The quest for sustainable development is increasingly recognized as a critical global challenge, necessitating integrated, multidimensional approaches to assessment and policymaking. As traditional economic indicators often fail to encapsulate the complex interplay between economic growth, social equity, and environmental protection, the development of composite indicators has emerged as a compelling solution. Composite indicators aggregate multiple dimensions of performance into a single metric, thus facilitating more informed comparisons across different entities be they countries, regions, or organizations [1]–[3]. However, their construction remains fraught with methodological challenges, including selection bias, weighting issues, and aggregation methodologies, which can significantly affect the interpretability and robustness of the indicators [4], [5].

Data Envelopment Analysis (DEA) provides a robust framework for constructing composite indicators, enabling researchers and policymakers to measure the relative efficiency of Decision-Making Units (DMUs) in converting multiple inputs into multiple outputs [6]. As a non-parametric method of frontier analysis, DEA accounts for the possibility that DMUs may operate under different conditions, thus offering a more nuanced evaluation of performance compared to traditional parametric approaches [7]–[9]. The adaptability of DEA to handle various types of input and output variables has led to its application across diverse sectors, including healthcare, education, and environmental management [10]–[12]. Moreover, the robustness of DEA has been enhanced through the incorporation of sensitivity analyses, which allow practitioners to evaluate the stability of efficiency scores against changes in data inputs [13].

Despite its strengths, DEA does not inherently address the subjective nature of indicator weighting, which is critical in the context of sustainable development, where priorities may differ among stakeholders [14]. This is where Multi-Criteria Decision Analysis (MCDA) plays a complementary role. MCDA encompasses a variety of decision-making frameworks designed to evaluate multiple conflicting criteria in a systematic manner [15]– [18]. Techniques such as the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) provide structured methodologies for prioritizing criteria and integrating stakeholder preferences into the decision-making process [19]. The combination of DEA and MCDA offers a pathway towards more transparent, participatory, and robust assessments of sustainability performance by allowing for quantitative efficiency analysis alongside qualitative stakeholder input.

Recent studies have begun to illuminate the potential of integrating DEA and MCDA methodologies to enhance the construction of composite indicators. For instance, Shao et al. [20] developed a hybrid model that combines DEA for efficiency measurement with AHP for weighting selection, resulting in a composite indicator framework applicable to urban sustainability assessments. Similarly, Arabi et al. [21] demonstrated the efficacy of employing DEA alongside TOPSIS for evaluating the sustainable performance of renewable energy projects, highlighting the versatility of an integrated approach. Such advancements underscore the necessity for a shift towards methodological pluralism in sustainability assessments, whereby the strengths of various analytical frameworks are harnessed to create more reliable and actionable indicators.

Despite the promising developments in integrating DEA and MCDA, numerous gaps remain in the literature. The absence of standardized protocols for integrating these methodologies can lead to inconsistencies in application and interpretation [22]. Furthermore, the dynamic and often contentious nature of sustainability objectives necessitates continuous engagement with stakeholders to ensure that the composite indicators reflect evolving priorities and values [23]. This article aims to bridge the existing gap by offering a comprehensive review of the theoretical foundations and practical implications of leveraging DEA and MCDA for the development of robust composite indicator frameworks in sustainable development. The objective is to provide a detailed roadmap that can guide future research and practice in constructing resilient and context-sensitive indicators that can withstand scrutiny and foster sustainable outcomes.

Combining DEA and MCDA for robust composite indicator frameworks in sustainable development is an intriguing and complex process. Below is a structured methodology that relies on both approaches to create a unified framework for evaluating sustainable development indicators.

The objective is to assess the efficiency of different provinces in Iran regarding their renewable energy utilization, with a focus on solar and wind energy. The analysis will help identify best practices and areas for improvement.

The integration of DEA and MCDA has been demonstrated as a robust framework for the evaluation of efficiency in sustainable resource management initiatives within Iran. The analysis of water resource management and renewable energy utilization has revealed significant performance disparities among provinces, highlighting the need for targeted interventions in areas displaying inefficiencies.

The results indicate that Tehran has achieved the highest efficiency score in renewable energy utilization. This result suggests that factors such as resource allocation, infrastructure investment, and policy support may contribute to the superior performance observed in this province. By identifying both high-performing and underperforming provinces, a clearer understanding of operational dynamics has been established, thereby guiding the development of tailored strategies aimed at optimizing resource use and fostering equitable access to sustainable energy solutions.

Moreover, the application of the integrated framework has facilitated a comprehensive analysis of the interplay between various sustainability indicators. The simultaneous evaluation of multiple criteria allows for the identification of synergies and trade-offs, thereby empowering decision-makers to make informed choices that align with the overarching goals of sustainable development.

While the findings are promising, additional research is warranted to explore the application of DEA in other contexts. The potential for MCDA to prioritize sustainability criteria tailored to local realities also warrants further investigation. As the complexity of sustainability challenges intensifies, the enhancement of analytical frameworks becomes essential for policy formulation, implementation, and evaluation.

In conclusion, a successful demonstration of the practical application of an integrated DEA and MCDA framework has been provided to assess sustainability in crucial sectors in Iran. Actionable insights into the efficiencies and inefficiencies of water resource management and renewable energy utilization have been generated, contributing to the discourse on effective, sustainable development practices.

The findings advocate for a strategic approach to resource management, emphasizing the importance of evidence-based interventions designed to improve performance across provinces. As Iran and similar contexts pursue their sustainability goals, the adoption of this integrated framework is expected to facilitate a nuanced understanding of resource dynamics, ultimately fostering decision-making that aligns with global sustainability imperatives.

The authors would like to express their sincere gratitude to the Department of Mathematics at the Isfahan (Khorasgan) Branch, Islamic Azad University, for their continuous support and valuable resources that facilitated this research. Special thanks are also extended to all experts and stakeholders who provided insightful feedback and contributions during the data collection and evaluation processes.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

The data that support the findings of this study are available from the corresponding author upon reasonable request. Hypothetical datasets used for demonstration purposes in the case studies are included within the article.