Forest area (% of land area)

The indicator 'Forest area (% of land area)' is a crucial metric that reflects the proportion of land area covered by forests relative to the total land area of a country or region. In 2022, the global median forest area stood at approximately 31.52%. This figure highlights the ongoing challenges and opportunities in forest management and conservation worldwide. Forests play an instrumental role in mitigating climate change, preserving biodiversity, and providing essential resources to human populations. Tracking this indicator helps nations and organizations understand their progress toward environmental sustainability and the health of their ecosystems.

The importance of measuring forest area cannot be overstated. Forests serve multiple functions: they act as carbon sinks, help regulate the water cycle, maintain soil health, and offer habitat for countless species. In addition, they provide livelihoods for millions of people, particularly in rural areas where communities depend on forest resources for food, medicine, and materials. A higher percentage of forest area often correlates with better ecological health and can contribute to enhanced resilience against climate-related disasters. In contrast, declining forest areas signal potential environmental degradation and loss of ecosystem services, which may exacerbate poverty and social inequality.

This indicator is intricately linked to others, including biodiversity, carbon emissions, and overall land use. For instance, regions with extensive forest areas tend to have richer biodiversity, as forests provide habitats for various species. Conversely, high carbon emissions often accompany deforestation, meaning that tracking these indicators together can reveal much about a region's environmental health. Countries with below-average forest area percentages may face significant ecological challenges, including increased erosion and diminished wildlife, which can lead to broader economic ramifications.

Several factors influence forest area percentages. Deforestation due to agriculture, urbanization, and logging has been a significant driver of forest loss, particularly in tropical regions. Economic incentives often prioritize short-term gains over long-term sustainability, leading to extensive land clearing for farming or development. Climate change also poses a threat to forests, with rising temperatures and altered rainfall patterns prompting shifts in forest composition and health. Additionally, government policies and enforcement play a critical role; countries that prioritize sustainable forestry practices and conservation will see more stable or growing forest areas, while those lacking regulation may experience rapid forest decline.

To address the ongoing challenges of forest area loss, comprehensive strategies must be implemented. One effective approach is the promotion of sustainable land management practices that balance economic needs with environmental conservation. This includes agroforestry systems, which combine agricultural and forestry practices, yielding benefits such as increased biodiversity and improved soil quality. Reforestation and afforestation initiatives are also essential in increasing forest area and restoring degraded landscapes. Furthermore, community engagement is vital; empowering local communities through education and sustainable livelihood programs can lead to better forest stewardship and protection.

International cooperation is another critical aspect of tackling forest loss. Multi-nation agreements aimed at climate change and biodiversity conservation can enhance efforts to protect forests globally. Support for indigenous rights and traditional land management practices can also lead to successful outcomes in forest preservation. Aligning economic incentives with environmental goals through mechanisms like payment for ecosystem services can further motivate sustainable practices that curtail forest loss.

Despite these strategies, there are flaws and challenges to consider. Economic development pressures often overshadow environmental concerns, leading to conflicts over land use. Additionally, illegal logging and unregulated land clearing remain substantial hurdles that can undermine conservation efforts. Lack of accurate data can hinder effective policy-making and monitoring; therefore, improving data collection and transparency is critical for informed decision-making and effective resource management.

Examining the latest figures reveals the disparities in forest area across the globe. For instance, the top five countries with the highest forest area percentages—Suriname (94.52%), Guyana (93.46%), Micronesia (92.11%), Gabon (91.23%), and Palau (90.37%)—demonstrate successful conservation strategies and extensive, undisturbed natural landscapes. These countries have vast tracts of rainforest and benefit significantly from their ecological assets, suggesting that capitalizing on these resources in sustainable ways can lead to both environmental health and economic opportunities.

Conversely, the bottom five countries—Monaco, Nauru, Qatar, Greenland, and Oman—exhibit minimal forest area percentages. The reasons for their low forest cover vary; for example, Monaco's limited land area makes extensive forestation impractical, while desert nations like Qatar and Oman face geographical constraints that limit their tree cover. Conversely, nations heavily reliant on industrial development may prioritize land for infrastructure over forest preservation.

Over the past three decades, global forest area percentages have shown a general decline, from 33.01% in 1992 to 31.14% in 2022. This downward trend underscores the urgent need for concerted efforts to reverse deforestation and promote sustainable land practices globally. Countries can look to successful models of forest management while forging new paths that honor both environmental integrity and human needs. Only with a commitment to protecting our forests can we hope to secure a healthier planet for future generations.

                    
# 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 = 'AG.LND.FRST.ZS'

# 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 <- 'AG.LND.FRST.ZS'

# 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))