A multicriteria decision-making approach for prioritizing renewable energy resources for sustainable electricity generation in Benin

Abstract

Benin’s rapid population, economic, and industrial growth will increase energy demand. Hence, Benin’s government is keen to increase its power capacity to meet future demand. Renewable energy sources are significant options that governments could explore when developing their sustainable energy strategies. Prioritizing energy sources for a country’s sustainable development requires a decision-making process. In this study, multicriteria decision-making (MCDM) methods are used to prioritize alternatives such as solar photovoltaic (PV), concentrated solar power (CSP), wind energy, hydropower, and bioenergy suitable for sustainable electrification in Benin. The study employed eighteen (18) criteria classified under the four pillars of sustainability (technical, social, economic, and environmental) to evaluate the alternatives. Thereafter, the criteria importance through inter-criteria correlation (CRITIC) and entropy methods, which were employed to compute criteria weights and incorporated into the evaluation based on distance from average solution (EDAS) method to rank the alternatives. The findings from the EDAS rankings show that solar PV is the optimal alternative, ranking in the first position, followed by wind energy in the second position. CSP, hydropower, and bioenergy rank in the third, fourth, and fifth positions. In addition, EDAS findings correlated fully with those of other MCDM approaches evaluated on the same decision matrix. This indicates that solar PV is Benin’s optimal technology for sustainable electricity generation. The study’s findings are critical for policymakers developing Benin’s renewable energy sector.

Keywords:

• Sustainable development
• Renewable energy technologies
• Multicriteria decision making
• Benin republic
• Energy planning

1. Introduction

Energy is the foundation for industrialization and the key to development in every nation. Transforming the global energy sector plays a significant role in achieving the 1.5°C target of the Paris Agreement and in providing access to affordable, reliable, and sustainable energy services to communities (I. U. W. B. W. IEA, 2021; D. B. IRENA et al., 2021. Renewable energy sources (RES) are expanding beyond supplying reliable electricity and preserving the environment (Owusu et al., 2016). New jobs and prospects for whole industries are rising, helping economic development. At the same time, reliable, sustainable energy is essential for ensuring people access necessities like safe drinking water and healthcare (D. B. IRENA et al., 2021. It has therefore become necessary to cover the ever-increasing energy demand to achieve sustainable development (Güney, 2019).

Many countries still heavily rely on fossil fuels like coal and crude oil to meet their energy needs (IEA, 2019). However, the reliance on these finite resources comes at a high cost to our environment. Burning fossil fuels releases harmful greenhouse gases, contributing to global climate change and air pollution (IEA, 2019). Continuous large-scale exploitation of non-renewable energy sources creates grave risks to humans with rapid increases in the earth’s average temperature (Harjanne & Korhonen, 2019). One part of the solution is the removal of fossil fuel subsidies to curb wasteful energy consumption and utilize more reliable and environmentally friendly energy sources.

RES, comprising solar, wind, hydro, geothermal, tidal energy, and biomass, are natural energy sources that replenish at a higher rate than consumed (Al Garni et al., 2018; Yazdani et al., 2020). Renewable energy technologies are increasingly important for addressing the world’s energy challenges and mitigating the impact of climate change. RES is essential for developing and implementing various technologies in different sectors, such as transportation, the digital economy, agriculture, and public health (Eslami et al., 2022; Saberi-Beglar et al., 2023). According to the I. U. W. B. W. IEA (2021), renewable energy consumption grew by 3% in 2020, while the need for other fuels decreased. This is due to the 7% growth in electricity production from RES. It is worth mentioning that the proportion of renewable energy in the world’s electricity generation mix jumped from 27% in 2019 to 29% in 2020 (IEA, 2021).

The use of RES has significantly increased in recent years but could not meet the overall rise in global energy demand (Hosseini, 2022). The intermittent nature of RES can pose challenges for grid integration. In view of this, these challenges can be addressed through the use of energy storage, backup power, and demand response programs (Golmohamadi, 2022; Javadi et al., 2022). Several techniques exist for integrating RES into the utility grid, making it more efficient and reliable (Aghdam et al., 2023; Ahmadi et al., 2022; Moafi et al., 2023). For example, new techniques such as artificial intelligence (Ahmad et al., 2022) and the Internet of Things (Mansouri et al., 2023) can significantly improve RES integration into the power grid, making it more efficient, reliable, and sustainable.

Benin, a West African country, is facing the challenge of providing sustainable electricity to its growing population. The country’s energy sector relies heavily on fossil fuels, making it vulnerable to price fluctuations and supply disruptions. Benin is among the West African nations that have targeted using RES to create jobs, enhance energy security, and boost the local economy. Currently, only 40% of the Beninese populace has access to electricity, and only 6% of rural and urban residents use clean cooking technologies (SEforALL, 2022; Stritzke et al., 2022). The government aspires to attain an electrification rate of 80% by 2025, with about 24.6% stemming from RES. This aims to boost its renewable energy production capacities, reduce the country’s long-standing energy shortage, and guarantee that its residents can access electricity by 2035 (MENSAH et al., 2022). However, implementing this target requires careful consideration of the available RES, their costs, and environmental impacts. Therefore, there is a need for a robust and effective multicriteria decision-making approach that can prioritize renewable energy resources for sustainable electricity generation in Benin.

Developing strategies to exploit RES, such as wind, solar, and hydropower, is essential for a sustainable environment (Hosseini, 2022; Razeghi et al., 2023). To address the issues of the energy-economy-environment nexus and achieve Sustainable Development Goals, several nations and institutions have adopted strategic plans that involve diversifying their energy mix and incorporating RES with other energy sources. Nearly all nations worldwide are currently promoting renewable energy programs, support policies, and targets (IEA, 2021). However, it is more challenging for decision-makers to select one specific RES over others when weighing all their advantages and disadvantages. As a result, choosing RES is a strategic and essential decision-making process for a nation based on various factors (Yazdani et al., 2020; Çelikbilek & Tüysüz, 2016).

Decision-makers must consider both qualitative and quantitative factors and environmental aspects to use RES safely, sustainably, and efficiently (Gülçin & Sezin, 2017). Relevant multicriteria decision-making (MCDM) methods have been developed to handle complicated decisions when selecting the most suitable RES in a particular country. MCDM methods are operations research-based models that address the decision-making problem when there are several competing objectives. For instance, conflicting criteria, incommensurable units, and challenges in designing and choosing alternatives are all aspects of these methods, which can handle both quantitative and qualitative criteria (Pohekar & Ramachandran, 2004).

MCDM applications enable handling complex issues with low requirements in energy planning (Dwivedi et al., 2023; F. Ali et al., 2022). It helps to ensure transparent and objective decision-making in selecting sustainable energy alternatives by providing a structured and rigorous process for evaluating alternatives based on multiple criteria and objectively weighing each criterion’s importance (Kaya et al., 2019). MCDM approaches are essential for selecting RES because they provide a systematic and structured way to evaluate and compare multiple criteria and objectives. Renewable energy alternatives often have different advantages and disadvantages that need to be considered, such as cost, environmental impact, reliability, and scalability. MCDM approaches allow decision-makers to weigh and prioritize these factors based on their relative importance, leading to more informed and rational decision-making.

MCDM methods, such as the analytic hierarchy process (AHP), Shannon entropy, best and worst (BWM), and decision-making trial and evaluation laboratory (DEMATEL), are used to determine the weight of criteria (Asadabadi et al., 2019; Diakoulaki et al., 1995; Torkayesh et al., 2020). Likewise, measurement of alternatives and ranking according to compromise solution (MARCOS), evaluation based on distance from average solution (EDAS), the technique for order preference by similarity to ideal solution (TOPSIS), and viekriterijumsko kompromisno rangiranje (VIKOR) are among the most useful MCDM methods in ranking alternatives (Amini et al., 2020; Torkayesh et al., 2020; Yazdani et al., 2020).

Several authors have used MCDM methods to evaluate RES for sustainable energy planning across the globe. For instance, Kabak and Daǧdeviren (2014) applied the benefits, opportunities, costs, and risks (BOCR) approach and the analytic network process (ANP) to prioritize RES in Turkey. The result showed that hydropower is Turkey’s most appropriate RES. Çelikbilek and Tüysüz (2016) proposed a grey-based approach that comprises DEMATEL, ANP, and VIKOR for evaluating RES. The authors found that MCDM methods are effective and applicable worldwide and are recommended for multicriteria evaluation problems. Georgiou et al. (2015) used AHP and PROMETHEE methods to evaluate five alternative energy generation topologies for desalination in the Aegean Islands. The findings indicate that the region should consider diesel generation in the case of economic priorities. Similarly, Şengül et al. (2015) used the fuzzy TOPSIS method to confirm the findings of (Kabak & Daǧdeviren, 2014), which show that hydropower is the best RES in Turkey for long-term energy planning.

Lee and Chang (2018) integrated the weighted sum model (WSM), VIKOR, TOPSIS, elimination and choice translating reality (ELECTREE), and Shannon entropy methods to prioritize RES in Taiwan. The author found hydropower is the best option, followed by solar and wind power in Taiwan. Also, Tasri and Susilawati (2014) applied the fuzzy analytic method to select the most appropriate RES in Indonesia. Based on several criteria, hydropower remains Indonesia’s most suitable renewable alternative, followed by geothermal, solar, wind, and biomass. Furthermore, Garni et al. (2016) evaluated renewable power generation sources in Saudi Arabia using AHP. It was found that solar photovoltaic (PV) and concentrated solar power (CSP) are the most appropriate renewable energy technologies in Saudi Arabia, followed by wind power.

Other notable studies include San Cristóbal (2011), who adopted AHP and VIKOR to evaluate RES in Spain. The authors found that a biomass power plant is the best choice for the country. Also, Yazdani et al. (2020) proposed a hybrid of Shannon entropy-EDAS techniques and investigated its feasibility for Saudi Arabia. The findings revealed that wind power is the region’s most suitable renewable energy choice. In addition, Streimikiene et al. (2020) employed MULTIMOORA and TOPSIS to prioritize sustainable electricity production technologies. The authors recommended future energy policies prioritizing sustainable energy technologies for power production while preserving the environment. Similarly, Büyüközkan and Güleryüz (2016) integrated the DEMATEL-ANP approach to select optimal RES. The authors revealed that combining economic, social, political, and technical attributes could help practitioners and investors decide best when selecting RES.

Van Thanh (2022) proposed a spherical fuzzy AHP and TOPSIS approach to select RES for Vietnam’s industrial complex. The authors found that solar energy is the best RES for the industrial sector, while wind energy is the worst alternative. Pathak et al. (2022) applied AHP to prioritize RES in India. The study considered nine economic, technical, and environmental criteria. The findings revealed that hydro energy ranks first, followed by solar and wind energy. Qazi et al. (2023) integrated entropy and TOPSIS to select the optimum RES alternative in Malaysia. Biomass was found to be the most appropriate RES for sustainable electricity generation. T. Ali et al. (2019) employed Entropy and EDAS methods to evaluate Bangladesh’s best renewable energy technology. The results revealed that the solar-wind-battery hybrid energy system is the best option. Afrane et al. (2021) adopted a fuzzy TOPSIS approach to select the best RES for electrification in Ghana. It was observed that hydropower is the best RES to satisfy Ghana’s energy needs, followed by solar, wind, and biomass. Asakereh et al. (2022) applied a fuzzy AHP method to determine the optimal RES for electricity generation in Iran. The results indicate that solar PV should be the primary focus for generating sustainable electricity in the Khuzestan province of Iran.

The above studies show that MDCM methods are widely used to evaluate and rank RES worldwide. The main objective of this study is to rank alternatives, comprising solar PV, CSP, wind energy, bioenergy, and hydropower, appropriate for sustainable electricity generation in Benin. This study makes several novel contributions to the literature on RES and sustainable electricity generation in Benin. Firstly, it proposes a comprehensive and systematic approach for prioritizing RES that incorporates stakeholder preferences and uncertainty. Secondly, the study identifies a set of criteria relevant and specific to the Benin context, which have not been explored in previous studies. Thirdly, the study evaluates the potential of RES, considering its technical, economic, social, and environmental aspects.

The study combines entropy, CRITIC, and EDAS MCDM methods to evaluate the suitability of different RES for sustainable electricity generation in Benin. The study findings aim to inform stakeholders, decision-makers, and policymakers on which RES could be prioritized for sustainable development. Also, it informs policymakers to consider drafting policies that would attract potential investors to venture into deploying the best renewable energy technology for the country’s socio-economic development.

2. Methodology

This section highlights the approaches used to prioritize the alternatives. Also, the criteria used for evaluating RES are explained. Additionally, the numerical algorithms attributed to the selected MCDM methods are highlighted.

2.1. Selecting alternatives

Benin’s primary energy resources include bioenergy, fossil fuels, mineral energy resources, hydro, solar energy, and wind energy (SEforALL, 2022). In view of this, since this study is focused on sustainable energy resources for electricity generation, only RES are considered for evaluation. These RES can significantly reduce the gap between energy demand and supply in Benin and increase electricity generation installed capacity (Odou et al., 2020). For example, solar energy can offer electricity to remote communities and be utilized for residential and industrial purposes. Benin’s solar radiation ranges from 3.9 to 6.1 kWh/m²/day from the south to the north, with annual productivity ranging from 1680 to 2118 kWh/m²/year. Solar energy can be harnessed using photovoltaic (PV) and concentrated solar power (CSP) technologies. A solar PV module converts sunlight into electricity. Whiles CSP generates electricity through mirrors. The mirrors concentrate and focus natural sunlight, subsequently transforming into heat. The heat produces steam, which spins a turbine to generate electricity.

Hydropower is a very efficient technology that can provide electricity for domestic and industrial applications in any country. It’s a mature technology and the most crucial energy source in many countries. Benin’s theoretical hydropower potential is estimated at 749 MW. Wind power can also provide energy for different purposes. It is also a mature technology with a relatively low cost. The wind speed at 10 m varies between 3 and 6 m/s in the coastal area of Benin. Benin has a considerable amount of agricultural waste. However, the bioenergy potential is currently untapped and mostly burned in the fields. Using 27% of its national tailings, Benin’s biomass reserve can supply its electrical capacity (Sotiman Yotto et al., 2021).

2.2. Selection of evaluation criteria

Criteria are required to make objective decisions about adopting the most suitable alternative. Thus, well-considered and carefully constructed selection criteria can make selection decisions transparent and acceptable. The study adopted four criteria, comprising social, economic, technical, and environmental, categorized under the “pillars of sustainability,” to evaluate the performance of the alternatives. Several criteria for each pillar were identified and selected to compare the alternatives from a specific point of view. This is the MCDM approach’s most crucial phase, which should be chosen in collaboration with the decision-makers.

The quantitative and qualitative data and information pertaining to the prospective criteria must be available before deciding how many criteria to use. The exploitation of RES possibilities is multidisciplinary, and single-criteria decision-making systems cannot handle it. A comprehensive literature review was done to compile the most common criteria used in sustainable energy planning in other countries (Büyüközkan & Güleryüz, 2016; Ertay et al., 2013; Garni et al., 2016; Georgiou et al., 2015; Kabak & Daǧdeviren, 2014; Lee & Chang, 2018; San Cristóbal, 2011; Streimikiene et al., 2020; Tasri & Susilawati, 2014; Torkayesh et al., 2020; Troldborg et al., 2014; Yazdani et al., 2020; Çelikbilek & Tüysüz, 2016; Şengül et al., 2015).

Based on the comprehensive literature review, 18 different criteria were identified and chosen for assessment. Table 1 presents the chosen criteria and their significant impact on decision-making. Table 1 shows that when making a decision, maximum values are better for positive attributes, and minimum values are better for negative attributes. Table A1 shows the data obtained for each criterion. The data were retrieved from reports published by notable international organizations such as the International Renewable Energy Agency (IRENA), the National Renewable Energy Laboratory (NREL), the Environmental Protection Agency (EPA), and scientific studies. Nevertheless, 10 experts were consulted to provide quantitative data for the criteria for which there was no available data in the literature. The experts were asked to provide data for the criterion based on a scale ranging from 1 to 5, where 1 is the worst score and 5 is the best. The profile of each expert is presented in Table 2.

Table 1. Decision-making processes criteria

Indicator	Criteria	Index	Attribute type	Implication
Technical	Efficiency	SC1	Positive	This criterion measures how efficiently a technology converts energy into electricity. It depicts how well technology can produce power from a source.
	Capacity factor	SC2	Positive	The rate at which technology operates throughout a specific period. It is the ratio of the annual energy the technology generates to its rated power capacity throughout the year.
	Technical potential	SC3	Positive	This criterion shows the energy potential that could be harnessed for electricity generation in Benin.
	Technology maturity	SC4	Positive	It depicts the maturity of the technology based on the existing installed capacity for electricity generation.
	Reliability	SC5	Positive	This criterion depicts the dependability of the technology for electricity generation throughout the day
	Ease of decentralization	SC6	Positive	It denotes the ability to deploy the technology closer to consumers.
Social	Job creation	SC7	Positive	This criterion defines the potential job opportunities created by deploying a particular technology.
	Social and political acceptance	SC8	Positive	This refers to the predicted degree of public and political satisfaction and their views towards each technology.
	Economic development	SC9	Positive	It measures the economic benefits of each technology to the national economy.
	Compatibility with the national energy policy	SC10	Positive	It indicates how the technology deployment aligns with the country’s aim of developing its power sector.
	Safety risks fatalities	SC11	Negative	It depicts the number of fatalities that occur during the lifecycle of a technology.
Economics	Cost of Energy	SC12	Negative	This indicates the cost of energy expected from technology during its lifespan.
	Capital cost	SC13	Negative	This is how much each technology costs per kW, comprising equipment cost, wages, installation, and infrastructure.
	O&M cost	SC14	Negative	It defines all the costs required for operating and maintaining each technology.
Environmental	Land use	SC15	Negative	This refers to the total land area required to install a particular technology.
	CO2 emissions	SC16	Negative	It refers to each technology’s total CO2, NOx, and SOx emissions during its lifecycle.
	NOx emissions	SC17	Negative
	SOx emissions	SC18	Negative

Table 2. Profile of experts

Measure	Variable	Frequency	Percentage
Institution	Academic and research	3	30
	Public agency	5	50
	Private agency	2	20
Job position	Teaching and research	3	30
	Lead consultant	2	20
	Manager	1	10
	Director	1	10
	Policy expert	3	30
Working experience	Below 5 years	1	10
	5–10 years	3	30
	11–20 years	3	30
	Above 20 years	3	30

2.3. Selecting the appropriate MCDM methods

Several MCDMs exist for evaluating alternatives. This study employed two weighting methods, entropy, and CRITIC, commonly used to compute criteria weights. The CRITIC and entropy approaches compute objective criteria weights when decision-makers think differently about the value of weights for criteria. These approaches prevent decision-makers from being biased when awarding weights to criteria compared to subjective methods such as BWM and AHP (Odoi-Yorke, Abofra, et al., 2022; Odoi-Yorke, Atepor, et al., 2022). The CRITIC and entropy weights are then integrated into the EDAS to rank the alternatives. EDAS was chosen because it is a newly developed MCDM that is resilient, evaluating alternatives with several contradictory criteria (Odoi-Yorke, Abofra, et al., 2022).

The study further applied other MCDMs, including TOPSIS, Combinative Distance-based Assessment (CODAS), Complex proportional assessment (COPRAS), Multi-Objective Optimization By Ratio Analysis (MOORA), and VIKOR, to the decision matrix to justify the findings from EDAS. The numerical computation steps for CRITIC, entropy, and EDAS are highlighted in Sections 2.3.1–2.3.3.

2.3.1. Shannon entropy method

The entropy algorithm estimates criteria objective weights using Eq. (1) - Eq. (3): (Yazdani et al., 2020); Odoi-Yorke, John, et al. (2022)

Step 1: Normalize the decision matrix using Eq. (2):

(1) ${\mathrm{S}}_{\mathrm{ij}}={\mathrm{Y}}_{\mathrm{ij}}/\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{ij}}\mathrm{i}=1,2,3,\dots ..\mathrm{n}$(1)

where, ${\mathrm{S}}_{\mathrm{ij}}$ is the normalized value, and ${\mathrm{Y}}_{\mathrm{ij}}$ is the decision matrix value.

Step 2: Compute the criteria entropy values as follows:

(2) $\mathit{\hspace{0.17em}}{\mathrm{N}}_{\mathrm{j}}=-\mathrm{R}\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{S}}_{\mathrm{ij}}\log {\mathrm{S}}_{\mathrm{ij},\mathit{\hspace{0.17em}}}\mathrm{j}=1,2,3.,\mathrm{n}$(2)

$\mathit{\hspace{0.17em}}$Where, $\mathrm{R}=\frac{1}{\mathrm{lo}{\mathrm{g}}_{\mathrm{a}}}\mathit{\hspace{0.17em}}$is a constant based on 0≤N_j≤1, ${\mathrm{N}}_{\mathrm{j}}$ is the entropy value, and a is the total alternatives.

Step 3: Using Eq. (3), calculate the criterion objective weight:

(3) $\mathit{\hspace{0.17em}}{\mathrm{W}}_{\mathrm{j}}=1-{\mathrm{N}}_{\mathrm{j}}/\sum _{\mathrm{i}=1}^{\mathrm{n}}1-{\mathrm{N}}_{\mathrm{j}}\mathit{\hspace{0.17em}},\mathrm{j}=1,2,\dots ,\mathrm{n}$(3)

where W_j is the weight for each criterion C_j.

2.3.2. CRITIC weighting technique

CRITIC estimates objective weights using the following steps: (Odoi-Yorke, Abofra, et al., 2022; Odoi-Yorke, John, et al., 2022):

Step 1: Normalizes the decision matrix $(\overline{{\mathrm{Z}}_{\mathrm{ij}}})$using Eq. (4):

(4) $\overline{{\mathrm{Z}}_{\mathrm{ij}}}=\frac{{\mathrm{Z}}_{\mathrm{ij}}-{\mathrm{Z}}_{\mathrm{j}}^{\mathrm{worst}}}{{\mathrm{Z}}_{\mathrm{j}}^{\mathrm{best}}-{\mathrm{Z}}_{\mathrm{j}}^{\mathrm{worst}}}\mathit{\hspace{0.17em}}$(4)

Where$,\mathit{\hspace{0.17em}}{\mathrm{Z}}_{\mathrm{ij}}$ are the decision matrix values; ${\mathrm{Z}}_{\mathrm{j}}^{\mathrm{best}}$are the best values in the decision matrix that exist in the column; ${\mathrm{Z}}_{\mathrm{j}}^{\mathrm{worst}}$ are the worst values in the decision matrix that exist in the column.

Step 2: Compute the standard deviation (S_j) value as follows:

(5) $\mathit{\hspace{0.17em}}{\mathrm{S}}_{\mathrm{j}}=\frac{\sqrt{{(\sum {\mathrm{Z}}_{\mathrm{ij}}-\mathit{\alpha })}^2}}{\mathrm{P}}\mathit{\hspace{0.17em}}$(5)

where, P is population size, α is the mean population, and ${\mathrm{Z}}_{\mathrm{ij}}$ is the normalized decision matrix value.

Step 3: Measure the criteria degree of conflict using Eq. (6):

(6) $\sum _{\mathrm{k}=1}^{\mathrm{m}}1-{\mathrm{f}}_{\mathrm{jk}}\mathit{\hspace{0.17em}}$(6)

where, f_jk is the symmetric matrix of n x n, which is the linear correlation coefficient between the vectors x_j and x_k

Step 5: Compute the criterion quantity of information C_j:

(7) ${\mathrm{C}}_{\mathrm{J}}={\mathrm{s}}_{\mathrm{j}}\ast \sum _{\mathrm{k}=1}^{\mathrm{m}}1-{\mathrm{f}}_{\mathrm{jk}}\mathit{\hspace{0.17em}}$(7)

Step 6: Estimate each criterion weight W_j using Eq. (8):

(8) $\mathit{\hspace{0.17em}}{\mathrm{w}}_{\mathrm{j}}=C_j/\sum _{\mathrm{i}=1}^{\mathrm{m}}\mathit{\hspace{0.17em}}{C}_j\mathit{\hspace{0.17em}}$(8)

2.3.3. EDAS method

The EDAS approach is based on the distance from the average solution, corresponding to the best alternative. This approach presents the positive distance from the average and the negative distance from the average as the first two metrics. These metrics can demonstrate how each alternative option differs from the average solution. EDAS ranks alternatives using the following steps (Odoi-Yorke et al., 2023; Yazdani et al., 2020):

Step 1: Find the criterion average solution $\left({\mathrm{N}}_{\mathrm{j}}\right)$ using Eq. (9):

(9) $\mathit{\hspace{0.17em}}{\mathrm{N}}_{\mathrm{j}}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Z}}_{\mathrm{ij}}}{\mathrm{a}}\mathit{\hspace{0.17em}}$(9)

Where, $\mathit{\hspace{0.17em}}{\mathrm{Z}}_{\mathrm{ij}}$ are the decision matrix values, and a is the total criteria.

Step 2: Compute the positive distance from average (PAij), where Eq. (10a) is applied if the criterion type is positive and Eq. (10b) is used if the criterion type is negative:

(10a) $\mathrm{P}{\mathrm{A}}_{\mathrm{ij}}=\frac{\max (0,({\mathrm{Z}}_{\mathrm{ij}}-{\mathrm{N}}_{\mathrm{j}}))}{{\mathrm{N}}_{\mathrm{j}}}\mathit{\hspace{0.17em}}$(10a)

(10b) $\mathrm{P}{\mathrm{A}}_{\mathrm{ij}}=\frac{\max (0,({\mathrm{N}}_{\mathrm{j}}-{\mathrm{Z}}_{\mathrm{ij}}))}{{\mathrm{N}}_{\mathrm{j}}}\mathit{\hspace{0.17em}}$(10b)

Step 3: Estimate the negative distance from average (NAij) where Eq. (11a) is applied if the criterion type is positive and Eq. (11b) is used if the criterion type is non-negative.

(11a) $\mathrm{N}{\mathrm{A}}_{\mathrm{ij}}=\frac{\max (0,({\mathrm{N}}_{\mathrm{j}}-{\mathrm{Z}}_{\mathrm{j}}))}{{\mathrm{N}}_{\mathrm{j}}}\mathit{\hspace{0.17em}}$(11a)

(11b) $\mathrm{N}{\mathrm{A}}_{\mathrm{ij}}=\frac{\max (0,({\mathrm{Z}}_{\mathrm{ij}}-{\mathrm{N}}_{\mathrm{j}}))}{{\mathrm{N}}_{\mathrm{j}}}\mathit{\hspace{0.17em}}$(11b)

Step 4: Calculate the weighted sum of PA_ij and NA_ij for the alternatives using Eqs. (12) and Eq. (13), respectively:

(12) $\mathrm{WS}{\mathrm{P}}_{\mathrm{i}}=\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{j}}\mathrm{P}{\mathrm{A}}_{\mathrm{ij}}\mathit{\hspace{0.17em}}$(12)

(13) $\mathrm{WS}{\mathrm{N}}_{\mathrm{i}}=\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{j}}\mathrm{N}{\mathrm{A}}_{\mathrm{ij}}\mathit{\hspace{0.17em}}$(13)

Where, $\mathrm{WS}{\mathrm{P}}_{\mathrm{i}}\mathit{\hspace{0.17em}}$is the weighted sum of PA_ij and $\mathrm{WS}{\mathrm{N}}_{\mathrm{i}}$ is the weighted sum of NA_ij.

Step 6: Normalize WSP_i and WSN_i values as follows:

(14) $\mathrm{LW}{\mathrm{P}}_{\mathrm{i}}=\frac{\mathrm{P}{\mathrm{A}}_{\mathrm{i}}}{\mathrm{ma}{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{PA}\right)}\mathit{\hspace{0.17em}}$(14)

(15) $\mathrm{LW}{\mathrm{N}}_{\mathrm{i}}=1-\frac{\mathrm{N}{\mathrm{A}}_{\mathrm{i}}}{\mathrm{ma}{\mathrm{x}}_{\mathrm{i}}\left(\mathrm{N}{\mathrm{A}}_{\mathrm{I}}\right)}\mathit{\hspace{0.17em}}$(15)

Where LWPi and LWNi are the normalized values for WSPi and WSNi, respectively.

Step 7: Calculate alternatives performance score (Z_i) using Eq. (16):

(16) $\mathit{\hspace{0.17em}}{\mathrm{Z}}_{\mathrm{i}}=\frac{1}{2}\left(\mathrm{LW}{\mathrm{P}}_{\mathrm{i}}+\mathrm{LW}{\mathrm{N}}_{\mathrm{i}}\right)\mathit{\hspace{0.17em}}$(16)

3.1. Criteria weights

As mentioned above, the criteria weights were computed using the CRITIC and entropy methods. Eq. (1) was used for the entropy method to normalize the decision matrix in Table 1A. After that, the criteria entropy values were computed using Eq. (2), and the final weights for each criterion were estimated using Eq. (3). On the other hand, the CRITIC algorithm for computing criteria weights is different from entropy. The CRITIC algorithm normalizes the decision matrix (Table A1) using Eq. (4). Eq. (5) was then used to calculate the criteria standard deviation. Also, the criterion quantity of information was evaluated using Eq. (6), and the criteria weights were finally estimated using Eq. (7). Different criteria influence the decision-making outcomes since each criterion’s weight affects the ranking. Despite different criteria, weights substantially determine which alternatives are preferred. The distribution of weight is based on the importance of the criteria. It considers the importance of each criterion in relation to the other criteria and is used to calculate the relative weights of the criteria for appropriate decision-making. A criterion with a higher weight greatly influences the decision-making process. Hence, estimating the weights of different criteria requires justifying the method employed. Figure 1 presents criterion weights estimated by CRITIC and entropy methods. MCDM methods are dependent on criteria weights in ranking alternatives. It should be noted that the total weight of the criteria is 1. It can be seen in Figure 1 that the top 5 criteria with the highest weights based on CRITIC methods are SC₂ (8.5%), SC₁ (6.7%), SC₁₁ (6.4%), SC₁₂ (6.3%), and SC₅ (6.2%). Similarly, for the entropy method, the top 5 criteria with the highest weight comprise SC₁₁ (21.4%), SC4 (19.2%), SC₁₅ (15.2%), SC₁₇ (9.7%), and SC₃ (8.7%). This result attests that criterion weight varies among the weighting methods. However, weights determined by the CRITIC method are nearly consistent. For example, the highest and lowest weight difference is approximately 77%. In contrast, the difference between the lowest and highest weight is almost 198% for the entropy method. Furthermore, weights obtained by CRITIC and entropy have a correlation coefficient of r_s = −0.086. This implies a very low negative correlation and an almost negligible relationship between the weights obtained by the two methods. To check the consistency of the criteria weight, the weights obtained from each method are compared to the no-priority approach, as shown in Figure 1. No-priority means that all criteria have the same weight. This was deduced by dividing the number of criteria (18) by 1. It can be observed that the weight obtained by the CRITIC method is nearly evenly distributed among the criteria compared to entropy. The CRITIC weights are roughly comparable to the no-priority weights. In fact, the highest weight obtained from the CRITIC method is just 46% higher than the no-priority weight of 0.056. Also, the lowest weight is about 33% lower than 0.056. That cannot be said for weights obtained by entropy methods. The highest weight is 116% higher than the no-priority weight of 0.056. Similarly, the lowest weight is about 190% lower than 0.056.

Figure 1. Estimated criteria weights.

3.2. Alternative’s performance based on the EDAS method

The performance of the alternatives (renewable energy resources) is evaluated using the EDAS approach. With regards to the EDAS approach, the average solution was obtained using Eq. (9), and the results are presented in Table A2. Next, Eq. (10a) and Eq. (10b) are applied to estimate the alternatives’ positive distance from average (PA) with respect to each criterion (Table A3). At the same time, Eq. (11a) and Eq. (11b) are used to compute the alternative negative distance from average (NA) for each alternative (Table A4). Thus, greater PA_ij and lower NA_ij values will indicate the best option. Higher PA_ij and NA_ij values indicate that the alternative is better than the average solution. After that, the WSP and NSP for the alternatives were evaluated using Eq. (12) and Eq. (13), respectively, as shown in Table 3. Furthermore, Eq. (14) and Eq. (15) were applied to normalize WSP and WSN values to obtain LWP, and LWN values, which are also presented in Table 3. Finally, the alternative performance scores (Z_i) were calculated using Eq. (16). The Z_i ranks the alternatives. The alternative with a higher Z_i is the best. It can be observed from Table 3 that alternative ranking is similar when using weights obtained from the CRITIC and entropy weight approaches. However, the Z_i for each approach differs due to different criterion weights. In light of this, the results show that solar PV technology, with the highest Z_i, is the most promising and best alternative. Wind energy, CSP, and hydropower rank in the 2nd, 3rd, and 4th positions, respectively. As seen in Table 3, bioenergy technology is the worst technology, ranking in the 5th position. In summary, the rankings of the alternatives are as follows: solar PV > wind energy > CSP > hydropower > bioenergy. This result indicates that solar PV technology should be prioritized as Benin’s first sustainable electricity generation technology.

Table 3. Alternative’s performance based on the EDAS method

Alternative	CRITIC approach						Entropy approach
	WSPi	WSNi	LWPi	LWNi	Zi	Rank	WSPi	WSNi	LWPi	LWNi	Zi	Rank
Solar PV	0.539	0.296	1	0.443	0.722	1	1.321	0.107	1.000	0.907	0.953	1
CSP	0.275	0.299	0.510	0.437	0.474	3	0.519	0.338	0.393	0.704	0.548	3
Bioenergy	0.154	0.532	0.287	0	0.143	5	0.210	1.143	0.159	0.000	0.080	5
Hydropower	0.283	0.330	0.526	0.380	0.453	4	0.271	0.993	0.205	0.131	0.168	4
Wind	0.327	0.122	0.607	0.771	0.689	2	0.521	0.260	0.395	0.772	0.583	2

3.3. Comparative analysis

In order to check the performance of the proposed approach and validate EDAS rankings, other five MCDM approaches, including COPRAS-Complex Proportional Assessment (Amoozad Mahdiraji et al., 2018), CODAS-Combinative Distance-Based Assessment (Raheja et al., 2022), TOPSIS- Technique for Order Preference by Similarity to Ideal Solution (Chodha et al., 2022), MOORA- Multi-Objective Optimization on the Basis of Ratio Analysis (Odoi-Yorke, Abbey, et al., 2022), and VIKOR- Viekriterijumsko Kompromisno Rangiranje (Odoi-Yorke, John, et al., 2022) was tested on same decision matrix to assess and rank the alternatives. Applying multiple MCDM approaches to the same decision matrix boosts each method’s benefits because each has its limitations. Thus, multiple MCDM approaches might give better results than one. It can be observed in Figure 2 that the positions of some alternatives are similar, while others are different in the MCDM approaches. This shows instabilities in the alternative ranks among the MCDM approaches. However, these variations could be attributed to the strengths and drawbacks of each MCDM technique. Figure 3 shows the estimated correlation coefficient for alternative ranks obtained from other methods compared to EDAS. Correlation coefficients range from −1 to+1. Both parameters increase in a positive correlation. A negative correlation means one variable’s rank increases while the other decreases. It can be deduced from the figure that there is a perfect positive correlation between the ranks obtained from MOORA + entropy and COPRAS + entropy compared to EDAS. Also, VIKOR+entropy and TOPSIS+CRITIC exhibit a very high positive correlation (r = 0.908) and dependable relationship with EDAS. Likewise, the MOORA+CRITIC, COPRAS+CRITIC, and CODAS+entropy methods have r = 0.723. This implies a high positive correlation and a marked relationship with the EDAS approach.

Figure 2. Comparison of alternative rankings to other MCDM methods.

Figure 3. Correlation coefficient between the proposed and other MCDM methods.

On the other hand, there is a moderately negative and significant relationship between VIKOR+CRITIC and the EDAS approach. In addition, CODAS+CRITIC shows a very low negative correlation (r = −0.015). Based on this correlation test, it can be said that the correlation between EDAS rankings and the other MCDM is high, except for VIKOR+CRITIC (moderate correlation) and CODAS+CRITIC (low correlation). In view of this, it can be said that the proposed method is validated. Thus, the ranks obtained from EDAS could be accepted in prioritizing the alternatives for sustainable electricity generation. The correlation coefficient test has proven that the order of prioritizing the alternatives is as follows: solar PV (first position), wind energy (second position), CSP (third position), hydropower (fourth position), and bioenergy (fifth position). Therefore, solar PV technology is the most promising and sustainable technology. The findings are similar to those obtained for other Sub-Saharan countries, including Ghana (Afrane et al., 2021), Kenya (Manirambona et al., 2022), India (Pathak et al., 2022) and South Africa (Naicker & Thopil, 2019), where solar PV, wind energy, and CSP emerged as the top 3 alternatives for sustainable energy planning. However, it deviates from a similar study in China by Abdul et al. (2022), where solar PV was considered the worst alternative for sustainable energy planning but wind energy emerged as the second optimal alternative.

Solar PV has been identified as a highly promising RES for Benin to invest in for electricity generation. Integrating solar PV into the existing energy mix in Benin can be achieved through a systematic approach. First, assessing the country’s current energy demand and supply is vital to determining the best solar PV installation locations. A feasibility study can follow this to evaluate the proposed solar PV project’s technical, financial, and environmental viability. Likewise, a comprehensive energy plan should be developed considering the existing energy infrastructure, transmission and distribution systems, and regulatory frameworks. This plan should also consider the potential impacts of solar PV integration on the grid, such as voltage stability, power quality, and reliability. Furthermore, policies and incentives can be put in place to encourage the adoption of solar PV by individuals, businesses, and industries. This can include feed-in tariffs, tax credits, and subsidies for solar PV installations.

Solar PV’s ranking as the most promising technology in Benin could be attributed to its maturity stage in the country’s electricity generation mix since the government and its stakeholders are making the necessary effort to utilize the PV technology for sustainable electrification. Recently, the government inaugurated its first utility-scale solar PV power plant. The plant has an installed capacity of 25 MW and can supply electricity to about 40,000 homes. Moreover, it would save about 23,000 metric tonnes of CO₂ annually (Anyango, 2022). This shows that the government is committed to using PV technology to improve economic growth, provide quality electricity, and mitigate greenhouse gas emissions for sustainable development.

Furthermore, the results show that CSP and wind energy outshine conventional hydropower and bioenergy. Notwithstanding this, CSP and modern bioenergy technologies are still in the developmental stage in Benin, with no installed capacity in the country’s electricity generation mix. In light of this, extra effort is required to initiate the deployment of CSP technology, especially in the northern region where solar irradiation is around 6.1 kWh/m²/day. Likewise, a significant amount of electricity could be generated using bioenergy technologies such as direct combustion gasification, pyrolysis, and anaerobic digestion. Therefore, stakeholders may continue to embrace these technologies because they are strongly linked to sustainability.

Benin, like many other countries, can promote the development of bioenergy and maximize the use of agricultural waste for electricity generation through a combination of policies and strategies that are tailored to its specific context. Based on this, the following policies and strategies can be put in place to promote the development of bioenergy in Benin and maximize the use of agricultural waste for electricity generation, taking into consideration the economic, environmental, and social benefits: (i) Developing a comprehensive policy and regulatory framework that encourages private sector participation in the bioenergy sector. The policy should provide incentives for private investment, set clear standards for quality and sustainability, and establish a transparent system for the allocation of resources; (ii) Implement feed-in tariffs to provide a guaranteed price for electricity generated from bioenergy sources. This will help incentivize the private sector to invest and ensure a stable and predictable revenue stream; (iii) Invest in research and development to improve bioenergy production efficiency and identify new and innovative ways to use agricultural waste for electricity generation; (iv) Develop public-private partnerships to encourage the private sector to invest in bioenergy projects. This can involve partnering with local communities to establish small-scale bioenergy projects that provide electricity to remote areas (v) Develop capacity-building programs to train local communities in bioenergy production and build their technical skills. This can help create local jobs and improve the country’s overall economic development; (vi) Developing an awareness-raising campaign to educate the public about the benefits of bioenergy and encourage the adoption of sustainable practices in agriculture

4. Conclusion

This study has utilized MCDM methods comprising CRITIC, entropy, and EDAS to prioritize five RES (solar PV, CSP, bioenergy, hydropower, and wind energy). Eighteen (18) criteria that stem from technical, economic, social, and environmental indicators were used to evaluate the performance of RES for sustainable electricity generation. The study’s key findings can be summarised as follows:

• Criterion weight varies among the weighting methods. Nevertheless, the CRITIC method produces nearly consistent weights. For example, the difference between the highest and lowest weights is around 77%. The difference between the lowest and highest weights is over 198% for the entropy technique.

• The correlation coefficient between CRITIC and entropy weights is −0.086. This indicates a minimal relationship and a very low negative correlation between the weights obtained by the two approaches.

• EDAS findings reveal that the order of priority of the renewable energy resources is as follows: solar PV > wind energy > CSP > hydropower > bioenergy. This indicates that solar PV is the most promising technology and should be considered first for sustainable energy planning. Likewise, bioenergy is the worst technology for electricity generation in Benin.

• In addition, the EDAS ranking fully correlates with the ranks obtained from the other MCDM tested on the decision matrix. This result demonstrates that mixing qualitative and quantitative data improves MCDM performance by eliminating disparities and bias. Also, using many testing methods ensures precise results. The more relevant criteria chosen, the better the study’s outcomes.

With the low level of bioenergy development in Benin, it is suggested that the government develop favorable policy and regulatory frameworks that provide tax breaks and subsidies to encourage investment in bioenergy and establish regulations to set production and transportation standards and distribution. Investments are required to develop the necessary infrastructure, including processing plants and transmission lines, and to develop a public awareness plan on the benefits of bioenergy to increase support for sustainable development. Private-public participation in renewable energy technologies can become more cost-effective with the help of state support and infrastructure development. Also, funding for capacity building can boost the growth of solar and wind power in Benin. This study is vital for prioritizing RES and policy development in Benin and other developing countries willing to implement RES. Future research should increase the total criteria and apply new optimization algorithms to improve results.

List of abbreviations and acronyms

Table

AHP	Analytic hierarchic process
ANP	Analytic network process
BOCR	Benefits, Opportunities, Costs, and Risks
BWM	Best Worst Method
C	Criterion
CO2	Carbon dioxide
CODAS	Combinative distance-based assessment
COPRAS	Complex proportional assessment
CSP	Concentrated solar power
DEMATEL	Decision-making trial and evaluation laboratory
EDAS	Evaluation based on distance from average
ELECTREE	Elimination and choice translating reality
EPA	Environmental Protection Agency
IRENA	International Renewable Energy Agency
LWN	Normalized value of WSP
LWP	Normalized value of WSN
MARCOS	Measurement of alternatives and ranking according to compromise solution
MCDM	Multi-criteria decision making
MOORA	Multi-objective optimization by ratio analysis
MULTIMOORA	Multi-objective optimization by ratio analysis plus the full multiplicative form
N	Entropy value
NA	Negative distance from average
NOX	Nitrogen oxides
NREL	National Renewable Energy Laboratory
P	Population size
PA	Positive distance from average
PV	Photovoltaic
RES	Renewable energy source
S	Standard deviation value
SC	Selection criteria
SOX	Sulphur oxides
TOPSIS	Technique for order preference by similarity to ideal solution
VIKOR	viekriterijumsko kompromisno rangiranje
W	Weight for each criterion
WSM	Weighted sum model
WSN	Weighted sum of negative distance from average
WSP	Weighted sum of positive distance from average
Y	Decision matrix values
Z	Alternative performance score

Acknowledgments

The authors acknowledge Mr. John Eshun Davies for assisting them conduct desk literature review.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Appendix A

Table A1. Decision matrix for MCDM methods

S/n	Criteria	Solar PV	CSP	Bioenergy	Hydropower	Wind
1	Efficiency (%)	15–20^d	23–35^e	36^e	90^g	20–40^f
2	Capacity factor (%)	17.2^e	42^e	68^e	45^e	39.2^e
3	Technical potential (MW)	3532^a	0	761^a	749^b	322^a
4	Technology maturity (MW)	8^c	0	0	0.6^c	0
5	Reliability*	3	3	4	4	3
6	Ease of decentralization*	5	3	5	2	2
7	Safety risks Fatalities/GW-yr)	0.000245^g	0.000245^g	0.0149^g	0.945^g	0.00189^g
8	Cost of energy (USD/kWh)	0.048^e	0.114	0.067^e	0.048^e	0.033^e
9	Capital cost (USD/kW)	857^e	4746	2353^e	2135^e	1325^e
10	O&M cost (USD/kW/yr)	9.5–13.5^g	75–80^g	50^g	20–60^g	27–39.5^g
11	Land use (m^2/kW)	35^g	40^g	5000^g	750^g	100^g
12	CO2 emissions (g/kWh)	49.174^g	13–19^g	8.5–130^g	2–20 g	3–41^g
13	NOx emissions (g/kWh)	0.178^g	0.054–0.082^g	0.08–1.7^g	0.004–0.06^g	0.02–0.11^g
14	SOx emissions (g/kWh)	0.257^g	0.035–0.049^g	0.03–0.94^g	0.001–0.03^g	0.02–0.09^g
15	Job creation (Total job-years/GWh)	0.87^g	0.23^g	0.21^g	0.27^g	0.17^g
16	Social and political acceptance *	5	3	4	4	3
17	Economic development *	5	2	3	4	3
18	Compatibility with the national energy policy*	5	2	3	5	3

Note: ^a(IRENA, 2018), ^b(Sotiman Yotto et al., 2021), ^c(MENSAH et al., 2022), ^d(Center for Sustainable Systems, 2022), ^e(IRENA, 2022), ^f(United States Environmental Protection Agency, 2013), ^g(Manirambona et al., 2022)

*Data obtained from experts based on a scale ranging from 1 to 5, where 1 is the worst score, and 5 is the best

Table A2. Criteria average solution with respect to the alternatives

Criteria	SC1	SC2	SC3	SC4	SC5	SC6	SC7	SC8	SC9	SC10	SC11	SC12	SC13	SC14	SC15	SC16	SC17	SC18
AVj	40.5	42.28	1072.8	1720	3.4	0.35	3.8	3.4	3.6	3.4	0.192	0.062	2283.2	42.45	1185	33.48	0.247	0.171

Table A3. Alternatives positive distance from average with respect to each criterion

Criteria	Entropy weighting method					CRITIC weighting method
	Solar PV	CSP	Bioenergy	Hydropower	Wind	Solar PV	CSP	Bioenergy	Hydropower	Wind
SC1	0.000	0.000	0.000	1.222	0.000	0.000	0.000	0.000	1.222	0.000
SC2	0.000	0.000	0.608	0.064	0.000	0.000	0.000	0.608	0.064	0.000
SC3	2.292	0.000	0.000	0.000	0.000	2.292	0.000	0.000	0.000	0.000
SC4	3.651	0.000	0.000	0.000	0.000	3.651	0.000	0.000	0.000	0.000
SC5	0.000	0.000	0.176	0.176	0.000	0.000	0.000	0.176	0.176	0.000
SC6	0.000	0.343	0.400	0.229	0.514	0.000	0.343	0.400	0.229	0.514
SC7	0.000	0.211	0.000	0.000	0.211	0.000	0.211	0.000	0.000	0.211
SC8	0.000	0.412	0.118	0.000	0.118	0.000	0.412	0.118	0.000	0.118
SC9	0.000	0.444	0.167	0.000	0.167	0.000	0.444	0.167	0.000	0.167
SC10	0.000	0.118	0.000	0.412	0.412	0.000	0.118	0.000	0.412	0.412
SC11	0.999	0.999	0.923	0.000	0.990	0.999	0.999	0.923	0.000	0.990
SC12	0.226	0.000	0.000	0.226	0.468	0.226	0.000	0.000	0.226	0.468
SC13	0.625	0.000	0.000	0.065	0.420	0.625	0.000	0.000	0.065	0.420
SC14	0.729	0.000	0.000	0.058	0.217	0.729	0.000	0.000	0.058	0.217
SC15	0.970	0.966	0.000	0.367	0.916	0.970	0.966	0.000	0.367	0.916
SC16	0.000	0.522	0.000	0.671	0.343	0.000	0.522	0.000	0.671	0.343
SC17	0.278	0.724	0.000	0.870	0.736	0.278	0.724	0.000	0.870	0.736
SC18	0.000	0.754	0.000	0.909	0.678	0.000	0.754	0.000	0.909	0.678

Table A4. Alternatives negative distance from average with respect to each criterion

Criteria	Entropy weighting method					CRITIC weighting method
	Solar PV	CSP	Bioenergy	Hydropower	Wind	Solar PV	CSP	Bioenergy	Hydropower	Wind
SC1	0.568	0.284	0.111	0.000	0.259	0.568	0.284	0.111	0.000	0.259
SC2	0.593	0.007	0.000	0.000	0.073	0.593	0.007	0.000	0.000	0.073
SC3	0.000	1.000	0.291	0.302	0.700	0.000	1.000	0.291	0.302	0.700
SC4	0.000	1.000	1.000	0.651	1.000	0.000	1.000	1.000	0.651	1.000
SC5	0.118	0.118	0.000	0.000	0.118	0.118	0.118	0.000	0.000	0.118
SC6	1.486	0.000	0.000	0.000	0.000	1.486	0.000	0.000	0.000	0.000
SC7	0.316	0.000	0.053	0.053	0.000	0.316	0.000	0.053	0.053	0.000
SC8	0.471	0.000	0.000	0.176	0.000	0.471	0.000	0.000	0.176	0.000
SC9	0.389	0.000	0.000	0.389	0.000	0.389	0.000	0.000	0.389	0.000
SC10	0.471	0.000	0.471	0.000	0.000	0.471	0.000	0.471	0.000	0.000
SC11	0.000	0.000	0.000	3.910	0.000	0.000	0.000	0.000	3.910	0.000
SC12	0.000	0.839	0.081	0.000	0.000	0.000	0.839	0.081	0.000	0.000
SC13	0.000	1.079	0.031	0.000	0.000	0.000	1.079	0.031	0.000	0.000
SC14	0.000	0.826	0.178	0.000	0.000	0.000	0.826	0.178	0.000	0.000
SC15	0.000	0.000	3.219	0.000	0.000	0.000	0.000	3.219	0.000	0.000
SC16	0.469	0.000	1.068	0.000	0.000	0.469	0.000	1.068	0.000	0.000
SC17	0.000	0.000	2.609	0.000	0.000	0.000	0.000	2.609	0.000	0.000
SC18	0.504	0.000	1.838	0.000	0.000	0.504	0.000	1.838	0.000	0.000