Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways

TitleProjecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways
Publication TypeJournal Article
Year of Publication2019
JournalEarth's Future
Volume7
Number4
Pages351-362
Date Published03/2019
Abstract / Summary

Improved understanding of the potential growth of urban areas at the national and global levels is needed for sustainable urban development. Current panel data analysis and local scale modeling are limited in projecting global urban area growth with large spatial heterogeneities. In this study, we developed country‐specific urban area growth models using the time series data set of global urban extents (1992–2013) and projected the future growth of urban areas under five Shared Socioeconomic Pathways (SSPs). Our results indicate the global urban area would increase roughly 40–67% under five SSPs until 2050 relative to the base year of 2013, and this trend would continue to a growth ratio of more than 200% by 2100. The growth of urban areas under relatively unsustainable development pathways (e.g., regional rivalry SSP3 and inequality SSP4) is smaller compared to other SSPs. Although developing countries would remain as leading contributors to the increase of global urban areas in the future, they may exhibit different temporal patterns, that is, plateaued or monotonically increasing trends. This variation is primarily attributed to the compounding effect of the growth in population and gross domestic product. Our urban area data set presents a first country‐level urban area projection under the five SSPs, spanning from 2013 to 2100. This data set has a great potential to support various global change studies, for example, urban sprawl simulation, integrated assessment modeling for sustainable development goals, and investigation of the impact of urbanization on atmospheric emissions, air quality, and human health.

URLhttp://dx.doi.org/10.1029/2019ef001152
DOI10.1029/2019ef001152
Funding Program: 
Journal: Earth's Future
Year of Publication: 2019
Volume: 7
Number: 4
Pages: 351-362
Date Published: 03/2019

Improved understanding of the potential growth of urban areas at the national and global levels is needed for sustainable urban development. Current panel data analysis and local scale modeling are limited in projecting global urban area growth with large spatial heterogeneities. In this study, we developed country‐specific urban area growth models using the time series data set of global urban extents (1992–2013) and projected the future growth of urban areas under five Shared Socioeconomic Pathways (SSPs). Our results indicate the global urban area would increase roughly 40–67% under five SSPs until 2050 relative to the base year of 2013, and this trend would continue to a growth ratio of more than 200% by 2100. The growth of urban areas under relatively unsustainable development pathways (e.g., regional rivalry SSP3 and inequality SSP4) is smaller compared to other SSPs. Although developing countries would remain as leading contributors to the increase of global urban areas in the future, they may exhibit different temporal patterns, that is, plateaued or monotonically increasing trends. This variation is primarily attributed to the compounding effect of the growth in population and gross domestic product. Our urban area data set presents a first country‐level urban area projection under the five SSPs, spanning from 2013 to 2100. This data set has a great potential to support various global change studies, for example, urban sprawl simulation, integrated assessment modeling for sustainable development goals, and investigation of the impact of urbanization on atmospheric emissions, air quality, and human health.

DOI: 10.1029/2019ef001152
Citation:
Li, X, Y Zhou, J Eom, S Yu, and G Asrar.  2019.  "Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways."  Earth's Future 7(4): 351-362.  https://doi.org/10.1029/2019ef001152.