Substantial reduction in population exposure to sea level changes along the Chinese mainland coast through emission mitigation

Abstract Rising sea level increases the exposure to flooding and related damage in coastal areas with high population density and substantial economic activity. As global temperatures continue to rise due to climate change, sea levels have been consistently increasing and are projected to continue this upward trend. This study assesses the future exposure at provincial and city levels populations coastal mainland China coast to local sea level changes under five greenhouse gas (GHG) emission scenarios from IPCC-AR6, as well as two low-confidence scenarios accounting for the potential impact of uncertain ice sheet processes with low- and high-GHG emissions. We incorporate spatial heterogeneity into regional sea level projections and population projections from 2020 to 2100, extreme sea levels (ESLs) of 10-, 50-, and 100 year return periods (RP), and local coastal protection standards. Our findings indicate that the inundated areas expand continuously within the century with heightened exposure under higher emission scenarios. Although the coastal population is projected to decline, the fraction of the coastal population exposed to flooding increases across all scenarios, with accelerated growth under higher GHG emissions and higher ESLs. Zhejiang and Jiangsu emerge as the provinces most exposed to sea-level rise, whereas Taizhou, Nantong, Wuxi, Panjin, and Huzhou are identified as the top five cities with the highest population exposure to local sea level rise (SLR). Transitioning towards a sustainable scenario (i.e. SSP1-2.6) rather than a fossil fuel-intensive one (i.e. SSP5-8.5) can reduce the local SLR and substantially mitigate these exposures. Compared to the median projections under SSP5-8.5, aligning GHG emissions with SSP1-2.6 could reduce population exposure substantially in all coastal provinces, especially in Jiangsu, where population exposure to 100 year RP coastal floods would be reduced by ∼1.6 M in 2050 and by ∼5.4 M in 2100.