Spatiotemporal evolution of land use carbon emissions and multi scenario simulation in the future—Based on carbon emission fair model and PLUS model

Abstract

With the increase of carbon dioxide concentration, global warming has become increasingly serious, and has posed a serious threat to human survival and the ecological environment. The land use carbon emissions (LUCE) are only second to those caused by fossil fuel combustion. The affected area of the lower Yellow River (ALYR) is an essential grain producing area in China. Studying its LUCE and future development has practical significance for the low-carbon development and food security of the basin. We analyzed the characteristics and drivers of LUCE in the ALYR from 2000 to 2018 and simulated the LUCE under different scenarios in 2030. The LUCE in the ALYR were in a continuous growth trend, with a growth rate of 27.94 %, and the economic efficiency and ecological carrying capacity remained stable. The built-up land area and the proportion of secondary industry in Gross Domestic Product (GDP) can promote the LUCE, while the per capita GDP has a suppressive effect. The LUCE will decrease in 2030, with the least LUCE under the cultivated land protection scenario. Compared to previous studies, this study enriches existing research from two aspects: economic/ecological fair of LUCE and prediction of different future scenarios. This provides a new research perspective for reducing LUCE and achieving regional carbon reduction. From a practical perspective, the results can not only provide reference for low-carbon land use in the ALYR, but also provide ideas for carbon reduction and sustainable development in other watersheds that are greatly disturbed by human activities. • Land use carbon emissions showed an increasing trend from 2000 to 2018. • The fair model of land use carbon emissions was constructed. • Built-up land plays a significant role in promoting land use carbon emissions. • In 2030, the land use carbon emissions under the three scenarios show a decreasing trend.