Assessing future land use demands in response to land degradation risk and Socio-Economic impacts
Ziyue Yu
a, Xiangzheng Deng
b,c,d,*, Ali Cheshmehzangi
e, Yunxiao Gao
caCollege of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
bBeijing Technology and Business University, Beijing, 100048, China
cInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
dUniversity of Chinese Academy of Sciences, Beijing 100190, China
eSchool of Architecture, Design and Planning, The University of Queensland, Brisbane 4072, Australia
A R T I C L E I N F O Keywords:
Land degradation Land use simulation Carbon sink CGELUC-DLS model Land use demands
A B S T R A C T
Land degradation in Shanxi Province threatens environmental services and socioeconomic growth. The region is a crucial resource-based location for the development of energy from coal mines in China. However, land degradation threatens sustainable development and ecological security. This study seeks to understand the complicated processes of land degradation and carbon stock variations in Shanxi Province’s degraded areas. The objectives include assessing the sensitivity of land degradation using a range of data (land use, soil, vegetation, topography, climate, and socio-economic) and predicting the dynamics of land systems under various environ- mental and socio-economic impacts and the spatial distribution of carbon stocks. The results reveal that the severity of land degradation has typically lessened after the implementation of the Grain-for-Green project. The proportion of the most severely degraded land at high risk of degradation decreased from 6.02% to 1.29%.
Additionally, the proportion of land that is mildly vulnerable has increased by 5.48%. However, rapid urbani- zation raises land degradation sensitivity in 2020. The overall percentage of critical land degradation sensitivity exceeds one third of the land in Shanxi Province. Therefore, faced with the challenge of balancing ecological conservation and socio-economic development in land use management, this study modeled the land use de- mands and spatial distribution, and carbon sinks under various scenarios. This study contributes to a better understanding of sustainable land management in fragile ecological zones and enriches the decision-making process for land resource management, particularly in the context of socio-economic policy changes.
1. Introduction
Growing human demands are placing more and more strain on the land, which serves as a habitat and a vital source of water, nutrition, and wellness (Assessment, 2005). In addition to being a vital natural resource, land is a useful tool for determining the likelihood of regional economic growth (Song et al., 2020). The negative impacts of climate change and increasing human stress are presently posing challenges to the sustainable development of land resources (Dooley and Kartha, 2018). Consequently, there are now several issues to deal with, including declining biodiversity, increasing greenhouse gas emissions, and worsening land degradation (Mbow et al., 2017). Environmental stress has increased in China as a result of the nation’s fast economic expansion, urbanization, and land degradation in the central and
western areas (Zhang et al., 2007; Wang et al., 2017). Increasing land demand is pushing extraordinary land use shifts. The state of land degradation is marked by a shift in vegetation cover, such as the con- version of grassland into sparse vegetation. In arid and semi-arid re- gions, drought exacerbates land degradation through the compounded effects of vegetation loss and exposed soil (Vogt et al., 2011; Yengoh et al., 2015; Mariano et al., 2018). While some researchers view land degradation as a transformative process, others consider it a conse- quence of such transformation (Glantz and Orlovsky, 1983). Conse- quently, land degradation results from unsustainable land use practices, leading to varying levels of impact on land resources arise.
Land degradation presents a multifaceted challenge that impacts both natural ecosystems and socio-economic structures. A comprehen- sive analysis of this issue requires an understanding of the various
* Corresponding author.
E-mail address: [email protected](X. Deng).
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
https://doi.org/10.1016/j.ecolind.2025.113529
Received 11 November 2024; Received in revised form 26 February 2025; Accepted 23 April 2025 Ecological Indicators 175 (2025) 113529
Available online 28 April 2025
1470-160X/© 2025 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).
factors driving land degradation (von Braun et al., 2013). Higher eco- nomic growth rates cause labor, technological advancements, and other production inputs to replace capital from a socioeconomic perspective (Xiao et al., 2022). This immediately affects production and consump- tion and, indirectly, land resources in the region. From an ecological standpoint, extensive human activities have significantly transformed the landscape in recent decades, leading to widespread land degradation worldwide (Grantham et al., 2020). Unsustainable land management practices such as land clearing, overgrazing, expansion of cropland and firewood, cultivation on steep slopes, and bush burning, as well as human disturbances such as significant groundwater resources, road construction, and mining development, all exacerbate degradation (Deng and Wen, 2011; Wassie, 2020). It is possible to mitigate land degradation and reduce greenhouse gas emissions by supporting ecological restoration programs through appropriate land use regula- tions (Chuai et al., 2013). Future land systems will undergo complex changes as a result of socioeconomic development and the unpredictable nature of related land rules. Natural land will eventually be sacrificed due to the irrational increase of urban land resources.
Traditional land degradation monitoring data sources are primarily hydro-meteorological data such as precipitation, temperature, evapo- ration, and runoff obtained from hydro-meteorological observation stations and field sampling data, which are then used to calculate various land degradation indicators. The traditional monitoring tech- nique has the advantage of high precision, but real-time data collecting is inadequate, limited in scope, and labor-intensive. Rather of depending only on expert knowledge and field study, land degradation monitoring is increasingly heavily reliant on remote sensing products and method- ologies (Reed et al., 2011). Utilizing remote sensing technology for monitoring land deterioration has proven to be a highly effective method. Even though there is considerable uncertainty when examining the results of numerous research that examine land degradation on
various temporal and spatial dimensions. Depending on the approach used to quantify land degradation, either the land’s final state or the continuing process is considered (e.g., Bai et al., 2008; Cai et al., 2011).
Thus, this study provides an overview of past datasets and methodolo- gies for estimating land degradation in order to establish a standard framework for land degradation estimation (Table 1).
The Chinese government’s green development strategy places a strong emphasis on the prudent management of land resources to ach- ieve sustainable development objectives, aiming to balance environ- mental conservation with economic growth (Luo et al., 2023). Land resources serve both as sources and sinks of carbon (Li et al., 2023).
Evaluating carbon emissions associated with changing land use, as well as carbon sequestration and storage in terrestrial ecosystems, is essen- tial. Anticipated population growth and rising agricultural production will further exacerbate carbon emissions from land use (Marques et al., 2019). This escalating land-use carbon emission challenges the objec- tives of dual carbon initiatives (Lai et al., 2016). Reducing carbon emissions from land use, while simultaneously enhancing carbon sequestration, is essential for promoting sustainable development and combating global warming. Additionally, alterations in land use signif- icantly affect local ecosystems’ capacity to store carbon (Chuai et al., 2013). Among various land use types, forests and grasslands stand out as important carbon sinks (Houghton and Hackler, 2003). Changes in land resources serve as a critical scientific foundation for government land management strategies, reflecting shifts in land use practices (Han et al., 2020). Research focused on forecasting the drivers of land use change is essential for informing policy recommendations that support sustainable development in the future.
Given the acute scarcity of land resources coupled with rising global demand, we face the significant challenge of promoting sustainable development while addressing immediate human needs. Striking a balance between these competing priorities and environmental Table 1
An overview of the methods and datasets used in this study.
Approach Data name References Benefits Limitations
Expert opinion the Global Assessment of Human- induced Soil Degradation (GLASOD)
Oldeman et al. (1990).
Dregne (2002). Symbolizes historical
degeneration Determines the extent of existing and potential decline
Take into consideration both soil and vegetation degradation
Inconsistent worldwide Qualitative and subjective Occasionally, actual and potential degradation are coupled.
Frequently, the state of degradation and the process of degradation are coupled.
Satellite- derived net primary
productivity The GIMMS radiometer
(AVHRR) data Bai et al. (2008). Consistent globally
Quantitative Readily repeatable Measures actual rather than potential changes
Neglects soil degradation Captures only the process of degradation that occurred after 1980, rather than the total state of the land Can be muddled by additional biophysical conditions Biophysical models the Harmonized World Soil
Database Cai et al. (2011). Consistent globally
Quantitative Restricted to existing croplands Does not cover degradation of vegetation.
Determines prospective degradation rather than actual degradation Mapping abandoned cropland the HYDE 3 land-use change
databaseMODIS
(Moderate ResolutionImaging Spectroradiometer)
satellite data
Field et al. (2008). Consistent globally Quantitative Captures changes 1700 onward
Outside of abandonment, disregards land and soil degradation
Includes lands that are not necessarily degraded
Actual rather than prospective changes are quantified
Trends.earth
CI, Lund University,NASA, GEF (Land degradation monitoring project)
2018 Available at:
https://trends.earth
Google Earth Engine Namibia (Mariathasan et al. 2019) South Africa (Venter et al. 2020;
Von Maltitz et al., 2019Global (Giuliani et al., 2020)
Generally applicable on different scales Open source, assess & data Can be updated with external data Step-by-step guideline
Resolution of default data might be not sufficient for accurate application on local scale
Source: Gibbs and Salmon, 2015
conservation is difficult (Wang et al., 2018). In regions like Shanxi province (eastern region of the Loess Plateau – China), rapid socio- economic development has intensified urban land use, creating a ten- sion between the dual goals of ecological restoration and economic growth. Shanxi is recognized as one of the most severely degraded areas in terms of land degradation, and the compromised state of its ecosys- tems hinders sustainable development and jeopardizes the region’s ecological security. This makes Shanxi province a significant potential area for expanding the swing space of the national development strategy in the northwest of China. A thorough and scientific understanding of the changes in land use brought about by human activity and climate change is required. This has the potential to improve land management, boost land use efficiency, and adjust land use to human activities and climate change.
2. Study area
Shanxi Province is situated in the eastern region of the Loess Plateau (Fig. 1), spanning latitudes from 34◦N to 40◦44
′
N and longitudes from 110◦14′
E to 114◦33′
E. The province comprises eleven municipalities:Taiyuan, Datong, Yangquan, Changzhi, Jincheng, Shuozhou, Yuncheng, Xinzhou, Linfen, and Luliang. Its complex topography features a diverse landscape of hills, mountains, and basins. However, this intricate terrain, combined with resource overexploitation, has led to severe soil erosion. In light of its ecological vulnerability, Shanxi necessitates robust ecological restoration projects aimed at enhancing soil conser- vation, boosting economic returns, and alleviating poverty. In 2002, the Grain for Green Program (GFGP) was expanded to encompass the entire province, with the goal of promoting ecological restoration by offering incentives to farmers who modify their land-use practices. Additionally, this government subsidy program provides local farmers with increased Fig. 1. The DEM map in Shanxi Province. (a) the location of study region in China; (b) Shanxi Province.
financial support.
Resource-based cities are pivotal to China’s economic development, as highlighted by Li et al. (2013). These communities often prioritize economic growth during urbanization, leading to an overreliance on local resources. Consequently, a significant portion of agricultural land is being converted for urban development, which poses risks to the environment in Shanxi Province. Additionally, the simultaneous expansion of industry and urbanization exacerbates existing environ- mental and ecological challenges (Lyu et al., 2018). The National Ter- ritorial Planning Outline (2016–2030) emphasizes that sustainable resource utilization and national development can only be achieved through enhanced protection measures. To mitigate land use conflicts, policymakers must evaluate the structural status of various land types
and ensure the efficient use of land resources, taking into account both socioeconomic and ecological contexts.
3. Methods
The Global Sustainable Development Goals (SDGs) aim for zero growth in land degradation by 2030. Achieving this objective necessi- tates an optimization of land use organization, as alterations in land use will ultimately affect key ecosystem services and socio-economic sys- tems. These changes will, in turn, feedback into land degradation trends and the structure of land use. To ensure an ecologically sustainable future, it is crucial to investigate the impacts of land-use changes on land degradation and carbon storage. This study begins with a narrow Fig. 2. Analytical framework.
definition of land degradation and focuses on the land degradation process and state (land degradation sensitivity assessment). On the basis of long-term series of remote sensing data products, the focus is on the disturbance of the original state of the land to its destruction and, consequently, its degrading surface. This study focuses on Shanxi Province to develop a GIS-based indicator system for assessing land degradation sensitivity with an emphasis on preventing potential land degradation. By integrating data on land use, soil quality, vegetation, topography, meteorology, and socio-economic factors, this research provides a comprehensive analysis of the regional and temporal char- acteristics of land degradation sensitivity, as well as trends in carbon stock changes.
Advancements in remote sensing technology have led to the wide- spread use of the Normalized Difference Vegetation Index (NDVI) for comprehensive assessments of land degradation. As a proxy for net primary production (NPP), NDVI provides an objective, historical view of changes in terrestrial ecosystems (Bai et al., 2008). However, fluc- tuations in NDVI and NPP are largely influenced by seasonal climate variability and shifts in land use (Yue et al., 2016). Determining land degradation solely by lowering the NDVI or NPP is imprecise. Due to the unique geographical position and climatic characteristics of Shanxi province, NPP data products suffer from low data accuracy and incomplete data in the Shanxi province, posing obstacles to long-term continuous monitoring utilizing NPP data. Thus, by combining the NDVI, expert knowledge, and albedo, it is feasible to dynamically monitor land degradation.
Land use is a critical factor influencing both land degradation and ecological protection. To explore the balance between socio-economic development and environmental conservation, this study focuses on resource-based provinces. Land use planning and management are employed to assess and analyze the structure of land resources in rela- tion to the impacts of socio-economic systems under different scenarios.
Subsequently, based on projected land use demands, the carbon sink capacity of terrestrial resources is evaluated by simulating their dynamic succession patterns across various scenarios. This approach allows for the development of scientific theories and policy recommendations aimed at fostering sustainable development and effective planning and management of land resources. By modeling land resource demands under diverse future scenarios, the study aims to delineate the spatial structure of land use and the corresponding distribution of carbon sinks.
The specific research framework and conceptual approach of this study are illustrated in Fig. 2.
3.1. Framework of environmentally sensitive area Index
Previous studies have frequently assessed land degradation on both global and regional scales. However, these analyses often share similar limitations, complicating the assessment of degradation risks. Addi- tionally, as mentioned earlier, land degradation is driven by a complex interplay of factors. Prior research only took into account one or more signs when evaluating the land degradation. In addition, in terms of land degradation’s driving mechanism, the majority of current research fo- cuses solely on climatic conditions and frequently disregards the impact of human activities. This ignores the possibility that a number of vari- ables could have more detrimental possible effects on land degradation.
Thus, more research on land degradation monitoring using remote sensing and its underlying mechanisms is required.
The assessment of land degradation has evolved from limited, qualitative evaluations to extensive, multi-dimensional quantitative analyses. This study, therefore, centers on a comprehensive set of key indicators—encompassing both human-driven and natural factors—that signal potential degradation risks. Specifically, it utilizes the environ- mentally sensitive area index (ESAI) to gauge land degradation sensi- tivity, leveraging the MEDALUS model to examine current and prospective degradation risks (Kosmas et al., 1999). By incorporating factors such as soil, vegetation, climate, and human activity, this
approach offers a comprehensive framework for assessing land degra- dation risk (Sepehr et al., 2007). A notable strength of the ESAI lies in its flexibility, enabling the adjustment of relevant components to align with the unique characteristics of specific study areas. The fourteen variables that comprise the ESAI are selected from four qualitative domains:
vegetation, climate, soils, and land management, which collectively inform the assessment of land degradation risk. This indicator has gained widespread application in evaluating land degradation sensi- tivity globally (Coluzzi et al., 2022; Rajbanshi and Das, 2021).
The environmentally sensitive area index (ESAI) is calculated as the geometric mean of four quality indices: Climate Quality Index (CQI), Soil Quality Index (SQI), Vegetation Quality Index (VQI), and Manage- ment Quality Index (MQI). Each of these indices is derived from a set of parameters detailed in the supplementary material, collectively reflecting the conditions of climate, soil, vegetation, and land manage- ment. The calculation method is expressed in Equation (1).
ESAI= (CQI×SQI×VQI×MQI)1/4 (1)
Assigned on a scale from 1 (showing the lowest sensitivity) to 2 (rep- resenting the maximum sensitivity), the ESAI values are based on the categorization scheme used by Kosmas et al. (1999)for the ESAI classes thresholds assignment. Eight subcategories and four major categories − not affected, potentially affected, fragile 1, fragile 2, fragile 3, critical 1, critical 2, and critical 3 − are used in this categorization approach to group the ESAI values (Table 3).
3.2. Carbon sequestration
Land use/land cover (LULC) change is a key factor influencing the carbon storage in terrestrial ecosystems (Chen et al., 2024). The carbon storage module of the InVEST model estimates carbon storage on a per- pixel basis by utilizing land use maps along with the carbon density associated with each land use type. Instead of calculating the carbon storage of a certain part in isolation, it takes multiple aspects into comprehensive consideration. For example, it simultaneously considers that regional terrestrial ecosystems encompass various carbon pools, including soil, dead biomass, belowground, and aboveground carbon.
For the configuration of model parameters, data on these four carbon sink pools were drawn from Tang et al. (2018)and Liang et al. (2021).
This research evaluated variations in carbon storage for each unit by considering land use, cover type, and the corresponding spatial distri- bution patterns of carbon density. For a given parcel x , the total amount of carbon sequestered over time is represented by:
Ctot =Ca+Cb+Cs+Cd (2)
where Ctot stands for the amount of carbon storage, Ca for aboveground carbon density, Cb for belowground biomass carbon density, Cs for soil carbon density, Cd for dead carbon density.
Moreover, the strength of the InVEST software lies in its ability to conduct scenario simulations (Li et al., 2024). Suppose there are Table 3
Classes and the related EASI score.
ESAI score Class Description
<1.175 N (Not affected) Insensitive to land degradation
1.175–1.22 P (Potentially
affected) Low land degradation sensitivity 1.23–1.26 F1 (Fragile 1) Moderately land degradation sensitivity 1.27–1.32 F2 (Fragile 2) Moderately land degradation sensitivity 1.33–1.37 F3 (Fragile 3) Moderately land degradation sensitivity 1.38–1.41 C1 (Critical 1) Highly or very highly land degradation
sensitivity
1.42–1.53 C2 (Critical 2) Highly or very highly land degradation sensitivity
>1.53 C3 (Critical 3) Highly or very highly land degradation sensitivity
different development scenarios in a certain region in the future, such as large − scale afforestation, urban expansion, or changes in land use patterns. The software can predict the possible changes in carbon stor- age in the future according to these set scenarios. Thus, it helps us make choices that are more conducive to carbon sequestration and ecological protection.
3.3. Simulation future land use demands based on government policy The CGELUC model, grounded in computational general equilibrium (CGE) theory, explores the mathematical relationships between eco- nomic production structures and consumption (Deng and Wen, 2011;
Bai et al., 2021). This model quantifies the interplay between land re- sources and socioeconomic systems by integrating land resource ac- counts into the social accounting matrix. As a result, the CGELUC model can quantitatively analyze the relationships between major industrial structures and land use. It effectively illustrates how changes in market demand and industrial structure drive the evolution of regional land use patterns. Importantly, the model also accounts for the effects of socio- economic policies on land use decisions. Furthermore, by incorpo- rating the Dynamics of Land Systems (DLS) model, the CGELUC framework enables the simulation of spatial distribution outcomes of land structure across various scenarios, providing a more comprehen- sive approach to sustainable land resource development as a unified socio-economic system than other models.
3.3.1. Social accounting matrix (SAM) with land resource account The development of computable general equilibrium models is fundamentally based on the use of a social accounting matrix (Campoy- Mu˜noz et al., 2017). When combined, these tools provide valuable op- portunities for policy analysis. However, traditional social accounting matrices often lack land resource accounts and ecological conservation accounts. In this study, the expanded social accounting matrix illustrates how various industrial models and regulations impact land use struc- tures. This expanded matrix consists of two main components: the traditional social accounting matrix and the land resource account (Table 4). The traditional matrix is organized according to the national economy’s industrial classification, encompassing the primary, sec- ondary, and tertiary sectors, with a total of 42 production sectors rep- resented. Institutional accounts are categorized into direct, indirect, and tariff accounts related to government taxes, alongside household and government accounts. The embedded land resource accounts classify six land use types: cropland, forest land, grassland, watersheds, built-up land, and unutilized land. To complete the social accounting matrix, this study assigns distinct industrial accounts to each type of land use (Deng and Wen, 2011; Deng et al., 2012; Zhou et al., 2017; Yu et al., 2023).
3.3.2. Simulation of future land use demands
The CGELUC model comprises nine key components: product pro- duction, quantitative analysis, equilibrium closure, product demand, land use conversion, pricing, taxation, international trade, and savings investment (Deng, 2011). The functioning of the CGELUC model is outlined as follows:
Step 1: Calculate the area of water, unutilized land, grassland, and other forest land in year t through the econometric analysis module.
Step 2: Estimate the area of cropland, forest area, and construction area in year t +1 by linking the estimation results with other mod- ules such as production and consumption behavior, land use transi- tion, commodity supply, price, trade, market equilibrium, and macro closure, and dynamic modules.
Step 3: Calculate the area of watershed, area of unutilized land, area of grassland, and other forested land in year t +1 using the estimated population, GDP, area of new afforestation, and proportion of urban residents in year t as explanatory variables in the econometric analysis module. Eventually, until the simulation of changes in land use structure throughout the entire period is finished, the afore- mentioned activities are repeated.
3.4. The land uses by Sectors: Spatial dynamics and Projection 3.4.1. Embedding grid-scale land use structures influenced by policies
In this study, the Dynamics of Land Systems (DLS) model in- corporates regional land use structures to achieve raster-scale geographical distributions of land use, utilizing the future land use structure generated by the CGELUC model in the previous stage (Deng and Wen, 2011; Jin et al., 2019). The future land use structure for the study area is expected to vary based on the implementation of different government subsidy programs and socioeconomic growth trajectories.
Consequently, when the DLS model is executed, the parameter settings will adapt to reflect these varied future scenarios. This research sets specific limitations on construction land within the green development framework. Additionally, by estimating the likelihood of land conver- sion, the study aims to safeguard vulnerable land. For example, land with significant ecological value, such as watersheds, is unlikely to be developed for other uses, while vacant properties are more prone to conversion.
3.4.2. Prediction of spatial land use distribution
The Dynamics of Land Systems (DLS) model simulates spatial pat- terns of land use by utilizing projected changes derived from the CGE- LUC model. This model can simulate land use patterns by incorporating various influencing factors, including socio-economic and natural environmental elements that drive land use changes. The model is described by the following equation:
logit(T) =ln ( pki
1− pki
)
=ln( exp(
αk0+αk1xi1+αk2xi2+⋯+αklxil+⋯+αkLxiL+rŷki) )
=αk0+αkXi+ryki
(5) pki represents the probability that the kth land use type will occur in grid i; xiL denotes the natural and socioeconomic variables; αki represents the natural and socioeconomic factor coefficient; αk =(αk1,αk2,⋯,αki,⋯,αkL) indicates the matrix of coefficients; yki represents the factor of spatial autocorrelation, and the coefficient is represented by r. A nonlinear model of the spatial patterns of land use is produced by calculating the Table 4
The simplified framework of Social Accounting Matrix (SAM).
Activity Factor Institution Tax Investment Rest of world Ecological conservation
Activity
Factor
Institution
Tax
Investment
Rest of world
Land resource
logarithmic likelihood ratio (logit(T)) of grid i to the kth land use following logarithmic transformation (Jin et al., 2021).
3.5. Scenario design
The research area is located on the Loess Plateau, which is recog- nized as one of the most severely affected regions in the world due to soil erosion (Wang et al., 2006). In response, local government initiatives have implemented ecological restoration and subsidy programs aimed at improving the ecological environment and achieving sustainable development. Furthermore, diverse climate changes are expected to give rise to varying socioeconomic policies in the future. This analysis for- mulates three prospective scenarios grounded in Shared Socioeconomic Pathway (SSP) to forecast alterations in land use within Shanxi Province by 2030 (Table 5). The SSP is a paradigm for forecasting future socio- economic growth trajectories at the local, national, and international levels (O’Neill et al., 2014). SSP1 is a pathway for sustainable devel- opment that tackles the problem of climate change; SSP2 is a moderate path in all respects; and SSP5 is a path that encourages rapid growth and development regardless of the challenges. The SSPs parameters are measured according to the research area scale. Three separate scenarios will then be developed by combining different local government- proposed policies with multiple future socio-economic scenarios that represent the research region.
Shanxi Province was selected as a crucial pilot by the Grain for Green Project (GFGP). In order to reduce the soil erosion and enhance the vegetation cover, the project extensively transforms sloping terrain into a forest or grassland. A number of laws are enacted by the government to provide farmers with incentives for carrying out the reforestation initiative. Therefore, the most direct influence on land use regulation comes from this ecological initiative. To guarantee steady grain pro- duction in Shanxi Province, the government has instituted a policy of supporting the preservation of farmed land as well as subsidies for ecological preservation. Shanxi Province’s land use development plan, which details the land use control rules, places restrictions on the development of underutilized land. If excessive amounts of cropland are turned into woods or grasslands, food production may suffer. Conse- quently, the land use development plan promulgated by the Shanxi province government stipulates the extent of agricultural protection.
4. Results
4.1. Spatiotemporal variation of individual quality index variables In this study, the degree of influence of each quality factor on land degradation sensitivity was assessed using a legend with a uniform threshold range. Combining the spatial results of each quality factor, the influence of climate factors on land degradation sensitivity was signifi- cantly enhanced, and the sensitivity of CQI gradually increased from the northern part to the southeastern part of Shanxi Province (Fig. 4).
During the study period, it was observed that the CQI sensitivity in the southern part of Shanxi Province was significantly lower than that in the northern part. This difference may be attributed to the gradual increase in rainfall from the northwest to the southeast, leading to a reduction in potential evapotranspiration. It was found that VQI sensitivity decreased overall during the fallow forest project, on the contrary, sensitivity increased in 2020. During the GFGP, the influence of VQI on the sus- ceptibility to land degradation rapidly declined as plant cover increased.
However, after the ecological restoration project given that human ac- tivities and land development activities gradually became more frequent, the VQI sensitivity increased in the area of building land expansion. Moreover, the results show that the areas of high VQI sensitivity overlap with the areas of high MQI sensitivity in 2020. These areas are mainly located near urban centers with high population den- sity. This is likely given that there is a lack of environmental controls or land use intensity policies. Given the economic growth and the conse- quent increase in population density, urbanization, and construction, there is a higher risk of land degradation due to urban land use.
4.2. Spatiotemporal variation of land degradation sensitivity
Based on the land degradation sensitivity evaluation framework, the results show that most of the regions in Shanxi Province are in the fragile to critical class of land degradation sensitivity (Fig. 5). During the period 1990–2015, land degradation in Shanxi Province has been mitigated (Fig. 6). The proportion of land at the most serious high risk of degra- dation decreased from 6.02 % to 1.29 %, while the proportion of mildly vulnerable land also increased by 5.48 %. There was also an increase from 1.36 % to 2.91 % in the percentage of land that is least susceptible to land degradation. The proportion of land without land degradation increased from 0.22 % to 0.94 %. In 2015, most of the lands with critical degradation sensitivity improved considerably, e.g., the proportion of lands with C2 was only 1.29 %. As a result, there is an overall increase in areas improving from critical to mild and moderate levels, especially in the eastern and southeastern parts of the study area, such as Yangquan, Jinzhong and Changzhi. In 2020, the overall land degradation situation in Shanxi Province was found to be more critical than in historical pe- riods, particularly in Datong and Shuozhou, where a high level of land degradation sensitivity was observed. The proportion of land area in the C2 level reached 15.4 %, while C1 level reached 20.86 %. The overall proportion of critical land degradation sensitivity exceeds one third of the land in Shanxi province.
The results showed that more than two-thirds of the land in Shanxi Province showed moderate degradation in terms of vulnerability to degradation sensitivity over the past 30 years. The land with a degra- dation sensitivity of Fragile (F) increased from 81.35 % in 1990 to 85.76
% in 2015, and then declined to 62.2 % in 2020 (Fig. 6). In 1990, the proportion of areas in categories F1, F2, and F3 was 3.88 %, 38.54 %, and 38.92 %, respectively. By the end of the GFGP, the least degraded F1 category land increased to 9.37 % and the proportion of the most likely degraded F3 category areas decreased to 33.7 %. From 1990 to 2015, most of the land at high risk of degradation was transformed into land at low risk of degradation and was not affected by degradation. Therefore, land degradation is mitigated by ecological restoration projects. On the contrary, in 2020, the risk of land degradation far exceeds the initial severity. Land with Critical (C) degradation sensitivity increases from 17.08 % in 1990 to 36.36 % in 2020. Most of the land with moderate degradation sensitivity is converted to critical degradation sensitivity.
In terms of spatial distribution, the land degradation sensitivity of Shanxi Province has been greatly mitigated from northwest to southeast with the implementation of the GFGP, which has significantly increased the vegetation cover (Fig. 7). In 2000 and 2015, most of the areas improved from heavy land degradation sensitivity to moderate, and even mild. The majority of the developed regions, including Taiyuan, the eastern portion of Xinzhou, the intersection of Luliang and Jinzhong, and the western portion of Linfen, are situated in comparatively mild Table 5
Scenario parameters and description
Scenario Parameter Description
Business-as-usual
scenario (BAU) Population SSP2
GDP SSP2
Policy
Subsidies Subsidies for farmed land: 67 yuan/
acre; subsidies for general farm-level payments: 1600 yuan/acre Green Development
scenario (GDS) Population SSP1
GDP SSP1
Policy
Subsidies Subsidies for GFGP: 1600 yuan per Economic Priority acre
Development scenario (EPD)
Population SSP5
GDP SSP5
Policy
Subsidies No subsidies
Fig. 4. Spatial and temporal distribution of quality factors for calculating ESAI in Shanxi province.
Fig. 5. Annual statistical results for each level of land degradation sensitivity in Shanxi province.
Fig. 6.Annual spatial and temporal distribution of ESAI in Shanxi province.
terrain. The spatial distribution shows a shift from large areas initially covered by red to yellow. This also indicates that land degradation in Shanxi Province has been curbed to some extent during the fallow forest project. However, as a resource-based province with poor natural background conditions, Shanxi Province continues to vigorously exploit fossil energy, which has a negative impact on the fragile ecological environment. Furthermore, as the population and economy have grown, more land has been available for development, posing a growing threat to the sensitive state of land degradation. According to the geographical trend of land degradation sensitivity studies, the majority of Shanxi Province is seriously at danger of land degradation, with just a few cities showing significant improvement. In light of the findings of the evalu- ation of land degradation, this study investigates the trend of changes in carbon stocks in order to contribute to the development of scientific recommendations for mitigating future degradation.
4.3. Spatiotemporal variation of carbon storage
Carbon sinks within ecosystems are essential for the management of terrestrial environments and for attaining carbon neutrality. One of the primary factors contributing to the uneven progression of the global carbon cycle is land use change. Soil carbon storage capacity has been probably adversely affected by land degradation. During 1990 and 2020, carbon sequestration in Shanxi Province exhibited a gradual in- crease in both geographic distribution and quantity, with only a minor segment of the territory demonstrating a decline (Fig. 8).
During the study period, the overall carbon stock in Shanxi Province remained relatively stable, with consistent spatial distribution charac- teristics each year. Specifically, the total carbon stock decreased from
767,377,282 Mg/C in 1990 to 762,263,541 Mg/C in 2020, and the total carbon sequestration increased from 2.379 ×109 Mg/ha to 2.384 ×109 Mg/ha from 1990 to 2015, which also indicates that the GFGP is conducive to the increase of the total carbon sequestration.
Shanxi province’s overall carbon storage has expanded dramatically in 2020, particularly in the areas south of Jincheng, east of Luliang, southwest of Linfen, and southwest of Linfen. As for the various land use types, the carbon sink of forest land will be approximately 4.12 ×108 Mg/C in 2020, making up 54.05 % of the total carbon storage; the carbon sinks of farmland and grassland, on the other hand, are antici- pated to reach 1.94 ×108 Mg/C and 1.43 ×108 Mg/C, respectively, making up 44.28 % of the carbon sink.
The findings indicate that forest land and grassland are the pre- dominant land use categories spread in the regions with large carbon stocks. The spatial results of carbon stock in the area with the distri- bution of building land showed lower carbon stock. This suggests that the primary land uses for carbon storage on land are grasslands and woodlands, which also have a large potential for reducing greenhouse gas emissions. In contrast, building land primarily emits carbon and has minimal capacity to act as a carbon sink. Furthermore, although farm- land produces agricultural carbon emissions, it also has some capacity to store carbon. Nevertheless, this reduces the potential for mitigation.
4.4. Simulation of land use demands under different scenarios
This study employed the CGELUC model, developed with GAMS software, to project land use demands in Shanxi Province across various scenarios for 2030. The simulations reveal a clear focus in Shanxi on developing and utilizing previously underused land across all modeled scenarios. Notably, under the economic-priority development scenario, the area of unused land is projected to decline sharply, decreasing by 66.33 % by 2030 (Fig. 9). Specifically, this scenario sees unused land reduced to just 34 km2 as previously undeveloped areas are repurposed to drive economic expansion. Moreover, built-up land exhibits the most significant land use change between 2020 and 2030, with an increase of 4,237 km2—an expansion rate of 33.02 %—reflecting a major driver of accelerated urbanization in the region.
In the green development scenario, expansion into developable lands, such as forests and grasslands, is highly restricted. Consequently, forest and grassland losses are minimized, with reduction rates of only 2.09 % and 1.65 %, respectively—the lowest across the three future scenarios. This outcome reflects a strong emphasis within the green development scenario on conserving natural ecosystems, as large areas of grassland and forest are allocated for ecological restoration and enhancement. Government subsidies further support these efforts, reinforcing ecological protection while promoting sustainable agricul- tural practices. Although cropland is projected to decrease by 2.91 % from 2020 to 2030, this decline is similar to that under the business-as- usual scenario. Notably, this green scenario limits the decline in grass- land area, showing a 7.10 % reduction compared to the business-as- usual model, highlighting its role in safeguarding these vital landscapes.
To evaluate the credibility of the model’s simulation, we compared the projected land use structure for 2020 with the actual land use data.
The high Kappa index of 0.89 in the simulation results indicates strong accuracy of the CGELUC-DLS model.
4.5. Spatial variation of land use simulation under different scenarios Fig. 10shows the spatial distribution patterns of land use transitions in Shanxi Province under various development scenarios. Cropland is most concentrated in areas of low topographic relief, including central Jincheng and Changzhi, central and southern Linfen and Yuncheng, and eastern Shuozhou. Forested areas, by contrast, are more common in elevated terrains, such as the central and western regions of Changzhi, southern and western Jincheng, and the central and western areas of Luliang. Grasslands are largely distributed in moderately elevated zones, Fig. 7.Spatial and temporal distribution of variation in ESAI in Shanxi prov-
ince during 1990–2020.
notably in northern Jinzhong and western Shuozhou. Built-up areas are primarily situated in high-density, high-GDP cities like Taiyuan, with smaller patches of construction land scattered across Jincheng, Yun- cheng, and Yangquan.
Under the EPD scenario, the most significant increase in building land is projected to occur by 2030, with most new construction areas situated near existing developed land or reclaimed regions. Simulation results indicate that in the southern parts of Shanxi, some cropland is converted to construction land, while in the western regions, certain grassland and forested areas are similarly repurposed. Although the BAU scenario shows a slower expansion of building land than the EPD sce- nario, it covers a comparable overall area. In contrast, the green development scenario projects minimal changes in building land dis- tribution, resembling that of 2020 with only moderate growth. As green development initiatives intensify, efforts to protect agricultural and ecological land result in a stabilized building land area by 2030 under this scenario.
4.6. Spatial variation of carbon storage under different scenarios The regional distribution of carbon sinks across Shanxi Province in 2030 under different scenarios closely aligns with land use patterns (Fig. 11), indicating that shifts in land use significantly influence carbon sink capacity. Areas with a high presence of forests, such as central Xinzhou and Lvliang, western Changzhi and Linfen, eastern Yuncheng, and southern Jincheng, exhibit elevated carbon stocks and are also the primary pilot sites of the GFGP initiative. However, as urbanization intensifies, regions with higher densities of built-up land, such as central Datong and Taiyuan, experience notable reductions in carbon reserves.
These variations highlight the differences in carbon sink capacity across regions, underscoring the need for coordinated regional development to support sustainable growth.
5. Discussion
The ability to deliver ecosystem services is diminished by the multifaceted and intricate process of land degradation (Wang et al., 2017). Moreover, unsustainable use of land resources may lead to po- tential land degradation risks. For example, urban land-use growth may encroach on agricultural areas or forests, which can lead to a decline in habitat quality and a decrease in carbon stocks due to soil degradation.
Therefore, a better understanding of land use changes due to land degradation can help us assess the enhancement of ecosystem services and optimize land use patterns. Most of the previous studies rely on a single data source or indicator to assess the land degradation process, which cannot fully reflect the actual situation. In addition, for the land degradation process, most studies only assess the role of climatic ele- ments and often ignore the impact of socio-economic issues. This research assesses the risk of land degradation in Shanxi Province for the years 1990–2020 based on a range of factors including soil, vegetation, climate, and human activity, with a focus on the province’s regional features. The implementation of the fallow forest return project has led to a significant increase in vegetation cover and improved the regional land degradation sensitivity. The findings, however, indicate that there is still a risk of land degradation and that it has gotten worse. This also shows that further land degradation cannot be fully prevented by considering only land use conversion and a single increase in vegetation planting. Considering the increasing demand for food production, land use planning must guarantee a certain amount of high-quality arable land. A sustained increase in food production will lead to a reduction in the area of arable land or its degradation. Furthermore, planting trees on currently cultivated land could potentially jeopardize food security. As a result, this study aims to assess the carbon sequestration capabilities of this vulnerable land area facing potential degradation. By considering the release of a certain amount of arable land under the condition of guaranteeing food security, we can actively restore the carbon Fig. 8.Spatial and temporal distribution of annual carbon stocks in Shanxi province.
sequestration capacity of degraded land to mitigate climate change.
Land use significantly impacts ecosystem services. Therefore, pre- dicting future land use changes under different scenarios can offer valuable scientific guidance for ecosystem protection and enhancement.
Unfortunately, there is a lack of studies forecasting land use demands for
balancing ecological protection and economic development under various policies and social-economic impacts. Determining the best course of action for sustainable regional land development is compli- cated by the paucity of research on the relationships between socio- economic systems and land resources. To address this issue and promote Fig. 9. Variation in area of each land-use type under different scenarios in Shanxi province (Unit: km2) Note: BAU represents the business-as-usual scenario; EPD represents the economic priority development scenario; GDS represents the green development scenario. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
regional sustainable development, this study creatively simulates sus- tainable land use by considering socio-economic policies. The CGELUC model is utilized in this work to investigate the mathematical relation- ship between land use demands and economic production structure based on social accounting matrix with land resource account. As a result, this study is better able to evaluate how the socioeconomic sys- tem is influencing the dynamic changes of the land system. The DLS model is then fed the land use demand outputs obtained from the CGELUC model. With consideration for the region’s socioeconomic development, the environment, and past trends in land use changes, the DLS model can combine macro-analysis of the land system’s dynamics with micro-econometric analysis at the raster scale to determine the land use’s dynamic structure and spatial distribution pattern under various scenarios.
Understanding the mechanisms behind land degradation processes
and how to counteract it are prerequisites to investigating the effects of land degradation on ecosystems and ecological restoration. The analysis of the macroscopic role of the natural environment on land use change is combined with the analysis of the impacts of socio-economic and human activities on land degradation and ecosystem risks, to synthesize and quantify the relative impacts of climate change and human activities on land degradation and ecosystem services and their interaction charac- teristics. In typical environmentally vulnerable places, it also measures the socioeconomic effects on land structure and determines demands and dynamic succession patterns of land resources under various sce- narios. In addition to strengthening the theoretical foundation of land degradation research in ecologically fragile areas and contributing to the scientific evaluation of the effects of human activity on ecologically fragile areas, it can serve to resolve the land use contradiction that arises from the need to protect the natural environment while also achieving Fig. 10.Spatial and temporal changes in land use types under different scenarios in Shanxi province.
regional economic development. Conversely, it offers the essential benchmark for the ensuing implementation of land degradation control, which is crucial for the all-encompassing administration of China’s mountains, rivers, forests, fields, lakes, and grasslands as well as for the creation of “Beautiful China”.
6. Conclusions
In 1990–2015, land degradation sensitivity in Shanxi Province was mitigated after the implementation of the fallow land reforestation project. The proportion of area with critical degradation sensitivity decreased, from 17.08 % to 10.39 %. However, the overall land degra- dation situation in Shanxi Province shows a more severe degree than in the historical period. Over one-third of Shanxi Province’s total land area is now more than the fraction of land with significant degradation
sensitivity, which grew to 36.36 %. Therefore, there is still a high risk of land degradation for cities with higher population density and faster economic development in Shanxi province. In the vicinity of the built-up land, the extremely sensitive region of land degradation is growing in 2020. Land, an unsustainable resource that stores carbon, is essential for both preventing climate change and maintaining ecological stability. To further prevent land degradation, the effects of ecological restoration projects still need to be further adjusted and consolidated. Blind con- version of land use types may exacerbate ecological degradation.
Therefore, rational management of land use structure can help balance economic development and ecological protection. The government should assess the ecological advantages, rationally manage land re- sources and strictly regulate land conversion.
Land structure transitions and land demand are influenced by a va- riety of factors, including socioeconomic dynamics and industrial Fig. 11.Spatial variations of carbon sink under different scenarios in Shanxi province.
composition. Additionally, different land use types vary in their soil carbon storage capacities. This study models future land use demand and structural changes under diverse socioeconomic scenarios and as- sesses associated carbon storage potential. Cultivated land currently dominates total land use, yet its availability may decline due to increased food production demands. While afforestation efforts on existing arable land could pose risks to food security, this study priori- tizes degraded lands and those at risk of degradation for restoration. By maintaining food security and focusing on rehabilitating idle and degraded lands for carbon sequestration, a portion of arable land could be repurposed, contributing to emission reductions. Consequently, the demand for arable land in Shanxi Province is projected to decline by 2030 relative to 2020 levels, while the proportion of forest and grassland as ecological land gains importance. Notably, under the green devel- opment scenario, forests and grasslands, with extensive areas, demon- strate the highest carbon sequestration and emission reduction potential. Our objective is to achieve carbon neutrality by establishing a landscape that balances economic development and ecological sustain- ability for the future.
2.1. Data
Land degradation sensitivity was assessed using a comprehensive array of datasets, including meteorological data, NDVI, topography, soil properties, land use, and socioeconomic indicators (Table 2). To enhance data accuracy and standardize spatial resolution, ArcGIS soft- ware was employed to resample the original raster data for all research parameters to a resolution of 1 km. The primary data for the social ac- counting matrix were sourced from the China Finance Yearbook, China Statistical Yearbook, Shanxi Provincial Statistical Yearbook, and China Rural Statistical Yearbook. Additionally, information regarding policies implemented by the Shanxi provincial government to promote food
production and enhance farmer livelihoods was utilized to define pa- rameters for the scenario design related to subsidies for ecological en- gineering and the preservation of arable land. In order to safeguard ecologically sensitive areas, guidelines from the Shanxi Provincial General Land Use Plan (2006–2020) were examined to establish con- version limits for various land use types.
CRediT authorship contribution statement
Ziyue Yu: Writing – original draft, Software, Methodology, Formal analysis, Data curation. Xiangzheng Deng: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. Ali Cheshmehzangi: Writing – review & editing, Validation, Supervision. Yunxiao Gao: Writing – review & editing, Validation, Investigation.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research was supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No.72221002).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolind.2025.113529.
Data availability
Data will be made available on request.
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Table 2
Data sources and their spatial and temporal resolution
Data Data sources Spatial
resolution Time range Land use maps Chinese Academy of
Sciences Resource and Environmental Science Data Center(https://www.resdc.
cn/)
1000 m 1990––2020
Meteorological
data the China Meteorological Sharing Service System (http s://cdc.cma.gov.cn/)
1000 m 1990––2020
Digital Elevation
Model (DEM) Chinese Academy of Sciences Resource and Environmental Science Data Center(https://www.resdc.
cn/)
30 m Static
Soil property Harmonized World Soil
Database (HWSD) 1000 m Static
GIMMS dataset (GIMMS3g) NDVI
the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High-Resolution Radiometer (AVHRR)
8000 m 1990––2015
NDVI data MODIS13A3 Normalized Difference Vegetation Index (NDVI) (Didan et al., 2015)
1000 m 2020
GDP (gross output
value/km2) Chinese Academy of Sciences Resource and Environmental Science Data Center(https://www.resdc.
cn/)
1000 m 1990–2020
Population density
(Number/area) Chinese Academy of Sciences Resource and Environmental Science Data Center(https://www.resdc.
cn/)
1000 m 1990–2020