Research Trends

Cai Wenjia, Department of Earth System Science, and Wang Can's group from the School of the Environment released data on future population and kilometer-level distribution in China under shared socioeconomic pathways

2020-03-13

     On March 9, Cai Wenjia, Department of Earth System Science, Tsinghua University, and Wang Can's group from the School of the Environment published an online article entitled " Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100” in the journal Scientific Data. The study for the first time constructed a population database (including gender, age, education level) and high-resolution (1km) grid of China's future provinces under five shared socioeconomic paths from 2010 to 2100. This work has impacted climate change. It is of great significance to assess and formulate precise climate change response strategies, and is important to carry out health risk assessment, public health policy formulation, resource demand and allocation, and social equity related policies.

     Shared socioeconomic pathways (SSPs) are a new generation of scenario portfolios constructed by the Intergovernmental Panel on Climate Change (IPCC) to promote comprehensive analysis of future climate change impacts, adaptation and mitigation. It describes five different development models of the future economic and social system, reflects the linkage between economic and social development and the challenge of climate change mitigation and adaptation, and is the core basis for conducting climate change impact assessments and formulating climate policies. At present, international organizations have established a national-scale database of socio-economic driving factors (population, GDP, and urbanization rate) long-term forecast databases. However, the database has two problems: one is that it does not fully reflect the reality of China in the setting of scenario parameters. The forecast data has a certain systematic deviation. The second problem is that the data is based on the country, it is difficult to evaluate the impact of climate change and climate policy with high spatial accuracy, and it is difficult to meet the actual needs of refined response to climate change. Therefore, constructing a set of sub-national scale and grid-based high-resolution SSPs basic element data to meet China's national conditions has urgent development needs and important research value.

     This study uses a multi-dimensional recursive model to estimate the structural information of China ’s provincial population and its gender, age, and education level from 2010 to 2100 under five socio-economic development paths. According to the urban grid and historical population grid of representative concentration paths (RCPs), the predicted population of the provinces is allocated as a population grid with a resolution of 1 km. Based on the latest statistical yearbook data of provinces and prefecture-level cities, and population gridded product data released by international agencies, the research verified and compared the total population, structured information, and gridded data of the provinces.

     The study found that China's population will peak between 2027 and 2034. In the medium development path (SSP2, which can be understood as the current policy scenario), China's population will reach a peak in 2029 with a peak of about 1.46 billion; the fastest peak (2027) under the uneven development path (SSP4), and the peak The population is the lowest, about 1.44 billion; the peak of the global regional competition path (SSP3) is the latest (2034), and the peak population is the highest, about 1.48 billion. By 2050, China's total population will not change much under different scenarios, ranging from 1.32 billion to 1.46 billion; by 2100, China's total population will vary greatly under different scenarios, and it may be possible to maintain the level of 1.35 billion, or it may be As low as 810 million.

     The data in this paper (including the population and population urbanization forecast data for China by province in the SSP scenario from 2010 to 2100, the provincial population sex, age, education level population data, and the 1km resolution population gridding data under the SSP-RCP scenario group) can be used for non-commercial research. The address of the article and the free download address can be found at the link at the end of the article.


The number of future population at the national level under five shared socio-economic development paths from 2010 to 2100
(The population distribution results under each combination of SSP-RCP can be downloaded and viewed for free)

     Chen Yidan, a 2017 Ph.D. student in the School of Environment, Tsinghua University, is the first author of the paper, and Associate Professor Cai Wenjia, Department of Earth System Science, Tsinghua University, is the corresponding author. The team of Cai Wenjia from the Department of Earth System Science, and Wang Can's group from the School of the Environment have long been committed to research on climate change economics, energy environment economic system simulation, and environmental and health impact assessment to mitigate climate change. This research was supported by the National Key Research and Development Plan (2017YFA0603602), the National Natural Science Foundation of China (No.71773061, No.71773062, and No.71525007) and other projects.


Paper link: https://www.nature.com/articles/s41597-020-0421-y
Data download address: https://doi.org/10.6084/m9.figshare.c.4605713


Contributed : Cai Wenjia

Reviewed : Luo Yong

Graphic editor: Fu Meijuan