China's provincial optimum population sizes under SSP-RCP scenarios

Abstract

Climate change poses new challenges to achieving a balanced population distribution. Predicting the impact of development patterns using climate change scenarios can offer more precise insights into environmental and social risks. By adopting the possibility–satisfiability (P–S) model, this study investigates the optimum population sizes for the 31 provincial districts of China by 2035 and 2050 under seven shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios. Results indicate that under SSP2-4.5 scenario, which maintains current development patterns, the optimum human population (OHP) ranges from 985 million to 717 million in 2035 and from 902 million to 623 million by 2050 at different P–S values, respectively. The SSP1-1.9 and SSP1-2.6 scenarios offer promising prospects by carrying the largest optimum population size. Therefore, China's future development depends on choosing a sustainable path that prioritizes the growth of the OHP. The optimum population sizes of provinces under unbalanced development routes SSP4-3.4 and SSP4-6.0 scenarios are mainly limited by economic development, while those of the regional competition route SSP3-7.0 scenario are limited by both economic and climatic pressures. Under the fossil fuel route SSP5-8.5 scenario, climate deterioration imposes severe constraints on optimum population size. The optimum population scales of the eastern coastal area with high urbanization levels and the northwest area with harsh environments are subject to natural conditions. In addition, the central and western regions encounter population size limitations due to insufficient economic development. Notably, China's population distribution remains concentrated in the east and sparse in the west, flanking the Hu Huanyong Line (Hu Line), with minimal expected changes in the future. By studying the spatial distribution of the optimum population under different SSP-RCP scenarios, this study provides theoretical support for government decision-making in the context of future climate change.