Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Research Area
2.2. Research Data Sources
2.3. Research Methods
2.3.1. Research Framework
2.3.2. PLUS Model
- Markov module
- 2.
- PLUS model
2.3.3. Estimation of Carbon Emissions from Land Use
- Direct carbon emission estimation
- 2.
- Indirect carbon emission estimation
- 3.
- Grid-based estimation of carbon emission intensity
- 4.
- Carbon Emission Forecast for 2030
3. Experimental Results
3.1. Spatiotemporal Analysis of Land Use
3.1.1. Analysis of Land Use Time
3.1.2. Spatial and Temporal Analysis of Land Use in 2030
3.2. Spatiotemporal Analysis of Carbon Emissions
3.2.1. Direct Carbon Emission Time Analysis
3.2.2. Indirect Carbon Emissions
3.2.3. Distribution of Total Carbon Emissions and Carbon Emission Intensity
3.2.4. Analysis of Carbon Emission Intensity Zoning
3.2.5. Spatial Autocorrelation Analysis of Carbon Emission Density
4. Discussion
4.1. Land Use Change and Carbon Emissions
4.2. The Relationship between Construction Land Expansion and Carbon Emission Intensity
4.3. Shortcomings and Prospects
4.4. Policy Recommendations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Name | Data Source |
---|---|
Land Use DEM, Nighttime Light, Precipitation, Temperature, GDP | RAESADCResource and Environment Science and Data Center (https://www.resdc.cn, accessed on 3 January 2023) |
Elevation, Slope, Slope Direction | GDC represented by Geospatial Data Cloud (http://www.gscloud.cn) |
Population Density | Worldpop (https://www.worldpop.org, accessed on 3 January 2023) |
Distance to Medical Facility Sites, Distance to City Center, Distance to Scientific and Educational Centers, Distance to Railways, Distance to Motorways, Distance to National Highways | ORM represented by Open Road Map database (http://www.openstreetmap.org, accessed on 3 January 2023) calculated using ArcGIS |
Energy Consumption Data | Government work: Tianjin Statistical Yearbook (https://stats.tj.gov.cn/tjsj_52032/tjnj/, accessed on 3 January 2023) |
Type of Land | Farmland | Woodland | Grassland | Unutilized Land | Waters | Construction Land |
---|---|---|---|---|---|---|
Neighborhood weight | 0.6 | −0.5 | 0.4 | 0.8 | 0.4 | 1 |
Type of Energy | Conversion Standard Coal Factor (t(C)·t−1) | Carbon Emission Factor (t(C)·t−1) |
---|---|---|
Coal | 0.7143 | 0.7559 |
Coke | 0.9714 | 0.855 |
Crude oil | 1.4286 | 0.5857 |
Gasoline | 1.4714 | 0.5538 |
Kerosene | 1.4714 | 0.5714 |
Diesel oil | 1.4571 | 0.5921 |
Fuel oil | 1.286 | 0.6185 |
Natural gas | 1.33 | 0.4483 |
Electricity power | 0.1229 | 0.2132 |
Landscape Type | Farmland | Woodland | Grassland | Construction Land | Unutilized Land | Water |
---|---|---|---|---|---|---|
2000 (km2) | 7988.72 | 204.64 | 266.67 | 1365.21 | 1.11 | 2107.08 |
2010 (km2) | 7429.32 | 226.93 | 220.79 | 2032.51 | 1.25 | 2019.66 |
2020 (km2) | 7228.81 | 215.90 | 257.16 | 2673.49 | 3.03 | 1543.18 |
2030 ED (km2) | 6152.93 | 205.08 | 227.25 | 3990.72 | 2.74 | 1350.96 |
2030 ND (km2) | 7001.01 | 214.78 | 262.96 | 3194.40 | 2.94 | 1275.49 |
2030 LC (km2) | 6971.84 | 245.46 | 263.51 | 3001.89 | 2.96 | 1435.92 |
Landscape Type (104 t) | Farmland | Woodland | Grassland | Unutilized Land | Water | Direct |
---|---|---|---|---|---|---|
2000 | 33.71 | −1.32 | −0.06 | −0.07 | 0.00 | 32.27 |
2010 | 31.35 | −1.46 | −0.05 | −0.10 | 0.00 | 29.74 |
2020 | 30.51 | −1.39 | −0.05 | −0.13 | −0.01 | 28.92 |
2030 ND | 29.54 | −1.38 | −0.06 | −0.16 | −0.01 | 27.94 |
2030 ED | 25.54 | −1.32 | −0.05 | −0.20 | −0.01 | 23.97 |
2030 LC | 29.42 | −1.58 | −0.06 | −0.15 | −0.01 | 27.63 |
2000.00 | 2010.00 | 2020.00 | 2030ND | 2030ED | 2030LD | |
---|---|---|---|---|---|---|
Direct | 32.27 | 29.74 | 28.92 | 27.94 | 23.97 | 27.63 |
Indirect | 2421.13 | 5344.75 | 5527.72 | 5984.93 | 6863.29 | 5784.43 |
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Li, X.; Liu, Z.; Li, S.; Li, Y.; Wang, W. Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin. Land 2023, 12, 2160. https://doi.org/10.3390/land12122160
Li X, Liu Z, Li S, Li Y, Wang W. Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin. Land. 2023; 12(12):2160. https://doi.org/10.3390/land12122160
Chicago/Turabian StyleLi, Xiang, Zhaoshun Liu, Shujie Li, Yingxue Li, and Weiyu Wang. 2023. "Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin" Land 12, no. 12: 2160. https://doi.org/10.3390/land12122160