Coupled PLUS-InVEST Modeling of Land Use Change and the Economic Valuation of Carbon Storage in Xi'an, China

Coupled PLUS-InVEST Modeling of Land Use Change and the Economic Valuation of Carbon Storage in Xi'an, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Coupled PLUS-InVEST Modeling of Land Use Change and the Economic Valuation of Carbon Storage in Xi'an, China Suliang Wang, Qiang Li, Longtan Qiao, Fangjiang Li, Guofeng Gao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7404029/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study develops and applies a coupled PLUS-InVEST modeling framework to examine the spatial dynamics of land use patterns and carbon storage in Xi’an (2000–2020). Four development scenarios—Business as Usual (BAU), Environmental Protection Scenario (EPS), Economic Profit (EP), and Cultivated Land Protection Scenario (CPS)—are constructed to assess and predict the spatiotemporal variations in land use carbon storage by 2030. Drawing on the theory of the time value of money, compounded present and future value formulas are employed to estimate the economic benefits derived from regional carbon storage over the period 2000–2030. Our results reveal pronounced structural shifts in land use, characterized by a sustained decline in cultivated land and accelerated expansion of construction land, contributing to a cumulative reduction of 2.0812 million tons of carbon storage over the two decades. Scenario-based projections demonstrate substantial variation in carbon storage by 2030: the EPS and CPS scenarios are expected to yield net increases of 541.4 and 63.5 thousand tons, respectively, while the BAU and EP scenarios result in declines, with the EP scenario exhibiting the greatest loss (352.7 thousand tons) due to intensified urban development. Between 2000 and 2020, the economic value of carbon storage in Xi’an expanded by 8.125 billion yuan, reflecting the significant appreciation of carbon prices over the two decades. Compared to the 2020 baseline, the value of carbon storage under the EPS would reach 26.389 billion yuan by 2030, significantly surpassing other scenarios. These findings highlight the ecological and economic benefits of the EPS pathway, offering a compelling reference for optimizing land resource allocation and promoting sustainable regional development. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Land Use Carbon Storage PLUS-InVEST Model Economic Valuation of Carbon Xi’an Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Carbon emissions continue to increase as a result of human activities, triggering global warming, melting glaciers, rising sea levels and frequent natural disasters., all of which pose serious threats to the sustainability and ecological security of the Earth's ecosystems [ 1 – 4 ]. In the context of accelerating economic development, global land use patterns are undergoing profound transformation. Meanwhile, growing societal demands and increasing spatial heterogeneity have intensified the complexity of interactions among ecosystem services, thereby affecting terrestrial carbon storage and constraining regional sustainable development [ 5 ]. Driven by rapid economic growth, urban land expansion in China has increasingly replaced carbon-rich land covers such as forests, cultivated fields, and grasslands, thereby intensifying carbon loss in urban ecological systems. [ 6 ]. Therefore, examining the role of land use transformation on land-based carbon reserves is critical to improving sequestration capacity and curbing global climate change [ 7 ]. By simulating multiple development scenarios, it becomes possible to forecast the dynamic evolution of future carbon storage and its economic value. It offers critical insights for promoting sustainable socio-economic development and achieving carbon neutrality targets [ 8 ]. Carbon storage estimation has become a prominent focus in ecosystem service research, with numerous studies exploring its spatial distribution and assessment methods [ 9 – 11 ]. The Carbon Storage and Sequestration (CSS) module within the InVEST framework has been broad application across domestic and international studies as a result of its low data requirements and high computational efficiency, earning recognition from many researchers [ 12 – 14 ]. However, this module has limitations in predicting carbon storage responses under dynamic landscape conditions, making it difficult to accurately reflect the potential correlation between land use patterns and carbon dynamics [ 15 ]. Anticipated changes in carbon storage are strongly guided by future land use dynamics and the demand for ecosystem services. The Patch-generating Land Use Simulation model (PLUS) has received increasing attention in recent years for its ability to simulate nonlinear mechanisms in multi-type land use transitions with high spatial accuracy[ 16 – 18 ]. Coupling the PLUS and InVEST models not only addresses the limitations of using either model independently, but also enables scenario-based prediction of future terrestrial carbon storage dynamics. This integrated approach provides scientific support for sustainable urban development [ 19 – 21 ]. Moreover, land use change affects carbon storage not only directly, but also indirectly influences its economic value by altering land functions [ 22 ]. Previous studies have often employed market valuation or the Swedish carbon tax method to estimate current carbon storage value, but rarely incorporated the time value of carbon pricing [ 23 ]. In the last decades, studies have advanced the understanding of addressing this gap by introducing the concept of the time value of money into carbon valuation [ 24 , 25 ]. However, comprehensive assessments of the economic value of future carbon storage under different development scenarios remain insufficient and warrant further research. Building on conducting comprehensive assessments of regional carbon storage and its economic value can provide critical insights for policymakers in optimizing land use planning and resource allocation [ 26 ]. Accurate estimation of the economic value of future carbon storage hinges upon rational carbon price setting, which is influenced by multiple factors, including the time value of money, market dynamics, policy uncertainties, and the stringency of carbon regulation[ 27 – 29 ]. Within the field of economics, the compound present value method-based on central bank discount rates-can be employed to estimate trends in future carbon pricing, effectively capturing its temporal value. This method is widely used across financial and commodity sectors and offers valuable input for guiding investment strategies [ 30 ]. Existing studies have demonstrated the validity of compound interest methods in long-term low-carbon economic assessments [ 31 , 32 ]. Based on this context, this study examines the patterns and temporal evolution of land use and carbon storage in Xi’an during the period 2000–2020. The coupled PLUS-InVEST model is developed, incorporating social and economic driving forces into a land use scenario simulation framework. Four development scenarios are constructed: Business as Usual (BAU), Environmental Protection Scenario (EPS), Economic Profit (EP), and Cultivated Land Protection Scenario (CPS), to project variation of land use patterns and carbon storage by 2030. Furthermore, the compound present and future value method is introduced to quantify the economic value of regional carbon storage under each scenario. This research contributes by offering an economic foundation for strategic sustainability planning and ecological enhancement in Xi’an. 2. Materials and Methods 2.1 The Study Area Xi’an, the capital of Shaanxi Province, is situated in northwestern China (107°40′E–109°49′E, 33°42′N–34°45′N, Fig. 1 ). Characterized by a warm-temperate and semi-humid continental monsoon climate, the city’s terrain declines from south to north, flanked by the Qinling Mountains and the Weihe Plain [ 33 , 34 ]. Covering approximately 1.01 million hectares, the region is predominantly composed of cultivated land, forest land, and urban land for construction [ 35 ] In recent years, accelerated urbanization and population growth (Xi’an Municipal Government) have intensified the mismatch between land use patterns, industrial development, and resource availability, resulting in mounting pressures such as land scarcity and water shortages. In response to these constraints, the Xi’an New Area was established in 2014 with approval from the State Council. As the nation’s seventh and the western region’s third national-level new area—it serves as a strategic platform to relieve ecological pressures and enhance coordinated regional development across Xi’an and Xianyang. 2.2 Sources of Data This study integrates multiple databases, containing land use datasets and driving factor datasets. Specifically, data land use in 2000, 2010, and 2020 with 30 meters spatial resolution were obtained from the Resource and Environmental Science and Data Platform. Based on research objectives, six land use categories were derived through classification: cultivated land, forest land, grassland, water bodies, construction land, and unused land. The fixed factors include six natural variables, two socio-economic variables, and nine accessibility indicators (see Table 1 for details). Slope data were derived from spatial analysis of DEM data using ArcGIS 10.8. Euclidean distance was employed derive accessibility indicators relative to various features in ArcGIS. All influencing factors datasets were resampled to a spatial resolution of 30 m × 30 m, converted into TIFF format, and projected to the WGS 1984 UTM Zone 48N coordinate system (see Fig. 2 ). Table 1 Sources of Multi-Source Data Category Data Data resource Basic Data Land Use Data (30 m), Xi'an, 2000/2010/2020 Resource and Environmental Science & Data Platform Natural Factors Elevation Geospatial Data Cloud (Derived from DEM data) Slope Mean Annual Temperature Earth Resources Data Cloud Mean Annual Precipation Soil Type Platform for Resource and Environmental Science Data Soil Erosion Socio-Economic Factors Population Density GDP Accessibility Factors Distance to County Government National Geographic Information Resources Catalogue Service System Distance to Expressway Distance to Provincial Road Distance to National Highway Distance to Railway Distance to Primary, Secondary, and Tertiary Roads Distance to Water System Restricted Area Ecological Function Protection Zone College of Urban and Environmental Sciences, Peking University Cultivated Land with Slope less than 6° Derived from Land Use data Stable Cultivated Land 2.3 Methods First, land use data for Xi'an from the years 2000, 2010 and 2020were used to construct a land category transfer matrix within ArcGIS. This matrix quantified transition directions, magnitudes, and temporal features of land conversion, thereby revealing the regional patterns of land use evolution. Next, the PLUS model was used to simulate the land use in 2020 and predict future spatial patterns depend on four development scenarios for 2030. The simulation incorporated both natural geographic and socioeconomic driving factors. Model reliability was verified through spatial coincidence analysis. Building on this foundation, the InVEST model was employed—using revised land-use-specific carbon density parameters—to estimate carbon storage for the years 2000–2020 and under various scenarios for 2030, with a focus on analyzing its spatiotemporal heterogeneity and economic value. Subsequently, a comparative analysis of regional characteristics was conducted to identify an optimal development pathway that reconciles ecological conservation with socioeconomic advancement (Fig. 3 ). 2.3.1 PLUS Model Simulation of Land Use Patterns The model simulates future land demand through historical land use raster data, which integrates the Land Expansion Analysis Strategy module (LEAS) and the Cellular Automata with Random Seeds for Multiple Types module (CARS) [ 36 ]. The combined use of the PLUS and Markov models enables direct calculation of future land use demand through the following formula [ 37 , 38 ]. $$\:{\text{S}}_{（\text{t}+1）}={\text{p}}_{\text{i}\text{j}}{\text{S}}_{\text{t}}$$ 1 Within the formula, the symbol S ( t + 1) denotes the land-use type at time step t + 1, pij indicates the area converted from use type i to type j before to transformation, and St represents the initial land-use type at time t . Based on the Markov chain module within the PLUS model, of the study predicts land use in 2020 using data from 2000 and 2010. The simulated results were compared with objective data of land use from 2020, yielding a Kappa coefficient of 0.88, an overall accuracy of 0.91, and coefficient of Fomx is 0.38. According to relevant studies, the model demonstrates high precision in simulating future land use changes and can be reliably applied to forecast future land use patterns[ 39 ], see Fig. 4 . The LEAS module derives the spatial pattern of land expansion from land use data across two time periods. It then applies the random forest algorithm to quantify the contribution of driving factors, using the following formula for detailed analysis: $$\:{P}_{i,k}^{d}=\frac{{\sum\:}_{n=1}^{M}I=\left({h}_{n}\right(x\left)\right)=d\:)}{M}$$ 2 The variable d can only take values of 0 or 1, which are used to indicate whether the land use type has been converted to type k or to other land use types, respectively. The vector x comprises multiple driving force factors, and the function I acts as the indicator function within the decision tree ensemble, ℎn (x) represents the predicted land use type of vector x under the nth decision tree. Additionally, m specifies the complete set of decision trees constructed. CARS: simulates future land use patches based on expanded land, and can predict the spatial patterns of land use patterns under different scenarios. Multi-development Mode Setting In accordance with the "Xi'an Territorial Spatial Master Plan (2021–2035)", the "Notice of the Shaanxi Provincial People's Government on the Shaanxi Province Main Function Zoning Plan", the "Notice of the Xi 'an Municipal People's Government on the '14th Five-Year Plan' for Ecological and Environmental Protection", the "Xi 'an Qinling Ecological and Environmental Protection Management Measures", and the "Work Plan for Comprehensively Improving the Construction of High-Standard Farmland in Xi 'an", relevant planning documents including the above-mentioned cases were referenced. Taking into account the current development situation of Xi'an City and the existing research results [ 40 – 44 ], the Business-as-Usual Scenario (BAU) andEPS were defined. Four development scenarios were applied: BAU, EPS, Economic Profit (EP), and Cultivated Land Protection Scenario (CPS). All scenarios designate the existing ecological protection area as the basic restricted area. BAU: Continuing the land use development probability from 2010 to 2020, the existing ecological protection areas are designated as restricted zones, serving as foundation for for simulating alternativedevelopment scenarios. EPS: In accordance with the relevant plans and policies for ecological and environmental protection in Shaanxi Province and Xi 'an City, a bidirectional transfer probability adjustment is implemented: the probability of transfer from cultivated land, forest land, grassland, and water bodies to construction land is decreased by 30%, while the rate of transfer from construction land to cultivated land, forest land, and grassland is increased by 50%, supporting the policy orientation of farmland protection and ecological restoration. EP: For the purpose of promoting regional economic development, construction land is expanding at a faster rate. Based on earlier findings and the characteristics of the region under investigation, transitions from construction land to alternative land types is restricted. and the rates of Cultivated land, forest land, grassland, waterbodies, and unused land were assigned increased probabilities- by 40%, 10%, 30%, 10%, and 50% respectively—for conversion into construction land [ 45 , 46 ]. CPS: As the core wheat-producing area in the Weihe Plain of Shaanxi Province, Xi'an undertakes the primary role of CPS and grain production stabilization.t Its policy system covers core tasks such as the delineation of basic farmland areas, the construction of high-standard farmland, the restoration of abandoned cultivated land, and equilibrium of occupation-compensation land. In constructing CPS, long-term stable cultivated land areas are identified by overlaying land use records for the years 2000, 2010 and 2020. According to the “Regulations for Grading Agricultural Land” and relevant policy studies, cultivated land with a slope of under 6° was identified as high-quality and, along with ecological conservation areas, designated as restricted conversion zones. With reference to existing achievements[ 47 – 49 ], outward conversion of cultivated land is strictly limited. Meanwhile, to implement protection policies, the conversion probabilities from unused land and grassland to cultivated land were increased by 50%. Parameter Setting The values of domain weights was 0 to 1. Values approaching 1 signify high expansion strength, while those near 0 denote low expansion intensity. This study adopts an established methodology[ 50 ]to calculate the domain weights of various development scenarios in 2030. The formula is as follows: $$\:{X}_{i}=\frac{\varDelta\:T{A}_{i}-{\varDelta\:TA}_{min}}{\varDelta\:{TA}_{max}-{\varDelta\:TA}_{min}}$$ 3 In the formula, Xi represents the neighborhood weight parameter for land type i ; represents the change amount of change in TA for land type during the study period; ΔTAmax and ΔTAmin represent the maximum and minimum change amounts of TA during the study period, respectively. The land use transfer matrix defines the conversion rules applied in land use types. When one land use type is allowed to be converted into another, the corresponding value in the matrix is 1; otherwise, it is 0. The settings of the four development scenarios considered in this study are detailed in Table 2 . Table 2 Transfer Matrices of Different Development Models Type BAU EPS EP CPS a b c d e f a b c d e f a b c d e f a b c d e f a 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 b 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 c 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 d 1 0 0 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 e 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 f 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 Table Note a: Cultivated land; b: Forest land; c: Grass land; d༚water; e༚Construction land; f༚Unused land;0༚Conversion restricted; 1༚Conversion allowed. 2.3.2 The InVEST model The Carbon module estimates regional carbon storage over a given time period primarily based on land use and four fundamental carbon pools (aboveground biomass carbon, belowground biomass carbon, soil carbon, and dead organic matter carbon) Within the InVEST model,. [ 51 ]. This sequestration module evaluates the carbon storage capacity of terrestrial ecosystems and the total regional carbon storage is computed using the following equation: $$\:{C}_{i}={C}_{i-above}+{C}_{i-below}+{C}_{i-soil}+{C}_{i-dead}$$ 1 $$\:{C}_{tot}={\sum\:}_{i=1}^{n}{C}_{i}\times\:{A}_{i}$$ 2 In this context, i represents a specific land use type; Ci is the carbon density associated with type i . This carbon density comprises four components: aboveground biomass carbon (Ci-above) 、belowground biomass carbon (Ci-below) 、soil organic carbon (Ci-soil) ,and dead organic matter carbon (Ci-dead) , each measured in tons per hectare (t/hm²) . The total ecosystem carbon storage (Ctot) is calculated by summing the products of the carbon density (t) ; and the area (Ai) of each land use type i across all n types [ 52 ]. This study refers to existing carbon density datasets and relevant academic research findings [ 53 ], and prioritizes data results that align with the characteristics of the study area. Based on comprehensive literature review and expert judgment, the correction values proposed by scholars such as Wang et al [ 54 ] are adopted as the carbon density parameters of the study area, and the specific values are presented inTable 3. Table 3 Carbon Densities of Different Land Use Types in Xi'an (t / hm 2 ) Type C i-above C i-below C i-soil C i-dead Cultivated land 4.2 31.1 77.19 0.63 Forest land 10.47 44.66 110.86 1.66 Grass land 8.72 33.33 71.35 0.39 waters 0.08 0 0 0 Construction land 0.62 0 74.61 0 Unused land 0.32 0 29.65 0 2.3.3 Methods of Compound Present and Future Value Calculation This study estimates the economic value of carbon storage using both the compound present value and future value methods [ 26 ]. Based on existing research, the carbon prices for the 2000, 2010, 2020 and 2030 years are set at 105 yuan, 128 yuan, 172 yuan and 211 yuan respectively [ 55 ]. 3. Results 3.1 Temporal and Spatial Dynamics of Land Use in Xi'an In 2000, 2010 and 2020, the land use structure was dominated by cultivated land, forest land and grassland, which together accounted for the three being 90.54% (in 2000), 87.38% (in 2010) and 85.74% (in 2020), respectively. Spatially, cultivated land was concentrated in the Guanzhong Plain, forest land was spread as a belt along the northern foothills of the southern Qinling Mountains, and grassland was widespread in the southern and east ern regions, and construction land is mainly distributed in the north-central area. Over the past two decades, significant land use transformation occurred: the areas of construction land, water bodies, unused land and forest land have shown an increasing trending, while cultivated land and grassland experienced continuous decline, with the eastern region being the primarily area of reduction, see Fig. 5 -a, b (2). Under the BAU scenario for 2030, the trajectories of cultivated land, water bodies, construction land, and unused land show slight deviations from the 2000–2020 trends, characterized by a reduction in forest area and an expansion of grassland. The EPS shows that the changing trends of cultivated land, forest land, grassland, waterbodies and construction land remain consistent with those from 2000 to 2020, while unused land under shows a significant decline. The CPS scenario also maintains the land use dynamics observed during 2000 to 2020, as illustrated in Fig. 5 -b(1) and Fig. 5 -c. 3.2Spatio-temporal Dynamics of carbon storage in Xi 'an City During 2000 to 2030, the carbon storage values and changes in Xi'an are illustrated in Figs. 6 and 7 a. Carbon storage decreased progressively over 2000, 2010 and 2020, reaching 126.6174 million tons, 125.5441 million tons and 124.5361 million tons, respectively. It declined by 1.0733 million tons during the first decade, and by 1.0080 million tons in the subsequent decade. The spatial pattern is characterized by a decline in values from southwest to northeast. The high-carbon areas are converged on the forest and grassland zones along the northern foothills of the Qinling Mountains in the southwest, while the low-carbon areas are located in the densely developed construction land near the confluence of the southern Wei River and western Ba River. Under the BAU scenario in 2030, a continued reduction in carbon storage is expected, consistent with the 2000–2020 trend. Figure 5 -b (1) shows that forest land exhibits the highest carbon storage, reaching 50.3462 million tons and accounting for 40.53% of the total. Compared to the BAU scenario, the EPS scenario resulted in an increase of 14000 hm² in cultivated land and 4700 hm² in forest land. As a result, total carbon storage increases by 845,100 tons. Under the CPS scenario, the four land use types expand by 0.92×10⁴ hm², 0.24×10⁴ hm², 0.25×10⁴ hm², and 0.01×10⁴ hm² respectively. The corresponding carbon storage increases by 1.0346 million tons, 396100 tons, 190600 tons and 2700 tons. Meanwhile, the areas of grassland and water bodies reduce by 11100 hm² and 3100 hm², leading to a decline in carbon storage by 1.2565 million tons and 300 tons, total carbon storage increased by 367,300 tons. The forest land, construction land, and unused land Under the EP scenario increased approximately 0.31×10⁴ hm², 1.18×10⁴ hm², and 0.01×10⁴ hm², respectively. The corresponding carbon storage increases by 518000 tons, 895200 tons, and 2900 tons, while total carbon storage decreases by 48900 tons due to reductions in other land types. By 2030, except for the EPS and CPS models, carbon storage under all scenarios is projected to fall below the 2020 level, which across scenarios follows the trend: EPS > CPS > BAU > EP. 4. Discussion The study is grounded in the spatial-temporal characteristics of land use changes in Xi'an (2000–2020). Using the coupled PLUS-InVEST model, it evaluates carbon storage under four development scenarios projected in 2030, revealing the impacts of land use transitions on ecosystem carbon stocks. Furthermore, the compound present and future value formulas are applied to quantify the worth of carbon storage, providing an in-depth assessment of its monetary significance in the region. 4.1 Effect of Land Use Change Carbon Storage in Xi’an (2000–2020) Our results have shown that carbon storage varies significantly across different s land use types. Cultivated land, forest land, and grassland demonstrate higher carbon storage levels in terrestrial ecosystems due to their high soil organic carbon density and carbon storage capacity[ 56 ]. Due to limited SOC retention capacity, construction and unused lands are more vulnerable to degradation, especially when land use is inefficient or poorly regulated—leading to risks such as erosion and desertification[ 57 ], which hinder the soil carbon sequestration process and make it difficult for carbon storage to accumulate effectively. This disparity is closely linked to vegetation coverage, the degree of human disturbance, and soil ecological functionality. With the progression of urbanization and population growth, Xi'an, similar to urban regions such as Beijing-Tianjin-Hebei, has experienced substantial shifts in land use patterns[ 58 , 59 ]. High-carbon-sequestration areas such as Cultivated land and grassland have been increasingly converted into construction land[ 60 ], as shown in Fig. 5 -b(2), resulting in fluctuations in regional carbon storage. This study reveals that carbon storage in Xi'an in 2020 declined by 1.64% compared to 2000,, primarily driven by the loss of cultivated land and grassland, reflecting a marked transformation of in land use structure and the intense of anthropogenic activities and ecosystem disturbances. To strengthen the region’s carbon sink function, Xi'an should prioritize the conservation of cultivated land resources to support ecosystem restoration and functional enhancement: first, preserve the integrity of grassland ecosystem and prevent non-ecological land conversion; second, promote sustainable agricultural and forestry practices, enhancing terrestrial carbon sequestration through technologies such as conservation tillage and sustainable forest management; third, embed the principle of "ecological priority" into urban planning, systematically designing urban green space networks and forest ecosystems, and boosting carbon sink capacity by increasing vegetation coverage. 4.2 Scenario-Based Analysis of Land Use Change Impacts on Carbon Storage by 2030 The ecosystem fragmentation driven by urbanization has triggered substantial land use transformation. The irreversible conversion of grassland, forest land and cultivated land into construction land and water bodies has compromised the integrity of native vegetation system, disrupted carbon cycle equilibrium, and resulted in a notable reduction in terrestrial ecosystem carbon storage [ 61 , 62 ]. This study shows that terrestrial carbon storage under the Economic Priority model is lower than the 2020 baseline, similar conclusions were drawn by LUO Shuqi et al [ 20 ].Research on the Shiyang River Basin indicates that carbon storage exceeds that of the Ecological Protection model under the CPS [ 63 ], primarily due to the expansion of cultivated land and the significant expansion in construction land under the EPS. However, in this study, the carbon storage patterns under the CPS and EPS diverge from those conclusions, mainly due to three mechanisms. First, cultivated land in Xi'an has continuously declined in the past two decades. The CPS strictly limits land conversion, focusing on preserving stable, high-quality cultivated land with urban areas. Second, Xi’an’s location- at the intersection of the ecologically fragile area Loess Plateau and the ecological restoration zone along the northern base of the Qinling Mountains- has shaped distinct land type evolution pathways [ 64 ]. Although the CPS curbs cultivated land conversion and mitigates area loss, it inadvertently leads to the occupation of high-carbon-density ecological lands, such as forest and grassland, becoming a primary driver of carbon storage decline. In addition, the warming and trend in Northwestern China[ 65 ], which coincides with carbon storage changes in Xi'an, serves as a secondary influencing factor. As anticipated, the Ecological Protection Model stronger carbon sequestration and storage capacity, consistent with the conclusions of Zhu et al [ 66 ].Under this model, terrestrial carbon storage in 2030 is projected to increase by 541,400 tons compared to 2020, by 845,100 tons compared to the Natural Development model, by 894,100 tons compared to the Economic Priority model, and by 477,900 tons compared to the CPS, demonstrating its significant positive impact on carbon accumulation. Nevertheless, carbon storage in 2030 remains substantially lower than in 2000, due to persistent challenges such as illegal villa construction at the northern foot of the Qinling Mountains, unregulated mining, excessive tourism development, construction land expansion, and water scarcity in the Weihe River. Moving forward, it is essential to integrate territorial spatial planning territorial spatial planning, strengthen land use controls for cultivated land, forest land and grassland, delineate construction land expansion boundaries, and enhance ecosystem carbon sink capacity through a "rigid constraints + flexible regulation" mechanism, thereby supporting the realization of "the goals of carbon peaking and carbon neutrality". 4.3 Economic Value of Carbon Storage under Land Use Change Scenarios in Xi’an Multiple economic dimensions are encompassed in the valuation of carbon storage, including greenhouse gas emission reduction, carbon market trading, and ecosystem service provision, thereby offering essential support for the development of a carbon trading system. Although existing studies have estimated ecosystem services values under various scenarios [ 67 , 68 ], empirical research remains underdeveloped on the economic valuation of carbon storage across differentiated development trajectories. This study quantifies the economic value of value of carbon storage in Xi'an between 2000 and 2020, and under four development scenarios projected for 2030, using compound present value and future value formulas (Fig. 7 b). Due to the relatively low economic value of carbon storage in water bodies, construction land, and unused land, these categories are not displayed in the figure for clarity. The study found that Xi'an experienced a 1.64% drop in carbon storage during 2000–2020, however, its economic value showed an upward trend, mainly because the carbon sequestration price in 2020 was 1.6 times and 1.3 times higher than that in 2000 and 2010, respectively. This conclusion differs from traditional studies[ 23 ]. Previous studies tend to overlook the time value of carbon prices, resulting in parallel shifts in storage quantity and valuation. This study is consistent with the analytical approach of Sui g et al[ 25 ], introducing the discount rate to retrospectively estimate earlier carbon price, it effectively reduces the impact of socioeconomic volatility and offers a new perspective for balancing development with conservation. In 2030, the economic value of carbon storage under different models i exceeds that in 2020 (Fig. 7 b), following a differentiated pattern: EPS Model > CPS Model > BAU Model > EP Model. Among them, the EPS model restricts unordered expansion, thereby indirectly avoiding carbon storage loss. The total value of cultivated land, forest land, and grassland reaches 24.554 billion yuan, accounting for 93.04% of the total economic value; The CPS model emphasizes sustainable agricultural land use, with cultivated land and forest land contributing 32.63% and 40.72% of the total value, respectively; under BAU model, forest land has the highest economic value, amounting to 10.623 billion yuan and representing 40.53% of the total; Under the EP model, although the value of construction land increases by 31.67% compared to 2020, the overall economic value is the lowest, revealing the limitations of development strategies driven solely by land economic orientation. The economic value of carbon storage exhibits a synergistic relationship with changes in storage changes, with the fixed carbon price mechanism acting as the primary explanatory factor. However, carbon prices are influenced by dynamic variables, such as market conditions, policy shifts, climate trends and other external drivers[ 28 ]. Current valuation models continue to face limitations, particularly in the calibration of discount rates and the accurate quantification of future carbon sink values, which remain sensitive to long-term socioeconomic and ecological uncertainties. 5. Conclusions This study constructs a land spatial optimization framework for future development based on the coupled PLUS-InVEST model [ 69 ], integrating natural, socioeconomic, and accessibility factors. However, constrained by the limited integration accuracy of multi-source data and the insufficient characterization of complex system interactions, the framework exhibits limitations in dynamic scenario deduction and cross-scale effect transmission analysis. Firstly, the InVEST model primarily focus on the carbon density attributes of land use types, without fully incorporating the impact of vegetation growth dynamics and species differences on carbon storage[ 70 ]. Future research should therefore enhance the analysis of plant growth patterns and vegetation type characteristics to improve the scientific rigor of carbon storage estimation; Secondly, the compound interest present value method assumes a tactic future carbon price, resulting in a mechanical linkage between valuation outcomes and carbon storage changes. This approach overlooks the inherent variability in dynamic of carbon prices and the complex transmission mechanisms under different development scenarios. In practice, factors such as urban expansion, industrial structuring, and policy regulation significantly influence carbon price formation across models, yet current methodologies have not effectively addressed this heterogeneity. Accordingly, it is recommended that future studies develop a multi-dimensional impact factor coupling model, integrating carbon storage valuation with dynamic carbon pricing mechanism, and incorporating regional development characteristics, policy instruments, and market volatility parameters. This direction will not only advance the theoretical framework of carbon accounting, but also provide scientific guidance for low-carbon urban spatial planning, foster innovation in green development strategies, and established a resilient basis for long-term strategic planning that promotes the coordinated advancement of economic, social, and ecological systems. Declarations Author Contributions： S.W.: Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing. H.Y.: Conceptualization, Methodology, Writing—review and editing. Q.L. and L.Q: Conceptualization, Project administration, Supervision. F.L., G.G. and G.L.: Writing—review and editing, Supervision. C.M. and Y.Z.: Supervision. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Projects of China Geological Survey (grant Nos.DD20230522). Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Acknowledgments: The insightful and constructive comments and suggestions from the anonymous reviewers are greatly appreciated. Conflicts of Interest: The authors declare no conflicts of interest. References Zhang, J.; Liu, Z.; Guan, Z.; Wang, L.; Zhang, J.; Han, Z. Balancing future urban development and carbon sequestration: A multi-scenario InVEST model analysis of China's urban clusters. Journal of Environmental Management . 2025 , 380 , doi:10.1016/j.jenvman.2025.125003. Wu, A.; Wang, Z. 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19:40:19","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176486,"visible":true,"origin":"","legend":"","description":"","filename":"4663d4b65e4f4cffb81da987917606d51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/260ba41c23610219465ae9b7.xml"},{"id":92028306,"identity":"7dded655-d4df-4b9c-b6ec-713563fe2c69","added_by":"auto","created_at":"2025-09-23 19:40:19","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191210,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/b0ffcf8092362dce6d32db59.html"},{"id":92028282,"identity":"8f4a2fd1-6778-4f01-93d2-b8597288785d","added_by":"auto","created_at":"2025-09-23 19:40:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246732,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use Overview Map of Xi’an\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/2270556cfc04214db0f71cf5.png"},{"id":92028285,"identity":"bc16dcf2-c3d9-42f9-abcb-870dc637ab96","added_by":"auto","created_at":"2025-09-23 19:40:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304018,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-source Data Preprocessing\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/f8f6d328c5c7ff0e6397f6ee.png"},{"id":92028284,"identity":"e136506d-ad70-45c7-8be0-b497c4466e22","added_by":"auto","created_at":"2025-09-23 19:40:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1681586,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Technical Framework\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/eb8aa2d81e0ba21400fbc711.png"},{"id":92028485,"identity":"0ea12bbf-7991-47b8-9ef7-d903b204c6ef","added_by":"auto","created_at":"2025-09-23 19:48:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335337,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between Predicted and Actual Results in 2020\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/a9aef8a3658230084fb4eebf.png"},{"id":92028487,"identity":"cd2bb0d7-d412-4ed2-ad5b-f69944c115ae","added_by":"auto","created_at":"2025-09-23 19:48:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1603150,"visible":true,"origin":"","legend":"\u003cp\u003ea） Proportions of Various Land Use Types from 2000 to 2030；（b） Areas of Various Land Use Types from 2000 to 2030 and Sankey Diagram from 2000 to 2020；(c) Chord Diagram from 2020 to Various Development Models.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/e5b1f823999cffb9481f75ad.png"},{"id":92028288,"identity":"bc2329ec-3c9a-486c-825f-68ad4c070c95","added_by":"auto","created_at":"2025-09-23 19:40:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":247824,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial-temporal changes of carbon storage in Xi'an from 2000 to 2030\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/fedb3a71ed96ecc832404554.png"},{"id":92028488,"identity":"d89dfd52-1602-44ce-86d6-d795b82cd4b4","added_by":"auto","created_at":"2025-09-23 19:48:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":443219,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon storage and price (a) and economic value of carbon storage changes (b) in Xi'an from 2000 to 2030\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/5cdbc52ae1234faf063b0a68.png"},{"id":95801464,"identity":"b7bfe682-fb6a-4da6-ace4-81f1fbe4550d","added_by":"auto","created_at":"2025-11-13 08:25:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5781770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7404029/v1/09d03c4e-7ce5-4a31-bbd1-877ba4325ecf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coupled PLUS-InVEST Modeling of Land Use Change and the Economic Valuation of Carbon Storage in Xi'an, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarbon emissions continue to increase as a result of human activities, triggering global warming, melting glaciers, rising sea levels and frequent natural disasters., all of which pose serious threats to the sustainability and ecological security of the Earth's ecosystems [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the context of accelerating economic development, global land use patterns are undergoing profound transformation. Meanwhile, growing societal demands and increasing spatial heterogeneity have intensified the complexity of interactions among ecosystem services, thereby affecting terrestrial carbon storage and constraining regional sustainable development [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Driven by rapid economic growth, urban land expansion in China has increasingly replaced carbon-rich land covers such as forests, cultivated fields, and grasslands, thereby intensifying carbon loss in urban ecological systems. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, examining the role of land use transformation on land-based carbon reserves is critical to improving sequestration capacity and curbing global climate change [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By simulating multiple development scenarios, it becomes possible to forecast the dynamic evolution of future carbon storage and its economic value. It offers critical insights for promoting sustainable socio-economic development and achieving carbon neutrality targets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCarbon storage estimation has become a prominent focus in ecosystem service research, with numerous studies exploring its spatial distribution and assessment methods [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Carbon Storage and Sequestration (CSS) module within the InVEST framework has been broad application across domestic and international studies as a result of its low data requirements and high computational efficiency, earning recognition from many researchers [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, this module has limitations in predicting carbon storage responses under dynamic landscape conditions, making it difficult to accurately reflect the potential correlation between land use patterns and carbon dynamics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Anticipated changes in carbon storage are strongly guided by future land use dynamics and the demand for ecosystem services. The Patch-generating Land Use Simulation model (PLUS) has received increasing attention in recent years for its ability to simulate nonlinear mechanisms in multi-type land use transitions with high spatial accuracy[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Coupling the PLUS and InVEST models not only addresses the limitations of using either model independently, but also enables scenario-based prediction of future terrestrial carbon storage dynamics. This integrated approach provides scientific support for sustainable urban development [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, land use change affects carbon storage not only directly, but also indirectly influences its economic value by altering land functions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous studies have often employed market valuation or the Swedish carbon tax method to estimate current carbon storage value, but rarely incorporated the time value of carbon pricing [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the last decades, studies have advanced the understanding of addressing this gap by introducing the concept of the time value of money into carbon valuation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, comprehensive assessments of the economic value of future carbon storage under different development scenarios remain insufficient and warrant further research.\u003c/p\u003e\u003cp\u003eBuilding on conducting comprehensive assessments of regional carbon storage and its economic value can provide critical insights for policymakers in optimizing land use planning and resource allocation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Accurate estimation of the economic value of future carbon storage hinges upon rational carbon price setting, which is influenced by multiple factors, including the time value of money, market dynamics, policy uncertainties, and the stringency of carbon regulation[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Within the field of economics, the compound present value method-based on central bank discount rates-can be employed to estimate trends in future carbon pricing, effectively capturing its temporal value. This method is widely used across financial and commodity sectors and offers valuable input for guiding investment strategies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Existing studies have demonstrated the validity of compound interest methods in long-term low-carbon economic assessments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Based on this context, this study examines the patterns and temporal evolution of land use and carbon storage in Xi\u0026rsquo;an during the period 2000\u0026ndash;2020. The coupled PLUS-InVEST model is developed, incorporating social and economic driving forces into a land use scenario simulation framework. Four development scenarios are constructed: Business as Usual (BAU), Environmental Protection Scenario (EPS), Economic Profit (EP), and Cultivated Land Protection Scenario (CPS), to project variation of land use patterns and carbon storage by 2030. Furthermore, the compound present and future value method is introduced to quantify the economic value of regional carbon storage under each scenario. This research contributes by offering an economic foundation for strategic sustainability planning and ecological enhancement in Xi\u0026rsquo;an.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 The Study Area\u003c/h2\u003e\u003cp\u003eXi\u0026rsquo;an, the capital of Shaanxi Province, is situated in northwestern China (107\u0026deg;40\u0026prime;E\u0026ndash;109\u0026deg;49\u0026prime;E, 33\u0026deg;42\u0026prime;N\u0026ndash;34\u0026deg;45\u0026prime;N, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Characterized by a warm-temperate and semi-humid continental monsoon climate, the city\u0026rsquo;s terrain declines from south to north, flanked by the Qinling Mountains and the Weihe Plain [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Covering approximately 1.01\u0026nbsp;million hectares, the region is predominantly composed of cultivated land, forest land, and urban land for construction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] In recent years, accelerated urbanization and population growth (Xi\u0026rsquo;an Municipal Government) have intensified the mismatch between land use patterns, industrial development, and resource availability, resulting in mounting pressures such as land scarcity and water shortages. In response to these constraints, the Xi\u0026rsquo;an New Area was established in 2014 with approval from the State Council. As the nation\u0026rsquo;s seventh and the western region\u0026rsquo;s third national-level new area\u0026mdash;it serves as a strategic platform to relieve ecological pressures and enhance coordinated regional development across Xi\u0026rsquo;an and Xianyang.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sources of Data\u003c/h2\u003e\u003cp\u003eThis study integrates multiple databases, containing land use datasets and driving factor datasets. Specifically, data land use in 2000, 2010, and 2020 with 30 meters spatial resolution were obtained from the Resource and Environmental Science and Data Platform. Based on research objectives, six land use categories were derived through classification: cultivated land, forest land, grassland, water bodies, construction land, and unused land. The fixed factors include six natural variables, two socio-economic variables, and nine accessibility indicators (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details). Slope data were derived from spatial analysis of DEM data using ArcGIS 10.8. Euclidean distance was employed derive accessibility indicators relative to various features in ArcGIS. All influencing factors datasets were resampled to a spatial resolution of 30 m \u0026times; 30 m, converted into TIFF format, and projected to the WGS 1984 UTM Zone 48N coordinate system (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSources of Multi-Source Data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData resource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLand Use Data (30 m), Xi'an, 2000/2010/2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResource and Environmental Science \u0026amp; Data Platform\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eNatural Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGeospatial Data Cloud (Derived from DEM data)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEarth Resources Data Cloud\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Precipation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePlatform for Resource and Environmental Science Data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil Erosion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSocio-Economic Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAccessibility Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to County Government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eNational Geographic Information Resources Catalogue Service System\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to Expressway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to Provincial Road\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to National Highway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to Railway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to Primary, Secondary, and Tertiary Roads\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistance to Water System\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRestricted Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEcological Function Protection Zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCollege of Urban and Environmental Sciences, Peking University\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCultivated Land with Slope less than 6\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDerived from Land Use data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStable Cultivated Land\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Methods\u003c/h2\u003e\u003cp\u003eFirst, land use data for Xi'an from the years 2000, 2010 and 2020were used to construct a land category transfer matrix within ArcGIS. This matrix quantified transition directions, magnitudes, and temporal features of land conversion, thereby revealing the regional patterns of land use evolution. Next, the PLUS model was used to simulate the land use in 2020 and predict future spatial patterns depend on four development scenarios for 2030. The simulation incorporated both natural geographic and socioeconomic driving factors. Model reliability was verified through spatial coincidence analysis. Building on this foundation, the InVEST model was employed\u0026mdash;using revised land-use-specific carbon density parameters\u0026mdash;to estimate carbon storage for the years 2000\u0026ndash;2020 and under various scenarios for 2030, with a focus on analyzing its spatiotemporal heterogeneity and economic value. Subsequently, a comparative analysis of regional characteristics was conducted to identify an optimal development pathway that reconciles ecological conservation with socioeconomic advancement (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 PLUS Model\u003c/h2\u003e\u003cp\u003e\u003cb\u003eSimulation of Land Use Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe model simulates future land demand through historical land use raster data, which integrates the Land Expansion Analysis Strategy module (LEAS) and the Cellular Automata with Random Seeds for Multiple Types module (CARS) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The combined use of the PLUS and Markov models enables direct calculation of future land use demand through the following formula [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{S}}_{（\\text{t}+1）}={\\text{p}}_{\\text{i}\\text{j}}{\\text{S}}_{\\text{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin the formula, the symbol \u003cem\u003eS\u003c/em\u003e(\u003cem\u003et\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1) denotes the land-use type at time step \u003cem\u003et\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1, \u003cem\u003epij\u003c/em\u003e indicates the area converted from use type \u003cem\u003ei\u003c/em\u003e to type \u003cem\u003ej\u003c/em\u003e before to transformation, and \u003cem\u003eSt\u003c/em\u003e represents the initial land-use type at time \u003cem\u003et\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBased on the Markov chain module within the PLUS model, of the study predicts land use in 2020 using data from 2000 and 2010. The simulated results were compared with objective data of land use from 2020, yielding a Kappa coefficient of 0.88, an overall accuracy of 0.91, and coefficient of Fomx is 0.38. According to relevant studies, the model demonstrates high precision in simulating future land use changes and can be reliably applied to forecast future land use patterns[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe LEAS module derives the spatial pattern of land expansion from land use data across two time periods. It then applies the random forest algorithm to quantify the contribution of driving factors, using the following formula for detailed analysis:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{P}_{i,k}^{d}=\\frac{{\\sum\\:}_{n=1}^{M}I=\\left({h}_{n}\\right(x\\left)\\right)=d\\:)}{M}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe variable \u003cem\u003ed\u003c/em\u003e can only take values of 0 or 1, which are used to indicate whether the land use type has been converted to type \u003cem\u003ek\u003c/em\u003e or to other land use types, respectively. The vector \u003cem\u003ex\u003c/em\u003e comprises multiple driving force factors, and the function \u003cem\u003eI\u003c/em\u003e acts as the indicator function within the decision tree ensemble, ℎn (x) represents the predicted land use type of vector x under the nth decision tree. Additionally, \u003cem\u003em\u003c/em\u003e specifies the complete set of decision trees constructed.\u003c/p\u003e\u003cp\u003eCARS: simulates future land use patches based on expanded land, and can predict the spatial patterns of land use patterns under different scenarios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-development Mode Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn accordance with the \"Xi'an Territorial Spatial Master Plan (2021\u0026ndash;2035)\", the \"Notice of the Shaanxi Provincial People's Government on the Shaanxi Province Main Function Zoning Plan\", the \"Notice of the Xi 'an Municipal People's Government on the '14th Five-Year Plan' for Ecological and Environmental Protection\", the \"Xi 'an Qinling Ecological and Environmental Protection Management Measures\", and the \"Work Plan for Comprehensively Improving the Construction of High-Standard Farmland in Xi 'an\", relevant planning documents including the above-mentioned cases were referenced. Taking into account the current development situation of Xi'an City and the existing research results [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the Business-as-Usual Scenario (BAU) andEPS were defined. Four development scenarios were applied: BAU, EPS, Economic Profit (EP), and Cultivated Land Protection Scenario (CPS). All scenarios designate the existing ecological protection area as the basic restricted area.\u003c/p\u003e\u003cp\u003eBAU: Continuing the land use development probability from 2010 to 2020, the existing ecological protection areas are designated as restricted zones, serving as foundation for for simulating alternativedevelopment scenarios.\u003c/p\u003e\u003cp\u003eEPS: In accordance with the relevant plans and policies for ecological and environmental protection in Shaanxi Province and Xi 'an City, a bidirectional transfer probability adjustment is implemented: the probability of transfer from cultivated land, forest land, grassland, and water bodies to construction land is decreased by 30%, while the rate of transfer from construction land to cultivated land, forest land, and grassland is increased by 50%, supporting the policy orientation of farmland protection and ecological restoration.\u003c/p\u003e\u003cp\u003eEP: For the purpose of promoting regional economic development, construction land is expanding at a faster rate. Based on earlier findings and the characteristics of the region under investigation, transitions from construction land to alternative land types is restricted. and the rates of Cultivated land, forest land, grassland, waterbodies, and unused land were assigned increased probabilities- by 40%, 10%, 30%, 10%, and 50% respectively\u0026mdash;for conversion into construction land [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCPS: As the core wheat-producing area in the Weihe Plain of Shaanxi Province, Xi'an undertakes the primary role of CPS and grain production stabilization.t Its policy system covers core tasks such as the delineation of basic farmland areas, the construction of high-standard farmland, the restoration of abandoned cultivated land, and equilibrium of occupation-compensation land. In constructing CPS, long-term stable cultivated land areas are identified by overlaying land use records for the years 2000, 2010 and 2020. According to the \u0026ldquo;Regulations for Grading Agricultural Land\u0026rdquo; and relevant policy studies, cultivated land with a slope of under 6\u0026deg; was identified as high-quality and, along with ecological conservation areas, designated as restricted conversion zones. With reference to existing achievements[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], outward conversion of cultivated land is strictly limited. Meanwhile, to implement protection policies, the conversion probabilities from unused land and grassland to cultivated land were increased by 50%.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParameter Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe values of domain weights was 0 to 1. Values approaching 1 signify high expansion strength, while those near 0 denote low expansion intensity. This study adopts an established methodology[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]to calculate the domain weights of various development scenarios in 2030. The formula is as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{X}_{i}=\\frac{\\varDelta\\:T{A}_{i}-{\\varDelta\\:TA}_{min}}{\\varDelta\\:{TA}_{max}-{\\varDelta\\:TA}_{min}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the formula, \u003cem\u003eXi\u003c/em\u003e represents the neighborhood weight parameter for land type \u003cem\u003ei\u003c/em\u003e; represents the change amount of change in \u003cem\u003eTA\u003c/em\u003e for land type during the study period; \u003cem\u003eΔTAmax\u003c/em\u003e and \u003cem\u003eΔTAmin\u003c/em\u003e represent the maximum and minimum change amounts of \u003cem\u003eTA\u003c/em\u003e during the study period, respectively.\u003c/p\u003e\u003cp\u003eThe land use transfer matrix defines the conversion rules applied in land use types. When one land use type is allowed to be converted into another, the corresponding value in the matrix is 1; otherwise, it is 0. The settings of the four development scenarios considered in this study are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTransfer Matrices of Different Development Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"25\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eBAU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c19\" namest=\"c14\"\u003e\u003cp\u003eEP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c25\" namest=\"c20\"\u003e\u003cp\u003eCPS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ee\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003ee\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003ea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003ec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003ed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003ee\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003ea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003ec\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c23\"\u003e\u003cp\u003ed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c24\"\u003e\u003cp\u003ee\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c25\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ec\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ee\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c23\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c24\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c25\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable Note\u003c/b\u003e a: Cultivated land; b: Forest land; c: Grass land; d༚water; e༚Construction land; f༚Unused land;0༚Conversion restricted; 1༚Conversion allowed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 The InVEST model\u003c/h2\u003e\u003cp\u003eThe Carbon module estimates regional carbon storage over a given time period primarily based on land use and four fundamental carbon pools (aboveground biomass carbon, belowground biomass carbon, soil carbon, and dead organic matter carbon) Within the InVEST model,. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This sequestration module evaluates the carbon storage capacity of terrestrial ecosystems and the total regional carbon storage is computed using the following equation:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{C}_{i}={C}_{i-above}+{C}_{i-below}+{C}_{i-soil}+{C}_{i-dead}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{C}_{tot}={\\sum\\:}_{i=1}^{n}{C}_{i}\\times\\:{A}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this context, \u003cem\u003ei\u003c/em\u003e represents a specific land use type; \u003cem\u003eCi\u003c/em\u003e is the carbon density associated with type \u003cem\u003ei\u003c/em\u003e. This carbon density comprises four components: aboveground biomass carbon \u003cem\u003e(Ci-above)\u003c/em\u003e、belowground biomass carbon \u003cem\u003e(Ci-below)\u003c/em\u003e、soil organic carbon \u003cem\u003e(Ci-soil)\u003c/em\u003e ,and dead organic matter carbon \u003cem\u003e(Ci-dead)\u003c/em\u003e, each measured in tons per hectare \u003cem\u003e(t/hm\u0026sup2;)\u003c/em\u003e. The total ecosystem carbon storage \u003cem\u003e(Ctot)\u003c/em\u003e is calculated by summing the products of the carbon density \u003cem\u003e(t)\u003c/em\u003e; and the area \u003cem\u003e(Ai)\u003c/em\u003e of each land use type \u003cem\u003ei\u003c/em\u003e across all \u003cem\u003en\u003c/em\u003e types [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study refers to existing carbon density datasets and relevant academic research findings [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and prioritizes data results that align with the characteristics of the study area. Based on comprehensive literature review and expert judgment, the correction values proposed by scholars such as Wang et al [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] are adopted as the carbon density parameters of the study area, and the specific values are presented inTable 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCarbon Densities of Different Land Use Types in Xi'an (t / hm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei-above\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei-below\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei-soil\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei-dead\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultivated land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrass land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewaters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruction land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnused land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Methods of Compound Present and Future Value Calculation\u003c/h2\u003e\u003cp\u003eThis study estimates the economic value of carbon storage using both the compound present value and future value methods [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on existing research, the carbon prices for the 2000, 2010, 2020 and 2030 years are set at 105 yuan, 128 yuan, 172 yuan and 211 yuan respectively [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Temporal and Spatial Dynamics of Land Use in Xi'an\u003c/h2\u003e\u003cp\u003eIn 2000, 2010 and 2020, the land use structure was dominated by cultivated land, forest land and grassland, which together accounted for the three being 90.54% (in 2000), 87.38% (in 2010) and 85.74% (in 2020), respectively. Spatially, cultivated land was concentrated in the Guanzhong Plain, forest land was spread as a belt along the northern foothills of the southern Qinling Mountains, and grassland was widespread in the southern and east ern regions, and construction land is mainly distributed in the north-central area. Over the past two decades, significant land use transformation occurred: the areas of construction land, water bodies, unused land and forest land have shown an increasing trending, while cultivated land and grassland experienced continuous decline, with the eastern region being the primarily area of reduction, see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-a, b (2).\u003c/p\u003e\u003cp\u003eUnder the BAU scenario for 2030, the trajectories of cultivated land, water bodies, construction land, and unused land show slight deviations from the 2000\u0026ndash;2020 trends, characterized by a reduction in forest area and an expansion of grassland. The EPS shows that the changing trends of cultivated land, forest land, grassland, waterbodies and construction land remain consistent with those from 2000 to 2020, while unused land under shows a significant decline. The CPS scenario also maintains the land use dynamics observed during 2000 to 2020, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-b(1) and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-c.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2Spatio-temporal Dynamics of carbon storage in Xi 'an City\u003c/h2\u003e\u003cp\u003eDuring 2000 to 2030, the carbon storage values and changes in Xi'an are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea. Carbon storage decreased progressively over 2000, 2010 and 2020, reaching 126.6174\u0026nbsp;million tons, 125.5441\u0026nbsp;million tons and 124.5361\u0026nbsp;million tons, respectively. It declined by 1.0733\u0026nbsp;million tons during the first decade, and by 1.0080\u0026nbsp;million tons in the subsequent decade. The spatial pattern is characterized by a decline in values from southwest to northeast. The high-carbon areas are converged on the forest and grassland zones along the northern foothills of the Qinling Mountains in the southwest, while the low-carbon areas are located in the densely developed construction land near the confluence of the southern Wei River and western Ba River.\u003c/p\u003e\u003cp\u003eUnder the BAU scenario in 2030, a continued reduction in carbon storage is expected, consistent with the 2000\u0026ndash;2020 trend. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-b (1) shows that forest land exhibits the highest carbon storage, reaching 50.3462\u0026nbsp;million tons and accounting for 40.53% of the total. Compared to the BAU scenario, the EPS scenario resulted in an increase of 14000 hm\u0026sup2; in cultivated land and 4700 hm\u0026sup2; in forest land. As a result, total carbon storage increases by 845,100 tons. Under the CPS scenario, the four land use types expand by 0.92\u0026times;10⁴ hm\u0026sup2;, 0.24\u0026times;10⁴ hm\u0026sup2;, 0.25\u0026times;10⁴ hm\u0026sup2;, and 0.01\u0026times;10⁴ hm\u0026sup2; respectively. The corresponding carbon storage increases by 1.0346\u0026nbsp;million tons, 396100 tons, 190600 tons and 2700 tons. Meanwhile, the areas of grassland and water bodies reduce by 11100 hm\u0026sup2; and 3100 hm\u0026sup2;, leading to a decline in carbon storage by 1.2565\u0026nbsp;million tons and 300 tons, total carbon storage increased by 367,300 tons. The forest land, construction land, and unused land Under the EP scenario increased approximately 0.31\u0026times;10⁴ hm\u0026sup2;, 1.18\u0026times;10⁴ hm\u0026sup2;, and 0.01\u0026times;10⁴ hm\u0026sup2;, respectively. The corresponding carbon storage increases by 518000 tons, 895200 tons, and 2900 tons, while total carbon storage decreases by 48900 tons due to reductions in other land types. By 2030, except for the EPS and CPS models, carbon storage under all scenarios is projected to fall below the 2020 level, which across scenarios follows the trend: EPS\u0026thinsp;\u0026gt;\u0026thinsp;CPS\u0026thinsp;\u0026gt;\u0026thinsp;BAU\u0026thinsp;\u0026gt;\u0026thinsp;EP.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study is grounded in the spatial-temporal characteristics of land use changes in Xi'an (2000\u0026ndash;2020). Using the coupled PLUS-InVEST model, it evaluates carbon storage under four development scenarios projected in 2030, revealing the impacts of land use transitions on ecosystem carbon stocks. Furthermore, the compound present and future value formulas are applied to quantify the worth of carbon storage, providing an in-depth assessment of its monetary significance in the region.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Effect of Land Use Change Carbon Storage in Xi\u0026rsquo;an (2000\u0026ndash;2020)\u003c/h2\u003e\u003cp\u003eOur results have shown that carbon storage varies significantly across different s land use types. Cultivated land, forest land, and grassland demonstrate higher carbon storage levels in terrestrial ecosystems due to their high soil organic carbon density and carbon storage capacity[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Due to limited SOC retention capacity, construction and unused lands are more vulnerable to degradation, especially when land use is inefficient or poorly regulated\u0026mdash;leading to risks such as erosion and desertification[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], which hinder the soil carbon sequestration process and make it difficult for carbon storage to accumulate effectively. This disparity is closely linked to vegetation coverage, the degree of human disturbance, and soil ecological functionality. With the progression of urbanization and population growth, Xi'an, similar to urban regions such as Beijing-Tianjin-Hebei, has experienced substantial shifts in land use patterns[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. High-carbon-sequestration areas such as Cultivated land and grassland have been increasingly converted into construction land[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-b(2), resulting in fluctuations in regional carbon storage. This study reveals that carbon storage in Xi'an in 2020 declined by 1.64% compared to 2000,, primarily driven by the loss of cultivated land and grassland, reflecting a marked transformation of in land use structure and the intense of anthropogenic activities and ecosystem disturbances. To strengthen the region\u0026rsquo;s carbon sink function, Xi'an should prioritize the conservation of cultivated land resources to support ecosystem restoration and functional enhancement: first, preserve the integrity of grassland ecosystem and prevent non-ecological land conversion; second, promote sustainable agricultural and forestry practices, enhancing terrestrial carbon sequestration through technologies such as conservation tillage and sustainable forest management; third, embed the principle of \"ecological priority\" into urban planning, systematically designing urban green space networks and forest ecosystems, and boosting carbon sink capacity by increasing vegetation coverage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Scenario-Based Analysis of Land Use Change Impacts on Carbon Storage by 2030\u003c/h2\u003e\u003cp\u003eThe ecosystem fragmentation driven by urbanization has triggered substantial land use transformation. The irreversible conversion of grassland, forest land and cultivated land into construction land and water bodies has compromised the integrity of native vegetation system, disrupted carbon cycle equilibrium, and resulted in a notable reduction in terrestrial ecosystem carbon storage [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study shows that terrestrial carbon storage under the Economic Priority model is lower than the 2020 baseline, similar conclusions were drawn by LUO Shuqi et al [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].Research on the Shiyang River Basin indicates that carbon storage exceeds that of the Ecological Protection model under the CPS [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], primarily due to the expansion of cultivated land and the significant expansion in construction land under the EPS. However, in this study, the carbon storage patterns under the CPS and EPS diverge from those conclusions, mainly due to three mechanisms. First, cultivated land in Xi'an has continuously declined in the past two decades. The CPS strictly limits land conversion, focusing on preserving stable, high-quality cultivated land with urban areas. Second, Xi\u0026rsquo;an\u0026rsquo;s location- at the intersection of the ecologically fragile area Loess Plateau and the ecological restoration zone along the northern base of the Qinling Mountains- has shaped distinct land type evolution pathways [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Although the CPS curbs cultivated land conversion and mitigates area loss, it inadvertently leads to the occupation of high-carbon-density ecological lands, such as forest and grassland, becoming a primary driver of carbon storage decline. In addition, the warming and trend in Northwestern China[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], which coincides with carbon storage changes in Xi'an, serves as a secondary influencing factor. As anticipated, the Ecological Protection Model stronger carbon sequestration and storage capacity, consistent with the conclusions of Zhu et al [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].Under this model, terrestrial carbon storage in 2030 is projected to increase by 541,400 tons compared to 2020, by 845,100 tons compared to the Natural Development model, by 894,100 tons compared to the Economic Priority model, and by 477,900 tons compared to the CPS, demonstrating its significant positive impact on carbon accumulation. Nevertheless, carbon storage in 2030 remains substantially lower than in 2000, due to persistent challenges such as illegal villa construction at the northern foot of the Qinling Mountains, unregulated mining, excessive tourism development, construction land expansion, and water scarcity in the Weihe River. Moving forward, it is essential to integrate territorial spatial planning territorial spatial planning, strengthen land use controls for cultivated land, forest land and grassland, delineate construction land expansion boundaries, and enhance ecosystem carbon sink capacity through a \"rigid constraints\u0026thinsp;+\u0026thinsp;flexible regulation\" mechanism, thereby supporting the realization of \"the goals of carbon peaking and carbon neutrality\".\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Economic Value of Carbon Storage under Land Use Change Scenarios in Xi\u0026rsquo;an\u003c/h2\u003e\u003cp\u003eMultiple economic dimensions are encompassed in the valuation of carbon storage, including greenhouse gas emission reduction, carbon market trading, and ecosystem service provision, thereby offering essential support for the development of a carbon trading system. Although existing studies have estimated ecosystem services values under various scenarios [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], empirical research remains underdeveloped on the economic valuation of carbon storage across differentiated development trajectories. This study quantifies the economic value of value of carbon storage in Xi'an between 2000 and 2020, and under four development scenarios projected for 2030, using compound present value and future value formulas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Due to the relatively low economic value of carbon storage in water bodies, construction land, and unused land, these categories are not displayed in the figure for clarity.\u003c/p\u003e\u003cp\u003eThe study found that Xi'an experienced a 1.64% drop in carbon storage during 2000\u0026ndash;2020, however, its economic value showed an upward trend, mainly because the carbon sequestration price in 2020 was 1.6 times and 1.3 times higher than that in 2000 and 2010, respectively. This conclusion differs from traditional studies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Previous studies tend to overlook the time value of carbon prices, resulting in parallel shifts in storage quantity and valuation. This study is consistent with the analytical approach of Sui g et al[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], introducing the discount rate to retrospectively estimate earlier carbon price, it effectively reduces the impact of socioeconomic volatility and offers a new perspective for balancing development with conservation. In 2030, the economic value of carbon storage under different models \u003cem\u003ei\u003c/em\u003e exceeds that in 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), following a differentiated pattern: EPS Model\u0026thinsp;\u0026gt;\u0026thinsp;CPS Model\u0026thinsp;\u0026gt;\u0026thinsp;BAU Model\u0026thinsp;\u0026gt;\u0026thinsp;EP Model. Among them, the EPS model restricts unordered expansion, thereby indirectly avoiding carbon storage loss. The total value of cultivated land, forest land, and grassland reaches 24.554\u0026nbsp;billion yuan, accounting for 93.04% of the total economic value; The CPS model emphasizes sustainable agricultural land use, with cultivated land and forest land contributing 32.63% and 40.72% of the total value, respectively; under BAU model, forest land has the highest economic value, amounting to 10.623\u0026nbsp;billion yuan and representing 40.53% of the total; Under the EP model, although the value of construction land increases by 31.67% compared to 2020, the overall economic value is the lowest, revealing the limitations of development strategies driven solely by land economic orientation.\u003c/p\u003e\u003cp\u003eThe economic value of carbon storage exhibits a synergistic relationship with changes in storage changes, with the fixed carbon price mechanism acting as the primary explanatory factor. However, carbon prices are influenced by dynamic variables, such as market conditions, policy shifts, climate trends and other external drivers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Current valuation models continue to face limitations, particularly in the calibration of discount rates and the accurate quantification of future carbon sink values, which remain sensitive to long-term socioeconomic and ecological uncertainties.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study constructs a land spatial optimization framework for future development based on the coupled PLUS-InVEST model [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], integrating natural, socioeconomic, and accessibility factors. However, constrained by the limited integration accuracy of multi-source data and the insufficient characterization of complex system interactions, the framework exhibits limitations in dynamic scenario deduction and cross-scale effect transmission analysis. Firstly, the InVEST model primarily focus on the carbon density attributes of land use types, without fully incorporating the impact of vegetation growth dynamics and species differences on carbon storage[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Future research should therefore enhance the analysis of plant growth patterns and vegetation type characteristics to improve the scientific rigor of carbon storage estimation; Secondly, the compound interest present value method assumes a tactic future carbon price, resulting in a mechanical linkage between valuation outcomes and carbon storage changes. This approach overlooks the inherent variability in dynamic of carbon prices and the complex transmission mechanisms under different development scenarios. In practice, factors such as urban expansion, industrial structuring, and policy regulation significantly influence carbon price formation across models, yet current methodologies have not effectively addressed this heterogeneity. Accordingly, it is recommended that future studies develop a multi-dimensional impact factor coupling model, integrating carbon storage valuation with dynamic carbon pricing mechanism, and incorporating regional development characteristics, policy instruments, and market volatility parameters. This direction will not only advance the theoretical framework of carbon accounting, but also provide scientific guidance for low-carbon urban spatial planning, foster innovation in green development strategies, and established a resilient basis for long-term strategic planning that promotes the coordinated advancement of economic, social, and ecological systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions：\u003c/strong\u003eS.W.: Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing\u0026mdash;original draft, Writing\u0026mdash;review and editing. H.Y.: Conceptualization, Methodology, Writing\u0026mdash;review and editing. Q.L. and L.Q: Conceptualization, Project administration, Supervision. F.L., G.G. and G.L.: Writing\u0026mdash;review and editing, Supervision. C.M. and Y.Z.: Supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by Projects of China Geological Survey (grant Nos.DD20230522).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe insightful and constructive comments and suggestions from the anonymous reviewers are greatly appreciated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang, J.; Liu, Z.; Guan, Z.; Wang, L.; Zhang, J.; Han, Z. Balancing future urban development and carbon sequestration: A multi-scenario InVEST model analysis of China\u0026apos;s urban clusters. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e. \u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e380\u003c/em\u003e, doi:10.1016/j.jenvman.2025.125003.\u003c/li\u003e\n\u003cli\u003eWu, A.; Wang, Z. 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Carbon Storage Assessment in the Upper Reaches of the Fenhe River under Different Land Use Scenarios. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e. \u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e41\u003c/em\u003e, 360-373, doi:DOI: 10.5846/stxb201909242005.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land Use, Carbon Storage, PLUS-InVEST Model, Economic Valuation of Carbon, Xi’an","lastPublishedDoi":"10.21203/rs.3.rs-7404029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7404029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops and applies a coupled PLUS-InVEST modeling framework to examine the spatial dynamics of land use patterns and carbon storage in Xi\u0026rsquo;an (2000\u0026ndash;2020). Four development scenarios\u0026mdash;Business as Usual (BAU), Environmental Protection Scenario (EPS), Economic Profit (EP), and Cultivated Land Protection Scenario (CPS)\u0026mdash;are constructed to assess and predict the spatiotemporal variations in land use carbon storage by 2030. Drawing on the theory of the time value of money, compounded present and future value formulas are employed to estimate the economic benefits derived from regional carbon storage over the period 2000\u0026ndash;2030. Our results reveal pronounced structural shifts in land use, characterized by a sustained decline in cultivated land and accelerated expansion of construction land, contributing to a cumulative reduction of 2.0812\u0026nbsp;million tons of carbon storage over the two decades. Scenario-based projections demonstrate substantial variation in carbon storage by 2030: the EPS and CPS scenarios are expected to yield net increases of 541.4 and 63.5 thousand tons, respectively, while the BAU and EP scenarios result in declines, with the EP scenario exhibiting the greatest loss (352.7 thousand tons) due to intensified urban development. Between 2000 and 2020, the economic value of carbon storage in Xi\u0026rsquo;an expanded by 8.125\u0026nbsp;billion yuan, reflecting the significant appreciation of carbon prices over the two decades. Compared to the 2020 baseline, the value of carbon storage under the EPS would reach 26.389\u0026nbsp;billion yuan by 2030, significantly surpassing other scenarios. These findings highlight the ecological and economic benefits of the EPS pathway, offering a compelling reference for optimizing land resource allocation and promoting sustainable regional development.\u003c/p\u003e","manuscriptTitle":"Coupled PLUS-InVEST Modeling of Land Use Change and the Economic Valuation of Carbon Storage in Xi'an, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 19:40:14","doi":"10.21203/rs.3.rs-7404029/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56d74b25-a559-47ab-8a77-7345c2a3c43e","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55056182,"name":"Biological sciences/Ecology"},{"id":55056183,"name":"Earth and environmental sciences/Ecology"},{"id":55056184,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55056185,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-11-12T14:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 19:40:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7404029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7404029","identity":"rs-7404029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}