HW6

Reference and disclaimer: This homework is attempting to generate figures, using World Bank data and google trend.

Task 1: The trend for Total GHG emissions of Top 10 countries

Background

As you know, the main culprit of climate change is greenhouse gas (GHG) emissions from human activities. Just 10 years ago, many people haave thought that developed countries in the Western regions have caused the GHG emissions. Therefore, the GHG mitigation commitments in Kyoto Protocol had been applied mainly to developed countries with the emphasis of historical responsibility. Also, according to their capacity, developing countries had been reporting the GHG emissions to UNFCCC every five years. However, according to the Durban outcomes in 2011, developing countries report their emissions biennially. Now, we can track the trend the GHG emissions for developing countries as well as for developed countries. Furthermore, according to the Paris Agreement in 2015, developing countries as well as developed countries should submit their Nationally Determined Contributions (NDC) to UNFCCC to prevent the climate change. Don’t they think it’s unfair?

Who are leading the increase in the world GHG emissions now?

The first chart is the figure on The trend for Total GHG emissions of Top emitters. (The data is collected by The World Bank API and the indicator is “EN.ATM.GHGT.KT.CE”.) The solid lines show the trends for developing countries, and the dotted lines indicate those for developed countries. To our surprise, the top country is China and the 6 developing countries belong to Top 10 countries. Furthermore, the total GHG emissions of develping countries are still increasing, which will widen the gap between the reality and global GHG mitigation goal. That’s why developing counties should also contribute to significant meaningful mitigation. Also, I think that the demands for the study related to the GHG mitigation in developing countries would increase continuously.

emission_var <- wb(indicator = "EN.ATM.GHGT.KT.CE")
country.emissions <- wb(country=c("CN", "USA", "IN", "RU", "JP", "BR", "DE", "ID", "MX", "IR"), 
  indicator = "EN.ATM.GHGT.KT.CE")
country.emissions <- mutate(country.emissions, value1=value/1000)
country.emissions <- select(country.emissions, "date", "value1", "iso2c")
country.emissions <- spread(country.emissions, key = iso2c, value = value1 )

g1 <- ggplot(country.emissions, aes(x=as.numeric(date))) +
  theme_classic() +
  ggtitle("Total GHG Emissions of Top 10 countries") +
  labs(x = "year", y = "Million ton CO2e") +
  theme(plot.title = element_text(hjust = 0.5, family="Arial", color="black", size=12, face="bold"),
      title = element_text(family="Arial", color="black", size=8, face="bold"),
      axis.ticks = element_blank()) + 
  geom_line(aes(y=CN, colour="aa"), linetype="solid", size=1.2) +
  geom_line(aes(y=US, colour="bb"), linetype="dotted", size=1.2) +
  geom_line(aes(y=IN, colour="cc"), linetype="solid", size=1.2) +
  geom_line(aes(y=RU, colour="dd"), linetype="dotted", size=1.2) +
  geom_line(aes(y=JP, colour="ee"), linetype="dotted", size=1.2) +
  geom_line(aes(y=BR, colour="ff"), linetype="solid", size=1.2) +
  geom_line(aes(y=DE, colour="gg"), linetype="dotted", size=1.2) +
  geom_line(aes(y=ID, colour="hh"), linetype="solid", size=1.2) +
  geom_line(aes(y=MX, colour="ii"), linetype="solid", size=1.2) + 
  scale_colour_manual(name=NULL, values = c("aa"="red", "bb"="blue", 
                                            "cc"="orange", "dd"="deepskyblue1",
                                            "ee"="skyblue1", "ff"="green",
                                            "gg"="slateblue1", "hh"="yellow4",
                                            "ii"="orange3", "jj"="tan1"),
                      labels = c("China", "United States",
                                 "India", "Russia",
                                 "Japan", "Brazil", 
                                 "Germany", "Indonesia",
                                 "Mexico", "Iran"))  
g1

Task 2: Interest in “Fine Dust” of Top 5 countries.

The second chart is the trend of ** the interest in Fine Dust of Top 5 countries**. You can see that the level of interest is sharply increasing from 2015. (The data is collected by “Google Trends”) Most of Korean people believe that these fine dusts would have been caused by the power plants of the Chinese estern part. Unfortunately, there is no interest in “Fine Dust” in China. Therefore, Korean Government is trying to find the scientific evidence that these particles were generated by China.

rm(list=ls())
googleData = gtrends(c("fine dust"), geo=c("KR", "PH", "SG", "AU", "ZA"), 
                     gprop = "web", time = "all")[[1]]
googleData = dcast(googleData, date ~ geo, value.var = "hits")

g2 <- ggplot(googleData, aes(x=date)) +
  theme_classic() +
  ggtitle("Interest in 'Fine Dust' of Top 5 countries") +
  labs(x = "year", y = "level of interest") +
  theme(plot.title = element_text(hjust = 0.5, family="Arial", color="black", size=12, face="bold"),
        title = element_text(family="Arial", color="black", size=8, face="bold"),
        axis.ticks = element_blank()) + 
  geom_line(aes(y=KR, colour="aa"), linetype="solid", size=1.2) +
  geom_line(aes(y=PH, colour="bb"), linetype="solid", size=1.2) +
  geom_line(aes(y=SG, colour="cc"), linetype="solid", size=1.2) +
  geom_line(aes(y=AU, colour="dd"), linetype="solid", size=1.2) +
  geom_line(aes(y=ZA, colour="ee"), linetype="solid", size=1.2) +
  scale_colour_manual(name=NULL, values = c("aa"="red", "bb"="blue", 
                                            "cc"="orange", "dd"="deepskyblue1",
                                            "ee"="skyblue1"),
                      labels = c("R.of Korea", "Philippines", 
                                 "Singapore", "Australia", "South Africa"))  
g2