The Influence of Tourism’s Spatiotemporal Heterogeneity on the Urban–Rural Relationship: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration, China

Abstract

The urban–rural imbalance, a social problem shared globally, is seeing a turnaround as a result of changes in production patterns. Tourism can not only provide employment but also drive the development of related industries, which is an effective measure to solve the urban–rural dichotomy. Against this background, we take the Beijing-Tianjin-Hebei (BTH) urban agglomeration as a sample, uses new urbanization and rural revitalization as a criterion for measuring urban and rural development, and quantifies the degree of urban–rural coordinated (URC) value in the BTH urban agglomeration from 2010 to 2019 by using the coupled coordination degree model. After that, the geographically and temporally weighted regression (GTWR) model is used to analyze the impact of tourism on the URC. The results show that: (1) there are large gaps within the BTH urban agglomeration in terms of urban and rural development, and there may be a threshold effect for the URC; (2) the impact of tourism on the URC shows spatiotemporal heterogeneity and the highest degree of diversity is high-quality intangible cultural heritage resources; (3) the density of highways exerts a negative impact on the URC. Finally, based on the findings, tourism is as an anchoring point to provide policy guidance for sustainable urban–rural development.

1. Introduction

Urban–rural relations have long been an important issue of global concern. In China, a developing country, development policies since the 1970s have led to a dichotomy between urban and rural (U&R) areas in terms of demographics and economic structure [1,2]. At the same time, changes in space and modes of production have fostered increasingly close links between U&R [3]. As for resource flows, U&R areas have unique resource advantages. Rural areas need urban areas not only to provide industrial products and financial services but also to transmit means of production, technology and management knowledge [4]. In turn, rural areas can provide urban areas with agricultural products and ecological resources, thus establishing a development system in which U&R areas have complementary advantages [5]. Moreover, revitalizing rural areas can not only provide affordable labor for urban areas but can also ease the irrational migration of the population to urban areas, thus alleviating the problems of urban congestion and hollow villages [6,7]. In this context, the complementarity of resources and the mobility of the population promote interdependence, making coordinated urban–rural development key to sustainable social growth [8,9]. Among these dynamics, tourism, with its strong economic drive and sustainable environmental friendliness, effectively permeates various industries, promoting regional development, and has thus become leverage for rural economic development and harmonious urban–rural relations [2,10,11].

In pioneering research on the interaction between U&R through tourism, Nadin and Stead underlined that tourism and recreational activities serve as crucial links connecting cities and the countryside [12]. They laid the groundwork for subsequent studies. Following this, Weaver and Lawton focused on residents and tourists in urban–rural fringe areas to understand their perceptions of tourism. Zhang et al. analyzed host–guest interaction mechanisms in similar regions subsequently [13,14,15]. These studies led Weaver to propose the concept of the urban–rural transition zone in 2005, which includes theme parks, tourist shopping villages, ecotourism, industrial tourism, and rural tourism [16]. Although this classification has evolved over time, it has opened new avenues of research beyond urban tourism by integrating Balaguer’s tourism-led growth (TLG) hypothesis [17].

Thereafter, scholars have examined the impact of tourism on urban–rural relationships. They discovered that tourism development not merely stimulates rural economic growth but also helps reduce the urban–rural income gap by enhancing employment rates, promoting urban–rural exchanges, improving infrastructure, driving industrial transformation, and altering residents’ perceptions of identity, thereby improving urban–rural relations [18,19,20]. However, some scholars argue that the benefits of tourism may be overestimated. According to their analysis, income from rural tourism tends to flow more toward urban areas, ultimately disadvantaging low-income rural populations [3]. Moreover, rural residents’ living spaces may be encroached upon, leading to a decline in their sense of self-efficacy [21]. Additionally, while tourism’s contribution to urban–rural coordination (URC) is primarily economic, variations in regional development and resource endowments can lead to different conclusions even within studies of the same country [22,23].

In comparative studies of inbound and outbound tourism, Shi and Zhang found that inbound tourism has a stronger capacity to adjust urban–rural income distribution [24,25]. However, when spatial spillover effects are considered, surrounding inbound tourism increases the local urban–rural income gap, which varies across different study areas [24]. This indicates that the impact of tourism on the URC may exhibit spatial heterogeneity.

In summary, the aforementioned studies provide a theoretical basis and direction for this research. However, the impact of tourism on urban–rural relations has a very complex mechanism, which still requires more detailed research. Firstly, most of these researches use the urban–rural income gap as the dependent variable and lack analyses beyond the economic dimension, making it difficult to provide an assessment of the overall picture of tourism’s impact on urban–rural relations. Secondly, most studies concentrate on the macro-national level and micro-cities or villages, and there is a lack of investigation of meso-urban agglomerations, which may limit comparative analyses. Finally, in research methodologies, temporal or spatial heterogeneity has been explored more widely, but combined analyses have been relatively limited.

Therefore, this study focuses the Beijing–Tianjin–Hebei (BTH) urban agglomeration in China, and we accomplished the following research objectives. (1) Taking China’s new urbanization and rural revitalization strategies as a criterion, the evaluation index system for urban–rural coordination development was established and linearly summed after determining the weights using the entropy value method. (2) We analyzed the temporal and spatial evolution patterns of urban, rural, and urban–rural coordination relationships in the BTH urban agglomeration from 2010 to 2019, and to visualize them using kernel density maps and radar maps. (3) A system of variables of tourism’s influence was established, and the optimal model was selected based on comparing the ordinary least squares (OLS) regression, time-weighted regression (TWR), geographic-weighted regression (GWR), and geographically and temporally weighted regression (GTWR). (4) We investigated the spatiotemporal heterogeneity of the impact of tourism on urban–rural coordination relations and offer policy recommendations based on findings from the context within which ecotourism-led industrial development has become a typical model of urban–rural coordination [26].

The study is structured as follows. The study area, framework, indicator system, and data sources are presented in Section 2. Section 3 calculates urban, rural, and urban–rural coordination relationships and analyzes temporal changes and spatial distributions. Within Section 4, we choose impact variables related to tourism and then examine spatiotemporal heterogeneity. Section 5 and Section 6 consist of the paper’s discussions and conclusions, accordingly.

2. Materials and Methods

2.1. Study Area

In this study, the Beijing–Tianjin–Hebei (BTH) urban agglomeration in China was selected as the study area; it consists of 13 prefecture-level cities, as shown in Figure 1. The BTH urban agglomeration is located in the North China Plain, bordered by the Bohai Sea to the east, the Taihang Mountains to the west, and the Yan Mountains to the north. This area features diverse terrain and distinct seasons: spring is dry and windy, summer is hot and rainy, autumn is clear and crisp, and winter is cold and dry. The BTH urban agglomeration plays a vital role in China’s economic development. Due to varying factors such as regional economy, geographical location and historical culture, the cities within the BTH urban agglomeration display different levels of economic growth, tourism development, resource endowment, and urban–rural relationship.

According to the 2023 National Economic and Social Development Statistics Bulletin (from the Chinese government website) for the BTH urban agglomeration, there is a remarkable gap between Hebei and Beijing and Tianjin. As municipalities directly under the central government, Beijing and Tianjin have a resident population of 21.85 million (87.8% urbanization rate) and 13.64 million (85.49%), respectively. In comparison, Hebei province has a resident population of 73.93 million and an urbanization rate of 62.77%. All three places have experienced negative population growth. In terms of economic development, Beijing (RMB 203.3 thousand) and Tianjin (RMB 122.8 thousand) have a strong foundation for economic development, while the per capita GDP of Hebei province is only RMB 59.3 thousand. Despite the large gap in population and economic growth, Hebei Province’s per capita tourism income (RMB 1132) is comparable to that of Beijing (RMB 1778) and Tianjin (RMB 939). In addition, tourists from Beijing and Tianjin accounted for more than 10% of the total number of tourists in Hebei. This phenomenon suggests not only that Hebei’s tourism industry has potential for growth but also that the coordinated development of the BTH urban agglomeration may become an increasingly influential trend in the future.

The natural attractions in the BTH urban agglomeration are exceptionally varied. Tianjin’s Haihe River scenic area is lined with lush trees. Additionally, Hebei features diverse natural landscapes, such as the seaside of Qinhuangdao, mountains of Chengde, and Baiyangdian Lake in Baoding. They all showcase the unique natural beauty of Hebei.

The cultural tourism resources in the BTH urban agglomeration are equally rich and diverse. Beijing’s ancient royal buildings and gardens, including the Forbidden City, the Summer Palace and the Temple of Heaven, highlight the grandeur of Chinese culture and imperial history. Tianjin’s Wudadao area, with its numerous well-preserved Western-style buildings, exemplifies the blend of cultures in modern China. In Hebei, the Summer Resort and surrounding temples in Chengde represent the epitome of Qing Dynasty royal culture. Other attractions like the Zhaozhou Bridge in Shijiazhuang and the Tomb of King Zhao of Yan in Handan draw many visitors all year round.

The unique natural landscapes, abundant resources, rich historical background and diverse cultural heritage, along with the vibrant blend of ethnic groups and their artistic contributions, are the key strengths that establish the BTH urban agglomeration as a significant highlight on China’s tourism map. At the same time, the capital city of Beijing has made this urban agglomeration more recognizable. Considering these distinctive attributes, we decided to conduct our research in this area.

2.2. Implementation of Analyses

In order to investigate the impact of tourism on the URC in the BTH urban agglomeration, this study adopts several widely recognized academic approaches. Firstly, the entropy method, known for its objectivity, is used to assess the importance of indicators, providing a solid foundation for subsequent analysis. Secondly, the coupling coordination degree, a model frequently utilized in academic research to measure the relationship between systems, is applied. Finally, the Geographically and Temporally Weighted Regression (GTWR) model is employed to determine the impact of tourism on the URC and their spatiotemporal variation, thus identifying the characteristics of different areas within the BTH urban agglomeration. The research framework is illustrated in Figure 2.

2.2.1. Entropy Method

The entropy method is used to calculate the relative weights of the urban and rural variables for linear summation. In information theory, entropy is a measure of the degree of disorder within a system that quantifies the valid information provided by the data. The entropy determines the weight of an indicator based on the amount of information conveyed by each indicator [27,28,29]. The greater the change in an evaluation indicator, the smaller its entropy value, indicating that the indicator contains and conveys more information. Therefore, the weight is also higher accordingly. The calculation processes are as follows:

1.

Non dimensional treatment:

When the variable is positive,

$${\mathrm{y}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\max {\mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{x}}_{\mathrm{i}\mathrm{j}}}}(\mathrm{i}=1,2,3,\dots \mathrm{n},\mathrm{j}=1,2,3,\dots \mathrm{n})$$

(1)

When the variable is negative,

$${\mathrm{y}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\max \mathrm{x}}_{\mathrm{i}\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{{\max \mathrm{x}}_{\mathrm{i}\mathrm{j}}{-\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{x}}_{\mathrm{i}\mathrm{j}}}}(\mathrm{i}=1,2,3,\dots \mathrm{n},\mathrm{j}=1,2,3,\dots \mathrm{n})$$

(2)

Standardization,

$${\mathrm{p}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{i}\mathrm{j}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{y}}_{\mathrm{i}\mathrm{j}}}}$$

(3)

To avoid a zero divisor, the results were uniformly translated.

2.

Entropy Calculation:

$${\mathrm{e}}_{\mathrm{j}}=-\mathrm{k}{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}{\mathrm{l}\mathrm{n}\mathrm{p}}_{\mathrm{i}\mathrm{j}}(\mathrm{k}={\displaystyle \frac{1}{\mathrm{l}\mathrm{n}\mathrm{n}}},0\le {\mathrm{e}}_{\mathrm{i}\mathrm{j}}\le 1)$$

(4)

where n = number of cities × year, in this study, n = 130.

3.

Weight Conversion

$${\mathsf{\omega }}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{g}}_{\mathrm{j}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{g}}_{\mathrm{j}}}}({\mathrm{g}}_{\mathrm{j}}=1-{\mathrm{e}}_{\mathrm{i}\mathrm{j}})$$

(5)

2.2.2. Coupling Coordination Degree Model

The coupling coordination degree is employed to evaluate the extent of mutual influence and coordination among cities in the BTH urban agglomeration during development [30]. The results reveal the patterns of synergistic development between systems [31]. Based on previous research, the calculation process is as follows [32]:

$$\left\{\begin{array}{c}\mathrm{C}=2\times \sqrt{{\displaystyle \frac{\mathrm{R}\times \mathrm{U}}{{\left(\mathrm{R}+\mathrm{U}\right)}^2}}}\\ \mathrm{D}=\sqrt{\mathrm{C}\times \mathrm{T}}\\ \mathrm{T}=\mathrm{\alpha }\times \mathrm{R}+\mathrm{\beta }\times \mathrm{U}\end{array}\right.$$

(6)

where C represents the coupling degree between urban and rural, indicating the synergy between the two systems. D denotes the coupling coordination degree, reflecting the high-quality synergistic development of two systems. C and D range between [0, 1]. U and R are the evaluation values of urban and rural development, respectively. T is the comprehensive coordination index, and α and β are the undetermined coefficients (with α + β = 1). Given the equal importance of the two systems, α and β are set to be equal.

2.2.3. Geographically and Temporally Weighted Regression Model

A geographically and temporally weighted regression (GTWR) model will be used to identify the spatiotemporal heterogeneity of tourism’s impacts on the BTH urban agglomeration. Compared to ordinary least squares (OLS) regression, the GTWR model not only accounts for the temporal variations of regression coefficients but also considers the potential spatial heterogeneity. Therefore, it allows for exploring the local characteristics of explanatory variables in the spatial dimension [33,34,35]. The significant differences in economic foundations, social backgrounds, and resource endowments among the cities in the BTH urban agglomeration make the GTWR model suitable for analysis. This study establishes a GTWR model to determine the impact of tourism on the URC at various spatial and temporal points, ensuring development strategies are tailored to specific conditions. The formula is as follows:

$${\mathrm{Y}}_{\mathrm{i}}={\mathsf{\beta }}_0\left({\mathrm{u}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}}\right)+{\sum }_{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{k}}\left({\mathrm{u}}_{\mathrm{i}},{\mathrm{v}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}}\right){\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(7)

In this model, u_i, v_i and t_i represent the spatial (longitude and latitude) and temporal (year) coordinates of the city i in the BTH urban agglomeration. β₀ is the constant term, and β_k is the regression coefficient of the k-th variable in the city i. X_it denotes the value of the k^th explanatory variable in year t in the city i. ${\mathsf{\epsilon }}_{\mathrm{i}}$ is the random disturbance term.

2.3. Data Source and Indicator System

This section will examine the extent to which the urban and rural areas of the BTH urban agglomeration have developed individually and in a coordinated manner over the period 2010–2019. New-type urbanization (NU) and rural revitalization (RR) have become core development policies for China’s urban and rural areas.

Unlike traditional urbanization, which concentrates on economic expansion, NU emphasizes non-economic dimensions of urban growth, with a primarily focus on being “people-centered”. As a result, in addition to population and economic indicators, factors such as infrastructure, public services, quality of life, resources, and the environment are crucial for assessing the effectiveness of NU initiatives [36]. Accordingly, this paper develops an evaluation indicator system for NU, integrating economic, demographic, social, spatial, and environmental benchmarks as core indices.

Rural revitalization (RR), which builds upon the targeted poverty alleviation initiative, focuses on the comprehensive development of rural areas. The strategic guidelines for RR issued by the Chinese government identify five key areas: industrial prosperity, ecological livability, cultural advancement in rural regions, effective governance, and improved livelihoods [37]. Consequently, the study introduces industry, environment, culture, governance, and livelihood as variables for evaluating RR [38,39]. To sum up, the evaluation indicator system is formulated, as presented in

Table 1. Indicator system of urban and rural development in the BTH urban agglomeration.

Variable	Criterion	Data	Type of Data
XU1—Economics	New Urbanization	Total retail sales of consumer goods	+
		Income gap between urban and rural residents	−
		Added value of the tertiary industry of GDP	+
XU2—Demography		Gross domestic product (GDP) per capita	+
		Population urbanization rate	+
		Proportion of employees in the secondary industry	+
		Proportion of employees in the tertiary industry	+
XU3—Societies		Number of students in higher general education institutions	+
		Number of hospital beds per 1000 population	+
		Financial education expenditure per capita	+
		Urban fixed asset investment	+
XU4—Spaces		Road area per capita	+
		Land urbanization rate	+
XU5—Environments		Industrial wastewater discharge	−
		Industrial fume emissions	−
		Urban green coverage	+
		Sewage treatment rate	+
		Non-hazardous domestic waste disposal rate	+
XR1—Industries	Rural Revitalization	Agriculture, forestry, livestock, fisheries output per capita	+
		Grain yield per hectare	+
		Total power of agricultural machinery	+
		Added value of the primary industry of GDP	+
XR2—Environments		Number of The Beautiful Leisure Villages in China	+
		Proportion of villages benefiting from piped water	+
		Agricultural fertilizer application per hectare	−
XR3—Governance		Areas of soil erosion under treatment	+
		Rural employment rate	+
		Number of village committees	+
XR4—Livelihood		Net income of rural inhabitants per capita	+
		Consumption expenditure per capita	+
		Engel coefficient for rural residents	+
		Rural share of total retail sales of consumer goods	+
		Average fixed asset investment per rural household	+
XR5—Civilization		Number of National Civilized Villages and Towns	+
		Proportion of villages with cable TV	+

(“type of data” represents the correlation between data and decision-making objectives).

This study takes 13 prefecture-level cities in the Beijing–Tianjin–Hebei urban agglomeration as the research object. Relevant data were obtained from the China Statistical Yearbook, Hebei Statistical Yearbook, China City Statistical Yearbook, municipal statistical bulletins, and government websites. Missing data were supplemented by interpolation.

3. Results

3.1. New Urbanization

The NU levels of the BTH urban agglomeration from 2010 to 2019 were visualized and analyzed using MATLAB (version 9.10.0) and Microsoft Excel (version 2019) software, as shown in Figure 3 and Figure 4. During the study period, the NU consistently showed three different distribution layers, indicating that the cities within the BTH urban agglomeration are roughly divided into three levels in terms of NU values. The peak kernel density gradually shifted to the right, suggesting a slow increment in the integral value. From 2010 to 2012, the average NU level increased by 7.62% annually. However, from 2013 to 2019, the growth rate slowed to 3.15%. This decline is likely associated with the Chinese government’s initial conversion to the NU in 2012, which prioritized ecological environment protection over traditional urbanization. Consequently, extensive production was restricted, and the intensive production required time to adapt, leading to a slowdown in the productivity.

Moreover, radar charts illustrate the spatial distribution of NU of the BTH urban agglomeration in 2010, 2015, and 2019. The distribution follows a descending order from Beijing to Tianjin to Hebei, with Beijing and Tianjin maintaining significantly higher levels than the cities in Hebei province. Overall, the spatial characteristic presents a pattern of “central > southern > northern”. This phenomenon can be attributed to the scale effect brought by higher initial NU levels in Beijing (China’s capital and a municipality), Tianjin (one of the municipalities), and Shijiazhuang (the capital of Hebei Province) combined with their locational, policy and financial advantages. On the other hand, cities with lower NU levels, such as Zhangjiakou, face constraints due to complex terrain and mountainous regions, limiting the expansion of transportation networks and urban spaces. This has resulted in slow progress in population and land urbanization. Therefore, the disparity in the NU is primarily related to economic foundations, industrial structure and geographical location.

3.2. Rural Revitalization

The RR levels of the BTH urban agglomeration from 2010 to 2019, as depicted in Figure 5 and Figure 6, show a temporal variation that correlates with policy changes. From 2010 to 2017, the annual growth rate was a modest 3.35%. However, during 2018–2019, as the Chinese government prioritized addressing issues related to agriculture, rural areas and farmers, the growth rate surged to 10.19%. The highest peaks of the kernel density plot rise and then fall, indicating that the number of cities with lower RRs experienced an increase and then a decrease. The rightmost peaks cluster and then separate, suggesting that cities with higher RRs experienced simultaneous gains. However, some of them dropped out at a later stage.

The spatial distribution of the RR exhibits a “central > northern > southern” pattern. Beijing and Tianjin still maintain a significant lead over Hebei province. It is worth noting that Qinhuangdao shows a notably low RR value. Possible causes are as follows. Firstly, Beijing and Tianjin, located in the central area, are renowned metropolises with rich historical and cultural heritage that attract tourists, leading to higher income and expenditure levels for rural residents compared to other cities in Hebei. Consequently, these areas have more advanced economies, infrastructure and tourism industries, and therefore higher levels of RR. Secondly, although Qinhuangdao is also a tourist city, its industry has a stable “tertiary–secondary–primary” structure that is heavily reliant on tourism revenue, with relatively underdeveloped agriculture in the primary sector. In brief, the divergence in the level of RR is mainly due to factors such as resource endowment and industrial structure.

3.3. Coupling Coordination

Figure 7 and Figure 8 show the urban–rural coordination (URC) of the BTH urban agglomeration from 2010 to 2019 during the research period. The average URC increased steadily during the study period, from 0.4266 to 0.5360. Concerning the type of coordination, the highest peak on the left side gradually shifted to the right, suggesting that cities with more dysfunctional urban–rural relationships are disappearing. At the same time, the peak on the left side indicates that the proportion of highly coordinating cities is increasing. However, the majority of cities in the BTH urban agglomeration are still stuck with a low degree of coordination.

The distribution of URC dispersion is smaller than the RR but larger than the UR, illustrating that the RR may be the major cause of URC’s dispersion. It can be noted that during the study period, there are increasing numbers of cities where URC is concentrated at 0.5 to 0.6, which may be related to the fact that rural development has reached a mature stage, implying that the capacity of the primary sector may be close to saturation. Meanwhile, the development of the secondary and tertiary industry has not yet reached its peak, slowing down the pace of rural areas’ pursuit of urban areas. The gap in Figure 7 is much smaller than that between the NU and RR, demonstrating that after a certain threshold, cities with simultaneously more backward NU and RR values may still obtain a higher URC. Moreover, Beijing and Tianjin are remarkably better than Hebei province in NU, RR and URC, which might imply that these two cities do not have a noteworthy spillover effect on Hebei province.

4. The Impact of Tourism

4.1. Impact Factor Selection and Model Comparison

4.1.1. Tourism Impact Factor Selection

The degree of URC is the explanatory variable, and the economic scale of tourism and tourism resource endowment are core explanatory variables, as shown in

Table 2. Tourism impact indicator system.

Criterion	Variable	Data Source
Tourism economic scale	F1—Total tourism revenue (REV)	China City Statistical Yearbook from 2011 to 2020
Tourism resource endowment	F2—A-level tourist attractions (AL)	https://www.gov.cn, accessed on 26 August 2024
	F3—Historical and cultural towns (villages) (TOWN)	https://www.mohurd.gov.cn, accessed on 26 August 2024
	F4—National intangible cultural heritages (INT)	https://www.ihchina.cn, accessed on 26 August 2024
	F5—National cultural relic protection units (RELIC)	https://www.gov.cn, accessed on 26 August 2024
Control variables	F6—Gross domestic product per capita (GDP)	China City Statistical Yearbook from 2011 to 2020
	F7—The proportion of total retail sales of consumer goods in rural areas (RETAIL)
	F8—Road density per capita (ROAD)

.

(1)

Tourism economic scale: Tourism economic scale is a key indicator for assessing the economic impact of tourist activities on the destination. Compared to the number of tourists, total tourism revenue directly captures the actual spending of visitors, providing a more accurate measurement of the industry’s contribution to urban and rural development [40]. Therefore, this article adopts the total tourism revenue (F1) as an indicator for analyzing the economic impacts of tourism.

(2)

Tourism resource endowment: As the expression of industrial activities on the supply side of tourism, tourism resource endowment represents the attractiveness of destination, and its quantity and spatial distribution play an important role in changing the income gap between rural and urban areas [41]. Thus, this article selects relevant tourism resources publicly recognized by the government as variables, including A-level tourist attractions (F2), historical and cultural towns (villages) (F3), national intangible cultural heritages (F4), and national cultural relic protection units (F5) [42,43,44,45].

(3)

Control Variables: Differences in the economic base, urban and rural development, and the infrastructure of tourism destinations will show different effects under the same scale of impact [46]. Therefore, this paper chooses GDP per capita (F6) (which represents the level of economic development), the proportion of total retail sales of consumer goods in rural areas (F7) (reflecting the gap between urban and rural expenditures), and the road density per capita (F8) (which measures the degree of transportation accessibility), as the control variables [47,48].

4.1.2. Model Comparison

ArcGIS software (version 10.8) was utilized to analyze multiple regression models, including ordinary least squares (OLS) regression, time-weighted regression (TWR), geographic-weighted regression (GWR), and geographically and temporally weighted regression (GTWR). According to

Table 3. Model comparison.

Model	OLS	TWR	GWR	GTWR
AIC	−427.6422797	−362.907	−604.079	−648.367
Adjusted R2	0.962790345	0.969371	0.995608	0.998132
RSS	0.247001677	0.192967	0.0276689	0.011769

, results show that the GTWR model outperformed the other models in terms of goodness of fit, the Akaike information criterion (AIC), and residual sum of squares (RSS). The variance inflation factor was calculated to be 4.26, which is below the threshold of 10, suggesting that the variables were independent and that multicollinearity was not present. In Moran’s I spatial autocorrelation test of the GTWR model residuals, the Z value was consistently less than 1.96, and the p value exceeded 0.1 across ten years. This suggests that the residuals were randomly distributed, confirming the significance of the GTWR model’s regression outcomes [49]. The results of the analysis are shown in

Table 4. Residual autocorrelation test.

Year	Moran’s I	Z-Value	p-Value
2010	−0.356399	−1.360982	0.173519
2011	0.003644	0.422617	0.672575
2012	−0.257927	−0.848789	0.395999
2013	−0.185275	−0.532295	0.594522
2014	0.042904	0.607621	0.543439
2015	−0.127275	−0.21148	0.832513
2016	0.007897	0.483963	0.628412
2017	−0.026484	0.273432	0.784521
2018	0.08992	0.836942	0.402625
2019	0.160046	1.165402	0.243856

.

4.2. Time Heterogeneity

The temporal heterogeneity of the coefficients of the impact of tourism on the URC in 2010, 2015, and 2019 is shown in Figure 9. The total tourism revenue (F1) in most cities had a positive impact on the URC, steadily growing over the research period. Initially, F1 negatively influenced URC in certain cities, but by 2019, all of the cities demonstrated a positive correlation between F1 and URC. This shift may be related to China’s all-for-one tourism initiative, which dismantled the traditional “benefit walls” of scenic spots, allowing tourism revenue to spread beyond operators to rural residents through job creation and infrastructure improvements [50].

The influence of four types of tourism resource endowments on the URC shows clear differences. First, A-level tourist attractions (F2), recognized as a quality benchmark in Chinese tourism, showed a declining impact over the research period. This trend might be due to the concentration of these spots near urban areas, where most of the benefits accrue in the cities. Second, historical and cultural towns and villages (F3) initially had a positive impact on the URC, but this influence decreased, possibly due to waning tourist interest. These towns and villages initially sparked great enthusiasm among urban visitors, becoming popular for weekend getaways, family gatherings, and educational trips. However, issues like commercialization and homogenization have reduced their appeal, thus slowing the flow of resources between urban and rural areas.

Furthermore, the impact of national intangible cultural heritage (F4) varied significantly and hindered URC towards the end of the research period. This may be due to the lack of specialists in rural areas, which has led to poor protection and promotion of F4. Lastly, national cultural relic protection units (F5) consistently promoted URC, though the trend showed an initial rise followed by a decline. This may be related to the fact that the spatial distribution of F5 is more densely urbanized, which limits its potential to enhance URC [51].

Among the control variables, per capita GDP (F6) consistently promoted URC development. In contrast, F7 and F8 had persistent downsides. Although the increase in rural ratio of the proportion of total retail sales of consumer goods (F7) has narrowed the consumption gap between urban and rural areas, the rise of rural tourism has just shifted consumption from rural areas to urban tourists, instead widening the gap between the two. The obstructive effect of F8 on the URC can be understood through the buffer zone theory. The highway construction in the BTH urban agglomeration is primarily concentrated in urban centers and along highways, creating a zonal and axial development pattern [52]. This creates an imbalance in transport infrastructure between urban and rural areas. As a result, the gap between high-density urban roads and low-density rural roads hinders effective urban–rural connectivity and economic interaction, thereby weakening URC.

4.3. Spatial Heterogeneity

The impact coefficients of cities in the BTH urban agglomeration for 2019 were graded using the Jenks natural breakpoint method in order to analyze the spatial heterogeneity of tourism-related impact factors (see Figure 10).

Apart from the total tourism revenue, the intensity of tourism-related variables exhibited spatial clustering. Specifically, the coefficients of F2 and F5 gradually decreased from east to southwest, while F4’s influence intensified from south to north. The impact of F3 increased progressively, with Beijing at its core. The reasons for this relate to economic development, geographical location, historical evolution, and social environment.

In Beijing, the impact of F1 was minimal. This could be related to tourists’ flow and consumption patterns. Compared to Hebei and Tianjin, most of Beijing’s famous tourism resources are concentrated adjacent to the city’s central axis (the connecting line of Beijing’s numerous well-known historical heritage sites), and thus tourists’ consumption behaviors and flow paths tend to be confined to the urban area [53]. Even with the complex industrial linkage effect, tourists’ economic activities remain more concentrated on neighboring urban facilities such as hotels, restaurants, and urban attractions rather than extending to the suburbs or rural areas [54]. Therefore, the growth of total tourism revenue does not play a significant role in promoting the urban–rural coordination. The variations in the coefficients of F2, F3 and F5 reflect the uniqueness of each city’s tourism resources. For instance, Chengde in the northeast is rich in historical and cultural heritage, while Qinhuangdao and Tangshan in the east boast coastal scenery, Great Wall relics and industrial landscapes. These cities have a balanced and diverse spatial distribution of tourist attractions and ancient towns, which contributes to city residents’ attraction to them.

The spatial variation of F4 could be due to two main factors: (1) the geographic proximity of northern cities to Beijing and Tianjin enhances their connectivity, making it easier to attract high-spending tourists from these two major cities; (2) many of the intangible cultural heritage resources in northern cities are closely linked to their 4A and 5A tourist areas (high-quality A-level tourist attractions (F2), which are generally well known and recognized [55]) and rural areas, such as the Summer Resort and surrounding temples in Chengde and the Grassland Highway in Zhangjiakou. These linkages enable tourists to experience local intangible cultural heritage programs and generate spillover effects while visiting scenic spots.

Table 5. Spatial variation coefficient of variables influencing tourism. Source: authors’ elaboration, starting from statistical data.

Variable	F1 (REV)	F2 (AL)	F3 (TOWN)	F4 (INT)	F5 (RELIC)
Spatial variation coefficient	0.4594	1.4076	1.9901	28.6880	0.8106

illustrates the spatial variability coefficients of the GTWR regression coefficients averaged over time. The spatial differences in all other tourism-related variables are clearly smaller than F4, indicating that the impact of F4 on URC varied most significantly among the cities in the BTH urban agglomeration.

5. Discussions

Coordinated urban and rural development is a key to sustainable economic development. Meanwhile, the ecological and industrial connectivity of tourism can serve as a driving force for adjusting urban–rural relations. The level of urban–rural coordination development is influenced by a multitude of factors, including economic foundation, policy advantages, industrial structure, geographic location, natural conditions, and resource distribution. Investigating internal mechanisms, with a focus on the interconnection of urban and rural resources and coordinate, is instrumental in reducing the wealth gap and enhancing social welfare. This study takes the Beijing–Tianjin–Hebei (BTH) urban agglomeration as a research sample, with 2010 to 2019 as the research period; we employed spatial analysis models to explore the impact of the core components of the current tourism industry on coordinated urban–rural development, building on an analysis of the existing state of this development. Our findings suggest that the backwardness of the rural industrial economy and infrastructure is the main factor behind the lack of coordination and the inability of the tourism industry to exert a driving effect. Additionally, cultural tourism attractions, particularly high-quality intangible cultural heritage, exhibit significant regional differences in their capacity to promote urban–rural coordination. Based on these findings, we present several recommendations for the promotion of more positive urban–rural relations:

• Focusing on rural areas and promoting the diversification of rural industries: Rural tourism significantly impacts urban–rural integration, addressing the underdevelopment of rural areas, which is a primary obstacle to this integration. Consequently, the core of rural development should focus on promoting industrial diversification through rural tourism. For instance, cities like Chengde and Qinhuangdao, which boast rich historical and cultural heritages, can leverage these assets to create unique tourism towns, thereby enhancing the economic vitality of rural areas. Furthermore, as the main sources of tourism in Hebei province, Beijing and Tianjin should proactively utilize their spillover effects by providing policy incentives. This approach can promote the export of tourist flows, thereby fostering the synergistic development of Beijing, Tianjin and Hebei.
• Improved infrastructure as a key channel for resource flows: As an intermediary in bridging the urban–rural gap, rural facilities are critical in facilitating the flow of resources and are a key factor in promoting urban–rural coordination [56]. The transport network forms the foundation for these resource flows, and currently, the road density in rural areas of the BTH urban agglomeration is insufficient to support effective urban–rural interaction. Consequently, regions with inadequate transport infrastructure, such as Zhangjiakou, must expedite the construction of rural infrastructure, particularly focusing on road, railway and air transport networks. By developing a public transport system with well-established main routes complemented by secondary routes, it is possible to ensure the rapid movement of tourists between urban and rural areas, thereby promoting short-distance rural tours. This enhancement will generate new revenue opportunities for rural areas, thus narrowing the urban–rural gap and improving urban–rural relations.
• Prioritizing the preservation and development of intangible cultural heritage resources for urban and rural advancement: The preservation and promotion of high-level intangible cultural heritage (ICH) resources should be a priority in urban and rural advancement due to their pronounced spatial heterogeneity in influencing urban–rural coordination. Cities like Zhangjiakou and Chengde, where ICH significantly impacts urban–rural relations, should emphasize developing these resources to foster coordinated growth. These cities can enhance their tourism appeal by organizing cultural festivals, establishing cultural heritage museums, and providing cultural heritage experiences. Additionally, it is essential to protect and transmit these ICH resources to ensure their sustainable development. For cities with fewer ICH resources, fostering cooperation and exchanges with other regions can help enrich local cultural tourism products by incorporating external resources.

6. Conclusions

Under the guidance of the relevant policies, this research delivers an examination of the evolution of new urbanization (NU), rural revitalization (RR) and urban–rural coordination (URC) in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2010 to 2019. Additionally, utilizing software such as MATLAB and ArcGIS, kernel density analysis and geographically and temporally weighted regression (GTWR) model were conducted to assess the impact of the core components of tourism on urban–rural relations. The findings are as follows.

• Urbanization trends: The findings revealed a steady increase in the NU levels within the BTH urban agglomeration, characterized by a distinct three-tier distribution. Between 2010 and 2012, the urbanization rate rose sharply. However, from 2013 to 2019, this growth rate decelerated. This slowdown is likely linked to the Chinese government’s policy shift in 2012, which prioritized ecological protection over traditional urban expansion. Spatial analysis showed that Beijing and Tianjin consistently exhibited higher NU levels than cities in Hebei province, following a “central > southern > northern” pattern. These variations can be attributed to differences in economic foundations, industrial structure, and geographical advantages.
• Insights into rural revitalization: The study also highlights a marked increase in the growth rate of rural revitalization in recent years, correlating with enhanced governmental focus on rural development. The spatial distribution of RR reveals that Beijing and Tianjin lead significantly, followed by the northern and southern regions of Hebei Province. Factors such as economic advantages, rich historical and cultural heritage, and diverse industry structures contribute to these regional differences. However, some areas, such as Qinhuangdao, lag due to their reliance on specific industries and less developed agricultural sectors.
• The development of urban–rural coordination: The results indicate that the URC within the BTH urban agglomeration has progressively improved over time, signaling a more balanced urban–rural relationship. However, despite this overall advancement, significant disparities persist among Beijing, Tianjin and Hebei. Furthermore, the research reveals that even when urban and rural areas are relatively underdeveloped, their URCs can still be high once a certain development threshold is surpassed. This suggests that a combination of value of the NU and RR is necessary to accurately assess the URCs of some cities. Additionally, the anticipated spillover effect from Beijing and Tianjin to Hebei is not clearly observable.
• The spatiotemporal heterogeneity of tourism’s impact: These analyses shed light on how the economic scale and resource endowment of tourism have had different impacts on the URCs across cities over time. From the temporal perspective, the impact of tourism on the URC has evolved throughout the study period. Initially, the benefits of tourism were concentrated in urban areas. However, with the adoption of policies aimed at strengthening rural tourism and improving infrastructure, these advantages have gradually been extended to rural areas. This shift became particularly evident from 2018 onwards, as policy preferences for the rural areas started to gain momentum, leading to a more even distribution of tourism revenues and a greater degree of the URC. This evolution suggests that well-planned tourism policies can effectively narrow the urban–rural divide. From the spatial perspective, the distribution of tourism resources, geographic location, uniqueness, and the richness of cultural assets significantly influence the spatial variation of tourism-related variables. Notably, high-quality intangible cultural heritage had the most substantial difference in the intensity of its impact across the BTH urban agglomeration. This implies that culture-centered tourism products are likely to become a core competitive advantage in the future.

In conclusion, this study emphasizes the importance of providing urban–rural adaptive policies based on regional differences. To achieve richer results, interdisciplinary research can be carried out while including other urban agglomerations in the research to identify commonalities and characteristics regarding spatiotemporal heterogeneity, intensity of impacts, and spillover effects.