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Article

Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data

by
Ziyu Jia
1,*,
Yan Jiao
2,
Wei Zhang
2 and
Zheng Chen
3
1
China Architecture Design and Research Group, Beijing 100037, China
2
China National Engineering Research Center for Human Settlement, Beijing 100037, China
3
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(7), 4147; https://doi.org/10.3390/su14074147
Submission received: 17 February 2022 / Revised: 16 March 2022 / Accepted: 17 March 2022 / Published: 31 March 2022
(This article belongs to the Topic Sustainable Smart Cities and Smart Villages)
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Abstract

:
Tourism has been seen and adopted as a vital means for achieving rural economic and social revitalization worldwide without harming sustainable development principles. For China, the evaluation of rural tourism competitiveness at the township level is essential for planning and developing the tourism industry as a basic administration and economic unit, but there is not enough research due to the lack of applicable data and systematic methods. Therefore, this study constructed a town-level rural tourism competitiveness evaluation and development mode classification model based on the modified Michael Porter’s Diamond Model using integrated multi-source data. By taking the 1806 township units in Henan Province, China as examples, we conclude four different modes based on the level of the comprehensive score and industrial internal balance (i.e., balanced development mode with multiple advantages, related and supporting industries driving mode, ecological resource-led mode, and rural landscape experience mode). Policy suggestions for the optimization of the rural tourism industry for Henan are discussed based on the results.

1. Introduction

As an active and extensive industry, rural tourism has been seen and adopted as a vital means for achieving rural economic and social revitalization worldwide without harming sustainable development principles [1,2]. The benefits of rural tourism as an engine for both social and economic upgrades have been discussed in previous literature, such as providing employment opportunities, improving the industrial structure, strengthening basic public service facilities, and narrowing the unbalanced development gap between urban and rural areas [3,4,5].
As a country with a 36.11% rural population, China in recent years has launched a series of national strategies, including the rural revitalization plan and the rural industry adjustment and promotion plan to continuously guide and simulate tourism development [6]. In recent decades, the central government has deployed a considerable number of national rural tourism demonstration bases at the town and village level, including China’s beautiful leisure villages, national rural tourism key villages, national pastoral complexes, etc. According to the National Rural Industry Development Plan (2020–2025), in 2019, the sector of rural tourism recorded 3.2 billion person-visits, generating over 850 billion RMB yuan in revenue, which was a year-on-year increase of 6.3%.
Driven by both policy guidance and market demand, China’s rural tourism industry has ushered in a stage of unprecedented development and reconstruction. At the same time, problems of regional homogenization and vicious competition began to emerge. How to enhance rural tourism competitiveness is a key spot that needs academic attention. Tourism competitiveness is essential for a destination to obtain a favorable position in the tourism market and sustain a competitive advantage [7]. Understanding the conception, influencing factors, development patterns, and mechanisms of the rural tourism industry is crucial for scientific regional tourism planning and management [8]. Most of the existing research focuses on regional scales at the provincial and municipal level and have different perspectives. There are several classical theories and corresponding evaluation models for the composition of regional tourism competitiveness: for example, the “market performance composition theory” that takes market share, profitability, productivity, and other intuitive market performance as evaluation elements; the “core competition theory” is based on core attraction factors such as tourism resources, quality of tourism products, and marketing capabilities, but to a certain extent, there are limitations such as lack of the explanatory effectiveness and incomplete consideration of influencing factors [9,10].
The evaluation of tourism competitiveness is a complex, systematic work that requires coordination with multiple elements from various departments. This is because the concept of tourism competitiveness is complex, multidimensional, and hard to quantify [11,12]. Due to the reason that tourism is primarily a service-driven industry, researchers had to adjust definitions, develop new models and identify factors that would be applicable [13,14]. Having high tourism competitiveness is to achieve balance between regional economy, ecological and cultural resource, public infrastructure and services, market demand, and so on [15]. The result is that it is difficult to identify a universal definition of tourism competitiveness; nevertheless, some popular and comprehensive conceptual definitions and models are often adopted based on Michael Porter’s Diamond Model [16,17,18]. The Porter’s Diamond Model, also known as the Theory of National Competitive Advantage of Industries, is derived from the analysis of competitive advantages of a certain country, region, or enterprise. It contains four main sub-determinants (factor conditions; demand conditions; related and supporting industries; firms’ strategy, rivalry, and structure) and two other affective sub-determinants (government; chance) [18]. The interrelationship of various determinants is easy to quantify and has strong universality, therefore has been a convincing basic framework for industrial competitiveness analysis [19]. On the basis, scholars modify the model according to characteristics of different industries, such as emphasizing the importance of direct government management and support to the tourism industry [20]; highlighting key determinants such as market demand and government intervention [21]; combing factor condition and related and supporting industries into a new determinant of stakeholders and resource integration capabilities [22].
As China’s most basic administration and economic unit, townships are not only at the forefront of rural construction and development but also represent the unique regional resources and industrial characteristics of various regions [23], therefore are the basic unit for planning and developing modern rural tourism industry. Under the current administrative management system, the planning and construction authority is concentrated on the upper county-level government, and the town units often do not have the power to determine their own development. In order for county-level governments to better plan the development of villages and town systems, it is necessary to identify the similarities and differences of each township within the provinces and cities, rather than conducting isolated studies on individual township units. However, due to the lack of extensive village and town-level surveys and statistical data, especially on rural tourism-related activities, current research is limited to the focuses of a qualitative summary of typical cases or quantitative analysis at a macro provincial, municipal, and county level. Research on the rural tourism industry lacks the support of comprehensive and refined data indicators, the diversity of the regional rural tourism development modes is not fully understood, making it hard to scientifically guide the differentiated development of the township tourism industry.
At the technical level, the maturing Internet tourism big data provides new ideas for this research gap. In recent years, the rapid development of network information technology has led to an explosive growth of various types and multi-scale tourism data [24]. Among them, user-generated data (UGD) represented by website text, image review data, and POIs (Points of interest) are mostly welcomed by scholars for advantages of easy access, large data volume, wide spatial and temporal coverage, and high precision [25,26]. A large number of studies have applied big data on tourism evaluation and pattern identification, such as analyzing the spatiotemporal patterns of the regional tourism industry through POI and online booking service data; analyzing the spatial differentiation and spatial structures of tourism resources using mobile big data; identifying regional tourism destination and hot spots using social media data [27,28,29,30,31]. However, the application of big data on identifying rural tourism competitiveness and development mode at the town level is relatively rare.
Therefore, the aim of the study is to fill the research gap by quantitatively evaluating rural tourism competitiveness and identifying general tourism industry development patterns at the town level.
The objectives of the study are:
  • Construct a town-level rural tourism competitiveness evaluation base on the modified Michael Porter’s Diamond Model using integrated multi-source data.
  • Identify typical patterns, influencing factors, and development mechanisms of the rural tourism industry.
  • Discuss strategies and policies for improving rural tourism competitiveness.
The model territory of the study is the 1806 township units in Henan Province, Central China.

2. Materials and Methods

2.1. Data and Technical Framework

This study quantifies the town-level rural tourism competitiveness through a multi-source data-based method and achieves comprehensive evaluation and development mode classification through statistical analysis. The theoretical model construction method is described in the next section.
Systematically and consistently documented statistics data are not available at the village and town levels in most areas in China. On the basis of limited statistics data, this study extensively collects and integrates multi-source tourism-related data to fully support model index quantification. The data involved come from three sources: (1) data released by the government (“Statistical Yearbook of Urban and Rural Construction—Villages and Towns”, “Statistical Data of Henan Provinces, Counties and Cities”, National A-level Tourist Attractions Directory, National and Provincial Rural Tourism Demonstration Site Directory); (2) internet big data (spatial data from OSM map, POI data from Baidu, user-generated content data from Ctrip); (3) land use data interpreted by satellite remote sensing (2020 data with accuracy of 30 m). The data are sorted and integrated through ArcGIS, and the initial processing methods used include name reverse search and spatial positioning, POI type classification according to resource types, remote sensing image supervision, and classification, etc.)
Exploratory factor analysis is used to synthesize multiple rural tourism industry indicators into several key factors, and comprehensive competitiveness evaluation score is calculated according to the factor extraction results. Then hierarchical cluster analysis is applied to classify the development mode. The statistical analysis is based on SPSS 25.

2.2. Model and Indicator System Building

China’s rural tourism industry is still in the initial stage from the perspective of industry life cycle; at this time, the rapid and high-quality development of the tourism industry cannot be supported by the supply or market forces alone. The practical experience in most areas in China and worldwide shows that rural tourism industry development is mostly determined by tourism resources and largely relies on related service facilities [19]. In addition, the impact of government policies is also quite significant, especially in China [32]. Based on literature review and field investigation, we construct a town-level rural tourism competitiveness evaluation model base on the modified Michael Porter’s Diamond Model. The modified model contains four main determinants (rural tourism production factor conditions; rural tourism market demand conditions; related and supporting industries; tourism firm strategy, rivalry, and structure) and one other sub-determinants (government support). It should be noted that this model is determined through the mutual adjustment of data and indicators. The specific structure and components are as follow (Figure 1):
Rural tourism production factor conditions: High-quality tourist attractions, scenic spots, and beautiful natural resources are the leading resources for the development of the tourism industry in villages and towns, which largely determine the scale and attractiveness of the tourism industry [33]. In terms of geographical environment factors, we focus on topography and landforms, as well as the scale of forests, cultivated land, and human settlements; in terms of tourist attractions, we include national A-level scenic spots and scenic POIs (from Baidu Maps), and they are classified into natural tourism attraction, historical and cultural tourism attraction, and leisure and wellness tourism attraction according to existing research [34,35,36].
Rural tourism market condition: The domestic demand condition has significant impact on the competitiveness of the industry; it is the driving force for the development and innovation of the tourism market [37]. Due to the lack of market demand data at town level, this paper quantifies the market demand through the number of city-level tourism receptionists and total tourism revenue and corrects the data through the number of popular reviews at town level from Ctrip tourist attractions to represent the real demand.
Firms’ strategy: Tourism is a highly complex industry. The tourism attraction resources are the front-end foundation of the industry chain, while accommodation, catering, shopping, and transportation facilities are the strategic cores that extend the backend of the industry chain, which are the necessary support for industrial integration and expansion [38,39]. There are diversified kinds of business entities in the rural tourism industry, from professional travel agencies and tourism and cultural companies with strong management capabilities to small business entities such as leisure farms and rural homestays run by farmers and rural cooperatives. The latter is often a potential new force to carry forward local culture innovation and promote endogenous development [40], and the organic combination of the two can produce diversified development models, which is of great significance to the sustainable development of the tourism industry in villages and towns.
Related and supporting industries: Tourism industry is a broad-linked industry, which has advantages in connecting with varied regional industries and economies, including agriculture, manufacturing industry, and service industry. At present, the focus of economic development in most rural areas in China is to promote agricultural modernization and develop secondary industries. The development of modern agriculture benefits from the guidance of large modern agricultural enterprises. Secondary industry, especially large-scale secondary industry enterprise, is of great significance for supporting the development of overall regional economy. In conclusion, a certain number and scale of primary and secondary industry enterprises in the region will provide strong support for the innovative and integrated development of the tourism industry [41,42].
National and local policy support: As an emerging industry, rural tourism cannot develop without government support and guidance [43]. In the past 20 years, national and local governments have selected and supported a series of typical villages and towns that are key to the development of the rural tourism industry, mainly including the national-level pastoral complexes, China’s beautiful leisure villages, national rural tourism key Villages. At the same time, local governments have also supported the development of a number of provincial-level rural tourism demonstration villages and towns, such as the Henan provincial rural tourism demonstration counties and villages. By planning rural tourism demonstration sites, the government not only provides basic funds and favorable policy environment to promote tourism development but also to attract capital investment and tourists, which is an external environmental factor for the rural tourism industry.
On the basis of the conceptual model, this paper comprehensively considers the availability and substitutability of data and uses the analytic hierarchy process to further construct the model indicators. The final indicator system includes 5 criterion layers, 7 subdivision criterion layers, and 18 indicators (Table 1).

2.3. Study Sample

Henan Province is located in the middle eastern part of mainland China with significant regional differences within the province (Figure 2). The three major mountain systems, the Yellow River, the Eastern Henan plains, and other different landforms in the territory form a varied natural geographical landscape. Henan has a long history of farming and has developed modern agriculture. At the same time, Henan has long been the center of China’s political economy and culture, and the rural areas are rich in cultural resources, including the Yangshao culture, loess cave-dwelling culture, and various folk arts. The rich natural history and agricultural landscape resources have prepared conditions for the development of various types of rural tourism in Henan Province.
As of 2021, there are 580 national A-level tourist attractions in the province, which could be divided into three types including natural (i.e., the Yuntai Mountain Natural Scenic Area), cultural (i.e., the Luoyang Baima Temple), leisure and wellness tourism attraction (i.e., the Luoyang Lavender Manor). At the same time, Henan has also vigorously developed agritainment tourism. As of 2021, it has constructed a wide range of national and provincial level rural tourism demonstration sites; among them, well-known brands include national-level pastoral complexes, China’s beautiful leisure villages, national rural tourism key villages, and Henan provincial rural tourism demonstration counties and villages.
Henan Province has 18 provincial-level municipal areas and 1892 townships under its jurisdiction. After removing some towns with missing data, the final sample size of our study is 1806 townships.

3. Result

3.1. Exploratory Factor Analysis Result of Rural Tourism Industry Competitiveness at Town Level

Factor analysis is performed on the evaluation indicator system. The variance contribution is 67.036% after six main factors are extracted, which meet the basic requirements of describing the overall characteristics of rural tourism industry competitiveness (KMO = 0.78, p < 0.0001). The factor loading values are shown in Table 2.
Factor rotation is carried out using the maximum variance method (Table 3). After rotation, the first main factor has a heavy load on the number of catering facilities, shopping facilities, public transport facilities, accommodation facilities, and the number of travel agencies and cultural tourism enterprises, which mainly reflects industry firm strategy, so it is named as firm strategy factor. The second main factor has a heavy load on terrain relief amplitude, terrain surface roughness, forest cover rate, township administrative area, and cultivated land area, so it is named as geographical environment factors. The third main factor has a heavy load on the number of enterprises, number of large-scale industrial enterprises, number of industrial enterprises, and number of employees in the enterprises, so it is named as related and supporting industry factor. The fourth main factor has a heavy load on total tourism revenue and the number of tourists, so it is named the market demand factor. The fifth main factor has a heavy load on the number of national and provincial rural tourism demonstration sites, so it is named as a policy support factor. The sixth main factor has a heavy load on the value of tourism attractions, so it is named the tourism attraction factor (Table 2 and Table 3).

3.2. Comprehensive Score of Tourism Industry Competitiveness at Town Level

The factor scores are estimated using the regression method, the proportion of the variance contribution rate of each factor is used for weighted summarization, and the comprehensive score (F) of tourism industry competitiveness of each township is obtained (Formula (1)).
F = (17.311 × F1 + 13.405 × F2 + 11.905 × F3 + 9.757 × F4+ 7.399 × F5 + 7.259 × F6)/67.036
After the data interval process (0–100), the comprehensive scores are classified and sorted into five grades using the natural discontinuity method and are visualized in ArcGIS. The spatial distribution is shown in Figure 3. It can be seen that the areas with relatively high comprehensive scores are gathered around the Taihang Mountains in northern Henan, the suburbs of Zhengzhou and Luoyang City, the Funiu Mountains in western Henan, and the Dabie Mountains in southern Henan. However, the comprehensive score are relatively low in the central and eastern Henan plains area.

3.3. Clustering Results of Rural Tourism Industry Development Mode

Hierarchical clustering analyses are used to classify the industry development mode quantified by the indicator systems. The hierarchical cluster diagram is obtained, and we adopt the classification results when the 1806 samples are divided into four main categories, the number of samples included in each category is similar and reasonable, and the number of categories is appropriate. The spatial distribution of the four categories is shown in Figure 4.

4. Discussion

4.1. Typical Mode of Rural Tourism Industry at Town Level

We analyze and interpret the classification results based on the main factor scores and comprehensive scores, and name the four categories based on the level of comprehensive development and industrial internal balance: (1) balanced development mode with multiple advantages; (2) related and supporting industries driving mode; (3) ecological resource-led mode; (4) rural landscape experience mode. The result of comprehensive score levels corresponding to the four categories of development mode is shown in the alluvial chart (Figure 5). It can be seen that the town samples from the first category generally rate the highest comprehensive score, while the second and third categories also have relatively high scores compared with the fourth one. Therefore, the first three modes are more competitive in developing rural tourism. The main factor scores of the four industrial development modes are shown in Figure 6.
(1) Balanced development mode with multiple advantages: This category has the highest average score in most main factors of the rural tourism industry. There is no short board, and the comprehensive score is generally high. Combined with literature review and the interpretation of the quantitative results, it is found that this mode often has dense and high-quality tourist attractions and rich natural environment resources as the industrial front-end base, and it is expanded and supported by the standardized and enriched service industries such as accommodation, catering, shopping, etc., so as to ensure a stable market [46]. Under the support of certain funds and policies from the government, business entities such as large cultural tourism companies are generally introduced, and small to medium-size entities such as farmers’ cooperatives will be developed to jointly participate in the construction of a multi-interactive operation mode.
For the study area, the number of townships in this development mode is the smallest, accounting for only 7.6% of the total sample. In terms of spatial distribution, it is relatively scattered, showing regional aggregation characteristics in the Taihang Mountain area in northern Henan and the Funiu Mountains in western Henan. This successful development model is more common at home and abroad, with national or international level tourism attractions as advantages; these townships have a stable and large-scale tourism market counting for million person-times per year, and have developed a “comprehensive tourism” model centered on the tourism attractions and large tourism investment entities [46]. At the same time, local villagers and small businesses are often involved, sharing the huge market together [46].
(2) Related and supporting industries driving mode: This type of township has a well-developed economy. The main difference between this mode to the first one is that the relatively high comprehensive score of tourism industry competitiveness is mainly due to the strong strength and scale of regional-related primary and secondary industries. Such advantages provide opportunities for industrial integration, which is an important means to implement comprehensive tourism and build a modern tourism economic, industrial system [47]. In terms of spatial distribution, the townships of this development model are mainly concentrated in the suburbs of big cities, such as Luoyang, Kaifeng, and Xuchang, with Zhengzhou City as a regional center, which shows obvious characteristics of spatial aggregation.
With location and economic advantages, this type of township should focus on strengthening the connection with the urban industrial chain and consumer market, for example exploring rural industrial integration by integrating production, exhibition and sales, cultural creativity, and leisure functions together to provide supporting resources for rural tourism [48]. It also creates new economic growth opportunities for industrial transformation and urban–rural integration.
(3) Ecological resource-led mode: This type of township has outstanding scores in geographical environment factors with high-quality ecological landscape resources. Generally, there are natural recreational tourist attractions such as national forest parks and national nature reserves, which are always protected and supported by national and local policies. Due to the influence of mountainous terrain and remote locations, this type of township has poor traffic conditions and tourism services, so most of them have not yet formed a large-scale and sustainable tourism industry. In terms of the spatial distribution of the study samples, this type of township mainly overlaps with the distribution of mountains and forest resources and is concentrated in areas with undulating terrain and sensitive ecology systems, such as the high altitude area of the Taihang Mountains in northern Henan, the Funiu Mountains in western Henan, and the Dabie Mountains in southern Henan.
Ecological resource-led townships need to appropriately develop the tourism industry on the basis of protecting the natural ecological environment, making rational use of mountain and forest resources, and strictly implementing the ecological protection red line policy and ecosystem management policy [49]. At the same time, the special livelihood mode and natural resource management method of mountain village communities should be protected and continued so as to ensure social ecosystem integrity and sustainable rural tourism development [50].
(4) Rural landscape experience mode: The factor scores and comprehensive scores of this type are all at a low level, except for the indicator of cultivated land area from the geographical environment factor. Townships of rural landscape experience mode generally located in traditional farming areas, such as, in this study, the eastern Henan Plain area. The various industries in this type of township are usually underdeveloped; therefore, it is not encouraged to give priority to the tourism industry but to focus on developing modern and characteristic agriculture to guarantee basic production and economic growth [51]. For towns with characteristic agricultural resources they can actively respond to the national support policies such as “one village one product” and “key counties of leisure agriculture”. Specifically, townships could encourage integrated development of agriculture, forestry, and animal husbandry with tourism to promote geographical, original products [52]. Townships should also make proper use of local human resources, especially unleash the potential ability of local residents in contributing to rural tourism development so as to maximize the improvement of the social economy and the lives of local residents.

4.2. More Keys to Sustainable Rural Tourism Industry Development

On this basis, we further discuss more socio-economic driving forces for sustainable rural tourism development to provide a more general perspective and key policy suggestions.
The core of rural revitalization is to break down the center-periphery structuring of urban and rural areas while preserving the natural features of the countryside. Therefore, the prosperity of rural industries is the foundation to promote the exchange of resources and narrow the gap between urban and rural development [53]. However, the common rural tourism industry in China nowadays, which is mainly sightseeing and low consumption, is attached and constrained by the consumer market from big cities. It mainly brings low-income jobs and environmental damages to the rural areas and cannot change the urban–rural dual structure. The integration and innovation of rural industries have become an inevitable choice to improve the efficiency of rural industries and link the two areas, and successful cases have been seen in Japan, South Korea, and other countries [54,55]. Rural industrial integration refers to the connection and integration of agriculture, secondary industry, and tourism services in the same industrial chain and network to achieve economic growth [56]. Under this mechanism, rural areas should pay attention to the primacy of the agricultural industry and strengthen the connection with the urban markets through agricultural product processing, sales, and tourism consumption in the original production area to keep the added value deprived by the secondary and tertiary industries within rural communities [56]. In this way, urban and rural economies can achieve long-term, effective and equitable linkages.
In addition, in the context of China’s new rural institutional reformation, the rural gentrification movement with tourism development as the main method is emerging and becoming the frontier of urban–rural relations [57]. With the advantages of better natural and social resources, rural areas around big cities are actively carrying out new tourism development and community building [58]. The influx of developers and the urban middle class has brought new opportunities for urban–rural social integration. However, it also brings problems such as high housing prices, privatization of resources, variation of local cultural landscapes, and class contradictions for the indigenous community [59,60]. Therefore, in China, the correct leadership of the government has played a major role in regulating the economic and social reformation of rural tourism gentrification [61]. Judging from the successful experience in southern China, the government has undertaken the basic work of infrastructure construction, land utilization control, and environmental protection, guiding social enterprises, new villagers (middle-class immigrants from cities), and local farmers to cooperate with each other for mutual benefit. The government is also responsible for propaganda, education, and promotion of harmony and sustainable development concepts [50]. The government not only maintains the balance between development and protection but also the interaction between villagers and enterprises. Of course, in successful cases, in addition to the elites, the initiative of local farmers is also very important to rural development.
For the vast underdeveloped rural area, indigenous people and organizations are valuable sources for rising tourism competitiveness. First, the indigenous culture and lifestyles of local residents are an indispensable part of the rural landscape, which is the core of tourism resources [62]. Second, the traditional indigenous knowledge and value of local farmers, such as appreciation of the intrinsic connection between culture and nature, is a scientific tool for natural resource management and developing a sustainable rural tourism industry [63,64]. Furthermore, as major stakeholders, they are more subjectively active in sustainable local industry building with the goal of improving their own life quality and well-being when given the correct guidance and financial assistance [43]. Therefore, rural landscape planning and management during tourism development should contribute to the preservation and enhancement of the inherent value and interest of the indigenous people [65]. Researches show that completely exogenous rural tourism development that relies on investors and government limits rural autonomy and resource allocation, while completely endogenous development is also flawed by the insufficient rural resources and capacity [66]. As a beneficial combination, the neo-endogenous development mode provides a more inclusive and practical view that it is necessary to value both endogenous forces from local residents and positive intervention from multi-sectoral government and exogenous investors [67].

5. Conclusions

The development of a sustainable tourism industry has a positive effect on rural revitalization and narrowing the gap between urban and rural areas. For rural towns where systematic research data are difficult to obtain, a scientific and comprehensive quantitative method is essential to evaluate rural tourism competitiveness and identify a typical pattern so as to provide policy guidance for sustainable tourism development.
According to the aims and objectives of this study:
(1)
We constructed a town level rural tourism competitiveness evaluation model containing six factors (i.e., rural tourism production factor conditions, rural tourism market condition, firms’ strategy, related and supporting industries, national and local policy support) based on the modified Michael Porter’s Diamond Model using integrated multi-source data.
(2)
Take the 1806 township units in Henan Province, China, as examples, we evaluate rural tourism competitiveness and identify typical development patterns at the town level and conclude four different modes based on the level of comprehensive competitiveness score and industrial internal balance (i.e., balanced development mode with multiple advantages, related and supporting industries driving mode, ecological resource-led mode, rural landscape experience mode).
(3)
By summarizing different development mechanisms and spatial distribution characteristics, we reaffirmed and emphasized the significant effects of tourism attraction resources, related supporting industries, mixed business entities, and policy guarantees in improving rural tourism competitiveness, which is basically consistent with the existing research.
(4)
Furthermore, in order to provide a more general and broader perspective, we also discussed the necessity of indigenous community participation and integrated urban–rural tourism development to achieve sustainable rural revitalization.
China’s rural tourism industry is in a special development period where opportunities and challenges coexist, and contradictions and risks are intertwined. Due to the inability to comprehensively understand the influencing factors of the rural tourism industry and scientifically evaluate the industrial characteristics of different townships, development problems prevail, including single tourism development mode, incomplete tourism infrastructure and service facilities, insufficient market vitality, and policy guarantees. In order to avoid homogeneous competition and disorderly development, it is necessary to find the appropriate development mode based on the comprehensive evaluation of township characteristics and smartly choose the right path to enhance the competitiveness of the rural tourism industry. In summary, this study fills a certain gap to the theory and method of rural tourism industry evaluation, mode classification, and development strategy at the scale and context of Chinese townships.

Author Contributions

Z.J.: Formal analysis, investigation, methodology, validation, writing—original draft and editing. Y.J.: Methodology, validation, writing—review and editing. W.Z.: funding acquisition, methodology, project administration. Z.C.: Data curation, investigation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key R&D Program of China (No.2019YFD1101302).

Institutional Review Board Statement

This article followed all ethical standards for carrying out research.

Informed Consent Statement

Not applicable for studies not involving humans or animals.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Su, L.; Huang, S.; Huang, J. Effects of destination social responsibility and tourism impacts on residents’ support for tourism and perceived quality of life. J. Hosp. Tour. Res. 2018, 42, 1039–1057. [Google Scholar] [CrossRef]
  2. Yoon, H.J.; Min, K.; Kim, C.W. Harmony of Sustainability and Productivity: Korean Templestay as Sustainable Tourism. Sustainability 2022, 14, 44. [Google Scholar] [CrossRef]
  3. The World Travel & Tourism Council. Travel & Tourism investment in the Americas. Available online: www.wttc.org (accessed on 15 February 2022).
  4. Gao, J.; Wu, B. Revitalizing traditional villages through rural tourism: A case study of Yuanjia village, Shaanxi province, China. Tour. Manag. 2017, 63, 223–233. [Google Scholar] [CrossRef]
  5. Kadiyali, V.; Kosová, R. Inter-industry employment spillovers from tourism inflows. Reg. Sci. Urban Econ. 2013, 43, 272–281. [Google Scholar] [CrossRef]
  6. Dai, B.; Jiang, Y.; Yang, L.; Ma, Y. China’s outbound tourism—Stages, policies and choices. Tour. Manag. 2017, 58, 253–258. [Google Scholar] [CrossRef]
  7. Leung, X.Y.; Baloglu, S. Tourism competitiveness of Asia Pacific destinations. Tour. Anal. 2013, 18, 371–384. [Google Scholar] [CrossRef]
  8. Saayman, M.; Du Plessis, E. Competitiveness of South Africa as a tourist destination. S. Afr. J. Res. Sport Phys. Educ. Recreat. 2003, 25, 57–65. [Google Scholar] [CrossRef]
  9. Sheng, J. A Study on the Constitution of the Competitiveness of Regional Tourism Industry Based on Competitive Nature. Tour. Trib. 2007, 22, 29–34. [Google Scholar]
  10. Cheng, W.; Li, Z.; Jian, W.H. Discussions on the core competitiveness of tourism industry. Hum. Geogr. 2005, 20, 59–62. [Google Scholar] [CrossRef]
  11. Crouch, G.I.; Ritchie, J.B. Tourism, competitiveness, and societal prosperity. J. Bus. Res. 1999, 44, 137–152. [Google Scholar] [CrossRef]
  12. Dwyer, L.; Kim, C. Destination competitiveness: Determinants and indicators. Curr. Issues Tour. 2003, 6, 369–414. [Google Scholar] [CrossRef]
  13. Medina-Mu Oz, D.R.; Chim-Miki, A.F. Tourism competitiveness assessment: The current status of research in Spain and China. Tour. Econ. 2013, 19, 297–318. [Google Scholar] [CrossRef]
  14. Abreu-Novais, M.; Ruhanen, L.; Arcodia, C. Destination competitiveness: What we know, what we know but shouldn’t and what we don’t know but should. Curr. Issues Tour. 2016, 19, 492–512. [Google Scholar] [CrossRef]
  15. Bazargani, R.; Kili, H. Tourism competitiveness and tourism sector performance: Empirical insights from new data. J. Hosp. Tour. Manag. 2021, 46, 73–82. [Google Scholar] [CrossRef]
  16. Novais, M.A.; Ruhanen, L.; Arcodia, C. Destination competitiveness: A phenomenographic study. Tour. Manag. 2018, 64, 324–334. [Google Scholar] [CrossRef]
  17. Ritchie, J.B.; Crouch, G.I. Competitiveness in International Tourism: A Framework for Understanding and Analysis; World Tourism Education and Research Centre, University of Calgary: Calgary, AB, Canada, 1993. [Google Scholar]
  18. Porter, M. The Competitive Advantage of Nations; Free Press: New York, NY, USA, 1990. [Google Scholar]
  19. Cronjé, D.F.; Plessis, E.D. A review on tourism destination competitiveness. J. Hosp. Tour. Manag. 2020, 45, 256–265. [Google Scholar] [CrossRef]
  20. Zhang, C. The Evaluation of Industrial Competitivenes of Regional Cultural Tourism Based on the Diamond Model. Chin. J. Manag. 2018, 15, 1781–1788. [Google Scholar]
  21. Yang, L.; Qiu, H. The theoretical development of “Diamond Model” and its enlightenment to China. Sci. Technol. Econ. 2008, 21, 55–58. [Google Scholar]
  22. Zhu, Z.; Di, H.; Shi, K. Research on the Competitiveness of Unmanned Economic Industry Based on Diamond Model. Sci. Technol. Manag. Res. 2019, 39, 152–159. [Google Scholar]
  23. Shi, T.; Li, X.; Xin, L.; Xu, X. The spatial distribution of farmland abandonment and its influential factors at the township level: A case study in the mountainous area of China. Land Use Policy 2018, 70, 510–520. [Google Scholar] [CrossRef]
  24. Li, X.; Law, R. Network analysis of big data research in tourism. Tour. Manag. Perspect. 2020, 33, 100608. [Google Scholar] [CrossRef]
  25. Kitsios, F.; Mitsopoulou, E.; Moustaka, E.; Kamariotou, M. User-Generated Content behavior and digital tourism services: A SEM-neural network model for information trust in social networking sites. Int. J. Inf. Manag. Data Insights 2022, 2, 100056. [Google Scholar] [CrossRef]
  26. Santos, F.; Almeida, A.; Martins, C.; Gonçalves, R.; Martins, J. Using POI functionality and accessibility levels for delivering personalized tourism recommendations. Comput. Environ. Urban Syst. 2017, 77, 101173. [Google Scholar] [CrossRef]
  27. Filipe, B.E.S.; Herrera, M.A.M.; Rosina, K.; Barranco, R.R.; Freire, S.; Schiavina, M. Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. Tour. Manag. 2018, 68, 101–115. [Google Scholar]
  28. Park, S.; Xu, Y.; Jiang, L.; Chen, Z.; Huang, S. Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Ann. Tour. Res. 2020, 84, 102973. [Google Scholar] [CrossRef]
  29. Paulino, I.; Lozano, S.; Prats, L. Identifying tourism destinations from tourists’ travel patterns. J. Destin. Mark. Manag. 2021, 19, 100508. [Google Scholar] [CrossRef]
  30. Li, Y.; Liu, H.; Wang, L.E. Spatial distribution pattern of the catering industry in a tourist city: Taking lhasa city as a case. J. Resour. Ecol. 2020, 11, 191. [Google Scholar]
  31. Zhang, Y.; Yang, C. Location Characteristics and Optimization Strategy of Commercial Hot Streets in Shichahai: From A Street Centrality Perspective. Tour. Trib. 2019, 34, 110–123. [Google Scholar] [CrossRef]
  32. Liu, C.; Dou, X.; Li, J.; Cai, L.A. Analyzing government role in rural tourism development: An empirical investigation from China. J. Rural Stud. 2020, 79, 177–188. [Google Scholar] [CrossRef]
  33. Mateoc-Sîrb, N.; Albu, S.; Rujescu, C.; Ciolac, R.; Țigan, E.; Brînzan, O.; Mănescu, C.; Mateoc, T.; Milin, I.A. Sustainable Tourism Development in the Protected Areas of Maramureș, Romania: Destinations with High Authenticity. Sustainability 2022, 14, 1763. [Google Scholar] [CrossRef]
  34. Zhang, D. Study on classification and evaluation of rural tourism resources in luoyang city. Chin. J. Agric. Resour. Reg. Plan. 2019, 40, 74–79. [Google Scholar] [CrossRef]
  35. Li, L.; Hou, G.; Xia, S.; Huang, Z. Spatial Distribution Characteristics and Influencing Factors of Leisure Tourism Resources in Chengdu. J. Nat. Resour. 2020, 35, 683–697. [Google Scholar] [CrossRef]
  36. Chen, Y.; Hao, F.; Yu, T. Spatial Pattern and Influencing Factors of Leisure Tourism Resources in Fuzhou under the Background of All-for-one Tourism. Resour. Dev. Mark. 2020, 36, 1127–1132. [Google Scholar]
  37. Jiang, L.; Wu, H.; Song, Y. Diversified demand for health tourism matters: From a perspective of the intra-industry trade. Soc. Sci. Med. 2020, 293, 114630. [Google Scholar] [CrossRef]
  38. Kuklina, M.; Dirin, D.; Filippova, V.; Savvinova, A.; Trufanov, A.; Krasnoshtanova, N.; Bogdanov, V.; Kobylkin, D.; Fedorova, A.; Itegelova, A.; et al. Transport Accessibility and Tourism Development Prospects of Indigenous Communities of Siberia. Sustainability 2022, 14, 1750. [Google Scholar] [CrossRef]
  39. Zhang, H.; Lu, L.; He, Y. Industry chain tourism: Concept and cases. World Reg. Stud. 2020, 29, 1006–1016. [Google Scholar]
  40. Ochoa-Jiménez, S.; Leyva-Osuna, B.A.; Jacobo-Hernández, C.A.; García-García, A.R. Knowledge Management in Relation to Innovation and Its Effect on the Sustainability of Mexican Tourism Companies. Sustainability 2021, 13, 13790. [Google Scholar] [CrossRef]
  41. Zhao, C. Research on the Integration of China’s Rural Tourism Industry Chain. Agric. Econ. 2007, 05, 18–19. [Google Scholar]
  42. Fong, V.; Hong, J.; Wong, I. The evolution of triadic relationships in a tourism supply chain through coopetition. Tour. Manag. 2021, 84, 104274. [Google Scholar] [CrossRef]
  43. Jia, Z.; Gao, M.; Xu, S.; Lyu, Y.; Feng, J.; Zhou, Z.; Yu, T.; Wu, W. Sociocultural vitality versus regulation policy and tourism development in preservation of traditional rural landscape: A case from Guizhou, China. Int. J. Sustain. Dev. World Ecol. 2021, 6, 179–192. [Google Scholar] [CrossRef]
  44. Jia, Z.Y.; Meng, C.Y.; Zhou, Z.X. A 3-D morphological approach on spatial form and cultural identity of ethnic mountain settlements: Case from Guizhou, China. J. Mt. Sci. 2021, 18, 1144–1158. [Google Scholar] [CrossRef]
  45. Zhao, C. Study on Sustainable Evaluation and Development Model of Comprehensive Tourism in Shandong Province; Shandong University: Jinan, China, 2019. [Google Scholar]
  46. Luo, Y.; He, J.; Mou, Y.; Wang, J.; Liu, T. Exploring China’s 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tour. Manag. Perspect. 2021, 37, 100769. [Google Scholar] [CrossRef]
  47. Zhao, C. The Integration of China’s Tourism Industry and three Industries and It’s Spatio-temporal Evolution. Inq. Into Econ. Issues 2019, 10, 165–174. [Google Scholar]
  48. Szromek, A.R.; Herman, K.; Naramski, M. Sustainable development of industrial heritage tourism—A case study of the Industrial Monuments Route in Poland. Tour. Manag. 2021, 83, 104252. [Google Scholar] [CrossRef]
  49. Mo, L.; Chen, J.; Xie, Y. Ecological Approach for the Evaluation of Structure and Sustainability in the Tourism Industry. Sustainability 2021, 13, 13294. [Google Scholar] [CrossRef]
  50. Zurayk, R.; El-Awar, F.; Hamadeh, S. Using indigenous knowledge in land use investigations: A participatory study in a semi-arid mountainous region of Lebanon. Agric. Ecosyst. Environ. 2001, 86, 247–262. [Google Scholar] [CrossRef]
  51. Gyawali, P.; Bhandari, S.; Shrestha, J. Horti-tourism; an approach for strengthening farmers’ economy in the post-COVID situation. J. Agric. Food Res. 2022, 07, 100278. [Google Scholar] [CrossRef]
  52. Domi, S.; Belletti, G. The role of origin products and networking on agritourism performance: The case of Tuscany. J. Rural Stud. 2020, 90, 113–123. [Google Scholar] [CrossRef]
  53. Yan, J.; Hao, C.; Xia, F. Toward improved land elements for urban–rural integration: A cell concept of an urban–rural mixed community. Habitat Int. 2018, 77, 110–120. [Google Scholar] [CrossRef]
  54. Ma, Q.; Wang, X. Effect and Promotion Strategy of Internet Plus on the Six Industrialization of Agriculture. Agric. Outlook 2016, 12, 48–52. [Google Scholar]
  55. Hisano, S.; Akitsu, M.; Mcgreevy, S.R. Revitalising rurality under the neoliberal transformation of agriculture: Experiences of re-agrarianisation in Japan. J. Rural Stud. 2018, 61, 290–301. [Google Scholar] [CrossRef]
  56. Jiang, C. “The Sixth-industrialization for Agriculture” in Japan and Promoting the Industrial Integration-development among Rural First Industry, Second Industry, and the Third Industry in China. Agric. Econ. Manag. 2015, 3, 5–10. [Google Scholar]
  57. Zhang, Q.; Lu, L.; Huang, J.; Zhang, X. Uneven development and tourism gentrification in the metropolitan fringe: A case study of Wuzhen Xizha in Zhejiang Province, China. Cities 2022, 121, 103476. [Google Scholar] [CrossRef]
  58. Atkinson, R.; Bridge, G. Gentrification in a Global Context: The New Urban Colonialism; Routledge: London, UK, 2005. [Google Scholar]
  59. He, S.J.; Qian, J.X.; Xu, Y.X.; Liu, B. Spatial-temporal evolution of rural gentrification amidst rapid urbanization: A case study of Xiaozhou village, Guangzhou. Acta Geogr. Sin. 2012, 67, 1044–1056. (In Chinese) [Google Scholar]
  60. Ghertner, D.A. Why gentrification theory fails in ‘much of the world’. City 2015, 19, 552–563. [Google Scholar] [CrossRef]
  61. Zhu, J.; Guo, Y. Social justice in spatial change:transition from autonomous rural development to integrated urbanization in China. Cities 2022, 122, 103539. [Google Scholar] [CrossRef]
  62. Zasada, I.; Häfner, K.; Schaller, L.; van Zanten, B.T.; Lefebvre, M.; Malak-Rawlikowska, A.; Nikolov, D.; Rodríguez-Entrena, M.; Manrique, R.; Ungaro, F.; et al. A conceptual model to integrate the regional context in landscape policy, management and contribution to rural development: Literature review and European case study evidence. Geoforum 2017, 82, 1–12. [Google Scholar] [CrossRef]
  63. Scheyvens, R.; Carr, A.; Movono, A.; Hughes, E.; Higgins-Desbiolles, F.; Mika, J.P. Indigenous tourism and the sustainable development goals. Ann. Tour. Res. 2021, 90, 103260. [Google Scholar] [CrossRef]
  64. Wang, N.; Fang, M.; Beauchamp, M.; Jia, Z.; Zhou, Z. An indigenous knowledge-based sustainable landscape for mountain villages: The Jiabang rice terraces of Guizhou, China. Habitat Int. 2021, 111, 102360. [Google Scholar] [CrossRef]
  65. Lee, C.H. Understanding rural landscape for better resident-led management: Residents’ perceptions on rural landscape as everyday landscapes—Science Direct. Land Use Policy 2020, 94, 104565. [Google Scholar] [CrossRef]
  66. Shucksmith, M. Endogenous development, social capital and social inclusion: Perspectives from LEADER in the UK. Sociol. Rural. 2002, 40, 208–218. [Google Scholar] [CrossRef]
  67. Olmedo, L.; Twuijver, M.V.; O’Shaughnessy, M. Rurality as context for innovative responses to social challenges—The role of rural social enterprises. J. Rural Stud. 2021, in press. [Google Scholar] [CrossRef]
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Figure 1. Conceptual Model for Rural Tourism Industry Competitiveness Evaluation at Town Level.
Figure 1. Conceptual Model for Rural Tourism Industry Competitiveness Evaluation at Town Level.
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Figure 2. Study sample.
Figure 2. Study sample.
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Figure 3. Comprehensive score of tourism industry competitiveness at town level.
Figure 3. Comprehensive score of tourism industry competitiveness at town level.
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Figure 4. Rural Tourism Industry Development Mode.
Figure 4. Rural Tourism Industry Development Mode.
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Figure 5. Alluvial chart of comprehensive score levels corresponding to the four categories of development mode.
Figure 5. Alluvial chart of comprehensive score levels corresponding to the four categories of development mode.
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Figure 6. Box plot of main factor scores of the four industrial development modes.
Figure 6. Box plot of main factor scores of the four industrial development modes.
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Table
Table 1. Indicator system of Rural Tourism Industry Competitiveness Evaluation Model at Town Level.
Table 1. Indicator system of Rural Tourism Industry Competitiveness Evaluation Model at Town Level.
Target LayerCriterion LayersSubdivision Criterion LayerIndicators LayerExplanation
Rural Tourism Industry Competitiveness Evaluation Model at Town LevelRural tourism production factor conditionsGeographical environment factorsForest cover rateRatio of forest area to township administrative area, based on land use data interpreted by satellite remote sensing images
Cultivated land areaArea of cultivated land in township administrative area, based on land use data interpreted by satellite remote sensing images
Township administrative areaBased on township administrative boundaries
Terrain relief amplitudeThe difference between the highest and the lowest altitude within township administrative area, based on DEM topographic map [44]
Terrain surface roughnessThe ratio of the surface area to its projected area within township administrative area, based on DEM topographic map [44]
Tourist attractionsValue of natural tourism attractionCalculated based on national A-level scenic attractions and Baidu map POI tourist attractions; Value = (5 × number of 5A scenic attractions + 2.5 × number of 4A scenic attractions + 1.5 × number of 3A scenic attractions + 0.75 × number of 2A scenic attractions + 0.15 × number of 1A scenic attractions + 0.1 * number of general POI tourist attractions)/township administrative area [45]
Value of historical and cultural tourism attraction
Value of leisure and wellness tourism attraction
Rural tourism market conditionMarket sizeNumber of touristsStatistical Data of Henan Provinces, Counties and Cities
Total tourism revenueStatistical Data of Henan Provinces, Counties and Cities
Internet popularityNumber of online reviews of scenic attractionsBased on the total number of Ctrip online reviews of tourist attractions in township area and use as correction factor to the statistical data at city level
Firms strategy Number of travel agenciesFilter by type and name of Baidu Map POI and count the number of different type of business entities within township administrative area
Tourism enterpriseNumber of leisure farms and rural homestays
Number of cultural tourism enterprises
Tourism service industryNumber of shopping facilitiesFilter by type and name of Baidu Map POI and count the number of different type of business entities within township administrative area
Number of accommodation facilities
Number of catering facilities
Number of public transport facilities
Related and supporting industriesRegional industrial capacityNumber of enterprisesStatistical Yearbook of Urban and Rural Construction—Villages and Towns
Number of employees in the enterprises
Industrial clusterNumber of industrial enterprises
Number of large scale industrial enterprises
National and local policy supportNational policyNumber of national rural tourism demonstration siteNumber of national-level pastoral complexes, China’s beautiful leisure villages, national rural tourism key villages, national historical and cultural towns and villages, Chinese traditional villages, national leisure agriculture and rural tourism demonstration site within township administrative area
Local policyNumber of provincial rural tourism demonstration siteNumber of provincial rural tourism demonstration counties and villages, provincial leisure agriculture and rural tourism demonstration site within township administrative area
Table
Table 2. Factor loading values.
Table 2. Factor loading values.
ElementInitial EigenvaluesRotational Load Sum of Squares
TotalPercent VarianceCumulative PercentageTotalPercent VarianceCumulative Percentage
15.17723.53023.5303.80717.30517.305
23.59516.34039.8702.94913.40430.709
32.0019.09648.9662.61911.90642.615
41.9108.68157.6472.1479.76052.375
51.1075.03362.6801.6287.40059.775
60.9584.35667.0361.5987.26267.036
70.8994.08871.124
80.7933.60674.730
90.7673.48578.216
100.7013.18881.404
110.6693.04384.446
120.6562.98287.429
130.5782.62790.056
140.4932.24092.296
150.4231.92194.217
160.3701.68495.901
170.3471.57797.478
180.2351.07098.548
190.2331.05899.605
200.0510.23099.835
210.0300.13699.971
220.0060.029100.000
Table
Table 3. Factor load matrix after rotation.
Table 3. Factor load matrix after rotation.
F1F2F3F4F5F6
Indicator Firm Strategy FactorGeographical Environment FactorRelated and Supporting Industry Factor.Market Demand FactorPolicy Support FactorTourism Attraction Factor
Number of catering facilities0.919
Number of shopping facilities0.908
Number of public transport facilities0.784
Number of accommodation facilities0.740 0.307
Number of travel agencies and cultural tourism enterprises0.671
Terrain relief amplitude 0.866 0.285
Terrain surface roughness 0.853 0.294
Township administrative area 0.728 0.252
Forest cover rate 0.644
Cultivated land area −0.5690.258
Number of industrial enterprises 0.860
Number of enterprises0.278 0.773
Number of employees in the enterprises0.293 0.760
Number of large scale industrial enterprises 0.691
Corrected total tourism revenue 0.957
Corrected number of tourists 0.950
Number of leisure farms, and ecological farms 0.256 0.676
Number of provincial rural tourism demonstration site0.216 0.651
Number of national rural tourism demonstration site 0.5680.364
Value of historical and cultural tourism attraction0.260 0.790
Value of natural tourism attraction 0.283 0.634
Value of leisure and wellness tourism attraction0.413 0.346 0.462
Extraction method: principal component analysis. The rotation has converged after 6 iterations.
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Jia, Z.; Jiao, Y.; Zhang, W.; Chen, Z. Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data. Sustainability 2022, 14, 4147. https://doi.org/10.3390/su14074147

AMA Style

Jia Z, Jiao Y, Zhang W, Chen Z. Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data. Sustainability. 2022; 14(7):4147. https://doi.org/10.3390/su14074147

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Jia, Ziyu, Yan Jiao, Wei Zhang, and Zheng Chen. 2022. "Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data" Sustainability 14, no. 7: 4147. https://doi.org/10.3390/su14074147

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