China’s New-Style Urbanization and Its Impact on the Green Efficiency of Urban Land Use
Abstract
:1. Introduction
2. Literature Review
2.1. Definition of GEULU
2.2. Methodology for Estimating GEULU
2.3. Driving Forces of GEULU
3. Materials and Methods
3.1. Study Area
3.2. Indicators and Data Sources
3.2.1. Input–Output System for GEULU
3.2.2. GEULU Under NU
3.2.3. Data Description
3.3. Methods
3.3.1. Super SBM-DDF-GML Model Involving Undesired Outputs
3.3.2. Entropy Weight Method
3.3.3. Nonparametric Kernel Density Estimation (KDE)
3.3.4. Exploratory Spatial Data Analysis (ESDA)
3.3.5. Geographically and Temporally Weighted Regression (GTWR) Model
4. Empirical Results
4.1. Measurement Results of GEULU and Its Characteristics of Spatiotemporal Evolution
4.1.1. Measurement Results of GEULU
4.1.2. Spatiotemporal Evaluation of GEULU at Urban Agglomeration Scale
4.1.3. Spatiotemporal Evaluation of GEULU at City Scale
4.2. Driving Mechanism of NU on the GEULU
4.2.1. A First Look: Why the GTWR Model?
4.2.2. The General Results of the GTWR
4.2.3. Spatiotemporal Heterogeneity of NU’s Impact on GEULU at Regional Scale
4.2.4. Spatiotemporal Heterogeneity of NU’s Impact on GEULU at Urban Agglomeration Scale
4.2.5. Spatiotemporal Heterogeneity of NU’s Impact on GEULU at City Scale
5. Discussion and Policy Implications
5.1. Interpretation of Findings
5.2. Policy Implications
5.3. Validation and Future Directions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
GEULU | Green efficiency of urban land use |
NU | New-Style Urbanization |
SDGs | Sustainable Development Goals |
SBM-DDF-GML | Slacks-based measure–directional distance function–global Malmquist–Luenberger |
SFA | Stochastic frontier analysis |
DEA | Data envelopment analysis |
ULUE | Urban land use efficiency |
GTWR | Geographically and temporally weighted regression |
KDE | Kernel density estimation |
ESDA | Exploratory spatial data analysis |
EU | Economic urbanization |
PU | Population urbanization |
LU | Land urbanization |
SU | Social urbanization |
EEB | Ecological and environmental benefits |
UDD | Urban development digitalization |
REC | Research and education clustering |
JJJ | Beijing–Tianjin–Hebei |
YRD | Yangtze River Delta |
GHM | Guangdong–Hong Kong–Macao Greater Bay Area |
CC | Chengdu–Chongqing |
MYR | Middle Yangtze River |
SP | Shandong Peninsula |
CP | Central Plains |
GZP | Guanzhong Plains |
GFZ | Guangdong–Fujian–Zhejiang Coastal Area |
BG | Beibu Gulf |
HC | Harbin–Changchun |
CSL | Central and Southern Liaoning |
CS | Central Shanxi |
CG | Central Guizhou |
CY | Central Yunnan |
HBEY | Hohhot–Baotou–Ordos–Yulin |
LX | Lanzhou–Xining |
NYR | Ningxia Yellow River |
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Urban Agglomeration | Abbreviation | Area (10,000 km2) | Population (Million Persons) | GDP (100 Million CNY) |
---|---|---|---|---|
Beijing–Tianjin–Hebei | JJJ | 21.80 | 107 | 86,000 |
Yangtze River Delta | YRD | 22.50 | 175 | 212,000 |
Guangdong–Hong Kong–Macao Greater Bay Area | GHM | 5.50 | 78 | 90,000 |
Chengdu–Chongqing | CC | 18.50 | 103 | 68,000 |
Middle Yangtze River | MYR | 31.70 | 130 | 92,000 |
Shandong Peninsula | SP | 7.40 | 103 | 73,000 |
Central Plains | CP | 28.70 | 160 | 81,266 |
Guanzhong Plains | GZP | 10.71 | 38.87 | 22,000 |
Guangdong–Fujian–Zhejiang Coastal Area | GFZ | 27.00 | 93.65 | 69,695 |
Beibu Gulf | BG | 11.66 | 44 | 22,000 |
Harbin–Changchun | HC | 26.40 | 44.09 | 20,468 |
Central and Southern Liaoning | CSL | 8.15 | 20 | 21,000 |
Central Shanxi | CS | 7.41 | 15.43 | 8937 |
Central Guizhou | CG | 5.38 | 11.78 | 12,600 |
Central Yunnan | CY | 11.14 | 19.56 | 12,800 |
Hohhot–Baotou–Ordos–Yulin | HBEY | 17.50 | 12 | 13,211 |
Lanzhou–Xining | LX | 9.75 | 10.61 | 5198 |
Ningxia Yellow River | NYR | 6.64 | 6.85 | 3568 |
Dimensions | Indicators | Association |
---|---|---|
Economic urbanization (EU) | Per capita GDP (CNY) | + |
Percentage of the secondary industry in the GDP (%) | + | |
Percentage of the tertiary industry in the GDP (%) | + | |
Per capita disposable income of urban residents (CNY) | + | |
Per capita retail sales of consumer goods (CNY) | + | |
Population urbanization (PU) | Urban population proportion (%) | + |
Urban population density (persons/km2) | + | |
Urban registered unemployment rate (%) | − | |
Per capita consumption expenditure of urban residents (CNY) | + | |
Land urbanization (LU) | Proportion of the built-up area to the total administrative area (%) | + |
Park green space area (hectares) | + | |
Per capita urban road area (square meters) | + | |
Social urbanization (SU) | Number of public transport vehicles per ten thousand people | + |
Number of health technical personnel per thousand people (per person) | + | |
Number of medical and health institution beds per thousand people (beds/1000 persons) | + | |
Per capita public library collection (volumes) | + | |
Engel coefficient of urban residents (%) | − | |
Ecological and environmental benefits (EEBs) | Proportion of environment protection-related words in the government work report (%) | + |
Urban domestic waste harmless treatment rate (%) | + | |
Industrial solid waste utilization rate (%) | + | |
Urban sewage treatment rate (%) | + | |
Green total factor productivity (%) | + | |
Urban development digitalization (UDD) | Proportion of digital-related words in the government work report (%) | + |
Number of internet broadband access users (10k households) | + | |
Proportion of total telecommunications business volume of GDP (%) | + | |
Number of mobile phone users (10k households) | + | |
Degree of digital transformation of listed companies | + | |
Research and education clustering (REC) | Proportion of education expenditure to fiscal expenditure (%) | + |
Number of faculty members and students in higher education institutions (10k persons) | + | |
Proportion of expenditure on science and technology to fiscal expenditure (%) | + | |
Number of patent applications | + | |
Degree of AI adoption by listed companies | + |
Years | Moran’s I | Z Value | Years | Moran’s I | Z Value |
---|---|---|---|---|---|
2006 | 0.035 ** | 2.333 | 2014 | 0.078 *** | 4.848 |
2007 | 0.046 *** | 2.984 | 2015 | 0.070 *** | 4.389 |
2008 | 0.027 ** | 1.999 | 2016 | 0.080 *** | 4.987 |
2009 | 0.055 *** | 3.491 | 2017 | 0.026 * | 1.846 |
2010 | 0.040 *** | 2.617 | 2018 | 0.028 ** | 2.019 |
2011 | 0.029 ** | 2.019 | 2019 | 0.097 *** | 6.265 |
2012 | 0.055 *** | 3.553 | 2020 | 0.092 *** | 5.855 |
2013 | 0.053 *** | 3.361 | - | - | - |
Models | Bandwidth | R2 | Adjusted R2 | AICc | Residual Squares |
---|---|---|---|---|---|
OLS | - | 0.004 | - | 15,821.930 | 10,193.040 |
GWR | 0.539 | 0.005 | 0.004 | 15,823.300 | 10,177.400 |
TWR | 0.710 | 0.006 | 0.004 | 15,820.700 | 10,173.200 |
GTWR | 0.414 | 0.008 | 0.006 | 15,818.500 | 10,151.600 |
Parameters | Minimum | Lower Quartile | Median | Upper Quartile | Maximum |
---|---|---|---|---|---|
EU | 3.293 | 4.632 | 5.172 | 5.682 | 7.825 |
PU | 1.451 | 1.898 | 2.114 | 2.289 | 2.914 |
LU | −5.491 | −3.781 | −3.161 | −2.389 | −1.272 |
SU | −2.434 | −1.208 | −0.881 | −0.506 | 0.572 |
EEBs | −6.269 | −1.217 | −0.265 | 0.053 | 1.221 |
UDD | −7.664 | −2.636 | −2.105 | −1.639 | 4.806 |
REC | 0.799 | 1.951 | 2.688 | 3.142 | 7.091 |
NU | −0.278 | −0.138 | 0.025 | 0.163 | 0.665 |
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Zhang, T.; Tan, Y.; Robinson, G.M.; Bai, W. China’s New-Style Urbanization and Its Impact on the Green Efficiency of Urban Land Use. Sustainability 2025, 17, 2299. https://doi.org/10.3390/su17052299
Zhang T, Tan Y, Robinson GM, Bai W. China’s New-Style Urbanization and Its Impact on the Green Efficiency of Urban Land Use. Sustainability. 2025; 17(5):2299. https://doi.org/10.3390/su17052299
Chicago/Turabian StyleZhang, Tingyu, Yan Tan, Guy M. Robinson, and Wenqian Bai. 2025. "China’s New-Style Urbanization and Its Impact on the Green Efficiency of Urban Land Use" Sustainability 17, no. 5: 2299. https://doi.org/10.3390/su17052299
APA StyleZhang, T., Tan, Y., Robinson, G. M., & Bai, W. (2025). China’s New-Style Urbanization and Its Impact on the Green Efficiency of Urban Land Use. Sustainability, 17(5), 2299. https://doi.org/10.3390/su17052299