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Ana Pueyo - Green Growth Diagnostics for Africa


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Presentation given at “Unlocking Investment in Africa’s Renewables: What are the Binding Constraints?” event, organised by the Institute of Development Studies and held on 19 January 2017 at the Wellcome Collection, London. For more information, please visit

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Ana Pueyo - Green Growth Diagnostics for Africa

  1. 1. Dr. Ana Pueyo – Institute of Development Studies (IDS) Closure event – 19th January Wellcome Trust, London Overview of the GGDA project
  2. 2. 1 Green Growth Diagnostics for Africa Principal Investigator Country leadership- co-investigators GHANA KENYA Technical and political economy partners Funding partners
  3. 3. 2 Clean energy access is a development priority
  4. 4. 3 What constraints RE investment in Ghana and Kenya? •Long list of constraints and related policies •But not all constraints are equally important. •Public funds are limited and they need to focus on the key constraints: those that if relaxed would deliver the biggest “bang for the buck”
  5. 5. 4 Growth Diagnostics Framework “there may be many reasons why an economy does not grow, but each reason generates a distinctive set of symptoms” (Haussmann, Rodrik and Velasco, 2004). “For this particular country, at this particular time, what is preventing the country from achieving higher sustained and shared growth?” (Haussmann, Klinger and Wagner, 2008: 4).
  6. 6. 5 Research questions •What are the binding constraints to investment in RE in Kenya and Ghana? •Which policies can remove the most binding constraint, are they politically feasible? •What will be the macroeconomic effect of increasing the share of RE and implementing the required policies? •What is the value of grid-scale variable renewable energy generation in Kenya and Ghana?
  7. 7. 6 GREEN GROWTH DIAGNOSTICS METHODOLOGY Binding constraints Political economy analysis CGEM Power Systems Reliability Analysis Dissemination and engagement
  8. 8. 7 3 steps to finding binding constraints 1. Decision tree analysis 2. Symptoms or signals that a constraint is binding – The shadow price of the scarce resource should be high – Movements in the constraint should produce significant changes in the desired outcome – Agents in the economy should be attempting to overcome or bypass the constraint – Agents less intensive in the constraint are more likely to survive and thrive 3. Syndrome or theory to explain why constraints emerge and persist
  9. 9. 8 PROBLEM: SUBOPTIMAL INVESTMENT IN RENEWABLE ENERGY Unattractive investments Insufficient supply of suitable finance Low returns High risks Inadequate access to savings Poor intermediation Why is there underinvestment in RE generation capacity?
  10. 10. 9 Unattractive investments Low returns High risks High costs Low revenues Installed O&M Financing Resource Prices Curtailment Link to finance decision tree Demand Project costs System costs Resource Are RE attractive enough?- Returns
  11. 11. 10 Are RE attractive enough?- Risks
  12. 12. 11 Is appropriate finance available?- Supply
  13. 13. 12 Is appropriate finance available?- Intermediation
  14. 14. 13 Kenya and Ghana case studies Kenya Ghana Population (2015) 46 Million 27.4 Million Income per capita 2015 (USD current) 1340 1480 Electricity access (%) 20% (in 2013) 55% (in 2016) 72% (in 2013) Electricity demand growth (2010-2015) 6% 7% Installed capacity (2015) 2,404 MW 2,831 MW % Renewable capacity 63% 56% % Renewables exc. hydro 29% 0.1% System losses (2013) 18% 21.5% Number of blackouts per month 6.3 8.4 Average duration of blackouts (hours) 5.6 6.6 % enterprises with generators 57.4% 52.1%
  15. 15. 14 Background Ghana •Chronic underinvestment in generation capacity and low utilisation of existing plants •Heavy cost for the economy of unreliable electricity supply •Expensive and dirty “short-term” solutions •RE not framed as part of the solution by the Government and WB •FiT approved and long pipeline of projects with provisional licenses but none with commercial operations to date
  16. 16. 15 Decision tree of constraints to RE in Ghana
  17. 17. 16 Syndrome: the overborrowing State •High fiscal deficit •State overcrowding private investment •Patronage •Bailout by IMF in 2015 •Potential further pressures: aggressive industrialisation and universal electrification
  18. 18. 17 Background Kenya •First SSA country to attract a significant number of IPPs through competitive bids •It has sufficient generation capacity at the moment and may reach overcapacity in the medium term •Ambitious geothermal programme and expectation that renewables will meet 90% of demand in the long term •But very low access rates and bad quality of access •FiT in place, but low implementation and a large number of unsolicited proposals
  19. 19. 18 Decision tree of constraints to RE in Kenya
  20. 20. 19 Syndrome: rent-absorbing political elite • High inflows of foreign aid channelled to the priorities of the political elite: large scale, flagship projects- wind and geothermal • Networking elements of the system and small scale provision for small dispersed rural areas not a priority • Local communities can effectively mobilise when they do not see a benefit in large infrastructure projects • FiT promoted by donors but so far seldom used • But new game-changing policies: Mini-grids regulation, geospatial mapping for on-grid off-grid planning; auctioning scheme for renewables; draft National Energy and Petroleum Policy 2015; Energy Bill 2015; local content regulations 2015.
  21. 21. 20 Focus of closure event •Planning •Regulation •Finance •Social acceptance •Political economy
  22. 22. 21 Conclusions •Useful methodology to reduce a large number of potential constraints to investment in RE to a manageable number •Policies to remove these constraints are not always politically feasible- PEA required •Binding constraints + PEA could significantly enhance effectiveness of policy support to RE in Africa •We invite donors and national policymakers to replicate this exercise to better target their support to green growth in developing countries
  23. 23. 22 Thank you Green Growth Diagnostics for Africa webpage: for-africa
  24. 24. 23 Costs and returns Kenya and Ghana LCOE (USD cents per kWh) Technology Ghana Kenya Wind 14.3 10.3 Solar 18.7 14.8 Hydro 7.9 10.7 Geothermal 7.3 Returns (%) Technology Ghana Kenya Wind 6.9 14.3 Solar Close to 0% 5.3 Hydro 33.5 5.3 Geothermal 16.8
  25. 25. 24 Prices in Kenya and Ghana Average electricity end-user tariffs in Kenya and Ghana USDc/kWh
  26. 26. 25 Offtaker risks in Kenya and Ghana Off-taker risk Topic Indicator Ghana Kenya Operational performance EBIT (if positive, EBIT margin) -123 Mill cedis 12% Liquidity Current liabilities/current assets 154% 60% cash and cash equivalents/ Accounts payable and short term borrowing 8% 91% Financial structure Short term debt/equity and LT debt 45% 19% System losses System losses (as % purchases) 23% 17.5% Credit risk Revenue collection to sales 89.9% Trade receivables past due by more than a year 13% 3% (over a year) 11% (plus impairment)
  27. 27. 26 Financial intermediation Intermediation Topic Indicator Ghana Kenya SSA LMI Poor intermediation Bank lending-deposit spread 2015 16 8.7 8.8 6.8 Competition Bank concentration (%) 2013 70.1 51.6 75.4 70.1 Boone indicator (2013) -0.13 -0.09 -0.05 -0.05 Cost Bank overhead costs to total assets (%) 5.7 4.7 5.2 3.4 Performance Bank return on equity (%, after tax) 2013 31.8 15.5 15.5 11.2 Bank non-performing loans to gross loans (%) 12 5 5.6 5.6 Stock market returns (%, year on year) 74.4 50 Short-termism Syndicated loan average maturity (years) 2013 1.1 9.5 4.5 5 Average maturity on new external debt commitments (2014) 12.1 (2014) 18 (2010- 14) 16.7 (2014) 28 (2010- 2014)
  28. 28. 27 Macroeconomic risks Macroeconomic risk Topic Indicator Ghana Kenya Currency risk Change in exchange rate (USD per local currency) -65% (2011-2015) -18 % (2015) -13% (2011-2015) -10 % (2015) Inflation Consumer Price Index year on year Dec 2015 17.7% (2015) 6.6% Fiscal stability Current account deficit -9.2% (2014) -7.1% Fiscal deficit -10.4% (2014) -7.3% (2015)