Assessing economic value of carbon storage and land use changes based on coupled PLUS-InVEST model in Hexi Region

Assessing economic value of carbon storage and land use changes based on coupled PLUS-InVEST model in Hexi Region | 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 Research Article Assessing economic value of carbon storage and land use changes based on coupled PLUS-InVEST model in Hexi Region Xue Hui, Hai Xinquan, Ma Yingchao, Fang Gang, He Lei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8150677/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 Situated within a unique “mountain-oasis-desert” ecological framework, Hexi region has undergone substantial shifts in land use, reshaping both its ecological integrity and socio-economic fabric. Understanding the economic implications of such changes, particularly in relation to carbon storage, holds critical value not only for guiding emission reductions and enhancing local incomes, but also for informing effective ecological compensation mechanisms. This research adopts Hexi region as a representative area, utilizing land use data from 2000 to 2020 in addition to the coupled Patch-generating Land Use Simulation with Integrated Valuation of Ecosystem Services and Tradeoffs (PLUS-InVEST) models integrated modeling approach to simulate land use and carbon storage dynamics under four distinct development pathways by 2030. To assess the cost-equivalent value derived from carbon storage in various land use types, this study applied compound interest models-specifically the present and future value formulas to determine monetary gains over time. Key outcomes are: (1) Over the period of 2000 to 2020, meadow and unutilized land continued to dominate Hexi region’s landscape, together comprising more than 90% of the total area. Over the same period, cultivated land expanded by 1,963.56 km 2 , water bodies grew by 448.75 km 2 , and construction land increased by 666.88 km 2 . In contrast, woodland, meadow, and unutilized land saw reductions of 17.92 km 2 , 338.11 km 2 , and 2,722.97 km 2 , respectively. (2) Total carbon storage increased by 2.1 million tons, showing a spatial gradient from higher levels in the southeast to lower in the northwest, with the ecological protection scenario in 2030 offering the most significant storage gains. (3) The economic valuation of carbon storage rose by 12.78 billion yuan from 2000 to 2020, and is projected to continue growing by 2030, with the highest value growth under the ecological protection scenario, followed by sustainable development, cultivated land protection, and natural development scenarios. Hexi Region Carbon Storage PLUS-InVEST Model Land Use Figures Figure 1 Figure 2 Figure 3 1 Introduction Climate change represents among the foremost environmental risks of our time, particularly evident in the rapid desertification of ecologically fragile zones and the growing imbalance in global water resource distribution, issues that have attracted extensive international concern[ 1 ]. As outlined in the United Nations Framework Convention on Climate Change (UNFCCC) and corroborated by the Paris Agreement, land use management is identified as a critical strategy for mitigating climate impacts. By transforming land use patterns, it is possible to influence the distribution and quantity of carbon storage, thereby enhancing carbon storage capacity and alleviating global warming. In response to these global imperatives, China has made firm policy commitments. The report of the 19th National Congress of the Communist Party in 2017 officially emphasized ecological restoration, and in September 2020, the country declared two strategic targets: achieving a “carbon peak” by 2030 and reaching “carbon neutrality” by 2060[ 2 ]. Recently, ecological and environmental challenges have drawn growing attention across global academic communities. A major area of inquiry has been the concept of ecological vulnerability, McCarthy et al.[ 3 ] linked to exposure, sensitivity, and adaptive capacity. Building on this, Rounsevell et al.[ 4 ] utilized the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) to model future land use patterns and assess their implications for ecosystem services across Europe. In a regional context, Tai Surigala et al.[ 5 ] developed an ecological vulnerability index system to examine spatial and temporal differentiation in the Yellow River basin. Another key research thread focuses on the ecological disturbances brought about by land use change a human-driven force of growing concern. As early as 1864, Marsh[ 6 ] underscored this issue in Man and Nature, analyzing how human land use reshapes natural landscapes. More recent works, such as that by Rani et al.[ 7 ] applied models like CA-Markov to predict land use trajectories in India’s Batinda district, while Shi[ 8 ] assessed carbon balance in Xinjiang through social network and spatial overlay analyses. Beyond diagnosis, scholars are increasingly exploring mechanisms to safeguard ecosystem health through natural capital accounting. Carbon storage valuation has emerged as a vital tool for internalizing ecological externalities and advancing ecological compensation strategies. Kira Tatsuo’s foundational work in Southeast Asian tropical forests during the 1960s highlighted the carbon storage function of biomass. Traditional carbon estimation methods such as field surveys and the IPCC inventory approach are constrained by their time intensity and limited scalability[ 9 ]. By contrast, integrated models like coupled Patch-generating Land Use Simulation with Integrated Valuation of Ecosystem Services and Tradeoffs (PLUS-InVEST) gained traction for their predictive accuracy and efficiency[ 10 ][ 11 ]. Liu et al.[ 12 ] combined the InVEST, PLUS, and Optimal Parameter-based Geographical Detector (OPGD) models to evaluate carbon dynamics in Ningxia, finding minimal loss under ecological protection but pronounced decline under urban expansion. Xiong et al.[ 13 ] reported significant carbon losses in karst areas from 1990 to 2020, and Lai et al.[ 14 ] used the models to guide sustainable land use planning in Nanjian County. In parallel, the economic valuation of carbon storage has evolved through methods like the afforestation cost approach, carbon tax estimation, and net present value analysis. Wu et al.[ 15 ] applied these techniques in Hunan Province to support policy design. Hussainzad et al.[ 16 ] employed the Net Present Value (NPV) method to evaluate carbon storage in Malaysia’s Pahang region, advancing green development strategies. Du et al.[ 17 ], comparing three methods, quantified Lishui’s forest carbon value from 2017 to 2021, yielding estimates ranging from 168 million to 3.484 billion yuan. Existing studies on ecological vulnerability have established a robust research framework, exploring this issue from multiple dimensions including theoretical foundations, influencing mechanisms, and practical applications. In the field of land use, research has increasingly relied on theoretical models and quantitative approaches to investigate the implications of land use alteration, monitor its dynamics, and assess the resulting carbon balance. Meanwhile, carbon storage evaluation particularly its economic dimension has evolved from traditional methods to more advanced model-based assessments, forming an integrated research process encompassing “data acquisition, model construction, and empirical analysis”. Building upon this foundation, the present study focuses on the “mountain-oasis-desert” composite ecosystem as a representative case, aiming to fill current gaps in research related to carbon storage and economic valuation in such complex ecological contexts. By integrating the market value method with compound present and future value approaches, this research explicitly incorporates temporal cost considerations into the carbon valuation process, thereby enhancing the reliability and applicability of the results for decision-making and policy formulation. Hexi region, recognized as one of China’s eight major ecologically fragile zones, represents a complex and sensitive ecosystem with limited resilience to external disturbances and ongoing degradation. This makes it a strategic priority for national ecological conservation initiatives. In response, local and regional governments have implemented a series of environmental policies aimed at improving ecological integrity and promoting sustainable land use. Within this context, evaluating carbon storage dynamics and their corresponding economic value becomes crucial for informing evidence-based policy decisions. Therefore, this study integrates an examination of land use dynamics and their influencing factors with a forward-looking assessment of carbon storage trends and valuation under multiple development scenarios. By focusing on the “mountain-oasis-desert” composite ecosystem, the research provides a theoretical basis and practical data support for optimizing land use structures and advancing Hexi region’s progress toward the national “dual carbon” objectives. 2 Overview and methods 2.1 Overview of Study Area Hexi region(Fig. 1 ), situated to the west of the Yellow River in Gansu Province, spans latitudes 37°10′ to 42°50′N and longitudes 93°20′ to 104°00′E. It encompasses five key cities: Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan. Geographically, Hexi serves as a vital transitional corridor connecting the Qinghai-Tibet Plateau (QTP) and the Mongolian Plateau along the north-south axis, while linking the Loess Plateau with the Tarim Basin from east to west. This region is characterized by a unique composite ecosystem of mountains, oases, and deserts, and functions as a critical ecological buffer against desertification, particularly from the Tengger and Badain Jaran deserts. Stretching from Gulang Gorge in the east to Xingxing Gorge in the west, Hexi covers approximately 266,000 km 2 and forms a strategic segment of China’s Silk Road Economic Belt. The climate is classified as temperate continental, marked by minimal precipitation, arid conditions, frequent sandstorms, and sharp temperature fluctuations. Annual rainfall ranges from 30 mm to 200 mm, generally decreasing westward, and the average annual temperature hovers around 12℃[ 18 ]. Major inland river systems in the region including the Shiyang, Heihe, and Shule Rivers support critical hydrological and ecological functions. Due to its distinct geographic setting and climate, Hexi region holds considerable research significance for the study of global arid ecosystems. 2.2 Data Collection Data utilized for the analysis are detailed in Table 1 . Geographic Information System (GIS) tools were employed to preprocess the land use data. Initially, the land use information for Hexi region was extracted using the “Extract by Mask” function. The raster projection was then standardized to WGS_1984_UTM_Zone_47N, resampled to 100-m resolution. To maintain data consistency across temporal layers, the “Reclassify” tool was used to recode land use types sequentially from 1 to 6, corresponding to arable land, forest, meadow, water bodies, urban land, along with unutilized land, respectively. Following this, the coordinate system, processing extent, and raster analysis environment were uniformly defined in the geoprocessing settings. The remaining land use datasets were further masked and reclassified to ensure alignment across all spatial layers. In addition to land use data, driving factor layers were incorporated. These were similarly extracted using the “Extract by Mask” function. For distance-based variables, vector datasets representing prominent landscape elements like rivers, roads, and railways were used. Euclidean distance calculations were then applied to convert these vector features into raster distance layers. Table 1 Types, years, and sources of data used in this analysis Category Date type Year Data source LUCC Land use/cover change 2000, 2010, 2020 Resource Environment Science and Data Center ( https://www.resdc.cn/ ) Socioeconomics Population density 2020 Resource Environment Science and Data Center ( https://www.resdc.cn/ ) Gross Domestic Product (GDP) Distance Distance to roads 2020 Open street map Distance to railways Nature Normalized Difference Vegetation Index (NDVI) 2020 Resource Environment Science and Data Center ( https://www.resdc.cn/ ) Digital Elevation Model (DEM) 2020 Geospatial Data Cloud Annual average temperature National Earth System Science Data Center Annual precipitation 2.3 Methods 2.3.1 InVEST Model This study employs InVEST model, a widely recognized ecosystem service assessment tool developed by Stanford University. Among its suite of modules including those for water yield, habitat quality, and nutrient retention the Carbon Storage and Sequestration module is applied for estimating terrestrial carbon storage. Within this module, the carbon accounting framework is structured around four major carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter[ 19 ][ 20 ]. The total carbon storage is determined as follows: $$\:\begin{array}{c}{\text{C}}_{\text{t}}={\text{C}}_{\text{a}}+{\text{C}}_{\text{b}}+{\text{C}}_{\text{s}}+{\text{C}}_{\text{d}} \left(1\right)\end{array}$$ where C t is the total carbon storage; C a is the aboveground biomass carbon storage; C b is the belowground biomass carbon storage; C s is the soil organic carbon storage; and C d is the dead organic carbon storage. $$\:\begin{array}{c}{\text{C}}_{\text{i}-\text{t}}=({\text{C}}_{\text{i}-\text{a}}+{\text{C}}_{\text{i}-\text{b}}+{\text{C}}_{\text{i}-\text{s}}+{\text{C}}_{\text{i}-\text{d}})\times\:{\text{A}}_{\text{i}} \left(2\right)\end{array}$$ where i is the average carbon density specific to each land use category and A i is the corresponding land area. The carbon density values applied for Hexi region are derived from prior empirical studies, with necessary adjustments made to reflect local environmental conditions and land classification standards[ 18 ].To ensure consistency in unit usage throughout the calculations, all carbon density values were standardized to units of ton·km − 2 . Table 2 Carbon density in Hexi Region (ton·km − 2 ) LUCC C-above C-below C-soil C-dead Cultivated land 0.1 1.5 9.1 0 Woodland 0.6 1.6 8.4 0 Meadow 0.8 2.2 15.8 0 Water bodies 0 0 0 0 Construction land 0 0 0 0 Unutilized land 0.01 0 0 0 2.3.2 PLUS Model PLUS model, proposed by China University of Geosciences, is widely recognized as a dynamic land use simulation framework that operates at the patch level. By capturing the underlying mechanisms of land transformation, the PLUS model has become a valuable tool in scenario-based planning and land management studies[ 21 ]. Scenario Setting: To simulate potential land use outcomes in 2030, four distinct development scenarios were established based on land transition probabilities derived from 2010 to 2020. Scenario 1 represents a natural development pathway, in which a Markov model is employed to forecast land demand and transition probabilities without the influence of policy interventions, serving as the baseline trajectory. Scenario 2, the ecological protection scenario, aligns with the objectives set forth in the Gansu Province 14th Five-Year Plan for Ecological Environment Protection, aiming to mitigate ecological degradation in this environmentally fragile region. Under this scenario, the potential of transforming forest and meadow into construction land is declined by 50%, and the potential of transforming cultivated land into construction land is decreased by 30%[ 22 ]. Scenario 3 focuses on cultivated land protection, reflecting the national requirement to maintain the “1.8 billion mu” redline. It emphasizes spatial optimization of farmland, ensuring that the total cultivated land area exceeds that of the natural development scenario. Ultimately, the potential of transforming cultivated land to construction land is declined by 60%, while the potential of converting forest and meadow to construction land is reduced by 20%, and the transformation from forest and meadow to cultivated land is surged by 30%[ 23 ]. Scenario 4 is designed around the concept of sustainable development, aiming to balance ecological integrity with land use efficiency. In this scenario, the probabilities of converting cultivated land and woodland into construction land are reduced by 10%, meadow and water bodies by 20%, while the potential of converting construction land into woodland is reduced by 20%, and its conversion into meadow, water, and unutilized land is lowered by 10%. Setting of Domain Weights: Domain weights are designed to quantify the relative difficulty of conversion between different land use types, serving as a critical parameter for constraining land use transitions within the PLUS model framework[ 24 ]. The domain weights are calculated using a normalization formula that incorporates the relative changes in land area between time periods, expressed as follows[ 25 ]: $$\:\begin{array}{c}{X}^{*}=\frac{X-{X}_{min}}{{X}_{max}-{X}_{min}} \left(3\right)\end{array}$$ where X * is the standardised deviation value; X is the change in area for each land use category between the two periods; X max is the maximum change in land area; and X min is the minimum change in land area. Through experimentation, the final weight of each land use category is determined as shown in Table 3 . Table 3 Field weight of land use types under different development scenarios Scenario Cultivated land Woodland Meadow Water bodies Construction land Unutilized land Natural development 0.82 0.73 0.78 0.88 1.00 0.01 Ecological protection 0.94 0.79 0.87 0.96 1.00 0.01 Cultivated land protection 1.00 0.66 0.60 0.82 0.82 0.01 Sustainable development 0.85 0.74 0.80 0.90 1.00 0.01 (3)Accuracy Validation: Accuracy validation was carried out through Kappa coefficient. Ranging from 0 to 1, the Kappa value reflects the degree of agreement, with larger values suggesting better simulation accuracy[ 26 ]. In this study, the Markov chain module embedded within the PLUS model was employed to simulate land use distribution in 2020 according to historical data from 2000 and 2010. The predicted outcomes were examined alongside the actual land use data for 2020, producing a Kappa coefficient of 0.898. This strong correlation demonstrates the robustness and reliability of the model, validating its suitability for projecting land use changes in Hexi region through to 2030. (4) Land Use Transfer Matrix: The land use transfer matrix is a two-dimensional analytical tool used to quantify and visualize the spatial conversion relationships between various land use categories within a defined temporal and geographic scope. In this framework, 1 demonstrates that the transformation between two land types is authorized, while 0 denotes a prohibited or restricted conversion pathway[ 27 ][ 28 ]. The matrix formulation is expressed as follows: $$\:\begin{array}{c}{\text{C}}_{\text{x}\text{y}}=\left[\begin{array}{ccc}{\text{C}}_{11}&\:\cdots\:&\:{\text{C}}_{1\text{n}}\\\:⋮&\:\ddots\:&\:⋮\\\:{\text{C}}_{\text{n}1}&\:\cdots\:&\:{\text{C}}_{\text{n}\text{n}}\end{array}\right] \left(4\right)\end{array}$$ where C xy is the area (km 2 ) of land use category y transferred to category x; while x and y are the land use types before and after the conversion (x = 1,2,3,···,n, y = 1,2,3,···,n). The land use transition matrix for each scenario is outlined in Table 4 : Table 4 Land use transfer Matrix under each scenario LUCC Scenario 1 Scenario 2 Scenario 3 Scenario4 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 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 0 B 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 1 0 0 0 0 C 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 D 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 E 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 F 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Note: A is cultivated land, B is woodland, C is meadow, D is water body, E is construction land, and F is unutilized land. 2.3.3 Present/Future Value Method of Compound Interest The compound present value and compound future value methods, widely applied in the fields of finance and management, are essential tools for assessing the time value of money, particularly in investment evaluation and risk analysis contexts. In this study, these financial techniques are adapted to estimate the economic value of carbon storage over time. Rather than relying solely on the 2020 carbon trading price, the analysis employs the average market prices from 2019 to 2021 as a more representative reference point. Using these values, reverse calculations and forward projections are carried out to derive the carbon trading prices for the years 2000, 2010, 2020, and 2030, based on compound present value and compound future value models[ 29 ]. The results of these computations are detailed in Table 5 . $$\:\begin{array}{c}{\text{P}}_{\text{n}}=F\times\:(\frac{\text{P}}{\text{F}},e,n） (\text{5}\text{)}\end{array}$$ where P n is the carbon price in period n; F is the final carbon price; (P/F,e,n) is the compound present value factor; e is the discount rate; and n is the number of periods. Table 5 Carbon trading price in Hexi Region (2000–2030) Year 2000 2010 2020 2030 Carbon price (yuan·ton − 1 ) 105 128 172 211 2.3.4 Carbon Storage Value Estimation The value of carbon storage reflects the ecological function and service potential of carbon retained within terrestrial ecosystems[ 30 ][ 31 ]. The model formulation is presented as follows: $$\:\begin{array}{c}V=\sum\:_{\text{n}=1}^{\text{n}}{\text{P}}_{\text{n}}\times\:{\text{C}}_{\text{n}} \left(6\right)\end{array}$$ where V is the economic value of a certain ecological carbon storage; C n is the total carbon storage of a certain ecosystem; and P n is the carbon trading price (yuan·ton − 1 ). 3 Results and analysis 3.1 Analysis of Spatiotemporal Evolution of Land Use Land use changes in Hexi region between 2000 and 2020 exhibit significant structural adjustments, as detailed in Table 6 . Throughout this period, the landscape was predominantly composed of unutilized land and meadow, which together constituted over 90% of the total area. Arable land and woodland followed, accounting for slightly more than 5% and 2%, respectively, while water bodies and construction land remained relatively minor components, each occupying less than 2% of the total land area. Over the two-decade span, substantial land conversions were observed: the area of arable land expanded by 1,963.56 km 2 , water bodies by 448.75 km 2 , and construction land by 666.88 km 2 , all showing consistent year-on-year growth. These increases are closely tied to major regional development initiatives. For example, to ensure national food security, the Gansu provincial government implemented large-scale land reclamation strategies, converting vast portions of unutilized land into productive farmland. Concurrently, driven by major policy interventions including the Comprehensive Management Project of the Heihe River Basin and the Western Development Strategy, additional areas of unutilized land were redirected toward water resource infrastructure and urban expansion. Consequently, unutilized land decreased markedly, from 169,516.84 km 2 in 2000 to 166,793.87 km 2 in 2020 an overall reduction of 2,722.97 km 2 . In contrast, the areas of woodland and meadow showed relatively moderate fluctuations: both experienced slight declines in the first decade, followed by modest recoveries in the second, resulting in net decreases of 17.92 km 2 and 338.11 km 2 , respectively. By 2030, land use patterns in Hexi region are projected to diverge moderately across different development scenarios. Under the natural development scenario, trends largely mirror those observed between 2000 and 2020, with continued expansion in the areas of arable land, water bodies, and construction land, while unutilized land undergoes a pronounced decline and woodland and meadow remain relatively stable. In the cultivated land protection scenario, policy-imposed restrictions on the conversion of farmland resulted in a notable surge in arable land area rising by 501.56 km 2 compared to 2020. Meanwhile, the areas of woodland, water bodies, and construction land also exhibit moderate increases of 18.91 km 2 , 237.17 km 2 , and 238.78 km 2 respectively, accompanied by decreases of 80.17 km 2 in meadow and 916.22 km 2 in unutilized land. Conversely, both the ecological protection and sustainable development scenarios demonstrate broadly similar patterns of land use adjustment. While unutilized land consistently declines across these two scenarios, arable land, meadow, woodland, water bodies, and construction land all register different levels of growth, showing a more balanced approach to ecological and development priorities. Table 6 Land use area under each scenario (km 2 ) LUCC Year 2000 2010 2020 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Cultivated land Area 13912.54 15777.12 15876.10 16027.88 16089.97 16377.66 16051.53 Proportion 5.62% 6.37% 6.41% 6.47% 6.50% 6.61% 6.48% Woodland Area 7377.77 7329.11 7359.85 7391.64 7396.71 7378.76 7392.06 Proportion 2.98% 2.96% 2.97% 2.98% 2.99% 2.98% 2.98% Meadow Area 53575.20 53136.44 53237.09 53352.93 53388.01 53156.92 53360.21 Proportion 21.63% 21.46% 21.50% 21.54% 21.56% 21.46% 21.55% Water bodies Area 2247.81 2435.00 2696.56 2932.06 2933.65 2933.73 2933.91 Proportion 0.91% 0.98% 1.09% 1.18% 1.18% 1.18% 1.18% Construction land Area 1017.18 1211.76 1684.06 2069.64 1968.68 1922.84 2047.89 Proportion 0.41% 0.49% 0.68% 0.84% 0.79% 0.78% 0.83% Unutilized land Area 169516.84 167757.81 166793.87 165873.42 165870.53 165877.65 165861.92 Proportion 68.45% 67.74% 67.35% 66.98% 66.98% 66.98% 66.97% From a spatial perspective (Fig. 2 ), cultivated land in Hexi region is primarily distributed in elongated strips within oasis zones formed by the alluvial plains of the Shiyang, Heihe, and Shule Rivers, particularly in areas surrounding Wuwei, Zhangye, and Jiuquan. Woodland and meadow are predominantly concentrated along the slopes and valleys of the Qilian Mountains, although small patches of meadow can also be observed scattered throughout the region. Construction land is mainly clustered in urban centers such as Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan, as well as along major transportation corridors. In contrast, unutilized land remains the dominant land use category in Hexi Corridor, extensively covering the desert and Gobi regions of the northwest. Due to the harsh natural conditions of these arid zones characterized by poor soil quality, low precipitation, and wind erosion such areas are poorly suited to extensive expansion or intensive land use. 3.2 Spatiotemporal Analysis of Carbon Storage In Table 7 , the total carbon storage in Hexi region exhibited a steady upward trajectory between 2000 and 2020, with an increase of approximately 1.11 million tons during the first decade and a more modest rise of 320,000 tons in the following ten years. For various land use categories, arable land was the primary contributor to the overall growth, adding a total of 2.1 million tons of carbon storage, with the most pronounced gains observed during the earlier period. This trend reflects the development of cultivated areas and the increasing importance of agricultural land as a carbon storage in the region. In contrast, unutilized land experienced a continuous reduction in both area and carbon storage capacity, resulting in a net loss of approximately 2,700 tons over the 20-year period. The dynamics of woodland and meadow followed a similar pattern: both witnessed declines in carbon storage during the first decade, followed by partial recoveries thereafter. Nevertheless, the cumulative losses remain substantial, with woodland and meadow carbon storage decreasing by 20,000 tons and 640,000 tons respectively. In the 2030 natural development scenario, the total carbon storage in Hexi region is projected to increase by approximately 380,000 tons compared to 2020, with arable land, woodland, and meadow contributing gains of 160,000 tons, 30,000 tons, and 190,000 tons respectively. Despite this overall growth, carbon storage in unutilized land is projected to decline slightly, decreasing by around 900 tons. Under the cultivated land protection scenario, the highest growth is observed in cultivated land, which adds approximately 520,000 tons of carbon. While woodland also experiences a modest gain, carbon storage in meadow and unutilized land decline by 140,000 tons and 900 tons respectively. In the ecological protection scenario, carbon storage continue to rise across arable land, woodland, and meadow, with respective increases of 220,000, 40,000, and 210,000 tons. Similarly, the sustainable development scenario yields increases of 180,000 tons in arable land, 30,000 tons in woodland, and 210,000 tons in meadow. Table 7 Carbon storage by land use types under each scenarios (10 6 ton) LUCC 2000 2010 2020 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Cultivated land 14.81 16.79 16.91 17.07 17.13 17.43 17.09 Woodland 7.85 7.80 7.83 7.86 7.87 7.85 7.86 Meadow 100.85 100.03 100.21 100.40 100.46 100.07 100.42 Waters 0 0 0 0 0 0 0 Construction land 0 0 0 0 0 0 0 Unutilized land 0.1694 0.1677 0.1667 0.1658 0.1658 0.1658 0.1658 Total 123.68 124.79 125.11 125.49 125.63 125.51 125.53 In Fig. 3 , the spatial distribution of carbon storage in Hexi region reveals a distinct gradient, characterized by larger values in the southeast and smaller values in the northwest. The southern Qilian Mountains constitute the principal high-carbon zone, where extensive coniferous forests contribute substantially to the region’s overall carbon storage capacity, making it the primary carbon storage in the area. In the central oasis belt, carbon storage values fall within a moderate range, driven by the dense vegetation cover of cultivated land, which acts as an effective transitional carbon pool between the mountainous forested areas and the arid zones. In contrast, the northwestern part of the region registers the lowest carbon storage levels. This pattern is largely due to harsh climatic conditions and sparse vegetation, which limit the carbon storage potential of desert and Gobi landscapes, thereby contributing to the region’s spatial heterogeneity in carbon distribution. 3.3 Analysis of Economic Value Changes in Carbon Storage Using the compound present and future value formulas, the economic valuation of carbon storage in Hexi region under different development scenarios from 2000 to 2030 is summarized in Table 8 . Between 2000 and 2020, the total financial value of carbon storage increased markedly, rising from 8.739 billion yuan to 21.519 billion yuan an overall growth of 146.24%. This surge is primarily attributed to the substantial rise in carbon pricing over the two decades. Among all land use types, meadow contributed the most significantly to this increase, both in terms of absolute value and growth rate, with an additional 1.011 billion yuan and a growth rate of 142.87%, establishing it as the dominant driver of financial value gains during this period. Other land types followed in the order of woodland, arable land, and unutilized land. Looking ahead to 2030, all four scenarios project further increases in the economic value of carbon storage, albeit with varying magnitudes. The ecological protection scenario is associated with the largest overall increase, while the natural development scenario shows the weakest growth trajectory. Across all scenarios, meadow continues to serve as the principal contributor to carbon value growth, with an average increase rate of 49.91%, followed by arable land and woodland. In contrast, unutilized land consistently exhibits the lowest economic return on carbon storage. Table 8 Financial value of carbon storage of various land types under each scenarios (10 6 yuan) LUCC 2000 2010 2020 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Cultivated land 1046.50 1763.03 2908.52 4390.54 4405.97 4483.13 4395.68 Woodland 554.70 819.04 1346.76 2021.65 2024.23 2019.08 2021.65 Meadow 7126.26 10503.61 17236.12 25823.67 25839.10 25738.79 25828.81 Water bodiess 0 0 0 0 0 0 0 Construction land 0 0 0 0 0 0 0 Unutilized land 11.97 17.61 28.67 42.65 42.65 42.65 42.65 Total 8739.47 13103.52 21518.92 32277.01 32313.02 32282.15 32287.30 4 Discussion Variations in land use patterns represent the most immediate factor influencing the dynamics of regional carbon storage. Land types such as cultivated land, meadow, and woodland characterized by dense vegetation cover and robust carbon storage capacity tend to store more carbon compared to construction land and unutilized land, which possess sparse vegetation and limited carbon storage potential[ 32 ].The spatial distribution of carbon storage in Hexi region follows a clear pattern of “high in the southeast and low in the northwest”, with the Qilian Mountains emerging as the dominant high-carbon zone. This finding is largely consistent with the conclusions of Liu[ 33 ].Over the past two decades, the extensive transformation of unutilized land to arable land has become the primary influencing factor of increased carbon storage in the region. This observation supports the conclusions from Ren et al.[ 34 ], which emphasized that converting land from low-carbon-density types to high-carbon-density types significantly enhances overall carbon storage. However, variations emerge when comparing projected carbon storage trends with other regional studies. While this study predicts that all four development scenarios will yield carbon storage gains by 2030 most notably under the ecological protection scenario this result diverges from Qingmiao et al.[ 35 ], who identified the cultivated land protection scenario as producing the highest carbon gains in the Shiyang River Basin. This discrepancy is likely attributable to regional land use trajectories; in the Shiyang River Basin, arable land experienced continuous growth since the 1980s, meaning that restrictive policies under a cultivated land protection framework have less influence. By contrast, Hexi region’s ecological protection scenario more effectively enhances the carbon balance by limiting degradation in high-density vegetation areas. Another notable outcome of this study is that, although woodland and meadow held higher carbon storage in 2000 than in 2010, their economic value in 2000 was lower due to the significantly lower carbon price at that time. This highlights that the financial value of carbon is jointly determined by actual carbon volume, the prevailing carbon price, and the applied discount rate[ 36 ]. Overall, Hexi region, as a typical composite ecosystem and one of China’s key ecologically fragile areas, holds high strategic ecological importance. A comprehensive assessment of its carbon economic value not only provides critical data to support long-term carbon emissions policy-making and carbon market development but also offers valuable guidance for promoting carbon balance and advancing integrated ecological-economic development in the region. 5 Conclusion This paper systematically examined land use changes in Hexi region throughout 2000–2020 period and employed InVEST and PLUS models to simulate carbon storage dynamics with four development scenarios natural development, cultivated land protection, ecological protection, and sustainable development projecting into 2030. Financial value of carbon storage was also assessed, leading to the following key conclusions over the period of 2000–2020: (1) Meadow and unutilized land remained the dominant land use types in Hexi region, together comprising over 90% of the total area. During this period, cultivated land, water bodies, and construction land expanded, while meadow, woodland, and unutilized land contracted. Among these, the largest growth occurred in cultivated land, while the most notable decline was observed in unutilized land, reflecting policy-driven land conversion aimed at improving agricultural productivity. (2) The total carbon storage increased by approximately 1.43 million tons, with the most rapid accumulation occurring in the first decade. Projections for 2030 suggest continued growth across all four scenarios, with the ecological protection scenario indicating the greatest increase, followed by the sustainable development, cultivated land protection, and natural development scenarios. These findings highlight the critical role of ecological restoration policies in enhancing carbon storage capacity. (3) From an economic perspective, the value of carbon storage rose from 8.739 billion yuan in 2000 to 21.519 billion yuan in 2020 a surge of 146.24% primarily driven by rising carbon prices and land type transitions toward higher carbon density. By 2030, all scenarios project further increases in carbon economic value. Declarations Ethical Approval Not applicable. Funding This study was supported by 2025 Annual Project of the Xi Jinping Economic Thought Research Center: "Research on Accelerating the Green and Low-Carbon Transformation of Industrial Structure Guided by Xi Jinping Economic Thought". Author Contribution X. H.: Data compilation, manuscript writing, chart creation.H. X.Q.: Methodology, article revision.M. Y.C.: Funding support, supervision.F. G. and H. L.: Revision suggestions. Data Availability The authors confim that the data supporting the findings of this study are availablewithin the article.The authors confim that the data supporting the findings of this study are availablewithin the article [and/or its supplementary materials]. References Hong C, Burney J A, Pongratz J, et al. Global and regional drivers of land-use emissions in 1961–2017[J]. Nature , 2021, 589(7843): 554–561. Friedlingstein P, O'sullivan M, Jones M W, et al. Global carbon budget 2022[J]. Earth system science data , 2022, 14(11): 4811–4900. IPCC.Climate change 2001:Impacts,adaptation and vulnerability.Cambridge:Cambridge University Press,2001. Rounsevell M D A, Reginster I, Araújo M B, et al. A coherent set of future land use change scenarios for Europe[J]. Agriculture, Ecosystems & Environment , 2006, 114(1): 57–68. Tai Surigala, Du Yuchunzi, She Rong, et al. Analysis of the spatiotemporal evolution characteristics and driving factors of ecological vulnerability in the middle and upper reaches of the Yellow River [J/OL]. Environmental Science ,1–20[2025-11-09]. Marsh,George Perkins.Man and Nature:Physical Geography as Modified by Human Action. Charles Scribner ,1864. Rani A, Gupta S K, Singh S K, et al. Predicting future land use utilizing economic and land surface parameters with ANN and Markov chain models[J]. Earth , 2023, 4(3): 728–751. Shi Xiujuan, Wang Zhiqiang. Study on the Carbon Balance of Land Use and Carbon Compensation Zoning in Xinjiang Counties [J/OL]. Arid Zone Geography , 1–17 [2025-11-09]. Gao Shuchao, Chen Yiqing, Chen Zongzhu, et al. Carbon storage and spatial distribution characteristics of forest ecosystems on Hainan Island[J]. Acta Ecologica Sinica , 2023, 43(9):3558–3570. Wang Z,Zeng J,Chen W.Impact of urban expansion on carbon storage under multi-scenario simulati ons in Wuhan,China[J]. Environmental Science and Pollution Research ,2022,29(30):45507–45526. Luo Shuqi, Hu Xiaomeng, Sun Yuan, et al. Multi-scenario land use change and its impact on carbon storage based on the Coupled PLUS-InVEST model[J]. Chinese Journal of Eco-Agriculture (Chinese and English) , 2023, 31(02):300–314. Liu Hao, Liu Weifan, Wan Menghu, et al. Analysis and prediction of the spatiotemporal evolution of carbon storage in the Ningxia Hui Autonomous Region based on the PLUS-InVEST-OPGD model [J/OL]. Environmental Science , 1–16 [2025-09-14]. Xiong Guolai, Wu Xuequn, Yang Minglong, et al. Analysis of spatiotemporal variations and influencing factors of carbon storage in karst areas based on the PLUS-InVEST model[J]. Journal of Soil and Water Conservation , 2025, 32(06):307–315, 326. Lai Xiaofeng, Li Heyan, Li Huan, et al. Spatio-temporal evolution and prediction of carbon storage in Nanjian County based on the PLUS-InVEST model [J/OL]. Journal of Hubei University (Natural Science Edition) , 1–13 [2025-09-14]. Wu Gezhi, Zhou Nidi. Research on Forest Carbon Storage and Its Economic Value in Hunan Province[J]. Journal of Central South University of Forestry and Technology , 2015, 35(8): 127–132. Hussainzad E A, Yusof M J M. Assessing the economic value of carbon sequestration in Taman Negara Pahang[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing , 2020, 540(1): 012058. Du Hao. Research on Forest Carbon Stock and Carbon Sink Economic Value in Lishui City[J]. Zhejiang Forestry Science and Technology , 2023, 43(03):87–93. Xue Jian, Li Zongsheng, Chen Xuejiao, et al. Analysis of the Spatiotemporal Changes and Influencing Factors of Ecosystem Service Functions in the Hexi Region from 2000 to 2020 [J]. Environmental Ecology , 2023, 5(07):1–12. He Huishuang, Dong Xinxin, Ding Jieyu. Multi-scenario simulation of the 'three lives' spatial evolution and carbon stock assessment in the Yellow River Basin based on the PLUS-InVEST model [J/OL]. Environmental Science , 1–18 [2025-09-16]. Zhang Kun, Wu Xinping, Liu Yongqiang, et al. Spatiotemporal evolution and prediction of carbon stocks in Xinjiang based on the PLUS-InVEST model[J/OL]. Arid Zone Research , 1–11[2025-09-16]. Jian L, Xia X, Liu X, et al. Spatiotemporal variations and multi-scenario simulation of urban thermal environments based on complex networks and the PLUS model: A case study in Chengdu central districts[J]. Sustainable Cities and Society , 2024,115. Gong Shengxuan, Zhang Yuhu, Li Yuhang. Changes and Predictions of Carbon Storage in Beijing-Tianjin-Hebei Based on the PLUS-InVEST Model [J]. Resources and Environment in Arid Areas , 2023, 37(6): 20–28. Yang Shuo, Su Hao, Zhao Guoping. Multi-Scenario Simulation of Urban Ecosystem Service Value Based on the PLUS Model: A Case Study of Hanzhong City [J]. Resources and Environment in Arid Areas , 2022, 36(10): 86–95. Wang Wenchao. Research on Urban and Rural Green Space Simulation and Prediction Based on the PLUS Model [D]. Shandong Jianzhu University , 2023. Yuan Xue. Research on Land Use Change Scenario Simulation and Landscape Ecological Risk in Wuhan City Based on the PLUS Model [D]. East China University of Technology , 2022. Niu Tongli, Xiong Lihua, Chen Jie, et al. Simulation and Multi-Scenario Prediction of Land Use Change in the Yangtze River Basin Based on the PLUS Model[J]. Journal of Wuhan University (Engineering Edition) , 2024, 57(02):129–141. Ren Jintong, Zhou Wei, Wang Jie, et al. Analysis of Ecological Quality Changes in Caohai National Nature Reserve Based on RSEI and CLCD [J/OL]. Journal of Western Human Settlements , 1–7 [2025-07-31]. Yang Shunfa, Zan Mei, Yuan Ruilian, et al. Carbon Stock Changes and Predictions in Xinjiang Based on the PLUS and InVEST Models [J]. Environmental Science , 2025, 46(01):378–387. Lei Xin, Hai Xinquan. Estimation of Land Use Change and Carbon Storage Economic Value in Lanzhou City Coupled with the PLUS-InVEST Model[J]. Geographical Sciences , 2025, 45(2): 339–348. Ayitursun·Shamushi, Zhou Hongtao, Shi Yansong, et al. Estimation of Land Use Change and Carbon Stock Economic Value in Xinjiang Based on the Coupled PLUS-InVEST Model [J/OL]. Environmental Science , 1–22 [2025-07-31]. Long Wenju, Bai Song. Research on Carbon Sequestration and Economic Value of Forests in Sichuan Province [J/OL]. Tianjin Agriculture and Forestry Science and Technology , 1–9 [2025-10-10]. Wang Yiming, Zhang Zengxin, Chen Xi. Analysis of the Temporal and Spatial Changes and Driving Factors of Carbon Stock in the Poyang Lake Eco-Economic Zone Based on the PLUS-InVEST-GeoDetector Model [J/OL]. Environmental Science , 1–24 [2025-09-19]. Liu Mengyuan. Assessment of Carbon Storage and Habitat Quality in Hexi Region Based on the InVEST Model [D]. Lanzhou University , 2023. Ren Xijin, Pei Tingting, Chen Ying, et al. Impact of Land Use Change on Carbon Storage in Gansu Province Based on Carbon Density Correction [J]. Ecological Science , 2021, 40(4): 66–74. Qingmiao, Zhao Jun, Feng Chao, et al. Response of ecosystem carbon storage services to land use change in the Shiyang River Basin, 1980–2030 [J]. Acta Ecologica Sinica , 2022, 42(23): 9525–9536. Sui Yuzheng, Chen Xiaoxuan, Li Shujuan, et al. Spatiotemporal Evolution of Blue Carbon in Coastal Zones and Its Service Value Assessment: A Case Study of Jiaozhou Bay [J]. Resources Science , 2019, 41(11): 2119–2130. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8150677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588118757,"identity":"d3cb651c-507a-4102-974e-fd5a97447031","order_by":0,"name":"Xue Hui","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Hui","suffix":""},{"id":588118758,"identity":"3275b566-5c46-4576-9d97-55f062629741","order_by":1,"name":"Hai Xinquan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNmbmAwYSFVAeDzFa+NjbEgoszpCiRY7njMKHyjZStLBJ5DBuuDmv1p5/RgLjg7dtDPLmhLXkHjacue144owbCcyGc9sYDHc2ENSSl2Ysue1YAsONBDZp3jaGBIMDhB1m/vvvnGP28jcS2H8Tp4XnjIGBZEMN4wagLczEaQEGsoHEsQOJG888bJacc07CcAMhLfLNoKisqbOXO5588MObMht5grZAwWEgZmwAEhLEqQeCOqJVjoJRMApGwQgEALXxP4QVxi+xAAAAAElFTkSuQmCC","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Hai","middleName":"","lastName":"Xinquan","suffix":""},{"id":588118759,"identity":"6d130aa1-fe30-419f-a490-3cbd4e78f62d","order_by":2,"name":"Ma Yingchao","email":"","orcid":"","institution":"Gansu Provincial Party School of the CPC","correspondingAuthor":false,"prefix":"","firstName":"Ma","middleName":"","lastName":"Yingchao","suffix":""},{"id":588118760,"identity":"30a8b212-b052-4ade-a0a8-4967fdd56721","order_by":3,"name":"Fang Gang","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Gang","suffix":""},{"id":588118761,"identity":"ebff9d2d-50cc-4b10-9921-518bacbf07e5","order_by":4,"name":"He Lei","email":"","orcid":"","institution":"Gansu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2025-11-19 04:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8150677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8150677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102446803,"identity":"5601de2d-2ecf-464b-9e27-9a8d3bc169c5","added_by":"auto","created_at":"2026-02-11 17:48:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171158,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location and administrative boundaries of study area\u003c/p\u003e","description":"","filename":"Figure1.Geographicallocationandadministrativeboundariesofstudyarea.png","url":"https://assets-eu.researchsquare.com/files/rs-8150677/v1/e2f319e58a2bf38db1cefa73.png"},{"id":102446804,"identity":"20ffb8e7-496f-4f06-bca3-e6fd25e37abd","added_by":"auto","created_at":"2026-02-11 17:48:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41272,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of land use types in Hexi Region\u003c/p\u003e","description":"","filename":"Figure2.SpatialdistributionoflandusetypesinHexiRegion.png","url":"https://assets-eu.researchsquare.com/files/rs-8150677/v1/e9582f7e934a7eb14ffc0faf.png"},{"id":102745873,"identity":"83a89016-b631-4291-b41e-9fbfa998a49d","added_by":"auto","created_at":"2026-02-16 08:54:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33710,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of carbon storage in Hexi Region\u003c/p\u003e","description":"","filename":"Figure3.SpatialdistributionofcarbonstorageinHexiRegion.png","url":"https://assets-eu.researchsquare.com/files/rs-8150677/v1/61b76f63caeb4b3b4562d23f.png"},{"id":104397296,"identity":"bbc1f074-6775-40bb-ac4a-180035010cfc","added_by":"auto","created_at":"2026-03-11 11:46:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1264684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8150677/v1/a239e51c-f3d1-445c-a713-0464e23bab30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing economic value of carbon storage and land use changes based on coupled PLUS-InVEST model in Hexi Region","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change represents among the foremost environmental risks of our time, particularly evident in the rapid desertification of ecologically fragile zones and the growing imbalance in global water resource distribution, issues that have attracted extensive international concern[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As outlined in the United Nations Framework Convention on Climate Change (UNFCCC) and corroborated by the Paris Agreement, land use management is identified as a critical strategy for mitigating climate impacts. By transforming land use patterns, it is possible to influence the distribution and quantity of carbon storage, thereby enhancing carbon storage capacity and alleviating global warming. In response to these global imperatives, China has made firm policy commitments. The report of the 19th National Congress of the Communist Party in 2017 officially emphasized ecological restoration, and in September 2020, the country declared two strategic targets: achieving a \u0026ldquo;carbon peak\u0026rdquo; by 2030 and reaching \u0026ldquo;carbon neutrality\u0026rdquo; by 2060[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, ecological and environmental challenges have drawn growing attention across global academic communities. A major area of inquiry has been the concept of ecological vulnerability, McCarthy et al.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] linked to exposure, sensitivity, and adaptive capacity. Building on this, Rounsevell et al.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] utilized the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) to model future land use patterns and assess their implications for ecosystem services across Europe. In a regional context, Tai Surigala et al.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] developed an ecological vulnerability index system to examine spatial and temporal differentiation in the Yellow River basin. Another key research thread focuses on the ecological disturbances brought about by land use change a human-driven force of growing concern. As early as 1864, Marsh[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] underscored this issue in Man and Nature, analyzing how human land use reshapes natural landscapes. More recent works, such as that by Rani et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] applied models like CA-Markov to predict land use trajectories in India\u0026rsquo;s Batinda district, while Shi[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] assessed carbon balance in Xinjiang through social network and spatial overlay analyses. Beyond diagnosis, scholars are increasingly exploring mechanisms to safeguard ecosystem health through natural capital accounting. Carbon storage valuation has emerged as a vital tool for internalizing ecological externalities and advancing ecological compensation strategies. Kira Tatsuo\u0026rsquo;s foundational work in Southeast Asian tropical forests during the 1960s highlighted the carbon storage function of biomass. Traditional carbon estimation methods such as field surveys and the IPCC inventory approach are constrained by their time intensity and limited scalability[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By contrast, integrated models like coupled Patch-generating Land Use Simulation with Integrated Valuation of Ecosystem Services and Tradeoffs (PLUS-InVEST) gained traction for their predictive accuracy and efficiency[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Liu et al.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] combined the InVEST, PLUS, and Optimal Parameter-based Geographical Detector (OPGD) models to evaluate carbon dynamics in Ningxia, finding minimal loss under ecological protection but pronounced decline under urban expansion. Xiong et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported significant carbon losses in karst areas from 1990 to 2020, and Lai et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] used the models to guide sustainable land use planning in Nanjian County. In parallel, the economic valuation of carbon storage has evolved through methods like the afforestation cost approach, carbon tax estimation, and net present value analysis. Wu et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] applied these techniques in Hunan Province to support policy design. Hussainzad et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] employed the Net Present Value (NPV) method to evaluate carbon storage in Malaysia\u0026rsquo;s Pahang region, advancing green development strategies. Du et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], comparing three methods, quantified Lishui\u0026rsquo;s forest carbon value from 2017 to 2021, yielding estimates ranging from 168\u0026nbsp;million to 3.484\u0026nbsp;billion yuan.\u003c/p\u003e \u003cp\u003eExisting studies on ecological vulnerability have established a robust research framework, exploring this issue from multiple dimensions including theoretical foundations, influencing mechanisms, and practical applications. In the field of land use, research has increasingly relied on theoretical models and quantitative approaches to investigate the implications of land use alteration, monitor its dynamics, and assess the resulting carbon balance. Meanwhile, carbon storage evaluation particularly its economic dimension has evolved from traditional methods to more advanced model-based assessments, forming an integrated research process encompassing \u0026ldquo;data acquisition, model construction, and empirical analysis\u0026rdquo;. Building upon this foundation, the present study focuses on the \u0026ldquo;mountain-oasis-desert\u0026rdquo; composite ecosystem as a representative case, aiming to fill current gaps in research related to carbon storage and economic valuation in such complex ecological contexts. By integrating the market value method with compound present and future value approaches, this research explicitly incorporates temporal cost considerations into the carbon valuation process, thereby enhancing the reliability and applicability of the results for decision-making and policy formulation.\u003c/p\u003e \u003cp\u003eHexi region, recognized as one of China\u0026rsquo;s eight major ecologically fragile zones, represents a complex and sensitive ecosystem with limited resilience to external disturbances and ongoing degradation. This makes it a strategic priority for national ecological conservation initiatives. In response, local and regional governments have implemented a series of environmental policies aimed at improving ecological integrity and promoting sustainable land use. Within this context, evaluating carbon storage dynamics and their corresponding economic value becomes crucial for informing evidence-based policy decisions. Therefore, this study integrates an examination of land use dynamics and their influencing factors with a forward-looking assessment of carbon storage trends and valuation under multiple development scenarios. By focusing on the \u0026ldquo;mountain-oasis-desert\u0026rdquo; composite ecosystem, the research provides a theoretical basis and practical data support for optimizing land use structures and advancing Hexi region\u0026rsquo;s progress toward the national \u0026ldquo;dual carbon\u0026rdquo; objectives.\u003c/p\u003e"},{"header":"2 Overview and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Overview of Study Area\u003c/h2\u003e\n \u003cp\u003eHexi region(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), situated to the west of the Yellow River in Gansu Province, spans latitudes 37\u0026deg;10\u0026prime; to 42\u0026deg;50\u0026prime;N and longitudes 93\u0026deg;20\u0026prime; to 104\u0026deg;00\u0026prime;E. It encompasses five key cities: Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan. Geographically, Hexi serves as a vital transitional corridor connecting the Qinghai-Tibet Plateau (QTP) and the Mongolian Plateau along the north-south axis, while linking the Loess Plateau with the Tarim Basin from east to west. This region is characterized by a unique composite ecosystem of mountains, oases, and deserts, and functions as a critical ecological buffer against desertification, particularly from the Tengger and Badain Jaran deserts. Stretching from Gulang Gorge in the east to Xingxing Gorge in the west, Hexi covers approximately 266,000 km\u003csup\u003e2\u003c/sup\u003e and forms a strategic segment of China\u0026rsquo;s Silk Road Economic Belt. The climate is classified as temperate continental, marked by minimal precipitation, arid conditions, frequent sandstorms, and sharp temperature fluctuations. Annual rainfall ranges from 30 mm to 200 mm, generally decreasing westward, and the average annual temperature hovers around 12℃[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Major inland river systems in the region including the Shiyang, Heihe, and Shule Rivers support critical hydrological and ecological functions. Due to its distinct geographic setting and climate, Hexi region holds considerable research significance for the study of global arid ecosystems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\n \u003cp\u003eData utilized for the analysis are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Geographic Information System (GIS) tools were employed to preprocess the land use data. Initially, the land use information for Hexi region was extracted using the \u0026ldquo;Extract by Mask\u0026rdquo; function. The raster projection was then standardized to WGS_1984_UTM_Zone_47N, resampled to 100-m resolution. To maintain data consistency across temporal layers, the \u0026ldquo;Reclassify\u0026rdquo; tool was used to recode land use types sequentially from 1 to 6, corresponding to arable land, forest, meadow, water bodies, urban land, along with unutilized land, respectively. Following this, the coordinate system, processing extent, and raster analysis environment were uniformly defined in the geoprocessing settings. The remaining land use datasets were further masked and reclassified to ensure alignment across all spatial layers. In addition to land use data, driving factor layers were incorporated. These were similarly extracted using the \u0026ldquo;Extract by Mask\u0026rdquo; function. For distance-based variables, vector datasets representing prominent landscape elements like rivers, roads, and railways were used. Euclidean distance calculations were then applied to convert these vector features into raster distance layers.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTypes, years, and sources of data used in this analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLUCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand use/cover change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000, 2010, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource Environment Science and Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSocioeconomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eResource Environment Science and Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross Domestic Product (GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOpen street map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to railways\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eNature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormalized Difference Vegetation Index (NDVI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResource Environment Science and Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Elevation Model (DEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeospatial Data Cloud\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual average temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNational Earth System Science Data Center\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Methods\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 InVEST Model\u003c/h2\u003e\n \u003cp\u003eThis study employs InVEST model, a widely recognized ecosystem service assessment tool developed by Stanford University. Among its suite of modules including those for water yield, habitat quality, and nutrient retention the Carbon Storage and Sequestration module is applied for estimating terrestrial carbon storage. Within this module, the carbon accounting framework is structured around four major carbon pools: aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The total carbon storage is determined as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{\\text{C}}_{\\text{t}}={\\text{C}}_{\\text{a}}+{\\text{C}}_{\\text{b}}+{\\text{C}}_{\\text{s}}+{\\text{C}}_{\\text{d}} \\left(1\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere C\u003csub\u003et\u003c/sub\u003e is the total carbon storage; C\u003csub\u003ea\u003c/sub\u003e is the aboveground biomass carbon storage; C\u003csub\u003eb\u003c/sub\u003e is the belowground biomass carbon storage; C\u003csub\u003es\u003c/sub\u003e is the soil organic carbon storage; and C\u003csub\u003ed\u003c/sub\u003e is the dead organic carbon storage.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{\\text{C}}_{\\text{i}-\\text{t}}=({\\text{C}}_{\\text{i}-\\text{a}}+{\\text{C}}_{\\text{i}-\\text{b}}+{\\text{C}}_{\\text{i}-\\text{s}}+{\\text{C}}_{\\text{i}-\\text{d}})\\times\\:{\\text{A}}_{\\text{i}} \\left(2\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere i is the average carbon density specific to each land use category and A\u003csub\u003ei\u003c/sub\u003e is the corresponding land area. The carbon density values applied for Hexi region are derived from prior empirical studies, with necessary adjustments made to reflect local environmental conditions and land classification standards[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].To ensure consistency in unit usage throughout the calculations, all carbon density values were standardized to units of ton\u0026middot;km\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCarbon density in Hexi Region (ton\u0026middot;km\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLUCC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-above\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-below\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-soil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-dead\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultivated land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoodland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeadow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnutilized land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 PLUS Model\u003c/h2\u003e\n \u003cp\u003ePLUS model, proposed by China University of Geosciences, is widely recognized as a dynamic land use simulation framework that operates at the patch level. By capturing the underlying mechanisms of land transformation, the PLUS model has become a valuable tool in scenario-based planning and land management studies[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eScenario Setting: To simulate potential land use outcomes in 2030, four distinct development scenarios were established based on land transition probabilities derived from 2010 to 2020. Scenario 1 represents a natural development pathway, in which a Markov model is employed to forecast land demand and transition probabilities without the influence of policy interventions, serving as the baseline trajectory. Scenario 2, the ecological protection scenario, aligns with the objectives set forth in the Gansu Province 14th Five-Year Plan for Ecological Environment Protection, aiming to mitigate ecological degradation in this environmentally fragile region. Under this scenario, the potential of transforming forest and meadow into construction land is declined by 50%, and the potential of transforming cultivated land into construction land is decreased by 30%[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Scenario 3 focuses on cultivated land protection, reflecting the national requirement to maintain the \u0026ldquo;1.8\u0026nbsp;billion mu\u0026rdquo; redline. It emphasizes spatial optimization of farmland, ensuring that the total cultivated land area exceeds that of the natural development scenario. Ultimately, the potential of transforming cultivated land to construction land is declined by 60%, while the potential of converting forest and meadow to construction land is reduced by 20%, and the transformation from forest and meadow to cultivated land is surged by 30%[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Scenario 4 is designed around the concept of sustainable development, aiming to balance ecological integrity with land use efficiency. In this scenario, the probabilities of converting cultivated land and woodland into construction land are reduced by 10%, meadow and water bodies by 20%, while the potential of converting construction land into woodland is reduced by 20%, and its conversion into meadow, water, and unutilized land is lowered by 10%.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSetting of Domain Weights: Domain weights are designed to quantify the relative difficulty of conversion between different land use types, serving as a critical parameter for constraining land use transitions within the PLUS model framework[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The domain weights are calculated using a normalization formula that incorporates the relative changes in land area between time periods, expressed as follows[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{X}^{*}=\\frac{X-{X}_{min}}{{X}_{max}-{X}_{min}} \\left(3\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003eX\u003c/em\u003e* is the standardised deviation value; \u003cem\u003eX\u003c/em\u003e is the change in area for each land use category between the two periods; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e is the maximum change in land area; and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e is the minimum change in land area. Through experimentation, the final weight of each land use category is determined as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eField weight of land use types under different development scenarios\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCultivated land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWoodland\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeadow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruction land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnutilized land\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcological protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultivated land protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSustainable development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(3)Accuracy Validation: Accuracy validation was carried out through Kappa coefficient. Ranging from 0 to 1, the Kappa value reflects the degree of agreement, with larger values suggesting better simulation accuracy[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, the Markov chain module embedded within the PLUS model was employed to simulate land use distribution in 2020 according to historical data from 2000 and 2010. The predicted outcomes were examined alongside the actual land use data for 2020, producing a Kappa coefficient of 0.898. This strong correlation demonstrates the robustness and reliability of the model, validating its suitability for projecting land use changes in Hexi region through to 2030.\u003c/p\u003e\n \u003cp\u003e(4) Land Use Transfer Matrix: The land use transfer matrix is a two-dimensional analytical tool used to quantify and visualize the spatial conversion relationships between various land use categories within a defined temporal and geographic scope. In this framework, 1 demonstrates that the transformation between two land types is authorized, while 0 denotes a prohibited or restricted conversion pathway[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The matrix formulation is expressed as follows:\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{\\text{C}}_{\\text{x}\\text{y}}=\\left[\\begin{array}{ccc}{\\text{C}}_{11}\u0026amp;\\:\\cdots\\:\u0026amp;\\:{\\text{C}}_{1\\text{n}}\\\\\\:⋮\u0026amp;\\:\\ddots\\:\u0026amp;\\:⋮\\\\\\:{\\text{C}}_{\\text{n}1}\u0026amp;\\:\\cdots\\:\u0026amp;\\:{\\text{C}}_{\\text{n}\\text{n}}\\end{array}\\right] \\left(4\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere C\u003csub\u003exy\u003c/sub\u003e is the area (km\u003csup\u003e2\u003c/sup\u003e) of land use category y transferred to category x; while x and y are the land use types before and after the conversion (x\u0026thinsp;=\u0026thinsp;1,2,3,\u0026middot;\u0026middot;\u0026middot;,n, y\u0026thinsp;=\u0026thinsp;1,2,3,\u0026middot;\u0026middot;\u0026middot;,n). The land use transition matrix for each scenario is outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLand use transfer Matrix under each scenario\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLUCC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eScenario 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eScenario 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eScenario 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eScenario4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"25\"\u003eNote: A is cultivated land, B is woodland, C is meadow, D is water body, E is construction land, and F is unutilized land.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3 Present/Future Value Method of Compound Interest\u003c/h2\u003e\n \u003cp\u003eThe compound present value and compound future value methods, widely applied in the fields of finance and management, are essential tools for assessing the time value of money, particularly in investment evaluation and risk analysis contexts. In this study, these financial techniques are adapted to estimate the economic value of carbon storage over time. Rather than relying solely on the 2020 carbon trading price, the analysis employs the average market prices from 2019 to 2021 as a more representative reference point. Using these values, reverse calculations and forward projections are carried out to derive the carbon trading prices for the years 2000, 2010, 2020, and 2030, based on compound present value and compound future value models[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The results of these computations are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{\\text{P}}_{\\text{n}}=F\\times\\:(\\frac{\\text{P}}{\\text{F}},e,n） (\\text{5}\\text{)}\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere P\u003csub\u003en\u003c/sub\u003e is the carbon price in period n; F is the final carbon price; (P/F,e,n) is the compound present value factor; e is the discount rate; and n is the number of periods.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCarbon trading price in Hexi Region (2000\u0026ndash;2030)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2030\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon price (yuan\u0026middot;ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4 Carbon Storage Value Estimation\u003c/h2\u003e\n \u003cp\u003eThe value of carbon storage reflects the ecological function and service potential of carbon retained within terrestrial ecosystems[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The model formulation is presented as follows:\u003c/p\u003e\n \u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equf\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}V=\\sum\\:_{\\text{n}=1}^{\\text{n}}{\\text{P}}_{\\text{n}}\\times\\:{\\text{C}}_{\\text{n}} \\left(6\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere V is the economic value of a certain ecological carbon storage; C\u003csub\u003en\u003c/sub\u003e is the total carbon storage of a certain ecosystem; and P\u003csub\u003en\u003c/sub\u003e is the carbon trading price (yuan\u0026middot;ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results and analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of Spatiotemporal Evolution of Land Use\u003c/h2\u003e \u003cp\u003eLand use changes in Hexi region between 2000 and 2020 exhibit significant structural adjustments, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Throughout this period, the landscape was predominantly composed of unutilized land and meadow, which together constituted over 90% of the total area. Arable land and woodland followed, accounting for slightly more than 5% and 2%, respectively, while water bodies and construction land remained relatively minor components, each occupying less than 2% of the total land area. Over the two-decade span, substantial land conversions were observed: the area of arable land expanded by 1,963.56 km\u003csup\u003e2\u003c/sup\u003e, water bodies by 448.75 km\u003csup\u003e2\u003c/sup\u003e, and construction land by 666.88 km\u003csup\u003e2\u003c/sup\u003e, all showing consistent year-on-year growth. These increases are closely tied to major regional development initiatives. For example, to ensure national food security, the Gansu provincial government implemented large-scale land reclamation strategies, converting vast portions of unutilized land into productive farmland. Concurrently, driven by major policy interventions including the Comprehensive Management Project of the Heihe River Basin and the Western Development Strategy, additional areas of unutilized land were redirected toward water resource infrastructure and urban expansion. Consequently, unutilized land decreased markedly, from 169,516.84 km\u003csup\u003e2\u003c/sup\u003e in 2000 to 166,793.87 km\u003csup\u003e2\u003c/sup\u003e in 2020 an overall reduction of 2,722.97 km\u003csup\u003e2\u003c/sup\u003e. In contrast, the areas of woodland and meadow showed relatively moderate fluctuations: both experienced slight declines in the first decade, followed by modest recoveries in the second, resulting in net decreases of 17.92 km\u003csup\u003e2\u003c/sup\u003e and 338.11 km\u003csup\u003e2\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eBy 2030, land use patterns in Hexi region are projected to diverge moderately across different development scenarios. Under the natural development scenario, trends largely mirror those observed between 2000 and 2020, with continued expansion in the areas of arable land, water bodies, and construction land, while unutilized land undergoes a pronounced decline and woodland and meadow remain relatively stable. In the cultivated land protection scenario, policy-imposed restrictions on the conversion of farmland resulted in a notable surge in arable land area rising by 501.56 km\u003csup\u003e2\u003c/sup\u003e compared to 2020. Meanwhile, the areas of woodland, water bodies, and construction land also exhibit moderate increases of 18.91 km\u003csup\u003e2\u003c/sup\u003e, 237.17 km\u003csup\u003e2\u003c/sup\u003e, and 238.78 km\u003csup\u003e2\u003c/sup\u003e respectively, accompanied by decreases of 80.17 km\u003csup\u003e2\u003c/sup\u003e in meadow and 916.22 km\u003csup\u003e2\u003c/sup\u003e in unutilized land. Conversely, both the ecological protection and sustainable development scenarios demonstrate broadly similar patterns of land use adjustment. While unutilized land consistently declines across these two scenarios, arable land, meadow, woodland, water bodies, and construction land all register different levels of growth, showing a more balanced approach to ecological and development priorities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use area under each scenario (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLUCC\u003c/p\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScenario 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eScenario 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eScenario 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13912.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15777.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15876.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16027.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16089.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16377.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16051.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7377.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7329.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7359.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7391.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7396.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7378.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7392.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53575.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53136.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53237.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53352.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53388.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53156.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53360.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2247.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2435.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2696.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2932.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2933.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2933.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2933.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstruction land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1017.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1211.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1684.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2069.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1968.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1922.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2047.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnutilized land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169516.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167757.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166793.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165873.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e165870.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e165877.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e165861.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.97%\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\u003eFrom a spatial perspective (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), cultivated land in Hexi region is primarily distributed in elongated strips within oasis zones formed by the alluvial plains of the Shiyang, Heihe, and Shule Rivers, particularly in areas surrounding Wuwei, Zhangye, and Jiuquan. Woodland and meadow are predominantly concentrated along the slopes and valleys of the Qilian Mountains, although small patches of meadow can also be observed scattered throughout the region. Construction land is mainly clustered in urban centers such as Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan, as well as along major transportation corridors. In contrast, unutilized land remains the dominant land use category in Hexi Corridor, extensively covering the desert and Gobi regions of the northwest. Due to the harsh natural conditions of these arid zones characterized by poor soil quality, low precipitation, and wind erosion such areas are poorly suited to extensive expansion or intensive land use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Spatiotemporal Analysis of Carbon Storage\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the total carbon storage in Hexi region exhibited a steady upward trajectory between 2000 and 2020, with an increase of approximately 1.11\u0026nbsp;million tons during the first decade and a more modest rise of 320,000 tons in the following ten years. For various land use categories, arable land was the primary contributor to the overall growth, adding a total of 2.1\u0026nbsp;million tons of carbon storage, with the most pronounced gains observed during the earlier period. This trend reflects the development of cultivated areas and the increasing importance of agricultural land as a carbon storage in the region. In contrast, unutilized land experienced a continuous reduction in both area and carbon storage capacity, resulting in a net loss of approximately 2,700 tons over the 20-year period. The dynamics of woodland and meadow followed a similar pattern: both witnessed declines in carbon storage during the first decade, followed by partial recoveries thereafter. Nevertheless, the cumulative losses remain substantial, with woodland and meadow carbon storage decreasing by 20,000 tons and 640,000 tons respectively.\u003c/p\u003e \u003cp\u003eIn the 2030 natural development scenario, the total carbon storage in Hexi region is projected to increase by approximately 380,000 tons compared to 2020, with arable land, woodland, and meadow contributing gains of 160,000 tons, 30,000 tons, and 190,000 tons respectively. Despite this overall growth, carbon storage in unutilized land is projected to decline slightly, decreasing by around 900 tons. Under the cultivated land protection scenario, the highest growth is observed in cultivated land, which adds approximately 520,000 tons of carbon. While woodland also experiences a modest gain, carbon storage in meadow and unutilized land decline by 140,000 tons and 900 tons respectively. In the ecological protection scenario, carbon storage continue to rise across arable land, woodland, and meadow, with respective increases of 220,000, 40,000, and 210,000 tons. Similarly, the sustainable development scenario yields increases of 180,000 tons in arable land, 30,000 tons in woodland, and 210,000 tons in meadow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon storage by land use types under each scenarios (10\u003csup\u003e6\u003c/sup\u003e ton)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScenario 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScenario 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eScenario 4\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.42\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnutilized land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125.53\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\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the spatial distribution of carbon storage in Hexi region reveals a distinct gradient, characterized by larger values in the southeast and smaller values in the northwest. The southern Qilian Mountains constitute the principal high-carbon zone, where extensive coniferous forests contribute substantially to the region\u0026rsquo;s overall carbon storage capacity, making it the primary carbon storage in the area. In the central oasis belt, carbon storage values fall within a moderate range, driven by the dense vegetation cover of cultivated land, which acts as an effective transitional carbon pool between the mountainous forested areas and the arid zones. In contrast, the northwestern part of the region registers the lowest carbon storage levels. This pattern is largely due to harsh climatic conditions and sparse vegetation, which limit the carbon storage potential of desert and Gobi landscapes, thereby contributing to the region\u0026rsquo;s spatial heterogeneity in carbon distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of Economic Value Changes in Carbon Storage\u003c/h2\u003e \u003cp\u003eUsing the compound present and future value formulas, the economic valuation of carbon storage in Hexi region under different development scenarios from 2000 to 2030 is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Between 2000 and 2020, the total financial value of carbon storage increased markedly, rising from 8.739\u0026nbsp;billion yuan to 21.519\u0026nbsp;billion yuan an overall growth of 146.24%. This surge is primarily attributed to the substantial rise in carbon pricing over the two decades. Among all land use types, meadow contributed the most significantly to this increase, both in terms of absolute value and growth rate, with an additional 1.011\u0026nbsp;billion yuan and a growth rate of 142.87%, establishing it as the dominant driver of financial value gains during this period. Other land types followed in the order of woodland, arable land, and unutilized land. Looking ahead to 2030, all four scenarios project further increases in the economic value of carbon storage, albeit with varying magnitudes. The ecological protection scenario is associated with the largest overall increase, while the natural development scenario shows the weakest growth trajectory. Across all scenarios, meadow continues to serve as the principal contributor to carbon value growth, with an average increase rate of 49.91%, followed by arable land and woodland. In contrast, unutilized land consistently exhibits the lowest economic return on carbon storage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinancial value of carbon storage of various land types under each scenarios (10\u003csup\u003e6\u003c/sup\u003e yuan)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScenario 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScenario 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScenario 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eScenario 4\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1046.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1763.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2908.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4390.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4405.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4483.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4395.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e554.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e819.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1346.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2024.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7126.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10503.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17236.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25823.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25839.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25738.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25828.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater bodiess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnutilized land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8739.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13103.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21518.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32277.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32313.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32282.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32287.30\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"},{"header":"4 Discussion","content":"\u003cp\u003eVariations in land use patterns represent the most immediate factor influencing the dynamics of regional carbon storage. Land types such as cultivated land, meadow, and woodland characterized by dense vegetation cover and robust carbon storage capacity tend to store more carbon compared to construction land and unutilized land, which possess sparse vegetation and limited carbon storage potential[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].The spatial distribution of carbon storage in Hexi region follows a clear pattern of \u0026ldquo;high in the southeast and low in the northwest\u0026rdquo;, with the Qilian Mountains emerging as the dominant high-carbon zone. This finding is largely consistent with the conclusions of Liu[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].Over the past two decades, the extensive transformation of unutilized land to arable land has become the primary influencing factor of increased carbon storage in the region. This observation supports the conclusions from Ren et al.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which emphasized that converting land from low-carbon-density types to high-carbon-density types significantly enhances overall carbon storage. However, variations emerge when comparing projected carbon storage trends with other regional studies. While this study predicts that all four development scenarios will yield carbon storage gains by 2030 most notably under the ecological protection scenario this result diverges from Qingmiao et al.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], who identified the cultivated land protection scenario as producing the highest carbon gains in the Shiyang River Basin. This discrepancy is likely attributable to regional land use trajectories; in the Shiyang River Basin, arable land experienced continuous growth since the 1980s, meaning that restrictive policies under a cultivated land protection framework have less influence. By contrast, Hexi region\u0026rsquo;s ecological protection scenario more effectively enhances the carbon balance by limiting degradation in high-density vegetation areas. Another notable outcome of this study is that, although woodland and meadow held higher carbon storage in 2000 than in 2010, their economic value in 2000 was lower due to the significantly lower carbon price at that time. This highlights that the financial value of carbon is jointly determined by actual carbon volume, the prevailing carbon price, and the applied discount rate[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Overall, Hexi region, as a typical composite ecosystem and one of China\u0026rsquo;s key ecologically fragile areas, holds high strategic ecological importance. A comprehensive assessment of its carbon economic value not only provides critical data to support long-term carbon emissions policy-making and carbon market development but also offers valuable guidance for promoting carbon balance and advancing integrated ecological-economic development in the region.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis paper systematically examined land use changes in Hexi region throughout 2000\u0026ndash;2020 period and employed InVEST and PLUS models to simulate carbon storage dynamics with four development scenarios natural development, cultivated land protection, ecological protection, and sustainable development projecting into 2030. Financial value of carbon storage was also assessed, leading to the following key conclusions over the period of 2000\u0026ndash;2020:\u003c/p\u003e \u003cp\u003e(1) Meadow and unutilized land remained the dominant land use types in Hexi region, together comprising over 90% of the total area. During this period, cultivated land, water bodies, and construction land expanded, while meadow, woodland, and unutilized land contracted. Among these, the largest growth occurred in cultivated land, while the most notable decline was observed in unutilized land, reflecting policy-driven land conversion aimed at improving agricultural productivity.\u003c/p\u003e \u003cp\u003e(2) The total carbon storage increased by approximately 1.43\u0026nbsp;million tons, with the most rapid accumulation occurring in the first decade. Projections for 2030 suggest continued growth across all four scenarios, with the ecological protection scenario indicating the greatest increase, followed by the sustainable development, cultivated land protection, and natural development scenarios. These findings highlight the critical role of ecological restoration policies in enhancing carbon storage capacity.\u003c/p\u003e \u003cp\u003e(3) From an economic perspective, the value of carbon storage rose from 8.739\u0026nbsp;billion yuan in 2000 to 21.519\u0026nbsp;billion yuan in 2020 a surge of 146.24% primarily driven by rising carbon prices and land type transitions toward higher carbon density. By 2030, all scenarios project further increases in carbon economic value.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by 2025 Annual Project of the Xi Jinping Economic Thought Research Center: \"Research on Accelerating the Green and Low-Carbon Transformation of Industrial Structure Guided by Xi Jinping Economic Thought\".\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX. H.: Data compilation, manuscript writing, chart creation.H. X.Q.: Methodology, article revision.M. Y.C.: Funding support, supervision.F. G. and H. L.: Revision suggestions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors confim that the data supporting the findings of this study are availablewithin the article.The authors confim that the data supporting the findings of this study are availablewithin the article [and/or its supplementary materials].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHong C, Burney J A, Pongratz J, et al. Global and regional drivers of land-use emissions in 1961\u0026ndash;2017[J]. \u003cem\u003eNature\u003c/em\u003e, 2021, 589(7843): 554\u0026ndash;561.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedlingstein P, O'sullivan M, Jones M W, et al. Global carbon budget 2022[J]. \u003cem\u003eEarth system science data\u003c/em\u003e, 2022, 14(11): 4811\u0026ndash;4900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC.Climate change 2001:Impacts,adaptation and vulnerability.Cambridge:Cambridge University Press,2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRounsevell M D A, Reginster I, Ara\u0026uacute;jo M B, et al. A coherent set of future land use change scenarios for Europe[J]. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, 2006, 114(1): 57\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTai Surigala, Du Yuchunzi, She Rong, et al. Analysis of the spatiotemporal evolution characteristics and driving factors of ecological vulnerability in the middle and upper reaches of the Yellow River [J/OL]. \u003cem\u003eEnvironmental Science\u003c/em\u003e,1\u0026ndash;20[2025-11-09].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh,George Perkins.Man and Nature:Physical Geography as Modified by Human Action.\u003cem\u003eCharles Scribner\u003c/em\u003e,1864.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRani A, Gupta S K, Singh S K, et al. Predicting future land use utilizing economic and land surface parameters with ANN and Markov chain models[J]. \u003cem\u003eEarth\u003c/em\u003e, 2023, 4(3): 728\u0026ndash;751.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Xiujuan, Wang Zhiqiang. Study on the Carbon Balance of Land Use and Carbon Compensation Zoning in Xinjiang Counties [J/OL]. \u003cem\u003eArid Zone Geography\u003c/em\u003e, 1\u0026ndash;17 [2025-11-09].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Shuchao, Chen Yiqing, Chen Zongzhu, et al. Carbon storage and spatial distribution characteristics of forest ecosystems on Hainan Island[J]. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, 2023, 43(9):3558\u0026ndash;3570.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z,Zeng J,Chen W.Impact of urban expansion on carbon storage under multi-scenario simulati ons in Wuhan,China[J].\u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e,2022,29(30):45507\u0026ndash;45526.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Shuqi, Hu Xiaomeng, Sun Yuan, et al. Multi-scenario land use change and its impact on carbon storage based on the Coupled PLUS-InVEST model[J]. \u003cem\u003eChinese Journal of Eco-Agriculture (Chinese and English)\u003c/em\u003e, 2023, 31(02):300\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Hao, Liu Weifan, Wan Menghu, et al. Analysis and prediction of the spatiotemporal evolution of carbon storage in the Ningxia Hui Autonomous Region based on the PLUS-InVEST-OPGD model [J/OL]. \u003cem\u003eEnvironmental Science\u003c/em\u003e, 1\u0026ndash;16 [2025-09-14].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong Guolai, Wu Xuequn, Yang Minglong, et al. Analysis of spatiotemporal variations and influencing factors of carbon storage in karst areas based on the PLUS-InVEST model[J]. \u003cem\u003eJournal of Soil and Water Conservation\u003c/em\u003e, 2025, 32(06):307\u0026ndash;315, 326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai Xiaofeng, Li Heyan, Li Huan, et al. Spatio-temporal evolution and prediction of carbon storage in Nanjian County based on the PLUS-InVEST model [J/OL]. \u003cem\u003eJournal of Hubei University (Natural Science Edition)\u003c/em\u003e, 1\u0026ndash;13 [2025-09-14].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Gezhi, Zhou Nidi. Research on Forest Carbon Storage and Its Economic Value in Hunan Province[J]. \u003cem\u003eJournal of Central South University of Forestry and Technology\u003c/em\u003e, 2015, 35(8): 127\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussainzad E A, Yusof M J M. Assessing the economic value of carbon sequestration in Taman Negara Pahang[C]//IOP Conference Series: Earth and Environmental Science. \u003cem\u003eIOP Publishing\u003c/em\u003e, 2020, 540(1): 012058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Hao. Research on Forest Carbon Stock and Carbon Sink Economic Value in Lishui City[J]. \u003cem\u003eZhejiang Forestry Science and Technology\u003c/em\u003e, 2023, 43(03):87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue Jian, Li Zongsheng, Chen Xuejiao, et al. Analysis of the Spatiotemporal Changes and Influencing Factors of Ecosystem Service Functions in the Hexi Region from 2000 to 2020 [J]. \u003cem\u003eEnvironmental Ecology\u003c/em\u003e, 2023, 5(07):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Huishuang, Dong Xinxin, Ding Jieyu. Multi-scenario simulation of the 'three lives' spatial evolution and carbon stock assessment in the Yellow River Basin based on the PLUS-InVEST model [J/OL]. \u003cem\u003eEnvironmental Science\u003c/em\u003e, 1\u0026ndash;18 [2025-09-16].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Kun, Wu Xinping, Liu Yongqiang, et al. Spatiotemporal evolution and prediction of carbon stocks in Xinjiang based on the PLUS-InVEST model[J/OL]. \u003cem\u003eArid Zone Research\u003c/em\u003e, 1\u0026ndash;11[2025-09-16].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJian L, Xia X, Liu X, et al. Spatiotemporal variations and multi-scenario simulation of urban thermal environments based on complex networks and the PLUS model: A case study in Chengdu central districts[J]. \u003cem\u003eSustainable Cities and Society\u003c/em\u003e, 2024,115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong Shengxuan, Zhang Yuhu, Li Yuhang. Changes and Predictions of Carbon Storage in Beijing-Tianjin-Hebei Based on the PLUS-InVEST Model [J]. \u003cem\u003eResources and Environment in Arid Areas\u003c/em\u003e, 2023, 37(6): 20\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Shuo, Su Hao, Zhao Guoping. Multi-Scenario Simulation of Urban Ecosystem Service Value Based on the PLUS Model: A Case Study of Hanzhong City [J]. \u003cem\u003eResources and Environment in Arid Areas\u003c/em\u003e, 2022, 36(10): 86\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Wenchao. Research on Urban and Rural Green Space Simulation and Prediction Based on the PLUS Model [D]. \u003cem\u003eShandong Jianzhu University\u003c/em\u003e, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Xue. Research on Land Use Change Scenario Simulation and Landscape Ecological Risk in Wuhan City Based on the PLUS Model [D]. \u003cem\u003eEast China University of Technology\u003c/em\u003e, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Tongli, Xiong Lihua, Chen Jie, et al. Simulation and Multi-Scenario Prediction of Land Use Change in the Yangtze River Basin Based on the PLUS Model[J]. \u003cem\u003eJournal of Wuhan University (Engineering Edition)\u003c/em\u003e, 2024, 57(02):129\u0026ndash;141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Jintong, Zhou Wei, Wang Jie, et al. Analysis of Ecological Quality Changes in Caohai National Nature Reserve Based on RSEI and CLCD [J/OL]. \u003cem\u003eJournal of Western Human Settlements\u003c/em\u003e, 1\u0026ndash;7 [2025-07-31].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Shunfa, Zan Mei, Yuan Ruilian, et al. Carbon Stock Changes and Predictions in Xinjiang Based on the PLUS and InVEST Models [J]. \u003cem\u003eEnvironmental Science\u003c/em\u003e, 2025, 46(01):378\u0026ndash;387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei Xin, Hai Xinquan. Estimation of Land Use Change and Carbon Storage Economic Value in Lanzhou City Coupled with the PLUS-InVEST Model[J]. \u003cem\u003eGeographical Sciences\u003c/em\u003e, 2025, 45(2): 339\u0026ndash;348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyitursun\u0026middot;Shamushi, Zhou Hongtao, Shi Yansong, et al. Estimation of Land Use Change and Carbon Stock Economic Value in Xinjiang Based on the Coupled PLUS-InVEST Model [J/OL]. \u003cem\u003eEnvironmental Science\u003c/em\u003e, 1\u0026ndash;22 [2025-07-31].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong Wenju, Bai Song. Research on Carbon Sequestration and Economic Value of Forests in Sichuan Province [J/OL]. \u003cem\u003eTianjin Agriculture and Forestry Science and Technology\u003c/em\u003e, 1\u0026ndash;9 [2025-10-10].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Yiming, Zhang Zengxin, Chen Xi. Analysis of the Temporal and Spatial Changes and Driving Factors of Carbon Stock in the Poyang Lake Eco-Economic Zone Based on the PLUS-InVEST-GeoDetector Model [J/OL]. \u003cem\u003eEnvironmental Science\u003c/em\u003e, 1\u0026ndash;24 [2025-09-19].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Mengyuan. Assessment of Carbon Storage and Habitat Quality in Hexi Region Based on the InVEST Model [D]. \u003cem\u003eLanzhou University\u003c/em\u003e, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Xijin, Pei Tingting, Chen Ying, et al. Impact of Land Use Change on Carbon Storage in Gansu Province Based on Carbon Density Correction [J]. \u003cem\u003eEcological Science\u003c/em\u003e, 2021, 40(4): 66\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQingmiao, Zhao Jun, Feng Chao, et al. Response of ecosystem carbon storage services to land use change in the Shiyang River Basin, 1980\u0026ndash;2030 [J]. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, 2022, 42(23): 9525\u0026ndash;9536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSui Yuzheng, Chen Xiaoxuan, Li Shujuan, et al. Spatiotemporal Evolution of Blue Carbon in Coastal Zones and Its Service Value Assessment: A Case Study of Jiaozhou Bay [J]. \u003cem\u003eResources Science\u003c/em\u003e, 2019, 41(11): 2119\u0026ndash;2130.\u003c/span\u003e\u003c/li\u003e\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":"Hexi Region, Carbon Storage, PLUS-InVEST Model, Land Use","lastPublishedDoi":"10.21203/rs.3.rs-8150677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8150677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSituated within a unique \u0026ldquo;mountain-oasis-desert\u0026rdquo; ecological framework, Hexi region has undergone substantial shifts in land use, reshaping both its ecological integrity and socio-economic fabric. Understanding the economic implications of such changes, particularly in relation to carbon storage, holds critical value not only for guiding emission reductions and enhancing local incomes, but also for informing effective ecological compensation mechanisms. This research adopts Hexi region as a representative area, utilizing land use data from 2000 to 2020 in addition to the coupled Patch-generating Land Use Simulation with Integrated Valuation of Ecosystem Services and Tradeoffs (PLUS-InVEST) models integrated modeling approach to simulate land use and carbon storage dynamics under four distinct development pathways by 2030. To assess the cost-equivalent value derived from carbon storage in various land use types, this study applied compound interest models-specifically the present and future value formulas to determine monetary gains over time. Key outcomes are: (1) Over the period of 2000 to 2020, meadow and unutilized land continued to dominate Hexi region\u0026rsquo;s landscape, together comprising more than 90% of the total area. Over the same period, cultivated land expanded by 1,963.56 km\u003csup\u003e2\u003c/sup\u003e, water bodies grew by 448.75 km\u003csup\u003e2\u003c/sup\u003e, and construction land increased by 666.88 km\u003csup\u003e2\u003c/sup\u003e. In contrast, woodland, meadow, and unutilized land saw reductions of 17.92 km\u003csup\u003e2\u003c/sup\u003e, 338.11 km\u003csup\u003e2\u003c/sup\u003e, and 2,722.97 km\u003csup\u003e2\u003c/sup\u003e, respectively. (2) Total carbon storage increased by 2.1\u0026nbsp;million tons, showing a spatial gradient from higher levels in the southeast to lower in the northwest, with the ecological protection scenario in 2030 offering the most significant storage gains. (3) The economic valuation of carbon storage rose by 12.78\u0026nbsp;billion yuan from 2000 to 2020, and is projected to continue growing by 2030, with the highest value growth under the ecological protection scenario, followed by sustainable development, cultivated land protection, and natural development scenarios.\u003c/p\u003e","manuscriptTitle":"Assessing economic value of carbon storage and land use changes based on coupled PLUS-InVEST model in Hexi Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 17:48:17","doi":"10.21203/rs.3.rs-8150677/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":"7d845ac6-0593-4de5-bc75-b7f63cd7dd7e","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T17:48:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 17:48:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8150677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8150677","identity":"rs-8150677","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}