The Spatial and Temporal Evolution and Influencing Factors of the Coupling and Coordinated Development of Basic Public Services, Urbanization, and Tourism in China
Abstract
:1. Introduction
2. Research Area Overview and Research Methods
2.1. Overview of the Study Area
2.2. Evaluation System Construction
2.3. Data Selection and Evaluation System Construction
2.4. Research Methods
2.4.1. Comprehensive Evaluation Model
2.4.2. Coupling Coordination Degree Model
2.4.3. Spatial Autocorrelation Model
2.4.4. Geographical Detector
3. Analysis of the Results
3.1. Research on the Development of the Coupling Coordination Degree of Basic Public Services, Urbanization, and Tourism
3.1.1. Time-Series Characteristics of the Comprehensive Development Index of Each Subsystem
3.1.2. Temporal Evolution Characteristics of Coupling Coordination Degree
3.1.3. Spatial Evolution of Coupling Coordination Degree
3.2. Spatial Agglomeration of Basic Public Services–Urbanization–Tourism Coupling Coordination Degree
3.2.1. Overall Coordination Level and Spatial Agglomeration Characteristics
3.2.2. Spatial Differentiation of Local Autocorrelation
3.3. The Influencing Factors of the Coordinated Development of Basic Public Services, Urbanization, and Tourism
3.3.1. Analysis of Influencing Factors
3.3.2. Significant Interaction
4. Discussion
5. Suggestions
6. Conclusions
- (1)
- From 2010 to 2020, there was comprehensive development: basic public services showed a rising trend, urbanization experienced a declining and fluctuating stability, and the tourism industry demonstrated an inverted U-shaped trend of rising first and then falling.
- (2)
- During the same period, for coupling coordinated development, the average value of the coupling coordination degree of the three systems was always in mild coordination and showed a slight upward trend; the stability of the coupling coordination degree for 18 provinces was low, and their levels changed. The coupling coordination degree for 13 provinces was relatively stable and remained unchanged. Spatially, the level of coupling coordination degree decreased from southeast to northwest, and the spatial heterogeneity of coupling coordination degree in each region was obvious. From 2010 to 2020, the coupling coordination degree of the eastern coastal and central regions increased slightly, while that of the northeast and western regions decreased slightly.
- (3)
- The spatial agglomeration of coupling coordination degree was revealed by the coupling coordination degree of the three systems, which demonstrates strong spatial autocorrelation, and with a tendency to gather in space; the agglomeration effect was obvious and interdependence between the systems exists; the coupling coordination degree of the three systems has an obvious spatial agglomeration effect, forming a hot-spot area with the southeast coast as the core, and a cold-spot area with the northwest inland area as the core. Both cold and hot spots gradually radiated outward, forming a spatial agglomeration distribution pattern of hot in the east and cold in the west.
- (4)
- In terms of influencing factors of coupling coordination degree, the coupling and coordinated development of China’s three systems is affected by many factors, and the influence of each factor is different in different years. The results of interaction detection showed different levels of Enhance, bi- and Enhance, nonlinear. The coupling and coordinated development of the three systems results from the combined effect of endogenous power (economic pulling power, infrastructure support power, industrial driving force, population agglomeration power) and exogenous power (government regulation power, market promotion power, social security power).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Target Layer | Element Layer | Index Layer |
---|---|---|
Basic public services | education and cultural services | Local fiscal expenditure on education (CNY one hundred million) |
Number of colleges and universities per ten thousand people (colleges/ten thousand people) | ||
The number of students in regular institutions of higher learning (ten thousand people) | ||
Number of full-time teachers in primary and secondary schools per ten thousand people (people) | ||
Local fiscal expenditure on science and technology (CNY one hundred million) | ||
The total collection of public libraries (ten thousand volumes) | ||
health and social security services | Number of medical and health institutions per ten thousand people (numbers/ten thousand people) | |
Number of beds in medical institutions per ten thousand people (numbers/ten thousand people) | ||
Number of professional doctors (ten thousand people) | ||
Number of social welfare homes per ten thousand people (numbers) | ||
Medical insurance coverage for urban workers (%) | ||
Basic pension insurance coverage of urban and rural residents (%) | ||
ecological environmental services | Comprehensive utilization rate of industrial solid waste (%) | |
Urban sewage treatment rate (%) | ||
Harmless treatment rate of municipal solid waste (%) | ||
Industrial wastewater discharge (million tons) | ||
Forest coverage (%) | ||
infrastructure as a service | Public transport vehicles per ten thousand people (numbers) | |
Urban water penetration rate (%) | ||
Urban gas penetration rate (%) | ||
Number of public toilets (numbers) | ||
information service | Number of post offices per ten thousand people (numbers) | |
Internet penetration (%) | ||
The number of mobile phone users per ten thousand people (numbers) | ||
Urbanization | population urbanization | Urbanization rate (%) |
Urban population density (person/km2) | ||
The proportion of employment in the second and third industries in the total employment (%) | ||
economic urbanization | GDP per capita (CNY) | |
The proportion of the tertiary industry in GDP (%) | ||
Per capita disposable income of urban residents (CNY) | ||
Urban fixed assets investment (one hundred million CNY) | ||
social urbanization | Urban registered unemployment rate (%) | |
The number of urban health technicians per ten thousand people (people) | ||
Engel coefficient of urban households (%) | ||
space urbanization | Urban built-up area per ten thousand people (km2/ten thousand people) | |
Per capita urban road area (m2/people) | ||
Green coverage rate of built-up area (%) | ||
Tourism | tourist economy | Domestic tourism revenue (CNY one hundred million) |
Foreign exchange earnings from tourism (USD ten thousand) | ||
The proportion of total tourism income to GDP (%) | ||
tourism market | Number of domestic tourists (one hundred million people) | |
The number of inbound tourists (ten thousand people) | ||
The growth rate of tourists (%) | ||
tourism resources | A-level scenic spot quality total score (score) | |
Total quality score of star hotels (score) | ||
tourism public services | Number of travel agencies (numbers) | |
The number of tourism practitioners (people) |
Coordination Degree | Coordination Type | Coordination Degree | Coordination Type |
---|---|---|---|
0.00~0.09 | Extreme imbalance | 0.50~0.59 | Slight coordination |
0.10~0.19 | Serious imbalance | 0.60~0.69 | Mild coordination |
0.20~0.29 | Moderate imbalance | 0.70~0.79 | Moderate coordination |
0.30~0.39 | Mild imbalance | 0.80~0.89 | High coordination |
0.40~0.49 | Slight imbalance | 0.90~1.00 | Extreme coordination |
Judgment Basis | Interaction |
---|---|
q(X1X2) < min [q(X1),q(X2)] | Weaken, nonlinear |
min [q(X1),q(X2)] < q(X1∩X2) < max [q(X1),q(X2)] | Weaken, uni- |
q(X1∩X2) > max [q(X1),q(X2)] | Enhance, bi- |
q(X1∩X2) = q(X1) + q(X2) | Independent |
q(X1∩X2) > q(X1) + q(X2) | Enhance, nonlinear |
Province | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.7601 | 0.7709 | 0.7536 | 0.7555 | 0.7585 | 0.7431 | 0.7504 | 0.7226 | 0.7401 | 0.7162 | 0.6840 |
Tianjin | 0.5483 | 0.5576 | 0.5501 | 0.5550 | 0.5594 | 0.5531 | 0.5853 | 0.5490 | 0.5052 | 0.5072 | 0.5050 |
Hebei | 0.5264 | 0.5334 | 0.5339 | 0.5136 | 0.5198 | 0.5155 | 0.5479 | 0.5427 | 0.5580 | 0.5587 | 0.5273 |
Shanxi | 0.4915 | 0.4984 | 0.5200 | 0.5159 | 0.5122 | 0.5108 | 0.5405 | 0.5071 | 0.5159 | 0.5152 | 0.4765 |
Inner Mongolia | 0.4766 | 0.4819 | 0.4914 | 0.4926 | 0.5012 | 0.4948 | 0.5275 | 0.5179 | 0.5144 | 0.5376 | 0.4831 |
Liaoning | 0.6219 | 0.6274 | 0.6223 | 0.6226 | 0.6238 | 0.5762 | 0.5834 | 0.5670 | 0.5596 | 0.5539 | 0.5201 |
Jilin | 0.4552 | 0.4608 | 0.4715 | 0.4761 | 0.4707 | 0.4751 | 0.5030 | 0.4707 | 0.4721 | 0.4711 | 0.4773 |
Heilongjiang | 0.5146 | 0.4986 | 0.5189 | 0.4885 | 0.4856 | 0.4922 | 0.5006 | 0.4853 | 0.4759 | 0.4781 | 0.4696 |
Shanghai | 0.7516 | 0.7232 | 0.7043 | 0.6888 | 0.6920 | 0.6759 | 0.7042 | 0.6756 | 0.6793 | 0.6528 | 0.7176 |
Jiangsu | 0.7140 | 0.7343 | 0.7314 | 0.7016 | 0.7213 | 0.7033 | 0.7140 | 0.7107 | 0.7115 | 0.7068 | 0.7155 |
Zhejiang | 0.7041 | 0.7151 | 0.7139 | 0.7055 | 0.7177 | 0.7134 | 0.7096 | 0.7153 | 0.7038 | 0.7003 | 0.6896 |
Anhui | 0.4970 | 0.5252 | 0.5348 | 0.5097 | 0.5226 | 0.5202 | 0.5487 | 0.5521 | 0.5608 | 0.5711 | 0.5577 |
Fujian | 0.5681 | 0.5751 | 0.5904 | 0.5767 | 0.5839 | 0.5799 | 0.5956 | 0.5984 | 0.5895 | 0.5958 | 0.6369 |
Jiangxi | 0.4736 | 0.4989 | 0.5110 | 0.4874 | 0.5076 | 0.5080 | 0.5375 | 0.5397 | 0.5699 | 0.5481 | 0.5509 |
Shandong | 0.6544 | 0.6732 | 0.6643 | 0.6527 | 0.6600 | 0.6543 | 0.6655 | 0.6638 | 0.6693 | 0.6599 | 0.6380 |
Henan | 0.5357 | 0.5344 | 0.5463 | 0.5225 | 0.5385 | 0.5337 | 0.5615 | 0.5603 | 0.5795 | 0.5812 | 0.6105 |
Hubei | 0.5400 | 0.5413 | 0.5501 | 0.5406 | 0.5601 | 0.5571 | 0.5822 | 0.5739 | 0.5768 | 0.5773 | 0.5587 |
Hunan | 0.5156 | 0.5127 | 0.5152 | 0.5073 | 0.5325 | 0.5284 | 0.5536 | 0.5572 | 0.5650 | 0.5801 | 0.5928 |
Guangdong | 0.7684 | 0.7709 | 0.7756 | 0.7585 | 0.7635 | 0.7686 | 0.7832 | 0.7788 | 0.7911 | 0.7815 | 0.7268 |
Guangxi | 0.4536 | 0.4636 | 0.4735 | 0.4551 | 0.4651 | 0.4690 | 0.4964 | 0.5162 | 0.5324 | 0.5447 | 0.5367 |
Hainan | 0.4150 | 0.4364 | 0.4280 | 0.4128 | 0.4148 | 0.4110 | 0.4277 | 0.4105 | 0.4153 | 0.4088 | 0.4386 |
Chongqing | 0.5011 | 0.5063 | 0.5222 | 0.4824 | 0.5037 | 0.4975 | 0.5159 | 0.5169 | 0.5243 | 0.5207 | 0.4827 |
Sichuan | 0.5099 | 0.5173 | 0.5372 | 0.5176 | 0.5324 | 0.5227 | 0.5490 | 0.5538 | 0.5830 | 0.5868 | 0.5816 |
Guizhou | 0.4195 | 0.4136 | 0.4356 | 0.4269 | 0.4273 | 0.4323 | 0.4783 | 0.4910 | 0.5123 | 0.5178 | 0.4977 |
Yunnan | 0.4698 | 0.4715 | 0.4867 | 0.4686 | 0.4845 | 0.4769 | 0.5081 | 0.5252 | 0.5345 | 0.5519 | 0.5334 |
Tibet | 0.4014 | 0.3818 | 0.4171 | 0.3986 | 0.3834 | 0.4209 | 0.3805 | 0.3551 | 0.3722 | 0.3737 | 0.3733 |
Shaanxi | 0.5190 | 0.5308 | 0.5432 | 0.5278 | 0.5409 | 0.5313 | 0.5428 | 0.5443 | 0.5569 | 0.5591 | 0.5166 |
Gansu | 0.3914 | 0.4018 | 0.4391 | 0.4164 | 0.4113 | 0.4220 | 0.4389 | 0.4464 | 0.4447 | 0.4422 | 0.4333 |
Qinghai | 0.3198 | 0.3383 | 0.3243 | 0.3428 | 0.3514 | 0.3567 | 0.3786 | 0.3854 | 0.3779 | 0.4136 | 0.3886 |
Ningxia | 0.2838 | 0.3131 | 0.3554 | 0.3802 | 0.3057 | 0.3207 | 0.3477 | 0.4152 | 0.3093 | 0.3739 | 0.3807 |
Xinjiang | 0.5111 | 0.4935 | 0.4937 | 0.4741 | 0.4789 | 0.4764 | 0.4921 | 0.4984 | 0.4972 | 0.5154 | 0.4728 |
mean value | 0.5262 | 0.5323 | 0.5405 | 0.5282 | 0.5332 | 0.5304 | 0.5500 | 0.5467 | 0.5483 | 0.5517 | 0.5411 |
Year | M (I) | Z (I) | P (I) |
---|---|---|---|
2010 | 0.196 | 2.983 | 0.003 |
2016 | 0.222 | 3.328 | 0.001 |
2020 | 0.247 | 3.619 | 0.000 |
Regional Type | 2010 | 2016 | 2020 | |||
---|---|---|---|---|---|---|
Amount | Proportion | Amount | Proportion | Amount | Proportion | |
high significant hot spots | 2 | 6.44 | 2 | 6.44 | 3 | 9.66 |
medium significant hot spots | 2 | 6.44 | 3 | 9.66 | 3 | 9.66 |
low significant hot spots | 1 | 3.22 | 2 | 6.44 | 2 | 6.44 |
not significant | 20 | 64.58 | 18 | 58.14 | 16 | 51.7 |
low significant cold spots | 2 | 6.44 | 1 | 3.22 | 2 | 6.44 |
medium significant cold spots | 4 | 12.88 | 4 | 12.88 | 4 | 12.88 |
high significant cold spots | 0 | 0 | 1 | 3.22 | 1 | 3.22 |
unstudied area | 3 | - | 3 | - | 3 | - |
Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2010 | |||||||||||
2016 | |||||||||||
2020 |
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Zhang, Z.; Gong, J.; Ma, H.; Zhang, J. The Spatial and Temporal Evolution and Influencing Factors of the Coupling and Coordinated Development of Basic Public Services, Urbanization, and Tourism in China. Sustainability 2023, 15, 11753. https://doi.org/10.3390/su151511753
Zhang Z, Gong J, Ma H, Zhang J. The Spatial and Temporal Evolution and Influencing Factors of the Coupling and Coordinated Development of Basic Public Services, Urbanization, and Tourism in China. Sustainability. 2023; 15(15):11753. https://doi.org/10.3390/su151511753
Chicago/Turabian StyleZhang, Zhongwu, Jian Gong, Huiqiang Ma, and Jinyuan Zhang. 2023. "The Spatial and Temporal Evolution and Influencing Factors of the Coupling and Coordinated Development of Basic Public Services, Urbanization, and Tourism in China" Sustainability 15, no. 15: 11753. https://doi.org/10.3390/su151511753
APA StyleZhang, Z., Gong, J., Ma, H., & Zhang, J. (2023). The Spatial and Temporal Evolution and Influencing Factors of the Coupling and Coordinated Development of Basic Public Services, Urbanization, and Tourism in China. Sustainability, 15(15), 11753. https://doi.org/10.3390/su151511753