Spatiotemporal Variation Characteristics of Ecosystem Carbon Storage in Henan Province and Future Multi-Scenario Simulation Prediction

Abstract

In response to a series of problems brought about by rapid economic development, such as global warming and the continuous deterioration of the ecological environment, China has taken the initiative to shoulder the responsibility of a major country and continued to contribute Chinese wisdom and Chinese solutions to the goal of “carbon peak and carbon neutrality” at an early date. In this paper, Henan Province has been selected as the study area, and the changes in land use and carbon storage in Henan Province from 2000 to 2020 have been analyzed spatially and temporally. The PLUS model is used to predict future land use changes under different scenarios, and the InVEST model is used to estimate carbon storage under the corresponding scenarios. The results showed that (1) During 2000–2020, the farmland in Henan Province has been in a decreasing trend, grassland and construction land showed a decreasing trend and then an increasing trend, and woodland showed a decreasing trend. From 2000 to 2020, Henan’s overall carbon storage showed a downward trend each year, with storage mainly in the western and southern regions of the province, with a spatial distribution of high storage in the west and low storage in the east. (2) Under the normal development scenario (SSP2-RCP4.5) from 2030 to 2050, the area of farmland and woodland basically showed a continuous downward trend, while construction land showed an upward trend annually, and farmland and construction land showed an increasing trend under the normal development scenario (SSP2-RCP4.5) and economic priority scenario (SSP5-RCP8.5). The decreasing trend of carbon storage was the smallest under the normal development scenario (SSP2-RCP4.5) and the ecological protection scenario (SSP1-RCP2.6). The results provide a basis for decision-making regarding low-carbon and circular developments and rational and optimal land use in Henan.