The Influence of Tourism’s Spatiotemporal Heterogeneity on the Urban–Rural Relationship: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Implementation of Analyses
2.2.1. Entropy Method
- 1.
- Non dimensional treatment:
- 2.
- Entropy Calculation:
- 3.
- Weight Conversion
2.2.2. Coupling Coordination Degree Model
2.2.3. Geographically and Temporally Weighted Regression Model
2.3. Data Source and Indicator System
3. Results
3.1. New Urbanization
3.2. Rural Revitalization
3.3. Coupling Coordination
4. The Impact of Tourism
4.1. Impact Factor Selection and Model Comparison
4.1.1. Tourism Impact Factor Selection
- (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
4.2. Time Heterogeneity
4.3. Spatial Heterogeneity
5. Discussions
- 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
- 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.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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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 | + |
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) |
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 |
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 |
Variable | F1 (REV) | F2 (AL) | F3 (TOWN) | F4 (INT) | F5 (RELIC) |
---|---|---|---|---|---|
Spatial variation coefficient | 0.4594 | 1.4076 | 1.9901 | 28.6880 | 0.8106 |
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Cong, Y.; Zhai, Y.; Dong, Y.; Zhao, Z.; Yang, G.; Shen, H. The Influence of Tourism’s Spatiotemporal Heterogeneity on the Urban–Rural Relationship: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration, China. Sustainability 2024, 16, 7468. https://doi.org/10.3390/su16177468
Cong Y, Zhai Y, Dong Y, Zhao Z, Yang G, Shen H. The Influence of Tourism’s Spatiotemporal Heterogeneity on the Urban–Rural Relationship: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration, China. Sustainability. 2024; 16(17):7468. https://doi.org/10.3390/su16177468
Chicago/Turabian StyleCong, Yi, Yanxia Zhai, Yubo Dong, Zhilong Zhao, Guang Yang, and Hejiang Shen. 2024. "The Influence of Tourism’s Spatiotemporal Heterogeneity on the Urban–Rural Relationship: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration, China" Sustainability 16, no. 17: 7468. https://doi.org/10.3390/su16177468