Impacts of land use and cover change on carbon storage: Multi-scenario projections in the arid region of Northwest China

doi:10.1016/j.regsus.2025.100248

Abstract

Abstract:

Carbon storage serves as a key indicator of ecosystem services and plays a vital role in maintaining the global carbon balance. Land use and cover change (LUCC) is one of the primary drivers influencing carbon storage variations in terrestrial ecosystems. Therefore, evaluating the impacts of LUCC on carbon storage is crucial for achieving strategic goals such as the China’s dual carbon goals (including carbon peaking and carbon neutrality). This study focuses on the Aral Irrigation Area in Xinjiang Uygur Autonomous Region, China, to assess the impacts of LUCC on regional carbon storage and their spatiotemporal dynamics. A comprehensive LUCC database from 2000 to 2020 was developed using Landsat satellite imagery and the random forest classification algorithm. The integrated valuation of ecosystem services and trade-offs (InVEST) model was applied to quantify carbon storage and analyze its response to LUCC. Additionally, future LUCC patterns for 2030 were projected under multiple development scenarios using the patch-generating land use simulation (PLUS) model. These future LUCC scenarios were integrated with the InVEST model to simulate carbon storage trends under different land management pathways. Between 2000 and 2020, the dominant land use types in the study area were cropland (area proportion of 35.52%), unused land (34.80%), and orchard land (12.19%). The conversion of unused land and orchard land significantly expanded the area of cropland, which increased by 115,742.55 hm². During this period, total carbon storage and carbon density increased by 7.87×10⁶ Mg C and 20.19 Mg C/hm², respectively. The primary driver of this increase was the conversion of unused land into cropland, accounting for 49.28% of the total carbon storage gain. Carbon storage was notably lower along the northeastern and southeastern edges. By 2030, the projected carbon storage is expected to increase by 0.99×10⁶, 1.55×10⁶, and 1.71×10⁶ Mg C under the natural development, cropland protection, and ecological conservation scenarios, respectively. In contrast, under the urban development scenario, carbon storage is projected to decline by 0.40×10⁶ Mg C. In line with China’s dual carbon goals, the ecological conservation scenario emerges as the most effective strategy for enhancing carbon storage. Accordingly, strict enforcement of the cropland red line is recommended. This study provides a valuable scientific foundation for regional ecosystem restoration and sustainable development in arid regions.

Key words: Land use and cover change (LUCC), Carbon storage, Carbon density, Ecological conservation Integrated valuation of ecosystem services and trade-offs (InVEST) model, Patch-generating land use simulation (PLUS) model

FENG Xuyu, ZHAO Xiao, TONG Ling, WANG Sufen, DING Risheng, KANG Shaozhong. Impacts of land use and cover change on carbon storage: Multi-scenario projections in the arid region of Northwest China[J]. Regional Sustainability, 2025, 6(4): 100248.

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Table S3

Configuration of various development scenarios for 2030."

Development scenario	Scenario description
ND	The land use and cover change (LUCC) patterns and transfer probabilities from 2000 to 2020 were retained. The 2020 land use data served as the baseline for simulating land use demand in 2030 under the ND scenario using the patch-generating land use simulation (PLUS) model.
CP	This scenario prioritizes cropland protection to ensure national food security by limiting its conversion into other land use types. Specifically, it reduces the probability of converting cropland and orchard land into construction land by 60.00%, decreases the likelihood of conversion into forest and grassland and water body by 30.00%, strictly prohibits the conversion of cropland into unused land, and increases the probability of converting unused land into cropland and orchard land by 40.00%.
EC	This scenario improves ecosystem quality by facilitating the conversion of land use types with low ecological quality index into ones with higher ecological quality index. Therefore, the conversion of forest and grassland to construction land is strictly restricted. Under this scenario, the probability of converting forest and grassland and water body into construction land was reduced by 60.00%, and the conversion of other land types into unused land was completely prohibited. The probability of converting cropland and orchard land into forest and grassland increased by 30.00%, and the probability of their conversion into construction land decreased by 40.00%.
UD	This scenario enhances urban greening by minimizing the conversion of construction land into other land use types, while increasing the conversion of other land use types into construction land. Under this scenario, the probability of converting cropland, orchard land, forest and grassland, and water body into construction land increases by 40.00%, while the conversion of unused land into construction land increases by 100.00%.

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Category	Data	Original resolution (m)	Source
Natural factor	DEM	30	Geospatial Data Cloud (http://www.gscloud.cn/)
	Slope	30	Geospatial Data Cloud (http://www.gscloud.cn/)
	Aspect	30	Geospatial Data Cloud (http://www.gscloud.cn/)
	Temperature	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)
	Precipitation	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)
	Soil type	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)
	Distance to rivers	1000	National Catalogue Service For Geographic Information (https://www.webmap.cn/)
Socio-economic factor	Population	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)
	GDP	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)
	Distance to primary roads	1000	National Catalogue Service For Geographic Information (https://www.webmap.cn/)
	Distance to railways	1000	National Catalogue Service For Geographic Information (https://www.webmap.cn/)
	Nighttime light	1000	Resource and Environmental Science Data Center (https://www.resdc.cn/)

Land use type	C_above (Mg C/hm²)	C_below (Mg C/hm²)	C_soil (Mg C/hm²)	C_dead (Mg C/hm²)	Source
Cropland	1.30	3.42	81.54	0.94	Zhu et al. (2021); Han et al. (2022); Du et al. (2024); Fu et al. (2024b); Li and Shao (2024)
Orchard land	1.57	4.10	97.85	1.13
Forest and grassland	8.66	5.34	109.67	2.16
Water body	0.19	0.06	0.00	0.00
Construction land	0.84	0.42	0.00	0.00
Unused land	0.63	0.16	26.27	0.00

Land use type	Cropland	Orchard land	Forest and grassland	Water body	Construction land	Unused land	Misclassification error (%)	Omission error (%)
Cropland	554	22	5	0	7	0	7.67	5.78
Orchard land	32	204	7	2	1	0	14.29	17.07
Forest and grassland	6	8	28	1	0	0	33.33	34.88
Water body	1	2	2	14	1	0	22.22	30.00
Construction land	4	2	0	0	17	2	34.62	32.00
Unused land	3	0	0	1	0	10	16.67	28.57

Land use type	Area (hm²)					Area proportion (%)
Land use type	2000	2005	2010	2015	2020	2000	2005	2010	2015	2020
Cropland	81,706.66	105,440.41	111,705.49	196,033.99	197,449.21	20.96	27.05	28.65	50.29	50.65
Orchard land	63,779.14	44,642.64	63,016.06	28,131.52	38,019.28	16.36	11.45	16.16	7.22	9.75
Forest and grassland	5403.16	22,533.50	23,990.74	29,573.92	32,811.16	1.39	5.78	6.15	7.59	8.42
Water body	53,667.46	46,484.16	36,075.14	31,525.75	33,086.16	13.77	11.92	9.25	8.09	8.49
Construction land	1223.64	2276.43	2775.46	4756.73	14,732.73	0.31	0.58	0.71	1.22	3.78
Unused land	184,054.38	168,456.64	152,271.16	99,812.72	73,736.16	47.21	43.21	39.06	25.60	18.91

2000	2020
2000	Cropland	Orchard land	Forest and grassland	Water body	Construction land	Unused land
Cropland	0.00	32.54	14.82	-7.11	-25.32	-1.80
Orchard land	-64.18	0.00	20.29	-34.99	-40.46	-4.66
Forest and grassland	-10.74	-1.81	0.00	-5.91	-5.30	-0.52
Water body	159.25	33.58	95.93	0.00	0.23	2.10
Construction land	5.90	1.35	2.04	-0.01	0.00	0.10
Unused land	503.42	43.59	106.33	-18.70	-13.03	0.00