Transcript Robust Decision Making and Climate Resilience

The Global Facility for Disaster
Reduction and Recovery
Making Good Decisions Under Uncertainty
Marianne Fay, World Bank
Good Decision Making Is Challenged
By Uncertainty and Disagreement
Rapid
Changes
Uncertain
Future
Competing
Priorities
Shanghai 1990 vs. 2010
Climate Change
Conservation vs.
Development
2
The stakes are high
3
Agenda
• A real case
• Traditional “Predict-then-Act” Planning
• Decision-making under uncertainty
4
Ho Chi Minh City As Context For Our
Discussion
Over 15 years, HCMC has planned
multi-billion dollar flood investments
using best available projections
5
Conditions have diverged from projections and
the city is at significant risk
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Today, HCMC seeks an innovative, integrated flood
risk management strategy
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Risk = Hazard x Exposure x
Vulnerability
Hazard
Exposure
• Future rainfall
intensity
• Population in the
study area
• Height of the
Saigon River
• Urban form
Vulnerability
• Vulnerability of
population to
flood depth
Risk Model
Risk = Expected Number of People
Affected By Floods Each year
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Model Calculates Risk From Six Parameters
Of Hazard, Exposure, Vulnerability
Rainfall
Increase
Increase
River Height
Population
Urban Form
Poverty Rate
Vulnerability
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Each Parameter Could
Take A Range of Values
Rainfall
Increase
Increase
River Height
Population
Urban Form
Poverty Rate
Vulnerability
+0%
+ 35%
20 cm
100 cm
7.4 M
19.1 M
Growth in Outskirts
2.4 %
Not Vulnerable
Growth in Center
25 %
Very Vulnerable
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We Can Use This To Make
Projections
Rainfall
Increase
Increase
River Height
Population
Urban Form
10%
30%
+0%
30 cm
20 cm
Vulnerability
+ 35%
70 cm
100 cm
11 M
17 M
19.1 M
7.4 M
Growth in Center
Growth in Outskirts
5%
Poverty Rate
This projection
assumes little climate
change and relatively
small population
2.4 %
Not Vulnerable
18%
25 %
This projection assumes more climate
change. It also has a larger, poorer, but
Very Vulnerable
less vulnerable population
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Traditional Planning Asks
“What Will The Future Bring?”
Predict
Act
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Most likely projection
We could rely on the most likely
projection for decision making…
10%
30 cm
11 M
5%
Risk Model
Risk From
Policy In
Your Projection
Policy
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But there can be disagreement about what is
the most likely projection
The Meteo-France model, from IPCC
Climate models can disagree with each
other
The Meteo-France and the Australian model, from IPCC
… and we have a lot of models…
(Epistemic uncertainty)
… whose results depend on future climate
policies and socio-economic trends…
It can be difficult to make good projections – even
with good data
Gross national product (trillions of 1958 dollars)
• In the early 1970s
forecasters made
projections of U.S
energy use based on
a century of data
2.2
2.0
1.8
1.6
1.4
1.2
1.0
.8
.6
.4
.2
0
1975 Scenarios
1973
1890
1900
1973
1970
1910
0
20
Historical
trend
continued
40
1960
1950
1940
1920 1929
60
80
100
120
140
Energy use (1015 Btu per year)
160 180
Believing Forecasts of the Unpredictable
Can Contribute to Bad Decisions
Gross national product (trillions of 1958 dollars)
• In the early 1970s
forecasters made
projections of U.S
energy use based on
a century of data
…they were all wrong
2.2
2.0
1.8
1.6
1.4
1.2
1.0
.8
.6
.4
.2
0
1975 Scenarios
2000
2000Actual
Actual
1990
1990
1890
1900
1973
1970
1910
0
20
Historical
trend
continued
1980
1980
1977
1977
1973
40
1960
1950
1940
1920 1929
60
80
100
120
140
Energy use (1015 Btu per year)
160 180
Scenario Planning asks:
What Might The Future Bring?”
Imagine
Possibilities
Act
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10%
30%
30 cm
11 M
We may chose to use several
different scenarios for decision
making…
17 M
18%
25cm
Risk Model
Risk From
Policy In Your
Scenarios
Policy
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1000 Scenarios
Few Scenarios Leaves Many Possible
Futures Uncovered…Instead Use
1000 Scenarios For Decision Making…
Risk Model
Risk From
Policy In 1000
Scenarios
Policy
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Use many scenarios to identify robust
decisions
• Decisions that perform well in many scenarios
even if not optimal in any single one.
• Examples…
Get an education even if you
hope to be a basketball star
Plant drought-resistant cassava even if
water-sensitive maize fetches higher prices
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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
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Under What Conditions Does The Current
Infrastructure Keep Risk Low?
It protects our city if…
< 6% increase in rainfall intensity
< 45 cm increase in river height
< 5% poverty rate
6%
45cm
5%
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Is it reasonable to assume these conditions will
hold?
45cm rise
Infrastructure
6% increase
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Should We Rely On Current Infrastructure?
Probably Not.
NOAA SLR + SCFC Subsidence
75 cm
Even if stakeholders disagree about the
future, they can probably agree that
current infrastructure will not be
sufficient
45cm rise
Infrastructure
MONRE SLR
Estimate 30 cm
6% increase
IPCC SREX
Mid Value (20%)
IPCC SREX
High Value (35%)
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What options usefully complement
the current infrastructure? And
How?
2. Raise Homes
1. Rely on current
infrastructure
4. Manage
Groundwater
3. Relocate Areas
5. Capture
Rain Water
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How Will Adding These Options Improve Our
Strategy?
Relocate
(17%, 100cm)
Rainwater
(10%, 100cm)
NOAA SLR +
SCFC Subsidence (75 cm)
Ministry of Urban Planning
Groundwater
(7%, 55cm)
Infrastructure
Ministry of
Environment
Elevate
(23%, 55cm)
Adding these measures
improves robustness
This “map” helps stakeholders
have structured discussions
MONRE SLR
Estimate 30 cm
IPCC SREX
Mid Value (20%)
IPCC SREX
High Value (35%)
What Are Tradeoffs
Between Robustness And Cost?
Rainwater
(10%, 100cm)
Relocate
(17%, 100cm)
Stakeholders debate about how
much robustness they can afford
(which is much more useful than
debating what the future will be)
NOAA SLR +
SCFC Subsidence (75 cm)
Groundwater
(7%, 55cm)
Elevate
(23%, 55cm)
Infrastructure
MONRE SLR
Estimate 30 cm
High Cost
IPCC SREX
Mid Value (20%)
IPCC SREX
High Value (35%)
Low Cost
Robust Decision Making Asks “What Are The Limitations of Our
Plan and How Can We Improve It?
Predict-then-act
Robust Decision Making
What will the future be?
Under what future
conditions does our
plan perform poorly?
What is the best near-term
decision given our
prediction?
How sensitive is our
decision to our prediction?
Can we modify the plan
to make it more robust?
What are the tradeoffs
between robustness,
costs, feasibility, etc.?
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.
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GFDRR’s Resilience to Climate
Change Thematic Program
Sofia Bettencourt, Lead Adaptation Specialist
GFDRR
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
The Resilience to Climate Change
Thematic Program
GFDRR Resilience to Climate Change Thematic Program
Help formulate enabling policies and investments integrating climate and disaster risk into development
PILLAR 1
PILLAR 2
PILLAR 3
Risk Identification
Risk Reduction
Preparedness
PILLAR 4
Financial
Protection
PILLAR 5
Resilient
Reconstruction
 A thematic funding window under the GFDRR Multidonor Trust Fund
 Supports same pillars and outputs, but
 Activities selected through a climate eligibility filter
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Climate Eligibility Criteria
Activities:
1. Linked to resilience to weather-related
disasters and climate change
2. Benefiting Low-Income or Low-toMedium 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
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Examples of Potential Activities
145° E
150° E
155° E
0
400
5° S
5° S
Kilometers
Lae
10° S
Port
Moresby
10° S
1. Climate and Disaster Risk Management
in the Andes –risk assessment applied to
“climate proof” priority sectors
100 200
Papua New Guinea
AAL / Asset Value
0% - 0.05%
0.15% - 0.2%
0.4% - 0.5%
0.05% - 0.1%
0.2% - 0.3%
0.5% - 1.15%
0.1% - 0.15%
0.3% - 0.4%
145° E
150° E
155° E
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Climate Resilient Infrastructure
Examples of Potential Activities
145° E
150° E
155° E
0
100 200
400
5° S
5° S
Kilometers
Lae
10° S
Port
Moresby
10° S
2. Africa Regional Hydro-Met –
strengthened capacity of regional and
national services
Papua New Guinea
AAL / Asset Value
0% - 0.05%
0.15% - 0.2%
0.4% - 0.5%
0.05% - 0.1%
0.2% - 0.3%
0.5% - 1.15%
0.1% - 0.15%
0.3% - 0.4%
145° E
150° E
155° E
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Hydromet – diagnosing weaknesses
Examples of Potential Activities
145° E
150° E
155° E
0
100 200
400
5° S
5° S
Kilometers
Lae
10° S
10° S
Port
Moresby
Papua New Guinea
AAL / Asset Value
0% - 0.05%
0.15% - 0.2%
0.4% - 0.5%
0.05% - 0.1%
0.2% - 0.3%
0.5% - 1.15%
0.1% - 0.15%
0.3% - 0.4%
145° E
150° E
155° E
3. Just-in-time Assistance - use of robust
decision making (e.g. Kazakhstan);
assistance to Togo on coastal erosion
control
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Managing Settlements at Risk
East
Coastline in 1955
LOME, TOGO
Coastline in 2009
West
Jan. 2011
Nov. 2007
Sept. 2013
Sept. 2008
Examples of Potential Activities
145° E
150° E
155° E
0
100 200
400
5° S
5° S
Kilometers
Lae
10° S
10° S
Port
Moresby
Papua New Guinea
AAL / Asset Value
0% - 0.05%
0.15% - 0.2%
0.4% - 0.5%
0.05% - 0.1%
0.2% - 0.3%
0.5% - 1.15%
0.1% - 0.15%
0.3% - 0.4%
145° E
150° E
155° E
4. Priority analytical work – Global study
on poverty, climate and disasters;
Knowledge notes on critical topics
(e.g. preventive resettlement);
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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
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For questions please
contact
GFDRR’s Climate Resilience Team:
Sofia Bettencourt,
Lead Adaptation Specialist,
GFDRR, Climate Change Group
Vice Presidency
[email protected]
Robert Reid
Disaster Risk Management and
Adaptation Specialist
GFDRR, Climate Change Group
Vice Presidency
[email protected]
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