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Article

Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China

by
Jun Shao
1,*,
Yuxian Wang
1,
Mingdong Tang
1,* and
Xinran Hu
2
1
School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430081, China
2
Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(16), 6736; https://doi.org/10.3390/su16166736
Submission received: 22 June 2024 / Revised: 2 August 2024 / Accepted: 4 August 2024 / Published: 6 August 2024
Browse Figures
Figure 1
<p>Study area.</p> "> Figure 2
<p>Research framework.</p> "> Figure 3
<p>Land use transfer chord map from 2000 to 2020.</p> "> Figure 4
<p>Land use transfer sankey map from 2000 to 2020.</p> "> Figure 5
<p>Land use status from 2000 to 2020.</p> "> Figure 5 Cont.
<p>Land use status from 2000 to 2020.</p> "> Figure 6
<p>Expansion probability of each land use type.</p> "> Figure 7
<p>Land use status under three scenarios.</p> "> Figure 8
<p>Variations in carbon storage and geo-averaged carbon density of terrestrial systems from 2000 to 2020.</p> "> Figure 9
<p>Spatial distribution of carbon storage from 2000 to 2020.</p> "> Figure 10
<p>Carbon stock changes from 2000 to 2020.</p> "> Figure 10 Cont.
<p>Carbon stock changes from 2000 to 2020.</p> "> Figure 11
<p>The change in carbon storage and carbon sink economy caused by land use type conversion from 2000 to 2020.</p> "> Figure 11 Cont.
<p>The change in carbon storage and carbon sink economy caused by land use type conversion from 2000 to 2020.</p> "> Figure 12
<p>Spatial distribution of carbon storage under three scenarios.</p> "> Figure 13
<p>The change in carbon storage and carbon sink economy caused by land use type conversion under three scenarios.</p> "> Figure 13 Cont.
<p>The change in carbon storage and carbon sink economy caused by land use type conversion under three scenarios.</p> "> Figure 14
<p>Driving factors.</p> "> Figure 15
<p>Importance of driving factors for each land use type.</p> ">
Versions Notes

Abstract

:
The carbon storage capacity of terrestrial ecosystems serves as a crucial metric for assessing ecosystem health and their resilience to climate change. By evaluating the effects of land use alterations on this storage, carbon management strategies can be improved, thereby promoting carbon reduction and sequestration. While county-level cities are pivotal to ecological conservation and high-quality development, they often face developmental challenges. Striking a balance between economic growth and meeting peak carbon emissions and carbon neutrality objectives is particularly challenging. Consequently, there is an urgent need to bolster research into carbon storage management. The study focuses on Jianli City, employing the InVEST model and land use data to examine the response patterns of land use changes and terrestrial system carbon storage from 2000 to 2020. Using the PLUS model, the study simulated the land use and carbon storage in Jianli City for the year 2035 under three scenarios: Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario. Our findings indicate the following: (1) Between 2000 and 2020, significant shifts in land use were observed in Jianli City. These changes predominantly manifested as the interchange between Cropland and Water areas and the enlargement of impervious surfaces, leading to a decrease of 691,790.27 Mg in carbon storage. (2) Under the proposed scenarios—Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario—the estimated carbon storage capacities in Jianli City were 39.95 Tg, 39.90 Tg, and 40.14 Tg, respectively. When compared with the 2020 data, all these estimates showed an increase. In essence, our study offers insights into optimizing land use structures from a carbon storage standpoint to ensure stability in Jianli’s carbon storage levels while mitigating the risks associated with carbon fixation. This has profound implications for the harmonious evolution of regional eco-economies.

1. Introduction

Under the severe situation of global climate change, human society confronts a myriad of challenges, including economic development [1], social risk [2], and so on. These challenges not only threaten the stability of natural ecosystems [3] but also affect the sustainable development of mankind [4]. The carbon cycle of the earth is the core of the climate system. Alterations in carbon storage directly affect the concentration of greenhouse gases globally, thereby determining the trend of climate change. A comprehensive understanding and precise prediction of the changes in carbon storage are crucial for mitigating climate change, preserving the ecological environment, and advancing sustainable development.
In the terrestrial ecosystem, carbon sequestration represents the most economically viable method for mitigating the escalating concentration of atmospheric CO2 in contemporary global society [5,6,7]. Land serves as a crucial element within the global climate system and significantly influences terrestrial ecosystems [8]. In the context of rapid urbanization and industrialization, Land Use and Cover Changes (LUCC) have emerged as the primary influence on carbon storage within terrestrial ecosystems. Consequently, the correlation between LUCC and terrestrial ecosystem carbon storage has garnered significant attention from both domestic and international academic communities.
The prediction of land use change aids in comprehending future trends in land utilization, thereby facilitating the formulation of urban development policies and planning strategies. Numerous spatial simulation prediction models for land use change have been developed by various research institutions to accurately forecast trends. These include the SLEUTH model [9,10], the CLUE-S model [11,12,13], and the FLUS model [14,15], among others. However, these models often fall short of adequately considering macro-level influencing factors. In 2020, the High Performance Spatial Computation Intelligence Laboratory (HPSCIL) introduced the PLUS model. This model can influence local land use competition at a macro level, thereby driving the amount of land use to meet future demands. Simultaneously, it can simulate the development and changes of various land-use conversion patches at a more detailed scale. It dynamically analyzes their driving factors and provides visual expression, making it widely used in academia [16,17,18].
Currently, researchers have extensively examined carbon storage within terrestrial ecosystems. This has been approached from various perspectives, including carbon stock estimation [19], factors influencing carbon stock [20,21], and the calculation of carbon stock value [22,23]. Additionally, numerous studies have integrated land use simulation models with the InVEST model to investigate the effects of LUCC on carbon stock under varying development scenarios. Yiling Wang et al. [24] employed land use data spanning 2000 to 2020, in conjunction with the InVEST model, to scrutinize the alterations in Hefei’s land use under various scenarios from 2000 to 2020 and their subsequent influence on carbon storage. Wenhao Wu et al. [25] underscored the significance of preserving carbon storage amidst rapid urbanization. The study predicted land use scenarios influenced by socio-economic factors. The InVEST model was employed to evaluate future ecosystem carbon storage in the Beijing–Tianjin–Hebei urban agglomeration, taking into account both dynamic environmental and anthropogenic influences.
Previous research has predominantly concentrated on large urban agglomerations and economic zones [25,26,27], human-clustered watershed areas [28,29], ecological protection areas with inherent natural characteristics [30,31], and provinces and cities possessing robust development resources [32,33,34]. However, there is a dearth of studies related to small and medium-sized cities. Despite their relatively smaller size, these cities are abundant in number, diverse in type, and expansive in scope. They constitute an integral part of the urban system and play a pivotal role in carbon emission reduction. In comparison to larger cities, small and medium-sized cities tend to be less developed. Their current economic foundation remains fragile, and they face significant challenges in achieving sustainable economic growth while balancing between peaking carbon emissions and attaining carbon neutrality. Consequently, there is a pressing need for more research and management guidance concerning the carbon storage strategies of these smaller cities.
The Jianghan Plain, a significant agricultural base in China, encompasses a substantial proportion of cultivated land that underpins regional and national food production [35]. However, the implementation of the central China rise plan has precipitated rapid urbanization, resulting in a considerable loss of cultivated land within the Jianghan Plain [36]. The compensation for this lost cultivated land has further exacerbated the erosion of other land uses. The escalating conflict between urban development, cultivated land expansion, and ecosystem protection in the Jianghan Plain area is not only impacting regional food security and economic development but also profoundly affecting the maintenance and alteration of carbon storage.
Jianli City, a representative county-level city in the Jianghan Plain, primarily relies on agriculture for its economic sustenance, with a particular emphasis on grain and aquaculture production. Alterations in agricultural production methods and land use structures within this region can serve as indicators of broader agricultural development trends in the Jianghan Plain. Consequently, investigating carbon storage in Jianli City can enhance the comprehension of the carbon cycling process within the Jianghan Plain region. This research is instrumental in resolving the tension between urban expansion, agricultural land acquisition, and ecosystem conservation in the Jianghan Plain area. Simultaneously, it furnishes robust support for the development of scientifically grounded land use planning and carbon reduction policies. This, in turn, provides a scientific foundation for regional sustainable development and the construction of an ecological civilization.

2. Materials and Methods

2.1. Study Area

Jianli City, situated on the northern bank of the middle reaches of the Yangtze River in Hubei Province’s central and southern regions, is positioned at the southern terminus of the Jianghan Plain. This city, characterized by its subtropical monsoon climate zone, boasts distinct seasons, a brief frost period, extended sunshine duration, and abundant rainfall. These climatic conditions facilitate superior agricultural conditions, establishing Jianli as a renowned agricultural hub within China. The city’s terrain is predominantly flat, with slightly elevated areas on the western, northern, and southern sides, and lower elevations in the central and eastern sectors. The city is enriched with water resources due to numerous rivers and canals, multiple trunk canals, branch canal water systems, and provincially protected lakes. Its total water resources rank first among those in the Jingzhou region (Figure 1).

2.2. Data Sources and Descriptions

The datasets used in this paper include land use data (due to the influence of the data and years of driving factors, in order to ensure the consistency and continuity of research data, this paper chooses land use data from 2000 to 2020 as an example), natural environment data, socio-economic data, and space-accessible data (Table 1).

2.3. Research Framework

This study integrates the PLUS and InVEST models, utilizing PLUS to simulate land use changes and InVEST to evaluate carbon storage. The research is structured into five stages (Figure 2): (1) Preprocessing of land use data from 2000 to 2020, which includes projecting rasters and conducting spatial corrections. (2) Spatiotemporal analysis of land use changes from 2000 to 2020, with the InVEST model employed to examine the spatiotemporal evolution of carbon storage during the same period. (3) Utilization of the LEAS module in the PLUS model to investigate the contribution values of various driving factors to different land class transformations, thereby determining the development probabilities of diverse land use types. (4) Employment of the CARS module in the PLUS model to generate multi-scenario predictions (for 2035) of land use, followed by an analysis of carbon storage variations under these scenarios. (5) Finally, the formulation of pertinent policy recommendations based on the findings.

2.4. Methods

2.4.1. Land Use Dynamics Degree

The land use dynamics degree serves as a reflection of the magnitude of changes in each land use type within a study area over a specified period, correlating both with region and time. It is employed to compare alterations in land use across different temporal frames within the same region or between distinct regions. The land use single dynamic degree elucidates the rate at which a specific land use class evolves per unit of time, expressed mathematically as (%)
K = S t 2 − S t 1 S t 1 × 1 t 2 − t 1 × 100 % ,
In the formula, K represents the single land use dynamic degree index of a certain land class from t1 to t2. St1 and St2 are the areas of a certain land class at the beginning and end of the study period, respectively.

2.4.2. Land Use Change Transfer Matrix

Spatial overlay analysis was conducted on the land use data using ArcGIS 10.8 software, yielding a detailed understanding of the transfer situation for each period. This information was subsequently classified and summarized to generate the land use transfer matrix. The matrix provides insights into the direction and extent of transfers between various land classes within a specific time frame in the study area. The formula is (ha)
A i j = [ A 11 A 12 ⋯ A 1 n A 21 A 22 ⋯ A 2 n ⋯ ⋯ ⋯ A n 1 A n 2 ⋯ A n m ] ,
In the formula, Ai is the area of class i land before transfer; Aj is the area of class j land after transfer; n is the number of land use types.

2.4.3. Patch-Generating Land Use Simulation Model

The Patch-Generating Land Use Simulation model (PLUS), developed by the High Performance Space Computing Intelligence Laboratory (HPSCIL), encompasses two modules: a Land Use Expansion Analysis Strategy (LEAS) and a CA model based on multi-type random patch seeds (CARS).
①
Land Expansion Analysis Strategy (LEAS)
The module extracts the portion of all types of land use expansion occurring between two distinct periods of land use change. It then samples from this increased segment to ascertain the probability of change and inertia for each type of land. The Random Forest Classification (RFC) algorithm is employed to investigate the contribution value of each driving factor towards the transformation of various types of land. Consequently, it provides insights into the development probability of each type of land use as well as the contribution of driving factors towards the expansion of each type of land use during this period. The formula is (%)
P i , k d ( x ) = [ ∑ n = 1 M l = [ h n ( x ) = d ] ] M ,
In the formula, P i , k d ( x ) represents the possibility of land use type k expanding in spatial unit i. hn(x) represents the predicted type of decision tree n. x represents the vector of multiple drivers. d represents the conversion from other land use types to land use type k, with a value of “1” indicating that it exists and a value of “0” indicating that it does not exist; l represents the indicator function of the set of decision trees; M represents the total number of decision trees.
②
CA model based on multi-type random patch seeds (CARS)
The CARS module is a scenario-driven land use simulation model. This model integrates both the “top-down” effects, which pertain to global land use demand, and the “bottom-up” effects, which are associated with local land use competition. Throughout the simulation process, the demand for land use exerts its influence on local land use competition via an adaptive coefficient. This mechanism propels the amount of land used towards meeting future demand. The formula is
O P i , k d , t = { P i , k d ( x ) × ( r × μ k ) × D k t i f Ω i , k t = 0 a n d < P i , k d ( x ) P i , k d ( x ) × Ω i , k t × D k t a l l o t h e r s ,
In the formula, O P i , k d , t denotes the overall evolution probability of land use type k. r denotes a random number ranging from 0 to 1. μk denotes the threshold of new land use type k. D k t denotes the influence of land use type k on future demand, depending on the gap between quantity and demand in t years that are iteratively derived. Ω i , k t denotes the neighborhood effect in spatial unit i regarding land use type k, which describes the proportion of coverage of the next neighborhood.

2.4.4. InVEST-Carbon Storage and Sequestration

InVEST is a comprehensive tool designed to investigate the impact of ecosystem changes on human benefits. It encompasses three primary ecosystems: terrestrial, marine, and freshwater, each of which is further divided into several modules. The carbon stock module discussed in this paper specifically pertains to the terrestrial ecosystem. The carbon stock within this ecosystem is primarily composed of four components: aboveground biomass (Cabove), belowground biomass (Cbelow), soil (Csoil), and dead organic matter (Cdead) (t/ha).
Σ C t o t a l = ( C a b a v e + C h e l o w + C s o i l + C d e a d ) × A i ,
In the formula, Ai is the total area of the i land use type in this area.
Carbon density serves as a crucial parameter in assessing carbon storage. Its data were sourced from the National Ecological Data Center Resource Sharing Service Platform (http://www.nesdc.org.cn/, accessed on 21 October 2023) and prior research studies [8,30,35,37,38,39]. However, these data are not actual measurements and thus require correction. Existing research indicates a significant correlation between carbon density and both annual average precipitation and temperature [40,41]. Consequently, this paper employs more comprehensive correction formulas to enhance the accuracy of calculating the carbon density data for Jianli City [42] (Table 2).
K B P = 0.03 × P ′ + 14.4 0.03 × P ″ + 14.4 , K B T = − 0.4 × T ′ + 43.0 − 0.4 × T ″ + 43.0 , K B = A v e r a g e ( K B P , K B T ) Σ K S P = 0.07 × P ′ + 79.1 0.07 × P ″ + 79.1 , K S T = − 3.4 × T ′ + 157.7 − 3.4 × T ″ + 157.7 , K S = A v e r a g e ( K S P , K S T ) , K D P = 0.001 × P ′ + 0.58 0.001 × P ″ + 0.58 , K D T = − 0.03 × T ′ + 2.03 − 0.03 × T ″ + 2.03 , K D = A v e r a g e ( K D P , K D T )
In the formula, KBP represents the biomass coefficient by precipitation, KBT represents the biomass coefficient by temperature, and KB represents the final biomass correction coefficient; KSP represents the soil coefficient by precipitation, KST represents the soil organic matter correction coefficient by temperature, and KS represents the final soil organic matter correction coefficient; KDP represents the dead organic matter correction coefficient by precipitation, KDT represents the dead organic matter correction coefficient by temperature, and KD represents the final dead organic matter correction coefficient; P′ represents the precipitation of Jianli City, P″ represents the national precipitation (mm); T′ indicates the temperature of Jianli City, T″ indicates the national temperature (°C).
This study quantifies the economic value shifts attributable to carbon sinks resulting from land-use conversion in Jianli City, spanning the years 2000–2035. The analysis is grounded on the InVEST model and encompasses three primary facets: firstly, it elucidates the societal cost associated with each metric ton of carbon emitted within the designated region; secondly, it ascertains the annual rate of fluctuation in carbon pricing; and thirdly, it establishes the market discount rate that society applies to immediate benefits [43]. The formula is
v a l u _ s e q x = V s e q u e s t x y r − f u t − y r − c u r ∑   t = 0 y r − f u t − y r − c u r 1 1 ( 1 + r 100 ) t ( 1 + c 100 ) t ,
In the formula, valu_seqx is the economic value of carbon sink caused by land-use conversion, x is the carbon sequestration grid, V is the value (Dollars) of sequestering or releasing one ton of carbon per year, according to relevant research, the value determined in this paper is 24 dollars/t [44], sequestx is the amount of carbon storage or loss per grid under current or different scenarios, yr_fut is the amount of carbon storage of terrestrial system in Jianli City under different scenarios in the future, yr_cur is the initial annual amount of carbon storage of terrestrial system in Jianli City, r is the market discount rate, which is determined as 10% in this paper [45], and t is the interannual change rate of carbon price, which is determined as 0 in this paper [46].

3. Results

3.1. Temporal and Spatial Evolution of Land Use

3.1.1. Temporal Evolution of Land Use

(1)
Analysis of land use structure change
Table 3 illustrates that Cropland was the predominant land use type in Jianli City from 2000 to 2020, with a significant trend variation. Notably, between 2000 and 2015, the area of Cropland experienced a year-on-year decline. However, from 2015 to 2020, despite a slight rebound, it continued to show an overall downward trajectory. The reduction from 326,958.39 hm2 in 2000 to 319,240.17 hm2 in 2020 reflected that part of agricultural land was inevitably occupied during the development process of Jianli City.
Water was the second largest land cover in Jianli City, and its area increased significantly from 2000 to 2010, which was due to the positive policies formulated by the Provincial Party Committee and Provincial Government in 2004 to build Honghu Lake into a beautiful and ecologically good wetland nature reserve with remarkable results. However, from 2010 to 2020, due to the demand for agricultural development, lake farming prevailed, and a large amount of water area was developed as paddy fields, resulting in a decrease in water area. Overall, from 2000 to 2020, the water area increased from 34,865.73 hm2 to 37,327.5 hm2, still showing an upward trend. It indicated that Jianli City had paid attention to the protection of water resources within its territory during its development process.
Throughout the research period, impervious land experienced a notable expansion, encompassing an area of 6358.86 hm2 in 2000 and escalating to 11,794.23 hm2 by 2020. This conspicuous growth trajectory unequivocally indicates that Jianli City is in a phase of rapid urbanization, characterized by an unabating acceleration of urbanization processes and a persistent increase in demand for urban land.
The proportion of Forest and Grassland within Jianli City is notably limited. This can be attributed to the city’s status as a significant agricultural county in the Jianghan Plain, characterized by its flat terrain devoid of natural topography such as mountains or hills. Consequently, the available space for the growth of forest grass is significantly constrained.
Based on the preceding analysis, notable disparities exist in both the area and the alteration of each land use type within Jianli City. Presently, the overarching land use structure of Jianli City is predominantly Cropland and Water, with a declining proportion of Cropland and an escalating proportion of Water and Impervious. This transformation in land use structure not only signifies the adjustment of Jianli City’s agricultural industry composition, transitioning from traditional farming to a blend of farming and distinctive aquaculture but also underscores the ongoing progression of urbanization in Jianli City. Throughout this agricultural evolution, certain agricultural lands have been converted into alternative types of land to accommodate urbanization; concurrently, water area alterations are influenced by various factors such as agricultural development and water resource conservation. Consequently, Jianli City must prioritize the judicious utilization of land resources and the preservation of its ecological environment for future development to achieve sustainable growth.
(2)
Analysis of land use dynamics degree
As illustrated in Table 4, the land use structure of Jianli City underwent significant changes between 2000 and 2020. Between 2000 and 2005, the land use single-dynamic degrees for Impervious and Water were positive, signifying rapid expansion in their respective areas. This trend underscores the robust pace of urbanization and water development. In contrast, land use single dynamic degree for all other categories was negative, with forests experiencing a more pronounced decline. From 2005 to 2010, while the area of Forest and Grassland saw minor increases, their dynamic degrees underwent notable shifts due to their smaller base sizes. Between 2010 and 2015, with the exception of Forest, the absolute value of each category’s dynamic degree declined. From 2015 to 2020, the land use single dynamic degree for Grassland and Impervious was positive, suggesting ongoing growth in Jianli City, indicative of the ongoing urbanization process. In summary, the alterations in Jianli City’s land use structure reflect both economic and societal developmental needs, as well as the complexities associated with reconciling ecological conservation with land utilization (Figure 3 and Figure 4).
Generally, the land use single-dynamic degrees of Impervious increased most significantly in Jianli City among changes in land use structure. This significant increase indicates that the urbanization process in Jianli City is currently in a rapid development stage. Concurrently, there was also a noticeable increase in Grassland, which may be attributed to the promotion of ecological restoration or greening engineering during the urban expansion process. Despite Cropland and Water being the primary types of land use in Jianli City and accounting for a large proportion, their change in the land use single-dynamic degrees was relatively minor due to their substantial base.
(3)
Analysis of land use transfer
Table 5, Table 6, Table 7 and Table 8 reveal significant changes in land use transfer within Jianli City from 2000 to 2020. The period from 2000 to 2005 saw the largest area of Cropland being transferred out, primarily transforming into Water and Impervious areas, with a notable shift towards Water. This suggests a marked expansion of water resources during this phase, indicating an acceleration in the urbanization process. Between 2005 and 2010, this trend was largely consistent, characterized by substantial transfers of Cropland and a significant influx of Water. From 2010 to 2015, there was a decline in the area of Cropland transferred out, with the primary land use change occurring between Cropland and Water, signifying a dynamic balance adjustment between these two categories. However, from 2015 to 2020, the pattern shifted, with Water becoming the predominant category being transferred out, predominantly converting into Cropland and Impervious areas. This shift may be attributed to escalating demands for agricultural development and urban expansion. In summary, the land use transfer trends in Jianli City transitioned from primarily transferring out Cropland to primarily transferring out Water, underscoring the evolving dynamics of economic growth, urbanization, and agricultural industrial structure adjustments in the region.
The land use transfer in Jianli City predominantly transpired between Cropland and Water categories, exhibiting distinct stage characteristics. Between 2000 and 2010, policies such as the conversion of farmland back to lakes and the enhancement of Honghu Lake’s ecological construction influenced the trend toward a shift from Cropland to Water. Subsequently, from 2010 to 2015, there was a near-equilibrium in the conversion between these two land classes. However, between 2015 and 2020, the development of characteristic industries in Jianli City, particularly the robust promotion of the aquaculture industry and paddy fields, led to a reversal in the trend towards conversion from Water to Cropland. Furthermore, it is important to highlight that the inflow contribution rate of Impervious consistently exceeded its outflow contribution rate throughout the study period. This observation underscores the rapid development of Jianli City, where the expansion of Impervious inevitably leads to encroachment on agricultural and ecological lands. Comparatively, both the inflow and outflow contributions of Grassland to Forest were minimal. This suggests that, as a major agricultural city in the Jianghan Plain, the proportion of forest and grassland in Jianli City remains relatively low. Consequently, its land use transitions have a limited impact on the overall land use structure.
In general, the characteristics of land use transfer in Jianli City are the product of a confluence of factors. Over recent decades, the rapid increase in population has led to a sharp rise in demand for land resources, resulting in continuous encroachment of Water by both Cropland and Impervious areas. Concurrently, under the auspices of ecological protection policies, efforts were made to expand the area of Water to maintain ecological equilibrium. However, as farmland protection policies have been progressively implemented, a portion of Water has been re-purposed as Cropland to cater to agricultural production needs.

3.1.2. Spatial Evolution of Land Use

The land use structure of Jianli City is predominantly Cropland, followed by Water and Impervious areas, with Forest and Grassland constituting a minimal proportion. Spatially, Cropland is extensively distributed across the city, particularly concentrated in the northwest region. Water primarily clusters along the eastern Honghu Lake coast and the southern Yangtze River coast, with internal river channels. Notably, the water area along the Honghu Lake coast expanded significantly from 2000 to 2010 but contracted from 2010 to 2020. Impervious land is mainly concentrated in the central urban area of the middle and south regions, with Xingou Town and Zhuhe Town also exhibiting significant concentrations of construction land. Other townships are scattered and relatively small in size. Over time, the construction land of each township has gradually expanded outward and interconnected, particularly along the Suizhou–Yueyang Expressway. In contrast, both Grassland and Forest are dispersed and constitute a minor proportion throughout the study period. This land use pattern not only reflects the agricultural dominance of Jianli City but also reveals the dynamic changes in land use during urbanization (Figure 5).

3.2. Land Use Projections

3.2.1. Growth Probabilities

Utilizing the land use data from Jianli City for both 2000 and 2020, the study superimposed these two datasets. By incorporating the grid cells that underwent changes in the LEAS module and integrating the driving factor data for sampling calculations, the study derived an expansion probability map for each land cover (Figure 6). It reveals a pronounced expansion trend for Cropland, with its expansion probability positively correlating to its proximity to water sources. This suggests that the primary driver of this expansion is Water. While Water also exhibits significant expansion, its likelihood of conversion to water is minimal in the central urban area south of the city center, indicating that Impervious is more resistant to conversion into Water. The expansion of Impervious is evident as well, with its highest probability of expansion observed in the southern region of the middle zone and a direct correlation between its proximity to roads and its expansion probability. In stark contrast, Forest and Grassland exhibit negligible expansion trends. This can be attributed to Jianli City’s location within the Jianghan Plain, coupled with the limited size of its forest and grassland areas, resulting in a minimal probability of future expansion.

3.2.2. Model Accuracy Verification

The study selected land use data from different time spans to carry out simulation analysis using the PLUS model in order to obtain the best simulation accuracy. First, the study adopted three five-year span data sets from 2000 and 2005, 2005 and 2010, 2010 and 2015, and one ten-year span data set from 2000 and 2010. Through the Markov Chain module of the PLUS model, the demand for each land use was predicted in 2020 (Table 9), and the neighborhood weights of each land use type were calculated (Table 10).
Then, the CARS module of the PLUS model was used to predict the land use distribution in 2020 separately. To verify the accuracy of the simulation results, actual land use data in 2020 were input into the validation module of PLUS, and the respective kappa coefficients and FOM accuracies were calculated (Table 11). Through comparative analysis, it was found that the five-year span data were more accurate than the ten-year span data. In addition, the data simulated for the 2010 and 2015 periods, which are closer to 2020 in time, had the best simulation accuracy.
Based on the above analysis, this study finally selects the land use data in 2015 and 2020 as the simulation basis to simulate the land use in 2035.

3.2.3. Multi-Scenario Simulation

Under the premise that the simulation accuracy is high enough, this study utilizes land use data from Jianli City in 2020 to forecast potential changes in future land use distribution. These predictions are based on three distinct scenarios (Table 12):
(1)
Natural Development Scenario: This scenario presumes that the conversion rates of various land use types will persist from past periods. It forecasts the natural progression trend of future land use without necessitating any external intervention or policy modification. This scenario serves as a benchmark for subsequent comparative analyses with other scenarios.
(2)
Urban Expansion Scenario: Taking into account the developmental needs of Jianli City and most counties within the Jianghan Plain, the research proposes an urban expansion scenario. This scenario predicts a 30% increase in the probability of Cropland, Grassland, and Water transitioning to Impervious land, mirroring the trend of construction land expansion during urbanization. Concurrently, it anticipates a 30% decrease in the likelihood of Impervious land transitioning back to Grassland, Cropland, or Water, which underscores the rigidity of land use during this urban expansion process.
(3)
Ecological and Food Security Scenario: Given the critical role of ecological preservation and food security in regional development, the research establishes an ecological and food security scenario. This scenario prioritizes the protection of the ecological environment and advocates for a shift towards “low-carbon” and “green” modes of development. It postulates that the likelihood of water transitioning to Cropland will increase by 30%, thereby emphasizing the necessity for developing reserve resources for cultivated land and establishing high-standard farmland. Concurrently, the conversion of forest and grassland to other land uses is limited to safeguarding the ecological functions of these forests and grasslands.
According to the analysis of prediction results (Figure 7, Table 13), the spatial distribution gap of future land use in Jianli City under three different scenarios is not significant, basically continuing the previous distribution law.
In the Natural Development scenario, the trends observed in each land use category mirrored those of the past five years. Specifically, there was a marginal increase in the areas of Cropland and Impervious, while Forest, Grassland, and Water experienced a decrease. Notably, the decline in Water was the most pronounced, amounting to 4652.16 hm2. This suggests that, under unregulated conditions, Water is the most susceptible to encroachment by other land-use types. If this trend persists, Jianli City could confront numerous challenges, including ecological degradation, flood hazards, and soil erosion. Furthermore, Cropland witnessed the most significant growth, suggesting a substantial conversion of Water into Cropland. While the area of Impervious also expanded, its growth rate has markedly decelerated, indicating a stabilization in Jianli City’s socio-economic development.
The Urban Expansion scenario underscores the profound influence of urban expansion on land use patterns. When juxtaposed with the Natural Development scenario, there is a marked increase in Impervious land, amounting to 390.11 hm2. Concurrently, the rate of growth in Cropland decelerates, whereas the areas designated for Water and Forest continue to diminish. This suggests that the surge in construction land during urban expansion predominantly stems from encroachments onto ecological lands.
In the Ecological and Food Security Scenario, the influence of policy orientation on land use is particularly pronounced. The area of Cropland expanded swiftly to 6350.63 hm2, with water reduction being the most notable change. This trend indicates that a significant amount of water was converted into Cropland under the impetus of policies promoting a balance between Cropland-to-compensation and food security. While this approach aids in ensuring food security, it may also result in irreversible harm to water resources.
In conclusion, the alterations in land use under various scenarios are indicative not only of the influence of natural and socio-economic factors but also of the significant impact of policy orientation on land use patterns. These elements must be thoroughly incorporated into the development of land use planning and policies to ensure the sustainable utilization of land resources and the healthy progression of ecosystems.

3.3. Carbon Storage Dynamics from 2000 to 2020

3.3.1. Temporal Evolution for Carbon Storage

Utilizing the carbon density data across various land classes and integrating it with the Jianli City land use raster map from 2000 to 2020, this study conducted a comprehensive analysis of the land system’s carbon storage fluctuations over time using the InVEST model. The findings indicate that the carbon storage in the Jianli City land system exhibited a pattern of initial decline followed by an increase over a span of 20 years. Specifically, the terrestrial system’s carbon storage in Jianli City exhibited a consistent decline from 2000 to 2010. The most pronounced decrease was observed between 2000 and 2005, resulting in a net reduction of 0.61 Tg. Although the rate of this decline decelerated between 2005 and 2010, the overall trajectory remained downward. This reduction in carbon storage can primarily be attributed to the encroachment on ecological and cultivated lands due to economic development and urban expansion. However, the terrestrial carbon storage in Jianli City began to rise from 2010 to 2020. Despite this, when compared to 2000, the total carbon storage of the terrestrial system in 2020 still decreased. This suggests that while some positive steps may have been taken for ecological protection in Jianli City in recent years, the long-term effects of land use change on carbon storage remain significant (Figure 8).
The trends in terrestrial system carbon stocks and geo-averaged carbon density across each township largely align with the broader situation in Jianli City. However, significant internal variations exist among these townships (Table 14). Notably, Hongcheng Town, Wangqiao Town, and Xingou Town exhibit the highest levels of terrestrial system carbon storage in Jianli City. This may be attributed to their unique land use structures and ecological protection measures. In contrast, areas such as Huanhu Farm, Qipan Town, and Shangchewan Town demonstrate relatively smaller carbon storage capacities.
When examining geo-averaged carbon density, the disparities between townships become more pronounced. In 2020, Fenyan Town emerged as the top performer with a geo-averaged carbon density of 103.87 t/hm2. In stark contrast, Qipan Township registered a significantly lower figure at 45.06 t/hm2. This substantial discrepancy underscores not only the varied land use and ecological protection approaches across townships but also the marked imbalance in carbon storage within the terrestrial system of Jianli City.
The imbalance in distribution has significant implications for the development of Jianli City. Future land use planning and ecological protection initiatives should take into account the unique circumstances and disparities of each township. This will enable the formulation of targeted policies and measures that promote the rational utilization of land resources and the healthy development of ecosystems. Concurrently, it is imperative to strengthen cooperation and exchange among townships. This joint effort will significantly contribute to enhancing carbon storage in the terrestrial system and achieving ecological balance in Jianli City.

3.3.2. Spatial Evolution for Carbon Storage

In terms of spatial distribution, the terrestrial system’s carbon storage in Jianli City exhibits a pronounced “western high and eastern low” regional pattern. The western region of Jianli City possesses relatively elevated carbon storage in terrestrial systems. This can primarily be attributed to the area’s dominance of cultivated land and its stable land use mode, both of which foster carbon accumulation. In stark contrast, the eastern Honghu Lake Coast region emerges as a region with low carbon storage. This disparity may be associated with the expansive water area, varied land use types, and significant human activity disruptions impacting the ecosystem within this region. The spatial distribution characteristic not only underscores the disparities in land use patterns across various regions of Jianli City but also illuminates the intricate relationship between carbon storage and land use types. For future land planning and carbon management strategies, it is imperative for Jianli City to fully acknowledge the variations in carbon storage across different regions. Targeted measures should be implemented to safeguard and augment carbon storage, particularly in areas with low-carbon storage, such as the eastern Honghu Lake coast (Figure 9).
As shown in Figure 10, by subtracting the carbon storage data from the two periods of the land system, the spatial distribution accompanying the change in terrestrial system carbon storage is obtained. From 2000 to 2020, the areas with obvious reductions in carbon storage are mainly distributed in Qiban Township, Qiaoshi Town, Rongcheng Town, and other townships with scattered distributions of carbon storage reduction; most townships have relatively concentrated areas with obvious increases in patches of land system carbon storage. Specifically, from 2000 to 2010, there were obvious areas of carbon storage reduction in Bianhe Town, Qipan Town, and Qiaoshi Town. From 2010 to 2015, Qiaoshi Town still had a relatively dense area of land system carbon storage reduction. Qipan Township and Bianhe Town began to have more areas of land system carbon storage increase from 2015 to 2020, except for Qiaoshi Town, which has a dense area of land system carbon storage reduction. The land system carbon storage basically remained unchanged or slightly increased in other townships of Jianli City.

3.3.3. Impacts of Land Use Change for Carbon Storage

The varying carbon densities across different land categories directly influence the transformation of regional terrestrial system carbon storage when these densities are converted into one another. This transformation encompasses not only the quantity of carbon storage but also alterations in the economic value of carbon sinks. The primary driver for significant shifts in regional terrestrial system carbon storage and the economic value of carbon sinks is the conversion between cultivated land, construction land, and water areas during the land use conversion process from 2000 to 2020 in Jianli City (Figure 11).
The direction of the economic changes in terrestrial system carbon stocks and sinks was roughly consistent between 2000–2005 and 2005–2010. The conversion of Cropland to water bodies was the most important reason for carbon loss (loss of 1,078,888.37 Mg and 1,101,986.01 Mg carbon stocks, respectively, with a decrease in the economic value of carbon sinks by 25,335.47 thousand USD and 25,877.87 thousand USD). The conversion of Cropland to construction land also caused a certain degree of reduction in carbon stocks, with losses of 50,922.35 Mg and 65,089.09 Mg carbon stocks, respectively. Meanwhile, part of the water was converted into Cropland, which led to a certain degree of increase in carbon stocks as well as the economic value of carbon sinks (increases of 510,403.90 Mg and 531,378.49 Mg terrestrial system carbon stocks, respectively, with an increase in the economic value of carbon sinks by 11,985.78 thousand USD and 12,478.33 thousand USD). Overall, the terrestrial system carbon stocks and the economic value of carbon sinks decreased, possibly due to the implementation of policies related to returning farmland to lakes.
From 2010–2015 to 2015–2020, a large amount of water area was converted into farmland, which brought about a greater increase in the carbon storage of the land system (the carbon storage of the land system increased by 708,994.35 Mg and 980,583.59 Mg, respectively, and the economic value of carbon sinks rose by 16,649.27 thousand USD and 23,026.98 thousand USD). At the same time, part of the farmland was converted into construction land, and some water areas also caused a decrease in the carbon storage of the land system and the economic value of carbon sinks, but overall, it still showed an upward trend.
In general, the conversion of cropland to water bodies caused a loss of 1,432,584.66 Mg of terrestrial system carbon storage and a decrease in the economic value of carbon sinks by 31,047.03 thousand USD from 2000 to 2020. However, the conversion of water bodies to cropland increased the terrestrial system carbon storage by 1,018,030.77 Mg, which brought about an economic value of carbon sinks of 22,062.80 thousand USD. The two-way conversion reached an approximate “balance of compensation.” At the same time, as a representative of the Jianghan Plain area, Jianli City has a land use structure dominated by water bodies and cropland, with relatively small proportions of forests and grasslands. Therefore, cropland is the largest carbon pool in Jianli City, and its changes in carbon storage have a decisive impact on the overall terrestrial system carbon storage in Jianli.
In the process of development in Jianli, with the changes in policy and natural conditions, the cultivated land and water area in Jianli City are also in a state of mutual transformation. In this process of dynamic equilibrium transformation, the carbon storage and economic value of carbon sinks in the terrestrial system of Jianli City also change accordingly. Therefore, when formulating land use policies and planning, Jianli City should fully consider the impact of land type transformation on carbon storage and the carbon sink economy.

3.4. Carbon Storage Dynamics from 2020 to 2035

3.4.1. Temporal and Spatial Evolution for Carbon Storage

Based on the simulation results, the carbon storage in 2035 under the natural development scenario, urban expansion scenario, and ecological protection scenario of Jianli City were 39.95 Tg, 39.90 Tg, and 40.14 Tg, respectively, compared with the carbon storage of 39.40 Tg in 2020, which indicated that the three scenarios had increased carbon storage, indicating that Jianli City would develop towards low-carbon in the future.
The spatial distribution pattern of future carbon storage in Jianli City will be consistent with the current storage; areas with higher values of carbon storage are mainly distributed in cultivated land, and areas with lower values of carbon storage are mainly distributed in towns and water bodies, which have a strong correlation with land cover (Figure 12).

3.4.2. Impacts of Land Use Change for Carbon Storage

Among the three future development scenarios in Jianli City, the conversion of Cropland, Water, and Impervious has a particularly significant impact on the carbon storage of terrestrial systems, while the changes in Forest and Grassland have a relatively small impact on carbon storage (Figure 13).
Under the Natural Development scenario, the terrestrial system carbon storage of Jianli City shows a growth trend. Among them, the transformation from Water to Cropland is the main reason for the increase in carbon storage, which increases the terrestrial system carbon storage by 517,038.42 Mg and brings an increase in the economic value of carbon sinks by 11,506.20 thousand USD. This shows that, under the natural development state, the transformation between Water and Cropland has a positive contribution to carbon storage.
The impact of land-use change on carbon stocks in the Urban Expansion scenario was similar to that in the natural development scenario, but the increase in carbon stocks and their economic value as carbon sinks due to Impervious to Cropland conversion was significantly reduced because of the restriction on construction land transfer. This indicates that during the urban expansion process, the conversion relationship between Impervious and Cropland needs to be treated more carefully to balance the needs of urban development and carbon stock protection.
Under the Ecological and Food Security Scenario, the changes in carbon storage and the economic value of carbon sinks in terrestrial systems are more significant. A large amount of Water and Impervious converted to Cropland, which promoted carbon storage by 674,788.60 Mg and 106,648.58 Mg, respectively, and the economic value of carbon sink increased by 15,016.77 thousand and 2373.36 thousand, correspondingly. Although part of Cropland was converted to Water and Impervious resulting in a decrease in carbon storage and its economic value of carbon sinks, overall, there is still a large increase in carbon storage under this scenario. This shows that, under the premise of focusing on ecological protection and food security, Jianli City can achieve effective growth in carbon storage through reasonable land use adjustments.

3.5. Driving Factors

Drawing upon relevant literature [26,30,31,33,47,48,49,50] and considering the evolving reality of Jianli City, we selected 19 driving impact factors. These were chosen based on the principles of data availability for driving factors, representativeness of the data, and quantifiability. The selection encompassed factors from the natural environment, socio-economic aspects, and accessibility, among others (Figure 14).
Given Jianli City’s distinctive geographical environment, its forest and grassland resources are notably limited. A comprehensive analysis of land use changes in Jianli City and their subsequent impact on carbon storage revealed that cultivated land and water bodies serve as the primary constituents of these changes. Moreover, they significantly influence the region’s carbon storage capacity. Consequently, alterations in cultivated land and water bodies have emerged as pivotal factors affecting carbon storage.
Specifically, cultivated land, the bedrock of agricultural production, experiences alterations in its area that directly influence crop growth and soil carbon cycling. Two primary factors affecting these changes are GDP and temperature, which underscore the profound impacts of economic development and climate change on cultivated land use. As GDP grows, there is a strengthening trend towards scale and intensification in agricultural production, potentially leading to a transformation in how cultivated land is utilized. Concurrently, global warming may indirectly affect the carbon storage of cultivated land by influencing crop growth cycles and moisture conditions. For water bodies, while the driving factors for their changes are relatively balanced, the significant roles of “Distance from urban secondary roads” and “Distance from provincial highway” underscore the guiding role of transportation networks in the distribution and evolution of water systems. This, in turn, affects the stability of water body carbon storage. Furthermore, alterations in impervious surfaces, a significant indicator of urbanization, are predominantly influenced by factors such as Night Lighting, Distance from urban secondary roads, and Distance from commerce. These elements collectively underscore the profound impact of human activities on land use, which subsequently influences regional carbon storage.
In conclusion, the alterations in land use within the City and their subsequent influence on carbon storage can be attributed to the interplay of multiple driving factors. These factors include both macroeconomic and climate change forces, as well as micro-constraints such as geographical location and topographical features, and even direct human intervention in land use (Figure 15).

4. Discussion

4.1. Suggestions for Carbon Storage Optimization

Jianli City, strategically situated in a pivotal geographical location, boasts abundant resource endowments and bears the significant responsibility of serving as the sub-center of Jingzhou city. These factors together determine the complex challenges and opportunities faced by Jianli City under multiple demands such as urbanization, development, food security, and ecological protection. Jianli City is the county-level city with the largest population in Jingzhou. However, its urbanization level remains relatively low, and its economic development is predominantly agricultural-based. Consequently, there is an urgent need to optimize its internal structure. To address this, Jianli City must implement scientific land use planning strategies. These strategies should aim to enhance the efficiency of land spatial utilization, stimulate the agglomeration and growth of non-agricultural industries, and foster the transformation and upgrading of its economic structure.
Meanwhile, Jianli City, a significant grain production hub, is tasked with the critical responsibility of safeguarding national food security. In light of stringent farmland protection and food production safety policies, the expansion of urban and rural construction land within Jianli City is subject to rigorous restrictions. Urban and rural planning must thoroughly consider the equilibrium between food security and land spatial utilization, optimize land use structures, enhance land use efficiency, and ensure the stability and sustainability of food production.
In the evolving landscape of ecological civilization construction, it is imperative for Jianli City to address both ecological preservation and high-quality development [51,52]. Jianli City should promote the intensive and efficient use of construction land on the premise of ensuring ecological security and food security so as to improve the overall benefit of land resources [53,54]. By optimizing the structure of land use, strengthening land consolidation and ecological restoration, and effectively increasing carbon storage, a solid foundation for low-carbon development is laid. Based on this, this study proposes the following three policy suggestions:
First, Jianli City should strictly fulfill its main responsibility for food security and give priority to the protection of arable land. Through strengthening the management and maintenance of arable land, the scale advantage of farmland areas will be transformed into the carbon sink advantage of the farmland ecosystem [45]. Arable land is not only an important base for grain production in Jianli City but also an important barrier to ecological protection in plain areas [38]. Therefore, in the process of promoting the transformation of agricultural development mode, Jianli City must adhere to the red line of arable land, prevent non-grain phenomena, and ensure the stability and health of farmland ecosystems. At the same time, Jianli City should rely on scientific and technological progress and scientific management to promote agriculture towards low-carbon and green development. By introducing advanced agricultural production technology and equipment, improving agricultural production efficiency, reducing the use of chemical fertilizers, pesticides, and other substances, and reducing carbon emissions during agricultural production. In addition, strengthening the construction of farmland water conservancy, improving the irrigation efficiency of farmland, and reducing water resource waste are also important ways to achieve low-carbon agriculture.
Second, Jianli City needs to strictly implement the ecological protection red line policy according to the ecological security pattern established by national land planning and ensure that key ecological barriers, ecological patches, and corridors are effectively protected and restored. By improving the management system of natural reserves, strengthening the carbon storage function of terrestrial ecosystems such as forests, wetlands, and grasslands, promoting high-carbon density land use, and enhancing the carbon storage level of Jianli City [54]. At the same time, with the development of ecological forestry as the core, forestry ecological construction is implemented to protect the Yangtze River coastal and old Jiang River island beach forest land, increase the total amount of forest resources, and enhance the carbon storage of the ecosystem. In addition, it focuses on developing key ecological corridors such as the ecological belt along the Yangtze River, perfecting the construction of natural reserves, taking the concept of ecological community as a guide, enhancing the stability of the ecosystem and carbon sequestration capacity, and achieving the goal of overall ecological protection and systematic restoration.
Thirdly, Jianli City should vigorously advocate for the development of new urban spaces. This initiative should primarily focus on enhancing the quality of urbanization, optimizing the intensity of construction land utilization, and mitigating carbon emissions resulting from unregulated expansion. By refining and streamlining the construction of urban green infrastructure, the carbon sequestration capacity of urban green spaces can be significantly augmented. In the forthcoming phases of urbanization, Jianli City must integrate national spatial planning to judiciously regulate the expansion of construction land. This would necessitate the formulation of stringent policy measures to achieve precise control over urban space. Concurrently, there should be a transition in the use of construction land from incremental development to stock excavation, thereby enhancing the efficiency and intensity of land use while preserving arable and ecological lands. Furthermore, Jianli City should actively expand the area of urban green spaces by promoting the construction of parks and road green belts. This would optimize the layout of green spaces, fostering a livable environment for citizens while simultaneously augmenting the city’s carbon sink capacity.

4.2. The Novelty of This Study

  • In terms of research area selection, the majority of current scholarly investigations into regional carbon storage, both domestically and internationally, predominantly concentrate on economically advanced large urban agglomerations and economic zones [24,28]. These areas often feature significant human concentrations within river basins, as well as ecological protection zones characterized by unique natural features and notable surface structure changes. However, there is a conspicuous lack of focus on related research in small to medium-sized cities at the county level [36]. This paper aims to address this gap by examining the impact of land use changes in Jianli City on carbon storage, thereby broadening the scope of research on carbon storage at the county scale.
  • From a research content standpoint, the majority of studies on carbon storage predominantly focus on analyzing spatial variations in carbon storage across different scenarios, neglecting to investigate the socio-economic implications resulting from these changes [26,34]. This study not only quantitatively examines the evolutionary characteristics of land and carbon storage under various conditions but also computes the alterations in the economic value of carbon storage corresponding to these situations. Furthermore, it investigates the primary factors driving the evolution of carbon storage and its spatial patterns in Jianli City. The study also evaluates the contributions of different driving factors to the spatiotemporal evolution of carbon storage, offering theoretical insights for the development of relevant carbon storage management policies.

4.3. Uncertainty of Assessment Results

  • Inaccuracies in Land Use Data
Wetlands play a pivotal role in carbon stock calculations, necessitating a detailed classification for precise estimations. However, the land use data employed in this study did not categorize wetlands with sufficient detail as an independent entity. This oversight could introduce errors during the carbon stock estimation process, potentially compromising the comprehensiveness and accuracy of the findings.
2.
Selection Criteria for Driving Factors.
While the study examined 19 driving factors to elucidate the mechanism behind land use change in Jianli City, the complexity of these factors, coupled with challenges in data acquisition and quantitative representation, renders it unlikely that all key driving elements are captured. Furthermore, certain data are elusive to obtain, and numerous factors present difficulties in quantification during practical operations, rendering them unsuitable for research. This could potentially introduce biases into the model simulation results, thereby complicating the accurate prediction of future land use spatial patterns.
3.
The Influence of Subjectivity on Model Parameter Determination
The parameter configurations within the PLUS model, particularly the transition matrix values and domain factors, exert a substantial influence on simulation outcomes. The parameter settings in this paper are mainly based on historical data analysis from Jianli City and the literature references and have been verified through debugging with PLUS V1.40 software. However, these settings exhibit a degree of subjectivity, which significantly impacts the model’s accuracy.

5. Conclusions

This study integrates the InVEST and PLUS models to conduct a comprehensive simulation and analysis of land use change and its subsequent carbon storage response in Jianli City, spanning from 2000 to 2035. The primary findings are delineated below:
  • Significant shifts in land use were observed in Jianli City from 2000 to 2020, primarily characterized by the reciprocal conversion between Cropland and Water, coupled with an expansion of Impervious areas. Over this timeframe, cumulative carbon storage of 691,790.27 Mg was diminished within the city. In terms of spatial distribution, the carbon storage exhibited a “high in the west and low in the east” regional pattern. The eastern Honghu Lake coast emerged as a region with minimal carbon storage, while other regions demonstrated higher concentrations. Predominantly, the areas experiencing significant reductions in carbon storage were attributed to the transformation of Cropland into Water in the eastern segment of the city.
  • The results of land use simulation show that under different development scenarios, the types of land use in Jianli City have undergone significant changes, which in turn affect the distribution and variation of carbon storage. Under the Natural Development scenario, there is a slight increase in Cropland and Imperviousness and a decrease in Forest, Grassland, and Water. The carbon storage and the economic value of carbon sinks increase under this scenario, mainly due to the conversion of water areas into cultivated land. Under the Urban Expansion scenario, the significant expansion of Imperviousness further exacerbates the changes in land cover, and its impact on carbon storage is more complex. The increase in Cropland slows down, and the continuous decrease in Water and Forest both pose challenges to the dynamic balance of carbon storage. In this scenario, special attention needs to be paid to carbon management strategies during urbanization to balance the relationship between economic development and ecological protection. Under the Ecology and Food Security scenario, the rapid increase in Cropland and the drastic decrease in Water and Imperviousness directly reflect the profound impact of policy orientation on land use and carbon storage. At the same time, the decreasing trend of Forest and Grassland is alleviated, and the overall carbon storage increases significantly. This scenario emphasizes the importance of carbon sequestration in cultivated land and also highlights the urgency of optimizing the use of construction land and improving the quality of urbanization while ensuring ecological and food security.

Author Contributions

Author Contributions: Conceptualization, J.S. and Y.W.; Methodology, Y.W. and M.T.; Software, M.T. and Y.W.; Validation, J.S. and Y.W.; Formal analysis, M.T; Resources, J.S.; Data curation, Y.W. and M.T.; Writing—original draft preparation, Y.W.; Writing—review and editing, J.S. and X.H.; Visualization, Y.W.; Supervision, J.S.; Project administration, J.S. and Y.W.; Funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the 2022 Hubei Provincial Building Technology Project (grant number 124-2022) and the 2022 Philosophy and Social Science Research Project of the Hubei Provincial Department of Education (grant number 22Q021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data employed for this study were obtained from https://zenodo.org/record/8176941 (accessed on 21 October 2023) and https://www.resdc.cn/ (accessed on 21 October 2023) and Jianli City Natural Resources and Planning Bureau.

Acknowledgments

The authors gratefully acknowledge the support of the funding.

Conflicts of Interest

All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Bozzola, M.; Lamonaca, E.; Santeramo, F.G. Impacts of climate change on global agri-food trade. Ecol. Indic. 2023, 154, 110680. [Google Scholar] [CrossRef]
  2. Yazar, M.; York, A.; Larson, K.L. Adaptation, exposure, and politics: Local extreme heat and global climate change risk perceptions in the phoenix metropolitan region, USA. Cities 2022, 127, 103763. [Google Scholar] [CrossRef]
  3. Varela, R.; de Castro, M.; Dias, J.M.; Gomez-Gesteira, M. Coastal warming under climate change: Global, faster and heterogeneous. Sci. Total Environ. 2023, 886, 164029. [Google Scholar] [CrossRef]
  4. Zhu, P.; Zhang, Z.; Lin, B. Understanding spatial evolution of global climate change risk: Insights from convergence analysis. J. Clean. Prod. 2023, 413, 137423. [Google Scholar] [CrossRef]
  5. Ning, K.; Chen, J.; Li, Z.; Liu, C.; Nie, X.; Liu, Y.; Wang, L.; Hu, X. Land use change induced by the implementation of ecological restoration Programs increases future terrestrial ecosystem carbon sequestration in red soil hilly region of China. Ecol. Indic. 2021, 133, 108409. [Google Scholar] [CrossRef]
  6. Xu, L.; He, N.; Li, M.; Cai, W.; Yu, G. Spatiotemporal dynamics of carbon sinks in China’s terrestrial ecosystems from 2010 to 2060. Resour. Conserv. Recycl. 2024, 203, 107457. [Google Scholar] [CrossRef]
  7. Yin, C.; Zhao, W.; Ye, J.; Muroki, M.; Pereira, P. Ecosystem carbon sequestration service supports the Sustainable Development Goals progress. J. Environ. Manag. 2023, 330, 117155. [Google Scholar] [CrossRef] [PubMed]
  8. Feng, Y.; Chen, S.; Tong, X.; Lei, Z.; Gao, C.; Wang, J. Modeling changes in China’s 2000–2030 carbon stock caused by land use change. J. Clean. Prod. 2020, 252, 119659. [Google Scholar] [CrossRef]
  9. Clarke, K.C.; Johnson, J.M. Calibrating SLEUTH with big data: Projecting California’s land use to 2100. Comput. Environ. Urban Syst. 2020, 83, 101525. [Google Scholar] [CrossRef]
  10. Varquez, A.C.G.; Dong, S.; Hanaoka, S.; Kanda, M. Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs. Sustain. Cities Soc. 2023, 91, 104442. [Google Scholar] [CrossRef]
  11. Luo, G.; Yin, C.; Chen, X.; Xu, W.; Lu, L. Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China. Ecol. Complex. 2010, 7, 198–207. [Google Scholar] [CrossRef]
  12. Liu, G.; Jin, Q.; Li, J.; Li, L.; He, C.; Huang, Y.; Yao, Y. Policy factors impact analysis based on remote sensing data and the CLUE-S model in the Lijiang River Basin, China. Catena 2017, 158, 286–297. [Google Scholar] [CrossRef]
  13. Peng, K.; Jiang, W.; Deng, Y.; Liu, Y.; Wu, Z.; Chen, Z. Simulating wetland changes under different scenarios based on integrating the random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration. Ecol. Indic. 2020, 117, 106671. [Google Scholar] [CrossRef]
  14. Liang, X.; Liu, X.; Li, X.; Chen, Y.; Tian, H.; Yao, Y. Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landsc. Urban Plan. 2018, 177, 47–63. [Google Scholar] [CrossRef]
  15. Lin, W.; Sun, Y.; Nijhuis, S.; Wang, Z. Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study. Sci. Total Environ. 2020, 739, 139899. [Google Scholar] [CrossRef] [PubMed]
  16. Li, X.; Fu, J.; Jiang, D.; Lin, G.; Cao, C. Land use optimization in Ningbo City with a coupled GA and PLUS model. J. Clean. Prod. 2022, 375, 134004. [Google Scholar] [CrossRef]
  17. Gao, L.; Tao, F.; Liu, R.; Wang, Z.; Leng, H.; Zhou, T. Multi-scenario simulation and ecological risk analysis of land use based on the PLUS model: A case study of Nanjing. Sustain. Cities Soc. 2022, 85, 104055. [Google Scholar] [CrossRef]
  18. Zhang, Z.; Hu, B.; Jiang, W.; Qiu, H. Spatial and temporal variation and prediction of ecological carrying capacity based on machine learning and PLUS model. Ecol. Indic. 2023, 154, 110611. [Google Scholar] [CrossRef]
  19. Sharma, H.; Pant, K.S.; Bishist, R.; Lal Gautam, K.; Ludarmani; Dogra, R.; Kumar, M.; Kumar, A. Estimation of biomass and carbon storage potential in agroforestry systems of north western Himalayas, India. Catena 2023, 225, 107009. [Google Scholar] [CrossRef]
  20. Chaudhary, S.; Aryal, B. Factors affecting the tree and soil carbon stock in Shorea robusta Gaertn. forests along the elevational gradient in Eastern Nepal. Acta Ecol. Sin. 2023, 44, 517–528. [Google Scholar] [CrossRef]
  21. Chen, X.; Yu, L.; Hou, S.; Liu, T.; Li, X.; Li, Y.; Du, Z.; Li, C.; Wu, H.; Gao, G.; et al. Unraveling carbon stock dynamics and their determinants in China’s Loess Plateau over the past 40 years. Ecol. Indic. 2024, 159, 111760. [Google Scholar] [CrossRef]
  22. Yoo, J.; Kim, J.; Kim, J.; Lim, J.; Kang, H. Soil carbon storage and its economic values of inland wetlands in Korea. Ecol. Eng. 2022, 182, 106731. [Google Scholar] [CrossRef]
  23. Parisa, Z.; Marland, E.; Sohngen, B.; Marland, G.; Jenkins, J. The time value of carbon storage. For. Policy Econ. 2022, 144, 102840. [Google Scholar] [CrossRef]
  24. Wang, Y.; Liang, D.; Wang, J.; Zhang, Y.; Chen, F.; Ma, X. An analysis of regional carbon stock response under land use structure change and multi-scenario prediction, a case study of Hefei, China. Ecol. Indic. 2023, 151, 110293. [Google Scholar] [CrossRef]
  25. Wu, W.; Xu, L.; Zheng, H.; Zhang, X. How much carbon storage will the ecological space leave in a rapid urbanization area? Scenario analysis from Beijing-Tianjin-Hebei Urban Agglomeration. Resour. Conserv. Recycl. 2023, 189, 106774. [Google Scholar] [CrossRef]
  26. Wu, F.; Wang, Z. Assessing the impact of urban land expansion on ecosystem carbon storage: A case study of the Changzhutan metropolitan area, China. Ecol. Indic. 2023, 154, 110688. [Google Scholar] [CrossRef]
  27. Bozkurt, S.G.; Kusak, L.; Akkemik, U. Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Turkey) metropolitan area between 1990 and 2018. Environ. Monit. Assess 2022, 195, 196. [Google Scholar] [CrossRef] [PubMed]
  28. Yang, B.; Chen, X.; Wang, Z.; Li, W.; Zhang, C.; Yao, X. Analyzing land use structure efficiency with carbon emissions: A case study in the Middle Reaches of the Yangtze River, China. J. Clean. Prod. 2020, 274, 123076. [Google Scholar] [CrossRef]
  29. Li, Y.; Geng, H. Spatiotemporal trends in ecosystem carbon stock evolution and quantitative attribution in a karst watershed in southwest China. Ecol. Indic. 2023, 153, 110429. [Google Scholar] [CrossRef]
  30. Long, X.; Lin, H.; An, X.; Chen, S.; Qi, S.; Zhang, M. Evaluation and analysis of ecosystem service value based on land use/cover change in Dongting Lake wetland. Ecol. Indic. 2022, 136, 108619. [Google Scholar] [CrossRef]
  31. Gao, M.; Xu, R.; Huang, J.; Su, B.; Jiang, S.; Shi, P.; Yang, H.; Xing, Y.; Wang, D.; Jiang, H.; et al. Increase of carbon storage in the Qinghai-Tibet Plateau: Perspective from land-use change under global warming. J. Clean. Prod. 2023, 414, 137540. [Google Scholar] [CrossRef]
  32. Peng, Y.; Cheng, W.; Xu, X.; Song, H. Analysis and prediction of the spatiotemporal characteristics of land-use ecological risk and carbon storage in Wuhan metropolitan area. Ecol. Indic. 2024, 158, 111432. [Google Scholar] [CrossRef]
  33. Fan, L.; Cai, T.; Wen, Q.; Han, J.; Wang, S.; Wang, J.; Yin, C. Scenario simulation of land use change and carbon storage response in Henan Province, China: 1990–2050. Ecol. Indic. 2023, 154, 110660. [Google Scholar] [CrossRef]
  34. He, Y.; Ma, J.; Zhang, C.; Yang, H. Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models. Remote Sens. 2023, 15, 1445. [Google Scholar] [CrossRef]
  35. Tang, L.; Ke, X.; Zhou, T.; Zheng, W.; Wang, L. Impacts of cropland expansion on carbon storage: A case study in Hubei, China. J. Environ. Manag. 2020, 265, 110515. [Google Scholar] [CrossRef] [PubMed]
  36. Zhou, H.; Tang, M.; Huang, J.; Mei, X.; Zhao, H. Driving mechanisms and multi-scenario simulation of land use change based on National Land Survey Data: A case in Jianghan Plain, China. Front. Environ. Sci. 2024, 12, 1422987. [Google Scholar] [CrossRef]
  37. Ke, X.; van Vliet, J.; Zhou, T.; Verburg, P.H.; Zheng, W.; Liu, X. Direct and indirect loss of natural habitat due to built-up area expansion: A model-based analysis for the city of Wuhan, China. Land Use Policy 2018, 74, 231–239. [Google Scholar] [CrossRef]
  38. Tang, L.; Ke, X.; Zhou, Q.; Wang, L.; Koomen, E. Projecting future impacts of cropland reclamation policies on carbon storage. Ecol. Indic. 2020, 119, 106835. [Google Scholar] [CrossRef]
  39. Song, S.; Kong, M.; Su, M.; Ma, Y. Study on carbon sink of cropland and influencing factors: A multiscale analysis based on geographical weighted regression model. J. Clean. Prod. 2024, 447, 141455. [Google Scholar] [CrossRef]
  40. Dai, W.; Huang, Y. Relation of soil organic matter concentration to climate and altitude in zonal soils of China. Catena 2006, 65, 87–94. [Google Scholar] [CrossRef]
  41. Li, X.; Li, F.; Wang, D.; Hou, J.; Wang, Z.; Cao, R.; Yang, W. Zonal patterns and driving factors of soil organic carbon density in Zhejiang Province, China. Geoderma Reg. 2023, 34, e00679. [Google Scholar] [CrossRef]
  42. Xu, C.; Zhang, Q.; Yu, Q.; Wang, J.; Wang, F.; Qiu, S.; Ai, M.; Zhao, J. Effects of land use/cover change on carbon storage between 2000 and 2040 in the Yellow River Basin, China. Ecol. Indic. 2023, 151, 110345. [Google Scholar] [CrossRef]
  43. Li, X.; Zhao, H.; Gu, B.; Chen, Y. Carbon sinks/sources’ spatiotemporal heterogeneity and their spatial response to economic-social factors in relatively developed regions: A case of China’s coastal area. J. Environ. Manag. 2024, 354, 120494. [Google Scholar] [CrossRef] [PubMed]
  44. Ricke, K.; Drouet, L.; Caldeira, K.; Tavoni, M. Country-level social cost of carbon. Nat. Clim. Chang. 2018, 8, 895–900. [Google Scholar] [CrossRef]
  45. Zuo, W.; Gu, B.; Zou, X.; Peng, K.; Shan, Y.; Yi, S.; Shan, Y.; Gu, C.; Bai, Y. Soil organic carbon sequestration in croplands can make remarkable contributions to China’s carbon neutrality. J. Clean. Prod. 2023, 382, 135268. [Google Scholar] [CrossRef]
  46. Zarandian, A.; Badamfirouz, J.; Musazadeh, R.; Rahmati, A.; Azimi, S.B. Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran. Environ. Monit Assess 2018, 190, 474. [Google Scholar] [CrossRef] [PubMed]
  47. Zhang, D.; Zhao, Y.; Wu, J. Assessment of carbon balance attribution and carbon storage potential in China’s terrestrial ecosystem. Resour. Conserv. Recycl. 2023, 189, 106748. [Google Scholar] [CrossRef]
  48. Zhu, L.; Song, R.; Sun, S.; Li, Y.; Hu, K. Land use/land cover change and its impact on ecosystem carbon storage in coastal areas of China from 1980 to 2050. Ecol. Indic. 2022, 142, 109178. [Google Scholar] [CrossRef]
  49. Gong, W.; Duan, X.; Sun, Y.; Zhang, Y.; Ji, P.; Tong, X.; Qiu, Z.; Liu, T. Multi-scenario simulation of land use/cover change and carbon storage assessment in Hainan coastal zone from perspective of free trade port construction. J. Clean. Prod. 2023, 385, 135630. [Google Scholar] [CrossRef]
  50. Li, L.; Huang, X.; Yang, H. A new framework for identifying ecological conservation and restoration areas to enhance carbon storage. Ecol. Indic. 2023, 154, 110523. [Google Scholar] [CrossRef]
  51. Hansen, M.H.; Li, H.; Svarverud, R. Ecological civilization: Interpreting the Chinese past, projecting the global future. Glob. Environ. Chang. 2018, 53, 195–203. [Google Scholar] [CrossRef]
  52. Meng, F.; Guo, J.; Guo, Z.; Lee, J.C.K.; Liu, G.; Wang, N. Urban ecological transition: The practice of ecological civilization construction in China. Sci Total Env. 2021, 755, 142633. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, L.; Wang, H.; Zhang, W.; Wang, C.; Bao, M.; Liang, T.; Liu, K.-d. Study on the development patterns of ecological civilization construction in China: An empirical analysis of 324 prefectural cities. J. Clean. Prod. 2022, 367, 132975. [Google Scholar] [CrossRef]
  54. Du, W.; Yan, H.; Feng, Z.; Yang, Y.; Liu, F. The supply-consumption relationship of ecological resources under ecological civilization construction in China. Resour. Conserv. Recycl. 2021, 172, 105679. [Google Scholar] [CrossRef]
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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. Land use transfer chord map from 2000 to 2020.
Figure 3. Land use transfer chord map from 2000 to 2020.
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Figure 4. Land use transfer sankey map from 2000 to 2020.
Figure 4. Land use transfer sankey map from 2000 to 2020.
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Figure 5. Land use status from 2000 to 2020.
Figure 5. Land use status from 2000 to 2020.
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Figure 6. Expansion probability of each land use type.
Figure 6. Expansion probability of each land use type.
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Figure 7. Land use status under three scenarios.
Figure 7. Land use status under three scenarios.
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Figure 8. Variations in carbon storage and geo-averaged carbon density of terrestrial systems from 2000 to 2020.
Figure 8. Variations in carbon storage and geo-averaged carbon density of terrestrial systems from 2000 to 2020.
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Figure 9. Spatial distribution of carbon storage from 2000 to 2020.
Figure 9. Spatial distribution of carbon storage from 2000 to 2020.
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Figure 10. Carbon stock changes from 2000 to 2020.
Figure 10. Carbon stock changes from 2000 to 2020.
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Figure 11. The change in carbon storage and carbon sink economy caused by land use type conversion from 2000 to 2020.
Figure 11. The change in carbon storage and carbon sink economy caused by land use type conversion from 2000 to 2020.
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Figure 12. Spatial distribution of carbon storage under three scenarios.
Figure 12. Spatial distribution of carbon storage under three scenarios.
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Figure 13. The change in carbon storage and carbon sink economy caused by land use type conversion under three scenarios.
Figure 13. The change in carbon storage and carbon sink economy caused by land use type conversion under three scenarios.
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Figure 14. Driving factors.
Figure 14. Driving factors.
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Figure 15. Importance of driving factors for each land use type.
Figure 15. Importance of driving factors for each land use type.
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Table 1. Data sources used in this study.
Table 1. Data sources used in this study.
Data TypeData NameTime FrameSpatial ResolutionData UnitData SourcesTreatment
Land useLand use2000–202030 m——https://zenodo.org/record/8176941, accessed on 21 October 2023Extraction by mask Project Raster
Natural environmentElevation202030 mmhttps://www.resdc.cn/, accessed on 21 October 2023.Resample
Slope202030 m°Resample Slope
Slope direction202030 m Resample Slope direction
Precipitation20201000 mmmResample
Temperature20201000 m°C
Vegetation coverage202030 m——
Soil type202030 m
Socio-economicPopulation density20201000 mPerson/km2https://www.resdc.cn/, accessed on 21 October 2023.Resample
Per capita GDP20201000 mmillion/km2
Night lighting20200.004°nW/cm2/sr
Space-accessibleDistance from motorway202030 mmhttps://www.resdc.cn/, accessed on 21 October 2023.Resample Euclidean distance
Distance from provincial highway202030 m
Distance from national highway202030 m
Distance from county road202030 m
Distance from township202030 m
Distance from urban primary roads202030 m
Distance from urban secondary roads202030 m
Distance from urban tertiary roads202030 m
Distance from primary schools202030 m
Distance from secondary school202030 m
Distance from medical institution202030 m
Distance from commerce202030 m
Distance from water202030 m
Table 2. Carbon density data of Jianli City (t/ha).
Table 2. Carbon density data of Jianli City (t/ha).
ClassCabove (t/ha)Cbelow (t/ha)Csoil (t/ha)Cdead (t/ha)
Cropland16.4910.8975.822.11
Forest30.146.03100.152.78
Grassland14.2917.1587.052.42
Water0000
Impervious7.611.5234.330
Table 3. Statistics of land use types and areas from 2000 to 2020.
Table 3. Statistics of land use types and areas from 2000 to 2020.
Class20002005201020152020
Area/hm2Weight/%Area/hm2Weight/%Area/hm2Weight/%Area/hm2Weight/%Area/hm2Weight/%
Cropland326,958.3988.70%321,743.3487.28%316,109.2585.75%315,899.5585.70%319,240.1786.60%
Forest447.030.12%281.520.08%299.790.08%279.180.08%261.720.07%
Grassland0.090.00%0.000.00%3.690.00%1.350.00%6.480.00%
Water34,865.739.46%39,118.0510.61%43,233.1211.73%42,676.4711.58%37,327.510.13%
Impervious6358.861.72%7487.192.03%8984.252.44%9773.552.65%11,794.233.20%
Total368,630.1100.00%368,630.1100.00%368,630.1100.00%368,630.1100.00%368,630.1100.00%
Table 4. Analysis of land use single-dynamic degrees.
Table 4. Analysis of land use single-dynamic degrees.
ClassLand Use Single-Dynamic Degrees
2000–20052005–20102010–20152015–20202000–2020
Cropland−0.32%−0.35%−0.01%0.21%−0.12%
Forest−7.40%1.30%−1.37%−1.25%−2.07%
Grassland−0.19%7.88%−3.59%9.58%3.38%
Water2.44%2.10%−0.26%−2.51%0.35%
Impervious3.55%4.00%1.76%4.13%4.27%
Table 5. Land use change transfer matrix from 2000 to 2005.
Table 5. Land use change transfer matrix from 2000 to 2005.
ClassThe 2005 Transition Matrix/hm2 Transfer-Out Area/hm2Transfer Out Contribution Rate/%
Class 1Class 2Class 3Class 4Class 5Total
The 2000 transition matrix/hm2Class 1317,363.943.780.008871.30719.37326,958.399594.4566.19
Class 2170.28276.300.000.450.00447.03170.731.18
Class 30.000.009.360.000.099.450.090.00
Class 44206.151.440.0030,182.58466.2034,856.374673.7932.24
Class 52.970.000.0054.366301.536358.8657.330.40
Total321,743.34281.529.3639,108.697487.19368,630.10————
Transfer-in area/hm24379.405.220.008926.111185.66——14,496.39——
Transfer in contribution rate/%30.210.040.0061.578.18————1
Table 6. Land use change transfer matrix from 2005 to 2010.
Table 6. Land use change transfer matrix from 2005 to 2010.
ClassThe 2010 Transition Matrix/hm2 Transfer-Out Area/hm2Transfer Out Contribution Rate/%
Class 1Class 2Class 3Class 4Class 5Total
The 2005 transition matrix/hm2Class 1311,707.0820.73.699102.78909.09321,743.3410,036.2666.03
Class 219.98260.640.000.90.00281.5220.880.14
Class 30.000.009.360.000.009.360.000.00
Class 44380.5718.450.0034,044.48665.1939,108.695064.2133.32
Class 51.620.000.0075.67409.977487.1977.220.51
Total316,109.25299.7913.0543,223.768984.25368,630.10————
Transfer-in area/hm24402.1739.153.699179.281574.28——15,198.57——
Transfer in contribution rate/%28.960.260.0260.4010.36————1
Table 7. Land use change transfer matrix from 2010 to 2015.
Table 7. Land use change transfer matrix from 2010 to 2015.
ClassThe 2015 Transition Matrix/hm2/hm2 Transfer-Out Area/hm2Transfer Out Contribution Rate/%
Class 1Class 2Class 3Class 4Class 5Total
The 2010 transition matrix/hm2Class 1310,034.252.971.174864.51206.36316,109.25607548.40
Class 222.86275.850.001.080.00299.7923.940.19
Class 30.180.009.540.003.3313.053.510.03
Class 45842.260.360.0037,288.1792.9743,223.765935.5947.29
Class 50.000.000.00513.368470.898984.25513.364.09
Total315,899.55279.1810.7142,667.119773.5536,8630.1————
Transfer-in area/hm25865.303.331.175378.941302.66——12,551.4——
Transfer in contribution rate/%46.730.030.0142.8610.38————1
Table 8. Land use change transfer matrix from 2015 to 2020.
Table 8. Land use change transfer matrix from 2015 to 2020.
ClassThe 2020 Transition Matrix/hm2 Transfer-Out Area/hm2Transfer Out Contribution Rate/%
Class 1Class 2Class 3Class 4Class 5Total
The 2015 transition matrix/hm2Class 1311,135.414.760.930781670.49315,899.554764.1535.59
Class 228.53246.960.003.690.00279.1832.220.24
Class 30.000.009.450.001.2610.711.260.01
Class 48069.220.005.2234,161.12431.5542,667.118505.9963.54
Class 57.020.000.2775.339690.939773.5582.620.62
Total319,240.17261.7215.8437,318.1411,794.23368,630.1————
Transfer-in area/hm28104.7714.766.393157.022103.3——13,386.24——
Transfer in contribution rate/%60.550.110.0523.5815.71————1
Table 9. Demand table of each land use type.
Table 9. Demand table of each land use type.
TypologyYearsCroplandForestGrasslandWaterImpervious
Demand for simulation2000–20053,963,961993119616,178141,103
2005–20103,929,5214328255633,523154,727
2010–20154,043,7783332120540,421134,702
2000–20103,934,2952784209634,323150,742
Actual20204,090,4383356206477,392150,961
Error rata2000–2005−0.030920161−0.704112038−0.4223300970.290717063−0.065301634
2005–2010−0.0393397970.2896305130.2378640780.3270498880.024946841
2010–2015−0.011407091−0.007151371−0.4174757280.132027768−0.107703314
2000–2010−0.038172685−0.1704410010.0145631070.328725659−0.001450706
Table 10. Neighborhood weights for land use types.
Table 10. Neighborhood weights for land use types.
ClassTimespan
Five YearsTen Years
2000–20052005–20102010–20152000–2010
Cropland0.8394036110.8321106380.8563057440.833121751
Forest0.0002102760.0009164920.000705580.000589537
Grassland2.51993 × 10−55.40 × 10−52.54 × 10−54.43 × 10−5
Water0.1304811120.1341540680.1144389250.134323504
Impervious0.0298798010.0327648030.0285243390.031920951
Table 11. Simulation accuracy verification.
Table 11. Simulation accuracy verification.
ModulusYears
2000–20052005–20102010–20152000–2010
Kappa0.648860.6945510.847090.620722
FOM0.2407150.2240340.4235060.204741
Table 12. The rule matrix of land use conversion under three scenarios.
Table 12. The rule matrix of land use conversion under three scenarios.
ClassNatural DevelopmentUrban ExpansionEcology and Food Security
abcdeabcdeabcde
a111111111111111
b110101101011000
c001010010100101
d101111011110111
e101111011110111
Table 13. Statistics of land use types and areas under three scenarios.
Table 13. Statistics of land use types and areas under three scenarios.
Class2020 TrueNatural DevelopmentUrban ExpansionEcology and Food Security
Area/hm2Weight/%Area/hm2Weight/%Dynamic/%Area/hm2Weight/%Dynamic/%Area/hm2Weight/%Dynamic/%
Cropland319,240.1786.60%323,833.1887.85%0.10%323,273.8887.70%0.08%325,590.8088.32%0.13%
Forest261.720.07%222.000.06%−1.01%221.610.06%−1.02%236.290.06%−0.65%
Grassland15.840.00%15.770.00%−0.03%15.850.00%0.00%15.850.00%0.00%
Water37,318.1410.12%32,665.988.86%−0.83%32,934.438.93%−0.78%31,549.718.56%−1.03%
Impervious11,794.233.20%11,893.173.23%0.06%12,184.343.31%0.22%11,237.463.05%−0.31%
Table 14. The carbon storage and geo-averaged carbon density of each township from 2000 to 2020.
Table 14. The carbon storage and geo-averaged carbon density of each township from 2000 to 2020.
TownCarbon Stock/TgLand Average Carbon Density/(t/hm2)
2000200520102015202020002005201020152020
Bianhe Town2.011.931.701.932.1381.3678.2268.7478.0786.07
Shangchewan Town0.920.940.930.940.9598.82100.2199.89100.37101.55
Sanzhou Town1.951.961.971.961.9784.4884.9585.1985.0885.31
Zhoulaozui Town2.062.062.062.062.05103.43103.58103.41103.33102.94
Chengji Town1.661.651.631.641.65103.55103.17102.23102.46103.08
Chiba Town1.891.901.901.891.9193.1193.4193.3893.0993.96
Hongcheng Town2.642.652.652.642.6299.75100.21100.1599.8799.01
Wangqiao Town2.172.172.162.172.18102.32102.19101.74102.19102.57
Qiaoshi Town1.831.661.581.381.4995.1586.5482.6072.1877.86
Bailuo Town1.601.601.611.591.6578.1778.1778.3777.4080.35
Zhuhe Town1.651.651.641.641.65100.22100.1599.5499.27100.25
Rongcheng Town1.161.181.171.150.9981.2082.4081.7880.2969.65
Huanghu Farm Management Area0.810.810.800.810.81102.91102.93101.88102.14102.60
Xingou Town2.182.172.172.162.15103.51103.28102.93102.58102.05
Zhemu Town2.202.152.112.082.1190.8688.7587.0785.8987.34
People’s Dwan Farms Management Area2.032.052.102.072.0693.9594.8696.8795.4694.98
Huangxiekou Town2.062.062.062.062.07102.67102.83102.67102.81103.11
Futiansi Town1.251.271.221.271.3294.8596.1592.8796.83100.09
Qipan Town1.270.910.670.680.8666.0947.3434.8435.3945.06
Wangshi Town1.281.281.281.281.27103.30103.72103.80103.61102.98
Gongchang Town1.501.491.511.511.51102.78102.26103.49103.42103.36
Maoshi Town1.871.841.861.881.8999.3998.1699.12100.08100.71
Fenyan Town2.102.082.092.102.10103.62102.93103.42103.91103.87
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Shao, J.; Wang, Y.; Tang, M.; Hu, X. Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China. Sustainability 2024, 16, 6736. https://doi.org/10.3390/su16166736

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Shao J, Wang Y, Tang M, Hu X. Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China. Sustainability. 2024; 16(16):6736. https://doi.org/10.3390/su16166736

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Shao, Jun, Yuxian Wang, Mingdong Tang, and Xinran Hu. 2024. "Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China" Sustainability 16, no. 16: 6736. https://doi.org/10.3390/su16166736

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