Arin’s Analysis
Looking at emissions through a few different lenses
First, a deeper look into the different inputs to a country’s emissions.
We looked in class at CO2 emissions per capita in emerging vs. developed economies in terms of cumulative CO2 consumption per capita. I wanted to break down some of that nuance a little further by looking into trade-based, consumption-based, and territorial CO2 emissions per capita, on average, in developed vs. emerging economies.
#Determine average per capital co2 emissions by category of emissions for developed vs. emerging economies every year
emissions_by_type <- emissions_dataset %>%
#compare for emerging markets and developed markets every year
group_by(em_dm, year) %>%
#take the average per capita co2 for each category, removing missing values from the analysis
summarize(avg_territorial_co2_per_capita = mean(territorial_co2_per_capita, na.rm = TRUE), avg_trade_co2_per_capita = mean(trade_co2_per_capita, na.rm = TRUE), avg_consumption_co2_per_capita = mean(consumption_co2_per_capita, na.rm = TRUE), avg_cum_co2_per_capita = mean(cumulative_co2_per_capita, na.rm = TRUE))## `summarise()` has grouped output by 'em_dm'. You can override using the
## `.groups` argument.#for the sake of visualization, I want to look at one year of data, so I will limit this data and pivot it into an easier format to make a graphic
emissions_by_type_2019 <- emissions_by_type %>%
#filter by 2019 when we have more data, keep simple
filter(year == 2019) %>%
#pivot longer to get data into shape for bar chart
pivot_longer(avg_territorial_co2_per_capita:avg_consumption_co2_per_capita)
#compare emerging and developed markets' co2 emissions by category in 2019, visually
ggplot(emissions_by_type_2019) +
aes(x = name, fill = em_dm, weight = value) +
geom_bar(position = "dodge") +
labs(x = "Category of CO2 Emissions", y = "Average Per Capita Tons CO2 Emissions", title = "Sources of Emissions in Emerging and Developed Markets") +
labs(color = "Economy Type") +
#change labels of bars
scale_x_discrete(labels = c('Consumption','Territorial','Trade')) +
scale_fill_hue(direction = 1) +
theme(axis.text.x = element_text(size = 10))
# Cumulative Emissions Over Time ## How long has the gap between
developed and emerging economies been this large?
We have seen above the large difference in emerging and developed economies when it comes to different components of cumulative per capita CO2 emissions. We will go beyond the emerging and developing economy categorization and look at the trajectory of emissions over the past 30 years using World Bank Income Group Designation.
emissions_by_wb_group <- wb_income_groups %>%
inner_join(emissions_dataset, by = c("country_name")) %>%
group_by(wb_income_group, year) %>%
summarize(avg_emissions = mean(cumulative_co2_per_capita, na.rm = TRUE)) %>%
arrange(desc(avg_emissions)) ## `summarise()` has grouped output by 'wb_income_group'. You can override using
## the `.groups` argument.#check out data structure and confirm things look ok
print(emissions_by_wb_group)## # A tibble: 120 × 3
## # Groups: wb_income_group [4]
## wb_income_group year avg_emissions
## <fct> <dbl> <dbl>
## 1 High 2019 580.
## 2 High 2018 574.
## 3 High 2017 567.
## 4 High 2016 561.
## 5 High 2015 556.
## 6 High 2014 551.
## 7 High 2013 546.
## 8 High 2012 540.
## 9 High 2011 533.
## 10 High 2010 525.
## # … with 110 more rowsggplot(emissions_by_wb_group) +
aes(x = year, y = avg_emissions, colour = wb_income_group) +
geom_line(size = 0.5) +
scale_color_manual(
values = c(High = "#F8766D",
`Upper Middle` = "#31B425",
`Lower Middle` = "#20AFEC",
Low = "#FF61C3")
) +
labs(
x = "Year",
y = "Cumulative CO2 Emissions Per Capita (Average)",
title = "Emissions Per Capita by Income Group",
subtitle = "Using World Bank Income Groups",
caption = "Sources: World Bank, Our World in Data",
color = "World Bank Country Group") +
theme_minimal()
# Cumulative Emissions Over in Developed Economies ## Who are the
biggest of the biggest emitters?
We have established both here and through the examples in class that high income countries have much higher cumulative emissions than lower income countries. We will use the more detailed economic group distinctions from the IMF groups to see how this differs among developed economies.
emissions_by_imf_group <- imf_wb_country_groups %>%
inner_join(emissions_dataset, by = c("country_name")) %>%
filter(group_type == "IMF") %>%
group_by(country_group, year) %>%
summarize(avg_emissions = mean(cumulative_co2_per_capita, na.rm = TRUE)) %>%
arrange(desc(avg_emissions)) ## `summarise()` has grouped output by 'country_group'. You can override using the
## `.groups` argument.#check out data structure and confirm things look ok
print(emissions_by_imf_group)## # A tibble: 510 × 3
## # Groups: country_group [17]
## country_group year avg_emissions
## <chr> <dbl> <dbl>
## 1 G7 2019 846.
## 2 G7 2018 840.
## 3 G7 2017 835.
## 4 G7 2016 829.
## 5 G7 2015 825.
## 6 G7 2014 820.
## 7 G7 2013 814.
## 8 G7 2012 808.
## 9 G7 2011 802.
## 10 G7 2010 796.
## # … with 500 more rowsemissions_by_imf_group %>%
#only look at developed economy groups
filter(country_group %in% c("G7", "Advanced G20", "Advanced Economies", "Euro Area",
"G20")) %>%
ggplot() +
aes(x = year, y = avg_emissions, colour = country_group) +
geom_line(size = 0.5) +
scale_color_hue(direction = 1) +
labs(x = "Year", y = "Cumulative CO2 Emissions Per Capita (Average)", title = "Emissions Per Capita by Income Group",
subtitle = "Using IMF Income Groups", caption = "Sources: Our World in Data, IMF", color = "IMF Country Group") +
theme_minimal()