Updating global urbanization projections under the Shared Socioeconomic Pathways

Abstract

Urbanization level is an important indicator of socioeconomic development, and projecting its dynamics is fundamental for studies related to global socioeconomic and climate change. This paper aims to update the projections of global urbanization from 2015 to 2100 under the Shared Socioeconomic Pathways by using the logistic fitting model and iteratively identifying reference countries. Based on historical urbanization level database from the World Urbanization Prospects, projected urbanization levels and uncertainties are provided for 204 countries and areas every five years. The 2010-2100 year-by-year projected urbanization levels and uncertainties based on the annual historical data from the World Bank (WB) for 188 of countries and areas are also provided. The projections based on the two datasets were compared and the latter were validated using the historical values of the WB for the years 2010-2018. The updated dataset of urbanization level is relevant for understanding future socioeconomic development, its implications for climate change and policy planning.

Fig. 1

Technical flowchart for estimating global urbanization dynamics. *For statistical purposes, the data for China do not include Hong Kong and Macao, Special Administrative Regions (SAR) of China, and Taiwan Province of China. ** Countries and areas were eliminated because of incomplete data, or the S-shape curve not being followed, or the urbanization level in 2010 having reached 100%.

Fig. 2

Comparisons between the simulated urbanization level (based on WB database) and historical values from 2010 to 2018 under different SSP scenarios. The gray dashed line representing a 10% gap of the 1:1 diagonal line. In each subplot, one array of points with the same color represents the changes in urbanization level from 2010 to 2018 for one country or area.

Fig. 3

Comparisons between different projections and historical urbanization level values in 2015 under different SSP scenarios. The rows in this panel represent the difference between this study’s simulated urbanization level (based on WUP 2018, WB, and WUP 2009 databases), Jiang & O’Neil’s simulated urbanization level (based on WUP 2009 database) and historical values in 2015 under different SSP scenarios, respectively. A total of 169 overlapping countries and areas are compared.

Fig. 4

Comparisons between the simulated urbanization level (based on WB database) and the simulated urbanization level (based on WUP 2018 database) from 2015 to 2100 (every five years) under different SSP scenarios. The gray dashed line representing a 10% gap of the 1:1 diagonal line. In each subplot, one color represents one country or area for the urbanization levels from 2015 to 2100 (every five years).

Fig. 5

Urbanization levels of the five example countries. For clarity of expression, we did not delineate the standard deviation but provided them in the ‘_SD.xls’ files.

Fig. 6

Urbanization levels of seven regions under different urbanization speed. The lines are the average values for different regions. For clarity of expression, we did not delineate the standard deviation but provided them in the ‘_SD.xls’ files.