Renewable Energy Financing: Trends, Challenges, and Innovations

Source: Internet

Motivation

Since the Industrial Revolution, fossil fuels have become the dominant energy source for most countries across the world. The interactive map below shows the share of energy that comes from fossil fuels globally. Fossil fuel combustion accounts for three-quarters of global greenhouse gas emissions. A rapid transition to low-carbon energy sources, such as renewables, is crucial to mitigate air pollution.

library(ggplot2)
library(plotly)
library(rnaturalearth)
library(sf)
library(dplyr)
library(readr)
library(leaflet)

# Adjust the path for cross-platform compatibility and assuming the script is run in an environment similar to RStudio
data_path <- "C:/Users/puxin/OneDrive/Documents/03_data_processed/fossil-fuels-share-energy (1).csv"
# For cross-platform compatibility, especially in a non-Windows environment, consider using file.path() or adjusting as necessary.

energy_data <- read_csv(data_path)

# Prepare the filtered data
filtered_data <- energy_data %>%
  filter(Year == 2022) %>%
  select(code, FossilFuelPercentage = `FossilFuelPercentage`) # Ensure column name matches

# Get the map data in sf format
world_map_data <- rnaturalearth::ne_countries(returnclass = "sf")

# Merge the data with the map data
merged_data <- world_map_data %>%
  left_join(filtered_data, by = c("iso_a3" = "code"))

# Prepare a color palette
pal <- colorNumeric(palette = "YlOrRd", domain = merged_data$FossilFuelPercentage, na.color = "white")

# Create the leaflet map
leaflet(merged_data) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    fillColor = ~pal(FossilFuelPercentage),
    color = "#BDBDC3", weight = 1, opacity = 1,
    fillOpacity = 0.7, smoothFactor = 0.5,
    highlightOptions = highlightOptions(
      weight = 3, color = "#666", bringToFront = TRUE
    ),
    popup = ~paste(
      "<strong>Country:</strong>", name, "<br/>",
      "<strong>Fossil Fuel Percentage:</strong>", FossilFuelPercentage, "%"
    )
  ) %>%
  addLegend(
    position = "topright", 
    pal = pal, 
    values = ~FossilFuelPercentage,
    labFormat = labelFormat(suffix = "%")
  ) %>%
  addControl(
    "<h4>Share of primary energy consumption from fossil fuels 2022</h4>
    <br/>Source: Energy Institute - Statistical Review of World Energy (2023)", 
    position = "bottomleft", 
    className = "map-source"
  )

In 2022, global investment in energy transition technologies hit a record USD 1.3 trillion. Yet, to meet the 1.5°C scenario outlined in IRENA’s 2023 World Energy Transitions Outlook, investments must quadruple annually. Renewable energy investments also reached a new high of USD 0.5 trillion, but this is less than a third of the yearly investment required.

This report aims to provide policymakers with insights into investment trends by answering the following questions, enabling them to use market instruments to develop and adapt policies that expand the global transition to renewable energy.

Investment Trends: What are the trends in investment by technology, sector, region, source of finance, and financial instrument?

Financing Gaps: What are the financing gaps in the deployment of renewable energy technologies, and how can these gaps be addressed through informed policy making?

Capital Markets Innovation: How can the use of innovative capital markets instruments open up important additional avenues for investors, thereby enhancing the flow of funds towards the energy transition?

Data Source and Methodology

To do this analysis, I use data from five data sets.

Energy Institute - Statistical Review of World Energy (2023): This dataset provides a comprehensive review of global energy statistics, focusing on primary energy sources before their transformation into electricity or other energy forms. Energy Institute

World Bank Open Data: This resource offers a wealth of information on global development indicators, including GDP per capita in current US dollars. World Bank

Global Carbon Budget (2023): This dataset focuses on per capita CO₂ emissions for the year 2022, excluding land-use changes but including emissions from fossil fuels and industry. Global Carbon Budget

IMF Climate Change Dashboard (Green Bonds): This dashboard highlights the growing sector of green bonds, financial instruments designed to support climate and environmental projects. IMF(Green Bonds)

IRENA Statistics Data (Finance and Investment): This dashboard sheds light on global trends in renewable energy investment, offering a overview of how funds are allocated across different renewable technologies and regions. IRENA

Private Investment

Between 2013 and 2020, the private sector funded about 75% of the world’s renewable energy investments, largely favoring established technologies due to a preference for lower-risk and financially lucrative opportunities. This trend is evident with 83% of solar PV investments in 2020 being privately financed, in contrast to hydropower, which rely predominantly on public finance, only 3% of its funding from private investors.

library(readxl)
library(dplyr)
library(ggplot2)
library(plotly)
library(tidyr)

data_path <- "C://Users//puxin//OneDrive//Documents//00_data_raw//2023_IRENA-CPI_RE_Investment_Trends.xlsx"
df <- read_excel(data_path)

df_source_private <- df %>%
  filter(Category == "Source", Product == "Private") %>%
  group_by(Year) %>%
  summarise(PrivateInvestment = sum(`Investment in USD billion`), .groups = 'drop')

df_source_total <- df %>%
  filter(Category == "Source") %>%
  group_by(Year) %>%
  summarise(TotalInvestment = sum(`Investment in USD billion`), .groups = 'drop')

# Join the data frames to calculate the proportion correctly
df_source_proportion <- df_source_private %>%
  left_join(df_source_total, by = "Year") %>%
  mutate(Proportion = PrivateInvestment / TotalInvestment)



df_technology <- df %>%
  filter(Category == "Technology") %>%
  group_by(Year, Product) %>%
  summarise(TotalInvestment = sum(`Investment in USD billion`), .groups = 'drop') %>%
  group_by(Year) %>%
  mutate(Proportion = TotalInvestment / sum(TotalInvestment))


# Prepare the colors for the stacked bar chart
unique_products <- unique(df_technology$Product)
colors <- RColorBrewer::brewer.pal(min(length(unique_products), 8), "Set3")

# Starting Plotly object with region investment bars
fig <- plot_ly()

# Adding bars for each 'Product' within 'Region'
for (i in seq_along(unique_products)) {
  product_data <- df_technology %>% filter(Product == unique_products[i])
  fig <- fig %>%
    add_bars(data = product_data, x = ~Year, y = ~Proportion, name = unique_products[i], marker = list(color = colors[i]), offsetgroup = i)
}

# Adding Private Investment proportion line
fig <- fig %>%
  add_lines(data = df_source_proportion, x = ~Year, y = ~Proportion, name = 'Private Investment in Source', line = list(color = 'blue')) %>%
  add_markers(data = df_source_proportion, x = ~Year, y = ~Proportion, name = 'Private Investment in Source', marker = list(color = 'red'))

# Adjusting layout for clarity
fig <- fig %>%
  layout(title = 'Proportion of Investments by Technology and Source Over Years',
         barmode = 'stack',
         xaxis = list(title = 'Year'),
         yaxis = list(title = 'Proportion', tickformat = ',.2%'),
         legend = list(title = list(text = 'Investment Type')),
         annotations = list(
           list(
             text = 'Bars: Investment Proportions by Technology within Regions<br>Line & Dots: Private Investment Proportion in Source',
             x = 0.5,
             xref = 'paper',
             yref = 'paper',
             y = -0.22,
             showarrow = FALSE,
             font = list(size = 12)
           )
         ))

# Display the interactive plot
fig

Private capital also tends to flow to countries with lower real or perceived risks, or into frontier markets only when effective risk mitigation facilities are provided, while a large portion of the world’s population remains underserved.

library(readxl)
library(dplyr)
library(ggplot2)
library(plotly)
library(tidyr)

data_path <- "C://Users//puxin//OneDrive//Documents//00_data_raw//2023_IRENA-CPI_RE_Investment_Trends.xlsx"
df <- read_excel(data_path)

df_source_private <- df %>%
  filter(Category == "Source", Product == "Private") %>%
  group_by(Year) %>%
  summarise(PrivateInvestment = sum(`Investment in USD billion`), .groups = 'drop')

df_source_total <- df %>%
  filter(Category == "Source") %>%
  group_by(Year) %>%
  summarise(TotalInvestment = sum(`Investment in USD billion`), .groups = 'drop')

# Join the data frames to calculate the proportion correctly
df_source_proportion <- df_source_private %>%
  left_join(df_source_total, by = "Year") %>%
  mutate(Proportion = PrivateInvestment / TotalInvestment)


# Assuming df has already been loaded and prepared
df_region_technology <- df %>%
  filter(Category == "Region") %>%
  group_by(Year, Product) %>%
  summarise(TotalInvestment = sum(`Investment in USD billion`), .groups = 'drop') %>%
  group_by(Year) %>%
  mutate(Proportion = TotalInvestment / sum(TotalInvestment))

# Prepare the colors for the stacked bar chart
unique_products <- unique(df_region_technology$Product)
colors <- RColorBrewer::brewer.pal(min(length(unique_products), 8), "Set3")

# Starting Plotly object with region investment bars
fig <- plot_ly()

# Adding bars for each 'Product' within 'Region'
for (i in seq_along(unique_products)) {
  product_data <- df_region_technology %>% filter(Product == unique_products[i])
  fig <- fig %>%
    add_bars(data = product_data, x = ~Year, y = ~Proportion, name = unique_products[i], marker = list(color = colors[i]), offsetgroup = i)
}

# Adding Private Investment proportion line
fig <- fig %>%
  add_lines(data = df_source_proportion, x = ~Year, y = ~Proportion, name = 'Private Investment in Source', line = list(color = 'blue')) %>%
  add_markers(data = df_source_proportion, x = ~Year, y = ~Proportion, name = 'Private Investment in Source', marker = list(color = 'red'))

# Adjusting layout for clarity
fig <- fig %>%
  layout(title = 'Proportion of Investments by Region and Source Over Years',
         barmode = 'stack',
         xaxis = list(title = 'Year'),
         yaxis = list(title = 'Proportion', tickformat = ',.2%'),
         legend = list(title = list(text = 'Investment Type')),
         annotations = list(
           list(
             text = 'Bars: Investment Proportions by Technology within Regions<br>Line & Dots: Private Investment Proportion in Source',
             x = 0.5,
             xref = 'paper',
             yref = 'paper',
             y = -0.22,
             showarrow = FALSE,
             font = list(size = 12)
           )
         ))

# Display the interactive plot
fig

Generally speaking, this means that the lowest income populations often pay the most for essential energy, hindering poverty alleviation and socio-economic progress. This necessitates a much stronger role for public financing in these contexts and not fully relying on private capital which may keep widening the disparities.

Public Investment

Globally, the public sector provided less than one-third of renewable energy investment in 2020. State-owned financial institutions and national DFIs were the main sources that year. Multilateral DFIs provided 9% of public finance – accounted for about half of international flows coming from the public sector. Commitments from bilateral DFIs in 2020 fell 70% compared to 2019.

library(readxl)
library(dplyr)
library(plotly)
library(DT)


# Load the data
data_path <- "C://Users//puxin//OneDrive//Documents//00_data_raw//IRENA_RE_Public_Investment_2022.xlsx"
df <- read_excel(data_path)

# Summarize the investment amount by agency
agency_summary <- df %>%
  group_by(Agency) %>%
  summarise(TotalAmount = sum(`Amount (2020 USD million)`), .groups = 'drop') %>%
  arrange(desc(TotalAmount))

# Keep only the top ten agencies and group the rest as 'Others'
top_agencies <- agency_summary %>%
  slice(1:10) %>%
  mutate(units = "2020 USD million")  # Add the "units" column for top 10 agencies

others_sum <- sum(agency_summary$TotalAmount[-(1:10)])
top_agencies <- bind_rows(top_agencies, tibble(Agency = "Others", TotalAmount = others_sum, units = "2020 USD million"))

# Assuming you want to display the datatable with the 'units' information
datatable(top_agencies,
          options = list(pageLength = 15, autoWidth = TRUE),
          rownames = FALSE) %>%
  formatCurrency(c('TotalAmount'), currency = 'USD', interval = 3, mark = ',') %>%
  formatStyle('TotalAmount', fontWeight = 'bold')

# For the pie chart, since 'units' is the same for all categories, it doesn't need to be directly included in the plot
# If you want to display it, consider adding it to the hover information
fig2 <- plot_ly(top_agencies, labels = ~Agency, values = ~TotalAmount, type = 'pie', textinfo = 'none',
                hoverinfo = 'label+percent+name', insidetextorientation = 'radial',
                hovertemplate = paste("<b>%{label}</b><br>Total: %{value} (%{percent})<br>Units: 2020 USD million")) %>%
  layout(title = 'Proportion of Investments by Agency (Top 10 and Others)',
         showlegend = TRUE)

# Display the plot
fig2

Due to limited public funds, governments have usually prioritized using available resources to make renewable energy projects more attractive to private investors by improving their risk-return profiles.

Sovereign guarantees have been commonly used to address credit risks, but they may affecting its credit rating and ability to finance essential infrastructure by increasing a country’s contingent liabilities.

This is particularly problematic for emerging economies already facing significant debt and inflation post-COVID-19. As these countries struggle to secure affordable capital or offer sovereign guarantees, there’s a pressing need for innovative financial instruments that enable energy transition without straining their economies.

Innovating Market Instruments: The Case of Green Bonds

Innovative capital-market instruments, such as green bonds, can open up crucial additional avenues through which investors can invest in renewables. Given the strong preference of institutional investors for indirect investments, ideally via listed and rated instruments, green bonds act as a bridge between such providers of capital and renewable energy assets.

According to the Climate Bond Initiative (CBI), the green bonds market has experienced a spectacular growth in the recent past, increasing from USD 36.6 billion worth of issuances in 2014 to USD nearly 500 billion in 2022.

Visual analysis:

The size of each point in the scatter plot below represents the total amount of green bonds issued by the country. The analysis reveals several key insights:

European Nations: European nations, despite high GDP per capita, issue fewer green bonds, likely due to smaller financial markets.

China: China leads in green bond issuance but also has high per capita carbon emissions, indicating a need for sustainable financial investment.

United States: The United States is noted for its high GDP per capita and carbon emissions per capita, yet it ranks seventh in green bond issuance. This suggests that, despite its substantial economic and environmental footprint, the U.S. possesses untapped potential in the green bond market.

library(readr)
library(dplyr)
library(plotly)

# Load the data
data_path <- "C:/Users/puxin/OneDrive/Documents/03_data_processed/Final project sovereign data with CO2.csv"
df <- read_csv(data_path)

# Filter for 2022 and select the top ten countries based on cumulative_bn_usd
df_2022_top10 <- df %>%
  filter(Year == 2022) %>%
  arrange(desc(cumulative_bn_usd)) %>%
  slice(1:10)

# Calculate a sizeref for better size scaling of markers
max_cumulative_bn_usd <- max(df_2022_top10$cumulative_bn_usd, na.rm = TRUE)
sizeref_value <- max_cumulative_bn_usd / 100 # Adjust 100 based on desired max marker size appearance

fig <- plot_ly(data = df_2022_top10, x = ~log(GDP_Per_Capita), y = ~log(Annual_carbon_emissions_percapita),
               type = 'scatter', mode = 'markers',
               marker = list(
                 size = ~cumulative_bn_usd, 
                 sizeref = sizeref_value, 
                 color = ~cumulative_bn_usd,  # Color by cumulative_bn_usd to add variety
                 colorscale = 'Viridis',      # Use the 'Viridis' colorscale for visibility
                 showscale = TRUE             # Show color scale
               ),
               text = ~country, hoverinfo = 'text+x+y') %>%
  layout(title = 'Top 10 Cumulative Green Bond Investment Countries 2022 (USD Billion)',
         xaxis = list(title = 'GDP per capita current US$ (log scale) '),
         yaxis = list(title = 'Per capita CO₂ emissions (log scale)'),
         plot_bgcolor = 'rgba(0,0,0,0)',  # Set plot background color to transparent or light color
         paper_bgcolor = 'rgba(0,0,0,0)', # Set paper background color to transparent or light color
         hovermode = 'closest')

# Display the plot
fig

Conclusion

The report finds that investments have become further concentrated in specific mature technologies and uses, and in a small number of developed countries/regions. It underscores the need to direct public funds to regions and countries that have considerable untapped potential but find it difficult to attract investment. Funding must be focused on supporting energy transition infrastructure development, as well as enabling policy frameworks to drive investment and address persistent socio-economic gaps.