Impact de la transition énergétique sur le développement
socio-économique des pays Ouest-africains
Impact de la transition énergétique sur le développement socio-économique des pays Ouest-africains
Author(s) :
Abstract
This study examines the link between the energy transition and sustainable development in West Africa from 2010 to 2023. Drawing on secondary data and empirical studies[6], this article explores how the deployment of renewable energy affects economic growth, human development, and environmental sustainability in the ECOWAS region. The data show that countries with a higher share of renewable energy, such as Cape Verde and Ghana, achieve better socio-economic outcomes and emit less CO₂[19]. However, progress remains uneven due to financial constraints, weak institutional frameworks, and persistent dependence on fossil fuels [24]. Regional disparities are particularly pronounced, with Eastern and Southern Africa outperforming West and Central Africa in renewable energy adoption [18]. The study concludes with policy recommendations to accelerate the regional energy transition towards inclusive and resilient growth, highlighting the essential role of governance quality, institutional capacity building, and regional cooperation (Tamasiga & Vea, 2024; Carnegie Endowment, 2025; PwC, 2024).
Keywords: Energy Transition, West Africa, Sustainable Development, Renewable Energy, Socio-economic Growth, Governance, Regional Disparities.
Introduction
Energy remains the cornerstone of sustainable development. In West Africa, population growth, urbanization, and economic expansion have significantly increased the demand for electricity. However, fossil fuels continue to dominate the regional energy mix, despite the continent’s abundant potential for renewable energy[3; 8; 27]. According to the World Bank (2023), more than 190 million people in ECOWAS countries still lack access to electricity, which poses a challenge for poverty reduction and inclusive growth[17]. The Africa Power Transition Factbook (2024) reveals that the average per capita electricity consumption in sub-Saharan Africa is only 750 kWh, compared to a global average of 3,150 kWh, a gap that significantly limits industrial development and quality of life [28]. The energy transition refers to the structural shift from fossil fuel-based systems to low-carbon, renewable, and efficient energy solutions[1]. It is closely linked to the Sustainable Development Goals (SDG 7 – Affordable and Clean Energy) and influences multiple dimensions of development: economic, social, and environmental [15]. Yet, the pace of the transition in West Africa remains slow, largely due to limited investment, weak governance, and inadequate infrastructure[10; 7; 24]. Recent analyses highlight significant regional disparities within Africa. While North, Southern, and East Africa have experienced relatively rapid growth in renewable energy, West and Central Africa continue to lag, reflecting deep-rooted inequalities in energy development, governance structures, and investment flows[18]. The IMF (2024) estimates that with about $25 billion in annual financial flows dedicated to climate and renewable energy, sub-Saharan Africa could increase its renewable electricity generation by up to 24% and boost annual GDP growth by 0.8 percentage points over the next decade[27]. The central question is: To what extent does the energy transition contribute to socio-economic development in West Africa? Answering this question helps determine how renewable energy policies can promote growth, equity, and environmental protection while accounting for heterogeneous development trajectories in the region.
Conceptual and Theoretical Framework
The theoretical foundation of the relationship between energy and development rests on the sustainable development model, which integrates three pillars: economic prosperity, social equity, and environmental sustainability [13]. Access to energy is both a driver and an outcome of development. Better access boosts productivity, industrialization, and quality of life [14].
Energy-Growth Nexus: Theoretical Perspectives
The relationship between energy consumption and economic growth has been widely debated in the literature, leading to four main hypotheses [29; 22]:
The Growth Hypothesis: Energy consumption drives economic growth (unidirectional causality from energy to GDP)
The Conservation Hypothesis: Economic growth leads to an increase in energy demand (unidirectional causality from GDP to energy)
The Feedback Hypothesis: There is bidirectional causality between energy and economic growth
The Neutrality Hypothesis: There is no significant relationship between energy consumption and economic growth Recent empirical data from West Africa and sub-Saharan Africa more broadly tend to support the growth and feedback hypotheses, suggesting that renewable energy development both stimulates and simultaneously responds to economic development [19; 22].
Sustainable Development Framework in the African Context
In the West African context, Diedhiou (2022) emphasizes the role of renewables in decoupling growth from emissions [6], while Boucetta (2024) argues that governance and technological innovation are prerequisites for sustainable progress [5]. Sambo (2021) identifies renewables as a catalyst for rural electrification and local value creation [12]. Furthermore, reports from the IEA (2024) and REN21 (2023) highlight Africa’s growing potential in solar, wind, and hydro power, even though structural and financial obstacles persist [8; 11].
The Role of Institutional Quality and Governance
Recent studies increasingly recognize that the success of energy transitions critically depends on the quality of institutions and governance structures [24; 29]. Pan et al. (2023) highlighted a complex relationship between governance quality and renewable energy development in 42 African countries, suggesting that institutional reforms must be carefully designed to avoid unintended consequences [24]. Kwakwa (2024) demonstrates that institutional quality, financial development, and trade openness positively influence access to clean fuels and technologies in 32 African countries. The study reveals that property rights and business freedom are particularly important determinants of renewable energy adoption [29]. Similarly, research on the governance of energy transitions highlights how historical backgrounds, political influences, and power dynamics shape contemporary energy systems and transition pathways.
Review of Empirical Studies
Empirical research consistently confirms a positive link between renewable energy development and economic growth in West Africa, with important nuances regarding causality, regional heterogeneity, and institutional mediators.
Economic Growth Dimension
Direct Economic Impacts: Diedhiou (2022) found that renewable energy consumption contributes to GDP growth while reducing carbon intensity in sub-Saharan Africa [6]. Afo-Loko (2021) demonstrated that foreign direct investment facilitates the adoption of renewables, but with mixed environmental outcomes [1]. Nathaniel et al. (2019) provide new evidence using a Dynamic Ordinary Least Squares (DOLS) method for West African countries, establishing a strong positive relationship between renewable energy consumption and economic growth [19]. Their findings are corroborated by Oluoch et al. (2022), who used advanced econometric techniques, including pooled mean group estimators, to examine 51 African countries between 1980 and 2018 [22]. Notably, Oluoch et al. (2022) found that both renewable and non-renewable electricity consumption stimulate growth, but with heterogeneous effects across regional economic communities (ECOWAS, COMESA, SADC, EAC), suggesting that uniform policies may be ineffective [22].
Macroeconomic and Employment Effects: The IMF (2024) provides compelling evidence that climate finance for renewables can generate substantial macroeconomic benefits. Their modeling suggests that $25 billion in annual climate finance could boost renewable electricity output by 24% and increase annual GDP growth by 0.8 percentage points, while also boosting labor demand in the electricity sector [27]. This finding underscores the potential of renewable energy investments to serve as engines for job creation and economic diversification.
Regional Heterogeneity: Carabajal et al. (2025) document significant regional disparities in the transition to renewables across Africa’s five major regions. East Africa has achieved relatively higher levels of renewable energy consumption, while West Africa continues to lag. Countries with strong governance structures and regulatory environments favorable to investment (North, Southern, and East Africa) have experienced faster renewable energy growth, while regions with weaker institutions face persistent bottlenecks [18].
Human Development Dimension
Social Impacts and Well-being: AGBOKPANZO et al. (2023) revealed that improving energy access increases the Human Development Index (HDI) and reduces poverty, particularly among women in WAEMU countries [2]. Ky (2020) highlighted the role of governance and policy coherence in ensuring an equitable distribution of energy benefits [10]. Kwakwa (2020) provides further evidence that economic well-being (measured by HDI) has an inverted U-shaped relationship with the share of renewables in total energy consumption in 32 African countries. A one percentage point increase in HDI is associated with a 5.54 percentage point increase in the renewable energy share, although this effect diminishes at higher development levels [29]. This suggests that middle-income African countries might be in an optimal position to accelerate renewable energy adoption.
Gender and Social Equity: The literature increasingly recognizes that energy transitions have differentiated impacts across social groups. Women, who bear the primary responsibility for household energy supply in many West African contexts, are expected to benefit disproportionately from access to modern energy services through time savings, reduced exposure to indoor air pollution, and improved economic opportunities [2; 29].
Environmental and Institutional Dimension
Environmental Performance: UNECA (2024) and ECREEE (2023) indicate that energy diversification has progressed, but fossil fuels still provide over 60% of total output in West Africa [16;7]. The Africa Power Transition Factbook (2024) reveals that fossil fuels accounted for 71% of the 820 TWh produced on the African continent in 2023, underscoring the continued dominance of carbon-intensive energy sources [28]. The IEA and AfDB (2023) note that renewable energy projects often face financing and maintenance difficulties, limiting their effectiveness in replacing fossil fuels [9]. However, falling costs of renewable technologies, due to innovation, economies of scale, and market dynamics, are making renewables increasingly competitive with traditional energy sources, leading to lower energy prices for consumers and increased profitability of renewable investments (African Renewable Energy Market, 2025).
Institutional and Governance Factors: Pan et al. (2023) present surprising findings on governance quality and renewable energy development in 42 African countries between 1996 and 2020. Contrary to conventional expectations, they find that improved governance quality is associated with a decrease in renewable energy development, suggesting complex and potentially counter-intuitive relationships that require further study [24]. This paradoxical result may reflect the reality that stronger governance is often accompanied by greater exploitation of conventional energy resources. Other studies, however, offer more nuanced perspectives. Kwakwa (2024) demonstrates that institutional quality strengthens the effects of urbanization, financial development, and trade openness on access to clean energy technologies [29]. The IMF (2024) emphasizes that governance reforms, business regulation, and external sector reforms are strongly associated with increased financial flows for climate (a 20% increase) and electricity generation (a 7% increase) over five-year periods [27]. The tension between diverse initiatives and the need for coherent transitions is a recurring theme across African contexts.
Cross-Country Comparisons and Performance Leaders
Cross-country comparisons show heterogeneous progress in West Africa. Cape Verde and Ghana are leading due to significant investments in renewables and strong institutional commitment, while Sahel states, such as Niger and Mali, are lagging due to structural fragility and weak capacities [11;1; 28].
Success Stories: Kenya’s experience, although located in East Africa, offers valuable lessons for West Africa. Kenya rapidly increased electricity access, from 36% in 2014 to 76% in 2023, through a combination of grid-connected and off-grid solutions, offering flexible financing for grid connection costs and home solar systems [28]. In West Africa, Ghana, Guinea, and Liberia have improved electricity access through government efforts, while Nigeria and Burkina Faso have progressed more slowly [28].
Policy Environment: Fewer than two-thirds (57%) of African markets tracked by BloombergNEF have implemented policies to organize reverse auctions or tenders for clean energy supply contracts. From 2014 to 2023, only 1.4 GW per year on average of new renewable projects signed power purchase agreements with governments or utilities on the continent, far behind Latin America (4.4 GW/year), India (14.9 GW/year), and China (45 GW/year) [28].
Discussion and Synthesis
The reviewed literature reveals several key lessons with important policy implications. ## Confirmed Positive Link with Important Caveats First, the deployment of renewables is correlated with stronger socio-economic indicators. Countries that progress in their energy transition tend to achieve higher GDP per capita, better education and health outcomes, and lower emissions [15; 4; 19; 22]. However, the magnitude and even the direction of effects vary significantly depending on the regional context, level of development, and institutional environment.
Heterogeneity is the rule, not the exception: The finding by Oluoch et al. (2022) that renewable and non-renewable electricity have different impacts across African regional economic communities challenges simplistic narratives about energy transitions [22]. This heterogeneity implies that policy prescriptions must be carefully tailored to specific regional and national contexts rather than applying one-size-fits-all approaches.
Governance Quality: A Double-Edged Sword
Second, governance quality and institutional coordination appear as decisive but complex factors [10 ;3 ;24 ;29]. Weak energy policies, inconsistent subsidies, and limited regional cooperation hinder progress. However, the paradoxical finding by Pan et al. (2023) that improved governance can sometimes be correlated with reduced renewable energy development suggests that institutional reforms must be carefully designed and sequenced [24].
Financial Viability: The Binding Constraint
Third, financial viability remains the primary constraint. Although donor support and private investment are increasing, funding gaps for large-scale renewable projects persist [8;9; 27]. The IMF (2024) estimates that sub-Saharan Africa receives only 2% of global investments in renewables, while it is home to 18% of the world’s population [27].
Innovative Financing Mechanisms: The Africa Power Transition Factbook (2024) notes that stable and enabling environments are essential to accelerate clean energy investment and deployment [28].
Regional Disparities and Energy Access Challenges
Fourth, regional disparities in Africa are significant and persistent. Carabajal et al. (2025) show that East Africa has achieved relatively higher levels of renewable energy consumption, while West Africa continues to lag [18]. This trend reflects not only differences in available resources but also variations in governance quality, investment climate, and infrastructure development. The Africa Power Transition Factbook (2024) report reveals that, although over 90% of African countries have implemented policies to improve energy access, their implementation has stalled in many regions of the continent since 2020 [28]. Grid extension to the entire population is often not economically viable due to low population density and rugged terrain, underscoring the importance of decentralized renewable solutions (mini-grids, home solar systems) for achieving universal access.
Convergence Between Energy and Development Agendas
Despite these challenges, the literature reports a gradual convergence between energy and development agendas in West Africa. Empirical findings confirm that the expansion of renewables promotes not only growth but also environmental resilience and social inclusion [12;14]. Carnegie Endowment (2025) argues that African countries should ensure that their education and vocational training systems can train a skilled workforce for energy-related industries, integrating curricula on clean energy systems, industrial maintenance, and automation. A regional approach leveraging transport corridors and cross-border cooperation is essential to develop energy value chains that support industrialization across the continent.
Policy Implications and Recommendations
To accelerate the energy transition and ensure sustainable outcomes, several evidence-based policy recommendations emerge:
Strengthen Regional Cooperation and Integration
Strengthen regional cooperation through ECREEE and ECOWAS to harmonize regulatory frameworks and integrate electricity markets [7]. The West African Power Pool should be reinforced to facilitate cross-border electricity exchanges and optimize the use of regional resources. Leverage the African Continental Free Trade Area to eliminate tariffs and harmonize trade laws, fostering infrastructure development in energy, transport, and telecommunications. This agreement is expected to boost foreign direct investment by 111% by 2035, which could catalyze large-scale renewable energy projects [27].
Increase and Diversify Investments in Renewables
Increase investments in renewables using blended finance mechanisms and national green funds [8; 9 ;17 ;27]. The IMF (2024) demonstrates that approximately $25 billion in annual climate finance dedicated to renewables could boost renewable electricity generation by up to 24% and increase annual GDP growth by 0.8 percentage points over the next decade [27]. Implement competitive procurement mechanisms: Fewer than two-thirds of African markets have auction systems for renewable energy contracts. Expanding competitive tenders can attract investment and reduce costs [28].
Prioritize decentralized solutions: Given the economic and technical challenges of extending conventional grids to dispersed rural populations, policies should support mini-grids and home solar systems through innovative financing (pay-as-you-go models) to accelerate access.
Strengthen Institutional Capacities and Governance
Strengthen institutional capacities to plan, monitor, and evaluate energy projects [3;16;24;29]. This includes: - Transparency and Accountability: Ensure transparent documentation of funding and involve civil society to ensure prudent resource management (Tamasiga & Vea, 2024) [30]
Capacity Building: Targeted training for skilled personnel and well-functioning institutions to mitigate risks for society and potential donors.
Regulatory Stability: Create predictable and consistent policy frameworks that reduce investor uncertainty
Anti-Corruption Measures: Combat corruption that undermines justice in clean energy transitions (Tamasiga & Vea, 2024) [30]
Promote Inclusive and Just Transitions
Prioritize energy access for marginalized groups to promote social inclusion and gender equality [2;29]. This includes:
Targeted Subsidies: Financial support for low-income households to enable access to modern energy services
Gender-Sensitive Policies: Recognition that women bear the primary responsibility for domestic energy and face specific barriers to access
Rural Electrification: Prioritize underserved areas by choosing appropriate technologies (grid extension or decentralized solutions)
Just Transition Frameworks: Protect workers and communities dependent on fossil fuel industries through retraining and support for alternative livelihoods (Tamasiga & Vea, 2024) [30]
Foster Innovation and Develop Human Capital
Encourage innovation and research partnerships in renewable technologies [11]. African governments should ensure that their education and vocational training systems can train a skilled workforce for energy-related industries. Curricula should integrate clean energy systems, industrial maintenance, and automation to meet growing demand. Connecting energy-related industries with local universities, technical schools, and research institutes is essential to strengthen local innovation capacity and reduce dependence on imported technology and expertise.
Align Energy with Broader Development Strategies
Integrate the energy transition with industrialization: PwC (2024) emphasizes that while the world will need fossil fuels during the energy transition, Africa must maximize the value of its resources. Recent discoveries of gas and oil in countries like Namibia, Mozambique, and Senegal can boost economic growth if properly managed as transition fuels, while developing renewable capacities.
Policy Coherence: African countries often treat climate, energy, and development as isolated policy areas. Yet, these areas are inextricably linked. Declining demand for oil and gas exports will impact revenues and thus the capacity to invest in decarbonization (Carnegie Endowment, 2025). These recommendations align with continental initiatives such as the Africa Energy Transition Programme, the Africa Renewable Energy Initiative, and the Sustainable Energy for All strategy [3]. Having established the theoretical foundations and empirical evidence regarding the relationship between the energy transition and socio-economic development in West Africa, this study identifies both opportunities and persistent knowledge gaps that warrant further quantitative investigation. While existing studies have made valuable contributions, several limitations appear:
Limited Regional Focus: Most publications examine Africa as a whole or individual countries, without paying sufficient attention to the specificities of West Africa.
Fragmented Analysis: Studies typically focus on economic, social, or environmental dimensions rather than integrating all aspects of sustainable development.
Lack of Typologies: There is no comprehensive classification of West African countries based on their energy transition progress and development trajectories.
Limited Spatial Analysis: The geographical dimensions of energy access and renewable energy deployment remain underexplored. To fill these gaps, the present study uses a comprehensive multivariate statistical approach integrating:
- Principal Component Analysis (PCA) to identify the underlying dimensions that structure the energy-development link
- Hierarchical Cluster Analysis (HCA) to develop a typology of West African countries
- Correlation and regression analyses to quantify the relationships between transition and development indicators
- Geographic Information Systems (GIS) to visualize spatial patterns and disparities The methodological framework, data sources, variable selection, and analysis techniques are detailed in the following chapter, which sets the stage for an empirical study covering 16 West African countries over the period 2010-2023.
METHODOLOGY
The adopted methodology combines a mixed-methods approach based on literature review, field data collection, spatial analysis, and advanced statistical processing. First, Zotero is used to organize the literature review. This tool allows for the systematic gathering, classification, and citation of scientific work on the energy transition and socio-economic development. This step results in the construction of the conceptual framework and the identification of key variables. Next, primary data collection is carried out using KoboCollect, via a structured questionnaire integrating socio-economic, energy, and geographical variables. Field surveys provide accurate information, supplemented by GPS coordinates of households and infrastructure, which facilitates spatial analyses. The geolocated data is then integrated into QGIS, software used to clean, spatialize, and map the collected information. This step allows for the visualization of spatial disparities, the identification of areas with high or low energy access, and the production of thematic maps useful for analysis. Finally, RStudio is used for statistical and econometric analyses. Data from KoboCollect and QGIS are processed to perform descriptive statistics, multivariate analyses (PCA, HCA), and explanatory models to understand the impact of the energy transition on socio-economic development. The articulation of these four tools ensures a rigorous, reproducible, and comprehensive approach, where bibliographic, field, spatial, and statistical data are integrated coherently to produce robust and scientifically valid results.
Map
Table of variables
library(readr)
ACPRTI <- read.csv("C:/Users/USER/Desktop/TD AD R/RTI 2025_2026/APC_RTI_A.csv", header = TRUE, sep = ";",
dec = ",", row.names=1)
ACPRTI ## PIB Elec.acess IDH EPEr CDT CarIntenElectr MRT.enfant
## Benin 3464.29 42.0 0.506 1.77 87.222 584.07 8.31
## Burkina Faso 2486.43 19.0 0.454 18.13 72.143 549.71 8.25
## Cote d'Ivoire 6045.20 71.1 0.557 23.88 89.167 431.72 7.14
## Gambia 2777.80 63.7 0.513 0.00 94.167 660.00 4.72
## Ghana 6607.50 86.3 0.618 34.78 95.556 478.26 4.02
## Guinea 3738.81 46.8 0.490 73.08 89.800 192.31 10.05
## GuineaBissau 2483.85 35.8 0.502 0.00 86.250 625.00 7.41
## Liberia 1539.21 29.8 0.503 34.21 91.500 421.05 7.74
## Mali 2796.23 53.4 0.414 39.09 70.000 416.24 9.69
## Mauritania 5836.57 47.7 0.553 28.48 88.750 474.68 4.03
## Niger 1585.60 18.6 0.413 2.82 68.375 704.23 11.91
## Nigeria 5491.70 59.5 0.554 20.66 83.091 522.70 11.13
## Senegal 4173.68 68.0 0.522 20.27 89.875 546.55 4.25
## Sierra Leone 2851.95 27.5 0.458 95.00 96.286 50.00 10.14
## Togo 2577.20 55.7 0.560 22.54 91.286 464.79 6.22
## ElecPhabitant ConsoEnPer Sante.Univ EPV
## Benin 84.24 1837.10 37.89 59.61
## Burkina Faso 77.74 1043.45 39.60 60.05
## Cote d'Ivoire 382.93 2234.78 42.78 60.29
## Gambia 194.10 792.09 46.15 63.85
## Ghana 678.99 3169.96 47.79 64.29
## Guinea 265.49 1526.84 39.92 59.37
## GuineaBissau 38.86 689.97 37.25 61.65
## Liberia 72.25 508.53 44.71 61.17
## Mali 175.98 1272.09 41.28 59.12
## Mauritania 333.69 3711.98 40.12 66.76
## Niger 28.98 445.61 34.98 59.54
## Nigeria 179.38 2345.25 38.42 53.46
## Senegal 386.74 2404.39 50.09 66.87
## Sierra Leone 24.71 688.85 41.00 60.26
## Togo 79.97 1246.70 44.01 61.33Data analysis
Data analysis was conducted using Principal Component Analysis (PCA), a statistical method for exploring multidimensional quantitative datasets. The main objective of this technique is to reduce the dimensionality of the data while retaining most of the information, in order to facilitate the interpretation of the relationships between the variables. PCA involves transforming the initial variables into a new set of uncorrelated variables, called principal components. The latter are prioritized so that the first component accounts for most of the total variance in the dataset, while the subsequent components capture decreasing shares. The analysis was carried out using the RStudio software, according to several methodological steps:
Data pre-processing
- Standardization of variables: In order to correct for differences in scale between variables, variables have been centered and reduced. This step ensures that measurements expressed in different units do not influence the results of the analysis.
Extraction and selection of major components
PCA is based on the decomposition of the variance-covariance matrix (or correlation matrix), in order to identify the factor axes that best explain the variability of the dataset. The factor axes are then selected according to their contribution to the total variance, so as to retain only those that contribute significant information to the overall interpretation.
Visualization and interpretation of results
Several graphical representations have been used to facilitate the reading of the results:
Correlation matrix: allows you to observe the strength and direction of the relationships between the different variables.
Factorial design: simultaneous projection of individuals (countries) and variables on the first two main axes, in order to visualize their groupings and structural relationships.
Correlation circle: illustrates the contribution of each variable to the formation of the main axes and their mutual correlation.
Explained variance graph: Determine the optimal number of components to be retained to effectively represent information.
Complementary classification
To deepen the interpretation, an Ascending Hierarchical Classification (CHA) was carried out from the PCA results. This approach makes it possible to group individuals (countries) into homogeneous classes according to their statistical similarities, thus facilitating the identification of typological profiles.
Results and interpretation
This section presents a synthesis of multivariate analyses aimed at characterizing the structural relationships between energy transition indicators and socio-economic development performance, and quantifying their contribution to the growth dynamics of West African countries.
Determining the optimal number of principal components to be retained
This subsection aims to identify the number of relevant principal components that can effectively represent the information contained in the data while limiting the loss of explanatory variance.
Principal Component Analysis (PCA) was used to reduce the dimensionality of the dataset while preserving most of the statistical information. The scree plot above shows the proportion of variance explained by each of the principal components. For the purposes of this study, the first two principal components alone summarize 80.08% of the total variance, broken down as follows:
- Axis 1: 60.40% of the variance explained.
- Axis 2: 19.68% of the explained variance. According to the Kaiser criterion, which recommends retaining only the components with an eigenvalue greater than 1, and given the presence of a marked bend on the scree graph, it is relevant to keep the first two components for the interpretation of the results.
Country contributions to Major Components 1 and 2 and the factor plan
Analysis without Cape Verde:
Determining the number of components to be retained:
Cape Verde’s removal lies in the principle of analytical robustness of Principal Component Analysis (PCA). In the initial dataset, Cape Verde had an out-of-the-way individual profile, with much higher GDP, access to electricity and life expectancy, and infant mortality much lower than the average of the other countries in the sample. In the presence of such a gap, Axis 1 of the APC would have been distorted and dominated by the opposition “Cape Verde vs. Rest of the Group”, masking the internal structure and nuances of the more comparable countries. The withdrawal of Cape Verde therefore reveals in the case of our analysis a more significant structure for the internal typology of West Africa.
The results of the correlation confirmed the interrelationship of the variables: significant relationships were established, proving that the variation in one indicator is strongly associated with the variation in other key indicators.
Strong correlations (|r| ≥ 0.7):
Between renewable energies (EPEr) and carbon intensity of electricity (CarIntenElectr): r = -0.98 The more electricity is renewable, the less carbon it is, which improves the environmental balance and sustainability. To accelerate this transition in West Africa, it is imperative to massively increase public and private investment in hydropower, solar and wind, to create attractive tax incentives for companies and investors, and to develop enhanced training of local engineers to ensure technological mastery, sustainable maintenance and national ownership of energy infrastructure.Between access to electricity (Elec.acess), GDP and HDI Widespread access to reliable and affordable energy directly drives GDP growth and improves living conditions, as reflected in the HDI. To achieve this in West Africa, it is crucial to massively scale rural electrification through the large-scale deployment of renewable mini-grids (solar, hybrid), to support these projects with targeted international financing and to actively involve local populations in the management, maintenance and governance of these infrastructures, in order to ensure their sustainability, their ownership and sustainable socio-economic impact.
Significant negative correlationsRevolving Share (EPEr) & Infant Mortality (MRT.child): The more renewable the energy, the better public health, as energy poverty and indoor air pollution decrease simultaneously. To achieve this impact in West Africa, it is necessary to prioritize the supply of clean energy to schools, hospitals and health infrastructures via autonomous or hybrid solar systems, and to launch massive campaigns to raise awareness and distribute clean cooking solutions (improved stoves, domestic biogas, solar cookers), in order to drastically reduce respiratory diseases, improve food security and strengthen the health resilience of communities.
Principal Component Analysis (PCA)
The variables such as GDP, Elec.access, EPV, ConsoEnPer, and ElecPhabitant are strongly correlated and describe a development axis (economic growth, energy access, and consumption). They align on the same gradient, contributing very strongly to the differentiation of countries according to the level of socio-economic development.
Variables such as EPEr (share of renewable energies), CDT (territorial control) are linked to the energy transition and energy policy aspect. Their alignment with Dim 2 shows that they embody a complementary dimension to development.
The variable such as CarIntenElectr (carbon intensity) is opposed to the development group (GDP, energy, etc.). Countries with high carbon intensity on electricity are generally less advanced in terms of access to clean energy.
MRT.child (infant mortality): This variable is also strongly opposed to the socio-economic axis, which means that infant mortality is high where development and access to energy are low.
Dim 1 (axe horizontal) This axis clearly distinguishes countries according to their level of development and access to energy: - Right: Countries such as Ghana, Côte d’Ivoire, and Senegal have a high GDP, good access to electricity (Elec.acess), high energy consumption per person (ConsoEnPer), and electricity consumption per capita (ElecPhabitant). - Left: Burkina Faso, Niger and Guinea-Bissau are on the negative side, associated with low economic development, energy poverty and low levels of consumption and access.
Dim 2 (axe vertical) This axis highlights the specificities related to the energy transition and health: - Top: Sierra Leone and Guinea are close to the EPEr arrow (share of renewables), indicating that they have cleaner energy production. - Bottom: Guinea-Bissau and Niger are on the side of CarIntenElectr (carbon intensity of electricity), signaling a very carbon-intensive energy mix. - On the MRT.child arrow: Liberia and Mali are the countries where infant mortality is statistically high.
Contribution of variables to principal component analysis (PCA)
Principal Component Analysis (PCA) highlights the structuring variables of the development dynamics of West African countries. This multidimensional statistical approach shows that GDP, access to electricity, energy consumption per capita and the share of renewable energies strongly influence the structuring of the dataset: they reflect the level of modernization, the intensity of economic transformations and the degree of investment in energy infrastructure. In contrast, the carbon intensity of electricity and infant mortality are evidence of energy deficits or poor social progress. By revealing the way in which these variables are distributed and grouped together, the ACP makes it possible to identify the major axes of regional development and to understand the factors, while guiding strategies for improving living conditions and growth in the Sahel region. This analytical approach thus offers a global vision of the trends and levers to be favoured to strengthen the socio-economic dynamic.
Most contributing variables (>10%)
CarIntenElectr vs EPEr
CarIntenElectr represents the carbon intensity of electricity production: the higher its contribution, the more it captures the opposition between countries that are very “carbon-intensive” and those that are not very carbon-intensive.
EPEr embodies the share of renewables in the energy mix: its strong contribution reflects the major gaps between countries that invest in clean energy and those still dependent on fossil sources.
Interpretation: While both reflect the energy dimension, CarIntenElectr mainly identifies polluting models, while EPEr points to the transition to marche. Their strong joint contribution indicates that the nature of energy (dirty vs. renewable) is the most structuring divide.
Important variables (8–10%)
ElecPhabitant vs MRT.child
ElecPhabitant (electricity consumption per capita) measures the actual access and capacity of populations to use electricity, a reflection of a modernised society.
MRT.enfant (infant mortality) reflects the overall quality of living conditions, particularly in terms of health, nutrition and the environment.
Interpretation: One expresses technological success, the other reveals social weaknesses. Their equivalent contribution suggests that progress is not only energy-related: access to electricity is combined with improved health to chart a solid development trajectory.
Moderately contributing variables (5–8%)
Elec.acess, IDH, PIB, ConsoEnPer
Elec.acess simply reports on the availability of electricity, without assuming usage or quality;
HDI aggregates economy, health, and education, giving a global vision of well-being;
GDP isolates the pure economic dimension;
ConsoEnPer (energy consumption per person) mixes industrial needs, transport, household equipment.
Interpretation: These variables draw a development gradient but each sheds light on a different facet: basic infrastructure (Elec.acess), wealth (GDP), lifestyle (ConsoEnPer), and global synthesis (HDI). Their moderate contribution shows that neither the economy alone, nor energy access alone, is enough to explain reality: everything depends on their interaction.
Dendrogram
The vertical axis (Height) represents the level of dissimilarity between the groups: the lower the merger between the countries or groups, the more similar these countries are. Conversely, the higher the junction, the more dissimilar the profiles. Three major homogeneous clusters emerge from the results:
Cluster 1: Ghana, Côte d’Ivoire, Mauritania, Senegal These countries are merging at a low level, marking a strong similarity in their development profile and access to essential services.
Likely Features: - Strong economic growth and relatively high level of development. - Good access to electricity and modern infrastructure. - More favourable social indicators (health, education). - Energy transition initiated or advanced. This cluster illustrates the leading countries of the sub-region, capable of driving the regional dynamic thanks to their resources, their openness and their strategic position.
Cluster 2: Sierra Leone, Guinea This duo also forms at a low height, indicating marked proximity.
Likely Features:
Energy transition underway (significant share of renewables).
Moderate automation and modernization; some social advances.
Persistent challenges but good evolutionary dynamics.
These two countries have specificities that distinguish them from the first group: intermediate profile, with potential for convergence towards the advanced group, but requiring targeted investments to accelerate socio-energy development.
Cluster 3: Niger, Gambia, Nigeria, Togo, Mali, Liberia, Burkina Faso, Guinea Bissau, Benin This group, the largest, is formed at a high intermediate level of dissimilarity. It brings together countries with fairly similar profiles but which have more difficulties.
Likely Features:
More limited access to energy, less developed infrastructure.
Lower human development indicators (health, education, sometimes high infant mortality).
Energy model dominated by fossil sources, low share of renewables.
Recurrent economic constraints, strong external dependence. This group embodies the most fragile area, which requires robust interventions: expanding access to electricity, accelerating the energy transition, investing in education and health to trigger the virtuous circle of socio-economic progress. Public policies and international aid will have to prioritize this cluster to catch up with the structural gap and reduce the gaps with the leading groups.
Regression:
model1
library(readr)
library(ggplot2)
library(dplyr)##
## Attachement du package : 'dplyr'## Les objets suivants sont masqués depuis 'package:stats':
##
## filter, lag## Les objets suivants sont masqués depuis 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lmtest)## Warning: le package 'lmtest' a été compilé avec la version R 4.5.2## Le chargement a nécessité le package : zoo## Warning: le package 'zoo' a été compilé avec la version R 4.5.2##
## Attachement du package : 'zoo'## Les objets suivants sont masqués depuis 'package:base':
##
## as.Date, as.Date.numericdata <- read.csv("C:/Users/USER/Desktop/TD AD R/RTI 2025_2026/APC_RTI_A.csv", header = TRUE, sep = ";",
dec = ",", row.names=1)
model_pib_epv <- lm(IDH ~ Elec.acess, data = data)
summary(model_pib_epv)##
## Call:
## lm(formula = IDH ~ Elec.acess, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.104569 -0.020996 0.008451 0.026039 0.046530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4052181 0.0277518 14.601 1.92e-09 ***
## Elec.acess 0.0021227 0.0005338 3.976 0.00158 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03962 on 13 degrees of freedom
## Multiple R-squared: 0.5488, Adjusted R-squared: 0.5141
## F-statistic: 15.81 on 1 and 13 DF, p-value: 0.001581# Visualisation de la régression
ggplot(data, aes(x = PIB, y = EPV )) +
geom_point(color = "steelblue", size = 3) +
geom_smooth(method = "lm", se = TRUE, color = "darkred") +
labs(
title = "Link between GDP and life expectancy",
x = "PIB",
y = "(EPV)"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
model2
library(readr)
library(ggplot2)
library(dplyr)
library(lmtest)
data <- read.csv("C:/Users/USER/Desktop/TD AD R/RTI 2025_2026/APC_RTI_A.csv", header = TRUE, sep = ";",
dec = ",", row.names=1)
model_pib_epv <- lm(IDH ~ Elec.acess, data = data)
summary(model_pib_epv)##
## Call:
## lm(formula = IDH ~ Elec.acess, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.104569 -0.020996 0.008451 0.026039 0.046530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4052181 0.0277518 14.601 1.92e-09 ***
## Elec.acess 0.0021227 0.0005338 3.976 0.00158 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03962 on 13 degrees of freedom
## Multiple R-squared: 0.5488, Adjusted R-squared: 0.5141
## F-statistic: 15.81 on 1 and 13 DF, p-value: 0.001581# Visualisation de la régression
ggplot(data, aes(x = CDT, y = CarIntenElectr )) +
geom_point(color = "steelblue", size = 3) +
geom_smooth(method = "lm", se = TRUE, color = "darkred") +
labs(
title = "Link between Percentage of government-controlled territory and Carbon intensity of electricity",
x = "CDT",
y = "(CarIntenElectr)"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
Discussion
Class 3 countries: Niger, Mali , Burkina Faso, Niger, Mali and Burkina Faso occupy a particularly fragile position in the West African energy landscape. Their high dependence on fossil fuels, combined with a low rate of access to electricity and insufficient infrastructure, severely limits their potential for socio-economic development. This structural vulnerability also exposes their populations to climate risks and limits the ability of states to respond effectively to basic needs. To overcome these challenges, it is essential to promote the implementation of off-grid solutions, adapted to rural realities: the deployment of solar microgrids and autonomous energy kits can significantly expand access to electricity, while stimulating local economic activities. Technological innovation must be accompanied by international financial support, mobilizing aid, investment and public-private partnerships to accelerate the development of modern and resilient infrastructure. Finally, the success of this transition depends on strengthening institutional capacities and promoting energy education within communities. The sharing of expertise and experiences with other regional countries, combined with the involvement of local actors, makes it possible to anchor the dynamic of inclusive and sustainable growth. Implementing these levers will offer the three countries the opportunity to reduce energy poverty and improve their resilience to climate and economic change.
Annexes 1
![Electricity generation per capita] (C:/Users/USER/Desktop/Carte RTI/Electricity generation per capita.png)
