Carbon storage simulation and analysis in Beijing-Tianjin-Hebei region based on CA-plus model under dual-carbon background

Abstract

Previous studies on carbon storage simulation had ignored the difference of carbon intensity among various vegetation types inner the same land use. In this paper, The PLUS model was used to predict the land use change under multi-scenarios from 2030 to 2060, and the vegetation type data were supplemented by CA model to obtain the land cover-vegetation datasets from 2030-2060. Combined with the carbon density table of vegetation type, the future land use carbon storage during 2030-2060 under multi-scenarios in Beijing-Tianjin-Hebei region were analyzed. The main conclusions were as follows: (1) The spatial distribution of carbon storage in Beijing-Tianjin-Hebei region showed a pattern of ‘high in northeast-southwest and low in southeast-northwest’; (2) The carbon storage in Beijing-Tianjin-Hebei region during 1990-2020 showed a decreasing trend; (3) During 2030-2060, the carbon storage in Beijing-Tianjin-Hebei region showed a continuous decreasing trend in the absence of policy intervention, while that under the ecological protection and farmland protection scenarios showed an increasing trend; (4) Under different development scenarios, there were obvious significances of carbon storage in spatial distribution.

Keywords:

• Carbon storage
• CA-PLUS model
• multiple scenarios
• land use
• Beijing-Tianjin-Hebei region

Data availability statement

The data that support the findings of this study are available from the corresponding author, Guo B, upon reasonable request.

Additional information

Funding

This work was supported by Natural Science Foundation of Shandong Province (grant no. ZR2021MD047); National Natural Science Foundation of China (grant no.42101306 and 41904008); Scientific Innovation Project for Young Scientists in ShandongProvincial Universities(grant no.2022KJ224); Open Research Fund of the Key Laboratory of Digital Earth Science, Chinese Academy of Sciences (grant no. 2019LDE006); A grant from State Key Laboratory of Resources and Environmental Information System; Open Fund of Key Laboratory of Meteorology and Ecological Environment of Hebei Province(grant no. Z202001H); Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR (grant no.2020NGCM02) and Agricultural Science and Technology Innovation Program (grant no. CAAS-ZDRW202201).