Influencing Factors of Peasant Households’ Willingness to Relocate to Concentrated Residences in Mountainous Areas: Evidence from Rural Southwest China

Abstract

Relocating poor people in mountainous areas to centralized settlement sites is an important poverty alleviation policy implemented by China and a key measure to promote stable poverty alleviation and sustainable rural development for the poor. Based on the survey data of 405 peasant households in the Panxi Area in 2021, this study constructed a structural equation model (SEM) to explore the influencing factors of peasant households’ willingness to relocate to a concentrated residence. The results showed the following: (1) Of the 405 peasant households surveyed, 20.98% were unwilling to move to centralized resettlement sites, making it more difficult to carry out the relocation policy for poverty alleviation. (2) Living environment, living conditions, important social groups, the economic benefits, living benefits, and survival benefits brought by concentrated residences, governments, and the village committees significantly influenced the willingness to relocate to a concentrated residence. In contrast, agricultural income, ecological benefits, and value benefits brought by the concentrated residence had little effect on the willingness to relocate to a concentrated residence. (3) Land force, human force, cognitive force, and national force significantly positively affected the peasant households’ willingness to relocate to a concentrated residence. This study is of great significance in promoting the implementation of poverty alleviation and relocation policy, improving the efficiency of relocation and promoting the wellbeing of peasant households.

1. Introduction

With the rapid development of urbanization and industrialization, the rural structure is constantly adjusted to meet the needs of agricultural modernization. As a form of rural structural adjustment, the relocation of peasant households to concentrated residences plays an important role in improving the efficiency of land use in rural areas, enhancing the level of rural public services and improving the level of rural production and the quality of life of peasant households [1]. In essence, encouraging peasant households to choose to reside in a concentrated residence is the process of rural housing reconstruction. Rural housing reconstruction is key to sustainable development. In the background of new rural construction, concentrated rural settlement (CRS) has been widely promoted [2]. Most scholars believe that the concentrated residence of peasant households is a new type of community residence with complete basic and supporting facilities, which scatters peasant households living in natural villages [3]. Among them, the concept of “population migration”, focused on the sharing of urban civilization by farmers [4], the concept of “village-to-community”, focused on rural transformation [5], and the concept of “land intensification”, focused on improving the efficiency of land use [6]. This study defines the concept of peasant households’ concentrated residences from multiple perspectives, that is, under the support of government policies, to promote the relocation of peasant households in poor areas to a concentrated settlement planned and constructed by the government, intending to promote improvement in the quality of life of peasant households, the intensive use of land, and the efficiency of agricultural production, focusing on lifting peasant households out of poverty. Compared with the dispersed living form of peasant households, promoting the concentrated residence of peasant households is an effective means of rural revitalization, which can enhance the development of rural areas and promote the coordinated development of urban and rural areas [7].

The advantages of the concentrated residence policy, which is promoted and controlled by governments at all levels from top to bottom, lie in the low cost required by peasant households and the direct and effective improvement of their living conditions [8]. As the main force and the ultimate beneficiary of the concentrated residence, peasant households’ willingness has a fundamental impact on the effectiveness of the concentrated residence policy. In reality, because some local governments have harmed the immediate interests of farmers and ignored the wishes of farmers by carrying out the policy of concentrated residence, conflicts between the government and farmers have arisen, which has reduced the wishes of farmers to live in concentrated residence [9,10]. This impedes the process of concentrated residence. Peasant households’ willingness to relocate is the core problem that affects the process of realizing the concentrated residence policy [11]. Understanding peasant households’ willingness and its influencing factors is an important guarantee to promote the process of concentrated residence. Many factors affect the peasant households’ willingness to relocate to a concentrated residence, including internal and external factors. Internal factors are summarized as individual characteristics (such as gender, age, education level) [12,13,14], household characteristics (such as household size, household income and expenditure, labor force) [15,16], and cultural characteristics (such as life expectancy, risk perception) [17,18,19]. External factors include attitudes of important social groups (such as family members, relatives, friends, and neighbors) [20,21,22], existing living conditions (such as the quality of houses, the years of construction of houses, the materials of houses) [23,24], living environment (such as natural disasters, the quality of cultivated land, infrastructure conditions) [25,26,27,28], and policy environment (such as the attitude and behavior of the government and village committee) [29,30]. Many domestic and foreign scholars have analyzed the factors affecting peasant households’ willingness to relocate to a concentrated residence through questionnaires and interviews [16,25]. The most common measurement models used are the Probit and Logit models [28,31]. The structural equation model (SEM) has been used increasingly in recent years [32], but mainly for studies on the plains. The Logit and Probit models are used to model the relationship between the dependent and independent variables; however, they cannot fit the complex nonlinear data well [33]. As a powerful statistical analysis tool, the structural equation model (SEM) can deal with many dependent variables simultaneously, allowing both independent variables and dependent variables to contain measurement errors and taking into account the direct and indirect relationships among multiple variables. This model can compensate for the deficiency in the Logit model and the Probit model, as they can only measure the direct effect among variables but cannot measure the intermediate effect and the total effect among variables [34]. To date, few studies have used the SEM model to determine the factors influencing peasant households’ willingness to relocate to a concentrated residence in mountainous areas.

Most scholars have paid attention to the concentrated residence of peasant households under the influence of meteorological disasters, such as floods and typhoons [35,36]. However, there are few that focus on poor peasant households concentrating their lives in regions threatened by geological disasters with the help of the government’s policy. Most of these study areas are in the plains, and only some scholars have explored the influencing factors of peasant households’ willingness to relocate to a concentrated residence in mountainous areas [37,38,39,40]. However, due to the differences in geographic location, resource conditions, development bases, and other factors, the rural areas in mountainous areas have lower economic development than the plains. Thus, the development of rural areas in mountainous regions deserves more attention, and the research on the influencing factors of peasant households’ willingness to relocate to a concentrated residence in mountainous areas should also be given more attention. In economically developed plains, concentrated residences will generally increase peasant households’ income to different degrees and speed up the urbanization process, and peasant households in these areas are more willing to do so. In remote mountainous areas where the economy is underdeveloped, the willingness of the peasant households is generally low if the policy is implemented in rural areas where farming is the main activity [3]. This dramatically slows down the government’s progress in promoting the policy, thus leading to these economically underdeveloped areas missing development opportunities, eventually forming a vicious circle. Although the proportion of the world’s poor has declined in recent years, the problem of poverty remains acute [41]. Statistics show that many poor people live in mountainous regions with poor natural and essential conditions [42]. In 2019, there were still 5.51 million poor people in rural China, mainly in the remote mountainous areas of central and western China [43]. Promoting the centralization of peasant households in mountainous areas in China will significantly eradicate poverty among the country’s peasant households and develop the country’s villages.

Against this background and based on the field survey data of 405 peasant households in Sichuan Province, the southwestern mountainous area of China, the influencing factors of peasant households’ willingness to relocate to a concentrated residence and the relationship of the influencing factors were explored. This study presents a structural equation model, in order to provide some reference for relevant policies to promote peasant households in mountainous areas to live in concentrated residences.

2. Theoretical Analysis and Research Hypotheses

2.1. The Theory of Planned Behavior

According to the theory of planned behavior, human behavior results from well-thought-out planning, and its variables include behavioral attitudes, subjective norms, perceived behavioral control, and behavioral will [44]. Behavioral attitude reflects an individual’s belief and evaluation of behavior results, such as peasant households’ rational cognitions about the implementation of the concentrated residence policy, including economic rationality, ecological rationality, survival rationality, and value rationality [45], defined as “cognitive force” in the study. Individual cognition has a significant predictive effect on their intention [46], and peasant households’ expectation of the result of concentrated residence will affect their own will and behavior. Subjective norms refer to individuals’ social pressure when deciding whether to act [47]. Especially in ethnic minority areas, where blood and geographic ties are predominant, peasant households will consider whether the important social groups (family, relatives, friends, neighbors) around them support their relocation to a centralized settlement, defined as “human force” in this study. If these important social groups around them support peasant households’ relocation behavior, then to a certain extent, it will strengthen peasant households’ decision-making confidence. Perceived behavioral control refers to the individual’s perception of the difficulty of performing a certain behavior, which reflects the actual control conditions. For example, peasant households’ willingness to relocate to a concentrated residence is affected by the environment of their residence (agricultural production, living, and ecological conditions) [16], defined as “land capacity” in this study. If the natural and living environments are poor, it will strengthen the farmers’ willingness to live together [48]. From the perspective of the theory of planned behavior, the following hypotheses are proposed in this paper:

H1. 

Land force has a positive effect on the peasant households’ willingness to relocate to a concentrated residence.

H2. 

Human force has a positive effect on the peasant households’ willingness to relocate to a concentrated residence.

H3. 

Cognitive force has a positive effect on the peasant households’ willingness to relocate to a concentrated residence.

2.2. The Social Cognitive Theory

Social cognitive theory emphasizes the interaction between the individual and the environment, which holds that human behavior and cognitive processes are formed by interacting with the environment. The theory emphasizes individual cognitive factors, including belief, memory, expectation, motivation, and self-reinforcement, essential in understanding and coping with the social environment [49]. Bandura put forward a three-dimensional interactive learning theory based on social cognitive theory: environment, behavior, and humans are mutually causal, and each has a two-way interactive and decisive relationship [18]. If the environmental conditions in which peasant households live cannot meet their living development needs, they will improve their living conditions [50]. Peasant households will imagine whether living conditions and quality of life will improve if they move into a concentrated residence with the government’s help [51]. If the results are expected to be better, this will strengthen the peasant households’ willingness to relocate to a concentrated residence. Families, relatives, friends, and neighbors are the people who have most contact with the peasant household. They are important channels for farmers to obtain information. The access to information will affect the peasant households’ cognition about the concentrated residence [52]. At the same time, they also provide significant emotional support for peasant households. When peasant households are faced with decision-making confusion, the support of these important social groups will remove some of the worries and concerns of peasant households, further strengthening peasant households’ willingness to relocate to a concentrated residence. From the perspective of the social cognitive theory, the following hypotheses are proposed in this paper:

H4. 

Land force has a positive promoting effect on peasant households’ cognitive force about concentrated residences.

H5. 

Human force has a positive promoting effect on peasant households’ cognitive force about concentrated residences.

H6. 

Cognitive ability plays a mediating role in land force, human force, and willingness to relocate to a concentrated residence.

2.3. The Policy Process Theory

The policy process theory emphasizes policymaking, implementation, evaluation, and the interaction of various stakeholders in this process. As the main body of policymaking, the national government’s behavior and attitude (such as policy orientation, value orientation, and decision-making efficiency) will directly affect the content and quality of policy [53]. Generally speaking, in the course of implementing state policy, it will bring a series of changes and influences to the important groups in society (such as enterprises, social organizations, and citizens) and the environment outside the place of residence (such as natural environment, social, and economic environment) [54]. Faced with some remote and poor mountainous areas with poor living environments and fragile ecological environments, traditional poverty alleviation measures may not be sufficient to achieve significant results [55,56]. In order to improve the living conditions of the poor households in these areas and realize poverty alleviation, the policy of poverty alleviation and relocation came into being. Implementing the policy is conducive to improving the land use efficiency and the living conditions of peasant households in mountainous areas by promoting the concentration residence of poor peasant households in mountainous areas [57]. The attitude and strength of the local government and village committee in publicizing the policy will also affect the degree of trust and support of the important social groups (families, relatives, friends, and neighbors) in the policy [58], defined as “national force” in this study. The range and intensity of the improvement of the living conditions and the perception and attitude of the important social groups around peasant households to the policy are all related to the policy support, and it indirectly affects the farmers’ willingness to live in a centralized way. From the perspective of the policy process theory, the following hypotheses are proposed in this paper:

H7. 

National force has a positive effect on land force.

H8. 

National force has a positive effect on human force.

H9. 

The relationship between land force, human force, and peasant households’ willingness to relocate to a concentrated residence is moderated by national force.

The research framework is shown in Figure 1.

3. Data and Methods

3.1. Data Source

Sichuan is a large agricultural province with a rural population of 42.92 million, 52.31% of the province’s resident population in 2019 [59]. The proportion of mountainous areas in Sichuan is over 77%, which profoundly impacts agricultural production and rural development in Sichuan. By the end of 2018, there were still more than 700,000 poor people, 1782 poor villages, and 38 poor counties in Sichuan Province, mainly concentrated in deeply impoverished areas of the province’s mountainous ethnic minorities (Tibetans and Yi) [33]. The Panxi Area, located in the mountainous area of southwestern Sichuan Province, was chosen as the study area in this study. The Panxi Area is the largest Yi settlement of Yi people in China, with an average altitude of 2500 m, rugged terrain, and inconvenient transport. Compared with rural areas in the plains, the development of rural areas in mountainous regions is slower and more difficult, which is closely related to the terrain. The mountainous terrain of the Panxi Area restricts contact and communication between the mountainous countryside and the outside world, leading to closed information and limited economic development in the mountainous countryside. At the same time, the mountainous countryside is threatened by geological disasters in the mountainous regions, which further exacerbates the problem of poverty in the Panxi Area [43,60]. In response to the poverty problem in the Panxi Area, the government in Sichuan has taken a series of practical measures to lift people out of poverty and has also actively promoted the work of poverty alleviation and relocation in inhospitable areas. The policy of relocation for poverty alleviation is a crucial way to address the issue of poverty where “the local environment cannot support the local people”. However, the mountain environment’s particularity, the ecological environment’s fragility, and the complexity of national culture all affect the progress of poverty alleviation relocation work to varying degrees [55]. Accelerating the progress of the policy of relocation for poverty alleviation in the Panxi Area is of great significance for achieving rural poverty eradication in the region.

The data used in this paper were mainly from a questionnaire survey conducted by the research team in August 2021. The 2016–2020 period was the phase of China’s poverty eradication campaign, and the policy of relocation for poverty alleviation, implemented in China in 2019, has made remarkable progress, laying a solid foundation for China to fully realize its poverty eradication goal in 2020. Therefore, the research designed in this study covered the basic situation of individuals and families, willingness to relocate, living place conditions, and knowledge of poverty alleviation and relocation policies at the end of 2019, and each questionnaire involved face-to-face interviews with farmers for 30–60 min. The research samples mainly adopted stratified sampling, followed by equal probability random sampling to ensure the typicality and representativeness of the selected samples [33]. The process is as follows: Firstly, according to the results of the coupling of the mountain disaster risk evaluation grade and spatial conflict [61], the conflict level was sorted and divided into four categories and combined with the national major function-oriented zones; Yanyuan County, Xichang City, Puge County, and Miyi County were selected as the sample area. Secondly, all townships in the study area were divided into two groups according to the sorting of contradiction rank (with the county median as the division standard, one group with a high contradiction rank and one group with a low contradiction rank), and two sample townships were randomly selected from the four sample areas. Then, two villages were randomly selected from each sample township in the same way. Finally, 25–30 households were randomly selected from the roster in each sample village using a random number table [45]. According to the above process, 16 villages and 405 peasant households were chosen. The distribution map of sample counties and villages is shown in Figure 2.

3.2. Selection of the Model Variables

3.2.1. Descriptive Statistical Analysis of Respondents

Among the 405 peasant households, 56.54% were more willing to move to a concentrated residence at the bottom than to live scattered on the hill, and 55 were highly willing to live in a concentrated residence. However, at the same time, 20.98% of peasant households were unwilling to move to centralized resettlement sites, and 22.47% were generally unwilling to live in a concentrated residence. As for the willingness to move to a town or village concentrated residence, almost 40% of peasant households chose not to do so. The above indicates that in the villages in the sample are, although the majority of peasant households were willing to live in a concentrated residence, there were still some peasant households who were reluctant to move to centralized resettlement sites, which to a certain extent made it more challenging to carry out the policy of relocation for poverty alleviation. Of the 405 peasant households, 74.57% were of Yi ethnicity, with an average age of 47.44 years, mostly male, and 42.72% were illiterate. Most peasant households were in relatively good health, with 73.83% working purely in agriculture and 62 households working part time, with an average of one elderly person and one child per household. Overall, the village populations were many middle-aged Yi males engaged in agricultural work, with a low level of education and a heavy family burden (

Table 1. Descriptive statistics of the respondents.

Variable	Definition	Mean	SD c
Gender	Respondents’ gender (female = 1, male = 0)	0.316	0.466
Ethnicity	Respondents’ ethnicity (Han = 1, Yi = 2, Tibetan = 3, other = 4)	1.746	0.436
Age	Respondents’ age (year)	47.444	15.171
Education	Respondents’ education level (year)	3.724	3.999
Health	Respondents’ health level (1 = very healthy–5 = very unhealthy)	2.273	1182
Occupation	Respondents’ occupation (1 = full-time farming, 2 = part-time farming, 3 = wage labor, 4 = other occupations)	1.509	1.089
Family scale	Total family population in 2019 (person)	4.624	1.747
Elderly people	Household count of individuals aged 64 and older (number of the persons)	0.615	0.787
Children	Household count of children aged under 6 (number of persons)	0.515	0.804
Family labor	Household labor force count within the age range of 16–64 (number of persons)	1.760	1.173

Note: ^c SD = standard deviation.

).

3.2.2. Selection of Variables

The dependent variable was concentration living willingness (Y1–Y3), which referred to the subjective attitude or psychological tendency of peasant households toward residing in a concentrated residence under the national poverty alleviation policy. The dependent variable was measured by the choice of residence type and the place of residence of the peasant households [62].

As the target of the national poverty alleviation policy, peasant households’ willingness to relocate to a concentrated residence is influenced by internal and external factors, mainly including personal, natural, social, and economic factors. Referring to existing studies and considering the actual situation of the study area [26,55,63,64,65,66], five independent variables were summarized as follows (

Table 2. Influencing factors of peasant households’ willingness to relocate to a concentrated residence.

Latent Variables	Observation Variables	Definition	Mean	SD c
Land force	Housing quality	DL1: Do you feel that the quality of the housing you live in is not good? a	3.479	1.089
	Geological hazards	DL2: Do earthquakes, landslides, mudslides, and other disasters occur frequently where you live? a	2.800	1.211
	Infrastructure conditions	DL3: Do you think the infrastructure in the village is in poor condition? a	3.217	1.07
	Living environment	DL4: Do you think the living environment is poor where you live? a	3.128	1.098
	Land quality	DL5: Do you think the land is infertile and the quality of the arable land is poor? a	2.926	1.043
	Agricultural income	DL6: Do you feel that income from farming is low where you live? a	3.472	1.03
Human force	Support from relatives and friends	RL1: Do you think your relatives and friends will support you in relocation? a	3.462	0.996
	Neighborhood support	RL2: Do you think your neighbors will support you in relocation? a	3.390	1.003
	Family support	RL3: Do you think your family will support you in relocation? a	3.449	1.079
Cognitive force	Economic rationality 1	RZL1: Do you think that concentrated residence will improve the standard of living of families? a	3.699	0.807
	Economic rationality 2	RZL2: Do you think that concentrated residence will improve the living conditions of families? a	3.891	0.825
	Ecological rationality	RZL3: Do you think that concentrated residence will be conducive to the efficient use of land? a	3.560	0.957
	Survival rationality	RZL4: Do you feel that concentrated residence will be good for future generations? a	3.837	0.916
	Value rationality	RZL5: Do you think it is a good thing for the government to organize concentrated residence for poverty alleviation? a	3.474	0.979
National force	Local government support	GL1: Do you feel that your local government supports you in relocation? a	3.617	0.847
	Village committee support	GL2: Do you feel that your village committee will support and guide you in your relocation? a	3.615	0.881
	Policy advocacy	GL3: Have you been informed by village cadres about the policy of relocation for poverty alleviation? a	3.398	1.098
Willingness to relocate to a concentrated residence	Form of living	Y1: Would you prefer to move to a concentrated residence than to live scattered in the hills? b	3.457	1.039
	Choice of living 1	Y2: Would you like to move to a concentrated residence in the town? b	2.985	1.156
	Choice of living 2	Y3: Would you like to move to a concentrated residence in the village? b	2.985	1.051

Note: ^a Likert 5-point scale, where 1 means strongly disagree and 5 means strongly agree; ^b Likert 5-point scale, where 1 means very unwilling and 5 means very willing; ^c SD = standard deviation.

): (1) Land force (DL1–DL6): Peasant households in mountainous areas usually live in remote areas where the local natural environment, living environment, arable land, and infrastructure conditions are insufficient to meet their daily production and living needs and where geological disasters threaten their living space. In such cases, peasant households preferred to move to centralized resettlement sites with the government’s help. (2) Human force (RL1–RL3): As the largest Yi settlement area in the country, ethnic minority peasant households in the Panxi Area are usually linked by blood and geographic ties. Peasant households’ family members, relatives, friends, and neighbors, as an important social group within peasant households, have an important influence on whether they choose to live in a concentrated residence or not by their support for the peasant households’ behavioral decision making. (3) Cognitive force (RZL1–RZL5): Peasant households exhibit certain characteristics of rational economics when making decisions, and their rational cognition is often a complex process in which multiple rationalities are intertwined. Therefore, this study focused on the economics, ecology, survival, and values of rational cognition to measure peasant households’ cognitions regarding concentrated residence. (4) National force (GL1–GL3): The local government and village committee are the implementers of the national poverty alleviation policy, and the implementation process and its effect are affected by the willingness of peasant households to relocate. At the same time, the intensity of peasant households’ willingness to relocate to a concentrated residence was directly related to the behavior and attitude of the local government and village committee. Therefore, when considering the variable of national force, this study mainly considered the attitudes and propaganda behaviors of local governments and village committees.

3.3. Methods

The structural equation model (SEM) is a statistical methodology based on statistical analysis techniques to explore and analyze complex multivariate research data, consisting of a measurement model and a structural model [34]. The measurement model is to describe the relationship between latent variables and measured variables, and its specific formula is as follows:

$$X={Ṿ}_x\psi +\omega$$

(1)

$$Y={Ṿ}_y\varphi +\theta$$

(2)

where $\theta$ is uncorrelated with $\psi$, $\varphi$, or $\omega$, and $\omega$ is uncorrelated with $\psi$, $\varphi$, or $\theta$, ${Ṿ}_x$ and ${Ṿ}_y$ are the factor loading on the indicator variable (x, y). In contrast, $\omega$ and $\theta$ are the measurement errors of the exogenous observation index $X$ and the endogenous observation index $Y$, respectively. $\psi$ and $\varphi$ are the exogenous latent variable and endogenous latent variable, respectively.

The structural model focuses on describing the relationship between two or more latent variables and is formulated as follows:

$$\varphi =\beta \varphi +\mathsf{ð}\psi +Ƞ$$

(3)

where $\beta$ is the n × n coefficient matrix representing the relationship between the endogenous latent variables; $\mathsf{ð}$ is the n × m coefficient matrix representing the effect of the exogenous latent variables on the endogenous latent variables. $Ƞ$ denotes the residual term of the structural equation, reflecting the unexplained part of $\varphi$ in the equation [33]. Analysis of the models in this study was carried out using AMOS 22.0.

Based on the research hypotheses and the construction of the theoretical model framework presented in the previous section, the initial model of structural equations for the factors affecting peasant households’ willingness to relocate to a concentrated residence in mountainous areas was constructed in AMOS 22.0 (Figure 3). e1–e20 are the measurement errors for each observed variable, and e21–e24 are the portion of the endogenous latent variables that cannot be explained or predicted by the exogenous latent variables, known as residuals or disturbance. The number 1 denotes the fixed parameter in the model.

4. Results

4.1. Validity and Reliability

In this study, each dimension’s internal consistency was first analyzed using Cronbach’s α reliability test. An α value below 0.6 is generally considered implausible, between 0.6 and 0.7 is plausible, between 0.7 and 0.8 is more plausible, between 0.8 and 0.9 is very plausible, and between 0.9 and 1 is extremely plausible [67]. As seen in

Table 3. Analysis of reliability and validity of questionnaire.

Latent Variables	Observation Variables	Cronbach’s Alpha	KMO	Bartlett’s Test of Sphericity
				Approximate Chi-Square	Degree of Freedom	p Value
Land force	DL1, DL2, DL3, DL4, DL5, DL6	0.818	0.866	706.892	15	0.000
Human force	RL1, RL2, RL3	0.893	0.749	712.814	3	0.000
Cognitive force	RZL1, RZL2, RZL3, RZL4, RZL5	0.743	0.787	472.674	10	0.000
National force	GL1, GL2, GL3	0.769	0.672	366.175	3	0.000
Willingness to relocate to a concentrated residence	Y1, Y2, Y3	0.808	0.748	698.660	3	0.000
Overall		0.871	0.848	2370.574	136	0.000

, the Cronbach’s α value for the total scale is 0.871, and the Cronbach’s α values for all dimensions are within the range of 0.7–0.9, thus indicating that the questionnaire used in this study had good internal consistency and good reliability. For validity analysis, factor analysis was used in this study for validation. The KMO (Kaiser–Meyer–Olkin) value for the total scale is 0.848 (>0.6), the KMO values for the four layers of the scale are all higher than 0.6, and the p value is 0.000 < 0.05, which passes the Bartlett sphere test. Thus, the questionnaire data are acceptable and suitable for factor analysis. In this study, four principal factors were extracted using principal component analysis and rotated by the maximum variance method to explain the factors better, and the cumulative variance contribution is relatively high (

Table 4. Factor loading matrix after rotation.

		Factor
		1	2	3	4
Land force	DL1	0.685	0.118	0.086	−0.020
	DL2	0.679	0.071	0.026	0.013
	DL3	0.774	−0.047	0.055	0.029
	DL4	0.777	0.157	0.108	0.122
	DL5	0.697	0.124	0.022	0.113
	DL6	0.693	−0.184	0.028	0.016
Human force	RL1	0.087	0.78	0.193	0.339
	RL2	0.042	0.858	0.131	0.266
	RL3	0.051	0.837	0.231	0.242
Cognitive force	RZL1	0.06	0.237	0.746	0.151
	RZL2	0.132	0.103	0.832	−0.048
	RZL3	−0.065	0.089	0.669	0.147
	RZL4	0.101	0.063	0.706	0.128
	RZL5	0.311	0.348	0.429	−0.302
National force	GL1	0.127	0.161	0.093	0.818
	GL2	0.079	0.284	0.119	0.806
	GL3	0.029	0.364	0.137	0.644
Cumulative variance contribution rate			68.136%

). Thus, it indicates that the questionnaire has good structural validity.

4.2. Fitting and Adaption of Models

As shown in

Table 5. Model fit indices.

Evaluation Indices	CMIN/DF	GFI	IFI	TLI	CFI	NFI	RMSEA	PGFI	PNFI
Initial Model	3.356	0.874	0.893	0.874	0.892	0.854	0.076	0.765	0.733
Modified model	2.447	0.910	0.935	0.923	0.935	0.895	0.060	0.787	0.063
Fit standard	≤3	>0.9	>0.9	>0.9	>0.9	>0.9	≤0.08	>0.5	>0.5

Note: CMIN/DF = χ²/df, GFI = goodness-of-fit index, IFI = incremental fit index, TLI = Tacker–Lewis index, CFI = comparative fit index, NFI = normed fit index, RMSEA = root mean square error of approximation, PGFI = parsimony goodness-of-fit index, PNFI = parsimony-adjusted NFI.

, the goodness-of-fit indices of the initial model, such as CMIN/DF, GFI, IFI, TLI, and CFI, are smaller than the reference value, which does not satisfy the criteria and, thus, needs to be modified. In general, the measurement model should be revised according to the modification indices (MI), in descending order, and only one at a time, and the cycle continues until most of the fitness metrics reflecting goodness-of-fit values align with the reference value criteria [68]. Using AMOS 22.0 software, the initial model was modified according to MI, and after adding three paths in turn, the modified model was obtained (Figure 4). Three paths of e5 ↔ e15, e17 ↔ e19, and e17 ↔ e20 passed the significance test and have positive values, indicating that the local village committee supported and guided the peasant households to relocate in favor of the efficient use of land, and the peasant households agreed that relocation was a good thing. They were more willing to relocate to the centralized resettlement sites in the village or the town for centralized residence.

With the modified model, CMIN/DF = 2.447 (<3), RMSEA = 0.060 (<0.08), the test results of GFI, IFI, TLI, and CFI are all higher than 0.9, and the NFI = 0.895, which is close to 0.9; in addition, the values of PNFI and PGFI are all higher than 0.5. Therefore, all the indicators of the modified model comply with the standard, indicating that the model’s overall fitness is good.

4.3. Modified Model Results

4.3.1. Modified Measurement Model Results

The non-standardized regression coefficients were calculated using the maximum likelihood method, and the results of the modified measurement model are shown in

Table 6. Fitting results of measured model.

Items			NSE	1 SE	CR	2 SE
DL1	←	Land force	1			0.620 ***
DL2	←	Land force	1.1	0.111	9.909	0.613 ***
DL3	←	Land force	1.128	0.102	11.031	0.712 ***
DL4	←	Land force	1.258	0.108	11.603	0.773 ***
DL5	←	Land force	0.993	0.097	10.262	0.642 ***
DL6	←	Land force	0.878	0.093	9.433	0.575 ***
RL1	←	Human force	1			0.854 ***
RL2	←	Human force	1.022	0.048	21.183	0.866 ***
RL3	←	Human force	1.089	0.052	20.935	0.858 ***
RZL1	←	Cognitive force	1			0.753 ***
RZL2	←	Cognitive force	1.024	0.077	13.284	0.755 ***
RZL3	←	Cognitive force	0.863	0.087	9.967	0.546 ***
RZL4	←	Cognitive force	0.91	0.083	10.937	0.604 ***
RZL5	←	Cognitive force	0.796	0.086	9.211	0.499 ***
GL1	←	National force	1			0.748 ***
GL2	←	National force	1.170	0.083	14.167	0.841 ***
GL3	←	National force	1.118	0.094	11.877	0.645 ***
Y1	←	Willingness to relocate to a concentrated residence	1			0.547 ***
Y2	←	Willingness to relocate to a concentrated residence	3.621	0.321	11.278	0.867 ***
Y3	←	Willingness to relocate to a concentrated residence	3.197	0.285	11.223	0.842 ***

Note: NSE = non-standardized estimate; ¹ SE = standard error; CR = critical ratio; ² SE = standardized estimate; *** significant at p < 0.001.

. The standard deviation and critical values of housing quality, support from relatives and friends, local government support, and form of living are blank because these four factors are assumed to have an inevitable effect on peasant households’ willingness to relocate to a concentrated residence when the model was initially constructed, so they are set as fixed parameters.

(1) Among land force (DL1–DL6), the path coefficient of housing quality is fixed at 1, and the standardized regression coefficient is 0.620. The standardized regression coefficients of the living environment, infrastructural conditions, land quality, geological hazards, and agricultural income of the place of residence are 0.773, 0.712, 0.642, 0.613, and 0.575, respectively. All six factors significantly positively affect peasant households’ willingness to relocate to a concentrated residence. Specifically, a bad living environment is the primary influencing factor, followed by poor infrastructure conditions in the settlement. This indicates that if the primary living conditions of the place do not meet the daily needs of the farmers, the farmers will be more willing to move to a centralized resettlement site with better conditions. The poor land quality in the settlement, coupled with the threat of geological hazards, may strengthen peasant households’ willingness to relocate to a concentrated residence. Low agricultural income in the settlement has a significant positive effect on the willingness of peasant households to relocate to a concentrated residence. However, its effect is relatively small compared with other land force factors. This may be because some peasant households carry out other odd jobs besides growing crops to supplement their income.

(2) Among the three observed variables reflecting human force (RL1–RL3), the standardized regression coefficients of the support from peasant households’ relatives and friends, neighbors, and family are higher than 0.85, all of which significantly positively affect peasant households’ willingness to relocate to a concentrated residence. This indicates that peasant households will have a stronger desire to live in concentrated residences if their relatives, friends, neighbors, and families supported them in relocating with the government’s help. Human beings are complex, and the behavioral intentions of peasant households are not entirely determined by themselves but also influenced by important social groups in the individual’s society (e.g., family, relatives, friends, and neighbors). The behavior, opinions, and attitudes of these important social groups close to the peasant household often significantly positively impact the peasant household.

(3) The standardized regression coefficients of the five observed variables of cognitive force (RZL1–RZL5) are 0.753, 0.755, 0.546, 0.604, and 0.499, respectively, which indicates that the rational cognitions (economic rationality, ecological rationality, survival rationality, and value rationality) of peasant households regarding relocation all positively affect the peasant households’ willingness to relocate to a concentrated residence. Among them, farmers pay more attention to economic rationality; that is, whether the living standard of their own families and the living conditions of their families will be improved after relocating to the centralized resettlement sites, which are the primary driving forces for the willingness of peasant households to live in a concentrated residence, followed by RZL4 (survival rationality). In contrast, the cognitions of RZL3 (ecological rationality) and RZL5 (value rationality) have a slighter positive influence on the peasant households’ willingness to relocate to a concentrated residence with the other cognitive force factors, probably because they are not closely related to peasant households’ interests.

(4) Among national force (GL1–GL3), the path coefficient of local government support is fixed at 1 with a standardized regression coefficient of 0.748, and the standardized coefficients of village committee support and policy advocacy are 0.841 and 0.645, respectively. These three factors have a significant and positive effect on peasant households’ willingness to relocate to a concentrated residence. Among them, the behavior and attitude of the village committee significantly positively impact the peasant households’ willingness to relocate to a concentrated residence. If the village committee supported and guided peasant households to relocate, it would significantly strengthen peasant households’ willingness to relocate to a concentrated residence. Most of the sample villages are in remote mountainous areas, where peasant households receive information in a closed manner, and the support and publicity of the local government and village committee would strengthen peasant households’ understanding of and trust in the relocation policy. Therefore, the attitude of the local government and the publicity of the relocation policy by village cadres also have a significant positive effect on the willingness of peasant households to live in a concentrated residence.

(5) Among the three observed variables reflecting the willingness to relocate to a concentrated residence, the path coefficient in the form of living is fixed at 1, with a standardized regression coefficient of 0.547. The standardized regression coefficients of Y1 and Y2 (choice of living) are 0.867 and 0.842, respectively. This suggests that the three factors affecting the preference of a concentrated residence and the willingness to live in centralized resettlement sites in a town or a village will also positively influence peasant households’ willingness to relocate to a concentrated residence.

4.3.2. Modified Structural Model Results

The standardized path coefficients and fitting results of the structural model are shown in

Table 7. Fitting results of structural model.

Items			NSE	1 SE	CR	2 SE
Willingness to relocate to a concentrated residence	←	Land force	0.049	0.017	2.819	0.121 **
Willingness to relocate to a concentrated residence	←	Human force	0.056	0.015	3.693	0.176 ***
Willingness to relocate to a concentrated residence	←	Cognitive force	0.263	0.035	7.565	0.590 ***
Cognitive force	←	Human force	0.324	0.042	7.758	0.454 ***
Cognitive force	←	Land force	0.176	0.052	3.394	0.196 ***
Human force	←	National force	0.888	0.081	10.967	0.661 ***
Land force	←	National force	0.254	0.066	3.872	0.239 ***

Note: NSE = non-standardized estimate; ¹ SE = standard error; CR = critical ratio; ² SE = standardized estimate; *** significant at p < 0.001; ** significant at p < 0.01.

. Land force significantly positively affects the willingness to relocate to a concentrated residence (path coefficient of 0.121, p < 0.01). This indicates that the production, living, and ecological spatial conditions of the place where peasant households live are poor, affecting their daily production and living activities. These factors strengthen peasant households’ willingness to relocate to a concentrated residence; thus, H1 is verified. Human force has a significant positive effect on the willingness to relocate to a concentrated residence (path coefficient of 0.176, p < 0.001), indicating that in addition to peasant households’ attitudes, the support of peasant households’ family members, friends, relatives, and neighbors in relocating will play a role in encouraging and motivating to a certain extent, which could strengthen the confidence and willingness of peasant households to choose to live in a centralized residence; thus, H2 is verified. Cognitive force significantly positively affects the willingness to relocate to a concentrated residence (path coefficient of 0.590, p < 0.001). The direct effect is much higher than that of land force and human force, indicating that the more comprehensive rational cognitions of peasant households about the impacts of relocating to the concentrated resettlement sites and the more long-term consideration, the more it will strengthen peasant households’ willingness to relocate to a concentrated residence; thus, H3 is verified. The path coefficients of the influence of land force and human force on cognitive force passed the significance test at the 1% level with standardized coefficients of 0.196 and 0.176, respectively, indicating that the rational cognitions of peasant households on the impact of the relocation policy is positively influenced by land force and human force; thus, H4 and H5 are verified. The path coefficients of the influence of national force on land force and human force were significant at the 1% level with standardized coefficients of 0.239 and 0.661, respectively, indicating that national force positively contributes to human force and land force. Thus, H7 and H8 are verified.

In order to explore the relationship between the latent variables further, the direct, indirect, and total effects between the variables are analyzed next. As shown in

Table 8. Comprehensive analysis of overall impact effects among variables.

Variables	Land Force			Human Force
	Direct Effects	Indirect Effects	Total Effects	Direct Effects	Indirect Effects	Total Effects
Land force
Human force
Cognitive force	0.176		0.176	0.324		0.324
Willingness to relocate to a concentrated residence	0.049	0.046	0.095	0.056	0.085	0.141
Variables	Cognitive Force			National Force
	Direct Effects	Indirect Effects	Total Effects	Direct Effects	Indirect Effects	Total Effects
Land force				0.254		0.254
Human force				0.888		0.888
Cognitive force					0.333	0.333
Willingness to relocate to a concentrated residence	0.263		0.263		0.150	0.150

, the total effects of land force, human force, cognitive force, and national force on peasant households’ willingness to relocate to a concentrated residence are 0.095, 0.141, 0.263, and 0.150, respectively. The indirect effects of land force, human force, and national force on peasant households’ willingness to relocate to a concentrated residence are 0.046, 0.085, and 0.150, respectively. The direct effects of land force and human force on the cognitive force are 0.176 and 0.324, and the direct effects of national force on land force and human force are 0.254 and 0.888, respectively. The direct effects of national force on land force and human force are 0.254 and 0.888, respectively. Through the analysis, it can be seen that the support of national force may directly increase the support of human force and also have a specific role in promoting the improvement of land conditions. At the same time, it may indirectly strengthen the willingness of local peasant households to live in a concentrated residence by improving their cognitive force. Therefore, H7, H8, and H9 are verified. The total effect of cognitive force on peasant households’ willingness to relocate to a concentrated residence is 0.263, and local peasant households’ willingness to relocate to a concentrated residence depends mainly on peasant households’ rational cognitions of relocation.

In order to test the mediating role of cognitive force between land force, human force, and the willingness to relocate to a concentrated residence, this study used AMOS 22.0 to conduct the mediating effect test by the Bootstrap method (Bootstrap was set to 2000 times, and the confidence interval was taken as 95%) [69]. In analyzing the mediating effect of land force and human force on the willingness to relocate to a concentrated residence, the confidence intervals of the indirect effect are [0.057, 0.122] and [0.017, 0.081], respectively. Since none of the confidence intervals contain 0, it indicates that the mediating effect of cognitive force between land force and human force and the willingness to relocate to a concentrated residence exists significantly, and thus, H6 is verified. The indirect effect of human force on peasant households’ willingness to relocate to a concentrated residence is higher than that of land force.

5. Discussion

Based on the survey data of peasant households in the Panxi Area, located in the southwestern mountainous area of China, this study analyzed peasant households’ willingness to relocate to a concentrated residence and the essential personal and family characteristics of peasant households. Then, a structural equation model was constructed to analyze the relationship between national force, human force, land force, cognitive force, and the willingness to relocate to a concentrated residence, and explored the main factors affecting peasant households’ willingness to relocate to a concentrated residence in mountainous areas. Compared with previous studies, this study has made the following novel contributions: Firstly, the research perspective is innovative. The construction of the theoretical model in this study is not based on a single theory but on a combination of three theories, namely, the theory of planned behavior, social cognition theory, and policy process theory, and has creatively constructed the theoretical model of “national force—human force—land force—cognitive force—willingness to relocate to a concentrated residence” from the perspective of the peasant households. Second is the innovation of the research content. The relationships between national force, human force, land force, cognitive force, and the willingness to relocate to a concentrated residence in mountainous areas in this study were not only empirically analyzed, but the direct, indirect, and total effects between the variables were also explored, which revealed in greater depth the mechanisms influencing the willingness to relocate to a concentrated residence, by using survey data of rural peasant households in southwestern China. Third is the innovation of the research method. Most existing studies about the influencing factors of peasant households’ willingness to relocate to a concentrated residence use traditional regression analyses, such as logit and probit models, which cannot measure the intrinsic relationship between the factors. The SEM model used in this study can analyze multicausal and multifunctional relationships and simulate the factors’ intrinsic logical relationships. In addition, there are some similarities and differences between the results of this study and those of existing studies.

This study obtained conclusions consistent with those of Montpetit (2017), Alam (2019), and Meng et al. (2023) [21,47,52]. That is, important social groups (family, relatives, friends, and neighbors) significantly impact the willingness of peasant households to relocate. When important social groups support peasant households’ decision making, it strengthens their confidence and motivation to implement the decision to some extent. In addition, these findings are consistent with research hypothesis H2, which agrees that human force has a positive effect on peasant households’ willingness to relocate. The important social groups of family members, relatives, friends, and neighbors play the roles of core, extended, and geographical relationships, respectively, in the social relationships of peasant households. Together, they form the social support network of the peasant households and profoundly impact the life, production, and development of peasant households. In this study, in villages inhabited by Yi people in the Panxi Area, peasant households are mostly linked by blood relationships and kinship, so peasant households in a village are very trusting of each other. If peasant households have the support of their neighbors, relatives, and friends when making decisions, it will greatly enhance their motivation to participate in it. Obtaining information from important social groups has been a common cognitive pathway in society, and people also infer information from their decisions and attitudes. Peasant households’ value-based decisions resulted from interactions with these important social groups [70]. Junquera (2022) argued that peasant households’ social relations affected their decision making, and that changes in social relations and rural structure were interrelated, so policymakers from the local to national level should recognize the contribution of peasant households’ diverse social support networks to rural development and pay attention to the role of important social groups in guiding important social groups [71], which is in line with this study’s conclusions.

Many countries have provided multifaceted government interventions to alleviate poverty [54,58,72], and while these strategies are cost-effective, policies aiming to reduce poverty tend to prioritize economic interventions. However, poverty alleviation policies that only consider economic support are limited in helping poor households. Leonardo et al. (2022) argued that there is also a need to consider addressing the psychological and social barriers of poor households [73], which is consistent with this study’s consideration of several factors of peasant households’ cognitive force, both of which pay attention to the psychological expectations and rational cognitions that peasant households have of the government’s poverty alleviation policies. This is consistent with hypothesis H3, which agrees that cognitive force has a positive effect on peasant households’ willingness. Hoang et al. (2024) argued that the government’s human support and living support could help reduce multidimensional poverty among mountainous peasant households [72]. That is consistent with the results of this study and hypothesis H9, which agrees that the behavior (such as the construction of resettlement areas) and attitudes (support and encouragement) of the local government and village cadres were conducive to strengthening the peasant households’ motivation to participate in the government’s poverty alleviation policies and to cooperate with the government, which in turn, would help them to combat poverty. Both this study and Whitaker et al. concluded that government policies impacted the wellbeing of peasant households in mountainous areas. However, in contrast to the results of this study and hypothesis H7, Whitaker et al. (2024) attributed the low wellbeing of peasant households in two mountainous regions in Italy to the implementation of government policies and regulations, which did not improve the wellbeing of peasant households in mountainous areas [66]. This may be because the policies and regulations implemented in rural mountainous areas in Italy were designed for the plains and did not apply to mountainous areas. The consequence of such implementation was that it eroded peasant households’ trust in government institutions. However, the research hypothesis H7 indicates that national force has a positive effect on land force. This shows that when the government formulates poverty alleviation policies for poor peasant households in mountainous areas, they should consider the local conditions and consider the peasant households’ real needs and development requirements from the peasant households’ perspective.

In addition, this study has certain limitations that can be addressed in future research. For example, this paper only discussed the peasant households’ willingness to relocate to a concentrated residence and the influencing factors of all peasant households in the study area; however, whether there is any heterogeneity in the willingness to live in a concentrated residence and its influencing factors among peasant households of different genders, ethnicities, and occupations must to be further explored. There are many factors affecting peasant households’ willingness to relocate to a concentrated residence; however, this study only explored the impact of important social groups in the human force on the peasant households’ willingness to relocate, without taking into account the individual characteristics of farmers, family characteristics, and cultural characteristics. The authors will attempt to construct other models to study this in the future. Additionally, further study will explore the simulation of the location and construction of ideal resettlement areas based on the factors that affect peasant households’ willingness to relocate to a concentrated residence in mountainous areas.

6. Conclusions and Implications

6.1. Conclusions

Promoting the relocation of poor peasant households in mountainous areas to concentrated residences is necessary for the successful implementation of the national poverty alleviation strategy and rural revitalization. This study explored the factors that influence peasant households’ willingness to relocate to a concentrated residence by constructing a structural equation model using data collected through a questionnaire survey of 405 peasant households in 2021 in the Panxi Area. The following major conclusions have been drawn:

(1) In the 16 villages surveyed in the Panxi Area, there were still 20.98% of peasant households in mountainous areas who were unwilling to choose the concentrated residence. (2) The poor living environment and infrastructure conditions in the places where peasant households lived, the support of important social groups (family, relatives, friends, and neighbors), economic benefits (the improvement of living standards and living conditions) brought by concentrated residence, and the support and guidance of the local government and village committee were important factors that strengthened peasant households’ willingness to relocate to a concentrated residence. (3) Low agricultural income, ecological benefits and value benefits brought by concentrated residence, slightly influenced the peasant households’ willingness to relocate to a concentrated residence. (4) Land force, human force, and cognitive force all had a significant positive effect on the willingness to relocate to a concentrated residence, and the mediating role of cognitive force between land force and human force and the willingness to relocate to a concentrated residence existed significantly.

6.2. Policy Implications

(1) The national government plays a crucial role in relocation [74]. Hypotheses H7, H8, and H9 of this study prove that the support of national force directly impacts human force and land force, and the relationship between land force, human force, and peasant households’ willingness to relocate to a concentrated residence is moderated by national force. It shows that in promoting the relocation of peasant households to centralized resettlement sites, it is essential to pay attention to the influence of the attitudes and behaviors of the local government and the local village committee on the peasant households. In implementing national policies, local governments should make appropriate adjustments to policies in light of the actual local situation and feedback from the public to meet better local development needs and the interests of the public. Village committees should assist the government in the specific implementation of relocation work, such as assisting in and resetting relocation targets. At the same time, village cadres should do a good job of publicizing the policy, so that peasant households have a comprehensive understanding of the relocation policy and can strengthen their understanding of and support for the state’s policy, thus strengthening their willingness to relocate to a concentrated residence.

(2) Peasant households’ cognitive force plays a crucial role in the decision-making process of moving to a centralized settlement. Hypotheses H3 and H6 in this study prove that cognitive force has a significant positive effect on the willingness of peasant households to live in centralized resettlement sites, and the direct effect is much higher than that of land force and human force. There is a significant mediating effect between land force, human force, and the willingness to relocate to a concentrated residence. It shows that before carrying out relocation work, it is essential to guide peasant households to understand the relocation policy correctly. In addition, farm households are more concerned about the economic benefits brought by concentrated residence [75]. Therefore, local governments and village committees can improve the acceptance and satisfaction of peasant households in mountainous areas with the relocation policy by strengthening the construction of concentrated residences and providing employment support, so that peasant households will willingly and actively participate in the relocation work with the correct knowledge.

(3) The importance of social support networks to individuals cannot be overstated. Important social groups, such as family, relatives, friends, and neighbors, play different roles in an individual’s life and form an important part of an individual’s social support network [76]. This study proves that the indirect effect of human force on peasant households’ willingness to relocate to a concentrated residence is higher than that of land force. It shows that in carrying out relocation work, it is essential to pay more attention to the influence of important social groups on the individual peasant household’s behavioral decisions and cognitive abilities. Important social groups can enhance peasant households’ willingness to relocate to a concentrated residence in many ways, such as by providing them with detailed information on the benefits of concentrated residence, guiding them to develop an optimistic mindset, and assisting them in moving. These measures will help dispel the doubts and worries of the peasant households and boost their confidence and expectations, thereby facilitating the smooth implementation of concentrated residence.