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Climate Change and Impacts in Africa (Python)

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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:

ColumnsDescription
C_group_IM24_shThe region of the world
Country_code_A3The country code
NameThe name of the country
Y_1970 - Y_2021The amount of CO2 (kt) from 1970 - 2021

IPCC 2006

These sheets contain the amount of CO2 by country and the industry responsible.

ColumnsDescription
C_group_IM24_shThe region of the world
Country_code_A3The country code
NameThe name of the country
Y_1970 - Y_2021The amount of CO2 (kt) from 1970 - 2021
ipcc_code_2006_for_standard_report_nameThe industry responsible for generating CO2

Instructions

The head of analytics in your organization has specifically asked you to do the following:

  1. Clean and tidy the datasets.
  2. Create a line plot to show the trend of CO2 levels across the African regions.
  3. Determine the relationship between time (Year) and CO2 levels across the African regions.
  4. Determine if there is a significant difference in the CO2 levels among the African Regions.
  5. Determine the most common (top 5) industries in each African region.
  6. Determine the industry responsible for the most amount of CO2 (on average) in each African Region.
  7. Predict the CO2 levels (at each African region) in the year 2025.
  8. Determine if CO2 levels affect annual temperature in the selected African countries.
# 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 solutions

Instruction 1: Clean and tidy the datasets

Tasks

  • Rename C_group_IM24_sh to Region, Country_code_A3 to Code, and ipcc_code_2006_for_standard_report_name to Industry in the corresponding African datasets.
  • Drop IPCC_annex, ipcc_code_2006_for_standard_report, and Substance from the corresponding datasets.
  • Melt Y_1970 to Y_2021 into a two columns Year and CO2. Drop rows where CO2 is missing.
  • Convert Year to int type.
# Rename columns in both datasets
columns_names = {'C_group_IM24_sh': 'Region', 
                 'Country_code_A3': 'Code', 
                 'ipcc_code_2006_for_standard_report_name': 'Industry'}

ipcc_2006_africa = ipcc_2006_africa.rename(columns=columns_names)

totals_by_country_africa = totals_by_country_africa.rename(columns=columns_names)


# Drop unrequired columns in both dataframes
drop_columns = ['IPCC_annex', 'ipcc_code_2006_for_standard_report', 'Substance']

ipcc_2006_africa = ipcc_2006_africa.drop(drop_columns, axis=1)

totals_by_country_africa = totals_by_country_africa.drop(['IPCC_annex', 'Substance'], axis=1)


#Melt the two dataframes into two columns 
ipcc_2006_africa = ipcc_2006_africa.melt(id_vars =['Region', 'Code', 'Name', 'Industry', 'fossil_bio'], var_name='Year', value_name= 'CO2')

totals_by_country_africa = totals_by_country_africa.melt(id_vars= ['Region', 'Code', 'Name'], var_name='Year', value_name='CO2')


#Drop null values in the CO2 column
ipcc_2006_africa.dropna(subset=['CO2'], inplace=True)

#Convert Year into the data type Int
#first remove 'Y_' from the years
ipcc_2006_africa['Year'] = ipcc_2006_africa['Year'].str.replace('Y_', '')
ipcc_2006_africa['Year'] = ipcc_2006_africa['Year'].astype(int)

totals_by_country_africa['Year'] = totals_by_country_africa['Year'].str.replace('Y_', '')
totals_by_country_africa['Year'] = totals_by_country_africa['Year'].astype(int)

print(ipcc_2006_africa.head())
print('')
print(totals_by_country_africa.head())

Instruction 2: Show the trend of CO2 levels across the African regions

Tasks

  • Using totals_by_country_africa, create a line plot of CO2 vs. Year in each Region to show the trend of CO2 levels by year.
# Trend of CO2 level emmissions per African 
sns.lineplot(x='Year', y='CO2', hue='Region', ci = None, data = totals_by_country_africa)
plt.ylabel('CO2 (kt)')
plt.title('CO2 levels across the African Regions between 1970 and 2021')

plt.show()

Instruction 3: Determine the relationship between time (Year) and CO2 levels across the African regions

Tasks

  • Using the totals_by_country_africa dataset, conduct a Spearman's correlation to determine the relationship between time (Year) and CO2 within each African Region.
  • Save the results in a variable called relationship_btw_time_CO2.
# Correlation between Year and CO2 per Region
relationship_btw_time_CO2 = totals_by_country_africa.groupby('Region')[['Year', 'CO2']].corr(method='spearman')

print(relationship_btw_time_CO2)

Instruction 4: Determine if there is a significant difference in the CO2 levels among the African Regions

Tasks

  • Using totals_by_country_africa, conduct an ANOVA using pingouin.anova() on the CO2 by Region. Save the results as aov_results.
  • Conduct a posthoc test (with Bonferroni correction) using pingouin.pairwise_tests() to find the source of the significant difference. Save the results as pw_ttest_result.
  • Is it true that the CO2 levels of the Southern_Africa and Northern_Africa region do not differ significantly? The previous task should provide you with the answer.
# Conduct ANOVA test on CO2 by Region
aov_results = pingouin.anova(data=totals_by_country_africa, dv='CO2', between='Region')

print(aov_results)

# Conduct pairwise_ttest on CO2 by Region
pw_ttest_result = pingouin.pairwise_ttests(data=totals_by_country_africa, dv='CO2', between='Region', padjust='bonf')

print('', pw_ttest_result, sep='\n')

# Is it true that the `CO2` levels of the `Southern_Africa` and `Northern_Africa` region do not differ significantly?

print('\nThe p-value for the contrast between this two regions is 0.079 which is greater that the usual significance level of 0.05, therefore it is okay to concude that CO2 between this two regions do not differ significantly')

Instruction 5: Determine the most common (top 5) industries in each African region.

Tasks

  • Group the ipcc_2006_africa data by Region and Industry.
  • Count the occurrences of each Industry within each Region and name it Count.
  • Sort the data within each region group by Count in descending order
  • Get the top 5 industries for each region
  • save it to variable top_5_industries for each region.
# Top FIVE industries in each African region
# Group data by Region and Industry and count occurrences of each Industry within each Region
grouped_ipcc_2006_africa = ipcc_2006_africa.groupby(['Region','Industry']).size().reset_index(name = 'Count')

# Sort data by Count in descending order in each region
grouped_ipcc_2006_africa_sorted = grouped_ipcc_2006_africa.sort_values(['Region', 'Count'], ascending=[True, False])

# Top 5 industries per region
top_5_industries = grouped_ipcc_2006_africa_sorted.groupby('Region').head(5).reset_index(drop = True)

print(top_5_industries)



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