Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective
Abstract
:1. Introduction
2. Theory Basis and Hypothesis
3. Research Methodology, Sample, and Data
3.1. Sample and Data
3.2. Variable Measures
3.2.1. Measurement: Network Importance of HSR Cities
3.2.2. Measurement: Quality of Urbanization
3.3. Empirical Model Selection
3.3.1. Spatial Econometric Model
3.3.2. Control Variables
- (1)
- Technology support: The quality of urbanization pursues the transformation and development of intellectualization. In this process, new technology such as IT and DT can play a key role. These technological innovations are closely related to primary work and produce a systemic change, which can transform life and work within a city significantly and fundamentally [51]. Thus, corresponding technology support is an important factor of urbanization development. In this study, the proportion of science and technology expenditure is used to reflect the government’s support for science and technology innovation.
- (2)
- Financial development: A stable capital chain and sustainable financial services can promote the expansion of reproduction activities in small- and medium-sized enterprises by encouraging innovative activities, preventing monopoly and forming a good market competition environment [52]. In the past, due to the backward movement of China’s investment and financing systems, urbanization quality has long been hindered by a capital bottleneck [53]. Thus, the development of financial industry can help to realize the further improvement in urbanization. According to Han et al. [54], the level of financial development is measured by the total loan balance of all financial institutions divided by the gross regional product.
- (3)
- Marketization degree: The market is one of the most active parts of urbanization quality. The total retail consumption of the market divided by the gross regional product is a reflection of the marketization degree. The higher the consumption, the more active the market. An active market is conducive to improving transaction efficiency and promoting economic development [55].
- (4)
- Economic openness: Openness can help to introduce the foreign advanced technology, talents, and management experience, improving the quality of city development [56]. In addition, the frequent communication with the foreign can also improve the reputation of the city, which accelerates the process of urbanization and internationalization. In this study, FDI was measured by the total amount of foreign investment actually utilized divided by the gross regional product.
4. Empirical Results
4.1. The Spatio-Temporal Evolution Characteristics of the HSR Network
4.2. Spatial Autocorrelation Test and Model Determination
4.3. The HSR Network and the Quality of Urbanization
4.4. HSR Network and the Quality of Urbanization: Dynamic Analysis
4.5. HSR Network and the Quality of Urbanization: Main Beneficiaries
4.6. HSR Network and the Quality of Urbanization: Robustness Test
5. Discussions and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
1 | In this table, hsr_dum, hsr_rdc, hsr_adc, hsr_rbc, hsr_abc denote the estimation results when the core explanatory variables (HSR) are the HSR dummy variable, the HSR relative centrality, the HSR absolute centrality, the HSR relative betweenness centrality and the HSR absolute betweenness centrality, respectively. W × hsr denotes the impact of the local HSR network on the quality of urbanization in other cities. Rtec, rfin, rmar, and rfdi are a series of control variables. pho is the spatial spillover coefficient. sigma2 is the individual variance. city FE and year FE denote individual-fixed terms and time-fixed terms, respectively. N is the total number of observations. logL is the maximum likelihood estimate. Vif is the variance inflation factor. LM-sar and LM-sem denote the Lagrange multiplier test results for the SAR and SEM models, respectively. |
2 | In this table, W × hsr denotes the impact of the local HSR network on the quality of urbanization in other cities. LR_hsr indicates the longer-term impact of the HSR network on urbanization. Con-var is a series of control variables. N is the total number of observations. logL is the maximum likelihood estimate. Vif is the variance inflation factor. LM-sar and LM-sem denote the Lagrange multiplier test results for the SAR and SEM models, respectively. |
3 | Data from China High-Speed Rail Timetable. |
4 | In this table, W × hsr denotes the impact of the local HSR network on the quality of urbanization in other cities. Con-var is a series of control variables. city FE and year FE denote individual-fixed terms and time-fixed terms, respectively. N is the total number of observations. |
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Variable | Units | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
qu | - | 0.181 | 0.187 | 0.018 | 2.734 |
hsr dum | - | 0.418 | 0.493 | 0 | 1 |
hsr rdc | % | 0.727 | 1.249 | 0 | 16.667 |
hsr adc | % | 0.858 | 1.154 | 0 | 6.919 |
hsr rbc | % | 2.289 | 5.374 | 0 | 45.729 |
hsr abc | % | 269.475 | 724.542 | 0 | 8738.812 |
rtech | % | 0.003 | 0.002 | 0 | 0.041 |
rmar | % | 0.381 | 0.108 | 0.026 | 1.013 |
rfin | % | 2.373 | 6.423 | 0.004 | 128.569 |
rfdi | % | 0.018 | 0.020 | 0 | 0.460 |
Index I | Index II | Weight (%) | Unit |
---|---|---|---|
Population urbanization | : Urban population density | 35.44 | Person/square kilometer |
: Proportion of urban district population | 35.40 | % | |
: Proportion of employees in secondary and tertiary industries | 0.40 | % | |
Economy urbanization | : Per capita GRP | 1.81 | Yuan |
: Proportion of GRP of secondary and tertiary industries | 1.18 | % | |
: GDP density of secondary and tertiary industries | 7.66 | 10 thousand yuan/km2 | |
Space urbanization | : Proportion of built-up area | 7.00 | % |
: Per capita built-up area | 0.78 | Square meter | |
: Per capita living area in urban | 0.54 | Square meter | |
: Per capita road area in urban | 0.79 | Square meter | |
Environment urbanization | : Per capita green area | 0.48 | Square meter |
: Green coverage rate of built-up district | 0.09 | % | |
: Domestic garbage treatment rate | 0.06 | % | |
: Wastewater treatment rate | 0.14 | % | |
Society urbanization | : Per capita education expenditure | 2.13 | Yuan |
: Medical facility beds per 1000 people | 0.98 | Set | |
: Books in public libraries per 100 people | 4.51 | Piece | |
: Average salary of urban employees | 0.95 | Yuan/Person |
Variables | Definition and Calculation Methods |
---|---|
hsr_dum | Dummy variable, whether a city has an HSR station (Yes = 1 and No = 0) |
hsr_rdc | HSR relative degree centrality, a city’s ability to connect directly in the global HSR network, calculated via Equation (1) (%) |
hsr_adc | HSR absolute degree centrality, a city’s ability to connect directly in the local HSR network, calculated via Equation (1) × (N − 1) (%) |
hsr_rbc | HSR relative betweenness centrality, a city’s ability to connect indirectly in the global HSR network, calculated via Equation (2) (%) |
hsr_abc | HSR absolute betweenness centrality, a city’s ability to connect indirectly in the local HSR network, calculated via Equation (2) × (N2 − 3N + 2) (%) |
rtec | Technology support, the proportion of science and technology expenditure (%) |
rfin | Financial development, the total loan balance of all financial institutions divided by gross regional product (%) |
rmar | Marketization degree, the total retail consumption of the market divided by the gross regional product (%) |
rfdi | Economic openness, the total amount of foreign investment actually utilized (FDI) divided by the gross regional product (%) |
City | HSR Lines | HSR Relative Degree Centrality | HSR Relative Betweenness Centrality | |||
---|---|---|---|---|---|---|
2009 | 2019 | 2009 | 2019 | 2009 | 2019 | |
Beijing | 1.0000 | 3.0000 | 5.5560 | 1.5000 | 0.0000 | 3.1010 |
Tianjing | 1.0000 | 5.0000 | 5.5560 | 2.0000 | 0.0000 | 20.1610 |
Shijiazhuang | 1.0000 | 3.0000 | 5.5560 | 2.0000 | 0.0000 | 10.9280 |
Qinhuangdao | 1.0000 | 2.0000 | 5.5560 | 1.0000 | 0.0000 | 17.7990 |
Nanjing | 1.0000 | 5.0000 | 5.5560 | 3.5000 | 0.0000 | 45.7290 |
Hefei | 2.0000 | 4.0000 | 11.1110 | 1.0000 | 1.9610 | 5.0090 |
Wuhan | 1.0000 | 2.0000 | 5.5560 | 3.0000 | 0.0000 | 9.7540 |
Huanggang | 1.0000 | 2.0000 | 11.1110 | 1.5000 | 1.9610 | 4.0860 |
Ji’nan | 1.0000 | 4.0000 | 5.5560 | 2.0000 | 0.0000 | 23.8520 |
Qingdao | 1.0000 | 4.0000 | 5.5560 | 1.5000 | 0.0000 | 4.5030 |
Zibo | 1.0000 | 2.0000 | 11.1110 | 1.5000 | 1.3070 | 6.1910 |
Weifang | 1.0000 | 2.0000 | 11.1110 | 1.0000 | 1.3070 | 5.3370 |
Taiyuan | 1.0000 | 3.0000 | 5.5560 | 1.5000 | 0.0000 | 6.5380 |
Shenyang | 1.0000 | 5.0000 | 5.5560 | 3.0000 | 0.0000 | 9.0960 |
Panjin | 1.0000 | 2.0000 | 16.6670 | 2.5000 | 1.9610 | 7.8020 |
Yangquan | 1.0000 | 1.0000 | 11.1110 | 1.0000 | 0.6540 | 6.3740 |
Jinzhou | 1.0000 | 1.0000 | 11.1110 | 2.0000 | 0.0000 | 7.9510 |
Huludao | 1.0000 | 1.0000 | 16.6670 | 1.5000 | 1.9610 | 17.0950 |
Liu’an | 1.0000 | 1.0000 | 11.1110 | 1.0000 | 2.6140 | 4.4090 |
national mean | 1.0526 | 1.6244 | 8.7722 | 1.1117 | 0.7224 | 5.2822 |
national sd | 0.2294 | 1.0107 | 3.8470 | 0.5128 | 0.9470 | 7.6108 |
Year | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|---|
Moran’s I | 0.055 | 0.052 | 0.058 | 0.054 | 0.055 | 0.055 | 0.062 | 0.061 | 0.065 | 0.047 | 0.063 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Variables | Dependent Variable: Quality of Urbanization | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
hsr_dum | hsr_rdc | hsr_adc | hsr_rbc | hsr_abc | ||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | |
hsr | −0.0087 ** | −0.0097 *** | −0.0052 *** | −0.0047 *** | −0.0001 | −0.0010 | 0.0002 | 0.0004 | 0.0001 | 0.0001 ** |
W × hsr | 0.0326 *** | 0.0067 | 0.0384 *** | 0.0198 * | 0.0100 *** | −0.0055 | 0.0014 * | −0.0007 | 0.0001 | −0.0001 ** |
rtec | 4.1260 *** | 4.1080 *** | 4.1606 *** | 4.1530 *** | 4.0645 *** | |||||
rfin | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |||||
rmar | −0.0788 *** | −0.0806 *** | −0.0807 *** | −0.0808 *** | −0.0841 *** | |||||
rfdi | −0.2950 *** | −0.2940 *** | −0.2903 *** | −0.2910 *** | −0.2821 *** | |||||
rho | 0.6600 *** | 0.2800 ** | 0.7770 *** | 0.2810 ** | 0.6686 *** | 0.2858 ** | 0.7640 *** | 0.2880 ** | 0.7471 *** | 0.2939 ** |
sigma2 | 0.0031 *** | 0.0030 *** | 0.0031 *** | 0.0030 *** | 0.0031 *** | 0.0030 *** | 0.0031 *** | 0.0030 *** | 0.0031 *** | 0.0030 *** |
city FE | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |
year FE | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |
N | 2730 | 2730 | 2730 | 2730 | 2730 | 2730 | 2730 | 2730 | 2730 | 2730 |
logL | 4008.9755 | 4059.2649 | 4011.5885 | 4062.8970 | 4005.8872 | 4056.2412 | 4002.2400 | 4056.5197 | 4003.6787 | 4059.3856 |
Vif | 1.0000 | 1.1100 | 1.0000 | 1.0700 | 1.0000 | 1.1100 | 1.0000 | 1.0700 | 1.0000 | 1.0700 |
LM-sar | 226.672 *** | 89.904 *** | 679.041 *** | 176.317 *** | 222.179 *** | 86.823 *** | 616.320 *** | 165.752 *** | 604.148 *** | 170.270 *** |
(Robust) | 0.475 | 7.478 *** | 54.134 *** | 3.527 *** | 1.536 | 10.985 *** | 10.747 *** | 0.543 | 44.508 *** | 0.768 |
LM-sem | 484.604 *** | 357.409 *** | 627.286 *** | 277.912 *** | 523.487 *** | 393.390 *** | 636.837 *** | 311.785 *** | 641.398 *** | 309.710 *** |
(robust) | 258.407 *** | 274.983 *** | 2.378 | 105.122 *** | 302.844 *** | 317.553 *** | 31.264 *** | 146.576 *** | 44.508 *** | 140.208 *** |
YEAR | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|---|
HSR cities | 19 | 41 | 57 | 69 | 91 | 110 | 146 | 167 | 172 | 186 | 197 |
New HSR cities | 19 | 22 | 16 | 12 | 22 | 19 | 36 | 21 | 5 | 14 | 11 |
HSR coverage | 0.07 | 0.15 | 0.21 | 0.25 | 0.33 | 0.40 | 0.53 | 0.61 | 0.63 | 0.68 | 0.72 |
Variables | Dependent Variable: Quality of Urbanization | |||||||
---|---|---|---|---|---|---|---|---|
Urban Agglomerations | Non-Urban Agglomerations | |||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
hsr_rdc | −0.0064 *** | −0.0058 *** | −0.0001 | 0.00001 | ||||
hsr_rbc | 0.0005 | 0.0007 * | −0.0013 *** | −0.0012 *** | ||||
W × hsr | 0.0350 *** | 0.0228 * | 0.0012 | −0.0001 | −0.0007 | −0.0030 | 0.0046 *** | 0.0020 ** |
LR_hsr | 0.1080 *** | 0.0231 | 0.0063 * | 0.0008 | −0.0037 | −0.0037 | 0.0075 *** | 0.0010 |
Con-var | No | Yes | No | Yes | No | Yes | No | Yes |
N | 2080 | 2080 | 2080 | 2080 | 650 | 650 | 650 | 650 |
LogL | 2792.262 | 2835.499 | 2784.876 | 2830.721 | 1819.698 | 1855.436 | 1850.296 | 1873.345 |
Vif | 1.00 | 1.06 | 1.00 | 1.07 | 1.00 | 1.06 | 1.00 | 1.06 |
LM-sar | 264.184 *** | 96.496 *** | 331.252 *** | 94.750 *** | 90.643 *** | 41.965 *** | 76.983 *** | 35.990 *** |
(Robust) | 42.021 *** | 0.000 | 2.567 | 0.217 | 17.562 *** | 46.500 *** | 40.031 *** | 41.224 *** |
LM-sem | 356.750 *** | 168.177 *** | 338.356 *** | 176.333 *** | 84.544 *** | 18.803 *** | 63.791 *** | 14.836 *** |
(Robust) | 137.587 *** | 71.681 *** | 9.671 *** | 81.8000 *** | 11.463 *** | 23.337 *** | 26.839 *** | 20.070 *** |
Variables | Dependent Variable: Quality of Urbanization | ||||||||
---|---|---|---|---|---|---|---|---|---|
Overall Regression | Urban Agglomerations | Non-Urban Agglomerations | |||||||
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
hsr_dum | −0.0068 * | ||||||||
hsr_rdc | −0.0061 *** | −0.0061 *** | −0.0009 | ||||||
hsr_adc | −0.0008 | ||||||||
hsr_rbc | 0.0007 ** | 0.0011 ** | −0.0009 *** | ||||||
hsr_abc | 0.0001 *** | ||||||||
W×hsr | 0.0294 *** | 0.0095 *** | 0.0118 *** | 0.0002 | 0.0001 | 0.0088 *** | 0.0002 | 0.0032 *** | 0.0012 *** |
Control | YES | YES | YES | YES | YES | YES | YES | YES | YES |
city FE | YES | YES | YES | YES | YES | YES | YES | YES | YES |
year FE | YES | YES | YES | YES | YES | YES | YES | YES | YES |
N | 2730 | 2730 | 2730 | 2730 | 2730 | 2080 | 2080 | 650 | 650 |
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Chen, J.; Li, W.; Wu, B.; Yu, Z. Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective. Systems 2023, 11, 523. https://doi.org/10.3390/systems11100523
Chen J, Li W, Wu B, Yu Z. Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective. Systems. 2023; 11(10):523. https://doi.org/10.3390/systems11100523
Chicago/Turabian StyleChen, Jingyu, Weidong Li, Bingyu Wu, and Zhen Yu. 2023. "Does High-Speed Railway Promote the Quality of Urbanization? From a Dynamic Network Perspective" Systems 11, no. 10: 523. https://doi.org/10.3390/systems11100523