Integrating Sustainability and Resilience Objectives for Energy Decisions: A Systematic Review

Abstract

There is a need for simultaneous attention to sustainability and resilience objectives while making energy decisions because of the need to address disruptions or shocks that can result from system-wide changes due to transitioning and existing threats to system performance. Owing to this emerging research area, this systematic review used the Scopus database to address the central question: What are the trends and practices that can enhance the integration of sustainability and resilience for energy decisions? The articles used are peer-reviewed, empirical research in the energy field and written in English. Articles that did not explicitly address energy systems (or any of the value chains) and gray literature were excluded from the study. The final screening of records resulted in the selection of 75 articles that effectively addressed the decision objective, context, and implementation (D-OCI), a classification scheme that supports 18 specific questions to identify practices for integrating the sustainability and resilience objectives. The highlighted practices are advantageous for decision evaluation and can provide valuable insights for formulating energy policies. This is particularly relevant because energy-related decisions affect households, organizations, and both national and international development. The study proposes ideas for future research based on the highlighted practices.

1. Introduction

Access to affordable, reliable, sustainable, and modern energy (SDG 7) [1] is being advocated globally because of its important contributions to household, community, and national lives. Using the popular definition of sustainable development from the Brundtland Report [2] as a basis, sustainable energy means providing energy to meet the requirements of the present generation without jeopardizing the ability of future generations to provide sufficient energy to their societies. There is an urgent need for a diversified energy mix to promote this agenda, leveraging the advantages of various sources while mitigating their potential drawback within the energy system. Apparently, upgrading and modernizing infrastructure to accommodate clean sources are essential. Furthermore, investing in new energy technologies is crucial to countering shortages in energy supply and meeting growing demands, especially in developing economies [3,4,5,6]. While ensuring that people have access to electricity is a necessary first step, a number of incidents show that power systems can be made much more resilient [7]. Changes in energy and environmental policies, new power plant types, and other major changes can cause disruptions during the transition to decarbonized energy systems. Other threats from nature and man-made actions and inactions are inevitable. Energy planning is imperative in anticipation of disruptive situations to ensure the maintenance of optimal energy system performance [8] because power outages have severe adverse effects on the healthcare sector, economy, and education, as well as sustainable development as a whole [9]. Thus, considering the practicalities of complex systems and a dedication to fulfilling sustainability obligations, a holistic approach [10] and an evaluation framework that integrates these factors is highly appropriate for this situation [11]. Research shows that simultaneous attention to sustainability and resilience issues is significant in promoting supply chain sustainability and competitiveness [12].

Sustainability aims to maintain environmentally, economically, and socially desirable human–environment interactions [13,14]. The incorporation of numerous sustainability criteria into the evaluation procedure is critical and a growing priority for low-carbon energy investment decisions. Assessing these criteria serves as a key tool in facilitating the transition to sustainability. This approach helps identify and implement sustainable alternatives for the future given their unique performance under different criteria [15,16]. It is crucial to assess the sustainability of energy systems and technologies from a wider perspective and throughout the system life cycle. A holistic approach requires an understanding of additional factors, including political, technological, and institutional factors [17] as decision-making transitions across local, national, and global levels. The criteria for measuring sustainability have unique indicators depending on the context of usage. When designing climate and energy policies, it must be clear that the best path depends upon the exact goal being addressed [18]. For example, renewable energy technologies were investigated via several critical sustainability indicators [19]. Some frameworks have been developed for assessing sustainability [20,21,22]. Frameworks for sustainability assessment offer interpretations through practical applications, such as utility analysis; cost–benefit analysis; and comparative value analysis, risk assessment, and multi-criteria analysis [23].

Resilience is a concept applied across various disciplines to describe the ability to withstand and adjust to disruptions while remaining functional [24]. It is “the capability to bounce back to normal from undesired change” [25]. It accounts for system behaviors under changing conditions and complex interactions among physical, informational, and human domains, thereby complementing conventional static system performance measures, such as sustainability [26]. Energy is one of the most important fields of application for resilience policies, particularly with respect to electricity [27]. Resilience is increasingly recognized as a critical requirement for energy transition [28]. Energy resilience (ER) is widely recognized as the adaptability of an energy system to respond to unforeseen shocks [29]. In theory, the ER can be understood as an energy system’s capacity to cope with changing conditions or disturbances [30]. For example, an energy system must continue supplying electricity even as it transitions to a renewable source [31]. Loss of resilience can reduce the ability of energy systems to deliver services, as well as sudden shifts into fundamentally new states and structures that can affect individuals, cultures, ecosystems, or knowledge systems [32]. Past studies have identified approaches for explaining resilience with respect to energy systems, including engineering resilience, which analyzes critical infrastructure such as the electricity grid to determine its ability to recover from tipping points [15,30,33]. Ecological resilience pertains to the capacity of a given system to endure disruptions and sustain its fundamental operations in the face of unpredictability. This system is capable of re-entering numerous equilibrium states [15,33]. Socio-ecological resilience is a soft approach that recognizes system dynamics, uncertainties, and natural disaster unpredictability. According to the resilience approach, which is dynamic and system-oriented, resilient social–ecological systems have adaptive capacity [25].

Regarding the relationship between the two concepts, they have been considered synonyms, or subsets of each other or distinct yet complimentary objectives [34]. Both the sustainability and resilience criteria have been conceived for broad application to evaluations of situations, options, and undertakings of various kinds, scales, and locations (i.e., [35]). Ensuring a high level of system resilience during a sustainability transition process minimizes the risk of system-level collapse [31]. Resilience and sustainability metrics often have overlapping goals; therefore, both can be included in composite indices [36] and can be applied to changes in system parameters [32], shortages and unequal distributions of energy resources [37], climate change [38], and energy management systems, among others [39]. Since the resilience of any infrastructure system is about sustaining capacity, especially during extreme events, it ought to be reflected in its analysis, measurement, tracking, enhancement, and planning [40]. Resilience criteria can enhance sustainability-based evaluation criteria by clarifying the necessary attributes for maintaining socio-ecological integrity [11]. However, uniting the concepts may be challenging when goals and strategies for achieving goals are different and when the equilibrium of the system is distinct [34]. Thus, case and context specifics are essential in every incorporation of the two objectives. To develop evaluation criteria for a specific case and context, it is necessary to combine general criteria with a focus on the specific factors relevant to the case and context, especially those that showcase aspirations and constraints [11].

The concepts of sustainability and resilience have been extensively discussed theoretically. While energy sustainability has gained wider recognition globally, energy resilience is trailing with some momentum [26]. Many energy decision studies have addressed the duo individually compared to joint application. For instance, refs. [41,42,43] addressed sustainability, whereas refs. [28,30,44,45,46,47,48,49,50,51,52,53,54,55] are on resilience. Additionally, single-objective studies have limited application to the increasing complexities of urban infrastructure systems, on the one hand, and more severe and more frequent natural disasters due to global climate change, on the other hand, which demands holistic considerations [46]. It is not enough to achieve sustainable transitions when interruptions due to low resilience persist in the system. This has forced researchers to consider resilience explicitly, along with assessing the sustainability of critical infrastructures [46]. The combination of the two for energy planning is still in its infancy, with a few on general transitions, renewable power supply, and the construction sector [25,47,48]. Mazur et al. [7] is an in-depth review of the development of general resilience frameworks for energy systems to promote sustainability goals. This study reiterates the likely synergy between the two but fails to address how they should be integrated in reality. Therefore, this study uses existing frameworks to present perspectives to address this gap. Based on current trends and practices, this review promotes an understanding of the elements or parameters or factors captured as “decision objective, context, and implementation” to enhance the integration of sustainability and resilience for energy decisions. The scope of this study is electrical energy since electricity plays a critical role in connecting different segments of the energy sector and supporting the fundamental operations of the residential, commercial, and industrial sectors [49] and for sustainable development [16]. As a result, in this study, ‘energy’ is used synonymously with electrical energy. Sustainability in this study takes a cue from SDG 7, whereas energy resilience is used to explain a system’s ability to withstand and recover quickly from any unanticipated shocks [50]. The achievement of these two objectives promotes SDG 7 without disruption in the system. A systematic approach is essential for facilitating effective decision-making and policy formulation to achieve the targets outlined in SDG 7 and, ultimately, to foster a sustainable energy transition [51].

The following sections of this article include situating the agenda of this study; the methodology used for this review; trends and practices that can improve the integration of the objectives; discussions of the findings; and conclusions with future research directions.

2. Overview of Existing Studies and Situating the Agenda of This Study

The body of knowledge contributes to the explanation of relevant concepts, generating theories, approaches, decision frameworks, and analyses of the sustainability and resilience of the energy system. The review by [42] aimed to develop methods for selecting, quantifying, evaluating, and weighting indicators, as well as general sustainability indicators and aggregation for renewable energy systems. Martín-Gamboa et al. [43] performed a systematic review of commonly used data sources, criteria, and tools that are useful for integrating data envelopment analysis and life cycle approaches to evaluate the sustainability of energy systems. To present the study’s results, both descriptive and in-depth analyses of the identified articles were used. Other studies include Lassio et al. [41] who focused on incorporating life cycle-based sustainability into the decision-making process concerning electricity generation by conducting frequency analysis of indicators. Dantas and Soares [51] identified challenges, methodological approaches, and sustainability indicators used to perform life cycle sustainability assessment via bibliometric, publication trend, and comparative analyses. Zhang et al. [52] focused on highlighting the limitations of the index system and assessment method for hydropower sustainability assessment, using historical studies as a means of improvement. On the other hand, Mola et al. [45] explored the resilience of electrical infrastructure systems, whereas a review by [31] investigated the application of resilience to energy systems. Gesser et al. [44] presented an overview of definitions of resilience across various scientific disciplines, and performed in-depth quantification and analysis of resilience assessment for energy systems. Ahmadi, Saboohi, and Vakili [53] ascertained the characteristics, frameworks, quantitative indicators, and methods of the analysis of energy system resilience. Jesse et al. [28] recently reviewed the definition of energy system resilience to gain a better understanding. Mazur et al. [7] presented a divergent approach. Insinuating the prospect of integrating sustainability and resilience objectives, the authors intend to promote the goal of global and local sustainability by conducting an in-depth review of existing frameworks to compile information on general resilience in energy systems with applications to rural areas. Boche et al. [54] undertook a review to generate a framework for comprehending scientific articles on the sustainability micro-grid system within technical and social fields, although the literature that addresses either sustainability or resilience was studied. Khan et al. [47] studied how introducing digital twins in renewable-based power plants contributes to sustainable practices and resilient systems, whereas [25] identified trends and key themes in the use of resilience to explain sustainable transition. Aside from clarifying the definition of concepts, ref. [55] investigated frameworks that address them together, along with the pros and cons of the frameworks. Nik et al. [56] presented an overview of energy system resilience to the climate, addressing concepts and assessment methods in urban spaces. The systematic review by [48] aimed to clarify the association between the two concepts, covering their definitions and integrated assessment within the construction sector. Shafiei et al. [57] addressed definitions and associations between concepts used to explain resilience, as well as indicators for assessment. Ref. [58] presented a taxonomy of high-impact, low-probability events, resilience analysis, and an assessment index in the context of electrical energy systems.

Table 1. Summary of some previous reviews.

Author	Objective	Scope of Review	Method	System Addressed
[7]	Resilience/ sustainability	Conceptualization	In-depth review of existing frameworks	Energy systems
[23]	Sustainability	Conceptualization	Mini review/qualitative	Some aspect of energy involved
[25]	Resilience perspective in sustainability	Conceptualization	Systematic	Transitions (energy, food transportation)
[28]	Resilience	Conceptualization	Bibliometric and descriptive analysis	Energy system
[30]	Resilience	Conceptualization and method	In-depth analysis	Energy systems
[41]	Sustainability	Method	Bibliometric and frequency analysis	Electricity generation
[42]	Sustainability	Method	Comparative analysis	Renewable energy systems
[43]	Sustainability	Method	Descriptive and in-depth analysis	Energy systems
[45]	Resilience	Conceptualization and method	Bibliometric analysis	Electrical infrastructural system
[47]	Resilience, Sustainability	Conceptualization	Systematic	Renewable power plants (digital twin role)
[48]	Resilience and sustainability	Methods	systematic	Construction sector
[51]	Sustainability	Method	Bibliometric, publication trends, and comparative analysis	Energy sector
[52]	Sustainability	Method	Comparative analysis	Hydropower
[53]	Resilience	Method	In-depth/comparative analysis	Energy system
[54]	Sustainability (or resilience)	Conceptualization and challenges	Systematic and comprehensive review	Micro-grid energy system
[55]	Resilience and sustainability	Conceptualization	Systematic	Environmental management
[56]	Resilience	Overview of concepts and methods	Extensive review	Energy system
[57]	Robustness and resilience	Conceptualization, Methods and strategies	Extensive review	Energy system
[58]	Resilience	Conceptualization	Comprehensive review	Electrical energy system
[59]	Sustainability	Conceptualization and method	Qualitative analysis, comparative analysis	Wind power
[60]	Resilience	Conceptualization and method	In-depth analysis	Natural gas supply chain
[61]	Sustainability	Method	Critical review	Waste-to-energy
[63]	Sustainability	Method	systematic reviews and meta-analyses	Energy management
[64]	Sustainability	Method	Descriptive and in-depth analysis	Energy system
[64]	Resilience	Overview and method	In-depth/comparative analysis	Energy systems
[65]	Sustainability	Method	Extensive review	Energy system
[66]	Resilience	Conceptualization	In-depth analysis	Energy system
[67]	Resilience	Conceptualization	Systematic review, qualitative analysis	Energy system

presents a summary of some previous reviews on sustainability and resilience. The table highlights the differences between the articles in terms of their objectives, scope, methods, and the type of system they addressed. The scope examined includes conceptualization, method, or both, whereas various types of reviews and analyses were utilized for the purpose, such as systematic, bibliometric, and meta-analyses, on the one hand, and in-depth, critical, descriptive, and comparative analyses, on the other. While some investigated the objectives individually, a few, including [7,25,47,48,55], examined both jointly, albeit within limited scope. These studies primarily focused on conceptualization or systems different from energy, such as environmental management [55] and construction [48]. Some of the studies address the entire energy system, which limits them to a specific objective or scope, either method or conceptualization, but a few, such as [56,57], still combined the two. Furthermore, some articles investigated specific technology, such as wind [59]; natural gas supply chain [60]; hydropower [52]; or micro-systems, such as waste-to-energy [61] and micro-grid [54]. These studies have focused mostly on the ‘what’ and ‘how’ of energy planning, addressing conceptual and methodological issues separately. However, the ‘which’ of planning is equally relevant for specificity [11], as explained under the D-OCI framework in the Materials and Methods section. Regarding the system boundary, none of the studies has specifically focused on electrical systems in particular, considering both sustainability and resilience objectives at the same time. Thus, the foregoing highlights the paucity of studies dedicated to investigating the integration of sustainability and resilience objectives in electrical energy system decisions. While the ‘what’, ‘which’, and ‘how’ questions remained largely unanswered, holistic planning can be strengthened when these questions are addressed jointly, given their interrelated nature. Consequently, this study seeks to answer key questions that provide guidance on incorporating the two objectives, drawing inspiration from “there is no clarity on what practices could jointly advance both areas” [62]. Its significance lies in examining practices that can enhance effective energy planning by offering more recent and comprehensive insights for energy evaluation.

3. Materials and Methods

This study adopted a systematic literature review design, as it provides a comprehensive framework and one of the best tools for exploring the literature [68]. It fits with the purpose of this study, which is to synthesize literature for up-to-date evidence-based practices for energy decisions and analysis and suggest policy and future research directions [69]. Its weakness is the narrow research questions it addresses, which might limit its suitability for more complex subjects [70]. However, as previous reviews [41,71] have demonstrated, this allows for a comprehensive investigation of the topic under consideration rather than a narrow focus. Figure S1 shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide [72] flow diagram for the study. The study implemented the following steps [73].

3.1. Formulating Review Questions

The main research question is as follows: What are the trends and practices that can enhance the integration of sustainability and resilience for energy decisions? The specific questions are as follows:

1.  

How are sustainability and resilience integrated?

2.  

How is sustainability assessed?

3.  

What types of disruptions are taken into account?

4.  

What are the types of resilience?

5.  

What are the anchors or attributes of resilience?

6.  

What are the approaches to energy resilience?

7.  

How is resilience assessed?

8.  

What are the system configurations addressed?

9.  

What are the technologies assessed?

10.

Which energy value chain(s) is/are considered?

11.

What is the scope of the decisions?

12.

What is the perspective on the assessment?

13.

What energy entity is used for decision-making?

14.

How inclusive is stakeholders’ participation in assessment?

15.

How are criteria/indicators identified?

16.

What are the sources of data for decision analysis?

17.

How are energy decisions analyzed?

18.

What are the types of data used for assessment?

The questions are motivated by a similar study on supply chain network design [71] and the necessity of expanding scope beyond the ‘what’ and ‘how’ of energy decisions—areas previously covered in review studies—to also incorporate ‘which’, as clarified in Section 3.4.

3.2. Locating the Studies

The Scopus database was used to identify the articles for review due to access to the database. Scopus has a larger journal collection and stronger interdisciplinary coverage. It has been used in several energy planning review articles to date [74,75]. To ensure the accuracy and completeness of the review, the keywords utilized for searching the database derived from a preliminary literature review, as recommended by [62], conducted between January and May 2023. With prior understanding and familiarity with relevant concepts associated with sustainability and resilience, specific keywords or terms identified were used for the search. However, refs. [62,71] provided guidance in framing the search strings because they share similar concepts, albeit in different knowledge areas [76]. The title, abstract, and author keywords were used for the search.

3.3. Selecting and Evaluating the Studies

The selected papers were in the English language, peer-reviewed, in the field of energy, and empirical research papers. The only exception to these inclusion criteria is [33], which has the attributes of a review paper; but Scopus regards it as a research article for unknown reasons. Exclusion criteria filter out irrelevant papers after the initial search results. These are papers not explicitly concentrating on any of the energy systems or any of the value chains, and grey literature, e.g., theses, project deliverables, and working papers. Most grey literature is developed into a paper, which will amount to duplication if it is included in the list of the literature reviewed. To search for the qualified articles, the term “energy” was included in the search string instead of “electr*” to capture studies that may not explicitly mention the electrical system but remain relevant. Note that the asterisk (*) after the search terms (i.e., sustain*) was used instruct the database to capture all variations of the terms. Given the broad initial results, an initial exclusion was necessary to ensure only papers directly related to energy systems were evaluated in the later stage of the review. To accomplish this, an initial scan of titles and abstracts from the search results was conducted. During this process, some keywords not focusing on energy system were identified. Subsequently, the keyword exclusion option on Scopus was used to refine the search, ensuring the exclusion of articles not addressing the energy system (see

Table 2. Advance search terms on Scopus.

TITLE-ABS-KEY(sustainab* AND resilien* AND energy AND assess* OR framework OR evalut* OR criteria OR indicator*) AND PUBYEAR > 2000 AND PUBYEAR < 2024 AND (LIMIT-TO (SUBJAREA,”ENER”)) AND (LIMIT-TO (LANGUAGE,”English”)) AND (EXCLUDE (EXACTKEYWORD,”Blockchain”) OR EXCLUDE (EXACTKEYWORD,”Architectural Design”) OR EXCLUDE (EXACTKEYWORD,”Information Management”) OR EXCLUDE (EXACTKEYWORD,”Air Quality”) OR EXCLUDE (EXACTKEYWORD,”Urban Resilience”) OR EXCLUDE (EXACTKEYWORD,”Urbanization”) OR EXCLUDE (EXACTKEYWORD,”Biodiversity”) OR EXCLUDE (EXACTKEYWORD,”Land Use”) OR EXCLUDE (EXACTKEYWORD,”Waste Management”) OR EXCLUDE (EXACTKEYWORD,”Building”) OR EXCLUDE (EXACTKEYWORD,”Food Supply”) OR EXCLUDE (EXACTKEYWORD,”Food Production”) OR EXCLUDE (EXACTKEYWORD,”Buildings”) OR EXCLUDE (EXACTKEYWORD,”Cooling Systems”) OR EXCLUDE (EXACTKEYWORD,”Competition”)) AND (LIMIT-TO (SRCTYPE,”j”)) AND (LIMIT-TO (DOCTYPE,”ar”))

for the advance search terms on Scopus). The articles from this exclusion search consisted of 314 items, of which 299 were available for download. Further review of the articles’ abstracts (and the body of the article where the abstract is not sufficient) led to the identification of 81 articles specific to electricity system, which were downloaded into Mendeley Reference Manager and a Microsoft Excel spreadsheet. Finally, 34 papers were selected for critical review following two successive thorough examinations of the entire texts, as they suitably addressed the decision objective, context, and implementation (based on the 18 specific questions). Another search was conducted in January 2025 using the same database and procedure, except setting a time band at 2023–2025. The motive is to include recently published articles after the initial search in May 2023. This search returned 344 items, of which 29 were not available for download at the time of the search; and 38 duplicates from the previous search were identified, implying that 277 were screened for relevance. A review of their abstracts identified 87 articles downloaded into Mendeley Reference Manager and a Microsoft Excel spreadsheet. After full-text readings, 41 articles were selected for critical review from the second search. Overall, 74 articles were used to achieve the objectives of this review. The list of articles is provided in Table S1 (Supplementary File). Figure S1 presents the PRISMA flow diagram for the study.

3.4. Analysis and Synthesis

To analyze the selected papers, a classification scheme was developed, as recommended [71]. A classification scheme reduces the complexity of a domain by identifying, describing, and structuring various elements according to their similarities, helping researchers or practitioners understand and analyze the domain [77,78]. It plays an important role in decision support [77]. Drawing insight from energy taxonomy [79], the taxonomy in this study helps identify and categorize various elements that influence energy decision-making. The scheme in this study, as shown in Figure 1, presents the contents of the articles under decision-making themes. Related content is synthesized to form a cohesive and comprehensive decision guide by grouping the 18 specific review questions under the themes. The themes are decision objective (D-O), decision context (D-C), and decision implementation (D-I) (D-OCI), which are linked to Q1–Q7, Q8–Q13, and Q14–Q18, respectively. Q1–Q18 are the specific questions. Aside from addressing ‘how’ (methodology aspect), the synthesis of the contents addressed the ‘what’ and ‘which’ questions to promote an optimal perspective and course of action for formulating energy policy. Within the frame of this study, the questions linked to D-O (what) help decision-makers focus on what they intend to achieve or what the decision is about. In this study, the focus is elements necessary to achieve the sustainability and resilience objectives in the energy system. The D-C questions (which) are the basis for D-O. Establishing the context clarifies which issues or factors can affect the outcome of energy decisions. D-I questions (how) cover how decisions are evaluated. Generally, the scheme addresses case- and context-specific energy decisions by providing decision-makers (DMs) with options from elements under different themes rather than generalizing strategies to achieve the objectives, which is unlikely to be effective because of the uniqueness of each element. Section 5 explains how D-OCI can be implemented.

The analysis and synthesis techniques used in the study include bibliographic and comparative presentations in tables, charts/figures, trends, and frequency/percentage distributions for ease of visualization. Qualitative justifications were also used to support these techniques.

3.5. Reporting and Using the Results

The results and discussion with practical implications are presented in Section 4.

4. Results

4.1. Trend Towards the Integration of Sustainability and Resilience

Figure 2a illustrates the number of articles published each year from 2001 to 2025. While the first articles appeared in 2001, the annual publication rate was minimal during the initial two decades (up to 2020), representing only 18.6 percent. However, this trend has since shifted, with a notable increase of 81.3 percent from 2021 to early 2025. Remarkably, five articles emerged by mid-January 2025, suggesting the potential for even greater publication numbers than in previous years. This trend underscores the rising importance of achieving sustainability and resilience objectives in the energy sector as the global community nears the SDG 7 timeline. Climate change, intermittent generation from renewables, natural disasters, political instability, and wars, among others, are likely causes of the surge in this research direction. The section on types of disruption (4.2.3) is another piece of evidence of the rising trends. Figure 2b displays the key terms utilized in the articles. The first two terms, ‘sustainab*’ and ‘resilien*’, highlight the focus of this study and their prominence as leading terms. Following these are ‘renewable’, ‘system’, ‘optimisation’, ‘electric*’, and ‘assessment’. This illustration further emphasizes the significance of renewable technologies within the electricity system and the need to evaluate candidate technologies during energy planning. Other key terms can be seen in the figure.

4.2. Decision Objectives (D-O)

This section addresses the core of energy decisions, elements of the decision objective, mostly from the conceptual perspective.

4.2.1. Types of Integration

The integration of the objectives can be achieved in two major ways, as deduced from the identified articles—implicit and explicit integrations. Articles categorized as implicit integration demonstrated that either of the concepts can be addressed implicitly when the other is the central theme, for example, when a study whose primary focus is sustainability addresses resilience latently and vice versa. There are two aspects to this:

(a)

Sustainability objective (with implicit integration of resilience issues) (SR)

According to some studies, the sustainability objective can promote resilience issues implicitly. Yue et al. [80] emphasized the resilience concept of ‘decentralization’ as a means to transition toward a sustainable energy future, while Wehbi [81] aggregated indicators of progress toward sustainability in the energy sector by introducing technologies that are renewable, introducing an economic perspective to resilience. The energy transition study by [82] introduced resilience thinking of the diversity of energy technologies in the energy portfolio, as applied to electricity generation in its sustainability assessment. In a quantitative life cycle assessment framework, Dantas and Soares [46] addressed the sustainability of energy systems, but resilience is seen as one of the two ways to achieve sustainability in an engineered system. The resilience aspect is the diversity of the portfolio of sources that have the highest chance of preventing disruptions. Similarly, resilience is one of the criteria in the sustainability assessment framework for power system planning [83]. The perspective of Maaloum et al. [84] was that energy transitions contribute to adaptive policies for responding to climate change. Thus, security was a key reflection of energy resilience in this study, as it assesses energy transition policy from a sustainable development perspective. Phoumin et al. [85] is concerned with the decision-making method for assessing hybrid energy systems from renewable sources for greening business, even as Dong et al. [86] focused on the sustainability performance of the smart grid, of which critical success factors to aid performance included resilience-related attributes or capabilities. Wehbi [87], justifying the role of sustainable energy planning and resiliency, developed a framework for a renewable energy transition based on five dimensions of sustainability. Li et al. [88] assessed the sustainability of geothermal power to promote the resiliency of the generation system. Hassan et al. [89] analyzed the economic and environmental aspects of transitioning to renewable energy and green hydrogen to promote energy security by 2030. The techno-economic study by Maaloum et al. 2024 [90] highlighted the resilience of future investment in green hydrogen when renewable technology is used for its production. A study on techno-economic assessment of a utility-scale wind power plant project promoted a low-carbon economy, increasing power generation capacity and resilience (diversification of the energy mix) [91]. Ref. [92] looked at how external effects can be incorporated into project sustainability assessments to support transition to renewable energy. The assessment addressed the social resilience of residents.

(b)

Resilience objective (with implicit integration of sustainability issues) (RS)

Here, the resilience objective can address sustainability objectives implicitly to achieve different energy goals. First, Mujjuni et al. 2021 [40] developed a framework for resilience assessment as a means to achieve development, with sustainability as one of its goals. Another article measured resilience from an economic perspective rather than from engineering or physical aspects, which are common in resilience studies, and examined the effect of resilience on CO₂ emissions [93]. Dong et al. [94] studied how energy resilience affects carbon dioxide (CO₂) emissions, thereby contributing to environmental sustainability. Using sustainability as the factor or strategy to achieve resilience, ref. [68] developed a resilient solar energy management system, but their later study identified and evaluated the enablers of resilience capability in the wind power sector to design, develop, and optimize a resilient management system for achieving sustainable energy management [39]. The scope of the study by González-Delgado et al. [95] was the economic and techno-economic resilience of power generation through large-scale production routes of hydrogen by indirect gasification using African palm fruit. Haro-baez et al. [96] are interested in strategies for promoting resilience in clean energy infrastructure on an island; the approach ensures energy security and sustainability. Other studies, such as ref. [97], developed a resilient integrated resource planning framework for transmission systems that aligned with energy sustainability goals, while ref. [98] undertook resilience decision support for electricity planning in rural areas with a focus on the techno-economic viability of power supply projects.

Articles under the explicit integration category revealed that the two objectives could be integrated without ambiguity. Specific issues related to the two concepts, such as the criteria/metrics/indicators, could be clearly identified as part of their evaluation process:

(a) Mixed integration (R-S): This involves incorporating sustainability and resilience assessment criteria distinctively, although not separately. That is, the contributions of the two objectives to energy decisions are not separate. For example, to present resilient energy system analysis and planning, both resilience and sustainability were included in the composite resilience index because of their overlapping goals [36]. In the case of Grafakos et al. [99], an integrated sustainability and resilience framework for low-carbon-energy technology assessments at the local level was developed, similar to assessing low-carbon-energy technologies against sustainability and resilience criteria [100]. Sustainability is the means to achieve resilience in the sustainable resilience of hydrogen energy systems [32], whereas resilience has been applied to sustainability criteria in the assessment framework for hydrogen energy infrastructure development [75]. Gholami [101] developed a multi-criteria framework to improve sustainability and resilience. This study was implemented to address the changing energy needs and the problems associated with adding renewable energy. Liu et al. [102] developed an inclusive framework that improves the efficiency and sustainability of energy systems, just as [103] evaluated and selected the optimal power grid options for multi-energy systems, taking into account resilience and sustainability indicators. A combined perspective of energy supply sustainability and resilience with respect to SDGs 7 and 13 and energy security was examined by ref. [104]. To strengthen grid resilience, ref. [105] used a case study to show how the interaction of economic and resilience-based assessments can be applied.

(b) Stand-alone integration (R_S): In this situation, both sustainability and resilience stand as individual objectives within the main goal of the energy transition. In strategic planning for balancing the trilemma of energy for energy system planning in coastal cities, different pathways for a sustainable transition have been evaluated, with resilience as one of the three objectives [106]. Salehi et al. [12] designed a resilient and sustainable biomass supply network via an optimization method. Among other factors, the study examined the associations between resilience factors and sustainability indicators by specifying both objectives within the biomass supply chain. In the dynamic sustainability framework for petroleum refinery projects, sustainability criteria were examined against some pillars of resilience [107]. Jing et al. [106] investigated economic and environmental criteria of sustainability. In a study on marine energy in coastal communities, Kazimierczuk et al. [108] listed community energy goals such as energy security, affordability, resilience, environmental sustainability, and economic growth. Gruber [109] measured both aspects of resilience and sustainability separately using economic and technical goals, respectively. Other articles in this category include refs. [110,111,112].

(c) Integration by strategy (IS): Emerging trends in the integration of both objectives have been observed from 2023 onward. There are more studies evaluating strategies that support both objectives simultaneously during the period. Therefore, it cannot be stated categorically whether an evaluation is meant to capture either objective, but there are explicit statements that justify classifying them as explicit integration based on the strategies pursued. Examples include the application of an integrated waste-to-energy approach to resilient energy system design for sustainable cities and communities [113]. To achieve SDG 7 through a reduction in fossil fuel and electricity costs, Eras-Almeida et al. [114] leveraged how to achieve a resilient energy supply through diverse options or a combination of options to implement this goal. The interest of Ala et al. [37] is achieving the resilience and long-term security of sustainable energy planning. Ba-alawi, Nguyen, Aamer, et al. [115] optimized sustainable green hydrogen energy storage to address the intermittent challenges of solar and wind energy production resulting from adverse weather conditions. The study conducted a sustainability analysis before determining which resilience option offers the greatest safety or minimal risk. Ba-alawi et al. [116] undertook a study to improve the strategic management and planning of water energy systems to reduce operational and emission expenses while maximizing system resilience within the generation and distribution network. Castrol et al. [117] used a techno-economic analysis to assess the significance and effects of incorporating storm hardening measures and insurance into a hybrid renewable energy system. Araria et al. [118] proposed a sustainable approach for offshore energy generation to address resilience concerns linked with intermittency. Dongyang et al. [119] examined dynamic adaptation strategies in power transmission to address problems that arise because of the growing reliance on renewable energy for the long-term management of power transmission systems. Tangi and Amaranto [120] created a decision–analytic framework that combines the single-objective simulation optimization model with multi-objective evolutionary algorithms to help plan the best, most sustainable, and resilient multi-energy systems. Different algorithms are evaluated and the most effective method is employed to derive ideal designs based on fluctuating renewable energy generation potential and energy cost. Elkholy et al. [121] address the implementation of an effective hybrid power system in two primary phases. Initially, it seeks to enhance the performance of individual renewable energy sources (RESs), guaranteeing their optimal efficiency. Secondly, it employs artificial intelligence to optimize energy distribution inside the micro-grid. The main emphasis is on guaranteeing the reliability, sustainability, and cost-efficiency of micro-grid operations using a hybrid optimization algorithm. Shah et al. [122] investigated the optimal system size for resilience and reliable load servicing of a 100% hybrid renewable energy system using the multi-objective genetic algorithm (MOGA) optimization technique, based on the premise that off-grid electrification of remote areas with sustainable energy systems (SES) is essential for achieving sustainable development goals.

As presented in Figure 3a, the number of articles classified as explicit integration (44) is more than those under implicit integration (31). IS (23) is the most adopted approach for integration, followed by RS (16). Both SR and R-S have an equal number of articles (15), while R_S (6) has the least number of articles. Furthermore, Figure 3b shows that the initial years were dominated by implicit integration, while explicit integration began to receive more attention from the research community from 2020 upwards.

4.2.2. Sustainability Objective

Table 3. Sustainability criteria and indicators.

Article	Economic								Environmental							Social
	EC	1	2	3	4	5	6	7	EV	1	2	3	4	5	6	SC	1	2	3	4	5	6	7	8
P1	X								X							X
P2	X								X							X
P3	X	X							X	X						X	X	X
P4	X								X
P5	X	X			X		X		X	X
P6	X								X					X
P7	X	X							X	X
P8																X
P9	X
P10									X	X
P11									X							X
P12
P13	X	X	X		X				X	X	X	X	X	X	X	X		X	X	X	X
P14	X	X	X		X				X	X	X	X	X	X	X	X		X	X	X	X
P15	X	X	X		X				X	X	X	X	X	X	X	X		X	X	X	X
P16	X	X		X	X		X		X					X	X	X	X	X				X	X
P17									X	X
P18
P19																X
P20	X	X		X	X				X					X
P21	X								X							X								X
P22	X		X						X	X						X		X	X
P23	X	X			X				X	X
P24	X								X
P25	X	X				X		X	X	X					X	X
P26									X	X
P27	X	X					X		X					X	X	X		X				X	X
P28									X	X
P29	X								X	X						X	X	X
P30	X	X							X	X						X
P31	X					X			X	X
P32									X	X
P33	X															X
P34	X	X				X			X	X						X
P35					X		X
P36	X	X			X		X	X	X	X					X			X				X
P37	X	X	X		X		X		X	X					X
P38	X								X
P39	X
P40	X				X				X	X
P41	X	X				X			X													X
P42	X	X	X		X			X	X	X
P43	X				X				X
P44	X				X			X	X				X
P45									X				X
P46	X	X	X		X				X	X			X	X		X		X	X	X		X	X	X
P47	X						X	X
P48	X
P49	X	X	X		X		X	X	X	X				X
P50	X
P51	X								X
P52	X								X
P53	X	X	X		X
P54	X	X			X		X		X	X
P55
P56	X								X							X
P57	X	X	X		X		X	X	X	X	X			X		X		X					X
P58	X	X			X		X		X	X				X	X	X	X	X					X	X
P59	X	X			X		X		X	X				X	X	X	X	X					X	X
P60	X	X	X		X		X	X
P61	X								X							X
P62	X	X	X		X		X		X	X
P63	X				X
P64	X						X	X	X	X
P65	X	X	X		X		X		X
P66	X	X					X	X	X	X
P67	X	X			X		X		X	X
P68	X
P69	X								X
P70	X	X	X		X	X			X	X	X			X	X	X	X
P71	X	X	X		X		X	X	X	X		X		X	X	X	X	X	X			X	X	X
P72									X	X
P73	X								X	X
P74
P75	X						X		X	X
Freq.	60	31	15	2	28	5	20	11	58	37	5	4	6	13	12	26	7	14	6	4	3	6	7	5
%	80.0	41.3	20.0	2.7	37.3	6.7	26.7	14.7	77.3	49.3	6.7	5.3	8.0	18.7	16.0	34.7	9.3	18.7	8.0	5.3	4.0	8.0	9.3	6.7

presents the sustainability criteria and indicators identified during the content analysis of the reviewed articles. The ticked boxes indicate the criteria/indicators identified in the articles. When at least one indicator associated with a criterion is identified, the criterion box is also ticked. Where it is not very clear which indicator(s) was addressed, only the criterion box was ticked if there was evidence that the criterion was addressed. In addition, the captions given to indicators represent general identifiers, which means that they may not be the actual terms used in some of the articles. According to the table, six criteria can be identified: environmental, economic, technical, social, technological, and institutional/political dimensions. The economic dimension was relevant in most of the articles, occurring in 80.0 percent of the articles. Its indicators include the cost of establishing power facilities or expansion, operation and maintenance, and repair, fuel cost, occupation, energy production, earning (NPV, IRR, ROI, NPC), and payback period (as well as lifespan of investment). Some indicators have limited usage, such as “investment and financing policy” [39], “grid electricity purchase” [85], “supply chain cost” [12], “economic performance” [107], cost of energy [123], insurance premium [117], and cost of balancing [124]. The environmental aspect has also gained much (77.3 percent) attention, although not to the same extent as the economic criterion. Frequently occurring indicators are CO₂ emissions and other air pollutants, ecosystem damage/impact (including health), resource usage (fuel, water, land, etc.), noise pollution, waste generation/reduction, and waste disposal (including infrastructure). Certain indicators are specific to particular studies, such as “unburned hydrocarbon” [85], ‘land and soil pollution’ [107], ‘environmental cost’ [121], ‘compatibility with national heritage’, and ‘green economy’ [101]. As evident in

Table 4. Sustainability criteria and indicators (continued).

Article	Technical									Technological								Institutional/Political
	TC	1	2	3	4	5	6	7	8	TE	1	2	3	4	5	6	7	IP	1	2	3	4	5	6
P1										X
P2	X
P3	X		X	X	X	X	X
P4	X		X							X	X
P5	X		X	X	X	X	X
P6	X		X							X								X			X	X	X
P7
P8										X	X	X
P9										X		X					X
P10
P11	X																	X
P12
P13
P14										X		X	X	X	X
P15										X		X	X	X	X
P16																		X						X
P17
P18
P19
P20
P21	X									X								X	X
P22	X		X		X	X	X			X						X		X	X			X
P23	X		X	X		X				X				X
P24	X
P25	X		X
P26
P27
P28																		X			X	X
P29										X						X
P30
P31	X		X
P32	X	X							X
P33	X																	X
P34	X
P35	X	X			X	X
P36	X				X					X						X
P37	X						X			X						X
P38	X
P39	X							X
P40	X				X	X				X				X
P41	X							X
P42	X		X
P43
P44	X							X
P45
P46	X		X															X		X	X	X	X
P47	X
P48
P49	X	X	X		X
P50										X
P51	X									X		X
P52	X						X			X		X	X			X		X				X
P53
P54	X						X
P55
P56
P57	X				X
P58	X	X						X		X	X	X			X			X					X	X
P59	X	X						X		X	X	X			X			X					X	X
P60			X
P61
P62										X
P63	X	X					X			X					X
P64	X					X
P65
P66	X	X
P67	X
P68
P69	X							X
P70	X							X		X	X							X
P71	X		X		X			X	X	X	X					X		X		X	X
P72	X
P73
P74										X	X						X
P75
Freq.	41	7	13	3	9	7	7	8	2	24	7	8	3	4	5	6	2	13	2	2	4	5	4	3
%	54.7	9.3	17.3	4.0	12.0	9.3	9.3	10.7	2.7	32.0	9.3	10.7	4.0	5.3	6.7	8.0	2.7	17.3	2.7	2.7	5.3	6.7	5.3	4.0

EV = Environmental criteria; EV1 = CO2 emissions and other air pollutions; EV2 = Noise pollution; EV3 = Radioactive waste; EV4 = Waste disposal (infrastructure); EV5 = Ecosystem damages (including health); EV6 = Resources usage (Fuel, water, land, others). EC = Economic criteria; EC1 = Cost of establishing power facilities or expansion; EC2 = Fuel cost; EC3 = Consumption/costs; EC4 = O&M (Operation and maintenance), repair; EC5 = Energy production; EC6 = Earnings (NPV, IRR, ROI, NPC, net); EC7 = Payback/lifespan. SC = Social criteria; SC1 = Social fairness in distribution of technology; SC2 = Employment (job creation, wages, etc.); SC3 = Social acceptance; SC4 = Aesthetic/functional impact; SC5 = Mortality, morbidity, accidents/fatalities; SC6 = Safety and security; SC7 = Welfare and Health. TC = Technical criteria; TC1 = Efficiency; TC2 = Capacity adequacy (capacity factor; rated capacity, unmet load); TC3 = Peak demand; TC4 = Electricity production; TC5 = Maximum capacity for energy technology; TC6 = Operational capabilities; TC7 = Reliability; TC8 = Security. TE = Technological criteria; TE1 = Flexibility; TE2 = Technical and non-technical innovations; TE3 = Technological maturity; TE4 = Market size (domestic); TE5 = Market size (potential); TE6 = Market size (potential); TE6 = Use of renewables; TE7 = Feasibility. IP = Institutional/Political criteria; IP1 = Organization; IP2 = Policy support; IP3 = Regulatory framework; IP4 = Political stability/acceptance; IP5 = Legal compliance.

, the technical criteria ranked third (54.7 percent) in terms of inclusion in the energy assessment. It is measured in terms of capacity adequacy (as a capacity factor; rated capacity, unmet load), maximum capacity for energy technology, maximum capacity for energy technology, peak demand, electricity production, and operational capabilities, which are the recurring sub-criteria in the articles. Other less common indicators are ‘permissible lowest/highest limitations of increased technologies in periods’ [37], ‘energy security’ [36,87], ‘technically accessible resource and resource availability’ [125], ‘service integrity (or reliability)’ [113], and ‘energy savings’ [126,127]. Social factors are fourth (34.7 percent) and have been used in the form of employment (job creation, wages, etc.), social fairness in the distribution of technology, welfare and health, social acceptance, safety and security, community engagement, aesthetic/functional impact and mortality, and morbidity accidents/fatalities (tendencies). The Human Development Index [123] is used in only one article.

The technological criteria (32.0 percent) can be distinguished from the technical criteria because they mostly address the concrete aspects of energy source or enablers that make the process (technical aspect) run smoothly to ensure continues energy usage. Compared with the earlier identified criteria, the technological aspect has fewer uses. Indicators include technical and non-technical innovations, market size (domestic), flexibility, technological maturity, market size (potential), and use of renewables. Other indicators with limited use are construction duration [37], techno-feasibility, operational and cross-border control smart design [39], and deployment viability (supply chain) [125], which are also relevant in specific situations. The dimension with the lowest degree of occurrence is institutional criteria (17.3 percent). The fact that the publications that featured it are recent confirms that it has recently gained recognition in sustainability assessment. The most occurring indicators include organization; policy support; regulatory framework; political stability/acceptance; legal compliance; and education, training, and skills, while institutional capacity occurred only in [87]. Furthermore, some articles combine two criteria, i.e., socio-economic [114] and techno-economic [39], among other articles.

4.2.3. Resilience Objective

Although both sustainability and resilience are important in energy transitions, it seems that the chances of effective energy planning can be attained when critical system issues, which are mostly associated with threats or shocks and/or resilience, are incorporated. As displayed in Figure 4, decisions should address a variety of threats, which were classified into 10 groups. Figure 4 depicts the cluster columns for each group. The highest cluster includes only climate-related disruptions with a frequency of 32, followed by ‘natural disasters’ (14) and ‘operational’ and ‘resources’ (8) in the second, third, and fourth positions. The next cluster includes ‘economic’ and ‘demand’ threats, which have seven articles each. The least common threats are ‘technical, technological’ (6), ‘policy’ (4), ‘pandemic’ (2), and other factors (2). Table S2 has the complete list of disruptions addressed in each of the articles. Table S3 presents the type disruptions associated with each cluster.

Furthermore, decisions should address different types of resilience. To determine the type of resilience addressed by the studies, the article title, abstract, and/or methodology sections were analyzed. When this was not entirely clear, the authors relied on the explanations provided in the articles in comparison to articles with similar contributions. According to Figure 4, energy system resilience (28) is the most common. Other types of resilience reported in at least two articles include micro-grids and power plants/supply (16), community energy (7), energy technology (5), energy generation (5), supply and demand (4), and business/management systems or organizations (4). Economic, environmental, and operational resilience are the least common. Approaches to resilience provide an explanation of how the articles aim to achieve resilience. Figure 4 shows that the approach to energy resilience is mostly adaptive (67). Other approaches include preventive, recovery, absorptive, and transformative approaches in descending order of occurrence. It is also possible to use more than one approach, such as in [33], which mentioned both adaptive and recovery, similar to [40], which combined adaptive, absorptive, anticipative, and transformative approaches, and [75], which focused on recovery, absorptive, and preventive strategies. Janta et al. [128] identified preparedness, absorbability, recoverability, and adaptability, whereas [129] mentioned adaptive, preventive, and anticipative methods.

Anchors of resilience help to achieve the listed approaches, reflecting attributes or initiatives that make the energy system or any of its value chains more resilient. A higher performance of these attributes increases the degree of resilience of the energy system. Figure 4 presents attributes that appear in at least two articles. Apparently, ‘diversity’ is the most measured attribute, occurring in 29 articles. Diversity can be related to the degree of functionality and responsiveness [130]. ‘Flexibility’ and ‘stability’ appeared in 16 and 10 articles, respectively, whereas ‘decentralization’, ‘reliability’, and ‘robustness’ were featured in 8 articles each. Meanwhile, ‘dispatch ability’ and ‘quality’ were addressed in seven articles, and ‘integration’ emerged in six articles.

Compared with sustainability, resilience assessment is more complex, as it requires different considerations, including the type of disruption (single or multiple), value chain (or entire system), and type of resilience, among others. Consequently, classifying criteria or indicators for measuring resilience in the same manner as sustainability proves challenging, if not impractical. Every assessment is likely to have unique components, except for adoption, where a new assessment is similar to an existing one. However, the aforementioned anchors of resilience have been used to measure the degree of resilience in some studies. These include stability of energy generation [99], flexibility [38,39], and integration [68,86]. Others are diversity [106,131], decentralization [36], reliability [84], robustness [75], and technology maturity [99], among others. Another trend in the evaluation of resilience is measurement—or optimization— through the lens of sustainability criteria. For instance, ref. [85] addressed the resilience index by using technical, social, economic, and organizational resilience. Some authors, refs. [95,117,132], conducted techno-economic assessment of resilience strategies. Gruber et al. [109] measured technical goals as a proxy for resilience, while [133] measured it directly using economic, environmental, and social criteria. Resilience was implied from environmental, technical, political, and economic factors [125]. The Supplementary Material summarizes how individual articles have assessed resilience. Table S3 has the list of articles and how resilience was evaluated.

4.3. Decision Context (D-C)

The decision context addresses the setting, issues, or factors that affect decision evaluation, such as the system configuration, value chain, technology, perspective to decision, decision term, entity or unit, and decision scope. The review revealed two main configurations: centralized and decentralized. Where an article is not specific about the configuration, it is assumed to be centralized because, based on findings during the review, those addressing decentralized systems are more specific about it than are those addressing traditional grid systems. According to Figure 5, in earlier years, articles focused on centralized systems until recently, when attention began to shift towards off-grid solutions. The shift is due to the disruptions in the larger system and their large-scale effects. Thus, decentralization may be an effective strategy to address sustainability and resilience issues simultaneously. To confirm this proposition, the correlation matrix in

Table 5. Correlation between the type and strategies for integration of objective.

	Implicit	Explicit	Decentralized	Centralized
Implicit	1
Explicit	0.779	1
Decentralized	0.980	0.885	1
Centralized	0.974	0.840	0.759	1

A value less than 0.3 is trivial [28].

highlights strong relationships between integration approaches and the system configuration employed.

Various technology options are available to boost performance in terms of sustainability and resilience. These technologies can be categorized into two categories: energy generation and storage. Those for generation can be grouped into renewables, non-renewables, and cleaner non-renewables. Renewable options include solar, biomass, wind, hydro, geothermal, tidal, and wave, although solar (52.0 percent), wind (32.0 percent), and biomass (20.0 percent) have been widely used to address energy transition (

Table 6. Types of energy technology.

	Renewables								Cleaner Non-Renewables			Non-Renewables				Storage	Others	Multi-Energy	Technology mix
	Biomass	Geothermal	Thermal	Hydro	Solar	Tide	Wave	Wind	Hydrogen	Nuclear/ Uranium	Gas	Oil	Diesel	Gasoline	Coal
P1									X									N	N
P2					X			X		X	X	X	X	X	X			Y	B
P3	X			X	X			X										Y	R
P4					X													Y	R
P5	X				X								X					Y	B
P6		X		X	X			X										Y	R
P7	X			X						X	X	X			X			Y	B
P8					X													N	R
P9																		-	-
P10																		-	-
P11	X				X			X		X								Y	B, F
P12																		-	-
P13	X			X	X			X		X	X				X			Y	B
P14	X			X	X			X		X	X				X			Y	B
P15	X			X	X			X		X	X				X			Y	B
P16												X						N	N
P17	X	X		X	X	X	X			X	X	X			X			Y	B
P18	X				X			X			X				X			Y	B
P19	X	X		X	X	X		X		X	X				X		X	Y	B
P20			CHP		X								X					Y	B
P21																		-	-
P22					X								X					Y	B
P23								X										N	R
P24*																		-
P25	X				X			X										Y	R
P26	X	X		X	X	X		X		X	X	X			X			Y	B
P27	X																	N	R
P28											X				X			Y	N
P29																		-	-
P30					X													N	R
P31									X									N	N
P32	X								X		X		X	X	X		X	Y	B
P33																		-	-
P34								X										N	R
P35						X		X					X					Y	B
P36					X	X	X	X					X			Li-Ion		Y	D
P37					X								X			X		Y	D
P38					X		X											Y	R
P39					X			X										Y	R
P40					X			X										Y	R
P41					X			X								X		Y	C
P42					X								X			Lithium		Y	E
P43*																		-	-
P44					X			X										Y	R
P45*																		-	-
P46					X													N	R
P47									X									N	N
P48	X				X											X	Elect. Veh.	Y	R
P49			X		X											X		Y	R
P50					X													N	R
P51					X			X										Y	R
P52					X			X	X									Y	B, F
P53																	Generators
P54				X	X			X										Y	R
P55					X													N	R
P56					X	X	X									X		Y	C
P57		X																N	R
P58																	Strategies
P59					X	X			X									Y	B
P60								X	X									Y	B, F
P61												X						N	N
P62							X	X	X				X			X		Y	E
P63						X			X							Lithium, Cryogenic		Y	E, G
P64						X										X		Y	C
P65			CHP													BESS		Y	C
P66						X			X				X					Y	B
P67					X	X			X									Y	B, F
P68						X			X				X			BESS		Y	E
P69		X	Heat Pump			X			X			X	X			BESS		Y	E
P70						X			X				X			BESS		Y	E
P71*																		-	-
P72						X			X									Y	B, F
P73									X				X			BESS		Y	E
P74			X		X	X							X			X		Y	E
P75					X	X			X									Y	B, F
Freq.	15	6	5	10	39	17	5	24	17	9	11	7	16	2	11	16
%	20.	8.0	6.7	13.3	52.0	22.7	6.7	32.0	22.7	12.0	14.7	9.3	21.3	2.7	14.7	21.3

Multi-energy decision: Y—Yes (49, 65.3 percent); N—No (15, 20.0 percent); N/A = (11, 14.6 percent). Type of mix: Renewable (R) = (21, 28.0 percent); Non-renewable (N) = (6, 8 percent); Both (B) = (22, 29.3 percent); R and storage (C) = (4, 5.3 percent); N and storage (D) = (2, 2.7 percent); R, N, and storage (E) = (8, 10.7 percent); R and cleaner non-renewable only (F) (6, 8.0 percent); Cleaner non-renewable with storage (G) (1, 1.3 percent); and N/A = (12, 16 percent). P24*, P71* addressed renewables but no specific technology was mentioned. P43*, P45*—Not applicable.

). Nuclear (a derivative of uranium), gas, and hydrogen are cleaner non-renewables, but hydrogen (22.7 percent) has received more attention. Under the non-renewables group, the diesel power engine (21.3 percent) is prominent as a backup to other technologies, although linked to a decentralized system. Others are coal (14.7 percent), oil (9.3 percent), and gasoline (2.7 percent). On the other hand, some technologies have been used to store excess energy, most especially in a system comprising Variable Renewable Energy (VRE), as revealed in 21.3 percent of the articles, which is why different storage strategies are being studied lately [124,134,135]. Common storage technologies include battery energy storage systems (BESS), lithium and cryogenic [136], lithium [117], and Li-Ion [123]. The descriptive statistics under Table 6 show that most energy decisions (65.3 percent) are multi-energy focused, some of which involve both renewables and non-renewables (29.3 percent) or only renewables (28.0 percent). Other combinations include renewable, non-renewable, and storage (10.7 percent); renewables and cleaner non-renewables only (8.0 percent); renewables with storage (5.3 percent); non-renewable with storage (2.7 percent); and cleaner renewables with storage (8 percent).

Furthermore, Figure 6 illustrates the combination of energy technologies implemented under various types of integration, as captured in the articles. ‘Renewables only’ are dominant in all integrations except R-S, while IS, RS, and R-S exhibit a greater diversity of technologies. The figure also indicates that SR primarily focuses on clean energy sources to highlight its commitment to sustainability, whereas RS, which is another form of implicit integration, permits non-renewables because it prioritizes system stability. In contrast, the diversity observed in IS and R-S integrations highlight the importance of achieving both objectives to ensure optimal decision-making regarding the energy mix.

Moreover, the value chains represent stages or activities necessary to make energy available for end usage. The current review investigated the value chains implemented in the identified studies. These include generation, transmission/transportation, storage, distribution, consumption, and general energy systems. The results presented in

Table 7. Energy value chains.

Article	GE	TR	ST	DS	CN	ES	Others	N of C
P1	X	X	X	X	X			5
P2						X		ES
P3	X							1
P4				X				1
P5	X		X					2
P6	X		X		X			3
P7	X							1
P8	X							1
P9	X							1
P10						X		ES
P11	X			X				2
P12							Sector	1
P13	X							1
P14	X							1
P15	X							1
P16	X	X	X	X	X			5
P17	X							1
P18						X		ES
P19						X		ES
P20	X	X			X			3
P21	X	X		X				3
P22	X							1
P23	X							1
P24	X	X		X			Demand	4
P25	X							1
P26						X		ES
P27				X				1
P28	X	X		X	X			4
P29						X		ES
P30						X		ES
P31	X							1
P32						X		ES
P33						X		ES
P34	X							1
P35	X							1
P36						X		ES
P37	X		X					2
P38	X							1
P39	X							1
P40	X			X				2
P41	X		X					2
P41	X							1
P43		X						1
P44	X							1
P45		X						1
P46	X							1
P47	X							1
P48				X				1
P49	X		X					1
P50	X	X						2
P51							Supply	1
P52	X							1
P53						X		ES
P54						X		ES
P55						X		ES
P56	X	X						2
P57	X							1
P58						X		ES
P59						X		ES
P60	X							1
P61							Supply	1
P62	X							1
P63	X						Supply, demand	2
P64	X							1
P65						X		ES
P66	X							1
P67	X							1
P68	X							1
P69	X
P70	X
P71						X		ES
P72						X		ES
P73	X							1
P74	X	X						2
P75							Supply	1
Count	48	11	7	10	5	18
%	64.0	14.6	9.3	13.3	6.7	24.0

GN = Generation; TR = Transmission; ST = Storage; DS = Distribution; CN = Consumption; ES = Energy System; No of C = Number of value chain. Single value chain = 39 (52 percent); Energy system = 18 (24.0 percent); Multiple value chains = 18 (24.0 percent).

show that 64.0 percent of the studies addressed ‘energy generation’. While 14.6 percent of the studies addressed ‘transmission’, 13.3 percent, 9.3 percent, and 6.7 percent focused on ‘distribution’, ‘storage’, and ’consumption’, respectively. Energy decisions can address the entire energy system (24.0 percent) of an entity or multiple value chains, as exemplified in 24.0 percent of the articles. Some examples are [32,107], which investigated five value chains, while refs. [86,131] investigated four value chains, and refs. [40,46,125] focused on three each. Refs. [113,114] focused on two each. Very few articles have addressed other aspects, such as energy demand [86], supply [90,96,137], supply and demand [138], and sector [33].

According to

Table 8. Scope of decision.

Scope	Number of Studies
National	14
Local/community	24
Power plant/energy generation/ project/specific system	9
Global	3
City/urban/municipal/county/sub-national/regional	11
Organizational	4
Sub- continent or continent	2
General	2
Not clear/stated	6

, nine levels of application are realistic. Ranked in the descending order based on the number of sources, they are local/community (24), national (14), city/urban/municipal (11), power plant/energy generation/project/specific system (9), organizational (4), global (3), and continent or sub-continent (2). Studies [99,139] are distinct in that they examined local energy transition issues but within a European context. Thus, they were not included in the list presented in Table 6. These findings demonstrate that studies have been more frequently applied to national, local/community, and specific system contexts. Continent or sub-continent have received comparatively little attention.

The review reveals that decisions should consider the time horizon of assessment (i.e., either current or future orientation).

Table 9. Time horizon and energy entities for decisions.

Time Horizon	Frequency	Entity	Frequency
Current (C)	9	Project-based	6
Future (F)	52	Technology-based	65
Current–Future (C,F)	14	General	4
Total	75		75

shows that most of the articles (52) are future-orientated, while a few are current (9) in nature. Another set of articles is both current and future-focused (14). The basis of this classification is whether an article is aimed at improving current performance or helping to make optimum decisions targeted at future times. The former prioritizes the current perspective, whereas the latter focuses on the future. Those that consider both perspectives invariably seek to improve the state of the art while looking forward to future decisions. The energy entity refers to the unit used for decision-making. The review identified both project-based and technology-based decision contexts. Technology-based energy planning appraisal has been the focus of most articles (65), whereas only 6 percent are project-based assessments, just as 94 are not specific about their basis of decision, at least based on the authors’ judgement (see Table 7).

4.4. Decision Implementation (D-I)

Decision implementation covers the methodological aspects of decision evaluation such as stakeholders’ inclusiveness, selection of criteria/indicators, type of data, and methods for aggregating criteria/indicators and models/methods. Energy decisions are multi-stakeholder-oriented. On the one hand, there are energy experts, policymakers, regulators, and researchers. On the other hand are users and investors, among others. Effective engagement of these various groups will not only achieve favorable decisions but also the cooperation of various stakeholders during decision implementation. According to Figure 7, 40.0 percent of the articles are outcomes of inputs from authors, experts, decision-makers, and other stakeholders, whereas 59.0 percent do not state whether stakeholders were engaged or not. This does not mean that they do not engage a few experts at a particular stage in their undertaking. For example, some are likely to consult relevant bodies of the literature for input data. It follows that they indirectly engage other stakeholders in an ‘informal’ manner. One of the functions of a decision lead or analyst is the selection of criteria/indicators and other techniques for measuring the achievement of the objectives of their assessment. Figure 5 presents how this aim has been achieved in the articles. Many of the studies (79 percent) relied solely on literature sources, whereas others requested contributions from major stakeholders while criteria were selected and validated. Authors like [131] engaged policymakers, academic/researchers, technical experts, and end users, totaling 30 participants during the Delphi survey, while [68] retrieved the opinions of 30 experts from academia and industry. Other activities requiring the support of stakeholders during decision-making, as found during the review, include actual assessment in the form of a survey [85,96,101,103] or interview [108,140].

The types of data observed in the articles (Figure 7) include quantitative data from secondary sources (61 percent) and qualitative data from primary sources through survey instruments (20). A combination of the two sources was found in some articles (15). An example is Ref. [85], where quantitative data on technical, economic, and environmental criteria were used in combination with qualitative data on socio-political criteria through questionnaire distribution. In such situations, data normalization may be required to ensure that the data are in the same unit of measurement for assessment purposes [133].

Figure 7 also presents methods for aggregating indicators in the articles. While the table provides insightful findings, grouping articles by methods can be complex, as different authors may understand a given approach in different ways. Thus, by applying the knowledge gained from [42,141], this review identified fundamental categories of indicators that can be differentiated from the articles by their construction methods and degree of aggregation—indicators, aggregated indicators, index, and composite indicators. Composite indicators are the most commonly used indicators, accounting for 48.0 percent of the articles. They involve relatively complex concepts, and the articles in this category sum numerous facets of a given phenomenon into a single number with a common unit. Common areas of application include optimization problems, such as hybrid/decentralized systems aiming for the best combination of criteria and options to achieve a particular objective [96,118,142,143]. Indicator-based methods accounted for 25 percent. The method is based on results from the processing and interpretation of primary data. Indices (14.0 percent) employ a form of dimensionless number that requires the transformation of data in various units for the most part in order to generate a single number. The aggregated indicators (7.0 percent) combine a number of components (indicators/sub-criteria) of the same units, mostly in an additive aggregation way. When data are not of the same unit, they can be normalized to have a common basis for assessment, such as in [133]. In addition to these, criteria can assume positive or negative values that are beneficial or non-beneficial for assessment purposes [19]. This affects the sizes of the weights that criteria assume and invariably affects the valuation of each decision alternative available.

It is important to identify methods for handling energy decision analysis. While most of the articles used at least two methods, Figure 8 identified 10 categories of methods that featured in at least two articles. Multi-Criteria Decision Analysis (MCDA) (discrete)-related methods have been widely used. Other multi-objective techniques (continuous versions of MCDA) identified are Multi-Objective Grey Wolf Optimization [37], the Multi-Objective Optimization Framework [106], the Multi-Objective Programming Model [37], and the Multi-Objective Resilience Metric Approach [36]. Hybrid Optimization of Multiple Energy Resources (HOMER) is also one of the top three methods identified, while the Capital Budgeting Model, the Descriptive Model (using descriptive statistics), Life Cycle Cost Analysis (LCCA), and Regression (such as Quantile regression [94]) have been used in two articles each. Other methods are listed in Table S4 (Supplementary Material).

5. Discussion

This study provides a comprehensive perspective on the trends and practices that can enhance energy sustainability and resilience decisions and analysis. Following the earliest article [80], efforts had been made to increase scientific knowledge and practices for achieving these objectives individually or together. The highest trends occurred in 2021, when publications started increasing beyond the previous two decades. Within the period under consideration, terms relating to the objectives have been prominent and supported by terms such as renewable, system, optimization, electric*, and assessment, among others. A recent study on resilience and sustainable goals in Global South urban strategies [144] highlighted these positive trends. Therefore, it is important to explore practices that can serve as guides in decision-making according to the D-OCI framework explained in Section 3.

5.1. Decision Objectives

One of the preliminary steps in the integration of sustainability and resilience is to clarify the issues relating to the goals to be achieved. In the first instance, the energy planners can achieve sustainability and resilience goals at the same time in several ways, which can be categorized into implicit integration, explicit integration, and integration by strategy. Implicit integration supports the existing notion that pursuing either sustainability or resilience can indirectly help one to achieve the other [55]. Although the implicit approach was common in the earliest studies, addressing the performance of a specific resilience aspect critical to addressing a specific threat may be difficult. An implied or aggregated performance value will not be adequate in this case. Mujjuni [40] recommended treating each element of the system affected by a threat as an entity, rather than adopting a ‘one-size-fits-all’ approach. An explicit approach could handle this challenge by addressing specific cases and contexts, which are subject to different factors or influences. Consequently, explicit integration has been used increasingly recently. Of the three forms of explicit integration identified in this study, standalone integration makes it easier to determine how each energy option achieves individual objectives prior to investment decisions and implements initiatives that will improve the aspects that are underperforming during the project life. Integration by strategy appears to be more appealing when different strategies that could promote the objectives are optimized to know which combination yields the best results, such as combining different technologies (i.e., generation and storage in a centralized or decentralized system). Most of the studies focused on the environmental, economic, technical, and social aspects of sustainability in energy planning. The position of technical factors among the triple bottom line (TBL) is not surprising since energy systems are made up of technical features. Contrary to the assertion that the Triple Bottom Line (TBL) encompasses a broader perspective [145] and includes minor issues, such as technical and operability [21], there is an increasing focus on technical, technological, and institutional/political sustainability as independent criteria. This corroborates the definition of sustainability as the ability to maintain an entity, outcome, or process over time through mutual relationships [146]. To maintain a sustainable energy system with minimal disruption, the right political will and institutional framework are needed. Technological sustainability is essential for meeting global dynamics and providing necessary technical support and enabling processes. Thus, a holistic approach to achieving sustainability objectives in a low-carbon economy is nothing short of integrating cross-cutting sustainability concerns into (energy) policy assessment procedures, thereby generating cohesive policymaking and better governance [147].

This review clearly demonstrates the diversity and distinct focus of resilience decisions. For instance, policymakers and practitioners may need to prioritize threats to the performance of the system. These are human/management, economic, technical/technological, resource, operational, policy, climate or natural, pandemic, demand, and others. While the entire energy system is open for energy planning, decisions could be made on the energy technology, organization/management systems, micro-grids/power plants, or power supply, among others. Moreover, while energy resilience strategies mostly cater to system adaptation, decision-makers have other options, such as recovery, absorptive, anticipative, transformative, and preventive strategies, which can be combined to attain the desired performance level of resilience. The strategies can manifest in various forms, but diversity, flexibility, stability, robustness, reliability, and decentralization have gained wider usage.

Given the aforementioned parameters, evaluating resilience can be a challenging task. As mentioned earlier, implicit integration of resilience with sustainability may not achieve an effective decision outcome, whereas it may be easier and more productive to aggregate assessment criteria if articulated explicitly, owing to the answer to the question: Resilience “of or for what”? Thus, taking a cue from articles on explicit integration, resilience assessment can be attribute-based, such as the diversity index [106], and resilience factors [12], or using anchors of resilience as assessment criteria. To evaluate resilience where integration by strategy is involved, sustainability criteria or indicators could be adopted as objectives to optimize [109,120]. These discussions indicate that the connection or differences in the sustainability and resilience depend on how they are integrated. In implicit integration, the focus is either how being sustainable could promote resilience or how being resilient could promote sustainability. In explicit integration, both goals may be pursued independently of each other or by a combination of strategies but collaboratively. Fundamentally, identifying the enabling factors and strategies provides a suitable tool for stakeholders to make better decisions in the energy industry [68], but it is also important to consider the decision context as explained next.

5.2. Decision Context

The decision objective is of little importance without establishing the context of the decision agenda. Two main system configurations are currently central to addressing energy challenges: centralized and decentralized systems. There is a strong link between the configuration of the system and the type of integration used, but there is not enough evidence to say whether one configuration performs better than another under a different approach or the other way around. However, reading carefully through articles on integration by strategy does show that decentralized, or off-grid, systems are good for the approach. The type of technology is another contextual issue, ranging from energy generation to storage. This review highlights a number of different technology choices, including different mixes of renewable, non-renewable, and storage technologies, to show how an energy system that uses only renewables could be a concern under unfavorable conditions. DMs should evaluate renewable and conventional technologies to determine which best achieves the two objectives or which should be mixed with others. The combination of renewables and cleaner conventional energy sources can serve as alternatives to dirty conventional energy sources [148]. They could address the intermittency challenge with renewable sources that are weather-dependent, such as wind and solar [149]. Consequently, it is not surprising that articles on hydrogen outnumbered those that addressed renewables other than solar and wind. A similar trend was observed for storage technologies to validate supporting renewable sources with cleaner non-renewables and storage technology. It is also evident from the comparative assessments of energy technologies associated with different types of integration that such diversity is useful for realizing sustainability and resilience objectives.

Another important decision context parameter is the value chain because the process of meeting the demand and supply of energy involves different value chains, such as generation, transmission, storage, distribution, and consumption, each of which has its own unique function, and factors or threats and should be treated accordingly. Energy is generated through different technologies (sometimes mixtures of different sources), but it must be transmitted to the distribution system using high-voltage lines through the grid. Next, it is delivered to consumers through poles and wires at low voltage [150]. Depending on which demand timely intervention, effort could be directed to the entire system or specific value chain, although more than 60.0 percent of previous studies are based on generation, most likely because of the dependence of other value chains on it. Nevertheless, different combinations of value chains from generation, transmission/transportation, storage, distribution, and consumption are realistic [86,131].

Furthermore, DMs need to specify the boundary of the decision. Available options include local/community, national, and power plant/energy generation/project/specific system scope. Localized/community, national, and urban options are the most studied. Efforts are being undertaken to meet the unique geographical location needs of the local community, which makes access to energy infrastructure difficult [128]. National energy mixes are examples for holistic assessment [51]. The benefits of nationwide energy decisions include providing top-level strategies that can guide lower levels. An example is the UN’s national commitment to SDGs [1], although developing plans and implementing policies would still require coordinated commitments from national, state, local, and other entities [151]. Furthermore, energy decisions can focus on either current or future systems/technologies or both. Performance evaluation is often required for the current system to improve its future performance, whereas evaluation of new energy investment is future-focused. There is the belief that the outcome of sustainability policy may not be felt in the short run (such as in intergenerational equality), unlike resilience policy, which will secure the system in the short term from potential threats. This is the main difference between the concepts [55,152]. The above argument could justify the use of sustainability indicators to assess resilience strategies to establish their long-term relevance. Some of the indicators are service life [122], lifetime costs inside the system [132], product lifetime analysis [90], operational lifetime [88], short-term and long-term perspectives [124], long-term economic and environmental benefits [89], and long-term climate resilience evaluations [135]. Nevertheless, both concepts work in tandem to guarantee uninterrupted access to energy.

With respect to the unit of decision, technology-based assessment has gained more popularity than project-based assessment since there is no basis for comparing projects that will always differ in technology, cost, size, etc. [153]. Technology-based assessments are useful for comparing or selecting among a number of alternatives/technologies, whereas project assessment may be more beneficial to a specific technology to determine feasibility.

5.3. Decision Implementation

As important as clarifying decision objectives and establishing the context, they depend on the synthesis of different considerations through an implementation strategy. Energy decisions involve multiple stakeholders. Stakeholders usually participate in the process of aggregating criteria for evaluation, but their inputs are also relevant during data collection. Nevertheless, 79.0 percent of the studies relied on the literature sources for criteria selection. Past studies are useful sources for aggregating criteria if existing criteria can be adapted, but policymakers, academics, technical experts, and end users could add value to the selection of criteria during the scientific validation process, such as in [99]. Moreover, indicators, indices, aggregated indicators, and composite indicators are the metrics for energy assessment based on their aggregation technique. From the decision assessment standpoint, the drawback in the use of individual indicators is that it does not provide the performance overview as a whole. Consequently, it is difficult to use individual indicators for comparability activity since they are entity-centric [23]. On the other hand, composite indices are extremely useful because information from several aspects is aggregated and simplified, enabling easy interpretation, although there may be data misinterpretation since each sub-index may have its own scope and limitations [50] and should not have been generalized. The trends in data collection for assessment include quantitative data from secondary sources and qualitative data from primary sources through survey instruments, and in some instances, a combination of the two is necessary. Combining different data sources is essential for easy data collection, availability, and measurability. Transparency in the assessment process is crucial, while involving stakeholders in key stages, such as validation and assessment of criteria, ensures stakeholder engagement. Conceivably, MCDA has gained popularity within the energy planning domain because it allows for a transparent process that engages as many stakeholders as possible. It can have a substantial effect on the effectiveness of the process [154].

Irrefutably, this study indicates the need for a shift from a narrow view of energy planning as a decision that involves static and narrow concepts to an embodiment of insights from a number of disciplines investigating the factors shaping it [155], for which sustainability and resilience considerations are inevitable. To apply D-OCI, the decision process should address the following question: Decision on what, for which setting, and how? The responses to the question will include choices among the elements of the framework. For example, under D-O, decision-makers can choose whether to integrate sustainability and resilience objectives implicitly or explicitly. Under D-C, decision-makers have the option to tailor integration to either energy generation or any other value chain, which will affect the choice of strategies to increase resilience. Under D-I, the type of data used for decision evaluation can be qualitative or quantitative, depending on the reasons stated in the subsection addressing the data type in Section 4. According to Figure 9, the first step in using the framework is to collect information on the energy decision problem from stakeholders. The information collected is input for establishing the decision objective, as well as contextualizing solutions in stages two and three. The framework may involve going back and forth between stages two and three to make sure that all of their parts are in agreement, especially those that have to do with resilience, before addressing issues related to decision implementation. For example, the choice of resilience strategy will require DMs to know the value chain being addressed. Furthermore, to guarantee effective assessment, criteria must be tailored to specific system configurations and value chains.

6. Conclusions

This review examines trends and practices that can enhance the integration of sustainability and resilience objectives in energy decision-making. Since energy transition involves disruptions in the energy system, in addition to both natural and man-made factors that threaten the resilience of the system, there is a need for effective strategies to address these disruptions in the context of sustainability. It can facilitate access to essential energy services without interruptions, tackle climate concerns related to fossil-based energy sources, and lessen vulnerability due to the integration of Variable Renewable Energy (VRE). Research in this area has emerged recently, and studies are needed to unravel effective practices within the energy-planning field.

This study concludes that integration can be enhanced by factoring practices within the D-OCI scheme, namely, the decision objective, decision context, and decision implementation. Under D-O, decisions should capture various types of integration, sustainability criteria, and specific issues related to resilience, such as types of disruption; types of resilience; and approaches to resilience, anchors of resilience, and resilience assessment. Under D-C, the focus should be on the type of strategy, type of energy technologies, value chains, scope of decision, and perspectives of decision and energy entities, whereas D-I should include the choice of stakeholders, selection of criteria/indicators, types of data, aggregation of criteria/indicators, and decision analysis methods. Based on the inadequate literature on these areas of decision, researchers and actors in the energy sector integrating sustainability and resilience should be explicit about these aspects for clarity, transparency, and effective planning, since the context often determines which strategy to adopt. Potential applications of the study include evaluating future energy investments to ensure that only those meeting certain sustainability and resilience criteria are executed, as well as evaluating the present system to identify its weaknesses in achieving the objectives, hence facilitating improvements. Moreover, it is feasible to adopt sustainable strategies pertinent to resilience and vice versa. The policy must be established as an essential component of a project, policy, or system to be upheld during and following a disruption [68].

Although this study has provided support for decision-making, empirical studies are needed to demonstrate these practices. In a broader sense, additional knowledge is required on the explicit approach of the two objectives in a specific case and context using factors presented by the decision context since few studies are available in this area. Important is holistic sustainability assessment specific to elements in the decision context using the criteria identified in this study. Studies on the subject are expected at the international, city/urban/municipal, and organizational levels. For example, assessments that address sub-regional energy decisions will benefit regional power pools, such as those in Sub-Saharan Africa, enabling optimal decisions on available technologies within the region. Similarly, the adoption of MCDA, a participatory action method, is necessary to engage all stakeholders in the selection of practices.

The study’s limitation is its use of only scientific journals, although the discussions of the findings include other sources. Future research could include technical documents and reports from development organizations, such as the IEA and IRENA, as these sources are crucial for national and international development decisions. This study relied on only one database due to limited access to others. However, this study relied on large article collections in the database and the existing precedent of other studies in this area of research that have used the same database. In addition, while this research area is trending one, separate studies need to be dedicated to other important issues to improve the decision-making of sustainable and resilient energy systems, such as climate targets, decarbonization, and even the water, energy, and food security nexus.

Without a doubt, by presenting important considerations, this study will reduce the complexity in integrating the objectives, thereby filling the existing knowledge gap. This study contributes to the SDGs on affordable and clean energy, resilient and sustainable cities and human settlements, and climate change action, and these practices are beneficial for policymaking in various settings, as energy decisions affect households, organizations, and national and international development.