Hierarchical Correlates of the Shrinkage of Cities and Towns in Northeast China
Abstract
:1. Introduction
2. Literature Review
2.1. Unbalanced and Dynamic Change of Cities and Towns
2.2. Factors Influencing the Shrinkage of Cities and Towns
2.3. Mutil-Level Cities and Towns in China
2.4. A Multi-Level Framework for Understanding the Shrinkage of Cities and Towns
3. Study Area, Data Sources, and Research Methods
3.1. Study Area
3.2. Indicators and Data Sources
3.2.1. Dependent Variable
3.2.2. Independent Variable
3.3. Research Methods
3.3.1. Identification of Shrinking Cities and Towns
3.3.2. Methods of Spatial Autocorrelation Analysis
3.3.3. Methods of Influencing Factors Analysis
4. Hierarchical Differences and Correlations of City and Town Shrinkage
4.1. Hierarchical Differences
4.2. Hierarchical Correlations
5. Associated Mechanisms of City and Town Shrinkage in Northeast China
5.1. Factors Affecting Shrinkage of Multi-Level Cities and Towns
5.2. A Multilevel Correlation Analysis of Factors Influencing Shrinking Cities and Towns
5.2.1. Cross-Level Impact of Prefecture-Level Cities on County-Level Towns
5.2.2. Cross-Level Impact of Prefecture-Level Cities on Ordinary Towns
5.2.3. Cross-Level Impact of County-Level Towns on Ordinary Towns
5.3. Associated Mechanisms of Multi-Level Shrinking Cities and Towns
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Categories | Variables | Prefecture Level | County Level | Town Level |
---|---|---|---|---|
Population | POP1 | |||
POP2 | 0.274 *** | |||
POP3 | 0.219 ** | −0.099 ** | ||
Economic | ECO1 | 0.347 * | 0.440 *** | |
ECO2 | 0.493 *** | |||
ECO3 | ||||
Industry | IND1 | 0.885 *** | −0.100 ** | |
IND2 | 0.341 ** | −0.037 | ||
IND3 | 0.342 * | 0.054 | ||
IND4 | 0.546 *** | 0.109 ** | ||
Infrastructure | INF1 | 0.013 | ||
INF2 | 0.180 * | |||
Environment | ENV1 | −0.236 *** | ||
ENV2 | 0.084 ** | |||
R2 | 0.384 | 0.378 | 0.165 |
1. | Data on the population of the townships in this study were obtained from the China Township Statistics and the China Statistical Yearbook (Township). Considering that the China Statistical Yearbook (Township) no longer counts the population of the built-up area of the town after 2018, statistics from 2018 were used for the town population. Using the average annual rate of population change (Equation (1)) to identify urban shrinkage minimizes the effect of time period on the identification of shrinking towns and comparisons between multiple levels. Missing data in this study are filled in using adjacent years. |
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Categories | Variables | Explanations |
---|---|---|
Population | Aging level (POP1) | Percentage of the population aged 65 and over |
Percentage of floating population (POP2) | Percentage of non-local resident population | |
Population centrality (POP3) | Urban population as a percentage of the total population | |
Economic | Economic development level (ECO1) | Rate of GDP (or nighttime light value) change |
Fixed asset investment (ECO2) | Fixed asset investment completion | |
Average wage (ECO3) | Average wage of urban workers in employment | |
Industry | Enterprise size (IND1) | Number of industrial enterprises above the scale |
Secondary industry share (IND2) | Secondary industry value-added ratio | |
Tertiary industry share (IND3) | Tertiary industry value-added ratio | |
Technology level (IND4) | Number of high-tech enterprises | |
Infrastructure | Medical level (INF1) | Number of medical and health institutions per 10,000 people |
Traffic level (INF2) | Length of road per capita | |
Environment | Air pollution index (ENV1) | PM2.5 average value |
Arable land (ENV2) | Arable land per capita |
Level | 2000–2010 | 2010–2020 | ||||
---|---|---|---|---|---|---|
Shrinkage | Slight Shrinkage | Significant Shrinkage | Shrinkage | Slight Shrinkage | Significant Shrinkage | |
Prefecture level | 5 | 5 | 0 | 20 | 14 | 6 |
14.7% | 14.7% | - | 58.8% | 41.2% | 17.6% | |
County level | 11 | 10 | 1 | 78 | 56 | 22 |
7.6% | 6.9% | 0.7% | 54.2% | 38.9% | 15.3% | |
Town level | 518 | 232 | 286 | 594 | 290 | 304 |
44.2% | 19.8% | 24.4% | 52.2% | 24.7% | 25.9% |
Categories | Variables | Prefecture Level | County Level | Town Level |
---|---|---|---|---|
Population | POP1 | |||
POP2 | 0.248 ** | |||
POP3 | 0.315 *** | −0.562 *** | ||
Economic | ECO1 | 0.577 *** | 0.396 *** | |
ECO2 | 0.268 * | |||
ECO3 | ||||
Industry | IND1 | 0.678 *** | −0.097 *** | |
IND2 | 0.315 ** | −0.337 *** | ||
IND3 | 0.639 *** | 0.101** | ||
IND4 | 0.552 ** | 0.313 *** | ||
Infrastructure | INF1 | 0.099 *** | ||
INF2 | 0.211 ** | |||
Environment | ENV1 | −0.085 *** | ||
ENV2 | 0.129 *** | |||
R2 | 0.514 | 0.311 | 0.338 |
County-Level Variables | Regression Coefficient | Prefecture-Level Variables | |||
---|---|---|---|---|---|
ECO1 | ECO3 | INF1 | INF2 | ||
POP2 | 0.248 ** | 4.386 | 0.045 | −4.017 * | −8.842 |
POP3 | 0.315 *** | 4.882 | −4.775 ** | 3.296 * | −1.241 |
ECO1 | 0.396 *** | 2.226 * | −0.504 | −2.115 | −0.302 |
IND2 | 0.315 ** | −1.715 | −0.560 | −3.406 * | −5.213 * |
Town-Level Variables | Regression Coefficient | Prefecture-Level Variables | |||||
---|---|---|---|---|---|---|---|
ECO3 | IND1 | IND3 | IND4 | INF1 | INF2 | ||
POP3 | −0.562 *** | 0.483 ** | 0.688 | 0.155 | −1.115 * | 0.344 * | 0.290 |
IND2 | −0.337 *** | −0.035 | −4.638 ** | 0.888 * | 3.576 | 0.792 | 0.898 |
IND3 | 0.101 ** | −0.690 | 1.006 | −0.196 | −0.702 | −1.975 ** | −1.410 ** |
IND4 | 0.313 *** | 0.471 | 1.095 | −1.194 * | 0.214 | −1.196 | −0.963 |
Town-Level Variables | Regression Coefficient | County-Level Variables | |||||
---|---|---|---|---|---|---|---|
POP3 | ECO1 | ECO3 | IND4 | INF1 | INF2 | ||
POP3 | −0.562 *** | −0.434 *** | 0.601 *** | 0.019 | −0.346 * | −0.039 | 0.046 |
IND1 | −0.097 *** | −0.011 | −0.074 | 0.437 *** | −0.111 | −0.086 | 0.066 |
IND2 | −0.337 *** | 0.059 | −0.771 | −0.027 | 0.656 | −0.591 | −0.209 |
IND3 | 0.101 ** | −0.620 ** | 0.299 | 0.078 | −0.566 | 1.382 | −0.302 * |
INF1 | 0.099 *** | −0.117 | 0.001 | −0.051 | 0.172 | 0.306 ** | −0.076 |
EVO1 | −0.085 *** | 0.084 | 0.193 | 0.234 | −0.051 | 0.350 ** | 0.139 |
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Liu, W.; Tong, Y.; Zhang, J.; Ma, Z.; Zhou, G.; Liu, Y. Hierarchical Correlates of the Shrinkage of Cities and Towns in Northeast China. Land 2022, 11, 2208. https://doi.org/10.3390/land11122208
Liu W, Tong Y, Zhang J, Ma Z, Zhou G, Liu Y. Hierarchical Correlates of the Shrinkage of Cities and Towns in Northeast China. Land. 2022; 11(12):2208. https://doi.org/10.3390/land11122208
Chicago/Turabian StyleLiu, Wei, Yao Tong, Jing Zhang, Zuopeng Ma, Guolei Zhou, and Yanjun Liu. 2022. "Hierarchical Correlates of the Shrinkage of Cities and Towns in Northeast China" Land 11, no. 12: 2208. https://doi.org/10.3390/land11122208
APA StyleLiu, W., Tong, Y., Zhang, J., Ma, Z., Zhou, G., & Liu, Y. (2022). Hierarchical Correlates of the Shrinkage of Cities and Towns in Northeast China. Land, 11(12), 2208. https://doi.org/10.3390/land11122208