Renewable energy consumption (% of total final energy consumption)

Renewable energy consumption as a percentage of total final energy consumption is a crucial indicator of how effectively a society is transitioning from fossil fuels towards more sustainable energy sources. This indicator encompasses a wide array of energy sources, including wind, solar, hydroelectric, biomass, and geothermal. The significance of this measure is underscored by the pressing global need to address climate change and to pursue development that is both sustainable and respectful of ecological systems.

The importance of renewable energy consumption cannot be overstated. As nations grapple with the realities of climate change and its effects, a shift towards renewable energy sources stands as a promising solution. Renewable energies are vital to reducing greenhouse gas emissions and fostering energy independence. In many developing nations, access to renewable energy can contribute greatly to local economies and improve the quality of life by providing consistent and sustainable energy sources. This shift is increasingly seen as a pathway to mitigate the factors exacerbating climate change, such as air pollution and habitat destruction.

An analysis of the latest data for 2022 demonstrates the heterogeneity in renewable energy consumption across different regions. The global median value for renewable energy consumption was 12.2%. While this figure may seem modest, it reflects significant progress in certain regions, particularly in those leading the charge towards renewable energy. The top five areas that showcase exceptional levels of renewable energy consumption are Somalia (95.4%), Liberia (92.8%), Central African Republic (90.9%), Uganda (90.9%), and Guinea-Bissau (87.4%). These countries exemplify how a heavy reliance on renewable resources can contribute to energy autonomy and decreased reliance on imported fossil fuels.

Conversely, the bottom five regions present a stark contrast, with some areas struggling to tap into renewable energy resources. The Cayman Islands, Sint Maarten, American Samoa, Northern Mariana Islands, and Turks & Caicos Islands each show alarmingly low percentages, with Cayman Islands and Sint Maarten reporting a complete absence of renewable consumption, at 0.0%. Such disparities highlight systemic challenges in these regions, which could include inadequate infrastructure, economic limitations, and lack of government policy favoring renewable investments.

Historically, the trajectory of global renewable energy consumption reflects a gentle but adamant upward trend since 1990, when the world registered a consumption of 16.66%. However, this figure saw fluctuations up until 2020 when it peaked at 19.77%. This upward arc poses a mixed narrative as the 1990 consumption levels initially suggested a high reliance on renewables, but subsequent years saw a decline, with a nadir around 2005 to 2010. It's evident that the transition has been uneven and often influenced by political, technological, and economic factors.

Factors affecting the renewable energy consumption indicator are multifaceted. Political decision-making and public policy play crucial roles. Government incentives, regulations, and targeted investments often encourage or discourage renewable energy development. Additionally, economic factors such as local availability of renewable resources, energy prices, and the financial capacity of consumers and industries heavily influence the transition to renewable energy. Technological advancements also dictate how efficiently renewable energy can be harnessed and integrated into existing energy grids, affecting consumption patterns significantly.

Various strategies have emerged to enhance renewable energy consumption. Countries are increasingly adopting ambitious renewable energy targets alongside investments in research and development. Promoting energy efficiency and conservation practices is also critical to complement renewable investments, allowing countries to maximize output from greener sources. Additionally, international cooperation and knowledge sharing can significantly boost the morale and capabilities of emerging economies looking to incorporate greener energy solutions.

However, the transition to renewable energy is not without its flaws. Many regions face challenges such as technology access and affordability, which can deter sustainable investments. The intermittency of some renewable sources, particularly solar and wind, requires robust energy storage solutions and diverse energy portfolios to ensure reliability. Moreover, regions with reliance on fossil fuel economies may encounter significant pushback from established interests, creating an antagonistic political landscape for renewable initiatives.

In conclusion, the renewable energy consumption percentage is a vital indicator reflecting the global shift towards more sustainable energy practices. While benchmark figures, such as the median of 12.2% in 2022, paint a mixed picture, they also highlight the significant progress made by various regions. As more countries strive for energy independence and sustainable economic growth, the future of renewable energy consumption looks promising, but it will require overcoming existing challenges through proactive policies, financial incentives, and technological innovation.

                    
# 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 = 'EG.FEC.RNEW.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 <- 'EG.FEC.RNEW.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))