# Evaluation of Provincial Tourism Competitiveness in the Yellow River Basin Based on Clustering Algorithm

Authors: Nana Geng; Xiuying Shao
Journal: Mobile Information Systems
 (2022)

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## Abstract

Tourism is an important part of China’s national economy. Promoting the development of regional tourism and improving the level of tourism competitiveness can promote the development of regional economy, society, and people's livelihood. Therefore, in the context of the HQDEV of the YRB, it is of great significance to study the evaluation and promotion strategies of tourism competitiveness along the Yellow River for promoting their HQDEV in terms of ecology, economy, and tourism. Taking the provinces as the research area, this article has constructed an evaluation index system of tourism competitiveness in the provinces and used the entropy weigh, the Topsis, and the natural breakpoint classification method. The economic and tourism industry subsystems and the overall tourism competitiveness level are comprehensively evaluated and analyzed, and on this basis, the promotion strategy of tourism competitiveness of the nine provinces along the Yellow River is proposed. According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, of which the index weight of the ecological protection level is 0.3057, and the environmental quality level is 0.0834. The index weight of the subsystem is 0.2691, of which the index weight of the economic innovation level is 0.1695, and the social and people's livelihood level is 0.0996. For the tourism competitiveness, the index weight of the ecological environment competitiveness subsystem is 0.3891, the index weight of the socioeconomic competitiveness subsystem is 0.2691, and the index weight of the tourism industry competitiveness subsystem is 0.3418. The results show that the level of tourism competitiveness along the nine provinces maintains a relatively stable form, but the overall level is not high and the intraregional differences are small.


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## Body

## 1. Introduction

The ecological protection and high-quality development (HQDEV) of the Yellow River basin (YRB) became a major national strategy of China. The strategy takes ecology and economy as the fundamental foothold and aims at promoting the HQDEV at multiple levels such as education, ecology, economy, society, people's livelihood, culture, and tourism. The nine provinces along the river are connected as a whole by it, forming an ecological corridor that runs through the eastern, central, and western regions of China and is an important economic region in China. Based on the strategy of ecological protection and HQDEV in the YRB, combined with the basic connotation of HQDEV, a comprehensive evaluation index system at multiple levels such as ecology, environment, economy, society, and tourism is formulated. On this basis, it is of great significance to measure, evaluate, and classify the tourism competitiveness(TC) level of these provinces for comprehensively analyzing the HQDEV level and spatial distribution of tourism under this strategy. Secondly, combined with the evaluation results, the promotion strategy of TC along the Yellow River is proposed, which can provide corresponding guidance and basis for the government to formulate high-quality tourism development policies and promote the HQDEV of the YRB.Kubickova and Li conducted a comprehensive evaluation and grading of the level of TC in the nine provinces along the YRB, analyzing the ecological environment, socioeconomy, tourism industry, and the overall level of TC in the nine provinces along the YRB in both time and space [1]. Demirovic et al.’s research found that government decisions greatly influence the level of TC and using time series analysis found that the degree of freedom of TC is influenced by the government and correlated with the stage of local development [2]. Measuring competitiveness has become a key factor in ensuring the success and sustainability of tourism and Romao and Nijkamp analyzed the applicability of the competitiveness model to assess the tourism strengths and weaknesses of Vojvodina as a rural tourism destination. The study found that key resources and attractions in rural Vojvodina were rated as superior to macro- and industry-related factors [3]. Tourism has a great potential to develop practice- and place-based innovation strategies. Zhao’study analyzed whether and how regional innovation systems influence the competitiveness of European tourism destinations. Labour-intensive regions for tourism services have lower levels of productivity, while regions with higher levels of education, innovation, and productivity are those where gross tourism value added is less important for the regional economy [4]. Based on the principles of systemic, hierarchical, and operational, Dias J G classified tourism condition, tourism environment, and tourism potential as TC and constructed a system of indicators to assess the TC of cities [5]. Rodriguez-Diaz and Pulido-Fernandez used validation and exploratory factors for the reliability analysis of environmental sustainability, and the test used was a validation analysis [6]. However, the above studies of TC in the YRB did not combine both with aggregation analysis.In order to analyze the impact of weights in the construction of the TC composite, Gryszel P applied these weights to the TTCI and to the four TC composites calculated by applying multicriteria techniques to analyze the different indicators proposed as being equally relevant [7]. The level of tourism destination competitiveness, due to its relatively complex nature, is a problematic area of study. Niyazbayeva A filled the existing gap by applying linear ranking and cluster analysis methods to assess the Sudety commune’s TC [8]. Li conducted a critical analysis of tourism cluster factors and the study investigated data on the tourism industry found competitive advantages and disadvantages affecting tourism cluster development [9]. Dutka et al. established a system of TC indicators by calculating the weight about each factor through AHP, which laid the foundation for evaluating TC scores [10]. The use of cluster models is important for the development of tourism. a Michalkova study of the possibilities and experience of introducing cluster models is relevant [11]. In order to assess the importance of regional competitiveness of tourism as a factor of regional growth, Petrova et al. obtained a profile of tourism concentration in the Slovak region and explained the impact of regional competitiveness of tourism on regional growth and regional tourism specialization profile by applying transfer share analysis [12]. However, the above aggregation analysis has limited research on the evaluation and analysis of TC.The novelty of this paper is that it takes the provinces along the Yellow River as the research area, constructs an evaluation index system of TC of these provinces, and uses the entropy weight method, the Topsis method, and the natural breakpoint classification method to evaluate the TC of the provinces. Eco-environment subsystem, socioeconomic subsystem, tourism industry subsystem, and overall TC level are comprehensively evaluated and analyzed, and on this basis, competitiveness enhancement strategies are proposed.


## 2. Evaluation and Methods of Provincial TC in the YRB

### 2.1. Tourism Competitiveness

According to different research subjects, tourism competitiveness can be divided into different levels such as international tourism, regional tourism, urban tourism, and tourism industry competitiveness. Regional TC refers to the ability of the tourism industry in a certain region to provide tourism services and tourism products to the market more effectively than other regions, and to obtain profits and sustainable industrial development [13, 14]. With the continuous and in-depth development of the tourism industry, the study of regional TC is of great significance for optimizing the allocation of regional tourism resources, promoting the rational and orderly development of regional tourism resources, and driving regional tourism and sustainable economic development [15]. It will build a comprehensive evaluation index system for tourism competitiveness from multiple levels such as ecology, environment, society, economy, and tourism. Re-measurement can achieve an innovative understanding of the concept of tourism competitiveness.


### 2.2. Evaluation Model of Urban TC

There are many factors that affect the competitiveness of urban tourism, both direct and indirect, including almost all aspects of economy, society, culture, education, and natural environment. Since it is impossible to define these fields, it is difficult to carry out research [16].Regional industrial competitiveness is a comprehensive competitiveness of regional industries that cannot be directly measured. The analysis of competitiveness must be based on a specific model. In the field of competitiveness research, the most effective and representative classical analytical model is the Porter diamond model, as shown in Figure1.Figure 1
Porter’s diamond model.As shown in Figure1, the competitive advantage theory has important guiding significance for the research on the TC of the nine provinces along the Yellow River. There are many factors that affect the competitiveness of regional tourism. By analyzing the key influencing factors of TC, it makes it possible to continuously improve the competitive advantage of the regional tourism industry and promote the HQDEV of tourism and economy in the YRB [7]. The IMD regional competitiveness model is shown in Figure 2.Figure 2
IMD regional competitiveness model.As shown in Figure2, IMD combines data processing and theoretical analysis through quantitative research and qualitative analysis to form a comparative study on the international competitiveness of countries around the world. The results of this study can describe the changing process of each country’s competitiveness in detail [17]. Although the research focus of the IMD model is mainly on national competitiveness, it is not suitable to directly apply the model to urban competitiveness. However, the model advocates the sustainable development of national competitiveness, which provides a powerful reference for constructing a city competitiveness model.


### 2.3. Methods of Spatial Pattern of Tourist Destination

Typically, exploratory spatial data analysis techniques such as nearest neighbor spatial distance analysis and spatial aggregation analysis are used for spatial pattern analysis. According to the needs of this research, the Euclidean distance analysis method is used to analyze the neighbors based on the distance analysis tool, as shown in the following formula:(1)NNI=D0−DE−.In formula (1), NNI represents the nearest neighbor index. The spatial distance analysis method in this paper is used to verify whether there are spatial agglomeration characteristics of tourist destinations in the region. In the process of spatial agglomeration analysis, the cluster analysis function and the kernel density estimation analysis method are applied, and the function formula required for its calculation is shown in the following formula:(2)Kr=Xn2∑i=1n∑j=1n1wijIruij,(3)LD=Kr∏−r,where LD represents the spatial peak, and the corresponding spatial distance is the maximum spatial aggregation scale.Kernel density estimation analysis is one of the commonly used hot spot analysis methods. Blind selection of this bandwidth can easily lead to its estimation bias. In order to reduce the bias, this paper has introduced the optimal spatial clustering distance of the cluster analysis function as the bandwidth required for its analysis. The kernel density estimation analysis is shown in the following formula:(4)fa=1nh∑i=1nKA−Aih,where fa is the kernel density calculation function at the spatial position X. The relevant data show that the larger the estimated value of fa, the denser the tourism resource points of this type. Spatial correlation analysis focuses on describing the correlation of attribute values between a certain location and neighboring locations in the study area. In this article, clustering is used to analyze the spatial correlation and agglomeration trend of the overall tourism resources. The calculation is shown in the following formula:(5)Moran,sI=n∑x=1nn∑y=1nxyax−a−ay−a−∑x=1n∑y=1nWab∑x=1nax−a−2.In the above formula, the cluster analysis is the actual Moran index of various and overall tourism resources.ax and ay represent the estimated kernel density of the research tourist destination, respectively; a− represents the average estimated kernel density of this type of resource in the study area; and Wab represents the space vector matrix, which defines the spatial relationship of regional units [18].


### 2.4. Influencing Factors of Urban TC

In the process of HQDEV of the YRB, it is necessary to adhere to the idea of system theory, grasp things from a holistic perspective, and conduct a comprehensive analysis of the system, so that the entire region can become an organic whole with a unified goal and jointly promote the HQDEV of the basin. The improvement of TC should also adhere to the theoretical thinking of the tourism system. According to the characteristics and functions of each element in the system, the top-level design of tourism planning and development is carried out from different subsystems, and a scientific and comprehensive tourism system structure is constructed to continuously promote the improvement of the overall TC [19].(1)
The dynamic principle. Urban TC is a dynamic comprehensive ability [20]. Therefore, when selecting indicators, it is necessary to select not only indicators that can reflect the current competition situation, but also that are dynamic and can reflect the continuous changes in competitiveness for a period of time in the future. Only by following the dynamic principle, the selected indicators can truly reflect the overall competitiveness of the city.(2)
Concise scientific principles. First of all, the selection of urban TC evaluation indicators should be based on science, and each indicator should be able to reflect the city's market ability in tourism competition and ensure that it can reflect the comprehensive competition level of the studied city. Secondly, the selection of these indicators should also follow the principle of simplicity and clarity. Each indicator can clearly and intuitively express the elements it represents. On the one hand, the selection of indicators should not be too cumbersome, resulting in repeated selection. On the other, it is too simple to include all elements.(3)
Quantification requires that the selected index data can be obtained through objective or scientific evaluation, and should be easy to analyze and calculate. The principle of feasibility requires that the selected indicator data are available, rather than fabricated and unfounded indicators [21].(4)
Being systematic. Starting from the universal applicability of the evaluation index system of urban tourism competitiveness, when selecting evaluation indicators, the universality of the research object should be fully considered, and the influencing factors should be added as comprehensively as possible. The evaluation index system can be used in the study of all cities, so general principles should be followed. However, it should be noted that the development of each city is unique, and the weights of indicators in the evaluation system constructed in our research have different results for different cities. Therefore, when researching the TC of a city, it is necessary to assign weights to the indicators according to the characteristics of the city, and the indicators with less impact can be appropriately ignored. This is the principle of specificity. It is necessary to comprehensively and comprehensively consider various indicators, and the selected indicators should fully reflect the basic characteristics of TC of the nine provinces along the Yellow River. Only a comprehensive and systematic evaluation index system can systematically evaluate the TC of the district, and at the same time, it is necessary to ensure the systematicness and logic of each index within the evaluation index system.(5)
Hierarchy principle. In the context of the HQDEV of the YRB, there are many factors that affect the TC of the nine provinces along the Yellow River. The principle of hierarchy requires that from the perspective of the whole, the influencing factors that constitute the TC of the nine provinces are divided into multiple levels from top to bottom. From the target layer, the criterion layer, the subdivision criterion layer to the index layer, it is gradually progressive, and the layers are in depth, forming a clear, intuitive, and targeted TC evaluation system. This can not only directly reflect the subordination between the indicators, but also comprehensively reflect the main factors affecting the competitiveness of tourism.(6)
Principle of operability. Operability mainly refers to the difficulty of data acquisition and the availability of data [22]. When designing and constructing the evaluation index system, the existing data and information should be used as the criterion, and the index data that is easy to obtain directly or obtained through calculation should be selected. At the same time, the characteristics of the TC of the research area should be fully reflected. The principle of operability is an important basis for ensuring the smooth progress of evaluation research. Only by constructing an evaluation index system based on the actual situation can it ensures the authenticity and reliability of the evaluation results.


### 2.5. AHP (Analytic Hierarchy Process)

The hierarchical unit ordering is the basis for determining the importance order of elements of a certain level relative to the elements of the previous level and the weight of the importance order of all elements of a certain level relative to the elements of the previous level:(6)YW=λmaxW,Hi=∏j=1nbiji=1,2,...,n.The calculated n-th root ofHi is shown in the following formula:(7)W−i=nHii=1,2,…,n.The calculation of the largest eigenroot is shown in the following formula :(8)λmax=∑i=1nAWinWi,where AWi is the ith component of vector AW.(9)CI=λmax−nn−1.If the semi-lift ladder-type fuzzy membership function is quantified, that is,(10)fa=a−a2a1−a2=1,0,a≥a1,a−a2a1−a2,a2<a<a10,a≤a2.,If the semi-drop ladder fuzzy membership function is quantified, then(11)fa=a1−aa1−a2=1,0,a≤a1,a−a2a1−a2,a2<a<a1,0,a≥a2,where a1 and a2 are the maximum and minimum values of each index attribute value in the same area at different times.(12)fj=∑j=1nWi×Ij,where fj is the evaluation score of a factor, Wi is the weight of a factor index, and Ij is the quantitative or standardized score.


### 2.6. Tourism Subsystem Relationship

The subsystem of urban tourism competitiveness is a complex set. There are many interrelated and mutually influencing factors within it and at the same time through the relationship between certain channels and certain factors intertwined to form a large and complex system [23]. From the perspective of system dynamics, improving the competitiveness of urban tourism is a dynamic feedback system. On the basis of comprehensively considering the interconnection between the subsystems, the interaction between the subsystems is studied. The relationship between the subsystems is drawn as shown in Figure 3.Figure 3
Relationship between subsystems.As shown in Figure3, the improvement of competitive performance can accumulate more wealth for the urban economy, strengthen resource construction and, at the same time, support the improvement of environmental competitiveness. It provides good conditions for the establishment of the city's image, which is conducive to attracting more tourists and forming a virtuous circle of economic development. On the other hand, a reasonable and appropriate environment is also more likely to attract foreign investment and provide per capita GDP, thereby enhancing the potential for competition. Only when there is a harmonious development situation among the competition performance, competition potential and supporting environment, can the promotion of urban TC form a positive interaction, and the urban TC can be improved.


### 2.7. Topsis Method

The Topsis method based on the entropy weight method is calculated based on the weight vector calculated by the entropy weight method weight model, which is more objective. The calculation steps of the Topsis method are as follows:(1)
A weighted normalized decision matrix is generated from the normalized decision matrixXmn and the weight vector Zm=Z1,Z2,…,Zj:(13)Ymn=Xmn∗Zm.(2)
The vector of the positive ideal solution is set asY+, and the vector of the negative ideal solution is set as Y−:(14)Y+=maxYmn|n=1,2,…,n=Y1+,Y1+,…,Yn+,Y−=minYmn|n=1,2,…,n=Y1−,Y2−,…,Yn−.(3)
Em+ and Em− are calculated separately, which is the Euclidean distance between each evaluation object and the positive and negative ideal solutions:(15)Em+=∑n=1kYmn−Yn+2,Em−=∑n=1kYmn−Yn−2.(4)
CalculateDm, the relative closeness of each evaluation object to the ideal solution:(16)Dm=Em−Em++Em−.The higher theDm score, the better the subject's overall score and vice verse.


## 2.1. Tourism Competitiveness

According to different research subjects, tourism competitiveness can be divided into different levels such as international tourism, regional tourism, urban tourism, and tourism industry competitiveness. Regional TC refers to the ability of the tourism industry in a certain region to provide tourism services and tourism products to the market more effectively than other regions, and to obtain profits and sustainable industrial development [13, 14]. With the continuous and in-depth development of the tourism industry, the study of regional TC is of great significance for optimizing the allocation of regional tourism resources, promoting the rational and orderly development of regional tourism resources, and driving regional tourism and sustainable economic development [15]. It will build a comprehensive evaluation index system for tourism competitiveness from multiple levels such as ecology, environment, society, economy, and tourism. Re-measurement can achieve an innovative understanding of the concept of tourism competitiveness.


## 2.2. Evaluation Model of Urban TC

There are many factors that affect the competitiveness of urban tourism, both direct and indirect, including almost all aspects of economy, society, culture, education, and natural environment. Since it is impossible to define these fields, it is difficult to carry out research [16].Regional industrial competitiveness is a comprehensive competitiveness of regional industries that cannot be directly measured. The analysis of competitiveness must be based on a specific model. In the field of competitiveness research, the most effective and representative classical analytical model is the Porter diamond model, as shown in Figure1.Figure 1
Porter’s diamond model.As shown in Figure1, the competitive advantage theory has important guiding significance for the research on the TC of the nine provinces along the Yellow River. There are many factors that affect the competitiveness of regional tourism. By analyzing the key influencing factors of TC, it makes it possible to continuously improve the competitive advantage of the regional tourism industry and promote the HQDEV of tourism and economy in the YRB [7]. The IMD regional competitiveness model is shown in Figure 2.Figure 2
IMD regional competitiveness model.As shown in Figure2, IMD combines data processing and theoretical analysis through quantitative research and qualitative analysis to form a comparative study on the international competitiveness of countries around the world. The results of this study can describe the changing process of each country’s competitiveness in detail [17]. Although the research focus of the IMD model is mainly on national competitiveness, it is not suitable to directly apply the model to urban competitiveness. However, the model advocates the sustainable development of national competitiveness, which provides a powerful reference for constructing a city competitiveness model.


## 2.3. Methods of Spatial Pattern of Tourist Destination

Typically, exploratory spatial data analysis techniques such as nearest neighbor spatial distance analysis and spatial aggregation analysis are used for spatial pattern analysis. According to the needs of this research, the Euclidean distance analysis method is used to analyze the neighbors based on the distance analysis tool, as shown in the following formula:(1)NNI=D0−DE−.In formula (1), NNI represents the nearest neighbor index. The spatial distance analysis method in this paper is used to verify whether there are spatial agglomeration characteristics of tourist destinations in the region. In the process of spatial agglomeration analysis, the cluster analysis function and the kernel density estimation analysis method are applied, and the function formula required for its calculation is shown in the following formula:(2)Kr=Xn2∑i=1n∑j=1n1wijIruij,(3)LD=Kr∏−r,where LD represents the spatial peak, and the corresponding spatial distance is the maximum spatial aggregation scale.Kernel density estimation analysis is one of the commonly used hot spot analysis methods. Blind selection of this bandwidth can easily lead to its estimation bias. In order to reduce the bias, this paper has introduced the optimal spatial clustering distance of the cluster analysis function as the bandwidth required for its analysis. The kernel density estimation analysis is shown in the following formula:(4)fa=1nh∑i=1nKA−Aih,where fa is the kernel density calculation function at the spatial position X. The relevant data show that the larger the estimated value of fa, the denser the tourism resource points of this type. Spatial correlation analysis focuses on describing the correlation of attribute values between a certain location and neighboring locations in the study area. In this article, clustering is used to analyze the spatial correlation and agglomeration trend of the overall tourism resources. The calculation is shown in the following formula:(5)Moran,sI=n∑x=1nn∑y=1nxyax−a−ay−a−∑x=1n∑y=1nWab∑x=1nax−a−2.In the above formula, the cluster analysis is the actual Moran index of various and overall tourism resources.ax and ay represent the estimated kernel density of the research tourist destination, respectively; a− represents the average estimated kernel density of this type of resource in the study area; and Wab represents the space vector matrix, which defines the spatial relationship of regional units [18].


## 2.4. Influencing Factors of Urban TC

In the process of HQDEV of the YRB, it is necessary to adhere to the idea of system theory, grasp things from a holistic perspective, and conduct a comprehensive analysis of the system, so that the entire region can become an organic whole with a unified goal and jointly promote the HQDEV of the basin. The improvement of TC should also adhere to the theoretical thinking of the tourism system. According to the characteristics and functions of each element in the system, the top-level design of tourism planning and development is carried out from different subsystems, and a scientific and comprehensive tourism system structure is constructed to continuously promote the improvement of the overall TC [19].(1)
The dynamic principle. Urban TC is a dynamic comprehensive ability [20]. Therefore, when selecting indicators, it is necessary to select not only indicators that can reflect the current competition situation, but also that are dynamic and can reflect the continuous changes in competitiveness for a period of time in the future. Only by following the dynamic principle, the selected indicators can truly reflect the overall competitiveness of the city.(2)
Concise scientific principles. First of all, the selection of urban TC evaluation indicators should be based on science, and each indicator should be able to reflect the city's market ability in tourism competition and ensure that it can reflect the comprehensive competition level of the studied city. Secondly, the selection of these indicators should also follow the principle of simplicity and clarity. Each indicator can clearly and intuitively express the elements it represents. On the one hand, the selection of indicators should not be too cumbersome, resulting in repeated selection. On the other, it is too simple to include all elements.(3)
Quantification requires that the selected index data can be obtained through objective or scientific evaluation, and should be easy to analyze and calculate. The principle of feasibility requires that the selected indicator data are available, rather than fabricated and unfounded indicators [21].(4)
Being systematic. Starting from the universal applicability of the evaluation index system of urban tourism competitiveness, when selecting evaluation indicators, the universality of the research object should be fully considered, and the influencing factors should be added as comprehensively as possible. The evaluation index system can be used in the study of all cities, so general principles should be followed. However, it should be noted that the development of each city is unique, and the weights of indicators in the evaluation system constructed in our research have different results for different cities. Therefore, when researching the TC of a city, it is necessary to assign weights to the indicators according to the characteristics of the city, and the indicators with less impact can be appropriately ignored. This is the principle of specificity. It is necessary to comprehensively and comprehensively consider various indicators, and the selected indicators should fully reflect the basic characteristics of TC of the nine provinces along the Yellow River. Only a comprehensive and systematic evaluation index system can systematically evaluate the TC of the district, and at the same time, it is necessary to ensure the systematicness and logic of each index within the evaluation index system.(5)
Hierarchy principle. In the context of the HQDEV of the YRB, there are many factors that affect the TC of the nine provinces along the Yellow River. The principle of hierarchy requires that from the perspective of the whole, the influencing factors that constitute the TC of the nine provinces are divided into multiple levels from top to bottom. From the target layer, the criterion layer, the subdivision criterion layer to the index layer, it is gradually progressive, and the layers are in depth, forming a clear, intuitive, and targeted TC evaluation system. This can not only directly reflect the subordination between the indicators, but also comprehensively reflect the main factors affecting the competitiveness of tourism.(6)
Principle of operability. Operability mainly refers to the difficulty of data acquisition and the availability of data [22]. When designing and constructing the evaluation index system, the existing data and information should be used as the criterion, and the index data that is easy to obtain directly or obtained through calculation should be selected. At the same time, the characteristics of the TC of the research area should be fully reflected. The principle of operability is an important basis for ensuring the smooth progress of evaluation research. Only by constructing an evaluation index system based on the actual situation can it ensures the authenticity and reliability of the evaluation results.


## 2.5. AHP (Analytic Hierarchy Process)

The hierarchical unit ordering is the basis for determining the importance order of elements of a certain level relative to the elements of the previous level and the weight of the importance order of all elements of a certain level relative to the elements of the previous level:(6)YW=λmaxW,Hi=∏j=1nbiji=1,2,...,n.The calculated n-th root ofHi is shown in the following formula:(7)W−i=nHii=1,2,…,n.The calculation of the largest eigenroot is shown in the following formula :(8)λmax=∑i=1nAWinWi,where AWi is the ith component of vector AW.(9)CI=λmax−nn−1.If the semi-lift ladder-type fuzzy membership function is quantified, that is,(10)fa=a−a2a1−a2=1,0,a≥a1,a−a2a1−a2,a2<a<a10,a≤a2.,If the semi-drop ladder fuzzy membership function is quantified, then(11)fa=a1−aa1−a2=1,0,a≤a1,a−a2a1−a2,a2<a<a1,0,a≥a2,where a1 and a2 are the maximum and minimum values of each index attribute value in the same area at different times.(12)fj=∑j=1nWi×Ij,where fj is the evaluation score of a factor, Wi is the weight of a factor index, and Ij is the quantitative or standardized score.


## 2.6. Tourism Subsystem Relationship

The subsystem of urban tourism competitiveness is a complex set. There are many interrelated and mutually influencing factors within it and at the same time through the relationship between certain channels and certain factors intertwined to form a large and complex system [23]. From the perspective of system dynamics, improving the competitiveness of urban tourism is a dynamic feedback system. On the basis of comprehensively considering the interconnection between the subsystems, the interaction between the subsystems is studied. The relationship between the subsystems is drawn as shown in Figure 3.Figure 3
Relationship between subsystems.As shown in Figure3, the improvement of competitive performance can accumulate more wealth for the urban economy, strengthen resource construction and, at the same time, support the improvement of environmental competitiveness. It provides good conditions for the establishment of the city's image, which is conducive to attracting more tourists and forming a virtuous circle of economic development. On the other hand, a reasonable and appropriate environment is also more likely to attract foreign investment and provide per capita GDP, thereby enhancing the potential for competition. Only when there is a harmonious development situation among the competition performance, competition potential and supporting environment, can the promotion of urban TC form a positive interaction, and the urban TC can be improved.


## 2.7. Topsis Method

The Topsis method based on the entropy weight method is calculated based on the weight vector calculated by the entropy weight method weight model, which is more objective. The calculation steps of the Topsis method are as follows:(1)
A weighted normalized decision matrix is generated from the normalized decision matrixXmn and the weight vector Zm=Z1,Z2,…,Zj:(13)Ymn=Xmn∗Zm.(2)
The vector of the positive ideal solution is set asY+, and the vector of the negative ideal solution is set as Y−:(14)Y+=maxYmn|n=1,2,…,n=Y1+,Y1+,…,Yn+,Y−=minYmn|n=1,2,…,n=Y1−,Y2−,…,Yn−.(3)
Em+ and Em− are calculated separately, which is the Euclidean distance between each evaluation object and the positive and negative ideal solutions:(15)Em+=∑n=1kYmn−Yn+2,Em−=∑n=1kYmn−Yn−2.(4)
CalculateDm, the relative closeness of each evaluation object to the ideal solution:(16)Dm=Em−Em++Em−.The higher theDm score, the better the subject's overall score and vice verse.


## 3. Evaluation of Provincial TC in the YRB Based on Cluster Analysis Algorithm

### 3.1. Indicator Selection and Construction Ideas

The ecological protection and HQDEV of the YRB has become a major national strategy, with ecology and economy as the fundamental foothold, so that the evaluation and development of TC along the nine provinces should not only focus on the development level of the tourism industry, but also comprehensively consider ecological environment, social economy, and other aspects. At the same time, with the development of modern tourism and the continuous updating of tourism formats, the development of tourism has shown a deep integration mode combining point, line, and surface. This requires that in the future tourism development process. Each region should not only pay attention to the development level of the local tourism industry, but also pay attention to factors such as the ecological environment and economic development level that are closely related to tourism. Establishing a comprehensive evaluation index system of tourism competitiveness covering multiple levels is an inevitable requirement for the evaluation of tourism competitiveness of the nine provinces and regions along the Yellow River under the new policy and new background.


### 3.2. Selection of TC Evaluation Model

Through literature analysis, in the research process of TC in China and other countries, the commonly used research methods mainly include the principal component analysis method, factor analysis method, Topsis method, analytic hierarchy process, grey theory, and weighted average method. On the basis of reading the relevant literature and considering the actual situation of the paper, this article has selected the entropy weight-Topsis method to evaluate and study the TC and finally classifies the TC level of the evaluation results through the natural breakpoint classification method. The geographical location along the provinces is shown in Figure4.Figure 4
Geographical map of the nine provinces along the Yellow River.As shown in Figure4, the nine provinces along the Yellow River include Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. The land area of the administrative area along the provinces is 3.5684 million square kilometers, accounting for about 1/3 of the country’s total land area. In 2019, 4.788 billion tourists from China and other countries were received along the nine provinces along the Yellow River, a year-on-year increase of 14.77%; the total tourism revenue totaled 5.58 trillion yuan, a year-on-year increase of 13.07%. The tourism economy in the region continues to develop, the tourism industry continues to grow, and the tourism service facilities are further improved. As of the end of 2018, there were a total of 8,582 travel agencies along the nine provinces along the Yellow River. There are 2,502 star-rated hotels, including 119 five-star hotels, 600 four-star hotels, and 1,332 three-star hotels.


### 3.3. Evaluation of TC Subsystem

#### 3.3.1. Evaluation of Ecological Environment Competitiveness Subsystem

The YRB is an important component of China’s ecological barrier. Therefore, the quality of the ecological environment in the YRB is the basis and condition for the development of tourism, and it is necessary to evaluate the competitiveness of the ecological environment in the areas along the Yellow River. According to the index weight and Topsis method, the eco-environmental competitiveness level of the provinces along the Yellow River is evaluated. The evaluation results are shown in Table1.Table 1
Evaluation results of the ecological environment competitiveness level of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.64510.64150.61240.61240.61240.6234Sichuan0.37540.37510.39840.36950.36120.4123Gansu0.37850.30420.29080.37250.36850.3528Ningxia0.24950.24960.22570.22140.25960.2654Inner Mongolia0.41210.42980.43230.39570.37680.3832Shaanxi0.28180.34120.30240.27590.27240.25475Shanxi0.16950.13650.17210.19240.19060.1912Henan0.19260.20080.20120.21450.21650.2387Shandong0.33850.33560.33230.34210.33450.3259It can be seen from Table1 that from 2014 to 2019, the eco-environmental competitiveness of the provinces and autonomous regions along the YRB has generally maintained a relatively stable level. Qinghai, Sichuan, and Inner Mongolia have the highest level of eco-environmental competitiveness; Gansu, Shandong, Ningxia, and Shaanxi are in the middle level; and Henan and Shanxi have the lowest level of eco-environmental competitiveness. At the same time, according to the evaluation results, a histogram of the competitiveness level and average value of the ecological environment along the provinces in 2019 is presented as shown in Figure 5.Figure 5
Histogram of eco-environmental competitiveness along the nine provinces in 2019.As shown in Figure5, as of the end of 2019, the relative closeness of the four provinces was higher than the average level of the nine provinces along the Yellow River, and the overall level of eco-environmental competitiveness was above the medium level. And Qinghai Province's eco-environmental competitiveness level is far ahead of other provinces. There are obvious differences between regions.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, of which the index weight of the ecological protection level is 0.3057, and the environmental quality level is 0.0834. The most important factors affecting the competitiveness of the ecological environment in each region are the per capita water resources and the coverage rate of nature reserves, the per capita park green space, and other ecological protection level indicators. Qinghai Province, Inner Mongolia Autonomous Region, and other western regions have vast territory, sparsely populated areas, superior ecological environment and natural resource conditions, and a high coverage rate of wetlands and nature reserves, which makes the western region have better ecological environment competitiveness. The eastern region mainly refers to Shandong Province, which is located in the North China Plain with relatively flat terrain. The Yellow River Delta develops here, and the wetland area is vast. Coupled with the further development of wastewater, waste gas, solid waste, and other treatment technologies, the competitiveness of the ecological environment in Shandong Province has gradually increased from a medium level to a relatively high level in recent years.


#### 3.3.2. Evaluation of Socioeconomic Competitiveness Subsystem

According to the index weight and Topsis method, the social and economic competitiveness level of the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table2.Table 2
Evaluation results of social and economic competitiveness of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.28450.29350.30010.33350.34320.3668Sichuan0.32750.36280.38350.43320.64350.6365Gansu0.40840.38320.38410.40860.40540.3535Ningxia0.34280.28320.29290.30310.31340.3320Inner Mongolia0.35620.33980.34210.37210.37210.3498Shaanxi0.62650.6230.63310.63590.65330.6431Shanxi0.39080.38280.37320.37060.40060.3532Henan0.26060.26120.27330.31450.33350.3235Shandong0.54210.51120.53350.59320.66340.6654It can be seen from Table2 that from 2014 to 2019, the level of social and economic competitiveness of the nine provinces and autonomous regions along the YRB has changed greatly, and some provinces have an obvious upward or downward trend. On the whole, Shandong and Shaanxi have the highest level of social and economic competitiveness, Sichuan, Qinghai, Gansu, Shanxi, and Inner Mongolia are in the middle level, and Ningxia and Henan have the lowest level of social and economic competitiveness. At the same time, according to the evaluation results, a bar chart showing the level of social and economic competitiveness and the average value of the provinces along the nine provinces along the Yellow River in 2019 is shown in Figure 6.Figure 6
Histogram of socioeconomic competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure6, by the end of 2019, the social and economic competitiveness levels of Shandong, Shaanxi, and Sichuan provinces were much higher than the average level of the nine provinces, and there was a large gap between the levels of the other six provinces and cities. According to the weight of the evaluation index system, the index weight of the social and economic competitiveness subsystem is 0.2691, of which the index weight of the economic innovation level is 0.1695, and the social and people's livelihood level is 0.0996. Therefore, the most important factors affecting the level of social and economic competitiveness of various regions are the economic innovation level indicators such as the proportion of the tertiary industry in GDP, the intensity of R&D expenditures, and the turnover of technology markets.


#### 3.3.3. Evaluation of Tourism Industry Competitiveness Subsystem

Promoting the HQDEV of the tourism industry in the YRB can not only drive the economic, social, and people's livelihood development in the YRB, but also an effective way to help the YRB get rid of poverty, and an important way to protect, inherit, and promote the Yellow River culture. According to the index weight and the Topsis method, the competitiveness level of the tourism industry in the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table3.Table 3
Evaluation results of the competitiveness level of the tourism industry along the provinces from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.13570.14560.13160.12980.13030.1356Sichuan0.39560.39950.38590.39230.42230.4795Gansu0.17680.17230.15560.19560.21540.2256Ningxia0.08240.07050.09230.08580.11980.1432Inner Mongolia0.55950.53650.53560.53560.53650.5295Shaanxi0.40650.41560.39230.41450.42950.4465Shanxi0.34360.35380.40230.35410.35650.3565Henan0.38650.38590.34230.33590.35650.3589Shandong0.60560.58290.56650.57650.57650.5989It can be seen from Table3 that the tourism industry competitiveness level of the provinces along the YRB was relatively stable from 2014 to 2019, with Shandong and Inner Mongolia having the highest level of tourism industry competitiveness, Sichuan, Shaanxi, Henan, and Shanxi in the middle level, and Gansu, Ningxia, Qinghai have the lowest level. At the same time, according to the evaluation results, a bar chart showing the competitiveness level and average value of the tourism industry along the provinces in 2019 is shown in Figure 7.Figure 7
Histogram of tourism industry competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure7, as of the end of 2019, the overall competitiveness of the tourism industry along the provinces was relatively good, and the four provinces reached above the average value in a decreasing trend.


#### 3.3.4. Evaluation of TC

To promote the implementation of the major national strategy of ecological protection and high-quality development in the Yellow River basin, we must jointly promote the ecological, economic, tourism, and other aspects, and use tourism development to drive the ecology and economy of the areas along the route. The coordinated development will gradually realize the high-quality development of ecology, economy, and tourism. Based on the analysis and evaluation of the three subsystems of the ecological environment, social economy, and tourism industry, the comprehensive TC of the provinces in the YRB is evaluated and ranked according to the index weight and Topsis method. Taking 2019 as an example, the calculation results of the comprehensive evaluation of TC along the nine provinces along the Yellow River in 2019 are shown in Figure8.Figure 8
Comprehensive evaluation results of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure8, from 2014 to 2019, the TC among the provinces along the YRB maintained a relatively stable situation, with Sichuan, Qinghai, and Shandong provinces having the highest levels of TC; and Inner Mongolia Autonomous Region, Shaanxi Province, Gansu Province is in the middle level; and Shanxi Province, Henan Province, and Ningxia Hui Autonomous Region are the lowest. However, on the whole, the TC of the provinces is not high, the relative closeness is below 0.5, and the intraregional differences are relatively small. In the same way, the TC levels of the provinces along the Yellow River and Jiuzhou from 2014 to 2019 were evaluated respectively, and the evaluation results are shown in Table 4.Table 4
Comprehensive evaluation results of TC of the nine provinces along the Yellow River in 2014–2019.
Provinces201420152016201720182019Qinghai0.46850.46560.46320.46650.46260.4765Sichuan0.37360.38130.39160.39560.44560.4768Gansu0.34120.29460.28650.34890.34320.3256Ningxia0.24120.22940.21890.23560.24560.2523Inner Mongolia0.44890.43640.44230.43560.42730.4168Shaanxi0.40650.42790.40590.40980.41320.4123Shanxi0.28560.28650.31020.29320.29680.2867Henan0.27060.27320.25560.27150.28090.2868Shandong0.45490.44560.43790.45680.46650.4731It can be seen from Table4 that the TC level of Sichuan Province has gradually increased from the fifth place to the first place in the past six years, and the relative closeness has increased from 0.37 to about 0.47. The TC level has improved most significantly. Sichuan Province has significant geographical advantages, good ecological environment and natural conditions, rich local product resources, and superior economic development level. At the same time, the level of tourism development ranks in the forefront of the country, making Sichuan Province's overall TC at a high level, with a relatively high level of competitiveness and strong tourism development potential.At the same time, according to the evaluation results, the TC level and average value of the provinces in 2019 are presented in a bar chart, as shown in Figure9.Figure 9
Histogram of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure9, as of the end of 2019, the overall TC of the provinces was not high, and the relative closeness was below 0.5. The difference in TC between provinces was small, and more than half of the provinces reached the average value.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, the index weight of the social and economic competitiveness subsystem is 0.2691, and the index weight of the tourism industry competitiveness subsystem is 0.3418. The three subsystems together have an impact on the TC of the provinces along the Yellow River, and the index of ecological environment has the greatest weight. The overall ecological resource endowment in the western region is good, the natural conditions are superior, the ecological environment quality is high, and it has strong ecological environment competitiveness. In particular, the ecological environment competitiveness of Qinghai Province is far ahead. However, the development of social economy and tourism industry is uneven between provinces. Therefore, in general, the overall tourism competition in the western region is high. The economic level of Shandong Province ranks first in the area along the Yellow River. The tourism resources in the region are complete in variety and quantity, the tourism brand effect is strong, and the tourism industry has a strong development momentum. At the same time, the environmental quality is improving day by day. Therefore, the overall level of TC is relatively high. The level of economic development and economic innovation in the central region is low, the ecological resource endowment in the region is lacking, the environmental quality is poor, the pollution is serious, the development level of the tourism industry is relatively weak, and the industrial competitiveness has not been formed, so the overall tourism competitiveness of the central region is low.


## 3.1. Indicator Selection and Construction Ideas

The ecological protection and HQDEV of the YRB has become a major national strategy, with ecology and economy as the fundamental foothold, so that the evaluation and development of TC along the nine provinces should not only focus on the development level of the tourism industry, but also comprehensively consider ecological environment, social economy, and other aspects. At the same time, with the development of modern tourism and the continuous updating of tourism formats, the development of tourism has shown a deep integration mode combining point, line, and surface. This requires that in the future tourism development process. Each region should not only pay attention to the development level of the local tourism industry, but also pay attention to factors such as the ecological environment and economic development level that are closely related to tourism. Establishing a comprehensive evaluation index system of tourism competitiveness covering multiple levels is an inevitable requirement for the evaluation of tourism competitiveness of the nine provinces and regions along the Yellow River under the new policy and new background.


## 3.2. Selection of TC Evaluation Model

Through literature analysis, in the research process of TC in China and other countries, the commonly used research methods mainly include the principal component analysis method, factor analysis method, Topsis method, analytic hierarchy process, grey theory, and weighted average method. On the basis of reading the relevant literature and considering the actual situation of the paper, this article has selected the entropy weight-Topsis method to evaluate and study the TC and finally classifies the TC level of the evaluation results through the natural breakpoint classification method. The geographical location along the provinces is shown in Figure4.Figure 4
Geographical map of the nine provinces along the Yellow River.As shown in Figure4, the nine provinces along the Yellow River include Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. The land area of the administrative area along the provinces is 3.5684 million square kilometers, accounting for about 1/3 of the country’s total land area. In 2019, 4.788 billion tourists from China and other countries were received along the nine provinces along the Yellow River, a year-on-year increase of 14.77%; the total tourism revenue totaled 5.58 trillion yuan, a year-on-year increase of 13.07%. The tourism economy in the region continues to develop, the tourism industry continues to grow, and the tourism service facilities are further improved. As of the end of 2018, there were a total of 8,582 travel agencies along the nine provinces along the Yellow River. There are 2,502 star-rated hotels, including 119 five-star hotels, 600 four-star hotels, and 1,332 three-star hotels.


## 3.3. Evaluation of TC Subsystem

### 3.3.1. Evaluation of Ecological Environment Competitiveness Subsystem

The YRB is an important component of China’s ecological barrier. Therefore, the quality of the ecological environment in the YRB is the basis and condition for the development of tourism, and it is necessary to evaluate the competitiveness of the ecological environment in the areas along the Yellow River. According to the index weight and Topsis method, the eco-environmental competitiveness level of the provinces along the Yellow River is evaluated. The evaluation results are shown in Table1.Table 1
Evaluation results of the ecological environment competitiveness level of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.64510.64150.61240.61240.61240.6234Sichuan0.37540.37510.39840.36950.36120.4123Gansu0.37850.30420.29080.37250.36850.3528Ningxia0.24950.24960.22570.22140.25960.2654Inner Mongolia0.41210.42980.43230.39570.37680.3832Shaanxi0.28180.34120.30240.27590.27240.25475Shanxi0.16950.13650.17210.19240.19060.1912Henan0.19260.20080.20120.21450.21650.2387Shandong0.33850.33560.33230.34210.33450.3259It can be seen from Table1 that from 2014 to 2019, the eco-environmental competitiveness of the provinces and autonomous regions along the YRB has generally maintained a relatively stable level. Qinghai, Sichuan, and Inner Mongolia have the highest level of eco-environmental competitiveness; Gansu, Shandong, Ningxia, and Shaanxi are in the middle level; and Henan and Shanxi have the lowest level of eco-environmental competitiveness. At the same time, according to the evaluation results, a histogram of the competitiveness level and average value of the ecological environment along the provinces in 2019 is presented as shown in Figure 5.Figure 5
Histogram of eco-environmental competitiveness along the nine provinces in 2019.As shown in Figure5, as of the end of 2019, the relative closeness of the four provinces was higher than the average level of the nine provinces along the Yellow River, and the overall level of eco-environmental competitiveness was above the medium level. And Qinghai Province's eco-environmental competitiveness level is far ahead of other provinces. There are obvious differences between regions.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, of which the index weight of the ecological protection level is 0.3057, and the environmental quality level is 0.0834. The most important factors affecting the competitiveness of the ecological environment in each region are the per capita water resources and the coverage rate of nature reserves, the per capita park green space, and other ecological protection level indicators. Qinghai Province, Inner Mongolia Autonomous Region, and other western regions have vast territory, sparsely populated areas, superior ecological environment and natural resource conditions, and a high coverage rate of wetlands and nature reserves, which makes the western region have better ecological environment competitiveness. The eastern region mainly refers to Shandong Province, which is located in the North China Plain with relatively flat terrain. The Yellow River Delta develops here, and the wetland area is vast. Coupled with the further development of wastewater, waste gas, solid waste, and other treatment technologies, the competitiveness of the ecological environment in Shandong Province has gradually increased from a medium level to a relatively high level in recent years.


### 3.3.2. Evaluation of Socioeconomic Competitiveness Subsystem

According to the index weight and Topsis method, the social and economic competitiveness level of the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table2.Table 2
Evaluation results of social and economic competitiveness of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.28450.29350.30010.33350.34320.3668Sichuan0.32750.36280.38350.43320.64350.6365Gansu0.40840.38320.38410.40860.40540.3535Ningxia0.34280.28320.29290.30310.31340.3320Inner Mongolia0.35620.33980.34210.37210.37210.3498Shaanxi0.62650.6230.63310.63590.65330.6431Shanxi0.39080.38280.37320.37060.40060.3532Henan0.26060.26120.27330.31450.33350.3235Shandong0.54210.51120.53350.59320.66340.6654It can be seen from Table2 that from 2014 to 2019, the level of social and economic competitiveness of the nine provinces and autonomous regions along the YRB has changed greatly, and some provinces have an obvious upward or downward trend. On the whole, Shandong and Shaanxi have the highest level of social and economic competitiveness, Sichuan, Qinghai, Gansu, Shanxi, and Inner Mongolia are in the middle level, and Ningxia and Henan have the lowest level of social and economic competitiveness. At the same time, according to the evaluation results, a bar chart showing the level of social and economic competitiveness and the average value of the provinces along the nine provinces along the Yellow River in 2019 is shown in Figure 6.Figure 6
Histogram of socioeconomic competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure6, by the end of 2019, the social and economic competitiveness levels of Shandong, Shaanxi, and Sichuan provinces were much higher than the average level of the nine provinces, and there was a large gap between the levels of the other six provinces and cities. According to the weight of the evaluation index system, the index weight of the social and economic competitiveness subsystem is 0.2691, of which the index weight of the economic innovation level is 0.1695, and the social and people's livelihood level is 0.0996. Therefore, the most important factors affecting the level of social and economic competitiveness of various regions are the economic innovation level indicators such as the proportion of the tertiary industry in GDP, the intensity of R&D expenditures, and the turnover of technology markets.


### 3.3.3. Evaluation of Tourism Industry Competitiveness Subsystem

Promoting the HQDEV of the tourism industry in the YRB can not only drive the economic, social, and people's livelihood development in the YRB, but also an effective way to help the YRB get rid of poverty, and an important way to protect, inherit, and promote the Yellow River culture. According to the index weight and the Topsis method, the competitiveness level of the tourism industry in the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table3.Table 3
Evaluation results of the competitiveness level of the tourism industry along the provinces from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.13570.14560.13160.12980.13030.1356Sichuan0.39560.39950.38590.39230.42230.4795Gansu0.17680.17230.15560.19560.21540.2256Ningxia0.08240.07050.09230.08580.11980.1432Inner Mongolia0.55950.53650.53560.53560.53650.5295Shaanxi0.40650.41560.39230.41450.42950.4465Shanxi0.34360.35380.40230.35410.35650.3565Henan0.38650.38590.34230.33590.35650.3589Shandong0.60560.58290.56650.57650.57650.5989It can be seen from Table3 that the tourism industry competitiveness level of the provinces along the YRB was relatively stable from 2014 to 2019, with Shandong and Inner Mongolia having the highest level of tourism industry competitiveness, Sichuan, Shaanxi, Henan, and Shanxi in the middle level, and Gansu, Ningxia, Qinghai have the lowest level. At the same time, according to the evaluation results, a bar chart showing the competitiveness level and average value of the tourism industry along the provinces in 2019 is shown in Figure 7.Figure 7
Histogram of tourism industry competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure7, as of the end of 2019, the overall competitiveness of the tourism industry along the provinces was relatively good, and the four provinces reached above the average value in a decreasing trend.


### 3.3.4. Evaluation of TC

To promote the implementation of the major national strategy of ecological protection and high-quality development in the Yellow River basin, we must jointly promote the ecological, economic, tourism, and other aspects, and use tourism development to drive the ecology and economy of the areas along the route. The coordinated development will gradually realize the high-quality development of ecology, economy, and tourism. Based on the analysis and evaluation of the three subsystems of the ecological environment, social economy, and tourism industry, the comprehensive TC of the provinces in the YRB is evaluated and ranked according to the index weight and Topsis method. Taking 2019 as an example, the calculation results of the comprehensive evaluation of TC along the nine provinces along the Yellow River in 2019 are shown in Figure8.Figure 8
Comprehensive evaluation results of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure8, from 2014 to 2019, the TC among the provinces along the YRB maintained a relatively stable situation, with Sichuan, Qinghai, and Shandong provinces having the highest levels of TC; and Inner Mongolia Autonomous Region, Shaanxi Province, Gansu Province is in the middle level; and Shanxi Province, Henan Province, and Ningxia Hui Autonomous Region are the lowest. However, on the whole, the TC of the provinces is not high, the relative closeness is below 0.5, and the intraregional differences are relatively small. In the same way, the TC levels of the provinces along the Yellow River and Jiuzhou from 2014 to 2019 were evaluated respectively, and the evaluation results are shown in Table 4.Table 4
Comprehensive evaluation results of TC of the nine provinces along the Yellow River in 2014–2019.
Provinces201420152016201720182019Qinghai0.46850.46560.46320.46650.46260.4765Sichuan0.37360.38130.39160.39560.44560.4768Gansu0.34120.29460.28650.34890.34320.3256Ningxia0.24120.22940.21890.23560.24560.2523Inner Mongolia0.44890.43640.44230.43560.42730.4168Shaanxi0.40650.42790.40590.40980.41320.4123Shanxi0.28560.28650.31020.29320.29680.2867Henan0.27060.27320.25560.27150.28090.2868Shandong0.45490.44560.43790.45680.46650.4731It can be seen from Table4 that the TC level of Sichuan Province has gradually increased from the fifth place to the first place in the past six years, and the relative closeness has increased from 0.37 to about 0.47. The TC level has improved most significantly. Sichuan Province has significant geographical advantages, good ecological environment and natural conditions, rich local product resources, and superior economic development level. At the same time, the level of tourism development ranks in the forefront of the country, making Sichuan Province's overall TC at a high level, with a relatively high level of competitiveness and strong tourism development potential.At the same time, according to the evaluation results, the TC level and average value of the provinces in 2019 are presented in a bar chart, as shown in Figure9.Figure 9
Histogram of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure9, as of the end of 2019, the overall TC of the provinces was not high, and the relative closeness was below 0.5. The difference in TC between provinces was small, and more than half of the provinces reached the average value.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, the index weight of the social and economic competitiveness subsystem is 0.2691, and the index weight of the tourism industry competitiveness subsystem is 0.3418. The three subsystems together have an impact on the TC of the provinces along the Yellow River, and the index of ecological environment has the greatest weight. The overall ecological resource endowment in the western region is good, the natural conditions are superior, the ecological environment quality is high, and it has strong ecological environment competitiveness. In particular, the ecological environment competitiveness of Qinghai Province is far ahead. However, the development of social economy and tourism industry is uneven between provinces. Therefore, in general, the overall tourism competition in the western region is high. The economic level of Shandong Province ranks first in the area along the Yellow River. The tourism resources in the region are complete in variety and quantity, the tourism brand effect is strong, and the tourism industry has a strong development momentum. At the same time, the environmental quality is improving day by day. Therefore, the overall level of TC is relatively high. The level of economic development and economic innovation in the central region is low, the ecological resource endowment in the region is lacking, the environmental quality is poor, the pollution is serious, the development level of the tourism industry is relatively weak, and the industrial competitiveness has not been formed, so the overall tourism competitiveness of the central region is low.


## 3.3.1. Evaluation of Ecological Environment Competitiveness Subsystem

The YRB is an important component of China’s ecological barrier. Therefore, the quality of the ecological environment in the YRB is the basis and condition for the development of tourism, and it is necessary to evaluate the competitiveness of the ecological environment in the areas along the Yellow River. According to the index weight and Topsis method, the eco-environmental competitiveness level of the provinces along the Yellow River is evaluated. The evaluation results are shown in Table1.Table 1
Evaluation results of the ecological environment competitiveness level of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.64510.64150.61240.61240.61240.6234Sichuan0.37540.37510.39840.36950.36120.4123Gansu0.37850.30420.29080.37250.36850.3528Ningxia0.24950.24960.22570.22140.25960.2654Inner Mongolia0.41210.42980.43230.39570.37680.3832Shaanxi0.28180.34120.30240.27590.27240.25475Shanxi0.16950.13650.17210.19240.19060.1912Henan0.19260.20080.20120.21450.21650.2387Shandong0.33850.33560.33230.34210.33450.3259It can be seen from Table1 that from 2014 to 2019, the eco-environmental competitiveness of the provinces and autonomous regions along the YRB has generally maintained a relatively stable level. Qinghai, Sichuan, and Inner Mongolia have the highest level of eco-environmental competitiveness; Gansu, Shandong, Ningxia, and Shaanxi are in the middle level; and Henan and Shanxi have the lowest level of eco-environmental competitiveness. At the same time, according to the evaluation results, a histogram of the competitiveness level and average value of the ecological environment along the provinces in 2019 is presented as shown in Figure 5.Figure 5
Histogram of eco-environmental competitiveness along the nine provinces in 2019.As shown in Figure5, as of the end of 2019, the relative closeness of the four provinces was higher than the average level of the nine provinces along the Yellow River, and the overall level of eco-environmental competitiveness was above the medium level. And Qinghai Province's eco-environmental competitiveness level is far ahead of other provinces. There are obvious differences between regions.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, of which the index weight of the ecological protection level is 0.3057, and the environmental quality level is 0.0834. The most important factors affecting the competitiveness of the ecological environment in each region are the per capita water resources and the coverage rate of nature reserves, the per capita park green space, and other ecological protection level indicators. Qinghai Province, Inner Mongolia Autonomous Region, and other western regions have vast territory, sparsely populated areas, superior ecological environment and natural resource conditions, and a high coverage rate of wetlands and nature reserves, which makes the western region have better ecological environment competitiveness. The eastern region mainly refers to Shandong Province, which is located in the North China Plain with relatively flat terrain. The Yellow River Delta develops here, and the wetland area is vast. Coupled with the further development of wastewater, waste gas, solid waste, and other treatment technologies, the competitiveness of the ecological environment in Shandong Province has gradually increased from a medium level to a relatively high level in recent years.


## 3.3.2. Evaluation of Socioeconomic Competitiveness Subsystem

According to the index weight and Topsis method, the social and economic competitiveness level of the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table2.Table 2
Evaluation results of social and economic competitiveness of the provinces along the Yellow River from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.28450.29350.30010.33350.34320.3668Sichuan0.32750.36280.38350.43320.64350.6365Gansu0.40840.38320.38410.40860.40540.3535Ningxia0.34280.28320.29290.30310.31340.3320Inner Mongolia0.35620.33980.34210.37210.37210.3498Shaanxi0.62650.6230.63310.63590.65330.6431Shanxi0.39080.38280.37320.37060.40060.3532Henan0.26060.26120.27330.31450.33350.3235Shandong0.54210.51120.53350.59320.66340.6654It can be seen from Table2 that from 2014 to 2019, the level of social and economic competitiveness of the nine provinces and autonomous regions along the YRB has changed greatly, and some provinces have an obvious upward or downward trend. On the whole, Shandong and Shaanxi have the highest level of social and economic competitiveness, Sichuan, Qinghai, Gansu, Shanxi, and Inner Mongolia are in the middle level, and Ningxia and Henan have the lowest level of social and economic competitiveness. At the same time, according to the evaluation results, a bar chart showing the level of social and economic competitiveness and the average value of the provinces along the nine provinces along the Yellow River in 2019 is shown in Figure 6.Figure 6
Histogram of socioeconomic competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure6, by the end of 2019, the social and economic competitiveness levels of Shandong, Shaanxi, and Sichuan provinces were much higher than the average level of the nine provinces, and there was a large gap between the levels of the other six provinces and cities. According to the weight of the evaluation index system, the index weight of the social and economic competitiveness subsystem is 0.2691, of which the index weight of the economic innovation level is 0.1695, and the social and people's livelihood level is 0.0996. Therefore, the most important factors affecting the level of social and economic competitiveness of various regions are the economic innovation level indicators such as the proportion of the tertiary industry in GDP, the intensity of R&D expenditures, and the turnover of technology markets.


## 3.3.3. Evaluation of Tourism Industry Competitiveness Subsystem

Promoting the HQDEV of the tourism industry in the YRB can not only drive the economic, social, and people's livelihood development in the YRB, but also an effective way to help the YRB get rid of poverty, and an important way to protect, inherit, and promote the Yellow River culture. According to the index weight and the Topsis method, the competitiveness level of the tourism industry in the nine provinces along the Yellow River is evaluated. The evaluation results are shown in Table3.Table 3
Evaluation results of the competitiveness level of the tourism industry along the provinces from 2014 to 2019.
Provinces201420152016201720182019Qinghai0.13570.14560.13160.12980.13030.1356Sichuan0.39560.39950.38590.39230.42230.4795Gansu0.17680.17230.15560.19560.21540.2256Ningxia0.08240.07050.09230.08580.11980.1432Inner Mongolia0.55950.53650.53560.53560.53650.5295Shaanxi0.40650.41560.39230.41450.42950.4465Shanxi0.34360.35380.40230.35410.35650.3565Henan0.38650.38590.34230.33590.35650.3589Shandong0.60560.58290.56650.57650.57650.5989It can be seen from Table3 that the tourism industry competitiveness level of the provinces along the YRB was relatively stable from 2014 to 2019, with Shandong and Inner Mongolia having the highest level of tourism industry competitiveness, Sichuan, Shaanxi, Henan, and Shanxi in the middle level, and Gansu, Ningxia, Qinghai have the lowest level. At the same time, according to the evaluation results, a bar chart showing the competitiveness level and average value of the tourism industry along the provinces in 2019 is shown in Figure 7.Figure 7
Histogram of tourism industry competitiveness of nine provinces along the Yellow River in 2019.As can be seen from Figure7, as of the end of 2019, the overall competitiveness of the tourism industry along the provinces was relatively good, and the four provinces reached above the average value in a decreasing trend.


## 3.3.4. Evaluation of TC

To promote the implementation of the major national strategy of ecological protection and high-quality development in the Yellow River basin, we must jointly promote the ecological, economic, tourism, and other aspects, and use tourism development to drive the ecology and economy of the areas along the route. The coordinated development will gradually realize the high-quality development of ecology, economy, and tourism. Based on the analysis and evaluation of the three subsystems of the ecological environment, social economy, and tourism industry, the comprehensive TC of the provinces in the YRB is evaluated and ranked according to the index weight and Topsis method. Taking 2019 as an example, the calculation results of the comprehensive evaluation of TC along the nine provinces along the Yellow River in 2019 are shown in Figure8.Figure 8
Comprehensive evaluation results of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure8, from 2014 to 2019, the TC among the provinces along the YRB maintained a relatively stable situation, with Sichuan, Qinghai, and Shandong provinces having the highest levels of TC; and Inner Mongolia Autonomous Region, Shaanxi Province, Gansu Province is in the middle level; and Shanxi Province, Henan Province, and Ningxia Hui Autonomous Region are the lowest. However, on the whole, the TC of the provinces is not high, the relative closeness is below 0.5, and the intraregional differences are relatively small. In the same way, the TC levels of the provinces along the Yellow River and Jiuzhou from 2014 to 2019 were evaluated respectively, and the evaluation results are shown in Table 4.Table 4
Comprehensive evaluation results of TC of the nine provinces along the Yellow River in 2014–2019.
Provinces201420152016201720182019Qinghai0.46850.46560.46320.46650.46260.4765Sichuan0.37360.38130.39160.39560.44560.4768Gansu0.34120.29460.28650.34890.34320.3256Ningxia0.24120.22940.21890.23560.24560.2523Inner Mongolia0.44890.43640.44230.43560.42730.4168Shaanxi0.40650.42790.40590.40980.41320.4123Shanxi0.28560.28650.31020.29320.29680.2867Henan0.27060.27320.25560.27150.28090.2868Shandong0.45490.44560.43790.45680.46650.4731It can be seen from Table4 that the TC level of Sichuan Province has gradually increased from the fifth place to the first place in the past six years, and the relative closeness has increased from 0.37 to about 0.47. The TC level has improved most significantly. Sichuan Province has significant geographical advantages, good ecological environment and natural conditions, rich local product resources, and superior economic development level. At the same time, the level of tourism development ranks in the forefront of the country, making Sichuan Province's overall TC at a high level, with a relatively high level of competitiveness and strong tourism development potential.At the same time, according to the evaluation results, the TC level and average value of the provinces in 2019 are presented in a bar chart, as shown in Figure9.Figure 9
Histogram of TC of nine provinces along the Yellow River in 2019.As can be seen from Figure9, as of the end of 2019, the overall TC of the provinces was not high, and the relative closeness was below 0.5. The difference in TC between provinces was small, and more than half of the provinces reached the average value.According to the weight of the evaluation index system, the index weight of the ecological environment competitiveness subsystem is 0.3891, the index weight of the social and economic competitiveness subsystem is 0.2691, and the index weight of the tourism industry competitiveness subsystem is 0.3418. The three subsystems together have an impact on the TC of the provinces along the Yellow River, and the index of ecological environment has the greatest weight. The overall ecological resource endowment in the western region is good, the natural conditions are superior, the ecological environment quality is high, and it has strong ecological environment competitiveness. In particular, the ecological environment competitiveness of Qinghai Province is far ahead. However, the development of social economy and tourism industry is uneven between provinces. Therefore, in general, the overall tourism competition in the western region is high. The economic level of Shandong Province ranks first in the area along the Yellow River. The tourism resources in the region are complete in variety and quantity, the tourism brand effect is strong, and the tourism industry has a strong development momentum. At the same time, the environmental quality is improving day by day. Therefore, the overall level of TC is relatively high. The level of economic development and economic innovation in the central region is low, the ecological resource endowment in the region is lacking, the environmental quality is poor, the pollution is serious, the development level of the tourism industry is relatively weak, and the industrial competitiveness has not been formed, so the overall tourism competitiveness of the central region is low.


## 4. Conclusions

The strategy of ecological protection and high-quality development in the Yellow River basin points out that the Yellow River basin has important ecological functions and economic status, and covers multiple aspects such as people’s livelihood, culture, and tourism. Tourism is a comprehensive industry. As an index to measure the ability of a region to maintain and improve its market position and market share in the process of continuous development, the level of TC plays an important role in guiding the development direction of regional tourism and promoting the level of economic development. Therefore, promoting the continuous improvement of the TC of the nine provinces along the Yellow River can not only promote the HQDEV of tourism in the regions, but also play a role in promoting regional ecological environmental protection and social and economic development. Since the strategy involves multiple aspects such as ecology, economy, society, people's livelihood, and tourism, the criterion layer of the evaluation system is composed of three subsystems: ecological environment, social economy, and tourism industry competitiveness. At the same time, in order to further reflect the connotation and requirements of “HQDEV,” the selection of indicators does not take absolute indicators as the main evaluation criteria, but pays more attention to relative indicators that can reflect the development structure and quality of development at all levels, with a reasonable combination of absolute quantity index and relative quantity index. On this basis, a comprehensive evaluation index system covering multiple aspects is established to study the development level of TC of the nine provinces along the Yellow River under the strategy of ecological protection and HQDEV in the YRB, thereby promoting the overall high-quality development of the YRB. In the future research work, the evaluation indicators can be selected according to the principle of combining qualitative and quantitative, and the evaluation index system can be further optimized to fully reflect the connotation of tourism competitiveness and high-quality development.



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*Source: 8904499-2022-08-13.xml*