Climate Change and Impacts in Africa
According to the United Nations, Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil, and gas.
The consequences of climate change now include, among others, intense droughts, water scarcity, severe fires, rising sea levels, flooding, melting polar ice, catastrophic storms, and declining biodiversity.
You work for a Non-governmental organization tasked with reporting the state of climate change in Africa at the upcoming African Union Summit. The head of analytics has provided you with IEA-EDGAR CO2 dataset which you will clean, combine and analyze to create a report on the state of climate change in Africa. You will also provide insights on the impact of climate change on African regions (with four countries, one from each African region, as case studies).
Dataset
The dataset, IEA-EDGAR CO2, is a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022) including or based on data from IEA (2021) Greenhouse Gas Emissions from Energy, www.iea.org/statistics, as modified by the Joint Research Centre. The data source was the EDGARv7.0_GHG website provided by Crippa et. al. (2022) and with DOI.
The dataset contains three sheets - IPCC 2006, 1PCC 1996, and TOTALS BY COUNTRY on the amount of CO2 (a greenhouse gas) generated by countries between 1970 and 2021. You can download the dataset from your workspace or inspect the dataset directly here.
TOTALS BY COUNTRY SHEET
This sheet contains the annual CO2 (kt) produced between 1970 - 2021 in each country. The relevant columns in this sheet are:
| Columns | Description |
|---|---|
C_group_IM24_sh | The region of the world |
Country_code_A3 | The country code |
Name | The name of the country |
Y_1970 - Y_2021 | The amount of CO2 (kt) from 1970 - 2021 |
IPCC 2006
These sheets contain the amount of CO2 by country and the industry responsible.
| Columns | Description |
|---|---|
C_group_IM24_sh | The region of the world |
Country_code_A3 | The country code |
Name | The name of the country |
Y_1970 - Y_2021 | The amount of CO2 (kt) from 1970 - 2021 |
ipcc_code_2006_for_standard_report_name | The industry responsible for generating CO2 |
1 hidden cell
# Setup
import pandas as pd
import numpy as np
import pingouin
from sklearn.linear_model import LinearRegression
from statsmodels.regression.linear_model import OLS
import seaborn as sns
import matplotlib.pyplot as plt
import inspect
plt.style.use('ggplot')
# The sheet names containing our datasets
sheet_names = ['IPCC 2006', 'TOTALS BY COUNTRY']
# The column names of the dataset starts from rows 11
# Let's skip the first 10 rows
datasets = pd.read_excel('IEA_EDGAR_CO2_1970-2021.xlsx', sheet_name = sheet_names, skiprows = 10)
# we need only the African regions
african_regions = ['Eastern_Africa', 'Western_Africa', 'Southern_Africa', 'Northern_Africa']
ipcc_2006_africa = datasets['IPCC 2006'].query('C_group_IM24_sh in @african_regions')
totals_by_country_africa = datasets['TOTALS BY COUNTRY'].query('C_group_IM24_sh in @african_regions')
# Read the temperatures datasets containing four African countries
# One from each African Region:
# Nigeria: West Africa
# Ethiopa : East Africa
# Tunisia: North Africa
# Mozambique: South Africa
temperatures = pd.read_csv('temperatures.csv')# The solution code and the test runner
import tests as runner
import solutionsInstruction 1: Clean and tidy the datasets
Tasks
- Rename
C_group_IM24_shtoRegion,Country_code_A3toCode, andipcc_code_2006_for_standard_report_nametoIndustryin the corresponding African datasets. - Drop
IPCC_annex,ipcc_code_2006_for_standard_report, andSubstancefrom the corresponding datasets. - Melt
Y_1970toY_2021into a two columnsYearandCO2. Drop rows whereCO2is missing. - Convert
Yeartointtype.
Hints
- Use
df.rename()method to rename columns. - The
df.drop()method can be used to drop columns. - You might find
df.melt()orpd.melt()useful. - The
df.column.astype(int)can be used to convert to a column to an integer type.
ipcc_2006_africa = ipcc_2006_africa.rename(columns={"C_group_IM24_sh":"Region", "Country_code_A3":"Code", "ipcc_code_2006_for_standard_report_name":"Industry"})
# -----------------------------------------------------------
totals_by_country_africa = totals_by_country_africa.rename(columns={"C_group_IM24_sh":"Region", "Country_code_A3":"Code"})
ipcc_2006_africa = ipcc_2006_africa.drop(["IPCC_annex", "ipcc_code_2006_for_standard_report", "Substance"], axis=1)
# ------------------------------------------------------------
totals_by_country_africa = totals_by_country_africa.drop(
['IPCC_annex', 'Substance'], axis=1)
value_vars = list(filter(lambda x: x.startswith('Y_'), ipcc_2006_africa.columns))
id_vars = list(set(ipcc_2006_africa.columns).difference(value_vars))
ipcc_2006_africa = ipcc_2006_africa.melt(id_vars=id_vars, value_vars=value_vars, var_name="Year", value_name="CO2")
# ------------------------------------------------------------
value_vars_t = list(filter(lambda x: x.startswith('Y_'), totals_by_country_africa.columns))
id_vars_t = list(set(totals_by_country_africa.columns).difference(value_vars_t))
totals_by_country_africa = totals_by_country_africa.melt(id_vars=id_vars_t, value_vars=value_vars_t, var_name="Year", value_name="CO2")
#ipcc_2006_africa = pd.melt(ipcc_2006_africa, id_vars=["Region", "Code", "Name", "Industry", "fossil_bio"], value_vars=ipcc_2006_africa.loc[:, "Y_1970":"Y_2021"], var_name="Year", value_name="CO2")
ipcc_2006_africa = ipcc_2006_africa.dropna(subset=["CO2"])
ipcc_2006_africa.Year = ipcc_2006_africa.Year.str.replace("Y_", "").astype(int)
# -----------------------------------------------------------
totals_by_country_africa = totals_by_country_africa.dropna(subset=["CO2"])
totals_by_country_africa.Year = totals_by_country_africa.Year.str.replace("Y_", "").astype(int)# DO NOT MODIFY THIS CELL
# Run this cell to determine if you've done the above correctly
# If there are no error messages, you are correct :)
runner.check_task_1(ipcc_2006_africa, totals_by_country_africa)# Uncomment and run to view solution one
#print(inspect.getsource(solutions.solution_one))Instruction 2: Show the trend of CO2 levels across the African regions
CO2 levels across the African regionsTasks
- Using
totals_by_country_africa, create a line plot ofCO2vs.Yearin eachRegionto show the trend of CO2 levels by year.
Hints
- Use
sns.lineplot()to create a line plot. - Your plot should be similar to the one shown below.
sns.lineplot(totals_by_country_africa, x="Year", y="CO2", hue="Region", ci=None)
plt.title("CO2 level across the African Regions between 1970 and 2021")
plt.ylabel("CO2 (kt)")
# Uncomment and run to view solution two
# print(inspect.getsource(solutions.solution_two))Instruction 3: Determine the relationship between time (Year) and CO2 levels across the African regions
Year) and CO2 levels across the African regionsTasks
- Using the
totals_by_country_africadataset, conduct a Spearman's correlation to determine the relationship between time (Year) andCO2within each AfricanRegion. - Save the results in a variable called
relationship_btw_time_CO2.
Hints
- Use
df.groupby()anddf.corr()methods. - Use the
corr()method'smethodparameter to set the correlation type.
relationship_btw_time_CO2 = totals_by_country_africa.groupby("Region")[["Year", "CO2"]].corr(method="spearman")
relationship_btw_time_CO2# DO NOT MODIFY THIS CELL
# Run this cell to determine if you've done the above correctly
# If there are no error messages, you are correct :)
# tests
runner.check_task_3(relationship_btw_time_CO2)# Uncomment and run to view solution three
# print(inspect.getsource(solutions.solution_three))