Spatiotemporal Changes in Synergy Effect Between Tourism Industry and Urban–Rural Integration Development in Yellow River Basin, China

Abstract

The imbalance between urban and rural development has become a global structural problem that needs to be solved urgently. In this context, the tourism industry, with its strong correlation and cross-regional integration characteristics, provides a key practical entry point and mechanism for systematically promoting integrated development by stimulating factor flow, reconstructing the value chain, and reshaping local identity. Based on the synergetic theory, this paper constructs the theoretical framework of the synergetic evolution of the tourism industry and urban–rural integration, and analyzes the synergetic effect of the tourism industry and urban–rural integration in 58 prefecture-level cities in the Yellow River Basin from 2007 to 2021 and the dynamic characteristics of its spatio-temporal evolution by using the entropy TOPSIS, Haken model, and spatial Markov chain methods. The results show the following: ① As the order parameter of synergistic evolution, the tourism industry dominates the evolution direction of the whole system, mainly showing positive feedback effect, showing a significant stage characteristic in general, and gradually reducing the difference from the initial regional differentiation to the middle stage, finally reaching a higher level of unity. ② The synergic evolution of the tourism industry and urban–rural integration in the Yellow River Basin presents significant temporal and spatial differences in the upstream, midstream, and downstream, with the overall characteristics of “collaborative improvement in the upstream, significant agglomeration in the midstream, and reverse decoupling in the downstream”. ③ The dynamic evolution of the synergistic development of the tourism industry and urban–rural integration in the Yellow River Basin has significant characteristics of spatial interaction and dynamic transfer. Its level has the effect of “path dependence”, showing a good trend of upward transfer, and the spatial neighborhood has a significant impact on the synergetic level transfer. The development trend of each region shows that “the upstream region is upward and stable, the midstream region has significant agglomeration and diffusion effects, and the downstream region is driven by polar nuclei and spatial differentiation”.

1. Introduction

The relationship between urban and rural areas is undergoing a historical transformation from a static “dual structure” to a dynamic “dual mutual structure”. In this process, tourism, with its long industrial chain, high industrial relevance, and strong regional driving force [1], has increasingly become the key engine and spatial link to promote economic diversification and regional balanced development [2]. Its development path has a profound impact on the depth, breadth, and sustainability of urban–rural integration. Against this realistic background, whether tourism can effectively bridge the gap between urban and rural development has become a global governance issue [3,4]. Therefore, it is necessary to systematically explore the following core issues: How do we scientifically recognize the key position of the tourism industry in the evolution of urban–rural integration? How do we analyze the synergy mechanism between the tourism industry and urban–rural integration? In particular, it is urgent to analyze its impact mechanism and path from the time dimension and space dimension, so as to provide a reference for China’s experience and an empirical paradigm to promote the coordinated development of urban and rural areas worldwide.

The Yellow River Basin is an extremely important geographical unit in China [5]. It is not only the birthplace of Chinese civilization, but also the epitome of the process of Chinese modernization [6]. As a complex geographical unit integrating the complexity of natural geography, the depth of history and culture, and the challenges of contemporary development, this region provides a highly valuable reference sample for the study of urban–rural relations in regions with different development stages and cultural backgrounds across the world. The development of tourism in the Yellow River Basin is deeply embedded in the local context, actively linking local advantageous industries, such as agriculture, culture, and ecology, and catalyzing the integration and symbiosis of multiple industries, so as to build a closer and mutually beneficial urban and rural economic and social network [7]. With the introduction of relevant policies to promote the high-quality development of the Yellow River Basin and the construction of the Yellow River Culture National Park, the role of the tourism industry in the regional socio-economic system has gradually increased, and it has become an important driving force and key factor in driving the integration of urban and rural development [8].

The path to achieve urban–rural integration is a core issue of widespread concern in the current academic community. Early studies are mostly based on the background of the urban–rural dual structure and unbalanced development [9], mainly focusing on theoretical construction [10,11], concept definition [12,13], and measurement and evaluation [14,15]. With the acceleration of urbanization and the promotion of rural revitalization, the focus of research has shifted to specific path exploration. As an important path to promote the connection between urban and rural areas, the tourism industry has gradually become a hot spot [16]. Relevant studies mainly focus on the internal impact of tourism development on urban–rural coordination [17], the spatial differentiation of tourism resources between urban and rural areas [18], and the promotion of rural tourism on urban–rural integration [19]. As Buckley et al. (2020) pointed out, world heritage tourism not only promotes the reverse migration of urban and rural areas, but also triggers profound changes in social structure [20]. Tan (2023) further revealed the positive impact of tourism development on urban–rural integration through empirical research on the Yangtze River Delta [21]. In addition, the tourism industry has significantly improved the flow efficiency of urban and rural resources and factors [22], effectively promoted an increase in employment opportunities [23], and optimized the income structure by promoting urban economic growth [24], promoting infrastructure construction, and enhancing internal and external exchanges of rural economy [25]. For example, the empirical analysis of Zhang (2023) shows that tourism has a non-linear impact on the rural income gap and urban–rural income inequality, and the regional differences are significant [26]. The existing research also emphasizes that the practice of the tourism industry in the construction of a recreational belt around a city [27] and the integration of urban suburbs [28] and rural tourism products, etc. [29], reflects the multidimensional promotion effect of tourism on urban–rural integration [30].

Existing studies mostly focus on the impact of the tourism industry on the single dimension of urban–rural integration [31], such as the role of tourism economic benefits in the urban–rural income gap [32], and seldom discuss the comprehensive synergistic effect of the tourism industry and urban–rural integration dual system [33]. Also, research mostly stays at the level of static analysis, so it is urgent to expand the analysis of the spatio-temporal evolution characteristics of synergy [34,35]. Based on this, this paper takes the Yellow River Basin as a case study and uses the Haken model to analyze the characteristics of the synergistic development of the tourism industry and urban–rural integration in the Yellow River Basin from the perspective of synergetic evolution. Based on the spatial Markov chain model, the synergistic evolution trend and spatial differentiation characteristics of the dual system in the Yellow River Basin are analyzed. The purpose of the study is to understand the synergy characteristics and development trend between the tourism industry in the Yellow River Basin and urban–rural integration, and provide a scientific basis and practical guidance for the integration of the tourism industry into regional economic and social development and an improvement in industrial contribution capacity.

The innovative significance of this paper lies in the following: First, the synergetics theory is introduced to build an analytical framework for the coordinated evolution of the tourism industry and urban–rural integration. It breaks through the traditional research paradigm based on linear economic logic, regards the tourism industry and urban–rural integration as a dynamic and interactive dual system, reveals its non-linear feedback, order parameter drive, and system synergy mechanism, and provides a new theoretical perspective for understanding the synergy between the tourism industry and urban–rural integration. Second, based on the empirical study of long time series and large regional scales, the characteristics and spatial pattern of the spatial synergistic evolution stage of a watershed are highlighted. The purpose of the study is to systematically reveal the characteristics and spatial pattern of the synergetic evolution stage of tourism and urban–rural integration in the Yellow River Basin and to show the spatial process mechanism of regional linkage, path dependence, and polar core drive. Third, we respond to the global issue of urban–rural integration and expand the theoretical and practical extension of tourism research. Taking the tourism industry as the breakthrough point to explore the path to solve the imbalance between urban and rural development provides regional experience and theoretical enlightenment for integrated governance based on diversified development and has strong international reference value and academic promotion significance.

2. Research Design

In order to systematically evaluate the synergetic development level of the tourism industry and urban–rural integration in the Yellow River Basin, this paper constructs a research technology path composed of “index evaluation-synergetic measurement—spatio-temporal evolution trend, respectively”, and uses a quantitative empirical modeling method overall, including the following three aspects: Firstly, a comprehensive index system is constructed based on the two subsystems of the tourism industry and urban–rural integration, and the entropy weight TOPSIS model is used to evaluate the comprehensive development level of 58 prefecture-level cities in the Yellow River Basin. Secondly, based on the synergetics theory, the improved Haken synergy model is used to measure the synergetic evolution process between the tourism industry and the urban–rural integration system. By identifying the master–slave relationship between the order parameter and the slave parameter, the study depicts the coupling mechanism and evolution direction of the two subsystems at different stages of development and further reveals the dominant role of the tourism industry in the urban–rural integration system and its positive feedback characteristics. Finally, in order to deeply analyze the spatial dynamic characteristics of the synergetic level of the tourism industry and the urban–rural integration system, this paper introduces the spatial Markov chain model and constructs the state transition probability matrix under the condition of spatial lag based on the traditional Markov transfer matrix to identify the impact of adjacent regions on the evolution path of the synergetic level and reveal the spatial dependence and spillover effect between regions.

2.1. Study Area and Data Sources

The Yellow River Basin is located across the three regions of western, central, and eastern China. It is the second-largest river basin in China and also a key area of tourism development and urban–rural integration [36]. According to official documents such as the plan for the protection, inheritance, and promotion of the Yellow River culture of the State Administration of cultural heritage and the Yellow River Water Conservancy Commission of the Ministry of water resources, the Yellow River Basin involves 69 prefecture-level administrative units (including prefecture-level cities, regions, autonomous prefectures, and leagues) [37]. On the basis of following the official boundary division, this paper comprehensively considers the structural integrity, availability, and time consistency of statistical data, excludes some autonomous prefectures and leagues in Qinghai, Ningxia, and Inner Mongolia with discontinuous statistics or a serious lack of indicators, and finally selects 58 prefecture-level cities in nine provinces where the Yellow River flows through as the research object, with a sample coverage of about 84.06% (Figure 1), which has a strong representativeness and data comparability. In order to better reveal the spatial difference characteristics of the tourism industry and the synergistic development of urban and rural integration in the Yellow River Basin, referring to the official classification standard of the Yellow River Water Conservancy Commission, this paper divides the main stream of the Yellow River into three sections, namely, the upstream, midstream, and downstream, and carries out a comparative analysis of these sections.

The data used in this paper are mainly from the China Statistical Yearbook, China Urban Statistical Yearbook, China urban and rural statistical yearbook, China Rural Statistical Yearbook, and China Social Statistical Yearbook, as well as the statistical yearbook of the nine provinces of the Yellow River, the national economic and social development statistical bulletins of cities at all levels, and China’s economic and social big data research platform from 2007 to 2021. Missing index data in individual years are supplemented and corrected by a linear interpolation method. In particular, due to the impact of the new crown epidemic, some areas of the Yellow River Basin have experienced abnormal fluctuations in tourism activities, traffic flows, and urban and rural statistics, and the release of relevant data has also lagged behind or been missing. In order to ensure the continuity of time series data and the stability of analysis results, the research period is set as 2007–2021.

2.2. Theoretical Basis

Synergy theory was proposed by the German physicist Herman Haken, which means that when the control parameters reach a critical point, the interaction between subsystems will produce a synergy effect, making the whole system enter an orderly state, and the overall function exceeds the simple addition of the functions of each part [38]. The two systems of the tourism industry and urban–rural integration involve a variety of interactive factors and variables, and the traditional single economic analysis framework struggles to fully explain this multidimensional problem.

Based on the synergy theory, this paper regards the tourism industry and urban–rural integration system as an open coordination system, and its synergetic evolution can be analyzed from the following four aspects (Figure 2): ① Openness. The tourism industry and urban–rural integration system continuously optimize the allocation of internal resources through internal and external interaction and resource interaction, thus promoting the synergistic development of various elements in the system [39]. ② Non-equilibrium. The tourism industry and urban–rural integration system is composed of several subsystems, which show a dynamic imbalance due to differences in geography, resources, economy, and culture. When a subsystem reaches relative equilibrium, changes in other subsystems may break the equilibrium and push the system as a whole into a new stage of synergistic evolution [40]. Therefore, the system is always in a non-equilibrium state, reflecting the differences in elements and spatial heterogeneity within the system. ③ Non-linearity. The interaction between subsystems within the system is shown through a complex non-linear feedback mechanism [41]. The development of the tourism industry depends on and affects the economic, cultural, and ecological elements of urban–rural integration and needs to be closely synergistic with policies and economic regulation. The non-linear interaction determines the overall behavior characteristics and evolution path of the system and ensures the toughness and stability of the system in uncertainty. ④ There are random fluctuations. In the tourism industry and urban–rural integration system, the fluctuations of various variables (such as market fluctuations and emergencies) promote the gradual evolution of the system from disorder to order.

The relationship between tourism development and urban–rural integration in the Yellow River Basin is complex and is mainly affected by economic resource endowment and geographical location differences, leading to an imbalance in urban–rural spatial economic development and restricting the integration process [42]. As a labor-intensive industry, tourism has strong industrial linkage and employment attraction. The vertical extension and horizontal integration of its industrial chain not only promote the sharing of economic achievements between urban and rural areas, but also bring multiple benefits at the social level [43]. On the one hand, the tourism industry has great employment flexibility, covering many fields such as accommodation, travel, shopping, and entertainment, creating a large number of jobs and effectively alleviating the problem of rural labor surplus [44]; on the other hand, the development of tourism has enhanced the willingness to return home for employment, alleviated the “hollowing out” of rural areas caused by the outflow of labor, and helped to alleviate employment inequality between urban and rural areas [45]. The spatial dependence of the tourism industry leads to significant spatial heterogeneity in its distribution and development, which profoundly constrains its driving effectiveness for urban–rural integration; at the same time, the high sensitivity of industries to the external environment leads to a volatile growth trajectory, and it is urgent to strengthen the stabilizing role of their socio-economic system through resilience enhancement mechanisms. It is worth noting that the non-linear superposition effect of multiple disturbance factors in the tourism industry may lead to discontinuous transitions in the development path of the industry, further exacerbating the complexity of spatial coordination in the tourism industry [46].

To sum up, the synergistic evolution of the tourism industry and the urban–rural integration system, relying on the circular mechanism of “factor flow subsystem adjustment non-linear feedback random fluctuation trigger”, has effectively promoted the transformation of the Yellow River Basin from geographical fragmentation to functional symbiosis, promoted the high-quality development of urban–rural integration, and injected sustainable vitality into the regional economy and society.

2.3. Construction of Index System

The tourism industry is a comprehensive economic activity focusing on meeting the needs of tourists, covering transportation, accommodation, catering, entertainment, shopping, and other fields [47]. Based on existing research, this paper measures industrial development ability, resource allocation ability, and spatial connection ability [48]. Urban–rural integration aims to achieve the synergistic development of the urban and rural economy, society, and ecosystem and promote deep integration between urban and rural areas by promoting the two-way flow of urban and rural resources and complementary advantages [49]. Based on existing research, this paper measures the integration of urban and rural areas from the following three aspects: production development integration, living services integration, and ecological environment integration [50]. See

Table 1. Indicator system for measuring the level of tourism industry development and urban–rural integration development.

Target Layer	Criterion Layer	Indicator Layer	Indicator Attribute	Indicator Weight	Definition of the Indicator
Tourism Industry	Industrial Development Capacity	Total tourist visits	+	0.128	This reflects the total number of tourists received by the region in a specific period [51].
		Proportion of tourism revenue in GDP	+	0.064	The ratio of tourism income to GDP reflects the importance of tourism to economic development [52].
		Per capita tourism consumption	+	0.032	The ratio of the total number of tourists to the total tourism income reflects the average expenditure of regional tourists on tourism activities [53].
		Coordination degree between tourism industry system and economic industry system	+	0.065	The ratio of the growth rate of tourism income to the growth rate of gross national product reflects the pull of the tourism industry on economic development [51].
		Coordination degree between tourism industry system and social system	+	0.024	The ratio of the growth rate of tourism employees to the growth rate of urban employment reflects the direct benefits that urban residents can obtain from the development of tourism industry [51].
	Resource Allocation Capacity	Tourism resource endowment	+	0.069	The grade weighting method is used to assign a value to A-level scenic spots1 to reflect the degree of tourism attraction [54].
		Number of beautiful leisure villages2	+	0.234	The number of beautiful leisure villages in a specific period reflects the development of rural tourism in the region [54].
		Number of travel agencies	+	0.122	The number of travel agencies owned by a region in a specific period reflects the scale of regional tourism development [55].
		Number of star-rated hotels3	+	0.056	The number of star-rated hotels in a specific period reflects the regional tourism reception capacity [55].
	Spatial Connectivity Capacity	Tourist traffic density	+	0.041	The ratio of the mileage of transportation routes to the total area of tourist cities reflects the supporting capacity of tourism transportation facilities [56].
		Tourism spatial density	+	0.124	The ratio of tourist arrivals to land space reflects the regional tourism reception capacity [57].
		Tourism density index	−	0.041	The ratio of the number of tourists to the number of local residents reflects the tolerance of urban residents for tourism activities [58].
Urban–Rural Integration	Production Development Integration	Per capita GDP	+	0.142	The ratio of the regional gross national product to the total population reflects the regional per capita economic output level [59].
		Industrial coordination and interaction ability	+	0.321	The ratio of the per capita output value of employees in the primary industry to that of employees in the secondary and tertiary industries reflects the differences in regional industrial structure and development level [60].
		Highway density	+	0.137	The ratio of highway mileage to land space reflects urban and rural traffic construction [61].
		Population density	+	0.047	The ratio of regional resident population to regional area reflects the density of regional population distribution [62].
		Urban rate	+	0.053	The ratio of regional urban population to regional total population reflects the degree of urbanization of the region [63].
	Living Services Integration	Ratio of urban and rural per capita income and expenditure structure4	−	0.011	The ratio of the difference between urban and rural residents’ per capita income and the difference between urban and rural residents’ per capita consumption expenditure reflects the balance of urban and rural economic interests [64].
		Ratio of urban and rural per capita consumption expenditure	−	0.021	The ratio of per capita consumption expenditure of urban residents to that of rural residents reflects the differences in consumption behavior between urban and rural residents [65].
		Ratio of urban and rural Engel coefficient	−	0.022	The ratio of Engel’s coefficient between urban residents and rural residents reflects the difference in the proportion of urban and rural residents’ consumption [65].
		Ratio of urban and rural per capita disposable income	−	0.025	The ratio of urban residents’ per capita disposable income to rural residents’ per capita disposable income reflects the difference in living standards between urban and rural residents [50].
		Ratio of health technical personnel5 per thousand people in urban and rural areas	−	0.012	The ratio of health technicians per thousand people in urban areas to those in rural areas reflects the level of regional medical security [64].
		Ratio of urban and rural social endowment insurance6 participation rate	−	0.044	The ratio of urban social endowment insurance participation rate to rural social endowment insurance participation rate reflects the regional social security level [66].
		Ratio of urban and rural per capita road area	−	0.013	The ratio of urban per capita road area to rural per capita road area is against the development level of transportation infrastructure and the fairness of distribution [67].
	Ecological Environment Integration	Application amount of pesticide and chemical fertilizer	−	0.071	This reflects the quality of urban and rural ecological environment [68].
		Per capita park green space area	+	0.064	The ratio of the total area of regional park green space to the total population of the region reflects the quality of urban environment and the comfort of residents [69].
		Greening coverage rate of built-up area	+	0.017	The proportion of green coverage area and regional area in the built-up area reflects the coverage degree of green facilities in the city [70].

for specific indicators.

2.4. Research Method

2.4.1. Entropy-Weight TOPSIS Method

The entropy-weight TOPSIS method is a multi-index decision analysis method that is mainly used to determine the weight of each index and sort the schemes [71]. First, through the construction of the index system, the data of each index is collected and standardized. Then, the entropy of each index is calculated and the weight of each index is determined. Finally, based on the weighted standardized matrix, the distance between each scheme and the ideal solution and the negative ideal solution is calculated, and the comprehensive evaluation index of each scheme is obtained and ranked. This study uses this method to evaluate the comprehensive level of cities in the Yellow River Basin in terms of the tourism industry and urban–rural integration. The specific calculation steps are shown in the literature.

2.4.2. Haken Model

The Haken model is an effective method to study the self-organization and synergistic evolution of complex systems. The basic idea is to divide the system variables into fast variables and slow variables, determine the fast variables and linear instability points of the system through calculation, and then eliminate the fast variables of the system by using the adiabatic approximation principle to obtain the order parameter equation and the evolution equation group [72]. This study calculates the synergistic evolution process of the tourism industry and urban–rural integration by constructing a Haken model.

In the Haken model, the system variables are divided into fast variables and slow variables. Suppose the evolution equation of the system is [41], as follows:

$$q_1=-{\gamma }_1{q}_1-a{q}_1{q}_2$$

(1)

$$q_2=-{\gamma }_2{q}_2+b{q}_1^2$$

(2)

where ${\gamma }_1$ and ${\gamma }_2$ are damping coefficients, $a$ and $b$ are the intensity coefficients of the interaction between state variables, and $q_1$ and $q_2$ are the time derivatives of state variables. When the system reaches a steady-state solution, that is, $q_1=q_2=0,$ if $\left|{\gamma }_1\right|\gg \left|{\gamma }_2\right|,$ and ${\gamma }_2>0$, it indicates that $q_2$ is a fast variable, which is the “adiabatic approximation hypothesis” of the motion system.

If the “adiabatic approximation hypothesis” is true, then $q_2=0$ can be obtained from Equation (2), as follows:

$$q_2\approx {\displaystyle \frac{b}{{\gamma }_2}}{q}_1^2$$

(3)

By introducing Equation (3) into Equation (1), the order parameter evolution equation of the system is obtained, as follows:

$$q_1=-{\gamma }_1{q}_1-{\displaystyle \frac{ab}{{\gamma }_2}}{q}_1^3$$

(4)

Equation (3) shows that a change in $q_1$ determines a change in $q_2$, so $q_2$ is the order parameter of the system and dominates the process of system coevolution. We take the opposite number to the right of the equal sign of the order parameter evolution equation and then integrate to obtain the potential function of the system, as follows:

$$V={\displaystyle \frac{1}{2}}{\gamma }_1{q}_1^2+{\displaystyle \frac{ab}{4{\gamma }_2}}{q}_1^4$$

(5)

The equilibrium point of the potential function is determined by $q_1=0$. When ${\gamma }_1\ast {\gamma }_2\ast a\ast b>0$, the equation has a unique stable solution: $q_1=0$; when ${\gamma }_1\ast {\gamma }_2\ast a\ast b<0$, the equation has three solutions, as follows: $q_1^{\ast }=0, q_1^{\ast \ast }=-\sqrt{\left|{\displaystyle \frac{{\gamma }_1{\gamma }_2}{ab}}\right|, }{q}_1^{\ast \ast \ast }=\sqrt{\left|{\displaystyle \frac{{\gamma }_1{\gamma }_2}{ab}}\right|}$. At this time, the zero solution of the equation is an unstable solution, which is generally not considered in practical analysis, while the non-zero solution is a stable solution. At this time, the system forms a new stable state through mutation.

In real development, the tourism industry system and the urban–rural integration system are both open symbiotic systems, which may not grow together with the development of time. The evolution trend of the two systems may show periodic declines or conflicts [40]. Therefore, it is difficult to judge whether the model hypothesis is tenable by complete adiabatic elimination. In this paper, according to existing research, the damping coefficients ${\gamma }_1$ > 0, ${\gamma }_2$ > 0 in the improved Haken model are set, and the absolute value of ${\gamma }_1$ is much greater than ${\gamma }_2$, that is, there is an order parameter $q_1$, and the variable $q_2$ is dominated. At the same time, because the data used in this paper is annual data, the Haken model is discretized as follows:

$$q_1\left(t\right)=\left(1-{\gamma }_1\right)q_1\left(t-1\right)-a{q}_1\left(t-1\right)q_2\left(t-1\right)$$

(6)

$$q_2\left(t\right)=\left(1-{\gamma }_2\right)q_2\left(t-1\right)+b{q}_1\left(t-1\right)q_1\left(t-1\right)$$

(7)

2.4.3. Spatial Markov Chain

The spatial Markov chain is used to analyze the spatiotemporal dynamic evolution characteristics of synergetic value [73]. Based on the transition probability matrix of the traditional Markov chain, this model introduces the concept of “spatial lag”, discretizes the synergetic value into $k$ types, and decomposes the $N\times N$ transition probability matrix into $k$ $N\times N$ transition conditional probability matrices. In the $N$th term conditional matrix, the element $m_{ij}\left(k\right)$ represents the probability that the residential environment grade of a city a will be transferred to grade $j$ from year $t$ to year $t+1$ when the spatial lag type is $k$. The calculation formula of spatial lag value is as follows:

$${Lag}_a={\sum }_{b=1}^n{Y}_b{W}_{ab}$$

(8)

where $Y_b$ is the observed value of area $b$; ${Lag}_a$ is the spatial lag value of region $a$; $n$ is the total number of cities; and the spatial weight matrix $W_{ab}$ represents the spatial relationship between region $a$ and region $b$. This paper uses the adjacency principle to define the spatial relationship, that is, the adjacent value of the region is 1, otherwise it is 0. At the same time, due to the island problem caused by the lack of statistical data, this paper defines the nearest city to a city as the adjacent city.

3. Results

3.1. Analysis on the Synergy Effect of Tourism Industry and Urban–Rural Integration

3.1.1. Synergetic Effect Model Construction and Order Parameter Identification

Based on the above evaluation index system, the entropy-weight TOPSIS method is used to calculate the tourism industry development level and urban–rural integration index of each prefecture-level city in the Yellow River Basin from 2007 to 2021. Taking these data as state variables, according to the improved Haken model, when the damping coefficient γ₁ > 0, γ₂ > 0 and the absolute value of γ₁ is far greater than γ₂, there is an order parameter $q_1$ and the variable $q_2$ is dominated. A calculation by EVIEWS 10.0 software shows that the development level of the tourism industry is the order parameter of the synergetic system, which dominates the evolution of the synergetic system (

Table 2. Order parameter identification results.

Model Assumptions	Equations of Motion	Parameter Data	Model Conclusions
$q_1=TD$ $q_2=URI$	$q_1\left(t\right)=0.762068q_1\left(t-1\right)+0.347441q_1\left(t-1\right)q_2\left(t-1\right)$ $q_2\left(t\right)=0.990098q_2\left(t-1\right)+0.03455q_1(t-1)q_1(t-1)$	${\gamma }_1$ = 0.237923, ${\gamma }_2$ = 0.009902, a = −0.347441, b = 0.347441	In this system, the equation of motion holds and the model assumptions are satisfied.

).

The evolution equation is $q_1$ = −219442$q_1$ + 1.08342${q_1}^3$.

The potential function is $V$ = 0.109721$q_1^2$ − 0.27085478$q_1^4$.

Let $q_1$ = 0 and find the following three solutions of the potential function: $q_1^{\ast }$ = 0, $q_1^{\ast \ast }$ = −0.45, and $q_1^{\ast \ast \ast }$ = 0.45. Since the development level of the tourism industry and the urban–rural integration index are positive, only the potential function $q$ > 0 is considered, so the stable point U(0.45, 0.011) of the synergistic effect of the binary system is obtained. The distance between any state parameter point A and the stable point of the system determines its state in the system, that is, the synergy value of the synergistic development of the tourism industry and urban–rural integration is $d$, as follows:

$$d=\sqrt{{\left(q-0.45\right)}^2+{\left(v\left(q\right)-0.011\right)}^2}$$

3.1.2. Spatio-Temporal Differentiation Characteristics of the Synergetic Evolution of Tourism Industry and Urban–Rural Integration

Stage Characteristics of Synergy Effect

The research period is divided into three stages according to the average value of the synergy between the tourism industry and urban–rural integration (

Table 3. Stage division of synergistic values.

Development Stage	Year	Upstream	Midstream	Downstream	Entirety
Initial Stage	2007	0.493	0.586	0.474	0.527
	2008	0.499	0.606	0.442	0.526
	2009	0.529	0.571	0.472	0.529
	2010	0.504	0.592	0.538	0.553
	2011	0.495	0.590	0.515	0.542
Mean	0.535		Standard Deviation	0.012
Medium-Term Stage	2012	0.504	0.609	0.484	0.543
	2013	0.480	0.605	0.375	0.500
	2014	0.548	0.665	0.429	0.559
	2015	0.592	0.689	0.449	0.587
	2016	0.646	0.716	0.473	0.620
Mean	0.562		Standard Deviation	0.045
Mature Stage	2017	0.654	0.713	0.464	0.617
	2018	0.678	0.711	0.415	0.606
	2019	0.649	0.706	0.409	0.595
	2020	0.649	0.678	0.410	0.583
	2021	0.663	0.687	0.429	0.597
Mean	0.600		Standard Deviation	0.013

). At the same time, Stata 12 was used to draw the kernel density curve of the synergetic value, and the Synergetic Evolution Process of the binary system was analyzed (Figure 3). ① Initial stage (2007–2011). At this stage, the average synergetic value was 0.535, indicating that regional synergetic development was in an unbalanced state. The nuclear density curve showed obvious multi-peak characteristics and tailing phenomena, which reflects the low level of synergetic development in some regions and large differences between regions. ② In the medium-term stage (2012–2016), the average synergetic value was 0.562, and the index changed significantly during the period, indicating that the synergetic development between regions had improved. During this period, the kernel density curve gradually moved to the right, with the wave crest concentrated in the range from 0.5 to 0.6, and the curve gradually became smooth, indicating that the difference in coordinated development between regions gradually narrowed. ③ In the mature stage (2017–2021), the average synergy value in this stage further rose to 0.600, indicating that the level of synergetic development had entered a relatively stable stage. The nuclear density curve showed that the multi-peak phenomenon gradually disappeared, and the tailing phenomenon also significantly weakened, reflecting that the coordinated development of various regions tended to be unified. Through the phased division of the synergy value, it was revealed that the difference between the synergetic development of the tourism industry and urban–rural integration in the Yellow River Basin gradually narrowed from the initial regional differentiation to the middle stage, and finally reached a higher level of unity, reflecting the synergetic evolution process of the dual system.

Temporal and Spatial Characteristics of Synergetic Effect in Different Regions

This paper uses the natural partition point method to classify the synergy value and urban–rural integration index of the dual system of 58 prefecture-level cities in the Yellow River Basin and compares the spatio-temporal evolution characteristics of the two values by sections (Figure 4).

In the initial stage of the upstream region, the development level of the tourism industry and the urban–rural integration index were at a low level. The positive impact of the tourism industry on urban–rural integration had not yet emerged, and the correlation with the development of the urban–rural integration system was weak. In the medium-term stage, the synergy value increased rapidly. In 2013, the “belt and road” initiative7 was put forward and implemented, and the tourism industry gradually showed its driving effect on the urban–rural integration system. From 2013 to 2018, the urban–rural integration index showed a low level of fluctuation and returned to a growth trend in 2019, reflecting the obvious lag effect of the tourism industry on economic and social development. Spatially, the urban–rural integration index of the upstream region was generally low, and the overall improvement was not obvious. Nevertheless, the synergy value steadily increased and the internal spatial heterogeneity was continuously enhanced, gradually forming a collaborative development cluster with Xining–Haidong and Lanzhou–Yinchuan as the dual core. As the core region for the implementation of the “belt and road” initiative, the policy dividend continues to release, injecting strong impetus into regional development. In particular, the booming tourism industry in node cities along the line, such as Xining and Lanzhou, has significantly enhanced its radiation effect as a growth pole, promoted the coordinated development of surrounding cities, and jointly entered a new stage of regional coordinated development.

In the initial stage of the midstream region, the urban–rural integration index and synergy value showed rapid growth, showing a positive trend for the development of the urban–rural integration. However, in the middle and mature stages, although the synergy value continued to grow, the urban–rural integration index gradually declined. There was an uneven convergence trend within the region, and spatial structural problems were gradually emerging. This unbalanced distribution of factors led to an increase in regional internal development differences, which weakened the overall synergetic development ability. This shows that although the tourism industry has promoted short-term economic growth, its benefits have not effectively covered all aspects of urban–rural integration. The spatial pattern of urban–rural integration in the midstream region was relatively stable, and only the level of urban–rural integration in Ordos and Xi’an reached a high stage. The synergy value showed an upward trend, and the synergy level of Shanxi Province was the highest, basically reaching an advanced synergy state. Due to its geographical proximity and the signing of tourism exchange and cooperation agreements, Xinzhou–Taiyuan in Shanxi Province formed a high synergetic development city cluster. This synergy effect spread to surrounding cities such as Jinzhong and Luliang, driving the overall regional synergy. Xi’an and Baoji in Shaanxi Province promoted the planning of Guanzhong Plain Urban Agglomeration and the common development of surrounding cities through the connection of high-speed rail.

During the whole research period of the downstream region, the downstream region showed a trend of reverse decoupling, that is, with the gradual decline in the synergy value, the urban–rural integration index increased in reverse. This indicates that the relevance between the tourism industry and the regional national economic system was weakening, and it failed to play its due role in optimizing the national economic structure and promoting spatial coordinated development. Spatially, the decoupling between the synergy value of the downstream region and the urban–rural integration index is also very obvious. Specifically, the overall spatial pattern of urban–rural integration remained stable. Jinan, Jining, and other cities in Shandong Province further promoted urban–rural integration through the construction of metropolitan areas and the rapid development of intercity transportation. However, the synergy value in Shandong Province was relatively low, and only the synergy value of Dezhou and Zibo increased slightly, with no obvious spatial cluster effect being formed. In contrast, the level of urban–rural integration in Zhengzhou, Jiaozuo, and other cities in Henan Province was relatively backward, but the synergy value of Zhengzhou and Luoyang was significantly higher, forming a “double core” growth pole, promoting Jiaozuo and other cities to form regional spatial clusters and gradually expand to the central and western regions.

3.2. Evolution Trend of Tourism Industry and Urban–Rural Integration Synergy Level

3.2.1. Transfer Trait of Synergy Level

In this paper, the Markov chain and spatial Markov chain are used to construct the level probability transition matrix of the Yellow River Basin tourism industry urban–rural integration collaboration. Based on the above spatial–temporal distribution characteristics of the binary system collaboration, the collaboration level is divided into the following five types: low level (LL), relatively low level (RLL), medium level (ML), relatively high level (RHL), and high level (HL), revealing its dynamic transition and evolution trend (Figure 5).

From the traditional Markov transition probability matrix, we can see the following results: ① The development characteristics of path dependence. The values on the diagonals of the matrix are greater than those on the non-diagonals, indicating that the evolution of the collaboration level has obvious “path dependence”, that is, the high-level and low-level states have a strong internal stability, and the probability of transition to other states is low. This indicates that after a long period of adjustment, the development of the tourism industry and the integration of urban and rural areas tend to be relatively stable. ② The good situation of upward transfer. The probability of the upward transition of lower-level, medium-level, and higher-level states is close to or greater than the probability of downward transition. This reflects the positive development potential of the synergy between urban tourism development and urban–rural integration, which is expected to achieve a higher level of synergetic development in the future.

According to the spatial Markov transition probability matrix, the following results can be obtained (Figure 5): ① The spatial neighborhood background has an important impact on the change of the synergetic level transition of the binary system. When the spatial neighborhood background is not considered, the probability of the transition from the medium level to the higher level is 26.19%; the probability of transitioning to the medium level is 28.57%, 31.03%, 23.33%, 33.33%, and 15.78%, respectively, when the field background is low level, low level, medium level, high level, and high level. ② The “self locking” effect in the transition path, that is, when the neighborhood conditions are different, the probability of keeping itself unchanged is greater than the probability of its upward or downward transition. ③ The influence of neighborhood type on collaboration level is significant. The transfer characteristic that the probability of self upward (downward) transfer increases significantly when it is adjacent to high (low)-efficiency provinces. The probability of a higher level transferring to a higher level is 9.3% when taking low-level cities as neighborhoods, while the probability of a higher level transferring to a higher level is 17.39% when taking high-level cities as neighborhoods.

3.2.2. Spatial Distribution of Synergy Level Transitions

Based on the above research, it is found that the coordinated development of the tourism industry and urban–rural integration in the Yellow River Basin is not isolated. More often, the development trend of a city will be affected by its neighbors and show a certain degree of spatial agglomeration. In order to more intuitively display its transfer situation, the spatial distribution pattern of the synergetic level transfer of the tourism industry and urban–rural integration in the Yellow River Basin under the condition of spatial lag was studied and drawn (Figure 6). In this study, we define an “upward” transition as the shift of a city from a lower coordination level to a higher one in consecutive time periods, reflecting improvements in the synergetic development of tourism and urban–rural integration. Conversely, a “downward” transition refers to a city’s regression from a higher level to a lower one, indicating a weakening of coordination.

The upstream region shows a steady upward trend. The urban development in the region is mainly self upward and self stable, and the spatial heterogeneity within the region is gradually weakening. It is worth noting that the Qilian Mountains region, as a special geographical and ecological region, presents a spatial collapse due to its own poverty and lack of resource endowment, which indicates that it has not fully enjoyed the dividends of coordinated development, leading to lagging development and a widening gap. There are obvious differences in the spatial development of the midstream region. There are relatively many regions that are stable and upward. In particular, in the cities at the junction of the Shaanxi, Shanxi, and Henan provinces, a positive upward development trend is generally seen, which fully verifies the strong growth potential and synergy of the tourism industry in boosting social economy and accelerating urban–rural integration. Yulin, Yan’an, Tongchuan, and other cities in Shaanxi Province have their own downward development trend, which reflects the urgency and complexity of realizing the comprehensive and balanced development of the region. The spatial structure of the downstream region shows the characteristics of polar nuclear effect. Luoyang in Henan Province and Jinan in Shandong Province form relatively stable spatial poles in their respective provinces, and continue to show their own upward development trend. Jining, Tai’an, Liaocheng, and eight other prefecture-level cities show their own downward development trend; Xinxiang, Zibo, Binzhou, and seven other prefecture-level cities maintain a relatively stable state of development. It is worth noting that the development of the tourism industry in Tai’an, Jining, and other cities with rich tourism resources in Shandong Province has not brought strong impetus to the local social economy as expected.

4. Discussion

Through an in-depth analysis of the Yellow River Basin, this study reveals the synergistic evolution characteristics of the tourism industry and urban–rural integration, and provides a profound understanding of the non-linear growth mechanism and spatial development path difference of the regional system. These findings not only expand the understanding of the key role of the tourism industry in promoting urban–rural integration, but also provide strong theoretical support for the formulation of regional development policies.

First, the development of urban–rural integration generally presents significant spatial differences in different regions, and the development of the tourism industry has strengthened this regional differentiation trend to a certain extent [3]. This is due to the differences in geographical location, resource endowment, infrastructure, and policy support among different regions, which makes the spillover effect and driving capacity of the tourism industry present an uneven distribution in space [74]. Taking the Yellow River Basin as an example, the upstream, midstream, and downstream have formed a differentiated regional development pattern due to different natural conditions, development bases, and historical paths. As an important force in the reconstruction of the regional economic structure, the development level and mode of the tourism industry have a profound impact on resource allocation and social development, thus amplifying the development differences between regions.

Second, as an external driving force, policy is an important mechanism to promote the non-linear growth of the tourism industry and the urban–rural integration system [75]. In multi-regional practice, development policy often plays a key role in activating the potential of system synergy and breaking the path dependence, but its effect is subject to the adaptability of regional conditions and the institutional environment [76]. Taking the Yellow River Basin as an example, especially in the upstream and midstream, the “belt and road” initiative and the regional coordinated development strategy have significantly enhanced the driving effect of the tourism industry, activated the positive feedback mechanism within the system, and promoted the formation of a growth pole. However, the development driven by policies is often accompanied by the risk of imbalance between regional infrastructure construction and resource allocation, especially in regions with weak resource endowment.

Third, the linkage between the tourism industry and urban–rural integration is non-linear and positively correlated [26], showing a “reverse decoupling” phenomenon in specific regions and stages, which reflects the complex risk of synergy between the two [77]. Even if the level of urban–rural integration continues to improve, if the development of the tourism industry fails to form an effective linkage with the local economic and social structure, it may weaken its substantive impetus to the integration process, resulting in a functional mismatch and power lag between systems [77,78]. Taking the downstream of the Yellow River Basin as an example, although the overall urban–rural integration index showed an upward trend, the synergy value continued to decline, indicating that the expansion of the tourism industry failed to continuously and effectively promote the process of urban–rural integration, but led to a weakening of the development momentum and the lagging of the system.

5. Conclusions and Policy Recommendations

5.1. Conclusions

Taking the Yellow River Basin as the research area, this paper divides it into the following three sections: upstream, midstream, and downstream. By constructing a comprehensive index system of tourism industry development and the urban–rural integration level, this paper uses a Haken model to analyze the synergetic system of the tourism industry and urban–rural integration and analyzes the synergetic development status of cities in the Yellow River Basin from the two dimensions of time and space. The spatial Markov chain model is used to predict the trend of regional synergetic development. The main research conclusions are as follows:

First, the tourism industry is the order parameter of the synergetic evolution of the tourism industry and urban–rural integration in the Yellow River Basin, which dominates the flow state and interaction of elements in the synergetic system. At this stage, the synergetic system of the tourism industry and urban–rural integration in the Yellow River Basin forms a positive feedback mechanism, and the level of synergetic evolution shows significant stage characteristics: the initial stage shows regional differentiation, the middle stage tends to narrow the differences, and finally a higher level of unity is achieved.

Second, the synergetic evolution of the tourism industry and urban–rural integration in the Yellow River Basin presents significant temporal and spatial differences in the upstream, midstream, and downstream. The overall characteristics are “collaborative improvement in the upstream, significant agglomeration in the midstream, and reverse decoupling in the downstream”: the upstream has a low start. Driven by the “belt and road” initiative, the synergetic value increases rapidly, forming a two-center synergetic development cluster with Xining–Haidong and Lanzhou–Yinchuan as the core. The synergy effect of the midstream of the region is significantly concentrated, and the regions with Xinzhou–Taiyuan and Xi’an–Baoji as the core form a relatively stable spatial synergy pattern in the middle and late stages. The downstream region shows a reverse decoupling trend, the synergy value decreases, and the urban–rural integration index increases, indicating that the driving effect of the tourism industry on the economy is weakened and the decoupling phenomenon still exists in space.

Third, the synergetic evolution of the tourism industry and urban–rural integration in the Yellow River Basin shows significant spatial interaction and dynamic transfer characteristics. The evolution of the synergetic level shows the characteristics of path dependence and self-locking, and the high- and low-level states have a strong stability and low transition probability. At the same time, the upward transition probability of a synergetic state is high, indicating that the Yellow River Basin has great potential for synergetic development. The spatial neighborhood has a significant impact on the synergetic level transfer, and the upward transfer probability increases significantly when it is adjacent to a high synergetic city, while it is lower on the contrary. Specifically, the development trend of each region is as follows: the upstream region itself is upward and stable, the effect of agglomeration and diffusion in the midstream region is significant, and the polar nucleus drive and spatial differentiation coexist in the downstream region.

This study theoretically deepens the understanding of the mechanism of regional synergetic evolution, and clarifies that tourism is the key driving force in promoting the coordinated evolution of tourism development and urban–rural integration. At the practical level, the study emphasizes that regional development strategies should be formulated according to local conditions—the upstream region should strengthen tourism driven development, the midstream region should consolidate the synergistic agglomeration effect, and the downstream region should focus on the “decoupling” problem. At the management level, the study suggests that decision makers should strengthen inter-city cooperation and optimize the spatial development pattern to promote regional coordinated development and long-term balance. Overall, the research successfully achieves its stated aim of exploring the spatio-temporal evolution and driving mechanisms of the synergetic development between tourism and urban–rural integration in the Yellow River Basin.

5.2. Policy Recommendations

Combined with the case study of this paper, the tourism industry in the Yellow River Basin and the integration of urban and rural development show an overall benign interactive trend. As the order parameter of the evolution of the leading synergetic system, the tourism industry has played a positive role in promoting the evolution of the composite system. However, there are still some structural problems in the process of coordinated development among regions, such as unbalanced development, the decoupling of tourism urban–rural integration, and a weak development foundation. In order to further optimize the regional spatial pattern and promote the coordinated development of the tourism industry and urban–rural integration, the following three policy suggestions are put forward:

Firstly, for underdeveloped regions, priority should be given to building inclusive mechanisms for infrastructure and resource sharing. In response to the lagging development of the tourism industry and insufficient momentum for urban–rural integration, infrastructure construction and resource sharing mechanisms should be prioritized as policy priorities. We should focus on strengthening the networked supply of basic public services and reduce the cost of factor flow through the interconnection of transportation and digital infrastructure. We should establish a cross-regional resource synergetic platform to systematically improve the carrying efficiency of tourism services and the resilience of urban and rural systems, especially focusing on guiding the directional flow of resource elements to low-lying areas through institutional design.

Secondly, to address the issue of regional coordination imbalance, we should establish a multi-center linkage governance framework. In response to the phenomenon of development clustering but insufficient regional coordination, a cross-regional coordination mechanism should be established to promote core city cooperation-driven development. We should promote the formation of a regional coordination mechanism driven by core node cities, deepening the complementarity of industrial functions and the sharing of public services. We should also focus on enhancing the efficiency of spatial connection between urban agglomerations, catalyze the regional integration of the tourism industry chain through high-speed transport corridors and digital platforms, and realize the synergetic release of economies of scale and network effects.

Third, to address the decoupling dilemma in industrial integration, it is essential to innovate the pathways of industrial integration and structural upgrading. In response to the phenomenon of “reverse decoupling”, where tourism develops rapidly while urban–rural integration lags behind, efforts should focus on promoting diversified and integrated industrial development. This includes guiding the construction of a diversified “tourism+” industrial ecosystem and fostering deep coupling between tourism and sectors such as cultural and creative industries, ecological agriculture, and technological innovation. At the same time, it is crucial to strengthen the systemic alignment between the tourism industry and urban–rural public services, labor markets, and environmental governance. By implementing institutional incentives to remove barriers to factor mobility, a sustainable development paradigm characterized by urban–rural functional complementarity can be cultivated.

5.3. Limitations and Prospects

Although this paper systematically reveals the spatio-temporal evolution pattern of the synergistic development of the tourism industry and urban–rural integration in the Yellow River Basin and puts forward targeted policy recommendations, there are still some shortcomings, and the follow-up research can be expanded and deepened considering the following aspects: ① The identification of the influencing factors of synergetic evolution is still insufficient. In the future, panel data regression, geographically weighted regression (GWR), and other methods can be introduced to identify the influencing factors of synergistic evolution. ② There are still some limitations in the construction of the index system. The existing indicators are mainly based on macro statistical data. Although they are representative, they do not pay enough attention to the micro level such as element interaction and residents’ perceptions in urban–rural integration. Follow-up research can be combined with multi-source data such as questionnaire surveys and big data to supplement more interactive and dynamic index content and improve the comprehensiveness of the evaluation system. ③ This paper focuses on the process of urban–rural integration from the perspective of the tourism industry, and aims to explore how tourism development affects the flow of urban–rural factors and the spatial reconstruction and coordination mechanism. It does not fully incorporate other influencing factors such as the political system, social structure, and governance mechanism. The research conclusion still has a certain applicable boundary. On the basis of this study, future research can further expand the perspective of integration and build a more comprehensive and multidimensional synergetic analysis framework.