Country

Indicator

Value type

Values present

Adjusted savings: net forest depletion (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	0.00213	-31.7%	93
Afghanistan	0.318	+33.5%	45
Angola	0.739	-15.3%	38
Albania	0.14	+2.89%	57
Argentina	0		100
Armenia	0.276	+0.481%	50
Australia	0.124	-16.9%	62
Austria	0.0291	+27.7%	75
Azerbaijan	0		100
Burundi	13.9	+1.05%	2
Belgium	0.00744	-1.5%	87
Benin	0		100
Burkina Faso	0		100
Bangladesh	0.0714	-7.17%	67
Bulgaria	0		100
Bahamas	0.0158	-21.5%	82
Bosnia & Herzegovina	0		100
Belarus	0		100
Belize	0.249	-29.3%	51
Bolivia	0		100
Brazil	0		100
Barbados	0.0121	-15.1%	84
Brunei	0.0486	-12.1%	72
Bhutan	2.55	-2.92%	23
Botswana	0.313	-6.65%	46
Central African Republic	0		100
Canada	0		100
Switzerland	0.00172	+34.3%	95
Chile	0		100
China	0		100
Côte d’Ivoire	0		100
Cameroon	2.64	-4.36%	20
Congo - Kinshasa	9.97	-2.26%	5
Congo - Brazzaville	3.11	-26%	17
Colombia	0		100
Comoros	1.62	+1.73%	32
Cape Verde	0.302	-7.39%	47
Costa Rica	0		100
Cyprus	0		100
Czechia	0.174	-21.6%	53
Germany	0		100
Djibouti	0.282	-15.2%	49
Dominica	0.033	-27.8%	74
Denmark	0.00531	+0.571%	89
Dominican Republic	0.0273	-25.2%	76
Algeria	0		100
Ecuador	0		100
Egypt	0.111	-15.7%	63
Spain	0		100
Estonia	0		100
Ethiopia	5.63	+3.43%	8
Finland	0		100
Fiji	1.2	+15.4%	36
France	0.0246	+7.03%	77
Micronesia (Federated States of)	0.0162	-10.1%	81
Gabon	3.14	-10%	16
Georgia	0.0579	-24.5%	69
Ghana	3.85	+2.52%	13
Guinea	5.12	-3.88%	10
Gambia	2.93	-4.86%	19
Guinea-Bissau	10.4	-5.02%	4
Equatorial Guinea	2.59	-13.8%	21
Greece	0.00861	-5.43%	86
Guatemala	0		100
Guyana	2.34	-40.8%	25
Honduras	0		100
Croatia	0.151	-13.5%	55
Haiti	0.327	-43.2%	44
Hungary	0		100
Indonesia	0		100
India	0.163	-13.5%	54
Ireland	0		100
Iran	0		100
Iraq	0.00327	-15.4%	91
Iceland	0.000112	+0.309%	98
Israel	0		100
Italy	0.00979	+0.216%	85
Jamaica	0.0617	-23.6%	68
Jordan	0.0204	-8.01%	79
Japan	0		100
Kazakhstan	0		100
Kenya	1.24	-3.75%	35
Kyrgyzstan	0		100
Cambodia	0		100
Kiribati	0.0239	-21.6%	78
South Korea	0		100
Laos	1.59	-5.79%	33
Lebanon	0		100
Liberia	17.5	-4.6%	1
Libya	0.0759	+11%	66
St. Lucia	0.0139	-29.3%	83
Sri Lanka	0.0558	-3.16%	70
Lesotho	3.79	-6.39%	15
Lithuania	0		100
Luxembourg	0.00621	-31.1%	88
Latvia	0		100
Morocco	0		100
Moldova	0		100
Madagascar	5.56	-3.76%	9
Maldives	0.00407	-25.7%	90
Mexico	0		100
North Macedonia	0		100
Mali	2.34	-2.93%	24
Myanmar (Burma)	2.23	+11.7%	26
Montenegro	0.135	-22.6%	58
Mongolia	0		100
Mozambique	0		100
Mauritania	0.653	-5.97%	39
Mauritius	0.0021	+3.65%	94
Malawi	4.28	+3.08%	11
Malaysia	1.75	-7.84%	31
Namibia	0.88	-1.99%	37
Niger	0		100
Nigeria	0		100
Nicaragua	0		100
Netherlands	0		100
Norway	0		100
Nepal	0.49	-4.5%	43
New Zealand	0		100
Oman	0.00145	-20%	96
Pakistan	0.13	-11.5%	59
Panama	0		100
Peru	0.128	-7.43%	61
Philippines	0		100
Papua New Guinea	2.04	-9.86%	27
Poland	0		100
Portugal	0.092	-5.11%	65
Paraguay	1.31	-23.2%	34
Qatar	0.00007	-25.8%	99
Romania	0		100
Russia	0		100
Rwanda	3.83	-2.99%	14
Sudan	0		100
Senegal	0		100
Singapore	0.000206	-26.4%	97
Solomon Islands	0		100
Sierra Leone	7.98	+6.64%	7
El Salvador	0.567	-29.7%	41
Somalia	11.3	-1.92%	3
Serbia	0		100
São Tomé & Príncipe	1.86	-4.86%	30
Suriname	1.9	-0.319%	28
Slovakia	0		100
Slovenia	0.149	-17.1%	56
Sweden	0		100
Eswatini	2.58	-14.9%	22
Seychelles	0.128	-7.93%	60
Turks & Caicos Islands	0.00323	-24.9%	92
Chad	3.96	-8.5%	12
Togo	2.98	-4.66%	18
Thailand	0		100
Tajikistan	0.642	-2.21%	40
Timor-Leste	0.103	+5.47%	64
Tonga	0.0386	+7.28%	73
Trinidad & Tobago	0.0545	-26.3%	71
Tunisia	0.211	-15.3%	52
Tanzania	0		100
Uganda	8.64	+1.4%	6
Ukraine	0		100
Uruguay	0		100
United States	0		100
Uzbekistan	0		100
St. Vincent & Grenadines	0.0194	-24.6%	80
Vietnam	0		100
Vanuatu	0.518	-9.67%	42
Samoa	0.285	-8.68%	48
South Africa	0		100
Zambia	0		100
Zimbabwe	1.87	-19.4%	29

Python
R

                    
# 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 = 'NY.ADJ.DFOR.GN.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 <- 'NY.ADJ.DFOR.GN.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))

Compare

Adjusted savings: net forest depletion (% of GNI)

Year: 2021

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