Effect of Fertility Policy Changes on the Population Structure and Economy of China: From the Perspective of the Shared Socioeconomic Pathways

Abstract

Beginning in 2016, all couples in China were allowed to have two children without any restrictions. This paper provides population and economic projections under five shared socioeconomic pathways (SSPs) and three fertility policies. By replacing the one‐child policy with the two‐child policy, the population is predicted to continue growing until 2025–2035, with a peak of approximately 1.39–1.42 billion, and then to decline under four SSPs, with the exception of the fragmented world SSP3. As a result, the two‐child policy will lead to mitigation of the pressure from labor shortages and aging problems to a certain extent. In addition, an increase in working‐age people with higher education level relative to projections based on the one‐child policy will lead to an increase in gross domestic product by approximately 38.1–43.9% in the late 21st century. However, labor shortages and aging problems are inevitable, and the proportion of elderly in China will be greater than 14% and 21% by approximately 2025 and 2035, respectively. Full liberalization of fertility is expected to reduce the share of elderly people by 0.7–1.0% and to lead to an increase in gross domestic product by 5.3–6.7% relative to the two‐child policy in the late 21st century. The full liberalization of fertility policies is recommended, supplemented by increases in pension and child‐rearing funds, improvement in the quality of health services for females and children, and extension of compulsory education to meet the needs of an aged society.

Projected increases in magnitude and socioeconomic exposure of global droughts in 1.5 and 2 °C warmer climates

“…To assess the exposures of populations and assets to droughts, which will eventually lead to higher drought losses in the future, instead of using a static socioeconomic scenario as many studies have (e.g., Hirabayashi et al, 2013;Smirnov et al, 2016), we employ the spatially explicit global shared SSPs. This dataset includes gridded population and GDP data under five SSPs, covering the period 2010-2100 at a spatial resolution of 0.5 • × 0.5 • (Jiang et al, 2017Su et al, 2018;Huang et al, 2019). It involves a sustainable scenario (SSP1), a pathway of continuing historical trends (SSP2), a strongly fragmented world (SSP3), a highly unequal world (SSP4), and a growth-oriented world (SSP5).…”

Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100

“…Population has direct influences on the challenges related to the mitigation of and adaptation to climate change by influencing economic growth and social development, affecting the amounts of resource consumption and pollutant emissions, and determining the number of residents exposed to pollutants and natural disasters [1][2][3][4][5] . As China is the most populous country in the world, estimations of China's population and its spatial distribution is important in global and China's scenario researches.…”

Each 0.5°C of Warming Increases Annual Flood Losses in China by More than US$60 Billion

“…Five socio-economic pathways (SSPs) were designed to represent different strategies of socioeconomic development and challenges of mitigation and adaptation (O'Neill et al, 2014). Considering the socio-economic challenges to mitigation and economic development under different SSPs (O'Neill et al, 2017;Huang et al, 2019), the "Sustainability" pathway SSP1 with low challenge to mitigation, "Middle of the road" SSP2 with medium challenge to mitigation and "Fossil-fueled development"…”

“…pathway SSP5 with high challenge to mitigation are separately combined with the RCP2.6, RCP4.5 and RCP8.5 to assess flood impacts at the 1.5°C, 2.0°C, 2.5°C, 3.0°C, 3.5°C and 4.0°C global warming levels. The provincial-scale socio-economy for 2010-2100 is projected under the SSPs parameter scheme (Leimbach et al, 2017) by considering the regional information on total factor productivity, capital stock and labor force from the latest census and the current universal two-child policy in China for labor force projection (Jiang et al, 2018;Huang et al, 2019). Gridded GDP is derived by scaling the SSP projections to 0.5° resolution, based on the weights of individual grid cells to total provincial GDP, which is from recorded data.…”