Renewable electricity output (% of total electricity output)

The indicator of renewable electricity output as a percentage of total electricity output plays a crucial role in assessing the energy landscape of any country or region. This metric signifies the proportion of electricity generated from renewable sources—such as solar, wind, hydroelectric, and biomass—relative to all electricity produced. The increasing focus on sustainable energy solutions brings this indicator to the forefront of discussions concerning climate change, energy independence, and economic development.

One of the primary reasons for monitoring renewable electricity output is its direct relation to greenhouse gas emissions. The burning of fossil fuels for electricity is a leading cause of CO2 emissions, contributing significantly to global warming. As nations pivot towards cleaner energy sources, the increase in renewable electricity output is vital for mitigating climate change impacts. Furthermore, as the urgency to address global challenges like air pollution, land degradation, and energy security grows, this indicator helps stakeholders evaluate their progress toward a sustainable future.

This metric does not exist in isolation; rather, it interacts with various other indicators. For example, the level of investment in renewable energy technologies correlates strongly with renewable electricity output. Countries that allocate financial resources toward solar, wind, and other renewable infrastructure typically see increases in their renewable share. Additionally, the political landscape can affect this indicator. Policies promoting clean energy adoption, such as subsidies for renewable projects or legislative mandates for cleaner grid standards, significantly boost renewable electricity contributions to total energy output.

Many factors influence the renewable electricity output percentage. Geographic factors play a crucial role; for instance, regions endowed with natural resources conducive to renewable energy (like high wind speeds or ample sunlight) are likely to achieve higher outputs. Moreover, technological advancements improve the efficiency and economics of renewable energy generation, further supporting higher output ratios. Economic factors also come into play, as countries with stronger economies often have more resources to invest in renewable infrastructure and research. Conversely, regions relying heavily on fossil fuels may have a lower percentage, reflecting the challenges of transitioning to greener alternatives.

Strategies to increase renewable electricity output typically encompass a multi-faceted approach. Policymakers may implement feeding tariff systems to guarantee fixed payments for renewable energy producers, incentivizing investments in these sectors. Incorporation of energy storage solutions, such as batteries, can help manage supply and demand discrepancies inherent in renewable energies, thus enhancing their contribution to the overall electricity mix. Additionally, public awareness campaigns can encourage energy conservation and a preference for cleaner energy sources among consumers.

Despite its importance, this indicator also has its flaws. Variability in renewable output, due to dependency on weather and climatic conditions, presents a challenge in maintaining a stable energy supply. For example, while wind and solar power can generate significant electricity output during favorable conditions, they can fall short during periods of low wind or reduced sunlight. Furthermore, the grid infrastructure in many regions may not be adequately designed to handle high levels of renewable energy, resulting in potential bottlenecks and inefficiencies.

Examining the latest data, we find that in 2019, the median value of renewable electricity output worldwide was 16.25%. Indonesia is presented as both a top and bottom performer with this figure, indicative perhaps of its unique energy profile or the challenges it faces in scaling local renewable efforts. It's worth noting that the global figures have fluctuated over the decades. For instance, the renewable electricity output percentage has shown declining trends from 19.36% in 1990 down to lower values in subsequent years, until peaking again around the mid-2010s.

The available statistics indicate that between 1990 and 2015, while the global population has increasingly recognized the need for greener energy solutions, the actual renewable share has faced substantial oscillations, hitting a low of 17.38% in 2003 before starting to rise again. The peak in 2012 at 20.9% and onwards shows an emerging positive trend toward integrating renewable energy sources, illustrating both international efforts and technological improvements.

In conclusion, the renewable electricity output indicator is a vital measure that encapsulates not only the potential to combat climate change but also signifies the broader economic and technological transitions that countries are undergoing. Continuous efforts must be directed towards overcoming existing challenges, creating actionable policies that encourage renewable investment, increasing public engagement, and pursuing innovations in the energy sector. By unlocking the full potential of renewable resources, regions can enhance energy security, improve public health through cleaner air, and contribute to a more sustainable global ecosystem.

                    
# 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.ELC.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.ELC.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))