Influencing Factors and Their Spatial–Temporal Heterogeneity of Urban Transport Carbon Emissions in China
Abstract
:1. Introduction
2. Research Methods and Data Sources
2.1. Research Methods
2.1.1. Spatial Econometric Model
2.1.2. GWR Model
2.2. Variable Selection and Data Source
3. Spatial Econometric Analysis of Influencing Factors of Urban Transport Carbon Emissions
3.1. Model Selection
3.2. Analysis of Empirical Results
4. Spatio-Temporal Heterogeneity Analysis of Influencing Factors
4.1. GDP per Capita (PGDP)
4.2. Population (POP)
4.3. Private Cars per Capita (PRC)
4.4. Urban Road Area (URA)
4.5. Urban Density (URD)
4.6. Public Transportation Effectiveness (PUB)
4.7. Government Environmental Protection (GEP)
5. Conclusions and Policy Recommendations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
PGDP | POP | PRC | URA | URD | PUB | GEP | |
---|---|---|---|---|---|---|---|
Tolerance | 0.2464 | 0.9414 | 0.3454 | 0.2388 | 0.3169 | 0.9707 | 0.3103 |
Variance inflation factor (VIF) | 4.0600 | 1.0600 | 2.9000 | 4.1900 | 3.1600 | 1.0300 | 3.2200 |
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Variable | Definition or Measurement | Unit |
---|---|---|
Urban transport carbon emission (TCE) | CO2 emission of urban transport industry (including road, rail, water, and air traffic) measured by MEIC model divided by the total population. | Tons per capita |
GDP per capita (PGDP) | GDP divided by the total population. | CNY per 10,000 people |
Population (POP) | Urban total population number at the end of year. | 10,000 people |
Private cars per capita (PRC) | Total number of private cars divided by the total population in a city. | Vehicles per 10,000 people |
Urban road area (URA) | Urban road area divided by the total population. | m2 per capita |
Urban density (URD) | Urban total population divided by built-up area. | 10,000 people per km2 |
Public transportation effectiveness (PUB) | The ratio of bus passenger traffic to the number of buses in operation. | Ten thousand passengers per car |
Government environmental protection (GEP) | Ratio of urban green land area to the total population. | Hectare per 10,000 people |
Variable | Obs | Mean | Std.dev. | Min | Max |
---|---|---|---|---|---|
TCE | 4260 | 0.5910 | 0.4240 | 0.0406 | 4.8900 |
PGDP | 4260 | 4.8360 | 5.0010 | 0.2760 | 50.6300 |
POP | 4260 | 440.7000 | 313.4000 | 16.4100 | 3416 |
PRC | 4260 | 0.0902 | 0.0957 | 0.0006 | 1.1340 |
URA | 4260 | 4.6700 | 6.1650 | 0.0036 | 75.9100 |
URD | 4260 | 0.3540 | 0.4250 | 0.0222 | 8.7490 |
PUB | 4260 | 0.0014 | 0.0009 | 9.78 × 10−6 | 0.0244 |
GEP | 4260 | 0.0018 | 0.0034 | 7.93 × 10−6 | 0.0481 |
Year | Moran’s I | E(I) | SD(I) | Z-Statistic | p-Value |
---|---|---|---|---|---|
2006 | 0.171 *** | −0.004 | 0.037 | 4.724 | 0.000 |
2007 | 0.164 *** | −0.004 | 0.038 | 4.425 | 0.000 |
2008 | 0.186 *** | −0.004 | 0.038 | 4.959 | 0.000 |
2009 | 0.178 *** | −0.004 | 0.030 | 4.724 | 0.000 |
2010 | 0.208 *** | −0.004 | 0.038 | 5.493 | 0.000 |
2011 | 0.226 *** | −0.004 | 0.039 | 5.888 | 0.000 |
2012 | 0.223 *** | −0.004 | 0.039 | 5.802 | 0.000 |
2013 | 0.223 *** | −0.004 | 0.040 | 5.724 | 0.000 |
2014 | 0.217 *** | −0.004 | 0.040 | 5.573 | 0.000 |
2015 | 0.197 *** | −0.004 | 0.040 | 5.068 | 0.000 |
2016 | 0.213 *** | −0.004 | 0.040 | 5.446 | 0.000 |
2017 | 0.229 *** | −0.004 | 0.040 | 5.824 | 0.000 |
2018 | 0.235 *** | −0.004 | 0.040 | 5.942 | 0.000 |
2019 | 0.235 *** | −0.004 | 0.040 | 5.933 | 0.000 |
2020 | 0.254 *** | −0.004 | 0.040 | 6.398 | 0.000 |
Test | Test Statistics | p-Value | ||
---|---|---|---|---|
LM test | LM | Spatial error | 1281.5010 *** | 0.0000 |
Spatial lag | 407.1780 *** | 0.0000 | ||
Robust LM | Spatial error | 934.6620 *** | 0.0000 | |
Spatial lag | 60.3390 *** | 0.0000 | ||
LR test | H0: SAR model | 224.6100 *** | 0.0000 | |
H0: SEM model | 114.6400 *** | 0.0000 |
Variable | SDM | OLS | ||
---|---|---|---|---|
Coefficient | Variable | Coefficient | ||
LNPGDP | 0.3560 *** | W*LNPGDP | −0.3260 *** | 0.1580 *** |
(0.0161) | (0.0248) | (0.0166) | ||
LNPOP | 0.3330 *** | W*LNPOP | −0.6030 *** | 0.0321 |
(0.0155) | (0.0457) | (0.0100) | ||
LNPRC | 0.2820 *** | W*LNPRC | 0.0964 *** | 0.2210 *** |
(0.0111) | (0.0152) | (0.0101) | ||
LNURA | 0.1210 *** | W*LNURA | 0.0784 *** | 0.1820 *** |
(0.0136) | (0.0257) | (0.0163) | ||
LNURD | −0.0403 ** | W*LNURD | −0.0230 | 0.1510 *** |
(0.0174) | (0.0317) | (0.0205) | ||
LNPUB | −0.0196 ** | W*LNPUB | −0.0613 *** | 0.0080 |
(0.0086) | (0.0146) | (0.0098) | ||
LNGEP | −0.0561 *** | W*LNGEP | −0.0678 *** | −0.0861 *** |
(0.0113) | (0.0219) | (0.0137) | ||
rho | 0.4630 *** | |||
(0.0159) | ||||
Sigma2_e | 0.1050 *** | |||
(0.0023) | ||||
No. | 4260 | 4260 | ||
R2 | 0.6180 | 0.6110 |
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Zhao, P.; Tian, B.S.; Yang, Q.; Zhang, S. Influencing Factors and Their Spatial–Temporal Heterogeneity of Urban Transport Carbon Emissions in China. Energies 2024, 17, 756. https://doi.org/10.3390/en17030756
Zhao P, Tian BS, Yang Q, Zhang S. Influencing Factors and Their Spatial–Temporal Heterogeneity of Urban Transport Carbon Emissions in China. Energies. 2024; 17(3):756. https://doi.org/10.3390/en17030756
Chicago/Turabian StyleZhao, Peng, Bei Si Tian, Qi Yang, and Shuai Zhang. 2024. "Influencing Factors and Their Spatial–Temporal Heterogeneity of Urban Transport Carbon Emissions in China" Energies 17, no. 3: 756. https://doi.org/10.3390/en17030756
APA StyleZhao, P., Tian, B. S., Yang, Q., & Zhang, S. (2024). Influencing Factors and Their Spatial–Temporal Heterogeneity of Urban Transport Carbon Emissions in China. Energies, 17(3), 756. https://doi.org/10.3390/en17030756