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 http://www.ids.ac.uk/events/unlocking-investment-in-africa-s-renewables-what-are-the-binding-constraints.

1. Dr. Ana Pueyo – Institute of Development Studies (IDS)
Closure event – 19th January
Wellcome Trust, London
Overview of the GGDA project

2. 1
Green Growth Diagnostics for Africa
Principal Investigator
Country leadership- co-investigators
GHANA KENYA
Technical and political
economy partners
Funding partners

3. 2
Clean energy access is a development priority

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. 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. 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. 6
GREEN GROWTH DIAGNOSTICS METHODOLOGY
Binding
constraints
Political
economy
analysis
CGEM
Power
Systems
Reliability
Analysis
Dissemination and engagement

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. 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. 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. 10
Are RE attractive enough?- Risks

12. 11
Is appropriate finance available?- Supply

13. 12
Is appropriate finance available?-
Intermediation

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. 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. 15
Decision tree of constraints to RE in Ghana

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. 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. 18
Decision tree of constraints to RE in Kenya

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. 20
Focus of closure event
•Planning
•Regulation
•Finance
•Social acceptance
•Political economy

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. 22
Thank you
A.Pueyo@ids.ac.uk
Green Growth Diagnostics for Africa webpage:
http://www.ids.ac.uk/project/green-growth-diagnostics-
for-africa

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. 24
Prices in Kenya and Ghana
Average electricity end-user tariffs in Kenya and Ghana USDc/kWh

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. 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. 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)