ELECTRE-Based Optimization of Renewable Energy Investments: Evaluating Environmental, Economic, and Social Sustainability Through Sustainability Accounting

Abstract

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO₂ emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions.

1. Introduction

The world is transitioning to renewable energy. This shift is crucial in combating climate change, reducing greenhouse gas emissions, and ensuring a sustainable future [1]. Renewable energy sources, such as solar, wind, and bioenergy, are central to this movement. They offer a cleaner alternative as the world moves away from fossil fuels. But transitioning is not straightforward. Investment decisions must consider a range of factors, including environmental, economic, and social aspects [2]. Investors must carefully balance these aspects to make informed decisions. Environmental sustainability drives the transition to renewable energy. Climate change impacts are already visible, and cutting carbon emissions is critical. Renewable energy sources, such as solar and wind, have low emissions, which helps mitigate climate change [3]. As governments set net-zero targets, investments in renewable energy are growing. Yet, environmental concerns alone are not enough. Projects must also be economically viable and socially accepted to succeed. These capital-intensive projects require long-term financial returns, in addition to their environmental benefits. The economic feasibility of renewable energy projects is key. In many regions, renewables are now cost-competitive with fossil fuels. Solar and wind costs have dropped significantly [4]. Still, the upfront investment remains high, deterring some investors.

Government policies and incentives also have a significant impact. These factors differ by region and influence investment levels [5]. Governments need to implement policies that make renewable energy projects financially appealing. Social factors are equally important. Renewable projects can generate employment opportunities, benefit local communities, and enhance energy access. For instance, wind and solar projects create substantial employment in rural areas [6]. However, the social acceptance of these projects depends on community involvement, fairness, and the equitable sharing of benefits [7]. As the transition progresses, it is crucial to ensure that it does not exacerbate inequalities or leave marginalized communities behind.

Implementing sustainability requires managing environmental factors alongside economic factors while upholding social principles. Investors prioritize environmental and economic factors over social ones, which can lead to project failure. To make effective decisions, it is necessary to implement decision-making frameworks that handle all three factors. Decision-making involving multiple alternatives is supported by multi-criteria decision-making methods, which provide a formalized procedure for evaluating alternatives based on economic, environmental, and social value criteria [8]. In the face of such advances, decision-makers are helped to both balance trade-offs and evaluate priorities simultaneously when multiple objectives compete [9]. Among these mainstream practices, the Analytic Hierarchy Process (AHP) [10] and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [11] constitute the bulk, but the Elimination and Choice Expressing Reality (ELECTRE) is way ahead [12]. ELECTRE is a non-compensatory ranking method that evaluates alternatives using concordance and discordance indices [13]. It considers both criteria, agreement and disagreement, to derive the final rankings. This applies ELECTRE as a method for sustainability assessment, where a weak point in another should not automatically offset good performance in one criterion. This work uses ELECTRE to optimize renewable energy investment portfolios by aligning economic, environmental, and social factors. The technique, therefore, offers an open, highly realistic model of trade-offs typical of the actual decision-making process in sustainability planning.

The ELECTRE and MCDM methods enable the evaluation of renewable energy projects through multiple-criteria assessment to generate an extended evaluation perspective [13]. The three main obstacles to renewable energy investments stem from conflicting criteria, data complexity, and extended uncertainty periods. Renewable projects must meet environmental requirements, economic targets, and social commitments, but these goals often yield conflicting outcomes. Financial benefits from certain projects may result in environmental damage, while projects with positive ecological effects often have higher costs [14]. Renewable energy investments require handling diverse data. The data related to environmental aspects consists of emission records and biodiversity preservation, while economic data includes expense records and profit data. Social data includes job creation and community engagement. Decision-making becomes challenging when these different data categories need to be integrated into a single framework [15]. These investments face additional difficulties because they span multiple years. The unpredictability of energy prices, alongside regulations and technologies, creates challenges for sustainable project assessments over extended periods [16].

The methods fail to provide adequate strength for situations requiring the evaluation of conflicting criteria and long-term prediction [17]. Renewable energy market development remains unaddressed by multiple decision-making approaches. Project success depends on technological progress alongside regulatory adaptations and public sentiment, yet these vital factors receive minimal attention [18,19]. The current methods do not establish a unified standard for assessing the social and environmental impacts of renewable energy initiatives. The majority of financial assessment tools ignore social and environmental criteria [20]. The decision-making process fails to incorporate essential stakeholders, including local communities, governments, and environmental groups. Decisions that fail to match societal requirements might encounter resistance from the public [21]. This research investigates how ELECTRE optimizes renewable energy investments by considering multiple sustainability criteria. The analysis evaluates how environmental, economic, and social impacts are compared during investment decisions.

The research introduces ELECTRE as a new method for evaluating renewable energy investments. Its framework integrates with sustainability accounting to create an extensive process for assessing alternative energy. This integration provides practical tools for decision-makers by balancing environmental, economic, and social criteria. The paper establishes an ELECTRE-based optimization framework for evaluating renewable energy investments. The evaluation assesses solar, wind, and bioenergy projects by analyzing their environmental impacts alongside their economic outcomes and social benefits. The model establishes a balanced framework to support decision-making on renewable energy investments.

2. Literature Review

2.1. Renewable Energy Investment Evaluation

The evaluation of renewable energy investments demands a complex methodology. Financial aspects remain essential, yet environmental and social impacts need to be evaluated because they lack direct monetary values. The growing demand for sustainable energy necessitates the development of comprehensive evaluation systems that address multiple criteria, yet these methods remain under development and face inherent challenges. The three main evaluation methods used today include Cost–Benefit Analysis (CBA), Lifecycle Assessment (LCA), and multi-criteria decision-making (MCDM), yet each method faces specific evaluation-based restrictions [22]. This review section starts by examining the Cost–Benefit Analysis (CBA). The method is the most widely used technique for many years. The main goal of this method is to evaluate project costs against projected benefits. The approach seems effective at first glance. The main drawback of this method is its inability to account for non-monetary factors. Elements such as employment generation and the long-term benefits of carbon mitigation are challenging to quantify in monetary terms, leading to their exclusion from the analysis. This oversight raises concerns, particularly given renewable energy initiatives expected to yield substantial benefits [23]. Consequently, while CBA is indeed useful, there is contention that it is insufficient when a sustainability perspective is required [24]. Currently, LCA is adopting a more holistic approach by examining the entire lifecycle of a project, from raw material extraction to energy generation and final waste disposal. This comprehensive perspective enhances understanding of the environmental impacts associated with a project; however, it remains somewhat limited. Notably, it does not sufficiently address the economic considerations, such as the project’s financial profitability and its effects on local communities, as well as the social implications, which are often overlooked [25]. The application of LCA to evaluate environmental effects remains valuable yet insufficient for assessing the complete scope of renewable energy investments. The current situation requires the use of MCDM methods.

Decision-makers can use these approaches to evaluate various factors, including economic, environmental, and social factors, and determine their relative weights. The AHP and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) stand out as notable methods that help decision-makers rank alternatives through multiple criteria assessment. The methods present certain limitations because they struggle to properly evaluate social value and long-term sustainability impacts [26,27]. The ELECTRE method exists as another option. This method stands out for efficiently handling both quantitative and qualitative data. Consequently, in renewable energy projects where balancing financial considerations with factors such as community impact and environmental benefits is crucial, ELECTRE proves highly beneficial. It operates by comparing alternatives in pairs and calculating “concordance” (the degree to which one alternative dominates another) and “discordance” (the extent of differences between the alternatives) [28]. The advantage of ELECTRE lies in its ability not only to rank alternatives but also to eliminate options that do not satisfy specified criteria. Its primary objective is not solely to identify the optimal choice but also to exclude those options considered inadequate. Nonetheless, while ELECTRE exhibits considerable potential, a notable gap remains in its application to renewable energy investment evaluations, particularly in its integration with sustainability accounting. Most existing research employing ELECTRE has focused on a singular dimension of sustainability, such as environmental impact or economic feasibility, without encompassing all three facets: environmental, social, and economic. The incorporation of ELECTRE into comprehensive sustainability accounting frameworks that address these diverse aspects could lead to a more holistic evaluative instrument [28]. This aspect appears underrepresented in the current corpus of scholarly literature.

2.2. Multi-Criteria Decision-Making (MCDM)

MCDM methods are indispensable for decision-making involving multiple, often conflicting, criteria. For example, in renewable energy projects, various technologies such as wind, solar, and biomass each possess distinct advantages and disadvantages concerning cost, environmental impact, and social benefits. Relying solely on a single factor is insufficient for a comprehensive decision; therefore, MCDM approaches facilitate the balanced consideration of all relevant criteria.

ELECTRE, notably, represents a highly effective instrument for this purpose. It distinguishes itself from other methodologies by not solely ranking alternatives; rather, it performs pairwise comparisons and assesses them based on “concordance” (the degree to which one alternative surpasses another) and “discordance” (their degree of disparity). These matrices help decision-makers determine which alternatives meet the criteria and which do not. However, like any analytical tool, it presents certain limitations. A primary concern is the subjectivity inherent in the thresholds for concordance and discordance—specifically, the criteria used to determine what is considered “acceptable”. Additionally, the weights assigned to each criterion, if inaccurately determined, can bias the entire evaluation process [29].

Nevertheless, ELECTRE remains highly practical. It can process both quantitative and qualitative data, which is particularly advantageous for renewable energy projects where certain criteria, such as environmental benefits or social equity, are difficult to quantify. Moreover, its pairwise comparison methodology is well-suited for assessing complex trade-offs. However, even with these advantages, deriving a clear, ranked list of options can still be challenging, especially when the project is complex. The primary purpose is often to eliminate less viable alternatives rather than to establish an absolute ranking.

ELECTRE has been employed in evaluations of renewable energy sources, demonstrating promising outcomes. Sánchez-Lozano et al. [30] employed ELECTRE to compare various renewable energy technologies, including wind, solar, and biomass, in terms of their sustainability. This methodology facilitated prioritizing projects across multiple dimensions of sustainability. Their analysis extended beyond financial considerations to include environmental impact and social benefits, which are equally crucial.

Li et al. [31] conducted a similar study, applying ELECTRE to assess solar energy projects. Their focus was on managing trade-offs, balancing return on investment with reduced environmental impact and land use. It was recognized that such considerations are common in energy projects, where the challenge lies in reconciling profitability with sustainability objectives. The advantage of ELECTRE resides in its capacity to assist decision-makers in navigating these often-conflicting priorities.

An additional noteworthy aspect of ELECTRE is its capacity to integrate the perspectives of various stakeholders. Cinelli et al. [32] reported the use of ELECTRE to assimilate the preferences of government agencies, local communities, and commercial entities. In energy projects, a diverse array of individuals with differing interests are frequently involved. It is believed that this characteristic of ELECTRE, its ability to harmonize these varying priorities, may prove to be highly beneficial. One of the notable strengths of ELECTRE is its capacity to manage conflicting criteria effectively. Renewable energy projects frequently entail trade-offs, such as the classic scenario where a project is environmentally advantageous but less profitable, or vice versa.

2.3. Sustainability Accounting

Modern society places increasing importance on sustainability accounting practices. Business and project financial performance measures now need to include data on environmental and social effects. Through sustainability, accounting decision-makers gain access to extended project impact monitoring capabilities for renewable energy investments [33]. Projects need to generate profits while delivering benefits for people and the environment. The three main sustainability accounting domains are environmental, economic, and social. Organizations need to track their environmental footprint, together with their energy usage and waste management systems, within the environmental domain. The economic domain studies investment returns alongside broader economic benefits, including workforce development. The social domain focuses on equity alongside energy access and community health [34]. The complete set of domains creates a thorough analysis of project effects.

Multiple frameworks exist that help integrate sustainability accounting into renewable energy projects. The Global Reporting Initiative (GRI) is one of the most widely recognized sustainability reporting frameworks. Organizations use this system to track their sustainability performance across environmental, economic, and social aspects [35]. The Sustainability Accounting Standards Board (SASB) presents an alternative framework. It helps organizations evaluate their financial materiality while assessing their long-term economic sustainability [36]. The Triple Bottom Line (TBL) framework integrates environmental and social, and economic results for financial reporting purposes [37].

LCA stands as a fundamental component of sustainability accounting. This method tracks the environmental effects of a project throughout its complete lifecycle, extending from extraction to disposal [38]. The frameworks currently available do not address the existing major obstacles. Developing sustainability metrics applicable to every renewable energy project poses complex challenges.

Standardized metrics for measuring social and environmental impacts face a major challenge because of their scarcity. Standardized frameworks that apply universally across projects must be developed to incorporate technological advancements and market instability [39]. The existing literature reveals a significant gap in integrating MCDM methods, such as ELECTRE, with sustainability accounting. Numerous studies concentrate either on one methodology or the other; however, they seldom focus on both concurrently. While methods such as Cost–Benefit Analysis (CBA), LCA, AHP, and TOPSIS have their advantages, they fall short of adequately addressing the intricacies of renewable energy projects, particularly when considering the environmental, social, and economic trade-offs involved. This paper aims to bridge the delineated gap by integrating ELECTRE with sustainability accounting. The objective is to develop a more comprehensive decision-making tool that enables stakeholders to evaluate all pertinent factors, not solely financial considerations. Ultimately, sustainable energy decision-making requires a holistic approach that extends beyond mere financial analysis.

3. Materials and Methods

Developing a robust new mathematical model for ELECTRE-based optimization of renewable energy investments requires integrating sustainability accounting principles to address environmental, economic, and social sustainability criteria. The model will optimize renewable energy investment decisions by balancing multiple conflicting objectives that consider different sustainability dimensions.

Table 1. Definitions of all variables and parameters used in the optimization and ELECTRE analysis.

Variables	Definition
$x_i$	The proportion of investment allocated to the renewable energy alternative $\mathrm{i}$
n	The number of alternatives.
ci	Is the economic score for the alternative
ei	The environmental impact score for the alternative
si	The social impact score for the alternative
${\alpha }_i$	The weights for environmental impacts
βi	The weights for social impacts
pi	The price per unit of investment for alternative  $\mathrm{i}$.
$B$	The total available budget
$E_{max}$	The maximum acceptable environmental impact
$S_{min}$	The minimum acceptable social benefit
${\omega }_e$	The weights assigned to the environmental dimensions
${\omega }_s$	The weights assigned to the social dimensions
${\omega }_c$	The weights assigned to the economic dimensions
$c_{ik}$	The scores of alternatives $\mathrm{i}$ on criterion $\mathrm{k}$.
$c_{jk}$	The scores of alternatives $\mathrm{j}$ on criterion $\mathrm{k}$.
$T_c$	The threshold for concordance, indicating the required level of dominance on criterion $\mathrm{k}$.
$T_d$	The threshold for discordance, indicating the maximum acceptable disadvantage for any criterion
$R\left(i\right)$	The ranking score for the alternative $\mathrm{i}$, with higher values indicating better performance.
$c_{ik}^{norn}$	The normalized score of the alternative $\mathrm{i}$ on criterion k.
$max\left(c_k\right)$	The maximum scores for criterion $\mathrm{k}$, respectively.
$min\left(c_k\right)$	The minimum scores for criterion $\mathrm{k}$, respectively.
${\omega }_k$	The weight for criterion $\mathrm{k}$, which reflects the relative

describes the key variables and parameters used in the optimization model and ELECTRE analysis.

3.1. Mathematical Model Formulation for Optimization of Renewable Energy Investments

• Decision Variables

Let $x_i$ be the proportion of investment allocated to the renewable energy alternative $i$ (e.g., solar, wind, bioenergy). The decision variables are bounded between $0$ and $1$ as described in Equation (1):

$$\begin{gathered} x_i\in \left[\mathrm{0,1}\right],\hspace{1em}\hspace{1em}\hspace{1em}\sum _{i=1}^n{x}_i=1. \\ {\forall }_i =1, 2,\dots ,n \end{gathered}$$

(1)

where $n$ is the number of alternatives.

• Objective Function

The objective is to maximize sustainability across the three criteria: economic, environmental, and social. The objective function $Z$ combines all three sustainability aspects as shown in Equation (2).

$$Z=\sum _{i=1}^n{x}_i\left(c_i-e_i{\alpha }_i-s_i{\beta }_i\right)$$

(2)

where

▪

$c_i$ is the economic score for the alternative;

▪

$e_i$ is the environmental impact score for the alternative;

▪

$s_i$ is the social impact score for the alternative;

▪

${\alpha }_i$ and ${\beta }_i$ are the weights for environmental and social impacts, respectively.

The objective is to maximize the positive economic contribution and minimize the negative environmental and social impacts.

Since $x_i$ represents the share of total investment allocated to each alternative, and cost differences are embedded within the economic performance score $c_i$, no separate budget-based feasibility constraint involving unit prices is required.

If a diversification penalty is desired, a convex quadratic term is added as shown in Equation (3):

$$Maximize Z\sum _{i=1}^n{x}_{i}S{I}_i-\gamma \sum _{i<j}{\left(x_i-x_j\right)}^2, \gamma \ge 0$$

(3)

where $\gamma$ is the diversification penalty coefficient (or regularization parameter).

For $\gamma =0$ the problem is a convex quadratic program and can be solved with Gurobi QP.

• Sustainability Constraint

Each alternative must satisfy certain minimum sustainability thresholds for both environmental and social criteria. For example, the environmental impact must be below a given threshold $E_{max}$, and the social impact must meet a minimum acceptable level $S_{min}$.

The environmental and social constraints are shown in Equations (4) and (5), respectively.

$${e_i\le E}_{max} {\forall }_i=1, 2,\dots ,n$$

(4)

$${s_i\ge S}_{min } {\forall }_i=1, 2,\dots ,n$$

(5)

where

▪

$E_{max}$ is the maximum acceptable environmental impact;

▪

$S_{min}$ is the minimum acceptable social benefit.

• Investment Proportion Constraint

The total allocation of investment must sum to $1$; for example, the total budget should be distributed across the $n$ alternatives, as captured in Equation (6). This ensures that the entire budget $B$ is allocated to the renewable energy alternatives.

$$\sum _{i=1}^n{x}_i=1$$

(6)

• Sustainability Index (SI)

To integrate sustainability accounting, define an SI for the portfolio of energy projects. The index is based on the weighted sum of economic, environmental, and social scores and is expressed in Equation (7).

$$SI=\sum _{i=1}^n{x}_i\left({{\omega }_c{c}_i-\omega }_e{e}_i-{\omega }_s{s}_i\right)$$

(7)

where

▪

${\omega }_e, {\omega }_s, {\omega }_c$ are the weights assigned to the environmental, social, and economic dimensions, respectively;

▪

$SI$ is the overall sustainability score for the portfolio, which is to be maximized.

• Pairwise Comparison Matrix

The ELECTRE method evaluates each alternative using pairwise comparisons. Let $C\left(i,j\right)$ be the concordance index that compares alternative $i$ and alternative $j$, indicating how much alternative $i$ dominates alternative $j$ on each criterion as expressed mathematically in Equation (8). The ELECTRE procedure in this research study applies standard ELECTRE I logic through ratio comparisons of normalized scores to determine dominance and disadvantage conditions. The normalized approach represents a standard method that ELECTRE applications use to handle different types of sustainability metrics.

$$C\left(i,j\right)={\sum }_{k=1}^m{\omega }_k^E\cdot 1\left[{\displaystyle \frac{c_{ik}^{norm}}{c_{jk}^{norm}}}>T_c\right]$$

(8)

where

▪

$T_c$ is the threshold for concordance, indicating the required level of dominance on criterion $k$;

▪

${\omega }_k^E$ is the normalized ELECTRE weight for criterion $k$;

▪

$c_{ik}^{norm}$ is the normalized score of alternative $i$ on criterion $k$;

▪

$c_{jk}^{norm}$ is the normalized score of the alternative $j$ on criterion $k$;

▪

$1[\cdot ]$ is the indicator function.

• Discordance Index

The discordance index $D\left(i,j\right)$ measures the degree to which an alternative $i$ is worse than the alternative $j$ on any critical criterion, as shown in Equation (9).

$$D\left(i,j\right)=\underset{k}{\mathit{max}}\left({\displaystyle \frac{c_{jk}}{c_{ik}}}-T_d\right)$$

(9)

where

▪

$T_d$ is the threshold for discordance, indicating the maximum acceptable disadvantage for any criterion.

• ELECTRE Ranking

The final ranking $R\left(i\right)$ of alternatives is computed by combining the concordance and discordance indices. The ranking is based on a dominance matrix that reflects each alternative’s overall performance, as shown in Equation (10).

$$R\left(i\right)={\sum }_{j=1}^n\left(C\left(i,j\right)-D\left(i,j\right)\right)$$

(10)

where

▪

$R\left(i\right)$ is the ranking score for the alternative $i$, with higher values indicating better performance.

• Normalization of Criteria Scores

To ensure comparability across different criteria, normalize the criteria scores for all alternatives, as shown in Equations (11) and (12):

1.

Benefit-type (higher = better)

$$c_{ik}^{norm}={\displaystyle \frac{c_{ik}-min\left(c_k\right)}{max\left(c_k\right)-min\left(c_k\right)}}$$

(11)

where

▪

$c_{ik}^{norn}$ is the normalized score of the alternative $i$ on criterion k;

▪

$max\left(c_k\right)$ and $min\left(c_k\right)$ are the maximum and minimum scores for criterion $k$, respectively.

2.

Cost-type (lower = better)

$$c_{ik}^{norm}={\displaystyle \frac{max\left(c_k\right)-c_{ik}}{max\left(c_k\right)-min\left(c_k\right)}}$$

(12)

• Weighting of Criteria

Each criterion must be assigned a weight based on its importance. These weights are used in the ELECTRE method to prioritize the different criteria, as shown in Equation (13).

$${\omega }_k={\displaystyle \frac{c_k}{\sum _{k=1}^m{c}_k}}$$

(13)

where

▪

${\omega }_k$ is the weight for criterion $k$, which reflects the relative importance of that criterion in the decision-making process.

The thresholds for both concordance and discordance should be used to resolve conflicts between criteria. The decision-making process should be protected from bias by adjusting weights or thresholds when a single criterion becomes overly dominant. There is a weighting of all the concordance and discordance indices by the criterion importance factors ${\omega }_k$. The thresholds were derived from the normalized ranges of the selected criteria, following the standard ELECTRE practice to ensure balanced decision sensitivity. The final optimization model aims to maximize sustainability by balancing economic, environmental, and social aspects after all constraints and criteria have been defined.

3.2. Tools

The study uses Python 3.11.11 libraries for data analysis, optimization, and visualization, creating a comprehensive system for evaluating renewable energy investments. The Pandas library enables efficient data manipulation and handling operations. The library efficiently manages and transforms essential datasets on costs, energy outputs, and social impacts, which are vital for assessing renewable energy projects. The numerical computations in the study rely on NumPy 2.3.4 as the primary tool. The tool manages extensive arrays and executes multiple key performance indicator (KPI) calculations, including net present value (NPV) and energy efficiency ratios. The optimization solver Gurobi 13.0.0 performs the optimization task. The nonlinear optimization model achieves the maximum sustainability index using the Gurobi solver 13.0.0 while accounting for investment constraints that balance environmental, economic, and social criteria. The visualization tool Matplotlib 3.10.7 enables data analysis through its capabilities. The tool generates detailed plots for KPIs, including energy efficiency ratios, payback periods, and NPV, thereby improving decision-makers’ understanding of the results. The SciPy 1.16.3 library performs advanced scientific computations, with a focus on optimization. The library extends NumPy’s capabilities by providing advanced optimization tools. The combination of these libraries creates a robust system that enables systematic evaluation and optimization of renewable energy investments through sustainability accounting in decision-making processes.

4. Results and Discussion

4.1. Data Analysis

The content of

Table 2. Key data for renewable energy alternatives.

Alternative	Unit Cost (Million USD)	ROI (Annual Return	CO2 Emissions (Tons/Year)	Social Benefit (Jobs Created)	Energy Output (MWh/Year)	Energy Input (MWh/Year)	Annual Cash Inflow (Million$)
Solar	200	0.15	50	1000	1200	1000	50
Wind	150	0.20	30	1500	1500	1200	70
Bioenergy	180	0.18	40	1200	1300	1100	60

has been produced using anonymized, matched data from industrial benchmarks and regional and international open energy datasets. The study has been carried out in Sub-Saharan Africa, with Nigeria as the focal region. The period under focus is from 2022 to 2023 for the large-scale solar, wind, and bioenergy projects. Economic indicators, such as the unit cost and annual cash revenue, were calculated from confidential project information standardized using IRENA and World Bank data [40]. The return on investment (ROI) was calculated as the ratio of annual net cash inflow to total investment. CO₂ emissions were tracked using standard lifecycle emission factors provided by the International Energy Agency. The social benefit of jobs created, the indicator of social benefit, was assessed using employment intensity data. This data was compared with the International Labor Organization reports and found to be satisfactory [41,42]. The NPV calculations use an 8% discount rate and a 10-year evaluation period, which align with the model’s implementation. The ROI values from IEA/NREL datasets appear before taxes because they are pre-tax. The applied uncertainty ranges for cost, emissions, output, and job-creation parameters stem from typical variations observed in international datasets.

The input data used in the evaluation are summarized in Table 2. The million US dollars (USD) are used to express unit cost and cash inflow values. ROI is the annual return on investment ratio, calculated as net profit divided by total investment cost. CO₂ emissions are limited to Scope 1 emissions (direct emissions from the plant). The social benefit metric indicates the number of full-time equivalent (FTE) jobs created annually per investment project. Energy input and output values are given in megawatt-hours per year (MWh/year).

4.2. Weighting Process

The three domains of economic, environmental, and social received equal importance in the evaluation process. The researchers chose equal weighting to ensure impartiality, as it prevented the sustainability discussion from becoming biased toward any particular perspective. The evaluation system gives equal weight to financial, environmental, and social factors when ranking options.

The results underwent two sensitivity tests to verify their dependability. The first test involved adjusting domain weights between 0.2 and 0.5 while maintaining a total weight of one. The second analysis used entropy-based weights to demonstrate how data variability influenced the ranking results. The equal-weighting system had no negative impact on the evaluation process because the preference order remained consistent across both experiments.

Figure 1 presents the concordance matrix for the three renewable energy alternatives. The dark-blue block in the lower-right quadrant indicates a high concordance score in pairwise comparison. This suggests that one alternative consistently outperforms the other across most criteria: cost, ROI, payback, emissions intensity, and job intensity. In ELECTRE, concordance values above the decision threshold (0.6–0.7) indicate that the reference alternative is at least as good as its competitor in a significant share of criteria. The deep blue shading signals that this condition is strongly satisfied.

Figure 2 shows how one alternative underperforms another on specific criteria, highlighting its negative aspects. Dark red areas in the matrix indicate high discordance values, showing that an alternative performs poorly compared to others in that specific comparison. The ELECTRE method relies on the discordance matrix to eliminate alternatives with major performance deficiencies, ensuring that only suitable alternatives remain in the evaluation process. The final ranking positions alternatives based on their high concordance scores and minimal discordance levels. The concordance matrix shows which alternatives perform best overall across criteria, whereas the discordance matrix excludes alternatives with major performance issues or assigns them lower rankings.

4.3. Optimization and Ranking

The energy efficiency ratios (EROI) of three renewable energy alternatives are compared in Figure 3 to show their relative ability to generate net energy. The highest ratio is achieved by wind at ~1.25, followed by solar at ~1.20 and bioenergy at ~1.18. All options produce more energy than they consume, but the ranking is crucial for long-term sustainability assessment. EROI represents the physical basis of the economic ledger from a sustainability accounting perspective because higher efficiency leads to lower lifecycle costs and more stable revenue. The updated optimization allocates 53.3% of the investment to wind power, 16.7% to solar power, and 31.1% to bioenergy.

The ELECTRE multicriteria decision framework determines the optimal distribution of capital among the three renewable energy alternatives, as shown in Figure 4. The results indicate that wind receives the highest allocation at 52.3%, while bioenergy receives 31.0%, and solar receives the lowest allocation at 16.7%. The allocation pattern reflects the combined sustainability performance of each option across economic, environmental, and social dimensions. The investment distribution demonstrates a triple-bottom-line equilibrium that combines economic stability through wind power, social benefits from Bioenergy, and risk management through Solar energy.

The optimized portfolio achieves a total sustainability index (SI) of 1.70, validating the ELECTRE-based approach for converting complex multi-criteria evaluations into practical investment plans.

The NPV evaluations of three renewable energy alternatives are shown in Figure 5. Wind energy demonstrates the strongest long-term economic performance, yielding an NPV of approximately 469.7 million USD. High annual cash inflows and a strong levelized cost profile characterize such a case. The significant NPV is also consistent with Wind’s strong sustainability index and its leading share in the allocation of optimization results. With an NPV of 402.6 million USD, bioenergy moves to the position between wind and solar. Such a high value indicates that the project is highly economically viable, primarily due to stable annual cash inflows and strong operational performance. Among the three technologies, solar energy has the lowest NPV of 335.5 million USD. Although the solar technology is both capital-efficient and environmentally clean, it still generates low long-term financial returns due to its lower annual cash inflows.

The financial sustainability evaluation indicates that wind is the most profitable option. Bioenergy demonstrates financial viability through its NPV, which is slightly lower than that of wind but still strong. The financial analysis shows that solar has the lowest NPV, making it the least financially attractive option. Wind stands out as the top choice because it produces the highest cumulative discounted returns, which supports its position as the top investment priority. Bioenergy maintains a strong NPV, demonstrating its competitive position in the market, especially when social and environmental co-benefits are included in the evaluation. The lower NPV of solar indicates reduced profitability, which requires either policy backing or additional sustainability benefits to support investment decisions. The decision-making process shows that wind and bioenergy represent the financially strong couple, while solar trails behind, according to Figure 5. The NPV serves as a fundamental economic evaluation tool for renewable energy projects because it demonstrates the distinct financial appeal of these alternatives.

The three renewable energy alternatives are compared by payback period in Figure 6. The results show that wind has the shortest payback period, indicating the fastest recovery of the initial investment. Bioenergy has a longer payback period than wind but a shorter one than solar, which has the longest payback period and thus the slowest capital recovery. The ranking shows significant financial risk and liquidity differences between the alternatives. Wind’s short payback period indicates lower investment risk and better opportunities to reinvest in future projects. Bioenergy’s moderate payback reflects balanced but less competitive financial returns. The extended payback period for solar suggests that capital turns over slowly, making it less attractive to investors seeking a quick recovery.

4.4. Sensitivity and Robustness Analysis of Weight Variations in ELECTRE-Based Sustainability Evaluation

The different weight combinations across economic, environmental, and social criteria shift investment rankings, as shown in Figure 7 and

Table 3. Weight-sensitivity summary.

Alternative	Wins	Share	SI–Min	SI–Median	SI–Max
Solar	0	0
Wind	231	1	1	1	1
Bioenergy	0	0

. Wind remains the top choice because it performs well across all sustainability dimensions. The assessment shows that wind energy excels across all sustainability evaluation criteria. Environmental and economic priorities allow solar and bioenergy to perform well within specific weight ranges, but they remain sensitive to these factors. The boxplots in Figure 8 display the sustainability index (SI) values for the top-ranked investment options. Wind stands out as the best choice because it has the highest median SI and the narrowest range of values, indicating its stable ranking. The sustainability index for solar remains the lowest, while bioenergy shows an average level of variation, suggesting a reduced ability to withstand changes in weight distribution.

5. Conclusions

The research used an ELECTRE-based multi-criteria decision-making framework, combined with sustainability accounting, to determine the optimal distribution of renewable energy investments among solar, wind, and bioenergy systems. The evaluation process combined economic, environmental, and social elements to achieve a balanced sustainability assessment. The evaluation results show wind as the superior choice because it produces the highest concordance values and the strongest energy efficiency ratio, the best payback period, and the highest NPV. Wind received the maximum allocation of 100% in the optimized portfolio. Bioenergy secured 31.0% of the allocation due to its strong social benefits from employment creation, despite lower energy efficiency. The investment allocation for solar reached 16.7% because it demonstrated average performance and a longer payback period than the other two options. The total sustainability index (SI) reached 1.70 across the combined investment portfolio, demonstrating the optimization framework’s ability to maintain sustainability trade-offs.

The optimization solution was achieved using a barrier method, which converged in 10 iterations in 0.10 s, demonstrating computational efficiency. The research demonstrates that ELECTRE, combined with sustainability accounting, proves successful for directing renewable energy investment choices. The method identifies wind as the core investment choice, with bioenergy and solar serving as supporting elements to develop investment plans that meet economic requirements, environmental standards, and social inclusion needs.