Aggregate Sustainability Indices in the Electricity Sector: A Review

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D. Gammanpila, Tharindi Raneesha, A. H.T.S. Kularathna, N. K. Jayasooriya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7395096/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2026 Read the published version in Sustainable Energy Research → Version 1 posted 6 You are reading this latest preprint version Abstract This review examines how sustainability in the electricity sector is measured using composite indices that integrate economic, environmental, social, and technological dimensions. A systematic screening of 579 records identified 50 peer-reviewed studies published between 2000 and 2024. These were grouped into four methodological categories which are multi-criteria decision analysis, statistical and econometric approaches, indicator-based composite indices, and review or comparative frameworks. Quantitative methods dominate, while aspects such as public acceptance, equity, resilience, and energy storage technologies remain largely absent. The analysis also reveals a persistent regional imbalance: although Sub-Saharan Africa (two studies) and the European Union (three studies) appear most frequently among regional groupings, this still represents limited scholarly coverage relative to their energy-transition relevance and population scale. South Asia and small island developing states remain almost entirely absent from the reviewed corpus. The findings highlight the need for more inclusive and locally relevant indices that address underrepresented social and technological factors, while preserving international comparability to support policy and planning for sustainable electricity transitions. Battery Energy Storage Systems (BESS) Composite Indicators Electricity Sector Energy Equity Multi-Criteria Decision Analysis (MCDA) Renewable Energy Social Readiness Sustainability Assessment Sustainability Index Technological Innovation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The global shift toward sustainable energy systems has created an urgent need for robust frameworks that can evaluate the economic, environmental, social, and technological performance of the electricity sector. As governments and utilities seek to balance economic growth, social equity, and environmental stewardship, composite sustainability indices have emerged as essential tools for benchmarking policy outcomes, guiding infrastructure investments, and tracking progress toward climate and development goals (Brown & Sovacool, 2007). However, the current landscape of electricity-sector sustainability assessment is fragmented. Many indices concentrate narrowly on specific dimensions such as carbon emissions, energy intensity, or economic contribution while overlooking equally critical aspects, including energy equity, resilience to supply disruptions, public acceptance, and the integration of emerging technologies like battery energy storage systems (BESS) (Narula & Reddy, 2015 ; Kılkış, 2015). Region-specific studies often reflect local policy agendas, but their methodological inconsistency limits international comparability and the transfer of best practices across contexts (Neves & Leal, 2010 ; Doukas et al., 2012 ). Evidence from recent research illustrates the importance of neglected dimensions. Studies on marine renewable energy in Japan, for example, have shown that local economic participation and environmental co-benefits can build strong community support (Kularathna & Takagi, 2018 ), and that decision-support tools such as the Decision Support Analytic Hierarchy Process (DS-AHP) can evaluate coexistence strategies under uncertainty (Kularathna et al., 2019 ). Social readiness modelling has demonstrated how targeted information flows can accelerate public acceptance of new energy projects (Ramachandran et al., 2021 ). Similarly, although BESS can enhance renewable energy integration, manage peak loads, and improve grid resilience, they are rarely included as explicit indicators in sustainability indices particularly in emerging markets where they could address pressing market and reliability challenges (Gammanpila et al., 2023 ). Methodological diversity further complicates the picture. Existing approaches range from indicator-based composite frameworks that often operationalised through Multi-Criteria Decision Analysis (MCDA) techniques such as AHP, TOPSIS, and PROMETHEE and statistical or econometric modelling, to qualitative assessments and system-dynamics modelling. While each method offers valuable insights, many rely on context-specific indicators and weighting schemes, reducing their applicability across different geographies (Hadian & Madani, 2015 ; Abu-Rayash & Dincer, 2019). The present study does not argue for universal or non-context-specific indices. Rather, it emphasises the need to balance contextual sensitivity with cross-regional comparability by establishing a minimal “core” indicator set that can be locally expanded. Such an approach aligns with sustainability-assessment best practices that advocate standardised methodological structures while allowing indicator contextualisation through region-specific data, normalisation, and weighting schemes (Singh et al., 2012 ; Böhringer & Jochem, 2007 ; Hadian & Madani, 2015 ).The challenge is especially acute in developing economies, where limited data availability and diverse governance contexts demand customised, yet comparable, assessment tools (Cucchiella et al., 2017 ; Ediger et al., 2007 ). Beyond methodological variation, it is equally important to consider energy justice and ethics in how sustainability indices are designed and applied. The literature on energy justice highlights three core dimensions which are distributional, procedural, and recognition justice which emphasise who benefits from energy transitions, whose voices are included in decision-making, and whose needs are recognised within policy frameworks. Many existing indices are designed from a technocratic perspective that prioritises data-rich, high-income contexts, inadvertently marginalising low-income, energy-deprived, or geographically isolated communities. Embedding justice-based criteria within sustainability assessment frameworks can ensure that indices reflect the lived realities and aspirations of affected populations, particularly in developing regions (Jenkins et al., 2016 ; Shortall & Davidsdottir, 2017 ; Streimikiene & Siksnelyte-Butkiene, 2020). This review addresses these gaps by systematically analysing 50 peer-reviewed studies on composite sustainability indices in the electricity sector, published between 2000 and 2024. The studies are classified by methodological approach, with particular attention to underrepresented social and technological indicators and to the uneven regional distribution of existing research. By mapping methodological trends and identifying neglected dimensions, the review provides a foundation for developing more inclusive, adaptable, and internationally comparable sustainability indices to support equitable electricity sector transitions worldwide. Methodology Systematic Review Process This systematic review was conducted to examine the development and application of composite sustainability indices in the electricity sector. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological transparency, reproducibility, and a clear audit trail of study selection (Page et al., 2021 )]. PRISMA was selected for its structured approach, which is widely recognised for enhancing the credibility of evidence syntheses in energy and environmental research. The review adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework to enhance methodological transparency and reproducibility. Originally developed for the health sciences (Moher et al., 2009; Page et al., 2021 ), PRISMA has been widely adapted in environmental, energy, and sustainability research where evidence synthesis requires structured screening and traceability (Siksnelytė-Butkienė, Streimikienė, & Zavadskas, 2018; Gunnarsdottir et al., 2020; Hadian & Madani, 2015 ). The framework organises the review into four sequential phases Identification, Screening, Eligibility, and Inclusion each governed by explicit inclusion/exclusion criteria, data-source documentation, and reviewer agreement procedures. In the present study, PRISMA was adapted for methodological mapping rather than clinical outcomes: the “intervention” is the methodological design of sustainability indices, and the “population” is electricity-sector literature. This adaptation enables systematic appraisal of how composite-index techniques evolve across geographies and contexts. The full PRISMA 2020 checklist (Table S1 ) and the flow diagram (Fig. 1 ) trace the screening of 579 records to 50 eligible peer-reviewed studies. The Boolean search strategy combined key terms relating to sustainability indices and the electricity sector. The core search string used was (“sustainable energy index” OR “electricity index” OR “composite indicators” OR “energy sustainability assessment” OR “sustainability frameworks”) AND (“electricity” OR “power sector” OR “grid” OR “energy systems”). Searches were conducted in Scopus and Google Scholar, restricted to English-language peer-reviewed journal articles published between 2000 and 2024. The final search was completed in June 2025. Also, Title, abstract screening and full-text review were carried out independently by two reviewers, with disagreements resolved by discussion with a third reviewer. The PRISMA 2020 checklist is provided in the Supplementary Appendix (Table S1 ). Studies were eligible for inclusion if they presented a composite or multi-indicator index directly applied to the electricity sector, contained quantifiable indicators related to electricity generation, transmission, distribution, or consumption, and employed a structured methodological approach such as Multi-Criteria Decision Analysis (MCDA), Principal Component Analysis (PCA), fuzzy logic, normalisation, or weighting schemes. Only peer-reviewed journal articles were included to ensure academic quality and methodological rigour. Studies were excluded if they focused on sectors unrelated to electricity, lacked a composite or structured index methodology, addressed general national sustainability without disaggregating electricity-specific indicators, or presented conceptual or theoretical frameworks without empirical or methodological application. Review papers without original indicator development or application, non-peer-reviewed sources such as conference papers, reports, and dissertations, as well as duplicate records and studies lacking sufficient methodological detail for assessment, were also excluded. The search identified a total of 579 records. After removing 116 duplicates, 463 unique records remained for screening. Title and abstract screening reduced this number to 135 articles that appeared to meet the inclusion criteria. These studies underwent full-text review, which resulted in the exclusion of 85 articles. The main reasons for exclusion at this stage were the absence of a composite indicator methodology (n = 42), lack of electricity-specific content (n = 28), and insufficient methodological detail (n = 15). Ultimately, 50 studies published between 2000 and 2024 met all eligibility criteria and were included in the final synthesis. Full texts of all included studies were accessible through institutional subscriptions. For each study, data were extracted on bibliographic details, geographic scope, methodological category, indicator dimensions, weighting and aggregation methods, and key findings with stated limitations. The full corpus of 50 included studies with metadata is provided in Supplementary Table S2 , detailing author/year, geography, scope, methodological approach, indicators (summary), weighting/normalization, and key limitations. However, study-level indicators were coded at the domain level (e.g., security/reliability, environmental performance, affordability/economics, access/renewables) and illustrative examples have been given rather than exhaustive lists. Key limitations were coded using a predefined rubric covering transparency of indicator selection and data sources, weighting/normalization subjectivity, validation/sensitivity analysis, data completeness/comparability, scope/transferability, stakeholder involvement, and construct coverage (equity, resilience, storage). This information was used to classify the studies into methodological categories, examine indicator coverage, and identify thematic and geographic gaps. The entire identification, screening, and inclusion process is summarised in the PRISMA flow diagram (Fig. 1 ), with counts matching those reported in this section. Scope and Analytical Approach This review applied the PRISMA framework to identify, screen, and analyse peer-reviewed studies on sustainability indices in the electricity sector. Each of the 50 included studies was examined according to its dominant methodological orientation Multi-Criteria Decision Analysis (MCDA), statistical modelling, composite index construction, or qualitative/non-MCDA approaches. This typology was chosen to ensure reproducibility and comparability across heterogeneous studies. Indicator-level details were also noted to capture how qualitative or stakeholder-driven factors were represented. While certain indices embed qualitative dimensions through coded variables or expert-based weighting, these remain largely quantitative in aggregation. The review therefore distinguishes between the numerical inclusion of qualitative proxies and genuinely interpretive or participatory qualitative frameworks. The analytical workflow of this review operates on two complementary layers. The first layer maps methodological architectures, classifying studies into analytical families such as MCDA, statistical/econometric, and composite-index approaches. The second layer captures indicator-level characteristics, recording the inclusion of economic, environmental, technical, and social indicators, as well as whether qualitative factors were incorporated either as coded variables (e.g., expert judgments, dummy variables) or as interpretive constructs (e.g., stakeholder interviews or participatory weighting). This dual-level coding provides a balanced representation of both methodological structure and content-level inclusion. The review therefore moves beyond simple taxonomy to interrogate how methodological choices shape the representation of qualitative sustainability dimensions. This approach aligns with the tradition of methodological mapping reviews in sustainability science (Mayer, Patterson, & Holling, 2004; Hadian & Madani, 2015 ) and complements quantitative systematic frameworks with contextual understanding. We acknowledge that composite indices can and do include qualitative proxies; however, evidence from the reviewed corpus indicates that interpretive and stakeholder-based qualitative integration remains scarce, validating the study’s call for more hybridized, participatory methodologies in future index development. Methodological Classification Framework To synthesise the methodological diversity observed across the 50 reviewed studies on composite sustainability indices in the electricity sector, each study was classified according to its primary methodological approach. This decision was guided by precedents in the literature, where reviews of energy sustainability assessment frameworks have categorised studies by methodological design to enable comparability and replication in future research (Cavallaro, 2020; Abu Bakar et al., 2015 ; Liu, 2014 ). Grouping by methodology facilitates a systematic understanding of the analytical tools employed, highlighting methodological convergence or divergence across contexts and enabling clearer identification of potential gaps in the evidence base. Across the reviewed corpus, methodological trends revealed a predominance of quantitative approaches, with Multi-Criteria Decision Analysis (MCDA) and statistical modelling accounting for the majority of studies. This finding mirrors patterns identified in prior reviews of energy sustainability assessment frameworks (Brown & Sovacool, 2007; Razmjoo, Sumper, & Davarpanah, 2019) suggesting a strong reliance on techniques that enable structured trade-off analysis and indicator aggregation. However, the relative scarcity of hybrid approaches that integrate quantitative robustness with qualitative stakeholder engagement highlights a persistent methodological gap. Such integration could strengthen the contextual relevance and policy uptake of sustainability indices, particularly in regions where socio-political factors and institutional readiness significantly shape electricity sector outcomes. By systematically mapping methodological prevalence against identified research gaps, this review provides a replicable evidence base to guide the development of more inclusive, adaptable, and decision-oriented composite indices in future scholarship. Results and Discussion Methodological Categorization of Sustainability Indicators To provide an objective, reproducible basis for comparison, the 50 reviewed studies were classified by their primary methodological framework rather than by thematic indicator domains. Study-level characteristics and methodological details are summarized in Supplementary Table S2. This choice reflects the technical design decisions underpinning each study, enabling methodological benchmarking and helping future researchers and policymakers identify approaches best suited to specific data environments and policy needs. Such an approach aligns with prior reviews that emphasise the methodological architecture of sustainability indices as the primary determinant of their robustness, transferability, and policy applicability (Abu Bakar et al., 2015; Liu, 2014; Ouedraogo, 2014) Among methodological approaches, Multi-Criteria Decision Analysis (MCDA) and statistical/econometric models each account for 28% of the reviewed studies (14 out of 50), followed by composite index-based frameworks (26%, n = 13) and review-based analyses (18%, n = 9). This distribution indicates a relatively balanced methodological representation, with quantitative approaches collectively dominating the literature. When considering MCDA, the frequently used techniques include Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). Several studies enhanced MCDA by incorporating fuzzy logic, geographic information systems (GIS), or scenario modelling to address uncertainty and improve spatial–temporal relevance. This structured weighting and ranking capability is particularly relevant in the electricity sector, where decisions involve balancing emissions reduction, cost, reliability, and social acceptance. Table 1: Studies employing MCDA approaches in electricity sector sustainability assessment Authors (Year) Brief Description Methodology Region Prokhorova, V., Budanov, M., & Budanov, P. (2024) Generalized integrated indicator for energy safety; ZNPP case Indicative analysis, AHP, expert estimation, automated monitoring Ukraine Cavallaro, F. (2020) Fuzzy Environmental Pressure Index for energy tech Fuzzy inference (Mamdani) Italy Qian, Q., et al. (2024) EEBD-3ES: impacts of coal reduction in China AHP and Entropy Weight, scenarios China Neofytou, H., Nikas, A., & Doukas, H. (2020) SETR Index for transition readiness Hybrid MCDA + stakeholder weighting Greece & EU Raza, S. S., et al. (2014) Selects energy storage for RES via sustainability index Weighted sum (MCDA) Pakistan Torul Yürek, Y., et al. (2021) Hybrid RES with BESS under uncertainty Pythagorean Fuzzy AHP and TOPSIS Turkey Goldrath, T., et al. (2015) Combined sustainability index for electricity efficiency measures MCDA, normalization, equal weighting Israel Afgan, N. H., & Carvalho, M. G. (2010) Sustainability Index for intelligent energy systems MCDA, normalization, weighted mean Portugal Cucchiella, F., et al. (2017) EU country sustainability comparison MCDA and AHP EU Liu, G. (2014) General Sustainability Indicator for renewables (review and build) Fuzzy AHP, weighted aggregation China Wang, Y., et al. (2020) Fuzzy evaluation for energy transition readiness Fuzzy comprehensive evaluation China Lin, B., & Zhu, J. (2021) Energy security assessment integrating renewable targets and constraints Entropy Weight and TOPSIS China Wu, J., et al. (2021) Low-carbon energy transition performance Improved TOPSIS and Entropy Weight China The second group comprises statistical and econometric approaches, which account for an equally large share being 28% of the reviewed literature. Techniques such as Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), entropy weighting, and regression-based econometrics allow for objective, data-driven indicator weighting and the exploration of causal relationships. These approaches are particularly valuable in contexts with large datasets and the need to identify underlying patterns or forecast impacts of policy scenarios. However, they may underrepresent qualitative aspects such as governance quality or public acceptance unless supplemented by other methods. Table 2: Studies using statistical and econometric methodologies for sustainability assessment Author(s) & Year Brief Overview Methodology (few words) Country / Region Liu Yan, Zheng AnGang, Shang HuaiYing (2020) NQI Capacity Index for smart energy meters PCA China Ospina Betancur, J. A., et al. (2022) Growth–energy links in Colombia via visuals Multivariate statistical analysis Colombia Doukas, H., et al. (2012) Rural community Energy Sustainability Index PCA Greece Ediger, V. S., et al. (2007) Fossil Fuel Sustainability Index (oil/gas/coal) Equal weights & PCA Turkey Yumashev, A., et al. (2020) HDI–energy–CO₂ interactions (OECD) 3SLS (simultaneous equations) OECD countries Mainali, B., et al. (2014) Rural household Energy Sustainability Index PCA-based weighting, normalization Nepal Wang, X., et al. (2020) China’s provincial energy sustainability Entropy Weight Method China Shah, S. A. A., et al. (2019) ESESI: South Asian energy security & environment DEA and Weighted Product South Asia Sarkodie & Adams (2020) HDI, inequality, electricity access vs governance/economy Panel data econometrics (robust, interactions) Sub-Saharan Africa Falbo, P., Fattore, M., & Stefani, S. (2010) Index for electricity spot market performance Statistical index (price volatility, efficiency, liquidity) Italy Ordu, B.M., & Soytaş, U. (2015) Energy commodity prices & electricity market performance Econometric time series; Granger causality Turkey Stein, E., et al. (2013) Climate change impact on energy systems & development Econometric simulation & scenario modelling Latin America & Caribbean Zieliński, T. (2021) whether Corporate Social Responsibility (CSR) participation impacts the profitability, market valuation, and stability of Poland’s four largest energy companies. Through descriptive statistics, index analysis, rankings, cluster analysis, and volatility measures. Poland Konara, K.M., & Tokai, A. (2020) Evaluates the energy metabolic system of Sri Lanka, assessing resource flows, efficiency, and sustainability perfor Material Flow Analysis (MFA), energy metabolism indicators Sri Lanka Note: Studies such as Sarkodie & Adams (2020) were retained because they employ multi-indicator quantitative frameworks that directly examine sustainability linkages within the electricity sector, despite not constructing a formal composite index. Inclusion criteria prioritized methodological diversity rather than index labelling. The third category covers indicator-based composite index frameworks without MCDA, representing 26% of studies. These approaches focus on selecting, normalising, and aggregating predefined indicators often drawing from international frameworks like the UNDP or IEA in order to benchmark performance and track progress. They offer transparency and ease of replication, making them suitable for policy communication, though they can be constrained by indicator availability and may perpetuate existing metric biases. Table 3: Studies applying indicator-based composite index frameworks Author(s) & Year Brief Overview Methodology (few words) Country / Region Brown, M. A., & Sovacool, B. K. (2007) Energy Sustainability Index (security, reliability, efficiency, environment) Indicator selection, normalization, weighting USA Kılkış, Ş. (2015) SDEWES Index for SEE cities Min–Max normalization, weighted aggregation South Eastern Europe Razmjoo, A., Sumper, A., & Davarpanah, A. (2019) New indicators for SEDI; comparative study Data collection, normalization, correlation analysis Iran Iddrisu, I., & Bhattacharyya, S. C. (2015) SEDI: multi-dimensional energy sustainability UNDP normalization, dimension averaging Sub-Saharan Africa Abu-Rayash, A., & Dincer, I. (2019) Integrated SDG-aligned index Weighted arithmetic & geometric means Canada Abu Bakar, N. N., et al. (2015) Energy Efficiency Index for buildings (review) Data normalization, benchmarking Malaysia De Vito, R., et al. (2017) WEF Nexus irrigation sustainability Index construction in nexus framework; case study Italy Kemmler, A., & Spreng, D. (2007) Energy-based indicators to track sustainability Energy framework integration, correlation lens Developing countries Neves, A. R., & Leal, V. (2010) Local energy sustainability indicators for municipalities Indicator selection, pilot testing Portugal Patlitzianas, K. D., et al. (2008) Sustainable energy policy indicators Literature synthesis; PSR/DPSIR framing Greece Wang, Q., et al. (2018) China’s energy security (modified indicator set) Improved index design, normalization China Zhang, X., et al. (2017) Urban energy sustainability index Indicator aggregation, weighting China Tsai, W.T. (2010) Taiwan’s TSDI applied to energy sustainability Indicator-based assessment Taiwan Vera, I., & Langlois, L. (2007) EISD: 30 indicators across social/economic/environmental International framework and case studies Global Razmjoo, A. A., Sumper, A., & Davarpanah, A. (2019) SDG/UN-Habitat aligned energy sustainability analysis Hybrid system modeling (HOMER) and indicator lens Iran Sharma, T., & Balachandra, P. (2014) Benchmarks sustainability of Indian electricity system Hierarchical indicator framework; normalization; aggregation India Neelawela, U.D., Selvanathan, E.A., & Wagner, L.D. (2019) Composite index for global electricity security Composite index construction; multi-dimensional indicators; normalization; weighting Global Huang, Y.-H. (2020) Determinants of residential electricity consumption in Taiwan LMDI; decomposition analysis Taiwan Salarvand, A., Mirzaeian, B., & Moallem, M. (2010) Power Quality Index (voltage, frequency, outage metrics) Weighting indicators of voltage, frequency, outage Iran Finally, review and comparative framework studies account for 18% of the literature. These syntheses assess existing indices, compare methodologies, and identify gaps, offering critical insights into conceptual strengths and weaknesses. While they do not generate new empirical scores, they play a vital role in shaping future methodological innovations and ensuring frameworks remain relevant to evolving sectoral priorities. Table 4: Reviews and comparative framework studies on electricity sustainability indices Author(s) & Year Brief Overview Methodology (few words) Country / Region Gunnarsdottir, I., et al. (2020) Review of sustainable energy development indicators SALSA review; thematic criteria Global Mayer, A. L., et al. (2004) Multidisciplinary sustainability indices review Comparative review Global Narula, K., & Reddy, B. S. (2015) Compares EAPI, ES Risk, ESI Indicator comparison, normalization Global Shortall, R., & Davidsdottir, B. (2017) Applicability of global indices to Iceland Participatory evaluation of national framework Iceland Axon, C.J., & Darton, R.C. (2021) Sustainability–risk frameworks for energy security Multi-disciplinary framework review UK Romero, J. C., & Linares, P. (2014) Exergy as a sustainability metric Thermodynamic LCA & thermoeconomics review Global Streimikiene, D., & Siksnelyte, I. (2016) Compares sustainability of electricity market models in developed countries Comparative assessment; qualitative and quantitative Developed countries Jamasb, T., & Pollitt, M.G. (2005) Reviews electricity market liberalization & integration progress in EU Policy/regulatory review EU This methodological mapping reveals a strong preference for quantitative, data-intensive approaches in electricity sector sustainability research, with qualitative synthesis playing a supporting yet strategically important role. By adopting a methodology-focused classification, this review not only charts current practice but also illuminates methodological blind spots, such as the absence of resilience-focused metrics in purely statistical approaches or the limited stakeholder engagement in many composite index designs. Methodological distribution of the studies The methodological approaches identified in the reviewed literature reveal distinct trends in how sustainability in the electricity sector is assessed. These approaches range from decision-support frameworks and statistical modelling to the direct adoption of composite sustainability indices and qualitative syntheses. Categorising the studies into four broad groups provides a clear view of prevailing analytical preferences and the relative prominence of each method. As shown in Figure 2, Multi-Criteria Decision Analysis (MCDA) and statistical or econometric techniques are equally dominant, each comprising 28 % of the reviewed studies. Composite index adoption accounts for 26 %, reflecting the significant influence of established sustainability frameworks on indicator selection and aggregation. Reviews, encompassing conceptual discussions, literature syntheses, and qualitative assessments, represent the remaining 18 %. This distribution demonstrates a clear preference for quantitative, data-intensive approaches in electricity-sector sustainability assessment. While such methods enable rigorous, replicable analysis, qualitative dimensions are often incorporated within these frameworks through expert scoring or categorical scaling; however, their numerical conversion can limit contextual richness and interpretive depth. The smaller share of explicitly qualitative and review-based studies therefore highlights the opportunity to integrate broader stakeholder perspectives, contextual insights, and interdisciplinary synthesis into future methodological development. Distribution of Sustainability Indicators Across Literature A frequency analysis of indicators across the 50 studies underscores a dominant focus on climate mitigation and energy transition metrics as shown in figure 3. Carbon emissions (CO₂/GHG) and renewable energy share appear in 80% and 70% of the indices respectively, reflecting global policy emphasis on decarbonisation. Energy intensity and GDP per capita are also common, linking energy performance to economic productivity. In contrast, socio-economic and resilience-oriented indicators such as affordability, diversity of supply, and system resilience appear far less frequently (under 12% of studies), and energy storage metrics are entirely absent. This is striking given the increasing importance of Battery Energy Storage Systems (BESS) for renewable integration, peak load management, and grid stability. The omission represents a critical methodological gap that limits the ability of current indices to capture the technological adaptability of modern electricity systems. Normalization, Weighting, and Aggregation Approaches Across the 50 studies reviewed, we observed diverse approaches to normalization, weighting, and aggregation. Most studies relied on min–max or z-score normalization, while a smaller share used distance-to-target or percentile ranks. Weighting was typically applied using equal or expert-based approaches such as the Analytic Hierarchy Process (AHP), with fewer studies adopting data-driven techniques such as entropy or principal component analysis (PCA). Aggregation was commonly achieved through arithmetic means, with occasional use of geometric means. Also, Sensitivity analysis is often applied by varying weights or normalization methods to test robustness of results. Regional observations of Indicators Geographical mapping of the reviewed studies reveals a marked imbalance in research coverage. China leads with 10 studies, reflecting its policy-driven sustainability agenda, extensive energy transition initiatives, and availability of detailed electricity sector data. OECD and European countries are also well represented, often focusing on emissions reduction, renewable integration, and energy security. By contrast, South Asia and Sub-Saharan Africa have fewer studies, and those that exist typically prioritise energy access and affordability in line with developmental needs.Several regions including large parts of Africa, Latin America, and small island states are represented by only one or two studies. This limited coverage constrains the applicability of global indices to these contexts, where climate vulnerability, grid instability, and energy poverty require tailored sustainability assessment frameworks. While global sustainability indices such as the Energy Trilemma Index, Energy Transition Index, and SDG 7 Progress Tracker provide comprehensive international benchmarks, their contextual applicability is constrained by limited regional data and locally validated indicators. Strengthening research coverage across under-represented regions would therefore improve the calibration and relevance of global sustainability assessments to diverse socio-technical realities. Geographical Distribution of Sustainability Assessment Studies The reviewed studies were mapped at two levels of geographic scope as country-specific analyses, where sustainability indices were developed or applied to a single nation, and regional or non-geographic analyses, where indices covered multiple countries within a defined region, economic grouping, or global context. Country specific studies The figure 4 below presents the country-wise distribution and the aim is to capture where empirical or methodological work has been concentrated at a national level. China dominates the literature with 10 studies, reflecting its expansive energy landscape, policy-driven sustainability reforms, and the availability of granular electricity-related datasets. China's emphasis on energy efficiency, decarbonization, and green innovation has made it a fertile ground for sustainability assessments. Also, several countries including the United States, Canada, Italy, Poland, India, Sri Lanka, Taiwan, and Turkey appear in two separate studies each, highlighting a moderate but significant engagement with electricity sustainability measurement. These countries often explore dimensions like energy transition, resilience, or environmental integrity through context-specific frameworks. Single-study countries include Iran, Portugal, Eastern European nations, Colombia, Pakistan, Israel, Iceland, Korea, and the UK. These cases typically adopt either focused methodological innovations or pilot-level indicator development, and represent diverse geographies ranging from the Middle East to Europe and Latin America. This country-level mapping reveals an uneven research distribution, with a concentration in high-emission and energy-intensive economies. It underscores the need for expanding empirical sustainability assessments to underrepresented countries, especially in Africa, Latin America, and parts of Southeast Asia, where data gaps and local challenges merit more targeted evaluation frameworks. Region specific studies The reviewed studies were classified according to their primary scope as reported by the authors. This included g eographic regions EU, Sub-Saharan Africa, South Asia) shown in blue, and non-geographic scopes such as economic or organisational groupings (“Developed countries,” “Developing countries,” “OECD countries”) and global analyses, shown in orange. This distinction avoids conflating location-specific literature coverage with studies adopting broad or economically defined perspectives. Including both categories in the same visualisation ensures completeness and transparency, while the colour separation allows readers to clearly interpret the relative representation of each scope type. The chart highlights that both the European Union (three studies) and Sub-Saharan Africa (two studies) are among the most frequently represented regional categories. However, this difference is minor, and overall regional representation remains limited, underscoring the need for broader geographic coverage in electricity-sector sustainability research. Among the geographic categories, the European Union (EU) and Sub-Saharan Africa appear most frequently, with 3 and 2 studies respectively, while other regional groupings such as South Eastern Europe, South Asia, and Latin America & Caribbean are each represented by a single study. The largest single category overall is “Global,” with 6 studies that address sustainability indicators at a worldwide scale. Non-geographic groupings which are “Developed countries,” “Developing countries,” and “OECD countries” each account for one study and reflect scope definitions based on economic classification rather than geography. This distribution highlights the concentration of literature in certain areas and the limited representation of others, suggesting that regional conclusions should be interpreted with caution where the sample size is small. Conclusions and Future Directions This systematic review of 50 peer-reviewed studies on composite sustainability indices in the electricity sector, published between 2000 and 2024, classified existing work into four methodological categories. Quantitative approaches particularly MCDA and statistical/econometric methods dominate, yet critical resilience, equity, and technological indicators such BESS remain largely absent. These dimensions are critical for electricity systems undergoing rapid transitions yet are seldom incorporated in existing frameworks. Resilience, equity/affordability, energy storage, and justice considerations are four critical dimensions that remain underrepresented in current sustainability indices for the electricity sector. Resilience goes beyond basic reliability and reflects the system’s ability to withstand and recover from shocks such as extreme weather, cyber threats, or renewable variability. Indicators such as SAIDI, SAIFI, reserve margin, and recovery time can capture this more effectively, yet are rarely applied. Equity and affordability address whether electricity is fairly priced and accessible to all, but most indices stop at general access without considering affordability gaps. Measures such as the share of household income spent on electricity, arrears rates, and tariff burdens on the lowest income quintile provide clearer insights into social fairness. Energy storage (BESS), though essential for integrating renewables and stabilizing grids, is almost absent from existing indices; indicators such as installed capacity per peak demand, round-trip efficiency, response time, and levelized cost of storage (LCOS) could reflect both technical and economic performance. Equally important is the integration of justice-oriented and participatory indicators, which remain largely missing from current frameworks and constrain their policy relevance. From an energy-justice perspective, sustainability assessments should capture not only technical and economic outcomes but also issues of fairness, inclusivity, and procedural representation. Including justice-related indicators such as stakeholder participation, affordability for vulnerable households, and regional equity in infrastructure investment would enhance the legitimacy and social resonance of sustainability indices, making them more relevant to modern power systems and policymaking aimed at a just and resilient energy transition Regional analysis further reveals major gaps, with limited coverage of Sub-Saharan Africa, South Asia, and small island states. This is largely due to data scarcity and inconsistent reporting practices. To support future work, three practical steps can be taken. First, use open datasets such as World Bank WDI, IEA, IRENA, and UN Energy Statistics, which provide baseline global coverage. Second, apply proxy indicators where direct data are missing (e.g., tariff schedules or household surveys for affordability, outage reports for resilience). Third, adopt a minimum “core” set of indicators to ensure comparability across contexts, including at least one environmental (CO₂/kWh), economic (retail tariff), social (access or affordability), and technical (system losses or reserve margin) measure. These steps provide a feasible pathway for extending sustainability indices into data-poor regions. Closing these gaps requires a recalibration of both design priorities and methodological strategies. Future indices should embed resilience, affordability, and storage-related metrics alongside established environmental and economic measures. Methodological innovation should prioritise hybrid models that combine the quantitative rigour of MCDA and statistical tools with the contextual nuance of qualitative stakeholder insights. Moreover, this process review has several limitations. It covered only English-language, peer-reviewed journal articles from Scopus and Web of Science, which may exclude relevant grey literature and regional studies. Publication bias may also be present, as well-designed or statistically strong studies are more likely to appear in indexed journals. Moreover, many reviewed works lacked transparency regarding stakeholder engagement, restricting insight into how social and policy perspectives influenced indicator selection. Future studies should adopt hybrid frameworks that integrate quantitative methods with participatory approaches such as Delphi panels or focus-group validation to improve inclusiveness, policy relevance, and contextual applicability of sustainability indices. Also, several recent studies illustrate promising efforts to balance quantitative robustness with qualitative contextualisation. The Sustainable Energy Transition Readiness (SETR) Index (Neofytou, Nikas, & Doukas, 2020)] integrates Multi-Criteria Decision Analysis with stakeholder-derived weights and qualitative scenario evaluation. The Integrated Sustainability Assessment Model (Abu Bakar et al., 2015)] aligns quantitative indicator aggregation with Sustainable Development Goal targets to ensure policy relevance. Similarly, a demonstration on how participatory consultation can adapt global indices to national contexts, maintaining comparability while capturing local socio-political realities have also been conducted (Afgan & Carvalho, 2010). These hybrid approaches exemplify how sustainability indices can evolve toward inclusivity without compromising analytical rigour. However, the reviewed hybridised frameworks demonstrate that methodological pluralism linking quantitative analysis with participatory and justice-based perspectives can generate sustainability indices that are both comparable and contextually grounded. Equally important is the localisation of indicator frameworks. Developing country- and region-specific indices that reflect infrastructure maturity, policy priorities, and socio-economic realities will yield more relevant and actionable results, while maintaining compatibility for cross-country benchmarking. Aligning such efforts with the United Nations Sustainable Development Goal 7 (“Affordable and Clean Energy”) particularly its targets to expand renewable energy share, improve energy efficiency, and ensure universal access can strengthen policy integration and attract international support. By broadening indicator scope, adopting hybrid methodologies, and tailoring frameworks to local contexts while keeping them SDG-aligned, researchers and policymakers can produce sustainability indices that are inclusive, adaptive, and directly applicable for guiding equitable and resilient electricity sector transitions worldwide. Although prior reviews (Streimikiene & Siksnelyte-Butkiene, 2020) have noted the conceptual value of hybrid and localised frameworks, the present analysis finds that their practical implementation within electricity-sector sustainability indices remains limited. This underlines the need for empirical, context-driven adaptation of existing global methodologies.Therefore, closing methodological, dimensional, and geographic gaps in electricity sector sustainability indices is essential for informed, equitable, and context-specific energy transition policies. Abbreviations The following abbreviations are used in this manuscript: AHP Analytic Hierarchy Process ANP Analytic Network Process BESS Battery Energy Storage Systems CSR Corporate Social Responsibility DEA Data Envelopment Analysis DPSIR Drivers-Pressures-State-Impact-Response EAPI Energy Architecture Performance Index EEBD Energy Efficiency and Emissions Balance Design (EEBD-3ES model) EISD Energy Indicators for Sustainable Development ESESI Energy Security and Environmental Sustainability Index ESI Energy Sustainability Index ESIOP Energy Security Index of Pakistan EU European Union GDP Gross Domestic Product GHG Greenhouse Gas GIS Geographic Information System HDI Human Development Index HOMER Hybrid Optimization Model for Multiple Energy Resources IEA International Energy Agency LCA Life Cycle Assessment LMDI Logarithmic Mean Divisia Index MCDA Multi-Criteria Decision Analysis MFA Material Flow Analysis NQI National Quality Infrastructure OECD Organisation for Economic Co-operation and Development PCA Principal Component Analysis PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PROMETHEE Preference Ranking Organization Method for Enrichment Evaluations PSR Pressure-State-Response RES Renewable Energy Sources SALSA Search, Appraisal, Synthesis, Analysis (review method) SDEWES Sustainable Development of Energy, Water and Environment Systems SDG Sustainable Development Goal SEDI Sustainable Energy Development Index SEE South Eastern Europe SETR Sustainability and Energy Transition Readiness Index TOPSIS Technique for Order Preference by Similarity to Ideal Solution UNDP United Nations Development Programme Declarations Availability of data and materials All data generated or analysed during this study are included in this published article. Additional materials, such as the list of reviewed studies and indicator classifications, are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the Research Council, University Research Grants, Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka (Grant No. RC/URG/FOE/2024/90). Authors’ contributions Conceptualization, W.D.G. and T.R.; Methodology, W.D.G.; Formal analysis, W.D.G. and T.R.; Resources, W.D.G. and T.R.; Data curation, W.D.G. and T.R.; Writing original draft preparation, W.D.G. and T.R.; Writing review and editing, W.D.G. and T.R.; Visualization, W.D.G. and T.R.; Supervision, A.H.T.S.K., N.K.J., and C.Y.; Project administration, A.H.T.S.K., N.K.J., and C.Y. All authors have read and approved the final version of the manuscript. 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Energies, 14 (3), 655. https://doi.org/10.3390/en14030655 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.pdf SupplementaryTableS2.pdf SupplementaryTableS3.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2026 Read the published version in Sustainable Energy Research → Version 1 posted Editorial decision: Accepted 20 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 10 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:00:37","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142932,"visible":true,"origin":"","legend":"","description":"","filename":"822ac46f6d7f416f83286469c0832e8f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/ca618a8e3bf32d24f8866ef5.xml"},{"id":95317079,"identity":"a57ff0f6-91df-45a5-b7fe-dc45e61d3d7a","added_by":"auto","created_at":"2025-11-06 16:00:37","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153122,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/403efb5ee309ebf690c3a4d2.html"},{"id":95316976,"identity":"304b0a32-19ef-4a73-a699-bc7a5e8e4f5e","added_by":"auto","created_at":"2025-11-06 16:00:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":414988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eillustrates the PRISMA flow diagram, tracing the process from 579 identified records to the final 50 included studies, ensuring transparency in article selection.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/874f5bd7c95b316200eed7f9.jpeg"},{"id":95317022,"identity":"b35eadb5-48e0-4b08-9cb9-9c231b5a6d92","added_by":"auto","created_at":"2025-11-06 16:00:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18182,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological distribution of the 50 reviewed studies: MCDA (28 %), statistical/econometric (28 %), composite indices (26 %), and review/qualitative frameworks (18 %).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/12079c970b412131ea4ea96d.png"},{"id":95316821,"identity":"a0acbe6d-d3f3-44f1-af87-baf3db69dcd6","added_by":"auto","created_at":"2025-11-06 16:00:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFrequency of sustainability indicators used in the 50 reviewed studies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/3be852ddc11a33b55b840ef1.png"},{"id":95316959,"identity":"4f05418c-3612-4771-86a2-308d32c82510","added_by":"auto","created_at":"2025-11-06 16:00:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":453072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCountry-level distribution of the 50 reviewed studies on electricity sector sustainability indices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/225433d7a6bdbfe9fb29f4e9.jpeg"},{"id":95317083,"identity":"9ca92fa9-aedc-49ea-bb33-c648c5ab9c8b","added_by":"auto","created_at":"2025-11-06 16:00:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRegional and non-geographic distribution of the 50 reviewed studies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/930cb75cc09900a748b22e77.png"},{"id":101151713,"identity":"ccedb616-0079-4301-982c-b8369577cba4","added_by":"auto","created_at":"2026-01-26 16:02:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2231855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/a7884c65-a289-497e-aca6-1ed8787de8ce.pdf"},{"id":95316936,"identity":"21366611-aa6b-46d4-a8cc-babe98ee480d","added_by":"auto","created_at":"2025-11-06 16:00:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":162788,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/6134fbfad74bd13748efd7e7.pdf"},{"id":95317018,"identity":"bb06d744-68bb-4593-8326-aeee43080d5e","added_by":"auto","created_at":"2025-11-06 16:00:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":219814,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/e43881b1b3522278ce1e9a00.pdf"},{"id":95316897,"identity":"74461683-0bef-405c-a5f3-d77a68a25618","added_by":"auto","created_at":"2025-11-06 16:00:25","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":213084,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7395096/v1/e316b0b43d5449b21df1545e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAggregate Sustainability Indices in the Electricity Sector: A Review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global shift toward sustainable energy systems has created an urgent need for robust frameworks that can evaluate the economic, environmental, social, and technological performance of the electricity sector. As governments and utilities seek to balance economic growth, social equity, and environmental stewardship, composite sustainability indices have emerged as essential tools for benchmarking policy outcomes, guiding infrastructure investments, and tracking progress toward climate and development goals (Brown \u0026amp; Sovacool, 2007).\u003c/p\u003e\u003cp\u003eHowever, the current landscape of electricity-sector sustainability assessment is fragmented. Many indices concentrate narrowly on specific dimensions such as carbon emissions, energy intensity, or economic contribution while overlooking equally critical aspects, including energy equity, resilience to supply disruptions, public acceptance, and the integration of emerging technologies like battery energy storage systems (BESS) (Narula \u0026amp; Reddy, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kılkış, 2015). Region-specific studies often reflect local policy agendas, but their methodological inconsistency limits international comparability and the transfer of best practices across contexts (Neves \u0026amp; Leal, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Doukas et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Evidence from recent research illustrates the importance of neglected dimensions. Studies on marine renewable energy in Japan, for example, have shown that local economic participation and environmental co-benefits can build strong community support (Kularathna \u0026amp; Takagi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and that decision-support tools such as the Decision Support Analytic Hierarchy Process (DS-AHP) can evaluate coexistence strategies under uncertainty (Kularathna et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Social readiness modelling has demonstrated how targeted information flows can accelerate public acceptance of new energy projects (Ramachandran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, although BESS can enhance renewable energy integration, manage peak loads, and improve grid resilience, they are rarely included as explicit indicators in sustainability indices particularly in emerging markets where they could address pressing market and reliability challenges (Gammanpila et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Methodological diversity further complicates the picture. Existing approaches range from indicator-based composite frameworks that often operationalised through Multi-Criteria Decision Analysis (MCDA) techniques such as AHP, TOPSIS, and PROMETHEE and statistical or econometric modelling, to qualitative assessments and system-dynamics modelling.\u003c/p\u003e\u003cp\u003eWhile each method offers valuable insights, many rely on context-specific indicators and weighting schemes, reducing their applicability across different geographies (Hadian \u0026amp; Madani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Abu-Rayash \u0026amp; Dincer, 2019). The present study does not argue for universal or non-context-specific indices. Rather, it emphasises the need to balance contextual sensitivity with cross-regional comparability by establishing a minimal \u0026ldquo;core\u0026rdquo; indicator set that can be locally expanded. Such an approach aligns with sustainability-assessment best practices that advocate standardised methodological structures while allowing indicator contextualisation through region-specific data, normalisation, and weighting schemes (Singh et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; B\u0026ouml;hringer \u0026amp; Jochem, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hadian \u0026amp; Madani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).The challenge is especially acute in developing economies, where limited data availability and diverse governance contexts demand customised, yet comparable, assessment tools (Cucchiella et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ediger et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond methodological variation, it is equally important to consider energy justice and ethics in how sustainability indices are designed and applied. The literature on energy justice highlights three core dimensions which are distributional, procedural, and recognition justice which emphasise who benefits from energy transitions, whose voices are included in decision-making, and whose needs are recognised within policy frameworks. Many existing indices are designed from a technocratic perspective that prioritises data-rich, high-income contexts, inadvertently marginalising low-income, energy-deprived, or geographically isolated communities. Embedding justice-based criteria within sustainability assessment frameworks can ensure that indices reflect the lived realities and aspirations of affected populations, particularly in developing regions (Jenkins et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shortall \u0026amp; Davidsdottir, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Streimikiene \u0026amp; Siksnelyte-Butkiene, 2020).\u003c/p\u003e\u003cp\u003eThis review addresses these gaps by systematically analysing 50 peer-reviewed studies on composite sustainability indices in the electricity sector, published between 2000 and 2024. The studies are classified by methodological approach, with particular attention to underrepresented social and technological indicators and to the uneven regional distribution of existing research. By mapping methodological trends and identifying neglected dimensions, the review provides a foundation for developing more inclusive, adaptable, and internationally comparable sustainability indices to support equitable electricity sector transitions worldwide.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSystematic Review Process\u003c/h2\u003e\u003cp\u003eThis systematic review was conducted to examine the development and application of composite sustainability indices in the electricity sector. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological transparency, reproducibility, and a clear audit trail of study selection (Page et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)]. PRISMA was selected for its structured approach, which is widely recognised for enhancing the credibility of evidence syntheses in energy and environmental research.\u003c/p\u003e\u003cp\u003eThe review adopted the \u003cem\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020)\u003c/em\u003e framework to enhance methodological transparency and reproducibility. Originally developed for the health sciences (Moher et al., 2009; Page et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), PRISMA has been widely adapted in environmental, energy, and sustainability research where evidence synthesis requires structured screening and traceability (Siksnelytė-Butkienė, Streimikienė, \u0026amp; Zavadskas, 2018; Gunnarsdottir et al., 2020; Hadian \u0026amp; Madani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The framework organises the review into four sequential phases Identification, Screening, Eligibility, and Inclusion each governed by explicit inclusion/exclusion criteria, data-source documentation, and reviewer agreement procedures.\u003c/p\u003e\u003cp\u003eIn the present study, PRISMA was adapted for \u003cem\u003emethodological mapping\u003c/em\u003e rather than clinical outcomes: the \u0026ldquo;intervention\u0026rdquo; is the methodological design of sustainability indices, and the \u0026ldquo;population\u0026rdquo; is electricity-sector literature. This adaptation enables systematic appraisal of how composite-index techniques evolve across geographies and contexts. The full PRISMA 2020 checklist (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and the flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) trace the screening of 579 records to 50 eligible peer-reviewed studies.\u003c/p\u003e\u003cp\u003eThe Boolean search strategy combined key terms relating to sustainability indices and the electricity sector. The core search string used was (\u0026ldquo;sustainable energy index\u0026rdquo; OR \u0026ldquo;electricity index\u0026rdquo; OR \u0026ldquo;composite indicators\u0026rdquo; OR \u0026ldquo;energy sustainability assessment\u0026rdquo; OR \u0026ldquo;sustainability frameworks\u0026rdquo;) AND (\u0026ldquo;electricity\u0026rdquo; OR \u0026ldquo;power sector\u0026rdquo; OR \u0026ldquo;grid\u0026rdquo; OR \u0026ldquo;energy systems\u0026rdquo;). Searches were conducted in Scopus and Google Scholar, restricted to English-language peer-reviewed journal articles published between 2000 and 2024. The final search was completed in June 2025. Also, Title, abstract screening and full-text review were carried out independently by two reviewers, with disagreements resolved by discussion with a third reviewer. The PRISMA 2020 checklist is provided in the Supplementary Appendix (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies were eligible for inclusion if they presented a composite or multi-indicator index directly applied to the electricity sector, contained quantifiable indicators related to electricity generation, transmission, distribution, or consumption, and employed a structured methodological approach such as Multi-Criteria Decision Analysis (MCDA), Principal Component Analysis (PCA), fuzzy logic, normalisation, or weighting schemes. Only peer-reviewed journal articles were included to ensure academic quality and methodological rigour. Studies were excluded if they focused on sectors unrelated to electricity, lacked a composite or structured index methodology, addressed general national sustainability without disaggregating electricity-specific indicators, or presented conceptual or theoretical frameworks without empirical or methodological application. Review papers without original indicator development or application, non-peer-reviewed sources such as conference papers, reports, and dissertations, as well as duplicate records and studies lacking sufficient methodological detail for assessment, were also excluded.\u003c/p\u003e\u003cp\u003eThe search identified a total of 579 records. After removing 116 duplicates, 463 unique records remained for screening. Title and abstract screening reduced this number to 135 articles that appeared to meet the inclusion criteria. These studies underwent full-text review, which resulted in the exclusion of 85 articles. The main reasons for exclusion at this stage were the absence of a composite indicator methodology (n\u0026thinsp;=\u0026thinsp;42), lack of electricity-specific content (n\u0026thinsp;=\u0026thinsp;28), and insufficient methodological detail (n\u0026thinsp;=\u0026thinsp;15). Ultimately, 50 studies published between 2000 and 2024 met all eligibility criteria and were included in the final synthesis. Full texts of all included studies were accessible through institutional subscriptions.\u003c/p\u003e\u003cp\u003eFor each study, data were extracted on bibliographic details, geographic scope, methodological category, indicator dimensions, weighting and aggregation methods, and key findings with stated limitations. The full corpus of 50 included studies with metadata is provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, detailing author/year, geography, scope, methodological approach, indicators (summary), weighting/normalization, and key limitations. However, study-level indicators were coded at the domain level (e.g., security/reliability, environmental performance, affordability/economics, access/renewables) and illustrative examples have been given rather than exhaustive lists. Key limitations were coded using a predefined rubric covering transparency of indicator selection and data sources, weighting/normalization subjectivity, validation/sensitivity analysis, data completeness/comparability, scope/transferability, stakeholder involvement, and construct coverage (equity, resilience, storage).\u003c/p\u003e\u003cp\u003eThis information was used to classify the studies into methodological categories, examine indicator coverage, and identify thematic and geographic gaps. The entire identification, screening, and inclusion process is summarised in the PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with counts matching those reported in this section.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eScope and Analytical Approach\u003c/h3\u003e\n\u003cp\u003eThis review applied the PRISMA framework to identify, screen, and analyse peer-reviewed studies on sustainability indices in the electricity sector. Each of the 50 included studies was examined according to its dominant methodological orientation Multi-Criteria Decision Analysis (MCDA), statistical modelling, composite index construction, or qualitative/non-MCDA approaches. This typology was chosen to ensure reproducibility and comparability across heterogeneous studies. Indicator-level details were also noted to capture how qualitative or stakeholder-driven factors were represented. While certain indices embed qualitative dimensions through coded variables or expert-based weighting, these remain largely quantitative in aggregation. The review therefore distinguishes between the numerical inclusion of qualitative proxies and genuinely interpretive or participatory qualitative frameworks.\u003c/p\u003e\u003cp\u003eThe analytical workflow of this review operates on two complementary layers. The first layer maps methodological architectures, classifying studies into analytical families such as MCDA, statistical/econometric, and composite-index approaches. The second layer captures indicator-level characteristics, recording the inclusion of economic, environmental, technical, and social indicators, as well as whether qualitative factors were incorporated either as coded variables (e.g., expert judgments, dummy variables) or as interpretive constructs (e.g., stakeholder interviews or participatory weighting). This dual-level coding provides a balanced representation of both methodological structure and content-level inclusion. The review therefore moves beyond simple taxonomy to interrogate how methodological choices shape the representation of qualitative sustainability dimensions.\u003c/p\u003e\u003cp\u003eThis approach aligns with the tradition of methodological mapping reviews in sustainability science (Mayer, Patterson, \u0026amp; Holling, 2004; Hadian \u0026amp; Madani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and complements quantitative systematic frameworks with contextual understanding. We acknowledge that composite indices can and do include qualitative proxies; however, evidence from the reviewed corpus indicates that interpretive and stakeholder-based qualitative integration remains scarce, validating the study\u0026rsquo;s call for more hybridized, participatory methodologies in future index development.\u003c/p\u003e\n\u003ch3\u003eMethodological Classification Framework\u003c/h3\u003e\n\u003cp\u003eTo synthesise the methodological diversity observed across the 50 reviewed studies on composite sustainability indices in the electricity sector, each study was classified according to its primary methodological approach. This decision was guided by precedents in the literature, where reviews of energy sustainability assessment frameworks have categorised studies by methodological design to enable comparability and replication in future research (Cavallaro, 2020; Abu Bakar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Grouping by methodology facilitates a systematic understanding of the analytical tools employed, highlighting methodological convergence or divergence across contexts and enabling clearer identification of potential gaps in the evidence base.\u003c/p\u003e\u003cp\u003eAcross the reviewed corpus, methodological trends revealed a predominance of quantitative approaches, with Multi-Criteria Decision Analysis (MCDA) and statistical modelling accounting for the majority of studies. This finding mirrors patterns identified in prior reviews of energy sustainability assessment frameworks (Brown \u0026amp; Sovacool, 2007; Razmjoo, Sumper, \u0026amp; Davarpanah, 2019) suggesting a strong reliance on techniques that enable structured trade-off analysis and indicator aggregation. However, the relative scarcity of hybrid approaches that integrate quantitative robustness with qualitative stakeholder engagement highlights a persistent methodological gap.\u003c/p\u003e\u003cp\u003eSuch integration could strengthen the contextual relevance and policy uptake of sustainability indices, particularly in regions where socio-political factors and institutional readiness significantly shape electricity sector outcomes. By systematically mapping methodological prevalence against identified research gaps, this review provides a replicable evidence base to guide the development of more inclusive, adaptable, and decision-oriented composite indices in future scholarship.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003ch4\u003e\u003cem\u003eMethodological Categorization of Sustainability Indicators\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eTo provide an objective, reproducible basis for comparison, the 50 reviewed studies were classified by their primary methodological framework rather than by thematic indicator domains. Study-level characteristics and methodological details are summarized in Supplementary Table S2.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis choice reflects the technical design decisions underpinning each study, enabling methodological benchmarking and helping future researchers and policymakers identify approaches best suited to specific data environments and policy needs. Such an approach aligns with prior reviews that emphasise the methodological architecture of sustainability indices as the primary determinant of their robustness, transferability, and policy applicability (Abu Bakar et al., 2015; Liu, 2014; Ouedraogo, 2014)\u003c/p\u003e\n\u003cp\u003eAmong methodological approaches, Multi-Criteria Decision Analysis (MCDA) and statistical/econometric models each account for 28% of the reviewed studies (14 out of 50), followed by composite index-based frameworks (26%, n = 13) and review-based analyses (18%, n = 9). This distribution indicates a relatively balanced methodological representation, with quantitative approaches collectively dominating the literature. When considering MCDA, the frequently used techniques include Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). Several studies enhanced MCDA by incorporating fuzzy logic, geographic information systems (GIS), or scenario modelling to address uncertainty and improve spatial\u0026ndash;temporal relevance. This structured weighting and ranking capability is particularly relevant in the electricity sector, where decisions involve balancing emissions reduction, cost, reliability, and social acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Studies employing MCDA approaches in electricity sector sustainability assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors (Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrief Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eProkhorova, V., Budanov, M., \u0026amp; Budanov, P. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGeneralized integrated indicator for energy safety; ZNPP case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eIndicative analysis, AHP, expert estimation, automated monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCavallaro, F. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eFuzzy Environmental Pressure Index for energy tech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eFuzzy inference (Mamdani)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eQian, Q., et al. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEEBD-3ES: impacts of coal reduction in China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eAHP and Entropy Weight, scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eNeofytou, H., Nikas, A., \u0026amp; Doukas, H. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSETR Index for transition readiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eHybrid MCDA + stakeholder weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGreece \u0026amp; EU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRaza, S. S., et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSelects energy storage for RES via sustainability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eWeighted sum (MCDA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTorul Y\u0026uuml;rek, Y., et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eHybrid RES with BESS under uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003ePythagorean Fuzzy AHP and TOPSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGoldrath, T., et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eCombined sustainability index for electricity efficiency measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMCDA, normalization, equal weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAfgan, N. H., \u0026amp; Carvalho, M. G. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSustainability Index for intelligent energy systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMCDA, normalization, weighted mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCucchiella, F., et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEU country sustainability comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMCDA and AHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eEU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLiu, G. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eGeneral Sustainability Indicator for renewables (review and \u0026nbsp;build)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eFuzzy AHP, weighted aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eWang, Y., et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eFuzzy evaluation for energy transition readiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eFuzzy comprehensive evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLin, B., \u0026amp; Zhu, J. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEnergy security assessment integrating renewable targets and constraints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eEntropy Weight and TOPSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eWu, J., et al. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eLow-carbon energy transition performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eImproved TOPSIS and Entropy Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe second group comprises statistical and econometric approaches, which account for an equally large share being 28% of the reviewed literature. Techniques such as Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), entropy weighting, and regression-based econometrics allow for objective, data-driven indicator weighting and the exploration of causal relationships. These approaches are particularly valuable in contexts with large datasets and the need to identify underlying patterns or forecast impacts of policy scenarios. However, they may underrepresent qualitative aspects such as governance quality or public acceptance unless supplemented by other methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Studies using statistical and econometric methodologies for sustainability assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor(s) \u0026amp; Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrief Overview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology (few words)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry / Region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLiu Yan, Zheng AnGang, Shang HuaiYing (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eNQI Capacity Index for smart energy meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOspina Betancur, J. A., et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eGrowth\u0026ndash;energy links in Colombia via visuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eMultivariate statistical analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eColombia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDoukas, H., et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eRural community Energy Sustainability Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEdiger, V. S., et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eFossil Fuel Sustainability Index (oil/gas/coal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eEqual weights \u0026amp; PCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYumashev, A., et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eHDI\u0026ndash;energy\u0026ndash;CO₂ interactions (OECD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e3SLS (simultaneous equations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eOECD countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMainali, B., et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eRural household Energy Sustainability Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePCA-based weighting, normalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWang, X., et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eChina\u0026rsquo;s provincial energy sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eEntropy Weight Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eShah, S. A. A., et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eESESI: South Asian energy security \u0026amp; environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eDEA and Weighted Product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSarkodie \u0026amp; Adams (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eHDI, inequality, electricity access vs governance/economy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePanel data econometrics (robust, interactions)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFalbo, P., Fattore, M., \u0026amp; Stefani, S. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eIndex for electricity spot market performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eStatistical index (price volatility, efficiency, liquidity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOrdu, B.M., \u0026amp; Soytaş, U. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eEnergy commodity prices \u0026amp; electricity market performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eEconometric time series; Granger causality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eStein, E., et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eClimate change impact on energy systems \u0026amp; development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eEconometric simulation \u0026amp; scenario modelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eLatin America \u0026amp; Caribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eZieliński, T. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003ewhether Corporate Social Responsibility (CSR) participation impacts the profitability, market valuation, and stability of Poland\u0026rsquo;s four largest energy companies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eThrough \u0026nbsp;descriptive statistics, index analysis, rankings, cluster analysis, and volatility measures.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKonara, K.M., \u0026amp; Tokai, A. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003eEvaluates the energy metabolic system of Sri Lanka, assessing resource flows, efficiency, and sustainability perfor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eMaterial Flow Analysis (MFA), energy metabolism indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSri Lanka\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Studies such as \u003cem\u003eSarkodie \u0026amp; Adams (2020)\u003c/em\u003e were retained because they employ multi-indicator quantitative frameworks that directly examine sustainability linkages within the electricity sector, despite not constructing a formal composite index. Inclusion criteria prioritized methodological diversity rather than index labelling.\u003c/p\u003e\n\u003cp\u003eThe third category covers indicator-based composite index frameworks without MCDA, representing 26% of studies. These approaches focus on selecting, normalising, and aggregating predefined indicators often drawing from international frameworks like the UNDP or IEA in order to benchmark performance and track progress. They offer transparency and ease of replication, making them suitable for policy communication, though they can be constrained by indicator availability and may perpetuate existing metric biases.\u003c/p\u003e\n\u003cp\u003eTable 3: \u0026nbsp;Studies applying indicator-based composite index frameworks\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"523\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor(s) \u0026amp; Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrief Overview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology (few words)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry / Region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBrown, M. A., \u0026amp; Sovacool, B. K. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEnergy Sustainability Index (security, reliability, efficiency, environment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIndicator selection, normalization, weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eKılkış, Ş. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSDEWES Index for SEE cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eMin\u0026ndash;Max normalization, weighted aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSouth Eastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRazmjoo, A., Sumper, A., \u0026amp; Davarpanah, A. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eNew indicators for SEDI; comparative study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eData collection, normalization, correlation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eIddrisu, I., \u0026amp; Bhattacharyya, S. C. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSEDI: multi-dimensional energy sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eUNDP normalization, dimension averaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAbu-Rayash, A., \u0026amp; Dincer, I. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eIntegrated SDG-aligned index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eWeighted arithmetic \u0026amp; geometric means\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAbu Bakar, N. N., et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEnergy Efficiency Index for buildings (review)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eData normalization, benchmarking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDe Vito, R., et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eWEF Nexus irrigation sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIndex construction in nexus framework; case study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eKemmler, A., \u0026amp; Spreng, D. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEnergy-based indicators to track sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eEnergy framework integration, correlation lens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eDeveloping countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNeves, A. R., \u0026amp; Leal, V. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eLocal energy sustainability indicators for municipalities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIndicator selection, pilot testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePatlitzianas, K. D., et al. (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSustainable energy policy indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eLiterature synthesis; PSR/DPSIR framing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWang, Q., et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eChina\u0026rsquo;s energy security (modified indicator set)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eImproved index design, normalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eZhang, X., et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eUrban energy sustainability index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIndicator aggregation, weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTsai, W.T. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eTaiwan\u0026rsquo;s TSDI applied to energy sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eIndicator-based assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVera, I., \u0026amp; Langlois, L. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEISD: 30 indicators across social/economic/environmental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eInternational framework and case studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRazmjoo, A. A., Sumper, A., \u0026amp; Davarpanah, A. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSDG/UN-Habitat aligned energy sustainability analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eHybrid system modeling (HOMER) and indicator lens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSharma, T., \u0026amp; Balachandra, P. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBenchmarks sustainability of Indian electricity system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eHierarchical indicator framework; normalization; aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNeelawela, U.D., Selvanathan, E.A., \u0026amp; Wagner, L.D. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eComposite index for global electricity security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eComposite index construction; multi-dimensional indicators; normalization; weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHuang, Y.-H. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eDeterminants of residential electricity consumption in Taiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eLMDI; decomposition analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSalarvand, A., Mirzaeian, B., \u0026amp; Moallem, M. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePower Quality Index (voltage, frequency, outage metrics)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eWeighting indicators of voltage, frequency, outage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFinally, review and comparative framework studies account for 18% of the literature. These syntheses assess existing indices, compare methodologies, and identify gaps, offering critical insights into conceptual strengths and weaknesses. While they do not generate new empirical scores, they play a vital role in shaping future methodological innovations and ensuring frameworks remain relevant to evolving sectoral priorities.\u003c/p\u003e\n\u003cp\u003eTable 4: Reviews and comparative framework studies on electricity sustainability indices\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"523\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor(s) \u0026amp; Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrief Overview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethodology (few words)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry / Region\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGunnarsdottir, I., et al. (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eReview of sustainable energy development indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSALSA review; thematic criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMayer, A. L., et al. (2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eMultidisciplinary sustainability indices review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eComparative review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNarula, K., \u0026amp; Reddy, B. S. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCompares EAPI, ES Risk, ESI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eIndicator comparison, normalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eShortall, R., \u0026amp; Davidsdottir, B. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eApplicability of global indices to Iceland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eParticipatory evaluation of national framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eIceland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAxon, C.J., \u0026amp; Darton, R.C. (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSustainability\u0026ndash;risk frameworks for energy security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eMulti-disciplinary framework review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eRomero, J. C., \u0026amp; Linares, P. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eExergy as a sustainability metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eThermodynamic LCA \u0026amp; thermoeconomics review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eStreimikiene, D., \u0026amp; Siksnelyte, I. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCompares sustainability of electricity market models in developed countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eComparative assessment; qualitative and quantitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eDeveloped countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eJamasb, T., \u0026amp; Pollitt, M.G. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eReviews electricity market liberalization \u0026amp; integration progress in EU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePolicy/regulatory review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eEU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis methodological mapping reveals a strong preference for quantitative, data-intensive approaches in electricity sector sustainability research, with qualitative synthesis playing a supporting yet strategically important role. By adopting a methodology-focused classification, this review not only charts current practice but also illuminates methodological blind spots, such as the absence of resilience-focused metrics in purely statistical approaches or the limited stakeholder engagement in many composite index designs.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eMethodological distribution of the studies\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe methodological approaches identified in the reviewed literature reveal distinct trends in how sustainability in the electricity sector is assessed. These approaches range from decision-support frameworks and statistical modelling to the direct adoption of composite sustainability indices and qualitative syntheses. Categorising the studies into four broad groups provides a clear view of prevailing analytical preferences and the relative prominence of each method.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, Multi-Criteria Decision Analysis (MCDA) and statistical or econometric techniques are equally dominant, each comprising 28 % of the reviewed studies. Composite index adoption accounts for 26 %, reflecting the significant influence of established sustainability frameworks on indicator selection and aggregation. Reviews, encompassing conceptual discussions, literature syntheses, and qualitative assessments, represent the remaining 18 %. This distribution demonstrates a clear preference for quantitative, data-intensive approaches in electricity-sector sustainability assessment. While such methods enable rigorous, replicable analysis, qualitative dimensions are often incorporated within these frameworks through expert scoring or categorical scaling; however, their numerical conversion can limit contextual richness and interpretive depth. The smaller share of explicitly qualitative and review-based studies therefore highlights the opportunity to integrate broader stakeholder perspectives, contextual insights, and interdisciplinary synthesis into future methodological development.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eDistribution of Sustainability Indicators Across Literature\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eA frequency analysis of indicators across the 50 studies underscores a dominant focus on climate mitigation and energy transition metrics as shown in figure 3. Carbon emissions (CO₂/GHG) and renewable energy share appear in 80% and 70% of the indices respectively, reflecting global policy emphasis on decarbonisation. Energy intensity and GDP per capita are also common, linking energy performance to economic productivity.\u003c/p\u003e\n\u003cp\u003eIn contrast, socio-economic and resilience-oriented indicators such as affordability, diversity of supply, and system resilience appear far less frequently (under 12% of studies), and energy storage metrics are entirely absent. This is striking given the increasing importance of Battery Energy Storage Systems (BESS) for renewable integration, peak load management, and grid stability. The omission represents a critical methodological gap that limits the ability of current indices to capture the technological adaptability of modern electricity systems.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eNormalization, Weighting, and Aggregation Approaches\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAcross the 50 studies reviewed, we observed diverse approaches to normalization, weighting, and aggregation. Most studies relied on min\u0026ndash;max or z-score normalization, while a smaller share used distance-to-target or percentile ranks. Weighting was typically applied using equal or expert-based approaches such as the Analytic Hierarchy Process (AHP), with fewer studies adopting data-driven techniques such as entropy or principal component analysis (PCA). Aggregation was commonly achieved through arithmetic means, with occasional use of geometric means. Also, Sensitivity analysis is often applied by varying weights or normalization methods to test robustness of results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegional observations of Indicators\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographical mapping of the reviewed studies reveals a marked imbalance in research coverage. China leads with 10 studies, reflecting its policy-driven sustainability agenda, extensive energy transition initiatives, and availability of detailed electricity sector data. OECD and European countries are also well represented, often focusing on emissions reduction, renewable integration, and energy security. By contrast, South Asia and Sub-Saharan Africa have fewer studies, and those that exist typically prioritise energy access and affordability in line with developmental needs.Several regions including large parts of Africa, Latin America, and small island states are represented by only one or two studies. \u0026nbsp;This limited coverage constrains the applicability of global indices to these contexts, where climate vulnerability, grid instability, and energy poverty require tailored sustainability assessment frameworks. While global sustainability indices such as the Energy Trilemma Index, Energy Transition Index, and SDG 7 Progress Tracker provide comprehensive international benchmarks, their contextual applicability is constrained by limited regional data and locally validated indicators. Strengthening research coverage across under-represented regions would therefore improve the calibration and relevance of global sustainability assessments to diverse socio-technical realities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGeographical Distribution of Sustainability Assessment Studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reviewed studies were mapped at two levels of geographic scope as country-specific analyses, where sustainability indices were developed or applied to a single nation, and regional or non-geographic analyses, where indices covered multiple countries within a defined region, economic grouping, or global context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCountry specific studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure 4 below presents the country-wise distribution and the aim is to capture where empirical or methodological work has been concentrated at a national level.\u003c/p\u003e\n\u003cp\u003eChina dominates the literature with 10 studies, reflecting its expansive energy landscape, policy-driven sustainability reforms, and the availability of granular electricity-related datasets. China\u0026apos;s emphasis on energy efficiency, decarbonization, and green innovation has made it a fertile ground for sustainability assessments. Also, several countries including the United States, Canada, Italy, Poland, India, Sri Lanka, Taiwan, and Turkey appear in two separate studies each, highlighting a moderate but significant engagement with electricity sustainability measurement. These countries often explore dimensions like energy transition, resilience, or environmental integrity through context-specific frameworks.\u003c/p\u003e\n\u003cp\u003eSingle-study countries include Iran, Portugal, Eastern European nations, Colombia, Pakistan, Israel, Iceland, Korea, and the UK. These cases typically adopt either focused methodological innovations or pilot-level indicator development, and represent diverse geographies ranging from the Middle East to Europe and Latin America.\u003c/p\u003e\n\u003cp\u003eThis country-level mapping reveals an uneven research distribution, with a concentration in high-emission and energy-intensive economies. It underscores the need for expanding empirical sustainability assessments to underrepresented countries, especially in Africa, Latin America, and parts of Southeast Asia, where data gaps and local challenges merit more targeted evaluation frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegion specific studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reviewed studies were classified according to their primary scope as reported by the authors. This included \u003cstrong\u003eg\u003c/strong\u003eeographic regions EU, Sub-Saharan Africa, South Asia) shown in blue, and non-geographic scopes such as economic or organisational groupings (\u0026ldquo;Developed countries,\u0026rdquo; \u0026ldquo;Developing countries,\u0026rdquo; \u0026ldquo;OECD countries\u0026rdquo;) and global analyses, shown in orange. This distinction avoids conflating location-specific literature coverage with studies adopting broad or economically defined perspectives. Including both categories in the same visualisation ensures completeness and transparency, while the colour separation allows readers to clearly interpret the relative representation of each scope type. The chart highlights that both the European Union (three studies) and Sub-Saharan Africa (two studies) are among the most frequently represented regional categories. However, this difference is minor, and overall regional representation remains limited, underscoring the need for broader geographic coverage in electricity-sector sustainability research.\u003c/p\u003e\n\u003cp\u003eAmong the geographic categories, the European Union (EU) and Sub-Saharan Africa appear most frequently, with 3 and 2 studies respectively, while other regional groupings such as South Eastern Europe, South Asia, and Latin America \u0026amp; Caribbean are each represented by a single study. The largest single category overall is \u0026ldquo;Global,\u0026rdquo; with 6 studies that address sustainability indicators at a worldwide scale. Non-geographic groupings which are \u0026ldquo;Developed countries,\u0026rdquo; \u0026ldquo;Developing countries,\u0026rdquo; and \u0026ldquo;OECD countries\u0026rdquo; each account for one study and reflect scope definitions based on economic classification rather than geography. This distribution highlights the concentration of literature in certain areas and the limited representation of others, suggesting that regional conclusions should be interpreted with caution where the sample size is small.\u003c/p\u003e"},{"header":"Conclusions and Future Directions","content":"\u003cp\u003eThis systematic review of 50 peer-reviewed studies on composite sustainability indices in the electricity sector, published between 2000 and 2024, classified existing work into four methodological categories. Quantitative approaches particularly MCDA and statistical/econometric methods dominate, yet critical resilience, equity, and technological indicators such BESS remain largely absent. These dimensions are critical for electricity systems undergoing rapid transitions yet are seldom incorporated in existing frameworks.\u003c/p\u003e\n\u003cp\u003eResilience, equity/affordability, energy storage, and justice considerations are four critical dimensions that remain underrepresented in current sustainability indices for the electricity sector. Resilience goes beyond basic reliability and reflects the system\u0026rsquo;s ability to withstand and recover from shocks such as extreme weather, cyber threats, or renewable variability. Indicators such as SAIDI, SAIFI, reserve margin, and recovery time can capture this more effectively, yet are rarely applied. Equity and affordability address whether electricity is fairly priced and accessible to all, but most indices stop at general access without considering affordability gaps. Measures such as the share of household income spent on electricity, arrears rates, and tariff burdens on the lowest income quintile provide clearer insights into social fairness. Energy storage (BESS), though essential for integrating renewables and stabilizing grids, is almost absent from existing indices; indicators such as installed capacity per peak demand, round-trip efficiency, response time, and levelized cost of storage (LCOS) could reflect both technical and economic performance.\u003c/p\u003e\n\u003cp\u003eEqually important is the integration of justice-oriented and participatory indicators, which remain largely missing from current frameworks and constrain their policy relevance. From an \u003cem\u003eenergy-justice\u003c/em\u003e perspective, sustainability assessments should capture not only technical and economic outcomes but also issues of fairness, inclusivity, and procedural representation. Including justice-related indicators such as stakeholder participation, affordability for vulnerable households, and regional equity in infrastructure investment would enhance the legitimacy and social resonance of sustainability indices, making them more relevant to modern power systems and policymaking aimed at a just and resilient energy transition\u003c/p\u003e\n\u003cp\u003eRegional analysis further reveals major gaps, with limited coverage of Sub-Saharan Africa, South Asia, and small island states.\u003c/p\u003e\n\u003cp\u003eThis is largely due to data scarcity and inconsistent reporting practices. To support future work, three practical steps can be taken. First, use open datasets such as World Bank WDI, IEA, IRENA, and UN Energy Statistics, which provide baseline global coverage. Second, apply proxy indicators where direct data are missing (e.g., tariff schedules or household surveys for affordability, outage reports for resilience). Third, adopt a minimum \u0026ldquo;core\u0026rdquo; set of indicators to ensure comparability across contexts, including at least one environmental (CO₂/kWh), economic (retail tariff), social (access or affordability), and technical (system losses or reserve margin) measure. These steps provide a feasible pathway for extending sustainability indices into data-poor regions.\u003c/p\u003e\n\u003cp\u003eClosing these gaps requires a recalibration of both design priorities and methodological strategies. Future indices should embed resilience, affordability, and storage-related metrics alongside established environmental and economic measures. Methodological innovation should prioritise hybrid models that combine the quantitative rigour of MCDA and statistical tools with the contextual nuance of qualitative stakeholder insights.\u003c/p\u003e\n\u003cp\u003eMoreover, this process review has several limitations. It covered only English-language, peer-reviewed journal articles from Scopus and Web of Science, which may exclude relevant grey literature and regional studies. Publication bias may also be present, as well-designed or statistically strong studies are more likely to appear in indexed journals. Moreover, many reviewed works lacked transparency regarding stakeholder engagement, restricting insight into how social and policy perspectives influenced indicator selection. Future studies should adopt hybrid frameworks that integrate quantitative methods with participatory approaches such as Delphi panels or focus-group validation to improve inclusiveness, policy relevance, and contextual applicability of sustainability indices.\u003c/p\u003e\n\u003cp\u003eAlso, several recent studies illustrate promising efforts to balance quantitative robustness with qualitative contextualisation. The Sustainable Energy Transition Readiness (SETR) Index (Neofytou, Nikas, \u0026amp; Doukas, 2020)] integrates Multi-Criteria Decision Analysis with stakeholder-derived weights and qualitative scenario evaluation. The Integrated Sustainability Assessment Model (Abu Bakar et al., 2015)] aligns quantitative indicator aggregation with Sustainable Development Goal targets to ensure policy relevance. Similarly, a demonstration on how participatory consultation can adapt global indices to national contexts, maintaining comparability while capturing local socio-political realities have also been conducted (Afgan \u0026amp; Carvalho, 2010). These hybrid approaches exemplify how sustainability indices can evolve toward inclusivity without compromising analytical rigour.\u003c/p\u003e\n\u003cp\u003eHowever, the reviewed hybridised frameworks demonstrate that methodological pluralism linking quantitative analysis with participatory and justice-based perspectives can generate sustainability indices that are both comparable and contextually grounded.\u003c/p\u003e\n\u003cp\u003eEqually important is the localisation of indicator frameworks. Developing country- and region-specific indices that reflect infrastructure maturity, policy priorities, and socio-economic realities will yield more relevant and actionable results, while maintaining compatibility for cross-country benchmarking. Aligning such efforts with the United Nations Sustainable Development Goal 7 (\u0026ldquo;Affordable and Clean Energy\u0026rdquo;) particularly its targets to expand renewable energy share, improve energy efficiency, and ensure universal access \u0026nbsp; can strengthen policy integration and attract international support.\u003c/p\u003e\n\u003cp\u003eBy broadening indicator scope, adopting hybrid methodologies, and tailoring frameworks to local contexts while keeping them SDG-aligned, researchers and policymakers can produce sustainability indices that are inclusive, adaptive, and directly applicable for guiding equitable and resilient electricity sector transitions worldwide. Although prior reviews (Streimikiene \u0026amp; Siksnelyte-Butkiene, 2020) have noted the conceptual value of hybrid and localised frameworks, the present analysis finds that their practical implementation within electricity-sector sustainability indices remains limited.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis underlines the need for empirical, context-driven adaptation of existing global methodologies.Therefore, closing methodological, dimensional, and geographic gaps in electricity sector sustainability indices is essential for informed, equitable, and context-specific energy transition policies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript: \u003c/p\u003e\n\u003cp\u003eAHP Analytic Hierarchy Process\u003c/p\u003e\n\u003cp\u003eANP Analytic Network Process\u003c/p\u003e\n\u003cp\u003eBESS Battery Energy Storage Systems\u003c/p\u003e\n\u003cp\u003eCSR Corporate Social Responsibility\u003c/p\u003e\n\u003cp\u003eDEA Data Envelopment Analysis\u003c/p\u003e\n\u003cp\u003eDPSIR Drivers-Pressures-State-Impact-Response\u003c/p\u003e\n\u003cp\u003eEAPI Energy Architecture Performance Index\u003c/p\u003e\n\u003cp\u003eEEBD Energy Efficiency and Emissions Balance Design (EEBD-3ES model)\u003c/p\u003e\n\u003cp\u003eEISD Energy Indicators for Sustainable Development\u003c/p\u003e\n\u003cp\u003eESESI Energy Security and Environmental Sustainability Index\u003c/p\u003e\n\u003cp\u003eESI Energy Sustainability Index\u003c/p\u003e\n\u003cp\u003eESIOP Energy Security Index of Pakistan\u003c/p\u003e\n\u003cp\u003eEU European Union\u003c/p\u003e\n\u003cp\u003eGDP Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eGHG Greenhouse Gas\u003c/p\u003e\n\u003cp\u003eGIS Geographic Information System\u003c/p\u003e\n\u003cp\u003eHDI Human Development Index\u003c/p\u003e\n\u003cp\u003eHOMER Hybrid Optimization Model for Multiple Energy Resources\u003c/p\u003e\n\u003cp\u003eIEA International Energy Agency\u003c/p\u003e\n\u003cp\u003eLCA Life Cycle Assessment\u003c/p\u003e\n\u003cp\u003eLMDI Logarithmic Mean Divisia Index\u003c/p\u003e\n\u003cp\u003eMCDA Multi-Criteria Decision Analysis\u003c/p\u003e\n\u003cp\u003eMFA Material Flow Analysis\u003c/p\u003e\n\u003cp\u003eNQI National Quality Infrastructure\u003c/p\u003e\n\u003cp\u003eOECD Organisation for Economic Co-operation and Development\u003c/p\u003e\n\u003cp\u003ePCA Principal Component Analysis\u003c/p\u003e\n\u003cp\u003ePRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\n\u003cp\u003ePROMETHEE Preference Ranking Organization Method for Enrichment Evaluations\u003c/p\u003e\n\u003cp\u003ePSR Pressure-State-Response\u003c/p\u003e\n\u003cp\u003eRES Renewable Energy Sources\u003c/p\u003e\n\u003cp\u003eSALSA Search, Appraisal, Synthesis, Analysis (review method)\u003c/p\u003e\n\u003cp\u003eSDEWES Sustainable Development of Energy, Water and Environment Systems\u003c/p\u003e\n\u003cp\u003eSDG Sustainable Development Goal\u003c/p\u003e\n\u003cp\u003eSEDI Sustainable Energy Development Index\u003c/p\u003e\n\u003cp\u003eSEE South Eastern Europe\u003c/p\u003e\n\u003cp\u003eSETR Sustainability and Energy Transition Readiness Index\u003c/p\u003e\n\u003cp\u003eTOPSIS Technique for Order Preference by Similarity to Ideal Solution\u003c/p\u003e\n\u003cp\u003eUNDP United Nations Development Programme\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eAll data generated or analysed during this study are included in this published article. Additional materials, such as the list of reviewed studies and indicator classifications, are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThis research was funded by the Research Council, University Research Grants, Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka (Grant No. RC/URG/FOE/2024/90).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Conceptualization, W.D.G. and T.R.; Methodology, W.D.G.; Formal analysis, W.D.G. and T.R.; Resources, W.D.G. and T.R.; Data curation, W.D.G. and T.R.; Writing original draft preparation, W.D.G. and T.R.; Writing review and editing, W.D.G. and T.R.; Visualization, W.D.G. and T.R.; Supervision, A.H.T.S.K., N.K.J., and C.Y.; Project administration, A.H.T.S.K., N.K.J., and C.Y. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors gratefully acknowledge the Faculty of Engineering, University of Sri Jayewardenepura, for providing institutional support and access to research resources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu Bakar, N. N., Hassan, M. Y., Abdullah, H., Rahman, H. A., Hussin, F., Abdullah, M. P., \u0026hellip; Said, S. M. (2015). Energy efficiency index as an indicator for measuring building energy performance: A review. \u003cem\u003eRenewable and Sustainable Energy Reviews, 44,\u003c/em\u003e 1\u0026ndash;11. https://doi.org/10.1016/j.rser.2014.12.018\u003c/li\u003e\n\u003cli\u003eAbubakar, I. R., Dano, U. L., Bala, K., \u0026amp; Hassan, A. (2015). 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CSR and financial performance of energy sector companies in Poland. \u003cem\u003eEnergies, 14\u003c/em\u003e(3), 655. https://doi.org/10.3390/en14030655\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sustainable-energy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Sustainable Energy Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Battery Energy Storage Systems (BESS), Composite Indicators, Electricity Sector, Energy Equity, Multi-Criteria Decision Analysis (MCDA), Renewable Energy, Social Readiness, Sustainability Assessment, Sustainability Index, Technological Innovation","lastPublishedDoi":"10.21203/rs.3.rs-7395096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7395096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis review examines how sustainability in the electricity sector is measured using composite indices that integrate economic, environmental, social, and technological dimensions. A systematic screening of 579 records identified 50 peer-reviewed studies published between 2000 and 2024. These were grouped into four methodological categories which are multi-criteria decision analysis, statistical and econometric approaches, indicator-based composite indices, and review or comparative frameworks. Quantitative methods dominate, while aspects such as public acceptance, equity, resilience, and energy storage technologies remain largely absent. The analysis also reveals a persistent regional imbalance: although Sub-Saharan Africa (two studies) and the European Union (three studies) appear most frequently among regional groupings, this still represents limited scholarly coverage relative to their energy-transition relevance and population scale. South Asia and small island developing states remain almost entirely absent from the reviewed corpus. The findings highlight the need for more inclusive and locally relevant indices that address underrepresented social and technological factors, while preserving international comparability to support policy and planning for sustainable electricity transitions.\u003c/p\u003e","manuscriptTitle":"Aggregate Sustainability Indices in the Electricity Sector: A Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 15:54:25","doi":"10.21203/rs.3.rs-7395096/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-11-20T15:53:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T16:58:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10563637262322760255603545881537945006","date":"2025-10-30T14:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T13:56:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T14:06:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sustainable Energy Research","date":"2025-10-10T16:06:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sustainable-energy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Sustainable Energy Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"49b3f1fe-3cdb-494b-8507-f7b7680c4439","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:00:14+00:00","versionOfRecord":{"articleIdentity":"rs-7395096","link":"https://doi.org/10.1186/s40807-025-00226-3","journal":{"identity":"sustainable-energy-research","isVorOnly":true,"title":"Sustainable Energy Research"},"publishedOn":"2026-01-22 15:57:08","publishedOnDateReadable":"January 22nd, 2026"},"versionCreatedAt":"2025-11-06 15:54:25","video":"","vorDoi":"10.1186/s40807-025-00226-3","vorDoiUrl":"https://doi.org/10.1186/s40807-025-00226-3","workflowStages":[]},"version":"v1","identity":"rs-7395096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7395096","identity":"rs-7395096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}