As climate change and global warming become more as more prominent, the future of Earth starts to concern many people. Carbon dioxide, a key greenhouse gas that drives glocal climate change, continues to rise every month, and its levels are at a record high. Greenhouse gas concentrations drive complex shifts in the planet's weather and climate systems, encompassing not only the rising average temperatures, but also extreme weather events, shifting wildlife populations and habitats, rising seas, and more. It is imminent we as a society take action to slow the worsening state of Earth. With many countries and companies pledging to becoming carbon neutral by 2030 or 2050, we need to first figure out which countries and from what sources carbon emissions come from in order to tackle this issue.
import pandas as pd
# Read CSV file into a DataFrame
df_emissions = pd.read_csv('GCB2022v27_MtCO2_flat.csv')
# clean up data by removing lines with NaN values
df_emissions = df_emissions.dropna()
# display first few rows of DataFrame
print(df_emissions.head())
Country ISO 3166-1 alpha-3 Year Total Coal Oil \
3232 Australia AUS 1990 278.154156 141.879819 88.842090
3233 Australia AUS 1991 279.528510 146.082840 88.245572
3234 Australia AUS 1992 284.525345 150.051381 87.916828
3235 Australia AUS 1993 288.870537 150.098575 90.386578
3236 Australia AUS 1994 293.696553 151.376241 91.924087
Gas Cement Flaring Other Per Capita
3232 34.454816 3.462872 7.272496 2.242063 16.315938
3233 32.786243 3.183033 7.001201 2.229622 16.184767
3234 33.970472 2.923411 7.303701 2.359551 16.293502
3235 35.670002 3.004698 7.136743 2.573941 16.383765
3236 37.032005 3.484276 6.880148 2.999795 16.494706
# below is the first row of the DataFrame
data_0 = df_emissions.iloc[0, :]
data_0
Country Australia ISO 3166-1 alpha-3 AUS Year 1990 Total 278.154156 Coal 141.879819 Oil 88.84209 Gas 34.454816 Cement 3.462872 Flaring 7.272496 Other 2.242063 Per Capita 16.315938 Name: 3232, dtype: object
The data is organized using the following columns: Country (the name of the country), ISO 3166-1 alpha-3 (the three letter code for the country), Year (the year of survey data), Total (the total amount of CO2 emitted by the country in that year), Coal (amount of CO2 emitted by coal in that year), Oil (amount emitted by oil) , Gas (amount emitted by gas) , Cement( amount emitted by cement) , Flaring(flaring emission levels ) and Other( other forms such as industrial processes ). In addition there is also one extra column Per Capita which provides an insight into how much personal carbon dioxide emission is present in each Country per individual.
Full data dictionary is provided below if interested.
# the countries that this dataset encompasses
df_emissions['Country'].unique()
array(['Australia', 'Austria', 'Belarus', 'Belgium', 'Brazil', 'Bulgaria',
'Canada', 'China', 'Croatia', 'Cyprus', 'Czech Republic',
'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece',
'Hungary', 'Iceland', 'Ireland', 'Italy', 'Japan', 'Kazakhstan',
'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Malta',
'Netherlands', 'New Zealand', 'Norway', 'Poland', 'Portugal',
'South Korea', 'Romania', 'Russia', 'Slovakia', 'Slovenia',
'Spain', 'Sweden', 'Switzerland', 'Taiwan', 'Turkey', 'Ukraine',
'United Kingdom', 'USA', 'Global'], dtype=object)
The dataset includes information on a lot of the major countries in the world, so it could be interesting to see how these key players in the economy compare/contrast against each other in emissions.
# the years that this dataset encompasses
years = df_emissions['Year'].unique()
print(years)
print(years.max())
print(years.min())
[1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959] 2021 1904
This dataset encompasses emissions trackings from 1904-2021, so there definitely is a wide range of data we could look at, and we could evaluate how the CO2 emissions have progressed over the years, what years categorized the most increase in emissions, and what the possible reasoning for that could be based on historical events.
# describe the Total emissions data
df_emissions['Total'].describe()
count 1619.000000 mean 1187.296340 std 4348.889189 min 0.141996 25% 38.908676 50% 111.280674 75% 459.921985 max 37123.850352 Name: Total, dtype: float64
We could evaluate the total emissions and emissions by category to see the difference in how different countries produce more/less CO2.
This dataset can be used to quantify CO2 emisison levels by country over time and understand the sources of these emissions can be valuable. And with insights from this dataset, we can begin to identify strategies to combat climate change through mitigating pollutants.
# turn csv values into a data dictionary
data_dict = df_emissions.to_dict(orient='list')