Warning: Paket 'tidyquant' wurde unter R Version 4.2.2 erstellt
Lade nötiges Paket: lubridate
Warning: Paket 'lubridate' wurde unter R Version 4.2.2 erstellt
Attache Paket: 'lubridate'
Die folgenden Objekte sind maskiert von 'package:base':
date, intersect, setdiff, union
Lade nötiges Paket: PerformanceAnalytics
Warning: Paket 'PerformanceAnalytics' wurde unter R Version 4.2.2 erstellt
Lade nötiges Paket: xts
Warning: Paket 'xts' wurde unter R Version 4.2.2 erstellt
Lade nötiges Paket: zoo
Warning: Paket 'zoo' wurde unter R Version 4.2.2 erstellt
Attache Paket: 'zoo'
Die folgenden Objekte sind maskiert von 'package:base':
as.Date, as.Date.numeric
Attache Paket: 'xts'
Die folgenden Objekte sind maskiert von 'package:dplyr':
first, last
Attache Paket: 'PerformanceAnalytics'
Das folgende Objekt ist maskiert 'package:graphics':
legend
Lade nötiges Paket: quantmod
Warning: Paket 'quantmod' wurde unter R Version 4.2.2 erstellt
Lade nötiges Paket: TTR
Warning: Paket 'TTR' wurde unter R Version 4.2.2 erstellt
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
library(scales)
Warning: Paket 'scales' wurde unter R Version 4.2.2 erstellt
Attache Paket: 'scales'
Das folgende Objekt ist maskiert 'package:purrr':
discard
Das folgende Objekt ist maskiert 'package:readr':
col_factor
library(ggrepel)
Warning: Paket 'ggrepel' wurde unter R Version 4.2.2 erstellt
library(readxl)
Warning: Paket 'readxl' wurde unter R Version 4.2.2 erstellt
library(readr)library(janitor)
Warning: Paket 'janitor' wurde unter R Version 4.2.2 erstellt
Attache Paket: 'janitor'
Die folgenden Objekte sind maskiert von 'package:stats':
chisq.test, fisher.test
library(here)
Warning: Paket 'here' wurde unter R Version 4.2.2 erstellt
here() starts at C:/Users/Bentje/Documents/JHU/Spring23/SustFin/CASD
library(rnaturalearth)
Warning: Paket 'rnaturalearth' wurde unter R Version 4.2.2 erstellt
library(countrycode)
Warning: Paket 'countrycode' wurde unter R Version 4.2.2 erstellt
library(wbstats)
Warning: Paket 'wbstats' wurde unter R Version 4.2.2 erstellt
Rows: 2016 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): country_name, iso3c, country_or_region, indicator, industry, partne...
dbl (2): year, energy_imports
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.
Rows: 6510 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): country_name, iso3c, country
dbl (2): year, GDP_PPP
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.
Rows: 4825 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): iso3c, country_name, Country/Region, unit
dbl (2): year, GHG_excl_LUCF
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.
Rows: 4825 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): country_name, Country/Region, iso3c, unit
dbl (2): year, GHG_incl_LUCF
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.
Rows: 2016 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): country_name, iso3c, country_or_region, indicator, industry, partne...
dbl (2): year, total_exports
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.
Rows: 2016 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): country_name, iso3c, country_or_region, indicator, industry, partne...
dbl (2): year, total_imports
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.
# joining sets# excluding LUCFdata_excl_LUCF <- final_ghg_ex_LUCF %>%inner_join(final_energy_imports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_total_exports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_total_imports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_gdp_ppp, by =c("iso3c", "country_name", "year")) %>%select(!`Country/Region`) %>%select(!country) %>%select(!country_or_region.y) %>%select(!country_or_region.x) %>%select(!unit.x) %>%select(!unit.x.x) %>%select(!unit.y) %>%select(!unit.y.y) write_csv(data_excl_LUCF, "./processed_02/data_excl_LUCF.csv")#including LUCFdata_incl_LUCF <- final_ghg_in_LUCF %>%inner_join(final_energy_imports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_total_exports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_total_imports, by =c("iso3c", "country_name", "year")) %>%inner_join(final_gdp_ppp, by =c("iso3c", "country_name", "year")) %>%select(!`Country/Region`) %>%select(!country) %>%select(!country_or_region.y) %>%select(!country_or_region.x) %>%select(!unit.x) %>%select(!unit.x.x) %>%select(!unit.y) %>%select(!unit.y.y) write_csv(data_incl_LUCF, "./processed_02/data_incl_LUCF.csv")
Aspect 1: Countries that do not differentiate between LUCF inclusive and exclusive
These countries roughly fall into three categories: They are either in a conflict situation (like Libya, Syria), an oil producing desert state (like Saudi Arabia, Kuwait), or a very small Pacific Island Nation (like Kiribati, Tuvalu). States in conflict may simply not have the current capacity to report these values, for oil producing desert states there is probably not much forestry to add but the Small Pacific Island Nations likely are home to important biodiversity and some rain forest. While they all emit less than a million tonnes of CO2e, there emissions might well turn negative if they reported LUCF inclusive data. They could actually present a high risk/high possible return investment if they try to preserve unique forestry but have a high risk of completely vanishing due to rising sea levels. would like to look at their sovereign debt structure, see whether there is data on their LUFC and risk of vanishing
visualization idea: regional map Oceania with referenced islands
biodiversity bonds? context Gabon deal? Can PCAF help with climate mitigation?
# comparison LUCF# How many countries differentiate between LUCF incl data and LUCF excl data?LUCF_comparison <- final_ghg_ex_LUCF %>%left_join(final_ghg_in_LUCF, by =c("iso3c", "country_name", "year", "Country/Region", "unit"))no_difference <- LUCF_comparison %>%filter (GHG_excl_LUCF == GHG_incl_LUCF) %>%filter (year ==2019) %>%mutate (class_ghg =ifelse (GHG_excl_LUCF <1, "less_than_1_M", ifelse(1< GHG_excl_LUCF & GHG_excl_LUCF<100, "1_to_100 M", "> 100 M")))# distinct (country_name)ggplot(no_difference, aes(x = class_ghg, y = GHG_excl_LUCF, size = GHG_excl_LUCF)) +geom_point()
ggplot(no_difference, aes(x = class_ghg, y = GHG_excl_LUCF))+geom_boxplot()
# visualization ideas: regional map Middle East and regional map Pacific Island Nations showing that the large GHG emitters not reporting any LUCF are mostly located in desert states and the very small emitters are Small Pacific Island Nations which would probably have negative scores if their LUCF was recorded but lack capacity for recording? # if incorporated, might lead to climate adaptation investment and protecting these islands???# more questions: How much LUCF do they have? What sort of Sovereign Debt Bonds do they offer? # problem: regional map always excludes Oceania, no matter which orderworld_map_data |>left_join(no_difference, by =c("iso_a3"="iso3c")) |># remove Antarctica. It takes up visual space, and isn't important to this analysisfilter(iso_a3 !="ATA") %>%#start your ggplot!ggplot(aes(fill = GHG_excl_LUCF)) +# this makes a map using the sf data. So easy, right?geom_sf() +coord_sf(xlim =c(105, -125), ylim =c(-50, 20))
no_difference
# A tibble: 30 × 8
iso3c country_name Country/Regi…¹ unit year GHG_e…² GHG_i…³ class…⁴
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
1 SAU Saudi Arabia Saudi Arabia MtCO… 2019 723. 723. > 100 M
2 ARE United Arab Emirates United Arab E… MtCO… 2019 244. 244. > 100 M
3 TKM Turkmenistan Turkmenistan MtCO… 2019 157. 157. > 100 M
4 KWT Kuwait Kuwait MtCO… 2019 137. 137. > 100 M
5 LBY Libya Libya MtCO… 2019 127. 127. > 100 M
6 QAT Qatar Qatar MtCO… 2019 115. 115. > 100 M
7 BHR Bahrain Bahrain MtCO… 2019 54.4 54.4 1_to_1…
8 SYR Syria Syria MtCO… 2019 48.5 48.5 1_to_1…
9 JOR Jordan Jordan MtCO… 2019 36.6 36.6 1_to_1…
10 YEM Yemen Yemen MtCO… 2019 25.3 25.3 1_to_1…
# … with 20 more rows, and abbreviated variable names ¹`Country/Region`,
# ²GHG_excl_LUCF, ³GHG_incl_LUCF, ⁴class_ghg
Aspect 2: For which states does the difference (LUCF inlcusive vs exclusive) really matter?
For China, Russia and the US GHG emissions are much less in absolute terms if included their LUCF data (several 100 Million tonnes). But for smaller developing countries the relative importance of accounting for LUCF or not is greater: Fiji’s GHG emissions are reduced by more than 90% if LUCF is included. These states thus tend to be the ones that would profit the most from coherent LUCF reporting.
Similarly, in absolute terms, countries with a lot of forest like Indonesia, Congo (COD), and Brazil have the highest increases in GHG when LUCF is included. For Indonesia, this means almost 1 billion tonnes of CO2e more in emissions. However, the relative change is most dramatic for the Solomon Islands (almost 5000% more GHG emissions, followed by Congo).
# A tibble: 10 × 9
iso3c country_name Country/Reg…¹ unit year GHG_e…² GHG_i…³ LUCF_…⁴ perce…⁵
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 CHN China China MtCO… 2019 12705. 12055. 650. 5.11
2 RUS Russia Russia MtCO… 2019 2477. 1925. 552. 22.3
3 USA United States United States MtCO… 2019 6001. 5771 230. 3.84
4 FRA France France MtCO… 2019 414. 352. 61.9 15.0
5 CHL Chile Chile MtCO… 2019 113. 55.3 57.7 51.0
6 KOR South Korea South Korea MtCO… 2019 698. 653. 45.8 6.56
7 BGR Bulgaria Bulgaria MtCO… 2019 51.3 17.5 33.8 65.9
8 POL Poland Poland MtCO… 2019 353. 320. 32.9 9.32
9 JPN Japan Japan MtCO… 2019 1167. 1134. 32.1 2.75
10 IND India India MtCO… 2019 3395. 3364. 31.3 0.921
# … with abbreviated variable names ¹`Country/Region`, ²GHG_excl_LUCF,
# ³GHG_incl_LUCF, ⁴LUCF_diff_excl_minus_incl, ⁵percent_change
LUCF_difference_2019_min
# A tibble: 10 × 9
iso3c country_name Country/…¹ unit year GHG_e…² GHG_i…³ LUCF_…⁴ perce…⁵
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 IDN Indonesia Indonesia MtCO… 2019 1002. 1960. -957. -95.5
2 COD Congo - Kinshasa Democrati… MtCO… 2019 55 680. -625. -1136.
3 BRA Brazil Brazil MtCO… 2019 1057. 1452. -394. -37.3
4 MMR Myanmar (Burma) Myanmar MtCO… 2019 133. 243. -110. -82.3
5 PER Peru Peru MtCO… 2019 101. 191. -89.9 -89.3
6 ZWE Zimbabwe Zimbabwe MtCO… 2019 30.5 118. -87.4 -286.
7 COL Colombia Colombia MtCO… 2019 187. 271. -83.2 -44.4
8 MYS Malaysia Malaysia MtCO… 2019 313. 396. -83.1 -26.5
9 BOL Bolivia Bolivia MtCO… 2019 60.8 139. -77.9 -128.
10 MOZ Mozambique Mozambique MtCO… 2019 35.4 107. -71.3 -202.
# … with abbreviated variable names ¹`Country/Region`, ²GHG_excl_LUCF,
# ³GHG_incl_LUCF, ⁴LUCF_diff_excl_minus_incl, ⁵percent_change
# goal: datasets with scope 1,2,3 emissions for country-years, possibly LUFC inclusive and exclusive# scope 1 emissions are the Climate Watch GHG emissions (LUCF excl.)# scope 2 emissions are the D35 OECD data emissions (energy imports)# scope 3 emissions are total imports - energy imports emissions, both OECDscope_emissions <- data_excl_LUCF %>%left_join(final_ghg_in_LUCF, by =c("iso3c", "country_name", "year")) %>%rename(scope_1_excl = GHG_excl_LUCF, scope_1_incl = GHG_incl_LUCF,scope_2 = energy_imports) %>%mutate(scope_3 = total_imports - scope_2) %>%select(iso3c, country_name, year, scope_1_excl, scope_1_incl, scope_2, scope_3, GDP_PPP) %>%group_by(country_name, year) %>%mutate(total_scopes_excl = scope_1_excl + scope_2 + scope_3, total_scopes_incl = scope_1_incl + scope_2 + scope_3) write_csv(scope_emissions, "./processed_02/scope_emissions.csv")
Exploring G7 dynamics and then for whom the different scope emissions increased the most (for scope 3 and scope 2 emissions: China, United States, India; China India and Saudi Arabia for scope 1 excluding and including LUCF)
g7_emissions <- scope_emissions %>%filter(country_name %in%c("United States", "United Kingdom", "Canada", "Japan", "Germany", "France", "Italy"))ggplot(g7_emissions, aes(x = year, y = total_scopes_excl)) +geom_point(aes(x = year, y = total_scopes_excl)) +geom_line(aes(x = year, y = scope_1_excl, color ="red")) +geom_line(aes(x = year, y = scope_2, color ="blue")) +geom_line(aes(x = year, y = scope_3, color ="green")) +facet_wrap(~ country_name)
# why does the legend associate blue with red?# to explore: trend in scope 2 and 3 emissions? -> regression? nesting like in datacamp courselibrary(broom)
Warning: Paket 'broom' wurde unter R Version 4.2.2 erstellt
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
# A tibble: 128 × 8
# Groups: country_name [64]
country_name data models term estimate std.e…¹ stati…² p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> (Intercept) -1.78e+4 9.42e+2 -18.9 4.15e-15
2 China <gropd_df> <lm> year 8.94e+0 4.69e-1 19.1 3.66e-15
3 United States <gropd_df> <lm> (Intercept) -1.17e+4 2.93e+3 -3.99 6.14e- 4
4 United States <gropd_df> <lm> year 5.98e+0 1.46e+0 4.10 4.76e- 4
5 India <gropd_df> <lm> (Intercept) -6.93e+3 4.24e+2 -16.3 8.81e-14
6 India <gropd_df> <lm> year 3.48e+0 2.12e-1 16.4 7.73e-14
7 Russia <gropd_df> <lm> (Intercept) -3.26e+3 5.91e+2 -5.52 1.50e- 5
8 Russia <gropd_df> <lm> year 1.64e+0 2.95e-1 5.58 1.31e- 5
9 Japan <gropd_df> <lm> (Intercept) -1.09e+3 8.69e+2 -1.25 2.24e- 1
10 Japan <gropd_df> <lm> year 6.02e-1 4.33e-1 1.39 1.79e- 1
# … with 118 more rows, and abbreviated variable names ¹std.error, ²statistic
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
scope_2_coefficients %>%arrange(desc(estimate))
# A tibble: 44 × 8
# Groups: country_name [44]
country_name data models term estimate std.error statistic p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> year 8.94 0.469 19.1 3.66e-15
2 United States <gropd_df> <lm> year 5.98 1.46 4.10 4.76e- 4
3 India <gropd_df> <lm> year 3.48 0.212 16.4 7.73e-14
4 Russia <gropd_df> <lm> year 1.64 0.295 5.58 1.31e- 5
5 South Korea <gropd_df> <lm> year 1.50 0.223 6.74 8.96e- 7
6 Australia <gropd_df> <lm> year 1.46 0.138 10.6 4.29e-10
7 Saudi Arabia <gropd_df> <lm> year 1.32 0.123 10.7 3.24e-10
8 Brazil <gropd_df> <lm> year 1.31 0.206 6.34 2.23e- 6
9 Indonesia <gropd_df> <lm> year 1.27 0.114 11.1 1.81e-10
10 Mexico <gropd_df> <lm> year 1.09 0.143 7.59 1.40e- 7
# … with 34 more rows
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
# A tibble: 128 × 8
# Groups: country_name [64]
country_name data models term estimate std.e…¹ stati…² p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> (Intercept) -4.86e4 2.43e+3 -20.0 1.32e-15
2 China <gropd_df> <lm> year 2.44e1 1.21e+0 20.1 1.17e-15
3 United States <gropd_df> <lm> (Intercept) -1.97e4 6.28e+3 -3.14 4.81e- 3
4 United States <gropd_df> <lm> year 1.02e1 3.13e+0 3.26 3.57e- 3
5 India <gropd_df> <lm> (Intercept) -1.98e4 1.12e+3 -17.7 1.74e-14
6 India <gropd_df> <lm> year 9.93e0 5.58e-1 17.8 1.52e-14
7 Russia <gropd_df> <lm> (Intercept) -7.72e3 1.36e+3 -5.66 1.09e- 5
8 Russia <gropd_df> <lm> year 3.89e0 6.80e-1 5.72 9.47e- 6
9 Japan <gropd_df> <lm> (Intercept) 4.79e3 2.06e+3 2.33 2.94e- 2
10 Japan <gropd_df> <lm> year -2.24e0 1.03e+0 -2.18 3.98e- 2
# … with 118 more rows, and abbreviated variable names ¹std.error, ²statistic
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
scope_3_coefficients %>%arrange(desc(estimate))
# A tibble: 53 × 8
# Groups: country_name [53]
country_name data models term estimate std.error statistic p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> year 24.4 1.21 20.1 1.17e-15
2 United States <gropd_df> <lm> year 10.2 3.13 3.26 3.57e- 3
3 India <gropd_df> <lm> year 9.93 0.558 17.8 1.52e-14
4 Saudi Arabia <gropd_df> <lm> year 4.13 0.325 12.7 1.37e-11
5 Russia <gropd_df> <lm> year 3.89 0.680 5.72 9.47e- 6
6 Indonesia <gropd_df> <lm> year 2.88 0.294 9.78 1.80e- 9
7 Brazil <gropd_df> <lm> year 2.79 0.493 5.65 1.10e- 5
8 South Korea <gropd_df> <lm> year 2.73 0.522 5.23 3.01e- 5
9 Australia <gropd_df> <lm> year 2.71 0.323 8.39 2.68e- 8
10 Turkey <gropd_df> <lm> year 2.44 0.262 9.31 4.35e- 9
# … with 43 more rows
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
# A tibble: 128 × 8
# Groups: country_name [64]
country_name data models term estimate std.e…¹ stati…² p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> (Intercept) -8.67e+5 4.10e4 -21.2 4.09e-16
2 China <gropd_df> <lm> year 4.36e+2 2.04e1 21.4 3.36e-16
3 United States <gropd_df> <lm> (Intercept) 5.90e+4 1.37e4 4.31 2.81e- 4
4 United States <gropd_df> <lm> year -2.62e+1 6.81e0 -3.84 8.83e- 4
5 India <gropd_df> <lm> (Intercept) -1.74e+5 6.94e3 -25.0 1.19e-17
6 India <gropd_df> <lm> year 8.76e+1 3.46e0 25.3 9.03e-18
7 Russia <gropd_df> <lm> (Intercept) -1.54e+4 3.64e3 -4.24 3.37e- 4
8 Russia <gropd_df> <lm> year 8.83e+0 1.82e0 4.86 7.43e- 5
9 Japan <gropd_df> <lm> (Intercept) 2.00e+3 2.29e3 0.874 3.92e- 1
10 Japan <gropd_df> <lm> year -3.65e-1 1.14e0 -0.319 7.53e- 1
# … with 118 more rows, and abbreviated variable names ¹std.error, ²statistic
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
# A tibble: 128 × 8
# Groups: country_name [64]
country_name data models term estimate std.e…¹ stati…² p.value
<chr> <list> <list> <chr> <dbl> <dbl> <dbl> <dbl>
1 China <gropd_df> <lm> (Intercept) -8.31e5 3.87e4 -21.4 3.11e-16
2 China <gropd_df> <lm> year 4.18e2 1.93e1 21.6 2.57e-16
3 United States <gropd_df> <lm> (Intercept) 4.87e4 1.35e4 3.61 1.56e- 3
4 United States <gropd_df> <lm> year -2.13e1 6.73e0 -3.16 4.52e- 3
5 India <gropd_df> <lm> (Intercept) -1.89e5 3.76e3 -50.4 3.19e-24
6 India <gropd_df> <lm> year 9.54e1 1.87e0 51.0 2.48e-24
7 Russia <gropd_df> <lm> (Intercept) 5.50e3 8.92e3 0.616 5.44e- 1
8 Russia <gropd_df> <lm> year -1.92e0 4.45e0 -0.432 6.70e- 1
9 Japan <gropd_df> <lm> (Intercept) -2.32e3 2.79e3 -0.831 4.15e- 1
10 Japan <gropd_df> <lm> year 1.75e0 1.39e0 1.26 2.21e- 1
# … with 118 more rows, and abbreviated variable names ¹std.error, ²statistic
Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
ℹ Please specify a name for each selection.
ℹ Did you want `data = -country_name`?
