Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methods
2.3. Model Developed Based on SD
2.3.1. Energy Subsystem
2.3.2. Food Subsystem
2.3.3. Water Subsystem
2.3.4. Urban-Rural Integration Subsystem and Land Use Subsystem
2.4. Data and Model Validation
2.5. Scenario Design
3. Results
3.1. Land Use Simulation Results
3.2. Scenario Comparisons
3.2.1. Food Safety
3.2.2. Urban Development
3.2.3. Ecological Civilization
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Year | Crop Land | Urban Land | Ecological Land | Rural Construction Land |
---|---|---|---|---|
2015 | 86,564 | 25,326 | 244,795 | 13,171 |
2016 | 87,635 | 25,633 | 243,137 | 13,318 |
2017 | 87,914 | 25,899 | 242,392 | 13,380 |
2018 | 90,308 | 25,980 | 239,919 | 13,421 |
2019 | 91,769 | 26,012 | 238,457 | 13,485 |
2020 | 87,628 | 26,176 | 242,965 | 12,953 |
2021 | 86,879 | 26,348 | 244,033 | 12,462 |
2022 | 86,879 | 26,531 | 244,321 | 11,992 |
2023 | 86,626 | 26,724 | 244,836 | 11,537 |
2024 | 86,868 | 26,928 | 244,813 | 11,113 |
2025 | 87,877 | 27,144 | 243,949 | 10,752 |
2026 | 88,844 | 27,371 | 243,089 | 10,419 |
2027 | 89,199 | 27,611 | 242,820 | 10,092 |
2028 | 89,879 | 27,865 | 242,196 | 9782 |
2029 | 90,842 | 28,134 | 241,261 | 9486 |
2030 | 90,432 | 28,418 | 241,678 | 9194 |
2031 | 89,338 | 28,719 | 242,752 | 8913 |
2032 | 87,937 | 29,037 | 244,111 | 8638 |
2033 | 86,611 | 29,372 | 245,368 | 8372 |
2034 | 85,498 | 29,726 | 246,385 | 8114 |
2035 | 84,417 | 30,102 | 247,341 | 7863 |
Year | Crop Land | Urban Land | Ecological Land | Rural Construction Land |
---|---|---|---|---|
2015 | 86,564 | 25,326 | 244,795 | 13,171 |
2016 | 87,635 | 25,633 | 243,137 | 13,318 |
2017 | 87,914 | 25,899 | 242,392 | 13,380 |
2018 | 90,308 | 25,980 | 239,919 | 13,421 |
2019 | 91,769 | 26,012 | 238,457 | 13,485 |
2020 | 87,259 | 26,140 | 243,370 | 12,953 |
2021 | 82,970 | 26,273 | 248,022 | 12,457 |
2022 | 79,563 | 26,412 | 251,770 | 11,976 |
2023 | 78,612 | 26,558 | 253,034 | 11,518 |
2024 | 78,797 | 26,712 | 253,119 | 11,095 |
2025 | 79,692 | 26,873 | 252,422 | 10,736 |
2026 | 80,579 | 27,042 | 251,698 | 10,403 |
2027 | 80,920 | 27,220 | 251,505 | 10,077 |
2028 | 81,519 | 27,407 | 251,029 | 9768 |
2029 | 82,390 | 27,604 | 250,256 | 9473 |
2030 | 82,064 | 27,811 | 250,665 | 9182 |
2031 | 81,088 | 28,028 | 251,705 | 8901 |
2032 | 79,834 | 28,256 | 253,005 | 8628 |
2033 | 78,623 | 28,495 | 254,241 | 8362 |
2034 | 77,608 | 28,747 | 255,263 | 8105 |
2035 | 76,626 | 29,013 | 256,228 | 7855 |
Year | Crop Land | Urban Land | Ecological Land | Rural Construction Land |
---|---|---|---|---|
2015 | 86,564 | 25,326 | 244,795 | 13,171 |
2016 | 87,635 | 25,633 | 243,137 | 13,318 |
2017 | 87,914 | 25,899 | 242,392 | 13,380 |
2018 | 90,308 | 25,980 | 239,919 | 13,421 |
2019 | 91,769 | 26,012 | 238,457 | 13,485 |
2020 | 87,259 | 26,110 | 243,400 | 12,953 |
2021 | 82,970 | 26,212 | 248,083 | 12,457 |
2022 | 78,892 | 26,317 | 252,536 | 11,976 |
2023 | 75,015 | 26,426 | 256,764 | 11,517 |
2024 | 71,327 | 26,539 | 260,773 | 11,083 |
2025 | 69,408 | 26,655 | 262,945 | 10,713 |
2026 | 71,387 | 26,775 | 261,169 | 10,391 |
2027 | 72,626 | 26,901 | 260,126 | 10,070 |
2028 | 73,157 | 27,032 | 259,772 | 9760 |
2029 | 73,936 | 27,168 | 259,152 | 9466 |
2030 | 73,693 | 27,311 | 259,542 | 9177 |
2031 | 72,834 | 27,459 | 260,533 | 8897 |
2032 | 71,727 | 27,613 | 261,759 | 8624 |
2033 | 70,635 | 27,774 | 262,955 | 8359 |
2034 | 69,716 | 27,942 | 263,963 | 8102 |
2035 | 68,832 | 28,118 | 264,919 | 7853 |
Appendix B
Indicators | 2019 | 2035 | ||
---|---|---|---|---|
Conservative Scenario | Smooth Transition Scenario | Rapid Transformation Scenario | ||
Crop land (ha) | 91,768.52 | 84,417.27 | 76,625.6 | 68,832.18 |
Urban land(ha) | 26,011.76 | 30,101.58 | 29,013.38 | 28,118.39 |
Ecological land (ha) | 238,457.06 | 247,340.56 | 256,228.32 | 264,919 |
Crop production (tons) | 523,290 | 590,464.79 | 535,814.32 | 481,150.81 |
Local urban food demand (tons) | 294,175.8 | 392,964.36 | 393,125.02 | 393,217.04 |
Local rural food demand (tons) | 90,266.96 | 44,416.97 | 44,274.43 | 44,192.79 |
Coal mining (kilo-tons) | 2047.08 | 38,594.11 | 25,974.97 | 15,687.23 |
Energy Footprint (hectares of woodland) | 5,697,712.65 | 17,971,390.06 | 12,378,342.86 | 7,712,356 |
Crop water demand (million m3) | 55.06 | 50.65 | 36.78 | 24.78 |
Urban domestic water demand (million m3) | 2.21 | 2.41 | 2.42 | 2.42 |
Rural domestic water demand (million m3) | 0.5 | 0.22 | 0.22 | 0.21 |
Industry water demand (million m3) | 11.91 | 14.16 | 14.16 | 14.16 |
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Variables | Coal Energy Consumption Ratio | Energy Consumption per Unit GDP | Comprehensive Irrigation Quota | Water Consumption per Unit Industrial Output Value | Grain Self-Sufficiency Rate |
---|---|---|---|---|---|
Crop land | 0 | 0 | 0.001 | 0 | 0.996 |
Urban land | 0.103 | 0.107 | 0 | 0 | 0.022 |
Ecological land | −0.012 | −0.013 | −0.005 | −0.001 | −0.343 |
Forest | 0 | 0 | −0.964 | −0.269 | −0.96 |
New mining land | 0.964 | 1 | 0 | 0 | 0 |
Average absolute value | 0.2158 | 0.224 | 0.194 | 0.054 | 0.4642 |
System | Parameter | Scenario | ||
---|---|---|---|---|
1 | 2 | 3 | ||
Energy | Coal energy consumption ratio | 0.84 | 0.72 | 0.6 |
Energy consumption per unit GDP (tons of standard coal/10,000 CNY) | 2.05 | 1.6 | 1.15 | |
Food | Grain self-sufficiency rate | 1.35 | 1.23 | 1.1 |
Water | Comprehensive irrigation quota (cubic meter/ha) | 6000 | 4800 | 3600 |
Area of Different Land Use Type | Land Use Trend | |
---|---|---|
2015–2020 | 2020–2035 (Simulate) | |
Urban land | Relatively rapid growth | Relatively slow growth |
Crop land | Keep growing | Decrease to the lowest point, then fluctuate |
Ecological land | Continuous decrease | Increase to the peak, then fluctuate |
Rural construction land | Continuous increase | Continuous decrease |
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Yu, X.; Shan, L.; Wu, Y. Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus. Land 2021, 10, 1013. https://doi.org/10.3390/land10101013
Yu X, Shan L, Wu Y. Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus. Land. 2021; 10(10):1013. https://doi.org/10.3390/land10101013
Chicago/Turabian StyleYu, Xujing, Liping Shan, and Yuzhe Wu. 2021. "Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus" Land 10, no. 10: 1013. https://doi.org/10.3390/land10101013