One of the leading causes of climate change from increased CO2 emissions has been the burning of fossil fuels such as oil, coal, and natural gas. Since humans started buring fossil fuels to create energy, there has been a drastic increase in global CO2 levels, resulting in changes to Earth's atmospheric composure and subsequently to Earth's climate.
The data set I plan to use for my projects consists of data displaying each country's fossil fuel consumption and how much CO2 it has produced for each fossil fuel ccategory, as well as the total CO2 output for each country. The data set contains data for each country from 1750 to 2021, and there is data for each year for each country.
Due to the challenges that current and past energy production has caused our planet, many governments, institutions, and corporations have placed great focus on methods to solve the issue of climate change. These plans require a transition from the burning of fossil fuels to more sustainable energy sources. Based on current discussions regarding these transitional periods, it seems that most countries or institutions will make more moderate changes to energy consumption and production over time. In this way, there will not be an imediate transition from natural gas to solar energy, for example. Therefore, it would make sense to ease down on the burning of the most harmful fossil fuels (the ones that produce the most CO2) during this transitional period.
I will use the data set I've chosen for this project to determine which fossil fuels are the most harmful to burn, which in turn might provide a more clear way to determine which fossil fuels to ease off of during transitional periods. In order to do this, I will cluster data of fossil fuel (coal, oil, natural gas, and total) usage for each country. Doing this will allow me to see which fuel source produces the most CO2 for each country. From there, I can cast a broader net and use the data for each country to tell me which form of fuel is the most polluting.
import pandas as pd
df_co2_emissions = pd.read_csv('CO2emissions.csv')
df_co2_emissions.head()
| Country | ISO 3166-1 alpha-3 | Year | Total | Coal | Oil | Gas | Cement | Flaring | Other | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | AFG | 1750 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | Afghanistan | AFG | 1751 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | Afghanistan | AFG | 1752 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | Afghanistan | AFG | 1753 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | Afghanistan | AFG | 1754 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
dict_co2_emissions = {"Country" : "Country", "ISO 3166-1 alpha-3" : "Country Code", "Year" : "Year", "Total" : "Total CO2 Emissions Per Country", "Coal" : "CO2 Emissions from Coal", "Oil" : "CO2 Emissions from Oil", "Gas" : "CO2 Emissions from Natural Gas"}
I will look at the CO2 emissions for each country in each fossil fuel category to determine which produces the most CO2.