Portfolio Details

Renewable Energy and Emission Reduction: A Case Study for Economic and Environmental Sustainability

The objective of this case study is to analyze a dataset of renewable energy projects that includes variables such as the type of renewable energy, installed capacity, energy production and consumption, storage capacity, grid integration level, initial investment, funding sources, financial incentives, greenhouse gas (GHG) emission reductions, air pollution reduction index, and jobs created. The aim is to understand the relationships between these variables and identify significant patterns or insights that can inform decision-making in real-world renewable energy projects. This analysis assumes that the data accurately represents the performance and impact of various renewable energy projects.

Result

Based on the data visualization results, the understanding gained is that the correlation matrix shows that most variables have very weak correlations (|r| <= 0.10) with each other. This indicates that no factors can be identified as having a significant influence on the efficiency of renewable energy projects based on the correlations in this dataset.

A notable example is the relationship between initial investment and job creation in renewable energy projects, which also shows a very weak correlation. This suggests that initial investment cannot be relied upon as a significant predictor for job creation.

However, the visualization also reveals that Biomass has the highest average reduction in greenhouse gas emissions, followed by Tidal and Hydroelectric. This indicates that although the correlations between variables are generally weak, there are some important findings related to the environmental impact of different types of renewable energy.