Spatio-temporal characteristics and scenario prediction of carbon emissions from land use in Jiangxi Province, China

Research on land use carbon emissions (LUCEs) is crucial for promoting low-carbon urban development. Previous studies have had difficulty in collecting carbon emission data at the micro-scale, so this study analyzed LUCEs at the long time series and raster scale using land use and nighttime lighting data. The STIRPAT model and scenario analysis methods were applied to explore the spatial evolution, drivers, emission peaks and scenario projections of LUCE in Jiangxi Province. The key findings show that: (1) The LUCEs in Jiangxi Province increased between 2000 and 2020, the spatial correlation of LUCEs at the grid scale was significant, and the high LUCEs areas were primarily distributed in the city as well as county centers; (2) Population size has the greatest influence on LUCEs, followed by energy intensity, industrial structure, per capita GDP, energy structure, and total fixed asset investment, respectively; (3) Scenarios are projected to peak in 2025–2035, with scenario 12 being the best way to meet rapid economic growth and reduce carbon emissions at a lower cost. This paper extends the scenario setting methodology by fully considering regional policies in the scenario projections. The results are helpful to deepen carbon emission reduction strategies and promote sustainable socio-economic and ecological development.