Initial Settings

# Package Loading======================== Package Loading========================
library(tidyverse)
library(ggthemr)
library(ggthemr)
library(ggcorrplot)
ggthemr("fresh", type = "outer", layout = "scientific", spacing = 2)
library(scales)
library(ggridges)
library(imputeTS)
# Data Loading===========================
biocpacity <- read_csv("Biocapacity by Source.csv")
co2emission <- read_csv("CO2e Emissions by SectorSource.csv")
company <- read_csv("CompanyData.csv")
ecofootprint <- read_csv("Ecological Footprint by Source.csv")

country_data <- co2emission %>% select(Year, population, gdp, land_area, forested_land, 
    percent_population, energy_use, renewable_energy)
co2emission <- co2emission %>% select(-population, -gdp, -land_area, -forested_land, 
    -percent_population, -energy_use, -renewable_energy)

General Assumptions

• No commission charges or extra fees during the carbon credits transaction

Data Manipulation

# Merge in columns =======================================
biocpacity <- biocpacity %>% pivot_longer(c("Built_up_Land", "Cropland", "Fishing_Grounds", 
    "Forest_Products", "Grazing_Land", "Biocapacity_Total"), names_to = "Type")
co2emission <- co2emission %>% pivot_longer(c("B", "E", "I", "O", "T", "W", "Emission_Total"), 
    names_to = "Type")
## Hold on to the rest: ecofootprint, company

Insights through Visulization

# Have a look ============================================= DATASET: co2emission
# General emission trend
co2emission %>% filter(Type == "Emission_Total") %>% ggplot(aes(x = Year, y = value)) + 
    geom_point(aes(size = log(value) * 100), alpha = 0.5) + geom_line(size = 1.5) + 
    scale_x_continuous(breaks = c(1999, 2005, 2011, 2015, 2019)) + geom_vline(xintercept = 2001, 
    linetype = 2, color = "orange", size = 1.2) + geom_vline(xintercept = 2011, linetype = 2, 
    color = "orange", size = 1.2) + labs(x = "Year", y = "Co2e Emission") + ggtitle("CO2e Emission Trend", 
    subtitle = "emission of co2e started dropping since 2011") + theme(panel.grid.major.x = element_blank(), 
    legend.position = "none")

## Trend in different type
co2emission %>% filter(Type != "Emission_Total") %>% ggplot() + geom_bar(aes(x = Year, 
    y = value, fill = value), stat = "identity") + facet_wrap(~Type) + ggtitle("Emission of Sectors", 
    "Max Emission Sector: Energy, Manufacturing and Construction ") + theme(legend.title = element_blank())

# DATASET: biocpacity The total biocpacity didn't had many alteration
biocpacity %>% filter(Type != "Biocapacity_Total") %>% ggplot() + geom_bar(aes(x = Year, 
    y = value, fill = Type), stat = "identity", position = "stack") + labs(x = "Year", 
    y = "Biocapacity Contribution") + ggtitle("Biocapacity Contribution Trend", "The total emission capacity and the proportion had little alteration")

Tricky Companies

all_zeros <- (company$`2019` == 0) & (company$`2018` == 0) & (company$`2017` == 0) & 
    (company$`2016` == 0) & (company$`2015` == 0)
have_zero <- (company$`2019` == 0) | (company$`2018` == 0) | (company$`2017` == 0) | 
    (company$`2016` == 0) | (company$`2015` == 0)
print(paste0("There are ", round(mean(all_zeros, na.rm = T), 4) * 100, "% of company that have no data"))

[1] "There are 15.95% of company that have no data"

print(paste0(round(mean(have_zero, na.rm = T), 4) * 100, "% of company have at least one 0"))

[1] "50.7% of company have at least one 0"

company <- company %>% filter(!((company$`2019` == 0) && (company$`2018` == 0) && 
    (company$`2017` == 0) && (company$`2016` == 0) && (company$`2015` == 0))) %>% 
    na_if(0)
round(colMeans(is.na(company)), 2)

Company ID     Sector   Location       2019       2018       2017       2016 
      0.00       0.00       0.00       0.24       0.30       0.28       0.27 
      2015 
      0.28 

table(company$`Company ID`) %>% as.data.frame() %>% arrange(-Freq) %>% head(10)

          Var1 Freq
1  5.48531e+12    6
2  2.45743e+13    4
3   1.1289e+13    2
4  2.02059e+13    2
5  2.73896e+13    2
6  3.66985e+13    2
7   8.7252e+13    2
8  8.82264e+13    2
9  1.26735e+11    1
10 1.50619e+11    1

temp_df <- company %>% filter(!(`Company ID` %in% c(5.48531e+12, 2.45743e+13, 1.1289e+13, 
    2.02059e+13, 2.73896e+13, 3.66985e+13, 8.7252e+13, 8.82264e+13))) %>% select(`2019`:`2015`)
rownames(temp_df) <- company %>% filter(!(`Company ID` %in% c(5.48531e+12, 2.45743e+13, 
    1.1289e+13, 2.02059e+13, 2.73896e+13, 3.66985e+13, 8.7252e+13, 8.82264e+13))) %>% 
    pull(`Company ID`)

d <- temp_df %>% rownames_to_column %>% gather(var, value, -rowname) %>% spread(rowname, 
    value)

d_noNA <- d %>% select(var) %>% cbind(na_interpolation(d[-1], option = "linear"))


rownames(d_noNA) <- d_noNA$var
new_company <- d_noNA %>% rownames_to_column %>% gather(var, value, -rowname) %>% 
    spread(rowname, value) %>% mutate(var = as.numeric(var)) %>% rename(`Company ID` = var) %>% 
    left_join(company[, c(1:3)], by = "Company ID") %>% select(`Company ID`, Sector, 
    Location, everything()) %>% mutate_at(vars(`2015`, `2016`, `2017`, `2018`, `2019`), 
    as.numeric) %>% rename(year_2015 = `2015`, year_2016 = `2016`, year_2017 = `2017`, 
    year_2018 = `2018`, year_2019 = `2019`) %>% na.omit() %>% mutate(Company_Total = year_2015 + 
    year_2016 + year_2017 + year_2018 + year_2019) %>% rename(`2015` = year_2015, 
    `2016` = year_2016, `2017` = year_2017, `2018` = year_2018, `2019` = year_2019) %>% 
    pivot_longer(`2015`:`2019`, names_to = "Year")

new_company %>% group_by(`Company ID`) %>% ggplot() + geom_density(aes(log(Company_Total), 
    fill = Sector, color = Sector), alpha = 0.8) + labs(x = "Log(Company Total Emissions)") + 
    ggtitle("Distribution of Company Total Emissions", "Waste and Transport are the dominant emission sources")

new_company %>% group_by(Location) %>% summarise(Total_Emission = sum(Company_Total)) %>% 
    ggplot() + geom_histogram(aes(log(Total_Emission)), fill = "darkred", alpha = 0.8) + 
    labs(x = "Log(Total Emission)")

new_company %>% ggplot() + geom_boxplot(aes(x = Sector, y = log(value), fill = Year)) + 
    labs(y = "Log(Company Yearly Emission)") + ggtitle("Sectors Weights & Yearly Trend", 
    "The transport sector generate the most CO2e\nNo significant yearly trend within the sectors")

Information about Pullanta

Total_emission <- co2emission %>% filter(Type == "Emission_Total")

corr_data <- country_data %>% left_join(Total_emission, by = "Year") %>% select(-Type) %>% 
    rename(Emission = value) %>% select(Year, Emission, everything())

corr <- round(cor(corr_data[-1] %>% scale()), 1)
p.mat <- cor_pmat(corr_data[-1] %>% scale())
ggcorrplot(corr, hc.order = T, type = "lower", p.mat = p.mat)

From the correlation graph, the CO2E emission is correlated with

• Land Area

• Energy Use

• Population

• GDP

Specially, the emssion of greenhouse gases is highly correlated with the land area. On the other hand, forested land, percent population, renewable energy consumption don’t lay much impact on the emission of CO2e, which is quite unexpected. (We did standardized the data by scaling and centralizing. But hence we are generating the correlation matrix, it doesn’t really matter.) As we look over the dataset, there exist huge gaps between renewable energy use and other energy consumptions. In other words, the renewable energy in Pullanta had slow development over these years.

Let’s go down to the factors that mostly effect the greenhouse gases emission.

GrowthRate <- country_data %>% # Population Rate
mutate(Previous_Year = lag(population, 1), Change = population - Previous_Year, Population_Growth_Rate = Change/Previous_Year * 
    100) %>% # GDP Rate
mutate(Previous_GDP = lag(gdp, 1), Change_GDP = gdp - Previous_GDP, GDP_Growth_Rate = Change_GDP/Previous_GDP * 
    100) %>% filter(Year != 2019, Year != 1995) %>% pivot_longer(c(Population_Growth_Rate, 
    GDP_Growth_Rate), names_to = "Growth_Rates")

GrowthRate_GDP <- GrowthRate %>% filter(Growth_Rates == "GDP_Growth_Rate") %>% ggplot(aes(x = Year, 
    y = value)) + geom_line(size = 1.5) + geom_smooth(linetype = 2) + scale_x_continuous(breaks = c(1995, 
    2000, 2005, 2010, 2015, 2018)) + labs(y = "Rate(%)") + ggtitle("GDP growth rate")
GrowthRate_Pop <- GrowthRate %>% filter(Growth_Rates == "Population_Growth_Rate") %>% 
    ggplot(aes(x = Year, y = value)) + geom_line(size = 1.5) + geom_smooth(linetype = 2) + 
    scale_x_continuous(breaks = c(1995, 2000, 2005, 2010, 2015, 2018)) + labs(y = "Rate(%)") + 
    ggtitle("Population growth rate")
gridExtra::grid.arrange(GrowthRate_Pop, GrowthRate_GDP, nrow = 1)

country_data %>% mutate(Previous_Year = lag(energy_use, 1), Change = energy_use - 
    Previous_Year, Energy_Growth_Rate = Change/Previous_Year * 100) %>% filter(Year != 
    2019, Year != 1995) %>% ggplot(aes(x = Year, y = Energy_Growth_Rate)) + geom_bar(aes(fill = Energy_Growth_Rate), 
    stat = "identity") + geom_smooth(linetype = 2, alpha = 0.3) + theme(legend.position = "top") + 
    labs(y = "Rate(%)") + ggtitle("Growth Rate of Energy")

country_data %>% mutate(Previous_Year = lag(land_area, 1), Change = land_area - Previous_Year, 
    Land_Growth_Rate = Change/Previous_Year * 100) %>% filter(Year != 2019, Year != 
    1995) %>% ggplot(aes(x = Year, y = Land_Growth_Rate)) + geom_bar(aes(fill = Land_Growth_Rate), 
    stat = "identity") + geom_smooth(linetype = 2, alpha = 0.3) + theme(legend.position = "top") + 
    labs(y = "Rate(%)") + ggtitle("Growth Rate of Land Use")

Revenue and Expense

Design Financial Instruments

Goverment perspective: Implementation

Enterprise perspective: Risk Management