Country

Indicator

Value type

Values present

Adjusted savings: net forest depletion (current US$)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	64,588	-18.5%	109
Afghanistan	47,376,783	-2.08%	60
Angola	458,784,517	+7.1%	22
Albania	25,282,158	+24.5%	64
United Arab Emirates	441,285	-6.8%	95
Argentina	0		112
Armenia	37,110,957	+8.63%	62
Australia	1,910,283,871	-1.58%	7
Austria	140,279,220	+40.3%	43
Azerbaijan	0		112
Burundi	387,979,514	+5.74%	29
Belgium	44,608,341	+11.3%	61
Benin	0		112
Burkina Faso	0		112
Bangladesh	312,966,727	+4.46%	31
Bulgaria	0		112
Bahrain	173,964	-6.05%	104
Bahamas	1,653,718	-11.2%	89
Bosnia & Herzegovina	0		112
Belarus	0		112
Belize	6,007,090	-15.6%	80
Bolivia	0		112
Brazil	0		112
Barbados	572,854	-12.4%	93
Brunei	6,844,237	+0.211%	78
Bhutan	60,791,709	+6.05%	54
Botswana	52,121,864	+5.92%	56
Central African Republic	0		112
Canada	0		112
Switzerland	13,678,335	+47.7%	69
Chile	0		112
China	0		112
Côte d’Ivoire	0		112
Cameroon	1,169,556,549	+6.23%	10
Congo - Kinshasa	5,183,414,416	+7.1%	3
Congo - Brazzaville	397,343,419	+6.13%	28
Colombia	0		112
Comoros	21,105,966	+7.73%	67
Cape Verde	5,734,358	+5.74%	81
Costa Rica	0		112
Cuba	68,828,464	-13.5%	50
Cyprus	0		112
Czechia	469,411,963	-9.67%	21
Germany	0		112
Djibouti	9,651,723	-5.82%	73
Dominica	183,256	-21.5%	102
Denmark	21,866,222	+13%	66
Dominican Republic	24,451,523	-10.8%	65
Algeria	0		112
Ecuador	0		112
Egypt	434,775,272	-6.66%	25
Eritrea	5,079,230	+9.07%	83
Spain	0		112
Estonia	0		112
Ethiopia	6,233,766,464	+6.97%	2
Finland	0		112
Fiji	48,409,315	+11.3%	59
France	748,244,629	+21.6%	13
Micronesia (Federated States of)	72,110	-12%	108
Gabon	534,893,958	+5.82%	16
Georgia	10,105,633	-12.8%	71
Ghana	2,914,483,929	+9.56%	6
Guinea	727,279,168	+6.26%	14
Gambia	58,232,092	+5.9%	55
Guinea-Bissau	170,872,863	+7.02%	41
Equatorial Guinea	236,781,155	+5.82%	35
Greece	18,428,625	+7.61%	68
Guatemala	0		112
Guyana	178,855,019	-15.5%	40
Honduras	0		112
Croatia	104,981,708	+2.1%	47
Haiti	68,622,620	-18.1%	51
Hungary	0		112
Indonesia	0		112
India	5,105,130,334	+2.74%	4
Ireland	0		112
Iran	0		112
Iraq	6,727,861	-4.37%	79
Iceland	27,386	+16%	111
Israel	0		112
Italy	210,086,237	+12%	38
Jamaica	8,779,780	-18.6%	75
Jordan	9,284,810	-5%	74
Japan	0		112
Kazakhstan	0		112
Kenya	1,348,734,997	+5.75%	9
Kyrgyzstan	0		112
Cambodia	0		112
Kiribati	90,059	-12.7%	107
South Korea	0		112
Kuwait	490,953	-6.01%	94
Laos	281,176,437	-6.65%	32
Lebanon	0		112
Liberia	578,298,695	+8.91%	15
Libya	32,773,512	-5.25%	63
St. Lucia	227,130	-22.6%	98
Liechtenstein	102,891	+14.4%	106
Sri Lanka	48,443,461	+1.29%	58
Lesotho	107,780,148	+6.04%	45
Lithuania	0		112
Luxembourg	3,705,649	-19.5%	84
Latvia	0		112
Morocco	0		112
Moldova	0		112
Madagascar	788,374,627	+7.67%	12
Maldives	200,105	+6.32%	100
Mexico	0		112
North Macedonia	0		112
Mali	427,706,388	+5.91%	26
Myanmar (Burma)	1,413,973,996	-7.4%	8
Montenegro	8,058,033	-4.48%	76
Mongolia	0		112
Mozambique	0		112
Mauritania	64,324,321	+11.6%	53
Mauritius	245,217	+4.36%	96
Malawi	530,351,178	+6.5%	17
Malaysia	6,355,359,642	+1.19%	1
Namibia	106,199,106	+12.3%	46
New Caledonia	1,241,758	+3.5%	90
Niger	0		112
Nigeria	0		112
Nicaragua	0		112
Netherlands	0		112
Norway	0		112
Nepal	178,858,444	+3%	39
New Zealand	0		112
Oman	1,195,255	-6.99%	91
Pakistan	448,133,718	+3.12%	24
Panama	0		112
Peru	265,103,876	-2.17%	33
Philippines	0		112
Papua New Guinea	520,635,408	-2.52%	18
Poland	0		112
North Korea	0		112
Portugal	230,658,079	+5.46%	37
Paraguay	503,192,580	-14.1%	20
French Polynesia	216,160	-6.69%	99
Qatar	118,807	-7.08%	105
Romania	0		112
Russia	0		112
Rwanda	415,762,302	+5.75%	27
Saudi Arabia	7,477,773	-4.96%	77
Sudan	0		112
Senegal	0		112
Singapore	718,503	-14%	92
Solomon Islands	0		112
Sierra Leone	318,893,050	+6.93%	30
El Salvador	153,775,541	-18.1%	42
Somalia	857,058,701	+8.7%	11
Serbia	0		112
South Sudan	236,213,063	+5.96%	36
São Tomé & Príncipe	9,924,243	+6.06%	72
Suriname	49,209,990	+5.61%	57
Slovakia	0		112
Slovenia	90,607,193	-5.45%	49
Sweden	0		112
Eswatini	112,570,356	+3.42%	44
Seychelles	1,754,733	+5.82%	88
Syria	3,074,370	-5.26%	85
Turks & Caicos Islands	30,821	-20%	110
Chad	451,240,446	+4.68%	23
Togo	251,014,752	+5.94%	34
Thailand	0		112
Tajikistan	67,816,501	+8.59%	52
Turkmenistan	240,007	+8.62%	97
Timor-Leste	1,945,293	-16.6%	87
Tonga	191,223	+1.02%	101
Trinidad & Tobago	13,282,239	-10.2%	70
Tunisia	95,069,135	-7.1%	48
Tanzania	0		112
Uganda	3,029,812,616	+6.54%	5
Ukraine	0		112
Uruguay	0		112
United States	0		112
Uzbekistan	0		112
St. Vincent & Grenadines	174,771	-21.9%	103
Venezuela	0		112
Vietnam	0		112
Vanuatu	5,432,637	-3.78%	82
Samoa	2,365,083	-9.76%	86
South Africa	0		112
Zambia	0		112
Zimbabwe	516,326,003	+6.01%	19

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.CD'

# 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.CD'

# 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 (current US$)

Year: 2021

Acquire full dataset with Python or R from source