Sustainable Spatial Distribution and Determinants of Key Rural Tourism Villages in China: Promoting Balanced Regional Development

Abstract

Understanding the spatial distribution and sustainable development of rural tourism is essential for promoting balanced regional growth and formulating optimized policy strategies. This study aims to provide insights into sustainable development and policy optimization. Utilizing geographic information system technology, dominance analysis, and Geodetector statistical methods, this research offers a comprehensive examination of the spatial patterns and determinants of these distributions. The findings reveal significant regional disparities and clustering, with a higher concentration of key villages in economically developed eastern and central regions and fewer in the less developed western regions. The dominance analysis highlights that provinces such as Zhejiang, Shandong, and Beijing demonstrate strong advantages across multiple dimensions, including ecological environment, economic development, tourism infrastructure, transportation accessibility, policy support, and social development. Conversely, regions such as Ningxia, Qinghai, and Tibet exhibit lower dominance scores, indicating challenges in rural tourism development due to limited resources and infrastructure. Key influencing factors include forest coverage, GDP per capita, the number of star-rated hotels, transportation network density, policy initiatives, and urbanization rates. The results underscore the importance of a multi-dimensional approach to enhance rural tourism competitiveness and suggest targeted strategies for underperforming regions. This study contributes to advancing the theoretical framework of sustainable rural tourism and provides actionable insights for policymakers to foster balanced regional development, ecological conservation, and community-centered tourism practices.

1. Introduction

With the rapid development of China’s economy and the steady improvement of living standards among urban and rural residents, rural tourism has emerged as a promising and rapidly expanding sector. It has become a focal point of interest for both scholars and policymakers. Rural tourism not only meets urban residents’ desires for nature and rural experiences but is also regarded as a vital pathway for transforming rural economies, promoting social development, and enhancing the quality of life for rural populations [1,2]. At the national level, a series of policies have been implemented to actively support and guide the development of rural tourism, aiming to achieve strategic goals such as coordinated urban–rural development and rural revitalization [3,4]. Key rural tourism villages are those that possess significant advantages in terms of resources, industrial foundation, and management, thereby playing a demonstrative and leading role in the development of rural tourism [5,6]. These villages often feature rich natural landscapes, unique historical and cultural elements, and distinctive local customs, along with well-developed infrastructure and services that provide high-quality tourism experiences. Such villages play an important exemplary role in promoting the high-quality development of rural tourism. In the selection process, the Ministry of Culture and Tourism of China takes into account various factors, including the village’s resource conditions, economic development level, social benefits, and sustainable development capacity, to select a representative group of key rural tourism villages [7].

The origins of rural tourism research can be traced back to the 1970s when tourism activities began expanding beyond urban areas into rural settings. Early studies in this field primarily focused on the socioeconomic impacts of tourism, with scholars examining how tourism could generate economic benefits and employment opportunities for rural communities [8]. The “rural revival” theory posits that tourism can contribute to rural development by attracting urban visitors, boosting local incomes, and improving infrastructure. Lane’s seminal work emphasized that the success of rural tourism depends not only on the exploitation of natural resources but also on infrastructure development, market promotion, and policy support [8]. During this period, research primarily concentrated on rural areas in Europe and North America, exploring how tourism could serve as a tool for revitalizing local economies. By the late 20th century, with the rise of global sustainability initiatives, researchers began to emphasize the importance of rural tourism in ecological conservation and cultural heritage preservation. Butler’s Tourism Area Life Cycle (TALC) model became a classic framework for understanding the development trajectory of tourism destinations [9]. This model examines the evolution of a destination from its initial exploration and development stages to maturity and eventual decline. In the context of rural tourism, the TALC model has been further developed to account for tourism’s role not only as an economic activity but also as a mechanism for protecting ecosystems and preserving cultural traditions. Consequently, scholars have increasingly explored how tourism resources can be developed in ways that ensure the sustainability of ecosystems while maintaining the continuity of cultural heritage.

In recent years, with the rapid advancement of information technology and globalization, the scope of rural tourism research has expanded significantly and theoretical frameworks have become more diversified. While Butler’s TALC model provides insights into the evolution of tourism destinations, its application in rural tourism has proven to be more complex [9]. Rural tourism is influenced by a myriad of factors, including natural resources, accessibility, policy support, and market demand. Compared with conventional mass tourism, the lifecycle of rural tourism is often subject to government intervention and external market forces, resulting in a non-linear and fluctuating development pattern. Building upon these earlier frameworks, sustainable tourism theory has emerged as a vital addition to the study of rural tourism. This theory advocates for minimizing the environmental and sociocultural impacts of tourism while achieving a balance between ecological preservation, economic growth, and social well-being [1]. Given that rural tourism often involves direct interaction with nature, there is a critical need to emphasize environmental sustainability, protecting local ecosystems, and fostering long-term community participation. Moreover, spatial distribution and regional economic theories provide essential frameworks for understanding the spatial layout of rural tourism. The interaction between tourism and regional economic development plays a critical role in determining the spatial distribution of tourism resources. In rural tourism, natural resources and economic disparities between regions are closely linked. Zhang et al. employed spatial analysis techniques to reveal that economically developed regions with well-established infrastructure and high demand are often the core areas of rural tourism, while less developed areas rely heavily on government support for resource allocation and policy intervention [10]. A growing number of scholars have adopted geographic information system (GIS) and spatial analysis methodologies to explore the spatial distribution patterns of rural tourism villages and the factors that influence them. For instance, Zhang et al. utilized GIS technology to study the spatial distribution of rural tourism villages in China, identifying key factors such as the abundance of natural resources, transportation accessibility, and the level of regional economic development [10]. Their findings highlight the significant role of resource endowments and infrastructure in shaping the spatial layout of rural tourism. In terms of sustainability, researchers have examined the role of rural tourism in ecological protection, cultural heritage preservation, and community participation. Cheng et al. demonstrated that the development of eco-tourism and green tourism can enhance the sustainability of rural tourism, while community involvement not only improves the tourism experience but also strengthens the well-being and sense of belonging among local residents [11]. Furthermore, Rosalina et al. underscored the necessity of resource integration and brand building in rural tourism, noting that these factors contribute to the creation of a differentiated competitive advantage, promoting the long-term development of rural tourism [12]. Meanwhile, research on the policy implications for rural tourism continues to evolve. Liu et al. emphasized that government involvement in infrastructure development, resource allocation, and policy support is critical to the success of rural tourism [3]. In resource-constrained areas, policy preferences and financial support significantly enhance the tourism appeal and development potential of these regions [7]^. This body of research suggests that policy support can not only mitigate resource and infrastructure deficits but also drive the growth of rural tourism villages through targeted funding and preferential policies.

However, most of these studies have focused on individual factors, such as economic development or resource availability, lacking comprehensive multi-factor analyses. Furthermore, the existing research on the spatial distribution of rural tourism villages in China is largely regional or local in scope, with limited nationwide macro-level studies. Many of these studies also overlook the application of spatial econometric methods, which may reduce the precision and reliability of their results. This paper aims to conduct a systematic analysis of the spatial distribution patterns of key rural tourism villages in China and the underlying driving factors using GIS technology and multivariate statistical analysis methods. Specifically, the study seeks to answer the following research questions: (1) What are the spatial distribution characteristics of key rural tourism villages in China? (2) What are the main driving factors influencing these spatial distribution patterns? (3) How do these factors interact to collectively shape the spatial distribution of rural tourism villages? This study integrates data on rural tourism resources, infrastructure development, and policy support and employs GIS and spatial econometric analysis to uncover the mechanisms driving the distribution of key rural tourism villages. This approach provides a new theoretical perspective, addressing gaps in current rural tourism research. On a practical level, the findings of this study will offer actionable insights for policymakers, particularly at the local government level, supporting more effective and sustainable rural tourism development. By providing a deeper understanding of the factors influencing rural tourism, this research will assist in optimizing development strategies and ensuring balanced regional growth in the rural tourism sector.

2. Materials and Methods

2.1. Data Sources

The data for this study were derived from several sources. The data on key rural tourism villages were obtained from the most recent list published on the official website of the Ministry of Culture and Tourism of China (https://www.mct.gov.cn/). Geographic information data were primarily sourced from the Resource and Environmental Science and Data Center of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (https://www.resdc.cn/DOI/), which includes high-resolution digital elevation models (DEM) and river system data. Socioeconomic data were mainly retrieved from the official website of the National Bureau of Statistics of China (https://www.stats.gov.cn/), covering various provincial and municipal economic development indicators, tourism industry data, and transportation network information. The integration and analysis of these data provide a reliable foundation for this study. The location data of rural tourism villages were transformed by geographic coordinates and matched by ArcGIS. Socio-economic data, such as GDP and traffic density, were standardized to ensure that the data were comparable. The mean filling method was used to process some missing data to reduce the bias caused by missing data.

2.2. Research Methods

2.2.1. GIS Spatial Analysis

Geographic information system (GIS) software, such as ArcGIS (v10.6) was employed to visualize and analyze the spatial data of key rural tourism villages. Techniques such as kernel density analysis and standard deviation ellipse were used to uncover the spatial distribution characteristics and clustering patterns of these villages [13,14]. These methods visually represented the spatial distribution of key villages, laying a foundation for further analysis.

Kernel density estimation (KDE) is a non-parametric method used to estimate the spatial density distribution of point data. By applying a kernel function to each data point, the density values of surrounding areas are calculated to generate a continuous density surface. In this study, KDE was utilized to analyze the spatial density distribution of rural tourism resources. The specific formula is as follows:

(1)$\mathrm{P}(\mathrm{x})={\displaystyle \frac{1}{\mathrm{n}}}\sum _{\mathrm{i}=1}^{\mathrm{n}}\mathrm{k}({\displaystyle \frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{h}}})$

where $\mathrm{k}({\displaystyle \frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{h}}})$ is the kernel function, $\mathrm{h}$ is the bandwidth, and ($\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}$) is the distance from the estimated point $\mathrm{x}$.

The standard deviation ellipse (SDE) is a spatial statistical method used to describe the directional trend and dispersion of data points in space. By calculating the center, long axis, and short axis directions, the SDE can reveal the spatial trends of data distribution. In this study, the SDE was employed to analyze the spatial distribution characteristics of rural tourism resources.

2.2.2. Dominance Analysis

Dominance analysis is a method used to evaluate the competitive advantages or disadvantages of different regions or entities in a specific domain. This approach involves calculating a dominance index for each region or entity based on various indicators, thereby determining their overall competitiveness [15]. The specific formula is as follows:

(2)$\mathrm{D}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{D}}_{\mathrm{i}}$

(3)${\mathrm{D}}_{\mathrm{i}}=\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}$

where D is the comprehensive dominance degree; $\mathrm{i}$ = 1,2... 6, represents different types of dominance; $\mathrm{n}$ is the type number of dominance; $\mathrm{j}$ is the influence factor of the ith type of dominance; $\mathrm{m}$ is the number of influence factors; $\mathrm{w}$ is the weight; and $\mathrm{X}$ is the normalized data.

2.2.3. Geodetector

Geodetector is a novel statistical method used to identify the driving factors behind spatial distribution phenomena. This method was used to quantify the influence of various factors on the spatial distribution of key rural tourism villages and determine the extent to which each factor explained the spatial distribution pattern of these villages. The application of Geodetector helps to deepen the understanding of the spatial distribution patterns of key rural tourism villages [16]. The specific formula is as follows:

(4)$\mathrm{q}=1-{\displaystyle \frac{1}{{\mathrm{N}\mathsf{\sigma }}^2}}\sum _{\mathrm{h}=1}^{\mathrm{L}}{\mathrm{N}}_{\mathrm{h}}{\mathsf{\sigma }}_{\mathrm{h}}^2$

where $\mathrm{L}$ represents the influencing factors of the spatial distribution of key rural tourism villages in China; ${\mathrm{N}}_{\mathrm{h}}$ and $\mathrm{N}$ represent the number of units in layer $\mathrm{h}$ and the entire region, respectively; ${\mathsf{\sigma }}^2\mathrm{a}\mathrm{n}\mathrm{d}{\mathsf{\sigma }}_{\mathrm{h}}^2$ represent the variance of the entire region and layer $\mathrm{h}$; and $\mathrm{q}$ is the detection value of the influencing factors on the spatial distribution of key rural tourism villages in China, where $\mathrm{q}\mathsf{ϵ}\left[\mathrm{0,1}\right]$. A larger value indicates a stronger explanatory power, while a smaller value indicates weaker explanatory power.

3. Results

3.1. Spatial Distribution Characteristics of Key Rural Tourism Villages

The distribution of key rural tourism villages (Table 1) showed a gradual increase in the number of villages in each successive batch, reflecting an increasing emphasis and effort on promoting rural tourism development. Notably, there was a significant increase in the number of key villages in the second batch, indicating substantial progress in rural tourism development during that phase. Additionally, the distribution demonstrated clear regional differences. The number of key villages in the eastern region consistently represented a large proportion across all batches, highlighting the intensity and maturity of rural tourism resource development in these areas. In contrast, the number of key villages in the central region increased in the second batch and then remained relatively stable. The western region had the highest number of key villages, especially showing a significant increase in the second and third batches. This trend indicates the rapid development of rural tourism in the western region, supported by national policies and increased resource development efforts, leading to a notable increase in the number of key villages.

The spatial distribution of the key rural tourism villages is characterized by extensive spread, noticeable clustering, and significant regional disparities (Figure 1). These villages are widely distributed across China, ranging from the eastern coast to the western frontier and from the northern plateaus to the southern hills, covering most provinces in the country. The eastern coastal and central regions have a higher density of key villages, particularly in provinces such as Zhejiang, Jiangsu, Shandong, Henan, Anhui, Hubei, Hunan, Sichuan, and Guizhou. The number of key rural tourism villages in the eastern region is significantly higher than in the western region, especially in the Yangtze River Delta and the Pearl River Delta, where the villages are densely distributed. These regions are economically more developed, with convenient transportation and abundant tourism resources, making them focal areas for rural tourism development. In municipalities like Beijing, Tianjin, and Shanghai, as well as provinces like Guangdong and Fujian, despite their smaller geographic sizes, the number of key villages is relatively concentrated, indicating a high level of rural tourism development.

The spatial distribution characteristics of key rural tourism villages reveal that they are not only widely spread across different regions but also show clear clustering patterns and significant regional differences (Figure 2). Key villages are densely concentrated in certain areas, particularly in the more economically developed eastern coastal and central regions, while being more sparsely distributed in the western regions. The kernel density analysis further highlighted these patterns, showing high-density clusters in provinces with rich tourism resources and strong economic foundations, such as the Yangtze River Delta and Pearl River Delta areas. This clustering pattern reflects the combined influence of economic development, accessibility, and tourism resource availability on the spatial distribution of key rural tourism villages. The kernel density analysis results indicate that high-density areas are mainly concentrated in the economically developed and well-connected eastern coastal and some central regions while low-density areas are primarily distributed in the remote and less accessible western and northern regions.

Based on the density distribution map of key tourism villages, we can observe the spatial density characteristics and their changing trends across different batches (Figure 3). In the first batch, regions with high density were mainly concentrated in East China, the central region, and the southwest, with Beijing, Jiangsu, and Zhejiang provinces having the highest densities. These areas have advantages in early rural tourism resource development, with developed economies, convenient transportation, and well-established infrastructure. The central regions, such as Henan and Hubei provinces, also show high village densities due to their geographical centrality, abundant natural resources, and rich historical and cultural heritage. The second batch of key villages maintains a high density in East China, particularly in Beijing, Jiangsu, and Zhejiang provinces. Central regions such as Henan and Anhui provinces also exhibit higher densities. In the third batch, high-density areas continue to be in East China, such as Beijing, Jiangsu, and Zhejiang provinces. There has been a significant increase in density in North China, especially in Hebei province, indicating progress in rural tourism development in this region. The fourth batch showed that East China, including Shandong, Jiangsu, and Zhejiang provinces, still has the highest density of key villages. The central regions, such as Henan and Anhui provinces, maintain relatively high density levels, while the southwest region, including Sichuan province, also has a high density. Across all four batches, the western regions consistently showed the lowest density, which is largely related to the vast area of these provinces.

3.2. Advantage Degree Analysis of Key Rural Tourism Villages

In this study, we evaluated the comprehensive development advantage of key rural tourism villages in China by calculating the standardized weights of various indicators. Five major dimensions were selected for the study: ecological environment, economic development, tourism industry, transportation facilities, and policy support. Specific variables within each dimension, such as the forest coverage rate, GDP, and per capita disposable income, were further refined. The weight of each variable was determined based on the Q statistic and then standardized (Table 2).

According to the Figure 4a, there are significant differences in the ecological environment dominance across regions. Provinces such as Yunnan, Fujian, and Sichuan are particularly prominent in terms of ecological environment dominance. Yunnan is well known for its unique geographical location and diverse biological resources, with high forest coverage and abundant ecological tourism resources, such as the Stone Forest, Erhai Lake, and Lugu Lake, ensuring its top position in ecological environment dominance. Fujian, with its favorable natural conditions and excellent ecological environments, such as Wuyi Mountain and Gulangyu Island, attracts many tourists. Effective ecological protection measures further enhance its ecological environment dominance. Sichuan is noted for its rich natural and cultural resources (such as world-class scenic spots like Jiuzhaigou and Mount Emei), high forest coverage, and diverse terrains, providing excellent conditions for ecological tourism. Other provinces such as Guangxi, Jiangxi, and Zhejiang also show significant ecological environment dominance; these areas have considerable natural resources and ecological foundations but still have room for improvements in resource development and protection. Regions like Jiangsu, Shanghai, and Tianjin have relatively low ecological environment dominance. Due to high levels of urbanization and industrialization, these areas have weaker ecological conditions, lower forest coverage, and higher PM2.5 concentrations, offering limited ecological support for rural tourism development.

Economic development dominance plays a critical role in the overall competitiveness of rural tourism, directly influencing the development and utilization of tourism resources (Figure 4b). In terms of economic development dominance, Beijing, Shanghai, and Zhejiang are particularly outstanding. Beijing, as China’s political and cultural center, has a strong economic foundation and a high level of per capita GDP, providing ample financial support and well-developed infrastructure for rural tourism development. Shanghai, leveraging its status as an international financial center, has rapidly developed its economy, with a high proportion of GDP from the tertiary sector. This not only drives overall economic growth but also creates significant market demand and consumption potential for rural tourism in surrounding areas. Zhejiang, with its developed private economy and favorable geographical location, has established a strong economic development trend, and high levels of disposable income have further enhanced the attractiveness and market competitiveness of rural tourism. Regions such as Guangdong, Jiangsu, and Shandong also demonstrate strong competitiveness. In contrast, regions like Gansu, Ningxia, and Qinghai face economic constraints due to their geographical locations and resource endowments, with relatively low levels of economic development, lower disposable incomes, and a smaller proportion of the tertiary sector, which limits the potential for rural tourism development to some extent.

Tourism industry dominance is important in measuring a region’s potential for rural tourism development, mainly reflecting the comprehensive strength of tourism infrastructure, service levels, and market attractiveness (Figure 4c). Shandong, Jiangsu, and Zhejiang provinces are particularly prominent in terms of tourism industry dominance. Shandong, with its rich historical and cultural resources and numerous A-level tourist attractions, has strong tourism appeal. The region not only has a large number of star-rated hotels and comprehensive tourism reception facilities but also demonstrates high levels of service capabilities across all segments of the tourism industry chain, collectively driving Shandong to become a hotspot for rural tourism. Jiangsu, relying on its advantageous geographical location and developed economic base, has created several well-known rural tourism brands. Zhejiang also performs well in tourism industry dominance due to its abundant tourism resources and strong marketing capabilities. Scenic spots such as West Lake and Qiandao Lake attract a large number of domestic and international tourists, promoting the rapid development of local rural tourism. Guangdong, Henan, and Sichuan provinces also show significant tourism development potential. However, regions like Ningxia, Qinghai, and Tibet face certain limitations in tourism infrastructure and service levels. Due to their remote locations, tourism infrastructure construction is relatively lagging, and tourism service capabilities are weak, resulting in lower tourism industry competitiveness.

Transportation infrastructure dominance reflects a region’s convenience of transportation and accessibility for tourists (Figure 4d). The completeness of transportation infrastructure directly affects the scale and quality of tourist flows, thereby determining a region’s competitiveness in the rural tourism market. Regions like Heilongjiang, Inner Mongolia, and Shandong are particularly prominent in terms of transportation infrastructure dominance. Heilongjiang, with its developed transportation network and high-density railway facilities, significantly enhances visitor accessibility and convenience. Inner Mongolia has a high density of transportation networks, especially near major tourist nodes, where well-developed transportation facilities make it easy for visitors to access natural landscapes and cultural sites. Shandong, with its advantageous geographical location and comprehensive transportation system (including well-developed road and railway networks), provides convenient travel conditions for tourists, enhancing its transportation infrastructure dominance. Henan, Sichuan, and Hebei provinces also show significant potential for transportation development. Meanwhile, regions such as Qinghai, Ningxia, and Tibet face certain limitations in transportation infrastructure development and service levels. Due to their remote locations and natural constraints, these areas have relatively weak transportation facilities and lower accessibility, restricting tourist mobility and tourism experiences.

Policy support dominance reflects the level of support from local governments in terms of policy formulation, financial investment, and project implementation (Figure 4e). Effective policy implementation can significantly enhance the quality and competitiveness of rural tourism development. Zhejiang, Shandong, and Liaoning are particularly prominent in policy support dominance. Zhejiang has promoted the construction of beautiful villages and the creation of rural tourism demonstration sites through a series of policy supports and financial investments. These policy measures have not only improved the infrastructure level of rural tourism but also increased the participation of and benefits for local residents. Similarly, Shandong has implemented active policies, such as supporting rural tourism demonstration counties and protecting traditional villages, significantly enhancing the efficiency of tourism resource development and sustainable development capacity. Liaoning’s policy support is reflected in the comprehensive development and protection of rural tourism resources, particularly in ecological protection and cultural heritage preservation, achieving notable success and promoting comprehensive rural tourism development. Henan, Hebei, and Hunan provinces also show significant policy support. However, regions like Ningxia, Qinghai, and Xinjiang have certain shortcomings in policy formulation and implementation. Due to a weak economic foundation and insufficient policy enforcement, rural tourism development in these areas is constrained.

Social development dominance plays a crucial role in assessing a region’s potential for rural tourism development (Figure 4f). It reflects the comprehensive capabilities of regional socio-economic structures, infrastructure levels, and the quality of public services. Regions with high social development dominance typically have high urbanization rates and well-developed public service facilities, providing a solid social foundation for rural tourism. Beijing, Jiangsu, and Shanghai are particularly prominent in terms of social development dominance. As the capital of China, Beijing leads in urbanization levels and has well-developed public service facilities, such as ample public toilets and high-quality healthcare and educational resources, significantly enhancing its social development dominance. Jiangsu, with its high level of economic development and urbanization, offers excellent public services and infrastructure, laying a solid foundation for rural tourism development. Shanghai, with its highly developed urban infrastructure and superior public service system, performs exceptionally well in social development dominance. This not only enhances its appeal to tourists but also improves tourism service quality and visitor satisfaction. Zhejiang, Guangdong, and Shandong provinces also show significant social development potential. However, regions such as Qinghai, Tibet, and Ningxia face limitations in socio-economic structure and public service levels.

The comprehensive advantage index plays a decisive role in assessing the development potential of rural tourism by considering multiple factors such as ecological environment, economic development, tourism industry, transportation infrastructure, policy support, and social development (Figure 5). Zhejiang, Shandong, and Beijing stand out in terms of comprehensive advantage. Zhejiang exhibits significant comprehensive advantages due to its excellent performance in ecological environment, economic development, and policy support. Its high forest coverage, economic vitality, and sustained policy support provide comprehensive support for rural tourism. Shandong’s strong tourism industry foundation, well-developed transportation infrastructure, and ample policy support significantly enhance its overall competitiveness. As China’s political, cultural, and economic center, Beijing benefits from highly developed infrastructure, favorable social development conditions, and strong policy support, which contribute to its outstanding performance in comprehensive advantage. Regions such as Guangdong, Jiangsu, and Fujian also show strong development potential in terms of comprehensive advantage. In contrast, Ningxia, Qinghai, and Tibet, despite possessing cultural heritage and certain natural resources, face limitations in several areas that constrain their potential for rural tourism development.

3.3. Analysis of Influencing Factors of Key Villages of Rural Tourism

Based on the comprehensive analysis using Geodetector (v2018), six factors—ecological environment, economic development, tourism industry, transportation infrastructure, policy support, and social development—play crucial roles in promoting rural tourism development (Figure 6). In terms of the ecological environment, forest coverage and terrain ruggedness are key indicators for enhancing ecological quality. High forest coverage not only provides beautiful natural landscapes for tourists but also improves air quality and increases green space, thereby significantly boosting the attractiveness of rural tourism. Diverse terrain features enhance the diversity of tourism resources and the uniqueness of landscapes, attracting more visitors. Regarding economic development, the proportion of the tertiary sector in GDP highlights the importance of the service industry in economic growth. A well-developed service sector can provide high-quality tourism services and supporting facilities, enhancing tourists’ experiences. The higher the economic level, the stronger the consumption power of residents and tourists, which in turn drives the development of the tourism industry. For the tourism industry, the number of A-level tourist attractions and star-rated hotels are important indicators for measuring tourism resources and service quality. Rich scenic resources and high-quality accommodation facilities can attract more tourists and enhance their travel experience and satisfaction. The number of local employees and travel agencies reflect the development level and market vitality of the tourism industry. The level of transportation infrastructure directly affects tourist accessibility and visitor flow. A well-developed transportation network and convenient facilities significantly enhance the attractiveness of tourist destinations and the ease of access for visitors. In terms of policy support, the number of leisure agriculture and rural tourism demonstration counties (spots) and beautiful leisure villages in China reflect the level of policy support for rural tourism. Active policy support and demonstration projects can promote the development of rural tourism, improve the efficiency of tourism resource utilization, and enhance sustainable development capabilities. Social development levels and the quality of public services have significant impacts on rural tourism development. High levels of urbanization and well-developed public service facilities not only improve the quality of life for residents but also enhance tourism service quality and visitor satisfaction, providing a strong social foundation for rural tourism development. Overall, these factors work together to promote the sustainable development of rural tourism.

4. Discussion

Although rural tourism in China has developed rapidly, there remain significant regional disparities in its spatial distribution and influencing factors, particularly between the eastern, central, and western regions. The density of rural tourism villages in the eastern and central regions is significantly higher than in the western and northern areas. This phenomenon is primarily influenced by regional economic development levels and infrastructure conditions. In the eastern regions, such as Zhejiang, Jiangsu, and Shandong, high levels of economic development, dense transportation networks, and well-developed tourism service infrastructure contribute to these areas becoming major clusters of rural tourism villages. In contrast, despite the rich natural ecological resources in the western and northern regions, these areas have sparser distributions of rural tourism villages due to inconvenient transportation, underdeveloped infrastructure, and relatively lagging economic development. This finding aligns with the existing literature, further validating the decisive role of transportation and economic development in the spatial distribution of rural tourism development [7]. Moreover, the driving factors for rural tourism development vary significantly across regions. Specifically, in the eastern and central regions, the level of economic development (such as per capita GDP) and transportation network density are key factors contributing to the dense distribution of rural tourism villages. Economically developed regions have greater resource development capabilities, allowing for the provision of well-established tourism services, while their advanced transportation networks improve the accessibility of tourism destinations, further accelerating the development of rural tourism in these areas. In contrast, although the western regions boast high ecological advantages, such as higher forest coverage and complex terrain, these areas have not fully leveraged these natural resources for tourism development due to inadequate infrastructure and lower economic levels. This result suggests that natural resource advantages alone are insufficient to drive regional tourism development; they must be combined with appropriate economic investment and infrastructure development. This finding adds to the existing literature by discussing the synergistic effects of ecological resources and economic development [10]. Additionally, policy support plays a crucial role in promoting the development of rural tourism villages, especially in the underdeveloped western and northern regions. Despite national policies that provide preferential support to these areas, the actual outcomes have not been as significant. One potential explanation for this is the regional differences in policy implementation. For instance, the more established policy execution systems and ample financial support in the eastern regions have led to more pronounced policy outcomes. In contrast, the western and northern regions, constrained by limited resources and weaker implementation capacities, have seen weaker effects from policy interventions. Therefore, enhancing the policy execution capacity in underdeveloped areas and narrowing the regional development gap will be key challenges for the future development of rural tourism.

Based on the research findings, optimizing the development of rural tourism in China should focus on regional differences and adopt multi-level strategies to promote balanced growth. Improving infrastructure, especially in the underdeveloped western and northern regions, is essential. These areas have rich ecological resources, but inadequate transportation limits tourism development. Priority should be given to expanding highways, railways, airports, and public transportation, alongside building tourism services like accommodations and sanitation facilities to enhance the tourist experience. In more developed eastern and central regions, the integration of rural tourism with other industries should be encouraged, promoting the “Tourism+” model. This can include agriculture, handicrafts, and cultural activities, which would extend the tourism chain and provide new income sources for local residents while creating more diverse experiences for tourists. While the western and northern regions have significant ecological advantages, these are underused due to slower economic development. Overdevelopment in the eastern and central regions has placed pressure on the environment. Future rural tourism development should emphasize ecological sustainability, following strict protection standards to avoid harmful impacts. The government should support this through clear ecological compensation mechanisms, promoting green and low-carbon tourism, and encouraging tourists to participate in environmental protection efforts. Policy support plays a critical role. The central government should continue to focus on underdeveloped areas, providing financial aid, tax breaks, and loan incentives. A comprehensive policy supervision system should be established to ensure proper implementation. Encouraging public–private partnerships can introduce more market resources, improving the management of rural tourism projects. The developed eastern regions have more advanced digital infrastructure, while western and northern regions face significant gaps. Governments and businesses should push for digital transformation in rural tourism by using technologies like big data and the Internet of Things to improve services. Digital platforms can help integrate and promote dispersed rural tourism resources, offering easy access to information and improving tourist experiences. Digital tools can also help manage tourist flows and optimize resources. The success of rural tourism also depends on community involvement. Governments should encourage local residents to participate in the development and management of tourism resources, ensuring they benefit directly. Training programs can improve local residents’ skills, increasing their employment and business opportunities. It is important to preserve local culture in tourism development, avoiding over-commercialization and ensuring both that tourism grows and community prospers [17,18,19,20,21,22,23].

Despite the valuable insights provided by this study on the spatial distribution and driving factors of key rural tourism villages in China, several limitations remain. The data, primarily sourced from publicly available databases, may lack timeliness and coverage, especially in remote areas where data quality issues could impact accuracy. Additionally, variables like tourism infrastructure and social development were limited to provincial or prefecture-level data, restricting finer spatial analysis at the village level. Methodologically, while GIS and multivariate statistical analyses were used, the complex interactions between factors such as policy support, infrastructure development, economic growth, and ecological protection were not fully explored, limiting a deeper understanding of these dynamics. The focus on macro-level data highlighted broad trends but missed micro-level development patterns and village-specific challenges. Future research should aim for more detailed, village-level data, especially regarding tourism infrastructure, economic development, and community participation. Remote sensing and big data could help address data gaps, particularly in remote areas, improving spatial analysis accuracy. Studies should also explore the dynamic interactions between factors using methods like structural equation modeling to better understand causal relationships, such as how policy support indirectly drives tourism through infrastructure development or how regions balance economic growth with ecological protection. Furthermore, micro-level case studies and fieldwork can uncover the unique development paths of different villages, offering more practical guidance and enriching the understanding of rural tourism development.

5. Conclusions

This study reveals the spatial distribution characteristics of key rural tourism villages in China and their main influencing factors. The results show that the distribution of key rural tourism villages exhibits significant regional differences and clustering characteristics, concentrating in the eastern coastal and central regions, such as Zhejiang, Jiangsu, Shandong, and Henan provinces, while being relatively sparse in the western and northern regions. The main influencing factors include ecological environment, economic development, tourism industry, transportation infrastructure, policy support, and social development. High forest coverage and terrain diversity enhance ecological attractiveness; economically developed regions have stronger tourism services and infrastructure guarantees; the number of A-level scenic spots and star-rated hotels directly influence the attractiveness of tourism resources. Higher densities of transportation networks and numbers of train stations improve accessibility, while policy support and public service facilities enhance tourism development quality and visitor experience.

Based on these findings, it is recommended that policymakers should focus on strengthening infrastructure and policy support in the western and northern regions to enhance rural tourism and achieve balanced development. Local governments should boost financial support, improve infrastructure, and foster public–private partnerships for sustainable tourism. Resource-limited areas should leverage ecological and cultural assets, build unique tourism brands, and enhance digital promotion. Community participation and capacity building should be prioritized to coordinate ecological protection with tourism development for sustainable resource use. While this study highlights the spatial distribution and influencing factors of rural tourism villages in China, some limitations remain. The reliance on macro-level data may have missed micro-level dynamics, suggesting the need for case studies in future research. Additionally, incorporating advanced spatial econometric models could improve the precision of future studies. As digital technologies and sustainability principles evolve, research should explore their integration into rural tourism for higher-quality growth.

Author Contributions

Conceptualization, Y.G. and H.Z.; methodology, Y.G.; software, Y.G.; validation, Y.G. and X.S.; formal analysis, Y.G.; investigation, Y.G.; resources, Y.G.; data curation, Y.G.; writing—original draft preparation, Y.G.; writing—review and editing, H.Z.; visualization, X.S.; supervision, Y.G.; project administration, Y.G.; All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Distribution map (a) and standard deviational ellipse (b) of key rural tourism villages.

Figure 2. Number of key rural tourism villages (a) and kernel density distribution (b) by province.

Figure 3. Kernel density spatial distribution of key rural tourism villages by first batch (a), second batch (b), third batch (c) and fourth batch (d).

Figure 4. Dominance of key rural tourism villages by province.

Figure 5. Comprehensive advantage index of key rural tourism villages by province.

Figure 6. Interaction detection results.

Table 1

Number of key rural tourism villages by batch.

Batch	East Region Quantity	Central Region Quantity	West Region Quantity	Total
First	126	64	130	320
Second	270	136	274	680
Third	79	42	78	199
Fourth	79	40	81	200

Table 2

Influencing factors of comprehensive development dominance of key rural tourism villages.

Category	Variable	Indicator Code	Unit/Description	Q Statistic	Normalized Weight
Ecological Environment	Forest Coverage Rate	X1	%	0.273998	0.54151
	PM2.5 Concentration	X2	µg/m3	0.036298	0.07179
	Terrain Ruggedness Index	X3	°	0.192997	0.3867
Economic Development	GDP	X4	Yuan per person	0.252739	0.2782
	Tertiary Industry as a Percentage of GDP	X5	%	0.510975	0.56274
	Per Capita Disposable Income of All Residents	X6	Yuan	0.181703	0.15806
Tourism Industry	Number of Star-Rated Hotels	X7	items	0.264158	0.22964
	Number of A-Level Tourist Attractions	X8	items	0.47759	0.41523
	Local Employees	X9	items	0.280197	0.24358
	Number of Travel Agencies	X10	items	0.139568	0.12128
	Total Number of Tourists Received	X11	10,000	0.212733	0.18528
Transportation Facilities	Transportation Network Density	X12	km/km2	0.110071	0.21289
	Train Stations	X13	items	0.407028	0.78711
Policy Support	Number of Beautiful Leisure Villages in China	X14	items	0.151152	0.21074
	Number of Demonstration Counties (Points) for Leisure Agriculture and Rural Tourism	X15	items	0.383066	0.53371
	Number of Traditional Villages	X16	items	0.180915	0.25555
Social Development	Urbanization Rate	X17	%	0.355609	0.67459
	Number of Public Toilets	X18	items	0.171819	0.32541

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