Country

Indicator

Value type

Values present

Coal rents (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Aruba	0		67
Afghanistan	0.0194	+177%	43
Angola	0		67
Albania	0.0323	+218%	38
United Arab Emirates	0		67
Argentina	0.000158	+63.9%	64
Armenia	0.00000	+85.5%	66
American Samoa	0		67
Antigua & Barbuda	0		67
Australia	0.786	+44.3%	8
Austria	0		67
Azerbaijan	0		67
Burundi	0		67
Belgium	0		67
Benin	0		67
Burkina Faso	0		67
Bangladesh	0.0133	+82.7%	49
Bulgaria	0.126	+116%	27
Bahrain	0		67
Bahamas	0		67
Bosnia & Herzegovina	0.302	+64.1%	18
Belarus	0		67
Belize	0		67
Bermuda	0		67
Bolivia	0		67
Brazil	0.0143	+133%	48
Barbados	0		67
Brunei	0		67
Bhutan	0		67
Botswana	0.5	+81.1%	13
Central African Republic	0		67
Canada	0.073	+65%	31
Switzerland	0		67
Chile	0.000152	-93.7%	65
China	0.608	+69%	12
Côte d’Ivoire	0		67
Cameroon	0		67
Congo - Kinshasa	0		67
Congo - Brazzaville	0		67
Colombia	0.726	+86.1%	9
Comoros	0		67
Cape Verde	17.2	+55.2%	1
Costa Rica	0		67
Curaçao	0		67
Cayman Islands	0		67
Cyprus	0		67
Czechia	0.0939	+72.6%	30
Germany	0.0145	+118%	47
Djibouti	0		67
Dominica	0		67
Denmark	0		67
Dominican Republic	0		67
Algeria	0		67
Ecuador	0		67
Egypt	0		67
Spain	0		67
Estonia	0		67
Ethiopia	0.000164	+96.8%	63
Finland	0		67
Fiji	0		67
France	0		67
Gabon	0		67
United Kingdom	0.00102	-0.0577%	59
Georgia	0.0079	+181%	53
Ghana	0		67
Guinea	0		67
Gambia	0		67
Guinea-Bissau	0		67
Equatorial Guinea	0		67
Greece	0.0146	+53.9%	46
Grenada	0		67
Guatemala	0		67
Guam	0		67
Guyana	0		67
Hong Kong SAR China	0		67
Honduras	0		67
Croatia	0		67
Haiti	0		67
Hungary	0.00937	+43.2%	51
Indonesia	1.22	+82%	6
India	1.28	+82.9%	5
Ireland	0		67
Iran	0.0094	+11.4%	50
Iraq	0		67
Iceland	0		67
Israel	0		67
Italy	0		67
Jamaica	0		67
Jordan	0		67
Japan	0.000357	+83.2%	62
Kazakhstan	0.849	+48.9%	7
Kenya	0		67
Kyrgyzstan	0.258	+49.3%	20
Cambodia	0.00481	+99.4%	56
Kiribati	0		67
St. Kitts & Nevis	0		67
South Korea	0.0011	+62.8%	58
Lebanon	0		67
Liberia	0		67
Libya	0		67
St. Lucia	0		67
Sri Lanka	0		67
Lesotho	0		67
Lithuania	0		67
Luxembourg	0		67
Latvia	0		67
Macao SAR China	0		67
Morocco	0		67
Moldova	0		67
Madagascar	0		67
Maldives	0		67
Mexico	0.0161	+44.8%	45
North Macedonia	0.00863	+84.4%	52
Mali	0		67
Malta	0		67
Myanmar (Burma)	0.0464	+91.1%	35
Montenegro	0.145	+56.5%	25
Mongolia	4.89	+10.2%	2
Mozambique	3.78	+108%	3
Mauritania	0.67	+30.4%	10
Mauritius	0		67
Malawi	0.0166	+96.4%	44
Malaysia	0.0252	+75.8%	41
Namibia	0		67
New Caledonia	0		67
Niger	0.0373	+88.2%	36
Nigeria	0.000478	+99.4%	61
Nicaragua	0		67
Netherlands	0		67
Norway	0.000758	+170%	60
Nepal	0.00606	+87.4%	54
New Zealand	0.0307	+57.3%	39
Oman	0		67
Pakistan	0.122	+72%	28
Panama	0		67
Peru	0.00249	+99.5%	57
Philippines	0.0944	+107%	29
Papua New Guinea	0		67
Poland	0.246	+70.7%	21
Puerto Rico	0		67
Portugal	0		67
Paraguay	0		67
Palestinian Territories	0		67
French Polynesia	0		67
Qatar	0		67
Romania	0.0241	+112%	42
Russia	0.613	+68.9%	11
Rwanda	0		67
Saudi Arabia	0		67
Sudan	0		67
Senegal	0		67
Singapore	0		67
Solomon Islands	0		67
Sierra Leone	0		67
El Salvador	0		67
Somalia	0		67
Serbia	0.235	+57.9%	22
São Tomé & Príncipe	0		67
Suriname	0		67
Slovakia	0.00515	+104%	55
Slovenia	0.0259	+45.6%	40
Sweden	0		67
Eswatini	0.137	+80.3%	26
Seychelles	0		67
Turks & Caicos Islands	0		67
Chad	0		67
Togo	0		67
Thailand	0.0336	+112%	37
Tajikistan	0.486	+93.8%	14
Timor-Leste	0		67
Tonga	0		67
Trinidad & Tobago	0		67
Tunisia	0		67
Turkey	0.0474	+93.9%	34
Tanzania	0.0494	+87.1%	33
Uganda	0		67
Ukraine	0.337	+88.4%	17
Uruguay	0		67
United States	0.166	+97.8%	24
Uzbekistan	0.0536	+83.1%	32
St. Vincent & Grenadines	0		67
Vietnam	0.377	+128%	16
Vanuatu	0		67
Samoa	0		67
Kosovo	0.379	+69.8%	15
South Africa	2.44	+51.3%	4
Zambia	0.231	+66.3%	23
Zimbabwe	0.29	+39.9%	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.GDP.COAL.RT.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.GDP.COAL.RT.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

Coal rents (% of GDP)

Year: 2021

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