Global Energy Transition Statistics

Overview

Access 2023 world energy and climate data and key decarbonisation indices through Enerdata's interactive data tool. This README file provides a summary of the dataset, including features and key points to consider during analysis.

Directory Structure

This directory contains the following files:

• world_energy_data.csv: Comprehensive data on the production, consumption, and trade of oil, gas, coal, power, and renewables, along with CO2 emissions from fuel combustion.
• README.md: This README file.
• scripts/: Directory for scripts used to analyze the data.

Features of the Dataset

• Comprehensive Data Access: Explore data on the production, consumption, and trade of oil, gas, coal, power, and renewables, along with CO2 emissions from fuel combustion.
• Global Coverage: Data encompasses 60 countries and regions worldwide, from 1990 to 2023.
• Exclusive Foresight: Gain insights into essential energy data and evaluate the COP28 pledge to determine if current trends support the tripling of renewable capacity and the doubling of energy efficiency by 2030.

Benefits of the Interactive Data Tool

• Animated Data Evolution: Visualize trends over time from 1990 to 2023.
• Interactive Map: Easily select areas with zoom in and out controls.
• Country Benchmarking: Compare data across different countries.
• Flexible Period Selection: Choose any time range to view data.
• Data Export: Export data globally or by specific energy sources.

Column Descriptions for world_energy_data.csv

• Country: Name of the country or region.
• Year: The year of the data point.
• Energy Type: Type of energy (e.g., oil, gas, coal, renewables).
• Production: Amount of energy produced.
• Consumption: Amount of energy consumed.
• Trade: Amount of energy traded.
• CO2 Emissions: CO2 emissions from fuel combustion.

Note: Some columns may have missing values, which should be considered during analysis.

How to Use This Data

Exploring the Data

1. Load the datasets into a data analysis environment like R or Python.
2. Merge the datasets if necessary to perform comparative analysis between different countries or regions.
3. Clean the data by handling missing values and standardizing column names for consistency.

Analyzing Trends

1. Analyze energy trends over time to identify any patterns or significant changes.
2. Compare the production, consumption, and trade of different energy types across countries.
3. Examine the CO2 emissions and their impact on climate change.

Visualizations

1. Create visualizations such as line charts, bar charts, and maps to illustrate energy trends and distributions.
2. Use tools like ggplot2 in R or Matplotlib in Python for visualization.

Reporting

1. Summarize findings in reports or presentations.
2. Highlight key insights and recommendations based on the analysis.

Start exploring and stay ahead with the latest energy trends and data.

Dataset Source :

https://energydata.info/dataset/global-energy-statistics-yearbook-dataset