In the field of clean energy, the “pressure indicators” selected in this paper do not directly apply the concept of “pressure” in the PSR model, but rather use its framework to evaluate various factors influencing the development of clean energy. In this context, “pressure indicators” are understood as those challenges or limitations that impact the development and utilization of clean energy, including energy scarcity situations.

Specifically, in the assessment of clean energy potential, pressure indicators mainly include the following categories. Resource availability: This refers to the actual presence and technically exploitable levels of renewable energy sources within a specific region. For example, the feasibility and scale of renewable energy projects can be determined through the evaluation of solar radiation or wind speed data in a given region. Technical and Economic Barriers: These encompass factors such as technological maturity, cost-benefit analysis, and market acceptance. If the cost of a particular clean energy technology is too high or its efficiency is low, it constitutes a “pressure” that hinders its widespread adoption and development. For instance, high initial investment costs for solar panels or wind turbines may deter investors and slow down project implementation. Social Acceptance and Infrastructure: Public attitudes towards new clean energy installations and the capability of existing power grids to effectively integrate and distribute new energy sources are crucial considerations. Environmental Impact: Although clean energy is more environmentally friendly compared to traditional fossil fuels, its production and operation can still cause certain environmental problems, such as noise from wind turbines and pollution during the manufacturing process of photovoltaic panels. These issues also constitute pressures on the development of clean energy.

The energy demand gap and energy security pressures in Africa are two complex and interrelated issues that collectively reflect the challenges faced by the continent in meeting the growing energy needs of its residents and industries. On one hand, with the rapid population growth and urbanization, the demand for electricity is increasing rapidly. However, existing infrastructure cannot keep up with this growth, leading to significant supply-demand imbalances. On the other hand, the acceleration of industrialization has increased the demand for stable and reliable energy, but many African countries still rely on traditional biomass energy (such as firewood), which is inefficient and causes serious indoor air pollution. Especially in rural areas, limited grid coverage results in a large portion of the population lacking access to the power system. According to data, only one-third of households in Sub-Saharan Africa’s rural areas have access to electricity. Moreover, despite Africa’s abundant renewable resources like solar and wind energy, insufficient funding and technological limitations have hindered their full exploitation.

Regarding energy security pressures, African countries face frequent power outages due to weak infrastructure and outdated technology, affecting daily life and industrial production. Fluctuations in international fossil fuel prices also affect the economic stability of African countries dependent on imported energy. High energy costs constrain corporate competitiveness and development potential. Additionally, frequent extreme weather events, such as droughts and floods, pose threats to critical energy facilities like hydropower stations, increasing the risk of energy supply.

Stress indicators involve numerous influencing factors. In this paper, the fuzzy comprehensive evaluation method is adopted to determine these indicators. This method is particularly suitable for handling complex issues with unclear boundaries and difficult quantification. It leverages concepts from fuzzy mathematics to convert non-quantifiable factors into forms that can be mathematically processed, thereby achieving a comprehensive assessment of multiple influencing factors.

Fuzzy comprehensive analysis is an evaluation method based on fuzzy mathematics theory, especially suitable for dealing with complex issues with unclear boundaries and difficulties in quantification. This method effectively converts qualitative information into quantitative data, thus enabling comprehensive consideration of multiple uncertain factors. Specifically, fuzzy comprehensive analysis first constructs a set of all potential influencing factors and sets corresponding evaluation criteria according to actual conditions. Next, through expert scoring or surveys, membership values of each factor relative to different evaluation grades are obtained, forming a fuzzy judgment matrix. Then, weight coefficients for each factor are calculated. Finally, combining fuzzy operations, the ultimate comprehensive evaluation result is derived. Through this systematic and structured approach, fuzzy comprehensive analysis helps identify solutions to overcome data insufficiencies or partial applicability issues inherent in traditional statistical methods, making the decision-making process more reasonable and forward-looking.

The energy state indicators discussed in this paper primarily refer to the basic reserves, development costs, and exploitable duration of wind energy, solar energy, and hydropower. The primary data sources for these indicators come from the Global Internet Development Cooperation Organization.

Hydropower Resources: It is essential first to clarify the river basin conditions within the area and the current stage of river development and utilization. If the rivers in the area are relatively flat and surrounded by many residents and farmland, the conditions for developing hydropower resources are less favorable, making it difficult to implement cascaded hydroelectric power stations. Therefore, hydropower resources in such areas are constrained. Typically, exploitable hydropower resources are concentrated in the upstream sections of rivers where there is a significant drop and canyon-like terrain, which is ideal for constructing dams to maximize hydropower utilization. The evaluation of hydropower resources is the foundation of hydropower resource development and utilization, with a focus on the evaluation of hydrological data such as water flow rate. This assessment mainly involves converting river hydropower into other forms of energy, most commonly electricity, and determining both technically and economically feasible development capacities.

Solar Energy: The state indicators for solar and wind energy focus on aspects such as the basic reserves, development costs, and exploitable duration of these renewable energy sources. Development Costs: It involves the technical and economic investments required to capture and convert these energies into usable forms, including equipment manufacturing, installation, and maintenance costs. Exploitable Duration: It refers to the length of time these energies can sustainably supply based on current technology levels and consumption rates.

Wind Energy: With the assistance of GIS technology, interpolation mapping of wind energy resources can be performed within the region. Based on the scale and distribution of wind farm construction land, the total exploitable wind energy resources can be calculated. Wind energy resources are generally more abundant during monsoon periods, especially in coastal hilly areas due to favorable terrain conditions suitable for large-scale wind farms. Wind energy assessments require collecting long-term wind data, including representative annual average wind speeds, monthly average wind speeds, hourly average wind speeds, and wind direction data. The evaluation criteria for wind energy resources should be based on wind power density, wind direction frequency, and the distribution of wind energy density directions.

Solar Energy: Assessing solar energy reserves relies on meteorological observation methods and surface radiation data, focusing on both photothermal and photovoltaic applications of solar energy. The amount of solar energy resources does not equate to the exploitable quantity. The obtainable amount depends on the land construction scale of photovoltaic power plants within the region. The number of photovoltaic power plants in regions rich in climate resources but unsuitable for agricultural or industrial development can be calculated to determine the regional solar energy resource scale and ultimately accurately assess the exploitable solar energy resources in the region by the GIS technology.

This paper focuses on the market regulation and fiscal support policies within the power industry. These elements are crucial for promoting the development of clean energy, optimizing energy structures, and achieving peak carbon emissions and carbon neutrality goals.

For clean energy, a reasonable market entry mechanism can ensure fair competition opportunities. Market entry also involves support policies for new entrants, such as priority access rights, which help enhance the investment attractiveness of clean energy projects, encouraging more capital and technology to flow into this sector. Pricing mechanisms are another key factor influencing the development of clean energy. Currently, many countries adopt renewable portfolio standards (RPS) or green certificate trading systems to encourage the use of clean energy. These mechanisms guide resource allocation through market price signals, incentivizing power generation companies to increase their investment in clean energy. Additionally, time-of-use pricing policies adjust electricity prices based on supply and demand at different times, making clean energy more economically valuable during peak hours, thus enhancing its competitiveness.

In terms of fiscal subsidies and support, direct subsidies are a common form of government financial support aimed at promoting clean energy development. They compensate for initial project costs, reduce investor risks, and improve project economic benefits. For instance, during the construction of wind farms, government-provided construction subsidies can cover part of the equipment purchase fees or installation costs.

Tax incentives are also significant fiscal support measures. By reducing or exempting related taxes or providing tax credits, they effectively alleviate the financial burden on clean energy companies. For example, exemptions from import tariffs on clean energy production equipment or reductions in corporate income tax not only lower operational costs but also promote the development of the entire industrial chain, from raw material supply to final product manufacturing and service provision, creating a virtuous cycle.

Furthermore, clean energy projects often require substantial upfront investments. Some governments provide financing facilitation through special loan programs, guarantee mechanisms, and other means. For example, establishing clean energy development funds specifically to offer low-interest or interest-free loans to qualified projects, or raising funds through issuing green bonds, expanding financing channels, and attracting more private capital to participate in the clean energy industry.

In summary, market regulation and fiscal support policies in the power industry play indispensable roles in the development of clean energy. Through market-oriented reforms, grid integration and management, direct subsidies, tax incentives, research and development funding, and financing facilitation, these policies not only create a favorable market environment for clean energy but also provide solid guarantees and support for investors. Therefore, clean energy response indicators, including market regulation in the power industry, fiscal support policies, land and labor support policies, etc., are selected for evaluation in this paper. Specific indicators are shown in Table 1 .

The scope of this paper covers 52 major countries and regions in Africa: Algeria, Egypt, Ethiopia, Angola, Benin, Botswana, Burkina Faso, Burundi, Equatorial Guinea, Togo, Eritrea, Cape Verde, The Gambia, Republic of the Congo (Congo-Brazzaville), Democratic Republic of the Congo (Congo-Kinshasa), Djibouti, Guinea, Guinea-Bissau, Ghana, Gabon, Cameroon, Comoros, Côte d’Ivoire, Kenya, Lesotho, Liberia, Libya, Rwanda, Madagascar, Mali, Mauritius, Mauritania, Morocco, Mozambique, Namibia, South Africa, Niger, Nigeria, Sierra Leone, Senegal, Seychelles, São Tomé and Príncipe, Eswatini (formerly Swaziland), Sudan, Somalia, Tanzania, Tunisia, Uganda, Western Sahara (non-autonomous territory), Zambia, Chad, and the Central African Republic.

This comprehensive coverage allows for an in-depth analysis of the energy landscape across these diverse nations and regions, providing valuable insights into their respective challenges and opportunities in the context of clean energy development and utilization.

First, the first-level evaluation indicators are established. Based on the evaluation indicator system, set up an evaluation indicator set. The evaluation indicator set includes various indicators that influence the risk of significant misstatement. The evaluation indicator set is represented as $U=\left\{U_1,U_2,\cdots ,U_n\right\}$. Next, the second-level evaluation indicators, denoted as $U=\left\{u_{i1},u_{i2},\cdots ,u_{in}\right\}$ ( $i=1,2,\cdots ,N$), indicating that $U_i$ contains several factors are established.

In the evaluation indicators, the relative importance of each indicator is its weight. Reasonably determining and appropriately adjusting the weights of the indicators reflects the distinction between primary and secondary aspects in comprehensive evaluations. When analyzing factors, follow the principle of prioritizing more critical elements over less important ones. The optimal factors are selected to analyze their impact and ensure objective and truthful evaluation. The evaluation set consists of all possible overall evaluation results that evaluators might make for the evaluated object. The size of the evaluation set can be determined based on the actual degree of subdivision and computational requirements. For example, it can be divided into five levels: Full, Good, Average, Low, and None.

Fuzzy Comprehensive Evaluation: The first-level fuzzy comprehensive evaluation set is:

$B_{ij}=A_{ij}{R}_{ij}=\left(a_{ij1},a_{ij2},\cdots ,a_{ijm}\right)\left[\begin{array}{ccc}{r}_{11}^{ij}& \cdots & r_{1k}^{ij}\\ ⋮& \ddots & ⋮\\ r_{m1}^{ij}& \cdots & r_{mk}^{ij}\end{array}\right]=\left(b_{ij1},b_{ij2},\cdots ,b_{ijm}\right)$

The vector $\left(b_{ij1},b_{ij2},\cdots ,b_{ijm}\right)$ represents the degree of membership of factors u to the evaluation criteria $V_1,V_2,\cdots ,V_n$. In the second-level fuzzy comprehensive evaluation, the principle followed is to evaluate based on all factors within the factor subset. Therefore, the single-factor evaluation matrix for U is:

$R=B_i=\left[\begin{array}{ccc}{b}_{11}& \cdots & b_{1k}\\ ⋮& \ddots & ⋮\\ b_{N1}& \cdots & b_{Nk}\end{array}\right]$

For the processing of evaluation indicators, commonly used methods include the Maximum Membership Degree Method, Fuzzy Distribution Method, and Weighted Average Method. These methods aim to assess the actual situation of the object from a more realistic, reliable, and comparable perspective, ultimately providing an overall evaluation that reasonably integrates these attributes or factors.

When the evaluation object involves qualitative indicators, the evaluation results are typically categorized into five levels: Excellent, Good, Average, Low, and None. The overall situation is then assessed based on the scores obtained.

To objectively determine the evaluation indicators, extensive literature reviews, surveys, and expert interviews are conducted. Ultimately, the evaluation indicators are finalized through these methods. By distributing questionnaires or conducting interviews, each respondent is asked to provide a rating for each second-level indicator. The final step involves statistical analysis to determine the membership degree of each second-level indicator, calculated as: Number of occurrences of this indicator/Total number of trials.

Determining Indicator Weights Using the Analytic Hierarchy Process

(1) Importance ranking of indicators: Experts rank the relative importance of both first-level and second-level indicators. The ranking is denoted as $U_1>U_2>\cdots >U_{n-1}>U_n$, where $U_i>U_j$, indicates that within the same level, indicator i is more important than or equally important to indicator j.

(2) Value assignment of indicator importance:

Define the importance ratio between two consecutive indicators at the same level, $U_{i-1}$ and $U_i$, as $r_i$.

$r_i=\frac{W_{i-1}}{W_i}$

where $W_{i-1},W_i$ are the weights of the i-1-th, i-th indicators, respectively. The assignment rules for $r_i$ are shown in Table 2 .

(3) Calculation of Indicator Weights

From the previous equations, we know:

${\displaystyle {\prod }_{i=k}^n{r}_i}=\frac{W_{k-1}}{W_k}\frac{W_k}{W_{k+1}}\cdots \frac{W_{n-2}}{W_{n-1}}\frac{W_{n-1}}{W_n}=\frac{W_{k-1}}{W_n}$

Summing from 2 to n, we can get that:

${\displaystyle {\sum }_{k=2}^n\left({\displaystyle {\prod }_{i=k}^n{r}_i}\right)}=1+{\displaystyle {\sum }_{k=2}^n\frac{W_{k-1}}{W_n}}=\frac{W_n}{W_n}+{\displaystyle {\sum }_{k=2}^n\frac{W_{k-1}}{W_n}}=\frac{1}{W_n}$

Therefore, once the relative weight of the last indicator is determined, the relative weights of the other indicators can be calculated. The weight of the last indicator can be calculated by:

$W_n={\left[1+{\displaystyle {\sum }_{k=2}^n\frac{W_{k-1}}{W_n}}\right]}^{-1}$

The weights of other indicators are: $W_{n-1}=r_n{W}_n$.

Note: The values 1.1, 1.3, 1.5, and 1.7 represent the median values between two adjacent scales.

The study of clean energy in Africa is a multi-layered and complex issue that can be addressed using a combination of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA). These methods allow for the integration and concretization of multi-layer indicators. The decision-making principles and bases of these two methods differ, providing good complementarity. This paper combines TOPSIS and GRA to establish an evaluation model for the potential of clean energy in Africa. The decision-making process of the model is as follows:

Step 1: based on the clean energy potential evaluation indicator system, the evaluation objects are determined that raw data are collected, and can be formed the initial decision matrix: $X={\left(x_{ij}\right)}_{m\times n}$.

Where $m$ is the number of evaluation units, $n$ is the number of evaluation indicators, $n=38$. $x_{ij}$ represents the attribute value of the i-th evaluation unit under the j-th indicator. The first step involves normalizing the indicators to obtain the normalized decision matrix: $V={\left(v_{ij}\right)}_{m\times n}$

$v_{ij}=\frac{x_{ij}-{\min }_n\left\{x_{ij}\right\}}{{\max }_n\left\{x_{ij}\right\}-{\min }_n\left\{x_{ij}\right\}}$ (forward pointer)

$v_{ij}=\frac{x_{ij}-{\min }_n\left\{x_{ij}\right\}}{{\max }_n\left\{x_{ij}\right\}-{\min }_n\left\{x_{ij}\right\}}$ (negative pointer)

Step 2: Entropy Weight Method is used to determine the Indicator Weights $w_j=\left(w_1,w_2,\cdots ,w_n\right)$

$p_{ij}=v_{ij}/{\displaystyle {\sum }_{i=1}^m{v}_{ij}}$

$e_i=-1/\ln \left(m\right){\displaystyle {\sum }_{i=1}^m{p}_{ij}\cdot \ln p_{ij}}$

$w_j=1-e_j{\displaystyle {\sum }_{j=1}^n\left(1-e_j\right)}$

Step 3: Based on index weights, the weighted normalized decision matrix Y is obtained:

$Y={\left(y_{ij}\right)}_{m\times n}={\left(w_i{v}_{ij}\right)}_{m\times n}$

Step 4: Determining the Positive Ideal Solution (PIS) $L_J^{+}=\left(f_1^{+},f_2^{+},\cdots ,f_n^{+}\right)$ and Negative Ideal Solution (NIS) $L_J^{-}=\left(f_1^{-},f_2^{-},\cdots ,f_n^{-}\right)$:

$f_j^{+}={\max }_i\left\{y_{ij}\right\}$, $f_j^{-}={\min }_i\left\{y_{ij}\right\}$

Step 5: Grey relational coefficients $r_i^{+}$ and $r_i^{-}$ are calculated based on grey relational analysis,

${\gamma }_{oij}^{+}=\frac{{\min }_i{\min }_j\left|f_j^{+}-y_{ij}\right|+\rho {\max }_i{\max }_j\left|f_j^{+}-y_{ij}\right|}{\left|f_j^{+}-y_{ij}\right|+\rho {\max }_i{\max }_j\left|f_j^{+}-y_{ij}\right|}$

${\gamma }_{oij}^{-}=\frac{{\min }_i{\min }_j\left|f_j^{-}-y_{ij}\right|+\rho {\max }_i{\max }_j\left|f_j^{-}-y_{ij}\right|}{\left|f_j^{-}-y_{ij}\right|+\rho {\max }_i{\max }_j\left|f_j^{-}-y_{ij}\right|}$

$r_i^{+}=\frac{1}{n}{\displaystyle {\sum }_{j=1}^n{\gamma }_{oij}^{+}},r_i^{-}=\frac{1}{n}{\displaystyle {\sum }_{j=1}^n{\gamma }_{oij}^{-}}$

Step 6: Euclidean distance $d_i^{+}$ and $d_i^{-}$ are calculated by TOPSIS

$d_i^{+}=\sqrt{{\displaystyle {\sum }_{j=1}^n{\left(f_j^{+}-y_{ij}\right)}^2}},d_i^{-}=\sqrt{{\displaystyle {\sum }_{j=1}^n{\left(f_j^{-}-y_{ij}\right)}^2}}$

Step 7: The $r_i^{+}$, $r_i^{-}$, $d_i^{+}$, $d_i^{-}$ are dimensionless-Processed

$R_i^{+}=r_i^{+}/\max \left\{r_i^{+}\right\}$, $R_i^{-}=r_i^{-}/\max \left\{r_i^{-}\right\}$

$D_i^{+}=d_i^{+}/\max \left\{d_i^{+}\right\}$, $D_i^{-}=d_i^{-}/\max \left\{d_i^{-}\right\}$

Step 8: relative proximity between the evaluation unit and the “ideal solution” can be calculated d:

$S_i^{+}=\alpha R_i^{+}+\beta D_i^{-}$

$S_i^{-}=\alpha R_i^{-}+\beta D_i^{+}$

$S_i=S_i^{+}/\left(S_i^{+}+S_i^{-}\right)$

where $\alpha$ and $\beta$ are preference coefficients, $\alpha =\beta =0.5$ in this paper. The larger the value, the higher the score of the evaluation unit, and the greater the potential.

Based on the fuzzy comprehensive evaluation analysis method, this paper reviewed a large number of literature sources and conducted interviews with 30 experts and relevant personnel from technology companies. Each interviewee was required to provide an evaluation for each secondary indicator, which was used to calculate the degree of membership for each secondary indicator. The evaluations were categorized into five levels: excellent, good, average, poor, and none. The final statistical results are shown in Table 3 . Each pair of data in the table represents, on the left, the frequency of responses falling within each evaluation category, and on the right, the calculated final degree of membership for each secondary indicator.

The final scores of each region are calculated as shown in Table 4 by the fuzzy comprehensive evaluation method.

The membership analysis for labor use policies will not be listed in detail here. The final scores for each indicator in each region are calculated as shown in Table 5 by the fuzzy comprehensive evaluation method.

Resource and environmental data were obtained from statistics compiled by the Global Energy Interconnection Development Cooperation Organization, as shown in Table 6 .

This translation accurately captures the methodology used to calculate the weights of the primary indicators by involving expert rankings and applying the $r_i$ value assignment rules, with the final results presented in Table 7 .

Based on the importance ranking of the primary indicators, the weights are calculated using equations. The resulting weight values for the primary indicators are shown in Table 8 .

By calculating the average of these 4 sets of data, we can obtain the weight vector for the primary indicators:

For these 3 sets of data, by calculating their average, we can obtain the weight vector for the primary indicators: W = (0.110, 0.111, 0.114, 0.114, 0.076, 0.070, 0.213, 0.191).

Similarly, another 4 experts from related fields were invited to rank the importance of the 8 secondary indicators and assign values to them. This resulted in the importance rankings and corresponding r_i values for each of the 8 secondary indicators, as shown in Tables 9-22 .

The weight vector of the secondary index of U₁ is obtained by averaging, W₁ = (0.495, 0.505).

The weight vector of the secondary index of U₃ is obtained: W₃ = (0.284, 0.325, 0.392).

The weight vector of the secondary index of U₄ is obtained: W₄ = (0.294, 0.316, 0.391).

The weight vector of the secondary index of U₅ is obtained: W₅ = (0.545, 0.455).

The weight vector of the secondary index of U₆ is obtained: W₆ = (0.550, 0.450).

The weight vector of the secondary index of U₇ is obtained: W₇ = (0.302, 0.341, 0.357).

The weight vector of the secondary index of U₈ is obtained: W₈ = (0.302, 0.341, 0.357).

The final comprehensive scores of each region are obtained according to part 2.3 of this paper, as shown in Table 23 .

Based on the results obtained from the table, the clean energy scores of African countries into three categories can be classified to high scores (≥0.55), medium scores (0.5 - 0.55), and medium-low scores (0.45 - 0.5).

Among the high-score potential countries, Western Sahara (non-autonomous territory) leads with a score of 0.6028, followed by Tanzania (0.5954), Algeria (0.5918), Mauritania (0.5807), Egypt (0.5759), Niger (0.5675), and Djibouti (0.5633). These countries possess superior natural resource conditions (such as solar, wind, or geothermal energy) or significant policy support. For instance, there are abundant solar energy resources here in North African countries such as Algeria and Egypt; Western Sahara’s geographical location is suitable for both wind and solar energy development. The medium-high potential countries include South Africa (0.4479), Ghana (0.5494), Botswana (0.5493), and Sudan (0.5502). There is a certain foundation in clean energy, but it may be limited by technology, funding, or infrastructure in some countries. For example, there is enormous solar potential here in South Africa, but social and economic balance issues need to be addressed in its energy transition process. The medium-low potential countries include Nigeria (0.3952), Senegal (0.4214), Democratic Republic of the Congo (0.5078), and Kenya (0.4828). Lower scores reflect uneven resource distribution, political instability, or insufficient investment. Eastern African countries such as Tanzania and Rwanda perform well overall, possibly due to the development of geothermal and hydropower resources. Although Western Sahara is not yet independent, its high score highlights its natural resource advantages and suggests it could become a focal point for regional cooperation in the future.

For high-score countries, local governments can prioritize large-scale projects to attract international investment. For medium-low score countries, there is a need to strengthen policy support and promote cross-border grid interconnections to share clean energy technologies. In addition, the clean energy potential in Africa shows significant regional differences here. There are obvious advantages here in North and East African countries, while West and Central African countries need to overcome policy and funding bottlenecks. Through regional cooperation and technology transfer, the overall potential can be maximized, contributing to global energy transition.