Introduction

We cannot live without electricity. The way it is connected to our daily lives shows how indispensable it is to people. However, power consumption, including use of electricity, is corrupting our future. Burning down fossil fuels, which is the common way in producing energy, is proven to be a hazard on air, water, and all sorts of natural substances. Economic alliances and several countries, leaded by group of OECD countries, are asserting mandatory control on energy consumption regarding its future threats.

This brings us question of whether the significance of energy consumption is well accepted to nations. With this in mind we would like to find out world power consumption level and the growth rate to see whether nations are acknowledging the potential threat. Furthermore, we would like to analyze continents and economic alliances that well reduced energy consumption to rank the leading groups in accomplishing their duties and get some insights from these countries. This would be useful to those with the highest power consumption growth rate. At last verification of external factors that influence energy consumption will be proceeded.

Research Question

  1. How has world energy consumption changed (measured by the original value and growth rate)
  2. What continent and economic alliances is leading the energy consumption reduction?
  3. What is an external factor that has influenced the reduction of energy consumption

Hypothesis

  1. The energy consumption varies greatly among different geographical locations and economic alliances(BRICS, CIS, and OECD).
  2. According to various situational variables (2008 financial crisis, COVID-19, etc.), consumption of energy will decrease temporarily.
  3. G2 countries(United States and China) will account for a large proportion of energy consumption.

Data Resources - Information, Definitions

The dataset outlines the quantity of terawatt (TWh) produced through various sets of energy, consists of both non-renewable energy and renewable energy.

1. RAW DATA

Source https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html

Who is the Enerdata? Enerdata is an independent research and consulting firm specialising in the analysis and modelling of the global energy markets and its drivers. Created in 1991, Enerdata now has over 25 years of experience on past and present issues shaping the energy industry. Their teams are made up of energy experts, analysts, engineers and IT specialists. Capitalising on its databases and forecasting models, Enerdata brings its expertise to cover the political, economic and environmental aspects of energy systems.

2. PREPROCESSING

Continent_consumption_TWH

* The dataset lists up the amount of energy consumption(TWh) group by 7 continents and 3 organizations from year 1990 to 2020. Specifically organizations analyzed in the dataset are OECD, BRICS, and CIS.

Country_consumption_TWH

* It is the dataset that lists up the amount of power consumption of 44 countries from year 1990 to 2020.

3. DATA COLUMN

  • Columns
    • Country
    • Year
    • OECD: An international organization with 38 countries providing a platform to compare policy experiences, seek answers to common problems, identify good practices and coordinate domestic and international policies of its members.
    • BRICS: Relatively diminished status of the existing developed countries in the 21st century and the emergence of five countries with large areas and populations led to the emergence of BRICS.
    • CIS: A coalition of former Soviet republics that became independent due to the dissolution of the Soviet Union
    • Power Generation: The process of producing electric energy or the amount of electric energy produced by transforming other forms of energy into electrical energy
    • Africa
    • Europe
    • Asia
    • North America
    • South America
    • Middle East
    • Pacific

library(tidyverse)
library(ggplot2)
library(rmarkdown)
library(knitr)
library(ggthemes)
cont_consump<-read.csv("Continent_Consumption_TWH.csv")
country_consump<-read.csv("Country_Consumption_TWH.csv")

Data Exploration

1) Continent Consumption
str(cont_consump)
unique(cont_consump$Year)
sum(is.na(cont_consump))
head(cont_consump,5)
summary(cont_consump)
2) Country Consumption
str(country_consump)
unique(country_consump$Year) 
sum(is.na(country_consump))
country_consump <- na.omit(country_consump) 
country_consump<-rename(country_consump,"Saudi Arabia" = "Saudi.Arabia")
country_consump<-rename(country_consump,"United Kingdom" = "United.Kingdom")
country_consump<-rename(country_consump,"United States" = "United.States")
country_consump<-rename(country_consump,"New Zealand" = "New.Zealand")
country_consump<-rename(country_consump,"South Africa" = "South.Africa")
country_consump<-rename(country_consump,"South Korea" = "South.Korea")
country_consump<-rename(country_consump,"United Arab Emirates" = "United.Arab.Emirates")
head(country_consump,5)

EDA of Energy Consumption

1. How Energy Consumption changed since 1990 to 2020?

ggplot(
    filter(gather(cont_consump, continent, Twh, World:CIS), continent == "World"), 
    aes(Year, Twh))+
    geom_point()+
    geom_line()

filter(gather(cont_consump,continent,Twh,World:CIS),continent == "World") %>% 
    mutate(percent_increase = (Twh - lag(Twh, default = first(Twh)))/ lag(Twh, default = first(Twh))) %>%
    ggplot(aes(Year, percent_increase))+
    geom_point() +
    geom_smooth(method = "lm")

  • Energy consumption has been increasing for a total of 30 years from 1990 to 2020.

  • If you look at the points where energy consumption suddenly decreased in 2009 and 2020, there are two outliers in the amount of change, and it is on the decline.

  • The previous graph is transferred to highlight the rate of percentage change from 1990 to 2020.

  • There are only two points that showed a negative percentage growth of energy consumption.

  • Each are found in 2009 and 2020, which each implies financial crisis and COVID-19, respectively.

2. How Energy Consumption changed for the continents?

ggplot(
    filter(gather(cont_consump,continent,Twh,World:CIS),continent != "World"& continent != "CIS" & continent != "BRICS" & continent !=  "OECD"),
    aes(Year,Twh, color = continent)) +
    geom_point() + 
    geom_smooth()

  • Asia showed the biggest increase in the energy consumption throughout the years due to two reasons.
  1. Asia showed the highest population growth over time.
  2. Regions in Asia went through the process of urbanization the most over time.
ggplot(
    filter(gather(cont_consump,continent,Twh,World:CIS),continent != "World"& continent != "CIS" & continent != "BRICS" & continent !=  "OECD" & continent !=  "Asia"),
    aes(Year,Twh, color = continent)) +
    geom_point() + 
    geom_smooth() +
    facet_wrap(~continent)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

  • The graph shows annual energy consumption of continents except Asia.
  • Asia was excluded to compare the other continents with similar scales so that we could examine the trend of energy consumption more precisely.
  • Europe and North America showed decreasing energy consumption unlike most other continents.
ggplot(
    filter(gather(cont_consump,continent,Twh,World:CIS),continent != "World"& continent != "CIS" & continent != "BRICS" & continent !=  "OECD",
           continent != "Asia",continent != "Africa",continent != "Latin.America",continent != "Middle.East",continent != "Pacific"),
    aes(Year,Twh, color = continent)) +
    geom_point() + 
    geom_smooth() +
    facet_wrap(~continent)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

  • The graph concentrates on the annual energy consumption of Europe and North America.
  • The 20% energy efficiency target was enacted with the adoption of the Energy Efficiency Directive 2012/27/EU in 2012.
  • The North American Energy Working Group (NAEWG) was established in 2001 to enhance North American energy trade and interconnections consistent with the goal of sustainable development.
  • Following a gradual decrease between 2007 and 2014, energy consumption increased between 2014 and 2017. ##The increase could partly be attributed to good economic performance since 2014 with low oil prices and colder winters.
  • In 2018, this growing trend was moderated, and primary energy consumption even declined compared to 2017.
  • The COVID crisis significantly hampered the economy and decreased the energy consumption in 2020.
  • However, unless the economy will become more energy efficient, the subsequent recovery will lead to a rebound in energy consumption.

3. How Energy Consumption changed for the Economic Alliances?

ggplot(
    filter(gather(cont_consump,continent,Twh,World:CIS),continent != "Africa" & continent != "Asia" 
           & continent !=  "Europe" & continent !=  "Latin.America" 
           & continent !=  "Middle.East" & continent !=  "North.America" 
           & continent !=  "Pacific"),
    aes(continent,Twh, fill = continent)) +
    geom_col()

  • When setting the world total energy consumption level as the standard, in the order of OECD, BRICs, and CIS the economic alliance has the highest total energy consumption level.
ggplot(
    filter(gather(cont_consump,continent,Twh,World:CIS),continent != "Africa" & continent != "Asia" 
           & continent !=  "Europe" & continent !=  "Latin.America" 
           & continent !=  "Middle.East" & continent !=  "North.America" 
           & continent !=  "Pacific" & continent !=  "World"),
    aes(Year,Twh, color = continent)) +
    geom_point() + 
    geom_line()

gather(select(cont_consump, "Year", "BRICS", "CIS", "OECD"), economic_alliances, Twh, BRICS:OECD) %>%
    ggplot(aes(Year, Twh, color = economic_alliances)) +
    geom_point() +
    geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

  • The energy consumption of OECD and CIS from 1990 to 2020 shows slight changes compared to BRICs energy consumption that increase abruptly from year 2000.
  • OECD’s total consumption level shows almost horizontal line from 2007 when Intergovernmental Panel on Climate Change (IPCC) issued the first scientific consensus on climate change. (CNN,2009)
  • The growth rate of BRICs’ energy consumption was caused by fierce economic growth.
  • Even though Brazils’ rainforest degradation rose as a global issue in 2009, vague implementation of ecological legislation and Russia’s ignorance on the issue resulted in continuous growth in power consumption of BRICs.
gather(select(cont_consump,"Year","BRICS", "CIS", "OECD"),economic_alliances, Twh, BRICS:OECD) %>% 
    ggplot(aes(economic_alliances, Twh, fill = economic_alliances))+ geom_col()

  • OECD produces the biggest amount of total energy consumption followed by BRICs and CIS.
gather(select(cont_consump,"Year","BRICS", "CIS", "OECD"),economic_alliances, Twh, BRICS:OECD) %>% 
    ggplot(aes( Twh, fill = economic_alliances))+ geom_boxplot()

  • OECD and CIS are located at opposite direction. OECD boxplot is on the upper right, but CIS boxplot is situated at middle left.
  • The shape of BRICs boxplot is stretched horizontally. The prominent shift of BRICs energy consumption level is related to industrialization which was held since 1990.
  • Short boxplot of OECD is partly due to the Paris agreement made in 2015 urging the energy transition and policy enforcement. The specific goals and implementation of environmental law halted the rapid increase of energy consumption.

4. How Energy Consumption changed for the countries?

ggplot(gather(country_consump, country, energy_consump, c(2:45)), aes(country, energy_consump, fill = as.factor(Year))) +
    geom_col() +
    theme(axis.text.x = element_text(angle = 90))

  • China and USA show the conspicuous amount of high energy consumption.
aggregate(energy_consump ~ country ,gather(country_consump, country, energy_consump, c(2:45)), FUN = "sum") %>% 
    arrange(desc(energy_consump)) %>%
    head(10) %>%
    ggplot(aes(country, energy_consump, fill = country)) +
    geom_col()

  • Top10 countries who consume the energy the most are included in top 20 countries with the largest population. Thus, detailed comparison can be made by per capita.

Top 10 Energy Consumption Countries

gat <- gather(country_consump,country,energy_consump, c(2:45))
agg <- aggregate(energy_consump ~ country ,gather(country_consump,country,energy_consump, c(2:45)), FUN = "sum")

ggplot(merge(gat, agg %>%
                 arrange(desc(energy_consump)) %>%
                 select(country) %>%
                 head(10)),
       aes(Year, energy_consump, color = country)) +
    geom_point() +
    geom_line() +
    geom_smooth(method = "lm") +
    facet_wrap(~country)

ggplot(merge(gat, agg %>%
          arrange(desc(energy_consump)) %>%
          select(country) %>%
          head(10)) %>%
    group_by(country) %>%
    arrange(country, Year) %>%
    mutate(percent_increase  =(energy_consump - lag(energy_consump, default = first(energy_consump))) / lag(energy_consump, default = first(energy_consump))),
    aes(Year, percent_increase, color = country)) +
    geom_point() +
    facet_wrap(~country) +
    geom_smooth(method = "lm")

  • The percentage by which the consumption of energy increases is decreasing or shows a downward trend with the exception of Russia and China.
  • The transition to market-based economy made both China and Russia highly focus on economic expansion rather than environmental protection, resulting in incessant growth of energy consumption (EIA, 2004)

Low 10 Energy Consumption Countries

aggregate(energy_consump ~ country, gather(country_consump, country, energy_consump, c(2:45)), FUN = "sum") %>%
    arrange(energy_consump) %>%
    head(10) %>%
    ggplot(aes(country, energy_consump, fill = country)) + geom_col() + theme(axis.text.x = element_text(angle = 90))

  • The Country with the lowest energy Consumption is New Zealand. Most countries with low energy consumption seem to be developing countries. This can be looked into further.

  • Developed countries with low energy consumption should be analyzed with attention.

  • New Zealand: high use of renewable energy combined with ardent legal action. (IEA,2020)

  • Norway: The abundant natural energy resources and a relatively small population made it easy for technology development leading to green shift. (Norsk Industry, 2021)

  • Portugal: Owns restriction toward carbon emission and coal- produced energy. (IEA,2020)

# Energy consumption/energy use over time period
ggplot(merge(gather(country_consump,country,energy_consump, c(2:45)),
             aggregate(energy_consump ~ country ,gather(country_consump,country,energy_consump, c(2:45)), FUN = "sum") %>% 
                 arrange(energy_consump) %>%
                 select(country) %>% 
                 head(10)),aes(Year,energy_consump, color = country)) + geom_point() + geom_line()
## Warning: Removed 20 rows containing missing values (geom_point).
## Warning: Removed 20 row(s) containing missing values (geom_path).

  • Algeria and Kuwait show upward slope with annual increase use of energy since energy is one of their exporting goods.
# percentage increase each year
ggplot(merge(gather(country_consump,country,energy_consump, c(2:45)),
             aggregate(energy_consump ~ country ,gather(country_consump,country,energy_consump, c(2:45)), FUN = "sum") %>% 
                 arrange(energy_consump) %>%
                 select(country) %>% 
                 head(10)) %>% 
           group_by(country) %>% 
           arrange(country,Year) %>% 
           mutate(percent_increase = (energy_consump - lag(energy_consump, default = first(energy_consump)))/lag(energy_consump, default = first(energy_consump))),
       aes(Year,percent_increase,color = country)) + geom_point() + facet_wrap(~country)+ geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).

  • Compared to top 10 countries with high energy consumption, countries with low energy consumption’s growth rate is stable without huge fluctuation.

External Factors

5. How Covid19 affected Energy Consumption?

  • We considered the Middle-East as both Economic Alliance and a continent at the same time.
    Cause, Middle-East has not only the feature of the close location of each country but also has been maintaining an important economic connection between those countries.
organization_consumption_rate <- cont_consump %>%
  rename("Middle-East" = "Middle.East", year = Year)  %>%
  select(year,OECD,CIS,"Middle-East",BRICS) %>%
  pivot_longer(names_to = "Economic_Alliance", values_to = "Consumption", -year) %>%
  group_by(Economic_Alliance) %>%
  arrange(Economic_Alliance, year) %>%
  mutate(Growth_Rate = 100 * (Consumption - lag(Consumption)) / lag(Consumption)) %>%
  filter(!is.na(Growth_Rate))
1) How COVID19 pandemic(2019-2020) affected Energy consumption by Economic Alliance?
Covid_Economic_Alliances <- organization_consumption_rate %>%
  filter(year == 2020)
  
Covid_EA <- ggplot(Covid_Economic_Alliances, aes(x = reorder(Economic_Alliance, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.3) +
  labs(title = "Impact of Covid19 Pandemic on Energy Consumption", x="\nEconomic Alliance", y="Growth Rate(%)\n") +
  theme_economist()

Covid_EA + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                 axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                 axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"))

  • The rate of decline on Energy Consumption due to COVID19.
Covid_Economic_Alliances1 <- Covid_Economic_Alliances %>%
  select(Economic_Alliance, Growth_Rate) %>%
  arrange(Growth_Rate)

Covid_Economic_Alliances1
## # A tibble: 4 x 2
## # Groups:   Economic_Alliance [4]
##   Economic_Alliance Growth_Rate
##   <chr>                   <dbl>
## 1 OECD                   -7.02 
## 2 CIS                    -5.49 
## 3 Middle-East            -1.23 
## 4 BRICS                  -0.147
  • This chart shows clearly the impact of COVID19 pandemic on energy consumption in the world.
  • In this chart, there was a significant decline in energy consumption of -7.02% in the OECD and -5.49% in the CIS, while Middle-East and BRICS showed a slight decline in energy consumption of -1.23% and -0.15%, respectively.
  • Relatively, OECD and CIS were significantly affected by COVID-19 compared to Middle-East and BRICS, and Middle-East and BRICS did not clearly show such a decline.
  • CIS and OECD showed significant minus growth rate on energy consumption, whereas BRICS and Middle-East relatively maintained a good performance.

Why BRICS did not get affected by COVID19 as much as OECD or CIS did?

  • BRICS Countries established a coordination mechanism to solve problems in energy supply and the resumption of enterprising production, tracked the supply and demand situation, and made every effort to ensure energy supply during the epidemic.
2) How COVID19 affected Energy Consumption by continent(2019~2020)?
cont_consump_rate <- cont_consump %>%
  rename("North-America" = "North.America", "Latin-America" = "Latin.America", "Middle-East" = Middle.East, year = Year) %>%
  select(year, Europe, "North-America", "Latin-America", Asia, Pacific, Africa, "Middle-East") %>%
  pivot_longer(names_to = "Continent", values_to = "Consumption", -year) %>%
  group_by(Continent) %>%
  arrange(Continent, year) %>%
  mutate(Growth_Rate = 100 * (Consumption - lag(Consumption)) / lag(Consumption)) %>%
  filter(!is.na(Growth_Rate))
Covid_continent <- cont_consump_rate %>%
  filter(year == 2020)


Covid_Continent <- ggplot(Covid_continent, aes(x = reorder(Continent, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.3) +
  labs(title = "Impact of Covid19 Pandemic on Energy Consumption", x="\nContinent", y="Growth Rate(%)\n") +
  theme_economist()

Covid_Continent + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                 axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                 axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"),
                 axis.text.x = element_text(size = 9, angle = 90, hjust = 1),
                 axis.text.y = element_text(size = 9))

  • The rate of decline on Energy Consumption due to COVID19.
# The rate of decline on Energy Consumption due to COVID19.
Covid_continent_Rate <- Covid_continent %>%
  select(Continent, Growth_Rate) %>%
  arrange(Growth_Rate)

Covid_continent_Rate
## # A tibble: 7 x 2
## # Groups:   Continent [7]
##   Continent     Growth_Rate
##   <chr>               <dbl>
## 1 North-America      -7.48 
## 2 Latin-America      -6.99 
## 3 Europe             -6.74 
## 4 Africa             -2.41 
## 5 Pacific            -1.94 
## 6 Middle-East        -1.23 
## 7 Asia               -0.468
  • COVID19 pandemic impacts considerably energy consumption of the six continents. Asia with china looses only -0.47% of its energy consumption against -6.74% for Europe and -7.48% for North America.
  • Pacific showed a decrease in energy consumption of -1.94%, Africa is -2.41%, and Latin America is -6.99%.
  • Relatively, Europe, Latin-America, North-America show a significant decline in energy consumption, whereas Asia, Middle-East, Pacific do not.

Why Europe, Latin-America, North-America showed a significant decline in energy consumption? And why Asia, Middle-East, Pacific did not?

  • The corona virus disease (COVID-19) has affected countries as a whole, but the effects have been more acutely felt in urban centers. While commercial, public, and social services were either halted or partially restricted during lockdown, there was still a need for Required Core Urban Service Standards.

  • These service standards are broadly crosscutting and while energy is directly needed in both heating and cooling and electricity, energy remains essential in delivering all core urban services.

  • In short, The more urbanized countries were, the more energy consumption would have decreased.

  • Actually, The rate of urbanization by continent is highest in North America, followed by Latin America and Europe, and Asia and Africa are the lowest.

3) How Covid19 affected Energy consumption(2019~2020) by countries?
  • We are going to look at the impact of COVID19 on energy consumption for each country.
  • To do so, we compute the change rate of energy consumption between 2019 and 2020.
  • After, we compare with the impact of economic crisis 2008-2009 on energy consumption.
country_consump_rate <- country_consump %>%
  rename(year = Year, "United States" = "United.States", "United Kingdom" = "United.Kingdom", 
         "South Korea" = "South.Korea", "New Zealand" = "New.Zealand",
         "South Africa" = "South.Africa", "Saudi Arabia" = "Saudi.Arabia",
         "United Arab Emirates" = "United.Arab.Emirates") %>%
  pivot_longer(names_to = "Country", values_to = "Consumption", -year) %>%
  group_by(Country) %>%
  arrange(Country, year) %>%
  mutate(Growth_Rate = 100 * (Consumption - lag(Consumption)) / lag(Consumption)) %>%
  filter(!is.na(Growth_Rate))
Covid19_country <- country_consump_rate %>%
  filter(year == 2020)
  
  
Covid_country <- ggplot(Covid19_country, aes(x = reorder(Country, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.4) +
  labs(title = "Impact of Covid19 Pandemic on Energy Consumption", x="\nCountry", y="Growth Rate(%)\n") +
  theme_economist()

Covid_country + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                      axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                      axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"),
                      axis.text.x = element_text(face = "bold", size = 8, angle = 90, hjust = 1),
                      axis.text.y = element_text(size = 9))

  • Only just 3 countries(China, Nigeria and Kuwait) have positive change rate. Only one countries of the BRICS outperforms but all countries in OECD is down.
  • COVID19 pandemic impacts seriously energy consumption of many countries but three countries are making good performance.
4) Top5 Country affected by COVID19
Top5_country_covid <- Covid19_country %>% select(Country, Growth_Rate) %>%
  arrange(Growth_Rate)

head(Top5_country_covid, 5)
## # A tibble: 5 x 2
## # Groups:   Country [5]
##   Country        Growth_Rate
##   <chr>                <dbl>
## 1 Venezuela           -19.4 
## 2 Mexico              -14.2 
## 3 Spain               -11.6 
## 4 France              -10.7 
## 5 United Kingdom       -9.41
  • Venezuela, Mexico, Spain, France, United Kindom had more significant impact of COVID19 than other countries.
5) Low5 Country affected by COVID19
Low5_country_covid <- Covid19_country %>% select(Country, Growth_Rate) %>%
  arrange(desc(Growth_Rate)) 

head(Low5_country_covid, 5)
## # A tibble: 5 x 2
## # Groups:   Country [5]
##   Country     Growth_Rate
##   <chr>             <dbl>
## 1 Kuwait            5.56 
## 2 China             2.18 
## 3 Nigeria           1.27 
## 4 New Zealand       0    
## 5 Iran             -0.372
  • Kuwait, China, Nigeria, New Zealand somehow had increased energy consumption as a impact of COVID19.

How China could maintain a balanced energy consumption?

  1. China strengthened the organization and guidance for power production
  2. China vigorously increased oil and gas exploration and development efforts, and focused on increasing reserves, production and imports to ensure a stable supply of oil and gas

6. How Economic Crisis affected Energy Consumption?

1) How Economic Crisis affected Energy consumption by Economic Alliance?(2008~2009)
Economic_Crsis_Alliances <- organization_consumption_rate %>%
  filter(year == 2009)

Crisis_EA <- ggplot(Economic_Crsis_Alliances, aes(x = reorder(Economic_Alliance, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.3) +
  labs(title = "Impact of Economic Crisis on Energy Consumption", x="\nEconomic Alliance", y="Growth Rate(%)\n") +
  theme_economist()

Crisis_EA + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                 axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                 axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"))

  • The rate of decline on Energy Consumption due to Economic Crisis.
Economic_Crisis_Alliances1 <- Economic_Crsis_Alliances %>%
  select(Economic_Alliance, Growth_Rate) %>%
  arrange(Growth_Rate)

Economic_Crisis_Alliances1
## # A tibble: 4 x 2
## # Groups:   Economic_Alliance [4]
##   Economic_Alliance Growth_Rate
##   <chr>                   <dbl>
## 1 CIS                     -7.64
## 2 OECD                    -4.43
## 3 Middle-East              3.06
## 4 BRICS                    4.01
  • This chart shows clearly the impact of economic crisis 2008-2009 on OECD and CIS.OECD and CIS show a relatively large drop in energy consumption due to the economic crisis.OECD was on a decline of -4.43%, and CIS was on a decline of -7.64% respectively.However, Middle-East and BRICS show increased energy consumption to 3.06% and 4.01%, respectively,and did not seem like to have had any significant impact on the economic crisis.
  • After economic crisis 2008-2009, OECD had lost 4.43% of its energy consumption and it see itself lose again 7.02% its energy consumption due to COVID19 pandemic. Contrarily to BRICS, Middle-East were most impacted by COVID19 pandemic.
2) How Economic Crisis affected Energy consumption?(2008~2009) by continent?
Economic_Crisis_continent <- cont_consump_rate %>%
  filter(year == 2009)


Economic_Crisis_Continent <- ggplot(Economic_Crisis_continent, aes(x = reorder(Continent, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.3) +
  labs(title = "Impact of Economic Crisis on Energy Consumption", x="\nContinent", y="Growth Rate(%)\n") +
  theme_economist()

Economic_Crisis_Continent + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                        axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                        axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"),
                        axis.text.x = element_text(size = 9, angle = 90, hjust = 1),
                        axis.text.y = element_text(size = 9))

  • The rate of decline on Energy Consumption due to Economic Crisis.
EC_continent_Rate <- Economic_Crisis_continent %>%
  select(Continent, Growth_Rate) %>%
  arrange(Growth_Rate)

EC_continent_Rate
## # A tibble: 7 x 2
## # Groups:   Continent [7]
##   Continent     Growth_Rate
##   <chr>               <dbl>
## 1 Europe              -5.35
## 2 North-America       -4.83
## 3 Latin-America       -2.52
## 4 Pacific              0   
## 5 Africa               2.19
## 6 Middle-East          3.06
## 7 Asia                 4.48
  • Due to the economic crisis, energy consumption in Europe, North America and South America fell by -5.45%, -4.83%, and -2.52%,while energy consumption in the Pacific, Africa, and Asia increased by 0%, 2.19%, and 4.48% respectively.
  • This suggests that the economic crisis had a relatively greater impact on energy consumption in Europe, North America, and South America.
3) How Economic Crisis affected Energy consumption (2019~2020) by countries?
Economic_Crisis_country <- country_consump_rate %>%
  filter(year == 2009)

EC_country <- ggplot(Economic_Crisis_country, aes(x = reorder(Country, -Growth_Rate), y = Growth_Rate)) + geom_col(width = 0.4) +
  labs(title = "Impact of Economic Crisis on Energy Consumption", x="\nCountry", y="Growth Rate(%)\n") +
  theme_economist()

EC_country + theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 15, color = "darkblue"),
                      axis.title.x = element_text(face = "bold", size = 9, color = "darkblue"),
                      axis.title.y = element_text(face = "bold", size = 9, color = "darkblue"),
                      axis.text.x = element_text(face = "bold", size = 8, angle = 90, hjust = 1),
                      axis.text.y = element_text(size = 9))

  • We have 11 countries that its change rate is positive. As we can see above graph, we can say that those countries in the Europe, North-America, South-America mostly had decline of energy consumption as impact of economic crisis.
4) Top5 countries affected by Economic Crisis
Top5_country_crisis <- Economic_Crisis_country %>% select(Country, Growth_Rate) %>%
  arrange(Growth_Rate)

head(Top5_country_crisis, 5)
## # A tibble: 5 x 2
## # Groups:   Country [5]
##   Country    Growth_Rate
##   <chr>            <dbl>
## 1 Ukraine         -15.6 
## 2 Romania         -12.5 
## 3 Kazakhstan      -10   
## 4 Sweden          -10   
## 5 Spain            -8.63
  • Ukraine, Romania, Kazakhstan, Sweden, Spain had huge amount of decrease in energy consumption due to the economic crisis.
5) Low5 countries affected by Economic Crisis
Low5_country_crisis <- Economic_Crisis_country %>% select(Country, Growth_Rate) %>%
  arrange(desc(Growth_Rate))

head(Low5_country_crisis, 5)
## # A tibble: 5 x 2
## # Groups:   Country [5]
##   Country              Growth_Rate
##   <chr>                      <dbl>
## 1 Algeria                    10.8 
## 2 India                       9.77
## 3 United Arab Emirates        8.47
## 4 China                       6.59
## 5 Indonesia                   5.95
  • Algeria, India, UAE, China, Indonesia had increased energy consumption.If we do comparison of theses two phenomena, we can conclude that Covid19 pandemic is impacted most several countries on energy consumption than economic crisis 2008-2009.

Why Ukraina especially got affected by Economic Crisis on Energy consumption?

  • Ukraina’s Lower disposable income of households, lower business revenues and credit crunch led to plunge in real consumption and investment. Slump in demand for Ukrainian exports negatively influenced metal and chemical industries, which caused the energy consumption to be decreased.

7. CONCLUSION

Hypothesis1 : The energy consumption varies greatly among different geographical locations and economic alliances(BRICS, CIS, and OECD).

  1. Global energy consumption has been increasing from 1990 to 2020.
  2. However, Europe and North America, where the proportion of developed countries is comparatively high, show a decrease in the energy consumption due to their performance in increasing the energy efficiency.
  3. OECD, the economic alliance with the highest energy consumption, shows decreasing growth rate because of their economic agreement and binding actions.

Hypothesis2 : According to various situational variables (2008 financial crisis, COVID-19, etc.), consumption of energy will decrease temporarily.

  1. If there is external variable, consumption of energy decrease temporarily.
  2. If we do comparison of theses two phenomena, we can conclude that Covid19 pandemic impacted most several countries on energy consumption than economic crisis 2008-2009.

Hypothesis3 : G2 countries(United States and China) will account for a large proportion of energy consumption.

  1. USA and China’s huge energy consumption stands out compared to the other countries.
  2. China’s energy consumption shows incessant growth whereas USA’s energy consumption is decreasing.

8. Several Topic that we can think or discusss in the future

  • Why Covid19 is impacted most several countries on energy consumption?

  • What will be the impact of COVID19 pandemic and its variants on energy consumption for year 2021, 2022 and 2023?

  • According to www.chooseenergy.com which stated that “the world’s food systems account for approximately 30% of global energy consumption”; we know that the Covid 19 pandemic is causing global energy consumption to drop significantly to -4%.

  • ARE WE IN THE AFTERMATH OF A GLOBAL FOOD CRISIS?

9. REFERENCE