ecological security system in coal resource-depleted rural areas, the key drivers vary among villages with differing levels of coupling coordination. This observation is consistent with prior research findings by multiple scholars [54,55]. However, this study highlights that for villages in the lagging stage of coordinated development, enhancing their response mechanisms—particularly by increasing technological responses to coal resource depletion—is critical. Finally, this study offers targeted policy recommendations for coal resource-exhausted villages at varying levels of ecological security coupling coordination.

4.2. Advantages of the Study Methodology

Research on the ecological security assessment of rural areas has long been a focus of academic attention. However, studies aimed at enhancing the ecological security resilience of coal resource-exhausted villages remain insufficient. Moreover, while existing research on identifying key factors affecting ecological security has predominantly focused on external factors such as land use structure [56] and landscape pattern and scale [57], there is a notable lack of studies examining the interrelationships among elements within the ecological security system—specifically, how local factor changes influence the overall stability of ecological security. Therefore, when assessing ecological security quality using the PSR model and SENCE theory, we have incorporated additional stress factors beyond traditional land use types, including the scale of resource exploitation, the level of resource utilization, and the degree of ecological function restoration. These factors were selected to more accurately reflect the coordination among the support, state characteristics, and response mechanisms of ecological security resilience in coal resource-exhausted villages. Furthermore, given that the system-wide response to local changes in rural ecological security is a long-term process, it is essential to consider both spatial and temporal dimensions simultaneously. To address this gap, this study innovatively constructs the “RS-SF-RM” model to explore the spatio-temporal dynamic impact of internal ecological security elements on rural ecological security levels. This approach helps clarify the complex relationships and coordination mechanisms among the subsystems of ecological security in coal resource-exhausted villages.

4.3. CREVs Ecological Security Strategies from the Perspective of Coupling Coordination

Although urban planners have recognized and attempted to enhance the ecological security quality of coal resource-exhausted villages through ecological restoration and coal mining subsidence control projects, few scholars have systematically examined how to improve the internal coordinated development level of the ecological security system from a systems science perspective. Based on the research findings regarding dynamic responses within the coupling coordination framework, this study proposes the following strategies for rural ecological security construction: (1) For regions with high coupling levels among the various subsystems of ecological security, such as well-coordinated and moderately coordinated coal resource-exhausted villages, they should consolidate their leading advantages in high-quality transformation and maintain relative strengths in ecological environment, technological innovation, and management services. Local governments, as basic governance units, should conduct dynamic monitoring of ecological security status characteristics and fully leverage the spatial spillover effects of technical response mechanisms to enhance the quality and regional prominence of ecological security construction. (2) For regions with relatively balanced coupling levels among the subsystems, such as primary-coordinated and barely coordinated rural areas, efforts should focus on preventing system imbalances caused by disorderly expansion of built-up areas and path dependence in resource utilization. Specifically, these rural areas should scientifically plan their industrial and land use layouts to avoid blindly promoting industrialization or urbanization, which could lead to overloading the carrying capacity of the ecological environment subsystem [58]. (3) For regions with lagging coordinated development among subsystems, such as slightly imbalanced or nearly imbalanced rural areas, first, they should strengthen ecological environment governance and environmental risk prevention capabilities, continuously restore mining area environments, and comprehensively manage coal mining subsidence zones to enhance ecosystem stability. Second, they should accelerate the development of diversified alternative industries, focusing on deep processing of agricultural and sideline products to increase product value-added, enhance the “blood-making” function of the rural economy, and gradually reduce excessive reliance on resource extraction.

4.4. Contributions and Limitations

Our study specifically examined the coupling and coordination of the ecological security subsystem in coal resource-exhausted villages, innovatively investigating the factors influencing the level of coordinated development in rural ecological security. However, our research has certain limitations. First, by using the internal elements of the rural ecological security system as the basis for constructing indicators, we may have overlooked the influence of location-specific factors under a geographical centrism framework on the ecological security of resource-exhausted villages. Second, with data collected only once every five years, minor changes occurring within this period might have been neglected, potentially affecting the comprehensiveness of the temporal scale. Finally, while exploring the key factors affecting the coupling and coordination of ecological security in coal resource-exhausted villages, we primarily relied on the grey relational degree model. However, some studies suggest that many variables in real-world ecological security issues exhibit nonlinear relationships. In future research, nonlinear models such as support vector machines (SVM) or neural network analysis could be considered to better capture complex nonlinear interactions, thereby providing more precise analytical results.

5. Conclusions

In this study, coupling degree and coupling coordination degree models are comprehensively used to quantitatively evaluate the coupling coordination among the ecological resilience support, state characteristics, and response mechanism of rural coal resources. Compared with most previous studies, this study innovatively proposed a “resilience support—state characteristics—response mechanism” ecological security assessment model for rural coal resource depletion, filling a gap in the research on the “man-land” mutual feed relationship and its evolution law in rural-scale coal mining areas. In terms of practical exploration, this study further extends the ecological security assessment research of coal resource-depleted areas to the rural scale, which is different from the previous urban and county scale. In terms of theoretical contribution, this study integrates the social–economic–natural complex ecosystem (SENCE) theory with the pressure-state-response (PSR) model, which solves the problem that the traditional PSR model has difficulty measuring the coupling coordination degree of the human–Earth relationship.

The results show the following: (1) From 2000 to 2021, the comprehensive index of ecological security in rural areas with depleted coal resources in Jiawang District is on the rise. The PAV and QSV ecological spatial spillover effect accelerated the improvement of ecological resilience in the south and west of Jiawang District. In addition, it is necessary for ZZV and DWV to optimize the industrial structure, strengthen the ecological compensation policy, and give full play to the role of the market and policy in resource allocation. (2) From the perspective of time series, the coupling coordination level among subsystems in Jiawang rural ecological security goes through six stages. From the perspective of spatial structure, the spatial distribution of the coupling coordination degree presents the characteristics of “high in the west and low in the east” and “high in the north and low in the south”. (3) Identify the dominant factors affecting the coupling and coordination of rural coal resource depletion. From the perspective of the coupling coordination of the ecological security subsystem, the high, medium, and low coupling coordination levels correspond to the state characteristics, resilience support, and response mechanism, respectively. From the perspective of Jiawang District as a whole, the state characteristics are the key factors affecting the coupling and coordination of ecological security in coal resource-exhausted villages; especially the state of resources and the state of the environment play a crucial role. From the perspective of rural areas, the leading factors of rural areas are different in different locations, resource conditions, and economic levels.

In summary, these research results provide key theoretical support and practical guidance for the coupling and coordination of ecological security subsystems in coal resource-exhausted villages. The study emphasizes the importance of combining resilience support, state characteristics, and response mechanisms in the construction of rural ecological security, which is particularly important in resource-depleted areas. These research results are helpful to formulate targeted improvement strategies for villages with different coupling and coordination levels and, at the same time, ensure the reduction of differences in ecological security development levels among different villages.