Climate Change and Low Income Countries

1. 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

2. 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.

3. MIT JP – Uncertainty Approach Integrated Global Systems Model –IGSM 70 Uncertain parameters 400 Monte Carlo Climate & Socio-Econ

4. MIT JP – Uncertainty to Risk Webster et al. (2010). MIT Joint Program Report #180.

5. 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.

6. Evolving View of Climate Change Environmental issue Humanist moral issue Global security issue UNU-WIDER views climate change as a global security issue.

7. Adaptation to Climate Change

8. Precipitation 2100: CCSM v. MIROC

9. 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…).

10. 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

11. 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.

12. Focus on Mozambique

13. 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

14. 2. Climate change impactsFour 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

15. 2. Climate change impactsAgriculture and crop yields Average change in yield from baseline, 2041-2050 (%) CLICROP models: 14 crops in 3 sub-national regions Captures daily T and P effects, water-logging and irrigation water demand (exclude CO2 fertilization)

16. 2. Climate change impactsHydropower generation Average annual production (Giga watt hours per year) 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

17. 2. Climate change impactsFlooding 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

20. 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

21. Effect of SLR on Return Period for the 100-year Storm Surge in Beira

22. 2. Climate change impactsDynamic CGE model Tanzania Malawi Zambia Zimbabwe 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) Recursive dynamic: Capital accumulation on past investment Exogenous TFP (linked to sector models) Autonomous adaptation (“typical farmer”)

23. 2. Climate change impactsBaseline “no climate change” scenario 3.7% average annual GDP growth Baseline specifies a future scenario reflecting development trends, policies, and priorities without climate change. Assumes a reasonable trajectory for growth and structural change until 2050 (e.g., falling agricultural GDP share). Consistent with sector models’ baselines (i.e., CGE captures individual baselines and their interactions within a market economy)

24. 2. Climate change impactsEconomywide damages Cumulative deviation in total absorption, 2003-2050 (5% discount rate) 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.)

25. 2. Climate change impactsDecomposition of impact channels Cumulative deviation in total absorption, 2003-2050 (5% discount rate) 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.

26. 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.

27. 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.

28. 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).

29. 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)