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Climate Change and Low Income Countries Channing Arndt United Nations University – World Institute for Development Economics Research and University of Copenhagen With contributions from many others Risk & 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. MIT JP – Uncertainty Approach • Integrated Global Systems Model –IGSM • 70 Uncertain parameters • 400 Monte Carlo • Climate & Socio-Econ MIT JP – Uncertainty to Risk Webster et al. (2010). MIT Joint Program Report #180. Observations • Projected temperature rise to 2100 is much more severe than it was only a few years ago due to: – Actual emissions growth. – Sensitivity of the climate system. • Very high levels of warming are possible. • Mitigation policy is powerful. – Expected temperature outcomes lower – Extreme temperature outcomes eliminated. • Considerable warming appears to be unavoidable. Adaptation policy also required. Evolving View of Climate Change 1. Environmental issue 2. Humanist moral issue 3. Global security issue UNU-WIDER views climate change as a global security issue. Adaptation to Climate Change Precipitation 2100: CCSM v. MIROC Some Regularities • Warmer – Higher temperatures will be observed throughout the globe. • Wetter – Due to speeding of the hydrologic cycle, precipitation globally is likely to increase. The distribution of this increase is unknown. – Note an indeterminate affect on the climate moisture index [CMI =f(P/T)]. • More intense. More intense rainfall and higher probability of extreme weather events (flooding, perhaps cyclones…). Recent African country studies • Malawi (2010) – Analysis of extreme weather events (i.e., droughts and floods) – Adaptation via drought-resistant seed varieties • Zambia (2009): – Contrasts historical variability and 3 climate change scenarios – Focuses exclusively on agriculture • Tanzania (2010): – Focus on agriculture and income distribution – Various crop modeling approaches Economics of Adaptation to Climate Change • Mozambique, Ghana, and Ethiopia (2010): – Considers 4 future climate change scenarios – Multi-sector approach: agriculture, energy, infrastructure, etc – Initial focus on the costing of adaptation options for COP15 in Copenhagen. • Current expansion into detailed country studies. Focus on Mozambique 1. Analytical Framework Climate change projections Temperature Precipitation PET Hydrology Stream-flow Runoff Net Evaporation Floods and Inundation Flood recurrence Crops Hydropower Infrastructure Crop yields Production levels E.g., road etwork length Dynamic Economy-wide CGE Model (WIDER) National/sector GDP Regional production Household welfare 2. Climate change impacts Four climate scenarios • Global wettest and driest scenarios • Local wettest and driest (i.e., for Mozambique) • Selected using “Climate Moisture Index” (CMI) – Global wet/dry are actually dry/wet scenarios for Mozambique – Global dry scenario is very wet scenario for Zambezi River Basin and SADC • Must use multiple GCMs • Must take regional approach Scenario CMI Global wet -0.6 Global dry +9.3 Local wet +33.0 Local dry -58.6 2. Climate change impacts Agriculture and crop yields • CLICROP models: 14 crops in 3 sub-national regions • Captures daily T and P effects, water-logging and irrigation water demand (exclude CO2 fertilization) Average change in yield from baseline, 2041-2050 (%) Global Wet Global Dry Cassava North region Center region South region Maize North region Center region South region Local Wet Local Dry 2.01 -4.75 -9.36 -3.44 -6.24 -3.27 -0.09 -3.10 0.36 -6.51 -6.21 -3.20 1.27 0.34 3.49 -1.32 0.64 6.37 -2.92 -3.04 -4.36 -1.87 -5.59 -3.95 2. Climate change impacts Hydropower generation • IMPEND model determines hydropower generation based on streamflow and installed/planned investments • Hydropower declines in all scenarios, but Mozambique remains a net energy exporter regardless Average annual production (Giga watt hours per year) 2003-2010 2011-2020 2021-2030 2031-2040 2041-2050 Baseline Global Wet 13,533 1.09 17,391 -2.35 26,991 -1.82 26,087 -3.94 25,479 -3.37 Change from baseline (%) Global Dry Local Wet 0.26 -3.07 -0.55 -7.36 0.40 -5.30 -0.62 -8.08 -0.98 -4.15 Local Dry -5.31 -6.62 -6.75 -7.26 -12.04 2. Climate change impacts Flooding and road infrastructure • CLIRUN: River basin models predict change in flood RPs • CLIROAD: Captures P, T and flood damages on roads • Global Dry has most flooding (regional basin effect) • This damages roads more than in other scenarios Global Wet Global Dry Local Wet Local Dry Change in national road network length relative to baseline, 2050 (%) -16.1 -22.4 -11.9 -2.1 Beira SLOSH Model Setup • Wind fields from the storm generation step generate storm surges in SLOSH • This “snapshot” shows storm surge above sea level at a time when a storm is offshore Effect of SLR on Return Period for the 100-year Storm Surge in Beira Return Time for Current 100 Year Flood 120 100 80 y = 105.65e-3.272x R2 = 0.9948 60 40 20 0 0 0.05 0.1 0.15 0.2 Low SLR (meters) 0.25 0.3 Medium 0.35 0.4 High 2. Climate change impacts Dynamic CGE model Tanzania Detailed economic structure: •4 regions (3x rural, urban) •33 sectors (17 in agriculture) •7 factors (3x land, 3x labor, capital) •20 households (rural/urban quintiles) Malawi Zambia Zimbabwe Recursive dynamic: •Capital accumulation on past investment •Exogenous TFP (linked to sector models) •Autonomous adaptation (“typical farmer”) 2. Climate change impacts Baseline “no climate change” scenario • Baseline specifies a future scenario reflecting development trends, policies, and priorities without climate change. • Consistent with sector models’ baselines (i.e., CGE captures individual baselines and their interactions within a market economy) Share of total GDP (%) • Assumes a reasonable trajectory for growth and structural change until 2050 (e.g., falling agricultural GDP share). 35 30 Agriculture Industry 25 20 15 3.7% average annual GDP growth 2. Climate change impacts Economywide damages Effects of climate change are negative and grow with time Large declines in national welfare by 2050 Wide variation in impacts across CC scenarios In worst scenario adaptation cost is about US$7.6 billion ($410 mil. p.a.) Cumulative deviation in total absorption, 2003-2050 (5% discount rate) 8 Discounted US$ billion (constant 2003 prices) • • • • 7 6 2.5 5 4 2040s 2.1 1.8 1.4 2020s 3 2 1 0 2030s 1.6 1.1 1.5 1.1 0.6 0.7 0.6 0.5 0.4 Global dry (CSIRO) Global wet (NCAR) Moz dry (UKMO) 1.2 2010s 1.0 2000s 0.7 Moz wet (IPSL) 2. Climate change impacts Decomposition of impact channels • Road network is the main impact channel due to major flooding within the trans-boundary water basin. • Crop yield damages are most severe in Local Dry scenario. • Hydropower reduces surplus energy exports but not welfare. Cumulative deviation in total absorption, 2003-2050 (5% discount rate) Change in per capita absoprtion growth rate from baseline (%-point) Global dry (CSIRO) Global wet (NCAR) Moz dry (UKMO) 0.0 -0.1 -0.2 -0.3 -0.4 Falling crop yields and rising sea level Detriorating transport system Declining hydropower generation Moz wet (IPSL) 3. Adaptation investments Step 1: Transport system investments • Sealing unpaved roads reduces worst case CC damages by 1/5 with little or no additional costs (i.e., advisable even under baseline). Step 2: Irrigation investments • 1 million hectares of new irrig. Land only slightly reduces CC damages. Step 3: Agricultural R&D or education investments • Raising agricultural productivity by a bit more than 1% each year offsets remaining damages. • Increasing rate of human capital accumulation also offsets damages. Baseline No climate change Global dry Global wet Moz dry Moz wet (1) 2.11 2.11 2.11 2.11 Impact With climate change (2) 1.73 1.85 2.02 1.91 Adaptation scenarios Transport infrastructure (3) = (2+) 1.81 1.92 2.04 1.97 Agriculture R&E Irrigation expansion Education (4) = (3+) 2.11 2.22 2.32 2.27 (5) = (3+) 1.84 1.95 2.07 2.00 (6) = (3+) 2.11 2.23 2.35 2.28 6. Summary of results • Without public policy changes, the worst scenario results in a net present value of damages of about US$7.6 billion. – Equal to an annual payment of US$420 million (5% discount rate). • Hardening rural roads reduces worst case impacts substantially, restoring approximately 1/5 of lost absorption. • Remaining welfare losses could be regained with improved agricultural productivity or human capital accumulation. • Investments costs required to restore welfare losses are subject to debate, but are reasonably less than US$400 million per year over 40 years. 6. Summary of Results • It is unlikely to be cost effective to protect the vast majority of coastal regions of Mozambique. • High value and vulnerable locations, such as the port of Beira, merit specific consideration. • Even relatively small levels of sea level rise dramatically increase the probability of severe storm surge events (assuming no change in the intensity and frequency of cyclone events). 6. Policy Recommendations • Best adaptation to CC is more rapid development leading to a more flexible and resilient society. – Effective strategies should reinforce development objectives • But climate-specific interventions include: – – – – Regional adaptation strategies (e.g., river basin management) Agricultural research & extension Seal unpaved roads (makes sense even if no CC) Soft adaptation where possible (e.g., Land use planning: most capital in low-income countries has not yet been invested) – Hard adaptation should be heavily scrutinized (e.g., dykes may reduce risk but increase exposure)