Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita)

Carbon dioxide (CO2) emissions excluding Land Use, Land-Use Change, and Forestry (LULUCF) per capita is a crucial environmental indicator that reflects the amount of CO2 emissions produced by an individual in a given region, measured in tons of CO2 equivalent per capita. This statistic is fundamental in understanding the impact of human activities on climate change and global warming, as CO2 is one of the primary greenhouse gases contributing to the greenhouse effect. When evaluating CO2 emissions on a per capita basis, it allows policymakers and researchers to analyze the emissions relative to population size, offering a clearer perspective on per person responsibility and aiding in identifying trends across different regions and timeframes.

The importance of monitoring CO2 emissions per capita resides in its role in shaping effective climate policies and sustainability efforts. A high per capita emission rate typically signifies greater fossil fuel consumption and industrial activity, which can lead to increased pressures on the environment and public health. By contrast, lower per capita emissions can indicate a transition towards more sustainable practices, renewable energy consumption, and energy efficiency measures. This indicator can serve as a benchmark for nations as they strive to meet international climate agreements, improve energy efficiency, and pursue sustainable development.

Several factors influence CO2 emissions per capita, such as energy sources, industrial activity, population density, and transportation methods. Countries that rely heavily on fossil fuels for energy production, such as coal or oil, typically have higher emissions. Conversely, nations investing in renewable energy technologies—such as solar, wind, and hydroelectric power—are likely to report lower emissions. Additionally, economic structures play a significant role; economies centered around manufacturing and industrial production often have higher carbon footprints compared to those emphasizing services or information technology.

A significant correlation exists between CO2 emissions per capita and other indicators, particularly Gross Domestic Product (GDP) and energy consumption. Generally, as a country's GDP grows, so does its energy demand; however, if a nation adopts cleaner energy technologies, it can decouple this relationship, achieving economic growth while reducing carbon emissions. Furthermore, urbanization rates can affect per capita emissions—densely populated urban centers might promote energy-efficient public transport systems, leading to lower per capita figures despite higher total emissions.

The latest data from 2022 reveals a median value of 2.36 tons of CO2e per capita globally. This figure indicates a broader trend towards decreasing emissions on a per capita basis in certain regions, but disparities still exist when we examine specific countries. The top five areas with the highest emissions per capita are concerning, led by Palau at an astounding 76.42 tons per person, followed by Qatar at 38.62 tons, Bahrain at 24.89 tons, Kuwait at 23.99 tons, and the United Arab Emirates at 21.72 tons. These figures highlight the substantial impact of oil and natural gas industries in these nations, along with high consumption lifestyles that contribute to elevated emissions.

Conversely, areas with the lowest emissions per capita demonstrate alternative pathways towards sustainability. The bottom five areas—Marshall Islands, Micronesia (Federated States of), Nauru, Northern Mariana Islands, and Tuvalu, all reporting 0.0 tons of CO2e per capita—suggest either limited industrial activities or significant reliance on renewable energy resources, though such low figures can also reflect unique geographical and socio-economic contexts that may not be directly comparable to larger or more industrialized nations.

Historical data shows that in 1970, the global CO2 emissions per capita were at 4.17 tons. Since then, emissions have fluctuated but generally remained above 4 tons until the pandemic in 2020 led to a temporary decline to 4.44 tons, as global activities slowed down dramatically. By 2021, emissions rebounded to 4.66 tons and slightly increased to 4.67 tons in 2022, emphasizing the ongoing challenges in reducing global carbon footprints despite recognized urgent calls for action against climate change.

Addressing the challenges associated with high CO2 emissions requires multi-faceted strategies. Transitioning towards renewable energy sources, promoting energy efficiency, enhancing public transport systems, and encouraging sustainable urban planning are essential elements of a strategy aimed at reducing per capita emissions. Additionally, raising public awareness and education about carbon footprints and adopting carbon taxes or cap-and-trade programs can incentivize individuals and businesses to reduce their emissions further.

However, there remain flaws in utilizing CO2 emissions per capita as a stand-alone metric. It can oversimplify complex issues regarding carbon production, especially in countries where emissions from exported goods are not accounted for. Nations with high production capabilities but lower populations may reflect a misleadingly low per capita figure compared to highly populated developing countries that may have lower industrial output but higher per capita emissions due to inefficient practices. Thus, it is vital to combine this indicator with others to gain a more comprehensive understanding of carbon emissions and their global implications.

In conclusion, monitoring carbon dioxide emissions excluding LULUCF per capita is paramount for understanding and addressing climate change. While it provides a clear benchmark for evaluating individual contributions to carbon emissions, its effectiveness can be enhanced when viewed alongside other metrics and contextual factors. Comprehensive strategies, including transitioning to renewable energy, enhancing educational outreach, and fostering international cooperation, are necessary steps towards creating a sustainable future and achieving meaningful reductions in global carbon emissions.

                    
# 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.CO2.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.CO2.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))