The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Influencing Factors Selection
2.3. Data Resources and Processing
2.4. Method
2.4.1. Correlation Analysis
2.4.2. Collinearity Diagnostics
2.4.3. Spatial Autocorrelation Analysis
2.4.4. Ordinary Least Squares (OLS)
2.4.5. Spatial Regression Model (SLM, SEM, SDM, and GWR)
2.4.6. K-Means Cluster Analysis
3. Results and Analysis
3.1. Spatial Distribution of Carbon Emissions
3.1.1. Overall Distribution of Land-Average Carbon Emissions
3.1.2. Spatial Autocorrelation of Land Average Carbon Emissions
3.2. Impacts of Road Traffic on Carbon Emissions
3.2.1. Explanatory Variables Selection
3.2.2. Comparison of OLS, SLM, SEM, SDM, and GWR Models
3.2.3. Cluster Partitioning
4. Discussion
4.1. Differences in the Spatial Distribution of Land-Average Carbon Emissions
4.2. The Impact of the Mechanisms of Road Traffic on Land-Average Carbon Emissions
4.3. Suggestions for Road Traffic Planning
4.4. Limitations and Future Prospects
5. Conclusions
- The distribution of urban land-average carbon emissions has obvious spatial differences, with hotspot areas primarily located around Beijing and Tianjin, which should be prioritized in low-carbon work.
- The global models (OLS, SLM, SEM, and SDM) and the local model (GWR) were built to analyze the impact mechanism, and it was discovered that the GWR model has a higher R2 value and a smaller AICc value, which has a better model performance.
- The three indicators of highway network density, city road length, and density of public transit network all have a significant effect on urban land-average carbon emissions, but the land use area system of streets and transportation has no effect. The emphasis should be on the highway network and public transit systems, particularly concerning initiatives to improve the efficiency of highway transportation organization and increase the proportion of public transportation trips.
- The GWR model’s results show that there is significant piecewise spatial differentiation in the impact of the three factors on the urban land-average carbon emissions. The highway network density has a relatively large impact on the northern region. The northwestern region is more affected by the density of the public transit network. The southwest are more affected by the city road length.
- By means of clustering, the study area was divided into four categories of dominant areas of different impact factors, and targeted traffic optimization recommendations were made. Class I areas are highway network dominant impact areas, where the focus should be placed on improving the efficiency of highway transport organization. Class II areas are public transit dominant impact areas, where the focus should be on optimizing the public transit network and improving the attractiveness of public transit. Class III areas are city road network dominant impact areas, where the focus should be on reducing traffic congestion on city roads and increasing the proportion of the green transportation trips. Class IV areas are multi-factor areas and they should be relatively focused on improving the attractiveness of public transit.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Target Layer | First-Order Index | Secondary Index | Units | Symbol |
---|---|---|---|---|
Urban Carbon Emissions (Y) | Highway network system | Highway network density [27] | km/km2 | X1 |
Highway network connectivity [60] | - | X2 | ||
City road network system | City road network density [27] | km/km2 | X3 | |
City road length [61] | km | X4 | ||
City road area per capita [62] | m2 | X5 | ||
Public traffic system | Length of public transit routes [63] | km | X6 | |
Density of public transit network [30] | km/km2 | X7 | ||
Land use system of street and transportation | Street and transportation land use area [64] | km2 | X8 | |
Street and transportation land use area ratio [27] | - | X9 | ||
Street and transportation land use area per capita | m2 | X10 |
Indicators | p | Collinearity Statistics | |
---|---|---|---|
Tolerances | VIF | ||
(Constant) | 0.137 | ||
Highway network density | 0.000 | 0.865 | 1.157 |
City road length | 0.000 | 0.936 | 1.068 |
City road area per capita | 0.660 | 0.935 | 1.070 |
Density of public transit network | 0.000 | 0.883 | 1.132 |
Model | R2 | Adjusted R2 | AICc |
---|---|---|---|
OLS | 0.51 | 0.49 | −8.14 |
SLM | 0.65 | 0.64 | −41.10 |
SEM | 0.65 | 0.64 | −38.76 |
SDM | 0.66 | 0.65 | −39.94 |
GWR | 0.74 | 0.67 | −42.25 |
Variable | Coefficient | p-Value | ||||||
---|---|---|---|---|---|---|---|---|
OLS | SLM | SEM | SDM | OLS | SLM | SEM | SDM | |
Highway network density | 0.2226 | 0.1379 | 0.1626 | 0.1128 | 0.0000 | 0.0000 | 0.0000 | 0.0049 |
City road length | 0.0012 | 0.0009 | 0.0010 | 0.0009 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Density of public transit network | 0.6735 | 0.5664 | 0.5566 | 0.5530 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
R-squared | 0.51 | 0.65 | 0.65 | 0.66 | ||||
AIC | −8.35 | −41.31 | −38.97 | −40.15 |
Indicators | Class I | Class II | Class III | Class IV |
---|---|---|---|---|
Number of cities | 30 | 4 | 35 | 48 |
Highway network density/km/km2 | 0.4711 | 0.1604 | 0.1095 | 0.2053 |
City road length/km | 0.0008 | 0.0005 | 0.0014 | 0.0010 |
Density of public transit network/km/km2 | 0.2348 | 1.3506 | 0.5587 | 0.4255 |
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Lei, H.; Zeng, S.; Namaiti, A.; Zeng, J. The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies. Land 2023, 12, 800. https://doi.org/10.3390/land12040800
Lei H, Zeng S, Namaiti A, Zeng J. The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies. Land. 2023; 12(4):800. https://doi.org/10.3390/land12040800
Chicago/Turabian StyleLei, Haiyan, Suiping Zeng, Aihemaiti Namaiti, and Jian Zeng. 2023. "The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies" Land 12, no. 4: 800. https://doi.org/10.3390/land12040800