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Journal of Geographical Sciences >

2025 , Vol. 35 >Issue 7: 1556 - 1574

DOI: https://doi.org/10.1007/s11442-025-2384-6

Research Articles

Relocation for improved ecosystem service and human wellbeing? Evidence from Fuping, Hebei, China

  • WU Pengxin , 1 ,
  • LIU Mingyao 1 ,
  • ZHENG Mingze 1 ,
  • LIAO Chuan 2 ,
  • HUA Xiaobo 3 ,
  • FEI Ding 4 ,
  • BAI Yansong 1, 5 ,
  • ZHOU Yuchen 1, 5 ,
  • ZHOU Yihan 5 ,
  • HUANG Qingxu , 1, 5, *
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  • 1. State Key Laboratory of Disaster Risk Reduction, Beijing Normal University, Beijing 100875, China
  • 2. Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
  • 3. College of Humanities and Development Studies, China Agricultural University, Beijing 100193, China
  • 4. Department of City and Regional Planning, Cornell University, Ithaca, NY, USA
  • 5. School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
* Huang Qingxu, Professor, specialized in rural and urban sustainability. E-mail:

Wu Pengxin, specialized in ecosystem services. E-mail:

Received date: 2024-12-25

  Accepted date: 2025-04-23

  Online published: 2025-08-26

Supported by

National Natural Science Foundation of China(42361144859)

Team Construction Project of Faculty of Geographical Science, BNU(2024-JXTD-03)

Team Construction Project of Faculty of Geographical Science, BNU(2024-KYTD-09)

The Beijing Normal University Tang Scholar(2021)

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Abstract

Migration is a potential strategy to reduce poverty in the Global South. In China, the Poverty-alleviation Relocation (PAR) is a government-led, large-scale migration initiative aimed at eliminating poverty and promoting environmental sustainability. To examine the ecological and socio-economic effects of the PAR, we quantified the changes in five types of ecosystem services (ES) as well as the subjective well-being of rural residents in Fuping county, Hebei province of China, by using ES mapping, household survey, and semi-structured interviews. We found that the PAR improves people’s quality of life, with the well-being scores associated with transportation, communication, education, and healthcare increasing by 0.45-0.81. Additionally, the PAR enhances the supply of ES, evidenced by the increases in four types of ES in both in-migration and out-migration areas. The ES growth rates in in-migration areas ranged from 0.7% to 3.9%, while in out-migration areas, the rates ranged from 0.4% to 2.5%. However, the changes in income and food well-being are minimal, with scores at 0 and 0.32, respectively. More importantly, the elderly and low-educated residents experience minimal improvements in well-being after relocation. Our findings suggest that for other developing countries seeking to adopt PAR, it is crucial to provide targeted support for livelihood transitions, particularly for marginalized social groups, restore out-migration areas, and strengthen cross-regional cooperation to better address ecological constraints on livelihoods.

Key words： ecosystem services; land use; rural sustainability; subjective wellbeing; social-ecological system; urbanization

Cite this article

WU Pengxin , LIU Mingyao , ZHENG Mingze , LIAO Chuan , HUA Xiaobo , FEI Ding , BAI Yansong , ZHOU Yuchen , ZHOU Yihan , HUANG Qingxu . Relocation for improved ecosystem service and human wellbeing? Evidence from Fuping, Hebei, China[J]. Journal of Geographical Sciences, 2025 , 35(7) : 1556 -1574 . DOI: 10.1007/s11442-025-2384-6

1 Introduction

‘No Poverty,’ the primary goal among the 17 Sustainable Development Goals (SDGs) proposed by the United Nations, has become one of the greatest challenges faced by developing countries in their pursuit of sustainable development (United Nations, 2015). Tremendous efforts by governments and non-governmental organizations worldwide have led to extraordinary declines in extreme poverty over the past three decades (Asadullah and Savoia, 2018). According to World Bank’s PovcalNet, between 1990 and 2014, the total population living on less than $2.15 per day fell by near 1.2 billion (World Bank, 2022).
Many developing countries have implemented various poverty alleviation strategies, among which relocation aims to move rural households out of unfavorable natural environment (Baird and Shoemaker, 2007; Hammond, 2008; Arnall et al., 2013) to address the ecological constraints of human well-being. That is because rural populations in various developing countries are often caught in a vicious circle of poverty and environmental degradation, often known as “spatial poverty traps” (Ravallion and Wodon, 1999; Cavendish, 2000; Pani and Carling, 2013). Concurrent with the relocation is land use change (Li et al., 2018a). Building new communities and demolishing previous houses will change the land use both in in-migration and out-migration areas, impacting the local ecological systems. Thus, relocation can be seen as a strategy coupling human and natural systems to achieve the twin goals of poverty alleviation and ecological restoration, benefiting sustainable development in poor regions (Lo et al., 2016).
The global drop in the percentage of population living in extreme poverty can be largely attributable to China’s poverty reduction efforts (United Nations, 2015). From 1990 to 2014, China eradicated poverty for more than 730 million people, accounting for 59.3% of the global reduction (World Bank, 2022). To lift remaining 70 million rural residents out of poverty, the Chinese central government implemented the Targeted Poverty Alleviation (TPA) in 2015 (Liu et al., 2018). A key strategy of the TPA is extensive relocation, which has involved millions of impoverished households and required substantial policy and financial support.
Poverty Alleviation Relocation (PAR) program is designed to relocate residents from remote mountainous villages into new apartments with efficient provision of infrastructure, services, and employment (Liu, 2007). The National Development and Reform Commission (NDRC) guides the PAR through the Poverty Alleviation Relocation Plan (NDRC, 2016), while county-level governments select relocation sites tailored to local contexts and demands. As reported by the central government (NDRC, 2020), the PAR Initiative achieved nationwide poverty eradication in 2020, with current efforts now prioritizing the consolidation of these achievements and the prevention of poverty relapse.
Globally, many studies have highlighted the close relationship between migration and poverty (Fuseini et al., 2023). The economic impact of spontaneous internal migration among the poor is well-researched. Cases from South Asia, Southeast Asia and West Africa show that migration reduces poverty at the individual level (Ranabahu, 2004; Anh, 2006; Afsar, 2009; Kwankye and Anarfi, 2011; Maimbo et al., 2015), and at the household level (Yang and Lu, 2010). However, research examining the multidimensional welfare reveals negative consequences, including deteriorating living condition (Asis, 2006), lack of labor rights (Khan and Harroff-Tavel, 2011), and inadequate care for left-behind family members (Hugo, 2002).
Unlike organized resettlement targeted poverty alleviation (Fuseini et al., 2023), large-scale resettlements for other purposes-such as disaster prevention, climate change adaptation and infrastructure development-are common worldwide. Previous studies indicate that without adequate planning, resources, and governance (Yadeta et al., 2022), such resettlements usually cause environmental degradation, due to unsustainable farming practices, intensified land use, and overextraction of natural resources (Nakayama, 1998; Schmidt-Soltau, 2003; Rogers and Wang, 2006). Therefore, to promote both social and environmental sustainability in resettlement areas, it is essential to consider the overall socio-economic and environmental impacts (Yeboah et al., 2022).
PAR, the largest poverty-related resettlement initiative, has been examined for its impact on households’ well-being. Based on survey at household level, many studies found that household incomes increased after relocation (Lo et al., 2016; Li et al., 2018a; Leng et al., 2021; Liu et al., 2023), while some others revealed declines in income (Ong, 2014), increases in income inequality (Li et al., 2021b) and larger gaps between income and living expense (Fan et al., 2015) after relocation. In addition, the overall change in household income masking differences among individuals. Tang et al. (2021) and Rogers et al. (2020) suggests that the socio-economic status of individuals significantly impacts changes in well-being after relocation. However, the environmental impact of the PAR has received less attention, partly due to the relatively short period since relocation. Recently, Feng et al. (2024) found that the PAR enhances the value of ecosystem services in the vulnerable Karst regions of Southwest China. Nonetheless, a research gap remains in integrating both well-being and ecological impacts of PAR from a holistic perspective.
This paper aims to investigate the effects of poverty alleviation relocating program on ecosystem services as well human wellbeing in Fuping county, Hebei province of China, which suffers from chronic poverty in mountainous areas. Specifically, we focus on two research questions: 1) How does poor households’ multi-dimensional well-being change at the individual level after relocation? 2) How do ecosystem services (ES) change in out-migration and in-migration sites after relocation? In the rest of this paper, we first contextualize the development of PAR in Fuping. Next, we introduce our methods, and then present results on PAR’s impact. Towards the end, we discuss how PAR changes the relationship between humans and ecosystems, and then summarize the efforts and challenges of the PAR. Our study provides policy recommendations and sheds lights on sustainable development pathways in underdeveloped regions.

2 Study area

2.1 Geographical characteristics and poverty alleviation context

Fuping, a county in Hebei province, North China, experiences a temperate monsoon climate (Figure 1). Winters are cold and dry, while summers are hot and humid, with rainfall concentrated between June and September. Covering an area of 2496 km2, the county is predominantly mountainous, with only 5.8% of the area under cultivation (Guo et al., 2022). Approximately 85% of the population (around 195,800 people) in Fuping relied on crop cultivation for their livelihoods in 2015 (Wei, 2021). Before the implementation of the TPA, Fuping was identified as one of the 832 impoverished counties in China (NRRA, 2014). According to the national Ministry of Environmental Protection, Fuping is identified as regions for water and soil conservation (Ministry of Environmental Protection, 2015), resulting in strict constraints on land-based development.
Figure 1 Location of the study area (Fuping county, Hebei province, North China) and the selected sample townships
PAR serves as the primary method for poverty-alleviation and ecological restoration in Fuping. In 2016, Fuping carried out 53 relocation projects, involving 598 settlements and 87,000 individuals (Guo et al., 2022). Following the relocation, the central government visited the area and publicly identified Fuping as a representative county in news reports. Rural households, including the poor, and the non-poor from targeted remote rural villages who have agreed to abandon their rural homesteads, were provided with housing in newly constructed high-rise resettlement communities. In addition, each relocated household receive a settling-in allowance of 8000 yuan per capita.
Under the policy framework of TPA, the local government integrated PAR with various anti-poverty measures, including industrial development, education supporting, and land consolidation. While supporting mushroom cultivation and fruit plantation, the government also offer the identified poor residents specific job opportunities in these rising industries (Guo et al., 2022). Rural residents are encouraged to transfer their barren farmland to enterprises. Some of the land is used for the intensive agricultural production of apples, pears and grapes, while the other is used for reforestation (Zhou et al., 2019).

2.2 Relocation process

Overall, the moving distance for resettled residents was quite short, with an average relocation distance of 1.08 km. All of six resettlement communities were constructed within the townships, of with two located near the town government, in the lowland areas (Figure 2). The other four communities were situated in the previous villages, where relocation was achieved by demolishing bungalows and constructing multi-story buildings. Most residents were relocated on-site, with a relocation distance of less than 1 km, accounting for 57.2% of the total. The resettlement areas in the original villages primarily accommodated local residents but also included residents from smaller mountainous villages. However, in the two resettlement areas near the government, 100% and 55.8% of residents moved more than 1 km, mostly involving people from other villages. Therefore, while the spatial location of residents became more concentrated after relocation, the actual moving distance was not substantial.
Figure 2 Relocation process in sample townships (relocation number from survey respondents)

3 Methods

We used a mixed methods approach in this research. First, we gathered policy documents and conducted semi-structured interviews with local officials, in order to clarify the specific goal of the PAR Initiative and the relocation process. Second, we conducted a survey to quantify the well-being changes of the relocated individuals and to compare these changes at the individual level. Third, based on land use data, we quantified the changes ES around the relocation sites.

3.1 Collecting questionnaires and conducting semi-structured interview

We conducted fieldwork in Fuping in 2023. There is total thirteen townships in Fuping. Excluding pilot sites and government-designated typical townships, we selected two representative townships: Longquanguan and Tianshengqiao (Figure 1). Characterized by significant ecological constraints and large relocated populations, the two townships located in the mountainous areas of Fuping, with less than 5% of land for agriculture. Longquanguan and Tianshengqiao relocated near half of its residents around 2017, with 2340 and 5292 individuals, respectively.
To clarify the relocation policy and process, we conducted semi-structured interviews with local officials responsible for the relocation work in each township. The interviews mainly covered issues such as the site selection of the in-migration area, reuse methods for the out-migration area, job availability, and the living security measures for residents. Moreover, the officials provided us with the accurate locations of the resettlement communities and the number of relocated residents, information that is not commonly disclosed to the public.
To measure well-being change of relocated individuals, we conducted surveys in all resettlement communities of the two townships. The sample size of the questionnaire was determined according to the formula proposed by Yamane (1973):
n = N 1 + N e 2
The sample size n is calculated to be from 199 to 382, where the error is taken as 5%-7% (i.e., e=0.05-0.07) and N is the total relocated population (N=8858).
The survey consists of four parts (Table S1). The first part covers the socioeconomic characteristics of respondents, including gender, age, occupation, education, non-farm income, and annual per capita household income. The next part addresses various sources of income and expenditures after relocation. The third part assesses the satisfaction of respondents with five well-being dimensions. During the survey, we also asked open-ended questions based on the respondents’ answers to gain a deeper understanding of specific aspects of changes in their well-being. When distributing questionnaires, we ensured that the respondents come from different households.
Table S1 Survey on household life satisfaction and socio-economic characteristics
Dear Sir/Madam,
We are from the Faculty of Geography, Beijing Normal University, and we are currently conducting a survey on household life satisfaction in Fuping. We invite you to complete this questionnaire. The survey is conducted anonymously and does not involve personal information such as your name or phone number. We will keep the results strictly confidential, so please feel free to answer truthfully. We sincerely appreciate your participation!
Satisfaction
Question description Strongly disagree Disagree No difference Agree Strongly agree
After relocation, the income of household members has
increased.		 ○ ○ ○ ○ ○
After relocation, you can have access to food from more sources, and it is sufficient and secure. ○ ○ ○ ○ ○
After relocation, you can have access to water from more sources, and it is sufficient and secure. ○ ○ ○ ○ ○
After relocation, the wireless communication (e.g., phone, internet signal) has improved. ○ ○ ○ ○ ○
After relocation, transportation in your area has become more convenient (you can reach your destination faster and more easily). ○ ○ ○ ○ ○
After relocation, you and your family have easier access to better educational resources (e.g., easier school enrollment, shorter distance to school). ○ ○ ○ ○ ○
After relocation, the quality of your housing has improved (e.g., per capita housing area, house quality). ○ ○ ○ ○ ○
After relocation, the number of green spaces (parks, squares, etc.) in your area has increased. ○ ○ ○ ○ ○
After relocation, your physical health has improved. ○ ○ ○ ○ ○
After relocation, your mental health has improved (e.g., better mood, reduced life stress). ○ ○ ○ ○ ○
After relocation, the ecological environment (air quality, greenery in the community, etc.) in your area has made you feel happier. ○ ○ ○ ○ ○
After relocation, your daily diet has become more diverse, nutritious, and healthy. ○ ○ ○ ○ ○
After relocation, you and your family have easier access to better medical resources (e.g., easier to buy medicine, see a doctor). ○ ○ ○ ○ ○
After relocation, the crime rate in your area has decreased, social security has improved, and your sense of safety has increased. ○ ○ ○ ○ ○
After relocation, the pollution (e.g., water pollution, air pollution) in your area has decreased, and the environmental quality has improved. ○ ○ ○ ○ ○
After relocation, the frequency of natural disasters (e.g., flash floods) in your area has decreased. ○ ○ ○ ○ ○
After relocation, your connection with community residents, relatives, and friends has become closer. ○ ○ ○ ○ ○
After relocation, you often participate in social activities (e.g., square dancing, community events). ○ ○ ○ ○ ○
After relocation, you are more actively involved in the decisions and implementation of village or town affairs. ○ ○ ○ ○ ○
Overall, your satisfaction with life has increased after relocation. ○ ○ ○ ○ ○
Realized ecosystem services
Type Indicator
Provisioning Annual income from crop farming (e.g., medicinal plants, fruit trees, mushroom cultivation, etc.):
≤ ¥0-1000 ≤ ¥1000-3000 ≤ ¥3000-5000 ≤ ¥5000-10,000 ≤ ¥10,000-50,000
≤ ¥50,000-100,000 ≤ > ¥100,000 ≤ None
Annual income from livestock farming (e.g., pig farming, fish farming, poultry farming):
≤ ¥0-1000 ≤ ¥1000-3000 ≤ ¥3000-5000 ≤ ¥5000-10,000 ≤ ¥10,000-50,000
≤ > ¥50,000 ≤ None
How much has your annual water bill increased after relocation:
≤ ¥0-100 ≤ ¥100-300 ≤ ¥300-500 ≤ ¥500-800 ≤ > ¥800 ≤ No difference ≤ Decreased
Did you use natural resources such as coal, wood, or straw for fuel before relocation? ≤ Yes ≤ No
How much has your annual electricity bill increased after relocation?
≤ ¥0-250 ≤ ¥250-500 ≤ ¥500-750 ≤ ¥750-1000 ≤ ¥1000-1500 ≤ > ¥1500
≤ No difference ≤ Decreased
Regulation Have you been affected by the following disasters?
≤ Drought ≤ Spring frost ≤ Sandstorm ≤ Flash flood ≤ Heavy rain ≤ Other_______
≤ None
Annual economic loss caused by natural disasters:
≤ ¥0-1000 ≤ ¥1000-3000 ≤ ¥3000-5000 ≤ ¥5000-10,000 ≤ ¥10,000-50,000
≤ > ¥50,000 ≤ None
Do you think the quality of drinking water has changed after relocation?
≤ Significantly decreased ≤Slightly decreased ≤ No difference ≤ Slightly improved
≤ Significantly improved
Do you think air quality has changed after relocation?
≤ Significantly decreased ≤Slightly decreased ≤ No difference ≤ Slightly improved
≤ Significantly improved
Compared to the original village, do you think the plant diversity in the community has changed after relocation?
≤ Significantly decreased ≤Slightly decreased ≤ No difference ≤ Slightly improved
≤ Significantly improved
Cultural Frequency of recreational activities such as hiking, walking by the river, or chatting in the community:
≤ 5-7 times a week ≤ 3-5 times a week ≤ 1-2 times a week ≤ Once every two weeks
≤ Rarely
Frequency of social or recreational activities such as going to squares or activity centers (outdoor):
≤ 5-7 times a week ≤ 3-5 times a week ≤ 1-2 times a week ≤ Once every two weeks
≤ Rarely
Annual income from tourism (including wages from working at natural scenic spots such as the
Tienshengqiao Waterfall Scenic Area):
≤ ¥0-5000 ≤ ¥5000-10,000 ≤¥10,000-50,000 ≤ ¥50,000-100,000 ≤ > ¥100,000
≤ None
Personal information
Gender ≤ Male
≤ Female		 Age ≤≤19 ≤ 20-29 ≤ 30-39 ≤ 40-49 ≤ 50-59 ≤≥60
Occupation ≤ Enterprise/Public institution ≤ Crop farming ≤ Livestock farming ≤ Handicraft
≤ Tourism ≤ Migrant work ≤ Other_______
Educational level ≤ No education ≤ Primary school ≤ Junior high school ≤ High school
≤ College or higher
Current resettlement area ≤ Gaoqu ≤ Ping Shitou ≤ Dajiaochang ≤ Zhujiaying ≤ Tienshengqiao ≤ Liyuanpu
≤ Bulaoshu
Administrative
village before
relocation		 ≤ Longquanguan ≤ Pingshitou ≤ Beiliuzhuang ≤ Heiyagou ≤ Gujiatai
≤ Xiliuzhuang ≤ Yinshashi ≤ Luotuowan ≤ Balizhuang ≤ Qingyanggou
≤ Dahubu ≤ Heilingou ≤ Tagou ≤ Luojiazhuang ≤ Dajiaochang ≤ Zhuajiaying
≤ Longwangmiao ≤ Bulaoshu ≤ Nanliyuanpu ≤ Tianshengqiao ≤ Beiliyuanpu
≤ Xixiaguan ≤ Yantai ≤ Hongcaohe ≤ Dachegou
Socio-economic characteristics
Annual non-agricultural income (e.g., wages, shop operation) ≤ ¥0-5000 ≤ ¥5000-10,000 ≤ ¥10,000-30,000 ≤ ¥30,000-50,000
≤ ¥50,000-100,000 ≤ > ¥100,000 ≤ None
Housing compensation
(Subsidy for original house during relocation)		 ≤ ¥0-1000 ≤ ¥1000-3000 ≤ ¥3000-5000 ≤ ¥5000-10,000
≤ ¥10,000-30,000 ≤ ¥30,000-50,000 ≤ > ¥50,000 ≤ None
Per capita household annual income ≤ ¥0-1000 ≤ ¥1000-3000 ≤ ¥3000-5000 ≤ ¥5000-10,000 ≤ ¥10,000-30,000
≤ ¥30,000-50,000 ≤ > ¥50,000
Do you have a pension? ≤ Yes ≤ No Are you a member of a poor household? ≤ Yes ≤ No

3.2 Quantifying and comparing well-being

Subjective well-being (SWB) refers to an individual’s perception and satisfaction with their state of life (Diener and Suh, 1997). It is measured through questionnaires by investigating the degree of happiness and satisfaction (Wang and Tang, 2016). Compared to objective well-being (OWB), which is commonly based on statistical data, SWB focused on well-being perception at the individual level.
Life satisfaction is one of the most common indicators of SWB, quantifying SWB by weighting and summing individuals’ ratings of multi-dimensional life satisfaction. First, based on the Millennium Ecosystem Assessment framework (MEA, 2005), 17 indicators in five dimensions of well-being were selected for the study (Table 1).
Table 1 Classification and definition of subjective well-being indicators
Subjective well-being Factors Definition
Essential materials
for a good life		 Food The ease of access to a diverse and abundant food supply
Fresh water The ease of access to an ample water supply
Communication The wireless communication such as phone and internet signal
Transport The level of transportation convenience
Education The ease of access to education such as distance
Housing The quality of housing, such as per capita housing area and
house quality
Public facilities The number of green spaces, such as parks and squares
Health Physical The level of physical health
Mental The level of mental health, such as mood and life stress
Diet Dietary diversity and nutritional quality
Medical conditions The accessibility and abundance of medical resources
Safety Social and public safety The crime rate and public security in the region
Productive Security The level of environmental pollution and the frequency of
natural disasters
Income Income Changes in income of household members
Social interaction Neighbours and friends The degree of closeness in connections with neighborhood
residents, relatives, and friends
Social activities The frequency of participation in social activities
Political activity The frequency of participation in the decision-making and
implementation of affairs in the village or town
Second, respondents scored each of the 17 indicators according to their perceived satisfaction after relocation. In our survey, satisfaction was scored on a scale from -2 to 2, where -2 represents “very dissatisfied”, -1 “dissatisfied”, 0 “average”, 1 “satisfied”, and 2 “very satisfied”. The well-being score can be expressed as follows:
W B i = ∑ j = 1 n S i j n
where WBi is the score for the type i of well-being, Sij is the satisfaction score for the evaluation indicator j of the type i of well-being. n is the number of indicators included in each type of well-being. Finally, the overall well-being score was calculated as the average of the respondents’ well-being scores from the questionnaire.
To compare the differences in well-being at the individual level, the samples were grouped according to their socioeconomic characteristics, including age, education, non- farm income, per capita annual income of the family and whether they were identified as poor households. After averaging each dimension of well-being separately, the significance of differences between groups was tested using the K-W test in SPSS 24.0. The K-W test is a non-parametric test that makes no assumptions about the overall distribution of the sample. Therefore, this method is not restricted by the distribution of the sample and has a wide range of application (Kruskal and Wallis, 1952).

3.3 Characteristics of the respondents

A total of 302 household surveys were performed in Fuping, of which 280 were valid, resulting in a response rate of 92.7% (Table 2). The official statistics showed that there were 48.5 thousand males in rural Fuping, accounting for 51.82%. Among the respondents, 47.9% were female and 52.1% were male, which aligns with the proportion of residents. Compared to the residents (28.85), the proportion of respondents over 60 years old was relatively high (40.4%). The majority of respondents (59.7%) had a primary school or junior high education, while this proportion for residents (84.05%) in Fuping is higher. Compared to respondents (16.8%), there were less uneducated residents (3.07%) in rural Fuping. Regarding income, 47.5% of respondents did not have non-agricultural income, and 41.1% of residents had an annual per capita income between 0-3000 yuan. Additionally, 46.1% of all respondents were identified as impoverished residents.
Table 2 Characteristics of respondents and residents in Fuping
Respondents’ characteristics Residents’ characteristics
Type Number Proportion Type Number Proportion
Gender Female 134 47.9% Female 45,140 48.2%
Male 146 52.1% Male 48,543 51.8%
Age 0-19 years old 33 11.8% 0-15 years old 19,526 20.8%
20-29 years old 11 3.9%
30-39 years old 19 6.8% 16-59 years old 47,134 50.3%
40-49 years old 53 18.9%
50-59 years old 51 18.2% 60 years old and above 27,023 28.9%
60 years old and above 113 40.4%
Education Uneducated 47 16.8% Uneducated 2808 3.1%
Primary school 68 24.3% Primary school 31,184 34.1%
Junior high school 99 35.4% Junior high school 45,749 50.0%
High school 44 15.7% High school 5695 6.2%
College and above 22 7.9% College and above 3392 3.7%
Non-agricultural
income
(104 yuan)		 0 133 47.5%
0-0.5 42 15.0%
0.5-1 33 11.8%
1-3 45 16.1% N/A
3-5 19 6.8%
5-10 5 1.8%
>10 3 1.1%
Per capita
household income
(104 yuan)		 0-0.1 79 28.2%
0.1-0.3 64 22.9%
0.3-0.5 33 11.8%
0.5-1 39 13.9% N/A
1-3 44 15.7%
3-5 11 3.9%
>5 10 3.6%
Identified
poor households		 yes 129 46.1% N/A
no 151 53.9%

3.4 Mapping ecosystem services

Ecosystem services (ES) refer to the benefits that humans obtain from ecosystems (MEA, 2005). These include supporting services (such as soil formation and nutrient cycling), provisioning services (such as the provision of food and clean water), regulating services (such as air purification and climate regulation), and cultural services (such as recreation and leisure) (Wu, 2013). ES ensure a good quality of life for people by offering opportunities to meet human needs (Qiu et al., 2021).
Considering the ecological characteristics of Fuping, we chose five indicators from 17 indicators in the MEA (Costanza et al., 1998) to quantify change after relocation (Tables 3, S2 and S3). Specifically, food provision is closely linked to the livelihood of rural households. Habitat quality indicates the ability of ecosystem to provide habitats for various species and influences the biodiversity. Carbon storage reflects the basic vegetation and surface cover characteristics of the study area. Considering the study area is located at the junction of semi-humid and semi-arid regions in China, and with high population density and an extreme shortage of water resources, water retention significantly impacts on the production and people’s life quality in Hebei province. Furthermore, air pollution is one of the main environmental problems in the province.
Table 3 Methods for quantifying selected ecosystem services
Ecosystem service Name Calculation methodology and interpretation References
Provisioning services Food provision F P = ∑ K = 1 K ∑ C = 1 C A c K ⋅ ρ c K
AcK is the area of the region occupied by food C in land use/cover type KρcK, the supply of food per unit area in land use/cover type K. The food supply in the study area was calculated based on croplands providing rice, oil crops, and vegetables; grasslands supplying meat and dairy products; and water bodies yielding aquatic products.		 Sharp et al., 2018
Supporting services Habitat quality Q x j = H j 1 − D x j z D x j z + k 2 Q x j
Qxj indicates the habitat quality of plot x in the jth land use/cover type; Hj is the habitat suitability of land use/cover type j; Dxj is the level of stress to which the xth plot in land use/cover type j is subjected; k is the half-saturation constant, usually half the maximum value of Dxj; and z is the normalisation constant, which usually takes the value of 2.5.		 Bai et al., 2011
Regulating services Carbon storage CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y D CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y D refers to pixel area, CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y D, CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y D, CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y D, CS K , x , y = A × φ K , x , y VA + φ K , x , y VB + φ K , x , y S + φ K , x , y Drefer to aboveground, belowground carbon density, and carbon density in soil organics and organic litter in each land use type, respectively. Sharp et al., 2018;
Ren et al., 2024
Water retention W R K , x , y = A × P x , y × C × R K , x , y
A represents the pixel area, Px,y is the amount of precipitation for image (x,y), C is the regional surface runoff coefficient, RK,x,y denotes the proportion of surface runoff intercepted for image (x,y) with land use/cover type K. In the study area, the value of C is 0.6. The proportions of surface runoff interception for cropland, forest, and grassland are 12%, 20%, and 11%.		 Yang et al., 2015
Air purification A P K , x , y = A × P M K , x , y
APK,x,y is PM10 retention and absorption for pixel (x,y) with land use/cover type, A is the pixel area, and PMK,x,y is PM10 adsorption per unit area for image (x,y) with land use/cover type. The PM10 adsorption per unit area for cropland, forest, and grassland are 9.2 kg/ha, 62 kg/ha, and 27 kg/ha.		 Landuyt et al., 2016
Table S2 Food supply per unit area of different land types in Fuping county
Croplands Grasslands Water bodies
Rice Oil crops Vegetables Meat Dairy products Aquatic products
Production per unit area (t/km2) 304.7 11.3 611.49 10.02 145.98 496.32
Table S3 Carbon density of each land use/cover type use
Land use/cover type Aboveground carbon density (t/ha) Belowground carbon density (t/ha) Carbon density in soil organics (t/ha) Carbon density organic litter (t/ha)
Croplands 5.0 0.7 108.4 0.0
Forest 59.8 10.8 185.3 7.8
Grasslands 0.6 2.8 99.9 0.0
Unutilized land 0.1 0.0 9.6 0.0
We primarily used land use data for 2015-2022, including seven land classes, including farmland, forest, shrubs, grassland, water bodies, bare ground and impervious surfaces, at a resolution of 30 m (Yang and Huang, 2023). The annual precipitation raster data is derived from the National Earth System Science Data Centre (http://www.geodata.cn). To unify the resolution, we resampled the 1 km precipitation data to a 30 m resolution. Additionally, considering the interannual variation of precipitation contributes to water retention instability, we averaged the annual precipitation from 2015-2022 for each raster to solely examine the impact of land use on water retention.
Given that the resettlement communities were mostly constructed in 2017, we classified 2015-2017 as the pre-relocation period and 2018-2022 as the post-relocation period. To explore the impact of relocation on changes in ES, we established a 3 km buffer zone around both the in-migration and out-migration areas. The average value of ES within each buffer zone was calculated for each year and each land use category.

4 Results

4.1 Impact of the PAR on well-being of relocated individuals

4.1.1 Overall well-being changes

Our results showed that respondents’ overall well-being improved after relocation, with an average change score of 0.45 (Figure 3a). However, there were substantial differences among individuals, indicated by a standard deviation of 1.07. Among the various dimensions of well-being, the most significant improvement was observed in material conditions, with a score of 0.46. Changes in the dimensions of health, safety, and social interaction were relatively small. Notably, respondents perceive that their income did not improve after relocation, with a change score of 0 and a standard deviation of 0.7.
Figure 3 The average changes in the well-being of relocated individuals (a. dimensions; b. indicators)
In the material dimension, respondents acknowledged that infrastructure had become more convenient after relocation (Figure 3b). The changes in scores for transportation, communication, and education well-being indicated high averages and low standard deviations. Specifically, the average change in transportation well-being was 0.81, with a standard deviation of 0.74. For communication, the average change was 0.55, with a standard deviation of 0.81. In terms of education, the average change was 0.45, with a standard deviation of 0.84. However, respondents perceived a shortage of food sources and a decline in water quality. The score for changes in food supply showed a low average (0.32) and a low standard deviation (0.82). The score of water quality showed a low average (0.27), and a high deviation (0.95).
In terms of health, respondents perceived a notable improvement in medical conditions after relocation. The change score for medical well-being had a low mean (0.62) and low standard deviation (0.76). However, there was no significant change in well-being after relocation for the other three indicators: physical health, mental health, and dietary structure. The corresponding mean change scores were 0.18, 0.16, and 0.27, respectively. In the safety dimension, respondents agreed that social security had improved after relocation, with an average score of 0.50 and a standard deviation of 0.74. However, the changes in well-being for natural disasters and environmental pollution were not significant, with average scores of 0.2 and 0.28 for well-being changes, respectively.

4.1.2 Well-being changes among interviewees

The differences in individual-level well-being were influenced by socio-economic characteristics (Table 4). For different ages, the higher the level of education, the smaller the improvement in well-being. This trend was reflected in overall well-being, as well as changes in well-being across four dimensions: income, health, safety, and social interaction. In the income dimension, the well-being of individuals over 50 years old decreased, with average changes in well-being of -0.14 for those aged 50-60 and -0.1 for those over 60 years old. The well-being of people under 50 years old has increased, with an average score change of 0.23. Additionally, people over 55 years old scored below 0.3 in the dimensions of health, safety, and social interaction.
Table 4 The differences of well-being changes among individuals with different socio-economic characteristics
Socio-economic characteristics Type Total Income Material Health Safety Social interaction
Age 0-19 1 0.545 0.703 0.687 0.596
20-29 0.727 0.273 0.709 0.424 0.303
30-39 0.474 0.158 0.232 0.421 0.035
40-49 0.377 0.057 0.215 0.384 0.377
50-59 0.373 -0.137 0.259 0.373 0.176
>60 0.327 -0.177 0.29 0.153 0.124
χ2 12.193* 30.681** 14.75* 22.171** 18.505**
Occupation Student 1.08 0.48 0.68 0.587 0.573
Government officials 0.5 0.083 0.517 0.444 0.778
Others 0.587 0.206 0.425 0.418 0.222
Farmer 0.414 -0.071 0.391 0.224 0.186
Migrant worker 0.468 -0.106 0.298 0.397 0.298
Handicrafts 0.167 -0.167 -0.1 0.389 -0.056
Tourism 0 -0.25 0.15 0.208 -0.083
Unemployed 0.082 -0.265 0.016 0.15 0.061
χ2 18.64** 24.924** 26.041** 15.658* 24.624**
Education Uneducated 0.213 -0.277 0.337 0.336 0.191
Primary school 0.015 -0.265 0.3 0.056 0.137
Junior high school 0.707 0.061 0.496 0.378 0.343
High school 0.705 0.364 0.649 0.459 0.583
College and above 0.636 0.409 0.63 0.709 0.636
χ2 20.276** 31.506** 12.146* 24.404** 21.483**

Note: **, *, and + indicate significant levels of 0.01, 0.05, and 0.1, respectively.

Respondents with higher education level perceived more significant improvement in well-being. This trend was reflected in overall well-being, as well as in the dimensions of income, material, health, and safety. Individuals with college or higher degree showed the most improvement in various aspects of well-being, with an average change score of 0.64. Specifically, the overall well-being change for individuals with a junior high school education or above (0.70) was better than that for individuals with lower education levels (0.10). The income well-being of individuals with a high school education or above improved (0.38), while those with lower education levels experienced a decrease (-0.12).
For respondents of different occupations, there have been significant changes in overall well-being as well as in the dimensions of income, health, safety, and social interaction. Students have experienced the largest changes in various forms of well-being, with an average score of 1.08 for changes. The overall well-being of government officials (0.50) and individuals with other professions (0.59) has improved. Individuals participating in tourism (0.00), handicrafts (0.17), and those unemployed ones (0.08) hardly saw their well-being changed. In terms of income, unemployed residents (-0.27), residents employed in tourism (-0.25) and handicrafts (-0.17), migrant workers (-0.11), and farmers (-0.07) have seen a decrease in satisfaction.

4.2 Impact of the PAR on ecosystem services

ES in the relocated areas (both in-migration and out-migration areas) have shown greater improvements compared to the entire county (Figure 4). From 2015 to 2022, there were an upward trend in carbon storage, water retention, air purification, and habitat quality in the relocated areas. Although these four types of ES have also increased in Fuping county, the growth in the relocated areas were larger. Interestingly, unlike the trend in Fuping, food supply services have declined in the relocated areas, as some cropland has been converted into residential zones to accommodate the concentrated population.
Figure 4 Changes in ES of out-migration and in-migration areas (2015-2022)
After the PAR, carbon storage in the relocated areas increased by 3.2%, from 11.15 t/ha to 11.50 t/ha, while Fuping saw a 2.3% increase, from 9.95 t/ha to 10.18 t/ha. Water retention in the relocated areas rose by 2.3%, from 43.18 mm to 44.17 mm, compared to a 1.2% increase, from 40.34 mm to 40.83 mm. Air purification in relocated areas improved by 3.1%, from 44.31 kg/ha to 45.66 kg/ha, whereas Fuping saw a 0.9% increase, from 39.58 kg/ha to 39.93 kg/ha. Habitat quality in the relocated areas increased by 0.5%, from 0.889 to 0.893, while Fuping experienced a slight decrease of 0.3%, from 0.851 to 0.848. In terms of food supply, the relocated areas experienced a decrease from 1.23 t/ha to 1.22 t/ha, a reduction of 0.7%, while Fuping saw an increase from 1.26 t/ha to 1.37 t/ha, an 8.1% rise. Overall, during the implementation of relocation projects, the ecological governance and restoration effects in the relocated areas have been relatively prominent.
Although there was a notable upward trend in carbon storage, water retention, air purification, and habitat quality in both the in-migration and out-migration areas from 2018 to 2020, the increase in the in-migration area was even greater. After relocation, the carbon storage of the in-migration area increased from 10.91 t/ha to 11.33 t/ha, representing a 3.9% increase, while the out-migration area saw an increase from 11.38 t/ha to 11.67 t/ha, a 2.5% increase. Water source retention in the in-migration area rose from 37.91 mm to 38.96 mm, marking a 2.8% increase, whereas the out-migration area increased from 39.81 mm to 40.55 mm, a 1.9% increase. Air purification in the in-migration area improved from 43.16 kg/ha to 44.79 kg/ha, a 3.8% increase, while the out-migration area increased from 45.45 kg/ha to 46.53 kg/ha, a 2.4% increase. Habitat quality of the in-migration area increased from 0.88 to 0.886, a 0.7% increase, whereas the out-migration area increased from 0.898 to 0.901, a 0.3% increase.

5 Discussion

This research investigates the PAR impact on relocated individuals’ well-being and ecosystem services in Fuping, Hebei province of China. The central and provincial governments issued strategic directions and policy guidelines for the PAR Initiative, while the local governments made detailed plan and implement construction. With huge investment and administrative effort, the PAR Initiative aimed to improve human-nature relationships in 832 poor counties over five years. In this section, by introducing the specific policy goal of the PAR Initiative, we first discuss how the PAR improved well-being and environment to a large extent, and then we reveal the emerging issues of the PAR that need to be address in future policy-making.

5.1 Positive impacts of the PAR on nature and society

Our findings reveal that the PAR increases relocated residents’ overall well-being by improving infrastructure and public services. This achievement is realized based on the clear guidance of the national PAR plan (NRDC, 2016). First, simply equipped housing was constructed for relocated residents, with a standard of 25 m2 per capita. Second, to enable relocated residents to have improved access to infrastructure, the relocation sites were selected near central villages or townships with convenient transportation, water supply, energy supply, and communication facilities. Third, to facilitate public services for relocated residents, schools, clinics and street markets were synchronously constructed along with housing. Though the government faces high upfront costs, relocated residents can keep long-term benefits from the infrastructure and public services (Liu et al., 2023). During the survey in Fuping, many rural residents mentioned that their children have improved access to schools and better-constructed roads provide more convenient transportation.
While previous studies focused on ES changes at county scale (Li et al., 2018; Zhu et al., 2023; Feng et al., 2024), we analyzed ES at the scale of relocation sites. Our analysis reveals that PAR enhances multiple ES, particularly through greater forest expansion in relocation areas than county-wide averages. Connected with national PAR guidelines (NDRC, 2016) prioritizing ecological restoration in vulnerable relocation areas, Hebei province exemplified this by integrating PAR with projects like Taihang Mountains reforestation, restoring one million mu of ecologically fragile land (HPRDC, 2016).
Thus, PAR demonstrates a nature-society win-win through enhanced land use efficiency and urbanization synergies (Huang, 2017; Rogers et al., 2020). There are two major benefits to concentrating rural residents into high-rise resettlement communities instead of investing in small, dispersed villages. First, it fully realizes the potential of existing resources as well as lowers the cost of building new housing and infrastructure, allowing more people to achieve improved life quality within limited funds and resources. Second, releasing fragmented lands for ecological regeneration. With the PAR Initiative guideline for land consolidation, many counties have witnessed forest or grassland area increase after relocation (Zhou et al., 2022; Zhu et al., 2023).

5.2 Unresolved problems of the PAR

Although the PAR improves rural individuals’ well-being in multiple dimensions, they perceived a shortage of food sources and a decline in water quality. After relocation, rural residents were forced to abandon their cropland after relocation, as the rugged terrain hindered their access to the cropland in the mountainous region - even when the direct distance was relatively short. Although the government encouraged cropland transfers to enterprises, the dividends were insufficient to cover living and food expenses (further details below). As for the water quality, relocated residents relied on tap water supplied by waterworks instead of the spring water they previously used. During our survey, many respondents complained about the poor tap water quality and their inability to have access to spring water in the in-migration areas.
Additionally, the PAR is yet to resolve the challenge of chronic poverty. In contrast to previous studies, which based on objective HWB, our results on subjective HWB indicate that residents’ satisfaction with income has seen only marginal increases. As rural residents experience urbanization through relocation, they are compelled to change and diversify their livelihoods (Li et al., 2021a). However, the national and provincial PAR plans do not provide clear guidance on how to increase relocated residents’ income. Without specific directives, local governments have the discretion to decide on the number of employment opportunities and allocation of welfare positions. Consequently, the livelihoods aimed at increasing income are often limited and unstable (Rogers et al., 2020; Ma et al., 2024).
Besides, the relocation distance is insufficient to overcome ecological limitation. Since the PAR is implemented at the township scale, all centralized relocation sites selected by the local government are confined to their administrative boundaries. As a result, ecological protection policies continue to constrain rural residents from diversifying their livelihoods, to a large extent.
Moreover, although land rent or dividends from land transfer can increase residents’ income, the expenditures after relocation have also increased. Many studies have shown that households were spending much more on housing, food, education and healthcare after relocation (Ong, 2014; Li et al., 2018). Our survey in Fuping found that relocated resident must purchase food after transferring their cropland. Additionally, the expenditure on electricity and tap water averagely increases 1044.63 yuan and 337.19 yuan per year, respectively. In the future, these costs may rise further, as the government subsidies for items such as fuel gas are phased out.
Our research highlights that marginalized social groups (the elderly, those with low education and low non-agricultural income) experience minimal improvement in their well-being. Many studies identified the elderly as a particularly vulnerable group when adapting to life after relocation, due to their reluctance to move (Huang, 2017), difficulties with social integration (Rogers and Wang, 2006), and challenges in changing their livelihoods (Ma et al., 2024). In addition to the elderly, our well-being survey in Fuping reveals that those with lower levels of education and those lacking non-agricultural income also face significant barriers to diversifying their livelihoods.
In terms of ecological restoration, our study indicates that out-migration areas have experienced less changes in ES compared to in-migration areas. In the in-migration areas, while some croplands have been converted into residential zones, there have also been significant efforts to restore surrounding grasslands and forests, aiming to improve the local environment where people have resettled. In contrast, out-migration areas receive far less attention. Interviews with local officials revealed plans to develop these out-migration villages for tourism, but many remain abandoned due to a lack of investment and viable development plans. This aligns with findings from a case study in Guizhou, which also highlights the challenges of effectively utilizing out-migration areas (Feng et al., 2024).

5.3 Limitations and future perspectives

Though our study identifies positive and negative impacts of the PAR on both nature and society, there are areas for improvement. First, since our survey was conducted during workdays, it likely missed data from individuals working outside the community, potentially leading to an incomplete representation of the population. Second, despite quantifying multi-dimensional well-being, our analysis focused on material needs of the relocated individuals. It is also crucial to reduce social exclusion and foster civic participation after relocation (Yan, 2018; Yang et al., 2020; Tang et al.2025). Future research should delve deeper into the well-being associated with social interaction. Third, our study did not analyze the changes in relationship between ecosystem services and human well-being after the migration. This line of research can be further conducted by the structure equation modelling. Fourth, given that the PAR involves multiple stakeholders, further studies should include participatory interviews to thoroughly consider the interests of the government, rural residents, and local companies.

6 Conclusions

Our research examines the impact of the Poverty Alleviation Relocation by combining assessments of individuals’ well-being and ecosystem services. Our study in Fuping contributes novel insights into the opportunities and challenges associated with relocation for poverty alleviation. We found that, the well-being of relocated individuals improved, particularly in term of infrastructure and public services. Additionally, four out of five types of ES around relocation areas increased greater than in the entire county. By concentrating resources and residents, people’s life quality improved and abandoned land for ecological restoration. Thus, the urbanization process associated with the PAR has had a positive impact on both society and nature.
However, the PAR has not fully resolved the challenge of chronic poverty. Our survey shows that income satisfaction showed only a marginal increase, driven by several factors. First, the relatively short relocation distance has not eliminated ecological constraints, limiting opportunities for alternative livelihoods. Additionally, the lack of clear employment policies for relocated residents has hindered their transition from agricultural work to other sectors. Moreover, rising living costs—such as electricity and water expenses—have further strained household finances. Marginalized social groups, including the elderly, individuals with low education levels, and those without non-agricultural income, face even greater challenges after relocation. Regarding ecological restoration, changes in ES in out-migration areas were less pronounced compared to in-migration areas. While in-migration areas improved the land use efficiency with adequate planning and resources, out-migration areas have received less attention. Many abandoned homesteads remain underutilized, neither effectively restored nor repurposed for tourism.
With huge investment and administrative effort, the PAR Initiative pursued a holistic approach to balance poverty alleviation with environmental protection. However, the campaign-style nature of the PAR Initiative, coupled with the intense pressure on local officials to meet their poverty eradication targets before 2020, hindered the thorough resolution of emerging issues. To address these challenges, it is necessary to integrate the PAR with future development policies (Liu et al., 2025), such as the Rural Revitalization strategy. This would enable the government to replan the use of out-migration areas, provide skill training for the low educated, and allocate funds to support the elderly.
Governments in the Global South can consider integrating migration-related strategies into their poverty alleviation. It is crucial to conduct sufficient pilot testing in diverse socioeconomic and environmental contexts before developing tailored guidelines. Beyond relocation planning, governments should take a holistic approach by incorporating employment opportunities and land restoration policies. Additionally, continuous monitoring of relocated residents’ well-being and environmental changes is essential to ensure both social and ecological sustainability. Furthermore, local governments should enhance cross-regional cooperation to better overcome ecological constraints on livelihoods, promoting a more participatory and sustainable approach of poverty alleviation relocation.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Afsar R, 2009. Unravelling the vicious cycle of recruitment: Labour migration from Bangladesh to the Gulf States. Geneva: ILO, 33.

[2]
Anh D N, 2006. Enhancing the development impact of migrant remittances and diaspora: The case of Viet Nam. Asia-Pacific Population Journal, 20(3): 111-122.

[3]
Arnall A, Thomas D S G, Twyman C et al., 2013. Flooding, resettlement, and change in livelihoods: Evidence from rural Mozambique. Disasters, 37(3): 468-488.

[4]
Asadullah M N, Savoia A, 2018. Poverty reduction during 1990-2013: Did millennium development goals adoption and state capacity matter? World Development, 105: 70-82.

[5]
Asis M M B, 2006. Living with migration: Experiences of left-behind children in the Philippines. Asian Population Studies, 2(1): 45-67.

[6]
Bai Y, Zhuang C, Ouyang Z et al., 2011. Spatial characteristics between biodiversity and ecosystem services in a human-dominated watershed. Ecological Complexity, 8(2): 177-183.

[7]
Baird I G, Shoemaker B, 2007. Unsettling experiences: Internal resettlement and international aid agencies in Laos. Development and Change, 38(5): 865-888.

[8]
Cavendish W, 2000. Empirical regularities in the poverty-environment relationship of rural households: Evidence from Zimbabwe. World Development, 28(11): 1979-2003.

[9]
Costanza R, d’Arge R, De Groot R et al., 1998. The value of the world’s ecosystem services and natural capital. Ecological Economics, 25(1): 3-15.

[10]
Diener E, Suh E, 1997. Measuring quality of life: Economic, social, and subjective indicators. Social Indicators Research, 40(1): 189-216.

[11]
Fan M, Li Y, Li W, 2015. Solving one problem by creating a bigger one: The consequences of ecological resettlement for grassland restoration and poverty alleviation in northwestern China. Land Use Policy, 42: 124-130.

[12]
Feng Q, Zhou Z, Chen Q et al., 2024. Quantifying the extent of ecological impact from China’s poverty alleviation relocation program: A case study in Guizhou province. Journal of Cleaner Production, 444: 141274.

[13]
Fuseini M N, Yendaw E, Abdulai I A, 2023. Migration and poverty in the Global South: An integrated review. SN Social Sciences, 3(12): 1-21.

[14]
Guo Y, Zhou Y, Liu Y, 2022. Targeted poverty alleviation and its practices in rural China: A case study of Fuping county, Hebei province. Journal of Rural Studies, 93: 430-440.

[15]
Hammond L, 2008. Strategies of invisibilization: How Ethiopia’s resettlement programme hides the poorest of the poor. Journal of Refugee Studies, 21(4): 517-536.

[16]
Hebei Province Development and Reform Commission, 2016. Hebei Province “13th Five-Year” Poverty Alleviation Relocation Plan.

[17]
Huang Y, 2017. Farewell to villages:Forced urbanization in rural China. In: China’s Urbanization and Socioeconomic Impact. Singapore: Springer, 207-227.

[18]
Hugo G, 2002. Effects of international migration on the family in Indonesia. Asian and Pacific Migration Journal, 11(1): 13-46.

[19]
Khan A, Harroff-Tavel H, 2011. The implications of the sponsorship system:Challenges and opportunities. In: Strengthening Dialogue to Make Migration Work for Development in the ESCAP and ESCWA Regions. Geneva: ILO, 28-30.

[20]
Kruskal W H, Wallis W A, 1952. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260): 583-621.

[21]
Kwankye S, Anarfi J, 2011. Migration impacts within the West African sub-region. Unpublished Paper Prepared for the Research Programme Consortium (RPC) on Migrating out of Poverty. Brighton: University of Sussex, 45.

[22]
Landuyt D, Broekx S, Engelen G et al., 2016. The importance of uncertainties in scenario analyses: A study on future ecosystem service delivery in Flanders. Science of the Total Environment, 553: 504-518.

[23]
Leng G, Feng X, Qiu H, 2021. Income effects of poverty alleviation relocation program on rural farmers in China. Journal of Integrative Agriculture, 20(4): 891-904.

[24]
Li C, Guo M, Li S et al., 2021a. The impact of the anti-poverty relocation and settlement program on rural households’ well-being and ecosystem dependence: Evidence from western China. Society & Natural Resources, 34(1): 40-59.

[25]
Li C, Li S, Feldman M W et al., 2018. The impact on rural livelihoods and ecosystem services of a major relocation and settlement program: A case in Shaanxi, China. Ambio, 47(2): 245-259.

[26]
Li C, Wang L, Feldman M W et al., 2021b. Poverty and income inequality effects of the relocation program in Shaanxi, China. Asian-Pacific Economic Literature, 35(2): 41-59.

[27]
Liu M, 2007. The case study on China’s resettlement poverty reduction. In: China Conference on Taking Action for the World’s Poor and Hungry People.

[28]
Liu M, Feng X, Zhao Y et al., 2023. Impact of poverty alleviation through relocation: From the perspectives of income and multidimensional poverty. Journal of Rural Studies, 99: 35-44.

[29]
Liu Z, Wang H, 2025. Regional revitalizing divergences and reasons for targeted policy-driven rural development in China: An assessment based on provincial-level data. Journal of Geographical Sciences, 35(3): 483-500.

[30]
Lo K, Xue L, Wang M, 2016. Spatial restructuring through poverty alleviation resettlement in rural China. Journal of Rural Studies, 47: 496-505.

[31]
Ma L, Zhang Y, Li T et al., 2024. Livelihood capitals and livelihood resilience: Understanding the linkages in China’s government-led poverty alleviation resettlement. Habitat International, 147: 103057.

[32]
Maimbo S M, McKechnie A, Ghani E S et al., 2015. Migrant labor remittances in the South Asia region. Washington, D.C.: Heidelberg University Library, 42.

[33]
MEA, 2005. Ecosystems and Human Well-being:Current State and Trends. Washington, DC: Island Press, 25.

[34]
Ministry of Environmental Protection, 2015. National Ecological Function Zoning (Revised Edition). https://www.mee.gov.cn/gkml/hbb/bgg/201511/t20151126_317777.htm. (in Chinese)

[35]
Nakayama M, 1998. Post-project review of environmental impact assessment for Saguling Dam for involuntary resettlement. International Journal of Water Resources Development, 14(2): 217-229.

[36]
National Development and Reform Commission, 2016. Poverty alleviation resettlement plan during the period of Thirteenth Five-year Plan. https://www.ndrc.gov.cn/xxgk/zcfb/tz/201610/W020190905517351522212.pdf. (in Chinese)

[37]
National Development and Reform Commission, 2020. The “13th Five-Year Plan” poverty alleviation resettlement task has been fully completed. https://www.ndrc.gov.cn/fzggw/wld/zcx/lddt/202012/t20201203_1252215.html. (in Chinese)

[38]
National Rural Revitalization Administration, 2014. List of 832 poor counties in China. https://nrra.gov.cn/art/2014/12/23/art_343_981.html. (in Chinese)

[39]
Ong L H, 2014. State-led urbanization in China: Skyscrapers, land revenue and “concentrated villages”. The China Quarterly, 217: 162-179.

[40]
Pani P, Carling P, 2013. Land degradation and spatial vulnerabilities: A study of inter-village differences in Chambal Valley, India. Asian Geographer, 30(1): 65-79.

[41]
Qiu J, Liu Y, Yuan L et al., 2021. Research progress and prospect of the interrelationship between ecosystem services and human well-being in the context of coupled human and natural system. Progress in Geography, 40(6): 1060-1072. (in Chinese)

[42]
Ranabahu R, 2004. The consequences of urban in-migration in Sri Lanka: A case study of Gampaha district. Sri Lanka Journal of Population Studies, 7: 71-87.

[43]
Ravallion M, Wodon Q, 1999. Poor areas, or only poor people? Journal of Regional Science, 39(4): 689-711.

[44]
Ren S, Jing H, Qian X et al., 2024. Spatial and temporal coordinated development research on ecosystem services and human well-being in the typical pastoral area of the Qinghai-Tibet Plateau. Journal of Geographical Sciences, 34(2): 252-288.

[45]
Rogers S, Li J, Lo K et al., 2020. China’s rapidly evolving practice of poverty resettlement: Moving millions to eliminate poverty. Development Policy Review, 38(5): 541-554.

[46]
Rogers S, Wang M, 2006. Environmental resettlement and social dis/re-articulation in Inner Mongolia, China. Population and Environment, 28(1): 41-68.

[47]
Schmidt-Soltau K, 2003. Conservation-related resettlement in Central Africa: Environmental and social risks. Development and Change, 34(3): 525-551.

[48]
Sharp R, Chaplin-Kramer R, Wood S et al., 2018. InVEST user’s guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund, 23-24.

[49]
Tang J, Xu Y, Ma W et al., 2021. Does participation in poverty alleviation programmes increase subjective well-being? Results from a survey of rural residents in Shanxi, China. Habitat International, 118: 102455.

[50]
Tang L, Liu C, Wang Y, 2025. Unveiling the role of social networks: Enhancing rural household livelihood resilience in China’s Dabie Mountains. Journal of Geographical Sciences, 35(2): 335-358.

[51]
United Nations, 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. New York: United Nations, 10.

[52]
Wang B, Tang H, 2016. Human well-being and its applications and prospects in ecology. Journal of Ecology and Rural Environment, 32(5): 697-702.

[53]
Wei Y, 2021. Research on the impact of resettlement on farmers’ livelihood in Fuping county[D]. Baoding: Hebei Agricultural University. (in Chinese)

[54]
World Bank, 2022. Poverty and Inequality Platform. Washington, D.C.: World Bank.

[55]
Wu J, 2013. Landscape sustainability science: Ecosystem services and human well-being in changing landscapes. Landscape Ecology, 28(6): 999-1023.

[56]
Yadeta T, Deribew K T, Getahun K et al., 2022. Recent resettlement programs, as drivers for Afromontane forest loss in the Hawa-Galan district of Ethiopia. Cogent Social Sciences, 8(1): 2088462.

[57]
Yamane T, 1973. Statistics:An Introductory Analysis. New York: Harper and Row, 123.

[58]
Yan F, 2018. Urban poverty, economic restructuring and poverty reduction policy in urban China: Evidence from Shanghai, 1978-2008. Development Policy Review, 36(4): 465-481.

[59]
Yang J, Huang X, 2023. The 30 m annual land cover datasets and its dynamics in China from 1985 to 2022 (Version 1.0.2).

[60]
Yang W, Dietz T, Kramer D B et al., 2015. An integrated approach to understanding the linkages between ecosystem services and human well-being. Ecosystem Health and Sustainability, 1(5): 12.

[61]
Yang W S, Lu M C W, 2010. Asian Cross-border Marriage Migration:Demographic Patterns and Social Issues. Amsterdam: Amsterdam University Press, 77.

[62]
Yang Y, de Sherbinin A, Liu Y, 2020. China’s poverty alleviation resettlement: Progress, problems and solutions. Habitat International, 98: 102135.

[63]
Yeboah A S, Baah-Ennumh T Y, Okumah M, 2022. Understanding the economic, socio-cultural, and environmental impacts of resettlement projects. African Geographical Review, 41(1): 71-92.

[64]
Zhou Y, Guo L, Liu Y, 2019. Land consolidation boosting poverty alleviation in China: Theory and practice. Land Use Policy, 82: 339-348.

[65]
Zhou Z, Feng Q, Zhu C et al., 2022. The spatial and temporal evolution of ecological environment quality in karst ecologically fragile areas driven by poverty alleviation resettlement. Land, 11(8): 1-20.

[66]
Zhu M, Zhou Z, Feng Q et al., 2023. Contribution of vegetation restoration to carbon sequestration driven by ex-situ poverty alleviation and relocation in ecologically fragile areas: Taking Guizhou, China, as a case. Land Degradation & Development, 34(13): 3905-3919.

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