Carbon dioxide (CO2) net fluxes from LULUCF - Organic Soil (Mt CO2e)

The Carbon Dioxide (CO2) net fluxes from Land Use, Land Use Change, and Forestry (LULUCF) specifically from Organic Soil are essential indicators that reflect the balance between carbon emissions and sequestration in organic soils. These fluxes are measured in metric tons of CO2 equivalent (Mt CO2e), and they play a crucial role in understanding the global carbon cycle, evaluating climate policies, and setting targets for greenhouse gas emissions reductions.

Organic soils, typically rich in carbon due to the accumulation of decomposed plant material, can either act as a carbon sink or a source of emissions. When these areas are disturbed by human activities such as agriculture, drainage, or deforestation, they can release significant amounts of CO2 into the atmosphere, contributing to climate change. Conversely, maintaining and restoring organic soils can enhance carbon storage, mitigating climate impacts.

The importance of monitoring CO2 net fluxes from LULUCF cannot be overstated. It provides insights into the effectiveness of various environmental policies aimed at reducing greenhouse gas emissions. Countries can assess their progress toward commitments made under international agreements like the Paris Agreement. Furthermore, these indicators help in understanding the role of soils in global carbon dynamics, assisting scientists in forecasting future climate scenarios based on soil management practices.

The data from 2020 indicates a world median value of 0.0 for CO2 net fluxes from organic soils. This figure suggests that, at a global scale, the emissions and sequestration from organic soils were relatively balanced in that year. However, this balance is deceptive; it masks significant regional disparities in carbon dynamics. The top five contributors to CO2 emissions from organic soils in 2020 were notably Indonesia, Russia, the United States, Germany, and Belarus. Indonesia led the erratic emissions with a staggering 854.61 Mt CO2e, largely driven by deforestation and land-use change, particularly for palm oil cultivation. Russia, with 115.0 Mt CO2e, and the United States at 43.9 Mt CO2e, also reflect patterns of land management and varying degrees of soil emission reduction efforts.

In contrast, the bottom five areas—Afghanistan, Albania, Algeria, Andorra, and Angola—reported a net flux of 0.0, indicating either minimal disturbance in their organic soils or perhaps lack of adequate measurement systems to capture emissions accurately. This discrepancy highlights the need for improved monitoring capabilities in lower-emission regions and demonstrates the varying conservation needs of different countries.

Over time, the global values for CO2 net fluxes from LULUCF illustrate a troubling trend. From a maintained value of 621.34 Mt CO2e in the 1990s, emissions soared until peaking astonishingly high in 2006 at 1663.65 Mt CO2e. This escalation reflects countries ramping up land conversion for agricultural practices and urbanization without proper carbon management strategies in place. However, there seems to be a slowing trend following 2016, which may suggest improvements made in policy and practices that aim to curb carbon emissions. For instance, the values in 2019 and 2020 indicate a slight increase after a downturn, with 1156.98 Mt CO2e and 1157.05 Mt CO2e, respectively.

Numerous factors affect CO2 net fluxes from organic soils. Land management practices such as tillage, crop rotation, and the introduction of cover crops can enhance soil structure and carbon sequestration potential. Climate variables—including temperature and precipitation—also influence organic matter decomposition rates and, consequently, carbon emissions. Moreover, socio-economic elements like urbanization trends, population growth, and economic incentives for sustainable practices significantly impact land use and management decisions.

Addressing the challenges associated with CO2 emissions from organic soils necessitates comprehensive strategies that harmonize environmental conservation with economic development. Introducing sustainable agricultural practices, enhancing afforestation and reforestation efforts, and employing better water management practices are critical. Education and outreach programs can help stakeholders understand the importance of protecting organic soils, thereby ensuring they contribute positively to carbon mitigation.

However, despite these strategies, flaws exist in the tracking and reporting mechanisms of these emissions. There can be significant gaps in data collection, especially in developing countries, leading to underreporting. Furthermore, the science surrounding soil carbon dynamics is complex, and predictive models sometimes lack precision, making it challenging to develop effective policy responses based on current data alone.

In conclusion, the measurement of CO2 net fluxes from organic soils provides crucial insights into global carbon dynamics. Stakeholders must recognize these emissions as a pivotal component of climate change and prioritize effective management strategies. Enhanced monitoring, policy reforms, and technological advances are essential to curtailing emissions from organic soils, thereby contributing to the greater goal of reducing global carbon footprints and achieving climate resilience.

                    
# 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.LU.OS.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.CO2.LU.OS.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))