Load all the necessary packages
library(tidyverse) ── 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
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(janitor)
Attaching package: 'janitor'The following objects are masked from 'package:stats':
chisq.test, fisher.testlibrary(here)here() starts at /Users/helenayue/sais_sustainable_financelibrary(readxl)
library(tidyr)
library(dplyr)
library(rnaturalearth)
library(countrycode)
library(wbstats)
library(ggplot2)
library(gganimate)
#install.packages('gganimate')
library(gapminder)
#install.packages('gifski')
library(gifski)Read the Scope 1 emission data
raw_domestic_scope_1 = read_excel("/Users/helenayue/Desktop/Sustainable Finance/historical_emissions.xlsx", col_names = TRUE)
raw_domestic_scope_1# A tibble: 193 × 31
Country Unit `2018` `2017` `2016` `2015` `2014` `2013` `2012` `2011` `2010`
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 China MtCO… 9853. 9446. 9225. 9151. 9296. 9274. 8832. 8572. 8143.
2 United … MtCO… 4744. 4588. 4663. 4571. 4688. 4673. 4537. 4755. 4990.
3 India MtCO… 2418. 2286. 2161. 2093. 2082. 1908. 1845. 1692. 1665.
4 Indones… MtCO… 1263. 1066. 1056. 1572. 1533. 1244. 1299. 1281. 772.
5 Russia MtCO… 1102. 1036. 1012. 905. 924. 945. 988. 1011. 849.
6 Japan MtCO… 1084. 1123. 1136. 1130. 1167. 1213. 1204. 1164. 1058.
7 Brazil MtCO… 818. 841. 831. 747. 774. 749. 716. 680. 1499.
8 Germany MtCO… 678. 703. 718. 713. 707. 747. 731. 717. 755.
9 Iran MtCO… 637. 626. 607. 599. 605. 584. 559. 552. 497.
10 Canada MtCO… 615. 603. 592. 694. 697. 691. 681. 684. 838.
# … with 183 more rows, and 20 more variables: `2009` <dbl>, `2008` <dbl>,
# `2007` <dbl>, `2006` <dbl>, `2005` <dbl>, `2004` <dbl>, `2003` <dbl>,
# `2002` <dbl>, `2001` <dbl>, `2000` <dbl>, `1999` <dbl>, `1998` <dbl>,
# `1997` <dbl>, `1996` <dbl>, `1995` <dbl>, `1994` <dbl>, `1993` <dbl>,
# `1992` <dbl>, `1991` <dbl>, `1990` <chr>Omit data entries
raw_domestic_scope_1 <- raw_domestic_scope_1 |>
na.omit()Change the emissions from catgorical to numerical for better data dorting later.
raw_domestic_scope_1$"1990" <- as.numeric(as.character(raw_domestic_scope_1$"1990")) # Convert one variable to numericWarning: NAs introduced by coercionMake the dataset longer
raw_domestic_scope_1 <- raw_domestic_scope_1 %>%
pivot_longer("1990":"2018",
names_to = "year",
values_to = "scope_1_emissions"
)Rename the data for standardization
raw_domestic_scope_1 <- raw_domestic_scope_1 |>
rename("country_name" = "Country")Create country code index function and iso3c index function
country_name_regex_to_iso3c <- function(country_name) {
country_name |>
countrycode(origin = "country.name",
destination = "iso3c",
origin_regex = TRUE)
}
iso3c_to_country_name <- function(iso3c) {
iso3c |>
countrycode(origin = "iso3c", destination = "country.name")
}Add a standardized country name and ISO code for each country.
raw_domestic_scope_1 <- raw_domestic_scope_1 %>%
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = country_name_regex_to_iso3c(country_name)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: MicronesiaDownload data for export emissions for each country from 1995-2018
scope_1_export = read_excel("/Users/helenayue/Desktop/Sustainable Finance/oecd_export.xlsx", col_names = TRUE)Tidy the data (to make the dataset longer)
scope_1_export <- scope_1_export |>
pivot_longer("1995":"2018",
names_to = "year",
values_to = "export_emissions"
)Standardize country names
scope_1_export <- scope_1_export |>
rename("country_name" = "Country")
scope_1_export <- scope_1_export |>
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = country_name_regex_to_iso3c(country_name)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: Rest of the Worldscope_1_export <- scope_1_export|>
na.omit()Combine domestic emissions and export emissions to generate the complete scope 1 emissions data.
scope_1_complete <-
full_join(raw_domestic_scope_1, scope_1_export, by = c("year" = "year", "country_name" = "country_name", "iso3c" = "iso3c"))Download data for scope 2 emissions for each country from 1995-2018
scope_2 = read_excel("/Users/helenayue/Desktop/Sustainable Finance/scope_2.xlsx", col_names = TRUE)New names:
• `` -> `...1`Tidy the data
scope_2 <- scope_2 |>
pivot_longer("1995":"2018",
names_to = "year",
values_to = "scope_2_emissions"
)Standardize the country name
scope_2 <- scope_2 |>
rename("country_name" = "...1")
scope_2 <- scope_2 |>
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = country_name_regex_to_iso3c(country_name)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: TürkiyeOmit missing data
scope_2 <- scope_2 |>
na.omit()Read data for scope 3 emissions for each country from 1995-2018 (here I did the manual calculation for scope 3 emissions in excel).
scope_3 = read_excel("/Users/helenayue/Desktop/Sustainable Finance/scope_3.xlsx", col_names = TRUE)Tidy the data
scope_3 <- scope_3 |>
pivot_longer("1995":"2018",
names_to = "year",
values_to = "scope_3_emissions"
)Standardize the country name
scope_3 <- scope_3 |>
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = country_name_regex_to_iso3c(country_name)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: TürkiyeOmit missing data
scope_3 <- scope_3 |>
na.omit()Combine scope 1 and scope 2 data
combine_scopes_1_2 <-
full_join(scope_1_complete, scope_2, by = c("year" = "year", "country_name" = "country_name", "iso3c" = "iso3c"))Combine scope 1, 2 and 3 data
complete_all_scopes_data <-
full_join(combine_scopes_1_2, scope_3, by = c("year" = "year", "country_name" = "country_name", "iso3c" = "iso3c"))Clean dataset to remove those with years without all kinds of emissions
complete_ready_scopes_data <- complete_all_scopes_data %>%
na.omit
complete_ready_scopes_data# A tibble: 1,512 × 8
country_name Unit year scope_1_emissions iso3c export_em…¹ scope…² scope…³
<chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
1 China MtCO₂e 1995 2739. CHN 442. 1.10 116.
2 China MtCO₂e 1996 2721. CHN 459. 1.03 123.
3 China MtCO₂e 1997 2785. CHN 500. 1.14 136.
4 China MtCO₂e 1998 2887. CHN 541. 1.09 141.
5 China MtCO₂e 1999 2804. CHN 509. 1.05 165.
6 China MtCO₂e 2000 2997. CHN 617. 1.39 216.
7 China MtCO₂e 2001 3197. CHN 612. 1.45 236.
8 China MtCO₂e 2002 3478. CHN 700. 3.56 266.
9 China MtCO₂e 2003 4084. CHN 986. 4.08 339.
10 China MtCO₂e 2004 4793. CHN 1342. 3.87 404.
# … with 1,502 more rows, and abbreviated variable names ¹export_emissions,
# ²scope_2_emissions, ³scope_3_emissionsGenerate a column to calculate the sum of scope 1, 2, and 3 emissions
complete_ready_scopes_data <- complete_ready_scopes_data %>%
mutate("consumption_emissions" = scope_1_emissions + scope_2_emissions + scope_3_emissions - export_emissions)Read GDP PPP data set
ppp_gdp_data = read_excel("/Users/helenayue/Desktop/Sustainable Finance/ppp_gdp.xlsx", col_names = TRUE)Tidy the data set
ppp_gdp_data <- ppp_gdp_data %>%
pivot_longer("1995":"2018",
names_to = "year",
values_to = "PPP_GDP_dollars"
)Omit missing data
ppp_gdp_data <- ppp_gdp_data %>%
na.omit()Standardize column names
ppp_gdp_data <- ppp_gdp_data %>%
rename("country_name" = "Country Name")
ppp_gdp_data <- ppp_gdp_data |>
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `iso3c = country_name_regex_to_iso3c(country_name)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: Africa Eastern and Southern, Africa Western and Central, Arab World, Caribbean small states, Central Europe and the Baltics, Channel Islands, Early-demographic dividend, East Asia & Pacific, East Asia & Pacific (excluding high income), East Asia & Pacific (IDA & IBRD countries), Euro area, Europe & Central Asia, Europe & Central Asia (excluding high income), Europe & Central Asia (IDA & IBRD countries), European Union, Fragile and conflict affected situations, Heavily indebted poor countries (HIPC), High income, IBRD only, IDA & IBRD total, IDA blend, IDA only, IDA total, Kosovo, Late-demographic dividend, Latin America & Caribbean, Latin America & Caribbean (excluding high income), Latin America & the Caribbean (IDA & IBRD countries), Least developed countries: UN classification, Low & middle income, Low income, Lower middle income, Middle East & North Africa, Middle East & North Africa (excluding high income), Middle East & North Africa (IDA & IBRD countries), Middle income, North America, Not classified, OECD members, Other small states, Pacific island small states, Post-demographic dividend, Pre-demographic dividend, Small states, South Asia, South Asia (IDA & IBRD), Sub-Saharan Africa, Sub-Saharan Africa (excluding high income), Sub-Saharan Africa (IDA & IBRD countries), Turkiye, Upper middle income, WorldAdd PPP-GDP data to data set with scope 1-3 emissions
scopes_GDP_PPP <-
full_join(complete_all_scopes_data, ppp_gdp_data, by = c("year" = "year", "country_name" = "country_name", "iso3c" = "iso3c"))Warning in full_join(complete_all_scopes_data, ppp_gdp_data, by = c(year = "year", : Each row in `x` is expected to match at most 1 row in `y`.
ℹ Row 5313 of `x` matches multiple rows.
ℹ If multiple matches are expected, set `multiple = "all"` to silence this
warning.Read nominal GDP data set
nominal_data = read_excel("/Users/helenayue/Desktop/Sustainable Finance/nominal_gdp.xlsx", col_names = TRUE)Tidy the data set
nominal_data <- nominal_data %>%
pivot_longer("1995":"2018",
names_to = "year",
values_to = "nom_GDP_dollars"
)Omit missing data
nominal_data <- nominal_data %>%
na.omit()Standardize column names
nominal_data <- nominal_data %>%
rename("country_name" = "Country Name")
nominal_data <- nominal_data %>%
rename("iso3c" = "Country Code")
ppp_gdp_data <- ppp_gdp_data |>
mutate(iso3c = country_name_regex_to_iso3c(country_name)) |>
mutate(country_name = iso3c_to_country_name(iso3c)) Add PPP-GDP data to data set with scope 1-3 emissions
scopes_GDP_PPP_nominal <-
full_join(scopes_GDP_PPP, nominal_data, by = c("year" = "year", "country_name" = "country_name", "iso3c" = "iso3c"))Clean the data set
clean_data <- scopes_GDP_PPP_nominal %>%
na.omit()Here I have all the necessary information and a clean dataset:)