Country

Indicator

Value type

Values present

Adjusted net savings, including particulate emission damage (% of GNI)

Source: worldbank.org, 01.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Angola	13.4	+48.6%	37
Albania	3.07	-218%	86
Argentina	11.6	+49.1%	45
Armenia	0.908	-64.2%	92
Australia	6.42	-10.3%	69
Austria	12.7	-1.96%	40
Azerbaijan	9.28	+16.1%	54
Belgium	12.1	+15.8%	42
Bangladesh	32.2	-4.94%	1
Bulgaria	8.53	+11.8%	58
Bahamas	4.82	-43.7%	77
Belarus	13.2	+9.67%	39
Belize	12	-20.3%	44
Bolivia	-2.04	-184%	103
Brazil	-0.246	-83.4%	96
Brunei	24	-22.8%	7
Bhutan	13.4	-26.8%	38
Botswana	8.5	+1.2%	59
Canada	7.58	+98.2%	65
Switzerland	16.2	+36.3%	29
Chile	-3.74	-181%	108
China	16.3	+6.75%	28
Cameroon	-1.61	-7.1%	100
Congo - Kinshasa	-11.2	-374%	115
Colombia	1.6	-66.8%	91
Comoros	5.41	+275%	75
Cape Verde	23.8	-8.11%	8
Costa Rica	16.9	+10.5%	26
Cyprus	1.92	+69.2%	90
Czechia	9.68	+12.8%	52
Germany	14.5	+10.1%	33
Denmark	20.4	+10.1%	12
Dominican Republic	21.6	+23.8%	10
Algeria	15.4	+10.8%	32
Ecuador	2.36	-61.2%	89
Egypt	-1.13	-130%	97
Spain	7.98	+16.8%	62
Estonia	16.5	+15.8%	27
Ethiopia	13.4	-12.5%	36
Finland	11.2	+5.01%	47
France	9.21	+55.4%	56
Georgia	-5.6	+67.9%	110
Ghana	3.78	-72.8%	82
Guinea	-16.3	+13.5%	116
Gambia	17.8	+139%	22
Greece	-2.16	-69.3%	104
Guatemala	8.6	+6.85%	57
Honduras	19.4	-15.1%	18
Croatia	8.27	+21.4%	60
Haiti	7.98	-32.1%	61
Hungary	11.5	+0.596%	46
Indonesia	10.8	+24%	50
India	15.4	+6.7%	31
Ireland	20.1	+42.1%	15
Iraq	9.44	+201%	53
Iceland	5.7	-27.2%	72
Israel	19.2	+2.21%	19
Italy	7.69	+40.2%	63
Jamaica	30.8	+32.7%	3
Jordan	4.17	-21%	81
Japan	3.55	-25%	83
Kazakhstan	-0.205	-103%	95
Kenya	7.15	+43.3%	66
Kyrgyzstan	-2.65	-120%	105
Cambodia	17.3	+4.18%	25
South Korea	17.7	+0.439%	23
Lebanon	-27.9	+38.9%	117
Lithuania	12.1	+6.17%	41
Luxembourg	20.4	+33.9%	13
Latvia	3.07	-39.8%	85
Morocco	21.5	+6.37%	11
Moldova	6.71	-2.38%	67
Madagascar	-3.26	-24.7%	106
Maldives	23	-3,448%	9
Mexico	3.45	-48.9%	84
North Macedonia	14.3	+23.8%	34
Mongolia	-8.08	+82.6%	112
Mozambique	-6.98	+70.6%	111
Mauritania	24.3	-13.6%	6
Mauritius	0.00364	-100%	94
Malaysia	0.36	+137%	93
Namibia	-1.83	-117%	101
Nigeria	18.5	+28.2%	21
Nicaragua	12.1	-30.4%	43
Netherlands	15.5	+13.6%	30
Norway	20.2	+48.8%	14
Nepal	24.4	-1.07%	5
New Zealand	9.86	-12.8%	51
Oman	-10.3	+17.4%	114
Pakistan	6.13	-5.11%	71
Panama	19.4	+5.25%	17
Peru	6.47	-40.9%	68
Philippines	7.68	-40.6%	64
Poland	10.8	-2.09%	49
Portugal	3.06	+97.1%	87
Paraguay	17.6	+3.48%	24
Qatar	25.5	+42.1%	4
Romania	4.95	-18%	76
Russia	6.37	-24.8%	70
Rwanda	-1.5	-64.5%	99
Sudan	-8.5	-148%	113
Singapore	31.6	+20.7%	2
El Salvador	5.59	-36%	74
Serbia	5.66	+1.01%	73
Slovakia	4.45	-9.25%	79
Slovenia	11.2	-3.29%	48
Sweden	19.8	+0.574%	16
Eswatini	4.67	+829%	78
Thailand	9.22	-6.74%	55
Timor-Leste	-81.6	+249%	118
Tonga	-3.35	-123%	107
Tunisia	-1.36	-67%	98
Uganda	-4.41	+286%	109
Ukraine	-2.03	+112%	102
Uruguay	13.9	-0.481%	35
United States	4.3	-24.3%	80
Uzbekistan	2.72	-73.7%	88
Vietnam	19	-4.28%	20

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.SVNG.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.SVNG.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 net savings, including particulate emission damage (% of GNI)

Year: 2021

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