5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland

1. Quantifying Future Vulnerability in
Scenarios of Socio-economic
Development
Dale S. Rothman, Professor and Senior Scientist
Frederick S. Pardee Center for International Futures
Josef Korbel School of International Studies
University of Denver
5Th International Disaster and Risk Conference
IDRC Davos 2014

2. Outline
Overview of the Shared Socioeconomic
Pathways
Estimating Future Vulnerability in the
Shared Socioeconomic Pathways
Caveats and Future Work

3. Shared Socioeconomic Pathways
• One component of a new set of integrated scenarios
describing future climate, societal, and environmental change
• Alternative pathways of societal change, which describe
plausible changes in demographic, economic, technological,
social, governance, and environmental factors other than
climate change.
• Designed to be combined with projections of climate change
and assumptions about climate policy to produce integrated
scenarios that include climate change policy, impacts, or both
• Designed to be extended to support integrated assessment
model scenarios of emissions and land use, as well as climate
impact, adaptation and vulnerability analyses
• Consist of qualitative narratives and quantitative projections

4. Uncertainty space defined by challenges
for mitigation and adaptation

5. Core quantitative indicators
regional and national population, GDP, educational
attainment, urbanization

6. Key Demographic Assumptions
SSP 1 SSP 2 SSP 3 SSP 4 SSP 5
• L = Low, M=Medium, H=High
• HFer = TFR > 2.9 in 2005-2010; LFer-Oth: TFR <= 2.9 and not
belonging to Rich-OECD; LFer-OECD : belonging to Rich-OECD as
per the definition of World Bank.
• For fertility, L, M, and H imply long-run (year 2200) TFR
convergence to 1.3, 1.75, and 2.2 children per woman, respectively.
HFer
LFer-
Oth
LFer-
OECD
HFer
LFer-
Oth
LFer-
OECD
HFer
LFer-
Oth
LFer-
OECD
HFer
LFer-
Oth
LFer-
OECD
HFer
LFer-
Oth
LFer-
OECD
Fertility L L M M M M H H L H L L L L H
Mortality L L L M M M H H H H M M L L L
Migration M M M M M M L L L M M M H H H

7. Key Economic Assumptions
SSP1 SSP2 SSP3 SSP4 SSP5
MFP/TFP growth at
frontier
M M L M H
Speed of Convergence H M L
LIC: L
MIC: M
HIC: M
• L = Low, M=Medium, H=High
• HIC = high income, MIC = Medium income, LIC = Low income
countries as per the definitions of the World Bank.
H

8. Replicating the SSPs in IFs
Total Population in High Fertility Countries

9. Replicating the SSPs in IFs
GDP per Capita in Low Income Countries
Countries

10. Low Income counties
More Sustained Poverty in SSPs3 and 4

11. Low Income counties
Less Access to Infrastructure in SSPs3 and 4

12. Low Income counties
Weaker Institutions in SSPs3 and 4

13. Low Income counties
Lower HDI in SSPs3 and 4

14. World Risk Index and its Components

15. Susceptibility

16. Low Income Countries
Higher Susceptibility in SSPs 3 and 4

17. Susceptibility across Income Groupings
varies by SSP

18. Caveats and Future Work
• The results presented here likely underestimate
the variation across the SSPs due to some issues
in the current representation of the SSPs in IFs
• We will continue to refine the representation of
the SSPs in IFs
• We will look to include additional indicators of
vulnerability in IFs
• We will also use IFs to check the internal
consistency of the SSPs

19. Access to IFs
• www.pardee.du.edu
• Some forecasts available
through Google Public
Data Explorer)
• Links from other sources
(e.g. Wikipedia ); also
African Futures Project;
Atlantic Council
IFs is
available
at :