Examining the Struggle for Electricity in Sub-Saharan Africa

Energy Access Globally Overtime

Over the past two decades, there has been a remarkable surge in global access to electricity. South Asia, which was ranked second lowest in terms of electricity access in 2000, has made significant improvements, nearly doubling its access rate to reach an impressive 98.77% by 2021. This progress underscores the region’s commitment to bridging the gap in energy accessibility. However, despite these advancements, Sub-Saharan Africa continues with only 50.57% of its population having access to electricity according to the most recent data available. This figure reflects the stark reality of energy poverty in Sub-Saharan Africa, making it one of the most underdeveloped regions globally in terms of energy access. Efforts to address this issue are crucial for the region’s socio-economic development and the improvement of the quality of life for millions of people living there.

options(repos = c(CRAN = "https://cran.rstudio.com/"))
#TLA: Latin America & Caribbean (IDA & IBRD)
#TSA: South Asia (IDA & IBRD)
#SSA: Sub-Saharan Africa (excluding high income)
#TEC: Europe & Central Asia (IDA & IBRD)
#MEA: Middle East & North Africa
#TEA: East Asia & Pacific (IDA & IBRD)
access_electricity <- esg_vars_long
access_electricity$Year <- as.numeric(access_electricity$Year)

access_electricity <- esg_vars_long %>%
  select(Year, Country.Code, Country.Name, EG.ELC.ACCS.ZS) %>%
  filter(Year >= 2000 &
         EG.ELC.ACCS.ZS != ".." &
         Country.Code %in% c("TLA", "TSA", "SSA", "TEC", "MEA", "TEA")) %>%
  arrange(Year)


p <- plot_ly(data = access_electricity, x = ~Year, y = ~EG.ELC.ACCS.ZS, type = 'scatter', mode = 'lines+markers', color = ~Country.Name)
htmlwidgets::saveWidget(as_widget(p), "plotly_chart.html")


#p <- plot_ly(data = access_electricity, x = ~Year, y = ~EG.ELC.ACCS.ZS, 
             #type = 'scatter', mode = 'lines+markers',
             #color = ~Country.Name, text = ~paste("Access:", EG.ELC.ACCS.ZS, "%"),
             #hoverinfo = 'text+x+y')


# Layout adjustments
p <- layout(p, title = 'Access to Electricity Over Time',
            xaxis = list(title = 'Year'),
            yaxis = list(title = 'Access to Electricity (%)'),
            hovermode = ('closest'),
            legend = list(font = list(size = 8),  
              xanchor = 'right',
              yanchor = 'top',
              bgcolor = 'rgba(255, 255, 255, 0.5)',
              margin = list(r = 120, t = 50, b = 50, l = 50),
              bordercolor = '#E2E2E2',
              borderwidth = 1,
              orientation = 'v'
            ))

# Render the plot
# Save the Plotly plot as a static image
#plotly::orca(p, file = "plotly_plot.png")

p

Why is the access to electricity so low in Sub-Saharan Africa?

options(repos = c(CRAN = "https://cran.rstudio.com/"))
access_electricity <- esg_vars_long
access_electricity$Year <- as.numeric(access_electricity$Year)

access_electricity_SA <- esg_vars_long %>%
  select(Year, Country.Code, EG.ELC.ACCS.ZS) %>%
  filter(Year >= 2000 &
         EG.ELC.ACCS.ZS != ".." &
         (Country.Code == "TSA" | Country.Code == "SSA")) %>%
  arrange(Year)

access_electricity_SA <- esg_vars_long %>%
  select(Year, Country.Name, EG.ELC.ACCS.ZS) %>%
  filter(Year >= 2000 &
         EG.ELC.ACCS.ZS != ".." &
         Country.Name %in% c("Angola", "Benin", "Botswana", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote D'Ivoire", "Equatorial Guinea", "Eritrea", "Eswatini", "Ethiopia", "Gabon", "Gambia, The", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mauritius", "Mozambique", "Namibia", "Niger", "Nigeria", "Rwanda", "Sao Tome And Principe", "Senegal", "Sierra Leone", "Somalia", "South Africa", "South Sudan", "Sudan", "Tanzania", "Togo", "Uganda", "Zambia", "Zimbabwe"
))

# Load the world shapefiles
world <- ne_countries(scale = "medium", returnclass = "sf")

# Filter for only African countries
african_countries <- world[world$continent == "Africa", ]

# Merge data with the filtered shapefiles
electricity_map_data <- merge(african_countries, access_electricity_SA, by.x = "name", by.y = "Country.Name")


electricity_map_data$EG.ELC.ACCS.ZS <- as.numeric(as.character(electricity_map_data$EG.ELC.ACCS.ZS))

#Latest year data
latest_data <- electricity_map_data %>%
  filter(Year == max(Year, na.rm = TRUE))

#Color
#palette_function <- colorNumeric(palette = "YlGnBu", domain = latest_data$EG.ELC.ACCS.ZS)

range_electricity <- range(electricity_map_data$EG.ELC.ACCS.ZS, na.rm = TRUE)
palette_function <- colorNumeric(palette = "YlGnBu", domain = range_electricity)


# Create the map
electricity_map <- leaflet(electricity_map_data) %>%
  addTiles() %>%
  addPolygons(
    fillColor = ~palette_function(EG.ELC.ACCS.ZS),
    color = "#BDBDC3",
    fillOpacity = 0.7,
    weight = 1,
    popup = ~paste(name, ":", EG.ELC.ACCS.ZS, "%")
  ) %>%
  addLegend(
    position = "bottomright",
    pal = palette_function,
    values = ~EG.ELC.ACCS.ZS,
    title = "Access to Electricity (%)",
    opacity = 1
  )

electricity_map <- electricity_map %>%
  setView(lng = 20, lat = 0, zoom = 2.5)

electricity_map

Landlocked Central Africa

Despite the low average access to electricity, the disparities in electricity access among countries in this area are more concerning. Lower than 10% of the population in land-locked countries such as Niger and Chad have the access to electricity, while the number countries such as Gabon and South Africa is above 80%.

Land-locked areas usually face greater challenges in infrastructure development. Limited transportation options will lead to higher costs of the potential energy projects and longer-than-usual construction period.

Additionally, land-locked areas generally have a larger underserved community, which add on more initial costs of energy development projects and maintenance cost in the subsequent years. Given the limited financial resources, the substantial financial outlay is an extreme burden for most of these land-locked countries.

options(repos = c(CRAN = "https://cran.rstudio.com/"))
library(esquisse)
library(ggplot2)

# Filter the data for TSA and SSA
tsa_ssa_data <- esg_vars_long %>%
  select(Year, Country.Code, EG.ELC.ACCS.ZS, NY.GDP.MKTP.KD.ZG) %>%
  filter(EG.ELC.ACCS.ZS != "..") %>%
  filter(NY.GDP.MKTP.KD.ZG != "..") %>%
  filter(Country.Code %in% c("TSA", "SSA"))

# Convert columns to numeric
tsa_ssa_data$Year <- as.numeric(tsa_ssa_data$Year)
tsa_ssa_data$NY.GDP.MKTP.KD.ZG <- as.numeric(tsa_ssa_data$NY.GDP.MKTP.KD.ZG)

# Check the data types again
str(tsa_ssa_data)

# Filter data for TSA
tsa_data <- tsa_ssa_data %>%
  filter(Country.Code == "TSA")

# Filter data for SSA
ssa_data <- tsa_ssa_data %>%
  filter(Country.Code == "SSA")
  

# Increase the size of the plot and points
options(repr.plot.width=10, repr.plot.height=6)

# Plot TSA and SSA with smoother lines
# Plot TSA and SSA with smoother lines
ggplot(tsa_ssa_data) +
  aes(x = Year, y = NY.GDP.MKTP.KD.ZG, colour = Country.Code) + 
  geom_point(shape = 16, size = 1)+  # Use larger points
  geom_smooth(data = tsa_data, aes(group = 1), method = "loess", se = FALSE, color = "coral", size = 1.5) +  
  geom_smooth(data = ssa_data, aes(group = 1), method = "loess", se = FALSE, color = "cornflowerblue", size = 1.5) + 
  labs(x = "Year", y = "GDP Growth", title = "Access to Electricity and GDP Growth Over Time") +
  theme_minimal(base_size = 14) +  
  scale_colour_manual(values = c("TSA" = "coral", "SSA" = "cornflowerblue"),
                      labels = c("TSA" = "South Asia",
                                 "SSA" = "Sub-Saharan Africa")) +
  theme(axis.text = element_text(size = 10),  
        legend.title = element_blank(),     
        legend.text = element_text(size = 9)) 

In comparison to other regions, South Asia, despite its limited resources and comprising mainly of developing countries, has demonstrated remarkable growth over the past two decades. When comparing the GDP growth and access to electricity between South Asia and Sub-Saharan Africa, we osberve a clear positive correlation. South Asia consistently exhibits a higher GDP growth rate alongside better access to electricity than Sub-Saharan Africa. This trend suggests a stronger economic growth in South Asia throughout this period. This correlation is intuitive; access to energy serves as a key indicator of productivity. Greater access to electricity, or other forms of energy, signifies a region’s possession of more advanced technology and human capital to facilitate the production of goods and services, therefore driving higher economic growth. Thus, enhancing access to energy stands as an indispensable initial step towards bolstering GDP growth.

How to increase energy access for landlocked Sub-Saharan countries?

Increase infrastructure budgets for landlocked countries:

Governments and international organizations should allocate more funds specifically for infrastructure development in landlocked countries. This can include investments in building power plants, expanding electricity grids, and improving transportation networks to facilitate the movement of equipment and materials necessary for energy projects.

Navigate more financing tools and infrastructure building domestically and internationally with the help of multilateral development banks and partner countries:

Landlocked countries can explore various financing options to fund energy infrastructure projects. Multilateral development banks, such as the World Bank and the African Development Bank, can provide loans and grants for energy projects. Additionally, partnerships with other countries and international organizations can bring in expertise, technology, and financial support to accelerate energy infrastructure development.

Maintain active communication with peer countries to learn from the experiences of energy finance projects by the neighboring countries in Sub-Saharan Africa:

Landlocked countries can benefit from sharing knowledge and experiences with neighboring countries that have successfully implemented energy finance projects. By participating in regional forums, workshops, and knowledge-sharing platforms, these countries can learn about best practices, innovative financing mechanisms, and lessons learned from past projects. This collaboration can help landlocked countries avoid common pitfalls and accelerate the implementation of energy infrastructure projects.