Coal as an energy source implies costly logistics and coal combustion is relatively difficult to control, both regarding power output and pollution. There are attempts to replace coal with hydrogen. The techniques are developed in Korea in the Swedish Hybrit joint venture company as well as in a similar Austrian project. The downside of using renewable energy is profitability. There are two solutions: lower renewable electricity cost or higher carbon emission cost. The growing use of renewables will affect the future energy mix. Mckinsey predicted that only the demand for natural gas will maintain a constant share. The demand for coal and oil will level off and then decline. Renewables share will increase. However, this report will find that until 2015, the demand for coal is still rising exponentially for the production of electricity. Although the western world’s industrialization over the past 200 years have enjoyed enormous benefits from fossil fuels, the transition of carbon-based economy to a more renewables-based economy is evitable because of its advantages. Although there are economic arguments against renewable energy sources, arguing that they are expensive, intermittent, and so on. Renewables are clean. The free a country from balance of payments burden. They generate employment.

The report obtained the most updated World Development Indicator (WDI) data set from its official website (uploaded the EXCEL into Rstudio) and from Indicators API (drew data directly from rstudio cloud).Major variables used are Electricity production from coal sources (% of total, Electricity production from hydroelectric sources (% of total), Electricity production from natural gas sources (% of total), Electricity production from nuclear sources (% of total), Electricity production from oil sources (% of total), Electricity production from renewable sources, excluding hydroelectric (% of total), and Electricity production from renewable sources, excluding hydroelectric (kWh).

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

On a global level, coal, natural gas, and hydroelectric energy are major energy sources for electricity production (Figure 1). Global renewable energy electricity production has increased from 2000 to 2015 (Figure 1). While the level of production using the six sources of energy remains stable, coal and renewable energy produced electricity have upward trends, while the other energy sources has a downward trend (Figure 2). Despite its environmental externalities, coal continues to be the major energy source for electricity production (Figure 2). Around May 2013, renewable energy surpassed oil became the 5th used electricity energy source. Around May 2014, renewable energy surpassed nuclear energy became the 4th used electricity energy source. Renewable energy electricity production is increasing exponential from year 2005 to 2015 (Figure 2)

Figure 1: Pie Charts

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

The report takes the definition and categorization of country income groups from the World Bank Database (WB). The WB differentiates country income level by their GNI per capita. High-income economies are those in which 2021 GNI per capita was more than $13,205. Upper middle income economies are those in which 2021 GNI per capita was between $4,256 and $13,205. Middle-income economies are those in which 2021 GNI per capita was between $1,086 and $13,205. Lower-middle-income economies are those in which 2021 GNI per capita was between $1,086 and $4,255. Low-income economies are those in which 2021 GNI per capita was $1,085 or less.

Gloabl Level Analysis

In terms of renewable energy produced electricity, all income groups uses higher level of renewable energy for electricity production. High income countries demonstrates the fastest increase (Figure 3). In terms of coal produced electricity, upper middle income economies and lower middle income economies are increasing their level of electricity produced by coal (Figure 4). This discovery is consistent with a classical theory: as economies grow, energy demand increases; if energy is constrained, GDP growth pulls back in turn (Bram Smeets, Namit Sharma, and Christer Tryggestad 2019). With the high energy demands, coal becomes their cheaptest energy source for economic development. At the same time, Figure 4 shows that high income economies’ use of coal as their electricity energy source decrease substantially from 2013 to 2015. There could be a correlation between income group and use of coal and renewable energy for electricity production.

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

The report takes the definition and categorization of region from the WB, differentiating them geographically into seven groups: East Asia & Pacific, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, North America, South Asia, Sub-Saharan Africa. Regionally, As one of the regions with the strongest economic performance and environmental awareness, Europe & Central Asia ranks first in using renewable energy for electricity production. East Asia & Pacific region rank second.

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

High income economies in East Asia & Pacific include Australia, Brunei, Guam, Hong Kong SAR China, Japan, Korea, Macao SAR China, Northern Mariana Islands, New Caledonia, Nauru, New Zealand, French Polynesia, Singapore. Low income economies in East Asia & Pacific include Dem. People’s Rep. Korea. Low Middle Income economies in East Asia & Pacific include Micronesia, Indonesia, Cambodia, Kiribati, Lao PDR, Myanmar, Mongolia, Philippines, Papua New Guinea, Solomon Islands, Timor-Leste, Vietnam, Vanuatu, Samoa. Upper Middle Income economies in East Asia & Pacific include American Samoa, China, Fiji, Marshall Islands, Malaysia, Palau, Thailand, Tonga, Tuvalu. Within the high income economies, Japan and Australia uses the most renewable energy in electricity production. Within the upper middle economies, China uses the most renewable energy in electricity production. Within the lower middle economies, Indonesia and Philippines uses the most renewable energy in electricity production. Those countries’ economic performance is better than their counterparts within the region, corroborating the correlation between income level and use of renewable energy in electricity production on a country level.

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

Lower Middle Income Economies Electricity Production using Renewable Energy

Although high income economies are using much higher level of renewable energy as their electricity production sources, lower middle income economies are experiencing a higher production of electricity using renewables too (Figure 9).

##       EnergySource Percentage2000
## 1             Coal           36.2
## 2      Natural Gas           26.2
## 3    Hydroelectric           15.9
## 4              Oil           12.1
## 5   Nuclear Energy            6.8
## 6 Renewable Energy            1.5
##       EnergySource Percentage2015
## 1             Coal           43.8
## 2      Natural Gas           26.7
## 3    Hydroelectric           12.6
## 4              Oil            7.4
## 5   Nuclear Energy              0
## 6 Renewable Energy            3.8
plot10<-ggplot(renewlowmidtime) +
  aes(x = date, y = EG.ELC.RNWX.ZS) +
  geom_bar(stat="identity", width=.5, fill="seagreen4")+
  labs(
    x = "Year",
    y = "Electricity Production by Renewable Energy Sources",
    tag = "Figure 10",
    title = "Electricity Produced by Renewable Energy",
    subtitle = "Lower Middle Income Economies Year 2015",
    caption = "Data Source: World Development Indicators",
  ) +
  theme_grey()
plot10

Conclusion and Future Outlook: World Map

As shown in the World Map 2 below, China ranks top 3 in using renewable energy producing electricity, depiste being categorized as a middle income economy. Since it is one of the fastest growing economies, along with the US and Europe, it uses the most renewable energy for electricity production. China has grown in to become a renewables superpower. China had installed 378 gigawatts (GW) of renewables capacity by 2014 to tap water, wind and sun to generate power. Under China’s 13th Five Year Plan, the country aims to have no less than 750 GW of renewables capacity available – more than all the countries of the OECD combined.

Renewables has more benefits in addition to the ones mentioned above. They enhance energy security. They respond to the economic imperative. This also offers a new way of framing industrial development strategies. They can pursue late-comer strategies, like the East Asian countries before them, and apply them to technologies, like wind turbines and solar panels, to build renewable energy systems that generate clean power, clear the skies, strengthen energy security and resolve balance of payments problems. Most importantly, when we think about how much gasoline our cars need to go, how much electricity needs to come out of a socket to make an appliance work, and how much coal, natural gas, or nuclear fuel must be fed into a power plant to generate the steam that turns the turbines, we naturally start with the amount of fuel inputs needed. With renewables, however, those metrics are practically meaningless. We don’t measure what fuels a solar panel or pushes a windmill—we measure the energy that comes out. The near total absence of any conversion loss is radically different: nothing is lost in the burning. Nor do sunshine or wind power need to be generated at large, centralized plants; companies, and indeed individual consumers, can in many cases harness the energy on-site. While most businesses will not be able to go completely or even largely off the grid, many will be able to lessen their electrical costs materially—and some, particularly large retailers, may even in certain locations produce a net energy surplus.

Figure 11: World Map 1 Income Level and Using Coal to Produce Electricity

# 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, elecoal, income, lat, lng)%>%
  mutate(elerenew3 = EG.ELC.COAL.ZS/2.5)

# Create a color palette with bins.
inccols = colorFactor( palette= c( "#f4a582","#ca0020", "#404040", "#bababa"), 
                       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 = ~elerenew3, 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

Figure 12: World Map 2 Income Level and Using Renewable Energy to Produce Electricity

# 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