Sage Journals: Discover world-class research

Abstract

The sustainable development of “industry-ecology-well-being” is a strategic goal of rural revitalization. This study selects 10 typical tourist village communities in China as research sites. Based on the adaptability theory of social-economic-natural ecosystems, this study constructs a coupling model of the “ecological dependence-livelihood well-being” of farmers' in terms of adaptability and identifies the mechanism underlying farmers' adaptability to rural tourism disturbance in the context a microsituation by way of a multinominal logistics model. The results found that are as follows. (1) Rural tourism development enhances farmers’ livelihood adaptability, and “high ecological dependence-high livelihood well-being” has become the optimal response to tourism disturbance. (2) Changes in the human-land relationship constitute a fundamental factor in the adaptability responses of farmers to tourism disturbance, and government tourism support institutions are an important external driving force. This study provides useful decision-making support and policy suggestions for identifying the model of dynamic adaptation and impact mechanism of farmers in the context of tourism disturbance, thereby promoting the development of tourism, the consolidation and expansion of the key achievements of poverty alleviation and comprehensive rural revitalization.

Keywords

Rural revitalization tourism community farmers' livelihood adaptability ecological dependence-livelihood well-being

Introduction

Rural tourism is an important channel for poverty alleviation in China and a key aspect of promoting the realization of the grand strategy of rural revitalization. Rural tourism is thus a “people-enriching project” that expands farmers' employment and increases farmers' income, and it has become an important driving force for the economic and social transformation and development of rural tourism communities (Rosalina et al., 2021). However, as an external force that affect rural communities, tourism activities can also cause negative externalities such as changes in the human-land relationship, resource structure, and ecological environment of the community. In the process of rural tourism development, because externalities are internalized in terms of the behavioral practices of microparticipants, farmers are not only the main participants and an important stakeholder group but also the most basic unit of social production and decision-making regarding consumption (Guo et al., 2017). The livelihood adaptability behavior of farmers in rural tourism communities reflects the process, approach and path that allow microeconomic actors to use natural resources and ecosystem services (including supply, regulation and cultural services), which directly determines the sustainability of the relationship between ecological protection and economic development (He et al., 2014). Therefore, the significance of this research lies in the facts that it evaluates the effects of rural tourism development on poverty reduction and enrichment from the perspective of the adaptability of microfarmers, that is focuses on the sustainable and modern transformation of community ecosystems, and that is clarifies not only the impact of rural tourism on the livelihoods and well-being of farming farmers’ but also the model of ecological dependence coupling.

Therefore, this study focuses on the livelihood responses of microsubject farmers in rural tourism communities, and constructs an analytical framework for farmers' livelihood response choices to tourism disturbance based on sustainable livelihood theory. We use a multinominal logistics (MNL) model to identify the dynamic responses associated with the livelihood adaptation strategies and livelihood adaptation capacities of community farmers in tourist destinations based on the researcher’s survey and interview data draw from the case sites. This study explores the dynamic response mechanism associated with farmers' livelihood adaptability in the context of tourism disturbance empirically, and provides policy inspiration concerning the sustainable development of rural tourism. This study also provides path support and a methodological reference for clarifying the relationship between ecosystem services and livelihood well-being in rural tourism destinations in further detail.

Literature review and analytical framework

Literature review

The Department for International Development (DFID, 2000) proposed a sustainable livelihood framework that focuses on the ability of farmers to cope with stress (risk) and the process by which they do so in the context of an adaptive behavior-oriented approach to livelihood (Singh and Hiremath, 2010). Scholarly researches have mainly focused on the impacts of land change (Vista, 2012), climate impact (Gebrehiwot and Van Der Veen, 2013), resources and environment (Twyman, 2001), and livelihood trajectory (Soltani et al., 2012) on sustainable livelihoods, the measurement of farmers’ livelihood capital (He et al., 2014), and the choice of livelihood strategies (Nielsen et al., 2013). However, there remains a lack of sustainable research concerning the way in which tourism disrupts farmers' livelihoods. The literature has mainly focused on static analyses of various components of farmers' incomes, but has not evaluated farmers' income sources in a dynamic manner. To expand the literature pertaining to the impact of rural tourism on farmers' livelihoods, this study focuses on changes in the dynamic interaction between farmers' livelihood strategies and livelihood capital.

Adaptability is closely related to terms such as vulnerability and resilience. This notion is often used to refer to the ability of individuals, families, and communities to maintain or restore their original state in response to external disturbances (threats or opportunities) (Biggs, 2011). The system path method is used to analyze the evolutionary process involved in the interaction between internal connections and disturbances to the system (Ruiz, 2010). Extant studies have provided important theoretical and methodological references that allow us to perform an adaptability analysis of farmers in rural communities in terms of their responses to tourism disturbance. Most such research has focused on overall social adaptability to system fragility, and less attention has been given to the adaptability response behavior and the motivations of subjects who live in the social ecological systems (SES) (Larsen et al., 2011). Based on the interrelationship between tourism disturbance and changes in the human-land relationship, this study analyzes the dynamic changes in rural farmers' decision-making with respect to adaptive behavior and adaptive capacity at the microsubject level of rural communities and attempts to identify the dynamic adaptation model and the influencing mechanism by which rural farmers respond to tourism disturbance.

Research pertaining to tourism disturbance has reported many results. Most existing studies have explored the direction of the effect of rural tourism from a single perspective, such as those of the ecological environment, socioeconomics or regional scales, through the use of static environmental capacity tools (Korstanje, 2011; Liu and Lv, 2010; Strickland-Munro et al., 2010). The research focuses on the participation behavior exhibited by microsubject farmers and the decision-making and effects associated with community tourism participation (Bao, et al., 2006; Zheng and Zhong, 2004). However, there remains a lack of research investigating the ways in which farmers and their families adapt to tourism development and improve their livelihoods from the perspective of human-land interaction. Sustainable livelihood theory provides a new analytical idea relevant to the study of farmers' livelihood choices in cases of tourism disturbance. Therefore, the purpose of this paper is to investigate the dynamic changes in farmers' decision-making with regard to adaptive behavior and their adaptive capacity at the microsubject level of rural communities by clarifying the relationship between tourism disturbance and changes in the human-land relationship. This paper also aims at identifying the dynamic adaptation model and the adaptability of farmers in cases of tourism disturbance. This research expands the scientific development of sustainable livelihood adaptability theory and provides useful decision-making support and policy suggestions for ongoing processes of tourism development, the consolidation and expansion of the achievements of poverty alleviation and the comprehensive promotion of rural revitalization.

Analytical framework

Rural tourism development inevitably alters the relationship between human being and land and disturbs the function and structure of the rural community ecosystem and the livelihood and livelihood strategies employed by residents (Figure 1). Firstly, rural farmers employ a livelihood adaptation strategy in response to tourism disturbance. The development of tourism has altered the conditions and methods of traditional land and woodland utilization in rural communities. The natural ecological environment in rural areas has been improved, and farmers’ resilience and livelihood status have been enhanced (Hoque et al., 2017; Wu et al., 2017). Farmers can establish a diversified family income structure strategy (Jia et al., 2021; Ma et al. (2021). Second, the results of the livelihood adaptability response of rural farmers to tourism disturbance reflect the relationship between community ecosystem services (ES) and human welfare (HW) on a microscale. Scholars initially proposed the concept of ecosystem service dependency and constructed an ecosystem service dependency index (Larondellea and Haase, 2013; Yang et al., 2013). Therefore, the conceptual framework of rural farmers' livelihood adaptability to tourism disturbance is constructed (Figure 1).

Figure 1.

Conceptual model of the livelihood adaptability response of residents of rural tourism communities.

Methods

Data sources

The research data are mainly drawn from the 2019 Henan Provincial Statistical Yearbook. In addition, the researcher selected 10 villages that exhibited unique characteristics as case sites for field investigations, in-depth interviews with village committees, and the administration of a farmer questionnaire. The questionnaire uses a five-point Likert scale to illustrate the development of various indicators and employs the entropy method to weight each indicator. The data collection is divided into two stages, namely, a preliminary investigation and a formal investigation. In June 2019, the research team conducted a 2-week preinvestigation of the tourism development status of the case sites. The subjects included the survey were farmers who were interviewed randomly. The results of the preinvestigation showed that village cadres have the best understanding of the development of rural tourism in their villages, including the role played by the government and tourism companies and the attitudes of farmers. Based on the actual situation, the authors revised and improved the questionnaire. Between August and October 2019, the researchers conducted formal investigations in the study area. As residents’ level of education was generally low, the method of “one question and one answer” was adopted for this survey. During the survey process, the researcher explained any indicators that the farmers did not understand. For low-educated individuals who were unable to complete the questionnaire, researchers completed the questionnaire in accordance with the individuals’ responses. A total of 1,750 questionnaires were distributed as part of this survey. Following the deletion of the invalid questionnaires, 1,658 valid questionnaires remained, which yielded a recovery rate of 94.7%. According to the differences in the intensity of tourism disturbance in rural communities, 612 effective samples were drawn from communities experiencing low-intensity tourism disturbance and 1,046 such samples were drawn from communities experiencing high-intensity tourism disturbance. Table 1 shows the basic data pertaining to farmer characteristics.

Table 1.

Residents’ well-being index evaluation system.

Criterion layer	Index layer	Interpretation or measurement of indicators	Weights
Natural capital	Per capita arable land area	Logarithm of per capita farmland area	0.520
Natural capital	Per capita forest area		0.481
Human capital	Total family labor capacity	6 years old and below = 0, 7–13 years old =1, 14–18 years old =3, 18–25 years old =4, 25–60 years old =5, 60–75 years old =2, 75 years old and above =0	0.622
	State of health	5-Point Likert scale ranging from 1 (unhealthy) to 5 (very healthy)	0.182
	Education	The highest level of education of a family member over 16 years old. College degree and above = 5, Junior college degree =4, High school/technical school degree =3, Junior high school degree = 2, Primary school degree =1, Illiterate =0	0.196
Social capital	Quality of relationships with neighbors		0.568
Social capital	Status of the farmer in society		0.432
Cognitive capital	Enthusiasm for participating in tourism		0.395
Cognitive capital	Ecological protection awareness		0.615
Material capital	Operational asset richness		0.177
Material capital	House value		0.823
Financial capital	Farmer annual income		0.585
	Ability to obtain loans		0.254
	Ability to receive subsidies		0.171

Data and indicators

Data standardization and weight determination

To prevent the occurrence of large errors resulting from a subjective determination of weights, we refer to the research by Cui et al. (2017), etc., and select the entropy method as an objective assignment method for calculating these weights (He et al., 2014). The specific calculation process is as follows:

Step 1: Construct the index evaluation matrix

E ′ : E' = {(e_{i j}^{'})}_{n \times m}

(1)

e_{i j}^{'}

is the evaluation value of the ith farmer under the jth index.

Step 2: Standardize the evaluation matrix $E'$ . We obtain

E : E = {(e_{i j})}_{n \times m}

(2)

Then, for $e_{i j} \in [0,1]$ , the standardized processing formula is

e_{i j} = \frac{e_{i j}^{'} - {e_{i j m i n}^{'}}}{{e_{i j \max}^{'}} - {e_{i j \min}^{'}}}

(3)

Step 3: Calculate the proportion of the evaluation of the ith farmer under the jth index by using the formula for

g_{i j} : g_{i j} = e_{i j} / \sum_{i = 1}^{m} e_{i j}

(4)

Step 4: Calculate the entropy value of the jth index according to the following formula for

h_{j} {: h}_{j} = - k \sum_{i = 1}^{m} g_{i j} \times \ln g_{i j}

(5)with

k = 1 / \ln m

(6)

Step 5: Calculate the entropy weight of the jth index in each dimension according to formula

w_{j} = (1 - h_{j}) / \sum_{j = 1}^{n} (1 - h_{j})

(7)

Residents’ well-being (RWB) index

Residents’ well-being (RWB) has been described from different perspectives by scholars in the fields of psychology, sociology, and economics, and this term refers to a state that reflects residents’ happiness in life, material satisfaction, spiritual growth, and physical health (Li et al., 2017). Based on the five livelihood capitals included in the sustainable livelihood analysis framework established by the DFID, the livelihood characteristics of farmers in the China family panel studies (CFPS), and the research results obtained by He et al. (2019), and Zhao et al. (2016) concerning the quantification of indicators for rural farmers’ livelihood capital, this study constructed a farmer well-being evaluation index system that includes natural capital, human capital, social capital, cognitive capital, material capital and financial capital (Wang et al., 2017).

We employed the entropy method to obtain the weights of various indicators and subsequently calculated RWB based on the standardized values and weights for each indicator. The formula for calculating RWB is

R W B_{i} = \sum_{j = 1}^{n} w_{i j} z_{i j}

(8)

R W B_{i}

is the value of the ith index capital,

w_{i j}

is the weight of the jth index of the ith index capital, and

z_{i j}

is the standardized value of the jth index of the ith livelihood capital.

z_{i j} = \frac{x_{i j} - \min {x_{i j}}}{\max {x_{i j}} - \min {x_{i j}}}

(9)

Index of dependence on ecosystem services

The IDES is an indicator that measures the degree of protection of the community ecosystem. The IDES is equal to the ratio between the net income of residents derived from ES and their total income (Li et al., 2017). A high IDES value indicates that residents are highly dependent on the ecosystem (Zhang and Chen, 2021). The difference in the costs paid by community residents affects our ability to quantify the degree of ecosystem service dependence accurately. This study employs the IDES to reflect the degree to which community residents depend on ES, which can improve our ability to compare different farmers. ES includes supply services, regulation services and cultural services. The formula of the IDES is as follows

I D E S_{i} = \frac{E N B_{i}}{| \sum_{i = 1}^{3} E N B_{i} + S N B |}

(10)

I D E S = \sum_{i = 1}^{3} I D E S_{i}

(11)

IDES represents the overall index of residents’ dependence on community ES, IDES_i represents the subindicator of residents’ dependence on the ith ES, ENB_i represents residents’ total net income from the ith ES, and SNB represents the income that residents receive from other economic activities.

In this study, a four-quadrant classification method was employed (Wang et al., 2017), featuring the RWB in the community as the ordinate and the community IDES as the abscissa; in addition, a coordinate quadrant chart was established to classify the patterns exhibited by the livelihood responses of community residents to rural tourism disturbance. To eliminate the influence of extreme values on these indicators, the median value was used as the boundary. The authors calculated the IDES and RWB of the overall effective sample and found that the median value of the IDES was 0.80 and that the median value of RWB was 4.61. The livelihood response models exhibited community residents can be divided into the following four models: model 1, “High dependence-High well-being” (H-H); model 2, “Low dependence-High well-being” (L-H); model 3, “Low dependence-Low well-being” (L-L); and model 4, “High dependence-Low well-being” (H-L). Figure 2

Figure 2.

The livelihood adaptability response results of community residents to tourism disturbance.

4 Data analysis and results

Descriptive statistics

Demographic characteristics

The average family size among farmers is 4.48 persons. The average number of members of the labor force within such a family is 2.24 persons. The average permanent farmer population is 3.17 persons. The proportions of families whose level of education is junior college and above, senior high school, junior high school, and elementary school and below are 10.8%, 22.1%, 47.3% and 19.8%, respectively. The average farmland area owned by farmers is 2593.31 m², and the average woodland area is 16206.50 m². The primary income sources of farmers are farming and external work, and the proportions of farmers’ diversified income sources that focus on tourism are 8.1%, 26% and 65.9% on average. The average family income level is $5817.28.

The livelihood adaptability strategy adopted by rural tourism communities

Based on the fact that a certain source of income accounts for more than 50% of total farmer income (Wen and Chen, 2020), this article classifies the livelihood adaptability strategies of rural tourism communities as agriculture-oriented (that is, income from family members engaged in agriculture, forestry and aquaculture accounts for more than half of total family income), migrant worker-dominant (that is, family members who leave their own villages to work outside the family for income accounts for more than half of total family income) or diversified (that is, family members engaged in tourism operations, agricultural and nonagricultural operations in their own villages, etc.). According to farmer income data pertaining to rural tourism communities in 2019, farmers engaged in agriculture-oriented, migrant labor-oriented, and diversified livelihoods at rates that accounted for 21.4%, 33.7%, and 44.9% of the total sample, respectively. The diversification of income is the main livelihood adaptation strategy employed in rural tourism communities.

Comparisons associated with the “ecological dependence-livelihood well-being” model among rural farmers in rural tourism communities

Comparison of rural farmers' livelihood adaptability strategies across different response models

Table 2 shows the livelihood strategies across the four types of livelihood adaptability responses on the part of the farmers in rural tourism communities. (1) From a general perspective, the selection of rural farmers’ livelihood adaptability strategies include diversified strategies, migrant work, and agriculture-led strategies according to their proportions. (2) From the perspective of various livelihood strategies, in the context of the agriculture-led strategy, the H-L model accounts for the highest proportion of the total. Farmers mainly rely on rural land to obtain income, which represents a single source of income and is associated with a high degree of risk. Therefore, increases in income for farmers who adopt this type of highly dependent land-based income model remain limited. With income from migrant work, farmers mainly obtain their own income by performing migrant work to improve the overall well-being of their families. However, these farmers must leave their hometowns, which results in a large amount of land becoming abandoned and a low dependence on the ecosystem, which thereby leads to a lack of protection for the community ecosystem. When the diversified livelihood strategy is employed, the H-H model accounts for a relatively high proportion of the total. This situation reflects the fact that rural tourism has a positive impact on the ecosystem of the local community. Tourism development increase the income of residents, which enables community residents to enjoy high rates of return from environmental protection and tourism development. As a result, farmers pay back into ecological protection and tourism development. A virtuous cycle of “eco-environment protection-sustainable tourism development-improvement of residents' well-being” is thus formed.

Table 2.

Comparison of the livelihood strategies employed residents in rural tourism communities across different models of “ecological dependence-livelihood well-being”.

Livelihood strategy	1. H-H	2. L-H	3. L-L	4. H-L	Total
Agriculture-oriented	N=176	N=18	N=18	N=217	N=367
	10.62%	1.09%	1.09%	13.09%	22.14%
Outside-work-oriented	N=4	N=320	N=134	N=21	N=541
	0.24%	19.30%	8.08%	1.27%	32.63%
Diverse livelihood strategies	N=424	N=141	N=92	N=93	N=750
	25.57%	8.50%	5.55%	5.61%	45.24%
Total	N=645	N=479	N=244	N=290	N=1658
	38.90%	28.89%	14.72%	17.49%	100%

Note: Pearson chi²(6) = 482.4637, Pr = 0.000.

Comparison of rural farmers' livelihood capital composition across different response models

Table 3 shows the livelihood capital of rural farmers with respect to different livelihood responses in rural tourism communities. 1) In the face of tourism development, the physical capital, social capital and financial capital of rural farmers are relatively high, and physical capital is the largest value in this context. This situation reflects an increase in the overall material available to and financial wealth of community residents in response to disturbance of the effects of rural tourism. Simultaneously, rural tourism can facilitate positive social relationships among farmers in the community. (2) In terms of the four response models, the overall livelihood capital is the largest according to the H-H model. This fact demonstrates that a diversification of livelihood strategies can increase livelihood capital. Diversified livelihood strategies can not only increase the overall livelihood capital of residents but can also facilitate the formation and maintenance of positive social relationships in rural communities as well as the protection and sustainable development of the ecological environment and the associated resources.

Table 3.

Comparison of residents' livelihood capital according to different models of “ecological dependence-livelihood well-being” in rural tourism communities.

Well-being	1.H-H	2.L-H	3.L-L	4.H-L	Mean
Well-being	N=645	N=479	N=244	N=290	N=1658
Natural capital	0.91^(2,3,4)	0.91^(1,3)	0.91^(1,2,4)	0.91^(1,3)	0.91
Human capital	0.98^(2,3,4)	0.96^(1,4)	0.59^(1,2,4)	0.67^(1,2,3)	0.86
Social capital	1.32^(2,3,4)	1.07^(1,4)	0.79^(1,2,4)	1.14^(1,2,3)	1.14
Cognitive capital	1.25^(2,3,4)	0.65^(1,3,4)	0.61^(1,2,4)	0.98^(1,3)	0.94
Physical capital	1.36^(2,3,4)	1.24^(1,3,4)	0.87^(1,2,4)	0.91^(1,2,3)	1.17
Financial capital	1.11^(3,4)	1.03^(1,4)	0.92^(1,4)	0.98^(1,2,3)	1.04
Livelihood capital	7.29	6.4	5.31	5.89	6.5

Note: The superscript numbers in parentheses indicate a significant difference between this model and other models at the 5% level.

Comparison of farmers’ IDES across different response models

Table 4 shows the situation of rural farmers’ response to the IDES across different livelihood adaptability response models in rural tourism communities. (1) In the IDES, the cultural service index is higher than the supply and regulation service index. As rural tourism develops, farmers can obtain tourism-related income either directly or indirectly. (2) In the supply, culture and regulation service index, the H-H model and the H-L model are both the highest, thus indicating that the development of tourism can increase the ecological dependence of farmers. (3) The cultural service index is the highest according to both the H-H and the H-L models. According to the L-H model, there are few differences in terms of the changes in various service indexes. The cultural service index in the L-L model is low. When farmers' level of ecological dependence is low, they mainly obtain high incomes by employ nonagricultural livelihood strategies such as traveling elsewhere to work. However, ecological and social problems such as leaving their hometowns, the neglect of arable land, and the abandonment of elderly individuals and children have gradually emerged.

Table 4.

Comparison of the IDES of residents across different “ecological dependence-livelihood well-being” models in rural tourism communities.

Ecological dependence index	1.H-H	2.L-H	3.L-L	4.H-L	Mean
Ecological dependence index	N=645	N=479	N=244	N=290	N=1658
IDES total index	1.315^(2,3)	0.685^(1,4)	0.365^(1,4)	0.8^(2,3)	0.87
Supply service index	0.48^(2,3)	0.21^(1,4)	0.185^(1,4)	0.23^(2,3)	0.3
Cultural service index	0.545^(2,3)	0.215⁽¹⁾	0.03⁽¹⁾	0.3^(2,3)	0.315
Adjusted service index	0.295^(2,3)	0.26^(1,3,4)	0.15^(1,3,4)	0.27^(2,3)	0.25

Note: The superscript numbers in parentheses indicate a significant difference between this model and other models at the 5% level.

Influencing mechanism of the livelihood adaptability response in rural tourism communities

Selection analysis model of the livelihood adaptability response model of rural farmers

In this paper, the selection probability of farmers' livelihood adaptability response model is used as the explained variable to regress the influencing factors in response to tourism disturbance. Since the livelihood adaptability response model of the explained variable counts as discrete data, the MNL regression analysis model employed by this article analyzes (Hausman and Mcfadden, 1984; Wang and Guo, 2001) the influencing mechanism of rural community farmers' adaptive response model selection in the face of tourism disturbance. The standardized unordered multiclass logistic regression model is

P (Y = i) = \frac{e^{^{β} i^{x} j}}{1 + \sum_{i = 1}^{4} e^{^{β} i^{x} j}}

(12)i represents the livelihood adaptability response model chosen by four types of farmers (i∈(1=H-H, 2=L-H, 3=L-L, 4=H-L)).

P (Y = i)

is the probability for a farmer to choose the ith livelihood adaptability response model. x is an explanatory variable that identifies the influencing factors associated with responses to tourism disturbance. Finally,

β_{i}

represents the coefficient to be estimated with the selected result i. The variables are compared with other types based on the H-H model. After transforming Equation (10), the estimation model of the relative ratio between the mode i selection and mode H-H selection is

\ln [P_{y = i^{'}} / P_{y = 1}] = α_{i^{'}} + \sum_{j = 1}^{n} β_{i^{'} j} x_{j}

(13)

i^{'} ϵ (2 = L - H; 3 = L - L; 4 = H - L)

. Three types of scenarios, namely, models (Ⅰ), (Ⅱ) and (Ⅲ), are constructed to conduct a regression analysis. Model (I) _i'=2 means that L-H takes H-H as the reference class. Model (II) _i'=3 indicates that L-L takes H-H as the reference class. Model (Ⅲ) _i'=4 signifies that H-L takes H-H as the reference class.

Influencing factors associated with the selection of the adaptability response model of rural farmers' livelihoods

Construction of the influencing factors and descriptive statistics

Based on the rural tourism-related literature and actual investigations, this research identifies the following influencing factors in the context of a microsubject perception evaluation: government tourism support; community participation; farmer tourism perception; community geographic characteristics; and family demographic characteristics (Chen et al., 2015; Freitag and Bciilmann, 2009; Le et al., 2015; Ngnkoo and Gursoy, 2016; Ouyang et al., 2017; Wang and Lu, 2014). Government tourism support includes four measurement indicators: resettlement subsidies, fiscal subsidies, tourism skills training, and inviting businesses and attracting investment (e.g. Nunkoo and So, 2016; Ouyang et al., 2017). Community participation includes the extent of participation in tourism planning and development, the extent of participation in tourism management, and the extent of participation in the distribution of tourism revenue (Zuo et al., 2017). The tourism perception of farmers includes: positive tourism perception and negative tourism perception. Positive tourism perception contains two measurement indicators: tourism development increases the income of local residents, and tourism development improves employment opportunities for local residents; negative tourism perception, in turn, includes tourism development causes local prices to rise, tourism development damages tourism resources and the community environment, and tourism development leads to crowds and traffic within the community (Nunkoo and So, 2016). Community geographical characteristics include two measurement indicators: the abundance of community resources and the level of community ecological conservation (He et al., 2014). Family demographic characteristics are mainly measured in terms of the number of laborers and level of education (Li et al., 2010). These indicators reflect the adaptive ability of farmers in rural communities to respond to disturbances resulting from rural tourism development. This study mainly evaluated the subjects' feelings and psychological cognitions on 5-point Likert scale (1 = very low, 5 = very high). The descriptive analysis of the variables is shown in Table 5.

Table 5.

Descriptive statistics concerning the influencing factor scale.

Variables	Items	Mean	SD
Government tourism support (GTS)	Resettlement subsidy (GTS1)	0.625	0.4903
	Fiscal subsidy (GTS2)	0.525	0.5057
	Tourism skills training (GTS3)	0.575	0.5006
	Invite business and attract investment (GTS4)	0.500	0.5064
Community participation (CP)	The extent of participation in tourism planning and development (CP1)	1.525	0.5057
	The extent of participation in tourism management (CP2)	2.500	0.5547
	The extent of participation in the distribution of tourism revenue (CP3)	2.325	0.9167
Positive resident tourism perception [PRTP]	Tourism development increases the income of local residents (PRTP1)	3.550	0.7143
Positive resident tourism perception [PRTP]	Tourism development improves employment opportunities for local residents (PRTP2)	3.700	0.7232
Negative resident tourism perception [NRTP]	Tourism development causes local prices to rise (NRTP1)	3.850	0.6222
	Tourism development damages tourism resources and the community environment (NRTP2)	2.875	0.4634
	Tourism development leads to crowds and traffic within the community (NRTP3)	4.400	0.5454
Community geographical characteristic (CGC)	The abundance of community resources (CGC1)	3.525	0.5057
Community geographical characteristic (CGC)	The level of community ecological conservation (CGC2)	3.450	0.6775
Family demographic characteristic (FDC)	Number of laborers (FDC1)	3.400	0.5905
Family demographic characteristic (FDC)	Education (FDC2)	3.100	0.6325

Reliability and validity tests for the common variance analysis of the influencing factors

To overcome the problem of common method deviation, first, the participants were informed of the purpose of the research when they completed the questionnaire survey, and this problem was similarly mitigated via the use of sampling program control and statistical methods (Lee and Kang, 2006). Harman’s single-factor test was used to assess the data. An exploratory factor analysis was performed with respect to all 17 measurement indicators, and the maximum explanatory variance among the factors was 27.98%, indicating that there was no common method deviation in the selected variable data. The Cronbach’s α coefficient of all variables ranged between 0.713 and 0.824, and the value of CR ranged between 0.718 and 0.895, suggesting that the index data exhibited high reliability and good stability. A confirmatory factor analysis (CFA) was conducted to test the convergence of the variable data and the discriminant validity. The variable factor loading coefficient was greater than 0.5, and the AVE value was between 0.496 and 0.724, indicating that the convergent validity of each variable index was good.

Results

In this study, Stata 15.0 software was used to conduct a multivariate and disordered logistic regression analysis concerning the choice of livelihood adaptability response model by the tourism community. The regression results are shown in Table 6.

Table 6.

Disorderly multiclass logistic regression results of livelihood adaptability response model selection by rural tourism communities in the context of farmers.

Explanatory variables		Model (Ⅰ) ln (Pi’=2/Pi=1)			Model (Ⅱ) ln (Pi’=2/Pi=1)			Model (Ⅲ) ln (Pi’=2/Pi=1)
		B	Std. Error	p	B	Std. Error	p	B	Std. Error	p
	Tourism Disturbance	2.313^∗∗	0.231	0.009	2.329^∗∗∗	0.227	0.000	1.151^∗∗∗	0.245	0.000
Government tourism support	GTS1	1.297^∗∗	0.450	0.012	2.113^∗∗∗	0.420	0.079	2.610^∗	0.491	0.031
	GTS2	1.673^∗∗∗	0.207	0.000	1.254^∗∗	0.190	0.000	1.621^∗∗	0.214	0.023
	GTS3	0.359∗	0.232	0.072	2.671^∗∗∗	0.262	0.000	2.312	0.232	0.331
	GTS4	3.110^∗∗∗	0.211	0.000	2.141^∗∗	0.042	0.013	2.312	0.218	0.236
Community participation	CP1	1.873^∗∗	0.238	0.011	1.312^∗∗∗	0.235	0.000	1.352^∗∗	0.242	0.006
	CP2	2.116^∗∗	0.175	0.015	1.871^∗	0.155	0.104	0.988^∗∗∗	0.195	0.000
	CP3	3.251	0.272	0.379	2.562^∗∗∗	0.260	0.000	2.213^∗∗∗	0.281	0.000
Positive resident tourism perception	PRTP1	1.349	0.216	0.710	2.231^∗∗	0.816	0.028	1.896^∗∗∗	0.809	0.000
Positive resident tourism perception	PRTP2	0.973	0.118	0.496	2.124^∗∗	0.108	0.013	1.993^∗∗	0.131	0.114
Negative resident tourism perception	NRTP1	−1.763	0.111	0.029	−0.334^∗∗	0.104	0.013	−0.875	0.118	0.616
	NRTP2	−1.432	0.249	0.009	−0.613^∗	0.242	0.027	−0.349	0.241	0.703
	NRTP3	−2.114	0.214	0.305	−0.321^∗∗	0.198	0.112	−0.563	0.222	0.406
Community geographical characteristic	CGC1	1.562^∗∗∗	0.231	0.000	2.531^∗∗∗	0.213	0.000	1.365^∗	0.245	0.075
	CGC2	2.312^∗∗∗	0.201	0.000	2.974^∗∗∗	0.057	0.000	1.978^∗	0.055	0.116
Family demographic characteristic	FDC1	1.542	0.118	0.004	2.123^∗∗	0.022	0.011	0.776^∗∗	0.025	0.042
Family demographic characteristic	FDC2	2.212	0.134	0.115	2.012^∗∗	0.694	0.029	0.981^∗∗	0.722	0.064
Constant term		3.692^∗	2.086	0.077	4.234	2.581^∗∗	0.010	4.459^∗	2.212	0.137
Prob > χ2		0.000
Pseudo R²		0.297

Note: ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001.

Model test

A multivariate logistic regression analysis was conducted to investigate the 17 factors associated with the impact indicators discussed above. The model prediction accuracy rate was 81.2%, and the model fit degree index was χ² = 229.211, p < 0.01, thus indicating that the constructed model has a significant degree of fit; the goodness-of-fit index was p > 0.05, which reflects goodness of fit. Pseudo R² value greater than 0.5 indicates that overall, the model passes the test.

Analysis of the empirical results

The results of the multivariate logit regression analysis of tourism disturbance in Models (I), (II) and (III) show that tourism disturbance has a significant impact on community residents’ livelihood response according to the H-H model at significance levels of 5% and 1%. The resident livelihood response model shifts from the L-H, L-L, and H-L models to the H-H model.

(1) Changes in the human-land relationship constitute a fundamental factor in the livelihood adaptability responses of rural farmers to tourism disturbance. Tourism disturbance breaks the balance between human beings and the land within the community. The expropriation of rural farmers' land resources and the availability of a large number of nonagricultural employment opportunities have prompted an adaptive change in agriculture-oriented and migrant-worker strategies. Diversified livelihood strategies have become the dominant model for farmers' adaptive strategies. Land acquisition is required for community tourism development. The main source of income in residents’ diversified livelihood strategies remains resources drawn from the community ecosystem, the environment, etc. Therefore, a loss of land resources resulting from tourism disturbance does not reduce the dependence of farmers on the community ecosystem or the level of farmers' well-being.

(2) Local government tourism support is an important external driving force that can improve the welfare of farmers. Local government tourism-supported resettlement subsidies, fiscal and tax subsidies, education and training, and investment promotion have significant impacts according to Models (I) and (II). Local government tourism support can improve farmers’ participation in tourism activities and allow them obtain benefits from such activities. The increase in the proportion of the value of the ES caused by tourism activities increases the probability of a shift in farmers’ livelihood adaptability towards the H-H model. However, education, training, and investment promotion are not significant according to Model (Ⅲ), which indicates that these three factors have no obvious effect on improving the well-being of farmers. This lack may be a result of the fact the farmers have restricted income due to tourism activities.

(3) The psychological perceptions of farmers (tourism participation and the tourism development effect) represent the endogenous force driving their livelihood adaptability responses. Farmers’ participation in tourism planning and development, tourism operation and management, and tourism income distribution have significant effects according to both Models (Ⅱ) and (Ⅲ). When farmers participate more in tourism, their livelihood adaptability responses are more likely to shift from the L-H and H-L models to the H-H model. The participation of farmers in the distribution of tourism income is not significant according to Model (Ⅰ). Farmers' perceptions of the ability of tourism to promote community economic development and increase employment significantly affects the L-L model, and thus the H-L model shifts to the H-H model. The positive promotion of the development of rural tourism with respect to the value of community ES can be perceived clearly by farmers.

(4) Family demographic characteristics and community geographic characteristics are basic factors associated with the livelihood adaptability responses of residents. Regarding family demographic characteristics, when the family population is larger and the education level of the family is higher, the number of residents who participate in community tourism activities in response to tourism disturbance is correspondingly higher. Tourism activities are more likely to improve RWB. However, an increase in population or level of education has no significant effect on the possibility of the livelihood model of residents shifting from L-H to H-H. There are few differences between the income earned by residents who operate as migrant workers and the income gained from community tourism activities. The abundance of community resources and the level of community ecological conservation have significant impacts according to Models (Ⅰ), (Ⅱ) and (Ⅲ). The status of the local ecosystem of the community can affect the well-being and dependence of farmers significantly. When the local ecosystem in the community is better, the probability that farmers will exhibit the H-H model in their responses to tourism disturbance is increased.

Conclusion and discussion

Research conclusion

Based on the theory of sustainable livelihood and Coleman’s rational choice theory, this paper analyzes the response model (the “ecological dependence-livelihood well-being” coupling model) of the adaptive livelihood strategy of rural community farmers to tourism disturbance. This study explores the influencing factors that affect the lively adaptability response model of rural farmers, which is a key factor in the realization of the strategic goal of rural revitalization. The main conclusions of this study are as follows.

Rural tourism development is helpful to the task of realizing the development strategy of rural revitalization. Rural communities use the thriving development of tourism to promote the ecological livability, rural civilization, and effective governance of their community as well as to pursue goal of living a prosperous life. The optimal choice for the adaptability response of community farmers' livelihoods to tourism disturbance is the “high dependence-high well-being” model. Tourism development increases the dependence of farmers on ecosystem service value as well as their well-being. The development of rural tourism helps farmers choose diversified livelihood strategies and mitigated seasonal problems relevant to agriculture and animal husbandry. The tourism business cycle is longer and provides greater benefits to residents, which prompts farmers to choose tourism as the leading means of diversifying their livelihoods.

Farmers' adaptability responses are a decision associated with farmers' rational survival choices under condition of tourism disturbance leading to rural community ecosystem imbalances. The government tourism support system is an important external driving force for farmers' adaptability response. The rational preferences of farmers in economic and social contexts (i.e. farmers' tourism participation and tourism perception effects) are internal driving forces for a variety of adaptability response choices. Geographical resource endowments and farmers' family demographic characteristics are basic factors associated with the differentiation of farmers' adaptive behavior choices.

Marginal contributions and directions for future research

Marginal contributions

The possible marginal contributions of this article are mainly reflected in the following aspects. (1) This study seeks to understand the impact of tourism with respect to the interaction between farmers and ecosystems in tourist destinations in rural communities. Based on a microsubject perspective, this research compares and analyzes the heterogeneity of farmers' livelihood capital, livelihood strategies, and ecological dependence in the context of different adaptability response models to rural tourism. (2) This research attempts to analyze the adaptability response results pertaining to microsubject tourism disturbance based on the coupling model of farmers' livelihoods and welfare and ecosystem service value. This research connects external stimuli, farmers' internal psychological perceptions and the results of farmers' adaptability response. Accordingly, this study is helpful with respect to the task of clarifying the influencing mechanism of farmers' adaptability response to tourism disturbance. (3) In practical, this research focuses on issues related to livelihood such as the livelihood and welfare of residents in rural tourism destinations and the sustainability of community ecosystems. This research can thus provide a reference for destination managers to formulate relevant policies for development based on people’s livelihood and to promote the realization of the dual goals of the high-quality survival of rural farmers in rural tourism areas and the ecological revitalization of communities.

Implications

Ways of increasing farmers' livelihood assets, improving the diversity of their livelihood activities, and reducing their livelihood vulnerability in the process of developing rural tourism are key aspect of local rural tourism development. Among these aspects, the creation of sustainable development capacity is a breakthrough point for reduce the livelihood vulnerability of farmers in the study area. First, in light of the currently low level of education among farmers, local government should focus on the cultivation of sustainable livelihood development by emphasizing human capita. It is necessary to consolidate rural compulsory education, promote rural vocational education and tourism skills training vigorously, and improve the employment skills and tourism management capabilities of the adult labor force among farmers. Second, the construction of tourism infrastructure should be improved and the reception capacity of rural tourism should be increased. Third, in light of the continuous expansion of land used for tourism development, a more equitable and reasonable compensation system for farmers should be designed.

Guidance by local governments should be strengthened. The government’s macrocontrol role should be emphasized, institutional guarantees should be established to allow farmers in relevant communities to participate in tourism, residents' right to participate should be protected, and residents' participation behavior should be standardized. Simultaneously, a variety of assistance policies should be implemented, such as the arrangement of special funds for tourism development, the adoption of various methods of improving the level of economic development in the community, use of the benign mechanism of tourism disturbance, the promotion of the benign optimization of the community structure, and improvement of the living standards of community residents.

Limitations and directions for future research

This study dose not include a cross-scale tracking analysis of multiperiod panel data according to different tourism development models for rural communities. This study mainly analyzes the impact mechanism from the perspectives of community farmers and the government, and the constructed adaptability response impact indicator system must be verified and improved further. This study also lacks an analysis of the impact of the livelihood adaptability response of community farmers to tourism disturbance from the perspective of tourist behavior selection, which can be used as a focus for follow-up research.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article. This work is sponsored in part by the Social Science Fund of Henan province, China (2020BJJ057).This work is sponsored in part by 2019 Henan Province Colleges and Universities Youth Key Teacher Training Plan,(2019GGJS006).This work is sponsored in part by 2023 Henan Province think tanks of colleges and universities, (2023ZKYJ22)

References

Bao

Sun

(2006) A contrastive study on the difference in community participation in tourism between China and the West. Acta Geographical Sinica 61(4): 401–413.

Biggs (2011) Understanding resilience in a vulnerable industry: The case of reef tourism in Australia. Ecology and Society 16(1). http://www.ecologyandsociety.org/voll6/issl/art30/

Chen

Yang

Wang

, et al. (2015) Vulnerability and influence mechanisms of rural tourism socio-ecological system: a household survey in China’s Qinling Mountain Area. Tourism tribune 30(3): 64–75.

Cui

Chen

Yang

(2017) Research on Sustainable Livelihoods Impacted by Rural Tourism——A Case Study of Ankang in Qinling and Bashan Mountainous Areas. Mountain Research 35(01): 85–94.

DFID (2000) Sustainable Livelihoods Guidance Sheets. London: Department for International Development, pp. 68–125.

Freitag

Bciilmann

(2009) Crafting trust:the role of political institutions in a comparative perspective. Comparative Political Studies 42(12): 1537–1566.

Gebrehiwot

Van Der Veen

(2013) Farm level adaptation to climate change: the case of farmer’s in the Ethiopian Highlands. Environmental Management 52(1): 29–44.

Guo

Zhou

Chen

, et al. (2017) Impact of farmers' livelihood capital on livelihood strategy in a typical desertification area in the inner mongolia autonomous region. Acta Ecologica Sinica 37(20): 6963–6972.

Hausman

Mcfadden

(1984) Specification tests for the multinomial logit model. Econometrica 52: 1219–1240.

10.

Fang

Liu

(2019) Influence of human capital on the livelihood strategy of farming households in poor mountainous areas: A case study of Liangshan Yi Autonomous Prefecture of Sichuan. China. Progress in Geography 38(9): 1282–1293.

11.

Yang

Chen

, et al. (2014) Impact of rural tourism development on farmer’s livelihoods-a case study of rural tourism destinations in northern slop of Qinling Mountains. Economics Geography 34(12): 174–181.

12.

Hoque

Quinn

Sallu

(2017) Differential livelihood adaptation to social-ecological change in coastal Bangladesh. Regional Environmental Change 18(1): 1–13.

13.

Jia

Xie

(2021) Vulnerability and influence mechanisms of social-ecological system in poor mountainous tourism destinations. Human Geography 36(01): 155–164.

14.

Korstanje

(2011) A review of "Tourism and Socio-environmental Effects. Tourism Geographies 13(1): 160–163.

15.

Larondellea

Haase

(2013) Urban ecosystem services assessment along a rural-urban gradient: a cross-analysis of European cities. Ecological Indicators 29(6): 179–190.

16.

Larsen

Calgaro

Thomalla

(2011) Governing resilience building in Thailand's tourism-dependent coastal communities: Conceptualizing stakeholder agency in social-ecological systems. Global Environmental Change 21: 481–491.

17.

Polonsky

Arambewela

(2015) Social inclusion through cultural engagement among ethnic communities. Journal of Hospitality Marketing and Management 24(4): 375–400.

18.

Lee

Kang

(2006) Identification for difference in differences with cross-section and panel data. Economics letters 92(2): 270–276.

19.

Kang

, et al. (2017) Impacts of relocation and settlement program on rural households' dependence on ecosystem. China Population, Resources and Environment 27(11): 115–123.

20.

Liang

Feldman

, et al. (2010) The Impact of The Policy of Returning farmland to Forest on Farmers' Livelihood: An analysis of sustainable livelihood from the perspective of family structure. Journal of Public Management 7(2): 1–10.

21.

Liu

(2010) Review on Community Participation in Tourism Devel. Progress in Geography 29(8): 1018–1024.

22.

Yuan

Wen

, et al. (2021) Impact of agriculture industrial development on farmers' livelihood: Based on the researchof four migrant villages in Hongsibu District, Ningxia. Progress in Geography 40(5): 784–797. DOI: 10.18306/dlkxjz.2021.05.006.

23.

Ngnkoo

Gursoy

(2016) Rethinking the role of power and trust in tourism planning. Journal of Hospitality Marketing & Management 25(4): 512–522.

24.

Nielsen

ØJ

Rayamajhi

Uberhuaga

, et al. (2013) Quantifying rural livelihood strategies in developing countries using an activity choice approach. Agricultural Economics 44(1): 57–71.

25.

Nunkoo

KKF

(2016) Residents’ support for tourism: Testing alternative structural models. Journal of Travel Research 55(7): 847–861.

26.

Ouyang

Gursoy

Sharma

(2017) Role of trust, emotions and event attachment on residents’ attitudes toward tourism. Tourism Management 63: 426–438.

27.

Rosalina

Dupre

Wang

(2021) Rural tourism: A systematic literature review on definitions and challenges. Journal of Hospitality and Tourism Management 47: 134–149.

28.

Ruiz

(2010) Social-ecological resilience and community-based tourism: An approach from Agua Blanca. Ecuador. Tourism Management 32(3): 1–12.

29.

Singh

Hiremath

(2010) Sustainable livelihood security index in a developing country: A tool for development planning. Ecological Indicators 10(2): 442–451.

30.

Soltani

Angelsen

Eid

, et al. (2012) Poverty, sustainability, and household livelihood strategies in Zagros, Iran. Ecological Economics 79: 60–70.

31.

Strickland-Munro

Allison

Moore

(2010) Using resilience concepts to investigate the impacts of protected area tourism on communities. Annals of Tourism Research 37(2): 499–519.

32.

Twyman

(2001) Natural resource use and livelihoods in Botswana's wildlife management areas. Applied Geography 21(1): 45–68.

33.

Vista

Nel

Binns

(2012) Land, landlords and sustainable livelihoods: The impact of agrarian reform on a coconut hacienda in the Philippines. Land Use Policy 29(1): 154–164.

34.

Wang

Guo

(2001) Logistic Regression Model: Methods and Applications. Beijing: Higher Education Press.

35.

Wang

(2014) Community tourism support model and its application based on social exchange theory: Case studies of gateway communities of Huangshan Scenic Area. Acta Geographica Sinica 69(10): 1557–1574.

36.

Wang

Zheng

Wang

, et al. (2017) Classification of the relationship between household welfare and ecosystem reliance in the Miyun reservoir watershed, China. Sustainability 9(12): 2290.

37.

Wen

Chen

(2020) Study on adaptation change of farmers’ lilivlihood and the influence mechanism under tourism development-A case study of rural tourism in yan’ an city. Chinese Journal of Agricultural Resources and Regional Planning 4(41): 250–259.

38.

Liu

Zhou

(2017) Peasant Households Adaptability under the Bankground of Rural Tourism Development in Traditional Villages-A Case Study of 4 Traditional Villages in Zhangjiajie. Economic Geography 12: 232–240.

39.

Yang

Dietz

Liu

, et al. (2013) Going beyond the Millennium Ecosystem Assessment: an index system of human dependence on ecosystem services. Plos One 8(5): e64581.

40.

Zhao

Yang

Wang

(2016) The relationship between livelihood capital and livelihood strategy based on logistic regression model in Xinping County of Yuanjiang dry-hot valley. Resources Science 38(01): 136–143.

41.

Zhang

Chen

(2021) Impact of Land Consolidation on the Coupling Relationship between Farmers’ Welfare Level and Ecosystem Dependence: Heterogeneity Analysis of Land Consolidation Models and Geomorphic Types. China Land Science 3(3): 88–96.

42.

Zheng

Zhong

(2004) A discussion of developing model of community- involved rural tourism. Tourism Tribune 19(4): 33–37.

43.

Zuo

Gursoy

Wall

(2017) Residents’ support for red tourism in China: The moderating effect of central government. Annals of Tourism Research 64: 51–63.

Tourism community farmers’ livelihood adaptability response in the process of rural revitalization —— A two-way coupling model based on ecological service dependence and livelihood well-being

Abstract

Keywords

Introduction

Literature review and analytical framework

Literature review

Analytical framework

Methods

Data sources

Data and indicators

Data standardization and weight determination

Residents’ well-being (RWB) index

Index of dependence on ecosystem services

4 Data analysis and results

Descriptive statistics

Demographic characteristics

The livelihood adaptability strategy adopted by rural tourism communities

Comparisons associated with the “ecological dependence-livelihood well-being” model among rural farmers in rural tourism communities

Comparison of rural farmers' livelihood adaptability strategies across different response models

Comparison of rural farmers' livelihood capital composition across different response models

Comparison of farmers’ IDES across different response models

Influencing mechanism of the livelihood adaptability response in rural tourism communities

Selection analysis model of the livelihood adaptability response model of rural farmers

Influencing factors associated with the selection of the adaptability response model of rural farmers' livelihoods

Construction of the influencing factors and descriptive statistics

Reliability and validity tests for the common variance analysis of the influencing factors

Results

Model test

Analysis of the empirical results

Conclusion and discussion

Research conclusion

Marginal contributions and directions for future research

Marginal contributions

Implications

Limitations and directions for future research

Footnotes

Declaration of conflicting interests

Funding

References