New Urbanization and Low-Carbon Energy Transition in China: Coupling Coordination, Spatial–Temporal Differentiation, and Spatial Effects
Abstract
:1. Introduction
2. Literature Review
2.1. Research on New Urbanization
2.2. Research on Low-Carbon Energy Transition
2.3. Research on the Relationship Between New Urbanization and Low-Carbon Energy Transition
3. The Coupling Coordination Mechanism Between NU and LCET
3.1. The Influence of NU on LCET
3.2. The Influence of LCET on NU
4. Data and Methods
4.1. Construction of Indication Systems
4.2. Research Methodology
4.2.1. Index Evaluation Methods
- Entropy weight method
- 2.
- Principal component analysis method (PCA)
4.2.2. Coupling Coordination Degree Model
4.2.3. Kernel Density Estimation
4.2.4. Dagum Gini Coefficient
4.2.5. Spatial Econometric Model
- Spatial autocorrelation model
- 2.
- Spatial Durbin model
5. Findings Analysis
5.1. Comprehensive Evaluation and Analysis of New Urbanization and Low-Carbon Energy Transition Subsystems
5.2. Spatiotemporal Analysis of Coupling Coordination Level
5.3. Characterization of the Evolution of Regional Disparities
5.4. Regional Spatial Differences and Decomposition
5.4.1. Overall and Intra-Regional Disparity Analysis
5.4.2. Inter-Regional Disparities Analysis
5.4.3. Decomposition and Analysis of Regional Variations
5.5. Spatial Effects Analysis
5.5.1. Global Spatial Autocorrelation Analysis
5.5.2. Identification and Testing of Spatial Effects Models
5.5.3. SDM Estimation Results and Analysis
5.5.4. Spatial Effects Decomposition
6. Conclusions and Suggestions
6.1. Conclusions
6.2. Suggestions
6.3. Limitation and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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System Layer | Standard Layer | Indicator Layer | Indicator Attributes |
---|---|---|---|
New urbanization | Urbanization of population | Urbanization rate of population (%) | + |
Population density (persons/sq.km) | − | ||
Growth rate of urban population (%) | + | ||
Urbanization of economy | GDP growth rate (%) | + | |
Per capita GDP (%) | + | ||
Per capita urban disposable income (CNY 10,000) | + | ||
Per capita fixed investment (CNY 10,000) | + | ||
Urbanization of society | Percentage of urban population with access to gas (%) | + | |
Number of public transport vehicles per 10,000 people (vehicles) | + | ||
Percentage of urban population with access to tap water (%) | + | ||
Urbanization of ecology | Green coverage rate in developed areas (%) | + | |
Urban wastewater treatment rate (%) | + | ||
Harmless treatment rate of domestic waste (%) | + | ||
Urbanization of industry | Share of GDP of the secondary and tertiary industry (%) | + | |
Proportion of main business income of high-tech industries (%) | + | ||
Urban–rural integration | Per capita disposable income ratio of urban and rural residents (%) | − | |
Per capita consumption expenditure ratio of urban and rural residents (%) | − | ||
Low-carbon energy transition | Environmentally friendly | CO2 emissions per person (tons) | − |
CO2 emissions CNY 10,000 (tons) | − | ||
Annual average concentration of fine particulate matter (PM2.5) (µg/m3) | − | ||
Comprehensive utilization rate of industrial waste (%) | + | ||
Green and low-carbon | Share of natural gas consumption (%) | + | |
Share of electricity in the total final energy consumption (%) | + | ||
Share of renewable energy in total social electricity consumption (%) | + | ||
Resilience and safety | Energy self-sufficiency rate (%) | + | |
Natural gas storage capacity (%) | + | ||
Energy production diversification index (%) | + | ||
Efficient | Energy consumption per CNY 100 million of GDP (10,000 tce) | − | |
The proportion of investment in the energy industry (%) | + | ||
Energy consumption elasticity coefficient (%) | − | ||
Equity | The proportion of household price index of transportation fuel to GDP per capita (%) | − | |
Per capita length of natural gas pipeline (km) | + |
D | Coupling Level | D | Coupling Coordination Level |
---|---|---|---|
[0, 0.10) | Extremely dysfunctional recession (Level 1) | [0.50, 0.60) | Barely coupled coordination (Level 6) |
[0.10, 0.20) | Severe dysfunctional recession (Level 2) | [0.60, 0.70) | Primary coupling coordination (Level 7) |
[0.20, 0.30) | Moderate dysregulation recession (Level 3) | [0.70, 0.80) | Intermediate coupling coordination (Level 8) |
[0.30, 0.40) | Mild dysregulation recession (Level 4) | [0.80, 0.90) | Good coupling coordination (Level 9) |
[0.40, 0.50) | On the verge of a dysfunctional recession (Level 5) | [0.90, 1] | Quality coupling coordination (Level 10) |
Variable | Symbol | Indicator Description |
---|---|---|
Economic development level | RGDP | GDP per capita in a region |
R&D intensity | RDTY | The ratio of internal R&D expenditure to GDP |
Social consumption level | CON | The ratio of total retail sales of consumer goods to GDP |
Opening to the outside world level | OPEN | The ratio of total imports and exports of goods and the product of exchange rates USD/CNY to GDP |
Rationalization of industrial structure | TL | The degree of coupling between factor input and output structure (Thiel index) |
Digitalization level | DIG | The ratio of total imports and exports of goods and the product of exchange rates USD/CNY to GDP |
Energy consumption level | ECS | The logarithm of the main business income (billion CNY) of the manufacturing industry of communication equipment, computers, and other electronic equipment |
Environmental regulation intensity | ENN | The ratio of a completed investment in industrial pollution control to industrial-added value |
Province | 2013 | 2016 | 2019 | 2022 | ||||
---|---|---|---|---|---|---|---|---|
D | Level | D | Level | D | Level | D | Level | |
China | 0.571 | 6 | 0.650 | 7 | 0.708 | 8 | 0.759 | 8 |
East | 0.720 | 8 | 0.761 | 8 | 0.815 | 8 | 0.850 | 9 |
Beijing | 0.846 | 9 | 0.861 | 9 | 0.891 | 9 | 0.954 | 10 |
Tianjin | 0.785 | 8 | 0.785 | 8 | 0.816 | 9 | 0.909 | 10 |
Hebei | 0.490 | 5 | 0.550 | 6 | 0.670 | 7 | 0.735 | 8 |
Shanghai | 0.817 | 9 | 0.857 | 9 | 0.857 | 9 | 0.844 | 9 |
Jiangsu | 0.873 | 9 | 0.863 | 9 | 0.910 | 10 | 0.959 | 10 |
Zhejiang | 0.785 | 8 | 0.835 | 9 | 0.892 | 9 | 0.927 | 10 |
Fujian | 0.728 | 8 | 0.768 | 8 | 0.829 | 9 | 0.871 | 9 |
Shandong | 0.725 | 8 | 0.779 | 8 | 0.779 | 8 | 0.809 | 9 |
Guangdong | 0.802 | 9 | 0.844 | 9 | 0.856 | 9 | 0.863 | 9 |
Hainan | 0.353 | 4 | 0.463 | 5 | 0.646 | 7 | 0.626 | 7 |
Central | 0.572 | 6 | 0.657 | 7 | 0.722 | 8 | 0.784 | 8 |
Shanxi | 0.412 | 5 | 0.502 | 6 | 0.584 | 6 | 0.689 | 7 |
Anhui | 0.645 | 7 | 0.725 | 8 | 0.769 | 8 | 0.820 | 9 |
Jiangxi | 0.597 | 6 | 0.677 | 7 | 0.751 | 8 | 0.840 | 9 |
Henan | 0.535 | 6 | 0.648 | 7 | 0.701 | 8 | 0.741 | 8 |
Hubei | 0.651 | 7 | 0.736 | 8 | 0.776 | 8 | 0.818 | 9 |
Hunan | 0.592 | 6 | 0.652 | 7 | 0.750 | 8 | 0.797 | 8 |
West | 0.456 | 5 | 0.579 | 6 | 0.642 | 7 | 0.704 | 8 |
Inner Mongolia | 0.561 | 6 | 0.625 | 7 | 0.621 | 7 | 0.732 | 8 |
Guangxi | 0.514 | 6 | 0.597 | 6 | 0.655 | 7 | 0.682 | 7 |
Chongqing | 0.658 | 7 | 0.754 | 8 | 0.790 | 8 | 0.838 | 9 |
Sichuan | 0.620 | 7 | 0.686 | 7 | 0.754 | 8 | 0.793 | 8 |
Guizhou | 0.147 | 2 | 0.520 | 6 | 0.630 | 7 | 0.656 | 7 |
Yunnan | 0.456 | 5 | 0.570 | 6 | 0.639 | 7 | 0.713 | 8 |
Shaanxi | 0.559 | 6 | 0.656 | 7 | 0.699 | 7 | 0.747 | 8 |
Gansu | 0.175 | 2 | 0.375 | 4 | 0.480 | 5 | 0.554 | 6 |
Ningxia | 0.477 | 5 | 0.542 | 6 | 0.609 | 7 | 0.658 | 7 |
Qinghai | 0.421 | 5 | 0.553 | 6 | 0.618 | 7 | 0.741 | 8 |
Xinjiang | 0.428 | 5 | 0.494 | 5 | 0.563 | 6 | 0.628 | 7 |
Northeast | 0.495 | 5 | 0.530 | 6 | 0.568 | 6 | 0.610 | 7 |
Liaoning | 0.580 | 6 | 0.560 | 6 | 0.624 | 7 | 0.652 | 7 |
Jilin | 0.521 | 6 | 0.604 | 7 | 0.604 | 7 | 0.630 | 7 |
Heilongjiang | 0.383 | 4 | 0.427 | 5 | 0.477 | 5 | 0.549 | 6 |
Year | Intra-Regional | Inter-Regional | Hypervariable Density |
---|---|---|---|
Contribution (%) | |||
2013 | 21.97% | 60.72% | 17.31% |
2014 | 21.82% | 56.30% | 21.89% |
2015 | 20.98% | 61.83% | 17.19% |
2016 | 21.12% | 59.99% | 18.90% |
2017 | 21.04% | 59.53% | 19.43% |
2018 | 20.32% | 64.26% | 15.42% |
2019 | 19.15% | 70.99% | 9.86% |
2020 | 20.18% | 65.08% | 14.74% |
2021 | 20.09% | 67.91% | 12.01% |
2022 | 20.46% | 67.74% | 11.81% |
Year | Moran’s I | p-Value |
---|---|---|
2013 | 0.391 | 0.000 *** |
2014 | 0.419 | 0.000 *** |
2015 | 0.473 | 0.000 *** |
2016 | 0.448 | 0.000 *** |
2017 | 0.452 | 0.000 *** |
2018 | 0.529 | 0.000 *** |
2019 | 0.608 | 0.000 *** |
2020 | 0.594 | 0.000 *** |
2021 | 0.554 | 0.000 *** |
2022 | 0.527 | 0.000 *** |
c | Spatial Adjacency Matrix | Economic Distance Matrix | |
---|---|---|---|
LM | LM-Error | 169.501 *** | 379.755 *** |
R-LM-Error | 130.498 *** | 311.708 *** | |
LM-lag | 46.809 *** | 73.371 *** | |
R-LM-lag | 7.805 *** | 5.325 ** | |
Hausman | 41.41 *** | 36.74 *** | |
LR | LR-SAR | 47.22 *** | 40.15 *** |
LR-SEM | 39.12 *** | 38.06 *** | |
Wald | Wald-SAR | 48.33 *** | 25.41 *** |
Wald-SEM | 57.70 *** | 43.27 *** |
Variables | Spatial Adjacency Matrix | Economic Distance Matrix | ||
---|---|---|---|---|
Main | Wx | Main | Wx | |
RGDP | 0.186 *** | −0.091 *** | 0.120 *** | −0.137 * |
(0.024) | (0.035) | (0.022) | (0.071) | |
RDTY | −0.009 | −3.963 * | −1.665 | −13.938 *** |
(1.275) | (2.370) | (1.218) | (3.649) | |
CON | 0.232 *** | −0.216 ** | 0.071 | −0.378 ** |
(0.058) | (0.099) | (0.055) | (0.174) | |
OPEN | 0.188 *** | −0.001 | 0.098 *** | 0.027 |
(0.034) | (0.081) | (0.035) | (0.100) | |
TL | −0.214 *** | 0.236 *** | −0.113 *** | −0.143 |
(0.040) | (0.075) | (0.038) | (0.137) | |
DIG | 0.002 | 0.006 | 0.002 | 0.008 |
(0.002) | (0.004) | (0.002) | (0.006) | |
ECS | 0.044 | 0.004 | −0.122 | −1.005 *** |
(0.116) | (0.248) | (0.106) | (0.261) | |
ENN | −1.682 * | −2.073 | −1.934 * | 12.016 *** |
(1.016) | (2.123) | (0.989) | (3.974) | |
rho | 0.241 *** | 0.178* | ||
(0.080) | (0.102) | |||
sigma2_e | 0.001 *** | 0.001 *** | ||
(0.000) | (0.000) | |||
Observations | 300 | 300 | 300 | 300 |
R-squared | 0.135 | 0.135 | 0.225 | 0.225 |
Number of IDs | 30 | 30 | 30 | 30 |
Variables | Spatial Adjacency Matrix | Economic Distance Matrix | ||||
---|---|---|---|---|---|---|
Direct | Indirect | Total | Direct | Indirect | Total | |
RGDP | 0.183 *** | −0.058 | 0.126 *** | 0.116 *** | −0.137 * | −0.020 |
(0.024) | (0.040) | (0.044) | (0.022) | (0.076) | (0.088) | |
RDTY | −0.362 | −4.645 | −5.008 | −2.221 ** | −17.013 *** | −19.234 *** |
(1.076) | (3.165) | (3.417) | (1.031) | (5.236) | (5.565) | |
CON | 0.228 *** | −0.193 | 0.034 | 0.067 | −0.415 * | −0.348 |
(0.061) | (0.129) | (0.132) | (0.063) | (0.233) | (0.272) | |
OPEN | 0.192 *** | 0.051 | 0.243 ** | 0.102 *** | 0.044 | 0.146 |
(0.038) | (0.108) | (0.124) | (0.038) | (0.129) | (0.126) | |
TL | −0.209 *** | 0.224 *** | 0.015 | −0.124 *** | −0.206 | −0.330 * |
(0.046) | (0.079) | (0.093) | (0.044) | (0.149) | (0.171) | |
DIG | 0.002 | 0.008 * | 0.011 * | 0.002 | 0.011 * | 0.014 * |
(0.002) | (0.005) | (0.006) | (0.002) | (0.007) | (0.007) | |
ECS | 0.049 | 0.017 | 0.066 | −0.146 | −1.215 *** | −1.361 *** |
(0.110) | (0.304) | (0.296) | (0.105) | (0.340) | (0.393) | |
ENN | −1.954 ** | −2.893 | −4.847 * | −1.721 * | 13.815 *** | 12.094 ** |
(0.929) | (2.496) | (2.691) | (0.944) | (4.969) | (5.399) | |
Observations | 300 | 300 | 300 | 300 | ||
R-squared | 0.135 | 0.135 | 0.225 | 0.225 | ||
Number of IDs | 30 | 30 | 30 | 30 |
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Wen, X.; Cao, X.; Wang, L.; Wen, J.; Yu, Z. New Urbanization and Low-Carbon Energy Transition in China: Coupling Coordination, Spatial–Temporal Differentiation, and Spatial Effects. Sustainability 2025, 17, 3352. https://doi.org/10.3390/su17083352
Wen X, Cao X, Wang L, Wen J, Yu Z. New Urbanization and Low-Carbon Energy Transition in China: Coupling Coordination, Spatial–Temporal Differentiation, and Spatial Effects. Sustainability. 2025; 17(8):3352. https://doi.org/10.3390/su17083352
Chicago/Turabian StyleWen, Xin, Xueqin Cao, Longqing Wang, Jiaxin Wen, and Zhibo Yu. 2025. "New Urbanization and Low-Carbon Energy Transition in China: Coupling Coordination, Spatial–Temporal Differentiation, and Spatial Effects" Sustainability 17, no. 8: 3352. https://doi.org/10.3390/su17083352
APA StyleWen, X., Cao, X., Wang, L., Wen, J., & Yu, Z. (2025). New Urbanization and Low-Carbon Energy Transition in China: Coupling Coordination, Spatial–Temporal Differentiation, and Spatial Effects. Sustainability, 17(8), 3352. https://doi.org/10.3390/su17083352