Supplementary Information

Financial De-risking to Unlock Africa’s Renewable Energy Potential

Bart Sweerts1,2, Francesco Dalla Longa2 and Bob van der Zwaan2,3,4,*

1 University of Amsterdam, Faculty of Science (IBED), Amsterdam, The Netherlands2 Energy research Centre of the Netherlands (ECN-TNO), Energy Transition Studies, Amsterdam3 Johns Hopkins University, School of Advanced International Studies (SAIS), Bologna, Italy4 University of Amsterdam, Faculty of Science (HIMS), Amsterdam, The Netherlands

* Corresponding author: bob.vanderzwaan@tno.nl

10 November 2018

1

Supplementary Notes

Supplementary Note 1. Levelized cost of electricity (LCOE) across Africa.

The LCOE values presented in the article (Figure 1 and 2) are calculated using Supplementary Equation 1, a single set of input factors per technology (Supplementary Table 1) and country-specific WACC values estimated for 2012 by Ondraczek et al. [1] (Supplementary Table 3) following the CDM executive board recommended approach [2]. To disaggregate the financing costs into debt and equity, a 70:30 debt to equity ratio was used.

Supplementary Note 2. WACC projection model.

The WACC projections for the years 2015-2050 are produced in three steps by estimating per year the contributions of: 1) country risk, 2) technology premium and 3) financial de-risking. The model yields yearly country and technology specific WACC values.

Step 1: country riskWACC values estimated for the year 2012 [1] are used as a starting point for the country base WACC. These WACC values are specific for the energy-generating sector [2] but do not include additional technology-specific premiums. From here, WACC values evolve through time as a result of different scenarios for GDP per capita (GDPPC) growth (Supplementary Table 2) according to the fitted line (Supplementary Equation 3; Fig. 3). However, the country-specific WACC values are weighed down by the starting point and are assumed to not increase as a result of the fitted line, as per the following equation:

WACC t ,cfinal=min(WACC t−1 ,c

final ,(tmax−t )tmax

⋅WACC t , cfit +( t

tmax )⋅WACCt−1 , cfinal )

Where the WACC value (WACC t ,cfinal) depends on years after present (t), the maximum value of t (

tmax=35¿ , the country (c), the fitted WACC curve value given the GDP per capita at year t and country c (WACC t ,c

fit ¿.

Step 2: technology premiumThe model assumes that each technology has a certain premium based on domestic skills capacity for that technology. This assumption is a simplification of the findings of a study on risk factors for renewable energy projects in EU member states [3]. To account for this, for each TIAM-ECN region (see Supplementary Note 3 for description), technology and year the technology premium is added, taking installed capacity as proxy:

Premc ,tech, y=max (0 , Premini1000ICc ,tech, y )

With the initial premium at zero deployment (Premini¿ and installed capacity in MW (ICc ,tech, y¿. Premini is set to 3% for all renewable energy technologies. The premium decreases linearly until the installed capacity reaches 1000MW, after which the technology premium is set to 0%. For fossil fuel technologies and hydropower, the technology premium is not included as these technologies are assumed to be well established.

Step 3: financial de-riskingTo incorporate the impact of direct financial de-risking on the WACC value for renewable energy technologies, the following steps were taken:

2

I. Estimate the total amount of financial de-risking (US$ / yr) that development finance institutions (DFIs) and the Green Climate Fund (GCF) will direct towards renewable energy projects across Africa (Supplementary Table 4).

II. Assign one of the following interest rates to each country in Africa: concessional (1% interest), sub market value (7% under current WACC), a mixture of both or no financial aid. These financial de-risking categories are based on the United Nations country classification (low-income, lower middle income, upper middle income and high-income respectively), which are based on the gross national income (GNI) per capita [4].

III. Determine the fraction of total investments for the renewable energy sector that comes from financial de-risking efforts, and recalculate the WACC accordingly. Finance originating from financial de-risking is assumed to have the same debt/equity ratio as finance provided by financial markets (See Supplementary Note 1).

To incorporate the effect of policy support, a portion of the technology premium was subtracted from the country-specific WACC value based on the nationally determined contributions assessment summarized in Supplementary Table 4. The policy support categories (0, 1, 2 and 3) relate to fixed percentage point decreases of the WACC (0%, 0.3%, 0.6% and 1.1%, respectively).

Supplementary Note 3. TIAM-ECN model description.

Like with other members of the TIMES family, TIAM is a technology-rich bottom-up energy system model. It is described in detail in Loulou and Labriet [5] and Loulou [6]. TIAM is a linear optimization model that minimizes energy system costs in each time-period with perfect foresight. The objective function includes capital, operation & maintenance, as well as fuel costs. Decommissioning and energy infrastructure costs are also included, albeit in an approximate way. Demand for energy services responds to changes in their prices through end-use price elasticities. Savings of energy demand are thereby accounted for in the objective function.

TIAM-ECN is built on a database of hundreds of energy-related processes and commodities, which allows for the simulation of the entire global energy system from resource extraction to end use. It is designed to cover a period of over 100 years and hence can be used to generate scenarios for the entire 21st century. For a general description of the reference energy system of TIAM-ECN see also Syri et al. [7]. Over the past years TIAM-ECN has been used successfully for analysis in several different domains, including on topics like developments in the transport sector (see e.g. [8]), the power sector [9], and burden-sharing among countries for global climate change control [10].

In the current set-up of TIAM-ECN, the world is disaggregated in 36 distinct regions [11], 17 of which are located in Africa as shown in Supplementary Figure 5.

3

Supplementary Figures

Supplementary Figure 1. Fourier amplitude sensitivity analysis of LCOE input parameters. The x-axis shows the normalized contribution of sensitive parameters to the total variance of the output parameter. Among the parameters tested are project lifetime, capacity factor, upfront investments, fixed O&M, variable O&M, fuel cost, heat rate, and WACC.

Supplementary Figure 2. Development Finance Institutions’ (DFIs) renewable energy investments in Africa by mechanism between 2009 and 2014 (IRENA Data & Statistics, 2017).

4

Supplementary Figure 3. Top 15 performing countries in terms of installed capacity of renewable energy (excluding large hydropower) and DFI investments.

Supplementary Figure 4. Scheme of the stylistic WACC model used to produce WACC projections.

5

Supplementary Figure 5. TIAM-ECN regional disaggregation of Africa.

0

1

2

3

4

5

6

7

8

Unifo

rmDi

vers

eCo

nces

siona

lDe

-risk

ed

Unifo

rmDi

vers

eCo

nces

siona

lDe

-risk

ed

Unifo

rmDi

vers

eCo

nces

siona

lDe

-risk

ed

Unifo

rmDi

vers

eCo

nces

siona

lDe

-risk

ed

Unifo

rmDi

vers

eCo

nces

siona

lDe

-risk

ed

2010 2020 2030 2040 2050

GHG

emiss

ions

[Gt C

O2e

]

Carbon dioxide (CO2) Methane (CH4) Nitrous Oxide (N2O)

Supplementary Figure 6. TIAM-ECN greenhouse gas (GHG) emissions projections under the WACC scenarios.

6

Supplementary Figure 7. Electricity production projections for Africa until 2050. Each bar represents the breakdown of electricity supply in a given scenario and simulation year. Strong (2DC) global climate policy is assumed.

7

Supplementary Tables

Supplementary Table 1. Techno-economic assumptions of the six different energy generating technologies [12–16] (EIA, 2016; IRENA, 2015; Pueyo et al, 2016; McKinsey, 2015; Ngugi, 2014)

Upfront investment (US$)

Fixed maintenance cost (US$)

Variable maintenance cost (US$)

Capacity factor (%)

Lifetime (years)

CSP 3500 65 0.003 20% 25Solar PV 2644 23.9 0 22% 25Wind onshore 1877 39.7 0 27% 20Small hydro 2468 98.7 0 50% 80Geothermal 4045 65 0.0116 90% 30Large hydro 1750 35 0 60% 80Hard coal 1653 52.86 0.0243* 85% 50Natural gas 978 11 0.1008* 56% 25Diesel 599.02 3.8621 0.2266* 54%** 30

* Based on authors calculations using BTU of fossil fuels and global fuel prices** Based on African petroleum-based electricity generators [17]

Supplementary Table 2. Real annual GDP per capita growth assumptions 2010-2050 [18,19].Region Scenario 2010 2020 2030 2040 2050Central Africa

High:Low:

4.7%4.7%

7.7%6.5%

7.3%6.2%

3.7%3.2%

3.0%2.5%

East Africa High:Low:

6.2%6.2%

8.4%7.1%

9.9%8.3%

9.8%8.3%

8.8%7.4%

North Africa

High:Low:

4.7%4.7%

5.8%4.9%

5.1%4.3%

4.9%4.1%

3.9%3.3%

Southern Africa

High:Low:

3.3%3.3%

4.1%3.5%

5.7%4.8%

6.3%5.3%

4.8%4.0%

West Africa

High:Low:

6.7%6.7%

9.4%7.9%

5.8%4.9%

5.2%4.4%

4.9%4.1%

Supplementary Table 3. WACC values estimated for African countries in 2012 following the Clean Development Mechanism’s Executive Board methodology [1].

See excel spread sheet ‘WACC_est_2012.xlsx’

Supplementary Table 4. Investment flows directed towards renewable energy in Africa [20]. Investment (US$ Billion yr1) 2010 2020 2030 2040 2050

Total investments*

- 3 12 25 40 60

Development Finance Institutions

Low support:Medium support:High support:

01.51.5

03.53.5

03.53.9

03.54.5

04.55

Green Climate Fund**

Low support:Medium support:High support:

000

03.333.33

03.3313.33

03.3315.72

03.3321.33

* Based on observed investments between 2009 and 2017, and projections of total investments in renewable energy calculated by TIAM-ECN for 2020 onwards. Investment needs are disaggregated to the country level based on GDP.** In line with current portfolio of the Green Climate Fund, 40% of total value is directed towards Africa of which 33% is directed towards financing renewable energy.

8

Supplementary Table 5. Nationally Determined Contributions per country, and corresponding policy support category (NDC interpretation taken from Cabre & Sokona [21]). Countries absent from Supplementary Table 5 have no NDCs and are categorized as no policy support. √ symbols denote unspecified contributions to a certain technology.

CountryTOTAL USD million

Total RE (MW)

PV (MW)

Hydro (MW)

Wind (MW)

Other renewable energy

Policy support category (0 - 3)

Algeria 9708 4525

4010 MW solar, 515 MW spread over wind, biomass and geothermal

1*contributions are conditional

Angola 1194 860 760 100 2

Benin 191 33 201 million solar PV lamps (13 MW)

2

Burkina Faso 918 √ √ √ 2

Cabo Verde 480810 GWh/yr

3

Chad 200 √ √ 3

Congo, Rep. 52 √ √ √ 1

Djibouti 3632 1510 250 60 1200 MW geothermal 3

Egypt 20680 8520 220 7200 1100 MW CSP 3

Eritrea e 300 3

Ethiopia 18687 14492 300 11660 1224731 MW biomass 577 MW geothermal

1

Gambia 77 35 2

Guinea 1635 5e 330 5e 2

Kenya 23845 11150 1200 3000 15005450 MW geothermal 10 MW biomass

3

Lesotho 364 1

Malawi 883 353 2 35120 million liter biofuel 2000 solar water heaters

2

Mauritania 10 √ 2000 PV water pumps

0* No support for utility scale renewables

Morocco 24105 10090 3350 1330 4200 1210 MW CSP 3

Niger 100 2

Nigeria 81570 25840 18000 5900 8001100 MW bioenergy 1000 MW CSP

3

Senegal 1137 454 160 144 15027500 bio-digesters 392 hybrid mini-grids

1

Seychelles 191 90 2

Somalia 30 15 15 1

South Africa 39280 17800 8400 8400 1000 MW CSP 3

Tanzania 5065 3258 150 2941 100 67 MW biomass 2

Tunisia 792 381 1

Uganda 5400 2471 3

Zimbabwe 405 27 1250 bio-digesters 1

TOTAL USD 241 bn 102 GW 34.2 26.4 25.7

Supplementary Table 6. Time-series of projected WACC-values across Africa in TIAM-ECN simulation years. The GDP-weighed average over several countries was taken to aggregate the values to the 17 TIAM-ECN African regions.

See excel spread sheet ‘WACC_proj_20152050.xlsx’

9

Supplementary Equations

Supplementary Equation 1. Weighted average cost of capital (WACC):

WACC= EV∙ Re+

DV∙Rd ∙ (1−Tc )(1)

With finance from equity (E), total financing (V), cost of equity (Re ¿, finance from debt (D), cost of debt (Rd ¿ and the corporate tax rate (T c¿ .

Supplementary Equation 2. Levelized cost of electricity (LCOE):

LCOE=∑t=1

n I t+M t+F t(1+r )t

∑t=1

n E t(1+r )t

(2)

With investment cost in year t (I t), operations and maintenance cost in year t (M t), fuel expenditures in year t (F t), electricity generation in year t (Et ¿, discount rate (r) and the lifespan of the project expressed in years (n).

Supplementary Equation 3. WACC in country c and year y as a fitted power function (plotted in Figure 3 in the main text) of GDP per capita (GDPpc):

WACCc , y=23.46−1.775 ∙GDPpcc , y0.2081(3)

Supplementary References

[1] Ondraczek J, Komendantova N, Patt A. WACC the dog: The effect of financing costs on the levelized cost of solar PV power. Renew Energy 2015. doi:10.1016/j.renene.2014.10.053.

[2] CDM – Executive Board. Annex 5: GUIDELINES ON THE ASSESSMENT OF INVESTMENT ANALYSIS. 2011.

[3] Noothout P, Jager D de, et al. The impact of risks in renewable energy investments and the role of smart policies. ClimateobserverOrg 2016.

[4] United Nations. World Economic Situation and Prospects 2014. 2014.[5] Loulou R, Labriet M. ETSAP-TIAM: The TIMES integrated assessment model Part I: Model

structure. Comput Manag Sci 2008. doi:10.1007/s10287-007-0046-z.[6] Loulou R. ETSAP-TIAM: The TIMES integrated assessment model. Part II: Mathematical

formulation. Comput Manag Sci 2008. doi:10.1007/s10287-007-0045-0.[7] Syri S, Lehtilä A, Ekholm T, Savolainen I, Holttinen H, Peltola E. Global energy and

emissions scenarios for effective climate change mitigation-Deterministic and stochastic scenarios with the TIAM model. Int J Greenh Gas Control 2008. doi:10.1016/j.ijggc.2008.01.001.

[8] Rösler H, Van der Zwaan B, Keppo I, Bruggink J. Electricity versus hydrogen for passenger cars under stringent climate change control. Sustain Energy Technol Assessments 2014. doi:10.1016/j.seta.2013.11.006.

[9] Keppo I, van der Zwaan B. The Impact of Uncertainty in Climate Targets and CO 2 Storage Availability on Long-Term Emissions Abatement. Environ Model Assess 2012. doi:10.1007/s10666-011-9283-1.

[10] KOBER T, VAN DER ZWAAN BCC, RÖSLER H. EMISSION CERTIFICATE TRADE AND COSTS

10

UNDER REGIONAL BURDEN-SHARING REGIMES FOR A 2°C CLIMATE CHANGE CONTROL TARGET. Clim Chang Econ 2014. doi:10.1142/S2010007814400016.

[11] van der Zwaan B, Kober T, Longa FD, van der Laan A, Jan Kramer G. An integrated assessment of pathways for low-carbon development in Africa. Energy Policy 2018. doi:10.1016/j.enpol.2018.03.017.

[12] US_Energy_Information. Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants. US Dep Energy 2013. doi:10.2172/784669.

[13] IRENA. Renewable Power Generation Costs in 2017. 2014. doi:10.1007/SpringerReference_7300.

[14] Pueyo A, Bawakyillenuo S, Osiolo H. Cost and Returns on Renewable Energy in Sub-Saharan Africa: A comparison of Kenya and Ghana. Evid Rep 2016.

[15] McKinsey & Company. Brigther Africa: The growth potential of the sub-Saharan electricity sector. 2015.

[16] Ngugi PK. What does geothermal cost? - The Kenya experience. 2012.[17] U.S. Energy Information Agency. International Energy Statistics n.d.

https://www.eia.gov/beta/international/data/browser/#/?c=4100000002000060000000000000g000200000000000000001&vs=INTL.44-1-AFRC-QBTU.A&vo=0&v=H&end=2017 (accessed October 10, 2017).

[18] Deutsche Bank (DB). North Africa - Mediteraenean neighbors on the rise. 2010.[19] International Monetary Fund. Regional Economic Outlook: Sub-Saharan Africa Restarting

the Growth Engine. 2017. doi:ISBN-13: 978-1-49832-984-2 (paper)\rISBN-13: 978-1-47551-995-2 (Web PDF).

[20] IRENA. Data and Statistics n.d. http://resourceirena.irena.org/gateway/dashboard/ (accessed October 10, 2017).

[21] Cabré MM, Sokona MY. Renewable Energy Investment in Africa and Nationally Determined Contributions (NDCs). n.d.

11