%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from pandas_datapackage_reader import read_datapackage
from countrygroups import EUROPEAN_UNION
Reading in the Data Package with ratification status of the Paris Agreement.
parties = read_datapackage("https://github.com/openclimatedata/paris-agreement-entry-into-force")
parties.head()
parties.count()
The European Union is contained both as a block and as single parties so let's look at EU parties only.
print(EUROPEAN_UNION)
assert(len(EUROPEAN_UNION) == 28)
parties.loc["EUU"]
parties.loc[EUROPEAN_UNION]
For the emissions shares the last available data submitted to the UNFCCC was used.
parties.Year.hist(bins=25 )
plt.title("Distribution of submission years");
And a look at how emissions are distributed, many countries with small shares.
parties.Percentage.hist(bins=20)
plt.title("Distribution of emissions shares for Entry Into Force");
not_ratified = parties[parties["Ratification-Acceptance-Approval"].isnull() != False]
ratified = parties[parties["Ratification-Acceptance-Approval"].isnull() == False]
Sum of emissions of parties that have ratified, subtracting EU as a group to avoid double-counting.
print(ratified.Percentage.sum() - parties.loc["EUU"].Percentage)
A list of the ten highest emitters who have not yet ratified.
not_ratified.sort_values("Emissions", ascending=False).head(10)