1. Introduction
Because of its unique advantages, poverty alleviation via the tourism industry has become an important issue for all sectors of society. According to the Poverty Alleviation Office of The State Council of China, more than 10 million people have been lifted out of poverty through the tourism industry, and an effective model has been formed in which the government and market play a synergistic role. However, after the achievement of comprehensive poverty alleviation, the development stage of poor areas has gradually changed, and problems such as homogenization, resource destruction and unclear boundaries between the government and the market are not conducive to the sustainable development of the tourism-based poverty alleviation industry; thus, the risk of returning to poverty is considerable. The role of social forces in the development of the industrial economy has become increasingly prominent, becoming the third “gentle hand” after the market and the government. In the future development process of the tourism-based poverty alleviation industry, whether the role status and function of the social, government and market factors should change and how to coordinate these factors in a manner that is more conducive to the long-term economic growth of poverty alleviation areas have become urgent questions to be resolved.
At present, studies on the long-term mechanism of tourism-based poverty alleviation can be roughly divided into qualitative and quantitative studies. The majority of qualitative studies include research on poverty alleviation through tourism [1], policy research on poverty alleviation through tourism [2], case studies on special areas, such as poor areas and ethnic minority areas, and macro analysis [3]. Quantitative research has focused on the construction of a performance evaluation system for tourism-based poverty alleviation [4], the factors affecting rural tourism participation [5], the factors affecting the performance of tourism-based poverty alleviation and other empirical studies [6]. The long-term mechanism of industrial poverty alleviation is a problem with multiple causes and complications [7]. Therefore, a long-term mechanism of tourism-based poverty alleviation should be constructed according to the long-term interaction of factors influencing different dimensions, whereas empirical studies focus on only a single causal relationship, and the generalizability of case studies is controversial. A noteworthy characteristic of the qualitative comparative analysis method is that it can incorporate the factors influencing different dimensions into a research framework and determine the configuration effect. Therefore, this paper, from the perspective of combined effects, investigates in depth a long-term path for poverty alleviation.
Based on the theory of collaborative governance, the theory of productive government and efficient markets and the theory of government failure and market failure, this paper uses 33 demonstration counties of tourism-based poverty alleviation distributed in eastern, central and western China as research samples. The analysis integrates the factors of government, market and society. Using qualitative comparative analysis of fuzzy sets (fsQCA) methods to explore the combined effects of these factors reveals the long-term path of tourism-based poverty alleviation. The results provide theoretical contributions and have practical implications for global research on tourism-based poverty alleviation in the context of environmental changes and the dynamic development of poverty alleviation.
2. Literature Review
In the late 1990s, the British International Development Agency proposed the concept of “pro-poor tourism”, that is, tourism that can improve the net income of poor people [8]. In 1991, the China Tourism Administration first proposed the concept of “poverty alleviation by tourism”, which became a hot topic in the field of poverty alleviation in China and led to a series of achievements observed in macrolevel research, case studies and empirical research. The related research content focuses mainly on the performance, mechanism, mode and obstacles and path of tourism-based poverty alleviation. Cao and Tang divided the research on tourism-based poverty alleviation in China into three temporal stages—early discussion, slow growth and maturity—and proposed that related research focus on important propositions such as the construction of a long-term mechanism of tourism-based poverty alleviation and the use of a multisubject participation mode to improve the efficacy of these efforts [9].
There are many studies on the long-term mechanism and relationships between various subjects’ participation in tourism-based poverty alleviation. From a global perspective, Diego R. et al. thought the contribution of private tourism enterprises to poverty alleviation is linked to voluntary social responsibility initiatives and the organization size [10]. Alam and Paramati argued that poverty alleviation tourism policies should provide more benefits to the poor, otherwise they will not be able to alleviate poverty [11]. Scholars in Ghana proposed the development of sustainable tourism through public–private partnerships to alleviate poverty in the country [12]. Researchers in Indonesia have found that the local community has a major role in implementing the program, among the various stakeholders [13]. Scholars in Cyprus noted that rural tourism can improve the well-being of villagers and built a conceptual framework to promote the sustainable development and competitiveness of the industry [14]. Gohori et al. stated that tourism development in Manicaland brought about community development through social, economic, environmental and cultural benefits [15]. Based on panel data from 15 Latin American countries, Dossou et al. found that a higher level of government governance can reduce poverty, while the development of tourism aggravates poverty [16]. However, China’s tourism industry has achieved widespread success in poverty alleviation. Therefore, Chinese scholars have conducted extensive research and discussion in this field. According to the leading forces driving poverty alleviation, scholars have proposed various development models, such as “government-led” and “enterprise-led” approaches. For example, Li et al. proposed that the government-led tourism-based poverty alleviation model is applicable to the entire Sanjiangyuan region [17] and Du and Shu noted that the tourism-based poverty alleviation model led by state-owned enterprises is highly sustainable [18]. However, it is difficult to achieve sustainable development by relying only on government-led tourism-based poverty alleviation, while relying only on enterprises may lead to disorderly development and the marginalization of farmers and tourism enclaves. Therefore, scholars have proposed a model that combines government and market entities. For example, Zhao et al.’s comprehensive research framework on anti-poverty tourism emphasizes the dual roles of government and the market [19]. With the continuous progress of society, an increasing number of scholars have investigated the role of social subjects and have come to believe that the effect of tourism-based poverty alleviation is greater when the government, the market and society cooperate; therefore, a mode of tourism-based poverty alleviation in which multiple subjects cooperate has been proposed. For example, Zou noted that enterprises, social organizations, individuals and the government should build a large social assistance network to support the sustainable development of the tourism-based poverty alleviation industry [20]. Liu et al. proposed that the multisubject collaborative model is the best model for poverty alleviation [21]. The role of the market and social forces in tourism-based poverty alleviation has been continuously recognized, and the government also supports the tourism-based poverty alleviation industry.
In addition, scholars have conducted considerable research on the factors influencing the long-term mechanism of tourism-based poverty alleviation. According to the sustainable livelihood analysis framework proposed by the DFID, natural capital plays an important role in improving livelihoods. Lewis et al. proposed that infrastructure affects the sustainable increase in the incomes of local poor people [22] and Manyara et al. noted that foreign capital control affects the sustainable development of rural tourism [23]. According to Deller et al., factors affecting the long-term development of rural tourism include local tourism resources, service level and tourists’ consumption power [24]. The above factors can be roughly classified into three levels—government, market and society—and Chinese scholars have proposed many influencing factors belonging to these three levels. The factors at the government level include the comprehensiveness of public infrastructure, government PS and the strength of tourism management, as proposed by Liu et al. [25]. The importance of government financial support was proposed by Su and Fang [26] and the factor of government subsidies was proposed by Wang Sangui [27]. Factors at the market level include tourism resource attraction, marketing influence and active project innovation, as proposed by Zhang et al. [28], and tourism enterprise competitiveness and tourism industry status, as proposed by Wang and Zhang [29]. Community support was proposed by Lai et al. [30] and farmers’ participation intentions were proposed by Liu et al. [25]. Factors at the social level include the scale of inbound tourists, proposed by Wan et al. [31], the participation of large social organizations, proposed by Peng and Wang [32], and the environment supporting social assistance to farmers, proposed by Li and Yi [33]. On the whole, the research content details a richly developed system from the macro level to the meso and micro levels, and the research methods show a development trend from qualitative analysis to quantitative analysis and then to the combination of qualitative and quantitative analysis.
In conclusion, the existing research on tourism-based poverty alleviation underscores the collaborative role of government, market forces and various social entities, while also presenting different levels of factors that impact the sustainable performance of tourism-based poverty alleviation. Therefore, this article starts from the perspective of “multi subject collaborative governance” and explores in depth the mechanism and effective path of the interaction of multiple subjects to promote the development of a long-term mechanism for tourism-based poverty alleviation.
3. Theoretical Analysis and Research Model
By combing the existing literature, this paper builds an analytical framework based on theories of collaborative governance, active government and efficient markets, and government failure and market failure.
3.1. Collaborative Governance Theory and Long-Term Mechanisms for Poverty Alleviation through Tourism
Collaborative governance is an overall open system composed of subsystems such as the government, non-governmental organizations, enterprises and individual citizens. All elements in the system coordinate with each other to form a structure and environment for sustainable operation within the system [34]. In the process of tourism-based poverty alleviation, multiple people play unique roles and collaborate, which is conducive to the efficient operation of the tourism-based poverty alleviation industry.
3.2. Active Government, Efficient Markets and Long-Term Mechanisms for Poverty Alleviation through Tourism
The theory of an efficient market and an active government is the core theory of new structural economics. The cooperative governance of the government and the market can promote long-term tourism-based poverty alleviation through the reasonable allocation of resources and the incentives of participants. First, regarding active governments, the degree of cash subsidies (CS), credit financing (CF), policy support (PS) and improved tourism public services (ITPS) are important factors affecting the establishment of a long-term mechanism for tourism-based poverty alleviation. CS are the most common way for the government to support the tourism-based poverty alleviation industry. The government provides subsidies to entities at various levels of rural tourism development, which can encourage more enterprises and poor households to participate in tourism-based poverty alleviation [35]. For example, Su and Fang proposed that government subsidies to rural households can stimulate households’ endogenous power to improve poverty alleviation [26]. CF targets the problem of farmers’ inability to continue participating in the tourism-based poverty alleviation industry due to the scarcity of productive capital [36]. The CF provided by the government can compensate for the lack of funds, thus expanding the scale of operation and producing sustainable poverty alleviation effects [37]. PS can guide farmers to actively participate in the tourism-based poverty alleviation industry. He and Jiang asserted that industrial policies in support of poverty alleviation can optimize the market environment [38]. Liu et al. suggested that with the strong support of government policies [25], rural tourism destinations can eliminate their location disadvantages and engage in sustainable development. Tourism public services are a basic necessity for the long-term development of the tourism-based poverty alleviation industry. The government provides public services, such as base construction, industrial technical services and personnel training in the development of the poverty alleviation industry, which can consolidate the industrial foundation for poverty alleviation [39]. This support can promote the high-quality development of rural tourism destinations [28]
In addition, in terms of the effective market, tourism resource endowments (TREs), farmer participation (FP), cooperative linkages (CLs) and enterprises’ drive (ED) are important factors affecting the establishment of long-term mechanisms for tourism-based poverty alleviation. TREs greatly affect the tourism market. Rural tourism destinations rich in tourism resources can enjoy a greater frequency of tourist visits [30], and tourism resources are important for the high-quality development of rural tourism destinations [40]. Farmers’ participation is the core of industrial poverty alleviation [41]. By providing farm catering, offering homestays, or acting as tour guides, participating farmers can improve their livelihoods [40]. Cooperatives provide organizational advantages for creating diversified profit channels and fair distribution and can provide direct support in job allocation and training guidance. The “enterprises + farmers + cooperatives” model is the ideal development model for the tourism-based poverty alleviation industry [42].
3.3. The Caring Society and Long-Term Mechanism for Poverty Alleviation through Tourism
Social forces promote common prosperity by driving employment, supporting cooperative management and sharing experiences [43]. A series of problems, such as inefficient allocation of resources caused by market failure and excessive expansion of government scale, enormous fiscal deficits, rent-seeking and corruption, increased transaction costs and low social and economic efficiency caused by government failure, hinder the sustainable development of the tourism-based poverty alleviation industry [44]. Therefore, a “caring society” is the third force for eliminating government failure and market failure. The motivation of social organizations (MSO) and the intensity of the social atmosphere supporting farmers (SASF) are important factors in the construction of long-term mechanisms for tourism-based poverty alleviation. Social subjects in the tourism-based poverty alleviation industry include people or groups who participate in all aspects of the industry, those who provide education and training opportunities for poor groups and those who coordinate relationships between farmers and other stakeholders [45]. Xu et al. proposed that the participation of social organizations in the tourism-based poverty alleviation industry can improve the participation of farmers and safeguard their interests [46]. An atmosphere supporting social assistance to farmers is an important factor affecting the public’s participation in consumer assistance and creating a long-term mechanism [47]. The sustainable development of the tourism-based poverty alleviation industry also requires an atmosphere that fosters social assistance to farmers. With the continuous progress of society, an increasing number of tourists spend money in rural tourism destinations for public welfare purposes, forming an atmosphere that supports farmers and promotes the sustainable development of the tourism-based poverty alleviation industry.
Combined with the above theoretical analyses, this paper proposes the following research model, as shown in Figure 1.
4. Research Design
4.1. Methodology
This paper uses QCA as the main tool to analyze the complex causal relationship between the conditional configuration and the construction of a long-term mechanism for tourism-based poverty alleviation. Different from the general perspective and contingency perspective, QCA does not assume the symmetry of causality [48], making this approach more in line with the actual characteristics of the complexity of sustainable tourism-based poverty alleviation in China. To specifically describe the state of the sustainable performance of tourism-based poverty alleviation (SPTPA) and the degree of influence of each factor, the data are measured using a calibrated set score, and fsQCA is adopted as an analysis tool.
In summary, this paper first uses fsQCA3.0 software to test whether various factors constitute the necessary conditions for the establishment of a long-term mechanism for tourism-based poverty alleviation, then analyses the complex driving mechanism between multidimensional factors and the SPTPA, explores which configuration of conditions can promote the long-term mechanism for tourism-based poverty alleviation and finally tests the robustness of the results.
4.2. Variable Measurement
First, for outcome variable measurement, the SPTPA is chosen as the result variable. The specific measurement methods are shown in Table 1:
Second, for conditional variable measurement, this paper argues that the more tourism enterprises there are in a region, the greater the contribution to local tourism-based poverty alleviation; thus, “the number of travel agencies” is used to measure the contribution of enterprises. The number of social organizations involved in tourism-based poverty alleviation is positively correlated with the performance of these efforts; therefore, “the number of social organizations per 10,000 people” is used to measure the contribution of social organizations in the county. The specific measurement methods are shown in Table 2.
4.3. Data Sources
4.3.1. Sample Selection
This study primarily focuses on China’s tourism-based poverty alleviation model counties as the research subjects. These model counties are exemplary county-level administrative regions in impoverished areas that leverage the development of tourism resources to achieve industrial poverty alleviation and stimulate local economic development. When examining the construction of a long-term mechanism for collaborative tourism-based poverty alleviation, high-performance model counties are more representative and valuable for reference compared to counties with low performance in sustainable tourism-based poverty alleviation. Therefore, this paper selects 10 model counties from Eastern China, 10 from Central China, and 13 from Western China, totaling 33 samples. These samples essentially cover all the impoverished areas in China.
4.3.2. Secondary Data
For the economic, social and ecological benefit indicators in the outcome variables, as well as the TRE, the CL, ED, CS, CF, PS, MSO and SASF in the conditional variables, all measurements are based on secondary data. Data sources include the “China Provincial Statistical Yearbook”, the “China County Statistical Yearbook,” statistical yearbooks of case counties, statistical bulletins and government work reports, among other official documents from the government, all of which are from the year 2022.
4.3.3. Primary Data
For the indicators of profitability of restoring force and anti-poverty willingness in the outcome variables, as well as the FP and the ITPS in the conditional variables, measurements are obtained through questionnaires, which are displayed in the Supplementary Materials. In September 2023, team members of the research project located farmers from the 33 case counties through various online and offline channels and distributed questionnaires to them. A total of 353 questionnaires were collected, with 42 invalid questionnaires excluded using SPSS 27.0, leaving 311 valid questionnaires. The sample efficiency rate was 88.10%, and it was ensured that each case county had at least 4 valid questionnaires, making it possible to conduct QCA analysis.
4.4. Calibration
The data in this paper include primary data and secondary data. Considering the differences between the two data types, different calibration methods are adopted. The two indicators of profitability resilience and farmers’ willingness to fight poverty were measured via questionnaires. The collected data were classified and aggregated by case county and then averaged to represent the values of each case county on the two indicators of profitability resilience and farmers’ willingness to fight poverty. The conditional variables measured by the questionnaire include the degree of farmers’ participation and the degree of completeness of tourism public services. Both indicators were measured on a seven-point scale. Considering that the standard deviation of the recovered values was small, the practice of Li Liyuan et al. was adopted [54], in which the mean plus the standard deviation was taken as the point of full membership, the mean was the crossing point, and the mean minus the standard deviation was the point of complete nonmembership. For the remaining variables, which were secondary data, the direct calibration method was used. The quantile values of 95%, 50% and 5% were used as the qualitative anchor points of full membership, crossing point and complete non-membership, respectively [55]. If the value of the calibrated variable was 0.5, 0.001 was added to this value to prevent the fsQCA software from eliminating the corresponding cases during calculation [56]. The resulting variables were obtained according to the methods of Wang Zhizhang et al. [57], who used the analytic hierarchy process to evaluate the performance of tourism-based poverty alleviation and determine the weights of indicators at all levels. The specific calibration results are shown in Table 3.
5. Results
5.1. Necessity Analysis of Conditioning Variables
This paper tests whether a single condition, including non-aggregation, constitutes a necessary condition for long-term poverty alleviation based on tourism to a low or high degree, and a consistency greater than 0.9 indicates a necessary condition [38]. fsQCA3.0 software was used to analyze the necessary conditions, and the results are shown in Table 4. The consistency level of all conditions was less than 0.9, with no consistency greater than or equal to 0.9; therefore, it is considered that there is no necessary condition influencing the long-term effect of tourism-based poverty alleviation.
5.2. Conditional Configuration Analysis
In this paper, fsQCA4.0 software is used to analyze the conditional configuration leading to long-term tourism-based poverty alleviation. The case frequency threshold is set to 1, the original consistency threshold is set to 0.80 and the PRI consistency threshold is set to 0.70 [57]. The configuration path for realizing long-term tourism-based poverty alleviation is determined according to the simple solution and the intermediate solution of the program output. The conditions that appear in both reduced solutions and intermediate solutions are the core conditions, while the conditions that appear only in intermediate solutions are edge conditions [48]. The software analysis results are shown in Table 5. A total of 10 configurations leading to long-term tourism-based poverty alleviation were generated by software analysis, and their consistency was much greater than the empirical critical value of 0.80 [55], indicating that the 10 configurations obtained were sufficient conditions for long-term tourism-based poverty alleviation. The overall coverage of 0.612 indicates that these 10 sets together explain the formation of long-term mechanisms for tourism-based poverty alleviation in 61.2% of cases.
Since grouping 4 to grouping 10 have extremely similar core conditions, they are named groupings 4a–4g in this paper. After Boolean simplification and considering the actual situation [58], the more typical configurations 1, 2, 3 and 4a were selected as the analysis objects. These four configurations were taken as the combination of sufficient conditions leading to long-term tourism-based poverty alleviation and summarized into the following four long-term paths for tourism-based poverty alleviation: the path via government financial support, the path via resource endowment, the path via ecosystem tourism and the path via multisubject collaboration.
First, there is a long-term path to poverty alleviation through government financial support for tourism. In configuration 1, government CF, as the core condition, and TRE, as the marginal condition, can lead to the establishment of a long-term mechanism of tourism-based poverty alleviation. The mechanism of this configuration is as follows: the government focuses on financial support by providing farmers with a source of funds for reproduction and encouraging farmers to make full use of local tourism resource endowments to carry out tourism business activities and obtain sustainable earnings.
The case county corresponding to this configuration is Pingjiang County in Hunan Province. The financial support of the government effectively promotes resource endowment and enables farmers to increase their sustainable income. For example, the Pingjiang Rural Commercial Bank has issued a total of CNY 98.6 million in tourism loans to support farmers in villages and towns along the trunk highway to develop 56 tourism projects, such as agricultural sightseeing tours, rural leisure tours, ecological health tours and self-picking orchards.
Second, there is a long-term path to poverty alleviation through resource-endowed tourism. In configuration 2, TRE, FP, CS and tourism public services are the core conditions leading to the establishment of a long-term mechanism of tourism-based poverty alleviation, and the original coverage of this configuration is the highest, indicating that configuration 2 is the path with the most empirical support. The mechanism of action is as follows: the government focuses on optimizing infrastructure construction to promote the quality and upgrading of the tourism industry while attracting and encouraging more farmers to participate in it through cash subsidies, fully tapping into the value of tourism resource endowment, and promoting the sustainable development of the tourism-based poverty alleviation industry.
The case counties corresponding to this configuration are Qingchuan County of Sichuan Province and Yingjiang County of Yunnan Province. On the basis of rich tourism resources, the government provides financial and facility support to attract farmers to participate to achieve sustainable development of the tourism-based poverty alleviation industry. Qingchuan County has seven 3A level and above scenic spots, and the government has invested CNY 102 million to support a tourism-based poverty alleviation demonstration village project and the use of “5G+” technology to systematically integrate tourism resources and information services. As of 2023, farmers operated 53 farmhouses.
Third, there is a long-term path to poverty alleviation through ecosystem-based tourism. In configuration 3, CS, CF, tourism public services and the atmosphere of social assistance to farmers are the core conditions, and farmers’ participation, CL and ED, as marginal conditions, can lead to the establishment of a long-term mechanism of tourism-based poverty alleviation. The mechanism of this configuration is as follows: the government provides all-round support for the development of the tourism-based poverty alleviation industry in this region, and its investment represents the primary source of support; however, tourists need to travel to the rural tourism destination to consume, form a virtuous ecological circle and promote the sustainable development of the tourism-based poverty alleviation industry.
The case county corresponding to this configuration is Changjiang County in Hainan Province. The synergy between the government and society has contributed to the long-term development of tourism-based poverty alleviation. Over the years, the county government has implemented discount loans related to tourism-based poverty alleviation and 46 projects, such as improving roads, drainage ditches, production water and other facilities in poor villages and settlement projects, totaling CNY 62.4072 million. In 2022, Changjiang Li Autonomous County ranked third in the province in terms of tourism reception, receiving 1,932,400 tourists and achieving a total tourism revenue of CNY 1,059 billion.
Fourth, there is a long-term path to poverty alleviation through tourism with the synergy of multiple subjects. In configuration 4a, the core conditions of TRE, CL, ED, CS, CF, ITPS and MSO and the marginal conditions of FP and SASF can support the establishment of a long-term mechanism of tourism-based poverty alleviation. From the case data, the number of jobs achieved by this model, the new consumption growth point of construction, and the income obtained are the highest among various path cases.
The case counties corresponding to configuration 4a are Penglai District in Shandong Province and Shitai County in Anhui Province. Shitai County has seven 4A scenic spots, 380 cooperatives, and 61 travel agencies. The relevant documents of the government of Taiwan proposed to subsidize poor families to run on-farm music events, introduced a special business of “ residential hostel loan” and built a new tourist service center. Anhui Province developed the “100 communities into 100 villages” project for the county tourism-based poverty alleviation industry to provide multiple types of support.
5.3. Robustness Check
This paper tested the robustness of the configurations that produce effective tourism-based poverty alleviation over the long term. With reference to the literature [59], the method of increasing case consistency is adopted to test the robustness of the effective configuration of long-term tourism-based poverty alleviation. The consistency threshold of the case is increased from 0.8 to 0.85, and the other processing methods remain unchanged. Through analysis, after the increase in the consistency threshold of the case, the number of configurations, core conditions and edge conditions, as well as the coverage and consistency of the solution, are found to be completely consistent with the original results, as is the new configuration after adjusting the threshold. This finding indicates that the research results in this paper are robust and reliable.
6. Discussion and Conclusions
6.1. Conclusions
Based on the actual development scenario of the tourism-based poverty alleviation industry and using data from 33 demonstration counties in eastern, central and western China, this paper discusses how multiple entities can jointly achieve tourism-based poverty alleviation over the long term. Based on the logic of synergy theory, the theory of efficient markets and active government and the theory of government and market failure, ten factors that may affect the long-term mechanism of tourism-based poverty alleviation are proposed, and fsQCA software is used for configuration analysis. The conclusions are as follows: First, the results of the necessary condition test of the fsQCA show that no single condition can promote long-term tourism-based poverty alleviation. Second, through configuration analysis, four equivalent paths are found to promote the long-term mechanism of tourism-based poverty alleviation. These are the paths of government financial support, resource endowment, ecosystem tourism and multisubject collaboration. Third, the government plays a role in each path, indicating that the establishment of a long-term mechanism for tourism-based poverty alleviation needs the support of the government. Fourth, the completeness of tourism public services is the core condition of all configurations, which shows that tourism public services are crucial to the sustainable development of the tourism-based poverty alleviation industry.
6.2. Theoretical Contribution
This paper uses the QCA method to explore the long-term path of tourism-based poverty alleviation with the participation of multiple subjects and provides the following theoretical contributions. First, most previous scholars simplified the complexity of the tourism-based poverty alleviation industry and believed that the construction of a long-term mechanism for tourism-based poverty alleviation depended on external conditions, such as an unchanging location, external investment and market demand, without accounting for the initiative of the farmers themselves [60,61]. This paper comprehensively analyses the configuration effects of external factors and farmers’ own dynamics on the long-term path construction of tourism-based poverty alleviation and adopts the QCA method to reduce the subjectivity of case studies and overcome the previous quantitative research limitations of considering single factors. Moreover, the fuzzy set analysis method is used to measure each variable on a more specific scale. Compared with the clear set, which adopts “0” and “1” to assign variables, fsQCA can more accurately describe the differences among the various conditions in the construction of the long-term mechanism of tourism-based poverty alleviation.
Second, this paper proposes four modes of long-term tourism-based poverty alleviation, which are applicable to different management practices and poverty alleviation governance situations and have good reference value for poor areas seeking to choose suitable tourism-based poverty alleviation governance modes according to their own economic development status or stage of rural tourism development. For example, economically underdeveloped counties focus on the development of government financial support modes. This finding further enriches the existing research in the field of long-term mechanisms for tourism-based poverty alleviation.
Third, previous studies have focused on the role of the government or the market in the tourism-based poverty alleviation industry [17,18], but analyses of other influencing factors are insufficient. This paper proposes that with the continuous improvement in global public quality, social organizations and individuals play an increasingly prominent role in promoting the establishment of long-term mechanisms for tourism-based poverty alleviation, and synergistic effects among government and market entities can better promote the sustainable development of tourism-based poverty alleviation. The results confirm the synergistic effect between the antecedent conditions, which expands the perspective of tourism-based poverty alleviation research.
Fourth, with reference to the literature, this paper incorporates profitability resilience and farmers’ willingness to engage in antipoverty efforts into the system used to measure the sustainable performance of tourism-based poverty alleviation, enriching the relevant theoretical research.
6.3. Practical Implications
First, the long-term path of government-supported tourism-based poverty alleviation is suitable for economically underdeveloped areas, the same applies to certain parts of the developing world, such as Nepal. This model emphasizes government financial support. Through specific credit policies, the government can provide low-interest loans and easy financing opportunities to support the tourism-based poverty alleviation industry and help enterprises and farmers address financial bottlenecks, such as by establishing special credit institutions or cooperative banks and providing targeted credit products.
Second, the long-term path of resource endowment for tourism-based poverty alleviation is suitable for areas rich in tourism resources, for example, Thailand, Malaysia and other countries with rich tourism resources around China. The model emphasizes high-quality tourism resources, the active participation of farmers and government support in funding and public services. While exploiting local tourism resources to obtain economic benefits, the government should adopt appropriate development strategies and management measures to protect and rationally utilize tourism resources to prevent the overexploitation and waste of these resources. Additionally, subsidies and public services should be provided for the development of the tourism-based poverty alleviation industry. Farmers should actively participate in tourism and provide tourism reception services, agricultural experience activities, etc.
Third, the long-term path of ecotourism-based poverty alleviation is suitable for areas with weak resource endowment foundations and nascent markets, such as China’s Hainan Province, Myanmar’s Bagan area and so on. The model emphasizes the government’s support in terms of funds, policies and public services, as well as the public’s support for the tourism-based poverty alleviation industry in all aspects. The government should not only provide software and hardware support for the tourism-based poverty alleviation industry but also help farmers. Individuals can choose to buy local agricultural products and handicrafts to help farmers increase their income. By participating in volunteer activities, the public can provide unpaid labor support for the tourism-based poverty alleviation industry in poor areas.
Fourth, the long-term path of multisubject collaborative tourism-based poverty alleviation is suitable for areas with relatively mature tourism industries. Such regions, characterized by an advanced state of tourism development, can customize this approach to fit their specific geographical and socio-economic contexts. The model’s efficacy and adaptability suggest that it warrants consideration for broader dissemination and implementation on a global scale. This model emphasizes the synergistic interaction of the three main bodies of the government, the market and society. The government, market players and society should establish collaborative governance relationships to jointly promote the sustainable development of the tourism industry. The government can establish cooperative relationships with the market and various social entities to provide necessary guidance and support. Market entities may cooperate with governments and social organizations to jointly develop tourism products and services. Social organizations can cooperate with the government and various market entities to jointly provide training for farmers, promote the concept of assisting farmers, and support the success of the tourism-based poverty alleviation industry.
6.4. Limitations and Future Studies
This exploratory study has several limitations. First, to facilitate the research, the selection of case counties is limited to China. Although the case counties are located in the eastern, central and western regions and have different levels of economic development, China, as a developing country, is different from developed countries in terms of consumption level, habits and the tourism market; therefore, the conclusions of this paper have certain regional limitations. Second, the data analyzed in this paper are cross-sectional, and panel data with time series could be analyzed in the future to reveal the long-term development trend of rural tourism areas. In addition, the number of influencing factors at the social level could increase. For example, factors such as the level of social consumption and consumers’ level of education can further enrich the participation of multiple subjects in the field of tourism-based poverty alleviation in the future.
W.L.: writing—review and editing as well as conceptualization. S.Z.: formal analysis, investigation, writing—original draft and conceptualization. C.L.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Guilin University of Technology.
Informed consent was obtained from all subjects involved in the study.
The datasets used during the current study are available from the corresponding author upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
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Outcome variables and their measurement.
| Outcome Variable | Level 1 Indicators | Level 2 Indicators | Level 3 Indicators | Level 4 Indicators | Literature Sources |
|---|---|---|---|---|---|
| SPTPA | Poverty alleviation goal | Economic benefit | Economic level | Economic growth rate | Chen, 2020 [ |
| GDP per capita | |||||
| Status of the tourism industry | Growth rate of gross tourism receipts | ||||
| Regional tourist arrivals | |||||
| Income level | Rural disposable income | ||||
| Local fiscal revenue per capita | |||||
| Sustainable goal | Social benefit | Structure and level of employment | Share of persons employed in the tertiary sector | ||
| Urban registered unemployment rate | |||||
| Level of social security | Pension insurance coverage | ||||
| Medical insurance coverage | |||||
| Coverage of the New Agricultural Cooperative Society (NACS) | |||||
| Ecological benefit | Ecological quality | Air quality excellence rate | |||
| Compliance rate of centralized drinking water sources | |||||
| Forest cover | |||||
| Profitability of restoring force | Buffering capacity; | Poll | Li et al., 2023 [ | ||
| Anti-poverty willingness | Farmers’ willingness to participate in the fight against poverty | Poll | Feng et al., 2017 [ |
Variable calibration.
| Variable Type | Variable Name | Calibration | ||
|---|---|---|---|---|
| Full Affiliation (95%) | Intersection Point (50%) | Completely Unaffiliated (5%) | ||
| Outcome variable | SPTPA | 273.79 | 104.13 | 17.43 |
| Conditional variable | TRE | 38.90 | 9 | 2.40 |
| FP | 5.60 | 4.43 | 3.26 | |
| CL | 228.94 | 32.99 | 1.85 | |
| ED | 77.50 | 16 | 1.70 | |
| CS | 4.27 | 0.92 | 0.49 | |
| CF | 28.73 | 7.30 | 3.85 | |
| PS | 0.79 | 0.30 | 0.01 | |
| ITPS | 5.70 | 4.75 | 3.80 | |
| MSO | 97.44 | 4.51 | 0.32 | |
| SASF | 101.80 | 17 | 4 | |
Conditional variables and measurements.
| Conditional Variable | Measurement Indicators | |
|---|---|---|
| Efficient market | TRE | Number of A-rated scenic villages; number of A-rated scenic spots [ |
| FP | Proportion of farmers in the county who are involved in rural tourism (farm restaurants, lodgings, scenic spot workers, sales of tourist commodities, provision of tourist transportation, acting as tour guides, singing and dancing performances, etc.) in the whole county (Poll) [ | |
| CL | Average number of cooperatives per 10,000 farmers [ | |
| ED | Number of tourism enterprises | |
| Active government | CS | Average general public budget expenditure per 10,000 population [ |
| CF | Average balance of agriculture-related loans per 10,000 farmers [ | |
| PS | Gross tourism receipts as a share of GDP [ | |
| ITPS | Quality of Tourism Public Information Services | |
| Caring society | MSO | Number of social organizations per 10,000 persons |
| SASF | Number of fairs organized; number of media and individual publicity campaigns [ |
Necessity and sufficiency tests for single conditional variables.
| Antecedents | Consistency Rate for Adequacy (Consistency) | Necessity Coverage (Coverage) | ||
|---|---|---|---|---|
| SPTPA | ~SPTPA | SPTPA | ~SPTPA | |
| TRE | 0.663 | 0.618 | 0.830 | 0.735 |
| ~TRE | 0.788 | 0.857 | 0.684 | 0.708 |
| FP | 0.783 | 0.724 | 0.748 | 0.658 |
| ~FP | 0.641 | 0.723 | 0.709 | 0.761 |
| CL | 0.570 | 0.541 | 0.762 | 0.688 |
| ~CL | 0.766 | 0.812 | 0.637 | 0.643 |
| ED | 0.617 | 0.526 | 0.740 | 0.601 |
| ~ED | 0.667 | 0.773 | 0.596 | 0.658 |
| CS | 0.694 | 0.583 | 0.841 | 0.672 |
| ~CS | 0.729 | 0.862 | 0.647 | 0.729 |
| CF | 0.652 | 0.533 | 0.771 | 0.600 |
| ~CF | 0.662 | 0.797 | 0.598 | 0.686 |
| PS | 0.601 | 0.630 | 0.669 | 0.668 |
| ~PS | 0.702 | 0.688 | 0.666 | 0.621 |
| ITPS | 0.818 | 0.732 | 0.769 | 0.655 |
| ~ITPS | 0.633 | 0.742 | 0.713 | 0.795 |
| MSO | 0.662 | 0.635 | 0.820 | 0.749 |
| ~MSO | 0.798 | 0.848 | 0.696 | 0.705 |
| SASF | 0.605 | 0.566 | 0.756 | 0.673 |
| ~SASF | 0.738 | 0.795 | 0.641 | 0.657 |
Configuration results.
| Antecedent | Long-Term Grouping of Tourism for Poverty Alleviation | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4a | 4b | 4c | 4d | 4e | 4f | 4g | |
| TRE | ᛫ | ● | ⊗ | ● | ● | ● | [Image omitted. Please see PDF.] | [Image omitted. Please see PDF.] | ⊗ | ⊗ |
| FP | ⊗ | ● | ᛫ | ᛫ | ⊗ | ⊗ | ● | ● | ● | ● |
| CL | ⊗ | [Image omitted. Please see PDF.] | ᛫ | ● | ● | ᛫ | ⊗ | ᛫ | [Image omitted. Please see PDF.] | ᛫ |
| ED | [Image omitted. Please see PDF.] | [Image omitted. Please see PDF.] | ᛫ | ● | ● | ᛫ | ● | [Image omitted. Please see PDF.] | ● | ● |
| CS | [Image omitted. Please see PDF.] | ● | ● | ● | ● | ● | [Image omitted. Please see PDF.] | [Image omitted. Please see PDF.] | ● | ● |
| CF | ● | [Image omitted. Please see PDF.] | ● | ● | ● | ᛫ | ⊗ | ● | [Image omitted. Please see PDF.] | ᛫ |
| PS | ⊗ | ⊗ | ● | ● | ● | ⊗ | ● | ● | ||
| ITPS | ⊗ | ● | ● | ● | ⊗ | ● | ● | ● | ● | ● |
| MSO | ⊗ | [Image omitted. Please see PDF.] | [Image omitted. Please see PDF.] | ● | ● | [Image omitted. Please see PDF.] | ● | ● | ● | ● |
| SASF | ⊗ | [Image omitted. Please see PDF.] | ● | ᛫ | ⊗ | ● | ● | ● | ⊗ | ⊗ |
| Consistency | 0.974 | 0.981 | 0.892 | 0.973 | 0.970 | 0.971 | 0.989 | 0.917 | 0.992 | 0.964 |
| Original coverage | 0.223 | 0.303 | 0.209 | 0.212 | 0.169 | 0.180 | 0.201 | 0.207 | 0.198 | 0.173 |
| Unique coverage | 0.038 | 0.086 | 0.025 | 0.007 | 0.021 | 0.015 | 0.017 | 0.038 | 0.007 | 0.020 |
| Coverage of solutions | 0.612 | |||||||||
| Consistency of solutions | 0.954 | |||||||||
Note: “●” represents the existence of core conditions, “᛫” represents the existence of edge conditions, “[Image omitted. Please see PDF.]” represents the absence of core conditions, and “⊗” represents the absence of edge conditions.
Supplementary Materials
The following supporting information can be downloaded at:
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