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Article

Spatiotemporal Variation in Carbon Storage in the Central Plains Urban Agglomeration Under Multi-Scenario Simulations

School of Geoscience & Technology, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(8), 1594; https://doi.org/10.3390/land14081594
Submission received: 7 July 2025 / Revised: 25 July 2025 / Accepted: 31 July 2025 / Published: 5 August 2025
(This article belongs to the Special Issue Integration of Remote Sensing and GIS for Land Use Change Assessment)

Abstract

Understanding changes in land use structures under multiple scenarios and their impacts on carbon storage is essential for revealing the evolution of regional development patterns and the underlying mechanisms of carbon cycle dynamics. This study adopted an integrated PLUS-InVEST modeling framework to analyze and predict changes in carbon storage in the Central Plains Urban Agglomeration (CPUA) under different scenarios for the years 2030 and 2060. The results showed the following: (1) From 2000 to 2020, the areas of forest land, water bodies, and construction land expanded, while the areas of cropland, grassland, and barren land decreased. Over this 20-year period, carbon storage showed a declining trend, decreasing from 2390.07 × 106 t in 2000 to 2372.19 × 106 t in 2020. (2) In both 2030 and 2060, cropland remained the primary land use type in the CPUA. Overall, carbon storage in the CPUA was higher in the southwestern area and decreased in the central and eastern parts, which was mainly related to the land use distribution pattern in the CPUA. (3) Carbon storage under the EP (ecological protection) and CP (cropland protection) scenarios was significantly higher than under the other two scenarios, and in 2030, carbon storage under the CP and EP scenarios exceeded that in 2020, while the UD (urban development) scenario had the lowest total carbon storage. This indicated that the expansion of construction land was detrimental to carbon storage enhancement, underscoring the importance of implementing ecological protection strategies. In summary, the results of this study quantitatively reflected the changes in carbon storage in the CPUA under different future development scenarios, providing a reference for formulating regional development strategies.

1. Introduction

As global climate change intensifies, the pivotal role of the carbon cycle in maintaining ecosystem functions and regulating the climate has become increasingly prominent. Terrestrial ecosystems, as major components of the global carbon cycle [1], store large amounts of vegetation carbon and soil organic carbon, and play a key role in regulating atmospheric CO2 concentrations and mitigating the greenhouse effect [2,3,4]. Land use change affects the ability of vegetation and soil within ecosystems to sequester carbon [5,6], thereby affecting the spatiotemporal distribution of carbon storage in terrestrial ecosystems [7]. At present, optimizing land use structure is considered a cost-effective approach to carbon sequestration [8], as it not only enhances local carbon sequestration capacity, but also enables the provision of ecological compensation to other high-emission regions. Therefore, quantitatively assessing and simulating regional land use and land cover change and ecosystem carbon storage is of great practical significance for mitigating global climate change and achieving the “dual carbon” strategic goals [9].
At present, most studies that assess and predict regional carbon storage rely on future land use conditions. Land use simulation serves as a critical tool for studying and predicting land use changes during urban development. It helps decision makers understand urbanization processes and enables rational resource allocation and management. The CLUE-S model focuses on land use demand and spatial allocation mechanisms, making it suitable for policy response simulations [10]. The CA-Markov model, which integrates the Markov chain and cellular automata, is simple to implement and well suited for short- to medium-term projections [11]; however, it suffers from low computational efficiency when applied to high-resolution data and large-scale areas [12]. The SLEUTH model [13] is primarily used for forecasting and analyzing urban expansion; however, its calibration process is time-consuming and computationally intensive. The SLEUTH-Suitability model [14] improved the calibration accuracy compared to the original SLEUTH. The FLUS model, which incorporates adaptive artificial neural networks, demonstrates strong multi-scenario simulation capabilities and supports spatial planning decisions [15]. The patch-generating land use simulation (PLUS) model [16,17], which combines machine learning with patch-generation mechanisms, can produce irregular land use patches that closely resemble real-world patterns [18]. It is particularly effective in simulating complex spatial patterns in urban fringe zones and ecological protection areas [19].
Carbon storage can be measured and estimated using two broad types of methods. Firstly, it involves field-based estimation using empirical models, in which plot surveys are conducted to collect data such as tree height, diameter at breast height, and biomass, which are then combined with carbon density coefficients to estimate carbon storage per unit area [20]. This method is highly accurate and suitable for small-scale studies, but is limited by high labor intensity and poor spatial representativeness. The second category includes remote-sensing and geoscience-based models for regional-scale carbon storage simulations, such as the InVEST model [21] and CASA [22]. These models use land use data, vegetation indices (such as NDVI), and climatic variables as inputs to achieve spatial and dynamic simulations of carbon storage across large temporal and spatial scales [23,24]. Among these, the InVEST model, which requires fewer parameters, is easy to operate, can be integrated with land use simulation models [25,26], and has been widely applied in regional carbon storage simulations and scenario analyses [27]. Nyamari et al. [28] assessed carbon storage under different development scenarios in Kenya by combining the InVEST model with the random forest algorithm. Ren et al. [29] investigated carbon sequestration capacity in Middle Eastern countries under SSP scenarios using the integrated PLUS and InVEST models. Combining the PLUS and InVEST models revealed not only the spatiotemporal distribution of carbon storage [30] but also supported multi-scenario analyses [31], providing a scientific basis for land use management and decision-making [32].
Research on the Central Plains Urban Agglomeration (CPUA) primarily focuses on urban land expansion characteristics [33], landscape patterns [34], and ecological and environmental effects [35]. Some studies have explored the effects of urban spatial structures on carbon emissions [36]. However, studies that integrate land use and carbon storage changes under different scenarios remain relatively limited. Therefore, this study aimed to simulate land use changes in the CPUA under various scenarios and to quantitatively assess the overall changes in its carbon storage. Based on observed land use changes in the CPUA from 2000 to 2020, the PLUS model simulated future land use patterns for 2030 and 2060 across different scenarios. Subsequently, the InVEST model was employed to assess carbon storage and its spatiotemporal dynamics under each scenario. The ecological background of the CPUA is fragile, and its ecosystem services have significantly degraded. This study aims to provide effective decision-making support for the future sustainable development of the CPUA. Specifically, we addressed the following key questions: (1) How has land use changed in the CPUA over the past two decades? (2) How will land use change across various future development scenarios? (3) How will these changes affect regional carbon storage?

2. Materials and Methods

2.1. Study Area

The CPUA is located in central China and covers 30 cities across five provinces (Henan, Shanxi, Hebei, Shandong, and Anhui). As a key region in implementing China’s central revitalization strategy, it plays a vital role in national development planning. The core area centers on Zhengzhou in Henan Province and extends to neighboring cities such as Luoyang, Kaifeng, Xinxiang, Jiaozuo, and Xuchang. Well-developed transportation infrastructure connects these cities, facilitating effective regional cooperation and coordinated development. The location of the CPUA is shown in Figure 1.

2.2. Data Sources

This study used land use data from 2000, 2010, and 2020 [37] along with basic geographic information and socioeconomic data (Table 1). All the datasets were resampled to 100 m spatial resolution and projected to WGS 1984 UTM Zone 50N to ensure the stability of the model. Distances to features, such as rivers, roads, and urban centers, were calculated using the Euclidean Distance tool in ArcGIS 10.8.

2.3. Methods

2.3.1. Land Use and Land Cover Change

Within a specific time interval, the land use dynamic degree model quantifies the rate at which land use quantities change in a study area. The single land use dynamic degree specifically measures the areal change rate of individual land use types within a region during the same period.
K = U j U i U j 1 T 100 %
K reflects the dynamic degree of a specific land use type over the study period. U i and U j represent the area at the beginning and end of this period, respectively, and T indicates its duration. A positive K value (K > 0) indicates an increase in the area of the land use type over time.
The land use transition matrix is a two-dimensional matrix derived from the changing relationships of land-cover conditions at different times within the same area, reflecting the conversion between different land use types [38]. Numerous scholars have utilized this model to analyze land use changes, where B i j represents the area of land use type i that transitions to land use type j, and n denotes the number of land use types.
B i j = B 11 B 12 B 1 n B 21 B 22 B 2 n B n 1 B n 1 B n n

2.3.2. Multi-Scenario Simulation of Urban Expansion

Traditional cellular automata (CA) models often struggle to simulate the synchronous evolution of land use patches and fail to capture complex land conversion rules. To address these limitations, the PLUS model integrates CA with a random forest algorithm and an adaptive inertia competition mechanism. This integration improves the model’s ability to identify land use drivers and enhances patch-scale simulation accuracy. The PLUS model first extracts the expanded land parcels between the two periods. Subsequently, development probabilities for each land category are estimated using a random forest method, after which a CA module with varied random patch seeds is used to simulate future landscape evolution.
First, based on the relevant existing studies [39,40], the practical conditions of the CPUA, and the availability of data, 12 driving factors were selected from three aspects: basic geographic information, socioeconomic elements, and transportation accessibility. These include elevation, slope, soil type, GDP, population, distance to primary roads, distance to secondary roads, distance to tertiary roads, distance to railways, distance to highways, distance to city center, and distance to water bodies. All the datasets were converted to raster format and unified to match the projection coordinate system and spatial resolution of the land cover data. Second, the Land Expansion Analysis Strategy (LEAS) module of the PLUS model was used to calculate the development probabilities of different land use types in the study area. Finally, by integrating the target cell counts for each land type, the transition cost matrix, and the probabilities for generating patch seeds, and neighborhood factors, a land use change simulation was conducted using the multi-type random patch seed-based CA model embedded in the PLUS framework.
(1)
Neighborhood Factor Weights: The neighborhood factor describes the interactions among different land use types and land units within a defined neighborhood. Their weights range from zero to one. A higher weight indicates that the land use type is more resistant to transformation and possesses a stronger capacity for expansion. In contrast, a lower weight suggests that the land use type is more likely to be converted or replaced by other land use categories.
w i = T A i T A m i n T A m a x T A m i n
where T A i is the expansion area change of land use type i, and T A m i n and T A m a x represent the minimum and maximum expansion area changes among all land use types, respectively [41].
This study utilized land use data from 2010 and 2020 and obtained the neighborhood factor weights as shown in Table 2 based on the results of the Markov chain analysis. In the weight calculation, setting the weight of cropland to 0 was deemed inconsistent with practical conditions; therefore, the weight for cropland was adjusted to 0.1.
(2)
Scenario Setting: Using land use data from 2000, 2010, and 2020 for the CPUA, land use in 2030 and 2060 was simulated under multiple scenarios, including natural development, ecological protection, cropland protection, and urban development. The Markov chain model was applied to constrain land use quantities by controlling the transition probabilities between categories, enabling the projection of land use distributions under different scenarios [42].
Natural development scenario: The NDs assume that the urban agglomeration maintains the land use change dynamics identified during 2000–2020, reflecting current urbanization trends. This scenario does not impose any restrictions on land use transitions or consider government or market interventions, serving as the baseline for simulating land use changes without additional constraints.
Ecological protection scenario: EPs aim to maximize the ecological benefits provided by various land use types. High population density and economic activities in the CPUA have already caused significant existing and potential ecological damage. This scenario emphasizes ecological protection as a priority for further optimizing the development of the region. Based on previous studies and actual conditions [43], this scenario reduces the transition probability from forestland and grassland to construction land by 50% and from cropland to construction land by 30%, and increases cropland-to-forest/grassland probability by 30%. Additionally, it treats regional water bodies as a constraint and prohibits any land use change.
Cropland protection scenario: The quantity and quality of prime cropland are crucial for national food security, and the CPUA is an important grain-producing region in China. Therefore, land use change simulations must incorporate cropland protection into the baseline scenario. Protecting croplands in urban agglomerations requires strict control of the conversion of prime cropland to other land use types to prevent its loss during urbanization, thereby maintaining the total area of prime cropland. In the scenario setting, the probability of transitioning from cropland to construction land was reduced by 60%, thereby ensuring strict cropland protection.
Urban development scenario: Rapid economic growth drives continuous urbanization. In the UD scenario, the transition probabilities from cropland, forest land, and grassland to construction land were reduced by 20%, whereas the transition probabilities from construction land to other land types, excluding cropland, were reduced by 30%.
For each development scenario, a cost matrix was established to represent the transitions between different land use types, where 1 indicates allowed conversion and 0 indicates prohibited conversion. In Table 3, A, B, C, D, E, and F represent the cropland, forest, grass, water, barren land, and construction land, respectively.
(3)
Driving factor settings: For future simulations, this study employed the LEAS and multi-type random patch seed (CARS) method, driven by a CA-Markov model, to generate the probability matrix of future land use demand. The LEAS analyzes historical land expansion trends and transforms them into predictive demands under different future scenarios. The CARS method simulates spatial patterns among different land use types by randomly generating seed patches.
The key driving factors incorporated into the model included the digital elevation model (DEM), slope, soil type, population, GDP, distance to expressways, distance to city centers, and distance to major roads (including primary, secondary, and tertiary roads). Figure 2 illustrates that all the raster datasets were standardized to a consistent spatial resolution and coordinate system.

2.3.3. Carbon Storage Calculation

InVEST, developed by Stanford University, the World Wildlife Fund, and other organizations, is an open-source modeling tool designed to quantify the value of natural capital and ecosystem services. This study utilized the carbon storage module of the InVEST model to estimate carbon storage in the CPUA across different time periods. The carbon storage was calculated using the following formula:
C t o t a l = C a b o v e + C b e l o w + C s i o l + C d e a d  
where C t o t a l represents the total carbon storage, and C a b o v e , C b e l o w , C s i o l , and C d e a d denote the carbon storage in the aboveground biomass, belowground biomass, soil, and dead organic matter, respectively.
Direct field measurements of carbon density in the CPUA are challenging and difficult to carry out. Therefore, the existing carbon density data were used for estimation and subsequently adjusted based on the specific climate conditions, soil properties, and land use types within the study area. Previous studies, both domestic and international, have demonstrated that aboveground and belowground biomass carbon densities are positively correlated with annual precipitation and mean annual temperature. Soil organic carbon has also been shown to be positively correlated with annual precipitation [44,45,46]. However, because the existing literature lacks a quantitative link between soil carbon density and mean annual temperature, only the relationship with precipitation was adjusted.
C S P = 3.3968 × P + 3996.1
C B P = 6.7981 e 0.00541 P
C B T = 28 × T + 398
In the formula, C S P represents the soil carbon density estimated based on precipitation, while C B P and C B T denote the biomass carbon densities estimated based on annual precipitation (mm) and mean annual temperature (°C), respectively. Here, P refers to mean annual precipitation, and T refers to mean annual temperature. According to climate data, the mean annual temperatures of China and the CPUA are 9.7 °C and 14.74 °C, respectively, with corresponding mean annual precipitations of 651 mm and 738 mm. By substituting these temperature and precipitation values for China and the CPUA into the above formulas, their respective soil carbon densities and biomass carbon densities were obtained. The ratio of these two values was then used as the correction factor.
K S = C S P C S P
K B P = C B P C B P
K B T = C B T C B T
K B = K B P × K B T
In the formula, C S P and C S P represent the soil carbon densities estimated based on precipitation for the CPUA and China, respectively. C B P and C B P represent the biomass carbon densities estimated based on precipitation for the CPUA and for China, respectively. C B T and C B T denote the biomass carbon densities estimated based on the mean annual temperature for the CPUA and China, respectively. K B and K S refer to the correction factors for biomass carbon density and soil carbon density, respectively, derived based on mean annual temperature and precipitation. The corrected carbon density is shown in Table 4.

3. Results

3.1. Land Use Change in the Central Plains Urban Agglomeration from 2000 to 2020

As shown in Figure 3, cropland was the dominant land use type in the CPUA, followed by forestland. Spatially, cropland is primarily located in the central and eastern areas of the region, and forests and grasses are mainly distributed in the western and southern high-altitude areas. Construction land was concentrated around urban centers and showed a noticeable increase from 2000 to 2020.
From 2000 to 2020, the areas of cropland, grassland, and barren land declined, whereas those of forestland, water bodies, and construction land expanded (Table 5). Cropland decreased by 13,690.66 km2 over the two decades, with relatively stable rates of change between the two intervals. The grassland area also declined, with a significant reduction of 4476.92 km2 between 2010 and 2020, and a dynamic rate of –2.4%. Forestland increased by 4606.54 km2 with dynamic rates of 0.64% and 0.50%, respectively. Driven by rapid economic growth and accelerated urbanization, construction land has expanded by 12,704.21 km2 from 2000 to 2020, with dynamic rates of 1.90% and 2.04%, respectively.
Based on the land use transition matrix, a Sankey diagram was generated to illustrate land use changes in the CPUA from 2000 to 2020, as shown in Figure 4. In the diagram, thicker lines represent larger areas of land conversion between types. During this period, the dominant pattern showed increasing construction land and a persistent reduction in cropland.
From 2000 to 2010, cropland was predominantly converted to construction land, totaling 7921.87 km2, followed by 2674.27 km2 converted to forestland. From 2010 to 2020, croplands continued to account for the most significant transitions, with 10,383.97 km2 converted into construction land and 2558.01 km2 converted into forestland. This trend reflects the rapid urbanization of the region and highlights the implementation of China’s “Grain for Green” reforestation policy [47].
Based on the LEAS module of the PLUS model, land use expansion maps for the study area were generated using the land expansion module. By incorporating these expansion maps along with various driving factors into the LEAS module, probability maps showing each land use type’s development potential and the impact of different driving factors were produced.
Due to the low responsiveness of barren land to various driving factors in land use change, barren land types were excluded from the analysis in this study. As shown in Figure 5, land use changes exhibit complex spatiotemporal dynamics and are driven by multiple factors. For croplands, DEM, slope, and distance to primary roads were the most influential factors, which may be attributed to the environmental requirements for crop growth and the intensity of human activities. For forestland, the DEM also plays a key role in its distribution, as forest expansion tends to occur in higher elevation areas farther from urban centers, where conditions are more favorable for forest growth. In the case of construction land, the most significant factors were the distance to roads and the DEM. As construction land is closely associated with human activities, roads serve as essential links that connect regions, stimulate economic development, and facilitate resource flow.

3.2. Spatiotemporal Evolution of Land Use Patterns in the CPUA

The land expansion atlas for various land use types was input into the CARS module, along with the calculated neighborhood factor weights, defined transition matrix, and land use demand derived from the Markov chain. The simulation generated a land use map of the CPUA for 2020. Model validation was performed by comparing the simulated land use in 2020 with the actual land use patterns. The PLUS model achieved a kappa coefficient of 0.80 (>0.75) and an overall accuracy of 0.89, indicating a high level of simulation accuracy. Therefore, the model is suitable for simulating land use distribution patterns for 2030 and 2060.
The predicted land use distribution maps for 2030 and 2060 (Figure 6) show that the overall land use pattern in the CPUA remains relatively stable. Construction land continues to be concentrated in the main urban areas of the cities, with Zhengzhou at the core. Cropland remains the dominant land use type across the area. The spatial distributions of forestland and grassland showed little change; they are predominantly concentrated in the mountainous and hilly regions of the western and southern agglomeration.
Based on the current land use proportions in 2020 and the projected scenarios for 2030 and 2060 (Table 6), the general land use structure in the CPUA remains relatively stable. Cropland, forest land, and construction land continue to dominate, whereas water bodies and barren land account for smaller proportions. Among the six land use types, the proportion of cropland showed a continuous decline, whereas forest land, water bodies, and barren land remained relatively constant. The share of construction land increased steadily over time, reflecting an ongoing urban development trend. Compared with the natural development scenario, both the ecological protection and cropland protection scenarios result in lower proportions of construction land by 2030 and 2060. Forest land and grassland covered larger areas under the ecological protection scenario compared to the others.

3.3. Carbon Storage Estimation in the Central Plains Urban Agglomeration

The InVEST model estimated carbon storage in the CPUA by analyzing land use data from 2000, 2010, and 2020, together with projected scenarios for 2030 and 2060. As shown in Figure 7, carbon storage in the CPUA showed a declining trend from 2000 to 2020. The total carbon storage was 2390.07 × 106 t in 2000. Compared to 2000, carbon storage decreased by 1.11 × 106 t in 2010 and further decreased by 16.77 × 106 t in 2020, reaching a total of 2372.19 × 106 t. This decline was mainly due to the large-scale change in cropland to construction land during 2000–2020, as construction land had very limited carbon sequestration capacity. In particular, from 2010 to 2020, fast economic growth produced extensive encroachment of construction land on cropland, resulting in a faster rate of carbon storage decline compared to 2000–2010.
The simulation for 2030 indicated that carbon storage under the CP and EP scenarios increased compared to 2020, with a greater increase under the CP scenario (an increase of 17.41 × 106 t) and an increase of 11.78 × 106 t under the EP scenario. In contrast, under the ND and UD scenarios, carbon storage showed a decline in 2030, with a decrease of 28.57 × 106 t under the UD scenario, resulting in a total of 2343.62 × 106 t. By 2060, carbon storage under all four scenarios was generally lower than in 2030. Consistently, the CP and EP scenarios maintained higher carbon storage than the other two scenarios, with carbon storage under the EP scenario reaching 2338.62 × 106 t, which was 49.52 × 106 t higher than that under the UD scenario.
Changes in carbon storage were mainly driven by land use transitions. The simulation results suggested that due to the low carbon density of construction land, the conversion of cropland, forest, and grassland to construction land led to an overall decline in carbon storage in the CPUA. Controlling the expansion of construction land and protecting cropland, as well as forest land with the highest carbon sequestration capacity, would be essential for increasing carbon storage.
Carbon storage estimated by the InVEST model is derived from the existing land use data, resulting in a spatial distribution pattern that closely reflects land use characteristics. As illustrated in Figure 8, the spatial distribution maps of carbon storage for 2000, 2010, and 2020 are presented, along with the forecasted maps for 2030 and 2060. Areas with dense forest cover exhibit higher levels of carbon storage. Carbon storage in the CPUA exhibits spatial variation, featuring elevated levels in the west and south and reduced amounts in the east and center. Among the cities in the CPUA, Nanyang City consistently maintained a relatively high level of carbon storage, mainly because it had a large proportion of forest land and a small fraction of construction land, which sustained a high carbon sequestration capacity. In contrast, cities such as Hebi, Huaibei, Luohe, and Xuchang had lower carbon storage values. On the one hand, this was due to their smaller land areas, and on the other hand, it was because cropland and construction land represented a large fraction of their land use, resulting in weaker carbon sequestration capacity.

4. Discussion

This study employed the PLUS model, which integrates the LEAS and CARS modules to generate land development probabilities and simulate the formation of land patches under these constraints, thereby significantly improving the simulation accuracy. Moreover, the PLUS model introduces an innovative approach to identify land expansion areas and incorporates a multi-seed growth mechanism to simulate land use dynamics more realistically. Compared to traditional land use simulation models, such as CLUE-S, CA-Markov, and FLUS, the PLUS model demonstrates clear advantages in both modeling accuracy and representation of process mechanisms [48].
According to the study, the land use pattern in the CPUA remained largely consistent over the period from 2000 to 2020. As key grain-producing areas, croplands have consistently been the dominant land use type. Based on the coupled PLUS-InVEST model, this study simulated land use change and carbon storage under different development scenarios for the key dual carbon target years of 2030 and 2060. The results show that cropland continues to dominate the land use structure in both 2030 and 2060, followed by forest land and construction land. From 2000 to 2020, carbon storage in the CPUA showed a continuous declining trend, indicating that urban expansion during periods of economic development led to reduced cropland and green space coverage. In 2030, carbon storage under the EP and CP scenarios increased, which suggested that restricting the increase in construction land contributed to enhanced carbon storage and regional carbon balance. Carbon storage under the CP scenario was slightly higher than that under the EP scenario because cropland accounted for a large proportion of land use in the CPUA, and the greater retention of cropland offset some of the carbon loss caused by its conversion to construction land. Although forest land had a higher carbon density than cropland, the short-term increase in forest area was limited, resulting in a smaller increase in carbon storage under the EP scenario compared to CP. By 2060, carbon storage under the EP scenario surpassed that under the CP scenario, indicating that from a long-term perspective, ecological protection policies better supported the improvement of regional carbon sequestration capacity. Under the UD scenario, carbon storage decreased due to the large-scale conversion of other land use types to construction land. Without effective control, this would hinder increases in carbon storage and obstruct the achievement of dual carbon goals.
This study has several limitations. Regarding data usage, the carbon density data did not distinguish between different vegetation types within the same land use category, despite their varying carbon storage capacities. For example, within forestlands, evergreen broadleaf forests typically have higher carbon densities than evergreen coniferous forests [49]. Future studies could enhance the precision of carbon storage estimates by adjusting carbon density values according to vegetation type. Additionally, although four scenarios—ND, CP, EP, and UD—were designed to ensure a comprehensive analysis, some parameters in the PLUS model, including the transition cost matrix, were established using empirical values. Because of challenges in acquiring data on nature reserves, only water bodies were designated as restricted development zones for land use prediction, which was not sufficiently comprehensive. Incorporating national and regional policy frameworks into future research is essential for enhancing the reliability and accuracy of scenario settings.

5. Conclusions

This study aimed to explore the spatiotemporal dynamics of land use and carbon storage in the CPUA under multiple development scenarios to better understand how different land use policies may influence regional carbon sequestration and support the achievement of dual carbon goals. The key findings are the following:
(1)
From 2000 to 2020, croplands remained the dominant land use type in the CPUA. During this 20-year period, forest land, water bodies, and construction land expanded, whereas croplands, grasslands, and barren land declined. Notably, construction land increased significantly by 12,704.21 km2, whereas croplands decreased by 13,690.66 km2.
(2)
According to the simulation results based on the PLUS model, cropland will continue to be the main land use type in 2030 and 2060. Under the UD scenario, construction land expanded significantly, accounting for 17.56% and 22.05% in 2030 and 2060, respectively. Compared with the ND scenario, both the EP and CP scenarios resulted in lower proportions of construction land.
(3)
From 2000 to 2020, carbon storage in the CPUA showed a declining trend, with a relatively stable spatial pattern. In both 2030 and 2060, carbon storage was higher under the CP and EP scenarios. Specifically, under the CP scenario in 2030, carbon storage increased by 17.41 × 106 t compared to 2020. In 2060, carbon storage under the EP scenario was the highest, exceeding that under the UD scenario by 49.52 × 106 t. The implementation of ecological protection and cropland protection policies can effectively enhance regional carbon sequestration capacity.
This research establishes a scientific basis for the CPUA’s future land use planning and sustainable development. However, there are still some limitations inherent in the data and models used. In the future, the adoption of more accurate data and more rigorous models may enhance the objectivity of such research.

Author Contributions

Conceptualization, J.W. and C.Z.; methodology, J.W. and C.Z.; validation, C.Z.; formal analysis, C.Z.; investigation, C.Z.; data curation, C.Z.; writing—original draft preparation, C.Z. and J.W.; writing—review and editing, C.Z., J.W., Z.S. and X.C.; visualization, C.Z.; supervision, J.W., C.Z., Z.S. and X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data can be found in Section 2.1.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CPUACentral Plains Urban Agglomeration
NDNatural Development
EPEcological Protection
CPCropland Protection
UDUrban Development
CACellular Automata
LEASLand Expansion Analysis Strategy
DEMDigital Elevation Model

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Figure 1. Location study area: Central Plains Urban Agglomeration(CPUA).
Figure 1. Location study area: Central Plains Urban Agglomeration(CPUA).
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Figure 2. The driving factors influencing land use and land cover change: (a) digital elevation model; (b) slope; (c) soil types; (d) GDP; (e) population; (f) distance to primary roads; (g) distance to secondary roads; (h) distance to tertiary roads; (i) distance to railways; (j) distance to highways; (k) distance to city center; (l) distance to water bodies.
Figure 2. The driving factors influencing land use and land cover change: (a) digital elevation model; (b) slope; (c) soil types; (d) GDP; (e) population; (f) distance to primary roads; (g) distance to secondary roads; (h) distance to tertiary roads; (i) distance to railways; (j) distance to highways; (k) distance to city center; (l) distance to water bodies.
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Figure 3. Land use in the CPUA from 2000 to 2020 ((a) 2000; (b) in 2010; (c) 2020).
Figure 3. Land use in the CPUA from 2000 to 2020 ((a) 2000; (b) in 2010; (c) 2020).
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Figure 4. Sankey diagram of land use change from 2000 to 2020.
Figure 4. Sankey diagram of land use change from 2000 to 2020.
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Figure 5. Contribution of driving factors to land use change (X1 to X12 represent the following variables: digital elevation model, slope, soil type, GDP, population, distance to primary roads, distance to secondary roads, distance to tertiary roads, distance to railways, distance to expressways, distance to city centers, and distance to water bodies.).
Figure 5. Contribution of driving factors to land use change (X1 to X12 represent the following variables: digital elevation model, slope, soil type, GDP, population, distance to primary roads, distance to secondary roads, distance to tertiary roads, distance to railways, distance to expressways, distance to city centers, and distance to water bodies.).
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Figure 6. 2030 and 2060 land use scenarios in the Central Plains urban agglomeration (NDS—natural development scenario; CPS—cropland protection scenario; EPS—ecological protection scenario; UDS—urban development scenario).
Figure 6. 2030 and 2060 land use scenarios in the Central Plains urban agglomeration (NDS—natural development scenario; CPS—cropland protection scenario; EPS—ecological protection scenario; UDS—urban development scenario).
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Figure 7. Carbon storage in the CPUA under different scenarios from 2000 to 2060.
Figure 7. Carbon storage in the CPUA under different scenarios from 2000 to 2060.
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Figure 8. Spatial distribution of carbon storage in the Central Plains urban agglomeration from 2000 to 2060.
Figure 8. Spatial distribution of carbon storage in the Central Plains urban agglomeration from 2000 to 2060.
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Table 1. Data types and data sources.
Table 1. Data types and data sources.
Data TypeAttributeSpatial ResolutionYearsSource
LUCC data 30 m2000, 2010, 2020https://zenodo.org/, accessd on 3 January 2025
Basic geographic information DEM30 m2020https://www.gscloud.cn/, accessd on 3 January 2025
Soil types1 km2020https://www.resdc.cn, accessd on 3 January 2025
River distribution1 km2020https://www.webmap.cn, accessd on 10 January 2025
Highway distribution1 km2020
Railway distribution1 km2020
Average annual temperature1 km2000–2020http://www.geodata.cn/, accessed on 10 January 2025
Annual average precipitation1 km2000–2020
Social and economicPopulation1 km2020https://www.resdc.cn, accessd on 10 January 2025
GDP1 km2020
City center 2020https://map.baidu.com/, accessd on 10 January 2025
Table 2. Weights of neighborhood factors.
Table 2. Weights of neighborhood factors.
Land Use TypeCroplandForestGrasslandWaterBarrenConstruction
weight0.10.6370.2940.4990.4861
Table 4. Carbon intensity of land use types (t/hm2).
Table 4. Carbon intensity of land use types (t/hm2).
TypesC_AboveC_BelowC_SiolC_DeadC_Total
Cropland3.880.9762.860.3968.09
Forest96.9223.2683.819.69213.69
Grassland3.8811.6378.570.9795.05
Water0.58031.43032.01
Barren2.52010.48013.00
Construction4.8508.38013.23
Table 3. Cost matrix settings.
Table 3. Cost matrix settings.
NDCPEPUD
ABCDEFABCDEFABCDEFABCDEF
A111111100000111111111111
B111111111011010100111011
C111111111111011100111111
D111111100100000100000101
E111111111111111111111111
F000001000001000001000001
Table 5. Land use area and dynamic degree in the Central Plains urban agglomeration.
Table 5. Land use area and dynamic degree in the Central Plains urban agglomeration.
Land Use TypeArea/km2K/%
2000201020202000~20102010~2020
Cropland201,989.28195,057.05188,298.62−0.34−0.35
Forest39,549.2942,067.8944,155.830.640.50
Grass12,918.9611,111.98442.04−1.40−2.40
Water2411.23065.923248.642.720.60
Barren13.777.694.55−4.42−4.08
Barren29,404.2634,976.642,108.471.902.04
Table 6. Land use proportions by scenario (%).
Table 6. Land use proportions by scenario (%).
CroplandForestGrasslandWaterBarrenConstruction
202065.7815.432.951.130.001614.71
2030ND63.7015.912.381.190.001416.82
CP65.7615.942.391.200.001414.71
EP64.3916.182.501.200.001415.74
UD63.0215.892.371.160.001317.56
2060ND59.0616.581.671.340.001321.35
CP60.9416.611.681.350.001419.43
EP59.6816.611.771.340.001420.37
UD58.4316.561.661.290.001222.05
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Wang, J.; Zhao, C.; Shi, Z.; Cheng, X. Spatiotemporal Variation in Carbon Storage in the Central Plains Urban Agglomeration Under Multi-Scenario Simulations. Land 2025, 14, 1594. https://doi.org/10.3390/land14081594

AMA Style

Wang J, Zhao C, Shi Z, Cheng X. Spatiotemporal Variation in Carbon Storage in the Central Plains Urban Agglomeration Under Multi-Scenario Simulations. Land. 2025; 14(8):1594. https://doi.org/10.3390/land14081594

Chicago/Turabian Style

Wang, Jinxin, Chengyu Zhao, Zhiyi Shi, and Xiangkai Cheng. 2025. "Spatiotemporal Variation in Carbon Storage in the Central Plains Urban Agglomeration Under Multi-Scenario Simulations" Land 14, no. 8: 1594. https://doi.org/10.3390/land14081594

APA Style

Wang, J., Zhao, C., Shi, Z., & Cheng, X. (2025). Spatiotemporal Variation in Carbon Storage in the Central Plains Urban Agglomeration Under Multi-Scenario Simulations. Land, 14(8), 1594. https://doi.org/10.3390/land14081594

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