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

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.

Future Climate is Uncertain: IPCC

MIT JP – Uncertainty to Risk

Webster et al. (2010). MIT Joint Program Report #180)

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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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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Wide Variation at Local Scale between Models

Precipitation2100

NCAR

Precipitation2100

MIROC

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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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 9

Modeling Framework

Infrastructure• Roads (CliRoad)• M&I Water• Floods

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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Climate Change Scenarios

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Scenario GCM CMI DescriptionBase 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 changeIn addition, the CC scenarios have two additional scenarios indicated by a suffix: “A” for adaptation and “AC” for adaptation with investment costs.

Adapt to what? – Global Wet and Dry

Two extreme GCMs used to estimate range of costs

Change in average annual precipitation, 2000 – 2050

CSIRO (DRY) NCAR (WET)

A2 SCENARIO

PRECIP CHANGES 2050

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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Five Agro-Ecological Zones

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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)

CC Impacts on Runoff in Abbay Basin

Blue Nile Percent Change in Flow

-40.00%

-20.00%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

2049

sresa1b_gfdl_cm2_1

sresa1b_ncar_ccsm3_0

sresa2_csiro_mk3_0

sresa2_ncar_ccsm3_0

FLOODS

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 490

20

40

60

80

100

120

History WET

REGION 3

Crop Yield

Total Hydropower Production in the 21 Ethiopia River Basins, Assuming Growing M&I Demands and Irrigation to 3.7 Million ha, 2001-2050

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

Export Share: Electricity

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0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

Perc

ent

Base trend Wet2 Dry2

Mean Decadal Changes in Hydropower Production Given Increasing M&I and Irrigation Demands, Relative to a No-Demand Scenario

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

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 income26

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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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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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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Discounted Absorption, Difference from Base

30-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

Wet2 Dry2 Wet1 Dry1

Perc

ent o

f dis

coun

ted

Base

GD

P

Discounted Absorption Difference from Base Scenario

Shock

Adapt

AdaptC

GDP, Deviations from Base

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-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2015 2025 2035 2045

Perc

ent d

evia

tion

fro

m B

ase

Deviation of GDP from Base Scenario

Wet2

Wet1

Dry1

Dry2

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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Total (D+I) Adaptation Costs as a Share of GFCF (%)

33

0.000

5.000

10.000

15.000

20.000

25.000

30.000

t1 t3 t5 t7 t9 t11 t13 t15 t17 t19 t21 t23 t25 t27 t29 t31 t33 t35 t37 t39 t41 t43

Perc

ent

WEt2AC Wet1AC Dry1AC Dry2AC

Residual Welfare Loss ($ billion)

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-2

0

2

4

6

8

10

12

t1 t3 t5 t7 t9 t11 t13 t15 t17 t19 t21 t23 t25 t27 t29 t31 t33 t35 t37 t39 t41 t43

$ bi

llion

s

WEt2AC Wet1AC Dry1AC Dry2AC

Benefit-Cost of Adaptation Projects

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Net Benefits and Adaptation Project Costs, $ billionsWelfare losses:

ScenariosWith

adaptationWithout

adaptation Net gainProject

costsBenefit-cost

ratioWet2 -61.48 -131.80 70.32 4.66 15.10Wet1 -17.67 -55.60 37.93 0.38 99.88Dry1 -32.67 -88.41 55.74 1.55 35.95Dry2 -124.06 -264.59 140.54 20.54 6.84Notes: Cumulated losses and costs 2010-2050, no discounting, in $ bil l ion

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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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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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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