Economics of climate change adaptation ethiopia
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Economics of climate change adaptation ethiopia

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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

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   Economics of climate change adaptation ethiopia Presentation Transcript

  • 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 RiskWebster 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 5
  • 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 6
  • Wide Variation at Local Scale between Models Precipitation 2100 NCAR Precipitation 2100 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 8
  • 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 11
  • Climate Change ScenariosScenario GCM CMI DescriptionBase Historical Climate Historical climate shocksWet2 Ncar_ccsm3_0-sres (A1b) 23% Ethiopia wet CC shocksWet1 Ncar_ccsm3_0-sres (A2) 10% Global wet CC shocksDry1 Csiro_mk3_0-sres (A2) -5% Global dry CC shocksDry2 Gfdl_cm2_1-sres (A1b) -15% Ethiopia dry CC shocksCMI: 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. 12
  • Adapt to what? – Global Wet and Dry Change in average annual precipitation, 2000 – 2050 CSIRO (DRY) NCAR (WET) A2 SCENARIOTwo extreme GCMs used to estimate range of costs
  • 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 15
  • Five Agro-Ecological ZonesSAM Region Temperature and Moisture RegimeR1 (Zone 1) Humid lowlands, moisture reliableR2 (Zone 2) Moisture sufficient highlands, cereals basedR3 (Zone 3) Moisture sufficient highlands, enset basedR4 (Zone 4) Drought-prone (highlands)R5 (Zone 5) Pastoralist (arid lowland plains) 16
  • CC Impacts on Runoff in Abbay Basin100.00% Blue Nile Percent Change in Flow80.00%60.00% sresa1b_gfdl_cm2_1 sresa1b_ncar_ccsm3_0 sresa2_csiro_mk3_040.00% sresa2_ncar_ccsm3_020.00% 0.00%-20.00%-40.00% 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
  • 120 FLOODS REGION 310080604020 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 History WET
  • Crop Yield
  • Total Hydropower Production in the 21 Ethiopia RiverBasins, Assuming Growing M&I Demands and Irrigation to 3.7 Million ha, 2001-2050
  • Climate Change Adaptation CostsShift projects within the development plan such that energy producedunder the Base scenario is matched or minimally exceeded Costs in 2010 USD; 5% discount rate
  • Export Share: Electricity 100.00 90.00 80.00 70.00 60.00Percent 50.00 40.00 30.00 20.00 10.00 0.00 Base trend Wet2 Dry2 22
  • 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 income 26
  • 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 27
  • 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 28
  • 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 29
  • 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
  • GDP, Deviations from Base Deviation of GDP from Base Scenario 2015 2025 2035 2045 0.00Percent deviation from Base -2.00 Wet2 -4.00 Wet1 -6.00 Dry1 Dry2 -8.00 -10.00 -12.00 31
  • 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 32
  • 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 33
  • Residual Welfare Loss ($ billion) 12 10 8$ billions 6 4 2 0 t1 t3 t5 t7 t9 t11 t13 t15 t17 t19 t21 t23 t25 t27 t29 t31 t33 t35 t37 t39 t41 t43 -2 WEt2AC Wet1AC Dry1AC Dry2AC 34
  • Benefit-Cost of Adaptation ProjectsNet Benefits and Adaptation Project Costs, $ billions Welfare losses: With Without Project Benefit-costScenarios adaptation adaptation Net gain costs 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 $ billion 35
  • 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 36
  • 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 37
  • 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 38