Emission levels in the East Asia and Pacific Region

The purpose of this analysis to examine which countries in the East Asia and Pacific Region emit the most CO2 by looking at total CO2 emissions and CO2 emissions per capita. As this is a region experience rapid, transformative economic growth, the relationship between GDP per capita and emissions per capita is also examined.

View data

Glimpse at the emissions dataset to see what variables are in the dataset

glimpse(emissions_dataset_full)

Rows: 6,702
Columns: 31
$ iso3c                                <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "A…
$ year                                 <dbl> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999…
$ gdp_usd_current_prices               <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.367, 4.5…
$ gdp_ppp_current_prices               <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19.661, 21…
$ gdp_pc_usd_current_prices            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 233.433, 2…
$ gdp_pc_ppp_current_prices            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1050.99, 1…
$ population                           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 18.707, 19…
$ govt_expenditure_pct_gdp             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 6.943, 11.…
$ debt_pct_gdp                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 345.977, 2…
$ territorial_co2                      <dbl> 2.603, 2.427, 1.379, 1.333, 1.282, 1.230, 1.165, 1.084, 1.…
$ trade_co2                            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consumption_co2                      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ cumulative_co2                       <dbl> 59.182, 61.610, 62.989, 64.322, 65.604, 66.834, 67.999, 69…
$ debt_usd                             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1510.8816,…
$ govt_expenditure_usd                 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 30.32008, …
$ territorial_co2_per_capita           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.05623563…
$ trade_co2_per_capita                 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consumption_co2_per_capita           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ territorial_co2_per_gdp              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.05350694…
$ trade_co2_per_gdp                    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consumption_co2_per_gdp              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ territorial_co2_per_debt             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.00069628…
$ trade_co2_per_debt                   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consumption_co2_per_debt             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ debt_usd_per_capita                  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 80.765572,…
$ territorial_co2_per_govt_expenditure <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.03469648…
$ trade_co2_per_govt_expenditure       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ consumption_co2_govt_expenditure     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ govt_expenditure_per_capita          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.620788, …
$ trade_pct_of_consumption_co2         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ cumulative_co2_per_capita            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.930561, …

Look at the different country group categorizations in the imf_wb_country_group dataset

unique(imf_wb_country_groups$country_group)

 [1] "Advanced Economies"                                                     
 [2] "Advanced G20"                                                           
 [3] "Emerging G20"                                                           
 [4] "Emerging Market and Middle-Income Africa"                               
 [5] "Emerging Market and Middle-Income Asia"                                 
 [6] "Emerging Market and Middle-Income Europe"                               
 [7] "Emerging Market and Middle-Income Latin America"                        
 [8] "Emerging Market and Middle-Income Middle East North Africa and Pakistan"
 [9] "Emerging Market Economies"                                              
[10] "Euro Area"                                                              
[11] "G20"                                                                    
[12] "G7"                                                                     
[13] "Low-Income Developing Asia"                                             
[14] "Low-Income Developing Countries"                                        
[15] "Low-Income Developing Latin America"                                    
[16] "Low-Income Developing Others"                                           
[17] "Low-Income Developing Sub-Saharan Africa"                               
[18] "Low-Income Oil Producers"                                               
[19] "Oil producers"                                                          
[20] "Arab World"                                                             
[21] "Central Europe and the Baltics"                                         
[22] "Caribbean small states"                                                 
[23] "Early-demographic dividend"                                             
[24] "East Asia & Pacific"                                                    
[25] "Europe & Central Asia"                                                  
[26] "Euro area"                                                              
[27] "European Union"                                                         
[28] "Heavily indebted poor countries (HIPC)"                                 
[29] "IBRD only"                                                              
[30] "IDA & IBRD total"                                                       
[31] "IDA total"                                                              
[32] "IDA blend"                                                              
[33] "IDA only"                                                               
[34] "Latin America & Caribbean"                                              
[35] "Least developed countries: UN classification"                           
[36] "Late-demographic dividend"                                              
[37] "Middle East & North Africa"                                             
[38] "North America"                                                          
[39] "OECD members"                                                           
[40] "Other small states"                                                     
[41] "Pre-demographic dividend"                                               
[42] "Pacific island small states"                                            
[43] "Post-demographic dividend"                                              
[44] "South Asia"                                                             
[45] "Sub-Saharan Africa"                                                     
[46] "Small states"                                                           
[47] "East Asia & Pacific (IDA & IBRD)"                                       
[48] "Europe & Central Asia (IDA & IBRD)"                                     
[49] "Latin America & Caribbean (IDA & IBRD)"                                 
[50] "Middle East & North Africa (IDA & IBRD)"                                
[51] "South Asia (IDA & IBRD)"                                                
[52] "Sub-Saharan Africa (IDA & IBRD)"                                        
[53] "World"                                                                  
[54] "High income"                                                            
[55] "Low income"                                                             
[56] "Lower middle income"                                                    
[57] "Upper middle income"                                                    
[58] "Sub-Saharan Africa (excluding high income)"                             
[59] "Europe & Central Asia (excluding high income)"                          
[60] "Latin America & Caribbean (excluding high income)"                      
[61] "East Asia & Pacific (excluding high income)"                            
[62] "Middle East & North Africa (excluding high income)"                     
[63] "Low & middle income"                                                    
[64] "Middle income"                                                          
[65] "Africa Eastern and Southern"                                            
[66] "Africa Western and Central"                                             
[67] "Fragile and conflict affected situations"                               

Data preparation

Convert country names (regular expression) to iso3c names:

country_name_regex_to_iso3c <- function(country_name) {
  country_name %>%
    countrycode(origin = "country.name", 
                                     destination = "iso3c",
                                     origin_regex = TRUE)
}

Create a new dataframe called imf_wb_country_groups_iso3c from the imf_wb_country_groups dataset. New dataframe includes a new variable called ‘iso3c’

imf_wb_country_groups_iso3c <- imf_wb_country_groups %>%
  mutate(iso3c = country_name_regex_to_iso3c(country_name))

Warning: Some values were not matched unambiguously: Channel Islands, Kosovo

imf_wb_country_groups_iso3c

Combine the imf_wb_country_groups_iso3c and the emissions_dataset_full datasets using the iso3c country names. New dataframe is emissions_data_combined.

emissions_data_combined <- imf_wb_country_groups_iso3c %>% left_join(emissions_dataset_full, by = "iso3c")

Filter the emissions_data_combined dataset by year 2019 and by country group ‘East Asia & Pacific’. Save filtered data as a new dataframe called emissions_data_EAP_2019. This dataframe contains 35 countries.

emissions_data_EAP_2019 <- emissions_data_combined %>%
  filter(year == 2019, country_group == "East Asia & Pacific")

emissions_data_EAP_2019

New dataframe to evalute CO2 emissions at the global level.

emissions_data_world_2019 <- emissions_data_combined %>%
  filter(year == 2019, country_group == "World")

Summary statistics

#Total territorial CO2 emissions in East Asia-Pacific
EAP_2019_emissions_sum <- sum(emissions_data_EAP_2019$territorial_co2, na.rm=TRUE)

EAP_2019_emissions_sum

[1] 14909.36

#Total global CO2 emissions
world_2019_emissions_sum <- sum(emissions_data_world_2019$territorial_co2, na.rm=TRUE)
world_2019_emissions_sum

[1] 35423.01

#East Asia and Pacific region's emissions as a percent of global emissions
(EAP_2019_emissions_sum / world_2019_emissions_sum )*100

[1] 42.08949

In 2019, the East Asia and Pacific region emitted 14,909.36 tons of CO2 and accounted for 42% of global CO2 emissions.

#Average territorial CO2 emissions in East Asia-Pacific
mean(emissions_data_EAP_2019$territorial_co2, na.rm=TRUE)

[1] 451.7988

#Average global CO2 emissions
mean(emissions_data_world_2019$territorial_co2, na.rm=TRUE)

[1] 174.4976

The average CO2 emissions level in the East Asia and Pacific region was 451.8 tons, which was also higher than the global average of 174.5 tons of CO2. East Asia and Pacific is therefore an important region for discussions of global emissions, given its high contribution to emissions level.

Analysis

Graph (1) Top CO2 emitters in the East Asia and Pacific region by total emission.

This graph shows the 15 countries with the highest total CO2 emissions within their physical territory in 2019, the majority of whom are developed or fast emerging economies, which reaffimrs that positive correlation between higher economic growth with higher emissions. China is the top emitter, with emission level far higher than all the remaining countries in the list. Japan, Indonesia, South Korea and Australia joins China in the top 5.

emissions_data_EAP_2019 %>%
  slice_max(order_by = territorial_co2, n = 15) %>%
  ggplot(aes(fct_reorder(country_name, territorial_co2),territorial_co2, fill(country_name)))+            
  geom_col(fill="#00A2E8") + 
  coord_flip() +
  scale_y_continuous(breaks = scales::pretty_breaks())+
  scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
  labs(
    x = "",
    y = "Territorial CO2 (Tons)",
    title = "Top CO2 emitters in the East Asia and Pacific region by total emission",
    subtitle = "15 countries with the highest total territorial CO2 emissions in 2019",
    caption = "Territorial Emissions is defined as the CO2 emissions created within a country's physical territory.
    Data source: World Bank, IMF"
    )

Graph (2) Countries with the highest CO2 emission per capita in the East Asia and Pacific region

This graph shows the 15 countries with the highest total CO2 emissions within their physical territory in 2019. The ranking of emitters have changed substantially, and the top 5 countries with the highest per capita emissions are Mongolia, Australia, South Korea, Taiwan and Japan. China is now ranked 9th in terms of per capita emissions.

emissions_data_EAP_2019 %>%
  slice_max(order_by = territorial_co2, n = 15) %>%
  ggplot(aes(fct_reorder(country_name, territorial_co2_per_capita),territorial_co2_per_capita, fill(country_name)))+            
  geom_col(fill="#00819C") + 
  coord_flip() +
  scale_y_continuous(breaks = scales::pretty_breaks())+
  scale_x_discrete (guide = guide_axis(n.dodge=1.5))+
  labs(
    x = "",
    y = "Territorial CO2 per capita (Tons)",
    title = "Countries with the highest CO2 emission per capita in the East Asia and Pacific region",
    subtitle = "15 countries with the highest per capita CO2 emission in 2019",
    caption = "Territorial Emissions per capita is defined as the CO2 emissions created within
    a country's physical territory, divided by its total population.
    Data source: World Bank, IMF"
    )

Several observations are noteworthy from graphs (1) and (2). Australia, South Korea and Japan are in the regional top 5 in terms of both total and per capita CO2 emissions. China, who ranks the highest in terms of total emissions, ranks much lower in terms of per capita emissions while Mongolia, who ranks much lower in terms of total emissions, takes first place in terms per capita emissions.

A point for discussion is whether developed countries in the East Asia-Pacific region are putting enough effort in their emission reduction, especially since developed countries have more capital and technological capacity than developing countries to enhance their sustainability initiatives. On the other hand, the fact that developing countries such as Indonesia and Mongolia ranks so high in terms of total and per capita emissions, respectively, indicate that further assistance are needed for developing countries to adopt stronger sustainability practices and standards.

China’s lower per capita emissions suggests that its high total emissions level may be partially due to its higher total population, but the country may have certain practices or mechanisms in place that are effective in lowering individual emissions level in its territory. This begs the question of which measures do we use to evalute whether a country is a major polluter: its total emissions level, or its per capita emissions level. It also suggests that discussions of a country’s emission level should also include the emission sources: is the emissions level high because of higher population, or does it reflect lacking sustainability practices?

Graph (3) Relationship between GDP per capita and CO2 emission per capita

This graph shows that there is a positive correlation between per capita CO2 emissions and GDP per capita. Countries that have higher GDP per capita tend to also have higher per capita emissions, with the exception of Mongolia as an outlier with low GDP per capita but much high per capita emissions. China is currently at the mid-way point in terms of both GDP per capita and per capita emissions. Given the pace at which the Chinese economy is growing, an interesting question is whether China will also have higher per capita emissions as it transitions from a middle-income to high-income economy.

emissions_data_EAP_2019 %>%
  drop_na(c("territorial_co2_per_capita")) %>%
  ggplot(aes(x = gdp_pc_usd_current_prices, y = territorial_co2_per_capita))+
  geom_point(color = "#4DB063")+
  scale_x_log10(guide = guide_axis(n.dodge=1.75))+ scale_y_log10()+
  geom_text_repel(aes(label = country_name), size = 2.25)+
  labs(
    x = "GDP per capita (USD, current prices)",
    y = "Territorial CO2 per capita (Tons)",
    title = "Relationship between GDP per capita and CO2 emission per capita",
    subtitle = "East Asia and Pacific region in 2019",
    caption = "Data source: World Bank, IMF"
    )