Total greenhouse gas emissions including LULUCF (Mt CO2e)

The indicator 'Total greenhouse gas emissions including LULUCF (Land Use, Land-Use Change, and Forestry)' measures the total output of greenhouse gases into the atmosphere, calculated in million metric tons of CO2 equivalent (Mt CO2e). It encompasses emissions from various sectors, including energy production, and agriculture, as well as carbon fluxes linked to land use changes. LULUCF is particularly significant as it accounts for both emissions and sequestration; for example, forests can sequester carbon but also release it due to deforestation and soil degradation. An analysis of this indicator provides insights into a country's contribution to global warming and climate change, reflecting the effectiveness of policies aimed at reducing emissions.

The importance of tracking total greenhouse gas emissions lies primarily in its role in mitigating climate change. With the evidence tipping towards an urgent climate crisis, carbon emissions are scrutinized for potential reductions across various sectors. Policymakers and scientists utilize this data to understand trends and formulate actionable strategies in line with international climate agreements such as the Paris Agreement, which focuses on keeping global temperature rise below 1.5 degrees Celsius. Monitoring this indicator also allows for comparison between regions, highlighting the progress of different countries in reducing their carbon footprints.

Diving into the relations with other indicators, total greenhouse gas emissions have a direct correlation with energy consumption and economic growth. Higher emissions often signify increased energy usage, especially from fossil fuels, driving up national economic productivity. Indicators such as per capita emissions reveal the rate of emissions relative to the population size, offering significant insights into the sustainability of a nation's growth. Moreover, the efficiency of industrial processes and the adoption of renewable energy sources also factor into the total emissions figures. Similarly, improvements in waste management practices and agricultural approaches can drastically influence overall greenhouse gas output.

Several factors affect total greenhouse gas emissions, including economic conditions, technological advancements, and regulatory frameworks. For instance, countries rich in fossil fuel reserves may rely heavily on those resources for energy, significantly increasing their emissions. On the other hand, advancements in renewable energy technologies—like solar and wind power—can lead countries to adopt cleaner energy sources, subsequently reducing their carbon emissions. Furthermore, effective government regulations aimed at emissions reductions, such as carbon pricing or cap-and-trade systems, can create economic incentives for industries to adopt greener methods.

Various strategies are employed globally to mitigate greenhouse gas emissions. Transitioning to renewable energy sources, improving energy efficiency, and increasing the carbon sequestration capacity of forests through LULUCF practices like afforestation are pivotal. Enhancing public transportation systems and encouraging carpooling and electric vehicle usage can mitigate emissions from the transportation sector. Additionally, promoting sustainable agricultural practices will not only help improve land use but also significantly reduce methane and nitrous oxide emissions stemming from livestock and fertilizer application. Engaging communities and raising awareness about individual contributions to carbon emissions can also catalyze change on a grassroots level.

However, the measurement of total greenhouse gas emissions is not without its flaws. One inherent flaw is the reliance on estimations based on indirect data sources, which can lead to discrepancies. Countries may also report their emissions differently, based on their capabilities to monitor and maintain accurate data. This inconsistency can result in the underrepresentation or overrepresentation of a country's actual emission levels. Furthermore, while LULUCF may show negative emissions due to increased carbon intake by forests, it can create a false sense of accomplishment, masking higher emissions in other sectors.

As of the latest recorded year of 2020, the total greenhouse gas emissions worldwide amounted to 50,068.76 Mt CO2e. This represents a notable decrease compared to previous years, particularly following a global shift in production and consumption patterns as a result of the COVID-19 pandemic. The median value of total emissions stands at 44.03 Mt CO2e, illustrating regional disparities and indicating areas where emissions can be significantly reduced.

When analyzing the top five areas contributing to emissions, China leads with an immense 13,706.06 Mt CO2e, followed by the United States with 4,795.79 Mt CO2e, and India with 3,208.86 Mt CO2e. These countries are prominent industrial giants where energy demands for production and consumption are high. Indonesia and Russia round out the top five, also highlighting the energy-intensive nature of their economies. On the stark opposite end, the bottom five areas include the Central African Republic with -216.11 Mt CO2e, indicating a net carbon sink, followed by Mali, Namibia, Gabon, and Cameroon, all also showing negative emissions figures. This information suggests that these countries may possess extensive forests or land management systems promoting carbon uptake.

Overall, analyzing total greenhouse gas emissions, including LULUCF, provides a comprehensive view of global efforts towards climate action, revealing both challenges and opportunities. Acknowledging the complex relationships between emissions, policies, and practical implementations can empower nations to devise more robust strategies for reducing their carbon footprints effectively. The importance of this indicator cannot be overstated, as it remains at the forefront of environmental policy and sustainability efforts worldwide.

                    
# 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.LU.MT.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.LU.MT.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))