Rows: 355 Columns: 42
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (12): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, ...
dbl (30): ObjectId, F1985, F1986, F1987, F1990, F1991, F1992, F1993, F1994, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Add Region
Use the countrycode package to add the region as a variable to green_debt_subset.
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `region = countrycode(Country, "country.name", "region")`.
Caused by warning:
! Some values were not matched unambiguously: World
cumulative_issuance_by_region <- green_bonds_long %>%group_by(region) %>%summarize(issuance_bn_usd =sum(issuance_bn_usd, na.rm =TRUE)) %>%arrange(desc(issuance_bn_usd))# Renaming NA values that emerged from world being a missing region to "world"cumulative_issuance_by_region$region[is.na(cumulative_issuance_by_region$region)] <-"World"Regions_without_world <- cumulative_issuance_by_region[cumulative_issuance_by_region$region !="World", ]
Visualize Cumulative Bond Issuance by Region
ggplot(Regions_without_world, aes(x =reorder(region, issuance_bn_usd), y = issuance_bn_usd)) +geom_col(fill ="lightgreen") +theme_minimal() +coord_flip() +geom_text(aes(label =sprintf("%.4g", issuance_bn_usd)), position =position_stack(vjust =0.5), color ="black") +scale_y_continuous(labels = scales::dollar_format(suffix ="bn", prefix ="$", scale =1)) +labs(x ="Region", y ="Cumulative Issuance (billion USD)", title ="Cumulative Issuance by Region in Billion USD",subtitle ="Europeans and Central Asians issue a lot of bonds per capita!",caption ="Data: IMF Climate Change Dashboard | Insight: Me!")
3.5.0.2 Homework problem 2
Use the full green_debt dataset
Use clean_names() to make the variable names snake_case
Filter out observations where type_of_issuer is “Not Applicable”
[1] "Banks" "International Organization"
[3] "Local and state Government" "Nonfinancial corporations"
[5] "Other financial corporations" "Sovereign"
[7] "State owned entities"
We can see here that we have 7 types of issuers, but that those are the only observations in the dataset. This means that type of issuer is “Not Applicable” for all but 7 issuers.
Use the tools taught in this chapter to provide a compelling data visualization and some repeatable factoids that provide actionable insights about green bond issuers.
Factoid 1: TOP 10 countries 2022 Issuance and Total Historical Issuance
# Factoid 1 TOP 10 countries 2022 Issuance and Total Historical Issuance# Calculate total issuances by country and typetotal_issuances_by_country <- green_bonds_long %>%group_by(Country, year) %>%summarise(issuance_bn_usd =sum(issuance_bn_usd), .groups ='drop')# Calculative total issuances by country over timecumulative_issuance_by_country <- total_issuances_by_country %>%arrange(Country, year) %>%group_by(Country) %>%filter(Country !="World") %>%mutate(TotalIssuances =cumsum(issuance_bn_usd))# Identify the top 10 countries based on TotalIssuances over timetop10_countries_issuances <- cumulative_issuance_by_country %>%ungroup() %>%arrange(Country, desc(year)) %>%distinct(Country, .keep_all =TRUE) %>%arrange(desc(TotalIssuances)) %>%slice(1:10)print(top10_countries_issuances)
# A tibble: 10 × 4
Country year issuance_bn_usd TotalIssuances
<chr> <dbl> <dbl> <dbl>
1 China, P.R.: Mainland 2022 99.4 325.
2 Germany 2022 89.1 295.
3 France 2022 33.3 271.
4 United States 2022 28.5 172.
5 Netherlands, The 2022 32.5 172.
6 United Kingdom 2022 7.90 122.
7 Italy 2022 27.5 82.7
8 Sweden 2022 15.8 80.4
9 Spain 2022 13.2 74.4
10 Japan 2022 23.0 61.8
Factoid 2: Top 5 countries by Issuance
# Factoid 2: Top 5 countries by Issuancetop5_countries_issuances <- cumulative_issuance_by_country %>%ungroup() %>%arrange(Country, desc(year)) %>%distinct(Country, .keep_all =TRUE) %>%arrange(desc(TotalIssuances)) %>%slice(1:5)print(top5_countries_issuances)
# A tibble: 5 × 4
Country year issuance_bn_usd TotalIssuances
<chr> <dbl> <dbl> <dbl>
1 China, P.R.: Mainland 2022 99.4 325.
2 Germany 2022 89.1 295.
3 France 2022 33.3 271.
4 United States 2022 28.5 172.
5 Netherlands, The 2022 32.5 172.
Graph 1: Top 5 Countries’ Green Debt Issuance by Year
Though China is a late entrant to green bonds, it now outperforms every single country in the dataset. We can also see a significant dip in green bonds issued in Western top performers in 2022. This could be due to the economic slowdown, but also due to the ESG backlash that many Western countries are currently experiencing.
top5_countries_over_time <- green_bonds_long %>%filter(Country %in% top5_countries) %>%group_by(Country, year) %>%summarise(issuance_bn_usd =sum(issuance_bn_usd), .groups ='drop')ggplot(top5_countries_over_time, aes(x = year, y = issuance_bn_usd, group = Country, color = Country)) +geom_line() +# Draw lines representing total yearly issuanceslabs(title ="Top 5 Countries' Green Bond Issuance Over Time",x ="Year",y ="Total Yearly Issuance (Billion USD)",color ="Country") +theme_minimal() +theme(legend.position ="bottom") +guides(color =guide_legend(nrow =3, title.position ='top', title.hjust =0.5))
Factoid 3: Total Yearly Green Bond Issuance by Region
We can see here that Europe and Central Asia currently dominates the global issuance of green bonds. East Asia and Pacific, though a late mover in the industry, is catching up with Europe and Central Asia. Though North America houses the US — the largest economy in the world, it is trailing behind Europe and East Asia considerably, which means it could most likely be doing more towards sustainable transition.
library(scales)
Warning: package 'scales' was built under R version 4.2.3
Attaching package: 'scales'
The following object is masked from 'package:purrr':
discard
The following object is masked from 'package:readr':
col_factor
total_yearly_issuance_by_region <- green_bonds_long %>%group_by(region, year) %>%filter(Country !="World") %>%filter(year >=2010) %>%summarise(total_issuance =sum(issuance_bn_usd), .groups ='drop')ggplot(total_yearly_issuance_by_region, aes(x = year, y = total_issuance, color = region)) +geom_line() +labs(title ="Total Yearly Green Bond Issuance by Region",x ="Year",y ="Total Issuance (Billion USD)",color ="Region") +scale_x_continuous(breaks =seq(from =2010, to =2022, by =1)) +theme_minimal() +theme(legend.position ="bottom") +guides(color =guide_legend(nrow =2, title.position ='top', title.hjust =0.5))
3.5.0.3 Homework problem 3:
Repeat the process from problem 2 for use_of_proceed and for principal_currency
What are green bond proceeds used for?
The proceeds are being used for a range of activities, mainly relevant to sustainability, financing of sustainability and infrastructure projects, as well as public goods provision.
What do we know about the currency of issuance? Is that changing over time?
We know that countries issue the bonds in their domestic currencies in 2022. But we don’t have data on past years and issuances designated “world”.
