CORRECTIONS

SUMMARY

From 2019 to 2021, global CO₂ emissions experienced significant fluctuations due to the COVID-19 pandemic. This exceptional period provides a unique opportunity to examine the evolution of the main drivers of emissions, as well as their environmental and socio-economic impacts. Through a comparative analysis based on key variables — including urban population, population density, precipitation, temperature, pollution index, and annual CO₂ emissions — this study explores the trends observed before, during, and after the pandemic. Ten countries with contrasting socio-economic and environmental profiles were selected for this analysis: Burkina Faso, Côte d’Ivoire, Nigeria, Senegal, Mali, Benin, Niger, Togo, Ghana and Cap vert. The global slowdown in human activity during the pandemic revealed the extent of the link between economic development and carbon emissions. This unprecedented context provides valuable insights into the mechanisms of CO₂ emissions and highlights new perspectives for sustainable and low-carbon development strategie.

INTRODUCTION

Carbon dioxide (CO₂), the main anthropogenic greenhouse gas, is a key indicator of environmental pressure. Largely produced by urbanization, industrial activity, and transportation, its emission levels closely reflect the intensity of human activities. Between 2019 and 2021, the COVID-19 pandemic abruptly disrupted these dynamics, leading to a significant global decline in CO₂ emissions.This unexpected shift presents a unique opportunity to examine the evolution of CO₂ emissions within a context of global slowdown. This study is framed around the following research question: What are the causes and impacts of CO₂ emissions, and how did the COVID-19 pandemic influence their trajectory ? The analysis draws on data from ten countries with diverse profiles, using variables such as population density, urbanization rate, temperature, precipitation, pollution index, and annual CO₂ emissions. The methodology is based on Principal Component Analysis (PCA) to identify key correlations among variables, supported by cartographic representation to highlight spatial and temporal contrasts across the study area.

Objectives of the Study Objective 1 : To identify and analyze the main anthropogenic and environmental factors driving CO₂ emissions in selected countries before, during, and after the COVID-19 pandemic.

Objective 2 : To assess the environmental and socio-economic impacts of CO₂ emissions across different regions and time periods.

Objective 3 : To evaluate the influence of the COVID-19 pandemic on the evolution of CO₂ emission patterns, highlighting any significant shifts or trends.

Objective 4 : To determine the statistical correlations between key variables such as urban population, density, temperature, precipitation, pollution index, and annual CO₂ emissions using Principal Component Analysis (PCA).

Objective 5 : To understand the environmental impact of the COVID-19 pandemic, particularly in relation to emission reductions and ecosystem responses. KEYWARDS AND DEFINITION

Comparative Study: An analytical approach that examines similarities and differences between multiple subjects. In this context, it refers to the comparison of CO₂ emissions and their drivers and impacts across different countries and time periods (before, during, and after COVID-19).

Causes of CO₂ Emissions: The primary factors responsible for the release of carbon dioxide into the atmosphere, including industrial activities, transportation, deforestation, urbanization, and energy consumption.[1]

Impacts of CO₂ Emissions: The consequences of elevated CO₂ levels on the environment and society, such as climate change, air pollution, public health issues, and economic disruptions.

Global Scale: The study covers multiple countries across different continents, aiming to capture both universal trends and regional specificities in CO₂ emissions and their effects.

COVID-19 Pandemic: A global health crisis caused by the SARS-CoV-2 virus, which began in late 2019 and significantly disrupted human activities worldwide. It serves as a key variable in this study due to its impact on economic activities and environmental indicators.

Before, During, and After COVID-19: A temporal framework used to observe the evolution of CO₂ emissions and related factors. “Before” refers to pre-pandemic conditions (2019), “during” to the peak years of disruption (2020), and “after” to the initial recovery phase (2021).

ASSESSMENT OF GLOBAL CO₂ EMISSIONS

This report provides an updated assessment of global carbon dioxide (CO₂) emissions trends up to 2011, extending last year’s analysis. The focus is on the changes in annual emissions from 2010 to 2011. Unlike the BP reports, which concentrate on fossil fuel combustion, this study incorporates a broader range of CO₂ emission sources. These include flaring of waste gas during oil production, cement clinker production, other limestone uses, non-energy fuel uses (such as feedstocks), and several smaller sources.[2]

CHANGES IN EMISSIONS BY COUNTRY

China, now firmly within the 6 to 19 tonnes per capita range[3], comparable to major industrial powers, reflects the ongoing scale of industrialization. During the study period, carbon emissions from transportation increased in the United States, China, India, Canada, Russia, and Brazil, while they decreased in Japan. This suggests that, in most of these countries, energy conservation strategies were not effectively implemented.

FACTORS INFLUENCING EMISSION INCREASES OR REDUCTIONS

In most countries, reductions in CO₂ emissions were primarily due to the decrease in carbon intensity, meaning more efficient energy use and the adoption of cleaner technologies. In contrast, emission increases were mainly driven by the structure of electricity generation (more or less dependent on fossil fuels) and economic growth. Therefore, to achieve sustainable emission reductions, optimizing energy structures and limiting the growth of private vehicle ownership are crucial.[4]

CASE STUDY: PHOENIX, ARIZONA

Recent research has highlighted a CO₂ “dome” in the Phoenix, Arizona metropolitan area, where CO₂ concentrations peak near the city center and are 50% higher than in the surrounding rural areas. This phenomenon is attributed to anthropogenic sources and the region’s physical geography. Human-related CO₂ emission data were collected from various government and NGO sources. Additionally, soil CO₂ efflux was measured across the dominant land-use types. Over 80% of emissions came from human activities, primarily automobile traffic. Natural desert ecosystems showed minimal emissions during hot, dry periods but responded rapidly to moisture. In contrast, human maintained vegetation types (such as golf courses, lawns, and irrigated agriculture) exhibited higher CO₂ efflux, which was both temperature and soil moisture dependent.[5]

COVID-19 AND THE IMPACT ON CO₂ EMISSIONS

Before the Pandemic Before COVID-19, global CO₂ emissions increased by about 1% per year, with significant growth in the transportation sector, while renewable energy was developing alongside fossil fuels.

Reduction in Emissions During Lockdowns Due to global lockdowns, daily global CO₂ emissions dropped by 17% in April 2020, mainly due to reduced transportation and industrial activities. The surface transportation sectorcontributed to nearly half of this reduction.[6]

IMPACT ON ANNUAL EMISSIONS

The reduction in annual emissions depended on the duration of the lockdowns. If conditions had returned to normal by mid-June, the decrease would have been around 4%. However, if restrictions had continued until the end of the year, the annual reduction could have reached 7%.[7]

Rebound After the Pandemic After the lockdowns were lifted, emissions rebounded, particularly in China and the United States, where economies restarted quickly, leading to a resurgence in CO₂ emissions.

Role of Post-COVID Policies Post-COVID economic policies, such as investments in renewable energy and the energy transition, will be crucial in determining whether the emission reductions observed during the pandemic can lead to lasting changes.

LESSONS FOR THE FUTURE

The pandemic showed that it is possible to temporarily reduce emissions, but for lasting impact, global policies aimed at decarbonization and reducing reliance on fossil fuels are essential.

CONSEQUENCES OF CO₂ EMISSIONS ON TEMPERATURE AND PRECIPITATION

The accumulation of CO₂ in the atmosphere leads to a global rise in temperatures, resulting in intensified heatwaves, ice melt, disruption of ecological balances, and increased evaporation of water resources. This dynamic heightens the vulnerability of both natural and human systems, particularly in arid or densely populated areas.

At the same time, precipitation patterns become more irregular and extreme. Some regions experience prolonged droughts, while others face intense and destructive rainfall events. These disruptions compromise food security, weaken water resources, degrade soils, and increase the risk of flooding.

These climate transformations trigger a cascade of impacts, affecting agriculture, biodiversity, water resources, infrastructure, and public health. They worsen food insecurity, intensify resource-related tensions, and increase the vulnerability of populations to climate-related hazard.[8]

The COVID-19 pandemic provided a unique insight into CO₂ emissions dynamics, highlighting their main causes and impacts. While lockdowns temporarily reduced emissions, the decline was not structural. However, the crisis demonstrated that rapid changes are possible, particularly through remote work and reduced travel. To achieve a lasting transition, investments in renewable energy and low-carbon policies are essential. This period should serve as a catalyst for accelerating the necessary transformations to combat climate change.

#II.TOOLS USED

KOBO TOOLBOX Kobo Toolbox is a suite of open-source tools designed to simplify the data collection process. It enables users to create and deploy forms and to collect data using various channels, such as webforms, mobiles, and tablets. It supports offline data collection, enabling users to collect data in remote areas without internet connectivity. Kobo Collect is the mobile app component of Kobo Toolbox and allows users to collect data on Android devices. It supports multiple languages, multimedia files, and collection of geospatial data. It helps monitor data .

RSTUDIO RStudio is an integrated development environment (IDE) for the R and Python programming languages. It offers a console, a syntax-highlighting editor, tools for direct code execution, as well as features for tracing, history, debugging, and workspace management. RStudio is available in both open-source and commercial versions, running on Windows, Mac, and Linux. Zotero Zotero is a free and open-source reference management software. It allows users to collect, organize, annotate, cite, and share research. Available for Mac, Windows, Linux, and iOS, Zotero facilitates the quick creation of bibliographies and provides automatic citation features in various styles.[9]

QGIS QGIS (Quantum Geographic Information System) is a free and open-source Geographic Information System (GIS) software. It enables users to create, edit, visualize, analyze, and publish geospatial information. QGIS is valued for its flexibility and support for multiple data formats, including raster and vector formats. MICROSOFT EXCEL Microsoft Excel is a spreadsheet program used to store, organize, and manipulate data. It represents data in tables composed of rows and columns, where each cell can contain different types of data, including text and numbers. Excel allows users to perform calculations using built-in formulas and functions, facilitating financial analysis, budgeting, and expense track #III.JUSTIFICQTION OF VARIABLES URBAN POPULATION The growth of the urban population is closely linked to increased economic activity, transportation, and energy consumption. This variable helps to understand how urbanization contributes to rising CO₂ emissions, especially in large cities.

DENSITY (POPULATION DENSITY) High population density can lead to a concentration of emission sources (transportation, buildings, industries). It also influences pressure on infrastructure and resources. This variable helps assess whether more densely populated areas emit proportionally more CO₂.

CO₂ EMISSIONS (TOTAL CO₂EMISIONS) This is the study’s primary indicator. It tracks the overall evolution of CO₂ emissions in each country, comparing the periods before, during, and after the pandemic. It is essential for identifying general trends.

POLLUTION INDEX This indicator reflects air quality by taking into account several pollutants, including CO₂. It helps link emissions to environmental and health impacts, complementing purely quantitative data.

PRECIPITATION Climate change induced by greenhouse gases can disrupt rainfall patterns. This variable examines whether changes in CO₂ emissions are associated with observed variations in precipitation.

TEMPERATURE CO₂ is a greenhouse gas contributing to global warming. Studying temperature trends helps to better understand the potential climatic impacts of emissions and to identify long-term correlations.[10]

CO₂ BY TRANSPORT Transportation is one of the largest contributors to CO₂ emissions, especially in urban areas. This variable measures the impact of mobility restrictions during the pandemic on emissions from this sector.[11]

CO₂ BY AGRICULTURE Agriculture contributes to emissions through deforestation, the use of nitrogen-based fertilizers, and machinery. This variable is key to analyzing a sometimes overlooked yet significant sector, particularly in countries with strong agricultural activity.

CO₂ BY INDUSTRY Industry is a major source of CO₂ due to its intensive use of fossil fuels. This variable helps evaluate the effects of industrial slowdown during the pandemic and the subsequent economic recovery.

CO₂ BY BUILDING This variable includes emissions from residential and commercial energy use (heating, air conditioning, electricity). It is relevant to understanding the effects of lockdowns on residential energy consumption.

Conclusion:These ten variables were selected to allow a comprehensive analysis combining demographic, environmental, and sectoral factors. They enable a detailed understanding of the causes of emissions, their temporal variations, and their environmental impacts in connection with the COVID-19 pandemic

setwd("C:/Users/HP/Desktop/DONNEES")

data = read.csv(file ="VARS2020NC.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:10]
##      Urb.Pop      Dens CO2.Emiss air_poll_CO     Precip  Temp.var Trans_CO2
## BFA  6573983  78.50399   5377314   967355.70  700.36096 0.3413738    760000
## BEN  6327922 115.91141   5517598   758637.56  968.50397 0.2877359   6340000
## CIV 14951022  90.92909  11015523  1588104.60 1150.19200 0.4674681   4270000
## CPV   343044 127.71315    516624    22725.79  217.64297 0.4147606    230000
## GHN 18287340 140.14583  19167768  1005131.10 1019.40770 0.5852895   9420000
## MLI  9534328  17.79546   6447858   461399.72  276.58563 0.5571928   4070000
## NER  3943290  18.72394   2461085   578675.75   96.28542 0.4446030   1160000
## SEN  8079308  49.92406  10820952   358039.94  688.11395 0.6394126   3270000
## TGO  3710640 159.39919   2438790   670994.06 1132.68330 0.5037856   1170000
##     Agri_CO2 Indus_CO2 CO2.build
## BFA   650000   2190000   1560000
## BEN  5900000   1540000   1050000
## CIV  6480000   3990000   2520000
## CPV   110000     20000     40000
## GHN 11520000   1490000   1620000
## MLI 37580000    970000   1840000
## NER 33460000    800000   1130000
## SEN  1320000     10000   4550000
## TGO  3370000   1860000    600000
setwd("C:/Users/HP/Desktop/DONNEES")

data = read.csv(file ="VARS2019NC.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:10]
##       Urb.Pop      Dens    CO2.Em  air.by.CO2    Precip    Temp.var CO2.trans
## BFA   6284393  76.61533   5620995   956987.80  645.5397  0.38889837    890000
## BEN   6091152 112.86592   5262159   739467.06 1217.6688  0.35727072   6070000
## CIV  14445816  88.65728  10535081  1548778.60 1446.8370  0.37437996   4340000
## CPV    346682 127.57221    630208    22809.67  113.8136 -0.21894820    250000
## GHN  18855948 137.38202  16418407   990579.44 1340.6803  0.64045095   8750000
## MLI   9088067  17.26650   5832595   455910.40  284.7253  0.09500599   3470000
## NER   4104353  18.11618   2513365   538513.25  122.5363  0.43761715   1200000
## NGA 107166569 230.00937 133823544 23266410.00 1263.9337  0.19188373  60640000
## SEN   7792658  84.93703  12713010   337061.53  454.4737  0.20600843   3770000
## TGO   3849488 155.59970   2318589   645950.50 1460.9044  0.59815997    890000
##     CO2.agri CO2.indus CO2.buld
## BFA   630000   2300000  1650000
## BEN  5950000   1450000  1030000
## CIV  6260000   3700000  2540000
## CPV   110000     20000    50000
## GHN 10280000   1380000  1550000
## MLI 35860000   1690000   930000
## NER 31850000    750000  1030000
## NGA 79810000  25690000 40780000
## SEN   960000     10000  4320000
## TGO  3040000   1840000   590000
setwd("C:/Users/HP/Desktop/DONNEES")

data = read.csv(file ="VARS2021NC.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:10]
##       Urb.Pop      Dens CO2.Emiss air_poll_CO     Precip   Temp.var Trans_CO2
## BFA   6871314  80.39199   6289259   978931.70  474.56122 0.73675490    900000
## BEN   6568819 118.95551   5759265   735911.70  848.26470 0.75479954   4360000
## CIV  15466014  93.20671  13669025  1629780.10 1263.70950 0.49540900   5160000
## CPV    346682 128.20224    545936    22600.85  142.82065 0.05482833    230000
## GHN  18855948 142.91844  20546160  1074800.40 1057.19100 0.73517436   1570000
## MLI  10002568  18.34849   6885983   467310.00  189.68820 1.11214000   4200000
## NER   4104353  19.34329   2712771   596817.00   53.87112 0.72120285    850000
## NGA 115265457 239.93904 135070380 23266248.00  888.83246 0.43889174  58980000
## SEN   8369341  89.44511  12309421   366049.84  462.96760 0.70436810   3890000
## TGO   3849488 163.23560   2670490   687294.06 1074.55210 0.82518244   1970000
##     Agri_CO2 Indus_CO2 CO2.build
## BFA   700000   2590000   1670000
## BEN  5890000   1600000    860000
## CIV  6460000   4220000   2620000
## CPV   110000     20000     50000
## GHN 10550000   1570000   1730000
## MLI 38500000   1950000   1000000
## NER 34940000    850000   1270000
## NGA 80290000  30380000  43520000
## SEN  1520000     10000   4830000
## TGO  3120000   1970000    610000
library(FactoMineR)
## Warning: package 'FactoMineR' was built under R version 4.4.3
pca_1 = PCA(X = data, scale.unit = TRUE, ncp = 10, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))

# Projections of variables on the axes:

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(factoextra)
## Warning: package 'factoextra' was built under R version 4.4.3
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
fviz_pca_var(pca_1, col.var = "cos2" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

fviz_pca_var(pca_1, col.var = "contrib" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

# Launching the PCA

library(FactoMineR)
pca_1 = PCA(X = data, scale.unit = TRUE, ncp = 10, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))
# Projections of variables on the axes:

library(ggplot2)
library(factoextra)
fviz_pca_var(pca_1, col.var = "cos2" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

fviz_pca_var(pca_1, col.var = "contrib" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

# Launching the PCA

library(FactoMineR)
pca_1 = PCA(X = data, scale.unit = TRUE, ncp = 10, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))
# Projections of variables on the axes:

library(ggplot2)
library(factoextra)
fviz_pca_var(pca_1, col.var = "cos2" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

fviz_pca_var(pca_1, col.var = "contrib" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

IN Africa Agriculture is an important sector .IN 2020 due to COVID 19 this sector probably was affects we can notice difference in the Circle of correlation. So we can confirm the covid 19 Impact but this is positive impact because we save our climate. But we notice in 2021 the pollution situation restarts to be the same.

Details In 2019, the variables were generally correlated. Population density contributed to the formation of the second axis, indicating its influence on the spatial variability of the data.

In 2020, the Covid effect disrupted this structure: the transport variable was no longer correlated, likely due to lockdowns. Density no longer contributed to the second axis, reflecting a reduced explanatory role due to the widespread decline in activity.

In 2021, there was a partial return to the pre-Covid structure: density once again contributed to axis 2. However, a new pattern emerged—temperature became correlated with precipitation, which was not the case in 2019. This may be explained by climate change or specific weather conditions that year.

# Classification of individuals

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)

plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

I. Detailed Analysis

  1. Country Distribution

NGA (Nigeria) is positioned far to the right on Dim1, with a very strong contribution (red color), which means it stands out significantly from other countries, likely due to high values in variables such as CO₂ emissions, urban density, or urban population. Countries like (CIV, SEN, MLR, BFA…) are clustered on the left and near the center, suggesting they have relatively low and similar values for the studied variables.

  1. Interpretation of the Dimensions

Dim1 (76.61%) likely captures the overall level of emissions or industrialization (countries further to the right are likely higher emitters).

Dim2 (13,27%) captures finer distinctions, potentially related to urban management or infrastructure.

REMARK The graph of individuals provides an overview of the similarities and differences between individuals. Then we can see the positions of the following countries: NGA located at the extreme right of the Dim 1,2 axis, it has very different characteristics from the other countries for this dimension; This graph helps identify groups of countries with marked similarities or differences along the main dimensions of the ACP. We find that NGA behave as atypical elements. For a better result, we removed NGA. After removing these countries from our data, we obtain the graph of individuals After a comparative study, we observed the impact of COVID-19 on our data. To achieve a better Principal Component Analysis (PCA), we decided to exclude NGA to prevent a single variable from dominating a class. We will resume the study to ensure a proper grouping and a relevant cartographic representation. For this part, we will focus on the year 2020.

ACP RESULTS 2020 Matrice of correlation

setwd("C:/Users/HP/Desktop/DONNEES")

data = read.csv(file ="VARS2020NC.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:10]
##      Urb.Pop      Dens CO2.Emiss air_poll_CO     Precip  Temp.var Trans_CO2
## BFA  6573983  78.50399   5377314   967355.70  700.36096 0.3413738    760000
## BEN  6327922 115.91141   5517598   758637.56  968.50397 0.2877359   6340000
## CIV 14951022  90.92909  11015523  1588104.60 1150.19200 0.4674681   4270000
## CPV   343044 127.71315    516624    22725.79  217.64297 0.4147606    230000
## GHN 18287340 140.14583  19167768  1005131.10 1019.40770 0.5852895   9420000
## MLI  9534328  17.79546   6447858   461399.72  276.58563 0.5571928   4070000
## NER  3943290  18.72394   2461085   578675.75   96.28542 0.4446030   1160000
## SEN  8079308  49.92406  10820952   358039.94  688.11395 0.6394126   3270000
## TGO  3710640 159.39919   2438790   670994.06 1132.68330 0.5037856   1170000
##     Agri_CO2 Indus_CO2 CO2.build
## BFA   650000   2190000   1560000
## BEN  5900000   1540000   1050000
## CIV  6480000   3990000   2520000
## CPV   110000     20000     40000
## GHN 11520000   1490000   1620000
## MLI 37580000    970000   1840000
## NER 33460000    800000   1130000
## SEN  1320000     10000   4550000
## TGO  3370000   1860000    600000
# Creation of correlation matrix
library(car)  # regression linéaire
## Warning: package 'car' was built under R version 4.4.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.4.3
library(carData)  # regression linéaire
library("clusterSim")
## Warning: package 'clusterSim' was built under R version 4.4.3
## Loading required package: cluster
## Loading required package: MASS
library(DataExplorer)
## Warning: package 'DataExplorer' was built under R version 4.4.3
library(FactoInvestigate)
## Warning: package 'FactoInvestigate' was built under R version 4.4.3
attach(data)
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.4.3
## corrplot 0.95 loaded
library(FactoMineR)  # lancement ACP
library(corrplot)
library(psych)
## Warning: package 'psych' was built under R version 4.4.3
## 
## Attaching package: 'psych'
## The following object is masked from 'package:car':
## 
##     logit
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
mat_cor = cor(data)
col = colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(mat_cor, method="color", col=col(200),  
         type="upper", order="hclust", 
         addCoef.col = "black", # Ajout du coefficient de corrélation
         tl.col="black", tl.srt=160, #Rotation des étiquettes de textes
         , sig.level = 0.1, insig = "blank", 
         # Cacher les coefficients de corrélation sur la diagonale
         diag=FALSE)

#rcorr(as.matrix(mat_cor[,1:10]))
# MATRICE DE CORRELATION ENTRE LES VARIABLES

pairs(data[, 1:10])  # Matrice de nuages de points

mat_cor <- cor(data[, 1:10])  # Matrice des corrélations de Pearson

• Colors: • Dark blue → Strong positive correlation (close to 1). • Red → Negative correlation (close to -1). • White → Weak correlation (close to 0). • Examples: • CO2.Emiss and Trans_CO2 have a very strong correlation (0.83), indicating they tend to move together. • Temp.var shows a negative correlation with some variables like indus_CO2 (-0.26), meaning that higher temperature variability is associated with lower precipitation. • Air pollution and Indus_CO2 are strongly correlated (0.94), suggesting that urban growth is linked to industrial CO2 emissions.

# Projections of variables on the axes:

library(ggplot2)
library(factoextra)
fviz_pca_var(pca_1, col.var = "cos2" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

fviz_pca_var(pca_1, col.var = "contrib" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )

The two graphs show a Principal Component Analysis (PCA). The first displays the quality of representation of the variables (cos²), and the second shows their contribution to the principal axes. Dim1 (43.1%) is related to urbanization,air pollution, and CO2 emissions (transport, buildings, industry). Dim2 (25.9%) appears to reflect climatic factors such as precipitation and temperature variation. Variables like CO2.build, Urb.Pop, air.pol, and ind.CO2 are linearly linked and strongly associated with Dim1. Precip and Temp var are opposed to pollution-related variables, suggesting an inverse relationship between climate évolution . DENDROGRAM The dendrogram below shows us that 3 main classes are created according to the similarities of individuals Dendrogram The dendrogram below shows us that 3 main classes are created according to the similarities of individuals

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)

plot.HCPC(res.HCPC,choice='tree',title='Arbre hiérarchique')

The three (3) classes thus created are: Based on the hierarchical dendrogram shown in the image and the study on CO₂ pollution, here is a suggested naming for the three classes, taking into account the grouping of countries: Class 1: CIV, GIN (Côte d’Ivoire, Guinea) Suggested name: Coastal countries with moderate industrialization These countries are located on the West African coast, with progressive economic growth and relatively moderate levels of CO₂ emissions. Their urbanization is increasing but remains moderate compared to other groups. Class 2: CPV, TGO, BFA, BEN (Cape Verde, Togo, Burkina Faso, Benin) Suggested name: Low-emission and climate-vulnerable countries This group mainly includes countries with low CO₂ emissions, modest levels of industrialization and urbanization, and high vulnerability to climate change impacts. Class 3: SEN, NER, MLI (Senegal, Niger, Mali) Suggested name: Sahelian countries with very low emissions These Sahel region countries are characterized by very low CO₂ emissions, often due to limited industrialization, but are highly exposed to climate hazards such as drought.

Class 1(Coastal countries with moderate industrialization): CIV, GHN Class 2(: Low-emission and climate-vulnerable countries): CPV, TGO, BFA, BEN Class 3(Sahelian countries with very low emissions) : SEN, NER, MLI

THE FACTORIAL PLAN The factorial design projects countries into the space of the first two principal dimensions (Dim1 and Dim2)

# Classification of individuals

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

3D REPRESENTATION OF HIERARCHICAL CLASSIFICATION

# Classification of individuals

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='3D.map',ind.names=FALSE,centers.plot=FALSE,angle=60,title='Arbre hiérarchique sur le plan factoriel')

PART IV. LINEAR REGRESSION DEFINITION Linear regression is a statistical method that models the relationship between a dependent variable (or response) and one or more independent variables (or predictors). It is based on the assumption that this relationship is linear, that is, that a change in the independent variable results in a proportional change in the dependent variable. SELECTION OF VARIABLES We will use the following variables: CO2 emission Transport

# Graphe de la régression linéaire simple
ggplot(data, aes(x =Trans_CO2 , y =CO2.Emiss)) +
  geom_point(color = "blue") +
  geom_smooth(method = "lm", se = TRUE, color = "red") +
  ggtitle("Régression linéaire : Effet de CO2.Em sur CO2.trans") +
  xlab("Trans_CO2") + ylab("CO2.Emiss") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

INTERPRETATION The image shows a linear regression graph illustrating the relationship between CO2 emission and Trans_CO2 (CO2 emissions from transportation). Graph Analysis: 1. Blue points: These represent observed data. Each point corresponds to a (CO2 emission, Trans_CO2) pair. 2. Red line: This is the linear regression line, which represents the general trend between transport emissions and CO2 emissions. 3. Gray area: This is the confidence interval, showing the uncertainty around the regression.

Interpretation: • The red line shows an increasing trend: as the urban population grows, CO2 emissions also increase. • The gray area is wider at the extremes, indicating more uncertainty in those regions. • The blue points do not perfectly align with the red line, meaning there is some data dispersion (suggesting that other factors influence CO2 emissions besides Transport emission). CONCLUSION: There is a positive relationship between CO2 from transport and CO2 emissionthe larger the urban population, the higher the emissions. However, the data dispersion indicates that the relationship is not perfect and that other variables may also play a role.

V : PRESENTATION OF SPATIAL

RESULT ON A MAP

I. STUDIED AREA

LIEN DU QUESTIONNAIRE

https://ee.kobotoolbox.org/x/WC3Mxbdj

Conclusion

The analysis conducted on the causes and impacts of CO₂ emissions before, during, and after the COVID-19 pandemic—based on a panel of ten countries—highlighted significant correlations between CO₂ emissions and variables such as population density, urbanization, pollution index, precipitation, and temperature. The Principal Component Analysis (PCA), carried out using R Studio, revealed insightful data structures, although the presence of two atypical individuals required their exclusion to ensure the robustness of the analysis.

Moreover, the methodological approach combined various digital and statistical tools: data mapping, source management with Zotero,questionnaire design using KoboCollect, and a thorough bibliographic review all contributed to enriching the study. Overall, this research provided a deeper understanding of CO₂ emission dynamics, their drivers, and their evolution in the unique context of the pandemic, while also opening up perspectives for future, more effective environmental policies

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