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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 3: 541 - 553

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.03.003

Rural Revitalization and Rural Tourism High-quality Development

Coupling Effect and Driving Mechanisms in High-quality Development of Rural Tourism and Rural Revitalization—Taking Hunan Province as a Case Study

  • LI Wei , * ,
  • JI Zuqiang ,
  • LIN Jin
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  • Rural Revitalization School, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
*LI Wei, E-mail:

Received date: 2023-06-22

  Accepted date: 2023-12-01

  Online published: 2024-05-24

Supported by

The Major Projects of Fujian Provincial Social Science Planning Fund(FJ2021Z005)

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Abstract

Rural tourism plays a crucial role in promoting rural revitalization and offers excellent opportunities for high-quality development. This study presents a coupling coordination model with Hunan Province as a case study. The model utilizes IAHP (Improved Analytic Hierarchy Process) and trophy-weighted TOPSIS (Technique for Order Preference by Similarity to Solution) to evaluate the dynamic evolution process of the coupling coordination between rural tourism and rural revitalization from 2010 to 2019. Additionally, it explores the dominant factors and driving mechanisms that impact the coupling of this system. The results demonstrate that the rural tourism and rural revitalization indices in Hunan Province increased significantly from 2010 to 2019, whereas the relative priority of tourism gradually declined. The degree of coupling between rural tourism and rural revitalization increased from 0.3 to 0.96 and progressed through three stages: low-level coupling, adjustment, and high-level coupling. The degree of coupling coordination has increased from 0.22 to 0.89. This increase can be divided into four stages: moderately imbalanced with a dual-low index, a transitional stage with a lower index, initial coordination with a moderate index, and relatively good coordination prioritizing revitalization. The relationship between rural tourism and rural revitalization is affected by factors, such as local general public budget revenue, rural tourism satisfaction, road network density, agricultural production conditions, the processing rate of agricultural products, and the Engel coefficient of rural residents. Effective approaches to improve coupling coordination include strengthening financial support, optimizing service facilities, and promoting industrial integration.

Key words： coupling effect; driving mechanisms; rural revitalization; rural tourism; TOPSIS

Cite this article

LI Wei , JI Zuqiang , LIN Jin . Coupling Effect and Driving Mechanisms in High-quality Development of Rural Tourism and Rural Revitalization—Taking Hunan Province as a Case Study[J]. Journal of Resources and Ecology, 2024 , 15(3) : 541 -553 . DOI: 10.5814/j.issn.1674-764x.2024.03.003

1 Introduction

The Chinese government proposed implementing a strategy for revitalizing rural areas in 2017. This strategy would cover various aspects of rural work, including integrating industries, conserving ecology, developing culture, improving rural governance, enhancing livelihoods, and promoting urban-rural integration. In recent years, due to continuous economic development and increasing urbanization, residents’ living standards have improved. Due to an increasing desire to reconnect with nature, rural tourism has experienced significant growth. According to data from the Ministry of Culture and Tourism, China had 3.09 billion rural tourism trips in 2019 before COVID-19 pandemic outbreak. This accounted for 50% of all domestic tourism trips. The Chinese government emphasized the need to comprehensively promote the strategy of rural revitalization in 2022. One of the key components of this strategy is to “develop rural characteristic industries and broaden the channels for farmers to increase income and get rich.” Tourism is an important part of rural industrial development, and it serves as an important starting point for promoting the construction of livable and workable villages. The construction of key villages and towns for rural tourism was included in the “Rural Revitalization Promotion Law of the People’s Republic of China” as a crucial task in June 2021. Rural tourism has been proven to be significant in enhancing the income levels of farmers, improving the cultural ecology in rural areas, and promoting the integrated development of rural industries.
Rural revitalization and rural tourism development are interdependent and have a mutually beneficial relationship. On one hand, rural tourism can serve as a crucial means to achieve rural revitalization. On the other hand, rural revitalization can provide excellent ecological resources and economic support for rural tourism (Li, 2019). Although rural revitalization and rural tourism are supposed to be complementary and mutually reinforcing, interference from various factors can lead to changes. Thus, a thorough exploration of the coordinated development path of these two sectors is crucial in guiding rural development scientifically. This will promote coupling, coordination, mutual integration, and mutual promotion of rural tourism and rural revitalization. Such an approach is highly significant for the successful implementation of the rural revitalization strategy.
Numerous studies have extensively researched the coordination between rural tourism and rural revitalization, primarily focusing on three aspects. The first aspect involves the interaction mechanisms between rural tourism and rural revitalization. Rural tourism has had a significant impact on rural revitalization by promoting the movement of people, goods, and capital towards rural areas, as well as facilitating the flow of information. This is known as the coupling effect, which has been demonstrated by various factors such as human migration, logistics, capital flow, and information flow toward rural areas (He et al., 2019). Implementing the rural revitalization strategy extends the boundaries of rural tourism. The growth of rural tourism, will make rural industries more attractive, leading to an increase in talented individuals returning to rural areas. This will help to talent will gradually return to revive rural culture and create a balance between urban and rural ecological conservation and development. Additionally, standardization of rural organizations will drive rural revitalization (Li, 2021; Li and Wu, 2022). The second aspect involves measuring and analyzing the coupling coordination between rural tourism and rural revitalization. Wang (2010) constructed a qualitative coupling model to study the relationship between rural tourism and new rural construction, examining resource allocation, development efficiency and development opportunities. Various quantitative models have been developed to aid decision-making processes. These models include the entropy weight method (Ma, 2019; Zhao and Lu, 2020; Lin and Zeng, 2021; Yin and Tang, 2021; Xiao et al., 2022), the coupling coordination degree model (Ma, 2019; Nie, 2019; Zhao and Lu, 2020; Lin and Zeng, 2021), the gray correlation model (Pang, 2019), the system simulation analysis model (Ma and Huang, 2020), and the principal component analysis method (Hu et al., 2015). Dong et al. (2020) evaluated the development level of rural revitalization and rural tourism based on the entropy weight method, analyzed the coupling relationship between rural revitalization and rural tourism in Shandong Province through the coupling function model, and explored the synergistic development path of the two. Pang (2019) developed a rural revitalization and tourism index for Henan Province, and used the gray correlation model to examine their relationship. The third aspect focuses on the coupling coordination development pathway of rural tourism and rural revitalization. Scholars outside of China have focused more on rural tourism and sustainable rural development (Petrović et al., 2017; Nooripoor et al., 2021), innovative development of rural tourism (Lane and Kastenholz, 2015), tourist satisfaction (Michalkó et al., 2015; Kastenholz et al., 2018), and the construction of tourism destinations (Su et al., 2019). Scholars within China are more likely to propose innovative strategies to promote high-quality rural tourism development based on rural revitalization (Yin and Li, 2018; Sun et al., 2023).
However, the current literature has certain limitations that need to be addressed. First, due to varying research perspectives, a unified evaluation index system has yet been established for rural tourism and rural revitalization. Further research is needed to calculate indicator weights. Second, there is a tendency for studies to view rural revitalization as a backdrop for analyzing rural tourism development. Few studies examine the coupling and synergy between the two, and employ quantitative calculations and analytic results. Third, further analysis is required to understand the driving mechanism behind the coupling and coordination of rural tourism and rural revitalization. Based on this, this study uses Hunan Province as a study area to scientifically construct a comprehensive evaluation indicator system. An improved analytic hierarchy process (IAHP) and an entropy weight method were used to determine the weights of the indicators, which makes up for the shortcomings that the subjective method relies too much on expert experience and the objective method only considers the actual data. The technique of the Order of Preference by Similarity to Ideal Solution (TOPSIS) model was employed to quantitatively measure the comprehensive development index and coupling coordination degree between rural tourism and rural revitalization, which objectively reflected the coordinated development level of rural tourism and rural revitalization. The gray correlation model was also used to analyze the dominant factors influencing the coupling coordination and reveal the underlying driving mechanisms. The correlation degree between the indicators was quantitatively analyzed. The aim is to provide theoretical support for the integrated development of rural tourism and rural revitalization.

2 Research areas and data sources

2.1 Research areas

Hunan Province is located in central China in the middle reaches of the Yangtze River (Fig. 1). The province has unique rural tourism and rural revitalization characteristics. Thus, it is a significant reference for other provinces in the country.
Hunan Province has a diverse selection of rural tourism resources, with a well-balanced distribution and numerous famous attractions such as Zhangjiajie, Huaminglou, and Shaoshan. The province boasts a rich rural cultural heritage. It is notable for the Shao Mountain Scenic Area, the Village of Eighteen Caves in Xiangxi, the first transformed village in the mountainous region, and Taohuajiang as the nest of beauties. These provide intrinsic driving forces for the development of rural tourism in Hunan. Among the 128 rural tourism boutique routes in China, Hunan accounts for six. From a spatial perspective, the rural tourism development in Hunan Province exhibits distinct characteristics and complementarity across different regions. The Xiangdong Urban Agglomeration, Xiangnan Mountainous Area, Xiangxi Ethnic and Cultural Area, and Xiangbei Agricultural and Fishery Area have unique features. Some areas are also developed centrally by villages and towns, forming clusters of interconnected rural tourism industry developments.
Hunan Province has four counties designated as national rural revitalization demonstration areas, six national-level agricultural industry clusters with distinctive advantages, and eight national-level modern agricultural industrial parks. The output value of the agricultural product processing industry has exceeded 2×1012 yuan, and there have been 7500 demonstration villages for scenic rural areas. The per capita annual income of urban and rural residents reached 47301 yuan and 19546 yuan, respectively, in 2022, an increase of 5.4% and 6.8% relative to 2021, which is higher than the GDP growth rate.

2.2 Data indicators and data sources

We have developed a rural tourism-rural revitalization index system (as shown in Table 1) based on relevant research findings from Hu et al. (2015), Ma (2019), Nie (2019), Pang (2019), Dong et al. (2020), Zhao and Lu (2020), Lin and Zeng (2021), and Xiao et al. (2022). The evaluation index system consists of four levels: goal, system, criterion, and indicators. The research goal is the “coupling coordination between rural tourism and rural revitalization” with rural tourism and rural revitalization as subsystems. The evaluation criteria for the rural tourism subsystem were selected based on their close connection to rural revitalization. These included overall effectiveness, resource base, market demand, and service support. To better align with the national and provincial strategies for rural revitalization, the criteria for the rural revitalization subsystem included thriving industries, ecological livability, civilized rural customs, and affluent livelihoods at the criterion level. The specific indicators were selected based on the research theme and objects, which followed the principle of “accurately reflecting the core elements of the criterion level.”
Table 1 Index system of rural tourism rural revitalization coupling system
Goal layer System layer Criterion layer Indicator layer Indicator interpretation Source Weight
Coupling and coordination of rural tourism and rural revitalization Rural tourism subsystem Overall quality and efficiency Number of visitors for leisure agriculture and rural tourism (T1) Measuring the development status of rural tourism from the perspective of tourist numbers Ma, 2019; Pang, 2019 0.07754
Income from leisure agriculture and rural tourism (T2) Measuring the development of rural tourism from the perspective of tourism revenue Pang, 2019; Nie, 2019 0.07729
Proportion of total tourism revenue to GDP (T3) Total tourism revenue/GDP Xiao et al., 2022 0.07580
Resource base Number of scenic spots above A-Level (T4) Reflecting the overall endowment of regional tourism resources Zhao and Lu, 2020; Yin and Tang, 2021 0.06634
Number of national-level leisure agriculture and rural tourism demonstration sites (T5) Measuring the development of high quality rural tourism demonstration sites Hu et al., 2015; Pang, 2019 0.05296
Number of five-star rural tourism areas (sites) (T6) Measuring the development of high-level rural tourism areas (points) Zhao and Lu, 2020 0.06395
Number of star-rated rural homestays (farmhouses) (T7) Measuring the development of high standard rural tourism market entities Hu, 2015; Zhao and Lu, 2020 0.06072
Market demand Urban-rural income ratio (T8) Per capita disposable income of urban residents/per capita disposable income of rural residents Ma, 2019;
Lin and Zeng, 2021		 0.06576
Urbanization rate (T9) Permanent population/total population Zhao and Lu, 2020 0.06946
Per capita tourism consumption expenditure(T10) Reflecting the consumption expenditure of urban and rural residents on tourism Yin and Tang, 2021 0.07412
Local general public budget revenue (T11) Characterizing the self investment capacity of rural tourism development Hu et al., 2015 0.05994
Service pack Number of homestay operators (T12) Measuring the reception capacity of rural tourism market Xiao et al., 2022 0.06464
Number of tourism enterprises (T13) Using the approximate replacement of “number of travel agencies” to measure the reception capacity of rural tourism market Yin and Tang, 2021; Xiao et al., 2022 0.07110
Level of satisfaction with rural tourism (T14) Using the “Business Environment Index” as an approximation to reflect the subjective satisfaction level of tourists with the rural tourism experience Ma, 2019; Dong et al., 2020 0.05297
Road network density (T15) Road network length/area, reflecting the level of transportation development Hu et al., 2015; Nie, 2019 0.06740
Rural revitalization subsystem Industrial prosperity Integrated grain production capacity (R1) Using the approximate replacement of “total grain crop yield” to reflect the ability and level of food security National Rural Revitalization Plan, 2018; Nie, 2019 0.04694
Agricultural production conditions (R2) Total power of agricultural machinery×rural power consumption Pang, 2019; Zhao and Lu, 2020 0.05599
Agricultural labor productivity (R3) Value added of primary industry/labor force in agriculture, forestry, animal husbandry, and fishery National Rural Revitalization Plan, 2018 0.04973
Processing rate of agricultural products (R4) Agricultural product processing output value/total agricultural output value National Rural Revitalization Plan, 2018 0.06133
Rural online retail sales (R5) Measuring the development of rural e-commerce Rural Revitalization Plan in Fujian Province, 2018 0.07145
Ecological livability Forest coverage rate (R6) Overall reflection of regional ecological environment Nie, 2019; Lin and Zeng, 2021 0.07240
Greenery coverage rate of villages (R7) Village green coverage area/total land area of the village Ma, 2019; Dong et al., 2020 0.05285
Percentage of villages with waste management (R8) Number of villages/total number of villages handling household waste Nie, 2019; Dong et al., 2020 0.08561
Coverage rate of sanitary toilets in rural areas (R9) Number of sanitary toilet renovations/total number of toilets Zhao and Lu, 2020 0.07054
Rural culture and civilization Coverage rate of comprehensive cultural service centers in villages (R10) Number of comprehensive cultural service centers in villages/total number of village Dong et al., 2020 0.06658
Proportion of civilized villages and towns at the county level and above (R11) Number of civilized villages and towns at or above the county level/total number of villages and towns Nie, 2019; Xiao et al., 2022 0.06842
Educational level of rural household leaders (R12) Reflecting the education status of rural residents Nie, 2019 0.05693
Live in affluence Per capita disposable income of rural residents (R13) Reflecting the richness of farmers and residents’ wallets Ma, 2019; Dong et al., 2020 0.05748
Ownership of household cars among rural residents (R14) Reflecting the modern lifestyle enjoyed by rural residents Designed by the author 0.06134
Engel coefficient of rural residents (R15) Total food expenditure of rural residents/total consumption expenditure of rural residents Dong et al., 2020; Xiao et al., 2022 0.05896
Rate of rural household access to piped water rate (R16) Number of villagers using tap water/total number of villagers Pang, 2019; Dong et al., 2020 0.06345
The data for each indicator were obtained from the corresponding year’s “Hunan Statistical Yearbook”, “China Cultural Relics and Tourism Statistical Yearbook”, “China Social Statistical Yearbook”, “China Environmental Statistical Yearbook”, “Hunan Province Tourism Development Report”, “Hunan Province E-commerce Report”, “Hunan Province Rural Revitalization Strategic Plan (2018-2022)”, “Hunan Homestay Industry Development Research Report (2019)”, “China Urban Business Environment Index Evaluation Report (2019)”, “Hunan Province Consumer Satisfaction Report (2018)”, and “China Business Environment Index Report (2017)”. Additionally, official websites, including the Ministry of Agriculture and Rural Affairs, Hunan Provincial Department of Culture, Tourism and Sports, and Hunan Provincial Forestry Bureau, were used as sources of information. Considering the different statistical methods used in the various statistical yearbooks, this article used the smoothing technique to fill in missing data for certain years.
Firstly, to determine the subjective weight of the evaluation index, we consulted with university teachers, as well as experts from relevant fields like Hunan Provincial Department of Culture and Tourism, Hunan Provincial Development and Reform Commission. We also combined relevant literature and reports on regional rural tourism and rural revitalization. The improved analytic hierarchy method (IAHP) was used for this purpose. Secondly, the objective weight of the index is calculated based on the entropy weight method. Finally, the combination weighting method is used to combine the subjective weight and the objective weight, resulting in a composite weight of each index. This approach effectively avoids the limitation problem of using a single method to determine the weight of the index.

3 Methods and models

3.1 Standardization of indicator data

The initial data were normalized using the extreme value method to obtain the standardized evaluation matrix X.
$X=\left[ \begin{matrix} {{x}_{11}} & {{x}_{12}} & \cdots & {{x}_{1n}} \\ {{x}_{21}} & {{x}_{22}} & \cdots & {{x}_{2n}} \\ \cdots & \cdots & \cdots & \cdots \\ {{x}_{m1}} & {{x}_{m2}} & \cdots & {{x}_{mn}} \\ \end{matrix} \right]$

3.2 Determination of weights

3.2.1 Improved analytic hierarchy process (IAHP)

First, construct the subjective comparison matrix:
$C={{[{{c}_{ij}}]}_{m\times m}}$
cij=$\left\{ \begin{align} & 1,\ \ \ \ \ i\ \text{is more important than}\ j \\ & 0,\ \ \ \ i\ \text{and}\ j\ \text{are equally important} \\ & -1,\ \ i\ \text{is less important than}\ j \\ \end{align} \right.$
Then, establish the sensory judgment matrix:
$S={{[{{S}_{ij}}]}_{m\times m}}$
where Sij=di–dj,${{d}_{i}}=\sum\limits_{l=1}^{m}{{{c}_{il}}}$and ${{d}_{_{j}}}=\sum\limits_{l=1}^{m}{{{c}_{jl}}}$
Next, establish the objective judgment matrix:
${{c}_{ij}}=R={{[{{r}_{ij}}]}_{m\times m}}$
where${{r}_{ij}}={{p}^{\left( \frac{{{S}_{ij}}}{{{S}_{t}}} \right)}}$,${{S}_{t}}=\underset{i,j}{\mathop{\text{max}}}\,({{S}_{ij}})=\text{max}({{d}_{i}})-\text{min}({{d}_{j}})$; and p is the range of scale expansion values, such as p equals 3 or 7. In this article, p is taken as 3.
Finally, normalize each column of the objective judgment matrix R to obtain the weight vector of the m criteria: ${{[{{\omega }_{1}},{{\omega }_{2}},{{\omega }_{3}},\cdots,{{\omega }_{m}}]}^{T}}$.

3.2.2 Entropy weight method

First, calculate the proportion of the j-th sample data under the i-th criterion as follows:
${{\rho }_{_{ij}}}={{x}_{ij}}/\sum\limits_{j=1}^{n}{{{x}_{ij}}}$
Next, calculate the entropy value for the i-th criterion as follows:
${{f}_{i}}=-d\times \sum\limits_{j=1}^{n}{{{\rho }_{ij}}}\times \ln {{\rho }_{ij}}$
where d is a constant; d = 1/lnn; and fi∈[0,1].
Then, calculate the utility value for the i-th criterion as follows:
${{\beta }_{i}}=1-{{f}_{i}}$
Finally, calculate the weights of each criterion as follows:
${{\omega }_{i}}={{\beta }_{i}}/\sum\limits_{i=\text{1}}^{m}{{{\beta }_{i}}}$

3.2.3 Combination weighting method

If the weight of a certain criterion calculated using the IAHP method is ωi1, and the weight calculated using the entropy weighting method is ωi2, then the composite weight ωi is:
${{\omega }_{i}}=\frac{\sqrt{{{\omega }_{i1}}\times {{\omega }_{i2}}}}{\sum\limits_{i=1}^{m}{\sqrt{{{\omega }_{i1}}\times {{\omega }_{i2}}}}}$

3.2.4 Weighted evaluation matrix

Multiply the weights of each corresponding indicator, denoted as ωi, by the matrix X to obtain the weighted evaluation matrix Y.

3.3 Distance calculation

In the weighted evaluation matrix Y, the maximum value of the i-th indicator among n samples is denoted as $y_{i}^{\text{max}}$. The corresponding dataset is denoted as ${{Y}^{\max }}$ and is referred to as the positive ideal solution. The minimum value is denoted as $y_{i}^{\text{max}}$. The corresponding dataset is denoted as ${{Y}^{\max }}$ and is referred to as the negative ideal solution.
.${{Y}^{\max }}=\left\{ \underset{1\le i\le m}{\mathop{\max }}\,{{y}_{ij}}\left| i=1,2,\cdots,m \right. \right\}=\left\{ y_{\text{1}}^{\text{max}},y_{\text{2}}^{\text{max}},\cdots,y_{m}^{\text{max}} \right\}$
${{Y}^{\text{min}}}=\left\{ \underset{1\le i\le m}{\mathop{\min }}\,{{y}_{ij}}\left| i=1,2,\cdots,m \right. \right\}=\left\{ y_{\text{1}}^{\text{min}},y_{\text{2}}^{\text{min}},\cdots,y_{m}^{\text{min}} \right\}$
where yij represents the weighted evaluation value of the j-th sample for the i-th indicator.
$K_{j}^{\max }=\sqrt{\sum\limits_{i=1}^{m}{{{({{y}_{ij}}-y_{i}^{\max })}^{2}}}}$
$K_{j}^{\min }=\sqrt{\sum\limits_{i=1}^{m}{{{({{y}_{ij}}-y_{i}^{\min })}^{2}}}}$
where $K_{j}^{\max }$ represents the distance between the j-th sample and the positive ideal solution and $K_{j}^{\min }$ represents the distance between the j-th sample and the negative ideal solution.

3.4 Integrated development index model of rural tourism and rural revitalization

Following the principle of being closer to the optimal solution and achieving a higher comprehensive index, the model was constructed as follows:
${{C}_{j}}=\frac{K_{j}^{\text{min}}}{K_{j}^{\text{max}}+K_{j}^{\text{min}}}$
where Cj is the comprehensive evaluation value of sample j, representing the degree of proximity to the optimal development level, referred to as proximity. This study represents the comprehensive development index of the rural tourism subsystem and rural revitalization subsystem based on the magnitude of proximity.

3.5 Coupling coordination model of rural tourism and rural revitalization

$C={{\left\{ \frac{T(x)\times R(y)}{{{\left[ \frac{T(x)+R(y)}{2} \right]}^{2}}} \right\}}^{k}}$
where C represents the coupling degree between the rural tourism subsystem and the rural revitalization subsystem; T(x) and R(y) represent the comprehensive development indices of the rural tourism and rural revitalization systems, respectively; and k is the discrimination coefficient, usually [2,5] and in this study, k=4.
To avoid high coupling at low development levels, the development levels of each subsystem were introduced as variables. The model for measuring the coupling coordination between rural tourism and rural revitalization was constructed as follows:
$R=\sqrt{C\times I}$
$I=a\times T(x)+b\times R(y)$
where R represents the degree of coupling coordination and I represents the comprehensive coordination index between rural tourism and rural revitalization. The parameters a and b are undetermined parameters, where a+b =1. In this study, a and b are both set to 0.5.

3.6 Gray correlation analysis of coupling coordination between rural tourism and rural revitalization

Using the coupling degree and coupling coordination degree as reference sequences and rural tourism and rural revitalization systems as comparative sequences, a gray correlation degree model was established:
${{\zeta }_{ij}}=\frac{\underset{1\le i\le j}{\mathop{\min \min }}\,\left| {{C}_{j}}-{{x}_{ij}} \right|+\underset{1\le i\le j}{\mathop{\rho \max \max }}\,\left| {{C}_{j}}-{{x}_{ij}} \right|}{\left| {{C}_{j}}-{{x}_{ij}} \right|+\underset{1\le i\le j}{\mathop{\rho \max \max }}\,\left| {{C}_{j}}-{{x}_{ij}} \right|}$
${{\gamma }_{i}}=\frac{1}{n}\sum\limits_{j=1}^{n}{{{\xi }_{ij}}}$
where ζij is the correlation coefficient between coupling degree Cj and system indicator xij for sample j; γi represents the gray correlation degree; Cj represents the coupling degree for sample j; and xij represents the normalized value of indicator Xi for sample j. The resolution coefficient isρ, and in this article, it is taken as 0.5. The correlation between the coupling coordination degree and the indicators of rural tourism and rural revitalization systems were calculated similarly.

4 Results and analysis

4.1 Analysis of the development level of rural tourism and rural revitalization

The study period for this research was from 2010 to 2019. The study aimed to meet the development requirements of the new era while also minimizing the impact of the epidemic. Furthermore, it aimed to reflect on the comparison before and after the introduction of the rural revitalization strategy. Using Equations 1-14 for quantitative calculation, the evaluation results of the comprehensive development index of rural tourism and rural revitalization in Hunan Province from 2010 to 2019 were obtained, as shown in Table 2.
Table 2 Comprehensive development index of rural tourism and rural revitalization (2010-2019)
Index 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Comprehensive Rural Tourism Index 0.245 0.308 0.375 0.419 0.470 0.490 0.550 0.599 0.723 0.750
Comprehensive Rural Revitalization Index 0.079 0.206 0.261 0.266 0.392 0.472 0.578 0.713 0.788 0.912
Relative Priority of Tourism Index 3.10 1.50 1.44 1.58 1.20 1.04 0.95 0.84 0.92 0.82
From 2010 to 2019, the comprehensive development index of rural tourism in Hunan Province increased from 0.245 to 0.75. This growth was reflected in the increasing number of visitors and annual income of leisure agriculture and rural tourism, which increased by 89.03% and 93.76% respectively. Additionally, the proportion of total tourism revenue to GDP increased from 9.15% to 24.56%, indicating that the tourism industry in the region has outpaced economic growth. Hunan Province boasts of numerous tourism resources, and this was reflected the number of A-level and above scenic spots, national leisure agriculture and rural tourism demonstration sites, and five-star rural tourism areas. These numbers increased by 2.07, 4.00, and 6.34 times, respectively, indicating that various regions have increased in importance in developing rural tourism. The rapid economic and social development has prompted strong market demand for rural tourism in Hunan Province. The improvement and optimization of supporting services have positively impacted consumer behavior.
From 2017, China implemented the rural revitalization strategy which was instrumental in increasing the comprehensive development index of rural revitalization in Hunan Province increased from 0.079 to 0.912. The strategy helped to bolster the rural industries, enhancing agricultural production conditions, increasing agricultural labor productivity by 11.02% annually, raising the processing rate of agricultural products by 1.67 times and increasing rural online retail sales by 96.66 times. In terms of ecological livability, forest cover showed an upward trend of 2.9 percentage points from a higher base, the greenery coverage rate of villages had increased from 13.8% to 23.01%, the rural domestic waste disposal rate had increased from 51.16% to 91.8%, and the coverage rate of sanitary toilets in rural areas had reached 90%. In addition, the coverage rate of comprehensive cultural service centers in villages had increased by 1.8 times, and the percentage of civilized villages and towns had increased by 23.45%. The per capita disposable income of rural residents had increased by 1.74 times, the ownership of household cars by rural residents had increased by 15.68 times, and the Engel coefficient of rural residents had decreased by 19.62.
To compare the development level between rural tourism and rural revitalization subsystems, the tourism relative priority model was introduced: P=T(x)/R(y). When P>1, it indicates that rural tourism development had relatively progressed; when 0.5<P≤1, it indicates that rural tourism development was at the same pace with rural revitalization; when P≤0.5, it indicates that rural tourism development was relatively lagging.
According to Table 2, the overall relative priority of tourism showed a gradually decreased from 3.10 in 2010 to 0.82 in 2019. This implies that rural tourism development has progressed at a similar pace as regional economic development. The development of rural tourism has been significant in driving regional economic growth and poverty alleviation, but it cannot solely promote rural revitalization. In 2016, the P-value entered the range of [0.51], indicating that rural comprehensive revitalization surpassed rural tourism development. This is consistent with the time when the central government proposed the implementation of the rural revitalization strategy. Rural tourism has been facing challenges such as insufficient development momentum, low development quality, and poor development benefits, which limit its role in promoting the rural revitalization strategy. Therefore, it is essential to respond promptly and adjust rural tourism development policies to transform rural tourism into high-quality development of “comprehensive revitalization of industry, talents, culture, ecology and organization.”

4.2 Analysis of the process characteristics of rural tourism and rural revitalization coupling coordination

Figure 2 displays the coupling and coordination degrees between the rural tourism subsystem and the rural revitalization subsystem using Equations 15-17 for each year of the study period.
Fig. 2 Dynamic evolution curve of coupling coordination of the rural tourism-rural revitalization system
The coupling process between the rural tourism and rural revitalization systems has gone through six stages of evolution. At the minimal coupling stage (C=0), there is no correlation and disordered development between the systems. At the low-level coupling stage (0<C≤0.3), the systems begin to show some correlation. At the transitional stage (0.3<C<0.5), the systems are in the process of changing. At the integration stage (0.5≤C<0.85), the systems are becoming more integrated. At the high-level coupling stage (0.85≤C<1), the systems are almost fully integrated. At the beneficial coupling resonance stage (C=1) the systems have converged towards a new ordered structure, resulting in a state of beneficial coupling resonance. The level of coupling coordination is classified into ten levels, from high to low, as follows: Excellent coordination, good coordination, moderate coordination, primary coordination, marginal coordination, mild imbalance, moderate imbalance, severe imbalance, and extreme imbalance. Based on this, the below analysis can be carried out.
The coupling degree of the rural tourism-rural revitalization system can be divided into three stages. From 2010 to 2019, the coupling degree between rural tourism development and rural revitalization underwent significant changes. In 2010, the coupling degree was below 0.3, indicating a low-level coupling. Although rural tourism played a role in revitalizing rural industries, its impact on rural ecological construction and cultural revitalization remained weak. Between 2011 and 2013, the coupling degree was consistently distributed in the range of 0.8 to 0.9, indicating that the rural tourism and rural revitalization subsystems were in the coordination phase or transitioning towards the high-level coupling phase. From 2014 to 2019, the coupling degree remained consistently above 0.95, indicating that rural tourism and rural revitalization entered a high-level coupling phase with strong interactions between the two. However, the dynamic evolution of the coupling degree revealed fluctuations indicated that the system might regress to previous coupling stages owing to factors such as policies, natural factors, and politics.
During the study period, the coupling coordination degree between rural tourism-rural revitalization system can be divided into four stages. Each stage is further divided into several processes. The first stage is characterized by a moderate degree of imbalance in the dual-low index. In 2010, the coupling coordination degree of rural tourism and rural revitalization was 0.2189, indicating a state of moderate imbalance. The comprehensive development index of rural tourism and rural revitalization were both low. During the second stage, there are relatively low tourism resources present in Hunan Province. The region has immense potential for the growth of rural tourism, which has already begun to aid poverty alleviation efforts. In 2011, despite being on the verge of imbalance; there were some improvements compared to 2010, potentially due to a significant increase in the rural revitalization index. In 2012-2013, there was weak coordination between the two systems, resulting in limited growth in both rural tourism and rural revitalization indices. The third stage represents the initial coordination stage and has a moderate index. This refers to the years 2014-2015. At this stage, the rural revitalization index still lags behind the rural tourism index, but the gap between them is decreasing. However, since both comprehensive indices remained below 0.5, the coupling coordination degree falls within the range of 0.6 to 0.7. Rural tourism is bringing certain social and economic benefits to rural revitalization and makes important contributions to expanding the rural tertiary industry to increase income and promoting the diversified development of rural economy. In the fourth stage, which covers the years 2016-2019, there was a focus on revitalizing rural areas with good coordination. One notable feature was that the rural revitalization index exceeded the rural tourism index, and both indices showed a steady increase. This stage can be further divided into two: The moderate coordination stage of 2016-2017 and the good coordination stage of 2018-2019. Despite significant progress made in the industrialization of rural tourism, the quality of its development still lags behind the comprehensive revitalization efforts in Hunan Province, which hinders its sustainable growth.
According to the empirical analysis, the coupling coordination degree is more reliable measure than the degree of coupling. It helps to avoid the high-level coupling state with low development levels and has higher stability and wider applicability. When development indices are introduced as variables, the coupling coordination degree is found to be lower than the coupling degree.

4.3 Analysis of the driving mechanisms of rural tourism and rural revitalization coupling coordination

By employing Equations 18 and 19, we were able to determine the coupling degree, coupling coordination degree, and gray correlation analysis between 15 indicators of rural tourism and 16 indicators of rural revitalization. The results are illustrated in Tables 3 and 4.
Table 3 Gray correlation analysis of the coupling degree, coupling coordination degree, and indicators of rural tourism and rural revitalization
Indicator Coupling degree Coupling coordination degree
Number of visitors for leisure agriculture and rural tourism (T1) 0.5911 0.7178
Income from leisure agriculture and rural tourism (T2) 0.5486 0.6733
Proportion of total tourism revenue to GDP (T3) 0.5571 0.6740
Number of scenic spots above A-Level (T4) 0.5695 0.7143
Number of national-level leisure agriculture and rural tourism demonstration sites (T5) 0.6451 0.7618
Number of five-star rural tourism areas (sites) (T6) 0.5974 0.7497
Number of star-rated rural homestays (farmhouses) (T7) 0.6094 0.5751
Urban-rural income ratio (T8) 0.7556 0.7472
Urbanization rate (T9) 0.6161 0.5129
Per capita tourism consumption expenditure (T10) 0.6229 0.7362
Local general public budget revenue (T11) 0.6842 0.8418
Number of homestay operators (T12) 0.4764 0.5792
Number of tourism enterprises (T13) 0.5503 0.6614
Level of satisfaction with rural tourism (T14) 0.6578 0.8643
Road network density (T15) 0.7112 0.8128
Table 4 Gray correlation analysis of the coupling degree, coupling coordination degree, and indicators of rural revitalization
Indicator Coupling degree Coupling coordination degree
Integrated grain production capacity (R1) 0.6363 0.7809
Agricultural production conditions (R2) 0.6619 0.8492
Agricultural labor productivity (R3) 0.5539 0.6681
Processing rate of agricultural products (R4) 0.6661 0.7605
Rural online retail sales (R5) 0.5022 0.5696
Forest coverage rate (R6) 0.6363 0.7809
Greenery coverage rate of villages (R7) 0.6246 0.7537
Percentage of villages with waste management (R8) 0.5616 0.6276
Coverage rate of sanitary toilets in rural areas (R9) 0.5756 0.6918
Coverage rate of comprehensive cultural service centers in villages (R10) 0.4930 0.5777
Proportion of civilized villages and towns at the county level and above (R11) 0.4862 0.5441
Educational level of rural household leaders (R12) 0.6448 0.6636
Per capita disposable income of rural residents (R13) 0.6007 0.7485
Ownership of household cars among rural residents (R14) 0.6271 0.7515
Engel coefficient of rural residents (R15) 0.7280 0.7583
Rate of rural household access to piped water rate (R16) 0.6045 0.7243
Table 3 illustrates that the urban-rural residents’ income ratio has the strongest correlation with the coupling degree in the rural tourism subsystem. Road network density, local general public budget income, and rural tourism satisfaction also show relatively strong correlations with the coupling coordination degree. In terms of the coupling coordination degree, rural tourism satisfaction, local general public budget revenue, road network density, the number of national-level leisure agriculture, and rural tourism demonstration sites, have relatively strong correlations. As a result, the primary factors influencing the coupling degree and coupling coor-dination degree are local general public budget revenue, rural tourism satisfaction, and road network density.
After conducting further analysis, it was discovered that, local general public budget revenue was significant positive correlation with the regional economy and residents’ income. This revenue reflects the level of prosperity and development in a region, indicating the market demand capacity and residents’ consumption potential. It also reflects the investment capacity and financial guarantee capability of rural tourism and revitalization. Therefore, increasing investment and providing financial support are necessary development of rural tourism and revitalization.
Tourist satisfaction with rural tourism is reflection of their overall perception towards the resources, local characteristics, cultural and ecological aspects, consumer environment, and supporting services of rural tourism destinations. It represents the soft and hard strengths of rural tourism destinations. In today’s highly competitive market, those destinations with good services and a favorable business environment can gain a good reputation and attract repeat customers. Improving tourist satisfaction with rural tourism development is a process of ecological revitalization, cultural revitalization, and organizational revitalization of rural areas.
Accessibility is a key aspect of rural tourism. One of the most important factors that determines the level of accessibility in a region is the density of road networks. Urban dwellers value their leisure time and prioritize efficiency, so it's crucial for rural tourism destinations to be within spatial and temporal reach. Improving rural tourism transportation services and reducing travel time in rural areas benefit the sustained and rapid development of rural tourism. Therefore, transportation convenience, including proximity to airports, presence of train stations, availability of highway exits, and density of highway networks, is crucial to the success of rural tourism destinations in attracting visitors from outside the region.. Building roads is also essential to ensure prosperity and the urgent development of rural revitalization. In summary, indicators such as local general public budget revenue, rural tourism satisfaction, and road network density play a collective role in the high-quality development of rural tourism and the implementation of rural revitalization strategies. They significantly influence the coupling degree and coupling coordination degree between the two aspects from the perspectives of market demand, resource base, supporting services, and accessibility.
In the rural revitalization subsystem, indicators such as the Engel coefficient of rural residents, forest coverage, the processing rate of agricultural products, and agricultural production conditions have a strong correlation with the coupling degree. Moreover, the indicators strongly associated with coupling coordination degree include agricultural production conditions, integrated grain production capacity, the processing rate of agricultural products, and the Engel coefficient of rural residents. In conclusion, agricultural production conditions, the processing rate of agricultural products, and the Engel coefficient of rural residents are the main factors affecting coupling and coordination degrees.
First, agricultural production conditions refer to the essential components necessary for agricultural production. These components include natural conditions, agricultural labor, arable land resources, agricultural water conservancy facilities, agricultural machinery and equipment, and agricultural science and technology. They are vital in ensuring the sustainable development of the agricultural industry and facilitate the integration of agriculture and tourism.
Second, the rate at which agricultural products are processed is an indicator of how well the agriculture and the secondary industries are integrated. To expand on this idea, encouraging the combined growth of the primary, secondary, and tertiary industries in rural areas is vital for increasing the value of agriculture, improving rural tourism, and reviving rural industrial.
Third, the Engel coefficient reflects the consumption structure and prosperity level of rural residents. If this coefficient decreases, it means that rural residents may have more disposable income to spend on rural tourism. This creates a wider market for rural tourism and indicates an improvement in the living standards and prosperity level of rural residents. The ultimate goal of rural revitalization is to achieve prosperity for rural communities. In conclusion, indicators such as indicators such as agricultural production conditions, the processing rate of agricultural products, and the Engel coefficient of rural residents play significant roles in agricultural production development and improving living standards. They collectively contribute to rural tourism and revitalization, significantly influencing the coupling degree and coordination between the two. In addition, forest coverage also has a significant impact, reflecting the level of ecological well-being.

5 Discussion

This study adopts the IAHP and trophy-weighted TOPSIS model to comprehensively evaluate the coupling and coordination process, characteristics, and mechanisms of high-quality development of rural tourism and rural revitalization in Hunan Province from 2010 to 2019. The study contributes to existing literature in three ways.
First, a novel indicator system was developed. A scientifically and logically constructed indicator system is the foundation for the smooth progress of subsequent research. In previous quantitative studies (Chen et al., 2020; Yin and Tang, 2021), mainly combined with the existing research results and rural revitalization development policies, respectively constructed rural tourism and rural revitalization index systems, the indicator systems were relatively simple, and the representation was not sufficiently comprehensive. To address this, we developed a novel indicator system with 15 indicators from four aspects—overall performance, resource base, market demand, and service support—to evaluate the high-quality development of rural tourism. We selected 16 indicators from four aspects: Thriving industries, ecological livability, civilized rural customs, and affluent livelihoods, through the four steps of “theoretical analysis-frequency statistics-expert feedback-realistic judgment”, to evaluate the rural revitalization strategy.
Second, the index weights were determined using scientific and rational methods. The weights of the different indicators played a crucial role in determining the final evaluation results. Compared with the studies of Pu et al. (2022) and Liu et al. (2023), this article adopted the AHP and the entropy weighting method and combined subjective and objective approaches. This approach helped eliminate the potential biases of subjective evaluations and avoid the limitations of purely objective evaluations, such as being influenced by data quality or imbalanced weights due to large standard deviations of certain data. By combining subjective judgments with objective calculations, this study provides a more scientific evaluation of each indicator’s relative importance, resulting in reliable results.
Third, in addition to evaluating the degree of coupling and coordination, a comprehensive analysis of the key factors influencing the coupling and coordination between the high-quality development of rural tourism and rural revitalization in this new era was conducted. It provides an in-depth explanation of the coupling and coordination mechanisms between the two research objects. During the analysis of the characteristics involved in coupling and coordination of rural tourism and rural revitalization, it is found that the first stage of rural tourism and rural revitalization index in Hunan Province is a double-low moderate dysregulation stage. This finding aligns with the previous results of Sheng and Zhang (2009). Meanwhile, besides answering “what”, this article also provides an explanation for “why”. It helps to demonstrate the underlying reasons from a dynamic perspective and provides new momentum for further promoting the coupled and coordinated development of rural tourism and rural revitalization.

6 Conclusions and recommendations

This study constructed a comprehensive evaluation of the relationship between rural tourism and rural revitalization. Based on historical data from Hunan Province from 2010 to 2019, an IAHP and entropy weighting method were employed to determine the weights of each indicator. The TOPSIS model was used to analyze the dynamic evolution process of evaluating the coupling and coordination between rural tourism and rural revitalization. Furthermore, the gray correlation model was employed to reveal the dominant factors and driving mechanisms of coupling coordination between rural tourism and rural revitalization. The main conclusions of this study are as follows:
(1) From 2010 to 2019, the comprehensive development index of rural tourism in Hunan Province increased from 0.245 to 0.75. The comprehensive development index of rural revitalization increased from 0.079 to 0.912. The relative priority of tourism gradually showed a downward trend of 3.10 in 2010 to 0.82 in 2019. Since 2016, rural comprehensive revitalization has been advancing faster than rural tourism development, marking a new era for rural revitalization.
(2) The coupling degree of rural tourism and rural revitalization increased from 0.3 to 0.96 and went through three general stages: low-level coupling, adjustment, and high- level coupling. The coupling coordination degree increased from 0.22 to 0.89 and can be divided into four stages: moderately imbalanced with a dual-low index, a transitional stage with a lower index, initial coordination with a moderate index, and relatively good coordination prioritizing revitalization. From 2018 to 2019, it entered a stage of good coordination.
(3) Local general public budget revenue, rural tourism satisfaction, and road network density contribute to the high-quality development of rural tourism and the implementation of a rural revitalization strategy. These are from the aspects of market demand, resource foundation, supporting services, and accessibility. Moreover, indicators such as agricultural production conditions, the processing rate of agricultural products, and the Engel coefficient of rural residents play a role in both the development of production and the improvement of living standards. These indicators collectively and significantly impact the coupling coordination and integrated development between rural tourism and rural revitalization.
Strengthening financial support, optimizing supporting services, and promoting industrial integration are effective approaches to enhancing the coupling coordination between rural tourism and rural revitalization. First, it is necessary to strengthen the financial foundation by fully utilizing the guiding role of government funds. Important steps would be to encourage the establishment of investment funds for rural tourism and rural revitalization as well as innovating financial products and service models. Optimizing services and support facilities is also essential. This can be done by improving public service facilities for rural tourism, including roads, parking lots, restrooms, communication, water and electricity supply, emergency response, and transportation tourism signage. Additionally, improving transportation conditions in rural tourism areas and underdeveloped rural areas is crucial. Finally, promoting the integrated development of the primary, secondary, and tertiary industries in rural areas is necessary. This can be achieved by focusing on high-quality development of the rural tourism industry, and enhancing tourism products related to sightseeing, experiences, leisure, vacationing, health and wellness, sports, and entertainment. Such efforts will stimulate the development of upstream and downstream industries, including agricultural cultivation, agricultural product processing, rural e-commerce, rural logistics, and rural cultural creativity. This integration will empower the comprehensive revitalization of rural areas.
Despite its significant contributions to the existing literature, this study has limitations. Owing to the difficulty of obtaining data, this study did not include the indicators related to the “effective governance” aspect as defined in the National Rural Revitalization Strategic Plan (2018-2022). In the future, it would be worth exploring how to utilize the coupling effect of rural tourism and rural revitalization and accurately measure the incremental contribution of rural tourism development to revitalizing industries, ecology, and culture in rural areas.

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