Total greenhouse gas emissions excluding LULUCF per capita (t CO2e/capita)

Total greenhouse gas emissions excluding LULUCF (Land Use, Land Use Change, and Forestry) per capita, expressed in tonnes of CO2 equivalent (t CO2e/capita), is a critical indicator of environmental health and sustainability. By analyzing this metric, we gain insights into a country's contribution to global warming and overall carbon footprint. It reflects not just the direct emissions from fossil fuel consumption but also other greenhouse gases released through various activities, shedding light on consumption patterns, industrial activities, transport systems, and residential energy use.

The importance of this indicator cannot be overstated. It serves a dual purpose. First, it enables policymakers to gauge the effectiveness of environmental regulations and policies targeting emissions. When emissions per capita decline, it often signifies effective governance and successful initiatives aimed at reducing carbon footprints. Second, it fosters global accountability by allowing for comparisons across nations. This fosters a competitive environment where countries strive to innovate and implement sustainable practices to lower their emissions.

The median value of total greenhouse gas emissions per capita in 2022 stood at 4.2 t CO2e/capita. This value serves as a benchmark, helping governments and organizations to assess their respective standings on a global scale. Comparatively, the top five regions demonstrate alarmingly high per capita emissions, with Palau leading at 79.85 t CO2e/capita, followed by Qatar at 73.25 t CO2e/capita, Bahrain at 45.9 t CO2e/capita, Kuwait at 36.57 t CO2e/capita, and Trinidad & Tobago at 33.61 t CO2e/capita. These figures illuminate a stark reality: certain nations, often with smaller populations or concentrated industrial activities, experience disproportionately high per capita emissions. This can drive discussions around equity in climate action, wherein larger, more populous nations may argue they have a greater responsibility to lead in emissions reductions.

Conversely, the lowest emission areas exhibit dramatically lower figures, with the Marshall Islands at only 0.06 t CO2e/capita, followed closely by Northern Mariana Islands at 0.08 t CO2e/capita, Nauru at 0.1 t CO2e/capita, American Samoa at 0.17 t CO2e/capita, and Guam at 0.25 t CO2e/capita. Such low emissions can be attributed to various factors, including limited industrial infrastructure, smaller populations, and a strong reliance on subsistence practices or tourism, which typically have lower carbon footprints compared to high-industrialized economies.

Additionally, total greenhouse gas emissions excluding LULUCF per capita is interconnected with various other indicators such as energy consumption per capita, electric vehicle adoption rates, and investments in renewable energy. For instance, countries with a higher percentage of renewable energy in their energy mix tend to exhibit lower emissions per capita. Thus, advancements in technology and shifts in public policy toward sustainability play critical roles in influencing this indicator.

Several factors affect total greenhouse gas emissions per capita. Population density and demographic structures juxtaposed against economic conditions can create significant variations in emissions. For example, countries with large urban populations may yield higher emissions due to the concentration of transportation and industrial activities. Conversely, rural regions or nations with low economic output may produce less greenhouse gas simply due to lesser industrial activity and consumerism.

Moreover, climate policies and international agreements, such as the Paris Agreement, exert a considerable influence on emissions levels. Countries committed to ambitious targets often find themselves investing heavily in low-carbon technologies and public awareness campaigns designed to encourage sustainable living practices among their citizens.

To mitigate total greenhouse gas emissions effectively, a multi-faceted approach incorporating diverse strategies and solutions is necessary. Transitioning to renewable energy sources such as solar, wind, hydro, and geothermal is essential. Enhanced energy efficiency in residential, commercial, and industrial buildings can significantly reduce emissions. Encouraging public transport, biking, and walking over personal vehicle use can further curb emissions. Comprehensive waste management practices aimed at minimizing landfill usage, alongside promoting recycling and composting, can also lessen the carbon footprint.

While this indicator is crucial in shaping climate policies, it is not without its flaws. A significant limitation lies in the exclusion of LULUCF, which can act as a carbon sink. Countries that implement extensive reforestation and sustainable agricultural practices may not benefit from these positive contributions in this specific metric. It can lead to discrepancies in how we assess and compare the environmental impact of different nations. Furthermore, relying solely on per capita values could obscure broader systemic issues such as socioeconomic inequalities and regional emissions disparities.

In conclusion, total greenhouse gas emissions excluding LULUCF per capita is an essential gauge of environmental responsibility and sustainability. It is interlinked with various critical indicators and influenced by numerous factors, demonstrating the complexity of climate issues. By utilizing this data effectively, nations and organizations can adopt targeted strategies to lower emissions, promote climate justice, and work collaboratively toward a sustainable future.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'EN.GHG.ALL.PC.CE.AR5'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'EN.GHG.ALL.PC.CE.AR5'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))