Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	0.0151	-36%	97
Albania	0.049	+1.03%	83
Argentina	2.41	+5.96%	14
Armenia	0.0452	+0.668%	84
Australia	3.43	+3.34%	9
Austria	0.0399	-5%	87
Azerbaijan	0.141	+3.3%	62
Belgium	0.0296	-18%	90
Bangladesh	2.82	+4.86%	10
Bulgaria	0.792	-0.615%	33
Bosnia & Herzegovina	0.003	-21.1%	104
Belarus	0.452	-3.56%	43
Brazil	13.2	+1.92%	3
Canada	3.79	+2.41%	8
Switzerland	0.0547	+13%	82
Chile	0.396	+3.77%	46
China	15.1	-2.71%	2
Côte d’Ivoire	0.136	+5.59%	64
Cameroon	0.0761	+2.28%	77
Colombia	0.64	-0.899%	36
Costa Rica	0.145	+0.346%	60
Cuba	0.0223	-13.6%	93
Cyprus	0.0036	0%	102
Czechia	0.136	-7.61%	65
Germany	0.566	-8.61%	38
Denmark	0.0558	-4.94%	81
Dominican Republic	0.187	+1.8%	53
Algeria	0.0931	-7.82%	74
Ecuador	0.546	+0.202%	39
Egypt	2.76	-0.947%	11
Spain	0.889	-2.75%	30
Estonia	0.071	0%	79
Ethiopia	0.472	+8.23%	40
Finland	0		108
France	2.23	+12.4%	17
United Kingdom	1.44	-1.09%	24
Georgia	0.0868	+1.76%	75
Greece	0.143	-3.83%	61
Guatemala	0.443	+1%	44
Croatia	0.061	-7.85%	80
Hungary	0.173	+3.17%	56
Indonesia	5.89	-0.362%	5
India	30.6	+2.27%	1
Ireland	0.179	+0.847%	55
Iran	1.69	+2.53%	20
Iraq	0.0369	-42.6%	88
Iceland	0.0008	+14.3%	107
Israel	0.0714	-0.557%	78
Italy	0.783	-2.25%	34
Jordan	0.0146	-1.35%	98
Japan	0.574	-0.933%	37
Kazakhstan	0.452	-1.67%	42
Kenya	0.2	+0.958%	51
Kyrgyzstan	0.128	0%	68
South Korea	0.455	0%	41
Kuwait	0.0031	0%	103
Lebanon	0.0053	0%	101
Libya	0.0269	-0.738%	91
Sri Lanka	0.137	-13.4%	63
Lithuania	0.415	+0.46%	45
Latvia	0.209	-0.428%	50
Morocco	0.167	-18.7%	57
Moldova	0.112	-0.532%	70
Mexico	2.65	-1.1%	12
North Macedonia	0.021	+0.962%	94
Mali	0.155	-5.01%	59
Myanmar (Burma)	1.2	+10.1%	26
Mauritius	0.0169	0%	95
Malaysia	1.4	-1.64%	25
Nigeria	1.68	+15%	21
Nicaragua	0.136	-0.658%	66
Netherlands	0.185	+16.3%	54
Norway	0.0053	0%	101
Nepal	0.258	+4.54%	48
New Zealand	0.907	+0.332%	29
Pakistan	5.62	+0.311%	7
Peru	1.19	-3.24%	27
Philippines	1.14	-4.86%	28
Poland	1.65	-1.59%	22
North Korea	0.0254	0%	92
Portugal	0.0321	-4.75%	89
Paraguay	0.097	+10.5%	72
Qatar	0.0018	0%	106
Romania	0.844	-1.31%	32
Russia	5.81	-2.63%	6
Saudi Arabia	0.278	-1.94%	47
Sudan	0.08	-23.1%	76
Senegal	0.0063	-11.3%	100
El Salvador	0.19	+1.01%	52
Slovakia	0.13	-0.0768%	67
Slovenia	0.0027	0%	105
Sweden	0.0153	-3.77%	96
Syria	0.0404	+3.06%	86
Thailand	2.1	-2.11%	18
Tajikistan	0.0433	+9.9%	85
Turkmenistan	0.854	-1.2%	31
Trinidad & Tobago	0.0094	0%	99
Tunisia	0.165	0%	58
Turkey	2.27	+0.0573%	16
Tanzania	0.123	-5.23%	69
Ukraine	2.39	-13.1%	15
Uruguay	0.661	+4.95%	35
United States	6.32	-0.529%	4
Uzbekistan	1.77	-3.46%	19
Venezuela	0.105	-14%	71
Vietnam	2.63	-4.6%	13
South Africa	1.55	+9.27%	23
Zambia	0.228	+6.24%	49
Zimbabwe	0.0963	-0.722%	73

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.AG.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.AG.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 Agriculture (Mt CO2e)

Year: 2023

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