Can Tourism Development Make Cities More Livable? Investigating 40 Cities in China
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Framework
4. Research Area and Data Sources
5. Research Design
5.1. Methodology: The Multilevel Model
5.1.1. Natural Environmental Attraction
5.1.2. Sociocultural Environment Attraction
5.1.3. Comprehensive Attraction
5.1.4. Personal Features
6. Empirical Analysis
6.1. Comparison of Livability Satisfaction between Cities
6.2. Analysis of Factors Influencing Urban Liveability
7. Discussion
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Attributes | Variables | Sample Size | Percentage |
---|---|---|---|
Hukou status | Non-local | 3283 | 35.20% |
Local | 6042 | 64.80% | |
Age | <20 | 997 | 10.70% |
20–29 | 1734 | 18.60% | |
30–39 | 2526 | 27.10% | |
40–49 | 1856 | 19.90% | |
50–59 | 1146 | 12.30% | |
≥60 | 1066 | 11.40% | |
Gender | Female | 4507 | 48.30% |
Male | 4818 | 51.70% | |
Education | Middle school and below | 1046 | 11.20% |
High school | 1720 | 18.40% | |
College | 2261 | 24.20% | |
Undergraduate | 2975 | 31.90% | |
Master and above | 1323 | 14.20% | |
Family’ month income (RMB) | <3000 | 1139 | 12.20% |
3000–4999 | 1826 | 19.60% | |
5000–9999 | 2703 | 29.00% | |
10,000–15,000 | 2392 | 25.70% | |
20,000–30,000 | 484 | 5.20% | |
>30,000 | 164 | 1.80% | |
location | Eastern region | 4675 | 50.10% |
Central region | 1835 | 19.70% | |
Western region | 2815 | 30.20% |
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Variable Properties | Explanation and Coding Scheme | Mean | Standard Deviation |
---|---|---|---|
Independent variable (objective) | |||
Natural environmental attraction | City parks: Number of city parks in the city | 114.95 | 154.03 |
Urban greening rate: Urban greening rate in the city | 39.32 | 5.32 | |
Air quality of cities: The number of days the air quality of cities reaches the standard | 269.00 | 54.88 | |
Natural scenic spots: The number of natural scenic spots in the city | 10.50 | 6.02 | |
Sociocultural environment attraction | City library: The number of libraries per 100 people in the city | 210.23 | 164.74 |
City museums: Number of city museums in the city | 39.49 | 26.11 | |
Culture heritage: The number of key cultural relics under protection | 7.56 | 5.31 | |
Tourist attraction | Tourism: Tourism as a share of GDP | 22.99 | 19.27 |
Tourism employees: The proportion of tourism employees | 24.78 | 14.22 | |
Scenic spots: The number of scenic spots above 4A level | 18.26 | 9.77 | |
Independent variable (subjective) | |||
Mediated Variable | Climatic comfort: Residents’ evaluation of climate comfort: 1–5 | 3.06 | 1.34 |
Afforestation coverage in the city: Residents’ evaluation of urban green coverage rate: 1–5 | 3.05 | 1.33 | |
Assessment of the natural environment: Residents’ evaluation of urban natural environment: 1–5 | 3.07 | 0.91 | |
Evaluation of urban cultural atmosphere: Residents’ evaluation of urban characteristic cultural atmosphere: 1–5 | 3.08 | 1.32 | |
Sociocultural environment evaluation: Residents’ evaluation of urban social and cultural environment: 1–5 | 3.05 | 0.87 | |
control variable | Rent and purchase of a house: 1 = Rent; 2 = No rent | 1.61 | 0.64 |
Living space: Respondents’ housing area(m3) | 72.87 | 32.11 | |
Age: 1 = (1,20]; 2 = (20,30]; 3 = (30,40]; 4 = (40,55]; 5 = (55,60]; 6 = (60,70]; 7 = 70 above | 3.38 | 1.48 | |
Gender:1 = male;2 = female | 1.48 | 0.50 | |
Education: 1 = high school or below; 2 = college degree or above | 1.46 | 1.50 | |
Census register: 1 = Nonlocal census register; 2 = Locality census register | 1.35 | 0.48 | |
Family income (RMB): 1 = [0, 3000]; 2 = (3000, 5000]; 3 = (5000, 10,000]; 4 = (10,000, 15,000]; 5 = (15,000, 20,000]; 6 = (20,000, 30,000]; 7 = 30,000 above | 3.17 | 1.39 | |
Family size: The number of family members | 3.10 | 1.18 |
Variable | Model 1 | Standard Error | Model 2 | Standard Error | Model 3 | Standard Error |
---|---|---|---|---|---|---|
Objective Variable | ||||||
City parks | 0.016 ** | 0.014 | ||||
Urban greening rate | 0.017 ** | 0.013 | ||||
Air quality of cities | 0.034 *** | 0.015 | ||||
Natural scenic spots | −0.015 * | 0.016 | ||||
City library | 0.008 | 0.009 | ||||
City museums | 0.009 * | 0.014 | ||||
Culture heritage | 0.035 * | 0.021 | ||||
Tourism | −0.008 * | 0.02 | ||||
Tourism employees | -0.022 | 0.013 | ||||
Scenic spots | −0.014 * | 0.017 | ||||
Control variable | ||||||
Rent and purchase of a house (Rent #) | 0.003 * | 0.014 | 0.004 * | 0.014 | 0.003 * | 0.014 |
Age | 0.003 ** | 0.012 | 0.005 ** | 0.002 | 0.005 ** | 0.002 |
Gender (male #) | −0.051 ** | 0.014 | −0.034 ** | 0.014 | −0.038 ** | 0.014 |
Education (Low education #) | 0.005 * | 0.002 | −0.020 ** | 0.009 | −0.021 ** | 0.009 |
Census register (local #) | −0.058 | 0.01 | −0.058 | 0.01 | −0.058 | 0.01 |
Family income | 0.022** | 0.009 | 0.007 ** | 0.004 | 0.007 ** | 0.004 |
Family size | 0.019 ** | 0.01 | 0.018 ** | 0.01 | 0.018 ** | 0.01 |
Constant | 0.287 | 0.019 | 0.283 | 0.02 | 0.285 | 0.02 |
AIC | 75,379.714 | 75,370.853 | 75,046.876 | |||
DIC | 11,534.783 | 11,524.664 | 11,528.111 | |||
Chi-square | 133.987 | 128.504 | 140.949 |
Variable | Model 4 | Standard Error | Model 5 | Standard Error | Model 6 | Standard Error |
---|---|---|---|---|---|---|
Objective variable | ||||||
City parks | 0.017 | 0.016 | ||||
Urban greening rate | 0.021 | 0.012 | ||||
Air quality of cities | 0.023 * | 0.013 | ||||
Natural scenic spots | 0.007 | 0.016 | ||||
City library | 0.031 | 0.009 | ||||
City museums | 0.027 * | 0.011 | ||||
Culture heritage | −0.004 | 0.015 | ||||
Tourism | ||||||
Tourism employees | ||||||
Scenic spots | ||||||
Subjective variable | ||||||
Climatic comfort | 0.025 * | 0.012 | ||||
Urban green coverage | 0.032 | 0.01 | ||||
Assessment of the natural environment | 0.195 ** | 0.014 | ||||
Evaluation of urban cultural atmosphere | 0.080 | 0.009 | ||||
Sociocultural environment evaluation | 0.057 | 0.009 | ||||
Rent and purchase of a house (Rent #) | 0.006 ** | 0.013 | 0.025 * | 0.017 | 0.048 ** | 0.012 |
Age | 0.006 ** | 0.002 | 0.009 ** | 0.013 | 0.006 ** | 0.002 |
Gender (male #) | 0.048 * | 0.013 | 0.006 *** | 0.002 | 0.006 *** | 0.013 |
Education (Low education #) | 0.023 *** | 0.008 | 0.009 * | 0.013 | 0.022 ** | 0.008 |
Census register (local #) | −0.062 | 0.009 | −0.005 | 0.011 | −0.061 | 0.009 |
Family income | 0.006 ** | 0.004 | 0.004 ** | 0.004 | 0.006 ** | 0.004 |
Family size | 0.02 | 0.009 | −0.008 | 0.011 | 0.018 | 0.009 |
Location (# Central) | ||||||
Location (West) | 0.014 ** | 0.017 | ||||
Location (East) | 0.020 ** | 0.017 | ||||
Constant | 0.29 | 0.018 | −0.003 | 0.017 | 0.272 | 0.02 |
AIC | 75,021.482 | 75,139.272 | 75,012.329 | |||
DIC | 10,996.143 | 10,876.642 | 10924.852 | |||
Chi-square | 130.392 | 111.25 | 128.466 |
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Kang, L.; Yang, Z.; Dang, Y.; Zhang, W.; Liu, C. Can Tourism Development Make Cities More Livable? Investigating 40 Cities in China. Int. J. Environ. Res. Public Health 2022, 19, 472. https://doi.org/10.3390/ijerph19010472
Kang L, Yang Z, Dang Y, Zhang W, Liu C. Can Tourism Development Make Cities More Livable? Investigating 40 Cities in China. International Journal of Environmental Research and Public Health. 2022; 19(1):472. https://doi.org/10.3390/ijerph19010472
Chicago/Turabian StyleKang, Lei, Zhaoping Yang, Yunxiao Dang, Wenzhong Zhang, and Caicai Liu. 2022. "Can Tourism Development Make Cities More Livable? Investigating 40 Cities in China" International Journal of Environmental Research and Public Health 19, no. 1: 472. https://doi.org/10.3390/ijerph19010472