Accounting for finance in electrification models for sub-Saharan Africa

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Articles https://doi.org/10.1038/s41560-022-01041-6

Accounting for finance in electrification models for sub-Saharan Africa Churchill Agutu   1,2 ✉, Florian Egli   1,3 ✉, Nathaniel J. Williams   2,4, Tobias S. Schmidt   1,5 ✉ and Bjarne Steffen   5,6 ✉ Electrifying 600 million people in sub-Saharan Africa will require substantial investments. Integrated electrification models inform key policy decisions and electricity access investments in many countries. While current electrification models apply sophisticated geospatial methods, they often make simplistic assumptions about financing conditions. Here we establish cost of capital values, reflecting country and electrification mode (that is, grid extension, minigrids and stand-alone systems), and specific risks faced by investors and integrate them into an open source electrification model. We find that the cost of capital for off-grid electrification is much higher than currently assumed, up to 32.2%. Accounting for finance shifts approximately 240 million people from minigrids to stand-alone systems in our main scenario, suggesting a more cost-effective electrification mode mix than previously suggested. In turn, electrification models based on uniform cost of capital assumptions increase the per kWh cost of electricity by 20%, on average. Upscaling and mainstreaming off-grid finance can lower electrification cost substantially.

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s of 2018, an estimated 600 million people in sub-Saharan Africa did not have access to electricity1. Because electrification has been identified as a key factor in eradicating poverty2, the United Nations Sustainable Development Goal (SDG) 7 has set a goal to ‘ensure universal access to affordable, reliable, sustainable and modern energy services’ by 20303. Historically, electrification has happened through grid extension. More recently, the emergence of off-grid solar-powered minigrids (MGs) and stand-alone systems (SASs) has created new electrification modes for rural regions, where most of the continent’s un-electrified population lives1,4,5. The diversity of electrification modes (grid extension, MGs and SASs) and the rapid cost declines of the off-grid modes has spurred the need for planning based on integrated geospatial electrification models, which inform electrification choices based on cost criteria, typically the levelized cost of electricity (LCOE). These models have reached relatively high levels of sophistication and have become prominent in the academic and grey literature1,6–12 and policy design13–15. While the LCOE of different electrification modes is typically derived from a detailed representation of equipment cost, the established models do not consider the cost of capital, specifically. They assume a standard discount rate across all electrification modes and geographies (typically around 8%) (refs. 6–8,16) with a few studies assessing sensitivity to this rate assumption but without differentiating between electrification modes7,9,17,18. To compare the LCOE of different electrification modes in a real world setting, however, the cost of capital needs to be taken into account19. A uniform discount rate of 8% does not reflect the actual financing situation and might introduce substantial bias20,21. Here we estimate representative cost of capital and implement them in a geospatial electrification model that assigns least-cost electrification modes to population clusters across sub-Saharan Africa. More specifically, we differentiate cost of capital by country and electrification mode and thereby extend the widely used Open Source

Spatial Electrification Tool (OnSSET)1,6,7,11,22 (Methods provide a detailed model description). The model derives least-cost pathways to 100% electrification of sub-Saharan Africa by 2030 with an intermediate target of 50% in 2025 (Methods). We first quantify the cost of capital for different electrification modes for all sub-Saharan African countries (Methods provide electrification-mode specifications). Second, we define four financing scenarios reflecting different possible sources of capital and maturities of the off-grid finance sector (Table 1). Third, we apply the model (Table 2 lists model inputs), to the financing scenarios in two defined grid-extension pathways that typically rely on government intervention for grid extension: an extended-area grid pathway where electrification of a cluster is solely based on the least-cost electrification mode optimization through grid extension, MG or SAS, and an existing-area grid pathway where grid extension beyond already electrified grid areas is halted in the model, while off-grid options are allowed. This second model is to account for the limited success in extending the grid in sub-Saharan Africa. We analyse results for all of sub-Saharan Africa and deep dive into focus countries, the Democratic Republic of the Congo (DRC), Ethiopia, Nigeria and Zimbabwe, to highlight the differing effects of the representative financing cost assumptions. Finally, we derive implications for electrification researchers, international organizations and national policymakers.

Cost of capital for electrification

In most sub-Saharan African countries, grid extension is primarily financed by the public sector (that is, from tax revenues or sovereign debt)4,20, while MGs and SASs are primarily financed by the private sector (that is, from privately owned companies)20,23. For public finance, the cost of capital varies widely across countries20,24 but represents the sovereign risk of lending to a government. For private finance, investors typically finance off-grid companies at a premium above the sovereign risk, which increases the cost of capital23,25.

Energy and Technology Policy Group, ETH, Zurich, Switzerland. 2Kigali Collaborative Research Centre (KCRC), Kigali, Rwanda. 3UCL Institute for Innovation and Public Purpose (IIPP), London, UK. 4Golisano Institute for Sustainability, Rochester Institute of Technology, Rochester, NY, USA. 5Institute of Science, Technology and Policy, ETH, Zurich, Switzerland. 6Climate Finance and Policy Group, ETH, Zurich, Switzerland. ✉e-mail: [email protected]; [email protected]; [email protected]; [email protected] 1

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Table 1 | Description of financing scenarios. Methods provide details on scenario description and the derivation of country- and mode-specific costs of capital Scenario

Description Grid extension

MGs

SASs

Uniform

• Standard assumption in the literature • Cost of capital is uniform (8%) across countries and electrification modes

Not specified

Not specified

Not specified

Public sector financing

• Electrification is solely financed by the government • Cost of capital is country specific and identical across electrification modes

Public sector

Public sector

Public sector

Niche (status quo) financing

• Status quo financing where the off-grid electricity sector is nascent (small caps, limited liquidity, low debt shares) • Cost of capital is country- and mode-specific

Public sector

Private sector (debt share 0%)

Private sector (debt share 50%)

Mainstream financing

• Potential future mainstream financing after the off-grid Public sector electricity sector has matured (larger caps, more liquidity, higher debt shares) • Cost of capital is country- and mode-specific

Private sector (debt share 40%)

Private sector (debt share 75%)

In this case, the cost of capital also depends on the institutional structure of the electricity sector4,26,27 and the local capital market development with respect to electrification financing23,28–30. Furthermore, there are differences in the cost of capital that private investors demand depending on the electrification mode they finance. MG investments are perceived as riskier, and therefore, the cost of capital for MGs is typically higher than that of SASs20,23,30. MGs are infrastructure-based systems, subject to risks faced by (low carbon) infrastructure investments in developing countries31. They depend on governing structures at national26 and sub-national levels and require long-term financing with long payback periods27,32,33. MGs are also disproportionally exposed to ‘grid-like’ regulations on electricity tariffs, licensing requirements and property rights20,27. This typically deters (conservative) debt providers23, resulting in close-to-zero debt financing in the MG sector34. In contrast, SASs can be characterized as a product rather than an infrastructure system, with a shorter payback period. As a result, the SAS sector has experienced non-negligible debt financing34 (Supplementary Table 2). Hence, they are less subject to governing structures at the (sub-)national level23,27. We use four financing scenarios shown in Table 1 to characterize the financing structures of different electrification modes: (1) uniform represents the current assumptions used in the literature, (2) public sector financing represents a scenario where electrification is financed exclusively by the public sector, (3) the niche (status quo) and (4) mainstream scenarios translate the different risk profiles between electrification modes described above into different debt shares, following the finance literature that demonstrates an increase of debt shares with decreasing investment risk. Differentiating weighted average cost of capital (WACC) between electrification modes can have a substantial effect on the LCOE and thus the technology selection of the model. The debt shares are based on empirical financing data and expert interviews (Methods). Furthermore, based on the finance literature, private sector finance in the niche scenario demands an illiquidity premium and a small cap premium, which disappear in the mainstream scenario as the sector matures (Methods). Figure 1 presents our estimates of the cost of capital for these four financing scenarios. The estimates illustrate that compared with representative costs of capital (‘niche’ scenario), the uniform rate of 8% is consistently too low for private sector financed off-grid electrification and additionally omits substantial variance between countries. Country differences range from 2.6% to 18.5% in sub-Saharan Africa. 632

Financing sources

Table 2 | Summary of electrification-mode assumptions used in the model (Table 2 provides more detail) Parameter

Capital costs (US$ kW−1)

Asset lifetime (years)

Capacity factor

Solar PV MG (including batteries)

2,950

20

Calculating by model using solar resource availability data

Hydro MG

3,000

30

0.5

Solar PV SAS (including batteries)

4,470–9,620 (depending on system capacity)

15

Calculating by model using solar resource availability data

Moreover, we see from Fig. 1 that accounting for mode-specific risks substantially increases the cost of capital in private sector finance scenarios (red/yellow parts). Accounting for mode-specific risks introduces a 5 percentage point (pp) cost of capital spread between MGs and SASs in the niche-financing scenario and a 2 pp spread in the mainstream financing scenario. In the niche scenario (representing the status quo), the MG cost of capital ranges from 15.7% to 32.2%, while the SAS cost of capital ranges from 9.8% to 26.0%.

Effects on least-cost electrification

In Fig. 2, we show the resulting least-cost electrification mixes for newly connected populations (referred to as new connections) and the total investment required in each scenario and for two grid-extension pathways. The extended-area grid pathway represents an optimistic pathway where grid extension is included as an electrification mode option in addition to off-grid electrification options by 2030. In this pathway, electrification through grid extension is possible for already grid-electrified clusters (due to population increase within clusters) and for un-electrified clusters through extension of the grid from the un-electrified clusters to the main grid infrastructure (which can be the already existing infrastructure or the planned grid infrastructure based on government grid-deployment plans). This pathway remains unlikely as historically, many sub-Saharan African countries have run behind schedule to expand their grids. For example, countries like DRC35,36 and Nigeria37, which combined are home to a quarter of sub-Saharan Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

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NATUrE EnErgy Sudan Somalia Mauritania Guinea Eritrea Liberia Niger Sierra Leone Burundi Mozambique Zimbabwe Central African Republic Malawi Chad South Sudan Lesotho Republic of the Congo DRC Ghana Zambia Gambia

Country

Mali Burkina Faso Equatorial Guinea Togo Cameroon Guinea-Bissau Sao Tome & Principe Rwanda Gabon Tanzania Uganda Madagascar Eswatini Comoros Angola Kenya Nigeria Benin Ethiopia Senegal

MG & SAS public-sector financing MG mainstream financing (premium) SAS mainstream financing (premium) MG niche financing (additional premium) SAS niche financing (additional premium)

Côte d'Ivoire Namibia South Africa Mauritius Botswana 0

5

8

10

15

20

25

30

35

Cost of capital (%)

Fig. 1 | Cost of capital estimates for the different financing scenarios and for the different electrification modes. The differences between the countries are based on the country default spreads (reflecting country-specific investment risk52), while the differences between the electrification modes are based on variations in the debt share (Methods). The differences between the financing scenarios are based on different sources of capital and on different maturities of the off-grid finance sector. The dotted line reflects the cost of capital for the uniform financing scenario.

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NATUrE EnErgy Extended-area extension pathway SAS

a

SAS

SAS

42%

New connections 2030 (%)

80

MG

11

13

12

14

14

7

10

10

54% 46% MG

23% MG

77% MG

11

15 36

80

60

56

55

51 31

40

75

76

80

40

78

20

20

Uniform

Public

Niche

0

Mainstream

d

350 Total investment (2018 billions of US$)

83% MG

100

SAS

SAS

17%

85%

MG

10

SAS

15%

b

55% 45%

60

0

c

65% MG

MG

100

35%

44% 56%

58%

Existing-area extension pathway SAS

SAS

300 228

228

200

42

44

50

47

150

0

49 23

100 50

244

136

136

Uniform

Public

34

34

34

Uniform

Public

Niche

Mainstream

300

300

305

43

45

350 300

250

34

232

250

48

200

64 149

33

150

212

210

100

172

299

190 111

151 50

Niche

0

Mainstream

Grid extension

MGs

45

45

45

45

Uniform

Public

Niche

Mainstream

SASs

Fig. 2 | Results showing the newly connected populations and total investments between 2018 and 2030 based electrification mode for the defined electrification pathways and financing scenario. a, Total new connections per electrification mode for the extended-area grid pathway (columns) and ratio of MGs to SASs for each financing scenario (pies). b, Total new connections per electrification mode for the existing-area pathway (columns) and ratio of MGs to SASs for each financing scenario (pies). c, Total investments needed to finance the extended-area grid pathway. d, Total investments needed to finance the existing-area grid pathway. Values have been rounded to the nearest integer; the same operation is carried out for all numbers to ensure consistency. Numbers in the bars show the percentage of new connections (a,b) and the total investment (c,d) for each electrification mode.

Africa’s un-electrified population1, have failed to reach their grid infrastructure development plans to date. The existing-area grid pathway, therefore, represents the other extreme where current grid infrastructure remains as is—grid extension is possible only for grid-electrified clusters and electrification of fully un-electrified clusters is entirely left to the off-grid electricity sector. Note that both scenarios are extremes, with the reality likely lying somewhere in between. We observe four high-level points for both electrification pathways across the different financing scenarios. First, the public sector scenario closely mirrors the uniform scenario concerning new connections and investment cost. This is because a uniform cost of capital of 8% is close to the weighted average of the public cost of finance for sub-Saharan African governments (compared with Fig. 1). Hence, using a uniform cost of capital produces reasonable electrification mix estimates for sub-Saharan Africa overall if one assumes that the public sector finances all electrification. Second, the estimated total investment cost across scenarios (2018 US$) is approximately US$228 billion for the extended-area grid pathway, which is equivalent to roughly US$19 billion per annum during 2018–2030, in line with past estimates from the International 634

Energy Agency of US$21 billion per annum1. The total investment cost is approximately 30% higher for the existing-area pathway. Third, the status quo (niche financing) departs from the uniform or public scenario substantially, resulting in a higher share of SASs and a lower share of MGs. The results depicted in column three of Fig. 2b,d show off-grid electrification shares of two-thirds, distributed almost equally among MGs and SASs (46–54%). Fourth, moving from the niche to the mainstream scenario (compared with Table 1) results in electrification shares more similar to the uniform scenario, albeit with higher SAS shares (plus 5 pp) and lower MG shares (minus 5 pp). This result illustrates the large potential effect of mainstreaming off-grid electrification finance on selection of MG. An analysis of the extended-area grid pathway, specifically (Fig. 2a), shows that in the niche-financing scenario, the share of MGs drops by 7 pp relative to the uniform scenario. MGs and SASs have a smaller contribution to the total new connections, accounting for 20% of the total new connections (compared with 24% in the uniform scenario). Most connections switch from MGs to grid extension in the extended-area grid pathway. This is mainly because high-density population clusters enable economies of scale Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

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NATUrE EnErgy Niche financing

Uniform financing

a

b

c

d

Extended-area grid pathway

Existing-area grid pathway

Grid extension MGs SASs Water bodies Countries with no data 0

500

1,000 km

Fig. 3 | Map of sub-Saharan Africa comparing financing scenarios for the defined electrification pathways. a, Uniform scenario extended-area grid pathway. b, Niche-financing scenario extended-area grid pathway. c, Uniform scenario existing-area pathway. d, Niche financing scenario existing-area pathway.

for grid extension. We also observe that the investment cost for the extended-area grid pathway (Fig. 2c) increases by 7% or US$16 billion due to the increased need for grid infrastructure compared with the uniform scenario. Increased grid infrastructure requires high up-front investments, which increase the total investment cost. The results from the existing-area pathway (Fig. 2b,d) show the importance of accounting for representative cost of capital values when analysing private sector financed off-grid electrification modes even more clearly. MGs and SASs contribute to 67% of new connections in the existing-area pathway for the uniform and public scenarios (Fig. 2b). In these scenarios, a larger portion of off-grid electricity connections would be directed towards MGs compared with SASs. In the niche scenario, MGs show a substantial decline contributing to 46% of the newly connected off-grid electrification modes compared with the uniform scenario where they account for 85%. Note that the observed grid investment costs in the existing-area grid pathway are the result of grid densification, that is, population densities in clusters already connected to the grid increase, which necessitates investment in the existing grid infrastructure to satisfy demand. In the mainstream scenario, the total new MG connections contribute to 77% of the total new off-grid connections in the existing-area grid pathway (Fig. 2b). We also see in the existing-area pathway that maturity of the sector can help foster deployment of MGs, which account for 51% of the total electrification mix in 2030 for the mainstream financing scenario. This is relatively similar to the public sector financing scenario (55%). However, the total investment cost for the mainstream financing scenario is the lowest across financing scenarios at only US$299 billion. This is mainly because the share of MGs is lower compared with the public and uniform scenario. Therefore, the distribution grid-infrastructure costs (which increase the up-front investment cost for MGs) are Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

reduced. Further, the share of SASs for these scenarios is not high enough that it would increase total investment costs as can be seen for the niche scenario.

Geographical effects

Figure 3 provides a granular view on the geographic location of new connections by electrification mode. The map shows the total new connections in 2030 for the uniform and niche-financing scenarios and both grid-extension pathways. We observe the largest changes in the share of MGs between scenarios in North Africa, West Africa and Madagascar for the extended-area grid pathway (Fig. 3a,b). In the existing-area grid pathway, large changes are observed in West Africa and Central and Southern Africa (Fig. 3c,d). The largest declines in the share of MGs for the extended-area grid pathway range between 24 pp and 42 pp for Sudan, Madagascar, Burkina Faso and Chad. For the existing-area grid pathway, MG share decreases are much larger, ranging between 44 pp and 58 pp in Mauritania, Eswatini, Senegal, Lesotho and Chad. Further, the assessment of the total investment needed to reach 100% electrification reveals large country differences in the investment per household between countries (Table 3). We focus on a single scenario (niche, status quo) because we see little variation in the total investment costs across the financing scenarios for the electrification pathways (compared with Fig. 2c,d). Table 3 shows that the required investment per household for most countries ranges between US$1,000 and US$1,850, with South Sudan having the highest per household investment cost. Three countries feature investment needs lower than US$1,000 per household. To set these numbers in context, about 40% of the sub-Saharan African population lived below the World Bank’s US$1.90 per day extreme poverty line in 201838. These numbers also stress the large variance between countries. Importantly, designing policies such as 635

Articles MG or SAS zones and incentives based on the (wrong) uniform cost of capital assumption may result in higher cost (LCOE) for the end consumer. Table 4 shows the average LCOE (US cents kWh−1) per country for optimal (least cost) electrification in the existing-area grid-extension pathway, assuming the niche (status quo) finance scenario. This is contrasted with the average LCOE, assuming the electrification approach chosen under a uniform assumption but actually financed with more representative niche (status quo) conditions (Methods). Electrification models based on uniform cost of capital assumptions increase the LCOE on average by about 20% in all of sub-Saharan Africa, with a range from 9% to 55% across all countries.

Explaining country variation

Finally, we aim to provide a better understanding of the mechanisms behind the shown variations between countries by zooming in on specific countries. Here we deep dive into DRC, Ethiopia, Nigeria and Zimbabwe. We choose these countries based on two characteristics: first, we focus on countries with a high relevance in achieving 100% electrification in sub-Saharan Africa; second, we focus on countries with a large variation in factors that explain the cost difference between electrification modes. These countries have some of the highest un-electrified populations (approximately 227 million in 201939) and are therefore crucial in the path to electrification for sub-Saharan Africa. Further, the difference in the cost of capital relative to the uniform rate together with the variance in population density across clusters are key factors driving mode-specific cost differences. As demonstrated above, an increase in the cost of capital compared with the uniform rate generally leads to lower MG shares because of their capital intensity. In high-density population clusters, this effect is mitigated because distribution infrastructure costs for MGs are lower. Supplementary Table 1 shows that the selected countries feature variation in the cost of capital (high for DRC and Zimbabwe; low for Ethiopia and Nigeria) and cluster densities (high for DRC and Nigeria; low for Ethiopia and Zimbabwe). We limit the analysis to the existing-area grid pathway and we omit the public sector scenario, as the changes in electrification mixes were minimal for these countries relative to the uniform scenario. Figure 4 (top row) shows that the share of MG decreases for all countries when electrification mode-specific cost of capital values are applied because the MG cost of capital is above the uniform rate for all countries. Yet, the effect differs substantially between countries (Zimbabwe: 42 pp decline; Ethiopia: 33 pp decline; Nigeria: 16 pp decline; DRC: 10 pp decline). Given the high cost of capital and the low population density of Zimbabwe (above), the largest effect was expected. In Ethiopia, the effect is still remarkable because the low population densities counter a comparatively lower cost of capital. Conversely, the high population-density clusters in DRC and Nigeria reduce the effect of the cost of capital on the share of MGs. This is because high-density clusters lead to economies of scale in the distribution infrastructure (a large contributor to up-front investment cost for MGs), reducing the effect of the cost of capital on the LCOE and making MGs a more competitive electrification mode. The competing effects between cost of capital and population density on the LCOE are also visible in the LCOE violin plots (bottom row of Fig. 4), which show the distribution of MG and SAS LCOE across clusters to be electrified for the different financing scenarios. Firstly, we observe a wider distribution for MGs compared with SASs, which is due to the distribution infrastructure costs which vary for MGs based on the geographic distribution of connections but do not factor in SAS LCOEs. Within MG plots, high LCOE values change more across financing scenarios compared with low LCOE values due to population-cluster densities. Lower population-cluster densities incur higher capital costs for distribution infrastructure which makes them more sensitive to the 636

NATUrE EnErgy Table 3 | Comparison of investment costs for different subSaharan African countries for the existing-area grid pathway in the niche-financing scenario Country

Average investment per electrified household (US$)

Total investment per electrification mode (% of total investment) Grid

MG

Total investment (billions of US$)

SAS

South Sudan

1,853

1

44

55

4.60

Rwanda

1,776

9

31

60

3.83

Chad

1,765

3

21

76

6.41

Burundi

1,761

5

64

31

6.50

Eswatini

1,730

22

3

74

0.44

Mozambique

1,699

8

15

76

13.96

Guinea

1,682

8

55

36

3.56

Madagascar

1,657

5

59

36

11.87

Central African Republic

1,650

5

57

38

1.45

Zimbabwe

1,629

9

3

88

5.77

DRC

1,624

5

78

17

36.53

Malawi

1,591

10

32

58

9.59

Lesotho

1,584

14

16

70

0.98

Zambia

1,562

12

4

84

5.60

Niger

1,538

9

52

39

7.28

Uganda

1,490

17

23

60

21.17

Mauritania

1,472

10

22

68

0.95

Burkina Faso

1,457

14

15

71

6.76

Sudan

1,407

9

36

54

7.75

Eritrea

1,389

9

45

46

1.17

Ethiopia

1,379

21

12

68

26.43

Somalia

1,378

10

64

26

3.64

Gambia

1,361

15

27

58

0.35

Senegal

1,361

14

6

80

2.07

Benin

1,357

19

42

39

4.31

Republic of the Congo

1,352

19

67

14

1.39

Liberia

1,338

26

35

39

1.49

Tanzania

1,331

17

14

69

19.84

Togo

1,323

27

31

43

2.42

Nigeria

1,306

17

51

32

48.08

Equatorial Guinea

1,300

27

49

24

0.24

Guinea-Bissau

1,254

23

14

63

0.46

Namibia

1,217

17

1

82

0.72

Cameroon

1,214

21

35

44

6.93

Angola

1,201

16

37

47

6.39

Kenya

1,191

32

17

51

12.32

Ghana

1,024

37

19

44

4.14

South Africa

990

43

2

55

6.82

Botswana

906

43

3

54

0.67

Gabon

891

59

17

24

0.17

Total sub-Saharan Africa

305

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NATUrE EnErgy cost of capital. In DRC, the average LCOE increases by 86% (72% in Ethiopia, 73% in Nigeria and 100% in Zimbabwe) in the niche scenario relative to the uniform scenario. Zimbabwe’s change is highest because it has many low population-density clusters, which makes distribution infrastructure expensive. Further, Zimbabwe has a high cost of capital for MGs. These effects are similar, albeit somewhat smaller for SASs because they do not have distribution infrastructure. Hence, the cost of capital is especially crucial for low population-density clusters. The same mechanisms are also visible in the mainstream scenario; however, the changes in the share of MGs is smaller than for the niche-financing scenario.

Discussion and policy implications

Our analysis shows that accounting for country- and mode-specific cost of capital changes the choice of cost-optimal electrification modes substantially. It can therefore prevent selection of less cost-efficient electrification modes, evident in models using generic and uniform assumptions. Future electrification models should therefore appropriately consider the institutional structures and financing conditions in the context of the analysis and apply region- and electrification mode-specific cost of capital values. This is crucial for high-risk, low population-density contexts where infrastructure-based electrification modes compete with product-based ones. On the basis of the cost of capital estimates in the niche (status quo) financing scenario, our results show that SASs may have a much larger role in electrifying the continent than previously observed in models which assume uniform financing conditions. Further, our comparison of average LCOEs for the niche and uniform financing scenario (assuming niche-financing conditions; Table 4) also indicates that failure to account for niche-financing conditions can result in a 20% increase in the average LCOE. In reality, governments that deploy technologies using models that assume uniform financing, risk slowing down electrification implementation given the higher costs of electrification and paucity of electrification funding in off-grid electricity sector currently34,40. Hence, governments that aim at efficient/least-cost electrification pathways should consider a bigger role for SAS in their electrification plans. Further, we observe that the cost of capital for private sector financed off-grid electrification modes (at niche-financing conditions (status quo)), remains relatively high (Fig. 1) compared with government financed off-grid modes (public financing conditions). Recognizing that funds for scaling private sector off-grid electrification modes remain scarce40,41, policymakers intending to attract large-scale, low-cost private sector capital need to focus on fostering maturity of the off-grid sector. Assuming mainstream financing conditions as a potential best-case target, we suggest two complementary levers to help mainstream off-grid assets for investors: de-risking and scaling. First, financial de-risking measures—for example, through guarantees or concessional debt—would help make both MGs and SASs cheaper23,42 by bringing down the cost of capital. Established institutions in electricity-sector finance, such as national development banks or international financial institutions (for example, World Bank or African Development Bank)43, can consider developing these options for scaling off-grid electrification options. Second, the nascence of private off-grid electrification companies and finance results in increased cost of capital through a small cap premium (Methods). Scaling up electrification companies can thus be a complementary lever to decrease the cost of capital of private sector-financed electrification. Research has shown that diverse off-grid portfolios can substantially reduce the overall risk due to portfolio effects25. While some off-grid companies have recently been taken over by large incumbent energy utilities (most prominently the French company ENGIE)44, several hurdles for company growth exist. Beyond access to capital (first point), technical standards for off-grid solutions differ between countries, requiring Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

Table 4 | Average LCOE for the niche finance scenario compared with uniform financing scenario (existing-area grid-extension pathway), assuming niche-financing conditions Country

(a) Mean LCOE (US cents kWh−1) Choice of electrification mode: based on niche scenario

(b) Mean LCOE (US cents kWh−1) Choice of electrification mode: based on uniform scenario

Additional cost (LCOE) of (b) over (a) in %

Chad

60.6

93.8

55

South Sudan

51.7

73.8

43

Burkina Faso

48.6

68.9

42

Mozambique

57.6

80.8

40

Malawi

53.7

69.8

30

Zimbabwe

49.3

61.7

25

Zambia

47.3

57.6

22

Namibia

33.2

40.5

22

Botswana

32.5

39.2

21

Madagascar

45.0

54.1

20

Uganda

45.4

54.2

19

South Africa

42.0

50.0

19

Rwanda

50.5

59.9

19

Gabon

53.7

63.2

18

Kenya

41.7

48.8

17

Ethiopia

41.4

48.3

16

Tanzania

43.5

50.5

16

Eswatini

49.7

57.4

16

Ghana

51.4

59.4

16

Benin

45.2

51.9

15

Cameroon

45.5

52.1

14

Angola

41.7

47.5

14

Togo

48.2

54.9

14

Nigeria

43.4

49.3

14

Somalia

50.1

56.8

13

DRC

50.0

56.5

13

Senegal

40.0

45.2

13

Liberia

60.2

68.0

13

Eritrea

48.1

54.2

13

Republic of the Congo

54.8

61.7

13

Mauritania

49.9

56.2

12

Guinea-Bissau

43.9

49.4

12

Niger

47.8

53.7

12

Guinea

52.9

59.3

12

Central African Republic

48.7

54.6

12

Sudan

51.8

57.8

12

Burundi

54.5

60.5

11

Equatorial Guinea

54.3

60.1

11

Lesotho

46.6

51.5

10

Gambia

44.2

48.4

9

Total sub-Saharan 46.8 Africa

56.3

20

637

Articles a

b

DRC 100

New connections 2030 (%)

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3

13

15

d

Nigeria 5

22

21

Zimbabwe

8 31

46

48

50

80

70

54

43

78

38

36

51

73 45

10

42 0

17

17

17

Uniform

Niche

Mainstream

e

LCOE (US cents kWh−1)

c

Ethiopia

5

Uniform

42

Niche

42

41

Uniform

Mainstream

41

Niche

41

Mainstream

f

g

h

1.50

1.50

1.50

1.50

1.25

1.25

1.25

1.25

1.00

1.00

1.00

1.00

0.75

0.75

0.75

0.75

0.50

0.50

0.50

0.50

0.25

0.25

0.25

0.25

0

Uniform

Niche

Mainstream

0

Uniform

Niche

Grid extension

0

Mainstream MGs

Uniform

Niche

Mainstream

29 3

0

25

25

25

Uniform

Niche

Mainstream

Uniform

Niche

Mainstream

SASs

Fig. 4 | a–h, Proportion of new connections for clusters to be electrified in 2030 in DRC (a), Ethiopia (b), Nigeria (c) and Zimbabwe (d), and the distribution of LCOE for clusters to be electrified in 2030 in DRC (e), Ethiopia (f), Nigeria (g) and Zimbabwe (h). For this analysis, 5% of the outliers are neglected. The colours on both types of plot correspond to the electrification mode. The numbers on the stacked bar plots (a–d) represent the percentage of total new connections in 2030 by electrification mode.

costly technology adjustments27. International organizations can consider incentivizing the adoption of a harmonized international standard such as the World Bank’s Lighting Global Quality Standard (in the case of SASs)45. Regarding the limitations of our analysis, first, it is worth noting that our approach does not capture the variance of risks within countries, which can be high in sub-Saharan Africa18,46. Future research should uncover within-country variance to provide even more granular electrification policy advice. Second, the model targets 100% tier 3 electrification irrespective of demand or ability to pay. In reality, however, most private sector investors will simply not invest in assets electrifying the lowest-income customer segments without government incentive. Hence, the aggregate numbers may be overly optimistic concerning the ability to attract capital. We note however, that to reach universal electrification by 2030, additional subsidies for low-income households would likely be required. Given the likely correlation of institutional quality and household income, SASs may be even more suited to electrify low-income regions because this electrification mode is less reliant on governance structures20. Future research should include demand analyses and the effect of targeted demand subsidies in optimal electrification planning. Moreover, companies may adapt their business models to different demand environments and size systems accordingly. These business model choices (for example, sale, lease-to-own, pay-as-you-go) may have an influence on the ability of a company to raise capital and on its cost of capital. In addition, the sizing of the system, in turn (for example, choice of electrification tier) influences the electrification mode choice with lower tiers favouring SASs and higher tiers favouring MGs or grid extension. Future research could look into these questions in comparative company case studies. Third, and related to this, our results have also shown vast differences in per household investment costs across countries (Table 3) and vast LCOE differences within countries (Fig. 4). 638

This variance could be investigated in future research, particularly with the notion of trade-offs in 100% electrification targets in mind as the opportunity cost of ‘going the last mile’ may be large given competing investment needs in other sectors such as agriculture, education or healthcare47.

Methods

This study intends to determine the influence of the cost of capital on least-cost geospatial electrification-model outcomes. We defined four financing scenarios and estimated country- and mode-specific cost of capital values for each scenario. We then applied the estimated cost of capital values to OnSSET, an open source integrated electrification model7 (Supplementary information). Estimation of cost of capital. Countries and electrification modes differ in the extent to which they can tap into debt finance (which is cheaper than equity because equity bears the first losses48), the cost of the debt (interest rates) and the cost of equity (or expected return on equity) for the remainder of the investment. Therefore the amount of debt that investors can access as part of their financing, the cost of debt and the cost of equity need to be quantified to determine the cost of capital. The basic expression of this overall cost of capital is the WACC19. For this analysis, we follow a standard notation (equation (1)) and estimate the cost of equity, the cost of debt and the debt share separately49. ( ) ( ) E D (1) WACC = × Ke,i + × Kd,i V V Ke,i and Kd,i are the cost of equity and the cost of debt, respectively, for investments in a specific country i. E, D and V denote total equity, debt and capital; the debt share equals DV . For simplification, this ‘vanilla WACC’ does not account for the country-specific tax rate due to the uncertainty about which entities would be liable to pay tax, especially for grid extension. We demonstrate in a sensitivity analysis for a representative set of clusters that the effect of including tax on the LCOE is comparably small (Supplementary Fig. 1). A higher debt share indicates lower investment risk because it translates in less equity being able to cover first losses49. We use this relationship and implement electrification mode-specific risk premiums by applying mode-specific debt shares for MGs and SASs. For SASs, we use debt shares from the ten largest off-grid electrification companies in developing countries by disclosed financing between Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

Articles

NATUrE EnErgy 2010 and 201834 (Supplementary Table 2). As of 2018, SASs had raised more than US$1.1 billion with overall more than 50% debt. For MGs, we estimated debt shares using expert interviews (Supplementary Table 3) together with grey literature. Our analysis confirmed that MG companies in sub-Saharan Africa are predominantly financed through grants and equity, and in some rare cases through concessional loans. Close to no financing is currently sourced from commercial debt. The cost of debt represents the interest rate on debt (for example, corporate bonds or loans). Data on the cost of debt for off-grid electrification companies is not publicly available. In such cases, the literature approximates the cost of debt (Kd,i) by starting from the risk-free rate (rf ) and adding a markup that represents the additional risk to the risk-free rate. Here the markup consists of two parts. First, the default spread to reflect country risk (CRpi) and, second, a corporate bond premium (Dp) to account for the fact that corporate bonds have higher risks and higher yields than government bonds50 because they can default more easily, which investors seek compensation for50. The cost of debt is therefore given by: Kd,i = rf + CRpi + Dp

(2)

For the cost of debt calculation, rf is based on the five-year US treasury bond yield in 201951 and the CRpi represents each country’s default spread52. The data were averaged over a period of five years (2015–2019), which is the longest period for which data are available for most of the countries. Further, this period also excludes effects from the financial crisis. The Dp value is set to 1.2% based on a literature estimate50. The cost of equity reflects an investor’s expected returns for investing in a company. It was calculated using equation (3): Ke,i = rf + ERpi + Ip + SCp

(3)

where ERpi is the equity risk premium, which varies by country (i) and accounts for the return that investors require as compensation for the risk they are taking on an investment in a publicly listed company in this particular country. In addition, companies in the off-grid electricity sector are still nascent. Investors, therefore, cannot easily liquidate their assets53. To reflect this, we add an illiquidity premium (Ip) to the equity risk premium. Finally, off-grid electricity companies are also still relatively small and so often have higher risk and higher returns compared with companies with large market capitalization54. This is because investors in small companies encounter higher search and monitoring costs, less transparency and a poorer track record55. We reflect this with a small cap premium (SCp). We average data on equity risk premium52 over five years, too (2015–2019). The illiquidity premium (3.6%) was determined by calculating the average markup on the cost of equity due to illiquidity across three different studies56–58, and the small market-cap premium is set to 3.8% based on a literature estimate55. We are aware of the fact that our approach of assuming additive risk premia and drawing on literature estimates partly from regions outside of sub-Saharan Africa is only a first approximation of country- and mode-specific cost of capital. Given the scarcity of financial data, however, we consider the approach appropriate for the purpose of our study as it reasonably well approximates the differences between countries, modes and financing scenarios. As a further reality test and validation, we triangulated the resulting cost of capital values using estimates from the literature for developing countries, where available (Kenya23, Malawi59 and Cambodia60), and through interviews with experts working in electrification finance, which confirmed that our results are reasonable approximations (Supplementary Table 5). Definition of financing scenarios. We defined three financing scenarios (in addition to the uniform scenario) with the intention of depicting possible financing options to reach 100% electrification in sub-Saharan Africa. Table 1 in the main text illustrates the breakdown for each component and each financing scenario (Supplementary Table 4 provides an illustration of calculations for all scenarios). For the public sector financing scenario, the public sector is the investor in all electrification modes. We assume that all investment is financed by increasing sovereign debt and define the corresponding cost of capital as the sum of a risk-free rate rf and the country-specific additional risk. For the niche-financing scenario, the investor in grid extension is the public sector and so the public cost of capital (above) is applied. Off-grid electrification is financed by the private sector. In this case, the cost of debt is equal to equation (2) and the cost of equity is equal to equation (3). The average debt share of the ten largest SAS companies is 50% (Supplementary Table 2), while the debt share for MG is 0%. For the mainstream financing scenario, grid extension is still financed by the public sector and remains unchanged. Off-grid electrification is financed by the private sector, however, it is now assumed to be mature. We therefore amend the cost of equity for MGs and SASs by removing the illiquidity premium and the small market-cap premium from equation (3). Further, as financial markets mature, debt shares tend to increase. We reflect this by assuming that all MG and SAS investments can attract the debt levels that current market leaders are able to attract. In this case, we use the debt shares of the market leaders among the ten largest off-grid companies34 (75% for SASs) and 40% for MGs based on interviews with experts in the off-grid electricity sector. Nature EnerGy | VOL 7 | July 2022 | 631–641 | www.nature.com/natureenergy

Application in electrification model. The integrated geospatial electrification model OnSSET7 was used for the electrification planning analysis. Detailed documentation and data can be accessed on the Global Electrification Platform7,11,22. OnSSET is a optimization energy-modelling tool which uses population-density data and geospatial properties for un-electrified clusters (at a resolution of 100 × 100 m), together with techno-economic data to size energy systems. The objective of the model is to meet total electricity demand for clusters to be electrified in a target year by calculating the LCOE for different electrification modes: grid extension, MG and SAS. The electrification mode with the lowest LCOE is then selected as the final electrification option for that particular cluster. In the model, the options for electrification through MGs include diesel-powered systems, solar PV plus batteries, hydro-power or wind power. SASs are powered using diesel generators or solar PV plus batteries. OnSSET uses the LCOE formula shown in equation (4) for each cluster: ∑n Im +O&Mm +Fm −Sm LCOE =

m=1

(1+r)m

Em m=1 (1+r)m

∑n

(4)

lm is the investment cost for an electrification mode in year m, O&Mm are the operation and maintenance costs, Fm are fuel costs, Sm is the salvage value (the value of the energy system at the end of its useful life), Em is the electricity generated, r the discount rate and n is the lifetime of the electrification mode in years. For grid extension, the LCOE equals the sum of the average national grid LCOE (based on an estimated country generation capacity mix for 2030) and the added LCOE of transmission and distribution infrastructure. The LCOE for MGs and SASs account for the generation capacity costs, distribution infrastructure costs (in the case of MGs), O&M costs and the fuel costs of the energy systems. OnSSET has been used for analysis by the World Bank11 and the International Energy Agency1. While there are possible geospatial electrification modelling alternatives to OnSSET—Reference Electrification Model (REM)9, IMAGE-TIMER model61, or the Network planner62—OnSSET is chosen because the code is open source, it uses publicly available data and it provides renewable energy technology options, which are representative of the current off-grid electrification landscape in sub-Saharan Africa. We modified the source code to implement country- and electrification mode-specific costs-of-capital values (referred to in the model as the discount rate). For this analysis, a cluster in the model refers to an area of land occupied by households, while the cluster density refers to the population within a particular cluster divided by the area of the cluster. The cluster density is one criterion used in the selection of countries for the deep dive analysis (Supplementary Table 1). The extended-area grid pathway was implemented by running the model as is implemented on the Global Electrification Platform11. For the existing-area pathway, we adjusted a model parameter to prevent extension of the existing-area network. For this analysis, OnSSET calculates the LCOE for different electrification modes in two target years—2025 (50% of the population electrified) and 2030 (100% of the population electrified). We omit fossil fuel-powered options from this analysis for three reasons: firstly, a review of literature34,41,63,64 and discussions with off-grid electricity sector experts showed that current business models for most off-grid companies deploy renewable energy technology due to the remoteness of the rural regions and the associated high cost of transporting fuel to meet energy requirements. SASs (used to power households) are now exclusively powered by solar PV plus batteries. Secondly, analyses comparing renewables to fossil fuel-powered options have already been extensively studied in literature6,7,23,65,66. Thirdly, our analysis aims to explore the viability of low-carbon technology options in the move towards meeting the SDGs. While all energy options would make it possible to meet SDG 7 (energy), the inclusion of fossil fuel-powered options would mean that SDG 13 (climate action) may not be reached. This is because the model outcomes would largely support deployment of diesel-powered systems over renewables because of the higher capital cost of renewables. The assumptions for the different electrification options are illustrated in Table 2. The end-year target demand per household is based on the World Bank’s multi-tier framework67. The multi-tier framework provides a standardized measure for household electricity demand using a spectrum of tiers ranging from 1 to 5. Each tier represents a set of energy services based on defined energy attributes resulting in a total amount of energy measured in kWh per household67. The target demand tier is set to tier 3 for both urban and rural areas across all countries. This is because detailed electricity-demand forecasts at the household level are unavailable for all countries in sub-Saharan Africa. Tier 3 level energy services offer a minimum of approximately 365 kWh annually per household67,68. While in practice, newly connected households tend to consume tier 1 and 2 level services69,70—these tiers do not typically support productive loads such as water pumps and refrigerators68,71—our analysis intends to allow for this possibility. Further, regions electrified through grid extension experience variations in energy services, for example, grid reliability37, and so are not bound by demand tiers. Nevertheless, despite this assumption, we would expect to see the same effect of the cost of capital across the different tiers; the main factor that would influence the outcome would be the population-cluster density. Increasing the tier would favour grid extension or MGs given more clusters will have higher energy requirements. 639

Articles This would reduce the LCOE, while lower tiers would favour SASs with exception of very high population-density clusters. We use country data and technology-specific modelling assumptions provided by the World Bank’s global electrification platform11 (Supplementary Table 7 provides model techno-economic inputs for grid extension, and Supplementary Table 8 provides techno-economic input parameters for off-grid modes). For the LCOE calculation in Table 4, the following assumptions are made. First, the mean is based on the average LCOEs for clusters where there is a difference between the LCOE at niche (status quo) financing conditions for uniform electrification outcomes and the niche-financing conditions for niche (status quo) electrification outcomes. Second, we carry out the estimation using only new connections in 2030 and only for clusters where there is at least one new connection. This is because 88% of total new connections after 2018 occur in 2030 (only 12% in 2025 to reach the 50% electrification target). Further, an analysis of the electrified clusters in 2025 (12% of the total) shows that 96% (uniform financing scenario) and 97% (niche (status quo) financing scenario) of these (almost 12%) retain the same electrification-mode choice in 2030. Third, the analysis is carried out for fully un-electrified sites in 2018. We therefore exclude grid-electrified sites because the LCOE remains unchanged because the model assumes an estimated LCOE for already grid-electrified clusters (in 2018).

Data availability

Country-level geospatial input data for the time period (2018–2030) is from the World Bank’s Global Electrification Platform11, which is, to our knowledge, the most complete and open source data for sub-Saharan African countries. The data have also been uploaded on an online public repository72. For this analysis, we use country input files that were generated by the Global Electrification Platform that can be accessed for each country on the platform website11. The data define physical properties of individual population clusters and are required to determine the electricity consumption by households in 2030. Scenario input data can also be found in Supplementary Table 9. The maps (Fig. 3) are plotted using cluster shape file data that is also available on the World Bank’s Global Electrification Platform11. The base layers used to plot the map include water bodies73 and administrative boundaries74.

Code availability

The modified OnSSET model used for this analysis can be accessed through the GitHub public repository (https://bit.ly/3jlre4G).

Received: 28 June 2021; Accepted: 3 May 2022; Published online: 9 June 2022

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Acknowledgements

We thank interviewees and technical experts who provided valuable feedback on empirical values used in the analysis and participants at the Sustainable Energy Transitions Initiative (SETI) virtual workshop 2020, the Centre of Economic Research at ETH Zurich (CER-ETH) PhD Seminar, the Swiss Association of Energy Economics (SAEE) workshop and EPG group members who provided feedback on earlier drafts of the paper. This project has received funding from the Engineering for Development (E4D) Doctoral Scholarship Programme, which is funded by the Sawiris Foundation for Social Development and the Swiss Agency for Development and Cooperation (C.A.), and from the European Union’s Horizon 2020 research (B.S.) and innovation programme (B.S.), European Research Council (ERC) (grant agreement number 948220, project GREENFIN) (B.S.).

Author contributions

T.S.S., B.S. and C.A. secured project funding; C.A., T.S.S., B.S., F.E. and N.J.W. designed the research; C.A., F.E. and B.S. coordinated the data research; C.A. carried out the modelling analysis; C.A., F.E., B.S. and T.S.S. conducted interviews; and C.A., F.E., T.S.S., B.S. and N.J.W. wrote the paper.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41560-022-01041-6. Correspondence and requests for materials should be addressed to Churchill Agutu, Florian Egli, Tobias S. Schmidt or Bjarne Steffen. Peer review information Nature Energy thanks the anonymous reviewers for their contribution to the peer review of this work. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. © The Author(s), under exclusive licence to Springer Nature Limited 2022

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