The Synergistic Evolution Characteristics and Influencing Factors of Tourism Economy and Urban Green Development Efficiency in the Yellow River Basin

Abstract

In the context of the “ecological priority and green development” strategy, examining the co-evolution between the tourism economy and the efficiency of urban green development can offer both theoretical insights and quantitative foundations to support ecological preservation and high-quality development in China’s Yellow River Basin. This research utilized approaches such as the Haken model and the geographically and temporally weighted regression model to investigate the spatiotemporal patterns, synergistic characteristics, and driving factors of the tourism economy and urban green development efficiency within the Yellow River Basin. The findings reveal the following: (1) Regional disparities in the tourism economy are progressively widening, whereas the efficiency of green development tends to decline. Furthermore, both the tourism economy and urban green development efficiency display “high-high clustering” and “low-low clustering” spatially. (2) The synergistic evolution of the two systems displays spatial characteristics of transitioning from polarization to trickle-down effects. (3) Natural factors such as topography and vegetation coverage, as well as human economic factors like industrial structure and the degree of openness, positively promote the synergy. However, elements such as temperature, precipitation, economic development level, and openness to innovation have a certain inhibitory effect on the synergistic evolution.

1. Introduction

With ecological conservation and high-quality development in the Yellow River Basin now elevated to a national strategic priority, balancing economic growth with environmental preservation is crucial for attaining this objective. Tourism, as a green sunrise industry, has emerged as a critical direction for the Chinese government at all levels to address complex issues related to economic development and ecological environment and to promote high-quality development in the Yellow River Basin [1]. In October 2021, China’s State Council released the Outline for Ecological Protection and High-Quality Development in the Yellow River Basin. This document clearly emphasizes the importance of fostering the integration of culture and tourism within the region, aiming to establish the cultural tourism sector as a key pillar of the local economy. However, tourism has the dual industrial attributes of environmental friendliness and resource consumption, which determines the dual contradictory relationship between tourism and ecological environment. Research has shown that 8% of global greenhouse gas emissions originate from the tourism industry, and tourism’s carbon emissions are projected to increase to 6.5 billion tons by 2025 [2]. This indicates that, under the strategic requirements of ecological protection and high-quality development, tourism must shift from “pursuing high-quality economic development” to “simultaneously promoting ecological protection and high-quality development”. Therefore, in the framework of ecological preservation and high-quality growth, it is essential to examine the interaction between the tourism economy and sustainable urban development while also investigating the driving forces behind their synergy. Such an emphasis has important theoretical and practical implications for enhancing ecological conservation and fostering high-quality regional economic progress in the Yellow River Basin.

Various international organizations, such as the United Nations, the International Monetary Fund, and the World Bank, acknowledge tourism as a vital component of green development, considering it a significant economic sector in the shift from a “brown” to a “green” global economy [3]. Scholarly discussions regarding the green industrial characteristics of the tourism economy frequently focus on whether tourism can inherently be considered a green industry. Some scholars have explored the ecological efficiency [4], carbon emission efficiency [5,6], and green development efficiency of tourism from the perspective of non-desirable outputs [7], indicating that tourism is a vital sector for low-carbon economic development. Additionally, some scholars have studied the eco-environmental effects of tourism through field measurement and remote sensing observation technology and found that tourism development has a significant promoting effect on the improvement of ecological environment [8]. While these studies lay a solid foundation for understanding whether tourism possesses green industrial attributes, they often overlook the broader question of whether tourism can promote overall regional green development.

In recent years, scholars have started to recognize the green industrial attributes of the tourism economy from a macro perspective, focusing on its green development effects. In theoretical exploration, researchers have used mathematical deductions and qualitative explanations. Most scholars agree that tourism can drive overall green development in tourist destinations while promoting economic growth. Zhong argues that tourism is a crucial pathway for transitioning economic development towards a green model [9]. Pan et al. introduced an interaction framework that explores the interaction between tourism and sustainability in economic, social, cultural, and environmental dimensions. They argued that tourism plays a role in driving changes within the green economic system [10]. Feng, using a material balance model, theoretically explained the mechanisms by which tourism promotes green development, indicating that tourism drives regional green development by directly or indirectly influencing economic output and the ecological environment of destinations [11]. In addition, several researchers have investigated the green development effects of the tourism economy from the perspective of evolutionary economic geography. For instance, Zhao’s research highlights how tourism destinations rely on and impact natural resources and the environment at various stages of their life cycle [12]. Napierala uncovers the ecological constraints encountered by the tourism industry throughout its development from an evolutionary standpoint and explores the co-evolutionary relationship between tourism and heavy industries [13]. On this basis, some scholars include local governments and tourism enterprises in the theoretical framework of their evolution analysis, arguing that the tourism industry has gradually established a green development path with regional characteristics through industrial upgrading and the adoption of green technologies [14,15]. However, due to variations in tourism resource endowments and historical development trajectories, different regions exhibit significant diversity in their green transformation processes [16,17]. On the empirical research front, researchers have begun to examine the economic and environmental externalities of tourism within empirical frameworks, focusing on quantitatively revealing tourism’s impact on economic growth and carbon emissions in destinations. Some scholars have studied China [18], South Korea [19], and Southeast Asian countries [20], and found that tourism not only boosts economic growth but also has a significant carbon reduction effect, indicating its green industrial attributes. However, research by Balli on Mediterranean countries [21] and Danish on BRICS nations [22] found that while tourism promotes economic growth, it increases regional carbon emissions and lowers environmental quality. Additionally, Sejdiu et al. explored how the concentration of the tourism industry affects regional green economic efficiency [23]; Banga used an auto-regressive distributed lag model to examine the connection between tourism growth, economic expansion, and environmental quality [24]; Mishra et al. highlighted the positive influence of the tourism economy on urban green development, as well as its spatial spillover effects at the municipal level [25]. With the deepening of research, some scholars believe that true sustainability is compared with economic growth in the context of limited environmental resources, and critically analyze the externalities of tourism economy. Gupta et al. found that while tourism can increase capital stock and national income in underdeveloped economies, it may lower environmental quality under new steady-state equilibrium, prompting the implementation of pollution control policies to ensure green growth [26]. Kosmas argues that in a neoliberal economy, excessive tourism growth can have a negative impact on natural and cultural resources as well as on society [27]. Chassagne argues that neoliberalism, universalism, and a Western-centric vision of tourism perpetuate a pattern of exploitation that ignores people, places, and the natural environment [28]. Wijesinghe uses a critical theory approach to question the discourses of “development”, “sustainable development”, and “sustainable tourism” to uncover their driving role in the neocolonialist agenda of domination and exploitation [29].

In summary, existing research often analyzes the green industrial attributes of tourism from the perspectives of its economic and environmental externalities but rarely explores the synergistic evolution patterns between tourism and green development levels. Furthermore, there is a lack of detailed depiction at the urban scale. Consequently, this study uses a detailed evaluation index to assess regional green development levels, aiming to perform empirical research on the synergistic evolution of the tourism economy and green development efficiency across a larger sample and more granular scale. The marginal contribution of this paper lies in integrating tourism economy and destination green development evaluation indicators within a unified research framework, addressing academic concerns regarding tourism’s green industrial externalities. It establishes a theoretical model for the co-evolution of the tourism economy and urban green development efficiency, utilizing spatiotemporal geographical weighted regression to examine the factors driving this synergy. On one hand, this enhances the theoretical system and quantitative expression of the synergistic evolution of tourism economy and urban green development efficiency. On the other hand, it summarizes the characteristics of their synergy, providing insights and suggestions for the Yellow River Basin’s tourism economy and urban green development.

2. Data Sources and Research Methods

2.1. Overview of the Study Area

The Yellow River originates from the “water tower” in the region where three rivers —Yellow River, Yangtze River, and Lancang River—begin. It flows through various geographical landscapes, including the Qinghai–Tibet Plateau, Inner Mongolia Plateau, Loess Plateau, and North China Plain, while traversing diverse climatic zones such as plateau mountains, temperate continental, monsoon regions, and areas with arid, semi-arid, and semi-humid precipitation patterns [30]. The interplay of these natural factors has shaped the distinctive structure of the Yellow River Basin. The upper and middle reaches are ecologically sensitive, making environmental sustainability a critical limiting factor for development. In the lower reaches, the river forms an overhanging linear structure, lacking a fully developed river network. The wide, shallow, and dispersed flow makes it challenging to establish a stable channel, significantly reducing the river’s navigability and impeding social and economic interactions between the upper and lower reaches as well as between the basin and external regions. This article, based on the natural boundaries of the Yellow River Basin defined by the Yellow River Conservancy Commission of the Ministry of Water Resources, focuses on several prefecture-level cities across Qinghai, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong as the area of study. To ensure data consistency, availability, and comparability, 65 administrative units were selected from 91 prefecture-level cities within these 8 provinces and regions for analysis (Figure 1). As of the end of 2018, the study area housed a total population of 271 million people and an economic output of 16.9 trillion yuan, forming the primary population and economic space of the Yellow River Basin.

2.2. Data Sources

The research data comprises three parts: (1) Basic Geographic Data: The vector administrative boundary maps are sourced from the National Geomatics Center of China’s Basic Geographic Information Data (http://www.ngcc.cn/) (accessed on 20 June 2024). This study selects 65 prefecture-level and above cities in the Yellow River Basin. To ensure spatial consistency and facilitate analysis, cities with inconsistent time-series data were excluded. (2) Natural and Environmental Data: Annual average temperature and precipitation data are sourced from the China Meteorological Data Service Center (http://data.cma.cn/site) (accessed on 19 May 2024) and processed using the Kriging interpolation technique to create raster data covering the period from 2002 to 2018. MODIS NDVI data are provided by the International Scientific Data Mirror Center, part of the Chinese Academy of Sciences’ Computer Network Information Center and are processed into monthly composites using ArcGIS 10.6 to produce yearly NDVI datasets. (3) Socioeconomic Data: Domestic and international tourism revenue data are sourced from the China Tourism Statistical Yearbook (2002–2018). Data on GDP per capita, passenger transport volume, number of university students, and environmental pollution control investment are mainly obtained from the China City Statistical Yearbook (2002–2018) and the China Economic Information Network Statistical Database (https://db.cei.cn) (accessed on 22 May 2024). Missing data are supplemented and adjusted using information from local government websites.

2.3. Indicator System Construction

Tourism economic development in the Yellow River Basin is represented by tourism specialization, which is the proportion of total tourism revenue to GDP in each city [31]. Since total tourism revenue is not directly available in statistical yearbooks, international tourism revenue is converted into RMB using the average annual exchange rate of the RMB to USD, and then combined with domestic tourism revenue to calculate total tourism revenue. Evaluating urban green development efficiency at the city scale is fundamental for exploring green development, with green total factor productivity from a performance perspective measured objectively from an input–output angle. Drawing on prior research and leveraging multiple data sources [32,33], this study develops a co-evolution index system to assess the interaction between the tourism economy and urban green development efficiency within the Yellow River Basin (

Table 1. Co-evolution index system of tourism economy and urban green development efficiency in the Yellow River Basin.

	Type	Primary Index	Secondary Index
Tourism economy	Domestic tourism	Domestic tourism economy	Domestic tourism revenue
	International tourism	International tourism economy	Inbound tourism revenue
Urban green development	Input index	Capital element	Total fixed assets of society
		Factors of labor force	Number of units employed at the end of the year
		Technical element	Total expenditure on science and technology at the end of the year
		Resource element	Total water supply; urban built-up area; electricity consumption of the whole society; artificial and natural gas supply; liquefied gas supply
	Output index	Expected output	Gross domestic product; average wages of urban workers; total retail sales of consumer goods; urban green area; green coverage rate; comprehensive utilization rate of industrial solid waste; rate of centralized sewage treatment; harmless treatment rate of household garbage
		Non-expected output	Industrial wastewater discharge; industrial SO2 discharge; industrial soot discharge

).

2.4. Research Method

2.4.1. SBM-Undesirable Model

Traditional radial DEA models fail to account for the slack variables in inefficient decision making units (DMUs), leading to biased efficiency measurements when undesirable outputs are present. The SBM-Undesirable model (slack-based model—undesirable) effectively addresses the slackness in non-expected output variables and the issue of undesirable outputs, providing an accurate assessment of urban green development efficiency [34]. This study employs an SBM-Undesirable model with constant returns to scale, which considers non-expected outputs from an input-oriented perspective to measure the green development efficiency of cities in the Yellow River Basin. The model’s formulation is as follows:

$$\rho =\mathit{min}{\displaystyle \frac{1-\frac{1}{N}{\sum }_{n=1}^N\frac{s_n^x}{x_{k^{\prime }n}^{t^{\prime }}}}{1+\frac{1}{M+1}\left(\frac{{\sum }_{m=1}^M{s}_m^y}{y_{k^{\prime }m}^{t^{\prime }}}+\frac{{\sum }_{i=1}^I{s}_i^b}{b_{k^{\prime }i}^{t^{\prime }}}\right)}}$$

$$s.t.\sum _{t=1}^T\sum _{k=1}^K{z}_k^t{x}_{kn}^t+s_n^x=x_{k^{\prime }n}^{t^{\prime }},(n=\mathrm{1,2}\cdots ,N)$$

$$\sum _{t=1}^T\sum _{k=1}^K{z}_k^t{x}_{km}^t+s_m^y=y_{k^{\prime }m}^{t^{\prime }},(m=\mathrm{1,2}\cdots ,M)$$

$$\sum _{t=1}^T\sum _{k=1}^K{z}_k^t{b}_{ki}^t+s_i^b=b_{k^{\prime }i}^{t^{\prime }},(i=\mathrm{1,2}\cdots ,N)$$

$$z_k^t\ge 0,s_n^x\ge 0,s_m^y\ge 0,s_i^b\ge 0,(k=\mathrm{1,2}\cdots ,K)$$

where the target efficiency is denoted by ρ, while N, M, and I correspond to the number of inputs, expected output, and unexpected output, respectively. (xⁱ_k′n, yⁱ_k'n, bⁱ_k′n) denote the value of k′ decision unit’s input–output in the t′ period; (S^x_n, S^y_n, S^b_n) refer to the relaxation of input, anticipated output, and unforeseen output, correspondingly; $Z_k^t$ is the weight vector of the decision unit.

2.4.2. Haken Model

The Haken model is useful not only for identifying the order parameters in a self-organizing evolutionary system and quantifying its level of organization but also for uncovering the order parameters that drive the system’s synergy, demonstrating how these parameters govern the system’s state far from equilibrium [35]. The specific approach is as follows: suppose there are two subsystems in the coordinated system, with order parameters $q_1$ and $q_2$, respectively. The equation of motion of the system is as follows [36]:

$$q_{1=-}{{\gamma }_{1}q}_1-a{q}_1{q}_2$$

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

where a and b are the strength coefficients of the interaction between the two subsystems; γ₁ and γ₂ are the damping coefficients of the two subsystems, γ₂ > 0 and |γ₂| > |γ₁|, which is said to satisfy the adiabatic approximation hypothesis of the system. At this time, immediately remove q₂, the order parameter q₁ has no time to change, and q₂ = 0, we can obtain the following:

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

By bringing it into the order parameter evolution equation, the system evolution equation can be obtained:

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

Find the inverse integral of q₁ to obtain the potential function of the system:

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

The Haken model was originally developed for the field of physics, and its application conditions can differ in social and economic domains. To scientifically simulate the relationship between the tourism economy and urban green development efficiency’s synergistic evolution, this paper makes the following improvements to the model: (1) The Haken model is designed for continuous random variables, so socioeconomic data need to be discretized. (2) Since the tourism economy and urban green development efficiency are intrinsic to urban systems, a constant term should be added to the Haken model. After these adjustments, the synergistic evolution equations become as follows:

$$q_{1\left(t\right)}=\left(1-{\gamma }_1\right)q_{1\left(t-1\right)}-a{q}_{1\left(t-1\right)}{q}_{2\left(t-1\right)}+C$$

$$q_{2\left(t\right)}=\left(1-{\gamma }_2\right)q_{2\left(t-1\right)}+b{q}_{1_{(t-1)}}^2+C$$

where C is the constant term of the equation; t is the time variable.

2.4.3. Spatiotemporal Geographically Weighted Regression Model

The spatiotemporal geographically weighted regression model is a localized model that builds on the geographically weighted regression by incorporating a temporal factor, considering that regression coefficients vary not only with spatial location but also over time [37]. The calculation formula is as follows:

$$Y_i={\beta }_0\left({\mu }_i,v_i,t_i\right)+\sum _k{\beta }_k\left({\mu }_i,v_i,t_i\right)X_{ik}+{\mathcal{E}}_i$$

where $Y_i$ is the dependent variable value for sample i; $x_{ik}$ is the independent variable;$t_i$ is the time coordinate for sample point i; ${\beta }_0\left({\mu }_i,v_i,t_i\right)$ is the spatiotemporal intercept for sample point i; ${\beta }_k\left({\mu }_i,v_i,t_i\right)$ are the regression coefficients associated with the sample point coordinates; and ${\mathcal{E}}_i$ represents the random error term at point i.

3. Synergistic Evolution Analysis of Tourism Economy and Urban Green Development Efficiency

3.1. Spatial and Temporal Evolution Characteristics

3.1.1. Temporal Evolution Characteristics

To investigate the temporal evolution of the tourism economy and urban green development efficiency in the Yellow River Basin, this study examines trends in their average values and variation coefficients from 2002 to 2018 (Figure 2). According to Figure 2, tourism economic development in the region shows a steady upward trajectory, while its coefficient of variation decreases initially before increasing. This result indicates that during the study period, the tourism economy of the Yellow River Basin experienced significant development, with its role in the progress of economic development becoming increasingly prominent. Especially in 2017, with the increase in the number of tourists and the rise in per capita travel expenses, the average value of the tourism economy has significantly improved. As the development process progresses, influenced by external factors such as tourism resource endowments, regional disparities in tourism economic development gradually emerged. Specifically, the coefficient of variation shows a narrowing disparity in tourism economic development levels across different cities within the Yellow River Basin from 2002 to 2012, decreasing from 0.89 to 0.61. However, from 2013 to 2018, regional disparities widened, with the coefficient of variation rising from 0.63 to 0.78. As a crucial area for ecological preservation in China, the Yellow River Basin has experienced a generally rising trend in green development efficiency over the study period, with the variation coefficient steadily declining. The average green development efficiency rose from 0.21 in 2002 to 0.63 in 2018, while the coefficient of variation decreased from 1.72 to 0.67 during the same timeframe. This suggests that, under the influence of green development principles, the efficiency of resource use in the Yellow River Basin has seen significant improvement. Especially during the “the Twelfth Five-year Plan” period, with the government’s increasing emphasis on structural adjustment, energy conservation and emission reduction, urban green efficiency grew rapidly in 2017. With the acceleration of the development process of regional integration, the strengthening of government policy guidance and implementation, and the improvement of scientific and technological innovation and technology, the level of green development in the basin has gradually narrowed the differences between regions in the process of rapid improvement.

To delve deeper into the dynamic evolution and temporal progression of the tourism economy and urban green development efficiency in the Yellow River Basin, the spatial density distribution of two variables is visually analyzed (Figure 3). Analysis reveals several agglomeration peaks in the kernel density distributions of both tourism economy and urban green development efficiency within the study area. The distribution of tourism economic development levels exhibits an inverted “U” shape, initially rising and then declining (Figure 3a), while urban green development efficiency shows an “N” shape, rising, declining, and then rising again (Figure 3b). The peaks in tourism economic development are primarily concentrated in the range of 0–0.4, while urban green development efficiency shows two agglomeration peaks within the ranges of 0–0.5 and 0.8–1.5. These findings indicate a certain degree of agglomeration in both the tourism economy and green development efficiency in the basin, although regional differences in tourism economy are smaller. Over time, the agglomeration peaks of the tourism economy have shown a downward trend with an expanding range, further illustrating the trend toward equilibrium in tourism economic development in the basin, aligning with China’s national strategic goal of common prosperity. Simultaneously, although the low-value agglomeration of urban green development efficiency is declining, the high-value agglomeration is rising, with the range of agglomeration further expanding. This trend is mainly due to a series of policy measures introduced by national and local governments to promote green development, as well as collaborative cooperation and resource integration between cities, effectively enhancing urban green development efficiency and expanding the range of high-value agglomeration within the basin.

3.1.2. Spatial Differentiation Characteristics

(1) Characteristics of spatial differentiation of tourism economy

Based on the measurement results of tourism economic development levels from 2002 to 2018, cities in the Yellow River Basin are classified into four levels: low (0–0.040), medium (0.041–0.090), relatively high (0.091–0.150), and high (0.151–0.228). During the study period, the tourism economy in the basin exhibited a “high-high agglomeration, low-low agglomeration” pattern in space, which has become increasingly pronounced over time and demonstrated a path-dependent effect of circular accumulation (Figure 4).

In 2002, the tourism economic index in the basin showed certain agglomeration characteristics, with high-value areas mainly concentrated in Xianyang, Weinan, Shuozhou, Sanmenxia, and Puyang in the middle and lower reaches (Figure 4a). The likely reasons for this are that these cities possess abundant tourism resources, and tourism occupies a significant proportion of their economic development. Moreover, during this period, tourism economic development primarily relied on an extensive resource-based development model, with tourism resources being the core elements of tourism economic development. Governments and enterprises mainly attracted tourists through the direct exploitation of natural landscapes and historical and cultural resources, with less attention paid to diversifying tourism products and enhancing service quality. Although this development model spurred rapid growth in tourism in the short term, it also posed challenges such as low resource utilization efficiency and significant environmental protection pressures. Overall, during this period, the high growth of the tourism economy was mainly driven by abundant resource endowments and policy incentives rather than fine management and innovation-driven development models.

By 2010, the tourism economic index in the basin exhibited a more cohesive distribution, forming the Shuozhou–Datong North Shanxi cultural tourism economic agglomeration area and the Zhongyuan cultural tourism economic agglomeration area of Luoyang–Shangluo (Figure 4b). During this period, as environmental awareness and cultural tourism demand increased, the Chinese government enhanced its support for the tourism industry, and the Yellow River Basin, leveraging its rich natural ecological resources and historical and cultural resources, further boosted its tourism appeal and economic vitality. Situated along the Yellow River, Shuozhou and Datong attracted numerous tourists with their natural attractions such as the Hukou Waterfall and abundant historical and cultural resources, enhancing the role of tourism in economic development. Furthermore, Shangluo and Luoyang, located in the cultural heartland of the Central Plains, leveraged their rich cultural tourism resources to promote local tourism economic development.

By 2018, the spatial pattern of the tourism economic index in the basin had undergone significant changes, showcasing the characteristic of similar-level areas developing in clusters (Figure 4c). This change is attributed to the widespread promotion of the all-for-one tourism concept, significantly enhancing the integration efficiency of tourism resources within the basin and promoting regional collaboration and the formation of tourism economic circles. During this period, the basin formed several tourism economic circles: the Central Plains cultural tourism economic circle centered on Luoyang, Zhengzhou, and Kaifeng; the Yellow River Golden Triangle tourism economic circle centered on Linfen, Yuncheng, and Weinan; the Northwest ecological and cultural tourism economic circle centered on Hohhot and Ulanqab; the Red tourism economic circle centered on Yan’an; and the Guanzhong cultural tourism economic circle centered on Xi’an and Baoji. The formation of these tourism economic circles not only fostered regional tourism cooperation and resource sharing but also drove overall tourism industry development and prosperity. Through more effective resource integration and coordinated development, these regions have significantly enhanced their tourism appeal and economic vitality.

(2) Spatial differentiation characteristics of urban green development efficiency

According to the calculation results of urban green development efficiency from 2002 to 2018, cities in the Yellow River Basin are classified into four levels: low efficiency (0–0.1), medium efficiency (0.11–0.20), relatively high efficiency (0.21–0.40), and high efficiency (0.41–1.11). Analysis reveals that the spatial heterogeneity of green development efficiency in the basin is significant, with partial spatial misalignment with the traditional economic landscape. Additionally, the spatial distribution evolution of urban green development efficiency within the basin exhibits a strong Matthew effect, with regional central cities and urban agglomerations gradually becoming crucial components of high-efficiency areas (Figure 5).

In 2002, high-efficiency and medium-high-efficiency areas of urban green development were primarily located in the middle and upper reaches of the basin, forming the Loess Plateau green development city belt centered on Xi’an and Yan’an and the Hetao Plain green development city belt centered on Ordos and Bayannur (Figure 5a). During this period, the basin experienced relatively slow industrialization and urbanization processes, with industrial development mainly following an extensive resource consumption model, resulting in relatively low overall green development levels within the basin. Additionally, the middle and upper reaches exhibited relatively high green development efficiency due to resource endowments, ecological protection policies, economic development models, and environmental management investments. In contrast, the downstream regions, with higher degrees of industrialization and urbanization, faced significant environmental pressures, leading to lower green development efficiency.

In 2010, with the promotion and application of green development concepts, cities in the basin effectively enhanced their green development efficiency during economic transformation, gradually forming high-efficiency areas centered on Longnan, Qingyang, Xi’an, Ordos, and Heze (Figure 5b). During this period, cities in the basin accelerated industrial structure adjustments, gradually transitioning from traditional high-pollution, high-energy consumption industries to high-value-added, low-pollution, low-energy consumption industries. For instance, Ordos and Qingyang focused on upgrading and transforming their coal and energy industries, promoting clean energy and circular economy development, thereby enhancing green development efficiency. Furthermore, cities within the basin strengthened regional cooperation and coordinated development through resource sharing, industrial synergy, and technological exchanges, collectively enhancing green development. For example, Heze, as an essential city in southwestern Shandong Province, engaged in extensive cooperation with surrounding areas in environmental protection industries and resource utilization, promoting regional green development efficiency. Meanwhile, technological innovation was a key driver in improving green development efficiency in the basin. As the hub of technological innovation in Northwest China, Xi’an actively advanced the growth of high-tech industries, promoting the adoption and spread of green technologies.

By 2018, high-efficiency and medium-high-efficiency areas further expanded, forming a cluster development model centered around urban agglomerations such as the Shandong Peninsula, Hohhot–Baotou–Ordos–Yulin, Guanzhong Plain, and Lanxi, which became the optimal regions for green development (Figure 5c). The emergence of these evolutionary characteristics can be attributed to the dual influences of policy incentives and industrial foundations in regional central cities and urban agglomerations, which have largely completed the transformation from labor-intensive industries to capital-intensive and knowledge-intensive industries, thereby reducing constraints related to resource environment and labor costs. Additionally, the continuous transformation and upgrading of input factor allocation, sustained increases in desired outputs, and consistent reduction in undesired outputs such as environmental pollution have maintained urban green development efficiency at a high level. Simultaneously, resource-based cities and traditional industrial cities such as Datong, Baoji, Baiyin, and Lanzhou, located in the middle and upper reaches, as well as cities such as Jiaozuo, Xinxiang, Jining, and Dongying, located in the lower reaches, faced challenges in transforming their industrial structures. Their green development efficiency was at a low level in this period due to factors such as industrial structure, resource endowment, and location conditions.

3.2. Construction of Synergistic Evolution Model and Identification of Order Parameters

Using tourism economy (TE) and urban green development efficiency (UDE) as state variables, we propose the Haken model hypothesis and employ the adiabatic elimination method to determine its validity. This approach ultimately reveals the system’s order parameters.

Table 2. Identification results of synergistic evolution parameters of tourism economy and urban green development efficiency in the Yellow River Basin from 2002 to 2018.

Model Assumption	Equation of Motion	Parameter Information	Model Conclusion
q1 = TE	q1 = 0.670 q1(t−1) − 0.213 q1(t−1)q2(t−1)	γ1 = 0.330, γ2 = 0.358	The motion equation is established; does not satisfy the adiabatic approximation rule; hypothesis is true
q2 = UDE	q2 = 0.642 q2(t−1) + 0.0387 q1(t−1)q2(t−1)	a = 0.213, b = 0.0387
q1 = UDE	q1 = 0.410 q1(t−1) + 0.170 q1(t−1)q2(t−1)	γ1 = 0.410, γ2 = 0.542	The motion equation is established; satisfies the adiabatic approximation rule; hypothesis is true
q2 = TE	q2 = −0.542 q2(t−1) + 0.008 q1(t−1)q2(t−1)	a = −0.170, b = 0.008

illustrates the parameters of the coordination model from 2002 to 2018: γ₁ = 0.410; γ₂ = 0.542; a = −0.170; b = 0.008. The motion equations for the synergistic development of the tourism economy and urban green development efficiency in the Yellow River Basin are $q_1=-0.410q_1+0.170q_1{q}_2$;

$$q_2=-0.542q_2+0.008q_1^2;$$

The order parameter equation is $q_1=-0.410q_1+0.003q_1^3$;

The potential function is given by $v=0.205{q_1}^2-0.001q_1^4$.

Setting q₁ = 0, we find three solutions for the potential function: $q_1^{*}$ = 0, $q_1^{**}$ = 12.535; $q_1^{***}$ = 34.677. Given that both the tourism economy and urban green development efficiency are positive, we only consider the potential function where q > 0. Consequently, we identify the stable point U (12.535, 34.677) for the synergistic evolution system of tourism economy and urban green development efficiency. The distance between any state parameter point A and the stable point determines the system’s state, represented by the synergy valued:

$$d=\sqrt{{\left(q-12.534\right)}^2+{\left[v\left(q\right)-34.677\right]}^2}$$

Research indicates that urban green development efficiency in the Yellow River Basin serves as the order parameter of the synergistic system, guiding its evolution. The control parameter, represented by the tourism economy, reflects the behavior of the synergistic system. Here, a < 0 indicates that from 2002 to 2018, the development of the tourism economy in the basin positively impacted the improvement of green development efficiency. Furthermore, b > 0 suggests that during the same period, efforts to enhance urban green development efficiency contributed positively to the advancement of the tourism economy. In the improved Haken model, the damping coefficients ${\gamma }_1$ > 0 and ${\gamma }_2$ > 0 indicate that both the tourism economy and urban green development efficiency in the basin exert positive feedback on the synergistic system.

3.3. Spatial and Temporal Differentiation Characteristics of Tourism Economy and Urban Green Development Efficiency Co-Evolution

3.3.1. The Temporal Characteristics

Figure 6a shows that the average level of the co-evolution of the tourism economy and urban green development efficiency in the Yellow River Basin exhibited a continuous upward trend from 2002 to 2018. At the same time, the coefficient of variation for co-evolution levels demonstrated a dynamic decline. This indicates a strong trend of coordinated development between the tourism economy and urban green development efficiency within the basin throughout the study period, with this trend gradually intensifying over time. Additionally, as the basin’s tourism economy grew and the level of green development improved, the co-evolution values increased, and the disparities showed a convergence trend.

3.3.2. Spatial Differentiation of the Synergistic Evolution between Tourism Economy and Urban Green Development Efficiency

This study employs the natural breaks method to classify the synergistic evolution level of the two systems in the prefecture-level cities of the Yellow River Basin into four stages: high-level synergy (>0.066), moderately high synergy (0.045–0.066), intermediate synergy (0.028–0.045), and primary synergy (0–0.028). The spatial distribution characteristics at three key time points, 2002, 2010, and 2018, are depicted. The analysis reveals that during the study period, the co-evolution of the tourism economy and urban green development efficiency in the basin exhibited a pattern of agglomerated and contiguous development, with a spatial evolution characterized by a shift from polarization to trickle-down effects (Figure 7).

In 2002, the number of cities in the higher synergy stage was limited and primarily concentrated in the mid-to-upper reaches of the Yellow River Basin. Two major high-value co-evolution zones were formed: one centered around Ordos and Bayannur in the Hetao Plain and another around Qingyang and Guyuan in the Guanzhong Plain (Figure 7a). These areas benefit from favorable natural ecological environments, excellent air quality, and abundant water resources, which facilitate green development. Furthermore, the good ecological conditions and rich cultural and tourism resources attract a significant number of tourists, boosting the tourism economy. In contrast, most cities in the lower reaches were at the intermediate co-evolution stage. This was mainly due to industrial activities exerting significant environmental pressure in the lower Yellow River region, coupled with insufficient development of tourism resources, which hindered the co-evolution of the tourism economy and green development efficiency. By 2010, the number of high-value co-evolution zones in the basin had increased to four, forming a trend of agglomerated and contiguous development around these high-value areas (Figure 7b). This reflects the improvement in the co-evolution of the tourism economy and urban green development under China’s “New Normal” economic phase and the “Five-in-One” development concept, where eco-tourism and cultural tourism have become trends. Additionally, strict environmental regulation policies have encouraged regions to actively pursue ecological civilization, further enhancing the synergy between tourism and green development. Compared to 2010, by 2018, the number of cities in the high and moderately high synergy stages had increased further, expanding gradually towards the mid-to-lower reaches of the basin. New emerging high-value agglomeration zones have formed around Taiyuan, Zhengzhou, and Jinan, complementing the existing high-value co-evolution clusters.

4. Analysis of Influencing Factors on the Synergistic Evolution of Tourism Economy and Urban Green Development Efficiency

4.1. Index Selection and Description

The synergistic evolution of the tourism economy and urban green development efficiency is the result of the intricate interplay between various natural background conditions and human societal development factors. Therefore, this paper seeks to establish a model to analyze the factors influencing the synergistic evolution of urban tourism economies and green development efficiency by examining the human–environment relationship in regional systems. The study systematically quantifies the mechanisms driving the synergistic evolution of urban tourism economies and green development efficiency under multiple factors. It aims to explore the factors influencing this synergy in the Yellow River Basin from two perspectives: natural background elements and human social development (

Table 3. Descriptive statistics of variables.

Variable Type	Variable Name	Variable Interpretation	Expected Impact
Explained variable	Co-evolution level	Calculated by using improved Haken model
Natural factors	Topographic condition	Mean topographic relief	−
	Temperature condition	Average annual temperature	+
	Precipitation condition	Annual precipitation	+
	Vegetation coverage	Vegetation coverage	+
Socioeconomic factors	Economic development	Per capita GDP (ten thousand yuan)	+
	Industrial structure	Industrial structure index	+
	Degree of openness	Actual use of foreign capital as a percentage of GDP (%)	+
	Scientific and technological innovation	Number of patent applications	+

).

(1) Natural Factors. The study initially identifies potential factors influencing the synergistic evolution of tourism economies and urban green development efficiency from four aspects: water, soil, air, and biology. ① Topography not only affects the type and distribution of tourism resources but also significantly impacts ecosystem stability and environmental protection [38]. Therefore, the average terrain relief is selected as the representative variable for topography. ② Precipitation affects the availability of water resources and vegetation cover, thereby influencing the richness of tourism resources and the quality of the ecological environment [39]. Thus, the annual average precipitation is used as the representative variable for precipitation conditions. ③ Due to latitude and topographical influences, there are significant temperature variations within the Yellow River Basin. Temperature is a key factor affecting tourism comfort and seasonal tourism activities, and appropriate temperature can enhance the stability of tourism economy [40]. Additionally, temperature conditions impact urban ecological resilience, affecting urban green development efficiency. Therefore, annual average temperature is selected as the representative variable for temperature conditions. ④ High vegetation coverage enhances the attractiveness of tourist attractions, and helps improve air quality, regulate climate, and protect biodiversity, thus affecting the quality and sustainability of regional ecological environments [41]. Given that vegetation coverage directly reflects the quality of regional ecological environments and ecosystem services, it is considered a potential variable influencing the synergistic evolution of tourism economies and urban green development efficiency.

(2) Socioeconomic factors. ① Economic development can drive the synergistic evolution of tourism economies and urban green development efficiency by increasing resource input, improving infrastructure, promoting technological innovation, and supporting policies. This ensures that economic growth is achieved while considering environmental protection and sustainable development [42]. Hence, per capita GDP is selected as the representative variable for economic development level. ② Industrial structure influences regional economic diversity and sustainability. A higher industrial structure index can promote economic diversification and the development of service industries while supporting green technologies and eco-friendly services, thereby driving the synergistic evolution of tourism economies and urban green development efficiency [43]. Thus, the proportion of the tertiary industry output to GDP is used to measure the rationalization and advancement of the urban industrial structure, and the industrial structure index serves as the representative variable for industrial structure rationalization. ③ Openness can attract foreign investment and advanced international technologies, introducing high-quality tourism projects and green development technologies. By optimizing resource allocation, it enhances the utilization efficiency of tourism resources and the technical level of urban green development, thus promoting improvements in tourism economies and urban green development efficiency [32]. In practice, the proportion of foreign capital to GDP is used as the representative variable for the region’s openness. ④ Technological innovation is a crucial driver for tourism and green development [44]. Higher scientific and technological expenditures indicate significant investment in technological innovation, enhancing the quality and efficiency of tourism services and promoting green development. Number of patent applications is used as the representative variable for technological innovation.

4.2. GTWR Model Calculation Results

The Geographically and Temporally Weighted Regression plugin in ArcGIS 10.6 was used to calculate and analyze the influencing factors. The ratio of spatial to temporal distance parameters was set to 1. The GTWR regression model parameters for the synergistic evolution of urban ecological resilience and tourism economy in Shandong Province from 2002 to 2018 are shown in

Table 4. GTWR parameters.

Model Parameter	Bandwidth	Residual Sum of Squares	Residual Estimated Standard Deviation	Akaike Information Criterion	R2	Adjusted R2	Spatiotemporal Distance Ratio
Value	18	3.58739	0.112193	−294.896	0.479622	0.500161	3.12837

. The model bandwidth is 18 cities, the corrected Akaike information criterion (AICc) is −294.896, and the adjusted R² is 0.500161, which is greater than the R² of the ordinary least squares (OLS) model (0.479622). This indicates that the GTWR model provides a better explanatory effect than the OLS model. Therefore, the GTWR model is chosen to explore the spatiotemporal non-stationarity of the influencing factors in the collaborative evolution of tourism economy and urban green development efficiency in the Yellow River Basin.

4.3. Analysis of Influencing Factors of the Synergistic Evolution

The factors influencing the synergistic evolution of tourism economy and urban green development efficiency in the Yellow River Basin have significant spatial heterogeneity, and each factor has obvious individual differences on the co-evolution (Figure 8).

Topographical conditions significantly and positively influence the synergistic evolution of the two systems, with the overall impact decreasing from the downstream to the upstream regions (Figure 8a). Specifically, the high regression coefficient for topography is primarily concentrated in the North China Plain of the downstream areas, an important origin of Confucian and Central Plains cultures, rich in cultural tourism resources and a good ecological environment. Variations in topography often lead to diversity in natural and cultural tourism resources, promoting tourism economy development while achieving a positive interaction between tourism and green development through the protective use of ecological environments. However, the mid-upper reaches are predominantly characterized by plateaus and mountains with fragile ecosystems. The landscape diversity formed by topographic differences promotes the development of tourism economy, but its influence on the synergistic evolution of two systems is weak due to the dual constraints of ecological protection and economic growth.

Temperature conditions predominantly exert an insignificant negative influence on the synergistic evolution of the two systems, showing a spatial characteristic of “higher in the north, lower in the south; stronger in the west, weaker in the east” (Figure 8b). The Yellow River Basin encompasses temperate monsoon, temperate continental, and plateau climates, and spans a wide latitudinal range, resulting in significant regional temperature variations. Cities in the southern part of the basin, located at lower latitudes than those in the north, are less affected by seasonal changes, and their biodiversity and ecosystem stability are relatively high, which enhances tourism economy and urban green development efficiency. Moreover, cities in the mid-lower reaches, situated in temperate monsoon zones with small annual temperature fluctuations, experience minimal temperature impact on tourism economic development and green development efficiency.

The impact of precipitation on the synergistic evolution of tourism economy and urban green development efficiency in the basin exhibits spatial heterogeneity (Figure 8c). The mid-lower reaches predominantly experience negative effects, while the upstream regions show positive influences. This phenomenon can be attributed to the low-lying terrain in the downstream areas, where heavy rainfall easily causes floods, damaging infrastructure and tourist sites, thereby hindering tourism attractiveness and green development efficiency. In contrast, the arid upstream regions benefit from moderate increases in precipitation, which alleviate drought conditions, promote vegetation growth and ecological restoration, enhance regional ecological resilience, and thus positively impact tourism economy and green development efficiency.

Vegetation coverage plays a role in promoting the synergistic evolution of tourism economy and urban green development efficiency, with regional variations (Figure 8d). The Hetao Plain in the middle reaches is most significantly affected by vegetation coverage in synergistic development process. Located in the mid-lower reaches of the Yellow River Basin, the Hetao Plain has relatively fragile soil and water resources. Due to long-term agricultural development and soil erosion, vegetation coverage is low, making the ecosystem heavily reliant on vegetation restoration and protection. Good vegetation coverage improves ecological environment quality and enhances the aesthetic value of natural landscapes and tourism resource attractiveness, thereby increasing the level of collaborative development between tourism economy and urban green development efficiency. In contrast, vegetation coverage has a relatively limited impact on these factors in the upstream and downstream areas due to its smaller influence on the local ecological environment and development model.

The economic development level significantly negatively impacts the synergistic evolution of tourism economy and urban green development efficiency, with the overall impact decreasing in a stepwise fashion from the inland west to the eastern coast (Figure 8e). This somewhat corroborates the environmental Kuznets curve theory. In the upstream areas of the Yellow River, lower economic development levels lead to inefficient resource utilization and weak environmental protection efforts, often accompanied by high pollution emissions and ecological damage, resulting in a more pronounced negative impact on tourism economy and green development efficiency. Conversely, in the downstream coastal areas, higher economic development levels, mature urbanization, and tourism development, along with more comprehensive environmental protection measures, improve the ecological environment and reduce the negative impact of economic growth, gradually forming a positive effect on synergistic evolution.

The industrial structure primarily exerts a positive impact on the synergistic evolution of the two systems, displaying a spatial pattern of “higher in the south, lower in the north; stronger in the east, weaker in the west” (Figure 8f). The Yellow River Basin exhibits noticeable differences in industrial development stages, with the overall industrial structure dominated by labor- and capital-intensive industries. Cities in the southern and eastern regions of the basin, leveraging location and resource advantages, boast strong economic power and optimized industrial structures, which drive the collaborative evolution process. In contrast, the mid-upper reaches face constraints due to resource endowment and economic development levels, with a single industrial structure that relies heavily on energy and heavy chemical industries, limiting the development of technology-intensive industries and hindering the collaborative evolution of tourism economy and green development efficiency.

The openness level primarily exerts a significant positive influence on the synergistic evolution of the two systems, presenting a band-like distribution pattern decreasing from the inland west to the eastern coast (Figure 8g). This result indicates that the Yellow River Basin has largely overcome the “pollution haven” dilemma common to developing regions. Specifically, foreign investment in urban clusters like the Lanzhou–Xining cluster, Hohhot–Baotou–Ordos–Yulin cluster, and Guanzhong Plain cluster in the mid-upper reaches significantly promotes the synergistic evolution of the two systems, while its positive impact is less pronounced in the downstream areas. This may be because cities in the downstream areas, with favorable location advantages and industrial development advantages, provide an excellent environment for foreign investment. Moreover, cities in the downstream areas have more mature in terms of the role transformation of domestic and foreign investment, and according to the law of diminishing marginal returns, the positive impact of openness gradually diminishes.

The regression coefficient for innovation capacity on the synergistic evolution of the two systems is generally negative, presenting a “central depression pattern” (Figure 8h). Spatially, low-value areas are concentrated in the Taiyuan urban cluster, Hohhot–Baotou–Ordos–Yulin region, and other coal-rich and processing areas. In these areas, mismatches between innovation input and output lead to innovation capacity not effectively translating into economic and green development dynamics. Additionally, insufficient infrastructure and policy support limit innovation capacity, putting central regions at a disadvantage in the synergistic evolution. High-value areas are mainly located in regions with significant industrial development and policy support, such as the Guanzhong–Tianshui Economic Zone and Shandong Peninsula urban cluster. This suggests that enhancing government-led innovation capacity is a viable strategy in the early stages of balancing high-quality development with ecological protection.

5. Discussion and Conclusions

5.1. Discussion

As a preliminary exploration of the synergistic evolution between tourism economy and urban green development efficiency in the Yellow River Basin, this study has certain limitations. Existing research has fully confirmed the validity of the tourism-led economic growth hypothesis in the Chinese context [45]. However, this study finds a spatial mismatch between the pattern of green development and the economic development in the basin at the prefecture level. This indicates that simply boosting economic output is insufficient to drive urban green development in the basin. Therefore, if the tourism economy solely promotes the economic output growth of tourism destinations may not effectively drive the overall green development of destinations. However, existing studies have demonstrated that the tourism economy significantly enhances the green development efficiency of destination cities [40]. This suggests that the green development effects of the tourism economy entail complex mechanisms. Future research can further examine the synergistic mechanisms between tourism economy and urban green development by constructing a theoretical framework for the green development effects of tourism economy. On one hand, employing mediation effect models and constructing interaction terms could allow for an in-depth investigation and comparison of the collaborative development mechanisms between the two systems. On the other hand, a more detailed decomposition of green development efficiency can be conducted to further investigate the driving forces behind the synergistic evolution of the two systems. In addition, airports and high-speed rail stations, as well as other transportation hubs, may also have an impact on the synergistic evolution of two systems. However, considering that this effect is mainly achieved through traffic efficiency and accessibility, the current research is limited by the availability of data, and the discussion in this area is relatively weak. Therefore, the impact of traffic factors such as transport accessibility and efficiency on tourism economy and urban green development efficiency should be further discussed in future studies.

5.2. Conclusions with Recommendations

5.2.1. Conclusions

(1)

Between 2002 and 2018, the Yellow River Basin exhibited significant dynamic changes in both tourism economy and green development efficiency. The tourism economy first experienced balanced growth, followed by increasing disparities, whereas green development efficiency showed a trend of narrowing differences. Furthermore, the tourism economy demonstrated a spatial pattern of “high-high clustering and low-low clustering,” which intensified over time and exhibited a path-dependent cumulative effect. Urban green development efficiency showed significant spatial differentiation, with regional central cities and urban agglomerations gradually forming high-efficiency areas, reflecting a strong Matthew effect.

(2)

Urban green development has been identified as the core sequence parameter of synergistic evolution, primarily exhibiting a positive feedback effect that dominates the entire system’s evolutionary direction. Specifically, the improvement of urban green development not only enhances environmental quality but also boosts the sustainability and attractiveness of the tourism economy, creating a positive feedback loop. This positive feedback effect drives the collaborative evolution between the tourism economy and urban green development. The synergistic evolution between tourism economy and urban green development shows a continuous upward trend and spatially presents a pattern of clustering and the transition from polarization to a trickle-down effect.

(3)

The influencing factors of the synergistic evolution of tourism economy and urban green development efficiency exhibit spatiotemporal differences. Natural factors such as topography and vegetation coverage and socioeconomic factors such as industrial structure and degree of openness have shown significant positive influences. In contrast, factors like temperature, precipitation, economic development level, and open innovation have posed certain hindrances to synergistic evolution. Specifically, topography, temperature, and industrial structure demonstrate a “strong in the east, weak in the west” stepped decreasing pattern, while precipitation, economic development level, and openness show the opposite. Vegetation coverage presents a spatial pattern of higher in the center and lower around the edges, whereas innovation capacity displays a “central collapse” spatial pattern.

5.2.2. Recommendations

Against the backdrop of ecological protection and high-quality development in the Yellow River Basin, promoting the high-quality collaborative development of tourism economy and urban green development efficiency is imperative. Firstly, this study focuses on the prefecture-level city scale to analyze the laws and heterogeneity of the collaborative evolution of tourism economy and urban green development efficiency more deeply and rationally. The Haken model is used to explore the co-evolution characteristics of the two systems, which enriches the research perspective of tourism economics and urban green development. Finally, this study investigates the influencing factors of the collaborative evolution of tourism economy and urban green development in the basin using the spatial–temporal geographically weighted regression model, aiming to provide a more scientific analysis of the synergistic evolution characteristics of the two systems. The policy implications of this study are as follows:

(1)

Local governments should fully leverage their unique resources and location advantages, providing stronger policy support to encourage tourism development and consider the tourism economy as a reliable policy tool to promote local urban green development. At the level of destination cities, although theory confirms that expanding the tourism economy scale can facilitate urban green development, in practice, it is crucial to emphasize promoting the sustainable development of the tourism industry within reasonable carrying capacity limits. At a broader regional level, it is crucial to prevent homogeneous competition among cities with similar resource advantages and geographical closeness, especially when they seek to grow their tourism economy. By creating mechanisms for inter-regional cooperation in and refining the spatial distribution of tourism products, a balanced tourism development model characterized by mutual support, healthy competition, and collaborative growth can be realized.

(2)

The local government needs to focus on improving the limited direct influence that the tourism economy currently has on promoting green development in the basin. Firstly, it is crucial to recognize that tourism is not a “smokeless industry”. As one of the major sources of global greenhouse gas emissions, and the government should give priority to greenhouse gas emission reduction and sustainable development under the constraints of the “dual carbon” target. Additionally, further magnifying the economic benefits of tourism, promoting the iteration and upgrading of tourism products, and breaking the reliance on the traditional low-efficiency “ticket economy” is essential. Furthermore, tourism development should promote a transition towards eco-friendly, lightweight, and shared approaches.

(3)

The inherent mechanism of positive feedback effects from both the tourism economy and urban green development efficiency on the synergistic system indicates that achieving a “win-win” scenario for green development and tourism economy is a crucial direction for future development in the Yellow River Basin. Regions within the basin should leverage the outstanding advantages of the cultural resources and natural ecology, emphasizing the “strong alliance” of quality resources to promote the formation of a Yellow River ecological and cultural tourism belt that links and integrates cultural and ecological resources from the upper, middle, and lower reaches of the basin. Given the regional differences in the synergistic evolution of tourism economy and urban green development efficiency, where central cities exhibit higher levels of collaborative evolution, regions within the basin should enhance cooperation among central cities. By forming a vanguard for ecological protection and high-quality development in the basin through mutual benefit alliances, the coordinated development of the entire basin can be advanced.