Ethiopian Development Research Institute and International Food Policy Research Institute (IFPRI/EDRI), Tenth International Conference on Ethiopian Economy, July 19-21, 2012. EEA Conference Hall
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Economics of climate change adaptation ethiopia
1. Economics of Climate Change
Adaptation: Ethiopia
Sherman Robinson (IFPRI),
Ken Strzepek (MIT),
Len Wright, Paul Chinowsky, (U of Colorado)
Paul Block (Columbia U)
Ethiopian Economics Association meeting
Addis Ababa, July 19-21, 2012
2. Risk and Uncertainty
• Knight (1921) :
– ―risk" refers to situations where the decision-
makers can assign mathematical probabilities
to the randomness which they face.
– "uncertainty" refers to situations when this
randomness cannot be expressed in terms of
specific mathematical probabilities.
4. MIT JP – Uncertainty to Risk
Webster et al. (2010). MIT Joint Program Report #180)
5. Some Implications
• Risk and uncertainty
– Model uncertainty
– Parameter estimation, confidence
– Policy uncertainty
– CC involves stochastic processes (chaotic?)
• Extremes matter
• Policy is powerful
• Robustness of adaptation strategies is crucial
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6. Climate Change Impact and
Adaptation Project
• World Bank: IFPRI, IDS, WIDER, MIT, U of
Colorado
• Core modeling team worked closely with:
– Country teams
– IFPRI: Emily Schmidt, Paul Dorosh (Ethiopia)
– Water/climate team: Ken Strzepek, Paul Block
• Case studies: Ethiopia, Mozambique, Ghana,
Bangladesh, Vietnam, Zambia and Tanzania
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7. Wide Variation at Local Scale between Models
Precipitation
2100
NCAR
Precipitation
2100
MIROC
8. Consistent Message from GCMs
• Increased daily precipitation intensity
– Increased frequency and intensity of storms
– More floods, even in ―dry‖ scenarios
• High degree of time (seasonal) and spatial
variation in precipitation
– High degree of uncertainty. Wide variation across
models
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9. Uses of History
• Uses of historical experience
– Future CC impacts are like past impacts with
some modifications to the distributions
– Future CC impacts are out of historical domain
and require different approach to analysis
• Models
– Reduced form models using historical data
– Deep structural models based on underlying
science and knowledge of technology/biology
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11. Ethiopian Case Study
• Parallel dynamic CGE models of Ethiopia,
Mozambique, and Ghana
– Related models of Bangladesh and Tanzania
• Dynamic recursive: to 2050
• Incorporate adaptation investment strategies
– Energy (hydropower)
– Agricultural investment (irrigation, technology)
– Roads
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12. Climate Change Scenarios
Scenario GCM CMI Description
Base Historical Climate Historical climate shocks
Wet2 Ncar_ccsm3_0-sres (A1b) 23% Ethiopia wet CC shocks
Wet1 Ncar_ccsm3_0-sres (A2) 10% Global wet CC shocks
Dry1 Csiro_mk3_0-sres (A2) -5% Global dry CC shocks
Dry2 Gfdl_cm2_1-sres (A1b) -15% Ethiopia dry CC shocks
CMI: Crop moisture index change
In addition, the CC scenarios have two additional scenarios indicated by a suffix:
“A” for adaptation and “AC” for adaptation with investment costs.
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13. Adapt to what? – Global Wet and Dry
Change in average annual precipitation, 2000 – 2050
CSIRO (DRY) NCAR (WET)
A2 SCENARIO
Two extreme GCMs used to estimate range of costs
15. Summary of background
• Ethiopia is heavily dependent on agriculture in
general and rainfed agriculture in particular.
• Climate models predict contrasting impacts for
Ethiopia
• Aggregate impacts obscure complexity—for
example spatial and seasonal variations
• Changes in occurrences of extreme events may
be more significant than changes in means
• Impacts on agriculture depend on various
assumptions—for example degree of autonomous
adaptation and effects of carbon fertilization
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16. Five Agro-Ecological Zones
SAM Region Temperature and Moisture Regime
R1 (Zone 1) Humid lowlands, moisture reliable
R2 (Zone 2) Moisture sufficient highlands, cereals based
R3 (Zone 3) Moisture sufficient highlands, enset based
R4 (Zone 4) Drought-prone (highlands)
R5 (Zone 5) Pastoralist (arid lowland plains)
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20. Total Hydropower Production in the 21 Ethiopia River
Basins, Assuming Growing M&I Demands and Irrigation to 3.7 Million
ha, 2001-2050
21. Climate Change Adaptation Costs
Shift projects within the development plan such that energy produced
under the Base scenario is matched or minimally exceeded
Costs in 2010 USD; 5% discount rate
23. Mean Decadal Changes in Hydropower Production Given Increasing
M&I and Irrigation Demands, Relative to a No-Demand Scenario
24.
25. Economywide: Methodology
• Computable General Equilibrium (CGE)
economywide model
• Regionalized
– Based on 5 agro-ecological zones
– Regional agricultural production
– Regional household incomes and consumption
• Disaggregated households
– Rural farm (by region)
– Small urban (rural non-farm) and large urban
centers
26. Data Base: EDRI 2004/05 Social
Accounting Matrix (SAM)
• Constructed as part of a project with IDS
(w/support of IFPRI-ESSP2)
• 65 production sectors, 5 Regions + urban
– 24 agricultural,
– 10 agricultural processing,
– 20 other industry,
– 11 services
• 14 Households by region and income
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27. Dynamics
• Model is run from 2006 to 2050
– Dynamic recursive specification. Exogenous
variables and parameters updated ―between‖
periods. CC shocks imposed.
– Model solved twice in each period:
• Solve after updating all exogenous variables to
determine ―desired‖ production decisions,
• Then fix agricultural factor inputs and solve again with
CC shocks on activities and factors
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28. Climate Change (CC) Shocks
• Temperature and water: direct impact on
agricultural productivity
– Crops (yields) and livestock by region
• Water shocks:
– Hydroelectric generating capacity
– Floods affect transport (roads) and agriculture by
regions
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29. Adaptation Investment
• Agricultural investment (e.g. irrigation, water
management, chemicals, technology)
• Dam construction: timing and more dams
• Road investment to reduce impact of flooding
on transport sector
• Increased road construction
• Investment to pave and ―harden‖ roads
– Linked to Ethiopia’s planned investment strategy
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30. Discounted Absorption, Difference
from Base
Discounted Absorption Difference
from Base Scenario
4.0
Percent of discounted Base GDP
2.0
0.0
Wet2 Dry2 Wet1 Dry1 Shock
-2.0
Adapt
-4.0
AdaptC
-6.0
-8.0
-10.0 30
31. GDP, Deviations from Base
Deviation of GDP from Base Scenario
2015 2025 2035 2045
0.00
Percent deviation from Base
-2.00
Wet2
-4.00 Wet1
-6.00 Dry1
Dry2
-8.00
-10.00
-12.00
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32. Adaptation Costs
• Direct costs of adaptation investment projects
• Indirect costs: opportunity cost of investment
resources diverted to adaptation projects
– Difference in absorption in adaptation scenario
with and without costed adaptation investments
• Residual welfare loss: Difference in
absorption between base run and adaptation
scenario with project costs
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33. Total (D+I) Adaptation Costs as a
Share of GFCF (%)
30.000
25.000
20.000
Percent
15.000
10.000
5.000
0.000
t1 t3 t5 t7 t9 t11 t13 t15 t17 t19 t21 t23 t25 t27 t29 t31 t33 t35 t37 t39 t41 t43
WEt2AC Wet1AC Dry1AC Dry2AC
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35. Benefit-Cost of Adaptation
Projects
Net Benefits and Adaptation Project Costs, $ billions
Welfare losses:
With Without Project Benefit-cost
Scenarios adaptation adaptation Net gain costs ratio
Wet2 -61.48 -131.80 70.32 4.66 15.10
Wet1 -17.67 -55.60 37.93 0.38 99.88
Dry1 -32.67 -88.41 55.74 1.55 35.95
Dry2 -124.06 -264.59 140.54 20.54 6.84
Notes: Cumulated losses and costs 2010-2050, no discounting, in $ billion
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36. Conclusions
• Negative impacts of CC shocks are significant
– Regional and sectoral variation across scenarios
– Especially severe in last decade
• Given growth scenario, planned hydroelectric
capacity meets demand under CC shocks
– CC shocks affect exports, not domestic supply
• Extreme ―wet‖ and ―dry‖ scenarios are worst
– increased incidence of droughts and floods are
especially damaging
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37. Conclusions
• Poor and rural households are similarly hurt
by CC shocks
– Lower mean incomes
– Higher coefficient of variation of incomes
• Somewhat worse for poor households
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38. Conclusions
• Adaptation investment
– Very beneficial, especially in extreme scenarios
– Reduces size and variance of CC impacts
– Reduces but does not eliminate negative impact
of CC shocks
– Benefits vary widely across CC scenarios.
• Need for analysis of investment under risk
– Consistent with Ethiopia’s agricultural
development strategy
• Infrastructure: roads, electricity, irrigation
• Technology, farm management, extension
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