Historical Changes and Multi-scenario Prediction of Land Use and Terrestrial Ecosystem Carbon Storage in China

Abstract

Terrestrial carbon storage (CS) plays a crucial role in achieving carbon balance and mitigating global climate change. This study employs the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs-RCPs) published by the Intergovernmental Panel on Climate Change (IPCC) and incorporates the Policy Control Scenario (PCS) regulated by China’s land management policies. The Future Land Use Simulation (FLUS) model is employed to generate a 1 km resolution land use/cover change (LUCC) dataset for China in 2030 and 2060. Based on the carbon density dataset of China’s terrestrial ecosystems, the study analyses CS changes and their relationship with land use changes spanning from 1990 to 2060. The findings indicate that the quantitative changes in land use in China from 1990 to 2020 are characterised by a reduction in the area proportion of cropland and grassland, along with an increase in the impervious surface and forest area. This changing trend is projected to continue under the PCS from 2020 to 2060. Under the SSPs-RCPs scenario, the proportion of cropland and impervious surface predominantly increases, while the proportions of forest and grassland continuously decrease. Carbon loss in China’s carbon storage from 1990 to 2020 amounted to 0.53 × 10¹² kg, primarily due to the reduced area of cropland and grassland. In the SSPs-RCPs scenario, more significant carbon loss occurs, reaching a peak of 8.07 × 10¹² kg in the SSP4-RCP3.4 scenario. Carbon loss is mainly concentrated in the southeastern coastal area and the Beijing-Tianjin-Hebei (BTH) region of China, with urbanisation and deforestation identified as the primary drivers. In the future, it is advisable to enhance the protection of forests and grassland while stabilising cropland areas and improving the intensity of urban land. These research findings offer valuable data support for China’s land management policy, land space optimisation, and the achievement of dual-carbon targets.