The Dynamics and Impact of Green Bonds in LatAm: Trends, Allocation and Efficiency

Nadia Yuning Xing

April 17,2024

Motivation

This study focus on analyzing the trends, capital allocation and efficiency of green bond issuance in Latin America, a choice based on several key considerations. First, as a region rich in biodiversity and facing significant environmental challenges, Latin America is in dire need of innovative financial instruments to balance its economic development with ecological preservation. Second, the region has demonstrated leading global innovation in green finance and sustainable development policies, particularly in carbon pricing and the development of green bond standards. In addition, Latin America’s economic structure and stage of development make it an ideal case study for examining how infrastructure finance can contribute to sustainable development. By exploring Latin America’s green bond market in depth, it will not only provide insights into the region’s own sustainable development, but also provide valuable references for the development of similar markets globally. The selection of Latin America as the region of focus for this study therefore aims to enhance our understanding of the role of green financial instruments in promoting environmental protection and economic growth, while also providing support and insights for global climate action.

This analysis seeks to provide an overview of green bonds in LaTAm regions by answering the following questions:

  • What is the trend in green bond issuance over time in Latin America and the world?

  • What is the primary investment focus of green bonds?

  • How effective are these bonds in terms of investment? In which areas do green bond investments perform better?

Dara Source and Methodology

To do this analysis, I combined five data sets from GBTP: https://www.greenbondtransparency.com/support/resources/

The data sets are respectively containing:

(1) bond’s general information

(2) allocation of proceeds to project categories using international standards (GBP, CBI)

(3) measurements of impact and outcome KPIs

(4) individual bond-tranche information

(5) detailed information of the projects receiving disbursements

Issuance Trends in LaTAm Compared with World

In the first paragraph, let us first explore the green bond issuance trend in Latin America from 2016 to 2022 and compare it with the global green bond issuance trend. From the line chart below, we can find that generally speaking, Latin America and the world have maintained the same trend, both growing first and then declining. The difference is that global green bond issuance has risen rapidly in 2021, and this has not also happened in the Latin America. In terms of growth rate, the growth rate of green bond issuance in Latin America and the world is basically the same, except for 2021.

# A tibble: 664 × 29
   link        handle ISIN  CUSIP FIGI  ticker bond_name issuer_name issuer_type
   <chr>       <chr>  <chr> <chr> <chr> <chr>  <chr>     <chr>       <chr>      
 1 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
 2 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
 3 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
 4 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
 5 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
 6 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
 7 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
 8 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
 9 https://ww… 0f91b… USP1… JK87… <NA>  <NA>   Banco Na… Banco Naci… Developmen…
10 https://ww… 0f91b… USP1… JK87… <NA>  <NA>   Banco Na… Banco Naci… Developmen…
# ℹ 654 more rows
# ℹ 20 more variables: issuer_jurisdiction <chr>, fund <lgl>,
#   framework_link <chr>, issuance_year <dbl>, issuance_date.x <chr>,
#   maturity_date <chr>, volume_usd_billion <dbl>, volume_usd <dbl>,
#   volume_bond_currency <dbl>, bond_currency <chr>, LAC_volume_usd <dbl>,
#   issuance_date.y <date>, `Mature markets` <dbl>, `Emerging markets` <dbl>,
#   `Offshore centers` <dbl>, Supranationals <dbl>, Global <dbl>, …

The following graph is showing the issuance volume among Latin America countries:

Allocation of Green bonds

In this paragraph, I want to show in which categories these green bonds are allocated. From the pie chart below, we can see 57% of green bonds invest in energy category, followed by water, biuldings and Land-use.

Investor Type

The third paragraph analyzes the investor type of these green bonds. The pie chart shows the majority are issued by Non-financial corporation (64.4%), followed by financial corporate (25.6%) . The sovereign green bonds only account for 3.13% in the total number of green bonds.

Conclusion (TBD)

Source Code
---
format: html
editor: visual
code-fold: false
code-tools: true
---

## The Dynamics and Impact of Green Bonds in LatAm: Trends, Allocation and Efficiency

Nadia Yuning Xing

April 17,2024

![](images/73822-maina.jpg)

### Motivation

This study focus on analyzing the trends, capital allocation and efficiency of green bond issuance in Latin America, a choice based on several key considerations. First, as a region rich in biodiversity and facing significant environmental challenges, Latin America is in dire need of innovative financial instruments to balance its economic development with ecological preservation. Second, the region has demonstrated leading global innovation in green finance and sustainable development policies, particularly in carbon pricing and the development of green bond standards. In addition, Latin America's economic structure and stage of development make it an ideal case study for examining how infrastructure finance can contribute to sustainable development. By exploring Latin America's green bond market in depth, it will not only provide insights into the region's own sustainable development, but also provide valuable references for the development of similar markets globally. The selection of Latin America as the region of focus for this study therefore aims to enhance our understanding of the role of green financial instruments in promoting environmental protection and economic growth, while also providing support and insights for global climate action.

This analysis seeks to provide an overview of green bonds in LaTAm regions by answering the following questions:

-   What is the trend in green bond issuance over time in Latin America and the world?

-   What is the primary investment focus of green bonds?

-   How effective are these bonds in terms of investment? In which areas do green bond investments perform better?

### Dara Source and Methodology

To do this analysis, I combined five data sets from GBTP: <https://www.greenbondtransparency.com/support/resources/>

The data sets are respectively containing:

\(1\) bond's general information

\(2\) allocation of proceeds to project categories using international standards (GBP, CBI)

\(3\) measurements of impact and outcome KPIs

\(4\) individual bond-tranche information

\(5\) detailed information of the projects receiving disbursements

### Issuance Trends in LaTAm Compared with World

In the first paragraph, let us first explore the green bond issuance trend in Latin America from 2016 to 2022 and compare it with the global green bond issuance trend. From the line chart below, we can find that generally speaking, Latin America and the world have maintained the same trend, both growing first and then declining. The difference is that global green bond issuance has risen rapidly in 2021, and this has not also happened in the Latin America. In terms of growth rate, the growth rate of green bond issuance in Latin America and the world is basically the same, except for 2021.

```{r}
#| echo: false
#| message: false
#| warning: false
#| code-fold: true
library(tidyverse)
library(readxl)
library(plotly)
library(ggplot2)
library(scales)
comparison <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/comparison.csv")
data_long_comparison <- comparison |> 
  pivot_longer(cols = c("LAC_volume_usd", "global_volume_usd"), names_to = "volume_type", values_to = "issuance_volume")
p <- ggplot(data_long_comparison, aes(x = issuance_year, y = issuance_volume, fill = volume_type)) +
  geom_col(position = "stack") +   
  scale_fill_manual(values = c("LAC_volume_usd" = "#377eb8", "global_volume_usd" = "#4daf4a")) +
  theme_minimal() +
  labs(title = "The Trend of Issuance of Green Bonds", x = "Year", y = "Issuance Volume (bn)", fill = "Volume Type")


ggplotly(p)




```

```{r}
#| echo: false
#| message: false
#| warning: false
#| code-fold: true
library(dplyr)
data_growth <- comparison %>%
  arrange(issuance_year) %>% 
  mutate(
    LAC_growth = (LAC_volume_usd - lag(LAC_volume_usd)) / lag(LAC_volume_usd) * 100,
    Global_growth = (global_volume_usd - lag(global_volume_usd)) / lag(global_volume_usd) * 100
  )

print(data_growth)
library(tidyr)
library(ggplot2)

data_growth_long <- data_growth %>%
  pivot_longer(cols = c("LAC_growth", "Global_growth"), names_to = "growth_type", values_to = "growth_rate")


p_growth <- ggplot(data_growth_long, aes(x = issuance_year, y = growth_rate, color = growth_type)) +
  geom_line() +
  geom_point() +
  scale_color_viridis_d(begin = 0.3, end = 0.9, option = "D") +  
  theme_minimal() +
  labs(title = "Annual Growth Rate of Green Bond Issuance", x = "Year", y = "Growth Rate (%)", color = "Growth Type")
ggplotly(p_growth)
```

The following graph is showing the issuance volume among Latin America countries:

```{r}
#| echo: false
#| message: false
#| warning: false
#| code-fold: true
library(leaflet)
library(countrycode)
library(rnaturalearth)
bonds_new <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/bonds_new.csv")
bonds_new$issuer_jurisdiction <- ifelse(bonds_new$issuer_jurisdiction == "Supranational", NA, bonds_new$issuer_jurisdiction)

bonds_new$ISO_code <- countrycode(bonds_new$issuer_jurisdiction, origin = "country.name", destination = "iso3c", warn = FALSE)

bonds_new <- bonds_new[!is.na(bonds_new$ISO_code), ]

bonds_summary <- bonds_new %>%
  group_by(ISO_code) %>%
  summarise(volume_usd_total = sum(volume_usd, na.rm = TRUE))

world_map <- ne_countries(scale = "medium", returnclass = "sf")
latin_america_caribbean_map <- world_map[world_map$region_un == "Americas" & 
                                         world_map$subregion %in% c("Central America", "South America", "Caribbean"), ]

latin_america_bonds <- merge(latin_america_caribbean_map, bonds_summary, by.x = "iso_a3", by.y = "ISO_code")


leaflet(latin_america_bonds) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(fillColor = ~colorQuantile("Blues", volume_usd_total)(volume_usd_total),
              color = "#BDBDC3",
              weight = 1,
              opacity = 1,
              fillOpacity = 0.7,
              highlight = highlightOptions(weight = 2,
                                           color = "#666",
                                           fillOpacity = 0.7),
              label = ~paste(iso_a3, ":", formatC(volume_usd_total, format = "f", big.mark = ","))) %>%
  addLegend("bottomright", pal = colorQuantile("Blues", latin_america_bonds$volume_usd_total), 
            values = ~volume_usd_total,
            title = "Issuance Volume (USD)",
            labFormat = labelFormat(big.mark = ","))
```

### Allocation of Green bonds

In this paragraph, I want to show in which categories these green bonds are allocated. From the pie chart below, we can see 57% of green bonds invest in energy category, followed by water, biuldings and Land-use.

```{r}
#| echo: false
#| message: false
#| warning: false
#| code-fold: true
library(dplyr)
library(ggplot2)
allocations_new <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/allocations_new.csv")
distinct_allocations <- allocations_new %>%
  distinct(bond_name, .keep_all = TRUE)

allocation_counts <- distinct_allocations %>%
  group_by(category) %>%
  summarise(count = n())

allocation_counts <- allocation_counts %>%
  mutate(percentage = count / sum(count) * 100)
library(plotly)
fig <- plot_ly(allocation_counts, labels = ~category, values = ~percentage, type = 'pie', textinfo = 'label+percent',
               insidetextorientation = 'radial') %>%
  layout(title = 'Proportion of Green Bond Allocation By Bonds Number')
fig

```

### Investor Type

The third paragraph analyzes the investor type of these green bonds. The pie chart shows the majority are issued by Non-financial corporation (64.4%), followed by financial corporate (25.6%) . The sovereign green bonds only account for 3.13% in the total number of green bonds.

```{r}
#| echo: false
#| message: false
#| warning: false
#| code-fold: true
library(dplyr)
library(plotly)
bonds_new_issuer <- bonds_new %>%
  distinct(bond_name, .keep_all = TRUE)

issuer_counts <- bonds_new_issuer %>%
  group_by(issuer_type) %>%
  summarise(count = n())

issuer_counts <- issuer_counts %>%
  mutate(percentage = count / sum(count) * 100)

fig <- plot_ly(issuer_counts, labels = ~issuer_type, values = ~percentage, type = 'pie', textinfo = 'label+percent',
               insidetextorientation = 'radial') %>%
  layout(title = 'Proportion of Issuer Type of Green Bonds in LAC')

fig
```

### Conclusion (TBD)