Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methods
3.1. Research Framework
3.2. Simulation Scenario Setting
3.3. SD Model
3.4. PLUS Model
3.5. Land Use Intensity
4. Results
4.1. Future Land Use Demand in Hangzhou
4.2. Future Land Use Spatial Distribution Patterns
4.3. Drivers of Future Land Use Expansion
4.4. Changes in Future LUI
5. Discussion
5.1. Comprehensive Framework Based on the SD-PLUS Model
5.2. LUCC in High-Density Urban Areas under Climate Change and Rapid Urbanization
5.3. Identifying Changes in LUI to Support Sustainable Land Management
5.4. Limitations and Prospects
6. Conclusions
- (1)
- From 2020 and 2050, cultivated land, unused land, and water are projected to show a declining trend across all three scenarios, while construction land is predicted to exhibit an increasing trend. This indicates that urbanization in Hangzhou will continue, underscoring the crucial need to enhance the preservation of cultivated land and water for the city’s sustainable development.
- (2)
- The expansion of construction land is most pronounced under the SSP585 scenario, while it is the least under SSP126.
- (3)
- Night light has the largest impact on the expansion of cultivated land and construction land, at 13.94% and 20.35% respectively, highlighting the crucial role of human activities in the urbanization process.
- (4)
- The average LUI in central urban districts markedly surpasses that in the surrounding suburban areas, with Xiacheng having the highest LUI. Measures such as rooftop gardens and vertical greening could be implemented to enhance greenery in this area. Chun’an has the lowest average LUI and should continue to strengthen ecological protection.
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data | Year | Source |
---|---|---|
Land use data | 2000, 2005, 2010, 2015, 2020 | https://www.resdc.cn (accessed on 9 May 2024) |
Historical GDP density | 2019 | |
Historical population density | 2019 | |
DEM | 2015 | https://www.gscloud.cn/ (accessed on 9 May 2024) |
Annual average temperature | 2000–2020 | https://www.geodata.cn (accessed on 9 May 2024) |
Annual precipitation | 2000–2020 | |
Night light data | 2020 | |
Socioeconomic data | 2000–2020 | https://tjj.hangzhou.gov.cn/ and http://tjj.zj.gov.cn/ (accessed on 9 May 2024) |
Road data | 2020 | https://www.openstreetmap.org (accessed on 9 May 2024) |
Future GDP density | 2020–2050 | [58] |
Future population density | 2020–2050 | [59] |
Future annual precipitation | 2020–2050 | https://worldclim.org/ (accessed on 9 May 2024) |
Future annual average temperature | 2020–2050 |
Main Equations | Unit |
---|---|
Population = INTEG (Population change, 701.70) | ten thousand |
Population change = Population × Population change rate | |
GDP = INTEG (GDP change, 1395.67) | hundred million |
GDP change = GDP × GDP change rate | |
Fixed investment = GDP × Fixed investment proportion × 10,000 | ten thousand |
Primary industry investment = Fixed investment × Primary industry investment proportion | |
Secondary and tertiary industry investment = Fixed investment × Secondary and tertiary industry investment proportion | |
Livestock investment = Primary industry investment × Livestock investment proportion | |
Forestry investment = Primary industry investment × Forestry investment proportion | |
Fishery investment = Primary industry investment × Fishery investment proportion | |
Agricultural investment = Primary industry investment × Agricultural investment proportion | |
Urban population = Population × Urban population ratio | |
Rural population = Population × (1 − Urban population ratio) | |
Livestock product demand = Population × Per capita livestock product demand × 10 | t |
Forestry product demand = Population × Per capita forestry demand × 10 | |
Aquatic product demand = Population × Per capita aquatic product demand × 10 | |
Food demand = Population × Per capita food demand × 10 | |
Urban construction land demand = Urban population × Per capita urban construction land demand | m2 |
Rural construction land demand = Rural population × Per capita rural construction land demand | |
Cultivated land = 4,113,653,430.269395 − 47.6988228 × Agricultural investment − 0.704891286 × Construction land − 33.3134561 × Food demand + 15,868,922.3 × Annual average temperature + 15722.8946 × Annual precipitation | |
Forest = 10,606,808,632.558468 + 0.26504743 × Cultivated land + 581,586.827 × Annual average temperature − 5629.89304 × Annual precipitation + 510.302416 × Forestry investment + 51.4650807 × Forestry product demand | |
Grassland = 502,040,720.9347341 + 0.0847 × Cultivated land − 2,489,243.88 × Annual average temperature + 3082.81077 × Annual precipitation − 26.5026233 × Livestock investment − 3.68764249 × Livestock product demand | |
Water = −30,982,590.42027104 − 418.865363 × Aquatic product demand −226.224045 × Fishery investment 18,513,262.7 × Annual average temperature − 15,452.7109 × Annual precipitation | |
Construction land = −30,982,590.42027104 + 1.18422594 × Urban construction demand + 0.94929414 × Rural construction land demand − 0.00273347 × Secondary and tertiary industry investment | |
Unused land = 16,858,054,800 − Construction land change − Grassland change − Forest change − Water change − Cultivated land change |
Category | Driving Factors |
---|---|
Socioeconomic factors | Distance to highway |
Distance to main roads | |
Distance to railroad | |
GDP density | |
Night light | |
Population density | |
Climate factors | Annual average temperature |
Annual precipitation | |
Environmental factors | DEM |
Slope |
2005 | 2010 | 2015 | 2020 | ||
---|---|---|---|---|---|
Cultivated land | Actual value (m2) | 3,242,344,500 | 3,197,394,000 | 3,033,113,400 | 2,903,287,500 |
Simulated value (m2) | 3,266,580,000 | 3,178,470,000 | 3,050,200,000 | 2,901,610,000 | |
Error rate | 0.75% | −0.59% | 0.56% | −0.06% | |
Forest | Actual value (m2) | 11,489,677,200 | 11,459,440,800 | 11,459,448,900 | 11,412,761,400 |
Simulated value (m2) | 11,491,500,000 | 11,469,400,000 | 11,456,200,000 | 11,417,800,000 | |
Error rate | 0.02% | 0.09% | −0.03% | 0.04% | |
Grassland | Actual value (m2) | 382,710,600 | 392,903,100 | 389,673,000 | 392,877,900 |
Simulated value (m2) | 386,699,000 | 390,392,000 | 392,301,000 | 392,722,000 | |
Error rate | 1.04% | −0.64% | 0.67% | −0.04% | |
Water | Actual value (m2) | 962,687,700 | 885,378,600 | 882,504,000 | 881,885,700 |
Simulated value (m2) | 937,958,000 | 904,677,000 | 864,375,000 | 867,438,000 | |
Error rate | −2.57% | 2.18% | −2.05% | −1.64% | |
Construction land | Actual value (m2) | 774,451,800 | 916,713,000 | 1,087,271,100 | 1,261,166,400 |
Simulated value (m2) | 775,904,000 | 916,645,000 | 1,086,000,000 | 1,263,170,000 | |
Error rate | 0.19% | −0.01% | −0.12% | 0.16% | |
Unused land | Actual value (m2) | 6,183,000 | 6,225,300 | 6,044,400 | 6,075,900 |
Simulated value (m2) | 5,863,560 | 6,036,450 | 5,719,400 | 6,113,900 | |
Error rate | −5.17% | −3.03% | −5.38% | 0.63% |
2030 | 2040 | 2050 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
SSP126 | SSP245 | SSP585 | SSP126 | SSP245 | SSP585 | SSP126 | SSP245 | SSP585 | ||
Cultivated land | Area (km2) | 2849.63 | 2829.42 | 2778.32 | 2803.94 | 2784.01 | 2648.15 | 2796.07 | 2759.22 | 2553.05 |
Proportion | 16.90% | 16.79% | 16.48% | 16.63% | 16.52% | 15.71% | 16.59% | 16.37% | 15.14% | |
Forest | Area (km2) | 11,419.24 | 11,415.14 | 11,407.29 | 11,415.21 | 11,411.68 | 11,392.56 | 11,416.79 | 11,408.88 | 11,382.77 |
Proportion | 67.74% | 67.71% | 67.67% | 67.72% | 67.69% | 67.58% | 67.72% | 67.68% | 67.52% | |
Grassland | Area (km2) | 393.79 | 393.95 | 394.35 | 394.35 | 394.36 | 395.69 | 394.19 | 394.35 | 396.36 |
Proportion | 2.34% | 2.34% | 2.34% | 2.34% | 2.34% | 2.35% | 2.34% | 2.34% | 2.35% | |
Water | Area (km2) | 857.41 | 856.84 | 853.22 | 849.72 | 850.59 | 836.75 | 848.96 | 850.09 | 826.97 |
Proportion | 5.09% | 5.08% | 5.06% | 5.04% | 5.05% | 4.96% | 5.04% | 5.04% | 4.91% | |
Construction land | Area (km2) | 1332.15 | 1356.88 | 1419.12 | 1389.21 | 1411.83 | 1579.60 | 1396.49 | 1439.96 | 1693.92 |
Proportion | 7.90% | 8.05% | 8.42% | 8.24% | 8.37% | 9.37% | 8.28% | 8.54% | 10.05% | |
Unused land | Area (km2) | 5.82 | 5.82 | 5.75 | 5.63 | 5.59 | 5.31 | 5.56 | 5.54 | 4.99 |
Proportion | 0.03% | 0.03% | 0.03% | 0.03% | 0.03% | 0.03% | 0.03% | 0.03% | 0.03% | |
Total land | Area (km2) | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 | 16,858.04 |
Proportion | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% |
2030 | 2040 | 2050 | |||||||
---|---|---|---|---|---|---|---|---|---|
SSP126 | SSP245 | SSP585 | SSP126 | SSP245 | SSP585 | SSP126 | SSP245 | SSP585 | |
Gongshu | 3.5881 | 3.6015 | 3.6289 | 3.6103 | 3.6260 | 3.6623 | 3.6134 | 3.6349 | 3.6784 |
Shangcheng | 3.5031 | 3.4259 | 3.4313 | 3.5083 | 3.4640 | 3.5291 | 3.5094 | 3.4676 | 3.5329 |
Xiacheng | 3.9586 | 3.9626 | 3.9690 | 3.9665 | 3.9683 | 3.9714 | 3.9671 | 3.9695 | 3.9746 |
Jianggan | 3.4659 | 3.4763 | 3.4908 | 3.4771 | 3.4902 | 3.5288 | 3.4786 | 3.4950 | 3.5412 |
Xihu | 2.8116 | 2.8154 | 2.8312 | 2.8232 | 2.8293 | 2.8713 | 2.8252 | 2.8366 | 2.8924 |
Binjiang | 3.4992 | 3.4715 | 3.4832 | 3.5047 | 3.4848 | 3.5243 | 3.5056 | 3.4877 | 3.5312 |
Xiaoshan | 2.9185 | 2.9242 | 2.9403 | 2.9307 | 2.9380 | 2.9906 | 2.9330 | 2.9460 | 3.0271 |
Yuhang | 2.8241 | 2.8292 | 2.8415 | 2.8338 | 2.8396 | 2.8664 | 2.8355 | 2.8451 | 2.8831 |
Fuyang | 2.3455 | 2.3479 | 2.3535 | 2.3484 | 2.3525 | 2.3658 | 2.3488 | 2.3547 | 2.3745 |
Linan | 2.1658 | 2.1667 | 2.1683 | 2.1662 | 2.1678 | 2.1715 | 2.1662 | 2.1683 | 2.1733 |
Tonglu | 2.2197 | 2.2212 | 2.2229 | 2.2203 | 2.2226 | 2.2263 | 2.2204 | 2.2231 | 2.2279 |
Chun’an | 2.0882 | 2.0879 | 2.0885 | 2.0882 | 2.0883 | 2.0902 | 2.0882 | 2.0885 | 2.0907 |
Jiande | 2.1712 | 2.1738 | 2.1755 | 2.1714 | 2.1761 | 2.1820 | 2.1715 | 2.1766 | 2.1846 |
Hangzhou | 2.3267 | 2.3294 | 2.3299 | 2.3285 | 2.3323 | 2.3341 | 2.3328 | 2.3442 | 2.3510 |
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Yao, S.; Li, Y.; Jiang, H.; Wang, X.; Ran, Q.; Ding, X.; Wang, H.; Ding, A. Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China. Buildings 2024, 14, 2165. https://doi.org/10.3390/buildings14072165
Yao S, Li Y, Jiang H, Wang X, Ran Q, Ding X, Wang H, Ding A. Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China. Buildings. 2024; 14(7):2165. https://doi.org/10.3390/buildings14072165
Chicago/Turabian StyleYao, Song, Yonghua Li, Hezhou Jiang, Xiaohan Wang, Qinchuan Ran, Xinyi Ding, Huarong Wang, and Anqi Ding. 2024. "Predicting Land Use Changes under Shared Socioeconomic Pathway–Representative Concentration Pathway Scenarios to Support Sustainable Planning in High-Density Urban Areas: A Case Study of Hangzhou, Southeastern China" Buildings 14, no. 7: 2165. https://doi.org/10.3390/buildings14072165