Context

At the onset of the COVID-19 pandemic, much was written about a “green recovery” emerging from the crisis. Coupled with the collapse in oil prices and improvement in air quality from temporary stay-at-home guidance, there was a belief that now was the time to begin seriously investing in the clean transition to create the green economy of the future. How and where has that panned out the most?

First we will start by compiling together all of the green financing instruments from the Institute of International Finance into a consolidated dataset for analysis.

Loading the Data Set

All of the IIF dataset is loaded into R

sus_debt <- process_iif_gb_issuance(range = "B4:G41", issuance_type = "Sustainable debt")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(sus_debt)

sustainable_bonds <- process_iif_gb_issuance(range = "I4:N41", issuance_type = "Sustainable bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(sustainable_bonds)

loans <- process_iif_gb_issuance(range = "P4:U41", 
                                           issuance_type = "Sustainable loans")
iif_issuance_data <- iif_issuance_data %>%
  bind_rows(loans)

green_bonds <- process_iif_gb_issuance(range = "W4:AB41", 
                                           issuance_type = "Green bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(green_bonds)

green_abs <- process_iif_gb_issuance(range = "AD4:AI41", 
                                           issuance_type = "Green ABS")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(green_abs)

sustainability_bonds <- process_iif_gb_issuance(range = "AK4:AP41", 
                                           issuance_type = "Sustainability bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(sustainability_bonds)

social_bonds <- process_iif_gb_issuance(range = "AR4:AW41", 
                                           issuance_type = "Social bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(social_bonds)

green_municipal_bonds <- process_iif_gb_issuance(range = "AY4:BD41", 
                                           issuance_type = "Green municipal bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(green_municipal_bonds)

sustainability_linked_bonds <- process_iif_gb_issuance(range = "BF4:BK41", 
                                           issuance_type = "Sustainability-linked bonds")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(sustainability_linked_bonds)

green_loans <- process_iif_gb_issuance(range = "BM4:BR41", 
                                           issuance_type = "Green loans")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(green_loans)

sustainability_linked_loans <- process_iif_gb_issuance(range = "BT4:BY41", 
                                           issuance_type = "Sustainability-linked loans")

iif_issuance_data <- iif_issuance_data %>%
  bind_rows(sustainability_linked_loans)
iif_issuance_data %>% glimpse()
## Rows: 2,405
## Columns: 4
## $ date            <date> 2013-03-31, 2013-03-31, 2013-03-31, 2013-03-31, 2013-…
## $ geography       <chr> "Mature markets", "Emerging markets", "Offshore center…
## $ issuance_bn_usd <dbl> 6.18958089, 0.45115568, 0.00000000, 1.15915118, 7.7998…
## $ issuance_type   <chr> "Sustainable debt (bonds and loans)", "Sustainable deb…

Stacked Area Chart

Looking at issuance of green debt across all geographies since the pandemic, it’s clear that emerging markets had the highest volume of green debt issuance indicating that green recovery efforts especially took off there compared to mature markets.

library(ggplot2)
library(dplyr)
library (ggthemes)
library(ggridges)

iif_issuance_data %>%
filter(date >= "2020-03-31", issuance_type == "Sustainable debt") %>%
ggplot(aes(x=date, y=issuance_bn_usd, fill=geography)) + 
  geom_area()+
  labs(
    x = "Date",
    y = "Issuance (USD Millions)",
    title = "Growth of Sustainable Debt Market Since the Pandemic",
    subtitle = "Sustainable Debt Volume From March 2020-March 2022",
    caption = "Data source: Institute of International Finance",
    legend = "Geography"
    )+
  theme_pander()

Density Ridges Chart

This graph provides a similar view of green debt across all geographies since the pandemic in a ridged format.

iif_issuance_data %>%
filter(date >= "2020-03-31") %>%
ggplot(aes(x = issuance_bn_usd, y = geography, fill = geography)) +
  geom_density_ridges() +
  theme_ridges() + 
  labs(
    x = "Issuance (USD Billions)",
    y = "Geography",
    title = "Distribution of Sustainable Debt Market Since the Pandemic",
    subtitle = "Sustainable Debt Volume From March 2020-March 2022",
    caption = "Data source: Institute of International Finance",
    legend = ""
    )+
  scale_color_discrete(name="")+
  theme_pander()+
  theme(legend.position = "none")
## Picking joint bandwidth of 15.3

Scatter Plot with Linear Trend

Diving into emerging markets more, I look into the growth of green bonds since the pandemic with a trend line and confidence interval showing that the market has steadily grown since March 2020.

iif_issuance_data %>%
filter(date >= "2020-03-31", geography =="Emerging markets", issuance_type == "Green bonds") %>%
ggplot(aes(x = date, y = issuance_bn_usd)) +
  geom_point() +
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE) +
  labs(
    x = "Date",
    y = "Issuance (USD Billions)",
    title = "Growth of Emerging Market Green Bonds Since the Pandemic",
    subtitle = "Green Debt Volume From March 2020-March 2022",
    caption = "Data source: Institute of International Finance",
    legend = ""
    )+
    theme_pander()
## `geom_smooth()` using formula 'y ~ x'