Country

Indicator

Value type

Values present

Carbon dioxide (CO2) emissions from Waste (Mt CO2e)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	0.0029	0%	43
Argentina	0.012	+1.69%	32
Australia	0.0158	0%	28
Austria	0.0008	0%	60
Azerbaijan	0.0024	0%	46
Belgium	0.0023	-4.17%	47
Benin	0.0004	0%	64
Bulgaria	0.0013	-18.8%	56
Bahamas	0.001	0%	58
Bosnia & Herzegovina	0.0038	0%	41
Belarus	0.0123	-6.11%	31
Bolivia	0.0026	0%	44
Brazil	0.303	+1%	8
Brunei	0.0018	0%	51
Canada	0.107	-3.35%	14
Switzerland	0.0085	-1.16%	38
Chile	0.0088	+3.53%	36
China	3.64	+2.18%	2
Congo - Brazzaville	0		68
Colombia	0.0189	+1.07%	25
Cuba	0.0005	0%	63
Czechia	0.0563	-0.53%	16
Germany	0.0309	-31%	23
Dominica	0.0001	0%	67
Denmark	0.001	0%	58
Dominican Republic	0.0005	0%	63
Algeria	0.0024	+4.35%	46
Ecuador	0.0047	+2.17%	40
Egypt	0.0005	+25%	63
Spain	4.18	+2.53%	1
Estonia	0.0001	0%	67
Ethiopia	0.0016	0%	53
France	2.33	+0.699%	4
Faroe Islands	0.002	0%	50
Gabon	0.0003	0%	65
United Kingdom	0.0543	-2.69%	17
Gibraltar	0.0006	0%	62
Greece	0.0017	-10.5%	52
Greenland	0.0011	0%	57
Guyana	0.0001	0%	67
Hong Kong SAR China	0.0112	+0.901%	33
Honduras	0.0334	+2.45%	22
Croatia	0		68
Hungary	0.0188	+1.62%	26
Indonesia	0.174	+2.11%	11
Ireland	0.0087	-8.42%	37
Iceland	0.0007	0%	61
Italy	0.405	-2.24%	7
Jamaica	0.0007	0%	61
Jordan	0.0002	0%	66
Japan	3.22	-1.63%	3
Kenya	0.0015	+7.14%	54
Cambodia	0.013	+3.17%	29
South Korea	1.61	+0.0186%	5
Kuwait	0.0004	0%	64
Laos	0.0158	+3.27%	28
Lebanon	0		68
St. Lucia	0		68
Lithuania	0.0025	+13.6%	45
Macao SAR China	0.0009	0%	59
Moldova	0.0024	0%	46
Maldives	0.0038	+2.7%	41
Mexico	0.506	+1.48%	6
North Macedonia	0.02	+0.503%	24
Mali	0.011	+4.76%	34
Malta	0.0003	0%	65
Myanmar (Burma)	0.047	+1.51%	20
Mongolia	0.0009	0%	59
Mauritius	0.0001	0%	67
Malaysia	0.078	+1.96%	15
Netherlands	0		68
Norway	0.0512	-7.91%	19
New Zealand	0.0021	-4.55%	49
Peru	0.0097	+2.11%	35
Philippines	0.154	+1.92%	13
Palau	0		68
Papua New Guinea	0.001	0%	58
Poland	0.196	-0.557%	10
North Korea	0.0112	+0.901%	33
Portugal	0.205	+1.53%	9
Paraguay	0.0001	0%	67
Qatar	0		68
Romania	0.0047	-4.08%	40
Singapore	0.042	+1.2%	21
Suriname	0.0002	0%	66
Slovakia	0		68
Slovenia	0.0049	-10.9%	39
Sweden	0.156	-0.256%	12
Togo	0.0002	0%	66
Thailand	0.0189	+1.61%	25
Timor-Leste	0.0002	0%	66
Turkey	0.0022	+15.8%	48
Ukraine	0.0128	+4.07%	30
Uruguay	0.0014	0%	55
United States	0.0034	0%	42
St. Vincent & Grenadines	0		68
Venezuela	0.0179	+1.13%	27
Vietnam	0.052	+2.77%	18

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 = 'EN.GHG.CO2.WA.MT.CE.AR5'

# 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 <- 'EN.GHG.CO2.WA.MT.CE.AR5'

# 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

Carbon dioxide (CO2) emissions from Waste (Mt CO2e)

Year: 2023

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