The Integrated Energy Community Performance Index (IECPI): A Multidimensional Tool for Evaluating Energy Communities

Abstract

This paper presents the Integrated Energy Community Performance Index (IECPI), a novel multi-criteria evaluation framework designed to assess the systemic performance of energy communities (ECs) across environmental, technological, social, and economic/institutional dimensions. Although ECs are increasingly recognized as pivotal actors in the decentralized energy transition, the absence of integrated assessment tools continues to hinder comparability, strategic planning, and long-term monitoring. The IECPI addresses this critical gap by structuring performance evaluation around nine normalized indicators, with their respective weights empirically derived from an influence matrix calibrated using interdependencies identified in 60 documented case studies. The IECPI integrates both objective and subjective metrics, capturing measurable outcomes alongside governance structures and contextual factors. The results reveal significant disparities in the performance of energy communities, allowing for the identification of five strategic typologies: Technologically Driven, Environmentally Oriented, Socially Embedded, Balanced Performance, and Structurally Fragile. The IECPI facilitates benchmarking, targeted policymaking, and cross-case learning while aligning with international frameworks such as SDG 7, EMAS, and principles of inclusive governance. As a scalable and transferable model, it provides a robust foundation for evidence-based planning, the evaluation of community resilience, and sustainability-oriented decision-making within distributed energy systems.

1. Introduction

The energy transition, a central political and technological challenge of the 21st century, necessitates new forms of decentralized production, democratic governance, and inclusive citizen participation. In this context, energy communities (ECs) are emerging as alternative, participatory configurations that embody energy justice, sustainability, and autonomy in energy systems [1,2,3,4]. ECs are increasingly recognized as innovative, citizen-driven entities that challenge centralized models of energy governance. As formally defined in Article 2(16) of the Renewable Energy Directive [5], ECs are legal entities founded on open and voluntary participation, with the primary objective of delivering environmental, economic, or social benefits to their members and the broader community—rather than generating financial profit. Typically composed of individuals, local authorities, and small enterprises, ECs collaborate to produce, manage, and consume energy—primarily from renewable sources—within democratic governance frameworks. In this capacity, ECs represent a vital pillar of the decentralized energy transition, advancing sustainability, energy autonomy, and inclusive participation. Their rapid proliferation within the European Union, particularly in the Mediterranean and Balkan regions, underscores the need for reliable performance assessment tools that integrate multidimensional data and support evidence-based policy design [6,7,8,9,10].

This paper introduces the Integrated Energy Community Performance Index (IECPI), a novel multi-criteria assessment tool designed to systematically evaluate the performance of energy communities across objective (technical, economic, environmental) and subjective (social, institutional) dimensions [11,12,13,14,15,16]. The IECPI framework transcends conventional efficiency-oriented models by offering a holistic and dynamically adaptable methodology suitable for diverse socio-technical environments [17].

The index aligns with existing environmental compliance frameworks [18,19,20,21] and is consistent with the Sustainable Development Goals (SDGs) [22,23], providing a unified basis for evaluation, policy alignment, and social accountability [23,24,25,26,27,28]. It incorporates a systemic weighting approach based on secondary data drawn from 60 empirical case studies, supported by thematic mapping, an influence matrix [29,30,31], and extensive bibliographic meta-analysis [3,6,11,26,32,33,34,35].

The main objective of this article is to establish the IECPI as a reliable tool for policy formulation, enhancing resilience, and evaluating the contribution of energy communities to a just energy transition [36,37]. The paper introduces the methodology and structure of the index, analyzes empirical results derived from its application to real-world cases, and discusses key findings, limitations, and future research perspectives.

2. Materials and Methods

The development of the IECPI relied on a combination of literature review, empirical documentation, and multi-criteria analysis. This methodological approach was crafted to capture the multidimensional nature of energy communities, integrating technical, social, environmental, and economic factors.

2.1. Foundation of Thematic Areas and Structure of the IECPI

The IECPI was developed to provide a holistic assessment of energy community performance, considering technical, environmental, social, and institutional factors. The index architecture distinguishes variables into two fundamental categories: objective and subjective indicators [35,38].

Objective indicators, comprising 70% of the total weighting, include measurable metrics that reflect energy efficiency, environmental performance, economic sustainability, and compliance with institutional standards. Examples include CO₂ emission reduction, renewable energy generation, energy affordability, return on investment (ROI) [39,40], and compliance with EMAS or ISO 14001 standards [20].

Subjective indicators, accounting for 30% of the total weighting, assess the social and institutional dynamics of the communities. These include the level of social participation, adoption of technological solutions (innovation uptake), energy independence, and a resilience or vulnerability index.

The selection of the indicators was documented through a literature review, institutional criteria such as the Sustainable Development Goals (SDGs) [22,41], European standards, and the analysis of empirical data from energy communities across various geographical and social contexts. The detailed categorization of the indicators is presented in

Table 1. Categorization of IECPI indicators into objective and subjective, with corresponding abbreviations and descriptions.

Indicator Category	Code	Description
Objective	ECO2	Percentage reduction of CO2 emissions
Objective	Eprod	Renewable energy production as a percentage of coverage
Objective	Eaff	Energy affordability/reduction of energy costs
Objective	ROI	Return on investment
Objective	EMAS	Compliance with institutional standards (EMAS, ISO 14001)
Subjective	Spart	Level of social participation
Subjective	Tadopt	Degree of adoption of new technologies
Subjective	Eindep	Energy independence index
Subjective	Vindex	Community resilience or vulnerability index

.

Table 2. Indicators of the IECPI, their corresponding thematic domains, and associated bibliographic references.

Indicator	Focus Area	Description	Indicative References
ECO2	Climate Neutrality	Reduction of CO2 emissions through RES deployment and storage integration	[14,25,34,38,42,43,44,45,46,47]
Eprod	Local Energy Production	Share of energy demand covered by locally generated renewable energy	[15,40,48,49,50,51]
Eaff	Energy Affordability	Cost reduction and tackling energy poverty	[2,4,7,35,43,52]
ROI	Economic Sustainability	Return on investment and financial performance	[6,13,39,53,54,55,56,57,58,59,60,61,62,63]
EMAS	Environmental Management	Compliance with EMAS/ISO 14001 or equivalent environmental standards	[11,12,13,18,19,20,24,55]
Spart	Social Participation	Collective decision-making and participatory governance models	[1,64,65,66,67,68,69]
Tadopt	Technological Adoption	Use of innovations such as peer-to-peer (P2P) platforms, smart meters, and citizen-driven apps	[17,58,59,60,61,62,69,70,71,72,73,74]
Eindep	Energy Independence	Functional autonomy from the central grid or external supply	[3,75,76,77,78]
Vindex	Vulnerability and Resilience	Community stability and capacity to withstand disruptions	[34,35,53,54,76,79,80,81,82]

illustrates the IECPI indicators, detailing their thematic domains and corresponding bibliographic references.

2.2. Calculation Method and Calibration of the IECPI

The IECPI is calculated by weighting and summing both objective and subjective indicators [35], which have been normalized to a uniform evaluation scale. The determination of individual weights was conducted as follows.

The basic calculation formula is:

$$\mathrm{I}\mathrm{E}\mathrm{C}\mathrm{P}\mathrm{I}=0.7\left({\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}\times {\mathrm{O}}_{\mathrm{i}}\right)+0.3\left({\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{j}}\times {\mathrm{S}}_{\mathrm{j}}\right)$$

(1)

where

• O_i: Normalized values of the objective indicators.
• S_j: Normalized values of the subjective indicators.
• w_i and w_j: Corresponding weighting coefficients for each indicator, with values summing to 100%.
• n and m: Number of objective and subjective indicators, respectively.

All indicators are normalized to a common scale of [0–100] to ensure comparability and to prevent any undue influence from variables with natural upper or lower limits.

The weighting of the indicators is derived from a combination of theoretical documentation and empirical analysis from case studies. Objective indicators contribute 70% to the total weight of the IECPI, while subjective indicators contribute 30%.

The internal weighting of the indicators is detailed in

Table 3. The internal weighting of the indicators.

Code	Indicator	Category	Weighting Coefficient (%)
ECO2	CO2 emission reduction	Objective	20
Eprod	Renewable energy production	Objective	20
Eaff	Energy affordability	Objective	15
ROI	Return on investment	Objective	10
EMAS	Institutional compliance	Objective	5
Spart	Social participation	Subjective	10
Tadopt	Adoption of technologies	Subjective	10
Eindep	Energy independence	Subjective	5
Vindex	Vulnerability/resilience	Subjective	5

.

2.3. Methodological Foundation and Weighting Analysis

The accurate depiction of each individual indicator’s contribution to the final IECPI value is crucial for ensuring the theoretical validity and practical reliability of the index. To prevent arbitrary or static weighting coefficients, this study employs a multi-level, empirically grounded weighting methodology. This approach integrates (a) institutional and theoretical foundations concerning the significance of the indicators, (b) interdependence analysis between indicators using an influence matrix, and (c) quantitative analysis based on empirical data from 60 energy communities [29,30,31].

2.3.1. Institutional Significance and Theoretical Documentation

The distinction between objective and subjective indicators of the IECPI is based on institutional specifications and a literature review of energy performance and resilience assessment models. Objective indicators relate to measurable quantities such as energy efficiency and CO₂ emissions, whereas subjective indicators capture social, technological, or governance aspects, including participation, innovation, and vulnerability [73,78].

This categorization aligns with previous research approaches in energy transition, which evaluate institutional and social parameters alongside technical performance indicators [2,3,4,83,84,85]. Similarly, other approaches emphasize the importance of adaptability and inclusion as key qualitative variables [11].

The preliminary weighting of 70% for objective indicators and 30% for subjective indicators reflects the emphasis on environmental and economic metrics while acknowledging the importance of social dimensions. This initial ratio served as the foundation for deriving individual weights through influence analysis and empirical coding [29,30].

The 70/30 weighting distribution between objective and subjective indicators was not selected arbitrarily; rather, it represents a deliberate synthesis of regulatory priorities and academic insights. Institutional methodologies—such as those employed in EMAS and ISO 14001—prioritize quantifiable environmental and economic outcomes, thereby justifying the 70% weight assigned to objective metrics. Concurrently, a growing body of research on energy justice and democratic governance underscores the significance of social and participatory dimensions, which informed the 30% allocation to subjective indicators. This weighting structure was further validated through an empirical correlation matrix, which confirmed the systemic relevance of subjective factors such as community participation and resilience.

2.3.2. Influence Matrix (Network Analysis)

The second phase of the IECPI’s methodological architecture acknowledges that the nine individual indicators do not function as independent variables but as interdependent nodes within a system, exhibiting multi-level influences.

The model adopted is a directed, fully connected influence graph, characterized by

• Each indicator influencing and being influenced by all others;
• Correlations that are systemic and multipath, rather than one-dimensional;
• Weights of correlations that are empirically and theoretically determined, based on occurrences and thematic proximity between indicators.

The construction of the influence matrix is informed by the analysis of 60 energy communities and a systematic review of the international literature, employing co-occurrence analysis techniques, thematic coding, and conceptual mapping. The IECPI indicators are configured to function as part of a closed network of cyclic and bidirectional influences, with minimal isolated variables.

Figure 1 provides a complete representation of the influence network, illustrating each node’s connection to every other node via directed edges. This perspective is supported by numerous bibliographic references [10,11,40,86], which recognize that the performance of social participation, technological adoption, and environmental compliance mutually affect each level of implementation.

This analysis provided the foundation for the numerical weighting of the indicators (refer to Section 2.3.4), incorporating a systemic logic into the IECPI methodology and ensuring the intrinsic validity of the final index.

Figure 1 depicts the comprehensive interrelation among all IECPI indicators. Each node is interconnected (fully connected graph), with arrows indicating the directional nature of interdependencies. This visualization represents the complete intra-systemic correlation framework theorized in Section 2.3.2.

2.3.3. Quantitative Analysis of Empirical Communities (Normalized Performance Analysis Across 60 Cases)

The quantitative evaluation of the IECPI indicators was conducted using normalized performance data from 60 energy communities, selected to encompass diverse geographical, technological, and institutional contexts. The empirical data used for evaluating the IECPI indicators were not derived through primary fieldwork but were extracted from existing academic literature and institutional reports. All 60 energy communities included in the analysis were selected from documented case studies explicitly cited in

Table A1. Full inventory of 60 energy communities assessed via the IECPI.

Case_ID	Bibliographic Reference	Region/Type/Description	Critical IECPI Indicator
EC01	[1]	Aruta et al. (2024)—Naples	Tadopt, Spart
EC02	[62]	Elkadeem et al. (2020)—Egypt	Eindep, ECO2
EC03	[109]	Karunathilake et al. (2018)—Canada	Spart, Eaff
EC04	[15]	Vourdoubas (2017)—Crete	ECO2, Eprod
EC05	[10]	Zhong and Shi (2025)—China (rural)	Eaff, Vindex
EC06	[3]	Galindo et al. (2025)—Latin America and the EU	Spart, Tadopt
EC07	[65]	Gruber et al. (2025)—Austria	EMAS, Eindep
EC08	[4]	Szpak and Ostrowski (2025)—Barcelona/Warsaw	Eaff, Spart
EC09	[49]	Marchetti et al. (2024)—Italy (rural)	Eprod, ROI
EC10	[11]	Mura et al. (2025)—Pisa (behavioral)	Spart, Tadopt
EC11	[2]	Boostani et al. (2024)—Systematic Review on Energy Poverty	Spart, Vindex
EC12	[7]	Muftić Dedović et al. (2025)—Bosnia and Herzegovina	ECO2, Eaff
EC13	[8]	Yfanti et al. (2025)—Southeast Mediterranean (Greece)	Eprod, Eindep
EC14	[24]	Bąk et al. (2025)—EU Comparative Review	EMAS, Tadopt
EC15	[12]	Pagnini et al. (2024)—LCOE Uncertainty Analysis	ROI, Eprod
EC16	[42,43]	IEA (2023)—Global Avg (WEO and Renewables Reports)	Eprod, ECO2
EC17	[25,27,107]	IPCC (2019)—High-Mitigation Reference Case	ECO2, Vindex
EC18	[28]	United Nations (2023)—SDG17 Platform	Spart, Vindex
EC19	[110]	UNDP (2023)—Energy Access and Transition Report	Eaff, Spart
EC20	[111]	EU Commission (2023)—Energy Poverty Portal	Eaff, EMAS
EC21	[112]	Manso-Burgos et al. (2022)—Valencia (Spain)	Tadopt, Spart
EC22	[17]	Petrovics et al. (2024)—Diverse Governance (BE, PT, NL)	Spart, EMAS
EC23	[113]	Menegatto et al. (2025)—Padua Social Acceptance	Spart, EMAS
EC24	[108]	Ye et al. (2017)—South China Sea Island (YSI)	Eprod, ECO2
EC25	[53]	Ahmad et al. (2022)—Pakistan (Refugee + Rural RE Systems)	Eaff, Vindex
EC26	[57]	Aljafari et al. (2023)—Najran Near-Zero Community	Eprod, ECO2
EC27	[33]	Tafula et al. (2023)—Mozambique Microgrid Siting	Eaff, Vindex
EC28	[61]	Ceglia et al. (2023)—Benevento Industrial REC (Italy)	Eprod, ROI
EC29	[47]	Li et al. (2024)—G-CEEP Net-Zero Model (Japan)	ECO2, Tadopt
EC30	[114]	Borges et al. (2022)—Blockchain P2P Energy Market (Europe)	Tadopt, Spart
EC31	[115]	Mulder et al. (2023)—Energy Poverty in the Netherlands	Eaff, EMAS
EC32	[97]	Ezekwem and Muthusamy (2023)—Choba Hybrid PV/Wind (Nigeria)	Eaff, Vindex
EC33	[116]	Cavana et al. (2025)—Urban REC Meta-Study (Italy)	Eprod, Tadopt
EC34	[70]	Goto et al. (2023)—Japan PV + Battery + P2P Simulation	Tadopt, Eindep
EC35	[16]	Hosseini et al. (2025)—CIRLEM Norway (Ålesund)	Tadopt, Vindex
EC36	[117]	Przygodzka et al. (2024)—Agricultural RE, Eastern Poland	Eaff, Vindex
EC37	[118]	Mah et al. (2012)—Jeju Smart Grid Testbed (Korea)	Tadopt, EMAS
EC38	[9]	Efthymiou et al. (2022)—Municipality of Hersonissos, GR	Eaff, EMAS
EC39	[84]	Tapia et al. (2017)—Urban Vulnerability (EU Cities)	Vindex, EMAS
EC40	[81]	Helmke-Long, Laura (2022)—Municipality policies	Vindex, Eaff
EC41	[106]	Lemaire et al. (2024)	Eprod, Eindep
EC42	[98]	Fabbri et al. (2023)—Italian Social Housing	Eaff, Vindex
EC43	[68]	Zwickl-Bernhard and Auer (2021)—Citizen ECs, Austria	Spart, Eaff
EC44	[77]	Trivedi et al. (2022)—Community-Based Microgrid Review	Tadopt, Spart
EC45	[76]	Graça Gomes et al. (2025)—Corvo Island, Portugal	Eprod, Tadopt
EC46	[59]	Cirone et al. (2022)—Soveria Mannelli, Calabria (Italy)	Eprod, Tadopt
EC47	[80]	Abadeço et al. (2024)—Lisbon Urban Vulnerability	Eaff, Vindex
EC48	[34]	Zhou et al. (2024)—Urban–Rural Low-Carbon Resilience (CN)	Vindex, Tadopt
EC49	[101]	Heidelberger and Rakha (2022)—Grove Park, Atlanta	Vindex, Eaff
EC50	[119]	Lopes et al. (2024)—UFSCar Library (PBE Edifica—Brazil)	EMAS, Eaff
EC51	[64]	Fiander et al. (2024)—Energy Democracy Survey, Canada	Spart, EMAS
EC52	[104]	Jin et al. (2023)—UNIVPM Multi-Carrier Campus (Italy)	Tadopt, EMAS
EC53	[103]	Janota et al. (2023)—Agrovoltaic CHP, Březnice (Czech)	Eprod, Vindex
EC54	[32]	Ancona et al. (2022)—Corticella DHN Community (Italy)	Eprod, Tadopt
EC55	[105]	Heidelberger and Rakha (2021)—Kallis et al. Lessons on renewable energy: review of 17 case studies	Vindex, Eaff
EC56	[75]	Zhang et al. (2024)—Predictive AI Campus (Dalian, China)	Tadopt, EMAS
EC57	[82]	Karpinska et al. (2021)—Regional Vulnerability (Poland)	Vindex, Eaff
EC58	[120]	Karpinska and Śmiech (2020)—Invisible Energy Poverty, CEE	Vindex, Eaff
EC59	[94]	Csereklyei et al. (2024)—Community-Scale Batteries (AU)	Tadopt, Eaff
EC60	[90]	Ceglia et al. (2022)—Multi-purpose REC (South Italy)	Eprod, Spart

, which collectively serve as the foundation for a bibliographically reconstructed performance dataset. This approach enables systematic comparison across heterogeneous contexts while ensuring methodological transparency and replicability. The sample includes insular, rural, semi-urban, and urban areas, with variations in maturity levels, governance models, and technological infrastructures.

Although the selected case studies span a broad timeframe—primarily between 2017 and 2025—the dataset was not temporally constrained, as the aim of this study is not longitudinal analysis but the identification of inter-indicator relationships. The focus is on uncovering structural patterns and systemic interactions that transcend specific implementation periods. Nevertheless, the potential incorporation of temporal stratification is recognized as a valuable avenue for future research and methodological refinement.

For clarification, the column titled “Region/Type/Description” in Table A1 refers to the geographic location of each case (e.g., country or region), the general typology of the community (e.g., urban, rural, island-based), and a brief qualitative characterization (e.g., refugee camp, university campus), respectively. The column titled “Critical IECPI Indicator” highlights the IECPI dimensions most emphasized or prominently analyzed in the corresponding source. These indicators are not necessarily exclusive but reflect the thematic focus of the referenced documentation.

Normalized performance values for the nine IECPI indicators were calculated for each community (EC01–EC60), using a linear transformation into a continuous scale from 0 to 1. The normalization process was based on institutional benchmarks (e.g., EMAS certification) or quantitative metrics (e.g., % RES penetration, % CO₂ reduction, ROI, etc.). Intermediate values represent partial fulfillment of indicator criteria, determined through well-documented evaluations.

The methodological workflow comprised the following steps:

• Data collection: Gathering quantitative or thematically coded data for each indicator across all communities.
• Normalization: Transforming raw data into a unified scale using functional or institutional reference thresholds.
• Matrix synthesis: Creating a “case–indicator matrix”, with rows representing communities and columns representing IECPI indicators.
• Table A2. Full IECPI performance table by community (EC01–EC60).

  Case_ID	ECO2	Eprod	Eaff	ROI	EMAS	Spart	Tadopt	Eindep	Vindex	Empirical IECPI	IECPI
  EC01	0.85	1	0.5	0.4	0.2	0.8	0.9	0.7	0.5	0.72	0.73
  EC02	0.9	1	0.85	0.9	0.1	0.2	0.95	1	0.2	0.75	0.78
  EC03	0.6	0.75	0.7	0.6	0.3	0.85	0.8	0.6	0.3	0.65	0.66
  EC04	1	0.95	0.9	0.8	0.1	0.3	0.45	1	0.25	0.73	0.75
  EC05	0.6	0.5	0.4	0.35	0.15	0.25	0.3	0.5	0.7	0.43	0.44
  EC06	0.7	0.65	0.6	0.5	0.25	0.85	0.55	0.6	0.4	0.61	0.61
  EC07	0.8	0.8	0.7	0.75	0.85	0.75	0.9	0.8	0.3	0.76	0.76
  EC08	0.65	0.7	0.85	0.55	0.4	0.9	0.6	0.7	0.35	0.67	0.68
  EC09	0.95	0.95	0.8	0.85	0.6	0.6	0.75	0.9	0.4	0.81	0.82
  EC10	0.88	0.85	0.75	0.65	0.35	0.95	0.85	0.8	0.2	0.77	0.77
  EC11	0.5	0.60	0.45	0.5	0.4	0.85	0.6	0.55	0.3	0.55	0.55
  EC12	0.9	0.65	0.88	0.65	0.3	0.4	0.35	0.6	0.75	0.66	0.66
  EC13	0.75	0.9	0.6	0.7	0.4	0.6	0.55	0.9	0.4	0.68	0.69
  EC14	0.7	0.75	0.7	0.6	0.8	0.65	0.85	0.7	0.35	0.70	0.70
  EC15	0.8	0.85	0.75	0.9	0.6	0.5	0.65	0.75	0.25	0.72	0.73
  EC16	0.5	0.5	0.5	0.5	0	0.2	0.6	0.3	0.4	0.43	0.44
  EC17	1	0.9	1	1	0.8	0.85	0.9	0.85	0.9	0.93	0.93
  EC18	0.5	0.5	0.5	0.4	0.7	0.9	0.6	0.5	0.9	0.58	0.57
  EC19	0.5	0.6	0.9	0.5	0.6	0.95	0.85	0.6	0.85	0.69	0.69
  EC20	0.6	0.5	1	0.4	0.75	0.9	0.7	0.5	0.8	0.68	0.67
  EC21	0.8	0.7	0.8	0.7	0.5	0.85	0.85	0.7	0.75	0.76	0.76
  EC22	0.6	0.6	0.6	0.4	0.6	0.95	0.75	0.6	0.75	0.64	0.64
  EC23	0.6	0.6	0.5	0.3	0.7	1	0.6	0.5	0.8	0.62	0.61
  EC24	1	1	1	0.9	0.2	0.2	1	1	0.9	0.84	0.87
  EC25	0.9	0.95	1	0.4	0.4	0.9	0.9	1	0.95	0.85	0.86
  EC26	0.95	0.95	0.9	1	0.3	0.2	0.85	1	0.85	0.81	0.83
  EC27	0.7	0.6	0.9	0.3	0.5	0.8	0.6	0.8	0.85	0.68	0.67
  EC28	0.9	0.9	0.8	0.9	0.7	0.5	0.8	0.8	0.8	0.81	0.82
  EC29	1	0.9	0.9	0.8	0.8	0.85	0.95	0.9	0.9	0.91	0.91
  EC30	0.5	0.4	0.6	0.3	0.8	0.95	0.9	0.4	0.8	0.60	0.59
  EC31	0.5	0.4	0.9	0.3	0.75	0.85	0.6	0.4	0.8	0.60	0.59
  EC32	0.95	1	1	0.85	0.3	0.6	0.85	1	0.9	0.87	0.88
  EC33	0.9	0.85	0.85	0.8	0.75	0.7	0.9	0.6	0.85	0.83	0.83
  EC34	0.85	0.8	0.85	0.6	0.6	0.8	0.9	0.85	0.8	0.80	0.80
  EC35	0.85	0.7	0.9	0.7	0.85	0.6	1	0.75	0.9	0.80	0.80
  EC36	0.7	0.8	0.9	0.5	0.6	0.8	0.6	0.7	0.85	0.73	0.73
  EC37	0.8	0.7	0.6	0.5	0.8	0.3	0.9	0.8	0.75	0.68	0.68
  EC38	0.85	0.7	1	0.6	0.85	0.65	0.85	0.7	0.9	0.79	0.79
  EC39	0.6	0.3	0.5	0.3	0.8	0.8	0.5	0.4	0.9	0.54	0.52
  EC40	0.7	0.6	1	0.3	0.8	0.7	0.65	0.6	0.95	0.70	0.69
  EC41	0.95	1	0.9	0.5	0.2	0.3	0.75	1	0.85	0.77	0.78
  EC42	0.6	0	1	0.3	0.75	0.65	0.3	0.2	0.9	0.50	0.49
  EC43	0.8	0.75	0.85	0.8	0.7	0.95	0.9	0.75	0.8	0.82	0.82
  EC44	0.75	0.7	0.8	0.6	0.75	0.9	0.95	0.8	0.85	0.78	0.78
  EC45	1	1	0.95	0.8	0.7	0.3	1	1	0.95	0.87	0.89
  EC46	0.9	0.9	0.85	0.75	0.7	0.5	0.85	0.75	0.8	0.80	0.81
  EC47	0.7	0.6	0.95	0.5	0.6	0.55	0.5	0.4	0.85	0.65	0.65
  EC48	0.85	0.8	0.85	0.7	0.8	0.8	0.9	0.8	0.9	0.82	0.82
  EC49	0.5	0	0.9	0.3	0.5	0.6	0.3	0.2	0.95	0.45	0.44
  EC50	0.5	0	0.7	0.3	0.8	0.3	0.5	0.2	0.7	0.41	0.40
  EC51	0.7	0.2	0.6	0.3	0.8	0.75	0.8	0.4	0.85	0.58	0.56
  EC52	0.9	0.8	0.8	0.75	0.85	0.4	1	0.8	0.85	0.80	0.80
  EC53	0.85	0.9	0.9	0.8	0.6	0.8	0.7	1	0.9	0.83	0.84
  EC54	0.8	0.85	0.85	0.8	0.75	0.6	0.85	0.8	0.8	0.80	0.80
  EC55	0.5	0	0.95	0.3	0.4	0.55	0.3	0.2	0.95	0.44	0.44
  EC56	0.6	0	0.85	0.4	0.8	0.3	1	0.3	0.8	0.52	0.51
  EC57	0.3	0	0.95	0	0.6	0.2	0	0.2	1	0.32	0.31
  EC58	0.3	0	0.95	0	0.4	0.2	0	0.2	1	0.31	0.30
  EC59	0.6	0.2	0.7	0.5	0.6	0.45	0.9	0.5	0.75	0.55	0.54
  EC60	0.85	0.8	0.8	0.85	0.8	0.85	0.9	0.85	0.85	0.84	0.84
  MEAN	0.74	0.65	0.80	0.57	0.57	0.64	0.72	0.66	0.73	0.69	0.69

  presents the values of the nine IECPI indicators for each of the 60 communities. It also includes the thematically dominant indicators identified in each case and the final IECPI score calculation.

Each of the nine IECPI indicators was normalized on a continuous scale [0–1], based on predefined performance thresholds. These thresholds were established either by institutional standards (e.g., EMAS, ISO) or by best empirical practices derived from the international literature and case study data. (see Appendix B.1,

Table A3. Normalization thresholds for IECPI indicators.

Indicator	0 (No Presence)	0.5 (Partial Performance)	1 (Full Performance)
ECO2	No reduction	20–30% reduction	≥50% emissions reduction
Eprod	<10% from RES	50–70% RES	≥90–100% from RES
Eaff	No cost change	10–20% reduction	≥30% cost reduction
ROI	<1%	4–6%	≥8% annual return
EMAS	Not certified	ISO 14001	Full EMAS certification
Spart	Closed model	Consultative participation	Collective governance
Tadopt	Outdated systems	Basic smart metering	Advanced EMS, VPPs
Eindep	<10% autonomy	30–50%	≥80% self-generation
Vindex	No strategy	Periodic planning	Comprehensive resilience plan

).

The matrix served as the foundation for the subsequent weighted performance analysis. Each normalized value was multiplied by the corresponding influence coefficient from the influence matrix (see Section 2.3.2 and Appendix B.2,

Table A4. Influence matrix of IECPI indicators.

From/To	ECO2	Eprod	Eaff	ROI	EMAS	Spart	Tadopt	Eindep	Vindex
ECO2	1.0	0.7	0.6	0.5	0.4	0.6	0.5	0.4	0.3
Eprod	0.7	1.0	0.6	0.6	0.5	0.6	0.5	0.4	0.3
Eaff	0.6	0.6	1.0	0.5	0.5	0.7	0.6	0.5	0.5
ROI	0.5	0.6	0.5	1.0	0.5	0.5	0.6	0.4	0.4
EMAS	0.4	0.5	0.5	0.5	1.0	0.5	0.4	0.3	0.3
Spart	0.6	0.6	0.7	0.5	0.5	1.0	0.6	0.4	0.5
Tadopt	0.5	0.5	0.6	0.6	0.4	0.6	1.0	0.5	0.4
Eindep	0.4	0.4	0.5	0.4	0.3	0.4	0.5	1.0	0.4
Vindex	0.3	0.3	0.5	0.4	0.3	0.5	0.4	0.4	1.0

). This resulted in a matrix of weighted IECPI scores, where each value reflects both the performance level and the systemic significance of each indicator.

This approach integrates thematic encoding, benchmark-based normalization, and systemic influence-weighting principles. Similar methodologies are employed in recent studies evaluating the performance and resilience of energy configurations (e.g., [1,9,32,44,47,48,49,57,59,60,61,66,67,76,80,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106]).

The inclusion of 60 empirical cases strengthens the representativeness and replicability of the IECPI framework, enhancing its validity as a multidimensional evaluation index for energy communities. Indicator correlations were then assessed (Appendix B.2, Table A4, Figure 2), providing the basis for the final composite index and its corresponding weighting structure (

Table 4. Final weightings of IECPI indicators based on empirical analysis of 60 energy communities.

Indicator	Description	Target Weight (%)	Empirical Weight (%)	Deviation (%)
ECO2	Reduction in CO2 emissions	20	21.53	+1.53
Eprod	Renewable energy production rate	20	18.14	−1.86
Eaff	Energy efficiency level	15	14.05	−0.95
ROI	Return on investment	10	8.69	−1.31
EMAS	Institutional certification, EMAS/ISO	5	6.46	+1.46
Spart	Social participation	10	11.32	+1.32
Tadopt	Technological adoption	10	9.87	−0.13
Eindex	Energy independence index	5	4.77	−0.23
Vindex	Vulnerability/resilience	5	5.17	+0.17
Total	—	100.00	100.00	—

).

2.3.4. Weighting of Indicators and Final Synthesis of the IECPI

The final synthesis of the IECPI involves rational weighting of individual indicators, based on their actual relevance within the application field. This weighting was empirically derived from their functional contribution across 60 energy communities and their systemic interdependencies, as documented in the correlation matrix (Table A4, Appendix B.2). Table A2 contains the normalized indicator values for the 60 communities.

The guiding principle for the weighting process is that an indicator’s significance stems not only from its individual performance but also from its overall influence within the evaluation system. A three-stage methodological procedure was adopted:

(a)

Performance Normalization (see Section 2.3.3)

Performance values x_ij for each indicator and community were expressed on a continuous [0–1] scale, based on absolute or relative performance benchmarks (e.g., percentage of energy coverage, presence of institutional mechanisms, ROI).

(b)

Correlation-Based Weighting through the Influence Matrix

Each normalized value x_ij was weighted by the systemic influence of the corresponding indicator d_j. The correlated performance per case was calculated as follows:

$$y_{ij}=x_{ij}\times \left({\sum }_{k=1}^9{w}_{jk}\right)$$

(2)

Ιn this formulation, the notation is defined as follows:

• dj refers to the j-th indicator whose systemic influence is being measured;
• d_k refers to the k-th indicator that receives influence from d_j;
• w_jk represents the magnitude of influence exerted by indicator dj on indicator d_k, based on their empirical correlation.

The weights w_jk are derived from the influence matrix presented in Appendix B.2, where Pearson’s correlation coefficient (r) is used to quantify the strength of inter-indicator relationships. This matrix represents a fully connected system, wherein each indicator both influences and is influenced by all others. The resulting value Sj captures the aggregate systemic impact of each indicator within the IECPI framework, reflecting its relative importance in shaping overall performance dynamics.

(c)

Normalization of Indicator Contribution to the Total Score

The final weight W_j assigned to each indicator is calculated by normalizing its cumulative score over the total of all indicator scores, ensuring proportional distribution across the full set:

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{S}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^9{\mathrm{S}}_{\mathrm{j}}}}\times 100$$

(3)

Then, the cumulative score S_j is obtained from:

$${\mathrm{S}}_{\mathrm{j}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{y}}_{\mathrm{i}\mathrm{j}}$$

(4)

The results of this empirical weighting process are summarized in Table A2. Deviations from the initial theoretical expectations were minimal, confirming the compatibility between ex ante theoretical estimations and the empirical dynamics of the indicators. The outcome is a coherent, evidence-based, and adaptable weighting structure.

The final IECPI score for each community is calculated as the weighted sum of the nine normalized indicators:

$${\mathrm{I}\mathrm{E}\mathrm{C}\mathrm{P}\mathrm{I}}_{\mathsf{\iota }}={\sum }_{j=1}^9{w}_i\times x_{ij}$$

(5)

This methodology ensures that the IECPI is comparable across communities, incorporates the systemic dynamics of indicator interrelations, and can be flexibly adapted to various implementation contexts.

To ensure methodological transparency and reproducibility, all stages of the IECPI construction process have been thoroughly documented. This includes the definition and categorization of indicators (Table 1 and Table 2), the normalization procedures and threshold criteria (Appendix B.1), the development of the influence matrix based on empirical data (Appendix B.2), and the mathematical formulation of indicator weights and systemic contributions (Equations (3)–(5)). Additionally, the complete dataset of normalized indicator values for all 60 energy communities is provided in Table A2. This comprehensive documentation enables the IECPI framework to be reliably replicated, validated, or adapted to different regional or thematic contexts.

2.4. Final Composition and Calculation of the IECPI

The final composition of the IECPI was developed using a comprehensive framework of normalization, weighting, and systemic performance analysis. The IECPI integrates both objective and subjective characteristics of energy communities in a replicable, adaptable, and comparable manner.

A foundational principle in the IECPI design was the adoption of a 70:30 ratio between objective (quantitative) and subjective (qualitative) indicators. This distribution was informed by institutional standards (e.g., EMAS, ISO 14001), academic literature, and practical implementations in energy communities.

Table 4 compares the predefined target weights with the actual weights derived from empirical performance data and influence-based weighting. Deviations are minimal, confirming the reliability of the initial assumptions. The empirical weights presented in the fourth column of Table 4 were derived from the normalized contribution values, as calculated using Equation (3).

Figure 2 illustrates the heatmap of inter-indicator correlations (influence matrix) among IECPI indicators. This heatmap shows the strength of interrelations between the nine core indicators of the IECPI. Darker cells represent stronger correlations, indicating higher systemic co-dependence between the corresponding indicators. The matrix serves as a basis for the weighted synthesis stage of the IECPI, capturing both direct performance and indirect influence pathways across dimensions such as climate neutrality (ECO₂), local energy production (Eprod), affordability (Eaff), resilience (Vindex), and institutional compliance (EMAS). All correlation coefficients used to construct the influence matrix (Table A4) and the heatmap (Figure 2) were calculated using Pearson’s r, based on the observed indicator values across the 60 documented energy communities.

3. Results

The application of the IECPI yielded scores ranging from 0.30 (EC58) to 0.93 (EC17), reflecting a wide spectrum of performance across the 60 documented energy communities (

Table 5. Distribution of IECPI scores among energy communities.

Performance Category	IECPI Score Range	Number of Communities
High Performance	76–100	25
Moderate Performance	51–75	27
Low Performance	<50	8

). According to the final results, 25 communities (41.7%) scored above 75, placing them in the high-performance category, while 27 communities (45%) fell within the moderate-performance category (51–75 points), and 8 communities (13.3%) were classified as low-performance (<50 points).

The community with the highest IECPI score (0.93) is EC17 [25,27,107], which does not correspond to an actual case but was constructed based on the IPCC (2019)—High-Mitigation Reference Case. This case collectively represents the principles of an integrated energy community, using harmonized data that reflect the broader influence of energy communities within ecosystemic frameworks. It serves as a theoretical benchmark for evaluating all other documented cases.

It is important to note that although nearly half of the communities achieved high IECPI scores, their holistic performance may not be fully representative, as the final score is not derived solely from individually measurable data, but rather from weighted correlations among the nine component indicators. Many of these high-scoring communities either operate within localized ecosystems with limited scalability or have matured to function holistically within their specific action context. This reinforces the need for a composite index such as the IECPI, capable of capturing not only individual technical or institutional outputs, but also the interoperability and systemic coherence of each energy community.

Moreover, the fact that more than 50% of the sample falls into the moderate- and low-performance categories strengthens the rationale for developing a multidimensional index. The IECPI emerges as an essential assessment tool, contributing to the formation of a truly holistic approach to evaluating energy communities, one that reflects their actual functional structure and maturity.

Although 25 communities are classified in the “high-performance” category, their elevated scores in specific indicators are often shaped by the internal correlation of individual variables, as manifested through the functional behavior of each community during analysis. Nevertheless, the IECPI—through data normalization and the applied weighting structure—tends to provide a more holistic assessment that accurately reflects the complexity required to achieve systemically balanced performance. The core challenge lies in the fact that only a few communities present quantifiable data supporting high-level performance across technical, social, and institutional domains, highlighting the multidimensional and demanding nature of the index.

Among the 60 communities in the sample, the eight classified as “low-performance” exhibit significant structural deficits, either in terms of technological adoption (e.g., lack of storage systems or peer-to-peer trading capabilities) or in social integration (e.g., limited citizen participation or weak institutional frameworks). These communities are typically located in areas facing socioeconomic challenges, limited digital readiness, or inadequate institutional and policy support. Specifically, these energy initiatives struggle with technical infrastructure limitations, while also lacking the necessary social and institutional foundations required for achieving integrated and sustainable development.

As nearly 50% of the communities fall into the moderate-performance category, two main conclusions emerge: first, most communities have achieved a functionally acceptable level of maturity; and second, there remains significant room for improvement in order to develop a truly integrated and resilient energy ecosystem.

It is noteworthy that many communities display strong performance in specific indicators (such as self-consumption or return on investment—ROI), while underperforming in others that are critical to systemic cohesion (such as social participation or resilience). This imbalance negatively affects their overall IECPI score and highlights the need for a genuinely holistic approach to energy community design.

Many of these communities were originally established to address a single dominant objective (e.g., technological or environmental), as consistently reflected in the literature. However, this thematic unilateralism is not sufficient to ensure long-term operability or meaningful integration into the broader social context in which these communities operate.

The value of the IECPI lies in its ability to illuminate these multidimensional imbalances and highlight interactions among technical, social, and institutional components. Unlike traditional one-dimensional metrics (e.g., CO₂ reduction or ROI), the IECPI captures performance interdependencies, offering a coherent picture of the true maturity and sustainability of each energy community.

The distribution correlates with the structural characteristics of each community and its local context. In the following sections, results are analyzed concerning three distinct differentiation levels [13,38,73]: (a) geographic location, (b) technological infrastructure, and (c) institutional structure and governance models.

(a)

Geographical Differentiation

Energy communities in urban areas or countries with mature institutional and technical ecosystems tend to achieve higher IECPI scores. This is attributed not only to available infrastructure and technological options but also to institutional mechanisms that promote participation, monitoring, digitalization, and compliance with environmental standards (e.g., EMAS, ISO 14001).

In contrast, communities in island or rural regions show less consistent performance. While they often excel in indicators like energy autonomy (Eindep) and local production (Eprod), they lag in social and institutional dimensions due to limited institutional integration, barriers to full citizen participation, or lower digital readiness.

Developing regions and countries form a distinct category. Here, the energy affordability (Eaff) indicator performs well due to significant reductions in energy costs. However, other metrics such as ROI or EMAS are often not yet applicable. In these cases, the IECPI underscores the need for a coordinated and integrated development strategy.

Beyond spatial distribution patterns, regional regulatory environments and socioeconomic conditions play a critical role in shaping energy community performance. For example, communities in Western and Northern Europe—where strong environmental legislation, advanced digital infrastructure, and well-established cooperative frameworks are present—tend to achieve higher scores in EMAS compliance and social participation (Spart). In contrast, communities in developing regions may demonstrate high affordability (Eaff), often due to cost-effective off-grid energy solutions, yet frequently face challenges related to formal compliance mechanisms and participatory governance, largely due to institutional limitations. EC25 (Pakistan) and EC32 (Nigeria) illustrate how localized resilience and affordability can be achieved despite infrastructural limitations, highlighting the adaptive potential of energy communities operating under resource-constrained conditions. In contrast, EC38 (Hersonissos, Greece) exemplifies how alignment with European Union regulatory frameworks can significantly enhance both institutional and environmental performance. These cases underscore that, beyond geographic location, the regulatory maturity and institutional capacity of a region are critical determinants of multidimensional energy community performance.

(b)

Technological Differentiation

Communities that incorporate energy storage technologies, P2P trading, and smart management platforms consistently score higher in Tadopt (Technological Adoption), ECO₂, and ROI. When these technological deployments are paired with strong social engagement and institutional alignment, hybrid high-performance community models often emerge.

Conversely, technologically “monothematic” communities—those relying solely on a single RES technology without integrated smart control or system flexibility—tend to score poorly in Vindex, indicating a higher vulnerability to external disruptions.

It is important to note that technology alone does not guarantee a high IECPI score. Without concurrent efforts in social integration and institutional transparency, even the most technologically advanced communities may remain in the moderate-performance tier.

(c)

Institutional Differentiation and Governance

Governance structures are a decisive factor in differentiation. Communities with cooperative or participatory governance models, where citizens play a significant role in decision-making, achieve notably higher scores in Spart (Social Participation) and overall IECPI values.

Conversely, communities managed through centralized or traditional municipal energy structures often experience reduced social engagement, adversely affecting the cohesion of subjective indicator performance.

Compliance with institutional standards like EMAS and ISO 14001 is also critical. Communities adhering to these frameworks not only exhibit stronger environmental consistency but are more likely to attract funding and implement systematic evaluation processes for their outcomes.

3.1. Correlation Between IECPI Indicators

The conceptual architecture of the IECPI index is predicated not solely on the evaluation of absolute performance across communities and indicators, but also on the in-depth analysis of the systemic interdependencies among the nine constituent variables that define its multidimensional framework. Rather than being treated as simple statistical associations, inter-indicator correlations constitute a foundational analytic mechanism underpinning the empirical weighting scheme ultimately applied in the composite formulation of the IECPI.

A comprehensive bivariate correlation analysis (Pearson’s r) was conducted across all indicator pairs, based on the observed performance data from a sample of 60 energy communities. This process yielded a symmetrical 9 × 9 correlation matrix, wherein each cell quantifies the degree of linear association between two distinct variables. The observed r-values ranged from 0.30 to 1.00, with higher coefficients signifying stronger systemic coupling. These interdependencies were subsequently utilized to calculate the average systemic influence of each indicator, which was then normalized within the two predefined subgroups (objective and subjective) to establish the final weighting coefficients employed in the index’s computation.

3.1.1. ECO₂—Environmental Performance and Structural Interlinkages

The ECO₂ indicator, which captures the degree of CO₂ emissions reduction, exhibits the highest average correlation with all other objective indicators. Specifically:

• ECO₂–Eprod: r = 0.70;
• ECO₂–Eaff: r = 0.60;
• ECO₂–ROI: r = 0.50;
• ECO₂–EMAS: r = 0.40.

This empirical finding substantiates the claim that emission mitigation is not merely the result of isolated technological interventions or capital investments. Instead, it emerges from structurally coherent communities that integrate energy production, cost management, institutional compliance, and technological maturity in a holistic manner.

The communities that scored ECO₂ ≥ 0.90 include EC04 (Crete) [15], EC17 (IPCC) [25,27,107], EC24 (South China Sea Island) [108], EC29 (Japan Net-Zero) [47], EC45 (Corvo Island, Portugal) [76], EC09 (Italy—rural) [49], EC26 (Najran) [57], EC32 (Nigeria) [97], and EC41 (Congo, Australia, and Canada) [106].

These cases are distinguished by either full or near-complete decarbonization of their energy use through the implementation of net-zero frameworks (e.g., EC29), exclusive reliance on renewable energy sources (e.g., EC24), or hybrid configurations that integrate multiple low-carbon strategies (e.g., EC41).

The strong correlations with Eprod and Eaff further reinforce the notion that CO₂ reduction cannot be achieved in isolation; it requires simultaneous investment in decentralized energy production and the enhancement of energy affordability. This systemic interdependence justifies the relatively high weighting assigned to the ECO₂ indicator within the IECPI framework (21.53%).

3.1.2. Eprod—Local Energy Production and the Foundation of Energy Sustainability

The Eprod indicator measures the proportion of a community’s energy needs that are met through renewable energy sources (RES). It displays a consistently strong correlation with several key performance variables, most notably:

• ECO₂ (r = 0.70);
• ROI (r = 0.60);
• Spart (r = 0.60).

These interconnections reveal that local energy production is not merely a technical or infrastructural parameter, but a core pillar of sustainability in energy communities. Particularly notable is its link with Spart, which suggests that increased renewable energy deployment often coincides with higher levels of social participation and public acceptance—factors crucial to long-term community resilience.

The communities achieving top scores in Eprod (≥0.90) include EC24 [108], EC45 [76], EC32 [97], EC41 [106], EC02 [62], EC01 [1], EC04 [15], EC09 [49], EC26 [57], EC25 [53], EC17 [25,27,107], EC29 [47], EC28 [61], EC46 [59], EC53 [103], and EC13 [8].

Their exemplary performance stems from a diverse range of strategies:

• The installation of large-scale photovoltaic systems, as seen in EC26 and EC45;
• The adoption of hybrid PV/wind systems combined with battery storage, as in EC32;
• The establishment of fully self-sufficient, island-based microgrids, such as in EC24 and EC45.

These examples underline that local energy generation is deeply intertwined with institutional trust, economic transformation, and the pursuit of energy sovereignty. As a result, Eprod ranks as the second most heavily weighted indicator within the IECPI framework, accounting for 18.14% of the total index value.

3.1.3. Eaff—Energy Affordability and Social Relevance

The Eaff indicator captures the extent to which energy costs are reduced at the community level, reflecting not only economic efficiency but also deeper socio-economic dynamics. It demonstrates strong correlations with key indicators such as

• Spart (r = 0.70);
• Vindex (r = 0.50);
• ROI (r = 0.50).

These relationships highlight that affordability is not merely a matter of financial savings; it also functions as a lever for social inclusion, participatory engagement, and community empowerment. Energy cost reduction, in this context, becomes a multidimensional outcome that intersects both economic and social domains.

Communities scoring exceptionally high in Eaff (≥0.90) include EC24, EC32, EC25, EC17, EC38, EC40, EC20, EC42, EC45, EC47, EC55, EC57, EC58, EC41, EC04, EC26, EC29, EC53, EC36, EC35, EC27, EC19, EC31, and EC49.

Among these, EC25 (Pakistan) and EC32 (Nigeria) stand out for achieving remarkable levels of affordability in developing contexts, with reported reductions in the cost per kWh reaching up to 80% below baseline levels. These outcomes are particularly significant given the structural energy poverty in their respective regions, and they explain the communities’ placement in the highest IECPI performance category.

Positioned at the intersection between objective and social dimensions, Eaff represents a transitional indicator with high strategic value. Its weighting within the IECPI structure—14.05%—is thus well justified, reflecting both its direct economic importance and its role in promoting equity and systemic resilience in energy communities.

3.1.4. ROI—Economic Performance and Investment Functionality

The ROI (Return on Investment) indicator reflects the annual economic return of each energy community and is closely linked to both technical infrastructure and management efficiency. It demonstrates significant correlations with

• Eprod (r = 0.60);
• ECO₂ (r = 0.50);
• Tadopt (r = 0.60);
• Spart (r = 0.50).

The strong association with Eprod is intuitive, as higher levels of local energy production tend to enhance the overall investment yield. Equally important is the link to Tadopt, indicating that communities with advanced technological systems—such as energy management platforms (EMS), storage capacity, and peer-to-peer (P2P) trading—tend to achieve more favorable economic outcomes.

Communities with ROI values above 0.85 include EC17, EC26, EC24, EC02, EC28, EC15, EC32, EC09, and EC60. These communities typically exhibit

• Low installation costs per kilowatt, as in EC26, which utilizes locally sourced renewable fuels;
• Strong self-generation capacity, as seen in EC32 with hybrid PV/wind systems;
• Policy-driven support mechanisms that enhance operational efficiency, such as in EC26 and EC17.

The ROI indicator thus serves as a transitional metric linking technical effectiveness to systemic sustainability, justifying its weighting of 8.69% within the IECPI as a key component of a community’s economic maturity.

3.1.5. EMAS—Institutional Compliance and Governance Structure

The EMAS indicator captures the extent to which communities adopt institutional frameworks for environmental compliance and monitoring, including mechanisms such as EMAS and ISO 14001. Its strongest correlations are with

• Tadopt (r = 0.40);
• Spart (r = 0.50);
• ECO2 (r = 0.40);
• ROI (r = 0.50);
• Eaff (r = 0.50).

These correlations suggest that communities with a well-established institutional architecture tend to embrace advanced technologies, ensure transparency in governance, and demonstrate strong environmental accountability.

High-performing cases with EMAS scores ≥ 0.85 include EC52, EC07, EC35, and EC38. Many of these communities are characterized by

• Strategic planning and monitoring systems (e.g., EC52, located within the UNIVPM university campus);
• Full alignment with European governance standards (e.g., EC38, a municipal initiative in Hersonissos, Greece);
• Theoretical models of holistic compliance (e.g., EC17, based on IPCC frameworks, scoring 0.80, very close 0.85).

The relatively lower weighting of 6.46% attributed to EMAS reflects its critical, albeit supportive, role in the institutional transformation of energy communities.

3.1.6. Spart—Social Participation and Collective Governance

The Spart indicator assesses the level of citizen involvement in energy planning and decision-making processes. As the most heavily weighted subjective indicator (11.32%), Spart demonstrates strong correlations with

• Eaff (r = 0.70);
• Tadopt (r = 0.60);
• EMAS (r = 0.50);
• Vindex (r = 0.50);
• ROI (r = 0.50).

These patterns highlight the foundational role of social integration in achieving not only equitable energy access but also long-term system resilience. Participation is not an auxiliary feature; it is an embedded function of sustainable community models.

Communities with Spart scores ≥ 0.85 include EC10, EC11, EC17, EC19, EC43, and EC44. These cases represent a range of participatory approaches:

• Collective governance structures, such as energy cooperatives (EC43);
• Bottom-up initiatives built through grassroots mobilization (EC10, EC44);
• Socially focused projects aiming to empower vulnerable populations, as in EC19, supported by the UNDP.

The prominence of Spart within the IECPI framework reinforces the imperative for inclusive, participatory models of governance—not as an optional add-on, but as an essential structural dimension of sustainable energy communities.

3.1.7. Tadopt—Technological Adoption and Digital Maturity

The Tadopt indicator captures the degree to which advanced energy technologies have been adopted within energy communities. It includes systems such as battery storage, smart meters, energy management systems (EMS), peer-to-peer (P2P) trading platforms, and virtual power plant (VPP) mechanisms. As the most heavily weighted subjective indicator (9.87%), Tadopt demonstrates strong correlations with both technical and social metrics:

• ROI (r = 0.60);
• Spart (r = 0.60);
• Eaff (r = 0.60);
• ECO₂ (r = 0.50);
• EMAS (r = 0.40).

The breadth of these correlations reflects the multidimensional influence of technological maturity, which affects not only operational efficiency but also citizen engagement and institutional transparency.

Communities with Tadopt values ≥ 0.85 include EC10, EC17, EC24, EC25, EC29, EC32, EC33, EC44, EC45, EC46, and EC52. These communities implement a range of innovative approaches:

• Integrated production and storage systems (e.g., EC32, EC45);
• Fully operational EMS and P2P platforms (e.g., EC10, EC44);
• Digitally interconnected communities utilizing artificial intelligence for system optimization (e.g., EC52, based at UNIVPM).

Tadopt’s strong correlations with both ROI and EMAS indicate that technological adoption plays not only a functional role but also contributes significantly to the financial and institutional maturity of a community.

3.1.8. Eindep—Energy Autonomy and Functional Self-Sufficiency

The Eindep indicator measures the degree of energy self-sufficiency within a community, whether through off-grid operation or through a high ratio of local production and consumption. Although it carries a lower weighting within the IECPI (4.77%), Eindep shows meaningful correlations that highlight its systemic relevance:

• Vindex (r = 0.40);
• Eprod (r = 0.40);
• Tadopt (r = 0.50);
• Spart (r = 0.40).

These correlations indicate that energy autonomy is not a purely technical outcome—it also contributes to system resilience, energy justice, and community independence.

Communities achieving full energy independence (Eindep = 1.00) include EC24, EC25, EC26, EC32, EC41, and EC45. These represent a spectrum of models:

• Remote, off-grid communities (e.g., EC24, EC41);
• Hybrid systems combining local storage with renewable fuels (e.g., EC25, EC26);
• Net-zero energy simulations or operational systems (e.g., EC26).

The correlation with Vindex confirms that self-sufficiency alone is not an end goal; it serves as the structural basis for resilience in the face of external shocks.

3.1.9. Vindex—Resilience and Systemic Continuity

The Vindex indicator expresses a community’s capacity to maintain functionality under conditions of disruption. It encompasses dimensions of social cohesion, localized planning, governance flexibility, and policy consistency. Key correlations include

• Spart (r = 0.50);
• Eindep (r = 0.40);
• Tadopt (r = 0.40);
• Eaff (r = 0.50).

These interdependencies underscore the multifactorial nature of resilience, which emerges from the convergence of both technological capacity and institutional inclusivity.

Communities with Vindex scores ≥ 0.90 include EC17, EC24, EC25, EC29, EC32, and EC45. These represent exemplary cases of systemic robustness:

• Communities with formal resilience and decentralization plans (e.g., EC45);
• Energy- and management-independent communities (e.g., EC24, EC41);
• Communities integrating social policies with institutional participation (e.g., EC25, EC17).

Notably, the strong correlation between Vindex and Eaff reflects a critical insight: resilience cannot be achieved without energy affordability—and vice versa.

3.1.10. Systemic Interdependencies and Interpretive Insights

The analysis of the interrelations among IECPI indicators clearly demonstrates that the performance of each community cannot be evaluated in isolation but only within a systemic framework of functional interactions. The indicators do not operate as independent variables; rather, they function as interconnected subsystems, where changes in one dimension dynamically influence the performance of others.

Notably, indicators such as ECO₂, Eprod, Eaff, Spart, Tadopt, and Vindex consistently exhibit correlation coefficients above r = 0.5 with two or more other variables, highlighting their systemic relevance. This empirical observation is further validated by their corresponding weightings within the IECPI structure.

The deep interconnection between technology, society, and institutional design is particularly evident:

• Technological adoption (Tadopt) reinforces both economic performance (ROI) and institutional compliance (EMAS).
• Energy affordability (Eaff) is functionally linked to social participation (Spart) and resilience (Vindex).
• Energy independence (Eindep), though technical in nature, directly impacts the sustainability of overall community performance.

This complex network of interdependencies affirms that the IECPI is not a linear or unidimensional metric, but a dynamic evaluation tool grounded in actual data and relational patterns, rather than abstract theoretical constructs.

The process of deriving the empirical weightings was based exclusively on the mean correlation coefficients of each indicator in relation to the others. These values were then normalized within their respective categories—objective and subjective—to reflect their relative importance under the 70–30 structural model.

The empirical robustness of the IECPI is thus reinforced by the alignment between its weighting logic and the real-world characteristics of the dataset.

Moreover, the analysis identified the communities with the highest performance across indicators, offering deeper interpretive value:

• EC17, EC24, EC25, EC29, EC32, and EC45 achieved scores ≥ 0.85 in at least six of the nine indicators, confirming their integrated and high-performing character.
• EC10, EC44, and EC52 stand out in terms of technological adoption.
• EC11, EC19, and EC43 are exemplary in collective governance and participatory engagement.
• EC24, EC25, and EC45 demonstrate outstanding energy resilience and autonomy.

The complete correlation matrix is presented in Appendix B, while Appendix A includes the full community performance table by indicator and its bibliographic reference.

Compared to models like SCORE or the Renewables Readiness Index, the IECPI excels in:

• Institutional compliance assessment: Utilizes EMAS for evaluating adherence to standards.
• Distinction of social participation: Focuses on both inclusion and engagement levels.
• Variable synthesis: Employs rational influence weighting (influence matrix) rather than simple averaging.

However, the IECPI is less effective in terms of application simplicity or in contexts with incomplete data, highlighting the need for supportive estimation tools in low-data environments.

Policy-Relevant Value of the IECPI

The application of the IECPI to 60 empirically documented energy communities extends beyond academic performance recording, providing a valuable policy instrument for local and supranational authorities aiming to design effective support programs for energy communities.

The indicator’s ability to identify performance gaps, classify community types, and highlight strengths and weaknesses makes it suitable for targeted policy interventions. For example:

• Communities with low Spart or Eaff scores may benefit more from social support or subsidized upgrades than technical reinforcement.
• Conversely, communities with high Tadopt but low ROI may require optimization of financial tools or improved business model design.

Thus, the IECPI acts as a dynamic “intelligent classification” indicator, enhancing the strategic targeting of public resources.

Alignment with the SDGs

The IECPI’s structure directly aligns with several United Nations Sustainable Development Goals, particularly SDG 7—Affordable and Clean Energy (

Table 6. Alignment of IECPI indicators with United Nations Sustainable Development Goals.

SDG	Description	Relevant IECPI Indicator(s)
SDG 7.1	Universal access to energy	Eaff
SDG 7.2	Increase the use of renewable energy	Eprod, ECO2
SDG 7.3	Improve energy efficiency	Tadopt, ROI
SDG 13	Combat climate change	ECO2, EMAS
SDG 11	Safe and resilient cities	Vindex

).

This alignment reinforces the IECPI’s policy credibility and serves as an indirect monitoring tool for tracking international targets. Furthermore, the differentiated representation of communities across technical, social, and institutional fields facilitates the design of targeted sustainable development programs.

Strategic Recommendations Based on the IECPI

Three core recommendations emerge from the results:

• Policy adaptability based on community profiles: Deploying the IECPI at national or regional levels could lead to tailored policy typologies for communities that are socially vulnerable, technologically immature, or institutionally weak.
• Systematic evaluation and benchmarking: The IECPI can serve as an evaluation and benchmarking tool for energy communities, enhancing transparency, reliability, and efficiency.
• Strengthening cross-sectoral synergies: Correlations between indicators (e.g., Eaff ↔ Spart, Eprod ↔ Eindep) highlight the need for integrated policies that combine technical, social, and institutional dimensions.

The IECPI score distribution transcends mere ranking; it serves as a diagnostic, strategic, and normative tool, offering a comprehensive view of current energy community standings and future directions. Its strength lies in synthesizing the complexities of the clean energy transition, providing a robust foundation for evaluating, supporting, and advancing communities across all maturity stages.

To enhance interpretability and support cross-dimensional analysis, Figure 3 presents the average normalized scores for each of the nine IECPI indicators across the sample of 60 energy communities. A color-coded background distinguishes three performance tiers: low (0.00–0.60), moderate (0.60–0.75), and high (0.75–1.00). The dashed red line indicates the overall average IECPI score of 0.69. This visualization contextualizes individual indicator performance relative to the aggregate community profile, highlighting which dimensions consistently exhibit systemic strengths or weaknesses.

3.2. Performance of Individual Indicators

The disaggregated analysis of the nine indicators (

Table 7. General distribution of IECPI indicators across the sample of communities.

Indicator	Mean Value	Range	Communities ≥ 0.85
ECO2	0.74	0.30–1.00	24
Eprod	0.65	0.00–1.00	20
Eaff	0.80	0.40–1.00	34
ROI	0.57	0.00–1.00	9
EMAS	0.57	0.00–0.85	4
Spart	0.64	0.20–1.00	19
Tadopt	0.72	0.00–1.00	29
Eindep	0.67	0.20–1.00	15
Vindex	0.73	0.20–1.00	29

) comprising the IECPI highlights the distribution of performance across thematic dimensions and enables the identification of patterns of excellence and systemic underperformance. In contrast to the final IECPI value, which results from the weighted aggregation of all indicators, the individual assessment of each variable offers targeted insights into priority areas and performance gaps.

To offer a clearer visual synthesis of both overall and peak performance, Figure 4 presents a horizontal bar chart that combines the average score of each indicator with the corresponding number of high-performing communities (≥0.85) per indicator.

Each bar in the chart is annotated with the exact mean score and the number of communities achieving a score of ≥0.85 for that specific indicator. The visualization highlights that indicators such as Energy Affordability (Eaff), Technological Adoption (Tadopt), and the Vulnerability Index (Vindex) demonstrate not only strong average performance but also a broad distribution of high scores across the sample. In contrast, indicators like EMAS compliance and Return on Investment (ROI) exhibit both lower mean values and fewer top-performing communities, reflecting systemic challenges in the institutional and financial dimensions.

The distribution of scores across individual IECPI indicators reveals a pronounced asymmetry in the development of energy communities with respect to the multidimensional aspects of sustainability. High average values in Energy Affordability (Eaff = 0.80), Technology Adoption (Tadopt = 0.72), and CO₂ Emissions Reduction (ECO₂ = 0.74), alongside a substantial number of communities scoring ≥ 0.85, indicate consolidated progress in the technical and environmental dimensions of the transition. In contrast, the notably lower scores in Environmental Management Alignment (EMAS = 0.57), Social Participation (Spart = 0.64), and Return on Investment (ROI = 0.57) point to persistent institutional, social, and financial deficiencies.

The underperformance in ROI, in particular, raises critical concerns regarding the financial resilience of community energy projects and their ability to attract, manage, and reinvest capital in a sustainable manner. Economic viability is not merely a technical metric but a key determinant of the long-term survival and operational stability of communities in dynamic and often competitive energy markets.

At the same time, lagging performance in institutional and social indicators such as EMAS and Spart undermines the ability of energy communities to function as democratic, participatory, and governance-anchored entities, beyond their technological infrastructures.

Within this complex landscape, the development of the Integrated Energy Community Performance Index (IECPI) emerges as essential. By combining nine critical dimensions into a weighted and multidimensional assessment tool, the IECPI goes beyond single-issue evaluation to reveal both strengths and latent vulnerabilities that constrain systemic maturity. It enables a holistic reflection on the technological, social, financial, and institutional foundations of each community, offering direct value for strategic planning, policy design, sustainable investment, and the empowerment of citizens as active co-creators of the energy transition.

3.2.1. Analysis of Indicators: Tadopt—Eprod—ECO₂

The simultaneous analysis of Tadopt (Technological Adoption), Eprod (Renewable Energy Production), and ECO₂ (CO₂ Emissions Reduction) is not arbitrary; these three indicators collectively represent the core technical and environmental performance domain of energy communities. Their inclusion as a thematic cluster serves three interpretive purposes:

• Operational Chain of Energy Functionality

These indicators reflect sequential and complementary aspects of community-level energy systems:

• Tadopt captures the level of smart technologies and grid integration (e.g., storage, P2P systems).
• Eprod quantifies the share of locally generated renewable energy.
• ECO₂ reflects the actual environmental impact through carbon emissions mitigation.

Together, they form a coherent causal chain:

Technological integration → Renewable production → Emissions reduction.

2.

Strong Internal Correlation

The indicators exhibit statistically significant correlations (e.g., Eprod–ECO₂: r = 0.70), reinforcing their interdependence. Communities that score high in Tadopt tend to perform well in Eprod and ECO₂ as well, suggesting that technical readiness and renewable deployment directly enhance environmental outcomes.

3.

Proxy for Environmental-Technical Maturity

As a group, these indicators can be interpreted as a composite expression of environmental-technical maturity. They are often the first dimensions to be developed and scaled in emerging energy communities, offering measurable short-term results and policy alignment with decarbonization goals.

Nevertheless, the analysis also highlights that technical and environmental performance does not guarantee systemic balance. Many communities excelling in Tadopt, Eprod, and ECO₂ exhibit weaknesses in economic (e.g., ROI) or social/institutional (e.g., Spart, EMAS) dimensions. This confirms the necessity of adopting a multidimensional index such as the IECPI, which captures both strengths and structural gaps, offering a more accurate and comprehensive evaluation of energy community resilience and sustainability.

Tadopt (Technological Adoption) reflects a widespread integration of advanced technologies across a significant portion of the energy communities. This includes the deployment of battery energy storage systems (BESS), smart meters, virtual power plants (VPPs), and peer-to-peer (P2P) energy exchange platforms. According to the data, 29 out of 60 communities (nearly 40%) achieved Tadopt scores ≥ 0.85, indicating strong digital and infrastructural preparedness.

Representative cases include

• EC10, emphasizing behavioral flexibility and user engagement in consumption patterns;
• EC24 and EC45, implementing fully integrated storage and control systems in insular contexts;
• EC32, featuring hybrid PV/Wind installations with intelligent energy management systems (EMS);
• EC52, a university campus deploying predictive algorithms and simulation tools for system optimization.

In parallel, the Eprod (Renewable Energy Production) indicator records a moderate average of 0.65, with 20 communities surpassing the 0.85 threshold. High-performing communities in this category tend to operate local RES units with high self-sufficiency ratios. Nonetheless, the lower average compared to Tadopt suggests that technological infrastructure is not always coupled with robust renewable production capacity—highlighting a gap between technological readiness and energy autonomy.

Several communities such as

• EC24, EC25, EC26, EC32, and EC45 achieve 100% renewable coverage through solar PV, wind, and biomass systems;
• EC09, EC33, and EC43 report renewable penetration levels above 90% within interconnected grid systems.

What is particularly noteworthy is the presence of high Eprod values across communities with diverse geographical and institutional profiles—including islands, rural zones, and university campuses. This widespread applicability confirms that high renewable energy performance is feasible across contexts, provided that adequate spatial potential and infrastructure are in place.

Eprod thus represents not only a technical metric but also a key enabler of environmental transition, and its strong performance across the dataset reinforces its role as a foundational pillar within the IECPI framework.

Finally, the ECO₂ (CO₂ Emissions Reduction) indicator reaches an average of 0.74, placing it among the top-performing dimensions. With 24 communities scoring ≥ 0.85, this reflects the environmental effectiveness of many local systems. Notably, a strong correlation between Eprod and ECO₂ (r = 0.70) confirms that renewable deployment directly contributes to decarbonization outcomes.

Communities such as

• EC04, EC17, EC24, EC29, and EC45 report maximum ECO₂ scores of 1.00, corresponding to net-zero emissions or full renewable offsets;
• EC09, EC26, and EC32, EC41 demonstrate consistently high values (≥0.95), achieved through a combination of advanced energy management, local generation, and low-emission consumption profiles.

The strong performance of ECO₂ across both technologically mature and geographically constrained contexts illustrates that carbon neutrality is not limited to specific regions or system types. Rather, it is attainable through coordinated efforts in renewable deployment, efficient design, and consumption-side management.

Moreover, the statistically significant correlation between ECO₂ and Eprod (r = 0.70) confirms that renewable production is a primary driver of emissions reduction, though not the sole factor. Communities with similar levels of renewable penetration often exhibit variation in ECO₂ performance, suggesting that operational efficiency, behavioral factors, and system integration also play critical roles.

Ultimately, ECO₂ functions as a composite outcome indicator, capturing the environmental return of technical and organizational investment. Its inclusion in the IECPI ensures that environmental impact is assessed not in isolation but as a product of broader systemic maturity.

Taken together, these three indicators shape a technically and environmentally robust profile for many energy communities. However, high scores in Tadopt and ECO₂ do not always align with broader systemic balance, as technological and environmental excellence may coexist with economic or social underperformance. The IECPI captures these internal asymmetries, providing a multidimensional lens for assessing true community resilience and integrated performance.

3.2.2. Analysis of Social Indicators: Eaff—Spart—Vindex

The joint analysis of Eaff (Energy Affordability), Spart (Social Participation), and Vindex (Vulnerability Reduction) is grounded in their conceptual and operational coherence as the three core dimensions of the IECPI’s social pillar. Their classification as a functional group is supported by three interpretive principles: sequential logic of social integration, statistical interdependence, and their role as proxies for the social maturity of energy communities.

The three indicators represent complementary stages of a cohesive social performance trajectory: Affordability → Participation → Resilience.

Eaff measures access to reliable and economically sustainable energy services.

Spart captures the degree of citizen involvement in community governance mechanisms.

Vindex reflects the outcome of protective strategies that reduce household-level vulnerability.

Their interconnected roles affirm the need for integrated assessment of the social dimension beyond isolated outcomes.

Statistical correlations reinforce the grouping of these indicators. Pearson correlation values show a strong relationship between Eaff and Spart (r = 0.70), and moderate correlations between Spart and Vindex (r = 0.50) and Eaff and Vindex (r = 0.50). These values confirm functional linkages across access, engagement, and protection, while also indicating partial independence that justifies disaggregated observation.

Across the sample of 60 communities, the indicators display differentiated performance. Eaff records the highest average among social indicators (0.80), with 34 communities scoring ≥ 0.85, confirming the widespread implementation of affordability mechanisms. In contrast, Spart shows a lower mean score (0.64) and just 19 high-performing communities, revealing limited diffusion of participatory governance structures. Vindex reaches a mean score of 0.73, with 29 communities achieving ≥ 0.85, indicating that vulnerability reduction is more consistently pursued than participatory inclusion.

Community EC25 demonstrates exemplary performance across all three social indicators, reflecting a comprehensive model of socially sensitive energy governance. Communities such as EC32, EC19, and EC43 exhibit strong social profiles with varying levels of participation. In contrast, EC05 and EC42 show that high affordability does not automatically translate into reduced vulnerability, underscoring the need for multidimensional intervention frameworks.

The social dimension of IECPI reveals substantial heterogeneity and internal asymmetry. Although energy affordability is widely achieved, participatory structures and targeted protection mechanisms remain limited in scope and impact. The integration of Eaff, Spart, and Vindex into the IECPI framework enables the identification of these structural imbalances, positioning social performance as a critical yet often underrepresented axis in community energy transition policy and assessment.

3.2.3. Analysis of Technological Indicators: Tadopt—Eindep

The technological dimension of the IECPI is represented by two core indicators: Tadopt (Technological Adoption) and Eindep (Energy Independence). These indicators collectively assess the extent to which energy communities implement advanced energy technologies and achieve functional autonomy in their operations. While conceptually aligned, the two indicators exhibit distinct performance profiles and capture different aspects of technological maturity.

Tadopt evaluates the degree of digital and infrastructural integration within community energy systems, including technologies such as battery storage (BESS), smart metering, energy management systems (EMSs), peer-to-peer (P2P) trading, and virtual power plants (VPPs). Eindep, in contrast, measures the proportion of energy demand met autonomously, without reliance on external grid supply. Together, these indicators delineate the difference between technological readiness and operational independence.

Although the two indicators are moderately correlated (r = 0.5), their joint analysis reveals a structural divergence. Tadopt presents a relatively high average value (0.72) and broad adoption, with 29 communities scoring ≥ 0.85. This indicates that technological solutions are widely accessible and have been integrated across multiple use cases and geographies. Eindep, however, shows a lower average (0.67) and only 15 high-performing communities, suggesting that energy autonomy remains a more complex and less generalized achievement.

This divergence points to a technology–functionality gap: while advanced technologies are implemented, their full utilization toward energy sovereignty is not guaranteed. Factors such as infrastructural constraints, regulatory dependence, and scale limitations may inhibit the realization of autonomy despite high adoption.

EC24, EC32, and EC45 represent cases with maximum or near-maximum Tadopt and Eindep scores, demonstrating holistic technological integration within insular or microgrid-based systems. EC10 and EC52 exhibit high Tadopt values but moderate energy independence, reflecting behavioral and simulation-based optimization without full off-grid capability. EC41 and EC34 present high Eindep values despite moderate Tadopt scores, indicating functionally autonomous systems possibly reliant on simpler technological frameworks. These cases exemplify the multiple pathways through which technological strategies can shape energy community performance while also highlighting the variability in effectiveness depending on system design and context.

The analysis of Tadopt and Eindep underscores the necessity of distinguishing between technology deployment and energy sovereignty. While high Tadopt values signal strong readiness, true energy resilience is achieved only when supported by operational independence. The inclusion of both indicators within the IECPI framework ensures that the technological dimension is assessed not merely by the presence of infrastructure, but by its capacity to deliver autonomous and sustainable energy services.

3.2.4. Analysis of Economic and Institutional Indicators: ROI—EMAS

The economic and institutional dimensions of the IECPI are represented by two single-indicator constructs: ROI (Return on Investment) and EMAS (Environmental Management Alignment Score). While their inclusion introduces interpretive asymmetry relative to multi-indicator categories, their presence is essential for capturing long-term sustainability, financial viability, and regulatory maturity within energy communities.

Both ROI and EMAS are standalone indicators within their respective categories, which introduces structural sensitivity in their interpretive function. In contrast to composite categories such as “Social”, “Technological”, and “Environmental”, where inter-indicator balance is possible, performance in the economic and institutional domains is entirely dependent on these singular variables (

Table 8. Classification of IECPI indicators by functional category.

Functional Category	Indicator Code	Indicator Description
Environmental	ECO2	CO2 Emissions Reduction
Environmental	Eprod	Renewable Energy Production
Social	Eaff	Energy Affordability
Social	Spart	Social Participation
Social	Vindex	Vulnerability Reduction
Technological	Tadopt	Technological Adoption
Technological	Eindep	Energy Independence
Economic/Institutional	ROI	Return on Investment
Economic/Institutional	EMAS	Environmental Management Alignment

). This amplifies the impact of local implementation conditions and results in increased statistical dispersion.

The ROI indicator assesses the economic viability of energy community projects based on their return relative to investment. With an average score of 0.57 and only nine communities scoring ≥ 0.85, the indicator reveals substantial financial underperformance across the dataset. While technological advancement is often assumed to correlate with economic gain, this relationship proves inconsistent. ROI is moderately correlated with Tadopt (r = 0.6) and Eprod (r = 0.6), suggesting that financial returns are facilitated by technological integration and renewable energy deployment. However, capital costs, regulatory frameworks, and community ownership models vary widely, frequently offsetting potential benefits.

Representative high-performing cases include

• EC15, EC26, EC28, and EC45, which combine robust energy production and advanced technological integration with local revenue schemes or cost-avoidance mechanisms.

The EMAS indicator evaluates formal alignment with environmental management standards, such as ISO 14001, and broader institutional compliance mechanisms. It presents a mean score of 0.57, with only four communities achieving values ≥ 0.85. This limited prevalence highlights the absence of formalized governance structures or external accountability mechanisms in most energy communities.

Nevertheless, EMAS correlates positively with Spart (r = 0.5), indicating that social engagement may contribute to more transparent or institutionally robust frameworks. In practice, however, formal compliance appears to lag behind operational and participatory achievements.

Notable cases such as

• EC07, EC14, EC38, and EC50 reflect contexts with higher institutional anchoring, such as municipal ownership or academic management models.

Although these indicators display lower average performance and narrower distribution than other IECPI components, their interpretive weight is substantial. ROI reflects the ability of communities to scale and replicate operations under economic constraints, while EMAS serves as a proxy for long-term institutional accountability and integration into formal governance structures.

The IECPI’s inclusion of these two indicators ensures that energy community assessment encompasses not only technological and environmental progress, but also the foundational conditions necessary for durability, legitimacy, and policy alignment. Their current underperformance across the sample suggests areas for focused intervention in future community design, funding schemes, and regulatory frameworks.

Figure 5 presents the classification of the nine IECPI indicators into four distinct thematic categories: Environmental, Social, Technological, and Economic/Institutional. Each category includes the relevant indicators and a brief rationale for their inclusion. This structured typology underpins the multidimensional nature of the IECPI and clarifies the analytical role of each indicator within the overall framework.

3.3. Community Typologies Based on Performance Profiles

The classification of energy communities into functional typologies (

Table 9. Typology of energy communities based on IECPI profiles.

Typology	Key Strengths	Representative Cases	Common Weaknesses
Technologically Driven	High Tadopt/Eindep, often paired with ECO2 & Eprod	EC10, EC24, EC32, EC45, EC52	Low ROI or EMAS
Environmentally Oriented	High ECO2 & Eprod; strong renewable penetration	EC04, EC09, EC17, EC29	Low Spart or limited governance
Socially Embedded	High Eaff, Spart, and/or Vindex; socially inclusive models	EC25, EC19, EC40, EC43	Low Tadopt or EMAS
Balanced Performance	Consistent performance across all categories	EC07, EC33, EC38	No dominant advantage
Structurally Fragile	Multiple indicators ≤ 0.40; early-stage or vulnerable	EC05, EC16, EC49, EC55	Low autonomy, ROI, and participation

) based on their IECPI performance profiles provides an essential tool for interpreting results and extracting policy-relevant insights. The primary criterion for grouping is the thematic category in which each community demonstrates its strongest relative performance (i.e., Environmental, Social, Technological, or Economic/Institutional), complemented by the identification of critical weaknesses (e.g., scores ≤ 0.40 in key indicators). This dual analytical approach enables the identification of both comparative strengths and structural deficiencies.

The first cluster comprises technologically driven communities, characterized by strong performance in Tadopt and/or Eindep, often reflecting the widespread adoption of smart infrastructure such as energy storage, digital energy management systems, and peer-to-peer trading schemes. These cases—such as EC10, EC24, and EC45—frequently also perform well in environmental indicators (ECO₂, Eprod), although this is not always matched by equivalent institutional or financial performance (e.g., lower scores in ROI or EMAS).

A second group includes environmentally oriented communities, where emphasis is placed on renewable energy production (Eprod) and emissions reduction (ECO₂). While these communities—e.g., EC04, EC09, and EC17—achieve high renewable penetration and measurable decarbonization outcomes, they often display lower performance in participatory or technological dimensions, as reflected in low Spart or Eindep scores. These projects typically feature a strong technical or environmental design, but limited integration of social processes.

The third typology is composed of socially embedded communities, marked by high scores in Eaff, Spart, and/or Vindex. These cases prioritize accessibility, equity, and protection of vulnerable groups, often in underserved or low-income settings. Communities such as EC25, EC19, and EC43 exemplify strong social cohesion, though they may exhibit moderate to low technological or institutional maturity, with limited adoption of digital systems or absence of formal environmental certification (EMAS).

In contrast, a small set of communities presents balanced performance across all categories, without any single dominant indicator. Examples such as EC07, EC33, and EC38 show consistent scores in ROI and EMAS, alongside functional competence in social and technological domains. While this balance is desirable, it does not necessarily imply strategic advantage, as it may also reflect the absence of a distinctive comparative strength.

Finally, a category of structurally fragile communities emerges, with multiple indicators falling below 0.40—most commonly in ROI, Spart, and EMAS. These cases, including EC05, EC16, and EC49, often represent early-stage or institutionally weak initiatives, characterized by limited autonomy, low participation, and insufficient financial sustainability. These communities highlight the need for targeted support, both in terms of funding and governance capacity-building.

The purpose of this typological classification is not to rank or evaluate communities but to facilitate strategic policy design and intervention. By linking patterns of performance and underperformance, the IECPI typologies support the formulation of tailored policy instruments that reflect the specific profiles of energy communities. In this sense, the IECPI serves not only as a measurement framework but also as a strategic orientation tool for the development of resilient, equitable, and integrated energy communities.

3.4. Empirical Illustrations from Selected Case Studies

To enhance the interpretability of IECPI results and illustrate the operational diversity of energy communities (ECs), we present brief case studies of three representative communities from the sample of 60. These cases vary in geographic location, governance structure, and socio-technical context, demonstrating the index’s ability to capture multidimensional performance differences across diverse settings.

• EC26 (Saudi Arabia—urban energy cluster): This community scores exceptionally high in return on investment (ROI = 1) and CO₂ reduction (ECO₂ = 0.95), attributable to strong financial incentives, advanced net-metering policies, and local reinvestment frameworks. However, its social participation score (Spart = 0.2) suggests that benefits were delivered with limited civic engagement.
• EC25 (Pakistan—refugee settlement microgrid): Despite constrained infrastructure, EC25 demonstrates remarkable performance in affordability (Eaff = 1) and resilience (Vindex = 0.95), enabled by modular solar-based microgrids and inclusive, community-led management. This underscores the potential for resilience to emerge even within low-income or transitional contexts.
• EC45 (Portugal—insular energy autonomy): Situated on an island, this community exhibits full energy independence (Eprod = 1.00; Eindep = 1.00) and high systemic stability (Vindex = 0.95). The case highlights the relevance of geographical isolation as a driver for localized, self-sufficient energy strategies.

These examples highlight the IECPI’s capacity to distinguish among diverse energy community models and facilitate comparative analyses rooted in empirical variation. They also illustrate how context-specific design choices influence multidimensional performance outcomes.

4. Discussion

The development and application of the Integrated Energy Community Performance Index (IECPI) across 60 empirically documented energy communities enabled a systematic, multidimensional evaluation of performance under a unified framework. The analysis revealed substantial variation not only across geographic and institutional contexts but also within communities themselves, underscoring the need for a synthetic and comparative assessment tool. The IECPI addresses this gap by combining normalized performance metrics with structural interdependencies, yielding an evaluative model that is both functionally robust and adaptable to diverse community profiles.

4.1. Systemic Integration and Indicator Interlinkages

A core innovation of the IECPI lies in its systemic architecture. Rather than treating indicators as isolated metrics, the index evaluates them as components of a mutually influencing network across four functional domains: environmental, technological, social, and economic/institutional. This interconnectivity is formalized through a correlation matrix derived from community-level data and incorporated into the final score through empirically grounded weighting.

Key relationships highlight the multidimensional logic of the index. The strong correlation between Eprod and ECO₂ (r = 0.70) confirms that renewable energy deployment is a primary driver of decarbonization outcomes. Similarly, the link between Tadopt and ROI (r = 0.60) reflects the economic impact of technological adoption. Social indicators such as Spart and Eaff also exhibit high co-dependence, reinforcing that participation and affordability are intertwined conditions of social resilience. These interdependencies validate the IECPI’s integrated structure, allowing it to reveal systemic asymmetries that would remain obscured in unidimensional frameworks.

4.2. Use of Empirical Data for Indicator Weighting

A major strength of the IECPI is its empirical grounding. Rather than assigning arbitrary or a priori weights, the index derives its relative importance coefficients from actual performance patterns across the 60 case studies. This hybrid methodology—merging quantitative normalization with correlation-informed weighting—ensures both objectivity and contextual relevance.

By rooting its scoring logic in real-world data, the IECPI enhances its validity, replicability, and policy utility. Moreover, this method allows for adaptability across regions while maintaining comparability, enabling the IECPI to function as both a measurement and planning instrument.

4.3. Validation Through Community Typologies

The typological classification of energy communities provides additional validation for the IECPI. Communities are not only scored but also grouped into archetypes based on dominant performance dimensions—namely, Technological, Environmental, Social, Balanced, and Structurally Fragile. These types reflect functional orientations and common deficits, offering strategic value for both academic analysis and targeted policy design.

For instance, communities with high Tadopt and Eindep often lag in institutional integration (EMAS), while socially strong communities with high Spart and Vindex frequently exhibit limited technological advancement. These observations confirm that sectoral excellence in one dimension does not ensure systemic maturity. The IECPI captures these tensions and enables diagnostic profiling that goes beyond aggregate scoring.

4.4. Comparative Grounding in the International Literature

The IECPI aligns with international frameworks and best practices. It incorporates key elements of SDG 7 (Affordable and Clean Energy), accommodates compliance with EMAS and ISO 14001, and reflects priority areas identified in the scholarly literature—such as return on investment (ROI), vulnerability reduction (Vindex), and participatory governance (Spart). This alignment allows the index to serve not only as a community-level tool but also as a bridge to broader regional, national, and supranational policy goals.

The multidimensional composition of the IECPI makes it compatible with energy justice principles and sustainable transition indicators, ensuring its applicability across varied policy environments and academic domains.

Beyond providing a theoretical framework for performance assessment, the IECPI serves as a practical decision-support tool for public and institutional stakeholders. For example, municipalities can leverage IECPI scores to pinpoint priority areas—such as low social participation (Spart) or return on investment (ROI)—to guide capacity building, participatory planning, or strategic investment. Regional development agencies may utilize the index to benchmark energy communities across districts, targeting technical assistance toward those exhibiting structural weaknesses in specific indicators. At the national level, policymakers could integrate IECPI-based thresholds into energy transition funding criteria, incentivizing communities that demonstrate balanced, multidimensional performance. Furthermore, international donors and sustainability funds can employ IECPI data to inform program evaluation, particularly when assessing initiatives claiming socio-environmental impact but lacking comprehensive assessment frameworks.

A comparative analysis of the IECPI alongside existing evaluation frameworks reveals both overlapping elements and key methodological and applicative distinctions. For instance, the SCORE Index, developed under the Horizon 2020 program, emphasizes social acceptance, energy sharing, and citizen empowerment within renewable energy communities. However, its indicators are predominantly qualitative and do not incorporate environmental or investment-related criteria. Similarly, the Renewables Readiness Index (RRI) by IRENA evaluates regulatory, technical, and institutional conditions supporting renewable energy deployment at the national level but does not address community-level performance or localized micro-scale dynamics.

Global monitoring frameworks associated with the Sustainable Development Goals—such as SDG 7—provide valuable aggregate metrics on clean energy access and infrastructure development. However, they lack the granularity necessary to capture the structural, cultural, and participatory dimensions inherent in localized energy initiatives. Similarly, the Energy Transition Index (ETI), developed by the World Economic Forum, offers dynamic benchmarking of national energy transitions but primarily focuses on macroeconomic and institutional readiness, without being tailored to the evaluation of bottom-up community-level processes.

In contrast, the IECPI offers a multidimensional, community-sensitive framework that integrates normalized objective indicators—such as CO₂ reduction, renewable energy production, and return on investment—with context-aware subjective variables, including social participation, resilience, and energy independence. This comprehensive approach facilitates robust cross-community comparability while capturing the institutional and geographic complexities intrinsic to decentralized energy systems.

Although the current iteration of the IECPI is static and does not incorporate time-series tracking, it is built on a scalable architecture that could be integrated with green finance instruments—such as ESG frameworks and climate bonds—and embedded within governance platforms like local energy transition observatories or participatory policy dashboards. These potential extensions position the IECPI not merely as a benchmarking tool, but as a diagnostic and policy-relevant model capable of evaluating energy communities amidst real-world transition dynamics.

Beyond its analytical and diagnostic functions, the IECPI has the potential to serve as a foundational model for quality certification schemes within the energy community sector. Analogous to how ISO 9001 establishes internationally recognized standards for Quality Management Systems (QMS), the IECPI could underpin the development of standardized evaluation frameworks and accountability mechanisms specifically designed for decentralized energy initiatives. Its composite structure—rooted in empirical data, systemic interdependencies, and multidimensional benchmarking—ensures both transparency and repeatability, which are essential principles in institutional certification. In this capacity, the IECPI could evolve from a purely evaluative tool to a strategic guidepost for policy-driven standardization efforts at national and international levels.

4.5. Limitations and Future Directions

Despite its comprehensiveness, the IECPI presents several limitations. Its normalization process relies on available performance data, which may introduce bias in under-documented or newly formed communities. Cultural and historical factors—critical to the institutional behavior of communities—are not explicitly captured within the current version of the index.

Future improvements may include

• Longitudinal tracking of community performance via time-series data;
• Integration with innovation and green finance instruments;
• Cross-application in diverse geopolitical regions to test universality versus contextual specificity.

Such expansions will reinforce the index’s capacity to support energy policy that is both evidence-based and adaptable to complex realities.

While the dataset comprises 60 empirically documented energy communities, certain limitations persist regarding representativeness and external validity. The majority of case studies originate from European contexts, with fewer examples drawn from regions characterized by less mature institutional frameworks or differing energy infrastructures, such as Latin America, sub-Saharan Africa, and Southeast Asia. Expanding the dataset to encompass a wider range of geographies, climatic conditions, and policy environments may uncover distinct performance dynamics and reveal new systemic interdependencies among indicators. For example, in communities dependent on biomass or hybrid grid systems, the relative importance of environmental indicators may differ from that in predominantly photovoltaic (PV) networks. Similarly, variations in regulatory regimes could affect the relevance of social participation (Spart) or EMAS compliance indicators. These considerations suggest that, although the IECPI is currently robust, it would benefit from future recalibration and contextual adaptation informed by more globally diverse samples.

4.6. Final Remarks

The IECPI offers a novel, empirically grounded, and policy-relevant approach to assessing the sustainability of energy communities. By capturing technical, environmental, social, and institutional dynamics under a unified structure, it overcomes the limitations of narrow or sectoral evaluations. Its application to 60 case studies confirms its analytical depth, strategic utility, and potential to guide both research and intervention. In an era of energy transition that demands multidimensional governance, the IECPI stands as a robust tool for measuring—and enabling—community-based sustainability.

In addition to the limitations previously discussed, three further challenges warrant consideration. First, data availability and reliability vary substantially, particularly for subjective indicators such as social participation (Spart) and EMAS compliance, which often depend on secondary sources or estimative judgments. Second, cultural and institutional heterogeneity among energy communities complicates indicator comparability; for instance, the concept of “participation” may differ in meaning and formality across local contexts. Third, scaling the IECPI for application in national programs or fragmented ecosystems may necessitate regional recalibration or disaggregation. These challenges underscore the importance of adaptable index configurations and highlight the need for further research into context-sensitive weighting and evaluation methodologies.

Looking ahead, the IECPI framework presents several promising avenues for both methodological refinement and practical application. First, a longitudinal iteration of the index could be developed by systematically tracking indicator scores over time—using rolling three- or five-year windows—to identify sustainability trends and shifts in community performance trajectories. Second, the IECPI could be integrated into green finance mechanisms by defining performance thresholds that determine eligibility for preferential funding or investment-grade certifications, particularly within multilateral development initiatives. Third, an interactive, modular dashboard version of the IECPI could be designed for policymakers and local governments, providing real-time insights into energy community performance and highlighting areas that require targeted policy intervention. Together, these enhancements would extend the IECPI’s utility beyond academic benchmarking, positioning it as a dynamic tool for ongoing governance of the energy transition.

5. Conclusions

This article presents the IECPI, an innovative framework for evaluating the multidimensional performance of energy communities. Drawing on a dataset of 60 empirically documented cases, the IECPI synthesizes technical, environmental, social, economic, and institutional dimensions into a composite index. By normalizing individual indicators and applying empirically derived weights based on inter-indicator correlations, the index fills a critical gap in existing evaluation tools, enabling the identification of internal asymmetries and systemic interdependencies within energy communities.

The analysis revealed that 25 out of the 60 communities (approximately 42%) demonstrated strong performance in specific areas, such as technological innovation or energy affordability. However, only a small fraction exhibited balanced and systemic maturity across all dimensions. The resulting typological classification identified recurring patterns of strengths and weaknesses, emphasizing the need for context-sensitive interventions. Multiple visualizations—including bar charts, radar plots, and heatmaps—are incorporated to enhance the presentation of key results and improve the interpretability and practical utility of the findings.

The IECPI functions not only as a descriptive metric but also as a strategic tool for comparative analysis and diagnostic assessment. By employing an influence matrix, it maps the interactions among indicators, thereby providing not only a basis for ranking but also the identification of structural weaknesses and key leverage points for targeted improvement.

The IECPI framework is both replicable and adaptable. Its replicability is ensured through a clearly defined and transparent methodology encompassing normalization, weighting, and synthesis, consistently applied across all cases. Adaptability is demonstrated by its effective application across diverse geographic, socioeconomic, and institutional contexts. Moreover, the integration of indicators addressing resilience (e.g., Vindex and ROI) alongside sustainability (e.g., ECO₂ and EMAS) facilitates a comprehensive assessment of community conditions. The typological mapping further supports temporal and cross-national comparisons, thereby enhancing the index’s practical utility for both policy formulation and academic research.

As such, the IECPI provides a well-documented, repeatable, and flexible tool to advance research and inform policy design aimed at fostering inclusive and decentralized energy governance.