1. The Global Facility for Disaster Reduction and Recovery Making Good Decisions Under Uncertainty Marianne Fay, World Bank

2. Good Decision Making Is Challenged By Uncertainty and Disagreement Shanghai 1990 vs. 2010 Rapid Changes Competing Priorities Conservation vs. Development Uncertain Future Climate Change 2

3. The stakes are high 3

4. Agenda A real case Traditional “Predict-then-Act” Planning Decision-making under uncertainty 4

5. Over 15 years, HCMC has planned multi-billion dollar flood investments using best available projections Ho Chi Minh City As Context For Our Discussion 5

6. Conditions have diverged from projections and the city is at significant risk 6

7. Today, HCMC seeks an innovative, integrated flood risk management strategy 7

8. Risk = Hazard x Exposure x Vulnerability Hazard Future rainfall intensity Height of the Saigon River Exposure Population in the study area Urban form Vulnerability Vulnerability of population to flood depth Risk Model Risk = Expected Number of People Affected By Floods Each year 8

9. Model Calculates Risk From Six Parameters Of Hazard, Exposure, Vulnerability Rainfall Increase Increase River Height Population Urban Form Poverty Rate Vulnerability 9

10. Each Parameter Could Take A Range of Values Rainfall Increase +0% + 35% Increase River Height 20 cm 100 cm Population 7.4 M 19.1 M Urban Form Growth in Outskirts Growth in Center Poverty Rate 2.4 % 25 % Vulnerability Not Vulnerable Very Vulnerable 10

11. We Can Use This To Make Projections Rainfall Increase +0% + 35% Increase River Height 20 cm 100 cm Population 7.4 M 19.1 M Urban Form Growth in Outskirts Growth in Center Poverty Rate 2.4 % 25 % Vulnerability Not Vulnerable Very Vulnerable 10% 30 cm 11 M 5% This projection assumes little climate change and relatively small population This projection assumes more climate change. It also has a larger, poorer, but less vulnerable population 30% 70 cm 17 M 18% 11

12. Traditional Planning Asks “What Will The Future Bring?” PredictAct 12

13. 10% 30 cm 11 M 5% Most likely projection Risk Model Policy Risk From Policy In Your Projection We could rely on the most likely projection for decision making… 13

14. But there can be disagreement about what is the most likely projection The Meteo-France model, from IPCC

15. Climate models can disagree with each other The Meteo-France and the Australian model, from IPCC

16. … and we have a lot of models… (Epistemic uncertainty)

17. … whose results depend on future climate policies and socio-economic trends…

18. It can be difficult to make good projections – even with good data In the early 1970s forecasters made projections of U.S energy use based on a century of data Gross national product (trillions of 1958 dollars) 2.2 2.0 1.8 1.6 1.4 1.2 1.0.8.6.4.2 0 180 Energy use (10 15 Btu per year) 0 Historical trend continued 1970 1920 1929 1940 1950 1960 1910 1973 1900 1890 20406080100120140160 1975 Scenarios

19. Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions Gross national product (trillions of 1958 dollars) 2.2 2.0 1.8 1.6 1.4 1.2 1.0.8.6.4.2 0 180 Energy use (10 15 Btu per year) 0 Historical trend continued 1970 1920 1929 1940 1950 1960 1910 1973 1900 1890 20406080100120140160 2000 Actual 1990 1980 1977 1975 Scenarios 2000 Actual 1990 1980 1977 In the early 1970s forecasters made projections of U.S energy use based on a century of data …they were all wrong

20. Scenario Planning asks: What Might The Future Bring? ” Imagine Possibilities Act 20

21. 10% 30 cm 11 M 25cm Risk Model Policy Risk From Policy In Your Scenarios We may chose to use several different scenarios for decision making… 30% 17 M 18% 21

22. 1000 Scenarios Risk Model Policy Risk From Policy In 1000 Scenarios Few Scenarios Leaves Many Possible Futures Uncovered…Instead Use 1000 Scenarios For Decision Making… 22

23. Use many scenarios to identify robust decisions Decisions that perform well in many scenarios even if not optimal in any single one. Examples… 23 Get an education even if you hope to be a basketball star Plant drought-resistant cassava even if water-sensitive maize fetches higher prices

24. Robust decision-making Uses very many scenarios to find –Limitations of current strategies –Strategies that are truly robust Focus on understanding policies & their weaknesses – not on making projections 24

25. 6% It protects our city if… < 6% increase in rainfall intensity < 45 cm increase in river height < 5% poverty rate 45cm 5% Under What Conditions Does The Current Infrastructure Keep Risk Low? 25

26. 6% increase 45cm rise Infrastructure 26 Is it reasonable to assume these conditions will hold?

27. 6% increase 45cm rise Infrastructure IPCC SREX Mid Value (20%) IPCC SREX High Value (35%) MONRE SLR Estimate 30 cm NOAA SLR + SCFC Subsidence 75 cm Probably Not. Even if stakeholders disagree about the future, they can probably agree that current infrastructure will not be sufficient Should We Rely On Current Infrastructure? 27

28. What options usefully complement the current infrastructure? And How? 1. Rely on current infrastructure 2. Raise Homes 3. Relocate Areas 4. Manage Groundwater 5. Capture Rain Water 28

29. Ministry of Environment Ministry of Urban Planning Infrastructure NOAA SLR + SCFC Subsidence (75 cm) How Will Adding These Options Improve Our Strategy? Relocate (17%, 100cm) Adding these measures improves robustness This “map” helps stakeholders have structured discussions Rainwater (10%, 100cm) Elevate (23%, 55cm) Groundwater (7%, 55cm) MONRE SLR Estimate 30 cm IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

30. Infrastructure NOAA SLR + SCFC Subsidence (75 cm) What Are Tradeoffs Between Robustness And Cost? Stakeholders debate about how much robustness they can afford (which is much more useful than debating what the future will be) Relocate (17%, 100cm) High Cost Low Cost Rainwater (10%, 100cm) Elevate (23%, 55cm) Groundwater (7%, 55cm) MONRE SLR Estimate 30 cm IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

31. Robust Decision Making Asks “What Are The Limitations of Our Plan and How Can We Improve It? Under what future conditions does our plan perform poorly? Can we modify the plan to make it more robust? What are the tradeoffs between robustness, costs, feasibility, etc.? Robust Decision Making What will the future be? What is the best near-term decision given our prediction? How sensitive is our decision to our prediction? Predict-then-act

32. For More Information…. Hallegatte et al. “Investment Decision Making under Deep Uncertainty: Application to Climate Change,” Policy Research Working Paper WPS6193, World Bank, Sept. 2012 Lempert et al., “Ensuring Robust Flood Risk Management in Ho Chi Minh City,” Policy Research Working Paper WPS6465, World Bank, May 2013. 32

33. GFDRR’s Resilience to Climate Change Thematic Program Sofia Bettencourt, Lead Adaptation Specialist GFDRR

34. GFDRR’s Resilience to Climate Change Thematic Program Based on “Building Resilience” report Recognizes that: Most disasters are weather-related Adaptation and DRM use similar approaches Internationally, the two agendas are coming together under Loss and Damage mechanism

35. 35 The Resilience to Climate Change Thematic Program  A thematic funding window under the GFDRR Multi- donor Trust Fund  Supports same pillars and outputs, but  Activities selected through a climate eligibility filter GFDRR Resilience to Climate Change Thematic Program PILLAR 4 Financial Protection PILLAR 1 Risk Identification Help formulate enabling policies and investments integrating climate and disaster risk into development PILLAR 2 PILLAR 3 Risk Reduction PILLAR 5 Resilient Reconstruction Preparedness

36. 36 Climate Eligibility Criteria Activities: 1.Linked to resilience to weather-related disasters and climate change 2.Benefiting Low-Income or Low-to- Medium Income Countries at high risk (including global and regional initiatives) 3.With potential to leverage wider investment and/or policy reforms 4.Supporting one or more of GFDRR strategic pillars and clearly contributing to its core results

37. 37 Examples of Potential Activities 1.Climate and Disaster Risk Management in the Andes –risk assessment applied to “disaster proof” priority sectors 2.Africa Regional Hydro-Met – strengthened capacity of regional and national services to better reach out to users 3.Just-in-time Assistance - use of robust decision making (e.g. Kazakhstan); assistance to Togo on coastal erosion control 4.Priority analytical work – Global study on poverty, climate and disasters; Knowledge notes on critical topics (e.g. preventive resettlement);

38. 38 Examples of Potential Activities 1.Climate and Disaster Risk Management in the Andes –risk assessment applied to “climate proof” priority sectors

39. Climate Resilient Infrastructure

40. 40 Examples of Potential Activities 2.Africa Regional Hydro-Met – strengthened capacity of regional and national services

41. Hydromet – diagnosing weaknesses

42. 42 Examples of Potential Activities 3.Just-in-time Assistance - use of robust decision making (e.g. Kazakhstan); assistance to Togo on coastal erosion control

43. Managing Settlements at Risk LOME, TOGO West East Coastline in 1955 Coastline in 2009

44. Jan. 2011 Sept. 2013 Nov. 2007 Sept. 2008

45. 45 Examples of Potential Activities 4.Priority analytical work – Global study on poverty, climate and disasters; Knowledge notes on critical topics (e.g. preventive resettlement);

46. 46 Proposed Steering Mechanism An Advisory Group on Climate Resilience as part of the CG Activities presented as a sub-set of the GFDRR Work Plan

47. 47 For questions please contact Sofia Bettencourt, Lead Adaptation Specialist, GFDRR, Climate Change Group Vice Presidency sbettencourt@worldbank.org Robert Reid Disaster Risk Management and Adaptation Specialist GFDRR, Climate Change Group Vice Presidency rreid1@worldbank.org GFDRR’s Climate Resilience Team: