Relation between CO2 emissions and the consumption of renewable energy (by country)

1) Describes and motivates a real-world problem where data science may provide helpful insights

The real-world problem that I that I believe we could provide helpful insights about using data science in the relations between Carbon Dioxide (CO2) emissions and renewable energy (wind and solar) consumption (by country). An article in 'Frontiers in Energy Research' ("Szetela B") states, "1 percentage point increase in renewable energy consumption leads to 1.25% decrease in CO2 emissions per capita", this shows that there is a significat relation between CO2 emissions and renewable energy (wind and solar) consumption.

Szetela B, Majewska A, Jamroz P, Djalilov B and Salahodjaev R (2022) Renewable Energy and CO2 Emissions in Top Natural Resource Rents Depending Countries: The Role of Governance. Front. Energy Res. 10:872941. doi: 10.3389/fenrg.2022.872941

2) Explicitly load and show your dataset.

# data set 1 - List of Countries by CO2 emissions 
import pandas as pd
co2_data = pd.read_csv('co2_emissions.csv')
# co2_data = co2_data.drop([0, 1, 2, 3])
co2_data.head()

	country	1980	1985	1990	1995	2000	2008	2009	2010	2011	2012	2013	2014	2015	2018
0	Afghanistan	0.1	0.3	0.2	0.1	0.0	0.2	0.2	0.3	0.4	0.4	0.3	0.3	0.3	0.3
1	Albania	1.9	2.7	1.7	0.7	1.0	1.5	1.5	1.6	1.8	1.7	1.7	2.0	1.6	1.6
2	Algeria	3.4	3.2	3.0	3.3	2.8	3.2	3.4	3.3	3.3	3.5	3.5	3.7	3.9	3.9
3	American Samoa	..	..	..	..	..	..	..	0.6	0.7	0.7	0.7	0.7	0.8	..
4	Andorra	..	..	7.5	6.7	8.0	6.4	6.1	6.1	5.9	5.9	5.9	5.8	5.9	6.0

Feature	Description
country	Name of country
1980	CO2 emission in 1980
1985	CO2 emission in 1985

(it continues with this pattern)

# alternate data set - List of Countries by CO2 emissions 
import pandas as pd
co2_data_1 = pd.read_csv('List of countries by carbon dioxide emissions.csv')
co2_data_1 = co2_data_1.drop([0, 1, 2, 3])
co2_data_1.head()

	Country	Fossil CO2 emissions(Mt CO2)	Unnamed: 2	Unnamed: 3	Fossil CO2 emissions	Unnamed: 5	2017 Fossil CO2 emissions	Unnamed: 7	2018 CO2 emissions	Unnamed: 9
4	Afghanistan	2.546	1.063	11.422	0.03%	348.60%	18	0.3	7.59	7.44
5	Albania	6.583	4.196	5.026	0.01%	-23.70%	175	1.7	5.32	5.56
6	Algeria	65.677	98.197	159.929	0.43%	143.50%	67	3.9	151.87	151.67
7	Angola	5.851	15.975	30.876	0.08%	427.70%	25	1	62.93	27.34
8	Anguilla	0.006	0.014	0.028	0.00%	366.70%	308	1.9	NaN	NaN

Feature	Description
country	Name of country
Fossil CO2 emissions	CO2 emission in 1990
Unnamed	CO2 emission in 2005

(it continues with this pattern) (this dataset would need to be re-structured if used)

# data set 2 - Renewable Energy Consumption (wind and solar)
import pandas as pd
# solar energy
solar_data = pd.read_csv('solar-energy-consumption.csv')
solar_data = solar_data.drop(solar_data.columns[1], axis=1)
solar_data.head()

	Entity	Year	Electricity from solar (TWh)
0	Afghanistan	2000	0.0
1	Afghanistan	2001	0.0
2	Afghanistan	2002	0.0
3	Afghanistan	2003	0.0
4	Afghanistan	2004	0.0

Feature	Description
Entity	Name of country
Year	The year of consumption
Electricity from solar (TWh)	The consumption of solar energy in Terawatt hour

# wind energy
wind_data = pd.read_csv('wind-generation.csv')
wind_data = wind_data.drop(wind_data.columns[1], axis=1)
wind_data.head()

	Entity	Year	Electricity from wind (TWh)
0	Afghanistan	2000	0.0
1	Afghanistan	2001	0.0
2	Afghanistan	2002	0.0
3	Afghanistan	2003	0.0
4	Afghanistan	2004	0.0

Feature	Description
Entity	Name of country
Year	The year of consumption
Electricity from solar (TWh)	The consumption of wind energy in Terawatt hour

3) Write one or two sentences about how the data will be used to solve the problem.

Using this data, to compare and analyse the exact relations between the two, would be beneficial as the countries wih higher renewable energy consumption could be used as examples and their methods of implementing renewable power could be used by other countries. This could lead to an extreme reduction of CO2 emissions in the world and could reduce the effects that global warming has on our everyday lives.