78
Intro to Socio-Economic Benefits of Climate Information Services
Intro to Socio-Economic Benefitsof
Climate Information Services
Format of Presentation
CLIMATE INFORMATION SERVICES
• Global economic cost of natural disaters
• Hydromet Hazards
• Forecast Verification
• CIS
CIS AND GLOBAL ECONOMIC COST OF DISASTERS
The reported global cost of natural disasters has risen significantly, with a 15-fold increase between the 1950s and 1990s. During the 1990s, major naturalcatastrophesare reported to have resulted in economic losses averaging an estimated US$66bn per annum (in 2002 prices). Record losses of some US$178bn were recorded in 1995, the year of the Kobe earthquake – equivalent to 0.7 per cent of global GDP (Munich Re, 2002).
It is also estimated that in developing nations losses are typically 10-14 % of GDP, Abramovitz, (2001),.
Global Distribution of Disasters Caused by Natural Hazards and their Impacts in Africa(1980-2007)
Number of disaster events - 1980-2007 (RA I)
Earthquake
3%
Epidemic
37%
Extreme Temperature
1%
Volcano
1%
Slides
1%
Insect Infestation
4%
Wild Fires
1%
Flood
32%
Drought
11%
Wind Storm
9%
Casualties - 1980-2007 (RA I)
Earthquake
1%
Epidemic
18%
Flood
2%
Drought
79%
Economic losses - 1980-2007 (RA I)
Drought
19.6%
Flood
18.5%
Wave-Surge
0.9%
Earthquake
48.9%
Wind Storm
11.8%
97% of events
99% of casualties
61% of economic losses
are related to hydro-
meteorological hazards and
conditions.
HYDROMET HAZARDS
Hydrometeorological hazards, typically droughts/floods when they intersect with vulnerability/exposure of communities wreak havoc on socio-economic development. Droughts of early 1990’s and recently 2015/16 over Southern Africa led to disruption in hydropower generation, massive food and non-food importation into the region at enormous costs. GDP were reversed due to economic damages.The visit of tropical cyclone Eline to Southern Africa in 2000 resulted in loss of lives, damaged to infrastructure such as roads and bridges, some of which are still in disrepair nearly two decades later. • Elsewhere in Africa, the stories are similar, the droughts that visited the
parts of the Greater Horn of Africa in the mid-1980’s and again in 2011 having led to losses of life. Lives are lost,
Some of which could have been avoided if early warning had been accompanied by early action.
Drought
Flooding
Wild fire
Thunderstorm
Typical devastating impacts of extreme climate variations in Africa
Disasters ranked according to (a) deaths and (b) economic losses (1970-2012).
(a) Disaster Type Year Country Number of Deaths
1 Drought 1983 Ethiopia 300000
2 Drought 1984 Sudan 150000
3 Drought 1975 Ethiopia 100000
4 Drought 1983 Mozambique 100000
5 Drought 1975 Somalia 19000
6 Flood 1997 Somalia 2311
7 Flood 2001 Algeria 921
8 Flood 2000 Mozambique 800
9 Flood 1995 Morocco 730
10 Flood 1994 Egypt 600
(b) Disaster Type Year Country Economic loss in USD
Billions
1 Drought 1991 South Africa 1.69
2 Flood 1987 South Africa 1.55
3 Flood 2010 Madeira 1.42
4 Storm (Emille) 1977 Madagascar 1.33
5 Drought 2000 Morocco 1.20
6 Drought 1977 Senegal 1.14
7 Storm (Gervaise) 1975 Mauritius 0.85
8 Flood 2011 Algeria 0.79
9 Storm 1990 South Africa 0.69
10 Storm (Benedicte) 1981 Madagascar 0.63
Source-wmo 2014
HYDROMET BENEFITS
Climate system can bring favourable conditions to communities, well distributed seasonal rains both temporally and spatially.
• This can lead to good agricultural production;
• Boosting the GDPs of the region, through availing agricultural commodities needed by locally industry for finished goods, or for international trade.
• Such would encourage other sectors of the economy to perform better.
However, it is not often that such favourable climate conditions are readily taken advantage of by communities.
This is in part due to inadequate investments in the NMHSs in order to:
• generate and disseminate CIS of highest quality;
• enable appropriate action to be taken by communities: appropriate seed varieties for maximum productivity, well-planned hydropower generation.
What needs to happen
• The negative impacts of hydrometeorological hazards on agriculture and food security, water resources oftentimes lead to disasters. Over 90% of natural disasters in Africa are a consecutive consequence of these hazards.
• Climate information Service (CIS) is an important component of the evidence base required to guide decisions regarding appropriate levels of investment to minimize negative potential impacts on the economy, ensuring uninterrupted delivery of critical services and infrastructure.
• Investing in the development of early warning systems (CIS) and contingency planning, impacted sectors (such as agriculture) is necessary to help protect socio-economic welfare.
CIC
Contributes to mitigation of adverse impacts of extreme climate variations on socioeconomic
development.
• This is achieved through the monitoring of near real-time climatic trends and generating medium-range (10-14 days) and long-range climate outlook products on monthly and seasonal (3-6 months) timescales.
• These products are disseminated in timely manner to the communities of the sub-region principally through the NMHSs, regional organizations, and also directly through email services to various users who include media agencies.
5 Weather • Climate • Water
Seamless hydrometeorological andclimate services
Evaluation and verification of the forecasts
12
• Many societal and economic systems are vulnerable to the impacts of climate variability and change.
• Decision-makers require high-quality, reliable, timely information on current, predicted and projected conditions for safety and security, and for adaptation strategies and measures.
• The requires that we evaluate and verify the forecast to assess their applicability.
13
Results
Forecast verification results help answer users’ questions about quality, not as a set of academic statistics.
14
0
10
20
30
40
50
60
70
80
90
2000 2002 2004 2005 2006 2007 2008 2011 2012 2013 2014
Ax
is T
itle
Axis Title
Trend of Hit Rate vs FAR
HIT RATE OND
FAR OND
Linear (HIT RATE OND)
Linear (FAR OND)
FAR TREND
HIT TREND
15
0
20
40
60
80
100
120
2000 2002 2004 2005 2006 2007 2008 2011 2012 2013 2014
Ax
is T
itle
Axis Title
Trend of Hit Rate vs False Alarm
Hit Rate JFM
FAR-JFM
Linear (Hit Rate JFM)
Linear (FAR-JFM)
SARCOF seasonal forecasts have on average period where study focuses (2001 – 2012); (2001-2012), and beyond
• A positive trend of 13% of HR has been observed (62 to 75%) on OND period and 20% on JFM season (68-88%);
• A reduction of FAR of 10% has been noticed (35 –25%) on OND period and 15% on JFM period (33-18%);
• Certain areas appear to perform better than others, potentially due to erratic tropical cyclone activity
Emerging Opportunities for National Meteorological and Hydrological Services ….
• Traditionally, disaster risk management has been focused on post disaster response in most countries!
• New paradigm in disaster risk management -Investments in preparedness and prevention through risk assessment, risk reduction and risk transfer ….– Adoption of Hyogo Framework for Action in 2005-
2015 by 168 countries (Kobe, Japan)
Implementation of the new paradigm in DRM would require meteorological, hydrological and climate
information and services!
Assessing the Socio-Economic Benefits (SEB) of
Climate Information Services (CIS)
March 2018
KnowlEdge Srl
Georg Pallaske
Project Manager, KnowlEdge SrlPh.D. candidate University of Bergen
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010Time
GDP growth rate
History Present Future
Rationale for SEB Analysis
Business as Usual
Policy Interventions
Business as Usual
Rationale for SEB Analysis
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010Time
GDP growth rate
History Present Future
Socio-Economic Benefits
The Socio-Economic Benefits of Climate Information Systems are many and varied.
• Some are direct (e.g. weather information, rainy days), some indirect (e.g. higher yield) some are induced (e.g. higher tax revenues).
• Some affect households (e.g. avoided damage to private property), others impact on businesses (e.g. avoided supply chain disruption) and the government (e.g. reduced infrastructure expenditure).
Socio-Economic Benefits (2)
The Socio-Economic Benefits of Climate Information Systems are many and varied.
• Some are expressed in economic terms, some others have social or environmental dimensions.
• Some appear immediately and on a continuousbasis, while some others will emerge over time (e.g. through improved systemic resilience).
Socio-Economic Benefits (3)
• The challenge is to estimate required investments, resulting avoided costs as well as added benefits.
• An opportunity would be missed if decisions only aim at mitigating costs and passively adapt to climate change.
– If a more active approach is taken, new opportunities may emerge, and avoided costs could be reinvested in more resilient economic activities.
Assessment of SEBs from CIS
System models and their use in decision making
Implementation
Science & analytics
Policy level Systems Model
Environ-mental models
Detailed place-based
model
Detailed spatially
explicit model
Human health models
Detailed spatially
explicit models
Economic models
Detailed
models
There is no single model that can address all the needs of decision makers and stakeholders at multiple scales
Theoretical framework of the models
• Combination of methods (e.g. optimization, econometrics and simulation).
• Unifying framework: System Dynamics
• Stakeholder engagement approach: Systems Thinking (with causal loop diagrams)
• Mathematical foundation: non-compensatory aggregation of indicators, differential equations
• Underlying drivers of change: stocks and flows, capturing feedback loops, delays and nonlinearity
Systems Thinking and System Dynamics
• Systems thinking attempts to understand a whole system rather than its parts, utilized to identify the most effective leverage points to stimulate change within the system
• Created by Jay Forrester in the late 1950s at the MIT, methodological foundation of “The Limits to Growth”, System Dynamics is an integrated and quantitative (modeling) approach utilized to understand situations for (complex) real world issues to guide decision making over time for achieving sustainable long term solutions (SD class, SPL – 2012).
youngfish
mature fish+
births
+
deaths+
catch
-
perceivedmature fish
+
+
carrying
capacity
+-
mature fishdensity
+
-
+
desired mature fish
-
System Dynamics allows…
• Understanding how structure leads to behavior (through causal relations, stocks and flows)
• Simulation across time scales (with semi-continuous runs, using differential equations)
• Disaggregated spatial assessments (with the possibility to use subscripts and use GIS as input)
• Modeling across disciplines (integrating optimization and econometrics in a single model framework)
Added value compared to other tools?
• High degree of customization.
• Broad stakeholder participation in the development of the tool, with emphasis not only on indicators but on causal relations also (with connections within and across sectors, for social, economic and environmental indicators).
• Integrated and dynamic modelling framework (starting simulations in the past to improve validation), targeting green growth policy formulation and assessment.
• Transparency of the approach (both for indicators and model) and accessibility.
• Represent the feedback structure of systems!
• Capture: • The hypotheses about the causes of dynamics• Mental models of individuals or teams• The important feedbacks driving the system
• Critical aspects:• Think in terms of cause-and-effect relationships• Focus on the feedback linkages among components of a system• Determine the appropriate boundaries for defining what is to be
included in the CLD
Causal Loop Diagrams (CLD)
• Reinforcing loops tend to increase and amplify everything happening in the system (i.e. action - reaction).
Example:Fold a paper (0,1 mm) 42 times:• What would be the final thickness of such paper?• The result is a thickness larger than the distance
between the Earth and the moon = 0,1*2^42 (43,980,465,111 cm = 439,804 Km)
Reinforcing Loops (1/2)
Population
800
600
400
200
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Population : Population1
populationbirths+
+R
R Self reinforcing
Reinforcing Loops (2/2)
• Negative loops are counteractive and oppose change.
• Balancing loops represent a self limiting process, which aims at finding balance and equilibrium.
Balancing Loops (1/2)
Population
100
75
50
25
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Population : Population
populationdeaths-
+B
B Self balancing
Balancing Loops (2/2)
Population
1,500
1,125
750
375
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Population : Population
-
+Bbirths
+
+R population deaths
Combining feedback loops
Population
2,000
1,500
1,000
500
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Population : Population
Population
1,500
1,125
750
375
0
0 10 20 30 40 50 60 70 80 90 100
Time (Month)
Population : Population
births population deaths
foodavailability
carrying
capacity
+
+ +
-
-
-
+
R B
B
Feedback Loops and Delays
Patterns of behavior created by feedback loops
Housing prices?
Population? Shellfish beds?
Employment creation? Congestion?
Potential Modes of Behaviour
Land-use, Water and Economies Dependent on infrastructure
Land-use, Water and Economies Dependent on infrastructure
green gdp
gdp
natural capital additions
+
consumption
demand of naturalresources
natural capital
+
+
natural capital
growth
+natural capital
extraction
natural capital
depletion
natural capitalreductions
+
+
+
- +
ecosystem
services
productivity(tfp)
+
++
physical capital+investment depreciation
+
+
+
ecologicalscarcity
-
-
human capitalemployed
populationjob creation
+
retirement
publicexpenditure
health
education
human capitalgrowth
training+
+
+
+
+
<human capital
growth>
+
privateprofits
+
+
+
wages
++
+
+
R
R
R
R
R
B
B
green gdp
gdp
natural capital additions
+
consumption
demand of naturalresources
natural capital
+
+
natural capitalgrowth
+natural capitalextraction
natural capitaldepletion
natural capitalreductions
+
+
+
- +
ecosystemservices
productivity(tfp)
+
++
physical capital+investment depreciation
+
+
+ecologicalscarcity
-
-
human capital employedpopulation
job creation
+
retirement
publicexpenditure
health
education
human capitalgrowth
training
+
+
+
+
+
<human capitalgrowth>
+
privateprofits
+
+
+
wages
++
+
+
R
R
R
R
R
B
B
gdp of the poor
+
<gdp>+
Systems analysis: value addition?
green gdp
gdp
natural capital additions
+
consumption
demand of naturalresources
natural capital
+
+
natural capitalgrowth
+natural capitalextraction
natural capitaldepletion
natural capitalreductions
+
+
+
- +
ecosystemservices
productivity(tfp)
+
++
physical capital+investment depreciation
+
+
+ecologicalscarcity
-
-
human capital employedpopulation
job creation
+
retirement
publicexpenditure
health
education
human capitalgrowth
training
+
+
+
+
+
<human capitalgrowth>
+
privateprofits
+
+
+
wages
++
+
+
R
R
R
R
R
B
B
gdp of the poor
+
<gdp>+
Systems analysis: value addition?
green gdp
gdp
natural capital additions
+
consumption
demand of naturalresources
natural capital
+
+
natural capitalgrowth
+natural capitalextraction
natural capitaldepletion
natural capitalreductions
+
+
+
- +
ecosystemservices
productivity(tfp)
+
++
physical capital+investment depreciation
+
+
+ecologicalscarcity
-
-
human capital employedpopulation
job creation
+
retirement
publicexpenditure
health
education
human capitalgrowth
training
+
+
+
+
+
<human capitalgrowth>
+
privateprofits
+
+
+
wages
++
+
+
R
R
R
R
R
B
B
gdp of the poor
+
<gdp>+
Systems analysis: value addition?
green gdp
gdp
natural capital additions
+
consumption
demand of naturalresources
natural capital
+
+
natural capitalgrowth
+natural capitalextraction
natural capitaldepletion
natural capitalreductions
+
+
+
- +
ecosystemservices
productivity(tfp)
+
++
physical capital+investment depreciation
+
+
+ecologicalscarcity
-
-
human capital employedpopulation
job creation
+
retirement
publicexpenditure
health
education
human capitalgrowth
training
+
+
+
+
+
<human capitalgrowth>
+
privateprofits
+
+
+
wages
++
+
+
R
R
R
R
R
B
B
gdp of the poor
+
<gdp>+
Systems analysis: climate impacts?
Climate
Climate
Climate
Climate
Climate impacts
Infrastructure ImpactsRoad networks
Electricity supply
Variability
Calibration of precipitation• Precipitation
The annual rainfall is distributed over the year to capture seasonalpatterns and their cascading effects.
seasonal precipitation
300
225
150
75
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
seasonal precipitation : Base2050 BAU 1980 sens
seasonal precipitation
400
300
200
100
0
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Mm
/(Y
ear*
Ha)
seasonal precipitation : Base2050 BAU 1980 sens
Climate variability and trends
Baseline simulation with constantseasonal precipitation and withoutvariation in precipitation.
Weather scenario assuming a decreasing trend in annualprecipitation and an increasingvariability in precipitation.
Selected Variables
400
350
300
250
200
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Mm
/Yea
r
Baseline Precipitation : Base2050 BAU month
precipitation : Base2050 BAU month
Selected Variables
400
350
300
250
200
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Mm
/Year
Baseline Precipitation : Base2050 Weather month
precipitation : Base2050 Weather month
Variability in precipitation to captureuncertainty
Base2050 BAU 1980 sens year
Sheet1
50.0% 75.0% 95.0% 100.0%
water resources internally produced
400 B
300 B
200 B
100 B
01980 1998 2015 2033 2050
Time (Year)
Small variabilities in seasonal precipitationcan, over the total area, cause large variations in the total amount of waterresources produced internally (total precipitation less evapotranspiration.
Base2050 BAU 1980 sens month
50.0% 75.0% 95.0% 100.0%
seasonal precipitation
400
300
200
100
01980 1998 2015 2033 2050
Time (Year)Base2050 BAU 1980 sens month
50.0% 75.0% 95.0% 100.0%
seasonal precipitation
400
300
200
100
02030 2030 2031 2032 2033
Time (Year)
Accounting for seasonal water needs
Selected Variables
300
225
150
75
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
annual crop water demand per hectare of agriculture land : Base2050 BAU 1980 sens month
seasonal precipitation : Base2050 BAU 1980 sens month
annual crop water demand per hectare of agriculture land
200
150
100
50
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
annual crop water demand per hectare of agriculture land : Base2050 BAU 1980 sens month
seasonal precipitation
300
225
150
75
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
seasonal precipitation : Base2050 BAU 1980 sens month
Crop water requirements arecompared to seasonal precipitation on a monthly base to derive the netirrigation requirements per hectare
Net irrigationrequirement
Seasonal shift
Selected Variables
400
300
200
100
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
annual crop water demand per hectare of agriculture land : Base2050 Season shift
seasonal precipitation : Base2050 Season shift
Selected Variables
300
225
150
75
0
1980 1980.20 1980.40 1980.60 1980.80 1981
Time (Year)
Mm
/(Y
ear*
Ha)
annual crop water demand per hectare of agriculture land : Base2050 BAU month
seasonal precipitation : Base2050 BAU month
The formulation of the model allows forcapturing a seasonal shift in precipitation.
In this example, the rainy season is shifted by2 months, from the start of the season.
A gradual shift in seasonal precipitation canbe included to see the impacts on theperformance of the system over time.
CLD Agriculture
population
agricutlture land
per capita
desired
agriculture land
+
+
gap in
agriculture land
+
agriculture
land
-
CLD Agriculture
population
agricutlture land
per capita
desired
agriculture land
+
+
gap in
agriculture land
land conversion for
agriculture
+
+
agriculture
land
+-
forest / fallow land
-
B
CLD Agriculture
population
agricutlture land
per capita
desired
agriculture land
+
+
gap in
agriculture land
land conversion for
agriculture
+
+
agriculture
land
+-
productive
agriculture land
+
forest / fallow land
-
yield per
hectare
+
B
agricuture
production+
CLD Agriculture
population
agricutlture land
per capita
desired
agriculture land
+
+
gap in
agriculture land
land conversion for
agriculture
+
+
agriculture
land
+-
productive
agriculture land
+
loss of agriculture land
due to flood / droughts
forest / fallow land
-
-YIELD PER
HECTARE
+
B
agricuture
production+
CLD Agriculture
population
agricutlture land
per capita
desired
agriculture land
+
+
gap in
agriculture land
land conversion for
agriculture
+
+
agriculture
land
+-
productive
agriculture land
+
loss of agriculture land
due to flood / droughts
forest / fallow land
-
-
impacts of floods / droughts
on agriculture productivity
-
yield per
hectare
+
B
-
agricuture
production+
First order impacts - AgricultureTotal Agriculture Land
200,000
175,000
150,000
125,000
100,000
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Ha
Total Agriculture Land : Base2050 Weather year
Total Agriculture Land : Base2050 BAU year
production yield agriculture land
21
20.5
20
19.5
19
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
To
n/(
Yea
r*H
a)
production yield agriculture land : Base2050 weather year
production yield agriculture land : Base2050 BAU year
total agriculture production rate
3 M
2.75 M
2.5 M
2.25 M
2 M
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
To
n/Y
ear
total agriculture production rate : Base2050 Weather year sens
total agriculture production rate : Base2050 BAU year
CLD Infrastructure
capital
total
production
total kilometer of
roads
total factor
productivity
+
+
+
CLD Infrastructure
investment
gross capital
formation
capital
total
production
TOTALKILOMETER OF
ROADS
total factor
productivity
+
+
+
+
+
+
R
CLD Infrastructure
investment
gross capital
formation
capital
total
production
total kilometer of
roads
road
construction
budget for roads
constructioncost per km of
road
desired kilometer
of roads
-
+
+
total factor
productivity
+
++
+
+
+
+
+
-
R
R
B
CLD Infrastructure
investment
gross capital
formation
capital depreciation
due to floods
capital
total
production
total kilometer of
roads
depreciation of roads due
to floods / droughts
road
construction
budget for roads
constructioncost per km of
road
desired kilometer
of roads
-
+
+
-
total factor
productivity
+
++
+
+
+
+ -
+
-
R
R
B
loss of roads due to floods
40
30
20
10
0
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Km
/Yea
r
loss of roads due to floods : WISER SEB CIS 23 Jan - CIS investment
loss of roads due to floods : WISER SEB CIS 23 Jan - BAU
First order impacts - Infrastructure
Functioning Roads
3000
2500
2000
1500
1000
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Km
Functioning Roads : Base2050 Weather year sens
Functioning Roads : Base2050 BAU year
The decreasing trend in precipitationleads to a reduced number of floods, andconsequently a reduced loss of roads andcapital.
Could reduced precipitation and highervariability lead to more volatile eventswhich cause more severe damage?
Capital
2 T
1.5 T
1 T
500 B
0
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Usd
Capital : WISER SEB CIS 23 Jan - CIS investment
Capital : WISER SEB CIS 23 Jan - BAU
CLD Macroeconomy
investment
gross capital
formation
capital depreciation
due to floods
capital
total
production
total kilometer of
roads
depreciation of roads due
to floods / droughts
road
construction
+
-
total factor
productivity
+
+
+
+
+ -
+
R
CLD Macroeconomy
investment
gross capital
formation
capital depreciation
due to floods
capital
total
production
total kilometer of
roads
depreciation of roads due
to floods / droughts
road
construction
+
-
total factor
productivity
+
+
+
+
+ -
+
R
literacy rate
energy
pricesaccess to health
care
++ +
CLD Macroeconomy
investment
gross capital
formation
capital depreciation
due to floods
capital
total
production
total kilometer of
roads
depreciation of roads due
to floods / droughts
road
construction
+
-
total factor
productivity
+
+
+
+
+ -
+
R
literacy rate
energy
prices
required health care
expenditure
total
population
+budget for
health care
access to health
care
+-+
+ +
R
CLD Macroeconomy
investment
gross capital
formation
capital depreciation
due to floods
capital
total
production
total kilometer of
roads
depreciation of roads due
to floods / droughts
road
construction
+
-
total factor
productivity
+
+
+
+
+ -
+
R
literacy rate
energy
prices
share of population affected
by adverse weather
required health care
expenditure
total
population
affected
population
+ +
++budget for
health care
access to health
care
+-+
+ +
R
Second order impacts - GDPBase2050 BAU 1980 sens year
Sheet1
50.0% 75.0% 95.0% 100.0%
real gdp
400 B
300 B
200 B
100 B
01980 1998 2015 2033 2050
Time (Year)
Base2050 BAU 1980 sens year
Sheet1
50.0% 75.0% 95.0% 100.0%
per capita implemented health expenditure
8000
6000
4000
2000
01980 1998 2015 2033 2050
Time (Year)
Base2050 BAU 1980 sens year
Sheet1
50.0% 75.0% 95.0% 100.0%
additional cost for reestablishing the road network
4 B
3 B
2 B
1 B
01980 1998 2015 2033 2050
Time (Year)
GDP represented as labor, capital andproductivity. Through the varyingperformance through all sectors, theconfidence intervals for GDP increase overtime.
In addition, the costs for maintaining theroad network and additional health carecosts are added to governmentexpenditures, and therewith decrease GDP even further.
Monthly VS Annual time stepBase2050 BAU 1980 sens year
50.0% 75.0% 95.0% 100.0%
total water demand
200 B
150 B
100 B
50 B
01980 1998 2015 2033 2050
Time (Year) Base2050 BAU 1980 sens month
50.0% 75.0% 95.0% 100.0%
total water demand
300 B
225 B
150 B
75 B
01980 1998 2015 2033 2050
Time (Year)
Due to the uncertainty about the amountof agriculture land and population, therange of total demand for water increasesin the long run, BUT …
… using seasonal data allows for a moredetailled planning of water demand, andhas the potential to provide informationabout possible water scarcity during thedry season. Therefore it is possible toanticipate eventual shortages.
SEB data analysis (1)
• The magnitude of adverse weather was estimated based on – Dataset with documented damages across 8 African
countries providing information on e.g.• Affected population• Affected agriculture land• Loss of livestock
– The respective stock value of the respective countries and years• Total population• Total agriculture land• Total livestock
SEB data analysis (2)
• The adverse weather indicators in the model are operationalized based on – Dataset with documented damages across African
countries
– Average monthly precipitation
• Thresholds for extreme events– Floods: 25% above average
– Droughts: 25% below average
• Impacts of adverse weather are implemented as non-linear functions
Impact of floods agriculture land
• The higher the flood indicator, the more agriculture land is affected
Impact of drought on livestock
• The share of livestock increases exponentially depending on the strength of the drought
SEB of Climate Information Services
• Climate impacts from different scenarios accumulate over time
• The reference scenario (green line) serves to assess added benefits and avoided costs
• The difference between the reference and CIS scenarios are benefits obtained from CIS
Cumulative Additional Cost For Reestablishing The Road Network
20 B
15 B
10 B
5 B
0
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Mu
r
Cumulative Additional Cost For Reestablishing The Road Network : WISER SEB CIS 19 Mar - CIS Investment
Cumulative Additional Cost For Reestablishing The Road Network : WISER SEB CIS 19 Mar - BAU
Cumulative Additional Cost For Reestablishing The Road Network : WISER SEB CIS 23 Jan - Reference
Cumulative Economic Loss From Livestock Due To Extreme Weather
200 M
150 M
100 M
50 M
0
1980 1990 2000 2010 2020 2030 2040 2050
Time (Year)
Mur
Cumulative Economic Loss From Livestock Due To Extreme Weather : WISER SEB CIS 19 Mar - CIS Investment
Cumulative Economic Loss From Livestock Due To Extreme Weather : WISER SEB CIS 19 Mar - BAU
Cumulative Economic Loss From Livestock Due To Extreme Weather : WISER SEB CIS 23 Jan - Reference
SEB of CIS: Cost benefit ratio
• Example results for 30% and 100% coverage
Scenario
Total
impacts
Total
SEBs
Total
investmentCost to
benefit
ratio(million USD)
(million
USD)
(million
USD)
Reference (0% CIS
coverage)
Full climate impacts
9'160.55
- - -
BAU (30% CIS coverage)
Impacts climate
8'159.32 1'001.23 208.31
4.81
CIS investment (100%
coverage by 2035)
CIS investment
3'027.19 6'133.36 845.14
7.26
Quality control: Validation of results
• The obtained simulation results were validated based on – Results obtained from the analysis of the dataset
• Comparison of simulation results to the range of impacts obtained from data analysis
– International reports• Assessment of whether the combined induced impacts
produced by the model are conform with publications on climate impacts in Africa
– Peer reviewed papers
Limitations (1)
• Use of average data obtained from a dataset covering 8 African countries – Customization of the tool to a country context
requires more specific data, such as• Share of area affected
• Local price assumptions on agriculture produce, livestock, roads, health care, etc.
• Impacts of adverse weather are estimated on monthly precipitation– Main cause of floods are dry spells followed by 2-3
days of heavy rain
Limitations (2)
• High level of aggregation for the assessment of impacts
– Some impacts might be caused by a combination of factors, and require more detailed causal relationships
• At this stage, investments in CIS are based on a fraction of GDP, not on specific costs of interventions
Summary
• The model captures social, economic and environmental dynamics
• Including climate variations in the analysis has cascading effects through all sectors
• The performance of the system changesdepending on the climate assumptions used
• Policy effectiveness has to be assessed using a variety of indicators, across sectors, actors, over time and space
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