Factors Influencing Livelihood Resilience of Households Resettled from Coal Mining Areas and Their Measurement—A Case Study of Huaibei City
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area and Resettlement Models of Villages Occupying Coal
2.1.1. Research Area
2.1.2. Resettlement Models for Coal Mining Villages
2.2. Data Source
2.3. Research Methods
2.3.1. Indicator System Construction
Data Standardization and Determination of Indicator Weights
- (1)
- Data standardization
- (2)
- Determination of indicator weights
Comprehensive Evaluation Using the TOPSIS Model
- (1)
- Construct the weighted matrix.
- (2)
- Find the optimal and worst value vectors for each indicator.
- (3)
- Calculate the weighted Euclidean distance.
- (4)
- Calculate the closeness.
- (5)
- Calculate the livelihood resilience Ri.
2.3.2. Analysis of Influencing Factors
3. Results and Analysis
3.1. Results of Livelihood Resilience Evaluation
3.1.1. Buffering Capacity
3.1.2. Self-Organizing Ability
3.1.3. Learning Ability
3.1.4. Total Livelihood Resilience
3.2. Factors Affecting Livelihood Resilience
3.2.1. Factors Affecting Buffering Capacity
3.2.2. Factors Affecting Self-Organizing Ability
3.2.3. Factors Affecting Learning Ability
4. Discussion
5. Conclusions
- (1)
- The overall livelihood resilience level of the four resettlement models was relatively low, ranked as TVC type > SC type > CVA type > MVC type. In terms of buffering capacity, the ranking was CVA type > MVC type > TVC type > SC type. In terms of self-organizing ability, the ranking was TVC type > SC type > CVA type > MVC type. In terms of learning ability ranking, it was SC type > CVA type > TVC type > MVC type.
- (2)
- The overall constraint factors of different resettlement models were similar, but the specific impact degrees differed. The main constraints on buffering capacity included the quantity of labor, the number of employees in mining enterprises, and arable land resources. The factors with strong constraints on self-organizing ability included traffic accessibility, policy awareness, leadership potential, etc. The main factors influencing learning ability included participation in village collective meetings, education level of the household head, and the number of days laborers work outside.
- (3)
- In the future, for CVA-type resettled families, providing employment opportunities in mining enterprises and strengthening the construction of rural infrastructure such as roads and schools will be important. For the MV- type resettlement model, it is necessary to improve transportation conditions and strengthen cadre training. For the TVC-type resettlement model, it is essential to enhance households’ policy awareness, increase the education and training opportunities for household heads, and improve the mechanization of arable land and the specialization level of farmers. For the SC-type resettlement model, it is crucial to strengthen the sanitation and environmental management of community households, enhance policy promotion, and increase investment in education.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Yang, Y.Q.; Xiao, W.; Li, S.C. A new concept of land relocation for coal-suppressed villages. Jiangsu Agric. Sci. 2018, 46, 238–241. [Google Scholar]
- Liu, H.F.; Bi, R.T.; Wang, G.F. Optimization and demonstration of land consolidation technical system in mining-under hilly billage region. Chin. J. Agric. Resour. Reg. Plan. 2019, 40, 80–88. [Google Scholar]
- Zhao, Y.L.; Peng, S.; Liang, S.; Tian, Y.; Gan, Y.X. On feasibility of turnover index return in integration of coal mining village migration with land transfer policy—A case study of mining areas in Shan Dong Province. Sci. Technol. Manag. Land Res. 2009, 26, 22–26. [Google Scholar]
- Zhao, Y.L.; Hu, Z.Q.; Chen, F.; Zhang, B.; Zhang, W.X. Study on problems and countermeasures in integrating village migration in coal mining areas with land transfer policy: A case of Yanzhou, Shandong. China Land Sci. 2007, 21, 60–64. [Google Scholar]
- Qi, Y.C. Anti-defonmation technology of buildings with high phreatic water level in mining area. J. Min. Strata Control Eng. 2010, 15, 39–41+67. [Google Scholar]
- Jia, Z.T.; Dong, G.J.; Peng, M.C.; Huang, W.F. Feasibility analysis of “increase-decrease linkage” of reclaimed land in mining area villages. Econ. Res. Guide 2016, 12, 33–34. [Google Scholar]
- Cheng, H. Analysis of the relocation and land acquisition issues of coal mine villages. China Collect. Econ. 2020, 20, 10–11. [Google Scholar]
- Sina, D.; Chang-Richards, A.Y.; Wilkinson, S.; Potangaroa, R. What does the future hold for relocated communities post-disaster? Factors affecting livelihood resilience. Int. J. Disaster Risk Reduct. 2019, 34, 173–183. [Google Scholar] [CrossRef]
- Quandt, A. Measuring livelihood resilience: The household livelihood resilience approach (HLRA). World Dev. 2018, 107, 253–263. [Google Scholar] [CrossRef]
- Li, X.P.; Shi, X.M. Smallholders’ Livelihood resilience in the dryland area of the yellow river basin in china from the perspective of the family life cycle: Based on Geodetector and LMG metric model. Land 2022, 11, 1427. [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]
- Nyamwanza, A.M. Livelihood resilience and adaptive capacity: A critical conceptual review. Jamba J. Disaster Risk Stud. 2012, 4, 1–6. [Google Scholar] [CrossRef]
- 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]
- Sina, D.; Chang-Richards, A.Y.; Wilkinson, S.; Potangaroa, R. A conceptual framework for measuring livelihood resilience: Relocation experience from Aceh, Indonesia. World Dev. 2019, 117, 253–265. [Google Scholar] [CrossRef]
- Gong, Y.; Zhang, R.; Yao, K.; Liu, B.; Wang, F. A livelihood resilience measurement framework for dam-induced displacement and resettlement. Water 2020, 12, 3191. [Google Scholar] [CrossRef]
- Kayastha, R.B.; Lee, W.K.; Shrestha, N.; Wang, S.W. Assessing the livelihood vulnerability of nomads to changing climate in the third pole region of Nepal. Land 2023, 12, 1105. [Google Scholar] [CrossRef]
- Chen, S.; Wu, J.; Zhou, K.; Li, R. Livelihood resilience and livelihood construction path of China’s rural reservoir resettled households in the energy transition. Front. Sustain. Food Syst. 2023, 6, 1046761. [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]
- Marschke, M.; Berkes, F. Exploring strategies that build livelihood resilience: A case from Cambodia. Ecol. Soc. 2006, 11, 42. [Google Scholar] [CrossRef]
- Chen, J.; Yang, X.J.; Yin, S. Measures of the resilience, effect and countermeasures of household poverty: The perspective of household structure. Chin. J. Popul. Resour. Environ. 2016, 26, 150–157. [Google Scholar]
- Hak, S.; Mcandrew, J.; Neef, A. Impact of government policies and corporate land grabs on indigenous people’s access to common lands and livelihood resilience in Northeast Cambodia. Land 2018, 7, 122. [Google Scholar] [CrossRef]
- Savari, M.; Damaneh, H.E.; Damaneh, H.E. Effective factors to increase rural households’ resilience under drought conditions in Iran. Int. J. Disaster Risk Reduct. 2023, 90, 103644. [Google Scholar] [CrossRef]
- Pagnani, T.; Gotor, E.; Caracciolo, F. Adaptive strategies enhance smallholders’ livelihood resilience in Bihar, India. Food Secur. 2021, 13, 419–437. [Google Scholar] [CrossRef]
- Liu, W.; Li, J.; Ren, L.; Xu, J.; Li, C.; Li, S. Exploring Livelihood Resilience and Its Impact on Livelihood Strategy in Rural China. Soc. Indic. Res. 2020, 150, 977–998. [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]
- 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]
- 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]
- Sarker, M.N.I.; Wu, M.; Alam, G.M.M.; Shouse, R.C. Livelihood resilience of riverine island dwellers in the face of natural disasters: Empirical evidence from Bangladesh. Land Use Policy 2020, 95, 106315. [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]
- 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]
- 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]
- Daniel, D.; Sutherland, M.; Speranza, C.I. The role of tenure documents for livelihood resilience in Trinidad and Tobago. Land Use Policy 2019, 87, 104008. [Google Scholar] [CrossRef]
- Hurlbert, M.A.; Gupta, J.; Verrest, H. A Comparison of drought instruments and livelihood capitals* combining livelihood and institutional analyses to study drought policy instruments. Clim. Dev. 2019, 11, 863–872. [Google Scholar] [CrossRef]
- 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]
- 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]
- Li, C.; Gao, M. Empirical study on impact of new urbanization on rural households’ livelihood resilience under migration and relocation for poverty alleviation. Stat. Decis. 2019, 35, 89–94. [Google Scholar]
- Ye, W.L.; Wang, Y.; Min, D.; Yang, X.J. Spatio-temporal evolution of the decoupling relationship between farmers’ livelihood resilience and multidimensional poverty in ecologically fragile areas: Jia County, Shaanxi Province. J. Arid Land Resour. Environ. 2021, 35, 7–15. [Google Scholar]
- Liu, C.F.; Liu, Y.Y.; Wang, C. Spatial characteristics of livelihood assets of poor farmers and its influential factors in Loess Hilly Region—A case study of Yuzhong County, Gansu Province. Econ. Geogr. 2017, 37, 153–162. [Google Scholar]
- Zhang, H.; Chen, H.; Geng, T.W.; Shi, Q.Q.; Liu, D. Spatial different and influential factors of farmers’ livelihood resilience in Hilly-Gully Region: A case study of Shigou Township in Mizhi County of Northern Shanxi. Int. J. Geogr. Inf. Sci. 2020, 36, 100–106. [Google Scholar]
- Su, F.; Luo, J.Q.; Zhu, X.Q.; Tong, L.; Zheng, Y.Y.; Xie, Y.J. Study on measurement and influencing factors of livelihood resilience in rural areas of Hubei Province. Sci. Adv. 2021, 36, 1117–1126. [Google Scholar]
- Deng, S.Q. Some insights on the survey of coal mining subsidence area in Huaibei City. West. Resour. 2020, 10, 65–67. [Google Scholar]
- Liu, H.J.; Jiang, Y.; Duan, J.; Gao, S.; Xu, Q. Exploration on spatial planning for transformation and sustainable development of resource-exhausted cities. Urban. Archit. 2021, 18, 7–11+87. [Google Scholar]
- Guo, X.X. Discussion on geological environment problems of mines in Huaibei City and significance of ecological restoration. Resour. Environ. Eng. 2021, 35, 364–368. [Google Scholar]
- Li, H.B.; Yan, Y.; Wang, X. Investigation and analysis on the basic situation of mining subsidence area in Huaibei City. West. Resour. 2021, 20, 89–91. [Google Scholar]
- Jiang, X.Y.; Wang, Q.H.; Zhou, H.X.; Tian, Y.B.; Li, X.L. Design and evaluation of coal mining scheme under Taibai Lake. Mine Surv. 2021, 49, 25–32+62. [Google Scholar]
- Yang, Y.Q.; Xiao, W.; Yu, Y.; Wang, P.F. Preferred relocation model for coal-suppressed villages based on hierarchical analysis and fuzzy judgement method. Jiangsu Agric. Sci. 2013, 41, 376–380. [Google Scholar]
- Qiao, J.; Xu, H.H. Exploring the relocation patterns of coal-suppressed villages around the world. Environ. Impact Assess. 2014, 10, 34–36. [Google Scholar]
- Fang, Y.P.; Zhu, F.B.; Qiu, X.P.; Zhao, S. Effects of natural disasters on livelihood resilience of rural residents in Sichuan. Habitat Int. 2018, 76, 19–28. [Google Scholar] [CrossRef]
- Thulstrup, A.W. Plantation livelihoods in central Vietnam: Implications for household vulnerability and community resilience. Nor. Geogr. Tidsskr. J. Geogr. 2014, 68, 1–9. [Google Scholar] [CrossRef]
- 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]
- 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]
- 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]
- Zuo, G.C.; Chen, Q. Challenges to sustainability of resource-exhausted cities: A case study of Lengshuijiang, China. Probl. Ekorozw. 2015, 10, 89–98. [Google Scholar]
- Chen, C.H. A novel multi-criteria decision-making model for building material supplier selection based on Entropy-AHP weighted TOPSIS. Entropy 2020, 22, 259. [Google Scholar] [CrossRef] [PubMed]
- Ji, T.N.; Zhou, Z.F.; Niu, Z.H.; Zhang, J.S. Comparative analysis of farmers’ livelihood resilience before and after relocation for poverty alleviation: A case study in the relocation site in Zhexiang Town of Zhenfeng County, Guizhou Province. J. Ecol. Rural Environ. 2022, 38, 1406–1414. [Google Scholar]
- Ren, C.F.; Cheng, Y.M.; Zheng, X.; Zhou, L.Z. Evaluation of ecological carrying capacity in Huaibei City based on the mean square deviation decision method. Ecol. Sci. 2019, 38, 168–177. [Google Scholar]
- He, Y.B.; Zhang, J.; Qiao, X.N.; Zhang, O.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]
- Li, T.; Cai, S.; Singh, R.K.; Cui, L.; Fava, F.; Tang, L.; Xu, Z.; Li, C.; Cui, X.; Du, J.; et al. Livelihood resilience in pastoral communities: Methodological and field insights from Qinghai-Tibetan Plateau. Sci. Total Environ. 2022, 838, 155960. [Google Scholar] [CrossRef]
- Luo, X.; Zhang, C.; Song, J.; Qiu, Z.; Li, W.; Wang, W. Do livelihood strategies affect the livelihood resilience of farm households in flooded areas? Evidence from Hubei Province, China. Front. Ecol. Evol. 2022, 10, 909172. [Google Scholar] [CrossRef]
- Liu, W.; Li, J.; Xu, J. Effects of disaster-related resettlement on the livelihood resilience of rural households in China. Int. J. Disaster Risk Reduct. 2020, 49, 101649. [Google Scholar] [CrossRef]
- Liu, J.L.; Ma, H.Q.; Xi, J.C.; Li, Z.; Li, W. The measurement and influencing factors of livelihood resiliencein heritage tourism destinations—A case study of Pingyao Ancient City, Shanxi Province. Tour. Trib. 2023, 38, 70–83. [Google Scholar]
- Li, C.; Li, S.; Feldman, M.W.; Li, J.; Zheng, H.; Daily, G.C. The impact on rural livelihoods and ecosystem services of a major relocation and settlement program: A case in Shaanxi, China. Ambio 2018, 47, 245–259. [Google Scholar] [CrossRef]
- Rogers, S.; Li, J.; Lo, K.; Guo, H.; Li, C. China’s rapidly evolving practice of poverty resettlement: Moving millions to eliminate poverty. Dev. Policy Rev. 2020, 38, 541–554. [Google Scholar] [CrossRef]
- Wang, W.L.; Ren, Q.; Yu, J. Impact of the ecological resettlement program on participating decision and poverty reduction in southern Shaanxi, China. Forest Policy Econ. 2018, 95, 1–9. [Google Scholar] [CrossRef]
- Lo, K.; Wang, M. How voluntary is poverty alleviation resettlement in China? Habitat Int. 2018, 73, 34–42. [Google Scholar] [CrossRef]
- Jie, L. Livelihood adaptation strategy and perceived adaptive capacity of rural relocated households in Southern Shaanxi Province, China. China Popul. Resour. Environ. 2016, 26, 44–52. [Google Scholar]
Sample Area | Resettlement Models | Resettlement Time | Geographical Location | Distance from the County Road | Construction Mode | Community Type | Resettlement Compensation |
---|---|---|---|---|---|---|---|
Penglou Village | Town-based village construction (TVC) | 7–9 years | Near Liuqiao Town | 0.2 km | Build according to the overall planning | Six stories plus attic and basement | 30 m2/person |
Beihunan and Miaoqian Village | Mining village combination (MVC) | 11–13 years | Near the coal mining area | 1.5–3.1 km | Build oneself | Rural community | 15,000 CNY/person |
Renhe community | Suburban community (SC) | 13–14 years | Close to the city | 0.25 km | Build according to the overall planning | New xix-story farmer village | 29.1 m2/person |
Renji Village | Central village agglomeration (CVA) | 12–14 years | Independent lot | 1.3 km | Build oneself | Rural community | 16,000 CNY/person |
Resettlement Model | Resettlement Scale | Distance from Town | Land Use Type | Types of Livelihood Activities | Mine Distribution |
---|---|---|---|---|---|
TVC | Bigger | Close | Cultivated land and construction land | Agricultural employees | More concentrated |
MVC | Less | Relatively far | Cultivated land and construction land | Agricultural employees and self-cultivated small farmers | More dispersed |
SC | Big | Near | Cultivated land and construction land | Agricultural employees and businesses | More concentrated |
CVA | Bigger | Far away | Construction land and commercial service land | Agricultural employees and agricultural business entities | More dispersed |
Variable | Option | Frequency | Percentage | Variable | Option | Frequency | Percentage |
---|---|---|---|---|---|---|---|
Sex | Male | 225 | 47.67% | Education level | Illiterate | 60 | 12.71% |
Female | 247 | 52.33% | Primary school | 194 | 41.10% | ||
Age | 20–40 | 146 | 30.93% | Junior high school | 136 | 28.81% | |
40–60 | 189 | 40.04% | High school | 61 | 12.92% | ||
>60 | 137 | 29.03% | College and above | 21 | 4.45% | ||
Resettlement model | TVC type | 135 | 28.60% | Health status | Good | 379 | 80.30% |
MVC type | 132 | 27.97% | General | 56 | 11.86% | ||
SC type | 101 | 21.40% | Serious illness without labor ability | 28 | 5.93% | ||
CVA type | 104 | 22.03% | Disability | 9 | 1.91% |
Dimension Layer | Indicator Layer | Indicator Definition and Assignment | Weight |
---|---|---|---|
Buffering capacity (0.36) | Household labor quantity (A1) | Labor capacity of household family members. Labor = 2, half labor = 1, no labor = 0 | 0.1433 |
Environmental quality status (A2) | Changes in the environment after relocation. Significantly better = 4; slightly better = 3; no significant change = 2; worse = 1 | 0.2087 | |
Risk perception (A3) | Changes in life risks after relocation. Significantly increased = 5; increased = 4; no change = 3; decreased = 2; significantly decreased = 1 | 0.1252 | |
Cultivated land resources (A4) | Existing cultivated land area, including land transferred and cultivated by the household (mu) | 0.1067 | |
Housing capital (A5) | A comprehensive representation of housing area and structure. Housing type: earth and wood = 1, brick and wood = 2, brick and mortar = 3, reinforced concrete = 4; housing area: 30 m2 = 1, 31–60 m2 = 2, 60–90 m2 = 3, 90–120 m2 = 4, >120 m2 = 5 | 0.0854 | |
Material capital (A6) | The main production and living materials owned by the household family | 0.0934 | |
Per capita income (A7) | The ratio of the total annual income of the household family to the total family population | 0.1016 | |
Number of people employed in the mining area (A8) | The number of family members working in the production unit | 0.0830 | |
Family member health status (A9) | The amount of money spent on medical treatment each year (CNY) | 0.0527 | |
Self-organization (0.33) | Understanding of resettlement policy (B1) | Understanding of resettlement policy. Relatively knowledgeable = 1, somewhat knowledgeable = 2, not knowledgeable = 3 | 0.1510 |
Attitude towards the development of coal mines (B2) | The satisfaction with coal mine development in meeting farmers’ livelihoods. Very dissatisfied = 1, somewhat dissatisfied = 2, generally satisfied = 3, relatively satisfied = 4, very satisfied = 5 | 0.1215 | |
Social integration degree (B3) | Well integrated = 1; able to integrate = 2; difficult to integrate = 3 | 0.1401 | |
Leadership ability of community cadres (B4) | Very low = 1, low = 2, average = 3, high = 4, very high = 5 | 0.1378 | |
Leadership potential (B5) | Number of family members who are party members or village cadres | 0.0760 | |
Transport accessibility (B6) | Distance to the nearest county road (km) | 0.2283 | |
Social network (B7) | Consists of surrounding neighbor relations and neighborhood trust degree. Very good = 1; relatively good = 2; average = 3; relatively poor = 4; terrible = 5 | 0.1452 | |
Learning ability (0.31) | Participation in village collective meetings (C1) | Whether to participate in village collective meetings. Yes = 1, no = 0 | 0.2543 |
Respondent’s education level (C2) | Below primary school = A, primary school = B, junior high school = C, high school and vocational school = D, college and above = E | 0.1641 | |
Respondent’s working time outside the village (C3) | Number of days the respondent works outside the village each year (days) | 0.1170 | |
Family education investment (C4) | The amount of family education investment each year (CNY) | 0.0611 | |
Information acquisition ability (C5) | Time spent watching TV, listening to radio, or browsing the internet daily (h) | 0.0896 | |
Ability to share knowledge (C6) | Very low = 1, low = 2, average = 3, high = 4, very high = 5 | 0.1682 | |
Highest education level of family members (C7) | Below primary school = 1, primary school = 2, junior high school = 3, high school and vocational school = 4, college and above = 5 | 0.1457 |
Type | Buffering Capacity | Self-Organizing Ability | Learning Ability | |||||||
---|---|---|---|---|---|---|---|---|---|---|
CVA Type | Obstacle Factor | A1 | A8 | A4 | B6 | B1 | B5 | C1 | C7 | C2 |
Obstacle Degree (%) | 14.43 | 13.55 | 12.49 | 25.50 | 21.72 | 13.65 | 26.83 | 16.69 | 16.27 | |
MVC Type | Obstacle Factor | A1 | A4 | A8 | B6 | B1 | B4 | C1 | C3 | C7 |
Obstacle Degree (%) | 15.59 | 12.91 | 12.56 | 36.13 | 18.28 | 12.23 | 33.91 | 13.88 | 12.45 | |
TVC Type | Obstacle Factor | A1 | A4 | A8 | B1 | B5 | B4 | C1 | C2 | C3 |
Obstacle Degree (%) | 17.16 | 15.39 | 12.47 | 29.54 | 19.31 | 18.17 | 27.83 | 15.03 | 13.34 | |
SC Type | Obstacle Factor | A4 | A1 | A2 | B1 | B5 | B2 | C1 | C7 | C2 |
Obstacle Degree (%) | 14.59 | 14.38 | 12.90 | 26.78 | 18.16 | 18.11 | 28.39 | 14.38 | 12.92 |
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Wang, P.; Wang, J.; Zhu, C.; Li, Y.; Sun, W.; Li, J. Factors Influencing Livelihood Resilience of Households Resettled from Coal Mining Areas and Their Measurement—A Case Study of Huaibei City. Land 2024, 13, 13. https://doi.org/10.3390/land13010013
Wang P, Wang J, Zhu C, Li Y, Sun W, Li J. Factors Influencing Livelihood Resilience of Households Resettled from Coal Mining Areas and Their Measurement—A Case Study of Huaibei City. Land. 2024; 13(1):13. https://doi.org/10.3390/land13010013
Chicago/Turabian StyleWang, Peijun, Jing Wang, Chunbo Zhu, Yan Li, Weijun Sun, and Jinyi Li. 2024. "Factors Influencing Livelihood Resilience of Households Resettled from Coal Mining Areas and Their Measurement—A Case Study of Huaibei City" Land 13, no. 1: 13. https://doi.org/10.3390/land13010013