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Article

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

by
Song Yao
1,2,
Yonghua Li
1,3,*,
Hezhou Jiang
1,
Xiaohan Wang
4,
Qinchuan Ran
1,
Xinyi Ding
1,
Huarong Wang
1 and
Anqi Ding
1
1
Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China
2
Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China
3
The Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China
4
Zhejiang University Urban-Rural Planning & Design Institute Co., Ltd., Hangzhou 310030, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(7), 2165; https://doi.org/10.3390/buildings14072165
Submission received: 9 June 2024 / Revised: 10 July 2024 / Accepted: 11 July 2024 / Published: 14 July 2024
(This article belongs to the Special Issue Planning for Sustainable Land Use and Built Environment in High-Density Cities)
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Abstract

:
Amidst the challenges posed by global climate change and accelerated urbanization, the structure and distribution of land use are shifting dramatically, exacerbating ecological and land-use conflicts, particularly in China. Effective land resource management requires accurate forecasts of land use and cover change (LUCC). However, the future trajectory of LUCC, influenced by climate change and urbanization, remains uncertain. This study developed an integrated multi-scenario framework by combining system dynamics and patch-generating land use simulation models to predict future LUCC in high-density urban regions under various Shared Socioeconomic Pathway (SSP)–Representative Concentration Pathway (RCP) scenarios. The results showed the following: (1) From 2020 to 2050, cultivated land, unused land, and water are projected to decrease, while construction land is expected to increase. (2) Future land use patterns exhibit significant spatial heterogeneity across three scenarios. Construction land will expand in all districts of Hangzhou, particularly in the main urban areas. Under the SSP585 scenario, the expansion of construction land is most significant, while it is the least under the SSP126 scenario. (3) Distinct factors drive the expansion of different land use types. The digital elevation model is the predominant factor for the expansion of forest and grassland, contributing 19.25% and 30.76%, respectively. Night light contributes the most to cultivated land and construction land, at 13.94% and 20.35%, respectively. (4) The average land use intensity (LUI) in central urban districts markedly surpasses that in the surrounding suburban areas, with Xiacheng having the highest LUI and Chun’an the lowest. Under the SSP126 scenario, the area with increased LUI is significantly smaller than under the SSP245 and SSP585 scenarios. These findings offer valuable guidance for sustainable planning and built environment management in Hangzhou and similarly situated urban centers worldwide.

1. Introduction

Land serves as a vital foundation for human existence and a crucial resource for economic development [1,2,3]. Land use and cover changes (LUCCs) reflect the interactions between human endeavors and the environment [4]. Over the past 1000 years, approximately three-fourths of the earth’s surface has been reshaped by human activities [5]. Unregulated exploitation and disorganized utilization of land have caused various degrees of ecological degradation and global climate change [6,7,8,9]. LUCCs have thus become a major concern for the global academic community and governments [10,11].
As the largest developing country, China has experienced a surge in urbanization from 17.92% in 1978 to 66.16% in 2023, following its economic reforms [12,13]. The rapid urbanization process has led to significant LUCC, with the swift expansion of construction land encroaching upon ecological spaces [14,15]. The escalating adverse effects on ecosystems have prompted a push for ecological civilization, affirming China’s dedication to sustainable development strategies [16,17]. Given China’s ongoing rapid urbanization [18], the strain on land resources and environmental degradation is expected to intensify [19,20]. Therefore, forecasting future LUCC is essential to understand its temporal and spatial evolution, and to accurately predict urban land use trends, which helps manage and develop land resources efficiently [21,22].
Recent academic efforts have aimed at predicting LUCC at various scales including global [23], national [24], urban agglomerations [25], and individual cities [26]. These studies enhance our scientific understanding of how human activities influence the natural environment and provide a crucial foundation for developing effective environmental conservation and land management policies. Specifically, LUCC prediction research focuses on the following three main aspects: scenario setting, land use demand prediction, and spatial pattern simulation. Establishing simulation scenarios is a critical first step in LUCC forecasting. Currently, there are three primary models. The first is the natural development scenario (NDS), which projects future land use demand based on historical data, extending existing LUCC trends without alterations [27]. For example, Han et al. simulated future LUCC in the Nanxi Lake watershed under NDS [28]. Secondly, actual policy scenarios are designed according to specific policy or planning documents [29]. For instance, Lin and Wang established a transportation planning scenario that incorporated planned expansions of metro lines, railways, and highways to simulate the LUCC of Guangzhou [30]. Thirdly, hypothetical policy scenarios use assumptions about future policy trends to establish restrictive rules [31]. For example, Zhao et al. designed various policy scenarios to simulate LUCC in the Dongting Lake basin [32]. With growing recognition of climate and socioeconomic impacts on LUCC [33,34], more scholars are beginning to couple Shared Socioeconomic Pathways (SSPs) with Representative Concentration Pathways (RCPs) to set simulation scenarios [35,36,37]. For example, Wu et al. used SSP-RCP scenarios to simulate LUCC and habitat quality from 2030 to 2050 [38]. Gao et al. predicted future LUCC and carbon storage in Heilongjiang Province under SSP-RCP scenarios [39].
Common models for quantitative land use simulation include the Markov model [26], multi-objective models [40], and the system dynamics (SD) model [41]. The SD model is particularly adept at handling nonlinear, higher-order, multi-feedback, complex system issues, making it well-suited for various policy scenarios and accurate in predicting future land use demands [42,43]. It has been widely used recently. For instance, Xu et al. applied the SD model to predict the future land use structure in the Beijing–Tianjin–Hebei region [44]. Wang et al. employed it to predict future land use demands in the Yellow River Delta [45]. Predictive models for spatial land use patterns include Cellular Automata (CA) [46], Agent-Based Models [47], the CLUMondo model [48], the future land use simulation model [49], and the Patch-Generating Land Use Simulation (PLUS) model [50]. The PLUS model, in particular, is extensively used because of its superior ability to explore the driving factors of LUCC, thereby providing more precise simulations of land use patterns [51,52]. For example, Wang et al. utilized the PLUS model to forecast future land use patterns in the Chang–Ji–Tu region [53]. Shi et al. used the PLUS model to simulate the distribution and driving factors of mangroves in Hainan Island [54]. Overall, this study combines the SD and PLUS models to predict future LUCC, enabling the precise identification of future land use structure and spatial patterns, as well as pinpointing the principal drivers of these changes.
Hangzhou is a significant economic center in Southeast China boasting a permanent population of over 10 million and high urban density [55,56]. The city is characterized by a densely populated urban area and concentrated development, especially in the metropolitan and adjacent areas, exemplifying a high-density urban setting. Over the past few decades, the rapid expansion of construction land in Hangzhou has encroached upon the forest and grassland, posing substantial threats to the ecological environment. Consequently, there is a critical need to simulate its LUCC. In this study, we propose a comprehensive framework based on SD-PLUS to simulate the land use structure and spatial patterns under SSP-RCP scenarios. This framework is tailored specifically to this high-density urban area. The objectives of this study are to (1) forecast land use demand under various SSP-RCP scenarios; (2) simulate spatial patterns; (3) quantify the dominant drivers of LUCC; and (4) identify changes in future land use intensity (LUI). The findings of this study offer valuable guidance for sustainable planning and built environment management in Hangzhou and similarly situated urban centers worldwide.

2. Study Area and Data

2.1. Study Area

Hangzhou, situated in Southeast China, serves as the administrative center of Zhejiang Province, covering an area of 16,850 square kilometers (Figure 1a). The city’s terrain is notably diverse, with its western region characterized by hills and the eastern region by plains (Figure 1b). Hangzhou is positioned in a subtropical monsoon area, which brings distinct seasonal variations and plentiful rainfall. This research utilizes the 2020 administrative division boundaries, as historical data for this study only extend up to that year, without including changes from the 2021 administrative adjustments. Hangzhou comprises 11 districts and counties, of which eight districts in the northeast are identified as the main urban regions. By 2023, Hangzhou’s gross domestic product (GDP) reached CNY 2005.9 billion, with a permanent population of 12.522 million and an urbanization rate of 84.2%, making it a typical high-density city. Therefore, predicting future LUCC is crucial for Hangzhou’s sustainable development.

2.2. Data

This study incorporated various datasets, including land use, geographic, socioeconomic, and climate data, as detailed in Table 1. Future precipitation, temperature, GDP, and population data under SSP-RCP scenarios were obtained from References [57,58,59]. All raster data were projected and resampled to a spatial resolution of 30 m × 30 m using ArcGIS 10.8 software.

3. Methods

3.1. Research Framework

This study’s methodology is structured into four distinct parts as follows: (1) setting the simulation parameters for different SSP-RCP scenarios; (2) forecasting future land use demands using the SD model; (3) simulating future land use patterns with the PLUS model; and (4) identifying changes in future LUI (Figure 2).

3.2. Simulation Scenario Setting

The Coupled Model Intercomparison Project Phase 6 (CMIP6) utilizes an integration of SSPs and RCPs to provide various scenarios for simulating future greenhouse gas emissions and socioeconomic changes [60,61,62]. Among these, the SSP1-RCP2.6 (SSP126) scenario represents a sustainable development pathway with lower emissions, SSP2-RCP4.5 (SSP245) aligns with ongoing patterns and medium emissions, and SSP5-RCP8.5 (SSP585) portrays a high-emission future driven by rapid economic growth reliant on fossil fuels [63,64]. Leveraging insights from prior studies [65,66], we selected four key future variables including GDP, population, temperature, and precipitation for land use demand predictions.

3.3. SD Model

We developed the Hangzhou land use demand SD model using Vensim PLE software (Figure 3). The model comprises the following four subsystems: population, economy, climate, and land use [34,67]. Its primary drivers include population density, GDP density, annual average temperature, annual precipitation, urbanization rate, per capita demand, and fixed investment. The model elucidates the dynamic interactions among economic growth, population dynamics, LUCC, and climate variability in Hangzhou, enabling scenario-based simulations to forecast future land use patterns.
In the population subsystem, the continuous increase in population leads to a rise in demand for urban construction, rural construction, food, livestock products, aquatic products, and forestry products, thereby affecting the area of construction land, cultivated land, grassland, water, and forest. In the economic subsystem, economic development inevitably impacts the utilization of land resources. For example, changes in fixed investment, GDP, and agricultural investment can all lead to LUCC. In the climate subsystem, variations in annual average temperature and annual precipitation have significant impacts on vegetation growth and the area of water, thereby affecting cultivated land transformation, forest, grassland, and water. In the land use subsystem, alterations in different land uses are often caused by multiple factors. For instance, changes in cultivated land area are determined by shifts in food demand, agricultural investment, construction land area, annual average temperature, and annual precipitation. Regression functions correlating land use types with influencing factors were established using SPSS 27 software (Table 2).
The model encompasses the entire geographic area of Hangzhou city, spanning from 2000 to 2050, with annual simulation time steps. It is structured into the following two key phases: a historical simulation from 2000 to 2020 and a predictive simulation from 2020 to 2050. The historical period is dedicated to verifying the simulation accuracy by comparing it against actual historical data. The predictive period focuses on estimating future land use requirements based on parameters derived from diverse SSP-RCP scenarios.

3.4. PLUS Model

The PLUS model, which is founded on CA, employs a random forest algorithm to analyze land use expansion between two periods [51]. This model effectively calculates the development probabilities and contributions of various driving factors for different land uses, thereby revealing potential reasons behind land use changes. Drawing on prior research and data availability [68,69,70,71], we identified three categories of driving factors, including socioeconomic, climatic, and ecological, to predict future land use patterns (Table 3).
The accuracy of the PLUS model was evaluated using the Kappa index and the figure of merit (FOM) index. These indexes have been widely used in previous LUCC research [72], and their formulas are as follows:
K a p p a = p 0 − p i 1 − p i
F O M = B A + B + C + D
where the Kappa index, ranging from 0 to 1, reflects accuracy, with values nearer to 1 indicating higher precision; p i represents the proportion of correct predictions in the simulation results; and  p 0 is the portion of the model simulation that matches the actual data. The FOM index also ranges from 0 to 1, where A denotes the area where LUCC occurred but the model predicted no change, B represents areas where the model’s simulations match the actual changes, C denotes areas where the model incorrectly simulated land use changes, creating error regions, and D represents areas where no change occurred in reality but the model simulated changes. A higher FOM index value indicates greater accuracy of the model outcomes [73].

3.5. Land Use Intensity

LUI measures the degree and strength of land utilization within a specific geographic area [74,75], which directly impacts the sustainability of land resources, ecological balance, and environmental quality of the region. In this study, we use the LUI index to quantify the level of LUI. Based on previous research, we established the intensity index for unused land at 1, for forest, grassland, and water at 2, for cultivated land at 3, and for construction land at 4 [76,77,78].

4. Results

4.1. Future Land Use Demand in Hangzhou

The accuracy of the SD model was verified by comparing its land use simulations from 2000 to 2020 with real-world data, indicating that most simulation errors were under 5% (Table 4). The largest error, at 5.377%, occurred in the category of unused land, likely because of its relatively small proportion in the dataset. Overall, the model demonstrated high fidelity in projecting Hangzhou’s land use demands.
Utilizing data on population, GDP, temperature, and precipitation under various SSP-RCP scenarios, we projected the land use demand in Hangzhou from 2020 to 2050 (Figure 4). During this period, cultivated land, water, and unused land exhibited a declining trend across all three scenarios, with the steepest decline under the SSP85 scenario and the gentlest under SSP126. In contrast, construction land was on the rise in all scenarios, with the greatest expansion under SSP585 and the smallest under SSP126. Grassland initially increased and then decreased in the SSP126 and SSP245 scenarios, while it continued to rise under SSP585, likely because of global warming effects associated with this scenario. Conversely, forest initially declined and then increased under SSP126 and SSP245 but continued to decline under SSP585.
We compiled the land use demands for 2030, 2040, and 2050 (Table 5). In Hangzhou, forest constitutes over 67% of the land use composition, predominantly located in the southwestern districts including Linan, Fuyang, Tonglu, Chun’an, and Jiande. Unused land represents a minimal proportion of only 0.03%. Although construction land comprises a relatively small percentage, it is concentrated in Hangzhou’s northeastern core urban areas, where the majority of the city’s population and GDP are also concentrated. This concentration poses increased governance needs and challenges.

4.2. Future Land Use Spatial Distribution Patterns

Using 2010 land use data as a training set, we projected the 2020 land use pattern and compared it with actual 2020 data. The overall accuracy was 90.975%, with a Kappa coefficient of 0.910 and an FOM value of 0.215, indicating that the model is highly credible. Figure 5 shows the future land use patterns in Hangzhou. Overall, the land use patterns show significant spatial heterogeneity across all scenarios (Figure 5). There is a noticeable expansion of construction land in all districts of Hangzhou, particularly the northeastern urban core. Under the SSP585 scenario, the expansion of construction land is most pronounced, with a substantial amount of cultivated land on the city’s periphery being converted into construction land, while the SSP126 scenario shows the least expansion.
For a detailed examination of localized land use changes, we focused on Areas A, B, and C in Hangzhou as projected for 2050 under three different scenarios (Figure 6). Area A is the central urban zone of Hangzhou, representing the area with the highest density of built environment. Area B, located in Tonglu County, features relatively concentrated construction land, exemplifying a construction-intensive area in central Hangzhou, and Area C, in the Qiandao Lake area of Hangzhou—a 5A tourist spot in China with high ecological and tourism value—functions as an ecological conservation area. The detailed map of Area A showed that by 2050, most of the Gongshu, Shangcheng, Xiacheng, and Jianggan districts will be covered by construction land—the core of central Hangzhou. Under SSP585, expansion is notably rapid, resulting in more extensive and cohesive patterns of construction land compared with the other scenarios. In Area B’s detailed map, although SSP585 still shows the greatest expansion of construction land, the expansion rate in Tonglu is much slower than the main urban area, with the forest remaining dominant and the ecological environment relatively intact. The detailed maps of Area C under all three scenarios are almost identical, likely because of Hangzhou’s protective policies for the Qiandao Lake area.

4.3. Drivers of Future Land Use Expansion

We employed the PLUS model to identify the development probabilities and dominant drivers (Figure 7 and Figure 8) that elucidate the underlying causes of LUCC (Figure 7 and Figure 8). The spatial distribution patterns of the development potential for each land use type were determined through the PLUS model’s LEAS module (Figure 7), where higher potential values suggest a greater likelihood of transition to that type of land. Overall, the development potential closely mirrors its current spatial distribution. Specifically, areas with high development potential for cultivated land are relatively scattered, primarily surrounding construction land and forest. Outside the main urban area, the development potential for forests in Hangzhou’s other regions is significant. In contrast, construction land has its highest potential values almost exclusively in the northeastern main urban area. Significant development potential for grasslands is found in the southern and central regions, including Chun’an, Jiande, Tonglu, and Fuyang. Hangzhou’s entire area shows high potential for water development, while unused lands exhibit very low potential.
We identified that the primary drivers of expansion for different types of land use vary significantly (Figure 8). The DEM substantially influences the expansion of forest and grassland, contributing 19.25% and 30.76% respectively. For cultivated land and construction land, the principal driving factor is night light, accounting for 13.94% and 20.35%, respectively. Population density is the main driver for water, accounting for 23.64%. For unused land, the predominant factor is the distance to main roads, contributing 29.28%.

4.4. Changes in Future LUI

We mapped the spatial distribution of LUI Hangzhou to reveal the extent of human activity’s impact on LUI, thereby supporting sustainable land use management (Figure 9). The spatial characteristics of LUI across different scenarios are consistently characterized by higher intensities in the central districts of Shangcheng and Xiacheng, which gradually decrease outward. The average land-use intensity increases over time (Table 6). Under the SSP585 scenario, Hangzhou experiences the highest average LUI, while it is lowest under the SSP126 scenario. Notably, the average LUI in central urban districts significantly exceeds that in the surrounding suburban areas. Among these, Xiacheng exhibits the highest LUI, nearing a value of 4, indicating that the area is almost entirely construction land. Conversely, Chun’an has the lowest average LUI because of its extensive water and forest, which contribute to its good ecological environment.
We employed GIS raster calculations to trace precise changes in LUI resulting from LUCC (Figure 10). Under the SSP126 scenario, most areas maintained the same LUI, with only scattered increases. In the SSP245 and SSP585 scenarios, there was a significant increase in areas with increased LUI, particularly concentrated in the northeastern parts of Hangzhou in Xihu, Xiaoshan, and Yuhang. This trend is due to the limited expansion opportunities in the other five central districts, which already had high levels of construction land in 2020. Conversely, Xihu, Xiaoshan, and Yuhang, being near the city center, possess ample space for development.

5. Discussion

5.1. Comprehensive Framework Based on the SD-PLUS Model

This study integrates the top-down SD model with the bottom-up PLUS model to better capture the nonlinear, dynamic, and systemic nature of LUCC and to simulate future land use patterns more accurately. The SD model adopts a top-down approach to predict land use demands, considering a complex array of social, economic, climatic, and land use factors, which reduces uncertainties in land use predictions and enables more scientific forecasting of future land use demands. Historical validation has shown the SD model to be highly accurate. We set future simulation parameters for the SD model using socioeconomic and climate data provided by CMIP6 under different SSP-RCP scenarios, which helped us derive future land use demand data.
On the other hand, the PLUS model simulates land spatial distribution from a bottom-up perspective, demonstrating a high degree of accuracy and landscape fidelity that surpasses other models. Utilizing 2010 land use data as a training set for historical validation, we found the Kappa coefficient to be 0.910 and an FOM value of 0.215. Compared with Li et al.’s research conducted in Hangzhou, our study achieves a higher FOM [40], which indicates that the model is highly reliable and meets the needs of this research. By integrating social, ecological, and climatic factors—ten driving factors in total—with the land use demand data output from the SD model, we successfully simulated the spatial patterns of future land use in Hangzhou.

5.2. LUCC in High-Density Urban Areas under Climate Change and Rapid Urbanization

LUCC reflects the complex interaction between human endeavors and the environment, influenced both by climate change and socioeconomic development. Following China’s economic liberalization, the country has experienced rapid urbanization—a trend expected to continue, exacerbating conflicts between human habitation and land, and deteriorating ecological conditions, particularly in high-density urban areas like Hangzhou. By utilizing future scenario data on precipitation, temperature, GDP, and population from CMIP6 and integrating this with the SD-PLUS coupled model, we identified the LUCC in high-density urban areas influenced by future climate changes and rapid urbanization.
In all scenarios considered, we observe a pronounced reduction in areas of cultivated land, unused land, and water, while construction land expands. This pattern aligns with the findings of Wang et al. [79], indicating that Hangzhou’s urban expansion is poised to continue, emphasizing the critical need to enhance the preservation of cultivated land and water for the city’s sustainable development. Future land use patterns exhibit significant spatial heterogeneity across the scenarios, with an expansion trend in construction land within all districts, which aligns with the findings reported by Li et al. [36]. This trend likely results from Hangzhou’s status as the capital city of Zhejiang Province, which boasts higher economic, technological, and educational resources, thus attracting populations from surrounding regions [80,81]. The expansion trends vary by area, with the main urban area experiencing more continuous and rapid expansion compared with the suburbs, underscoring the central area’s greater appeal. The expansion of construction land within ecological reserves has been effectively controlled, demonstrating the success of Hangzhou’s ecological management policies.
Distinct factors drive different types of land use expansion. Notably, night light contributes significantly to the expansion of cultivated land and construction land. The data on night light, closely associated with human activities [82,83], reveal that these land types have the highest land use intensity indices, highlighting the pivotal role of human activities in the urbanization process.

5.3. Identifying Changes in LUI to Support Sustainable Land Management

The process of urban expansion often coincides with an increase in LUI. Identifying changes in LUI can help pinpoint areas crucial for management and assist local governments in adjusting policies. Our study indicates that under the SSP126 scenario, the increase in LUI was considerably less than under the SSP245 and SSP585 scenarios. This suggests that the socioeconomic development paths and energy use structures effectively control the rate of increase in LUI. Consequently, Hangzhou could consider optimizing its economic development framework in the future, shifting from rapid to high-quality growth and encouraging the adoption of renewable energy sources to replace fossil fuels [34]. Moreover, dynamic monitoring and management of land use are also essential [84].
Regions with increased LUI are primarily located in the northeastern districts of Xihu, Xiaoshan, and Yuhang. These are key regions for land use planning and built environment management, where establishing ecological red lines and urban growth boundaries could control disorderly expansion [85,86]. In all scenarios, Xiacheng exhibits the highest average LUI, nearing a value of 4, indicating that almost the entire area is developed. In this district, because of the scarcity of available land, implementing green initiatives like rooftop gardens and vertical greening could help enhance the greenery [87,88,89]. Chun’an has the lowest average LUI and is among the areas in Hangzhou with the best ecological environments. It is crucial to continue strengthening ecological protection there and consider establishing ecological compensation policies to ensure the sustainability of the area [90].

5.4. Limitations and Prospects

This study forecasted LUCC under the impacts of climate change and rapid urbanization and identified changes in LUI to support sustainable land use management in Hangzhou. Despite yielding meaningful conclusions, this research still faces several limitations.
Firstly, in the setting of simulation scenarios, we selected only the three most representative scenarios—SSP126, SSP245, and SSP585 [91]. Future research could explore a broader range of development scenarios to conduct more detailed studies on LUCC in high-density urban areas, thereby better supporting sustainable land use planning [92]. It is essential to highlight that our future projections for LUCC presume that the driving factors across the subsystems of population, economy, climate, and land use will not qualitatively diverge from their historical patterns. This assumption of continuity could lead to substantial errors in forecasting, thereby greatly affecting our simulation results.
Secondly, the calculation of LUI in this study was based on a simplified LUI index. Future studies could consider integrating multi-source data such as urban inputs, urban outputs, and population metrics to identify LUI more precisely [93]. This approach would provide improved support for the precise management of the built environment.

6. Conclusions

This research developed an integrated SD-PLUS framework to model LUCC under SSP-RCP scenarios. This framework enhances our ability to capture the nonlinear, dynamic, and systemic nature of LUCC and to simulate future land use patterns more accurately. Tailored specifically for Hangzhou, this framework is also adaptable to other high-density urban areas globally, offering valuable insights for sustainable planning. The principal findings of this study include the following:
(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.
To better validate the applicability of our framework, further studies involving more high-density urban case studies are necessary. Additionally, research into optimizing the built environment in high-density urban areas at a more detailed scale is also essential.

Author Contributions

Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, S.Y.; formal analysis, S.Y.; investigation, S.Y., Y.L., H.J., X.W., Q.R., X.D., H.W. and A.D.; resources, S.Y.; data curation, S.Y.; writing—original draft preparation, S.Y.; writing—review and editing, S.Y.; visualization, S.Y.; supervision, Y.L.; project administration, S.Y.; funding acquisition, Y.L. All authors have read and agreed to the published version of this manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 51878593), and Zhejiang University (Grant No. Qiushifeiying20240053).

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data used in this study appear in Section 2.2 of this article.

Conflicts of Interest

Yonghua Li was employed by the Architectural Design and Research Institute of Zhejiang University Co., Ltd. Xiaohan Wang was employed by Zhejiang University Urban-Rural Planning & Design Institute Co., Ltd. The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Study area. (a) Hangzhou’s position in China and (b) digital elevation model (DEM) in Hangzhou.
Figure 1. Study area. (a) Hangzhou’s position in China and (b) digital elevation model (DEM) in Hangzhou.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. SD model causal feedback chart in Hangzhou.
Figure 3. SD model causal feedback chart in Hangzhou.
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Figure 4. Projections of land use demand in Hangzhou.
Figure 4. Projections of land use demand in Hangzhou.
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Figure 5. Future land use distribution patterns in Hangzhou.
Figure 5. Future land use distribution patterns in Hangzhou.
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Figure 6. Local land use distribution in 2050.
Figure 6. Local land use distribution in 2050.
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Figure 7. Spatial distribution of development potential of each land use type in Hangzhou.
Figure 7. Spatial distribution of development potential of each land use type in Hangzhou.
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Figure 8. Contributions of driving factors to the expansion of each land use type.
Figure 8. Contributions of driving factors to the expansion of each land use type.
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Figure 9. Spatial distribution of future LUI in Hangzhou.
Figure 9. Spatial distribution of future LUI in Hangzhou.
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Figure 10. Spatial distribution in future LUI changes in Hangzhou.
Figure 10. Spatial distribution in future LUI changes in Hangzhou.
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Table 1. Data information.
Table 1. Data information.
DataYearSource
Land use data2000, 2005, 2010, 2015, 2020https://www.resdc.cn
(accessed on 9 May 2024)
Historical GDP density2019
Historical population density2019
DEM2015https://www.gscloud.cn/
(accessed on 9 May 2024)
Annual average temperature2000–2020https://www.geodata.cn
(accessed on 9 May 2024)
Annual precipitation2000–2020
Night light data2020
Socioeconomic data2000–2020https://tjj.hangzhou.gov.cn/ and http://tjj.zj.gov.cn/
(accessed on 9 May 2024)
Road data2020https://www.openstreetmap.org
(accessed on 9 May 2024)
Future GDP density2020–2050[58]
Future population density2020–2050[59]
Future annual precipitation2020–2050https://worldclim.org/
(accessed on 9 May 2024)
Future annual average temperature2020–2050
Table 2. Main equations of the SD model.
Table 2. Main equations of the SD model.
Main EquationsUnit
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 × 10,000ten 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 × 10t
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 demandm2
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
Table 3. Driving factors in the PLUS model.
Table 3. Driving factors in the PLUS model.
CategoryDriving Factors
Socioeconomic factorsDistance to highway
Distance to main roads
Distance to railroad
GDP density
Night light
Population density
Climate factorsAnnual average temperature
Annual precipitation
Environmental factorsDEM
Slope
Table 4. Relative error test of the land use simulation data in Hangzhou.
Table 4. Relative error test of the land use simulation data in Hangzhou.
2005201020152020
Cultivated landActual value (m2)3,242,344,5003,197,394,0003,033,113,4002,903,287,500
Simulated value (m2)3,266,580,0003,178,470,0003,050,200,0002,901,610,000
Error rate 0.75%−0.59%0.56%−0.06%
ForestActual value (m2)11,489,677,20011,459,440,80011,459,448,90011,412,761,400
Simulated value (m2)11,491,500,00011,469,400,00011,456,200,00011,417,800,000
Error rate 0.02%0.09%−0.03%0.04%
GrasslandActual value (m2)382,710,600392,903,100389,673,000392,877,900
Simulated value (m2)386,699,000390,392,000392,301,000392,722,000
Error rate 1.04%−0.64%0.67%−0.04%
WaterActual value (m2)962,687,700885,378,600882,504,000881,885,700
Simulated value (m2)937,958,000904,677,000864,375,000867,438,000
Error rate −2.57%2.18%−2.05%−1.64%
Construction land Actual value (m2)774,451,800916,713,0001,087,271,1001,261,166,400
Simulated value (m2)775,904,000916,645,0001,086,000,0001,263,170,000
Error rate 0.19%−0.01%−0.12%0.16%
Unused landActual value (m2)6,183,0006,225,3006,044,4006,075,900
Simulated value (m2)5,863,5606,036,4505,719,4006,113,900
Error rate −5.17%−3.03%−5.38%0.63%
Table 5. Future land use configuration in Hangzhou.
Table 5. Future land use configuration in Hangzhou.
203020402050
SSP126SSP245SSP585SSP126SSP245SSP585SSP126SSP245SSP585
Cultivated landArea (km2)2849.632829.422778.322803.942784.012648.152796.072759.222553.05
Proportion16.90%16.79%16.48%16.63%16.52%15.71%16.59%16.37%15.14%
ForestArea (km2)11,419.2411,415.1411,407.2911,415.2111,411.6811,392.5611,416.7911,408.8811,382.77
Proportion67.74%67.71%67.67%67.72%67.69%67.58%67.72%67.68%67.52%
GrasslandArea (km2)393.79393.95394.35394.35394.36395.69394.19394.35396.36
Proportion2.34%2.34%2.34%2.34%2.34%2.35%2.34%2.34%2.35%
WaterArea (km2)857.41856.84853.22849.72850.59836.75848.96850.09826.97
Proportion5.09%5.08%5.06%5.04%5.05%4.96%5.04%5.04%4.91%
Construction landArea (km2)1332.151356.881419.121389.211411.831579.601396.491439.961693.92
Proportion7.90%8.05%8.42%8.24%8.37%9.37%8.28%8.54%10.05%
Unused landArea (km2)5.825.825.755.635.595.315.565.544.99
Proportion0.03%0.03%0.03%0.03%0.03%0.03%0.03%0.03%0.03%
Total landArea (km2)16,858.0416,858.0416,858.0416,858.0416,858.0416,858.0416,858.0416,858.0416,858.04
Proportion100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%
Table 6. Future average LUI of each district in Hangzhou.
Table 6. Future average LUI of each district in Hangzhou.
203020402050
SSP126SSP245SSP585SSP126SSP245SSP585SSP126SSP245SSP585
Gongshu3.58813.60153.62893.61033.62603.66233.61343.63493.6784
Shangcheng3.50313.42593.43133.50833.46403.52913.50943.46763.5329
Xiacheng3.95863.96263.96903.96653.96833.97143.96713.96953.9746
Jianggan3.46593.47633.49083.47713.49023.52883.47863.49503.5412
Xihu2.81162.81542.83122.82322.82932.87132.82522.83662.8924
Binjiang3.49923.47153.48323.50473.48483.52433.50563.48773.5312
Xiaoshan2.91852.92422.94032.93072.93802.99062.93302.94603.0271
Yuhang2.82412.82922.84152.83382.83962.86642.83552.84512.8831
Fuyang2.34552.34792.35352.34842.35252.36582.34882.35472.3745
Linan2.16582.16672.16832.16622.16782.17152.16622.16832.1733
Tonglu2.21972.22122.22292.22032.22262.22632.22042.22312.2279
Chun’an2.08822.08792.08852.08822.08832.09022.08822.08852.0907
Jiande2.17122.17382.17552.17142.17612.18202.17152.17662.1846
Hangzhou2.32672.32942.32992.32852.33232.33412.33282.34422.3510
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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

AMA Style

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

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Yao, 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

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