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Article

Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China

by
Peijun Wang
1,2,3,*,
Jing Wang
2,
Yan Li
2,
Yuan Ren
2 and
Jiu Shi
2
1
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
School of Public Policy and Management, China University of Mining and Technology, Xuzhou 221116, China
3
Research Center for Land Use and Ecological Security Governance in Mining Area, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(6), 1233; https://doi.org/10.3390/land14061233
Submission received: 20 April 2025 / Revised: 29 May 2025 / Accepted: 6 June 2025 / Published: 7 June 2025
(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)
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Abstract

The application of livelihood resilience theory in mining-under village relocation areas, coupled with the assessment of the coupling coordination degree between farming household livelihood resilience and ecological livability, is crucial for advancing sustainable development in mining regions and revitalizing rural communities. To examine whether a synergistic enhancement effect exists between the livelihood resilience and ecological livability of relocated farming households, this study utilizes a dataset of 1027 survey responses from farming households in typical mining-under-relocated villages within the eastern plain mining region of China. A measurement index system for farming household livelihood resilience was developed, encompassing three dimensions: buffering capacity, self-organization capacity, and learning capacity. Simultaneously, an evaluation index system for farming household ecological livability was constructed, focusing on three key dimensions: green production, green living, and green ecology. Using these frameworks, the coupling coordination degree between farming household livelihood resilience and ecological livability, along with its influencing factors, was analyzed. The findings reveal the following: (1) The overall livelihood resilience of relocated farming households in mining-under villages is relatively low, with the ranking being buffering capacity > learning capability > self-organization ability. The central village aggregation model demonstrates significantly greater resilience compared to the mine-village integration model. (2) The ecological livability across different relocation models is generally high, and farming households in the town-dependent village construction model, the central village aggregation model, and the suburban community model exhibit significantly higher ecological livability levels compared to those in the mine-village integration model. (3) The coupling coordination degree between livelihood resilience and ecological livability varies across relocation modes, with most modes demonstrating moderate to high-quality coordination. (4) Leadership potential and the presence of water-flush toilets are the most significant factors influencing the coupling coordination degree between livelihood resilience and ecological livability. This study provides valuable insights into the interplay between livelihood resilience and ecological livability in relocated farming households, offering practical implications for sustainable development and rural revitalization in mining regions.

1. Introduction

As global natural and anthropogenic disasters increase in frequency, alongside the rapid pace of socio-economic development, rural communities that rely on land for production and living face significant challenges [1,2], particularly in the mining-under village relocation areas of eastern China. The relocation process for mining-under villages in high groundwater-level plain mining regions diverges from typical relocations. In these regions, underground coal mining induces severe surface subsidence and waterlogging, which irreparably damages the original village sites, necessitating the relocation of farming households to other areas [3]. Relocated farming households encounter dual challenges due to shifts in their local production and living environments, compounded by socio-economic transitions, leading to substantial alterations in their livelihood activities [4]. Concurrently, the new village sites established after the relocation of mining-under villages represent reconstructed ecosystems, requiring an assessment of their ecological livability. The Central Committee of the Communist Party of China and the State Council’s Opinion on Learning and Applying the Experience of the ‘Thousand Village Demonstration, Ten Thousand Village Improvement’ Project to Vigorously and Effectively Promote Comprehensive Rural Revitalization underscores the importance of advancing key tasks such as rural industrial development, rural construction, and governance levels to build livable and productive beautiful villages. Furthermore, ecological livability is highlighted as one of the overarching requirements for rural revitalization. Consequently, to achieve sustainable development in mining areas and facilitate rural revitalization, it is essential to examine the livelihood resilience levels of relocated farming households from mining-under villages, as well as the coupling coordination degree between livelihood resilience and ecological livability, along with their influencing factors.
Based on the definitions of resilience by scholars such as Holling [5], Walker [6], and Folke [7], resilience is understood as the capacity of a system to endure, cope with, and adapt to changes following disturbances. Livelihood is typically defined as the means by which individuals, households, or groups sustain themselves. As livelihood systems increasingly come under the influence of ecological, economic, and social changes, the notion of livelihood resilience has garnered greater attention. The concept of livelihood resilience was initially introduced by ecologists such as Chambers and Conway [8] (1992) within the framework of sustainable livelihood theory. Livelihood resilience is characterized by the ability of a community or household’s livelihood system to adapt to environmental changes, recover from adverse impacts, and undergo transformation [9]. Previous research has explored the effects of various disturbances on the livelihood resilience of farming households, with a primary focus on climate change [10,11,12,13,14,15,16], public health events [17], natural disasters [18,19,20], food security [21,22], and poverty alleviation relocation [23,24]. Several research frameworks for livelihood resilience have been developed, including the sustainable livelihood framework and the livelihood resilience framework. Among these, the framework encompassing buffering capacity, self-organizing capacity, and learning capacity proposed by Speranza [25] has seen widespread application [20,26]. Research efforts have primarily concentrated on the measurement of livelihood resilience [27,28] and the analysis of influencing factors [29]. Additionally, some scholars have examined the relationship between livelihood resilience and livelihood vulnerability [30], as well as the impact of policy implementation on livelihood resilience [31].
The concept of ecological livability was initially deconstructed into rural ecological sustainability and rural livability sustainability [32]. Among them, rural ecological sustainability is defined as the relationship between rural residents and their environment, while rural livability sustainability is interpreted as a safe, healthy, convenient, and comfortable rural living environment. Earlier research has mainly focused on related concepts, indicator evaluation, spatiotemporal characteristics, and influencing factors of ecological livability [32,33,34,35]. Through extensive practice, several typical relocation models of mining-under villages have been established in eastern China, each linked to distinct policy frameworks. However, the mechanisms by which these relocation policies achieve mutually beneficial outcomes, such as optimizing household livelihood structures and improving ecological livability, remain underexplored. In particular, research addressing challenges like “relocated but unable to retain residents” and “poverty resulting from relocation” is currently scarce. Thus, it is very essential to examine the relationship between the livelihood resilience and ecological livability of farming households relocated from mining-under villages.
To fill the research gap, the following hypotheses were made in this study: The livelihood resilience and ecological livability of farming households exhibit a synergistic enhancement effect following relocation; specifically, the improvement of farming households’ ecological livability can strengthen their livelihood resilience, while the enhancement of livelihood resilience can, in turn, promote the intrinsic motivation for ecological protection, thereby further improving ecological livability. To test these hypotheses, the frameworks of livelihood resilience and ecological livability are integrated in this study, drawing on survey data from 1027 farming households across 12 representative villages in four provinces: Jiangsu, Anhui, Henan, and Shandong. An evaluation index system for the livelihood resilience and ecological livability of relocated farming households is developed. The entropy weight-TOPSIS model is applied to calculate the comprehensive index of indicators, and the coupling coordination degree between the two is subsequently measured. Furthermore, the influencing factors are analyzed. This study is intended to offer references for advancing sustainable development in mining areas and facilitating the rural revitalization of mining-under villages.

2. Materials and Methods

2.1. Overview of the Study Area and Relocation Modes

2.1.1. Overview of the Study Area

It has been statistically determined that the total coal resources beneath the “three categories” (buildings, railways, and water bodies) in China reach an estimated 13.79 billion tons. Of these, coal resources located beneath villages represent approximately 60% of the total coal resources found under buildings. These resources are largely concentrated in the northern region of Anhui Province, the eastern region of Henan Province, the northern region of Jiangsu Province, and the southwestern part of Shandong Province, impacting a population that could reach 13.21 million. This research centers on 12 distinct relocated villages situated in Huaibei City of Anhui Province, Shangqiu City of Henan Province, Xuzhou City of Jiangsu Province, and Jining City of Shandong Province. The geographical distribution of these villages is illustrated in Figure 1. Household surveys were conducted in these 12 relocated villages (communities) using a random sampling method. The number of valid questionnaires collected is depicted in Table 1.

2.1.2. Relocation Modes for Mining-Under Villages

The need to relocate mining-under villages arises due to the substantial coal resources located beneath these areas. As coal extraction continues, the ground beneath residential areas becomes hollowed out, resulting in widespread surface subsidence, as well as the cracking or collapsing of houses and courtyard walls. These conditions compromise the safety of villagers’ homes, markedly endangering the lives and property of residents. Thus, it becomes essential to relocate the affected villages or residents completely. Over years of practice and exploration, four primary models for mining-under village relocation have been established in China: the town-dependent village construction (TVC) model, the mine-village integration (MVI) model, the suburban community (SC) model, and the central village aggregation (CVA) model. The characteristics for each model, such as relocation scale, the distance from the town after relocation, land-use types, and types of livelihood activities, can be found in Table 2.

2.2. Data Sources

The methodology of this study was predominantly based on a questionnaire survey approach. Field research was executed in eight separate phases from 16 September 2022 to 24 July 2023, across the 12 selected villages. Before conducting field research, foundational data regarding the social, economic, and ecological aspects of the mining-under villages were collected from the relevant county and township government departments of the new villages established after the relocation. This was done to gain an understanding of the general conditions of the region and to determine the sample size for the research based on the total population of each relocated village (community). During the research, key interviews were conducted with township officials, village heads, or leaders of villager groups to obtain an overall understanding of the livelihoods and ecological livability in the village. With the assistance of village officials, a random selection of farming households from various villages (communities) was determined using a lottery method. Subsequently, under the guidance of leaders of villager groups, random household surveys were carried out to gather baseline data on the actual livelihood levels and ecological livability of the farming households.
The research in this paper is divided into the following three stages. (1) Preliminary Research: Prior to the formal research, a preliminary investigation was conducted in May 2022, randomly selecting 8–10 households in Nanzhongshan Village, Xuzhou City, Jiangsu Province, to gather information on the population, rural governance, and ecological environment of the new village post-relocation, aiming to understand its overall status. (2) Formal Research: Based on the preliminary research, the original questionnaire was supplemented and revised. Random sampling surveys were conducted in each relocated village, with each household survey taking approximately 25 min. (3) Supplementary Research: In September 2023, supplementary research was conducted to address missing and incomplete data. A total of 1089 questionnaires were disseminated. Following the exclusion of invalid responses, 1027 valid questionnaires were acquired, leading to an effective response rate of 94.31%. The distribution of the sample is outlined in Table 3.

2.3. Research Methods

2.3.1. Measurement of Farming Household Livelihood Resilience

This study employs the livelihood resilience framework articulated by Speranza [25] and constructs a measurement framework encompassing three primary dimensions: buffering capacity, self-organization capacity, and learning capacity. Buffering capacity denotes the system’s ability to endure external shocks while seizing new opportunities to enhance livelihood outcomes amidst disturbances. It serves as the foundation and prerequisite for maintaining the stability and functionality of the livelihood system and is typically gauged by livelihood capitals, including human, natural, physical, and financial resources [36,37]. Self-organization capacity focuses on collective agency, indicating the system’s capability for self-governance and self-management under the combined influences of institutional structures, social networks, and community organizations. This capacity is often evaluated using indicators such as social networks, leadership potential, and the life confidence index [26,38]. Learning capacity reflects adaptive management, encompassing the ability to apply existing knowledge to current livelihood practices while actively acquiring new knowledge and skills in a changing external environment. In this study, learning capacity is measured by indicators such as the educational level of the household head, investment in education, and participation in village collective meetings [39,40,41]. The detailed indicator system is presented in Table 4.
To mitigate the discrepancies among indicators, the range method was applied to standardize the original data. Subsequently, the entropy weight-TOPSIS model was utilized to conduct a comprehensive assessment of the livelihood resilience level of farming households following their relocation from mining-under villages [42].

2.3.2. Evaluation Index System for Ecological Livability Level

Grounded in the theoretical framework of ecological livability and informed by prior research [43,44], an evaluation index system was developed to assess the ecological livability level of relocated farming households from mining-under villages. This system integrates three dimensions: green production, green living, and green ecology, encompassing seven key indicators, as shown in Table 5. The evaluation utilized the entropy weight-TOPSIS model [42].

2.3.3. Coupling Coordination Degree Model

The coupling coordination degree model is applied to assess the extent of coordinated development between different phenomena and to evaluate the quality of their interaction. The coupling coordination index serves not only to gauge the interaction level between the livelihood system of relocated farming households in mining-under villages and the ecological livability level but also to determine whether these two factors are mutually reinforcing or inhibiting. The formula is as follows:
C = 2 R i × D i ( R i + D i ) 2
D = C × ( α u 1 + β u 2 )
where C represents the coupling degree, D signifies the coupling coordination degree between livelihood resilience and ecological livability, and Ri and Di denote the livelihood resilience index and ecological livability index of farming households, respectively. The coefficients α and β, as derived from relevant literature [43], are both set at 0.5 in this study. To precisely capture the coupling coordination degree between livelihood resilience and ecological livability levels, and based on prior research [42,45,46], the coupling coordination degree is categorized into ten distinct grades, as outlined in Table 6.

2.3.4. Random Forest Model

The random forest model is a classic, robust, and efficient machine learning method commonly employed in environmental modeling research, which demonstrates a relatively strong resilience in addressing collinearity among predictive variables and handling covariate data that may contain noise [47]. This study, based on R language (R 4.4.2), utilizes the randomForest package to quantify the importance of various indicators within the livelihood resilience system and the ecological livability system concerning their contribution to the coupling coordination degree. The increase in mean squared error (%IncMSE) is employed to rank the indicators, followed by the use of the rfPermute package to obtain significance level estimates for the importance of each variable in the random forest. This analysis aims to elucidate the relative importance of different variables in relation to the coupling coordination degree, with the decision tree set to 1000 and categorical variables transformed into dummy variables.

2.3.5. Statistical and Analytical Methods

The organization and standardization of the questionnaire survey content were accomplished using Excel 2024. The significant difference analysis of the livelihood resilience of relocated farmers across different models, as well as the disparities in various dimensions and ecological livability, was conducted using Tukey’s post hoc multiple comparison method. The graphical representations were generated using Origin 2024.

3. Results and Analysis

3.1. Livelihood Resilience of Farming Households Relocated from Mining-Under Villages

3.1.1. Overall Situation of Livelihood Resilience for Farming Households Relocated Under Different Modes

The livelihood resilience measurement outcomes for villages in different regions were classified based on various relocation models, resulting in the overall assessment of buffering capacity, self-organizing capacity, learning capacity, and livelihood resilience for each relocation mode, as presented in Table 7. The buffering capacity of households relocated under the CVA and MVI models does not differ significantly. Similarly, no significant differences are observed between the TVC and SC models. However, both the CVA and MVI models exhibit significantly higher buffering capacities than the TVC and SC models. There is no significant difference in the self-organizing capacity of households among the TVC, CVA, and MVI models; however, all three models demonstrate significantly higher self-organizing capacities compared to the SC model. The learning capacity of households does not differ significantly among the TVC, CVA, and SC models. However, all three models exhibit significantly higher learning capacities compared to the MVI model. There is no significant difference in household livelihood resilience among the TVC, CVA, and SC models. However, the CVA model demonstrates significantly greater resilience compared to the MVI model. Overall, the results suggested that the livelihood resilience of relocated farming households from mining-under villages was relatively low, at only 0.1799. Among the components, self-organization capacity was the lowest at 0.1454, while buffering capacity and learning capacity were at 0.2059 and 0.1781, respectively.

3.1.2. Detailed Analysis of Livelihood Resilience for Farming Households Under Different Relocation Modes

Buffering Capacity
To thoroughly examine the variations in different dimensions of livelihood resilience among farming households relocated under diverse models, the 12 surveyed villages were categorized according to their respective relocation models. Grouped box plots were utilized to depict the buffering capacity levels of farming households across different models, as illustrated in Figure 2. Within the TVC model, no significant differences were observed in the household buffering capacities between Nanzhongshan Village, Sanli Community, Yangtun Village, Penglou Village, and Fangzhuang Community. Similarly, no significant differences were found in the household buffering capacities of Renhe Community, Honggang Village, and Yunhewan Community, all of which belong to the SC model. Significant differences in household buffering capacity levels were observed between the two villages under the CVA model. Renji Village demonstrated a buffering capacity level of 0.353, which was substantially higher than Xingxing Village’s level of 0.151. Villages analyzed under the MVI model exhibited relatively higher household buffering capacity levels overall. Wanglou Village recorded a level of 0.304, notably exceeding Beihunan Village’s buffering capacity. Additionally, the box plot for Wanglou Village displayed a “longer box,” indicating greater internal disparities within the village.
Self-Organizing Capability
Figure 3 illustrates that the variations in self-organizing capacity among farming households across different relocation models and villages are relatively minor, with overall distribution patterns that tend to be clustered. Within the TVC model, no significant differences were identified in the household self-organizing capacities across Nanzhongshan Village, Sanli Community, Yangtun Village, Penglou Village, and Fangzhuang Community. Likewise, within the CVA and MVI models, no notable differences were observed in the household self-organizing capacities between Renji Village and Xingxing Village, as well as between Beihunan Village and Wanglou Village, respectively. The self-organizing capacity of farming households within the SC model, particularly in the Yunhewan community, demonstrates a markedly higher level compared to that observed among residents in the Renhe community and Honggang village. The upper section of Figure 3 reveals the presence of outliers, which can be attributed to the disproportionately high weight given to the indicator “The number of family members who are party members or village officials”. However, as families with party members and village officials constitute less than 20% of the total, these cases have been identified as outliers in the calculation process.
Learning Capability
Figure 4 depicts that no significant differences were found in the learning capabilities of farming households across the five villages (communities) under the TVC relocation model, the two villages under the MVI model, and the three villages (communities) under the SC model. However, significant differences were observed under the CVA model. Farming households in Renji Village, located in Huaibei City, Anhui Province, demonstrated significantly higher learning capabilities than those in Xingxing Village, situated in Jining City, Shandong Province.
Comprehensive Livelihood Resilience
As illustrated in Figure 5, similar to the learning capacities of farming households, there are no significant differences in the overall livelihood resilience among the five villages (or communities) under the TVC relocation model, the two villages under the MVI model, and the three villages (or communities) under the SC model. However, significant differences were observed under the CVA model. Specifically, farming households in Renji Village, located in Huaibei City, Anhui Province, demonstrated a significantly higher level of comprehensive livelihood resilience than those in Xingxing Village, located in Jining City, Shandong Province.

3.2. Evaluation Results of Ecological Livability Index for Farming Households Relocated from Mining-Under Villages

3.2.1. Overall Ecological Livability Level of Relocated Farming Households Under Different Models

As illustrated in Figure 6, the ecological livability across various relocation models is relatively high, and there are no significant differences in the overall ecological livability levels of farming households between the TVC, CVA, and SC relocation models. However, the ecological livability levels of farming households in these three models are significantly higher than those in the MVI model. Under the TVC relocation model, no significant differences in overall ecological livability were observed among households in Sanli Community, Yangtun Village, and Nanzhongshan Village, all located in Peixian County, Xuzhou City, Jiangsu Province. However, the ecological livability in these areas was markedly higher compared to that of Panlou Village in Suixi County, Huaibei City, Anhui Province, and Fangzhuang Community in Weishan County, Jining City, Shandong Province. Under the CVA relocation model, no significant difference is observed in the overall ecological livability between households in the two villages. However, under the MVI model, significant differences in ecological livability are found between households in the two villages, and under the SC model, differences are observed across the three villages (or communities). Specifically, under the MVI model, Beihunan Village demonstrates a significantly higher level of ecological livability compared to Wanglou Village, and Wanglou Village exhibits the lowest overall ecological livability level among all the relocated villages. Under the SC model, the ecological livability rankings are as follows: Yunhewan Community > Renhe Community > Honggang Village.

3.2.2. Detailed Ecological Livability Levels of Farming Households Relocated Under Different Models

TVC Model
As illustrated in Figure 7, the green production indicators for farming households within the TVC model vary from 0.153 to 0.196. Among the villages, Nanzhongshan Village records the highest green production indicator, while Yangtun Village and Fangzhuang Community exhibit similar levels of green production. In the green living dimension, Nanzhongshan Village, Sanli Community, and Yangtun Village consistently show high levels, whereas Fangzhuang Community demonstrates the lowest level at 0.259. With regard to the green ecology dimension, the overall differences among the five villages (communities) are relatively minor, with Fangzhuang Community having the highest green ecology index for farming households and Yangtun Village having the lowest.
CVA Model
The ecological livability levels of the two villages adhering to the CVA model are largely comparable. However, specific dimensions reveal notable differences. In the green ecology dimension, Renji Village (0.347) exceeds Xingxing Village (0.324). In contrast, Renji Village exhibits lower levels of green production and green living than Xingxing Village.
MVI Model
The ecological livability level of farming households relocated through the MVI model is relatively low, particularly in Wanglou Village, where the green living index is a mere 0.006. Beihunan Village also exhibits a low green living index of 0.136, reflecting that this relocation model places a significant environmental burden on the daily lives of farming households. Moreover, Beihunan Village records the lowest green production index among all relocated villages, at just 0.142. The green ecology indices for Beihunan Village and Wanglou Village are 0.331 and 0.306, respectively, showing little variation from those observed in the other three relocation models.
SC Model
The ecological livability levels across the three villages (or communities) under the SC model are generally consistent. Yunhewan Community reports the highest green ecological indicators among these villages, indicating a superior ecological environment and greater resident satisfaction. The green production and green living indicators of Renhe Community are comparable to those of Yunhewan Community, reflecting a relatively high overall ecological livability level. Although Honggang Village displays high green living indicators, its green production and green ecological indicators remain comparatively low. Notably, the green production indicator for Honggang Village is only 0.161, which is below the average value of 0.184 for this relocation model.

3.3. Coupling Coordination Degree

Table 8 presents the coupling coordination degree between livelihood resilience and ecological livability of farming households across different relocation models for mining-under villages. Among the five villages (or communities) utilizing the TVC model, coordination levels vary from moderate to good. Penglou Village, Nanzhongshan Village, and Fangzhuang Community exhibit good coordination, whereas Sanli Community and Yangtun Village display moderate coordination. The CVA model reveals significant differences in coupling coordination between its two villages. Renji Village demonstrates excellent coordination with a high degree of 0.967, while Xingxing Village shows moderate coordination with a degree of 0.715. The MVI model records the lowest coupling coordination level among the four relocation models. Beihunan Village approaches a near-disorder state with a coordination degree of 0.439, while Wanglou Village is in a moderate disorder state with a coordination degree of just 0.223. The three villages (or communities) under the SC model exhibit considerable variation in their coupling coordination degrees. Honggang Village is characterized by a moderate disorder state with a low coordination degree of 0.297, Renhe Community demonstrates moderate coordination at 0.777, and Yunhewan Community achieves good coordination.

4. Discussion

4.1. Livelihood Resilience of Farming Households

Farming households display varying degrees of livelihood resilience, influenced by factors such as relocation destination and origin, housing standards, and timing of relocation within different resettlement models. This study specifically examines livelihood resilience across various resettlement models, which contrasts with prior research focused on livelihood resilience under different livelihood strategies. Previous research on livelihood resilience has predominantly targeted ecologically fragile areas and impoverished regions, with limited attention given to farming households in mining-under village relocation contexts. Liu and Yu. (2023) 45 explored the impact of relocation on farming households’ livelihood resilience, finding that varying resettlement models exert differing effects on livelihood resilience, aligning with the results of this study. However, the resettlement models analyzed in their research differ from the relocation models discussed herein.
The findings of this study reveal that the overall livelihood resilience of farming households relocated through different mining-under village models remains relatively low. Livelihood resilience represents the capacity of these households to sustain, resist, and adapt to external changes. Farming households in mining-under villages experience significant and prolonged impacts due to relocation, leading to substantial alterations in their livelihood capital and strategy choices post-relocation. The resilience of farming households varies across different relocation models. In the TVC model, the variation in livelihood resilience is minimal, indicating that their resilience is less influenced by the specific location of the administrative village. The CVA model, which centers on using central villages as construction sites, enables farming households to construct their own houses rather than high-rise apartments. The close proximity between the relocation destination and the original site, along with the preservation of neighborhood relationships, contributes to a relatively higher level of livelihood resilience in this model. Similar to the CVA model, the MVI model exhibits strong buffering and self-organization capacities. However, households in this model are situated farther from urban areas, with only about 35% of the relocated households residing in the new area. Consequently, their learning capability is diminished, resulting in a lower overall level of livelihood resilience. The SC model adheres to a “unified planning and construction” standard, with buildings typically constructed as apartments of five or more stories in accordance with local government regulations. Farming households in this model occupy smaller living spaces, often lacking storage for agricultural tools and crops. This situation frequently leads them to transfer their farmland, which in turn reduces their buffering capacity. Moreover, interactions among farming households residing in high-rise apartments are less frequent, leading to lower self-organization capacity. However, the proximity to urban areas provides these households with more learning opportunities and enhances their learning capabilities.

4.2. Ecological Livability Level of Farming Households

Ecological livability serves as a fundamental aspect of rural revitalization, embodying the capacity for sustainable development. For farming households transitioning from mining-under villages to new residential areas, the presence of a favorable ecological environment and a high ecological livability level is paramount. Before relocation, these households were adversely impacted by underground coal mining, contending with severe issues like ground subsidence and water pollution. Following relocation, their living environment quality has undergone significant improvement. As a result, the ecological livability level of these relocated farming households has substantially increased, now generally reflecting a relatively high standard.
The research findings suggest that the overall ecological livability level of farming households across various relocation models is generally high. In the TVC model, the CVA model, and the SC model, the ecological livability levels all exceed 0.800. However, the ecological livability level for farming households in the MVI model is comparatively lower. This disparity arises primarily because this model depends on the roads and infrastructure of the mining area during relocation. Coal transportation frequently causes road surface damage, while dispersed coal dust contributes to the degradation of the regional ecological environment. Moreover, the distance between relocated farming households and urban centers in this model ranges from 3 to 5 km, which is notably greater than in the TVC model and the SC model. As a result, the ecological livability level of farming households in the MVI model is relatively low.

4.3. Influencing Factors of Coupling Coordination

To investigate the influencing factors of the coupling coordination degree between livelihood resilience and ecological livability for farming households relocated from mining-under villages, a random forest model was employed to analyze the impact of 19 indicators from the livelihood resilience index system and 7 indicators from the ecological livability index system on coupling coordination. Finally, the combined influence of indicators from both systems on coupling coordination was examined, with the results illustrated in Figure 8, Figure 9 and Figure 10, while the meanings of the indicators are detailed in Table 4 and Table 5.
In the indicator system for livelihood resilience, the ranking of indicators affecting the degree of coupling coordination from most to least important is as follows: B1 > C4 > C2 > C3 > A5 > A3 > A4 > A1 > C5 > A2 > B4 > C1 > C6 > B6 > C7 > B2 > B5 > B3 > A6. The indicators with an importance exceeding 30% are B1 (leadership potential), C4 (family investment in education), C2 (highest education level among family members), C3 (duration of the labor force working away from home), A5 (per capita income), and A3 (housing capital). Among these, B1 (leadership potential) holds a relative importance significantly greater than that of the other indicators, whereas C7 (knowledge sharing ability), B2 (neighborhood relations), B5 (degree of social integration), B3 (policy awareness), and A6 (health status of family members) exhibit comparatively lower relative importance regarding the degree of coupling coordination. In the indicator system for ecological livability, the ranking of indicators affecting the degree of coupling coordination from most to least important is as follows: E2 > E1 > F1 > F3 > F2 > D1 > D2. The top three indicators are E2 (presence of flush toilets), E1 (traffic accessibility), and F1 (vegetation coverage), with E2 (presence of flush toilets) demonstrating a relative importance that far exceeds that of the other indicators. When synthesizing the indicators for livelihood resilience and ecological livability, the ranking of indicators affecting the degree of coupling coordination from most to least important is as follows: B1 > C4 > E2 > C2 > C3 > A5 > A3 > A4 > A1 > C5 > A2 > B4 > C1 > E1 > C6 > F1 > B6 > C7 > F3 > B2 > F2 > B5 > D1 > B3 > A6 > D2. The indicators with an importance exceeding 30% are B1 (leadership potential), C4 (family investment in education), E2 (presence of flush toilets), C2 (highest education level among family members), C3 (duration of the labor force working away from home), A5 (per capita income), and A3 (housing capital). Notably, B1 (leadership potential) exhibits a relative importance that is significantly greater than that of the other indicators, while C7 (knowledge sharing ability), F3 (household water supply quality), B2 (neighborhood relations), F2 (environmental quality), B5 (degree of social integration), D1 (cultivated land quality), B3 (policy awareness), A6 (health status of family members), and D2 (use of chemical fertilizers and pesticides) demonstrate comparatively lower relative importance with respect to the degree of coupling coordination.

4.4. Policy Recommendations

Based on the above findings, the following policy recommendations are proposed. The relocated households in Sanli Community, characterized by the TVC model; Beihunan Village and Wanglou Village, which relocated under the MVI model; and Renhe Community and Honggang Village, representing the SC model, exhibit relatively low self-organizing capacity. It is necessary to enhance the leadership potential of farmers and the leadership abilities of community cadres within these communities (villages). Households in Xingxing Village, which follow the CVA model; Beihunan Village and Wanglou Village, which were relocated under the MVI model; and Honggang Village, representing the SC model, demonstrate limited learning capacity. This can be improved through increased household investment in family education, greater participation in village collective meetings, and enhanced information acquisition capabilities. Relocated households in Sanli Community, following the TVC model, and Beihunan Village, which relocated under the MVI model, display relatively low levels of green production. These communities (villages) should adopt environmentally friendly production methods, reduce the consumption of resources such as fertilizers and pesticides during agricultural activities, and continuously improve arable land quality. Relocated households in Beihunan Village and Wanglou Village, which were relocated under the MVI model, have a relatively low level of green living. It is essential to vigorously promote the conversion of dry toilets to water-flush toilets, focus on infrastructure development such as roads, and enhance transportation accessibility in these villages. To enhance the coupling and coordination between household livelihood resilience and ecological livability across various relocation models, several strategies should be implemented. These include fostering leadership potential among farmers, increasing household investment in education, extending the duration of labor force engagement away from home, and raising per capita income and housing capital. Additionally, efforts should be made to improve the highest educational attainment within families and to implement measures such as transitioning from dry toilets to water-flush toilets.

5. Conclusions

This study analyzed 1027 survey data samples from mining-under village relocation areas to systematically classify the types of farming household relocations. A detailed comparison of the livelihood resilience and ecological livability levels across different mining-under village relocation models was conducted, followed by an analysis of the coupling coordination between these two factors under various relocation models and an exploration of the influencing factors. The conclusions drawn from the research are as follows:
(1)
The overall livelihood resilience of relocated farming households in mining-under villages is relatively low, with the ranking being buffering capacity (0.2059) > learning capability (0.1781) > self-organization ability (0.1454). The CVA model demonstrates significantly greater resilience compared to the MVI model.
(2)
The ecological livability across different relocation models is generally high. Farming households in TVC, CVA, and SC models exhibit significantly higher ecological livability levels compared to those in the MVI model.
(3)
The coupling coordination degree between livelihood resilience and ecological livability varies across different relocation models for mining-affected villages. The TVC model typically demonstrates a coordination level ranging from good to moderate. In contrast, the CVA model exhibits coordination from high-quality to moderate levels. The MVI model tends to show coordination from near-disorder to moderate disorder, while the SC model displays a spectrum of coordination from moderate to good, including instances of moderate disorder.
(4)
The degree of coupling coordination between household livelihood resilience and ecological livability is significantly influenced by several key indicators, including leadership potential, family investment in education, the presence of flush toilets, the highest education level attained by family members, the duration of labor force members working away from home, per capita income, and housing capital.
(5)
To enhance the livelihood resilience and ecological sustainability of households, it is recommended to implement several countermeasures. These include strengthening the leadership capabilities of both farmers and community leaders, increasing household investments in education, adopting environmentally sustainable production practices, upgrading dry toilets to water-flush systems, and improving transportation accessibility.
This study inevitably has some limitations. Despite including 1027 households, the research captures livelihood and ecological levels from a single period, lacking longitudinal monitoring of relocated farming households. Future studies might explore the coupling coordination level between livelihood resilience and the ecosystem across various time periods for relocated farming households. Furthermore, this research primarily examines specific relocation areas without accounting for spatial interrelationships. Subsequent research could leverage geospatial technology to analyze the spatial relationships between livelihood resilience and ecological livability levels of farming households relocated from mining-under villages.

Author Contributions

Methodology, P.W. and J.W.; software, J.W. and Y.L.; validation, J.W. and Y.L.; formal analysis, J.W.; data curation, J.S.; writing—original draft preparation, P.W. and J.W.; writing—review and editing, P.W. and J.S.; visualization, Y.L. and Y.R.; supervision, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Postdoctoral Science Foundation. (Grant No. 2020M680664).

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Acknowledgments

We sincerely thank Jianwei Zhang of Huaibei Land Consolidation Center and Baishan Gong, the Coal Division of the Energy Bureau of Jining City, for their help during the questionnaire survey process.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic location of the research area.
Figure 1. Geographic location of the research area.
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Figure 2. Buffering capacity of farming households under different relocation models. Note: Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
Figure 2. Buffering capacity of farming households under different relocation models. Note: Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
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Figure 3. Self-organizing capacity of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
Figure 3. Self-organizing capacity of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
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Figure 4. Learning capability of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
Figure 4. Learning capability of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
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Figure 5. Comprehensive livelihood resilience of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
Figure 5. Comprehensive livelihood resilience of farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
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Figure 6. Overall levels of ecological livability for farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
Figure 6. Overall levels of ecological livability for farming households under different relocation models. Different lowercase letters indicate significant differences in indicators among the villages (communities) with different relocation models (p < 0.05).
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Figure 7. Detailed levels of ecological livability for farming households under different relocation models.
Figure 7. Detailed levels of ecological livability for farming households under different relocation models.
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Figure 8. Importance ranking of livelihood recovery indicators for farming households relocated from mining-under villages.
Figure 8. Importance ranking of livelihood recovery indicators for farming households relocated from mining-under villages.
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Figure 9. Importance ranking of ecological livability indicators for farming households relocated from mining-under villages.
Figure 9. Importance ranking of ecological livability indicators for farming households relocated from mining-under villages.
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Figure 10. Importance ranking of comprehensive indicators for farming households relocated from mining-under villages.
Figure 10. Importance ranking of comprehensive indicators for farming households relocated from mining-under villages.
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Table 1. Valid questionnaires from relocated village surveys across cities.
Table 1. Valid questionnaires from relocated village surveys across cities.
CityCounty (District)TownshipVillage (Community)Number of Valid Questionnaires
HuaibeiSuixi CountyNanpingRenji104
WugouBeihunan76
Wanglou56
LiuqiaoPenglou135
Xiangshan DistrictRenweiRenhe101
ShangqiuYongcheng CountyChengxiangHonggang122
XuzhouPei CountyLongguSanli45
YangtunNanzhongshan14
Yangtun66
JiningWeishan CountyHuanchengFangzhuang104
Zoucheng CountyTaipingXingxing109
Rencheng DistrictAnjuYunhewan95
Table 2. Basic information on different relocation models.
Table 2. Basic information on different relocation models.
Relocation ModeRelocation ScaleDistance from TownLand Use TypeTypes of Livelihood Activities
TVCBiggerCloseCultivated land and construction landAgricultural employees
MVISmallRelatively farCultivated land and construction landAgricultural employees and self-cultivated small farmers
SCBigNearCultivated land and construction landAgricultural employees and businesses
CVABiggerFar awayConstruction land and commercial service landAgricultural employees and agricultural business entities
Table 3. Distribution characteristics of the questionnaire survey samples.
Table 3. Distribution characteristics of the questionnaire survey samples.
IndexCategoryFrequency NumberFrequency RateIndexCategoryFrequency NumberFrequency Rate
GenderMale48947.61%Degree of educationIlliterate12011.68%
Female53852.39%Primary school35734.76%
Age20–4035734.76%Junior school42140.99%
40–6047946.64%Senior high school12011.68%
>6019118.60%University or above90.88%
Relocation modelTVC36435.44%Health conditionGood74872.83%
CVA21320.74%Common15214.80%
MVI13212.85%Seriously ill and unable to work969.35%
SC31830.96%Physical disability313.02%
Table 4. Evaluation index system for livelihood resilience of relocated farming households in mining-under villages.
Table 4. Evaluation index system for livelihood resilience of relocated farming households in mining-under villages.
Dimension LayerIndicator LayerIndicator Definitions and AssignmentsAttributeWeight
Buffering capacity (0.21)Number of family laborers (A1)The labor capacity of farming household members is defined as follows: full labor = 2, partial labor = 1, and no labor capacity = 0.+0.2071
Cultivated land area (A2)The current cultivated land area includes both transferred and self-cultivated land (in mu).+0.4474
Housing capital (A3)Housing is characterized by a combination of living space and structure. Housing types are classified as: earth-wood = 1, brick-wood = 2, brick-concrete = 3, reinforced concrete = 4. Living space is categorized as: 30 m2 = 1, 31–60 m2 = 2, 60–90 m2 = 3, 90–120 m2 = 4, >120 m2 = 5.+0.0884
Material capital (A4)The quantity of main production and living materials owned by the farming household.+0.0822
Per capita income (A5)The ratio of total annual household income to the total number of family members.+0.1657
Health status of family members (A6)The annual medical expenses (in yuan).-0.0092
Self-organizing capacity (0.34)Leadership potential (B1)The number of family members who are party members or village officials.+0.7719
Neighborhood relations (B2)Satisfaction levels are rated as: excellent = 5, good = 4, average = 3, poor = 2, terrible = 1.+0.0321
Policy awareness (B3)The level of understanding of relocation policies is categorized as: well-informed = 3, somewhat informed = 2, and uninformed = 1.+0.0488
Attitude towards coal mine development (B4)The effectiveness of mining area development in meeting farmers’ livelihood needs. Satisfaction levels are rated as very dissatisfied = 1, somewhat dissatisfied = 2, moderately satisfied = 3, fairly satisfied = 4, and very satisfied = 5.+0.0516
Degree of social integration (B5)Integration levels are classified as: well-integrated = 3, can integrate = 2, and difficult to integrate = 1.+0.0092
Leadership ability of community cadres (B6)Levels are rated as: very low = 1, relatively low = 2, average = 3, relatively high = 4, very high = 5.+0.0864
Learning capacity (0.45)Education level of household head (C1)Education levels are categorized as: below primary school = 1, primary school = 2, junior high school = 3, high school or technical secondary school = 4, university and above = 5.+0.0655
Highest education level among family members (C2)Education levels are categorized as: below primary school = 1, primary school = 2, junior high school = 3, high school or technical secondary school = 4, university and above = 5.+0.0399
Duration of the labor force working away from home (C3)The total number of working days per year for all laborers in the household (in days).+0.1086
Family investment in education (C4)The annual investment in education (in yuan).+0.2579
Participation in village collective meetings (C5)Participation in village collective meetings is indicated as: yes = 1, no = 0.+0.3908
Information acquisition ability (C6)The daily time spent watching TV, listening to the radio, or browsing the internet (in hours).+0.0924
Knowledge sharing ability (C7)Levels are rated as: very low = 1, relatively low = 2, average = 3, relatively high = 4, very high = 5.+0.0449
Note: In the table, “+” denotes positive indicators, indicating that a higher value is preferable, while “-” denotes negative indicators, indicating that a higher value is less desirable.
Table 5. Evaluation index system for ecological livability level of farming households relocated from mining-under villages.
Table 5. Evaluation index system for ecological livability level of farming households relocated from mining-under villages.
Dimensions and IndicatorsBasic IndicatorsIndicator Definitions and AssignmentsIndicator DirectionWeight
Green productionCultivated land quality (D1)Very poor = 1, poor = 2, average = 3, good = 4, very good = 5+0.1906
Use of chemical fertilizers and pesticides (D2)Yes = 1, no = 0-0.0575
Green livingTraffic accessibility (E1)Distance to county road (km)-0.3327
Presence of flush toilets (E2)Flush toilet = 1, pit toilet = 0+0.0037
Green ecologyVegetation coverage (F1)Ratio of cultivated land, forest land, and grassland to total land area+0.1183
Environmental quality (F2)Environmental changes after relocation. Significantly worse = 1, slightly worse = 2, no change = 3, slightly better = 4, significantly better = 5+0.1277
Household water supply quality (F3)Very poor = 1, poor = 2, average = 3, good = 4, very good = 5+0.1694
Note: In the table, “+” denotes positive indicators, indicating that a higher value is preferable, while “-” denotes negative indicators, indicating that a higher value is less desirable.
Table 6. Criteria for the classification of coupling coordination degree levels.
Table 6. Criteria for the classification of coupling coordination degree levels.
D Value IntervalCoordination LevelCoupling Coordination TypeD Value
Interval		Coordination LevelCoupling Coordination Type
(0.0–0.1)1Extreme disorder[0.5–0.6)6Reluctant coordination
[0.1–0.2)2Severe disorder[0.6–0.7)7Primary coordination
[0.2–0.3)3Moderate disorder[0.7–0.8)8Intermediate coordination
[0.3–0.4)4Mild disorder[0.8–0.9)9Good coordination
[0.4–0.5)5Near-disorder[0.9–1.0)10High-quality coordination
Table 7. The buffering capacity, self-organizing capacity, learning capacity, and overall livelihood resilience of farming households under different relocation models.
Table 7. The buffering capacity, self-organizing capacity, learning capacity, and overall livelihood resilience of farming households under different relocation models.
Relocation ModelsBuffering CapacitySelf-Organizing
Capacity		Learning CapacityLivelihood Resilience
TVC0.1459 ± 0.0031 b0.1581 ± 0.0100 a0.1994 ± 0.0095 a0.1924 ± 0.0077 ab
CVA0.2492 ± 0.0102 a0.1578 ± 0.0129 ab0.1979 ± 0.0116 a0.1984 ± 0.0094 a
MVI0.2710 ± 0.0119 a0.1474 ± 0.0176 ab0.1315 ± 0.0123 b0.1573 ± 0.0113 b
SC0.1575 ± 0.0041 b0.1185 ± 0.0079 b0.1836 ± 0.0097 a0.1715 ± 0.0071 ab
Overall0.20590.14540.17810.1799
Note: Different lowercase letters indicate significant differences in indicators among the different relocation models (p < 0.05).
Table 8. Coupling coordination degree of livelihood resilience and ecological livability for relocated households in mining-under villages.
Table 8. Coupling coordination degree of livelihood resilience and ecological livability for relocated households in mining-under villages.
Relocation ModesRelocation VillagesCoupling Degree C ValueCoordination Index T ValueCoupling Coordination Degree D ValueCoordination LevelCoupling Coordination Degree
TVCPenglou0.9940.7260.8499Good coordination
Nanzhongshan0.9670.7900.8749Good coordination
Sanli0.8410.5950.7078Intermediate coordination
Yangtun0.8980.6510.7658Intermediate coordination
Fangzhuang0.9910.7240.8479Good coordination
CVARenji0.9980.9370.96710High-quality coordination
Xingxing0.8700.5870.7158Intermediate coordination
MVIBeihunan0.8830.2190.4395Near-disorder
Wanglou0.3870.1290.2233Moderate disorder
SCRenhe0.9270.6510.7778Intermediate coordination
Honggang0.2230.3960.2973Moderate disorder
Yunhewan0.9820.8230.8999Good coordination
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Wang, P.; Wang, J.; Li, Y.; Ren, Y.; Shi, J. Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China. Land 2025, 14, 1233. https://doi.org/10.3390/land14061233

AMA Style

Wang P, Wang J, Li Y, Ren Y, Shi J. Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China. Land. 2025; 14(6):1233. https://doi.org/10.3390/land14061233

Chicago/Turabian Style

Wang, Peijun, Jing Wang, Yan Li, Yuan Ren, and Jiu Shi. 2025. "Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China" Land 14, no. 6: 1233. https://doi.org/10.3390/land14061233

APA Style

Wang, P., Wang, J., Li, Y., Ren, Y., & Shi, J. (2025). Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China. Land, 14(6), 1233. https://doi.org/10.3390/land14061233

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