Shaping climate-resilient development - a framework for decision making

Shaping Climate-resilient
Development – A Framework
for Decision-making
Dr. David N. Bresch, Head Sustainability, Swiss Re
on behalf of the Economics of Climate Adaptation Working Group

Climate-compatible development
requires both mitigation and adaptation
Development
Achieving the Millennium
Development Goals

Low-carbon Climate-
development resilient
development

Climate-
compatible
development

Mitigation Adaptation
Climate-
proofed
abatement

Please find the full study at www.swissre.com/climatechange

2

Economics of climate adaptation (ECA)
study group

Partner consortium:


3

Climate-resilient development needs to
address total climate risk
Objectives:
 Provide decision makers with the facts and methods necessary to design and execute
a climate adaptation strategy
 Supply insurers, financial institutions, and potential funders with the information
required to unlock and deepen global risk transfer markets
Key features:
 Developed a methodology to quantify local total climate risks, meaning it looked at the
combination of
 today’s climate risk,
 the economic development paths that might put greater population and value at risk
 the additional risks presented by climate change.
Swiss Re’s role:
 Lead contributor to the research. Swiss Re defined the assessment and risk modelling
approach and provided overall risk assessment knowledge

4

Today's
How we address total climate risk focus

 Measure success
and adjust  Identify areas
strategies as What are the Where most at risk given
scenarios change outcomes and and what relevant hazards,
lessons? is the population, and
threat? economic value

TCR  Develop
 Implement a How do manage- frequency and
severity scenarios
holistic climate we
execute?
ment for most relevant
risk strategy that What is hazard(s)
overcomes at stake?
barriers, and  Quantify value at
launch initiatives risk
How could  Determine vulner-
we respond? ability to the
hazard
 Price tag on total
 Select time frame for measure climate risk
analysis
 Identify potential adaptation
measures
 Determine feasibility
 Determine societal costs and benefits www.swissre.com/climatechange

5

We simulate natural catastrophes in
great detail

6

We simulate hundred thousands of
possible events

7

The probabilistic set

historic
~100 years

probabilistic
~10‘000 years

8

The working group studied eight
regions with diverse climate hazards

U.K. / Hull
China
Mali North, Northeast

Florida India
Maharashtra

Tanzania
Samoa
Samoa

Guyana


9

TEST CASE ON
MAHARASHTRA, INDIA – FOCUS ON
DROUGHT RISK TO AGRICULTURE
10

1. Where and what is the threat?
Focus on drought due to its large
impact on agriculture and human livelihood
India, Maharashtra case study


11 Source: ECA group

2. What is at stake?
Three scenarios for climate change
to capture uncertainty
• Predicting local climate is inexact 2030 scenarios Description
given limited data. Therefore, 1 Today’s • Historic rainfall and
3 scenarios were developed for climate drought data used to
rainfall change in the 2030 estimate rainfall frequency
timeframe
– Based on temp and precipitation
predictions from 22 global 2 “Moderate” • Average change based on
climate models change the mean rainfall
– Distribution in rainfall varied predicted from 22 GCMs1
from 92-102% of today’s value

• While some regional climate models 3 “High” • Extreme change based on
change average of 90th percentile
exist assessing at a higher
values for predicted rainfall
resolution and smaller grid area
from 22 GCMs
than GCMs, the science behind
these models is still developing
GCM results consistent with output from
• Climate scenarios were later used regional models (A2 and B2) for Maharashtra
to develop 3 hazard scenarios

1 22 GCMs for Maharashtra, run with the A1B scenario
SOURCE: Results for GCMs from Prof. Reto Knutti, ETH Zurich; RCM results for A2 and B2 from Prof. Krishna Kumar,
Indian Institute of Tropical Meteorology
www.swissre.com/climatechange


The economic value at risk – driven by
economic growth and climate change
Expected loss from exposure to climate
High climate change scenario, 2008 USD millions
570 • Expected loss is
driven by current risk,
23% of 2030 total
agricultural growth,
expected loss
200 and climate change
• Agriculture income
growth would contribute
132 to an additional 23%
238 of 2030 upper bound
35% of 2030
loss
total
expected • Climate change
loss (occurring in
combination with
income growth) will
2008, Incremental Incremental 2030, total account for 35% of
Today’s increase from increase from expected 2030 upper
expected economic climate loss bound loss
loss growth; no change
climate change


3. How could we respond?
Managing total climate risk requires
a cost-effective adaptation portfolio
Portfolio of
responses

Hazards Infrastructure
and asset- based
responses

Technological
and procedural
Total optimization
responses
Climate
Risk Systemic and
Vulnerability behavioral
responses

Risk transfer and
Value contingent
financing



Measures are analyzed in respect of
costs and benefits (averted loss) in great detail
Measure* Cost (mn $) Benefit (mn $) Cost/Benefit ($/$) Loss averted (mn $)
1 Drainage systems (rf) -80 74 -2.13 3
2 Soil techniques -197 1,109 -0.18 21
3 Drainage systems (ir) -74 447 -0.16 16
4 Irrigation controls 14 1,438 0.01 59
5 Drip irrigation 139 7,978 0.02 547
6 Crop engineering (ir) 81 1,155 0.07 64
7 Sprinkler irrigation 285 3,280 0.12 225
8 Integrated Pest Mgmt. (ir) 49 551 0.09 36
9 IPM (ir) 146 1,374 0.11 91
10 Watershed +rwh 534 4,545 0.12 312
A Last mile irrigation 1,553 5,467 0.28 227
B Rehab. of irrigation systems 966 2,733 0.35 113
C Ground water pumping 1,837 2,733 0.67 113
11 Crop engineering (rf) 271 1,384 0.73 35
D Planned irrigation projects 8,987 12,027 0.75 499
E Canal lining 16 20 0.81 1
12 Insurance 1,035 1,035 1.00 1,036
Relief and rehabilitation NA NA NA 556

Totals 2,200 24,370 NA 3,000
• Only 80% of the expected loss can be mitigated by 12 measures. The remaining 20% is
“residual” loss, which will require additional penetration of insurance, or relief and rehabilitation to
address
*All figures are in terms of PV values, in current prices, up to 2030


Adaptation measures were prioritized
according to their costs and benefits


16

In addition to agricultural ‘best practice’,
index-based micro insurance is a powerful tool


1 Estimated present value out to 2030 at 2009 dollars

Micro insurance ( a form of risk transfer)
reduces the volatility



TEST CASE ON
SAMOA – FOCUS ON RISKS
CAUSED BY SEA LEVEL RISE
19

Huge economic value is already at risk
from the climate –risks will rise as the
climate changes and economies grow
Expected loss from exposure to climate
SAMOA EXAMPLE
High climate change scenario, USD millions
77
Potential
impact from
economic 26
growth
x 3.1
Potential
26 impact
from
25 change in
climate

2008, Economi Climate 2030, total
today’s c growth change expected
expected loss
loss Incremental
increase www.swissre.com/climatechange

20

How could we respond?

Approx. 60% of expected loss can be
avoided cost effectively

60%


21

Risk transfer is an efficient way of
providing coverage for high-severity /
low-frequency events
SAMOA EXAMPLE

Expected loss for 250-year event
Percent of GDP

34
Loss covered
5 Percent of residual Annual cost
risk to be covered USD millions
18
Further risk
11 mitigation 49% 23
measures

Total Maximum Loss Residual Risk transfer 100% 7
expect bearable averted risk
-ed loss by cost to be
loss efficient covered
measures


22

Annualized losses of 1-12% of GDP
today are likely to rise up to
19% of GDP by 2030


23

Between 40 and 68 percent of the
expected economic loss in the regions
studied can be averted cost-effectively

Introduction
of cash crops


david_bresch@swissre.com on behalf of the Economics of Climate Adaptation Working Group 24

Shaping climate-resilient development - a framework for decision making

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