solutii

solutii.R

# Load required libraries
library(readxl)
library(dplyr)
library(ggplot2)
library(gridExtra)

# Set working directory (adjust as needed)
# setwd("path/to/your/excel/files")

cat("=== ENVIRONMENTAL ECONOMICS PRACTICE EXAM SOLUTION ===\n\n")

# =============================================================================
# PART 1: CARBON EMISSIONS ANALYSIS (25 points)
# =============================================================================

cat("PART 1: CARBON EMISSIONS ANALYSIS\n")
cat("==================================\n\n")

# Load emissions data
emissions_data <- read_excel("emissions_data.xlsx")

# Question 1: Calculate emissions per capita for 2023
cat("Question 1: Emissions per capita for 2023\n")
emissions_2023 <- emissions_data %>%
  filter(year == 2023) %>%
  mutate(emissions_per_capita = co2_emissions / population)

mean_emissions_per_capita <- round(mean(emissions_2023$emissions_per_capita, na.rm = TRUE), 1)
median_emissions_per_capita <- round(median(emissions_2023$emissions_per_capita, na.rm = TRUE), 1)

cat("Mean global emissions per capita for 2023:", mean_emissions_per_capita, "tonnes CO2 per person\n")
cat("Median global emissions per capita for 2023:", median_emissions_per_capita, "tonnes CO2 per person\n\n")

# Question 2: Carbon intensity by region for 2023
cat("Question 2: Carbon intensity of GDP by region for 2023\n")
emissions_2023 <- emissions_2023 %>%
  mutate(carbon_intensity = co2_emissions / gdp_usd * 1000) # tonnes CO2 per million USD

regional_intensity <- emissions_2023 %>%
  group_by(region) %>%
  summarise(mean_carbon_intensity = round(mean(carbon_intensity, na.rm = TRUE), 1)) %>%
  arrange(region)

print(regional_intensity)
cat("\n")

# Question 3: CAGR of CO2 emissions 2000-2023
cat("Question 3: Top 5 countries with highest emissions growth rates (2000-2023)\n")
emissions_growth <- emissions_data %>%
  filter(year %in% c(2000, 2023)) %>%
  group_by(country_code) %>%
  summarise(
    emissions_2000 = co2_emissions[year == 2000],
    emissions_2023 = co2_emissions[year == 2023],
    .groups = 'drop'
  ) %>%
  filter(!is.na(emissions_2000) & !is.na(emissions_2023) & emissions_2000 > 0) %>%
  mutate(
    cagr = ((emissions_2023 / emissions_2000)^(1/23) - 1) * 100
  ) %>%
  arrange(desc(cagr)) %>%
  slice_head(n = 5)

top_5_growth <- sort(emissions_growth$country_code)
cat("Top 5 countries with highest emissions growth (alphabetical order):", paste(top_5_growth, collapse = ", "), "\n\n")

# Question 4: Scatter plot GDP per capita vs CO2 per capita for 2023
cat("Question 4: Creating scatter plot for 2023 data\n")

# Calculate GDP per capita for plotting
plot_data_2023 <- emissions_2023 %>%
  mutate(gdp_per_capita = gdp_usd * 1000 / population) %>% # Convert to per capita
  filter(!is.na(gdp_per_capita) & !is.na(emissions_per_capita))

# Create the scatter plot
p1 <- ggplot(plot_data_2023, aes(x = gdp_per_capita, y = emissions_per_capita, color = region)) +
  geom_point(alpha = 0.7, size = 2) +
  geom_smooth(method = "lm", se = FALSE, color = "black", linetype = "dashed") +
  scale_x_continuous(limits = c(0, 80000), breaks = seq(0, 80000, 10000),
                     labels = scales::comma_format()) +
  scale_y_continuous(limits = c(0, 25), breaks = seq(0, 25, 5)) +
  labs(
    title = "CO2 Emissions vs GDP per Capita (2023)",
    x = "GDP per Capita (USD)",
    y = "CO2 Emissions per Capita (tonnes)",
    color = "Region"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom"
  )

print(p1)
cat("Scatter plot created with trend line and regional color coding\n\n")