Major Trend: Global Electricity Energy Source

Progress of Renewable Energy Replacing Coal: by Income Groups

telerenew <- WDIE %>%
  rename(Indicator = "Indicator Code")%>%
  rename(Country = "Country Code")%>%
  pivot_longer('1990':'2021', names_to = "year") %>%
  mutate(year = as.numeric(year)) %>%
  filter(!is.na(value)) %>%
  filter(Indicator %in% c("EG.ELC.RNWX.ZS","EG.ELC.COAL.ZS","EG.ELC.HYRO.ZS","EG.ELC.NGAS.ZS","EG.ELC.NUCL.ZS","EG.ELC.PETR.ZS"))

telerenew$Indicator[telerenew$Indicator == "EG.ELC.COAL.ZS"] <- "elecoal"
telerenew$Indicator[telerenew$Indicator == "EG.ELC.HYRO.ZS"] <- "elehydro"
telerenew$Indicator[telerenew$Indicator == "EG.ELC.NGAS.ZS"] <- "elengas"
telerenew$Indicator[telerenew$Indicator == "EG.ELC.NUCL.ZS"] <- "elenuc"
telerenew$Indicator[telerenew$Indicator == "EG.ELC.PETR.ZS"] <- "eleoil"
telerenew$Indicator[telerenew$Indicator == "EG.ELC.RNWX.ZS"] <- "elerenew"

Global Electricity Energy Sources Trend

telerenew4<- telerenew %>% 
  filter(Indicator %in% c("elecoal","elehydro","elengas","elenuc","eleoil", "elerenew"))%>%
  filter (Country %in% c("HIC", "LMC","MIC", "UMC", "LIC"))%>%
  group_by(year, Indicator)%>%
  summarize(sum = sum(value))

ggplot(telerenew4, aes (x = year, y = sum, group = Indicator))+
  geom_line(aes(colour = Indicator))+
  geom_point(aes(x = year, y = sum, colour = Indicator))+
  labs(
    title = "General Trend: Electricity Produced with Six Energy Sources",
    subtitle = "Cross Energy Sources (Year 1990 - 2015)",
    caption = "Data from World Bank Dataset.",
    tag = "Figure 2",
    x = "Year",
    y = "% of total electricity production",
    colour = "Energy Sources"
  )+
  theme_gray()

Cross-Regional Comparison Based on Income Groupings

Gloabl Level Analysis

telerenew5<- telerenew %>% 
  filter(Indicator == "elerenew")%>%
  filter (Country %in% c("HIC", "LMC","MIC", "UMC", "LIC"))%>%
  filter (year < "2015")%>%
  group_by(year, Country)%>%
  summarize(sum = sum(value))

p1<- ggplot(telerenew5, aes (x = year, y = sum, group = Country))+
  geom_line(aes(colour = Country))+
  geom_point(aes(x = year, y = sum, colour = Country))+
  labs(
    title = "Global Trend: Electricity Produced by Renewable Energy",
    subtitle = "Cross Income Groups (Year 1990-2014)",
    caption = "Data from World Bank Dataset.",
    tag = "Figure 3",
    x = "Year",
    y = "% of the energy source",
    colour = "Country"
  )+
  theme_gray()

telerenew5<- telerenew %>% 
  filter(Indicator == "elecoal")%>%
  filter (Country %in% c("HIC", "LMC","MIC", "UMC", "LIC"))%>%
  filter (year < "2015")%>%
  group_by(year, Country)%>%
  summarize(sum = sum(value))

p2 <- ggplot(telerenew5, aes (x = year, y = sum, group = Country))+
  geom_line(aes(colour = Country))+
  geom_point(aes(x = year, y = sum, colour = Country))+
  labs(
    title = "Global Trend: Electricity Produced by Coal",
    subtitle = "Cross Income Groups (Year 1990-2014)",
    caption = "Data from World Bank Dataset.",
    tag = "Figure 4",
    x = "Year",
    y = "% of the energy source",
    colour = "Country"
  )+
  theme_gray()

grid.arrange(p1, p2, nrow = 2)

Inter-Regional Analysis

plot2<- renewreg %>%
 filter(date >= 2015L & date <= 2015L) %>%
 ggplot() +
  aes(x = country, y = EG.ELC.RNWX.KH/1000000000) +
  geom_bar(stat="identity", width=.5, fill="darkgreen")+
  labs(
    x = "Regions",
    y = "Electricity Production by Renewable Energy in Billion kwh",
    tag = "Figure 5",
    subtitle = "Electricity Production by Renewable Energy",
    caption = "By Region (2015)"
  ) +
  theme_minimal()+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  scale_x_discrete(labels=c("East Asia\n& Pacific", "Europe \n& Central Asia", "Latin America \n& Caribbean", "Middle East \n& North Africa", "North America", "South Asia", "Sub-Saharan Africa"))
plot2

Country Level Analysis within East Asia & Pacific Region

plota<-ggplot(reneweaphi, aes(x = date, y = EG.ELC.RNWX.KH/1000000000, color = country, shape = country, group = country)) +
  geom_point() + 
  geom_line() +
  labs(title = "Electricity Produced by Renewable Energy: High Income Economies") +
  labs(subtitle = "(2000-2015)") +
  xlab("Year" ) +
  ylab("Electricity production from renewable sources in billion (kWh)") +
  labs (tag = "Figure 6")+
  scale_x_continuous(breaks = seq(2000,2015,5))+
  theme_grey() +
  theme(panel.grid.minor.x = element_blank())
plota

plotc<- ggplot(reneweapeumi, aes(x = date, y = EG.ELC.RNWX.KH/1000000000, color = country, shape = country, group = country)) +
  geom_point() + 
  geom_line() +
  labs(title = "Electricity Produced by Renewable Energy: Upper Middle Income Economies") +
  labs(subtitle = "(2000-2015)") +
  xlab("Year") +
  ylab("Electricity production from renewable sources in billion (kWh)") +
  labs (tag = "Figure 7")+
  scale_x_continuous(breaks = seq(2000,2015,5))+
  theme_grey() +
  theme(panel.grid.minor.x = element_blank())
plotc

plotb<- ggplot(reneweaplmirev, aes(x = date, y = EG.ELC.RNWX.KH/1000000000, color = country, shape = country, group = country)) +
  geom_point() + 
  geom_line() +
  labs(title = "Electricity Produced by Renewable Energy: Lower Middle Income Economies") +
  labs(subtitle = "(2000-2015)") +
  xlab("Year") +
  ylab("Electricity production from renewable sources in billion (kWh)") +
  labs (tag = "Figure 8")+
  scale_x_continuous(breaks = seq(2000,2015,5))+
  theme_grey() +
  theme(panel.grid.minor.x = element_blank())
plotb

Conclusion and Future Outlook: World Map

# create a vector of the desired indicator series
indicators <- c(elecoal, elehydro, elengas, elenuc, eleoil,
                elerenew)
countries <- WDI(country="all", indicator = indicators, 
     start = 1990, end = 2015, extra = TRUE)
# convert geocodes from factors into numerics
countries$lng <- as.numeric(as.character(countries$longitude))
countries$lat <- as.numeric(as.character(countries$latitude))
# Remove groupings, which have no geocodes
countries <- countries %>%
   filter(!is.na(lng))

# filter to 2015 and select relevant variables
wdi98 <- countries %>% filter(year == 2015)%>%
  select(country,year, elerenew, income, lat, lng)%>%
  mutate(elerenew2 = EG.ELC.RNWX.KH/5000000000)

# Create a color palette with bins.
inccols = colorFactor( palette= c( "#b2df8a","#33a02c", "#a6cee3", "#1f78b4"), 
                       levels = c("High income", "Upper middle income", 
                                  "Lower middle income", "Low income"))
#order income levels
wdi98$income <- ordered(wdi98$income, levels = c("High income", "Upper middle income", "Lower middle income", "Low income"))
#create leaflet map for 1998
w98 <- leaflet(data = wdi98) %>%
  addTiles() %>% setView(lng = 10, lat =0, zoom = 1.5) %>%
  addCircleMarkers(~lng, ~lat,popup = ~as.character(country),
                   color = ~ inccols(income),
                   radius = ~elerenew2, stroke = FALSE, fillOpacity = .9)%>%
  addLegend(pal = inccols, values = ~income,opacity=0.9,
            title = "Income Category", position = "bottomleft" ) %>%
  addControl("Income and Population in 2015", position = "bottomright");w98

1. Through which type of energy is electricity made from in each country/ in each region.
2. Which country does best in using renewable sources to produce electricity
3. Which country does the worst in using renewable energy sources to produce electricity?
4. What is the level of GHG emission & CO2 emission in that country?
   
5. What is the CO2 emission from electricity and heat production
6. (What is the level of PM2.5 comparing the worst and best country)
7. What is their renewable electricity output?

• Run cross sectional multiple regression: Y is level of electricity consumption across countries. X1 is number covid 19 cases. Control variables include: population density, rural vs urban ratio, # vaccinations…
• Include an interaction term of the ICT electricity use because research has found that ICT sector development is positively correlated to a electricity usage. Or include a qualitative analysis of how shifting towards working from home expedited the digitization process and the ICT sectors.