Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.3. Kernel Density Estimation
2.4. Markov Chain Model
2.5. Measurement of UL
2.6. Bivariate Spatial Autocorrelation Analysis
2.7. Multiscale Geographically Weighted Regression
3. Results
3.1. Spatiotemporal Patterns of CEs Intensity in Chinese UAs
3.1.1. Spatiotemporal Patterns of CEs Intensity
3.1.2. Dynamic Evolution of CEs Intensity
3.2. Spatiotemporal Patterns of UL in Chinese UAs
3.3. Spatial Autocorrelation Analysis of CEs Intensity and UL in Chinese UAs
3.4. Regression Results
3.4.1. Global Regression Results
3.4.2. Spatial Heterogeneity Analysis Based on Multi-Scale GWR Model
Multi-Scale Analysis
Multi-Scale GWR Model Regression Analysis
3.4.3. Spatial Heterogeneity Analysis of Local Parameters in Multi-Scale GWR Model
4. Discussion
4.1. Summary of Findings
4.2. Policy Implications
4.2.1. Controlling Urban Expansion and Optimizing Spatial Layout
4.2.2. Implementing Differential Carbon Reduction Policies
4.2.3. Promoting the Low Carbon Transition Development of New-Type Urbanization
4.3. Limitations and Future Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Time Slice | Spatial Lag | Type | L | ML | MH | H |
---|---|---|---|---|---|---|
10 | L | L | 0.804 | 0.192 | 0.004 | 0 |
ML | 0.014 | 0.521 | 0.465 | 0 | ||
MH | 0 | 0 | 0.606 | 0.394 | ||
H | 0 | 0 | 0 | 1 | ||
ML | L | 0.697 | 0.297 | 0.006 | 0 | |
ML | 0.010 | 0.599 | 0.383 | 0.008 | ||
MH | 0 | 0 | 0.681 | 0.319 | ||
H | 0 | 0 | 0 | 1 | ||
MH | L | 0.731 | 0.263 | 0.006 | 0 | |
ML | 0.002 | 0.500 | 0.485 | 0.013 | ||
MH | 0 | 0 | 0.646 | 0.354 | ||
H | 0 | 0 | 0 | 1 | ||
H | L | 0.733 | 0.267 | 0 | 0 | |
ML | 0 | 0.465 | 0.535 | 0 | ||
MH | 0 | 0 | 0.616 | 0.384 | ||
H | 0 | 0 | 0 | 1 | ||
20 | L | L | 0.669 | 0.322 | 0.009 | 0 |
ML | 0.022 | 0.222 | 0.756 | 0 | ||
MH | 0 | 0 | 0.167 | 0.833 | ||
H | 0 | 0 | 0 | 1 | ||
ML | L | 0.495 | 0.480 | 0.025 | 0 | |
ML | 0.004 | 0.312 | 0.665 | 0.019 | ||
MH | 0 | 0 | 0.325 | 0.675 | ||
H | 0 | 0 | 0 | 1 | ||
MH | L | 0.429 | 0.551 | 0.020 | 0 | |
ML | 0.004 | 0.172 | 0779 | 0.045 | ||
MH | 0 | 0 | 0.204 | 0.796 | ||
H | 0 | 0 | 0 | 1 | ||
H | L | 0 | 1 | 0 | 0 | |
ML | 0 | 0 | 0.800 | 0.200 | ||
MH | 0 | 0 | 0.058 | 0.942 | ||
H | 0 | 0 | 0 | 1 |
Variables | 2000 | 2010 | 2020 | ||||||
---|---|---|---|---|---|---|---|---|---|
Coef | t_Stat | Prob | Coef | t_Stat | Prob | Coef | t_Stat | Prob | |
UL | 4.567 *** | 7.890 | 0.000 | 5.175 *** | 11.240 | 0.000 | 3.265 *** | 8.901 | 0.000 |
PD | 0.001 *** | 3.770 | 0.000 | 64.265 *** | 11.680 | 0.000 | 50.268 *** | 10.586 | 0.000 |
ED | 0.257 *** | 3.790 | 0.000 | −0.247 *** | −4.410 | 0.000 | −0.201 *** | −4.365 | 0.000 |
COHESION | 0.024 *** | 8.110 | 0.000 | 0.039 *** | 13.640 | 0.000 | 0.055 *** | 17.711 | 0.000 |
AI | −0.009 *** | −5.450 | 0.000 | −0.013 *** | −7.810 | 0.000 | −0.016 *** | −7.845 | 0.000 |
DEM | −0.001 *** | −11.750 | 0.000 | 0.000 *** | −6.720 | 0.000 | 0.000 *** | −7.699 | 0.000 |
RD | 0.811 *** | 4.590 | 0.000 | 1.199 *** | 7.670 | 0.000 | 0.000 *** | 0.037 | 0.971 |
VC | −1.312 *** | −12.390 | 0.000 | −1.244 *** | −12.700 | 0.000 | −1.011 *** | −10.782 | 0.000 |
Con_s | 5.323 *** | 47.880 | 0.000 | 5.060 *** | 41.580 | 0.000 | 4.860 *** | 0.000 | 1.000 |
OLS method diagnosis | N | R2 | A_VIF | N | R2 | A_VIF | N | R2 | A_VIF |
1769 | 0.637 | 2.52 | 1769 | 0.726 | 3.38 | 1769 | 0.765 | 4.16 |
Variables | Multi-Scale GWR | GWR | ||||
---|---|---|---|---|---|---|
2000 | 2010 | 2020 | 2000 | 2010 | 2020 | |
UL | 1662 | 1074 | 1753 | 113 | 155 | 191 |
PD | 155 | 170 | 184 | 113 | 155 | 191 |
ED | 1673 | 1766 | 1766 | 113 | 155 | 191 |
COHESION | 1607 | 198 | 1768 | 113 | 155 | 191 |
AI | 1673 | 644 | 1766 | 113 | 155 | 191 |
DEM | 79 | 157 | 1768 | 113 | 155 | 191 |
RD | 450 | 47 | 49 | 113 | 155 | 191 |
VC | 191 | 43 | 179 | 113 | 155 | 191 |
Con_s | 354 | 548 | 43 | 113 | 155 | 191 |
R2 | 0.824 | 0.836 | 0.836 | 0.830 | 0.827 | 0.841 |
Adj. R2 | 0.797 | 0.814 | 0.824 | 0.797 | 0.803 | 0.824 |
AICc | 2498.758 | 2299.793 | 2075.145 | 2572.331 | 2413.653 | 2156.661 |
Year | Variable | Mean | STD | Min | Max | p ≤ 0.01 | p ≤ 0.05 | p ≤ 0.1 | + (%) | − (%) |
---|---|---|---|---|---|---|---|---|---|---|
2000 | UL | 0.224 | 0.032 | 0.176 | 0.281 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 |
PD | 0.176 | 0.123 | 0.019 | 0.629 | 49.41 | 62.52 | 74.05 | 100.00 | 0.00 | |
ED | 0.062 | 0.002 | 0.06 | 0.074 | 0.00 | 0.00 | 6.90 | 100.00 | 0.00 | |
COHESION | 0.245 | 0.098 | 0.05 | 0.495 | 82.14 | 94.12 | 96.21 | 100.00 | 0.00 | |
AI | −0.069 | 0.06 | −0.171 | 0.012 | 33.63 | 35.27 | 40.81 | 3.11 | 96.89 | |
DEM | −0.246 | 0.27 | −0.887 | 0.394 | 56.70 | 70.49 | 73.94 | 17.81 | 82.19 | |
RD | 0.213 | 0.285 | −0.233 | 1.346 | 21.93 | 29.85 | 40.31 | 78.86 | 21.14 | |
VC | −0.128 | 0.218 | −0.742 | 1.184 | 25.16 | 34.26 | 39.80 | 23.29 | 76.71 | |
Con_s | 0.12 | 0.112 | −0.071 | 0.267 | 53.70 | 58.00 | 66.31 | 77.44 | 22.56 | |
2010 | UL | 0.208 | 0.004 | 0.2 | 0.216 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 |
PD | 0.178 | 0.113 | 0.037 | 0.556 | 46.52 | 69.87 | 82.42 | 100.00 | 0.00 | |
ED | 0.054 | 0.002 | 0.051 | 0.064 | 0.00 | 0.00 | 4.92 | 100.00 | 0.00 | |
COHESION | 0.333 | 0.001 | 0.33 | 0.336 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 | |
AI | −0.125 | 0.003 | −0.133 | −0.115 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 | |
DEM | −0.467 | 0.001 | −0.469 | −0.464 | 100.00 | 100.00 | 100.00 | 0.00 | 100.00 | |
RD | 0.173 | 0.187 | −0.109 | 0.891 | 23.29 | 34.65 | 40.87 | 83.89 | 16.11 | |
VC | −0.051 | 0.128 | −0.316 | 0.184 | 33.13 | 51.16 | 58.90 | 35.73 | 64.27 | |
Con_s | 0.153 | 0.431 | −0.683 | 1.477 | 47.99 | 55.96 | 61.16 | 65.23 | 34.77 | |
2020 | UL | 0.212 | 0.008 | 0.197 | 0.235 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 |
PD | 0.165 | 0.097 | 0.004 | 0.468 | 54.55 | 66.53 | 72.87 | 100.00 | 0.00 | |
ED | 0.004 | 0.008 | −0.008 | 0.03 | 0.00 | 0.00 | 0.00 | 60.66 | 39.34 | |
COHESION | 0.55 | 0.011 | 0.529 | 0.571 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 | |
AI | −0.144 | 0.006 | −0.157 | −0.133 | 100.00 | 100.00 | 100.00 | 100.00 | 0.00 | |
DEM | −0.315 | 0.392 | −1.258 | 0.75 | 51.10 | 59.81 | 65.52 | 21.14 | 78.86 | |
RD | 0.037 | 0.07 | −0.045 | 0.286 | 13.85 | 19.39 | 27.42 | 59.81 | 40.19 | |
VC | −0.071 | 0.099 | −0.281 | 0.108 | 30.02 | 38.33 | 43.19 | 24.65 | 75.35 | |
Con_s | −0.069 | 0.202 | −0.328 | 0.2 | 76.31 | 84.28 | 86.09 | 52.57 | 47.43 |
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Li, W.; Wu, J.; Yang, L.; Chen, W.; Cui, X.; Lin, M. Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations. Land 2024, 13, 1303. https://doi.org/10.3390/land13081303
Li W, Wu J, Yang L, Chen W, Cui X, Lin M. Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations. Land. 2024; 13(8):1303. https://doi.org/10.3390/land13081303
Chicago/Turabian StyleLi, Weisong, Jiahui Wu, Liyan Yang, Wanxu Chen, Xinghua Cui, and Mingyu Lin. 2024. "Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations" Land 13, no. 8: 1303. https://doi.org/10.3390/land13081303