Coupling Coordination Between Livelihood Resilience and Ecological Livability for Farming Households Relocated from Mining-Under Villages in Eastern China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area and Relocation Modes
2.1.1. Overview of the Study Area
2.1.2. Relocation Modes for Mining-Under Villages
2.2. Data Sources
2.3. Research Methods
2.3.1. Measurement of Farming Household Livelihood Resilience
2.3.2. Evaluation Index System for Ecological Livability Level
2.3.3. Coupling Coordination Degree Model
2.3.4. Random Forest Model
2.3.5. Statistical and Analytical Methods
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
3.1.2. Detailed Analysis of Livelihood Resilience for Farming Households Under Different Relocation Modes
Buffering Capacity
Self-Organizing Capability
Learning Capability
Comprehensive Livelihood Resilience
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
3.2.2. Detailed Ecological Livability Levels of Farming Households Relocated Under Different Models
TVC Model
CVA Model
MVI Model
SC Model
3.3. Coupling Coordination Degree
4. Discussion
4.1. Livelihood Resilience of Farming Households
4.2. Ecological Livability Level of Farming Households
4.3. Influencing Factors of Coupling Coordination
4.4. Policy Recommendations
5. Conclusions
- (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.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Park, B.; Ochieng, H.K. The impacts of rural development project on resilience to climatic disasters: The case of Bangladesh. World Dev. 2024, 180, 106631. [Google Scholar] [CrossRef]
- Su, H.; Zhao, X.; Wang, L.; Li, Y. How rural community resilience evolves after a disaster? A case study of the eastern Qinghai-Tibetan Plateau, China. Appl. Geogr. 2024, 165, 103238. [Google Scholar] [CrossRef]
- Yang, Y.H.; Xiao, W.; Li, S.C. New concepts for relocation land of mining-under villages. Jiangsu Agric. Sci. 2018, 46, 238–241. [Google Scholar]
- Ge, X.C.; Chen, S.J. Reactivation and activation: The livelihood transformation and development of relocated migrants in the post poverty alleviation era. J. Arid. Land Resour. Environ. 2021, 35, 15–21. [Google Scholar]
- Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Evol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
- Walker, B.; Holling, C.S.; Carpenter, S.R.; Kinzig, A.P. Resilience, adaptability and transformability in social-ecological systems. Ecol. Soc. 2004, 9, 5. [Google Scholar] [CrossRef]
- Folke, C. Resilience: The emergence of a perspective for social-ecological systems analyses. Glob. Environ. Chang. 2006, 16, 253–267. [Google Scholar] [CrossRef]
- Chambers, R.; Conway, G. Sustainable rural livelihoods: Practical concepts for the 21st century. IDS Discuss. Pap. 1992, 296, 5–9. [Google Scholar]
- Tanner, T.; Lewis, D.; Wrathall, D.; Bronen, R.; Cradock-Henry, N.; Huq, S.; Lawless, C.; Nawrotzki, R.; Prasad, V.; Rahman, M.A.; et al. Livelihood resilience in the face of climate change. Nat. Clim. Chang. 2015, 5, 23–26. [Google Scholar] [CrossRef]
- Dinh, N.C.; Tan, N.Q.; Ty, P.H.; Phuong, T.T.; Linh, N.H.K. Bridging climate vulnerability and household poverty: Perspectives from coastal fishery communities in Vietnam. Local Environ. 2025; 1–18, Early Access. [Google Scholar] [CrossRef]
- Namgyal, P.; Sarkar, S.; Kumar, R. Vulnerability assessment of rural households to climate change using livelihood vulnerability framework approach in the trans-Himalayan region of Ladakh, India. Anthr. Sci. 2025, 49, 100467. [Google Scholar] [CrossRef]
- Sanusi, M.M.; Dries, L. Smallholder rice farmers’ resilience to water insecurity in Ogun State Nigeria. Reg. Environ. Chang. 2025, 25, 30. [Google Scholar] [CrossRef]
- Aschinger, R.; Boillat, S.; Speranza, C.I. Smallholder livelihood resilience to climate variability in South-Eastern Kenya, 2012–2015. Front. Sustain. Food Syst. 2023, 7, 1070083. [Google Scholar] [CrossRef]
- Fan, Y.; Shi, X.; Li, X.; Feng, X. Livelihood resilience of vulnerable groups in the face of climate change: A systematic review and meta-analysis. Environ. Dev. 2022, 44, 100777. [Google Scholar] [CrossRef]
- Ye, W.; Wang, Y.; Yang, X.; Wu, K. Understanding sustainable livelihoods with a framework linking livelihood vulnerability and resilience in the semiarid loess plateau of China. Land 2022, 11, 1500. [Google Scholar] [CrossRef]
- Mohammed, K.; Batung, E.; Kansanga, M.; Nyantakyi-Frimpong, H.; Luginaah, I. Livelihood diversification strategies and resilience to climate change in semi-arid northern Ghana. Clim. Chang. 2021, 164, 53. [Google Scholar] [CrossRef]
- Rudiarto, I.; Elisa, D.N.; Insani, T.D.; Handayani, W.; Dewi, S.P. Livelihood resilience and adaptive cycles of farming households during the implementation of social restriction policies due to COVID-19 in Indonesia: Case study from Grobogan Regency, Central Java. Environ. Dev. Sustain. 2025; 1–30, Early Access. [Google Scholar] [CrossRef]
- Usman, M.; Ali, A.; Baig, S.A.; Radulescu, M.; Abbas, A.; Akram, R. Food security in Punjab, Pakistan: Rural views on climate disasters and their impacts. Environ. Dev. Sustain. 2025; 1–23, Early Access. [Google Scholar] [CrossRef]
- Liu, H.; Pan, W.L.; Su, F.; Huang, J.Y.; Luo, J.Q.; Tong, L.; Fang, X.; Fu, J.Y. Livelihood resilience of rural residents under natural disasters in China. Sustainability 2022, 14, 8540. [Google Scholar] [CrossRef]
- Lu, H.; Zheng, J.; Ou, H.; Liu, Y.; Li, X. Impact of natural disaster shocks on farm household poverty vulnerability-a threshold effect based on livelihood resilience. Front. Ecol. Evol. 2022, 10, 860745. [Google Scholar] [CrossRef]
- Badewa, A.S.; Dinbabo, M.F. Multisectoral intervention on food security in complex emergencies: A discourse on regional resilience praxis in Northeast Nigeria. GeoJournal 2023, 88, 1231–1250. [Google Scholar] [CrossRef]
- Wassie, S.B.; Mengistu, D.A.; Birlie, A.B. Agricultural livelihood resilience in the face of recurring droughts: Empirical evidence from northeast Ethiopia. Heliyon 2023, 9, e16422. [Google Scholar] [CrossRef]
- Li, C.; Wang, L.; Kang, B.W.; Gao, M. Measurement and influencing factors of livelihood resilience of relocated migrants. J. Xi’an Jiaotong Univ. Soc. Sci. 2019, 39, 38–47. [Google Scholar]
- Liu, W.; Li, J.; Xv, J. Evaluation of rural household’s livelihood resilience of the relocation and settlement project in contiguous poor areas. Arid. Land Geogr. 2019, 42, 673–680. [Google Scholar]
- Speranza, C.I.; Wiesmann, U.; Rist, S. An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics. Glob. Environ. Chang. 2014, 28, 109–119. [Google Scholar] [CrossRef]
- Fachrista, I.A.; Irham; Masyhuri; Suryantini, A. Livelihood resilience of vegetable farmers: Efficacy of organic farming in dealing with climate change in Java, Indonesia. Appl. Ecol. Environ. Res. 2019, 17, 11209–11232. [Google Scholar] [CrossRef]
- Wen, T.F.; Shi, Y.Z.; Yang, X.J.; Wang, T. The resilience of farmers’ livelihoods and its influencing factors in semiarid region of the Loess Plateau—A case study of Yuzhong County. Chin. J. Agric. Resour. Reg. Plan. 2018, 39, 172–182. [Google Scholar]
- He, Y.B.; Zhang, J.; Qiao, X.N.; Zhang, Q.L. Rural households’ livelihood resilience in poor mountainous areas under the background of targeted poverty alleviation: A case study of Qinba mountain areas in Henan province. J. Arid. Land Resour. Environ. 2020, 34, 53–59. [Google Scholar]
- Zheng, D.Y.; Huang, X.J.; Wang, C. Farmers’ livelihood resilience and its optimization strategy in Loess Plateau of north Shaanxi province. J. Arid. Land Resour. Environ. 2020, 34, 9–16. [Google Scholar]
- Bauer, T.; de Jong, W.; Ingram, V.; Arts, B.; Pacheco, P. Thriving in turbulent times: Livelihood resilience and vulnerability assessment of Bolivian Indigenous forest households. Land Use Policy 2022, 119, 106–146. [Google Scholar] [CrossRef]
- Zhao, M.; Chen, H.; Shao, L.; Xia, X.; Zhang, H. Impacts of rangeland ecological compensation on livelihood resilience of herdsmen: An empirical investigation in Qinghai Province, China. J. Rural Stud. 2024, 107, 103245. [Google Scholar] [CrossRef]
- Li, X.; Yang, H.; Jia, J.; Shen, Y.; Liu, J. Index system of sustainable rural development based on the concept of ecological livability. Environ. Impact Assess. Rev. 2021, 86, 106478. [Google Scholar] [CrossRef]
- Jiang, X.L.; Wu, Q.L.; Lin, J.; Xu, W.X. An interval comprehensive evaluation of rural ecological livability in Yangtze River Economic Belt. J. Environ. Prot. Ecol. 2021, 22, 189–196. [Google Scholar]
- Yu, J.; Li, X.; Guan, X.; Shen, H. A remote sensing assessment index for urban ecological livability and its application. Geo-Spat. Inf. Sci. 2024, 27, 289–310. [Google Scholar] [CrossRef]
- Xiao, Y.; Li, Y.; Tang, X.; Huang, H.; Wang, R. Assessing spatial-temporal evolution and key factors of urban livability in arid zone: The case study of the Loess Plateau, China. Ecol. Indic. 2020, 140, 108995. [Google Scholar] [CrossRef]
- Matter, S.; Boillat, S.; Ifejika Speranza, C. Buffer-capacity-based livelihood resilience to stressors-An early warning tool and its application in Makueni County, Kenya. Front. Sustain. Food Syst. 2021, 5, 645046. [Google Scholar] [CrossRef]
- Stanford, R.J.; Wiryawan, B.; Bengen, D.G.; Febriamansyah, R.; Haluan, J. The fisheries livelihoods resilience check (FLIRES check): A tool for evaluating resilience in fisher communities. Fish Fish. 2017, 18, 1011–1025. [Google Scholar] [CrossRef]
- Zhao, X.; Chen, H.; Zhao, H.; Xue, B. Farmer households’ livelihood resilience in ecological-function areas: Case of the Yellow River water source area of China. Environ. Dev. Sustain. 2022, 24, 9665–9686. [Google Scholar] [CrossRef]
- Zhao, X.; Xiang, H.; Zhao, F. Measurement and spatial differentiation of farmers’ livelihood resilience under the COVID-19 epidemic outbreak in rural China. Soc. Indic. Res. 2023, 166, 239–267. [Google Scholar] [CrossRef] [PubMed]
- Zhou, W.; Guo, S.; Deng, X.; Xu, D. Livelihood resilience and strategies of rural residents of earthquake-threatened areas in Sichuan Province, China. Nat. Hazards 2021, 106, 255–275. [Google Scholar] [CrossRef]
- Zuo, G.; Chen, Q. Challenges to sustainability of resource-exhausted cities: A case study of Lengshuijiang, China. Probl. Sustain. Dev. 2015, 10, 89–98. [Google Scholar]
- Yang, Z.; Li, C.W.; Ren, Z.Y.; Li, P.; Xu, Y.T.; Han, J.C.; Pei, L. Evaluation of land use performance in Ningxia, China based on Entropy-weight TOPSIS model and diagnosis of its obstacle factors. J. Earth Sci. Environ. 2023, 45, 796–805. [Google Scholar]
- Liu, N.R.S.; Wu, R.G.; Jin, L. Coupling coordinated evaluation of sustainable livelihoods and ecological livability of farming and herding households in semi-agricultural and semi-pastoralist areas of northern China. J. Arid. Land Resour. Environ. 2023, 37, 74–81. [Google Scholar]
- Xiao, L.M.; Zhang, R.J.; Xiao, Q.L. Dynamic evolution of ecological livable level in rural China and its regional disparity: An empirical study based on non-parametric estimation and Dagum Gini coefficient decomposition. Chin. J. Agric. Resour. Reg. Plan. 2021, 42, 119–130. [Google Scholar]
- Liu, W.; Yu, Q.Q. Impact of poverty alleviation resettlement on rural household livelihood resilience in Southern Shaanxi. Geogr. Geo-Inf. Sci. 2023, 39, 105–110. [Google Scholar]
- Shu, X.L.; Gao, Y.B.; Zhang, Y.X.; Yang, C.Y. Study on the coupling relationship and coordinative development between tourism industry and eco-civilization city. Chin. J. Popul. Resour. Environ. 2015, 25, 82–90. [Google Scholar]
- Abera, W.; Tamene, L.; Abegaz, A.; Hailu, H.; Piikki, K.; Soderstrom, M.; Girvetz, E.; Sommer, R. Estimating spatially distributed SOC sequestration potentials of sustainable land management practices in Ethiopia. J. Environ. Manag. 2021, 286, 112191. [Google Scholar] [CrossRef] [PubMed]
City | County (District) | Township | Village (Community) | Number of Valid Questionnaires |
---|---|---|---|---|
Huaibei | Suixi County | Nanping | Renji | 104 |
Wugou | Beihunan | 76 | ||
Wanglou | 56 | |||
Liuqiao | Penglou | 135 | ||
Xiangshan District | Renwei | Renhe | 101 | |
Shangqiu | Yongcheng County | Chengxiang | Honggang | 122 |
Xuzhou | Pei County | Longgu | Sanli | 45 |
Yangtun | Nanzhongshan | 14 | ||
Yangtun | 66 | |||
Jining | Weishan County | Huancheng | Fangzhuang | 104 |
Zoucheng County | Taiping | Xingxing | 109 | |
Rencheng District | Anju | Yunhewan | 95 |
Relocation Mode | Relocation Scale | Distance from Town | Land Use Type | Types of Livelihood Activities |
---|---|---|---|---|
TVC | Bigger | Close | Cultivated land and construction land | Agricultural employees |
MVI | Small | Relatively far | Cultivated land and construction land | Agricultural employees and self-cultivated small farmers |
SC | Big | Near | Cultivated land and construction land | Agricultural employees and businesses |
CVA | Bigger | Far away | Construction land and commercial service land | Agricultural employees and agricultural business entities |
Index | Category | Frequency Number | Frequency Rate | Index | Category | Frequency Number | Frequency Rate |
---|---|---|---|---|---|---|---|
Gender | Male | 489 | 47.61% | Degree of education | Illiterate | 120 | 11.68% |
Female | 538 | 52.39% | Primary school | 357 | 34.76% | ||
Age | 20–40 | 357 | 34.76% | Junior school | 421 | 40.99% | |
40–60 | 479 | 46.64% | Senior high school | 120 | 11.68% | ||
>60 | 191 | 18.60% | University or above | 9 | 0.88% | ||
Relocation model | TVC | 364 | 35.44% | Health condition | Good | 748 | 72.83% |
CVA | 213 | 20.74% | Common | 152 | 14.80% | ||
MVI | 132 | 12.85% | Seriously ill and unable to work | 96 | 9.35% | ||
SC | 318 | 30.96% | Physical disability | 31 | 3.02% |
Dimension Layer | Indicator Layer | Indicator Definitions and Assignments | Attribute | Weight |
---|---|---|---|---|
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 |
Dimensions and Indicators | Basic Indicators | Indicator Definitions and Assignments | Indicator Direction | Weight |
---|---|---|---|---|
Green production | Cultivated 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 living | Traffic accessibility (E1) | Distance to county road (km) | - | 0.3327 |
Presence of flush toilets (E2) | Flush toilet = 1, pit toilet = 0 | + | 0.0037 | |
Green ecology | Vegetation 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 |
D Value Interval | Coordination Level | Coupling Coordination Type | D Value Interval | Coordination Level | Coupling Coordination Type |
---|---|---|---|---|---|
(0.0–0.1) | 1 | Extreme disorder | [0.5–0.6) | 6 | Reluctant coordination |
[0.1–0.2) | 2 | Severe disorder | [0.6–0.7) | 7 | Primary coordination |
[0.2–0.3) | 3 | Moderate disorder | [0.7–0.8) | 8 | Intermediate coordination |
[0.3–0.4) | 4 | Mild disorder | [0.8–0.9) | 9 | Good coordination |
[0.4–0.5) | 5 | Near-disorder | [0.9–1.0) | 10 | High-quality coordination |
Relocation Models | Buffering Capacity | Self-Organizing Capacity | Learning Capacity | Livelihood Resilience |
---|---|---|---|---|
TVC | 0.1459 ± 0.0031 b | 0.1581 ± 0.0100 a | 0.1994 ± 0.0095 a | 0.1924 ± 0.0077 ab |
CVA | 0.2492 ± 0.0102 a | 0.1578 ± 0.0129 ab | 0.1979 ± 0.0116 a | 0.1984 ± 0.0094 a |
MVI | 0.2710 ± 0.0119 a | 0.1474 ± 0.0176 ab | 0.1315 ± 0.0123 b | 0.1573 ± 0.0113 b |
SC | 0.1575 ± 0.0041 b | 0.1185 ± 0.0079 b | 0.1836 ± 0.0097 a | 0.1715 ± 0.0071 ab |
Overall | 0.2059 | 0.1454 | 0.1781 | 0.1799 |
Relocation Modes | Relocation Villages | Coupling Degree C Value | Coordination Index T Value | Coupling Coordination Degree D Value | Coordination Level | Coupling Coordination Degree |
---|---|---|---|---|---|---|
TVC | Penglou | 0.994 | 0.726 | 0.849 | 9 | Good coordination |
Nanzhongshan | 0.967 | 0.790 | 0.874 | 9 | Good coordination | |
Sanli | 0.841 | 0.595 | 0.707 | 8 | Intermediate coordination | |
Yangtun | 0.898 | 0.651 | 0.765 | 8 | Intermediate coordination | |
Fangzhuang | 0.991 | 0.724 | 0.847 | 9 | Good coordination | |
CVA | Renji | 0.998 | 0.937 | 0.967 | 10 | High-quality coordination |
Xingxing | 0.870 | 0.587 | 0.715 | 8 | Intermediate coordination | |
MVI | Beihunan | 0.883 | 0.219 | 0.439 | 5 | Near-disorder |
Wanglou | 0.387 | 0.129 | 0.223 | 3 | Moderate disorder | |
SC | Renhe | 0.927 | 0.651 | 0.777 | 8 | Intermediate coordination |
Honggang | 0.223 | 0.396 | 0.297 | 3 | Moderate disorder | |
Yunhewan | 0.982 | 0.823 | 0.899 | 9 | Good 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
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 StyleWang, 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 StyleWang, 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