Linking land use and cover change to Sustainable Development Goals and carbon storage: A case study in the Yellow River Basin, China

doi:10.1016/j.regsus.2026.100390

Abstract

Abstract:

Land use and cover change plays a pivotal role in the achievement of Sustainable Development Goals (SDGs) and carbon storage (CS). However, few studies have linked them within Shared Socioeconomic Pathway (SSP)-Representative Concentration Pathway (RCP) scenario frameworks. In this study, a patch-generating land use simulation (PLUS) model was used to analyse historical land use data alongside Land-Use Harmonization 2 (LUH2) dataset-based SSP-RCP scenarios, generating detailed high-resolution land use simulations for the years 2030 and 2060. The three scenarios, including SSP1-2.6, SSP2-4.5, and SSP5-8.5, represent low-, medium-, and high-emission pathways, respectively. The Yellow River Basin (YRB) in China was selected as the study area, where we assessed city-level SDG indicator scores and estimated CS through the integrated valuation of ecosystem services and tradeoffs (InVEST) model. A Spearman correlation analysis was conducted to identify synergies and trade-offs among SDGs and between SDGs and CS. The results reveal notable disparities in SDG indicator scores across cities in the YRB. Under SSP1-2.6 scenario, the mean value of SDG 15.1F (forestland coverage) increases significantly, from 0.16 in 2030 to 0.23 in 2060, whereas under SSP5-8.5 scenario, the mean value of SDG 2.1 (land reclamation rate) is the highest among the three scenarios, and the value of SDG 11.2 (urbanization rate) reaches its peak of 0.23 by 2060. The ecological protection-oriented scenario leads to a marked improvement in CS, primarily driven by expanding forestland areas. Furthermore, synergies are observed between SDG 11.2 and SDG 2.1, while SDG 15.1G (grassland coverage) exhibits trade-offs with SDG 11.2 and SDG 2.1. This study provides an integrated framework that can aid in assessment of local SDGs and CS and offers valuable insights for the development of sustainable urban planning strategies amid future climate and policy uncertainties.

Key words: Land use and cover change (LUCC), Sustainable, Development, Goals (SDGs), Carbon storage (CS), Patch-generating land use simulation (PLUS) model, Yellow River Basin (YRB)

LI Man, ZHAO Lin, HE Hao, YU Huijie, DAI Siyuan, Rathnayaka Mudiyanselage Dinesh Madhushanka KARUNARATNAA, Abu Reza Muhammad Towfiqul ISLAM, WU Dongli, LI Qi, HU Zhenghua. Linking land use and cover change to Sustainable Development Goals and carbon storage: A case study in the Yellow River Basin, China[J]. Regional Sustainability, 2026, 7(5): 100390.

Figures/Tables 16

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Table S1

Table S2

Table S3

Table S4

Fig. S1.

Fig. S2.

Fig. S3.

References 79

[1]	Allen C., Nejdawi R., El-Baba J., et al., 2017. Indicator-based assessments of progress towards the sustainable development goals (SDGs): A case study from the Arab region. Sustainability Science. 12(6), 975-989. doi: 10.1007/s11625-017-0437-1
[2]	Bacani V.M., Machado da Silva B.H., Ayumi de Souza Amede Sato A., et al. 2024. Carbon storage and sequestration in a eucalyptus productive zone in the Brazilian Cerrado, using the Ca-Markov/Random Forest and InVEST models. Journal of Cleaner Production. 444, 141291, doi: 10.1016/j.jclepro.2024.141291.
[3]	Cao M., Tian Y., Wu K., et al., 2023. Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios. Science Bulletin. 68(7), 740-749. doi: 10.1016/j.scib.2023.03.012 pmid: 36934012
[4]	Cao S.X., Liu Z.X., Li W.M., et al., 2021. Balancing ecological conservation with socioeconomic development. Ambio. 50(5), 1117-1122. doi: 10.1007/s13280-020-01448-z
[5]	Chen L., Yao Y., Xiang K.M., et al., 2024. Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration. Journal of Environmental Management. 368, 122261, doi: 10.1016/j.jenvman.2024.122261.
[6]	Cheng Y.L., Chen Y.B., 2024. Spatial and temporal characteristics of land use changes in the Yellow River Basin from 1990 to 2021 and future predictions. Land. 13(9), 1510, doi: 10.3390/land13091510.
[7]	Chu X., Zhan J.Y., Li Z.H., et al., 2019. Assessment on forest carbon sequestration in the Three-North Shelterbelt Program region, China. Journal of Cleaner Production. 215, 382-389.
[8]	Colglazier W., 2015. Sustainable Development Agenda: 2030. Science. 349(6252), 1048-1050.
[9]	Ding Y.H., Wu P., Liu Y.J., 2022. Modulation of sea surface temperature in three oceans on precipitation increase over Northwest China during the past 60 years: A review. Frontiers in Climate. 4, 1015225, doi: 10.3389/fclim.2022.1015225.
[10]	Doelman J.C., Stehfest E., Tabeau A., et al., 2018. Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Global Environmental Change. 48, 119-135.
[11]	Fan L.Y., Cai T.Y., Wen Q., et al., 2023. Scenario simulation of land use change and carbon storage response in Henan Province, China: 1990-2050. Ecological Indicators. 154, 110660, doi: 10.1016/j.ecolind.2023.110660.
[12]	Fuso Nerini F., Sovacool B., Hughes N., et al., 2019. Connecting climate action with other Sustainable Development Goals. Nature Sustainability. 2(8), 674-680. doi: 10.1038/s41893-019-0334-y
[13]	Gao L., Tao F., Liu R.R., et al., 2022. Multi-scenario simulation and ecological risk analysis of land use based on the PLUS model: A case study of Nanjing. Sustainable Cities and Society. 85, 104055, doi: 10.1016/j.scs.2022.104055.
[14]	Gao M.N., Xu R.H., Huang J.L., et al., 2023. Increase of carbon storage in the Qinghai-Tibet Plateau: Perspective from land-use change under global warming. Journal of Cleaner Production. 414, 137540, doi: 10.1016/j.jclepro.2023.137540.
[15]	Getzner M., Bröthaler J., Neuhuber T., et al., 2025. Socio-economic, political and fiscal drivers of unsustainable local land use decisions. Land Use Policy. 153, 107537, doi: 10.1016/j.landusepol.2025.107537.
[16]	González-García A., Arias M., García-Tiscar S., et al., 2022. National blue carbon assessment in Spain using InVEST: Current state and future perspectives. Ecosystem Services. 53, 101397, doi: 10.1016/j.ecoser.2021.101397.
[17]	Guo H.D., Huang L., Luo L., et al., 2023. Progress on achieving environmental SDGs assessed from Big Earth Data in China. Science Bulletin. 68(24), 3129-3132. doi: 10.1016/j.scib.2023.09.017 pmid: 37739841
[18]	Han L.J., Zhang L.M., Zhou W.Q., et al., 2022. China’s urban-rural expansion and natural habitat loss since the 1980s: Retrospective analysis and future suggestions. Frontiers in Environmental Science. 10, 65174, doi: 10.3389/fenvs.2022.1065174.
[19]	He Y.T., Liang Y.J., Liu L.J., et al., 2023. Loss of green landscapes due to urban expansion in China. Resources, Conservation and Recycling. 199, 107228, doi: 10.1016/j.resconrec.2023.107228.
[20]	Huang M.L., Mamitimin Y., Abulizi A., et al., 2024. Integrated assessment of land use and carbon storage changes in the Tulufan-Hami Basin under the background of urbanization and climate change. International Journal of Applied Earth Observation and Geoinformation. 135, 104261, doi: 10.1016/j.jag.2024.104261.
[21]	Hurtt G.C., Chini L., Sahajpal R., et al., 2020. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. Geoscientific Model Development. 13(11), 5425-5464. doi: 10.5194/gmd-13-5425-2020
[22]	IPCC (Intergovernmental Panel on Climate Change), 2021. Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
[23]	Jia K., Sheng Q.Y., Liu Y.H., et al., 2024. A framework for achieving urban Sustainable Development Goals (SDGs): Evaluation and interaction. Sustainable Cities and Society. 114, 105780, doi: 10.1016/j.scs.2024.105780.
[24]	Kong W.L., Shen W.C., Yu C.Y., et al., 2025. The neglected cost: Ecosystem services loss due to urban expansion in China from a triple-coupling perspective. Environmental Impact Assessment Review. 112, 107827, doi: 10.1016/j.eiar.2025.107827.
[25]	Li C.X., Lei L.T., Jia X., et al., 2025a. Accuracy and applicability assessment of vegetation types of the land cover products produced in China. Ecological Indicators. 174, 113506, doi: 10.1016/j.ecolind.2025.113506.
[26]	Li H.J., Zhang K., Liu Y.Q., et al., 2025b. Spatiotemporal evolution of land use and carbon storage in China: Multi-Scenario simulation and driving factor analysis based on the PLUS-InVEST model and SHAP. Environmental Research. 279, 121860, doi: 10.1016/j.envres.2025.121860.
[27]	Liang X., Guan Q., Clarke K.C., et al., 2021. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Computers, Environment and Urban Systems. 85, 101569, doi: 10.1016/j.compenvurbsys.2020.101569.
[28]	Liao W.L., Liu X.P., Xu X.Y., et al., 2020. Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China. Science Bulletin. 65(22), 1935-1947. doi: 10.1016/j.scib.2020.07.014 pmid: 36738059
[29]	Lin X., Jiao X., Tian Z., et al., 2023. Projecting diversity conflicts of future land system pathways in China under anthropogenic and climate forcing. Earth’s Future. 11(10), e2022EF003406, doi: 10.1029/2022EF003406.
[30]	Liu Q., Qiao J.J., Li M.J., et al., 2024. Spatiotemporal heterogeneity of ecosystem service interactions and their drivers at different spatial scales in the Yellow River Basin. Science of The Total Environment. 908, 168486, doi: 10.1016/j.scitotenv.2023.168486.
[31]	Liu X.P., Liang X., Li X., et al., 2017. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning. 168, 94-116.
[32]	Liu X.P., Wang S.J., Wu P.J., et al., 2019. Impacts of urban expansion on terrestrial carbon storage in China. Environmental Science & Technology. 53(12), 6834-6844. doi: 10.1021/acs.est.9b00103
[33]	Liu Y., Huang B., Guo H.D., et al., 2023. A big data approach to assess progress towards Sustainable Development Goals for cities of varying sizes. Communications Earth & Environment. 4(1), 66, doi: 10.1038/s43247-023-00730-8.
[34]	Liu Y.X., Zhao M.Y., 2024. Coupling coordination between carbon storage protection in alpine wetlands and SDGs: A case study of the Qinghai-Tibet Plateau, China. Journal of Cleaner Production. 483, 144260, doi: 10.1016/j.jclepro.2024.144260.
[35]	Liu Z.H., Zhou M., Li D.Y., et al., 2025. Co-benefit of forestation on ozone air quality and carbon storage in South China. Nature Communications. 16(1), 2429, doi: 10.1038/s41467-025-57548-5.
[36]	Lu Y.W., Chen S.L., 2024. Exploring the realization pathway of carbon peak and carbon neutrality in the provinces around the Yangtze River of China. Journal of Cleaner Production. 466, 142904, doi: 10.1016/j.jclepro.2024.142904.
[37]	Luo L., Wang Y.N., Liu Y.C., et al., 2022. Where is the pathway to sustainable urban development? Coupling coordination evaluation and configuration analysis between low-carbon development and eco-environment: A case study of the Yellow River Basin, China. Ecological Indicators. 144, 109473, doi: 10.1016/j.ecolind.2022.109473.
[38]	Ma S.L., Huang J.L., Wang X.X., et al., 2025. Multi-scenario simulation of low-carbon land use based on the SD-FLUS model in Changsha, China. Land Use Policy. 148, 107418, doi: 10.1016/j.landusepol.2024.107418.
[39]	Mikunda T., Brunner L., Skylogianni E., et al., 2021. Carbon capture and storage and the sustainable development goals. International Journal of Greenhouse Gas Control. 108, 103318, doi: 10.1016/j.ijggc.2021.103318.
[40]	National Bureau of Statistics of China, 2022. China Statistical Yearbook 2021. Beijing: China Statistics Press (in Chinese).
[41]	Næss J.S., Cavalett O., Cherubini F., 2021. The land-energy-water nexus of global bioenergy potentials from abandoned cropland. Nature Sustainability. 4(6), 525-536. doi: 10.1038/s41893-020-00680-5
[42]	O’Neill B.C., Kriegler E., Ebi K.L., et al., 2017. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21^st century. Global Environmental Change. 42, 169-180.
[43]	Peng K.F., Jiang W.G., Ling Z.Y., et al., 2021. Evaluating the potential impacts of land use changes on ecosystem service value under multiple scenarios in support of SDG reporting: A case study of the Wuhan urban agglomeration. Journal of Cleaner Production. 307, 127321, doi: 10.1016/j.jclepro.2021.127321.
[44]	Popp A., Calvin K., Fujimori S., et al., 2017. Land-use futures in the shared socio-economic pathways. Global Environmental Change. 42, 331-345.
[45]	Pradhan P., Costa L., Rybski D., et al., 2017. A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Future. 5(11), 1169-1179. doi: 10.1002/eft2.2017.5.issue-11
[46]	Qiao W.Y., Huang X.J., 2022. The impact of land urbanization on ecosystem health in the Yangtze River Delta urban agglomerations, China. Cities. 130, 103981, doi: 10.1016/j.cities.2022.103981.
[47]	Qiao X., Liu H.G., Hao Z.X., et al., 2025. Simulating land use change for sustainability: A case study of the northern slope of Tianshan Mountains. International Journal of Digital Earth. 18(1), 2524055, doi: 10.1080/17538947.2025.2524055.
[48]	Runting R.K., Bryan B.A., Dee L.E., et al., 2017. Incorporating climate change into ecosystem service assessments and decisions: A review. Global Change Biology. 23(1), 28-41. doi: 10.1111/gcb.13457 pmid: 27507077
[49]	Sachs J., Kroll C., Lafortune G., et al., 2021. Sustainable Development Report 2021. Cambridge: Cambridge University Press.
[50]	Tang X.J., Woodcock C.E., Olofsson P., et al., 2021. Spatiotemporal assessment of land use/land cover change and associated carbon emissions and uptake in the Mekong River Basin. Remote Sensing of Environment. 256, 112336, doi: 10.1016/j.rse.2021.112336.
[51]	Tang X.L., Zhao X., Bai Y.F., et al., 2018. Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey. Proceedings of the National Academy of Sciences. 115(16), 4021-4026.
[52]	Tebaldi C., Debeire K., Eyring V., et al., 2021. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics. 12(1), 253-293. doi: 10.5194/esd-12-253-2021
[53]	Tobler, W.R., 1979. Cellular geography. In: GaleS., OlssonG., (eds.). Philosophy in Geography. Dordrecht: Springer.
[54]	Van Beek L., Hajer M., Pelzer P., et al., 2020. Anticipating futures through models: The rise of integrated assessment modelling in the climate science-policy interface since 1970. Global Environmental Change. 65, 102191, doi: 10.1016/j.gloenvcha.2020.102191.
[55]	Van Vuuren D.P., Riahi K., Calvin K., et al., 2017. The shared socio-economic pathways: Trajectories for human development and global environmental change. Global Environmental Change. 42, 148-152.
[56]	Verburg P.H., Soepboer W., Veldkamp A., et al., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management. 30(3), 391-405. pmid: 12148073
[57]	Wang Q., Liu C., Hou Y.T., et al., 2022. Study of the spatio-temporal variation of environmental sustainability at national and provincial levels in China. Science of The Total Environment. 807, 150830, doi: 10.1016/j.scitotenv.2021.150830.
[58]	Wang S.T., Bai X.M., Zhang X.L., et al., 2021. Urbanization can benefit agricultural production with large-scale farming in China. Nature Food. 2(3), 183-191. doi: 10.1038/s43016-021-00228-6 pmid: 37117442
[59]	Wang Y.C., Huang C.L., Feng Y.Y., et al., 2020. Using earth observation for monitoring SDG 11.3.1-Ratio of land consumption rate to population growth rate in mainland China. Remote Sensing. 12(3), 357, doi: 10.3390/rs12030357.
[60]	Wei N., Xia J.Y., 2024. Robust projections of increasing land carbon storage in boreal and temperate forests under future climate change scenarios. One Earth. 7(1), 88-99. doi: 10.1016/j.oneear.2023.11.013
[61]	Weiskopf S.R., Isbell F., Arce-Plata M.I., et al., 2024. Biodiversity loss reduces global terrestrial carbon storage. Nature Communications. 15(1), 4354, doi: 10.1038/s41467-024-47872-7. pmid: 38778013
[62]	Wiedmann T., Allen C., 2021. City footprints and SDGs provide untapped potential for assessing city sustainability. Nature Communications. 12(1), 3758, doi: 10.1038/s41467-021-23968-2. pmid: 34145276
[63]	Wood S.L.R., Jones S.K., Johnson J.A., et al., 2018. Distilling the role of ecosystem services in the Sustainable Development Goals. Ecosystem Services. 29, 70-82.
[64]	Wu H., Yang Y., Li W., 2024. Spatial optimization of land use and carbon storage prediction in urban agglomerations under climate change: Different scenarios and multiscale perspectives of CMIP6. Sustainable Cities and Society. 116, 105920, doi: 10.1016/j.scs.2024.105920.
[65]	Wu X.T., Fu B.J., Wang S., et al., 2023. Three main dimensions reflected by national SDG performance. The Innovation. 4(6), 100507, doi: 10.1016/j.xinn.2023.100507.
[66]	Xiao H.J., Bao S., Ren J.Z., et al., 2024. Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective. Nature Communications. 15(1), 500, doi: 10.1038/s41467-023-44679-w. pmid: 38216549
[67]	Xu C.L., Zhang Q.B., Yu Q., et al., 2023. Effects of land use/cover change on carbon storage between 2000 and 2040 in the Yellow River Basin, China. Ecological Indicators. 151, 110345, doi: 10.1016/j.ecolind.2023.110345.
[68]	Xu L., Saatchi S.S., Yang Y., et al., 2021. Changes in global terrestrial live biomass over the 21^st century. Science Advances. 7(27), eabe9829, doi: 10.1126/sciadv.abe9829.
[69]	Xu W.B., Xu H.Z., Li X.Y., et al., 2024. Ecosystem services response to future land use/cover change (LUCC) under multiple scenarios: A case study of the Beijing-Tianjin-Hebei (BTH) region, China. Technological Forecasting and Social Change. 205, 123525, doi: 10.1016/j.techfore.2024.123525.
[70]	Yang J., Huang X., 2021. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth System Science Data. 13(8), 3907-3925.
[71]	Yang J.X., Gong J., Tang W.W., et al., 2020. Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration. Computers, Environment and Urban Systems. 79, 101402, doi: 10.1016/j.compenvurbsys.2019.101402.
[72]	Yin D.Y., Yu H.C., Shi Y.Y., et al., 2023. Matching supply and demand for ecosystem services in the Yellow River Basin, China: A perspective of the water-energy-food nexus. Journal of Cleaner Production. 384, 135469, doi: 10.1016/j.jclepro.2022.135469.
[73]	Zhang C., Dong J.W., Ge Q.S., 2022a. Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis. Computers and Electronics in Agriculture. 197, 106946, doi: 10.1016/j.compag.2022.106946.
[74]	Zhang H.Y., Li X., Luo Y.P., et al., 2024a. Spatial heterogeneity and driving mechanisms of carbon storage in the urban agglomeration within complex terrain: Multi-scale analyses under localized SSP-RCP narratives. Sustainable Cities and Society. 109, 105520, doi: 10.1016/j.scs.2024.105520.
[75]	Zhang J.J., Yang J., Liu P.F., et al., 2024b. Effects of land use/cover change on terrestrial carbon stocks in the Yellow River Basin of China from 2000 to 2030. Remote Sensing. 16(10), 1810, doi: 10.3390/rs16101810.
[76]	Zhang X.Y., Liu K., Li X.K., et al., 2022b. Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China. Physics and Chemistry of the Earth, Parts A/B/C. 125, 103106, doi: 10.1016/j.pce.2022.103106.
[77]	Zhang Y.L., Zhao Z.Y., Zhu J., et al., 2024c. The dynamic patterns of critical ecological areas in the Yellow River Basin are driven primarily by climate factors but threatened by human activities. Journal of Environmental Management. 371, 123282, doi: 10.1016/j.jenvman.2024.123282.
[78]	Zhao X., Ma X.W., Chen B.Y., et al., 2022. Challenges toward carbon neutrality in China: Strategies and countermeasures. Resources, Conservation and Recycling. 176, 105959, doi: 10.1016/j.resconrec.2021.105959.
[79]	Zhu K., Song Y.L., Lesage J.C., et al., 2024. Rapid shifts in grassland communities driven by climate change. Nature Ecology & Evolution. 8, 2252-2264.

Category	Data	Source
Land use	China Land Cover Dataset (CLCD)	Wuhan University CLCD-Land use and cover change (LUCC) Product (https://doi.org/10.5281/zenodo.5210928)
Land use	Land-use harmonization 2 (LUH2) data	Land Use Harmonization 2 project (https://luh.umd.edu/)
Carbon density	Carbon density data	Ecosystem Research Network Data Center (https://www.cnern.org.cn/)
Socioeconomic factor	Gross regional product (GRP)	Resource and Environment Science and Data Center (https://www.resdc.cn/)
Socioeconomic factor	Population data	Liu et al. (2024) and https://doi.org/10.6084/m9.figshare.c.4605713 (Chen et al., 2020)
Natural factor	Temperature	National Earth System Science Data Center (https://www.geodata.cn/data/)
	Precipitation	National Earth System Science Data Center (https://www.geodata.cn/data/)
	Digital elevation model (DEM)	Resource and Environment Science and Data Center (https://www.resdc.cn/)
	Slope	Resource and Environment Science and Data Center (https://www.resdc.cn/)
	Normalized difference vegetation index (NDVI)	Resource and Environment Science and Data Center (https://www.resdc.cn/)
Accessibility data	Distance to road, railway, and water	National Catalogue Service for Geographic Information (https://www.webmap.cn/)
Accuracy validation data	Global live vegetation biomass carbon dataset	https://doi.org/10.5281/zenodo.4161694 (Xu et al., 2021)

Land use type in the LUH2	Land use type in the CLCD	Land use type in the PLUS model
Forested primary land	Forest	Forestland
Potentially forested secondary land	Shrub	Forestland
Rangeland	Grassland	Grassland
Managed pasture	Grassland	Grassland
C₃ annual crops	Cropland	Cropland
C₃ perennial crops	Cropland	Cropland
C₄ annual crops	Cropland	Cropland
C₄ perennial crops	Cropland	Cropland
C₃ nitrogen-fixing crops	Cropland	Cropland
Urban land	Urban	Construction land
Non-forested primary land	Barren	Barren land
Potentially non-forested secondary land	Barren	Barren land
None	Water body	Water body
None	Snow/Ice	Water body
None	Wetland	Water body

Land use type	C_above (t/hm²)	C_below (t/hm²)	C_soil (t/hm²)
Forestland	31.47	8.20	75.76
Grassland	0.75	4.70	72.10
Cropland	1.97	0.30	65.28
Construction land	0.73	7.99	0.00
Barren land	0.38	0.00	6.28
Water body	0.09	0.00	0.00

Characterization index	Quantification method	Unit	Attribute	Source	SDG
Land reclamation rate	Cropland area/total land area	%	+	Qiao et al. (2023)	SDG 2.1
Per capita cropland	Cropland area/total population	km²/capita	+	Jia et al. (2024)	SDG 2.4
Urbanization rate	Construction land area/total land area	%	+	Yin et al. (2023)	SDG 11.2
Forestland coverage	Forestland area/total land area	%	+	Xu et al. (2020)	SDG 15.1F
Grassland coverage	Grassland area/total land area	%	+	Zuo et al. (2025)	SDG 15.1G