Comparative Analysis of the Energy Transition and Electricity
Access in West Africa
Comparative Analysis of the Energy Transition and Electricity Access in West Africa
Author(s) :
Introduction
Access to electricity and the energy transition are now two major challenges for sustainable development in West Africa. Despite notable progress over the past two decades, the region still faces a significant energy deficit: a substantial portion of the population, particularly in rural areas, remains without reliable and affordable access to electricity. This situation not only limits economic growth prospects but also hinders improvements in living conditions and the integration of new technologies.
At the same time, the energy transition is becoming a necessity in the face of environmental challenges and the persistent reliance on fossil fuels. West African countries, however, possess considerable potential in renewable resources, particularly solar, hydroelectric, and wind energy. The challenge, therefore, is to reconcile the expansion of electricity access with a diversification of the energy mix, in order to meet growing needs while reducing the carbon footprint.
This research project fits within this framework by proposing a comparative analysis of the policies, strategies, and outcomes observed in different West African countries. The objective is to highlight the convergences and divergences in terms of energy transition and electrification, to identify the factors of success or failure, and to outline pathways for a more coherent and sustainable regional approach. This study thus aims to contribute to a better understanding of West African energy challenges and to inform reflection on solutions adapted to local realities.
I. Research Problem and Formulation of Research Questions
Theme: Comparative Analysis of the Energy Transition and Electricity Access in West Africa
o Research Problem: In a global context of energy transition, West Africa faces major challenges regarding electricity access, grid reliability, and the integration of renewable energy. How can disparities between countries in terms of electricity access, energy production, and the use of clean energy be analyzed in order to propose recommendations for a sustainable energy transition?
o Research Questions :
- What is the current state of electricity access in different West African countries, and how is it evolving?
- What are the main determinants of electricity production (fossil fuels, renewables, hydro, solar, etc.)?
- How do countries differ in their adoption of renewable energy?
- What correlations exist between the level of electrification, economic growth, and human development?
- Which energy policies could be recommended to improve access to clean, reliable, and affordable energy?
II. Materials and Methods
1. Materials Used
Introduction to Zotero
Zotero is a free and open-source reference management software that allows users to collect, organize, and cite academic references. It is multiplatform (Windows, Mac, Linux) and integrates directly with Word, LibreOffice, and Google Docs to insert citations and automatically generate bibliographies. In the context of our project, it was used to produce the bibliography.
Introduction to RStudio
RStudio is an integrated development environment (IDE) designed for the R programming language. R is a language specialized in statistical processing, data analysis, and graphical visualization. The software provides a user-friendly interface that facilitates writing scripts, executing calculations, creating graphics, and managing analytical projects. In the context of the project, it was used to analyze statistical data, process databases, and visualize results.
Introduction à QGIS
Quantum GIS (QGIS) is a free and open-source Geographic Information System (GIS) software.
It allows users to visualize, edit, and analyze geospatial data, including maps, satellite images, GPS data, and geographic databases.
For our project, the software allowed us to map the representative maps of the variables used throughout the work.
2. Methods Used
a) Presentation of the Experimental Framework
Our study was based on the West African countries of Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mauritania, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo.
First, we identified our study variables using the Our World in Data database. We thus obtained data on the different countries, which serve as our observations. These data were then analyzed and interpreted.
b) Description of Methodological Tools
Data collection was carried out on the following websites: https://ourworldindata.org/ and https://www.worldbank.org.
Next, we transferred the data to RStudio for Principal Component Analysis (PCA). In parallel, we performed the mapping of our data using RStudio.
III. Literature Review
Context and Objective
Access to modern, reliable, and affordable energy is a fundamental pillar of socio-economic development and a key goal of the 2030 Sustainable Development Agenda (SDG 7). Yet, access remains a major challenge for many regions of the world, particularly in West Africa.
The most recent data illustrate the extent of this situation. In 2021, the average electricity access rate in West African countries was 51.3%, ranging from 18.7% in Niger and 19% in Burkina Faso to 93.7% in Cape Verde and 86.3% in Ghana, well below the global average of 91.4%. In 2011, this rate was 37.1%, with the countries experiencing the highest growth over the decade being Liberia, Guinea-Bissau, and Sierra Leone (Trésor, Economie Gouv).
Beyond national borders, a deep energy divide also exists within countries, between urban and rural areas. The case of Senegal is illustrative in this regard. In 2021, while 97% of urban residents had access to electricity, this rate dropped to only 18% in rural areas (DataWorld Bank). This inequality in access perpetuates a cycle of poverty, hampers agricultural development, and limits access to essential services such as health and education, making rural electrification a top priority for governments in the region.
Within this framework, this study aims to conduct a comparative analysis of the situation in the 16 West African countries. By cross-referencing data on electricity access, the energy mix, socio-economic development indicators, and environmental impact, it seeks to identify the levers that will enable the region to address its energy challenge while pursuing a path of more sustainable and resilient development.
Current Status and Disparities in Electricity Access
Access to electricity remains a major challenge for the development of West Africa, a region where energy demand is rapidly increasing due to population growth and urbanization. Despite notable progress over the past decade, the situation is characterized by overall low electrification and deep disparities, both between countries and between urban and rural areas.
In 2021, the average electricity access rate in West African countries was only 51.3% (World Bank, 2023). This figure is well below the global average of 91.4% and highlights the region’s significant lag. However, this regional average masks extremely contrasting national situations. A clear divide is observed between countries such as Cape Verde (93.7%) and Ghana (86.3%), which are approaching universal access, and Sahelian countries like Niger (18.7%) and Burkina Faso (19.0%), where the vast majority of the population remains without this basic service (World Bank, 2023). This heterogeneity reflects differences in political stability, investment capacity in infrastructure, and the implementation of energy policies.
Beyond national borders, a deep energy divide exists between urban and rural areas. Rural electrification remains the region’s main challenge. The data are unequivocal: while capitals and major cities are often well served, rural areas are largely neglected. In Senegal, for example, the access rate was 97.4% in urban areas compared to only 18.2% in rural areas in 2021 (World Bank, 2023). This stark disparity, observed in most countries of the region, is explained by the prohibitive costs of extending the national grid to low-density areas, making electrification projects unattractive for private operators (Abdul-Salam & Phimister, 2021). This situation perpetuates a cycle of poverty, limits access to education and healthcare, and hinders the development of economic activities in rural areas.
Access to Energy: A Pillar of Socio-Economic Development
Access to electricity is a crucial driver of socio-economic development in West Africa, where its impact goes far beyond mere convenience to directly influence economic growth and the well-being of the population.
The correlation between energy and growth is well established. According to Mensah (2021), a 1% increase in per capita electricity consumption in Sub-Saharan Africa leads to an approximate 0.3% growth in per capita GDP. This relationship is explained by electricity’s role as an essential input across all productive sectors, from agriculture to industry. World Bank data (2023) confirm that West African countries with the highest per capita energy use generally exhibit the highest per capita GDP.
The impact on the Human Development Index (HDI) is equally significant. Electrification enables the operation of medical equipment and the refrigeration of vaccines, thereby improving health indicators (UNDP, 2022). In education, access to electricity promotes school attendance and digital access, as demonstrated by Lenz et al. (2019) in Senegal, where rural electrification boosted secondary school enrollment rates.
Accessible energy transforms local economies by enabling the development of micro-enterprises and improving agricultural productivity. The World Bank (2020) emphasizes that the transition to electricity reduces energy poverty, frees up time for women and children, and decreases health problems related to indoor air pollution.
Thus, addressing the energy deficit in West Africa is far more than a technical challenge: it is an essential condition for unlocking economic development potential and sustainably improving the living conditions of the population.
Energy Mix, Renewable Potential, and Dependence
The West African energy mix remains dominated by fossil fuels, with thermal power plants providing the bulk of electricity production (IEA, 2022). This dependence creates vulnerability to international hydrocarbon prices and places a burden on the economies of importing countries.
However, the region possesses exceptional renewable potential. Hydropower remains the main renewable source, but photovoltaic solar represents the most promising opportunity, with a potential capable of covering several times the current regional demand (IRENA, 2022). Despite this, the share of modern renewable energy in the electricity mix remains marginal, often below 5% (World Bank, 2023).
The energy transition faces several major challenges: high initial investment costs, intermittency of renewable sources, and insufficient regulatory frameworks (ECREEE, 2021). The West African Power Pool (WAPP) seeks to address these issues by promoting grid integration and resource pooling (WAPP, 2021).
Thus, realizing West Africa’s energy potential requires accelerated investments, a more favorable regulatory framework, and strengthened regional cooperation to reduce dependence on fossil fuels. This energy growth, still largely reliant on fossil fuels, is driving a rapid increase in emissions. The annual CO2 emission rate rose by 40% between 2010 and 2021 (Our World In Data, 2023), while deforestation for fuelwood reaches 2.5% annually in some countries such as Burkina Faso and Niger (FAO, 2022).
Urban air pollution frequently exceeds WHO limits (2023), posing major health challenges. Yet, urban renewable potential remains underutilized, with less than 15% of solar capacity connected to urban grids (IRENA, 2023). Integrated solutions combining energy efficiency and renewable energy could reduce urban emissions by 30% (AfDB, 2023), but they require strengthened municipal capacities and innovative financing.
Synthesis and Identification of the Research Gap
Our literature review reveals that the energy issue in West Africa is characterized by complex interrelationships across several critical dimensions. First, there is a close and bidirectional link between electricity access and socio-economic development, where energy acts both as a driver and a consequence of growth (Mensah, 2021; World Bank, 2023). Second, the region’s energy mix, still largely dependent on fossil fuels, presents economic vulnerability while offering considerable and underutilized renewable potential, particularly in solar energy (IEA, 2022; IRENA, 2022). Finally, the dynamics of urbanization exacerbate demand and intensify environmental pressures, such as rising CO2 emissions and deforestation, creating a pressing dilemma between development and sustainability (United Nations, 2022; Our World in Data, 2023).
While the literature is rich in national case studies or sectoral analyses (either purely technical or economic), a major gap remains. Few studies provide a comparative, multidimensional, and quantitative analysis that simultaneously integrates all key variables—electricity access rates, GDP per capita, Human Development Index (HDI), share of renewable energy, CO2 emissions, and urbanization rates—across all 16 countries in the region in a synchronized manner (ECREEE, 2021; IEA, 2022). Existing approaches tend to isolate these dimensions, preventing a holistic understanding of national energy profiles and potential synergies.
Our research aims precisely to fill this gap. It proposes an integrated methodology that cross-references all these variables to develop a robust typology of West African countries. By using advanced quantitative analysis methods (such as factor analysis and classification), we will identify groups of countries sharing similar characteristics in terms of energy performance, development, and environmental impact. This unprecedented socio-energy mapping will allow us to move beyond generalizations and formulate differentiated and targeted public policy recommendations, tailored to the specific challenges and opportunities of each identified country group, thereby contributing to a more effective roadmap for the regional energy transition.
#IV. Design of Data Collection Tools
1. Data Collection
To gather our data, we used the KoboCollect application to design questionnaires. The ultimate goal was to administer these questionnaires to a sample of the population.
Research Project and Information Processing (RTI) S7 GEE Group 2
Comparative Study of Electricity Access in West Africa
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#I. General Information
2. Presentation of the Maps
Figure 1: Annual CO2
Emissions
The map illustrates the variation in annual CO2 emissions in West Africa between 2000 and 2023. The countries with the highest emissions are Nigeria, Gambia, Ghana, Mali, Senegal, and Côte d’Ivoire, indicating that they emitted the most CO2 during this period. This suggests a higher level of development in countries with high CO2 production compared to those in the mid-range category, such as Niger, Benin, Burkina Faso, Mauritania, and Guinea. The countries with the lowest annual CO2 emissions are Togo, Cape Verde, Liberia, Sierra Leone, and Guinea-Bissau.
Figure 2: CO2 Emissions per Capita
This map shows the variation in per capita CO2 emissions in West Africa between 2000 and 2023. The countries with the highest per capita emissions are Benin, Mauritania, Gambia, Senegal, and Ghana. This indicates that population size also influences CO2 emissions, meaning that countries leading in total annual CO2 production are not necessarily the leaders in per capita emissions. The mid-range category includes Mali, Burkina Faso, Sierra Leone, and Guinea. Finally, the countries with the lowest per capita CO2 emissions are Togo, Liberia, Niger, Nigeria, Côte d’Ivoire, and Guinea-Bissau.
Figure 3: Electricity Generation
The map shows the variation in electricity generation in West Africa between 2000 and 2023. The countries with the highest production are Nigeria, Gambia, Ghana, Mali, Senegal, and Côte d’Ivoire. These countries experienced the greatest growth in electricity generation during this period compared to the mid-range category, which includes Benin, Togo, Burkina Faso, Mauritania, and Guinea. The low category comprises Niger, Liberia, Cape Verde, Sierra Leone, and Guinea-Bissau, which are the countries with the lowest electricity generation rates.
Figure 4: GDP per Capita
The map illustrates the variation in GDP per capita in West Africa between 2000 and 2023. The countries with the highest GDP per capita are Nigeria, Ghana, Guinea, and Côte d’Ivoire, indicating a population with a higher standard of living and greater access to electricity compared to the mid-range category, which includes Benin, Burkina Faso, Mauritania, Senegal, Sierra Leone, and Gambia. The countries with the lowest GDP per capita are Togo, Liberia, Niger, Mali, Cape Verde, and Guinea-Bissau.
Figure 5: Net Electricity Imports
The map depicts the variation in electricity imports in West Africa between 2000 and 2023. The countries with the highest levels of electricity imports are Nigeria, Niger, Burkina Faso, and Togo. The mid-range category includes Benin, Mali, Côte d’Ivoire, Mauritania, Senegal, and Gambia. Finally, the countries with the lowest import levels are Ghana, Liberia, Niger, Sierra Leone, Cape Verde, Guinea, and Guinea-Bissau.
Figure 6: Primary Energy Consumption
The map illustrates the variation in primary energy consumption in West Africa between 2000 and 2023. The countries in the highest category are Benin, Ghana, Mali, and Mauritania. The mid-range category includes Nigeria, Burkina Faso, Côte d’Ivoire, Guinea, Senegal, and Gambia. Finally, the countries in the lowest category are Togo, Liberia, Sierra Leone, Guinea-Bissau, Cape Verde, and Niger.
Figure 7: Electricity Production from Renewables
The map illustrates the variation in electricity production from renewable sources in West Africa between 2000 and 2023. The countries in the highest category are Sierra Leone, Liberia, and Guinea. The mid-range category includes Benin, Mali, Mauritania, Guinea-Bissau, Guinea, Senegal, Liberia, Sierra Leone, and Gambia. Finally, the countries in the lowest category are Togo, Ghana, Burkina Faso, and Nigeria.
Figure 8: Electricity Production from Hydropower
The map shows the variation in hydropower electricity production in West Africa between 2000 and 2023. The countries in the highest category are Sierra Leone, Liberia, Guinea, and Mali. The mid-range category includes Benin, Mauritania, Guinea-Bissau, Senegal, Côte d’Ivoire, Niger, and Gambia. Finally, the countries in the lowest category are Togo, Ghana, Burkina Faso, and Nigeria.
Figure 9: Share of Population with Access to Electricity
The map shows the variation in the population’s access to electricity in West Africa between 2000 and 2023. The countries in the highest category are Togo, Ghana, Guinea-Bissau, and Mali. The mid-range category includes Benin, Mauritania, Guinea, Senegal, and Gambia. Finally, the countries in the lowest category are Niger, Sierra Leone, Burkina Faso, Nigeria, Côte d’Ivoire, and Liberia.
Figure 10: Annual Change in Primary Energy Consumption
The map illustrates the variation in the annual change of primary energy consumption in West Africa between 2000 and 2023. The countries in the highest category are Sierra Leone, Niger, Burkina Faso, Guinea-Bissau, and Mauritania. The mid-range category includes Nigeria, Togo, Côte d’Ivoire, Senegal, Liberia, and Gambia. Finally, the countries in the lowest category are Benin, Ghana, Mali, and Guinea.
Figure 11: Human Development Index
The map shows the variation in the Human Development Index (HDI) in West Africa between 2000 and 2023. The countries in the highest category are Niger, Côte d’Ivoire, and Sierra Leone. The mid-range category includes Nigeria, Burkina Faso, Guinea, Guinea-Bissau, Senegal, Gambia, Ghana, Togo, and Mali. Finally, the countries in the lowest category are Benin, Liberia, Sierra Leone, and Mauritania.
Figure 12: Share of Urbanization
The map shows the variation in urbanization rates in West Africa between 2000 and 2023. The countries in the highest category are Nigeria, Ghana, Cape Verde, Mauritania, and Mali. The mid-range category includes Benin, Burkina Faso, Côte d’Ivoire, and Togo. Finally, the countries in the lowest category are Niger, Gambia, Liberia, Sierra Leone, Guinea-Bissau, and Guinea.
V. Results and Interpretations
I. Principal Component Analysis (PCA)
Introduction
To analyze survey data, several methods can be used, each with its advantages and limitations. Since our dataset contains many variables, we will adopt an approach that simplifies interpretation: factor analysis.
In our project, we will use Principal Component Analysis (PCA). This method transforms correlated variables into new independent variables called principal components. It reduces the number of variables while retaining most of the information. There are different types of factor analysis:
- PCA: suitable for quantitative variables;
- MCA (Multiple Correspondence Analysis): used for qualitative variables;
- FAMD (Factor Analysis of Mixed Data): combines quantitative and qualitative variables.
Our dataset contains 13 variables, with 1 qualitative and 12 quantitative variables, making PCA the most appropriate method. To implement it, we will use RStudio, which allows data import, calculations, and the production of various visualizations.
a. Steps of the Analysis
- First, we will select the variables used in the study.
- Next, we will import the data table into RStudio.
- Then, we will perform the necessary calculations for the PCA and display the initial results.
- After that, we will check for any size effects among the variables.
- If present, we will apply a rotation to correct for this effect.
- Subsequently, we will display the new results after correction.
- Finally, we will present the graphs generated from the analysis to facilitate understanding.
b. Additional Step: Classification
Once the PCA is completed, we will perform a classification to group countries based on their similarities.
There are two types of classification:
- Unsupervised classification, which automatically creates groups;
- Supervised classification, which aims to determine the group to which an individual belongs.
In our case, we will use unsupervised classification, specifically Hierarchical Ascending Classification (HAC). This method progressively groups the two closest elements, whether individual countries or groups of countries. It allows us to highlight clusters of countries with common characteristics and understand how they differ from one another.
By inserting the code lines (see appendix) and importing the dataset
named ACP_RTI2_diff into RStudio, the PCA generated the
following components:
This representation highlights the
total distribution of inertia, or in other words, the variance explained
by the total number of components in our study. The first dimension
accounts for approximately 31.05% of the variance, while the second
accounts for 24.24%. We propose to limit the analysis to the first two
axes, as these components explain 55.29% of the total inertia. This
observation suggests that only these axes carry meaningful information.
Consequently, the description of the analysis will be restricted to
these two axes.
Table 2 confirms the previous analysis by showing the percentage of variance for each dimension as well as their cumulative values. In PCA, not all dimensions are retained. To select which ones to keep, the elbow rule is used: the graph of eigenvalues is examined, and only the axes before the “elbow” are retained. Following this criterion, the first two dimensions were chosen as the principal components of the analysis. Together, they represent 55.29% of the total information.
a. Representation of Individuals
After extracting the principal components, each individual (country) is plotted on a plane formed by the selected axes. The closer an individual is to the origin, the less well it is represented on this plane. In Figure 2, some countries such as Cape Verde, Ghana, and Nigeria clearly stand out from the others. Since such observations can be misleading, the analysis will continue to obtain more reliable conclusions.
b. Representation of
Variables
The correlation circle shows how the variables are positioned in the plane of the principal components. Variables located near the circle are well represented, whereas those near the center (the origin) are less well represented. We notice that some variables, such as the population’s access to electricity and the total urbanization rate, are less well represented. In contrast, all other variables are well positioned on the circle, indicating that they contribute significantly to the formation of the axes to which they are related.
B. Variable
Analysis
Contribution and Quality of Representation of Variables
The contribution of variables helps identify those that most influence the construction of the different principal components. Recall that, for each individual, the quality of its representation on an axis is given by the cos², i.e., the square of the cosine of the angle between the principal axis and the axis on which it is projected. The closer this cos² is to 1, the more accurate and faithful the individual’s representation on that axis.
In the corresponding Figure 5, we observe that the variables contributing most to the formation of Axis 1 include: GDP per capita (14.781%), energy imports per capita (13.306%), urbanization rate (12.273%), share of electricity (11.982%), renewable energy production (11.184%), energy use per capita (11.110%), and annual CO2 emissions.
Examining the cos² values, the best-represented variables on Axis 1 are GDP per capita, energy imports per capita, urbanization rate, share of electricity, renewable energy production, energy use per capita, and annual CO2 emissions. In contrast, variables such as CO2 emissions per capita and energy use per capita are less well represented on this axis.
II. Hierarchical Ascending Classification
Hierarchical ascending classification first shows how individuals (countries) cluster or group together. It also helps identify those that stand out the most or the groups that are clearly distinct from the others.
A. Dendrogram
After executing the script, the first graph used in the classification process is the dendrogram shown in Figure 7. It shows that the observations can be grouped into three main clusters: Nigeria and Ghana form one group distinct from the others, as do Cape Verde and Mauritania.
B. Factorial Plane and
Hierarchical Tree on the Factorial Plane
Figures 6 and 7 show the classification according to the factorial plane and the hierarchical classification tree.
C.Caractérisation
Class 1 consists of countries such as Liberia, Niger, and Sierra Leone. This group is characterized by a strong dependence on energy imports per capita, while local energy consumption remains low. These countries also have low GDP per capita, limited urbanization, and low annual CO2 emissions, reflecting a reduced level of economic and industrial development. Overall, these are predominantly rural nations with low energy consumption, dependent on external sources to meet their energy needs.
Class 2 consists of countries such as Cape Verde and Mauritania. This group is characterized by high energy consumption per capita, as well as elevated CO2 emissions per capita and a significant GDP per capita, reflecting a higher level of economic development than Class 1. These countries therefore combine intensive energy use with a higher standard of living, resulting in a larger individual carbon footprint.
Class 3 consists of countries such as Ghana and Nigeria. This group is characterized by high annual CO2 emissions and very high electricity production in TWh, indicating significant national energy production and consumption. In contrast, these countries have low energy imports per capita and a small share of electricity from renewable sources, highlighting a strong dependence on fossil fuels (oil and gas) for non-renewable local production to meet their energy needs.
After the first PCA, we identified two outlier countries: Ghana and Nigeria. Once these countries were removed from the analysis, two other countries appeared as atypical: Cape Verde and Mauritania. Indeed, Ghana contributed strongly to Axis 1 (around 45%), followed by Nigeria on Axis 1 (around 35%). After their removal, Cape Verde stood out with a contribution to Axis 1 of nearly 67%, distinguishing it from the rest of the group, particularly Mauritania (less than 20%). The latter cannot be considered atypical due to its low representation.
For this reason, these three countries (Ghana, Nigeria, and Cape Verde) were removed to achieve a more coherent data distribution, making the results easier to interpret.
III. SECOND PRINCIPAL COMPONENTS ANALYSIS (Excluding Cape Verde, Ghana, and Nigeria)
1. Identification of Principal Components
We repeated the PCA in RStudio after removing the data for the atypical countries (Cape Verde, Ghana, and Nigeria) from the dataset. The figure shows the total distribution of inertia, and following the elbow rule, we retained the first two dimensions.
From the graph, we can estimate that Dimension 1 accounts for approximately 48.49% of the information, while Dimension 2 accounts for 27.28%. These two selected dimensions therefore represent 75.77% of the total information. Dimensions 3 to 5 account for approximately 37.95% of the information, with values ranging from 17.16% (Dimension 3) to 9.73% (Dimension 5).
The eigenvalues produced by the PCA
in the table further confirm the relevance of retaining the first two
principal components. Indeed, Dimension 1 explains
27.28% of the total variance, while Dimension 2
accounts for 21.21%. Together, these two axes cumulate 48.49% of
the initial information, indicating a sufficiently faithful
representation of the data structure.
2. Representation of Individuals
The results produced by RStudio are shown in Figure 9 after running the script:
3. Representation of Variables
Figure 11 shows the representation of the variables on the correlation circle.
Contribution and Quality of Representation of Variables
In Figure 14, we observe the contributions and cos² values of the variables. The variables energy use per capita, CO2 emissions per capita, and annual CO2 emissions contribute strongly to Axis 1, with values between 16% and 20%. For Axis 2, the variables energy imports per capita, Human Development Index, and electricity access rate dominate, with contributions between 12% and 19%.
The cos² values confirm that the variables contributing the most are also the best represented, ensuring the statistical robustness of our interpretation.
A. Hierarchical Ascending Classification
1.Dendrogram
Running the classification script generated the dendrogram shown in Figure 12, which reveals the formation of three distinct groups.
2.Factorial Plane and Hierarchical Tree on the Factorial Plane
3.Characterization of
Classes
Class 1 consists of individuals such as Liberia. This group is characterized by low values of GDP per capita (GDP_per_capita_USD) and annual CO2 emissions (Annual_CO2_emission), ranging from the lowest to moderately low values. These indicators reflect limited economic and industrial development, as well as a low individual carbon footprint. Overall, this group represents nations where economic and energy potential remains relatively low.
Class 2 includes countries such as Côte d’Ivoire, Mali, Mauritania, and Sierra Leone. These nations are distinguished by high values of GDP per capita and annual CO2 emissions, from the highest to moderately high. This reflects a higher level of economic development and significant energy consumption, indicating notable economic and industrial activity compared to Class 1.
Class 3 is composed of countries such as Burkina Faso and Niger. This group is characterized by high energy imports per capita (Energy_Imports_per_capita) and low electricity access rate for the population (Electricity_Access_Rate_of_population). These indicators show strong external energy dependence and limited electricity access, highlighting constraints in local energy development.
IV. CONCLUSION AND RECOMMENDATIONS
The Principal Component Analysis (PCA) conducted in this study provided a clearer understanding of the energy and socio-economic dynamics of the countries analyzed. The results highlighted significant disparities between nations, revealing distinct energy profiles that reflect their level of development, local production capacity, and dependence on external resources.
The PCA identified major components explaining a large portion of the data variability, particularly the importance of energy consumption, CO₂ emissions, and GDP per capita in structuring national profiles. The identification of atypical countries (Nigeria, Ghana, Cape Verde) underscores the existence of very different energy trajectories, linked to higher production capacities or more industrialized economic models.
Hierarchical clustering confirmed these contrasts by distinguishing three groups of countries with well-defined characteristics:
- Group 1: low economic development and high energy dependence;
- Group 2: higher energy consumption and a higher standard of living;
- Group 3: strong imbalance between external dependence and limited electricity access.
These findings indicate that energy issues are central to the sustainable development of the studied countries. Effective resource management, equitable access to electricity, and a controlled transition toward cleaner sources are essential levers to reduce the observed inequalities.
Recommendations
Based on the insights from this analysis, several recommendations can be proposed:
Strengthen local energy production capacities, especially in countries heavily dependent on imports, to reduce vulnerability and support economic development.
Improve population access to electricity, particularly in Group 3 countries, where low access rates hinder economic activity, education, healthcare, and overall quality of life.
Accelerate the transition to renewable energy, especially in countries heavily reliant on fossil fuels, to reduce CO₂ emissions and promote a more sustainable energy model.
Implement differentiated national policies tailored to the energy profile of each country group:
- Support infrastructure development for the least advanced countries,
- Encourage energy efficiency in high-consumption countries,
- Promote programs to reduce external dependence in intermediate countries.
Enhance regional cooperation, particularly regarding electrical interconnection and knowledge sharing, to foster more resilient and better-coordinated energy systems.
In summary, this study demonstrates that energy planning is a strategic lever for economic growth and environmental stability in the region. An integrated approach, combining statistical analysis, tailored public policies, and targeted investments, will help reduce inequalities and promote sustainable and equitable development across West Africa.
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