Can the Spatial Function Division of Urbanization Promote Regional Coordinated Development? Evidence from the Yangtze River Economic Belt in China
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Analysis and Research Hypotheses
3.1. The SFDU and Regional Coordinated Development
3.2. The SFDU, Allocating of Factors and Regional Coordinated Development
4. Methodology and Data
4.1. Model Specification
4.1.1. Basic Regression Model
4.1.2. Dynamic Panel Model
4.1.3. The Mediation Effect Model
4.2. Variable Construction and Measurement
4.2.1. Explained Variable: Regional Coordinated Development Level
- Use the improved extreme value method to standardize the value of each indicator:
- 2.
- Calculate the information entropy and weight of each indicator:
- 3.
- Calculate the RCDI according to the indicator weight:
4.2.2. Explanatory Variable: The SFDU
4.2.3. Mediating Variable: Resource Allocation Efficiency (RAE)
4.2.4. Control Variables
- (1)
- Physical capital level (PCL). We use the proportion of the city’s annual fixed-asset investment to GDP as a proxy.
- (2)
- Human capital level (HCL). We use the proportion of college students in the total registered population to measure this level.
- (3)
- Urbanization Level (URR). We use the proportion of the population of municipal districts to the total population of the city to represent this.
- (4)
- Degree of government intervention (GIL). We use the proportion of fiscal expenditure to GDP to express this level.
- (5)
- Level of opening-up (FDI). We use the proportion of foreign direct investment as a proxy.
- (6)
- Infrastructure level (INF). We use per capita urban road area to measure this level.
- (7)
- Industrial structure (IS). We use the ratio of urban tertiary industry added value to secondary industry added value as a proxy.
4.3. Study Area and Data Description
5. Empirical Results
5.1. Preliminary Observation
5.1.1. The Regional Coordinated Development Level
5.1.2. The Level of SFDU
5.2. Basic Regression Results
5.3. Regression Results of Sub-Regional and Coordination Dimensions
5.4. Mechanism Test
5.5. Robustness Test
5.5.1. Robustness Test I: Replacing the Explained Variable
5.5.2. Robustness Test II: Quantile Regression
6. Discussion
6.1. Environmental Impacts of the SFDU on the Yangtze River Ecosystems
6.2. Socio-Economic Impacts of the SFDU on the Local Population Involved
6.3. Spatial-Regional Impacts of the SFDU on the Geo-Morphologic and Land Use Changes Reported
6.4. Governmental Policies and Measures Taken to Support the SFDU
- (1)
- Change the status quo of policy homogenization, coordinate urban industrial policies based on the SFDU, and strengthen its promotion in relation to regional coordinated development. Change and adjust the existing form of the traditional division of labor between departments or products, stop the homogenization of industrial policies between cities, formulate urban industrial policies oriented towards the SFDU, and clarify the structures of various industries from the perspective of the industrial chain. The functional positioning of cities promotes the SFDU and effective collaboration between cities based on different industrial chain links.
- (2)
- Refine the strategy of the industrial division of labor between cities by region and by industrial chain node, strengthen advantages, make up for shortcomings, and guide the rational and orderly transfer of industries. On the one hand, remove the boundaries created by administrative divisions, take economic belts and urban agglomerations as units and combine the resource endowments, location advantages, and future development goals of each region to clarify the development orientation of urban industries. Realize the effective matching between the SFDU and the actual situation of the region and guide reasonable and orderly transfers of industries in the upstream, middle, and downstream areas. On the other hand, each region should also clarify its own shortcomings in promoting regional coordinated development through the SFDU and make targeted improvements. For example, the Pan-Yangtze River Delta urban agglomeration and the Pan-Chengdu–Chongqing urban agglomeration should pay more attention to the adverse impact on the ecological environment that occurs during the development of the SFDU and increase supporting policies such as green innovation or environmental regulation, so that the SFDU and the development of the ecological environment can be undertaken with more coordination.
- (3)
- Innovate and improve the market mechanism, optimize the allocation of resources between cities, and unblock the channels and mechanisms by which the SFDU can promote regional coordinated development. On the one hand, optimize and adjust the capital allocation structure, appropriately disperse and transfer the areas with excess capital allocation, guide capital inflow through policies to the areas with insufficient capital allocation, improve the efficiency of capital allocation, and better enable the SFDU to play a channeling role in promoting regional coordinated development. On the other hand, in view of the current situation in which the SFDU in the YREB is not well-matched with the development of the labor market, speed up the development of labor marketization, break down administrative barriers, rationally regulate and guide the current “war for talents” in major cities, and promote the orderly and reasonable flow of labor based on the value chain, as well as realizing the effective allocation of labor resources in different regions.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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First-Level Indicators | Secondary Indicators | Third-Level Indicators | Indicator Explanation | Unit |
---|---|---|---|---|
Coordinated development of regional economy (RCDI_E) | Total output (B1) | GDP per capita (C1) | GDP/total population | Yuan |
Public input (B2) | Public budget expenditure per capita (C2) | Total public budget expenditure/total population | Yuan | |
Innovation input (B3) | R&D expenditure as a percentage of GDP (C3) | R&D spending/GDP | % | |
Coordinated development of regional people’s livelihood (RCDI_P) | Urban and rural income (B5) | Urban–rural income gap (C5) | Per capita disposable income of urban residents/per capita disposable income of rural residents | times |
Traffic condition (B6) | Urban road area per capita (C6) | Urban road area/total population of municipal districts | sqm/person | |
Medical condition (B7) | Medical beds per capita (C7) | Number of medical beds/total population | beds/10,000 people | |
Education condition (B8) | Education expenditure as a percentage of public budget expenditure (C8) | Education spending/public budget spending | % | |
Coordinated development of regional green ecology (RCDI_G) | Energy consumption (B9) | Pollutant emissions per 10,000 yuan of GDP (C9) | (Industrial wastewater + industrial sulfur dioxide + industrial soot emissions)/GDP | ton/10,000 yuan |
Green governance (B10) | Comprehensive utilization rate of industrial solid waste (C10) | Utilized industrial solid waste/total industrial solid waste | % | |
Ecological outcomes (B11) | Urban green space per capita (C11) | Urban green space area/total population of municipal districts | sqm/person |
Type | Abbreviation | Variable | Explanation |
---|---|---|---|
Explained variable | RCDI | Regional coordinated development level | Index |
Explanatory variable | NDI | The SFDU | Index |
Mediating Variable | RAE | Resource allocation efficiency | Calculated by model |
Control variables | PCL | Physical capital level | The city’s annual fixed asset investment/GDP |
HCL | Human capital level | College students/The total registered population | |
URR | Urbanization Level | The population of municipal districts/The total population of the city | |
GIL | Degree of government intervention | Fiscal expenditure/GDP | |
FDI | Level of opening-up | Foreign direct investment/GDP | |
INF | Infrastructure level | Per capita urban road area | |
IS | Industrial structure | Urban tertiary industry added value/Secondary industry added value |
Variables | Mean | Standard Deviation | Min | Max | Sample Volume |
---|---|---|---|---|---|
RCDI | 0.7634 | 0.0588 | 0.5802 | 0.9148 | 1080 |
NDI | 2.0396 | 1.2194 | 0.2035 | 8.0343 | 1080 |
PCL | 0.7364 | 0.2490 | 0.2268 | 2.1761 | 1080 |
HCL | 0.0172 | 0.0226 | 1.34 × 10−5 | 0.1270 | 1080 |
URR | 0.3185 | 0.1941 | 0.0468 | 1 | 1080 |
GIL | 0.1840 | 0.1070 | 0.0154 | 1.5751 | 1080 |
FDI | 0.0225 | 0.0192 | 5.92 × 10−5 | 0.1185 | 1080 |
INF | 10.9057 | 6.0315 | 0.59 | 71.6393 | 1080 |
IS | 0.7893 | 0.3142 | 0.2723 | 3.17 | 1080 |
Baseline Regression | Fixed Effects | Mixed Effects | Random Effects | SYS-GMM | |
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | |
L.RCDI | 0.432 *** | ||||
−0.0186 | |||||
NDI | 0.0028 ** | 0.005 *** | 0.0043 *** | 0.0056 *** | 0.0057 *** |
(0.0012) | (0.0013) | (0.0012) | (0.0012) | (0.0006) | |
PCL | −0.0106 ** | −0.0007 | −0.0140 *** | 0.0088 * | |
(0.0049) | (0.0059) | (0.0048) | (0.0046) | ||
HCL | 0.103 | 0.679 *** | 0.680 *** | 0.233 *** | |
(0.2060) | (0.0801) | (0.1410) | (0.0602) | ||
URR | −0.0064 | 0.0863 *** | 0.0478 *** | 0.0472 *** | |
(0.0193) | (0.0087) | (0.0146) | (0.0095) | ||
GIL | −0.0266 ** | −0.132 *** | −0.0429 *** | −0.133 *** | |
(0.0107) | (0.0146) | (0.0107) | (0.0118) | ||
FDI | 0.0716 | 0.481 *** | 0.299 *** | 0.0903 ** | |
(0.0872) | (0.0771) | (0.0793) | (0.0364) | ||
INF | −0.0019 *** | −0.0001 | −0.0016 *** | −0.0003 ** | |
(0.0002) | (0.0003) | (0.0002) | (0.0001) | ||
IS | 0.0010 *** | 0.0003 | 0.0008 *** | 0.0007 *** | |
(0.0003) | (0.0002) | (0.0002) | (0.0001) | ||
Constant | 0.758 *** | 0.749 *** | 0.720 *** | 0.723 *** | 0.396 *** |
(0.0026) | (0.0109) | (0.0084) | (0.0097) | (0.0155) | |
Observations | 1080 | 1080 | 1080 | 1080 | 972 |
R-squared | 0.006 | 0.103 | 0.486 | ||
F | 0.000 | 0.000 | 0.000 | ||
Region fixed | YES | YES | |||
Year fixed | YES | YES | |||
AR(1) AR(2) | 0.000 0.120 | ||||
Hansen | 0.175 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Overall Regression | Economic Coordination | People’s Livelihood Coordination | Ecological Coordination | |
L.RCDI | 0.664 *** | |||
(0.0393) | ||||
L.RCDI_E | 0.650 *** | |||
(0.0345) | ||||
L.RCDI_P | 0.696 *** | |||
(0.0419) | ||||
L.RCDI_G | 0.553 *** | |||
(0.0516) | ||||
NDI | 0.0117 *** | 0.0231 *** | 0.0172 *** | −0.0157 ** |
(0.0024) | (0.0030) | (0.0025) | (0.0067) | |
PCL | −0.0063 | −0.0488 *** | 0.0165 | −0.0656 *** |
(0.0104) | (0.0102) | (0.0123) | (0.0159) | |
HCL | 0.341 ** | 0.586 ** | 1.499 *** | −0.856 *** |
(0.1600) | (0.2770) | (0.3350) | (0.1920) | |
URR | 0.0147 | 0.0486 *** | 0.0278 * | 0.0342 |
(0.0335) | (0.0155) | (0.0371) | (0.0859) | |
GIL | −0.0350 * | −0.0114 | 0.0640 * | −0.183 *** |
(0.0202) | (0.0277) | (0.0386) | (0.0276) | |
FDI | 0.0415 ** | 0.00225 ** | 0.139 * | 0.0986 |
(0.1460) | (0.3840) | (0.2750) | (0.4660) | |
INF | −0.00001 | −0.0024 *** | −0.0011 | 0.0022 ** |
(0.0006) | (0.0007) | (0.0007) | (0.0010) | |
IS | 0.0005 * | 0.0026 *** | −0.0025 *** | 0.0016 *** |
(0.0003) | (0.0003) | (0.0005) | (0.0005) | |
Constant | 0.206 *** | 0.163 *** | 0.238 *** | 0.310 *** |
(0.0317) | (0.0273) | (0.0329) | (0.0540) | |
Observations | 279 | 279 | 279 | 279 |
AR(1) | 0.002 | 0.000 | 0.000 | 0.000 |
AR(2) | 0.444 | 0.312 | 0.312 | 0.710 |
Hansen | 0.875 | 0.865 | 0.865 | 0.692 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Overall Regression | Economic Coordination | People’s Livelihood Coordination | Ecological Coordination | |
L.RCDI | 0.572 *** | |||
(0.0331) | ||||
L.RCDI_E | 0.731 *** | |||
(0.0228) | ||||
L.RCDI_P | 0.522 *** | |||
(0.0408) | ||||
L.RCDI_G | 0.494 *** | |||
(0.0438) | ||||
NDI | 0.0117 *** | 0.0230 *** | 0.0104 *** | 0.0094 *** |
(0.0012) | (0.0018) | (0.0014) | (0.0020) | |
PCL | 0.0157 | 0.0545 *** | 0.0736 *** | −0.0764 *** |
(0.0117) | (0.0074) | (0.0136) | (0.0187) | |
HCL | 0.383 *** | 0.748 *** | 0.172 ** | −0.0992 |
(0.0696) | (0.1350) | (0.0682) | (0.1400) | |
URR | −0.014 | −0.0885 *** | −0.0929 *** | 0.0699 *** |
(0.0196) | (0.0218) | (0.0326) | (0.0243) | |
GIL | −0.280 *** | −0.414 *** | −0.656 *** | −0.0695 |
(0.0475) | (0.0441) | (0.0943) | (0.0569) | |
FDI | −0.0989 | 0.151 | −0.325 ** | −0.111 |
(0.1550) | (0.4400) | (0.1410) | (0.3500) | |
INF | −5.27 × 10−5 | −0.005 *** | 0.0014 *** | 0.0039 *** |
(0.0004) | (0.0005) | (0.0004) | (0.0010) | |
IS | 0.0011 ** | −8.18 × 10−5 | 0.0019 *** | 0.0026 *** |
(0.0005) | (0.0005) | (0.0004) | (0.0008) | |
Constant | 0.294 *** | 0.227 *** | 0.351 *** | 0.311 *** |
(0.0351) | (0.0221) | (0.0327) | (0.0420) | |
Observations | 324 | 324 | 324 | 324 |
Regions | 36 | 36 | 36 | 36 |
AR(1) | 0.000 | 0.004 | 0.000 | 0.003 |
AR(2) | 0.153 | 0.255 | 0.247 | 0.298 |
Hansen | 0.802 | 0.655 | 0.958 | 0.795 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Overall Regression | Economic Coordination | People’s Livelihood Coordination | Ecological Coordination | |
L.RCDI | 0.424 *** | |||
(0.0202) | ||||
L.RCDI_E | 0.836 *** | |||
(0.0300) | ||||
L.RCDI_P | 0.205 *** | |||
(0.0341) | ||||
L.RCDI_G | 0.469 *** | |||
(0.0321) | ||||
NDI | 0.0058 *** | 0.0148 *** | 0.0046 *** | −0.0115 *** |
(0.0009) | (0.0018) | (0.0012) | (0.0024) | |
PCL | 0.008 | 0.0166 | 0.0720 *** | 0.0312 *** |
(0.0055) | (0.0110) | (0.0079) | (0.0087) | |
HCL | 0.069 | 0.803 *** | 0.464 *** | −1.398 *** |
(0.1240) | (0.2000) | (0.1690) | (0.1360) | |
URR | 0.0479 *** | −0.0133 | 0.103 *** | 0.0549 ** |
(0.0164) | (0.0312) | (0.0156) | (0.0277) | |
GIL | −0.0810 *** | −0.0271 | −0.0679 *** | −0.236 *** |
(0.0028) | (0.0284) | (0.0252) | (0.0399) | |
FDI | 0.560 *** | 0.831 *** | 0.614 *** | 0.238 *** |
(0.0659) | (0.0425) | (0.0428) | (0.0670) | |
INF | −0.0004 ** | −0.0063 *** | −0.004 *** | 0.0054 *** |
(0.0002) | (0.0006) | (0.0006) | (0.0004) | |
IS | 0.0006 ** | 0.0012 *** | 0.0009 *** | 0.0013 *** |
(0.0003) | (0.0004) | (0.0003) | (0.0003) | |
Constant | 0.393 *** | 0.0807 *** | 0.506 *** | 0.368 *** |
(0.0163) | (0.0255) | (0.0286) | (0.0259) | |
Observations | 369 | 369 | 369 | 369 |
Regions | 41 | 41 | 41 | 41 |
AR(1) | 0.000 | 0.000 | 0.000 | 0.000 |
AR(2) | 0.217 | 0.841 | 0.129 | 0.152 |
Hansen | 0.615 | 0.516 | 0.482 | 0.443 |
RAE_K | RAE_L | RCDI | RCDI | RCDI | |
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | |
NDI | 2.878 *** | −6.151 *** | 0.0032 *** | 0.0051 *** | 0.0057 *** |
(0.7100) | (1.9090) | (0.0007) | (0.0006) | (0.0006) | |
PCL | −17.09 *** | 7.799 | 0.0160 *** | 0.000498 | 0.00876 * |
(5.2980) | (8.2600) | (0.0036) | (0.0039) | (0.0046) | |
HCL | 479.0 *** | −588.4 *** | 0.0255 | 0.296 *** | 0.233 *** |
(80.7300) | (201.2000) | (0.0737) | (0.0438) | (0.0602) | |
URR | −66.08 *** | 35.52 | 0.0628 *** | 0.0270 *** | 0.0472 *** |
(17.4300) | (30.5300) | (0.0132) | (0.0043) | (0.0095) | |
GIL | 53.81 *** | −91.60 ** | −0.136 *** | −0.135 *** | −0.133 *** |
(17.0200) | (40.8500) | (0.0116) | (0.0080) | (0.0118) | |
FDI | −1163 *** | 1495 *** | 0.269 *** | 0.311 *** | 0.0903 ** |
(87.2400) | (273.0000) | (0.0296) | (0.0317) | (0.0364) | |
IS | 0.076 | 1.314 *** | 0.0005 *** | 0.0007 *** | −0.0003 ** |
(0.1930) | (0.3130) | (0.0001) | (0.0001) | (0.0001) | |
L.YL | −0.0962 *** | 0.0007 *** | |||
(0.0089) | (0.0001) | ||||
RAE_K | 0.00032 *** | ||||
(0.0000) | |||||
RAE_L | −0.0001 *** | ||||
(0.0000) | |||||
Constant | 35.54 *** | −34.36 *** | 0.387 *** | 0.449 *** | 0.396 *** |
(6.1010) | (9.4130) | (0.0176) | (0.0194) | (0.0155) | |
Observations | 972 | 972 | 972 | 972 | 972 |
AR(1) | 0.003 | 0.006 | 0 | 0 | 0 |
AR(2) | 0.675 | 0.987 | 0.129 | 0.17 | 0.12 |
Hansen | 0.312 | 0.978 | 0.16 | 0.274 | 0.175 |
(1) | (2) | (3) | |
---|---|---|---|
Fixed Effects | Random Effects | SYS-GMM | |
L.JN | 0.521 *** | ||
−0.0097 | |||
NDI | 0.0174 *** | 0.0167 *** | 0.0048 *** |
(0.0031) | (0.0031) | (0.0006) | |
PCL | −0.0412 *** | −0.0481 *** | −0.101 *** |
(0.0158) | (0.0156) | (0.0048) | |
HCL | 1.304 *** | 1.309 *** | 0.243 *** |
(0.1880) | (0.1880) | (0.0866) | |
URR | 0.162 *** | 0.161 *** | 0.0531 *** |
(0.0204) | (0.0204) | (0.0116) | |
GIL | −0.363 *** | −0.366 *** | −0.426 *** |
(0.0346) | (0.0346) | (0.0205) | |
FDI | 0.624 *** | 0.678 *** | 0.825 *** |
(0.1900) | (0.1890) | (0.0441) | |
INF | 0.0024 *** | 0.0023 *** | 0.0013 *** |
(0.0007) | (0.0007) | (0.0002) | |
IS | 0.0007 | 0.0006 | 0.001 *** |
(0.0005) | (0.0005) | (0.0001) | |
Constant | 0.610 *** | 0.623 *** | 0.378 *** |
(0.0244) | (0.0264) | (0.0081) | |
Control variables | Control | Control | Control |
Observations | 1080 | 1080 | 972 |
R-squared | 0.485 | ||
AR(1) | 0.000 | ||
AR(2) | 0.051 | ||
Hansen | 0.163 |
Median Position | (1) | (2) | (3) |
---|---|---|---|
25% | 50% | 75% | |
Explained variable | RCDI | RCDI | RCDI |
NDI | 0.0052 ** | 0.0076 *** | 0.0046 ** |
(0.0016) | (0.0014) | (0.0020) | |
Control variable | Yes | Yes | Yes |
Observations | 1080 | 1080 | 1080 |
R-squared | 0.236 | 0.376 | 0.395 |
Region | Yes | Yes | Yes |
Year | Yes | Yes | Yes |
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Guo, S.; Ma, H. Can the Spatial Function Division of Urbanization Promote Regional Coordinated Development? Evidence from the Yangtze River Economic Belt in China. Sustainability 2022, 14, 7115. https://doi.org/10.3390/su14127115
Guo S, Ma H. Can the Spatial Function Division of Urbanization Promote Regional Coordinated Development? Evidence from the Yangtze River Economic Belt in China. Sustainability. 2022; 14(12):7115. https://doi.org/10.3390/su14127115
Chicago/Turabian StyleGuo, Siliang, and Heng Ma. 2022. "Can the Spatial Function Division of Urbanization Promote Regional Coordinated Development? Evidence from the Yangtze River Economic Belt in China" Sustainability 14, no. 12: 7115. https://doi.org/10.3390/su14127115
APA StyleGuo, S., & Ma, H. (2022). Can the Spatial Function Division of Urbanization Promote Regional Coordinated Development? Evidence from the Yangtze River Economic Belt in China. Sustainability, 14(12), 7115. https://doi.org/10.3390/su14127115