WORK IN PROGRESS -- Analysis of Renewable Energy Adoption and its Impact on CO2 Emissions in Europe and Globally

Objective

The primary objective of this project is to analyze the adoption rates of renewable energy sources and their impact on CO2 emissions. The analysis will utilize the owid_energy and owid_co2 datasets to draw insights into renewable energy consumption trends, the effectiveness of renewable energy policies, and their correlation with carbon emissions reduction. The goal is to identify trends, correlations, and potential areas for policy intervention to further promote renewable energy use.

Datasets

• owid_energy: Contains detailed information on various types of energy consumption, electricity generation, and their shares in the total energy mix.
• owid_co2: Contains data on carbon emissions from various sources, including fossil fuels and other industries, as well as indicators like GDP and population.

Key SQL Concepts

• Data Aggregation: Using functions like SUM(), AVG(), and COUNT() to summarize and group data to provide meaningful insights (e.g., total energy consumption, average emissions).
• Joins: Combining data from owid_energy and owid_co2 using INNER JOIN, LEFT JOIN, etc. to provide a comprehensive analysis.
• Subqueries: Utilizing nested queries to perform more complex data retrieval.
• Window Functions: Calculating cumulative totals, moving averages, and year-over-year changes.
• Handling Time-Series Data: Analyzing trends over time by leveraging date-based functions.

Research Hypotheses

• H1: Countries with higher adoption rates of renewable energy sources have significantly lower CO2 emissions (per capita).
• H2: There is a positive correlation between the percentage change in renewable energy consumption and the reduction in total greenhouse gas emissions.
• H3: European countries have a higher average adoption rate of renewable energy compared to the global average.
• H4: Economic growth (measured by GDP) is positively correlated with the adoption of renewable energy sources.
• H5: The share of renewable energy in total electricity generation has increased over the last decade globally.
• H6: Population growth influences the rate of renewable energy adoption and overall energy consumption patterns.
• H7: The transition to renewable energy sources leads to a measurable decrease in fossil fuel consumption and associated CO2 emissions.
• H8: Solar and wind energy show the most significant growth trends among renewable energy types across different countries.

Detailed Analysis Steps:

1. Data Cleaning and Preparation:

• Ensure data consistency and handle missing values.
• Standardize date formats and ensure proper data types for each column.

2. Descriptive Analysis:

• Calculate the total, average, and percentage share of different energy sources (biofuel, coal, fossil fuels, hydro, nuclear, solar, wind) for each country and year.
• Determine the overall and per capita CO2 emissions for each country.

3. Aggregation and Joins:

• Aggregate energy consumption data by country and year.
• Join owid_energy and owid_co2 datasets on country and year to create a unified dataset for analysis.

4. Trend Analysis:

• Analyze the trends in renewable energy adoption and CO2 emissions over the years.
• Use window functions to calculate year-over-year changes in energy consumption and emissions.

5. Correlation Analysis:

• Calculate the correlation between renewable energy adoption and CO2 emissions reduction.
• Investigate the relationship between GDP and renewable energy adoption.

6. Comparative Analysis:

• Compare the renewable energy adoption rates and CO2 emissions across different regions (focus on Europe vs. global data).
• Identify the top and bottom performers in renewable energy adoption.

7. Hypothesis Testing:

• Test the formulated hypotheses using statistical methods.
• Use SQL subqueries to isolate and test specific segments of the data.

8. Visualization:

• Create visualizations (charts, graphs) to illustrate key findings and trends.
• Highlight significant insights using visual tools like time-series plots, bar charts, and scatter plots.

9. Reporting:

• Summarize the analysis in a comprehensive report.
• Provide actionable insights and recommendations based on the findings.

Key Questions

1. What are the total, per capita, and average renewable energy consumption trends for each country over the years?
2. How do different countries compare in terms of renewable energy adoption rates?
3. What is the correlation between renewable energy adoption and CO2 emissions reduction?
4. How has the share of renewable energy in total electricity generation changed over the years?
5. Are there significant differences in renewable energy adoption rates between European countries and the global average?
6. What is the impact of economic growth on the adoption of renewable energy sources?
7. How do GDP and population growth affect renewable energy consumption and carbon emissions?
8. What are the trends in specific types of renewable energy (e.g., solar, wind) over time?
9. How do the top renewable energy adopters compare in terms of carbon emissions reduction?

Expected Outcomes

• Identification of countries leading in renewable energy adoption and their impact on CO2 emissions.
• Insights into the effectiveness of renewable energy policies and their contribution to emission reductions.
• Comparative analysis highlighting regional differences in renewable energy adoption.
• Actionable recommendations for policymakers to enhance renewable energy adoption and reduce carbon footprints.

This project aims to provide a comprehensive understanding of the relationship between renewable energy adoption and its impact on carbon emissions, contributing to the broader goal of mitigating climate change.