Public private partnerships investment in energy (current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 187,330,000 +866% 21
Argentina Argentina 720,000,000 +109% 12
Armenia Armenia 26,000,000 -29.7% 34
Azerbaijan Azerbaijan 348,000,000 +39.2% 16
Burundi Burundi 1,000,000 -93.6% 44
Benin Benin 9,600,000 -39.3% 39
Bangladesh Bangladesh 247,550,000 -59.6% 19
Bulgaria Bulgaria 293,140,000 +176% 18
Bosnia & Herzegovina Bosnia & Herzegovina 495,230,000 +529% 13
Brazil Brazil 3,830,070,000 -25.7% 3
Botswana Botswana 60,000,000 +1,100% 29
China China 38,416,240,000 +11,634% 1
Cameroon Cameroon 100,000,000 -92.7% 28
Congo - Kinshasa Congo - Kinshasa 50,000,000 -63% 30
Colombia Colombia 720,900,000 +23.1% 11
Cape Verde Cape Verde 30,000,000 -62.5% 33
Dominican Republic Dominican Republic 858,020,000 +177% 10
Ecuador Ecuador 181,000,000 +62.5% 22
Egypt Egypt 869,400,000 -16.3% 9
Gabon Gabon 30,000,000 -80.3% 33
Honduras Honduras 370,000,000 +42.5% 15
Indonesia Indonesia 171,000,000 +14% 23
India India 4,467,300,000 +87.7% 2
Kazakhstan Kazakhstan 194,530,000 +597% 20
Kyrgyzstan Kyrgyzstan 117,990,000 25
Cambodia Cambodia 447,500,000 +986% 14
Laos Laos 959,000,000 +1,857% 8
Sri Lanka Sri Lanka 8,320,000 -38.8% 40
Madagascar Madagascar 48,000,000 +61.5% 31
Mexico Mexico 14,650,000 -96.7% 37
Mozambique Mozambique 30,000,000 -76.9% 33
Mauritius Mauritius 23,170,000 -66.7% 35
Malaysia Malaysia 130,790,000 +174% 24
Nigeria Nigeria 116,000,000 +190% 27
Nicaragua Nicaragua 12,500,000 -83.6% 38
Peru Peru 1,787,600,000 +106% 5
Philippines Philippines 2,081,560,000 +749% 4
Papua New Guinea Papua New Guinea 1,200,000 -98.2% 43
Rwanda Rwanda 40,000,000 -89% 32
Senegal Senegal 297,450,000 +452% 17
Sierra Leone Sierra Leone 1,200,000 -97% 43
Somalia Somalia 5,670,000 42
Chad Chad 100,000,000 +40.2% 28
Tanzania Tanzania 30,000,000 +2,400% 33
Uganda Uganda 19,000,000 -13.6% 36
Uzbekistan Uzbekistan 1,611,940,000 -46.8% 6
Vietnam Vietnam 117,000,000 -95.3% 26
South Africa South Africa 1,010,200,000 -33.2% 7
Zambia Zambia 7,500,000 -97.8% 41 

                    
# 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 = 'IE.PPN.ENGY.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 <- 'IE.PPN.ENGY.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))