Global urbanization projections for the Shared Socioeconomic Pathways

By Leiwen Jiang, Brian C. O'Neill. 2017

565 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management Science and Operations Research, Transportation

View Paper: https://www.semanticscholar.org/paper/436098eb5e1f8bebdbc71eff5c7947f053191045

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. D. P. Vuuren
2. R. McDonald
3. Martina Flörke
4. C. Schneider
5. D. V. van Vuuren
6. Brian C. O'Neill
7. Keywan Riahi
8. Bas J. van Ruijven

In Management Science and Operations Research

1. Elmar Kriegler
2. D. V. van Vuuren
3. Gunnar Luderer
4. Bas J. van Ruijven
5. Martina Flörke
6. C. Schneider
7. R. McDonald
8. Marian Leimbach

In Transportation

1. C. Schneider
2. Martina Flörke
3. R. McDonald
4. Leiwen Jiang
5. Brian C. O'Neill
6. Perry Sadorsky

Citations in the corpus

• Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation (2018, unknown): “These estimates depend on population growth and rates of urbanization (Jiang and O’neill, 2017).”
• Long-term model-based projections of energy use and CO2 emissions from the global steel and cement industries (2016, methodology): “As starting point for future projections, we use the Shared Socioeconomic Pathway 2 (SSP2) scenario (Chateau et al., 2015; Jiang and O’Neill, 2015; Samir and Lutz, 2015; O’Neill et al., 2015) as implemented in the IMAGE model (Van Vuuren et al., in review).”
• How much energy will buildings consume in 2100? A global perspective within a scenario framework (2018, background): “Because urbanisation will continue rising in the 21st century [28], and floor space per capita is lower in urban than rural regions, we decrease the income elasticity over time in most scenarios.”
• The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview (2017, methodology): “For each SSP, a single population, education (KC and Lutz, 2016) and urbanization projection (Jiang and O’Neill, 2016) was developed, while three different economic modeling teams participated in the development of the GDP projections (Crespo Cuaresma, 2016; Dellink et al.”
• Water competition between cities and agriculture driven by climate change and urban growth (2018, methodology): “(2) Calculation of sectoral water withdrawals and consumption driven by socio-economic data (SSP2) and climate scenarios for the entire time period of 1971–2070 and allocated to (sub)basin scale.”
• Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling (2016, unknown): “The urban and nonurban populations are obtained by dividing the SSP country population scenarios using share of urban populations projected by [18].”