Unlocking Renewable Energy Investment in Africa: The Power of Creative Financing

Alyssa Anderson
April 24, 2024

Photo of Solar Panels and Wind Turbines – The Nature Conservatory (2020)

Access to energy is a fundamental driver of economic development and quality of life worldwide. However, according to recent data from the International Energy Agency (IEA), the number of people living without electricity is on the rise, reaching nearly 775 million in 2022, marking the first global increase in two decades.

The United Nations’ Sustainable Development Goals (SDGs) prioritize Affordable and Clean Energy Access as a crucial component for achieving broader development objectives. Energy access directly influences health outcomes, poverty alleviation, gender equality, education access, economic growth, and the availability of basic services such as cooking, heating, refrigeration, transportation, and sanitation.

Additionally, individuals lacking access to affordable electricity often rely on dirtier energy sources, such as coal, which increase greenhouse gas emissions and exacerbate climate change. Clean energy technologies, not only decrease carbon emissions, but can also be more efficient in providing energy at lower costs over the long term compared to traditional fossil fuels. Increasing investment in sustainable energy sources for developing populations can have the dual effect of helping countries meet Net Zero emissions targets, while improving development outcomes.

In this study, I examined three datasets to pinpoint regions with the lowest concentrated total energy investments, identify populations in those regions that encounter energy access barriers, and highlight the countries in these regions with the highest debt-to-GDP ratio. Many of the countries with the lowest access to energy also face the highest levels of debt, creating a vicious cycle where limited resources are further constrained by financial obligations. To break this cycle and address the pressing need for renewable energy investment, financial markets and Development Finance Institutions (DFIs) must implement innovative financing mechanisms to support those most in need.

Allocation of Energy Investment in Emerging Markets

In the first analysis, I examined energy investment data by region from the International Energy Agency (IEA). To focus on emerging markets, and mitigate the influence of outliers, particularly driven by the Middle East’s substantial oil resources, the analysis excluded Europe, North America, and the Middle East. The graph revealed that Africa and Latin America have the lowest total investments in energy, particularly in clean energy. Despite housing approximately 83% of the world’s population, emerging markets receive only one-third of the world’s total energy investment, with Africa receiving a mere 3%.

options(repos = c(CRAN = "https://cran.rstudio.com/"))
library(tidyverse)
library(janitor)
library(countrycode)
library(dplyr)
library(ggplot2)
library(lubridate)
library(readxl)
library(xfun)
library(esquisse)
library(stringr)
library(readxl)
library(RColorBrewer)
library(grid) 
library(ggrepel)
library(viridis)
options(verbose = FALSE)

total_invest_long_1 <- read_excel("C:/Users/alyss/OneDrive - Johns Hopkins/Desktop/MIEF Courses/Sustainable Finance/03_data_processed/long_investment_1.xlsx")

#graph of investment amounts per region

ggplot(total_invest_long_1, aes(x = region, y = Investment_Amount, fill = Investment_Type)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_manual(values = c("total_clean_investment" = "forestgreen", "total_investment" = "darkblue"),
                    labels = c("Investments in Clean Energy", "Total Energy Investments")) +
  labs(title = "Comparison of Energy Investments by Region (2015-2023)",
       x = "",
       y = "Investment (USD billions)",
       subtitle = "The African continent only receives 3% of the world's total investment in energy",
       caption = "Data: International Energy Agency",
       fill = "Investment Type") +
  theme_minimal(base_family = "Helvetica") +
  theme(axis.text.x = element_text(size = 8, angle = 45, hjust = 1, vjust = 1),  # Rotate x labels for better fit
        axis.text.y = element_text(size = 9),
        axis.title = element_text(size = 9),
        plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
        legend.title = element_text(size = 9),
        legend.text = element_text(size = 9),
        legend.position = "top",
        legend.key.size = unit(1, "lines"),  # Bigger legend keys
        plot.caption = element_text(size = 8, hjust = 0))

Which Countries in Africa Have the Lowest Access to Energy?

While energy access can encompass the availability of fuel for transportation or cooking, the primary data collected is on electricity usage. The subsequent data, sourced from Our World In Data, concentrates on Africa due to its low energy investments. The graph compares each country’s electricity access to its GDP per capita in 2020. As we can see by the linear trend, access to electricity tends to increase with income. Sub-Saharan Africa has the lowest rates of electricity access, with around 53% of the population not having reliable access to this commodity. The IEA projects that industrialization and a growing population may double demand for energy on the continent in the coming decades, worsening the energy crisis.

# import data 

access_to_electricity_vs_gdp_per_capita_1 <- read_excel("C:/Users/alyss/OneDrive - Johns Hopkins/Desktop/MIEF Courses/Sustainable Finance/03_data_processed/access-to-electricity-vs-gdp-per-capita (1).xlsx")

# fill in columns
elect_gdp_data <- access_to_electricity_vs_gdp_per_capita_1 %>%
  group_by(Entity) %>%   # Group the data by country
  fill(Continent, .direction = "downup") %>% # Fill NA values down and up within each group
  ungroup()

# filter for Africa 
elect_gdp_africa <- elect_gdp_data %>%
  filter(Continent == "Africa")

# rename long column names 
names(elect_gdp_africa)[5] <- "gdp_per_cap"
names(elect_gdp_africa)[4] <- "access_to_elect"

# filter for 2020
elect_gdp_africa_2020 <- elect_gdp_africa %>%
  filter(Year == 2020)

# create logged Gdp
log_gdp_africa_2020 <- elect_gdp_africa_2020 %>%
  mutate(log_gdp = log(gdp_per_cap))

# graph

ggplot(log_gdp_africa_2020, aes(x = log_gdp, y = access_to_elect, color = Entity)) +
  geom_point() +
  geom_text_repel(
    aes(label = Entity), 
    vjust = -0.5, 
    hjust = -0.5,
    segment.color = NA  # This removes the lines connecting labels to points
  ) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Access to Electricity vs. Log GDP per Capita in Africa 2020",
    x = "Natural Log of GDP per Capita (USD)",
    y = "Access to Electricity (%)",
    color = "Country",
    subtitle= "Income levels and access to energy are intertwined",
    caption = "Data | Our World In Data"
  ) +
  theme_minimal() +
  scale_color_viridis(discrete = TRUE) +  # Apply Viridis 
  guides(color = FALSE) 

How to Incentivize Investment?

From the earlier analysis, it’s evident that the African continent faces the dual challenge of low energy investment and significant barriers to energy access. Addressing under-investment in universal energy access should be a central focus of development finance institutions, governments, and private investors, with estimates suggesting that achieving universal electricity access by 2030 would require substantial investments of $30 billion per year. However, due to high debt levels in many African countries, investors are cautious about investing in renewable energy expansion on the continent.

Using data from the International Debt Statistics database, it becomes apparent that among the African countries in the lowest quadrant of the ‘Access to Electricity vs. GDP per Capita’ graph, their debt-to-GDP ratios are steadily increasing over time. Creative financing techniques such as concessional financing, blended finance, and debt-for-nature swaps will be essential to aid these countries in need.

# import data 

debt_to_gdp <- read_excel("C:/Users/alyss/OneDrive - Johns Hopkins/Desktop/MIEF Courses/Sustainable Finance/03_data_processed/debt_gdp_ratio_country_ts.xlsx")

# convert to numeric

debt_to_gdp[-which(names(debt_to_gdp) %in% c("year"))] <- lapply(debt_to_gdp[-which(names(debt_to_gdp) %in% c("year"))], function(x) as.numeric(as.character(x)))

# pivot long 
long_debt_gdp <- pivot_longer(debt_to_gdp, 
                        cols = -year,  # Exclude the Year column from pivoting
                        names_to = "country",
                        values_to = "value")

#rename values column
long_debt_gdp <- long_debt_gdp %>% rename(debt_to_gdp_ratio = value)

#remove N/As
debt_gdp_clean <- na.omit(long_debt_gdp)


#pulling bottom obs from last graph 

sorted_data <- log_gdp_africa_2020 %>%
  arrange(log_gdp, access_to_elect)

# Select the bottom 20 observations
bottom_20 <- head(sorted_data, 20)

bottom_20_countries <- bottom_20$Entity
filtered_debt_gdp <- debt_gdp_clean %>%
  filter(country %in% bottom_20_countries)


# creating graph

# filter select countries
selected_countries <- c("Africa", "Guinea-Bissau", "Mozambique", "Rwanda", "Gambia", "Sierra Leone", "Niger", "Mali", "Uganda", "Burundi", "DR Congo")

debt_gdp_10 <- debt_gdp_clean %>% filter(country %in% selected_countries)
debt_gdp_10_2011 <- debt_gdp_10 %>% filter(year >= 2011)

# make end label
label_data <- debt_gdp_10 %>%
  group_by(country) %>%
  slice_tail(n = 1) 

my_colors <- viridis_pal(option = "viridis")(15)


ggplot(data = debt_gdp_10_2011, aes(x = year, y = debt_to_gdp_ratio, group = country, color = country)) +
  geom_line() +
  geom_text(data = label_data, aes(label = country, fontface = "bold"), hjust = 1, vjust = -0.5, size = 3, nudge_y = c(-0.025, 0, 0, 0, 0, 0, 0.1, -0.05, 0, 0, 0.1)) +
  #Africa, Gambia,G-B,Mali,Mozambique, Niger, Rwanda, sierra leone,uganda 
  labs(title = "Debt to GDP Ratio by Country Over Time",
       x = "Year",
       y = "Debt to GDP Ratio (%)",
       subtitle = "The Countries with the lowest electricity access often have the highest levels of debt",
       caption = "Data | International Debt Statistics Database"
       ) +
  scale_x_continuous(breaks = unique(debt_gdp_10_2011$year)) +
  scale_color_manual(values = my_colors) +
  theme_minimal() +
  theme(legend.position = "none")

Debt-For-Nature Swaps Offer a Potential Solution

One solution is using debt-for-nature swaps (DFNS). These swaps convert debts of low- and middle-income countries into commitments for environmental conservation. Debt-for-nature-swaps, or debt-for-energy-swaps in this case, offer an alternative to traditional financing when access to capital markets or loans are limited, as observed in the countries above. Using DFNS instead of traditional debt could encourage more investment in clean energy. While DFNS may not be suitable for every situation, they represent a positive step forward and encourage investors to explore innovative approaches beyond traditional financing methods.

Universal access to renewable energy is not only a moral imperative but also essential for reducing the effects of climate change, ultimately contributing to environmental sustainability, resilience, and promoting economic growth and prosperity for all.