This research project aims to conduct a comprehensive statistical analysis of the correlation between renewable energy production and the environmental footprint, with a specific focus on CO2 emissions. Key features of the study include:
To conduct this analysis, a comprehensive dataset was collected, encompassing information on renewable energy production and CO2 emissions over a specified timeframe. The datasets include data from the “Our World in Data” organization1, covering a range of countries and regions, allowing for a global perspective on the topic.
Before performing the statistical analysis, the collected data underwent a pre-processing process to ensure its quality and consistency. The following steps were taken:
Two subsets were created: “filtered_owid” for CO2 levels and “filtered_energy” for the TWh of energy produced in a specific year.
The statistical analysis consisted of several steps, starting from descriptive statistics to gain a general understanding of the dataset. R software and libraries were used for the analysis, including:
These tools allowed for a comprehensive comparison and visualization of the datasets, providing an overview of the phenomenon under study. Linear charts were used to facilitate the comparison of the resulting values.
Additionally, the Pearson Coefficient was computed to measure the correlation between renewable energy production and CO2 emissions. The Pearson Coefficient is a statistical measure that quantifies the linear relationship between two variables. A result of -0.8866505 suggests a strong inverse correlation between renewable energy production and CO2 emissions2.
This statistical study was conducted by Francesco Silvano and Giacomo Noghera. The data and libraries used belong to their respective owners.
This project is licensed under the MIT License. See the LICENSE file for more information.