A theory-based decision support framework for energy transition: pluralized perspective

This study presents a theory-based decision support framework for energy transitions that addressed the requirement for comprehensive approach beyond routine problem-solving techniques. The decision support framework integrates value-focused thinking, sustainability and resilience theories, and multi-criteria decision analysis (MCDA). The four theoretical lenses were synthesized to address three core support areas: alternatives identification, evaluation criteria development, and method selection. The resulting conceptual framework is structured into foundational, evaluation, and decision-making stages, each underpinned by stakeholder engagement and context alignment. To demonstrate usefulness, the article provides a detailed illustrative application to a hypothetical energy planning scenario in a Sub-Saharan Africa nation, an epicenter of energy access challenges. The framework offers transparency, adaptability, and rigor for methodological roadmap for national energy planning. This work provides a clear guide for policymakers and researchers to apply in diverse geographic and system scales, bridging the gap between theory and practical energy policy evaluation.

1 Introduction

There is an urgent need to transition to an energy system that is more efficient and emits significantly fewer greenhouse gases (GHG) to avoid the worst impacts of climate change on people and the planet. A symptom of uneven energy transition has occurred in the form of uneven and inadequate energy access in developing countries and poor and remote communities, leading to an overall increase in energy poverty (World Economic Forum, 2024). A satisfactory measure of a successful energy transition includes sustainability in the energy future, energy security and resilience, as well as equity and inclusivity, which is collectively referred to as the energy triangle (World Economic Forum, 2024).

The energy sector of Sub-Saharan Africa (SSA) is characterized by immense, untapped energy potential for clean energy, and a significant and persistent deficits. Despite its growing population, the region has over 600 million citizens lacking electricity. This represents the lowest rates of energy access in the world and is a major barrier to economic development, with attendant issues of endemic poverty and public health concerns (UNCTAD, 2023; World Bank, 2025a). The heavy reliance on traditional biomass is reflected in nearly half of the region’s energy mix (Climate Analytics, 2022). Dirty fuels, including coal and oil, dominate a significant proportion of the electricity generation in some countries, yet their per capita electricity consumption remains extremely low, a mere fraction of the global average (Agoundedemba et al., 2023). The low access is evident by a large urban–rural divide, skewed towards the rural population. The existing infrastructure are notably behind in technological advancement to offer solutions to unstable and insufficient current and escalation future demands while high cost of grid extension makes it more challenging to reach rural communities (Climate Analytics, 2022).

Notwithstanding these negative peculiarities, SSA is endowed with abundant and unexploited renewable resources for energy transition estimated to be 1,000 times greater than the energy demand in 2040 (KFW Development Bank, GIZ, and IRENA, 2021). Leveraging the regions vast energy potential for the achievement of universal energy access goal such as SDG 7, and similar commitments like Mission 300 and agenda 2063 will require substantial investment and supportive policy environment. While public funding and development finance institutions play a crucial role, a significant increase in private funding is inevitable although investors are often reluctant to commit funds to weak regulatory frameworks, political instability and other risks. Consequently, the implementation of clear, stable and investor friendly policies is necessary (Cai et al., 2024; World Bank, 2025a). Implementing energy transition initiatives is as complex as addressing global concerns altogether. As would be expected, it has to encompass multiple objectives, development sectors, interests, options, perspectives, types of information, system configurations, and assessment matrices, among other factors – elements which require a structured analytical approach. The critical factors are rooted in the essential need for holistic and effective decisions, as identified in various energy transition literature. Consequently, they are governed by the methodological requirements of multi-criteria decision analysis (MCDA) (Grafakos and Flamos, 2017), and the necessary integration of the concepts like sustainability and resilience (Bundhoo et al., 2018) to manage system goals and threats to performance. Crucially, these factors involve systemic and often conflicting relationships, defining the core complexity of transition, as detailed below:

• Multiple objectives: Considering the importance of balancing competing goals (Kaya et al., 2019), the more the aspects integrated into decision-making, the higher the effectiveness and efficiency of energy planning. As Saputra et al. (2025) argued, multiple perspectives are crucial for energy planning, as relevant tools are designed to explore trade-offs different sustainability aspects. This ensures that transition is not only about changing technology but is also a holistic evolution process of the system.

• Development sectors: Energy is a service-orientated capital, a major input to make other systems or sectors work. A study by Sovacool et al. (2020) emphases the centrality of energy to proper functioning of critical sectors such as transport, security, water, food, communication, manufacturing, and others. A review produced further evidence by showing how the financial sector, through supports like green finance, acts a critical enabler of energy transition (Fan et al., 2025), making interdependency with other sectors an important consideration when determining transition pathways.

• Interests: There is a strong interest of different groups of industry actors that feel directly or indirectly affected by the outcome of the decision (Grafakos, 2016). Therefore, seeking the views of civil society and local communities can help to build consensus and prevent conflicts that might stall renewable energy project (Radtke and Renn, 2024). The participatory approach ensures that decisions are both technically sound and socially and politically viable. Conversely, those that are not part of the decision may be unsatisfied with the outcome of the decision process.

• Value chains: Although the transition relies mostly on energy generation, it is also important to consider transmission, distribution, and storage in light of the World Economic Forum’s (WEF) energy triangle mentioned above. Each of these components has a unique approach to being an enabler of transition (World Economic Forum, 2024). This systemic approach is necessary for grid stability as the integration of variable renewable sources is increasing.

• Information: Transition decision-making cannot be effective without considering different sources and types of information. This has been shown in the evaluation of suitable hybrid renewable energy system, where both quantitative and qualitative data were analyzed (Odoi-Yorke et al., 2022). Real-time data from recent innovations such as micro-grids has been found to support capacity flexibility for broader grids (Wärtsilä Energy and AVK, 2025). Clearly, there is an increasing need for robust analytical methods to handle complex and sometimes uncertain information.

• Technology options: Several technologies compete to have a share in the energy mix. While early thought on transition favors increasing the share of the renewables, the challenges associated with these sources pitch non-renewables against them. For instance, Grafakos and Flamos (2017) provide a decision analysis framework for evaluating competing energy sources that promote low-carbon system, whereas another study (Tan and Li, 2024), detailed advances in perovskite solar cells towards identifying power conversion efficiency and challenges of scaling up for commercial production. Both studies depict the importance of holistic assessment as key consideration for evaluating future energy mix.

• Configuration: Although research increasingly supports initiatives to shift from centralized to decentralized systems, the choice of configuration will depend on its impact on the stability of the power system, which is influenced by various specifications and requirements for infrastructure and management. According to ESCAP (2012), the ideal system will be determined by an assessment of the ability to meet the aforementioned unique needs.

• Temporal scale: Transition planning and implementation involve both short- and long-term initiatives, but the impact must accurately predict what the situation will look like in the future while the system is still operational. Oliveira et al. (2025) introduce a novel socio-temporal and spatial approach to equitable home energy transition that promotes understanding of how social practices evolve over time, allowing for predictions of future energy demand and planning for a resilient system.

• Matrices: Decision assessment techniques are in a large number; selecting the most accurate or appropriate can be confusing.

• Geographical scope: The decision will be location dependent because technical feasibility, technological maturity, political situations and economic conditions are not the same everywhere. Mumuni and Issah (2025) revealed that tailored strategies are essential for reducing emissions and improving energy access in SSA, underscoring the fact that successful energy transitions are highly contextual and geographically specific.

Energy transition is widely viewed through the lens of sustainability, most especially with the pursuit of SDG 7 and other initiatives in the global community. Regarding the policy options available to achieve this goal, there is evidence surrounding the limitations of sustainability assessment in capturing the dynamics within systems that incorporate cleaner sources of energy—the disruptions that might likely occur due to the different configurations of technologies involved. In the traditional approach to addressing system disruption, resilience has been used to capture how a system can maintain stability when a vulnerable system is affected by some threats. The importance of the concept is noticeable in the several studies on energy resilience assessments (Bundhoo et al., 2018; Hosseini and Parvania, 2021). Its principle can be applied to the investigation of the adaptive performance of energy systems to the potential disruption by introducing new technology. This initiative helps to undertake comprehensive assessments of potential impacts of integrating new technologies in the area of policy and investment strategies to pursue (Buytaert et al., 2020). Aasa et al. (2025) revealed an increasing understanding of how to integrate resilience and sustainability to achieve the energy transition goal, as well as practices that could enhance energy planning performance in this field.

A close examination of existing energy transition frameworks reveals a focus on technical, policy, and socio-economic aspects, albeit from varying viewpoints. For instance, in frameworks on integrated approaches for decentralized energy systems, the focus is on environmental, social, technical, political, and economic factors (Wehbi, 2024). Evro et al. presents a policy-ready framework for linking renewable deployment with emissions outcomes, to serve as a tool for policymakers to simulate interventions and track progress (Evro et al., 2026). Another study investigated different policy levels such as micro, meso, and landscape to address potential failures in innovation policies related to energy systems (Sousa, 2024). A study on energy literacy emphasizes the importance of an awareness for successful low-carbon shifts through a transition conceptual framework (Gladwin and Ellis, 2023). The “enabling framework to support the sustainable energy transition” identifies 11 categories, including environmental protection, societal behavior, and finance, to help overcome barriers at a national level (Blohm, 2021). Additionally, the “conflicted energy transition” framework examines conflicts that arise from physical manifestations of energy projects like wind farms, using a neopragmatic approach to combine theoretical perspectives on landscape and conflict (Blohm, 2021). The conceptual framework for the United Kingdom explores transitions that decarbonised systems by analyzing changes in terms of reproduction, de-alignment, and reconfiguration, identifying key drivers like economic, technological, and policy imperatives (Shackley and Green, 2007).

Despite these differing perspectives, none of the existing frameworks comprehensively integrates multiple theoretical lenses for transition decision-making from resilience and sustainability perspectives. Considering that, the decision-making process is intrinsically complex, multidimensional, and crucial across various areas (Savaşkan et al., 2025); this article proposes a pluralized framework that combines value-focused thinking (VFT), sustainability and resilience theories, and multi-criteria decision analysis (MCDA) to offer a more transparent, adaptable, and structured approach.

While MCDA is well-known as an analytical tool for addressing multi-dimensional decisions, the approaches to implementing it are generally underdeveloped in SSA compared to other regions of the world (Harichandan et al., 2022; Visentin et al., 2020), including integrating Problem Structuring Methods (PSMs) with MCDA (Marttunen et al., 2017). Furthermore, the effective practice adopted where MCDA is well-developed may not be feasible in developing countries with limited decision-making capacities (Ezbakhe and Pérez-Foguet, 2020). Meanwhile the unique challenge in SSA is not merely the choice of technological infrastructure to implement, but also the need to design pathways that address low energy access, significant infrastructure deficits, and high investment risk, while simultaneously leveraging the immense renewable resources in the region to promote sustainable and inclusive business model (World Bank, 2025b). Such objectives must be achieved without threatening system stability due to the integration of variable alternatives. Also, existing literature on PSM-MCDA often omits critical methods like VFT for structuring context-specific values and alternatives (Marttunen et al., 2017), and fails to integrate the theories of resilience and sustainability required to navigate SSA’s volatile political and environmental landscape. Consequently, the general need for PSMs in MCDA, as noted in the broader literature, becomes a far more urgent and context-specific innovation gap when considering the complexity of decision-making for energy access and development in SSA. This article addresses this gap by proposing a theoretically enhanced framework specifically tailored to capture the pluralized objectives and systemic risks inherent in SSA’s transition.

The strength of the current study is providing a specific field and theoretically enhanced instance of the general decision guidance incorporating the observed knowledge gaps and ensuring context alignment, making it particularly useful for national energy planning in diverse geographic and systemic contexts of SSA. The requirement for a theory-based decision-support framework is most pressing in SSA, the global epicenter of the energy access deficit, where about 565 million people lack electricity (World Bank, 2025b). The article defines the SSA context for energy transition using three main interconnecting concerns that traditional, single-theory MCDA approaches have not adequately explored:

• Stakeholders’ diverse values: Energy planning is affected by conflicting stakeholders’ priorities, such as among national governments (grid extension), international donors (sustainability and climate finance), and local private operators (profitability of decentralized renewable energy systems). Such conflicts should be unified as suggested by Cho (1999). The VFT is useful to reconcile these competing values into a unified set of strategic objectives before decision analysis.

• Comprehensive factors: Achieving universal access requires planning that integrates various aspects beyond either technical feasibility, economic viability, or even the triple bottom line (social-economic-environmental). The emphasis on sustainability theory ensures that the MCDA criteria cover all relevant factors across institutional, economic, technical, social, environmental, and technological dimensions, directly supporting major continental goals like the Mission 300 initiative and Agenda 2063.

• Systemic vulnerability: The region faces climate shocks, political instability, and other threats to system performance. Thus, the resilience theory ensures that all alternatives are evaluated for the sustainability aspect but also for their ability to withstand and recover from existing system failures or external shocks from integrating new technology into the system.

To address these challenges, this study proposes a decision support framework that synthesizes VFT, sustainability, and resilience with the analytical power of MCDA. Thus, the focus of the following sections is the theoretical perspective and conceptual framework that can help to integrate these considerations for effective energy transition planning in sections 2.0 and 3.0, respectively. To demonstrate the application of the conceptual framework, section 3.0 also included a hypothetical validation in developing countries’ planning context, while section 4.0 concludes the study. The methodology employed was a purposive review, enabling researchers to comprehensively reflect on the study’s issue by utilizing current research, theories, and philosophies from both their discipline and other domains (Cook, 2019). A purposeful review—a more extensive review—was used to provide a wider view of the theories rather than limiting them to the energy field alone (Cook, 2019). The literature covers research articles, reviews, organizational publications, and other documented sources of information. These sources were supported by a prior systematic review article to gain specific perspectives on energy decisions.

To guide this review and ensure relevance to energy transitions in developing contexts exemplified by SSA, the discussion is based on four perspectives according to the following criteria:

• direct support for decision-making processes (identification of objectives, alternatives, or criteria);

• ability to address both static goals (sustainability) and dynamic shocks (resilience);

• complementarity in generating evaluation metrics (synthesizing method) and structuring decision values; and

• proven application in energy planning literature.

The criteria ensure that each theory not only enriches intellectual understanding but also directly influences the development of a practical, integrated decision framework.

2 Theoretical perspectives

A theoretical framework is a structure that summarizes concepts and established theories and previously tested and published knowledge, which serves as a general foundation for a research study (Kivunja, 2018; Mensah et al., 2020; Schad et al., 2021). A theoretical framework can provide a map of the current state of knowledge about a phenomenon and offer evidence-based explanations for why the particular problem connects to particular phenomena (Andre Hughes, 2019). It ultimately provides support for the entire investigation and helps a researcher analyze and interpret data within a research problem context. It provides a broader perspective on the research by outlining the main theme, reviewing existing literature, identifying the problem being investigated, formulating proposed questions, detailing the procedure for data collection and analysis, discussing findings, and concluding (Chukwuere, 2021). The legitimacy of sustainable development (or any of its supporting areas, such as energy) as a field of study will be linked to the quality of theories that can explain the attitudes and various dispositions towards its agenda (Enders and Remig, 2014) while addressing the various concerns. In energy planning, a theoretical framework can help understand the complex dynamics of energy transition, such as explaining how introducing new technology to the system complements the traditional sources (Geels, 2010).

This study is grounded in value-focused thinking, sustainability theory, resilience theory, and multi-criteria decision analysis to evaluate energy transition decisions in SSA. The review of these theories provides the basis for building a new conceptual framework, knowing that a literature review can be a valuable research design when aiming to develop one. The theories collectively address the three important and related areas of decision support for the energy transition: potential alternatives, criteria for recommending alternatives, and decision technique for evaluating transition alternatives. The focus on these areas is based on the observed knowledge inadequacy for context alignment in SSA, the status quo that has lingered long enough to receive overwhelming attention. For proper structuring of the review, value-focused thinking is introduced first.

2.1 Value-focused thinking

This study relies on value-focused thinking (VFT) to structure the energy transition problem around context-driven objectives and alternatives from stakeholders’ values associated with the strategic goal rather than predefined sets of options. VFT, originated by Keeney (1996), is defined as the problem-structuring method that identifies fundamental and means objectives from stakeholder values and then derives alternatives that align with the objectives (Keeney, 2008). Common decision analysis methodologies are often implemented without addressing an important aspect of decision-making: problem structuring. Rather than being proactive, DM is reactive and lacks control over the decision situation (Keeney, 1996). In most cases, the starting point is defining criteria and identifying alternatives for resolving the problem, thereby assuming the position of “given the problem” (Colorni and Tsoukias, 2018). The decision analysis literature primarily focuses on “how to choose” an alternative without considering where it comes from (Colorni and Tsoukias, 2018) and its likely contributions to the long-run stability of the system and not just an immediate remedy. The decision process should answer the questions: What are the sources of alternative solutions and criteria for their evaluation? What is the core problem regarding “values,” and how are they relevant to the strategic position in the context of the solution? What is the overall goal? Thus, VFT is a tremendous asset in this regard.

VFT helps decision-makers to identify and structure value-based objectives (Françozo and Belderrain, 2022). VFT is associated with identifying desirable decision opportunities and generating alternatives before solving the decision problem. Value is what we care about in a decision, knowing that with proper judgement, it can influence the future for desirable outcomes (Keeney, 2008). Value orientation clarifies objectives by ensuring decision-makers define core values and priorities before determining and evaluating options for addressing the decision problem (Bortoluzzi et al., 2021). Just like in a research project, objectives are set to guide the research, and significance is also stated to prove it is a worthwhile undertaking in the field of study. The following are the steps for creating alternatives using VFT:

• First, identify objectives: several techniques can help in the compilation of an initial list of objectives.

• Second, structure objectives: the objectives are categorized as means or ends objectives and logically structured.

• Third, create alternatives: several procedures assist in using the objectives to create alternatives.

• Fourth, the objectives are examined to identify worthwhile decision opportunities (Keeney, 1996).

DMs start with determining the values in addressing the problem presented. These values are translated into two objectives: the fundamental and the means. Fundamental objectives are possible outcomes that are important to the situation. The extent to which these objectives are achieved relies on the means objectives (Keeney, 2008), which not only serve as a yardstick for screening a set of candidate solutions but also help in generating new alternatives that go beyond existing options. This procedure has been demonstrated in some energy studies, as presented next.

2.1.1 Application of VFT in the energy sector

The strength of VFT is aligning decision-making in any area to the overall goal because strategic objectives are fundamental to exploring decision opportunities (Höfer et al., 2019). In modern times, where the global environment is overwhelmed with diverse challenges, VFT helps integrate different decision areas, optimize resource usage, and improve risk management. Figure 1 further illustrates the benefits of applying VFT to decision-making situations.

Figure 1

Figure 1. Benefits of using VFT for decision structuring. Source: Kamari et al. (2017).

Regarding offering support for decision analysis with multiple criteria within the energy sector, VFT establishes criteria for evaluating the energy system (Keeney et al., 1987). It creates strategic goals needed to prioritize energy planning (Mirakyan and Guio, 2014), combines with PROMETHEE to choose how to distribute energy generation (Bortoluzzi et al., 2021) and facilitates group decision-making for the 2030 energy transition (Höfer et al., 2019). Thus, it is beneficial to MCDA wherein, in most cases, there is no major procedure for determining alternatives that are evaluated but also establish objectives relevant for generating criteria for evaluating alternatives in MCDA. Focusing solely on long-term forecasts of energy demand and environmental impact without investigating the quality of alternatives to meet this demand would be incomplete. For instance, Höfer et al. (2019) reveals that most stakeholders prefer the alternative with the highest tendency to limit climate change. This illustrates how VFT can help identify possible options for resolving problems and ensure that only quality alternatives are evaluated by MCDA to determine which option provides the best solution. Having addressed its benefits, the discussion shifts to VFT’s limitations.

2.1.2 Criticism of VFT

Despite the contributions of VFT to decision-making, experts acknowledge that its usefulness and effectiveness depend on specific decision situations, and it is also limited in terms of cognitive effort and time required, value definition, and bias in subjective views. To use VFT to generate new alternatives will involve several iterations, a situation that will demand availability of participants throughout the process (Keeney, 2012).

Notwithstanding, it is significantly more beneficial than alternative-focused thinking, which has been widely used in regular decision-making for both identifying decision opportunities and creating alternatives (Keeney, 1992). VFT reframes energy planning by emphasizing the integration of strategic values before alternatives are investigated. This ensures that energy planning is not reactive but grounded in core values peculiar to the domain of application, and enabling the development of an impact-making framework. Despite its conceptual strength, VFT has limited application in energy transition studies, with many studies neglecting the upstream structuring of values, particularly in SSA. This study addresses that gap by incorporating VFT into the initial stage of the decision-making process, ensuring alternatives for achieving the transition goal emanate from context-specific values, considering international, regional and national initiatives such as SDG 7, Agenda 2063, Mission 300, rather than generic assumptions. Consequent upon this critique, the next session presents sustainability theory to support evaluation criteria mechanism.

2.2 Sustainability theory

This study adopts sustainability theory to frame energy-mix choices as a balance between human needs and ecological limits under the influence of several factors. Several studies have traced the emergence of ‘sustainability’ with ‘sustainable development (SD)’ and overwhelmingly addressed the terms conceptually; examples are (Diaz-Iglesias et al., 2021; Enders and Remig, 2014; Harrington, 2016). For instance, Bossel (1999) stated that the sustainability goal translates more accurately into SD goal. Supporting this assertion, Joseph et al. (2022) noted that while sustainability is the desired state of continued human life sustenance, SD is the means of achieving the said state. Fundamentally, the underlying principle on which sustainability theory is anchored in this study is whether “human activity successfully maintains itself and its goals without exhausting the resources on which it depends” (Jenkins, 2009). Put differently, the concern is “the tension between the aspirations of human beings towards a better life on the one hand and the limitations imposed by nature on the other hand” (Afsordegan, 2015). This definition sets up the exploration of “sustainability” as a concept for explaining transition in this decade of sustainable development.

The ultimate objective of establishing the concept of sustainability as an organizing principle for the planet is to foster well-functioning alignment between individuals, society, the economy, and the regenerative capacity of the planet’s life-supporting ecosystems. This alignment represents a particular type of dynamic equilibrium in the interaction between a population and the carrying capacity of its environment. The equilibrium has been shifted over time due to the increasing activities on the planet earth and consequential wastages “that exceed the planet’s regeneration and absorption capacities” (Ben-Eli, 2018). The International Resource Panel (IRP) report revealed that natural resource extraction and processing account for nearly 50% of global greenhouse gas emissions (IRP, 2020). Another study found strong correlations between ecological footprint, CO₂ emissions, and resource overexploitation (Amer et al., 2024). Consequently, the earlier submission by Russell et al. (1995) that sustainability is the “measure of how the growth, maintenance, or degradation of a resource or set of resources affects a population’s ability to sustain itself” remains relevant. The emphasis is on “maintaining” the desirable aspects of natural or social conditions and, when possible, improving them, including the status of natural resources over a long period of time (Harrington, 2016), justifying intergenerational equity and equality.

Threats to the sustainability of a system require urgent attention if their rate of change begins to approach the speed with which the system can adequately respond. As the rate of change overwhelms this ability to respond, the system loses its viability and sustainability (Bossel, 1999). In most informed fields of human endeavor, sustainability has become firmly associated with appreciation of complexities entwined in human and ecological systems (multiple interacting factors and dynamic self-organizing processes in multiple interacting systems, at various scales, with pervasive and inevitable uncertainties, etc.) (Gibson, 2006). This may explain the two contrasting approaches to sustainability discussed in section 2.2.1.

2.2.1 Approaches to sustainability

There are two approaches to explaining sustainability – weak and strong, as presented in Table 1. The main difference between them is whether there is a specific obligation or not. Weak sustainability imposes no specific obligation. It ensures that the present generation uses resources responsibly to prevent the future generation from losing access to the same resources. Strong sustainability prioritizes the preservation of natural capital, whether locally or on a larger scale (Döring and Muraca, 2010). One practical empirical study is using data from the World Bank in a series of regression models to test the possibility or impossibility of replacing natural capital with physical capital (Norouzi and Fani, 2021). The study demonstrated that natural capital retains a direct, positive, and independent variable in explaining sustainable development even with the introduction of human, physical, and social capital indicators in the equations. This result implies that natural capital cannot be fully replaced, thus supporting the strong sustainability position over the weak one. However, both are paramount in assessing impact, considering the orientations of different practitioners, such as ecologists and other environmental scientists that assume the major role of a strong approach, whereas economists favor a weak approach as a basis for using their models (Afsordegan, 2015). These two approaches provide complementary vantage points for evaluating resource use and ecological limits, framing our examination of sustainability assessment criteria as detailed in 2.2.2.

Table 1

Table 1. Approaches to sustainability.

2.2.2 Dimensions and assessment of sustainability

Considering the broader scope, based on the complexities that recent knowledge highlights on what constitutes sustainability, the above approaches are not enough to assess the impact of any means to the desired state of sustaining life to explain. The concept extends beyond just environmental impact (Khalili, 2011). The challenge sustainability addresses is the investigation of definite ways to pursue distinct goals (such as SDGs) that conform to their mutual relations (Jenkins, 2009). Sustainability can be understood as consisting of distinct principles that in relation to human-environment interaction (Ben-Eli, 2018), influenced in part by the various economic, social, and environmental factors present in today’s world (Harrington, 2016). Also known as “triple pillars,” “dimensions,” “constraints,” or “bottom lines,” the factors are said to be interrelated in the sense that any change in one, whether positive or otherwise, affects the others almost in the same proportion. Any effort to achieve sustainability involves simultaneously “maximizing and minimizing” the different aspects to attain balanced development (Mondini, 2019). It has informed the development of assessment frameworks. The OECD sustainability impact assessment aptly captured the usefulness of such tools for integrated policy development, as well as investigating the effects of policies, plans, strategies, and programs in these three aspects (OECD, 2010). Although the factors are regarded as the main sustainability assessment criteria, other criteria might as well be integrated into a sustainability assessment depending on the type, scope, and scale of the system (Moslehi and Reddy, 2019). For instance, the institutional dimension was explained alongside the triple pillars in urban transitions (Chen et al., 2023), and introduced to monitor the effectiveness of institutions in UN Agenda 21 (Spangenberg, 2002). Together, any incorporated dimensions should underpin SDG agenda as discussed in the following section on application of sustainability to energy planning.

2.2.3 Application of sustainability in the energy sector

Sustainability assessment is an effective approach to clarify and address sustainable urban transitions (Chen et al., 2023). The theory has been widely adopted in the energy field (Chowdary et al., 2025; Colapinto et al., 2020; Khan, 2021; Osorio-tejada et al., 2022; Pal and Shankar, 2023; Williams and Robinson, 2020; World Bank, 2024). Energy sustainability encompasses a broad spectrum of concerns, ranging from environmental stewardship and resource conservation to social equity and economic development (Panarello et al., 2024). According to the SDG7 Initiative for Africa, sustainability is one of the key pillars that facilitates investment aimed at supporting the SDGs through long-term financing for clean energy solutions, environmental sustainability, and business sustainability (United Nations Economic Comission for Africa, 2020). The various dimensions of sustainable energy development regarding how energy is generated and used broadly underpin the concept of the sustainable energy transition, transcending both technological innovations and institutional and behavioral changes (Akrofi et al., 2022). Energy technologies have been assessed for sustainability from different dimensions in the literature (Shortall et al., 2015), but not all are suited for a particular energy source. Sustainable Development Goals Impact Assessment Framework for Energy Projects (SDGs-IAE) presents interlinkages between energy-related projects and the SDG’s targets. It enables users to identify a longlist of possible ways a project can positively or negatively affect global and local attainment of SDGs (Castor et al., 2020). However, it is crucial that any assessment framework be of an elaborate perspective and throughout the system lifecycle. Such a holistic approach to low-carbon economy will require an understanding of political, technological, and institutional factors in addition to the traditional dimensions (Verma et al., 2023). One of the ways this comprehensive measurement can be attained is by situating energy decisions within the general developmental sector to account for its relationship with other sectors, as elaborated upon subsequently.

Current policies treat energy or the energy sector in isolation from other sectors, with policies and plans developed just for this sector. Consequently, implementing integrated, cross-sectoral, and collaborative planning has been identified as a key suggestion for amplifying the contribution of sustainable energy in meeting the SDGs. The energy sector should be effectively assessed and managed to accomplish the fruits of sustainable electricity production (Akber et al., 2017). Depending on the objective and purpose, scientific articles have assessed alternatives for addressing energy problems using single criteria with sub-criteria or indicators or multiple criteria with individual indicators from economic, technical, institutional, environmental, and social/ethical sustainability (Ilskog, 2008; Shojaeimehr and Rahmani, 2022) (see Table 2). However, generally speaking, the framework for sustainability assessment of energy comprises at least one of the following criteria: social, environmental, economic, technical, technological, or political/institutional criteria (Aasa et al., 2025). DMs can choose to evaluate alternatives using selected variables that meet specific requirements, but they can also combine more than two aspects. Different sources of energy produce impacts whose magnitudes are not equivalent to others in terms of these aspects. This study advocates for holistic assessment because the wider the scope of criteria, the greater the effectiveness of sustainability assessment in distinguishing the likely impact of alternatives on transitioning. The next section is about major criticisms of sustainability theory.

Table 2

Table 2. Description of sustainability criteria.

2.2.4 Criticism of sustainability theory

Critics argue that the conceptual relevance of sustainability is compromised by its susceptibility to competing ideals (Javanmardi et al., 2023; Lindsey, 2011), but such contention does not invalidate its usefulness because the core of the theory remains focused on maintaining equilibrium between the human system and the ecological system that sustains it. In fact, the inevitable nature of moral and political discussions is evident in these diverse and differing opinions (Jenkins, 2009). In addition, the obvious expectation is that any idea presented on sustainability will not be the same everywhere. In real application, sustainability theory confronts the society with pertinent questions, which must be put into the context of decision-making: “What must be sustained?. Which goals must be pursued? And what is the shared foundation for doing so?” (Jenkins, 2009). Furthermore, choice among pathways to sustainability, place of application (local, national, or international), scale (in both space and time), systems thinking while addressing issues of concern, limits (considering the limitation in earth’s resources), change as an essential challenge and consideration, interconnected concepts, and the identity of sustainability (as a normative concept) are relevant factors when applying sustainability theory (Harrington, 2016). Another limitation in its application to the energy system is that it does not explicitly address the dynamic aspect of the system that affects its stability when the system faces immediate or distant threats. This necessitates the resilience theory presented in the next section.

Irrefutably, sustainability theory is an input to integrated criteria for evaluating energy systems across social, environmental, economic, technical, technological, and institutional dimensions, which is in alignment with long-term energy planning goals. However, most applications of sustainability assessment in energy transitions fail to account for all the dimensions identified simultaneously. This study addresses the gap by operationalizing sustainability using contextually validated criteria and indicators within a decision framework that reflects the realities of SSA. Having established the criteria for holistic sustainability assessment, the dynamic aspect of a system undergoing transition is presented via resilience theory as a complementary lens.

2.3 Resilience theory

This study employs resilience theory to promote energy system adaptive capacity to shocks. According to the modern resilience theory (Holling, 1973), “resilience determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persists.” (Holling, 1973). The theory of resilience concerns the behavior of systems during and aftershocks (Jesse et al., 2019). An accurate and holistic conception of infrastructure resilience can facilitate proactive preparation, response and mitigation plans against system shocks (Nateghi, 2018). What makes energy clean and sustainable comprises two closely related dimensions, namely the fuel source of the energy and the nature of the supply infrastructure in relation to actual and suppressed demand (UNHABITAT, 2022). The condition of the supply system can be described as how resilient it is. Resilience is to sustainability what ‘tail’ in a coin is to the ‘head’. Because of climate change, increased efforts have been made to ensure energy systems are more stable, making resilience gain significant importance (Jesse et al., 2019). However, to transition the energy system to a cleaner form, natural and man-made threats, operational, economic, policy, human (such as war), resource, demand, and technological factors are equally sources of concern to the systems which resilience should address (Aasa et al., 2025). An example is the extended droughts in the Zambezi River basin in 2023, which made the Kariba Dam operate at only 40 per cent of its rated capacity, causing long periods of blackouts across Zimbabwe and Zambia and emphasizing the susceptibility of hydropower-dependent systems to climate stressors (World Bank, 2023). Consequently, resilience theory provides the conceptual foundation for assessing a system’s capacity to withstand such threats, as explored in section 2.3.1.

2.3.1 Capacities and dimensions of resilience

Resilience has been explained from different perspectives. There are three core capacities for explaining resilience (Jesse et al., 2019; Sharifi and Yamagata, 2015): absorptive capacity, which is the degree to which a system is able to absorb shocks posed by a disruption; adaptive capacity, the degree to which a system is able to adapt itself temporarily to new disrupted conditions; and restorative capacity, the degree to which a system is able to restore itself if adaptive capacity is ineffective (Hosseini et al., 2016). However, literature has informed that the resilience approach is a dynamic and system-oriented process that views adaptive capacity as a core feature of resilient social–ecological systems (Brien and Hope, 2010). Therefore, adaptive capacity provides the conceptual foundation for assessing resilience performance in energy systems, which is examined in section 2.3.2.

2.3.2 Resilience assessment

The assessment of the resilience of a system by indicators qualitatively or quantitatively is often a complicated issue due to the large number and abstract nature of definitions of resilience in energy systems (Jasiunas et al., 2021). However, resilience performance can be assessed in three major ways:

• Resilience curve: The resilience curve is a visual representation to show how performance is changing with time over several phases of a disruptive event.

• Indicators: These are also effective for measuring performance. They are qualitative or quantitative metrics that serve as proxy for the resilience curve.

• Models and methods: In models and methods, historical data or simulated data performance indicators can be used to predict resilience for existing or future systems using a multitude of methods from various fields (Jasiunas et al., 2021).

Furthermore, two types of resilience metrics have been identified, such as attribute - and behavior/performance-based. Attribute metrics are based on resilience indicators that measure a system’s ability or capacity to absorb disruptions, whereas other resilience indicators, which measure a system’s behavior/performance in the presence of disruptions, are consistent with performance-based metrics (Martišauskas et al., 2022).

Since system changes that improve resilience against one threat may be completely ineffective or even decrease resilience to another threat, every resilience evaluation is likely to have unique components (Aasa et al., 2025). The ultimate measure of the effectiveness of resilience models and indicators is their usefulness in guiding planning for resilience as well as their impact on everyday life (Hosseini et al., 2016; Jasiunas et al., 2021). Another intricacy arises from system characteristics. As a characteristic of the system, resilience is often influenced greatly by the system structure and relationships and interactions between different components rather than by the performance of the individual components of the system (Månsson et al., 2014). Thus, it may be better to consider a set of threats to specific resiliencies instead of just general resilience (Jasiunas et al., 2021). For instance, the process of meeting demand and supply of electricity, for instance, involves generation, transmission and distribution, each of which has its unique disruption factors or threats. In addition, as power is being transmitted and distributed, there is an increasing need for a socio-technical view to achieve resilience.

In the context of the energy system, threats to energy generation are more central to addressing energy access and stability than those related to transmission and distribution, which are considered subordinate and thus important for this study. Key elements of resilient energy technology, such as redundancy, efficiency, diversity, risk reduction, and adaptive capacity (Grafakos et al., 2017), are useful input for attribute-based metrics to ensure the generation technologies in the energy mix can adequately cater for various threats. The attribute-based indicators generally demonstrate characteristics of energy technology that provide it the ability or capacity to adapt to disruptions. The indicators measure what makes the energy system more or less resilient (Aasa et al., 2025). In particular, this study emphasizes that adaptive attributes are particularly relevant to the energy system (Molyneaux et al., 2016) and deserve more attention in energy (see Table 3). The criticism of resilience theory is presented next.

Table 3

Table 3. Attributes for measuring resilience.

2.3.3 Criticism of resilience theory

Despite its usefulness for explaining the dynamics of a system, it has been criticized for its limitations in some areas (Jesse et al., 2019). First, there is no standard approach for quantifying resilience in energy systems; generalizing indicators is difficult because they are often context-specific, which significantly limits their practical usefulness and policy relevance. Nevertheless, this approach can be beneficial because of the complexity of the energy system. By specifying measurements associated with a value chain, the system-specific threats can be addressed effectively. Another threat linked to measurement is limited integration with socio-technical transitions and lack of consideration for human and institutional dimensions. The theory does not adequately feature dynamics of long-term transition like change in governance, behavior, and technology. Moreover, resilience models usually focus on technical and infrastructural aspects, overlooking the important roles of social, political, and institutional dynamics. In both cases, the narrow focus leads to a limited assessment of system vulnerability and capacities. Fortunately, the missing aspects can be adequately captured by the sustainability assessment previously discussed, and this justifies an integrated sustainability and resilience approach.

This theory contributes to energy transition planning by introducing dynamic capacity that captures how systems respond to shocks and uncertainties. These attributes are especially relevant in countries facing infrastructure fragility and climate variability and disruptions linked to introducing new technology to the system. However, resilience assessments are typically generalized for the entire energy system or scope, which leads to limitations in effectively managing uncertainties caused by threats that are often unique to each value chain. This study bridges that gap by integrating resilience attributes peculiar to energy generation – the most critical value chain – and within adaptive capacity, directly into the evaluation stage of the conceptual framework. This approach guarantees the comparison of energy alternatives not only based on sustainability criteria but also by their capacity to withstand and recover from disruptions. In the next section, the review presents an integrated sustainability and resilience approach towards displaying practices that unite the two objectives in energy planning.

2.4 Integrated sustainability and resilience approach

Integrated sustainability and resilience approach (ISRA) is introduced in this study as a new decision-making approach for energy planning. It is a growing interest in scientific discussions, most especially environmental management disciplines. For instance, in 2001 a pilot decentralized wind-power project in Hokkaido, Japan, integrated local wind turbines with the existing distribution network and demonstrated a 30 % reduction in outage duration during extreme winter storms—an early real-world validation of resilience-inclined sustainability integration (Yue et al., 2001). Thereafter, ISRA has been applied in general environmental management (Marchese et al., 2018), building assessment frameworks (Roostaie et al., 2019), building material management to minimize carbon footprints (Almulhim et al., 2020), assessment of health systems’ contribution to global priorities (Cristiano et al., 2021), and low carbon targets in the construction industry (Sesana and Oro, 2024). Moslehi and Reddy (2019) identified two dimensions to “sustainability” in an energy system. First, designing, operating, managing, and supporting a system in such a manner that its environmental impacts and costs are minimal – this is the concept of energy-efficient design and operation. Designing in such a way that it is robust to disruptions and shocks posed by natural, manmade, or random events and, if possible, should be able to dynamically transform and adapt and be able to quickly recover and deal with the aftermaths.

One of the early studies on ISRA in the energy field responded to the demand of the World Summit on Sustainable Development held in the following year by aggregating a set of indicators inclusive of resilience to external trade to track progress toward electricity sustainability (Spalding-Fecher, 2003). A recent review reported several empirical studies have been implemented and are still counting (Aasa et al., 2025). Its usage as a holistic decision-making approach is emerging as an alternative to the existing narrow approaches. As a result, review studies are being conducted to enquire into necessary parameters for understanding the relationship between sustainability and resilience in energy decision-making. The role of digital twins in achieving the objectives (Khan et al., 2025) and challenges to integrating them in the energy supply chain (Masood et al., 2023) are a few examples. These studies are veritable inputs to strengthen the ISRA application.

Early proponents have argued for a context-specific strategy because the energy system consists of several subsystems, each with unique characteristics that distinguish them from other subsystems within the main system (Gaudreau and Gibson, 2010). Rising to this challenge, Marchese et al. (2018) present the similarities and differences between sustainability and resilience in environmental management, identifying three frameworks: (1) sustainability as a component of resilience, (2) resilience as a component of sustainability, and (3) resilience and sustainability as separate objectives. A review of trends and practices for enhancing a resilience-inclined sustainability framework came up with a decision objectives, agenda and implementation (DOAI) framework for an integrated sustainability and resilience decision-making system for a specific context, as displayed in Table 4. It explains various considerations for incorporating the ideas of the two principles. The application of DOAI by DM is guided by three questions relating to the three agendas: What? (Context) (Gaudreau and Gibson, 2010) Which? (Conceptual) (Mazur et al., 2019) How? (Methodology) (Dantas and Soares, 2022). Under the decision agenda, DM determines the type of integration, sustainability criteria, nature of disruption addressed, types of resilience, anchor of resilience, and measurement of resilience (Aasa et al., 2025). Decision context explores integration strategies, types of technologies, value chain, scope of decision, temporal scale and decision entity. Lastly, decision implementation addresses stakeholders’ engagement and type, the type of criteria for evaluation, the selection of criteria and the type of information needed for decision-making. The article cited can be explored further for the details of these areas of decision evaluation.

Table 4

Table 4. Considerations for integrated sustainability and resilience approach using energy transition perspective.

The type of integration to be adopted is an important consideration when developing an evaluation framework under ISRA. It includes the implicit approach, a popular technique in early studies, which is relevant where either objective is promoted while addressing the other latently (Marchese et al., 2018; Roostaie et al., 2019). The explicit approach is more recent as an alternative to the implicit approach. It encompasses stand-alone integration (Gruber et al., 2024; Kazimierczuk et al., 2023), mixed integration (Liu et al., 2024; Yazdanie, 2023) and strategy integration (Araria et al., 2024; Tangi and Amaranto, 2025) exemplified by the cited studies. The main difference between these integrations is whether the performances or contributions of alternatives to sustainability and resilience can be clearly accounted for during preliminary assessment and post-implementation monitoring.

This study places a strong emphasis on standalone integration because it allows for a distinct accounting of objectives, unlike mixed integration, and it is generally underdeveloped. The integration of strategies involves combining various energy alternatives that simultaneously enhance the sustainability and resilience of the system. Despite the usefulness of the decision, this integrated approach and specific technique must be used to evaluate decision alternatives against the integrated criteria it presents. To operationalize ISRA, section 2.5 presents MCDA as an adopted analytical tool for addressing its multiple and conflicting factors.

2.5 Multiple criteria decision analysis

Multiple criteria decision analysis (MCDA) is adopted as an analytical procedure for integrating VFT-generated alternatives with sustainability and resilience criteria. The field of decision-making that involves multiple evaluation criteria is expanding (Uzun et al., 2021), particularly the multiplicity of various dimensions of sustainability and the attributes for measuring resilience. Modern application of multi-criteria decision-making theory involves using computational methods that incorporate several criteria and orders of preference for evaluating and selecting the best option among many alternatives based on the desired outcome (Belton and Stewart, 2002; Ishizaka and Nemery, 2013). Generally, some characteristics of decision-making that make MCDA useful for syntheses and arriving at tangible solutions include addressing uncertainty, complexity, multiple goals, and multiple stakeholders (Grafakos, 2016). Hybrid measurement of criteria (qualitative and quantitative) and a mixture of deterministic and probabilistic attributes are also allowed (Xu and Yang, 2001).

2.5.1 MCDA application in energy planning

MCDA has been widely used for energy investment analysis, policy evaluation, and strategic decision-making to enhance transparency and stakeholder collaboration. Some recent applications include selection of energy supplier (Hussain et al., 2024); evaluation of sustainability of battery technologies (Das et al., 2025), clean energy technologies for envisioning SDG 7 (Elavarasan et al., 2024); assessment of hydrochars from the hydrothermal carbonization of agrowaste (Isigonis et al., 2024); and urban energy sustainability (Haddad et al., 2021). MCDA methods are useful because they help manage multiple conflicting criteria in a structured way, allowing for the consideration of various preferences regarding those criteria. This approach is particularly important in energy debates, where there are many criteria and even more different and usually opposing views of different stakeholders (Santoyo-castelazo and Azapagic, 2020). The various applications can be categorized by the type of problem addressed, as depicted in Figure 2. These identified elements of MCDA, together with the others in the CAUSE checklist, which include decision Criteria, Alternatives, Uncertainty consideration, Stakeholders or decision makers, and Environment (Belton and Stewart, 2002), are key to effective decision analysis.

Figure 2

Figure 2. Type of decision problem in MCDA. Source: Afsordegan (2015), Cajot et al. (2017), Chen (2006), Habenicht et al. (2002), Marle et al. (2015), and Talukder et al. (2017).

2.5.2 Selection of MCDA methods

Selecting the most appropriate method is key to effective problem solution in MCDA. Numerous methods have been developed to handle a wide range of decision problems (Taherdoost and Madanchian, 2023). Thus, it is relevant to know which is useful in a particular situation. The methods are generally classified into two main groups: Multi-Objective Decision Making (MODM), which is designed to solve optimal design problems by simultaneously achieving multiple objectives, and Multi-Attribute Decision Making (MADM), which involve the assessment of multiple discrete alternatives based on independent criteria (Afsordegan, 2015; Bagheri Moghaddam et al., 2011). The methods also fall into different “school of thought” based on their approach:

• Value measurement approach (also known as the American school) or full aggregation in which a score is evaluated for each criterion (local level) to show the degree to which one option may be preferred to another. Thereafter, the scores are aggregated into a higher level (global score) to show preference based on all the criteria. These scores produce an order of preference for the alternatives such that a is preferred to b (a < b) if and only if V (a) > V (b) (e.g., AHP, ANP, MAUT, and MACBETH) (Afsordegan, 2015).

• Goal, aspiration or reference level approach in which desirable or satisfactory levels of achievement are established for each criterion, and then identify options close to achieving these desirable goals or aspirations (e.g., TOPSIS, DEA, VIKOR and goal programming). of this category.

• The outranking approach (also referred to as the French school), in which alternative courses of action are compared pairwise, initially in terms of each criterion, to identify the extent to which a preference for one over the other can be asserted. In aggregating such preference information across all relevant criteria, the model seeks to establish the strength of evidence supporting the selection of one alternative over another (e.g., methods in the ELECTRE and PROMETHEE families) (Belton and Stewart, 2002; Ishizaka and Nemery, 2013).

As could be observed, there are multiple methods under each classification of MCDA, but each method has its uniqueness, which brings method selection concern to the forefront in developing decision support frameworks. The challenge of identifying the most suitable method for a specific situation has resulted in the use of multiple methods to ensure robust results. For instance, AHP and Fuzzy TOPSIS (Ahmed et al., 2020); DEMATEL and ANP (Büyüközkan and Güleryüz, 2016); fuzzy AHP and VIKOR (Wang et al., 2019); AHP and VIKOR (Büyük€ozkan and Karabulut, 2017); and Principal Component Analysis (PCA) and Analytic Network Process (ANP) (Yeo et al., 2020). This development is not without limitation. Decision-making is structured to meet the demand of the method, whereas it should be the other way because methods are meant to provide optimum solutions (Watróbski et al., 2019). Consequently, researchers are beginning to recognize this limitation, and a recent development involves using tools with a predefined taxonomy of characteristics peculiar to the available methods. The tool recommends potential methods based on the characteristics the analyst deems most appropriate to the decision (Cinelli et al., 2022; Watróbski et al., 2019). This method also presents a challenge related to evaluating methods using limited variables; in some cases, multiple methods are recommended. However, the development of a framework based on the index of suitability provides a solution to this obstacle.

Under the index of suitability, the appropriateness of any MCDA method is determined by assessing a range of methods, utilizing both decision problem variables and method variables, which generate values known as the index of suitability; this index is an aggregate of each method’s values based on these variables. The method with the highest index is usually preferred to those with lower value. The problem variable is the ‘external’ scenario in which the MCDA method will be implemented, which might involve several decision-making issues based on the rules and other measures in the domain where evaluations are conducted (Guarini et al., 2017, 2018). This implies that the context of the evaluation must be considered while choosing a method. The variables include the size of the evaluation elements, the type of indicator, the number of decision-makers, the type of solution, and the level of technical support required (Guarini et al., 2017; Ziemba, 2022). The MCDA method variables include the type of problem, the solution approach, the ease of use, the implementation procedure, the output typology, the software support, the degree of usage, the problem solution, and the structure of criteria (Cinelli et al., 2020; Guarini et al., 2017; Ziemba, 2022). The index of suitability is applied to determine the most suitable method for addressing a problem based on the unique characteristics that each method has regarding these variables. The generated method in turn determines how data collection will be structured and synthesized to fit the method’s procedure.

2.5.3 Criticism of MCDA

An important concern in applying MCDA as an analytical technique is the uncertainty about the use of MCDA results and the extent to which they are taken into account in real decisions and in policy implementation, which has been questioned (Turner et al., 2000). This premise is one of the most striking arguments for Cost Benefit Analysis (CBA), explaining its extensive use in practical policy making and offering the possibility to directly influence the market mechanism, which is still the dominating driving force in human societies (Grafakos, 2016). Similarly, the prior selection of criteria can be viewed as a disadvantage compared to classic indices that contain predefined sets of indicators. However, it allows DMs to choose criteria tailored to a specific decision problem, without excluding the possibility of using a predefined set of indicators derived from one of the classic sustainability indices (Ziemba, 2022). It has also been stated that it is necessary to develop related methods and models able to move from multi-discipline and inter-discipline toward trans-discipline and holism to identify the emergent properties related to sustainability problems (Sala et al., 2015). This suggestion is relevant to ensuring optimality in the results presented by any method applied. The flexibility allowed on the assessment metric is an added advantage and beneficial for the integrated sustainability and resilience proposed in the previous section. Meanwhile, the metric and other contextual factors are important variables for identifying the most appropriate method.

This study assumes that the structured process in MCDA will contribute positively to addressing the complexity of the energy transition in SSA by providing analytical aid in evaluating energy alternatives across multiple conflicting criteria. Its structured approach enables transparent comparison and supports decision-makers in navigating trade-offs among sustainability and resilience dimensions. While MCDA is widely applied in energy planning though with limited application in SSA, it can be difficult to determine the exact method for resolving a specific problem among the many that are available. As mentioned earlier in this section, several articles have employed multiple methods to address the same problem; however, this does not ensure that the best solution is found in a real-world context, as this approach ultimately compares solutions derived from different methods. Thus, it is important to determine from the outset the most suitable method, making this article propose this important step explicitly to MCDA procedure. If the most suitable method is not determined, the analyst will have to adjust the problem to fit a specific method instead of letting the problem dictate which method should be used. The few available frameworks to address this issue are either complex, evaluate fewer variables for methods, assess fewer methods for suitability, or serve as general-purpose tools. These limitations could be the reason for low adoption of MCDA in SSA with limited technical expertise. Moreover, many studies treat MCDA as a standalone tool, in isolation from upstream value structuring or downstream policy translation, although its procedure does not have a formal remedy for identifying alternatives to address energy problems. Additionally, few studies explicitly integrate both sustainability and resilience metrics within the same MCDA framework, most especially in the SSA context. This study resolves these limitations by embedding MCDA within a broader conceptual framework that begins with VFT and culminates in actionable decision-making, ensuring methodological coherence and contextual relevance. It also accounts for determining the most suitable method for the energy transition context using the suitability framework. The last section under this theoretical perspective is dedicated to explaining the synergy among the theories before the conceptual framework section.

2.6 Integrative synthesis and theoretical mapping

The combined perspectives of VFT, sustainability theory, resilience theory, and MCDA are the multi-dimensional foundation upon which this study stands. Each theory contributes unique but complementary insights into energy planning. Value-focused thinking is linked to identifying strategic priorities and structuring decision objectives for identifying potential alternatives. Sustainability theory is linked to assessing long-term ecological, economic, and social goals, emphasizing technical, technological, and institutional concerns related to potential options. Resilience theory complements sustainability by incorporating the capacity to adapt to change to reinforce the need to account for disruptive factors that are likely to affect the system performance. MCDA can operationalize these principles through integrated evaluation, trade-off analysis, and scenario comparison. To clarify their integration, these theories are sequentially nested rather than operating in parallel. VFT is the foundational element, applied in the initial stage to lead stakeholder engagement and establish the core objectives (the strategic ‘what’). Subsequently, the principles of sustainability and resilience theories are applied as theoretical filters to these VFT-derived objectives, defining the necessary dimensions and scope (the operational ‘how’) that the evaluation criteria for decision analysis (using MCDA) must cover. This hierarchy ensures the criteria are not only stakeholder-driven but also theoretically robust for both long-term viability and shock resistance. Table 5 maps each theory to its functional role and corresponding research focus. Then, a unified conceptual framework is presented in 3.0.

Table 5

Table 5. Mapping theories to research focus.

Building upon the multi-dimensional scope of energy sustainability defined in Section 2.2.3, the framework refines its theoretical commitment through a two-level process to ensure analytical clarity. In the first instance, the framework aligns with the strong sustainability approach, as detailed in Table 1 (Hobson, 2013). This stance is essential because its end goal – a ‘multilevel socio-political transformation’ – is necessary for addressing the structural challenges inherent in achieving energy equity and development in the Sub-Saharan African context. To translate this agenda into an actionable evaluation system, the framework operationalizes sustainability using the six-dimensional model of sustainable energy planning (social, environmental, economic, technical, technological, and institutional), as detailed in Table 2. These dimensions serve as the practical mechanism for criteria development, in addition to resilience adaptive aspects as discussed 2.3.1. This dual-level approach ensures that the VFT process focuses on the transformational agenda and then operationalized using comprehensive criteria. This structure ensures the criteria reflect both the environmental mandate (‘clean energy’) and the essential continuity and welfare concerns (‘continuous supply’) within a single, coherent evaluation.

3 Conceptual framework

With the theoretical lenses for the research clarified, this section synthesizes the theories into a conceptual framework (Figure 3) that operationalizes VFT-generated alternatives, theory-driven criteria, and MCDA method selection and implementation in a unified decision-making process.

Figure 3

Figure 3. Conceptual Framework for Integrated Decision Support for Energy Transition. Source: Authors’ conceptualisation.

A conceptual framework is a researcher-developed model that explains specific concepts and variables relevant to a particular study and is usually drawn from multiple existing theories and empirical studies. It provides direction that is missing in a theoretical framework, acting as a roadmap or research paradigm, which makes it easier to identify input as well as outputs, thereby being advantageous for generating solutions to research problems and a more specific and practical tool than a theoretical framework (Mensah et al., 2020). In energy decision-making, it could be presented as a diagram outlining specific variables and proposing a specific relationship among them. It also provides guidance in the selection of specific metrics and development of a framework for decision analysis (Nweke, 2022).

Energy planning is not like recurring operational activities; strategies for providing services must imbibe the principle of inclusivity, which implies catering for several interacting factors to serve other sectors effectively. The approach to determining solutions for energy problems must shift from the common techniques used in routine or operational issues, where choices are made arbitrarily; this philosophy has infiltrated strategic decision-making in the energy sector, where long-term value should prompt questioning existing methods and generating new alternative solutions that align with an entity’s overall goals. Likewise, standard metrics are needed to justify the qualification of solutions as a requirement to ensure access to a clean and uninterrupted supply of energy services. These supports are incomplete without understanding the scientific support – determining which method to use to analyze options that best meet the transition goal. Thus, based on the suggestion by Mickwitz et al. (2021), an interdisciplinary decision framework proposed in this study relied on the theories presented in the previous sections (Figure 3). The framework presents the relationship between decision contexts (based on VFT), ISRA, and MCDA as unified support for energy transitions. The framework operationalizes the perspectives by modifying VFT for transition-specific alternative generation, merging sustainability and resilience into an explicit integrated evaluation framework, introducing MCDA method determination, and structuring the entire process with MCDA. The synthesis is presented across three distinct sequential aspects: foundational, intermediate and decision-making, hereafter discussed in succession. Since the process requires deliberate input from various interest groups, the team aggregation and structure are discussed first.

While the conceptual framework is structured into the three systematic stages – foundational, evaluation, and decision-making that characterize most complex decision support tools, its novelty – lies in the pluralistic theoretical inputs that define the content and output of each stage. This framework is not a modification of an existing MCDA procedure but a unique synthesis designed to address specific methodological gaps in energy planning for the SSA context. Its three primary differentiators that establish its originality are:

• VFT-driven alternatives: The foundational stage explicitly begins with VFT, which moves the framework beyond simple alternative identification to strategic structuring and investigation of alternatives that align with long-term value, a crucial step absent in conventional MCDA models.

• Theoretically determined criteria: The criteria for decision analysis under MCDA are not generic but filtered by the principles of resilience theory and a commitment to strong sustainability (as discussed in Section 2.2.3), guaranteeing that the final metrics are effective against shocks and focused on transformative energy access.

• Method suitability integration: Introduced a mandatory step for determining the most suitable MCDA method using the Index of Suitability to ensure that the analytical technique aligns with the contextual variables of the problem, to maximize the objectivity of the outcome.

Thus, these integration elevates existing conventional analytical procedure to a robust and theory-driven decision support roadmap.

3.1 Team aggregation and structure

Public participation is an important element in a democratic setting where people can express different values and believes (Marttunen et al., 2015). A decision support team (DST) is mandatory for the implementation of the integrated decision support. The team should be multidisciplinary, comprising decision-makers (DMs) who provide strategic vision, technical experts for data collection and validation, general stakeholders to ensure the acceptability of decisions, and at least an individual facilitator/analyst to manage the analytical process. Stakeholders’ engagement is crucial during the entire process, both directly and indirectly to ensure that the analytical outcomes can be translated into practical and acceptable policies. The direct approach involves active solicitation of ideas and input from stakeholders for acceptability and feasibility of any proposed solution. Secondary documentations, such as repositories of previous decisions and technical knowledge adaptable to the current context are used for indirect engagements.

3.2 Foundational stage

This initial stage focuses on problem structuring, alternative generation, criteria development and selecting appropriate analytical tool. The needs and constraints of the process should be carefully analyzed at this early stage of the process (Marttunen et al., 2015).

3.2.1 Sub-stage 1: alternative determination

It directly applies the principles of VFT to establish the context for the decision. It shift emphasis from merely existing options to proactively screening available the existing and identifying new ones that align with core values and evidence of strategic impact expected of the system. The task at this stage is summarized in Table 6.

Table 6

Table 6. Alternatives determination.

3.2.2 Sub-stage 2: evaluation framework development (sustainability and resilience)

This sub-stage establishes a holistic and integrated scorecard my incorporating the principles of sustainability and resilience theories to measure the performance of the generated alternatives as displayed in Table 7.

Table 7

Table 7. Evaluation framework development.

3.2.3 Sub-stage 3: MCDA method determination

This step select the most appropriate MCDA method using index of suitability mentioned in 2.3.5.3 by assessing a range of methods, utilizing both decision problem variables and method variables to ensure the method fits problem’s complexity, evaluation framework and inherent data uncertainty. The step is presented in Table 8. This step depends on information already gathered from sub-stages 1 and 2.

Table 8

Table 8. Steps for MCDA method determination.

3.3 Intermediate stage/evaluation stage

With the completion of the foundational stage, DST can now execute multi-criteria assessment using the determined inputs and the selected MCDA method as follows:

3.3.1 Sub-stage 4: IC-MCDA application

The stage conduct analysis that integrates the IC with the selected method procedure. The procedure is the same from methods except the mathematical computation technique (Table 9).

Table 9

Table 9. Procedure for IC-MCDA application.

3.4 Decision-making stage

In the later segment of the framework, the results from the evaluation are used to make informed decisions.

3.4.1 Sub-stage 5: policy formulation

This step translates the quantitative results into qualitative policy outcomes and requires mandatory stakeholder engagement (Table 10).

Table 10

Table 10. Steps for generating policy form the evaluation stage.

The proposed conceptual framework provides a structured mechanism for three decision support areas in energy transition planning. By linking VFT for problem conceptualization and alternatives generation, an evaluation system for multi-dimensional evaluation, and the suitability method selection and analysis, this framework prepares the ground to ensure that energy policies are not only technically sound but also strategically aligned with the core objectives of sustainability and resilience, making it highly valuable for DMs operating in complex, developing contexts. Over time, the framework can be adjusted to accommodate lesson learn in previous usage (Inotai et al., 2018). In the following, an illustrative application of the framework is presented.

4 Illustrative application: prioritizing energy alternatives in a hypothetical country

To bridge the limitation between concept and practice, this section presents an empirical application of the integrated decision support framework of Nigeria that addresses the critical challenge of reconciling the nation’s immediate Mission 300 energy access target with its long-term Net-Zero by 2060 commitment (Mission 300 Africa Energy Summit, 2025; Nigeria Ministry of Environment, 2021). It is also relevant to the Nigeria Integrated Resource Plan (NIRP, 2024), which is a comprehensive approach to national power system planning that includes on the supply side a holistic assessment of national energy resources and on the demand side opportunities for energy efficiency to derive a least-cost combination of supply and demand measures. NIRP further national objectives such as energy security and access, social equity, decarbonization and environmental sustainability (Federal Ministry of Power, 2024).

4.1 Team aggregation

The Nigeria Federal Ministry of Energy convened a DST workshop comprising the technical team that developed the ETP and NIRP, representing different groups including financing/development organizations, the private sector, government agencies and academic/research institutions. The selection ensures a wide range of sector experts from across the electricity delivery value chain (Federal Ministry of Power, 2024). The team implemented the integrated decision support, following the three stages.

4.2 Foundational stage

The DST workshop featured the conceptualization of energy transition problem considering various policies mentioned in the introduction to identify the potential alternatives, criteria for their measurement and suitable method for the analysis.

A. Alternative determination

Since the ETP supports sustainable and resilient energy access with little or no interruptions in the country, similar to national objectives in NIRP, the FOs were automatically set as sustainability and resilience. Further discussion led to identifying key MOs, which included achieving universal electricity access by 2030 (in line with the Mission 300 Nigeria Compact to double access growth); reducing carbon intensity of the power sector (in line with ETP 2022’s Net-Zero by 2060 target); and enhancing grid security and modularity (‘resilience’ in support of NIRP 2024). With this target set, three mutually exclusive alternatives ( $A_i$ ) were identified (Table 11) based on Nigeria’s current energy policy landscape.

B. Integrated sustainability and resilience criteria

Table 11

Table 11. Potential alternatives for energy transition.

Following the identification of alternatives, there was a consensus on indicators of seven criteria ( $C_j$ ) for sustainability and resilience evaluation as presented in Table 12.

C. MCDA method determination

Table 12

Table 12. Integrated sustainability and resilience criteria for evaluation of alternatives.

To evaluate the alternatives using IC, the analyst initially identified a suitable MCDA method by applying the suitability framework (Guarini et al., 2018). Based on the 11 variables in the framework, the analyst used the information from contextualization, alternative determination and evaluation criteria to determine the current decision problem’s expected properties. Applying the properties to the suitability framework, the IS values for common MCDA methods in energy planning were calculated and ranked. The ranking placed the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ahead of other methods, making it the most suitable. The fundamental concept of the method is to define both the positive ideal solution (PIS) and the negative ideal solution (NIS). PIS is a solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the NIS maximizes the cost criteria and minimizes the benefit criteria. The best solution is the one that is closest to the PIS and farthest from the NIS (Alptekin, 2015; Zhao et al., 2022). Calculating PIS and NIS is useful for evaluating the beneficial (pros) and non-beneficial (cons) aspects of a decision (Jia and Wang, 2023). Aside simplicity in computation to support domain with little or technical expertise on MCDA, TOPSIS accepts unlimited range of evaluation elements (criteria/indicators and alternatives), explicit trade-offs and interactions among indicators, preferential ranking of alternatives with numerical values that help in comprehending their similarities and differences, and simplicity in computation (Govindan et al., 2013).

Despite the suitability of this method, the analyst considered a likely approach to address the ambiguity and uncertainty associated with human judgement during preference evaluation. Fuzzy set theory was adopted (Yuan et al., 2018) due to its simplicity compared to the trapezoidal fuzzy model. Thus, the Fuzzy TOPSIS MCDA method was employed due to its ability to handle the qualitative and uncertain nature of expert judgements using Triangular Fuzzy Numbers (TFNs). The model uses a 5-point hedonic scale, shown in Table 13, to show the qualities used for rating energy options and weighing the criteria. The scale is mapped to Triangular Fuzzy Numbers (TFNs) [l, m, u] on the [0, 1] interval for weights and the [1, 5] interval for ratings. The next paragraphs describe the procedures for IC-MCDA integration.

Table 13

Table 13. Linguistic scale and TFN mapping.

4.3 Intermediate stage

This stage describes the evaluation of the transition alternatives using IC-MCDA.

A. Data Collection and aggregation

The study utilizes a hypothetical panel of three experts (E1- E3) whose linguistic judgements on criteria weights ( ${\tilde{w}}_j$ ) and alternative ratings ( ${\tilde{x}}_{\mathit{ij}}$ ) were collected. The linguistic terms are mapped to TFNs as defined in Table 13. Tables 14, 15 present the ${\tilde{w}}_j$ and ${\tilde{x}}_{\mathit{ij}}$ respectively, and their corresponding TFNs.

B. Group fuzzy aggregation

Table 14

Table 14. Expert judgments for criteria weights.

Table 15

Table 15. Expert judgments for alternative ratings (${\tilde{x}}_{\mathit{ij}}$).

The arithmetic mean method ( $\tilde{A}$ ) is used to derive the aggregated fuzzy decision matrix ( ${\tilde{x}}_{\mathit{ij}}$ ) and the aggregated fuzzy weighting vector ( ${\tilde{w}}_j$ ), which are requirement for the Fuzzy TOPSIS calculation (Table 16).

C. Fuzzy TOPSIS computation

$\tilde{A}=\frac{\sum _{k=1}^3{\tilde{x}}_{\mathit{ijk}}}{K}$

Table 16

Table 16. Aggregated fuzzy decision matrix and the aggregated fuzzy weighting vector.

The Fuzzy TOPSIS computation involves the following steps.

Step 1: The aggregated matrix is normalize to produce decision matrix $\tilde{R}=\left[{\tilde{r}}_{\mathit{ij}}\right]$ (Table 17): Calculated with the following formulas:

Table 17

Table 17. Normalized fuzzy decision matrix.

For benefit (B) criteria: ${\tilde{r}}_{\mathit{ij}}=\frac{l_{\mathit{ij}}}{C_j^{+}},\frac{m_{\mathit{ij}}}{C_j^{+}},\frac{u_{\mathit{ij}}}{C_j^{+}};$ where $C_j^{+}={max}_i{u}_{\mathit{ij}.}$

For cost (C) criteria: ${\tilde{r}}_{\mathit{ij}}=\frac{a_j^{-}}{u_{\mathit{ij}}},\frac{a_j^{-}}{m_{\mathit{ij}}},\frac{a_j^{-}}{l_{\mathit{ij}}};$ where $a_j^{-}={min}_i{l}_{\mathit{ij}}$

Step 2: Calculation of the weighted normalized matrix ( ${\tilde{v}}_{\mathit{ij}}$ )

The normalized matrix $\tilde{R}$ is multiplied by the corresponding criteria weight ${\tilde{w}}_j$ (from the aggregated fuzzy weighting) to yield the weighted normalized matrix $\tilde{V}$ = [ ${\tilde{v}}_{\mathit{ij}}$ ] in Table 18. The formula is given as:

${\tilde{v}}_{\mathit{ij}}={\tilde{r}}_{\mathit{ij}}\otimes {\tilde{w}}_j=(l_{\mathit{ij}}^r.l_j^w,m_{\mathit{ij}}^r.m_j^w,u_{\mathit{ij}}^r.u_j^w)$

Table 18

Table 18. Weighted normalized fuzzy decision matrix.

Step 3: Determining ideal solutions using the following equations.

Fuzzy Positive Ideal Solution ( $\mathit{FPI}{S}^{+}$ ): The FPIS represents the best possible performance across all criteria. It is constructed by taking the maximum component of the TFNs ( ${\tilde{v}}_{\mathit{ij}}$ ) from the Weighted Normalized Matrix ( $\tilde{V}$ ) for each criterion $j$ (Table 19).

$\mathit{FPI}{S}^{+}={\tilde{v}}_j^{+}={max}_i{\tilde{v}}_{\mathit{ij}}=\left({max}_i\right(l_{\mathit{ij}}),{max}_i(m_{\mathit{ij}}),{max}_i(u_{\mathit{ij}}\left)\right)$

Table 19

Table 19. Ideal Solutions decision matrix.

Fuzzy Negative Ideal Solution ( $\mathit{FNI}{S}^{-}$ ): The FNIS represents the worst possible performance across all criteria. It is constructed by taking the minimum component of the TFNs ( ${\tilde{v}}_{\mathit{ij}}$ ) from the Weighted Normalized Matrix ( $\tilde{V}$ ) for each criterion $j$ (Table 19).

$\mathit{FNI}{S}^{-}={\tilde{v}}_{\mathit{ij}}^{-}={min}_i{\tilde{v}}_{\mathit{ij}}=\left({min}_i\right(l_{\mathit{ij}}),{min}_i(m_{\mathit{ij}}),{min}_i(u_{\mathit{ij}}\left)\right)$

Step 4: Calculate the Euclidean distance $d$ between two TFNs is used to find the separation measure for each alternative from the ideals (Table 20).

$d(\tilde{A},\tilde{B})=\sqrt{\frac{1}{3}[{(l_A-l_B)}^2+{(m_A-m_B)}^2+{(u_A-u_B)}^2]}$

Table 20

Table 20. Final fuzzy TOPSIS ranking.

Distance to $\mathit{FPI}{S}^{+}$ : $d_i^{+}=\sum _{j=1}^{7}d({\tilde{v}}_{\mathit{ij}},{\tilde{v}}_j^{+})$

Distance to $\mathit{FNI}{S}^{-}$ : $d_i^{-}=\sum _{j=1}^{7}d({\tilde{v}}_{\mathit{ij}},{\tilde{v}}_j^{-})$

Step 5: Calculate of the closeness coefficient ( $C{C}_i$ )

The $C{C}_i$ is calculated as the ratio of the distance from the negative ideal solution ( $d_i^{-}$ ) to the sum of the distances from both ideal solutions ( $d_i^{+}$ and $d_i^{-}$ ). This ensures the coefficient is normalized between 0 and 1.

$C{C}_i=\frac{d_i^{-}}{d_i^{+}+d_i^{-}}$

Step 6: The alternatives are ranked in descending order based on $C{C}_i$ (Table 21).

D. Presentation baseline results

Table 21

Table 21. Weighting schemes for sensitivity analysis and rankings.

According to Table 20, the final ranking is A2 > A3 > A1, confirming that the DRE focus is the most suitable energy transition strategy, mainly driven by its high scores on highly weighted criteria, access expansion rate (C1) and modularity and scalability (C5), where it achieved the FPIS. A2 and A1 have closeness to the idea values that are above 0.5, an indication that the duo have a higher chance of being considered in the energy mix. Although A1 performs well on economic cost (C3), the low weight of this criterion prevents it from competing with the overall utility of the energy transition options. Additionally, none of the alternatives is the perfect solution to the sustainability and resilience challenges of the system since none has CC that is equal to or very close to 1.0. Notwithstanding these results, it is equally important to examine how the ranking stability changes under different policies, which are indicated by variations in the weights of assessment criteria.

E. Sensitivity analysis

To ensure the suggested alternative is stable against changes in policy objectives, a sensitivity analysis was performed across three distinct policy scenarios (Table 21). According to the results in Table 21, A1 consistently ranked last (Rank 3) in all four scenarios. This finding is highly robust, meaning its sub-optimality is independent of the specific strategic priorities chosen by the government. On the other hand, the ranking between A2 and A3 is sensitive to the policy changes. A2 is preferred when access/modularity (Base, Scenario 2) is dominant, leveraging its decentralized strengths. A3 achieves the first position when policy prioritizes long-term stability and cost-effectiveness (Scenarios 1 and 3), leveraging its low-emissions profile and high technical efficiency. The switching position of A2 and A3 appears to suggest that the most resilient policy involves investing in both.

4.4 Decision-making stage

On examination of the various empirical outcomes from the integrated framework, DST provides two definitive policy mandates:

4.4.1 Robust consideration

The finding that the gas expansion (A1) alternative consistently ranks last (Rank 3) in all four scenarios is the most robust outcome. In terms of policy, the outcome implies that future planning should mandate a phased reduction of investment in gas expansion to make funds available for cleaner solutions since gas expansion is a sub-optimal decision.

4.4.2 Strategic mandate

The fluctuation in the top rankings between A2 and A3 requires a diversified investment strategy to manage risk and address the dilemma between immediate access and long-term deep decarbonization. Thus, policy-making should focus on implementing a dual-track energy policy:

• Track one (focus on A2): Prioritize investment in DRE to leverage its strength in highly weighted access and modularity to meet social objectives and equity as contained in Mission 300.

• Track two (accelerate A3): promote planning and investment in Grid-RE + storage, leveraging its strong position on GHG intensity and system resilience to achieve long-term environmental and systemic goals (net-zero 2060). This future-proofs the system for the Net-Zero 2060 target.

The conceptual framework, therefore, guides the decision from a simple preference ranking to a strategic resource allocation plan that is optimized for overall utility and resilient to shifting policy priorities.

4.5 Linking policy mandates to the multi-level perspective

The final policy mandates derived from the Fuzzy TOPSIS analysis are not merely a quantitative ranking; they represent a strategic prescription for managing the systemic transitions which can be described by the Multi-Level Perspective (MLP). MLP has been prominently used in energy transition to explain the interaction processes within and among three analytical levels: niches, socio-technical regimes and a socio-technical landscape (El Bilali, 2019; Ye et al., 2024). The MLP provides the necessary theoretical lens to discuss the results as a roadmap for niche-regime interaction and governance intervention:

1. Invalidating the regime (de-alignment): The consistent ranking of the gas expansion as in the last position provides quantitative evidence of the socio-technical regime’s failure to meet the new, non-negotiable demands imposed by the Landscape Level (specifically, GHG reduction and resilience mandates). The policy mandate for a phased reduction of the energy alternative is thus theorized as a necessary act of regime de-alignment to prevent technological lock-in.

2. Strategic intervention in the niche Level: The parallel mandate (A2 and A3) recommendation moves the decision from simply selecting an optimal technology to designing a strategy for niche acceleration. The framework’s ability to distinguish between A2’s strengths (access, modularity) and A3’s strengths (decarbonization, resilience) allows for the design of a coherent policy mix tailored to each niche:

• Policies supporting DRE should focus on immediate market-pull mechanisms to leverage its social utility.

• Policies supporting Grid-RE + storage should focus on framework policies (e.g., grid infrastructure policy) to institutionalize its technical advantages and facilitate niche-regime co-evolution in the centralized system.

5 Conclusion

The successful application of the three-stage conceptual framework in the hypothetical case study provides strong inferences regarding its validity, utility, and theoretical coherence as a decision-support protocol for complex energy transitions in SSA.

The framework’s effectiveness was validated in the three core areas. First, the foundational stage confirmed the framework’s theoretical coherence by successfully operationalizing VFT, sustainability theory, and resilience theory. This phase allowed the problem to be structured based on the explicit priorities of the decision-makers, which directly link strategic values to the evaluation criteria. Second, the intermediate evaluation stage used Fuzzy TOPSIS to demonstrate its application in managing uncertainty. By processing ambiguous expert judgements via TFNs, the framework delivered a robust, single, quantitative ranking that mathematically resolved the inherent trade-offs between conflicting criteria such as A1’s strength in LCOE and. A2’s strength in access. Third, the decision-making stage proved the framework’s policy practicality and theoretical power. The integral sensitivity analysis confirmed that the core strategic finding that the A1 alternative, which represents the current socio-technical regime, is sub-optimal regardless of policy focus is robust. This finding provides the quantitative justification for a regime de-alignment policy. In contrast, the fluctuation between A2 and A3 (the niches) supported a two-pronged energy policy, which, as articulated by the MLP, constitutes a strategic approach to support two viable niches concurrently. This process uncovers how the VFT/MCDA methodology can explicitly mediate preferences across the MLP levels (landscape pressures driving regime exit, niche potential driving investment) and inform the design of a coherent policy mix for systemic change.

Although the decision framework promotes the components of the energy trilemma presented in the introduction section as a fundamental decision-making reality, perfect attainment is impossible. The primary utility of the decision framework from this study is not achieving balance but providing the structured process to manage inevitable trade-offs. The framework ensures transparent and comprehensive analysis by linking VFT defined values and the integrated evaluation criteria with the MCDA process, culminating in the determination of the optimal compromise solution, that is, the result with the highest overall value within the constraints of the SSA context.

Ultimately, the case study concludes that the conceptual framework is a transparent and resilient governance tool that converts abstract sustainability and resilience objectives into tangible, risk-mitigation initiative, ensuring that energy planning is both value-driven and robust to future uncertainties. The framework is a timely complement for enhancing large-scale energy optimization and capacity expansion models. While these models excel at finding economically least-cost solutions, it is often difficult to incorporate non-monetary, qualitative, or uncertain strategic objectives like resilience, social equity, or institutional feasibility. The framework provides the necessary interfaces to embed these critical values, thereby bridging the gap between technical optimization and policy-driven decision-making. There are two main agendas that can be pursued with respect to addressing this gap.

5.1 Front-end integration: informing the optimization problem

The framework’s pre-evaluation stages serve as a robust input generator for quantitative modelling:

• Translating fuzzy weights into stochastic constraints: The most significant contribution is the translation of the expert-generated fuzzy criteria weights into inputs for stochastic or robust optimization. Instead of forcing the model to use a single, crisp carbon price or social cost of energy (which is highly uncertain), the TFN ranges define a probability distribution or a range of acceptable risk for cost/penalty parameters related to that criterion. This ensures the model optimizes for a value-weighted cost that is resilient to uncertainty in policy priorities.

• Defining multi-objective functions: The seven dimensions from the integration of sustainability and resilience can be used to construct a multi-objective function, moving the model beyond cost optimization. For instance, criteria like modularity or vulnerability are quantified as auxiliary objectives or hard constraints on system attributes (e.g., maximum centralized capacity, minimum storage requirement), ensuring that the resulting least-cost plan is also strategically resilient.

• Strategic scenario definition: The participatory VFT-driven identification of the alternatives can effectively constrain the solution space of the optimization model. By focusing the model’s simulation on the pre-screened and policy-relevant mix of alternatives, the framework will ensure the model output is directly comparable to political mandates to promote the generation of both technically optimal and strategically viable pathways.

5.2 Back-end integration: validation and evaluation of model outputs

Once an energy model generates a set of optimized scenarios, the framework can act as a powerful qualitative filter:

• Non-monetary performance evaluation: Model outputs (which are optimized for cost) can be fed back into the framework and rated by experts against the full set of criteria. Applying the MCDA method to the model’s output provides a final performance ranking that accounts for multiple utilities. This allows policymakers to critically assess how well the least-cost technical solution aligns with the most-preferred policy utility.

• Scenario comparison and trade-off resolution: When a model produces multiple technical scenarios (such as an “economic maximizing” scenario against a “decarbonization maximizing” scenario), the MCDA output clarifies the trade-offs between them. For instance, the framework can quantify exactly how much utility is lost in pursuit of a marginal cost reduction, providing a transparent basis for final political choice.

5.3 Future agenda

The main limitation of the current article is that the application remains illustrative and hypothetical. Although this successfully validates the framework’s theoretical coherence and analytical functionality, the ultimate proof of usefulness lies in its application to a dynamic, real-world planning environment. Future research must transition this methodology into an empirical setting, for instance, by applying it to the actual planning dilemmas of a specific developing economy. This undertaking will require engaging a real panel of policymakers and industry experts whose elicited judgments will carry the full weight of the seven evaluation criteria. With respect to methodological limitations, the reliance on expert judgment, even within the fuzzy environment, introduces limitations regarding subjectivity and the handling of deep uncertainty. To address this challenge, future study should explore the combination of subjective weight with objective weighting technique.

Author contributions

OA: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. SP: Supervision, Writing – review & editing. RJM: Supervision, Writing – review & editing. IM: Supervision, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was made possible by funding received from the Intra-Africa Mobility Scheme of the European Union in partnership with the African Union. This support falls under the Africa Sustainable Infrastructure Mobility (ASIM) project (grant number: 624204-PANAF-1- 2020-1-ZA-PANAF-MOBAF). The opinions and conclusions expressed in this research are entirely those of the authors and do not necessarily reflect the views of ASIM.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that Gen AI was used in the creation of this manuscript. The authors used Google Gemini (2.5) to assist in synthesizing and addressing feedback from reviews. The authors subsequently reviewed and edited the content as needed and take full responsibility for the content of the published article.

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