Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan
Abstract
1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources
3. Methods and Model
3.1. Carbon Emission Calculation Method
3.2. Urban Development Scenario Prediction Method
3.2.1. ANN-CA Model
3.2.2. Development Scenario Setting
3.2.3. Experimental Process
3.3. Numerical Simulation Method
3.3.1. WRF-UCM Model
3.3.2. Case Setting
3.3.3. Setting of Simulation Parameters
3.3.4. Verification of Model Effectiveness
4. Results
4.1. Carbon Emission Calculation and Forecast Results of Wuhan City
4.2. Prediction of Land Use Evolution in Wuhan City
4.3. Analysis of Thermal Environment Change in Land Expansion Scenario in Wuhan
4.3.1. Temperature Curve Analysis
4.3.2. Analysis of Temperature and Difference Field
4.3.3. Analysis of Urban Heat Island Intensity
5. Discussion
5.1. Reasons for Thermal Environment Difference in Urban Scenarios
5.2. Development Suggestions for Wuhan in Response to Heat Stress
5.3. Limitations and Prospects of This Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Artificial neural network | a computational model inspired by the human brain |
Anthropogenic heat | heat generated by human activities |
Atmospheric boundary layer | the lowest part of the atmosphere |
Cellular automaton | a discrete model studied in computability theory |
Carbon neutrality | achieving net-zero carbon footprint |
Carbon peak | carbon emissions reach their highest level |
Carbon sink | a process or entity that absorbs and stores carbon |
Carbon source | a process or entity that releases carbon into the atmosphere |
Impervious surface | areas covered by materials that do not allow water to penetrate |
Latent Heat Flux | heat exchange per unit area under the constant temperature |
Plot ratio | the ratio of a building’s total floor area to the size of the land |
Urban heat island effect | urban areas are significantly warmer than their surrounding rural areas |
Sensible Heat Flux | the turbulent heat flux between the surface and the atmosphere |
Shortwave Radiation Flux | the shortwave radiation flux from the surface down |
Surface albedo | the fraction of solar radiation that is reflected by a surface |
Surface Flow | the heat flux of heat conduction between the surface and the lower layer |
Urban canopy layer | the top layer of an urban area, consisting of buildings, trees, and other structures |
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Land Use | Carbon Emission Coefficient (t/hm2) | Energy | Fold Standard Coal Coefficient (t/t) | Carbon Emission Coefficient (tCO/t) |
---|---|---|---|---|
cultivated land | 0.497 | coal | 0.7143 | 0.7559 |
forest land | −0.581 | coke | 0.9714 | 0.855 |
grassland | −0.021 | crude oil | 1.4286 | 0.5857 |
water | −0.359 | gasoline | 1.4714 | 0.5538 |
unused land | −0.005 | diesel | 1.4571 | 0.5921 |
fuel oil | 1.4268 | 0.6185 | ||
kerosene | 1.4571 | 0.5714 |
Physical Process | Physical Scheme |
---|---|
mp_physics | New Thompson |
ra_lw_physics | RRTM |
ra_sw_physics | Goddard |
bl_pbl_physics | MYJ Monin–Obukhov |
sf_sfclay_physics | Monin–Obukhov (Janjic Eta) |
cu_physics | Kain–Fritsch (New Eta) |
Factor | Low (31) | Medium (32) | High (33) |
---|---|---|---|
Roof_level [m] | 15 | 21 | 27 |
Frc_urb [fraction] | 0.6 | 0.65 | 0.7 |
Roof_width [m] | 25 | 40 | 55 |
Road_width [m] | 20 | 20 | 20 |
Anthropogenic heat [W/m2] | 160 | 214 | 277 |
Albedo | 0.15 | 0.15 | 0.15 |
Observation Station | Observed Value (°C) | Simulated Value (°C) | Mean Deviation | RMSE | Correlation Coefficient |
---|---|---|---|---|---|
JiangXia | 34.21 | 30.83 | 3.38 | 3.77 | 0.92 * |
CaiDian | 32.97 | 31.08 | 1.89 | 2.09 | 0.97 * |
HuangPi | 33.65 | 31.11 | 2.54 | 2.74 | 0.96 * |
Case | UHPI | SUHPI | ||||
---|---|---|---|---|---|---|
Before Dawn | Morning | Afternoon | Night | Afternoon | Night | |
SQ | 20.77% | 39.72% | 68.17% | 50.54% | 29.16% | 7.93% |
TG-SQ | 1.96% | 1.46% | −0.88% | 1.65% | 1.66% | −0.48% |
NG-SQ | 1.75% | 4.31% | 0.48% | 4.00% | 4.25% | 0.22% |
EP-SQ | 1.50% | −1.87% | −2.48% | −0.73% | −1.37% | −1.65% |
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Lin, K.; Zhan, Q.; Xue, W.; Shu, Y.; Lu, Y. Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan. Buildings 2025, 15, 208. https://doi.org/10.3390/buildings15020208
Lin K, Zhan Q, Xue W, Shu Y, Lu Y. Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan. Buildings. 2025; 15(2):208. https://doi.org/10.3390/buildings15020208
Chicago/Turabian StyleLin, Kai, Qingming Zhan, Wei Xue, Yulong Shu, and Yixiao Lu. 2025. "Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan" Buildings 15, no. 2: 208. https://doi.org/10.3390/buildings15020208
APA StyleLin, K., Zhan, Q., Xue, W., Shu, Y., & Lu, Y. (2025). Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan. Buildings, 15(2), 208. https://doi.org/10.3390/buildings15020208