Scenario-based assessment of land-use change and urban carbon storage: spatiotemporal dynamics and drivers in Beijing, China
Year: 2026
Type: article
Abstract: Understanding how land-use change reshapes terrestrial carbon storage (CS) is important for China’s dual-carbon goals in rapidly urbanizing regions. Taking Beijing as a megacity case, we integrated GIS-based land-use analysis with the InVEST CS module to quantify CS dynamics from 1980 to 2020. We then used the Geodetector to identify dominant drivers of spatial heterogeneity and coupled a Markov chain with the PLUS model to simulate land use and CS in 2030 under alternative development pathways. Results show that cultivated land, forest land, and construction land dominated Beijing’s land-use structure. From 2000 to 2020, cropland decreased by 2,240.25 km 2 , while forest land and construction land increased by 177.22 km 2 and 2,130.58 km 2 , respectively. Total CS followed a “decline–rebound” trajectory, decreasing from 2.9462 × 10 8 t (1980) to 2.7828 × 10 8 t (2010) and then rising to 2.8352 × 10 8 t (2020). Spatially, CS exhibited significant positive spatial autocorrelation throughout the study period (Global Moran’s I = 0.883–0.900), with high–high clusters concentrated in the mountainous northwest and low–low clusters mainly distributed across the built-up plains in the center and southeast, as further confirmed by LISA and hot/cold spot patterns. Geodetector results indicate that topography and climate dominate CS heterogeneity, with slope showing the strongest explanatory power (q = 0.62). Scenario simulations suggest that CS would increase more under a carbon-sink priority scenario (+2.71%) than under an inertial-development scenario (+1.76%) by 2030, highlighting the value of ecological-priority land-use strategies for carbon-sink enhancement.
Authors: Chuzhi Li, Bo Li
Institutions: Hunan First Normal University, Central South University
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7129108711