Project 2 - Supply and Demand Gap Analysis

Detailed Documentation of Renewable Energy Share Analysis

By Yeragudipati Anupama

Aim:

The aim of this project is to analyze the renewable energy share across various regions from 1965 to 2021, focusing on identifying global trends, regional differences, and the impact of economic development on the adoption of renewable energy. The analysis also seeks to provide actionable insights for policymakers and investors to guide future investments and strategies for promoting sustainable energy growth worldwide.

Introduction:

Renewable energy has become a crucial component in addressing global energy challenges, including climate change, energy security, and economic development. The transition to renewable energy sources, such as wind, solar, and hydropower, is critical for reducing dependency on fossil fuels and achieving sustainable development goals (SDGs). This project leverages data from 104 regions to assess renewable energy share, identify trends, and provide insights on how different regions are progressing toward a cleaner energy future.

Problem Statement:

While global renewable energy share is growing, there are significant disparities between regions. Some countries have made remarkable progress in adopting renewable energy, while others, particularly in developing regions, remain heavily reliant on fossil fuels. Understanding these trends is essential for guiding global policy, investment decisions, and efforts to mitigate the effects of climate change. This analysis aims to:

• Investigate renewable energy adoption trends across different regions.
• Analyze the impact of economic development and other factors on renewable energy share.
• Provide actionable recommendations for enhancing renewable energy adoption in regions with low penetration.

Methodology:

To perform a comprehensive analysis of renewable energy share, the following methodology was used:

1. Data Collection and Preprocessing:

   • A dataset containing renewable energy share data for 104 regions from 1965 to 2021 was used.
   • The data includes key columns such as Region Name, Year, and Renewable Energy Share.
   • The data was cleaned using Power BI Power Query addressing missing values, outliers, and inconsistent formatting.

2. Data Transformation and Analysis:

   • Power BI was used to visualize and analyze trends over time across different regions.
   • Python was used for statistical analysis, such as trend analysis, regional comparisons, and correlation analysis.
   • Key analysis steps involved aggregating the data by region and year, and performing temporal trend analysis to detect patterns.

3. Insights Extraction:

   • Key insights were drawn from the visualizations and statistical analysis, focusing on regions with the highest and lowest renewable energy shares, temporal trends, and correlations between economic factors and renewable energy adoption.

Analysis:

Data Sheets: The analysis was based on a dataset that included the following key columns:

• Entity (Region Name): The name of the region/country.
• Year: The year for which the renewable energy share data is available.
• Renewable Energy Share: The percentage of total primary energy consumption derived from renewable sources.

The data was analyzed in two primary ways:

1. Regional Trends: A comparison of renewable energy share across 104 regions.
2. Temporal Trends: Tracking renewable energy share over time, from 1965 to 2021, for different regions.

Key Analysis Steps:

• Trend Analysis: Renewable energy adoption trends were visualized using line charts and bar charts in Power BI to identify periods of significant growth or stagnation.
• Regional Comparisons: Comparisons were made between high-income and low-income countries to understand the impact of economic development on renewable energy adoption.
• Correlation Analysis: Python's Pandas and SciPy libraries were used to compute correlations between renewable energy share and factors like GDP and industrial growth.

Insights:

1. Regional Insights:

   • Developed Regions: European countries, North America, and parts of Asia (e.g., Japan, South Korea) showed consistent growth in renewable energy adoption, with some reaching over 50% renewable energy share by 2021.
   • Developing Regions: Sub-Saharan Africa, South Asia, and parts of Latin America displayed slower growth, with adoption rates remaining below 20% in many countries.

2. Temporal Trends:

   • Global Growth: A clear upward trend in renewable energy adoption was observed globally, especially post-2010, driven by technological advancements and stronger policy support.
   • Policy Impact: Regions with strong governmental policies, such as Germany and Denmark, showed the most significant increases in renewable energy share.

3. Economic Development Impact:

   • Higher-income countries have shown faster renewable energy adoption, likely due to better access to technology, financial resources, and infrastructure.
   • Low-income regions are progressing at a slower rate, highlighting the need for international support to foster sustainable energy growth.

Recommendations:

1. Investment in Developing Regions:

   • To accelerate renewable energy adoption, it is essential to focus investments in developing regions, especially in Africa and parts of Asia. International financial aid, technological assistance, and infrastructure development can facilitate renewable energy growth in these areas.

2. Support for Innovation in Developed Countries:

   • Continue to support technological innovation in renewable energy, especially in high-income countries. Investments in grid integration, energy storage, and smart grids will help maintain the momentum of renewable energy adoption.

3. Regional Policy Reforms:

   • Governments should consider policies that incentivize renewable energy adoption, such as tax credits, subsidies, and public-private partnerships. These policies should be tailored to regional economic conditions.

4. Focus on Low-Adoption Countries:

   • For countries with low renewable energy adoption, it is crucial to identify and overcome barriers such as lack of infrastructure, high initial costs, and limited access to financing.

Conclusions:

The analysis reveals significant disparities in renewable energy adoption across regions, with developed countries leading the transition and developing countries lagging behind. Despite this, global renewable energy adoption is on the rise, and the transition is gaining momentum. Policymakers and investors must focus on tailored strategies for each region to ensure the achievement of global sustainability goals. By investing in renewable energy infrastructure, supporting technological innovation, and enacting region-specific policies, we can accelerate the transition to a more sustainable and cleaner energy future.

This project has successfully provided a comprehensive view of renewable energy share trends across different regions, and the insights drawn will be valuable for guiding future investments and policy decisions in the renewable energy sector.

By Yeragudipati Anupama