Blackrock Data

Author

Joe Aumuller

Data Analysis Homework 2 - Data Visualization

ESG Investments are on the rise, but are they really helping solve CO2 intensity of investing?

Visualization 1 - ESG Launches Over Time

library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.2.2

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 1.0.0

Warning: package 'ggplot2' was built under R version 4.2.2

Warning: package 'tidyr' was built under R version 4.2.2

Warning: package 'readr' was built under R version 4.2.2

Warning: package 'purrr' was built under R version 4.2.2

Warning: package 'dplyr' was built under R version 4.2.2

Warning: package 'stringr' was built under R version 4.2.2

Warning: package 'forcats' was built under R version 4.2.2

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

# assign the url to `github_raw_csv_url`
github_raw_csv_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/blackrock_etf_screener_2022-08-30.csv"

# read in the data, and assign it to the object `blackrock_etf_data`
blackrock_etf_data <- read_csv(github_raw_csv_url)

Rows: 393 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (14): ticker, name, incept_date, net_assets_as_of, asset_class, sub_asse...
dbl  (8): gross_expense_ratio_percent, net_expense_ratio_percent, net_assets...

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.

ggplot(data = blackrock_etf_data) + 
  geom_bar(mapping = aes(x = year_launched, fill = is_esg))

Compared with data reaching back to 1995, there is an increasing trend in ESG funds but is the new fund type and rating system improving the carbon intensity of investments?

Visualization 2 - Ratings

bar <- ggplot(data = blackrock_etf_data) + 
  geom_bar(
    mapping = aes(x = sustainability_characteristics_msci_esg_fund_ratings_msci_esg_fund_rating_aaa_ccc, fill = sustainability_characteristics_msci_esg_fund_ratings_msci_esg_fund_rating_aaa_ccc), 
    show.legend = FALSE,
    width = 1
  ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

bar + coord_flip()

bar + coord_polar()

Most assets appear to be rated A or above, with the most rated as AA. Despite this, many funds are not ESG funds and do not uniformly reflect a lower carbon intensity of their assets.

Visualization 3 - CO2 Intensity of Investments

ggplot(blackrock_etf_data, aes(x = msci_weighted_average_carbon_intensity_tons_co2e_m_sales, y = msci_esg_quality_score_0_10, color = is_esg)) +
  geom_point()

Warning: Removed 62 rows containing missing values (`geom_point()`).

Looking at a scatter plot of carbon emission intensity and esg scores there appear to be some outliers (1 ESG, severl Regular Funds) that have higher ratings despite relatively intensive CO2 emissions. What else is driving these ratings if not solely the carbon emissions?