Prioritizing the European Investment Sectors Based on Different Economic, Social, and Governance Factors Using a Fuzzy-MEREC-AROMAN Decision-Making Model

Abstract

This study tackles the challenge of identifying optimal investment sectors amid the growing importance of environmental, social, and governance (ESG) factors, which are often complex and conflicting. This research aims to effectively evaluate and prioritize ten investment sectors based on twelve ESG criteria by integrating expert evaluations with two advanced multi-criteria decision-making (MCDM) methods. Three expert teams assessed each sector’s performance based on these criteria using fuzzy logic to manage uncertainties in expert judgments. The MEREC (MEthod based on the Removal Effects of Criteria) identified biodiversity and land use as the most critical factor, while transparency and disclosure was least significant. The AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization) method was further used to rank the ten alternative sectors, with impact investing funds emerging as the top choice, followed by renewable energy and sustainable responsible investment funds. Conversely, ESG-compliant stocks, ESG-focused exchange-traded funds, and ESG-focused real estate investment trusts ranked the lowest. The study’s findings were validated through comparisons with other MCDM tools and sensitivity analysis, confirming the robustness of the proposed model. This research offers a valuable framework for investors looking to incorporate ESG considerations into their decision-making, promoting sustainable and responsible investing practices.

1. Introduction

In recent years, the importance of ESG factors in investment decisions has gained substantial traction. As global awareness of climate change, social inequities, and corporate governance issues increases, investors are recognizing the need to align their portfolios with sustainable and responsible principles. ESG investing, which integrates these three critical dimensions, aims to create long-term value not only for shareholders but also for the broader society and environment [1]. Traditional investment strategies, primarily focused on financial returns, are increasingly being supplemented or even replaced by approaches that consider ESG performance. This shift reflects the growing recognition that companies excelling in ESG factors are better positioned to mitigate risks and capitalize on opportunities, leading to more resilient and sustainable financial performance.

The significance of incorporating ESG factors into investment decision-making cannot be overstated. Environmental factors, such as carbon emissions and energy efficiency, directly impact climate change and resource sustainability [2]. Social factors, including labor practices and community engagement, influence social equity and corporate reputation. Governance factors, like board diversity and transparency, affect corporate accountability and risk management. Together, these factors shape the overall sustainability and ethical footprint of investment choices. Prioritizing investment sectors based on ESG factors helps investors identify opportunities that align with their values and risk profiles. Moreover, it promotes corporate responsibility and encourages companies to improve their ESG performance [3]. This research aims to develop a robust and comprehensive decision-making model to prioritize ten European investment sectors based on twelve ESG factors that addresses the complexities and uncertainties inherent in ESG assessments, thereby providing clear and actionable insights for investors. The existing models often struggle with the complexity and subjectivity inherent in ESG assessments, leading to less reliable or overly simplistic rankings. Furthermore, many of these models fail to integrate the nuanced and sometimes conflicting nature of ESG criteria effectively.

The motivations for conducting this research are manifold. Firstly, there is a growing demand from investors for tools and methodologies that can accurately evaluate and rank investment options based on ESG criteria. Current models often lack the sophistication to handle the subjective and sometimes conflicting nature of ESG data. Secondly, the European investment landscape, with its diverse sectors and stringent regulatory environment, presents a unique opportunity to apply and test advanced decision-making frameworks [4]. This study aims to bridge the gap by integrating fuzzy logic with MEREC [5] and AROMAN [6] models. Fuzzy logic is particularly suited for dealing with the ambiguity and imprecision in expert judgments, while MEREC and AROMAN provide robust frameworks for evaluating criteria weights and prioritizing investment sectors. The European market is especially ripe for such analysis due to its progressive ESG regulations and the variety of sectors with differing ESG impacts. Sectors such as renewable energy and impact investing funds are already recognized for their positive contributions to sustainability, whereas others, like real estate investment trusts (REITs), face greater scrutiny. By focusing on Europe, this research taps into a region where ESG considerations are both highly relevant and complex [7]. Furthermore, the integration of expert opinions through fuzzy logic enhances the model’s reliability by accommodating diverse perspectives and reducing bias. Experts from environmental science, social governance, and financial analysis contribute to a holistic evaluation, ensuring that the resulting sector prioritizations are comprehensive and well rounded.

The primary objective of this study is to propose the optimum investment sectors among ten alternatives based on twelve conflicting ESG factors using the concept of hybrid MCDM. This study introduces the Fuzzy-MEREC-AROMAN model, a hybrid MCDM approach that combines fuzzy logic with the MEREC and AROMAN methods. This model is particularly well suited for handling the ambiguities and uncertainties in expert judgments, which are common in ESG assessments. By employing the Fuzzy-MEREC-AROMAN decision-making model, this research aims to provide a sophisticated approach to ESG investment prioritization [2,3,4,5,6]. This research contributes to the growing body of literature on ESG investing by introducing a novel decision-making model that provides valuable insights for both academic researchers and practical investors [8]. The Fuzzy-MEREC-AROMAN model offers a more transparent, systematic, and comprehensive evaluation of investment sectors compared to existing models, addressing the limitations of previous approaches. By promoting a more structured and nuanced approach to ESG assessment, this study encourages the adoption of sustainable and responsible investing practices. It also highlights the importance of considering a wide range of ESG factors in investment decisions, thereby fostering a more sustainable and equitable global economy.

The model is applied specifically to the European market, a region characterized by progressive ESG regulations and a diverse range of sectors with varying ESG impacts. This application not only demonstrates the model’s effectiveness in a real-world context but also provides actionable insights for investors operating within this regulatory environment. By focusing on Europe, the research taps into a region where ESG considerations are both highly relevant and complex, making the findings particularly valuable. Furthermore, this study enhances the reliability of the model by incorporating expert opinions from environmental science, social governance, and financial analysis. This integration ensures that the sector prioritizations are comprehensive and reflect a balanced perspective, reducing the potential for bias. The involvement of experts from different fields allows for a holistic evaluation of the sectors, ensuring that the model accounts for the diverse and sometimes conflicting nature of ESG criteria.

In conclusion, this research aims to provide a robust and reliable tool for prioritizing ten European investment sectors based on twelve ESG factors [9]. Through the integration of expert evaluations, fuzzy logic, and advanced MCDM techniques, the proposed model offers a comprehensive framework for sustainable and responsible investment decision-making. This research not only contributes to the growing body of literature on ESG investing but also provides a practical tool for investors looking to prioritize sectors based on ESG factors. The Fuzzy-MEREC-AROMAN model developed in this study offers a novel and sophisticated approach to ESG investment prioritization, promoting a more structured and nuanced evaluation process. By addressing the complexities and uncertainties inherent in ESG assessments, this study provides clear and actionable insights that can support sustainable and responsible investment decisions, ultimately fostering a more equitable and sustainable global economy.

2. Literature Review

The integration of ESG factors into investment decisions has gained significant traction in recent years. Investors and policymakers recognize the importance of sustainable and responsible investment practices. This literature review aims to provide a comprehensive understanding of the methodologies and frameworks employed in prioritizing investment sectors based on ESG factors [9]. Research in the ESG field has been extensive and multifaceted, addressing various aspects of ESG integration and its implications. The relevance of ESG factors in investment decision-making is well documented. This literature review is divided into three distinct sections covering different aspects of ESG and decision-making scenarios. The first section focuses on research articles involving ESG as the main area of concern, including its significances, benefits, and performance [10]. The second section particularly focuses on the application of different MCDM tools in ESG sectors, highlighting its ability in prioritizing the ESG criteria across diverse sectors and regions. The third section solely focuses on the implications of MEREC and AROMAN MCDM models in various fields.

2.1. Aspects of ESG Integration and Its Implications

The integration of ESG factors into investment strategies has been increasingly recognized as crucial for fostering sustainability and ethical financial practices. This literature review synthesizes the methodologies and findings from recent studies on ESG investment strategies, highlighting the diversity of approaches and their contributions to sustainable finance and investment decision-making globally [4,7,8,10]. Research in the ESG field has been extensive and multifaceted, addressing various aspects of ESG integration and its implications. Extensive research has been conducted on the integration of ESG factors into investment decision-making. Studies by Sassen et al. [11] and Sládková et al. [12] have shown that integrating ESG criteria can lead to improved long-term returns and risk management. A study by Lupu [13] highlights the growing importance of ESG integration in European investment decisions. The integration of ESG factors in investment decisions has evolved from a niche approach to a mainstream practice. Studies have shown that ESG considerations can mitigate risks and uncover opportunities for better financial performance [11,12,13]. The Global Sustainable Investment Alliance (GSIA) reported that sustainable investments reached USD 30.7 trillion in 2022, indicating a growing trend [14]. ESG criteria cover a broad spectrum, including environmental impact, social responsibility, and governance practices, making the evaluation process complex and multi-dimensional.

Ionescu et al. [15] conducted a meta-analysis of over 2000 empirical studies on ESG and financial performance, concluding that the majority of studies find a positive relationship between ESG and corporate financial performance. Their work highlights the financial benefits of incorporating ESG factors into investment decisions. Park and Jang [16] investigated the materiality of ESG factors and found that firms with good performance on material ESG issues outperform those with poor performance on these issues. This research emphasizes the importance of focusing on material ESG factors that are relevant to the firm’s industry. Kotsantonis et al. [17] explored the value-creating potential of ESG performance, suggesting that ESG integration can lead to better risk management, innovation, and competitive advantage. Their review of the academic and practitioner literature provides a comprehensive understanding of the business case for ESG. Wanday and Ajour El Zein [18] examined the impact of corporate social responsibility (CSR) on access to finance, finding that firms with better CSR performance face lower capital constraints. This study underscores the financial advantages of strong ESG performance in terms of easier access to capital. Meira et al. [19] compared high-sustainability firms to low-sustainability firms over an 18-year period, showing that high-sustainability firms significantly outperform their counterparts in terms of stock market and accounting performance. This longitudinal study provides robust evidence of the long-term benefits of sustainable practices.

Iazzolino et al. [20] developed a framework for integrating ESG factors into credit risk analysis, highlighting the impact of ESG considerations on credit ratings and risk assessments. This research is crucial for understanding how ESG factors influence the creditworthiness of companies. Dmuchowski et al. [21] investigated the impact of ESG on firm value, finding that ESG activities enhance firm value by improving stakeholder relationships and reducing information asymmetry. This study supports the view that ESG integration can lead to value creation. De Lucia et al. [22] explored the relationship between environmental performance and financial performance, finding that reductions in emissions can lead to increased profitability. This early work highlights the economic benefits of environmental sustainability. Abate et al. [23] analyzed the impact of corporate social performance on stock returns, showing that companies with strong social performance tend to have better stock market performance. This research supports the positive financial impact of social sustainability practices. Iamandi et al. [24] investigated the role of governance structures in firm performance, finding that strong governance mechanisms are associated with improved financial outcomes. This study highlights the importance of governance factors in investment decisions.

Egorova et al. [25] studied the impact of ESG on corporate bond performance, finding that companies with strong ESG performance have lower credit spreads. This research emphasizes the importance of ESG factors in fixed-income investments. Maiti [26] surveyed institutional investors to understand their perspectives on ESG integration, finding that investors consider ESG factors to be financially material, and they integrate these factors to enhance investment performance. This study provides insights into the practical application of ESG integration by investors. Van Duuren et al. [27] examined the impact of ESG on real estate investment performance, finding that sustainable properties have higher rental rates and lower vacancy rates. This research highlights the financial benefits of sustainability in the real estate sector. Helfaya et al. [28] investigated the relationship between corporate sustainability performance and firm financial performance, finding that firms with higher sustainability scores have better financial performance. This study adds to the growing evidence of the financial benefits of ESG integration. La Torre et al. [29] analyzed the impact of CSR on firm risk, showing that firms with strong CSR performance have lower risk profiles. This research underscores the risk management benefits of ESG practices. Janicka and Sajnóg [30] conducted a meta-analysis on the relationship between corporate social performance and financial performance, finding a positive relationship. This comprehensive study supports the view that socially responsible practices lead to better financial outcomes.

Auer and Schuhmacher [31] examined the effect of socially responsible investing on stock returns, showing that socially responsible firms outperform their counterparts. This study highlights the market’s recognition of the value of ESG practices. Buallay [32] explored the impact of ESG disclosure on firm value, finding that transparent ESG reporting enhances firm value. This research emphasizes the importance of transparency and accountability in ESG practices. Eccles et al. [33] investigated the impact of sustainability on organizational processes and found that sustainable firms have better operational efficiency and lower cost of capital. This study adds to the understanding of the operational benefits of ESG integration. Schramade [34] analyzed the financial and social performance of socially responsible investments, finding that these investments yield competitive financial returns while delivering social benefits. This research supports the dual benefits of ESG integration. Di Simone et al. [35] conducted a meta-analysis on the relationship between CSR and financial performance, concluding that CSR has a small but positive impact on financial performance. This study provides a nuanced view of the financial implications of CSR practices.

2.2. Integration of MCDM Concept with ESG

MCDM models are essential for evaluating and prioritizing options based on multiple criteria. The complexity of ESG factors requires sophisticated MCDM approaches. The European investment landscape presents a diverse range of sectors with varying levels of ESG performance. Applying suitable MCDM models can help identify which sectors are leading in sustainability and which ones require more attention and improvement [36]. The application of diversified decision-making models in ESG evaluations is exemplified in various studies. For instance, the model can be used to prioritize investment sectors based on their ESG performance, helping investors allocate resources to sectors that align with their sustainability goals. This approach is particularly relevant in the European investment landscape, where ESG considerations are increasingly prioritized. Investment decision-making and the adoption of ESG criteria are critical areas of research in finance and management [37]. This literature review synthesizes recent studies exploring the determinants of investment decisions, the barriers to ESG adoption, and the evaluation of sustainable and socially responsible investments across various industries.

Sood et al. [38] delve into the determinants of investment decisions by examining the priorities of investors. The study employs a comprehensive approach to identify key factors influencing investment choices, such as financial performance, risk, and market trends. The findings highlight the importance of psychological and behavioral aspects in investment decisions, providing insights into how investors prioritize different determinants. Liou et al. [39] explore the key barriers to ESG adoption in enterprises. The study identifies several challenges, including lack of awareness, insufficient regulatory frameworks, and perceived costs. By using a systems and soft computing approach, the authors provide a nuanced understanding of the obstacles that enterprises face in integrating ESG criteria, emphasizing the need for targeted strategies to overcome these barriers. Sharma and Kumar [40] focus on prioritizing the attributes of sustainable banking performance. The study uses an MCDM approach to evaluate various performance attributes such as financial stability, customer satisfaction, and environmental impact. The findings suggest that sustainable banking practices can be effectively prioritized using MCDM techniques, enhancing the overall performance and sustainability of banks. Petrillo et al. [41] proposed a non-financial ranking system for socially responsible mutual funds in the Italian market. The study integrates multiple criteria to assess the social and environmental performance of mutual funds, providing a framework for investors to make informed SRI choices. The authors highlight the growing importance of non-financial criteria in investment decision-making.

Verheyden and Moor [42] discuss methods to define and evaluate socially responsible investments using multi-criteria decision analysis (MCDA). The study provides a comprehensive overview of different MCDA methods and their applications in SRI, emphasizing the need for a balanced approach that considers both financial and non-financial criteria. Bhuvaneskumar et al. [43] assess the performance and ranking of socially responsible companies in India using FAHP, TOPSIS, and Altman Z-score. The study demonstrates the effectiveness of combining different MCDM techniques to evaluate the financial health and social responsibility of companies, providing a robust framework for SRI evaluation. Reig-Mullor and Brotons-Martinez [44] evaluate the performance of commercial banks in Spain using intuitionistic fuzzy numbers. The study highlights the application of fuzzy logic in handling uncertainty and complexity in bank performance evaluation, providing insights into the effectiveness of different banking strategies. Gündoğdu et al. [45] examine ESG risks and environmentally sensitive competitive strategies in a multinational logistics company. The study underscores the importance of integrating ESG criteria into corporate strategies to mitigate risks and enhance competitive advantage, providing a case study of successful ESG implementation. Paat et al. [46] assess ESG risks for future phosphorite mining in Estonia using a fuzzy analytical hierarchy process. The study illustrates how fuzzy MCDM techniques can be applied to evaluate environmental and social risks in the extractive industries, contributing to more sustainable mining practices.

Er et al. [47] propose a decision support tool based on fuzzy Multimoora for project portfolio selection in the oil and gas industry. The study demonstrates the application of advanced MCDM methods to prioritize and select projects that align with strategic objectives and ESG criteria, enhancing decision-making in the oil and gas sector. Biswas et al. [48] introduce the Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) method to compare firm performance in the energy sector. The study highlights the effectiveness of the ERUNS method in evaluating and benchmarking firm performance based on multiple criteria, including ESG factors. Zatonatska et al. [49] provide a comprehensive analysis of best practices in applying ESG criteria within the energy sector. The study identifies key factors driving ESG adoption and provides recommendations for enhancing ESG performance in energy companies. Zopounidis et al. [50] explore the environmental, social, and corporate governance framework for corporate disclosure using a multi-criteria dimension analysis approach. The study highlights the importance of transparent ESG disclosure in improving corporate accountability and performance. Llanos et al. [51] rate ESG key performance indicators in the airline industry. The study uses a comprehensive approach to evaluate the ESG performance of airlines, providing insights into the sustainability practices and challenges faced by the industry.

The reviewed literature demonstrates the diverse applications and continuous advancements in MCDM methods for investment decision-making and ESG adoption. The integration of fuzzy logic, intuitionistic fuzzy sets, and other hybrid approaches with traditional MCDM methods has enhanced the robustness and accuracy of decision-making processes. These advancements facilitate better handling of uncertainty, improve the precision of criteria evaluation, and offer more comprehensive solutions to complex decision-making problems in various sectors [52]. Future research should continue to explore the integration of new techniques and their applications, ensuring that MCDM methods remain relevant and effective in addressing emerging challenges. However, some more studies have been discussed in

Table 1. MCDM models to evaluate and prioritize ESG factors.

References	Objective	Methodology	Key Findings
Lo and Lin [1]	To develop a Z-fuzzy-based decision-making model for selecting investment trust companies based on ESG indicators.	Utilized Z-fuzzy logic to handle uncertainties in ESG data.	Identified critical ESG indicators and proposed a framework for selecting trustworthy investment companies.
Meng and Shaikh [2]	Evaluated ESG criteria and green finance investment strategies using fuzzy AHP and fuzzy WASPAS.	Applied fuzzy Analytic Hierarchy Process (AHP) and Weighted Aggregated Sum Product Assessment (WASPAS) to prioritize ESG factors.	Highlighted effective strategies for integrating green finance into investment decisions.
Sood et al. [3]	Assessed investor preferences for ESG factors in India using fuzzy AHP.	Conducted a fuzzy AHP analysis to rank ESG criteria based on investor priorities.	Provided insights into how Indian investors prioritize ESG factors in their investment strategies.
Li et al. [4]	Assessed ESG factors and policies for green finance investment decisions in China using fuzzy AHP and fuzzy DEMATEL.	Applied fuzzy AHP and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate policy impacts on sustainable development.	Proposed policy recommendations to enhance the sustainability of green finance investments in China.
Yu et al. [7]	Developed an integrated MCDM framework for evaluating ESG sustainable business performance.	Integrated multiple-criteria decision-making techniques to assess environmental, social, and governance aspects.	Provided a holistic approach to measuring and improving ESG performance across business operations.
Nguyen et al. [8]	Introduced an integrated DEA and MCDM framework using spherical fuzzy sets to assess ESG in Vietnam’s wire and cable sector.	Combined Data Envelopment Analysis (DEA) and MCDM with spherical fuzzy sets to evaluate sector-specific ESG performance.	Demonstrated the applicability of the framework in enhancing ESG efficiency in a specific industrial context.
Teodorescu et al. [9]	Developed an integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection.	Integrated MCDM and optimization techniques to select portfolios based on ESG criteria.	Provided a robust framework for constructing socially responsible investment portfolios.
Hoang et al. [10]	Assessed ESG performance in the global electronics industry using an integrated MCDM approach with spherical fuzzy sets.	Applied spherical fuzzy sets in MCDM to evaluate industry-wide ESG performance.	Identified strengths and weaknesses in ESG practices across global electronics firms.
Yazo-Cabuya et al. [53]	To prioritize organizational risks related to sustainability using DEMATEL and AHP methods.	Combined DEMATEL and AHP to assess and rank risks.	Provided a structured approach to identifying and prioritizing sustainability risks within organizations, facilitating informed decision-making for sustainable practices.
Roy [54]	Developed an ESG-based credit rating model to enhance sustainable investments and green economy.	Utilized ESG criteria to create a credit rating framework that supports green financing.	Demonstrated that incorporating ESG factors into credit ratings can improve the credibility and effectiveness of green financing, promoting sustainable economic growth.
Tan et al. [55]	To establish an ESG country ranking system using performance contribution analysis and MCDM approaches.	Applied a combination of MCDM techniques to evaluate and rank countries based on their ESG performance.	Offered a comprehensive ranking of countries, highlighting their strengths and weaknesses in ESG performance, thereby guiding international investment decisions.
Lin et al. [56]	Identified key financial, environmental, social, governance, bond, and COVID-19 factors affecting global shipping companies.	Employed a hybrid MCDM method integrating various criteria to evaluate the impact on shipping companies.	Provided a detailed understanding of how different factors, including the pandemic, influence the ESG performance of shipping companies, aiding in strategic planning and risk management.
Quayson et al. [57]	Designed a decision support tool to integrate ESG considerations into the natural resource extraction industry.	Used the ordinal priority approach to develop a tool for sustainable development in resource extraction.	Presented a practical tool for industry stakeholders to incorporate ESG factors, thereby enhancing sustainability and responsible resource management.
Bilbao-Terol et al. [58]	Analyzed Global Reporting Initiative (GRI) sustainability reports using multi-criteria analysis for socially responsible investment (SRI).	Applied multi-criteria decision-making to evaluate and interpret GRI reports.	Demonstrated the utility of multi-criteria analysis in assessing the comprehensiveness and impact of sustainability reports, supporting more informed SRI decisions.
Xidonas and Essner [59]	To construct ESG portfolios using a multi-objective optimization approach.	Integrated multiple objective optimization techniques to create portfolios that balance financial performance with ESG criteria.	Provided a robust framework for building investment portfolios that align with ESG principles while achieving desirable financial outcomes.

(Source: Author’s own elaboration.)

emphasizing the integration of MCDM in the ESG field.

This literature review synthesizes the diverse methodologies and findings from recent studies on ESG and sustainable investment strategies. The research highlights the critical role of MCDM models, optimization techniques, and decision support tools in evaluating and integrating ESG criteria across various sectors and geographical regions [28,31,32,33,34,35,38,41]. Each study contributes valuable insights into the development and implementation of sustainable investment practices, emphasizing the importance of ESG factors in driving long-term sustainability and ethical investment decisions.

2.3. Past Studies on Fuzzy-MEREC-AROMAN MCDM Model

The European investment landscape presents a diverse range of sectors with varying levels of ESG performance. Applying the Fuzzy-MEREC-AROMAN model can help identify which sectors are leading in sustainability and which ones require more attention and improvement. To effectively prioritize investment sectors based on ESG criteria, sophisticated decision-making models are required [60]. The Fuzzy-MEREC-AROMAN decision-making model combines fuzzy logic, the MEREC method, and the AROMAN method, providing a comprehensive framework for such evaluations. The field of MCDM has seen significant advancements in recent years, with various methods being developed and applied to solve complex decision-making problems across different sectors. This literature review aims to explore and synthesize the findings from recent research articles on MCDM methods, particularly focusing on the MEREC-AROMAN method and its applications. The literature review explores the components of this model and its application in diverse fields [61]. However, very few research applications of MEREC and AROMAN have been recorded to date. Being newly developed tools, MEREC and AROMAN have found very limited applications in the MCDM field. Some of them have been discussed in

Table 2. Fuzzy-MEREC-AROMAN method and its applications.

Domain	References	Explanations
Fuzzy-MEREC-AROMAN method hybrid MCDM methods	Kara et al. [60]	They presented the MEREC-AROMAN method for determining sustainable competitiveness levels, using Turkey as a case study. The study demonstrates how this hybrid method can effectively integrate multiple criteria to assess and rank the sustainable competitiveness of different sectors. The authors highlight the method’s robustness in handling both qualitative and quantitative data, making it a versatile tool for policymakers and researchers.
	Kiptum et al. [65]	They discussed the implementation of effective supply chain management practices in the National Oil Corporation of a developing country using an integrated BWM-AROMAN approach. The study demonstrates the method’s ability to optimize supply chain performance by prioritizing key criteria and addressing various challenges faced by the oil sector. The authors highlight the approach’s effectiveness in enhancing decision-making accuracy and operational efficiency, making it a valuable tool for supply chain management in resource-constrained environments.
	Alrasheedi et al. [66]	They presented an interval-valued intuitionistic fuzzy AROMAN method for selecting sustainable wastewater treatment technologies. This method integrates intuitionistic fuzzy sets with the AROMAN approach to handle the inherent uncertainty in decision-making processes. The study emphasizes the method’s capability to evaluate and rank different wastewater treatment technologies based on sustainability criteria, providing a comprehensive framework for environmental decision-making.
	Esangbedo and Tang [61]	They evaluated enterprise decarbonization schemes using a grey-MEREC-MAIRCA hybrid MCDM method. This study underscores the importance of integrating grey systems theory with MCDM to handle uncertainty in decision-making processes. The hybrid approach allows for a comprehensive evaluation of decarbonization strategies, providing a nuanced understanding of their effectiveness and feasibility.
	Güçlü [67]	They conducted a comparative analysis of various MCDM methods with multiple normalization techniques. The study combines MPSI with DNMARCOS, AROMAN, and MACONT methods, offering insights into the strengths and weaknesses of each hybrid model. The findings suggest that the choice of normalization technique can significantly impact the outcomes of MCDM processes, emphasizing the need for careful method selection in practical applications.
	Elsayed [68]	They evaluated enterprise decarbonization schemes using a grey-MEREC-MAIRCA hybrid MCDM method. This study underscores the importance of integrating grey systems theory with MCDM to handle uncertainty in decision-making processes. The hybrid approach allows for a comprehensive evaluation of decarbonization strategies, providing a nuanced understanding of their effectiveness and feasibility.
	Hu et al. [69]	They introduced an intuitionistic fuzzy SWARA-AROMAN decision-making framework for sports event management. The study showcases how this hybrid approach can effectively manage and prioritize various factors influencing sports events, such as logistics, safety, and participant experience. The integration of SWARA with AROMAN allows for a more nuanced evaluation of criteria, enhancing the decision-making process in the context of sports management.
	Zhang et al. [70]	They proposed a comprehensive multistage approach for measuring the efficiency of logistics processes in the presence of a mismatch between sales and logistics. This approach incorporates multiple MCDM techniques to address the complexities of logistics management. The study highlights the practical implications of using such a multistage approach to optimize logistics efficiency, particularly in scenarios where there is a disconnect between sales forecasts and logistics capabilities.
Sector-specific applications	Taşcı [71]	They used a hybrid MCDM method for multi-dimensional performance evaluation in the Turkish non-life insurance sector. The study illustrates how the integration of different MCDM techniques can enhance the accuracy and reliability of performance assessments. The hybrid approach enables a comprehensive analysis of various performance dimensions, offering actionable insights for industry stakeholders.
	Gangwar et al. [72]	They conducted a comparative analysis of various MCDM techniques to optimize W-DLC coatings for tool materials. This study provides a detailed examination of the performance of different MCDM methods in a specific industrial context, showcasing their potential in improving material selection processes. The findings emphasize the practical benefits of MCDM in enhancing the efficiency and effectiveness of manufacturing operations.
	Bouraima et al. [73]	They applied an integrated multi-criteria approach to formulate and assess healthcare referral system strategies in developing countries. The study illustrates how MCDM methods can be tailored to address specific challenges in healthcare, such as resource allocation and service delivery optimization. The findings underscore the potential of MCDM in improving healthcare systems by providing a structured framework for evaluating and prioritizing various strategic options.
	Önden et al. [74]	They explored the adoption of the metaverse and chat generative pre-trained transformer (GPT) technology using a single-valued neutrosophic Dombi Bonferroni-based method for selecting software development strategies. This study highlights the application of advanced MCDM methods in the technology sector, particularly in the context of emerging technologies like the metaverse and GPT. The method’s ability to handle uncertainty and multiple criteria makes it a valuable tool for technology adoption and strategy selection.
Advances in MCDM techniques	Liu et al. [75]	They explored optimized grid partitioning and scheduling in multi-energy systems using a hybrid decision-making approach. The study demonstrates the applicability of advanced MCDM techniques in complex energy systems, highlighting their potential to improve operational efficiency and sustainability. The hybrid approach integrates various criteria to ensure balanced decision-making, addressing both technical and economic considerations.
	Elsayed et al. [76]	They investigated the application of digital twin technology in the energy sector using MEREC and MAIRCA methods. This research underscores the potential of MCDM methods in evaluating and implementing innovative technologies. The combination of MEREC and MAIRCA provides a robust framework for assessing the feasibility and impact of digital twin applications, contributing to the advancement of smart energy solutions.
Comparative studies and evaluations	Dündar [77]	They evaluated hands-on entrepreneurship training programs using fuzzy BWM and AROMAN methods. The study provides a comprehensive assessment of training effectiveness across different regions, highlighting the role of MCDM in educational program evaluation. The findings suggest that the integration of fuzzy logic with MCDM can enhance the precision and reliability of evaluation processes.
	Gao et al. [78]	They presented an integrated spherical fuzzy multi-criterion group decision-making approach for digital marketing technology assessment. The study showcases the application of advanced fuzzy MCDM techniques in the digital marketing sector, providing valuable insights into technology evaluation and selection. The integrated approach facilitates a more nuanced understanding of the various factors influencing technology adoption decisions.
	Biswas et al. [79]	They conducted a multi-criteria-based comparison of electric vehicles using q-rung orthopair fuzzy numbers. This study highlights the effectiveness of fuzzy MCDM methods in handling the inherent uncertainty and complexity of electric vehicle evaluation. The findings contribute to the growing body of knowledge on sustainable transportation solutions, offering practical insights for consumers and policymakers.

(Source: Author’s own elaboration.)

. Fuzzy logic, introduced by Zadeh [62], addresses the uncertainty and imprecision inherent in human reasoning by allowing for partial membership in sets. This characteristic makes fuzzy logic particularly suitable for decision-making scenarios where criteria are not strictly binary. In the context of ESG evaluation, fuzzy logic helps capture the nuanced and often subjective nature of ESG factors. Numerous studies have highlighted the application of fuzzy logic in decision-making. For instance, Kahraman et al. [63] utilized fuzzy-MCDM in evaluating performance criteria, demonstrating its effectiveness in handling ambiguity. Similarly, Alimohammadlou and Khoshsepehr [64] applied fuzzy logic to supplier selection, showcasing its flexibility in diverse decision-making contexts.

The reviewed articles collectively highlight significant advancements in MCDM techniques, emphasizing their adaptability and effectiveness in various sectors. The reviewed literature demonstrates the diverse applications and advancements of MCDM methods, particularly the hybrid approaches that combine different techniques to enhance decision-making processes [68,69]. The MEREC-AROMAN, BWM-AROMAN and intuitionistic fuzzy AROMAN method, along with other hybrid MCDM methods, have shown significant potential in addressing complex decision-making challenges across various sectors including supply chain management, environmental sustainability, sports event management, logistics, healthcare, and technology adoption [78]. The Fuzzy-MEREC-AROMAN decision-making model offers a comprehensive framework for prioritizing investment sectors based on ESG factors. By combining the strengths of fuzzy logic, the MEREC method, and the AROMAN method, this model addresses the complexities and uncertainties inherent in ESG evaluations [72,73,74,75,76]. The extensive research in the ESG field underscores the importance of integrating ESG factors into investment decisions, highlighting the relevance and applicability of the Fuzzy-MEREC-AROMAN model.

2.4. Novelty and Research Gaps

The review of the existing literature reveals several gaps that highlight the need for further research in the area of prioritizing European investment sectors based on different ESG factors using the Fuzzy-MEREC-AROMAN decision-making model. The identified gaps pertain to methodological approaches, geographic focus, sector-specific analysis, and integration of ESG factors. While various studies have integrated fuzzy logic with other MCDM methods (e.g., FAHP, TOPSIS), there is limited research specifically combining fuzzy logic with the MEREC-AROMAN model. This integration is crucial for handling the inherent uncertainty and imprecision associated with ESG factors. Many studies focus on standalone MCDM methods without exploring the potential benefits of hybrid models. The combination of Fuzzy-MEREC-AROMAN could offer a more robust framework for ESG prioritization, but this hybrid approach has not been extensively studied before. In contrast, much of the existing literature, such as the studies by Bhuvaneskumar et al. [43] and Kiptum et al. [65], focuses on non-European contexts. There is a paucity of research specifically addressing European investment sectors, which have unique regulatory, economic, and social environments affecting ESG criteria. Within Europe, the varying regulatory landscapes and market conditions across different countries necessitate region-specific studies. Existing research often treats Europe as a homogeneous entity, overlooking the diversity in ESG priorities across different European regions.

Recent works by Sharma and Kumar [40] and Zatonatska et al. [49] address ESG criteria at a broad sectoral level. Detailed, sector-specific studies within Europe are required to understand the unique ESG challenges and opportunities in sectors like energy, finance, technology, and manufacturing. There is a lack of focus on emerging sectors such as renewable energy and digital technologies. These sectors are critical for Europe’s sustainable development goals, and prioritizing them based on ESG factors is essential for informed investment decisions. Studies by Liou et al. [39] and Gündoğdu et al. [45] identified key ESG barriers and risks, but do not provide a comprehensive analysis of how these factors should be prioritized for different sectors. A detailed prioritization framework is needed to guide investors in aligning their portfolios with ESG goals. Moreover, ESG factors are dynamic and evolve with regulatory changes, technological advancements, and societal expectations. The existing literature often lacks a dynamic framework that can adapt to these changes. The Fuzzy-MEREC-AROMAN model could address this gap by incorporating real-time data and evolving criteria. While theoretical models are well explored, there is a gap in practical decision support tools that investors can use to apply these models in real-world scenarios. Research by Er et al. [47] hinted at the potential of decision support tools but does not provide comprehensive solutions. Effective prioritization of investment sectors requires input from various stakeholders, including investors, regulators, and civil society. Existing studies, including by Verheyden and Moor [42], often overlook the importance of stakeholder engagement in the decision-making process.

Addressing these research gaps will enhance the robustness and applicability of the Fuzzy-MEREC-AROMAN decision-making model for prioritizing European investment sectors based on ESG factors. This research presents a novel approach to prioritize the European investment sectors by integrating the Fuzzy-MEREC-AROMAN decision-making model, addressing several identified gaps in the existing literature. The authors intend to fulfill all of the above-stated research gaps in this research article. This research significantly advances the field of investment decision-making based on ESG factors by integrating these novel elements and provides valuable insights for investors in the European context.

3. Research Design

The research design for prioritizing the European investment sectors based on different ESG factors involves a structured approach integrating fuzzy logic with the MEREC [5] and AROMAN [6] decision-making models. This comprehensive approach addresses the complexities and uncertainties that are inherent in ESG assessments, providing a robust framework for investors. The methodology consists of the following steps, which have also been depicted using a flow diagram shown in Figure 1:

• Step 1: The first and foremost step is to set the primary goal of the ongoing research. This study aims at choosing the optimum investment sector from a list of ten alternative sectors and proposes a preference ranking order based on twelve conflicting ESG factors using a newly developed fuzzy-integrated MEREC-AROMAN hybrid MCDM model.
• Step 2: This step involves meeting with the session expert members to define the potential ESG criteria and investment sector alternatives.
• Step 3: Fuzzy logic is employed to manage the uncertainties and variabilities in the expert judgments.
• Step 4: MEREC is applied to determine the relative importance of each ESG criterion.
• Step 5: AROMAN is further used to prioritize the investment sectors based on their overall performance across all criteria.
• Step 6: Validation and sensitivity analysis is conducted to ensure the consistency, reliability, and stability of the ranking order.

Figure 1. Flow diagram of the entire hybrid MCDM model. (Source: Author’s own elaboration.)

Figure 1. Flow diagram of the entire hybrid MCDM model. (Source: Author’s own elaboration.)

3.1. Collecting Expert Opinions

Initially, a team of panel expert members were formed to identify the crucial ESG parameters and a few European investment sectors based on which the following analysis has been carried out. Experts were selected based on their knowledge and experience in three key areas, environmental science, social governance, and financial analysis. The experts were chosen based on their academic qualifications, professional experience, publications, and recognition in their respective fields as provided in

Table 3. Demographic details of the panel expert members.

Experts	Area of Expertise	Designation	Academic Qualifications	Professional Experience	Publications	Recognition/Awards
Environmental field experts (Expert team 1)
E1	Environmental science	Senior research scientist	Ph.D. in environmental science	15 years in climate research	50+ journal articles	Nobel prize in environmental studies
E2	Environmental science	Environmental engineer	MSc in environmental engineering	10 years in renewable energy	30+ journal articles	Best research paper award in environmental engineering
E3	Environmental science	Professor of ecology	Ph.D. in ecology	20 years in biodiversity research	60+ journal articles, 3 books	International biodiversity conservation award
Social governance field experts (Expert team 2)
E4	Social governance	Director of social policy research	Ph.D. in sociology	18 years in social policy	45+ journal articles	Distinguished sociologist award
E5	Social governance	Senior social worker	MSc in social work	12 years in community engagement	25+ journal articles	National social work excellence award
E6	Social governance	Human rights advocate	Ph.D. in human rights	14 years in human rights advocacy	40+ journal articles	Global human rights award
Financial governance field experts (Expert team 3)
E7	Financial analysis	Chief investment officer	MBA in finance	20 years in investment banking	20+ journal articles, 2 books	Top financial analyst award
E8	Financial analysis	Professor of finance	Ph.D. in finance	25 years in sustainable finance	70+ journal articles	Lifetime achievement award in sustainable finance
E9	Financial analysis	ESG investment analyst	MSc in financial management	15 years in ESG investing	35+ journal articles	Best ESG researcher award

(Source: Author’s own elaboration.)

. A balanced expert committee was ensured to capture diverse perspectives. The panel comprised nine experts categorized into three teams of three members each, particularly focusing on three key areas. This number strikes a balance between having enough diversity of opinion and maintaining manageability in data collection and analysis.

The selection of ESG factors and investment alternatives for this analysis was guided by a comprehensive review of multiple credible sources. Scopus and Web of Science (WoS) databases were extensively searched for published articles and reviews that discuss relevant ESG factors and their impact on investment sectors. Keywords such as “ESG factors”, “sustainable investment”, “environmental finance”, “social governance”, and “investment sector analysis” were used. Articles from top journals in environmental science, social governance, and financial analysis were reviewed to understand the prevailing trends and critical factors [14,15]. The expert team also follows reports from major financial and consulting firms, such as McKinsey, Deloitte, and PwC, which frequently publish insights on sustainable investing and ESG criteria. Moreover, websites of organizations such as MSCI, Sustainalytics, and FTSE Russell, which provide ESG ratings and insights, were consulted. Publications from entities like the United Nations (e.g., UNPRI), the European Commission, and the World Wildlife Fund (WWF) also offered guidelines and frameworks on ESG factors [22]. Systematic reviews and meta-analyses on ESG criteria and sustainable investing helped consolidate the understanding of key factors.

A series of brainstorming sessions were conducted with a panel of nine expert members, categorized into three teams focusing on environmental science, social governance, and financial analysis [43,44,45,46]. The objective of these sessions was to identify and define various ESG factors influencing different European investment sectors and to determine the key alternatives for investment. After thorough discussions, the panel identified twelve ESG factors categorized into environmental, social, and governance factors, and selected ten European investment sector alternatives for the analysis [61,62]. To define the key ESG factors relevant to European investment sectors, the environmental science team focused on identifying factors related to environmental sustainability and impact, such as carbon emissions, resource efficiency, biodiversity, and waste management. The social governance team highlighted the importance of labor practices, diversity, community engagement, and customer safety [76,77]. The financial analysis team emphasized the governance aspects, including board composition, executive compensation, transparency, and anti-corruption measures.

Similarly, to identify potential European investment sectors that align with the defined ESG factors, the environmental science team focused on sectors like renewable energy and green bonds that contribute to environmental sustainability. The social governance team focused on sectors such as social bonds and impact investing funds that promote social welfare and responsibility [39]. The financial analysis team considered sectors like ESG-focused ETFs and corporate bonds from ESG leaders that provide good financial returns while adhering to governance standards. Lastly, refinement was performed to finalize the ESG factors and investment alternatives and ensure that all perspectives were considered. Each team reviewed the factors and alternatives proposed by the other teams, providing feedback and suggestions for refinement [50,51]. Through iterative discussions, the panel reached a consensus on the final list of ESG factors and investment alternatives provided in

Table 4. List of ESG factors and their significances.

ESG Factors	Designation	Significance	Relevance
Environmental Factors (EFs)
Climate change and carbon emissions [14,15,20,21,25]	CCCE	Climate change is a critical global issue, and carbon emissions are a major contributor. This factor assesses a company’s carbon footprint and its efforts to reduce greenhouse gas emissions. Critical for reducing global warming and aligning investments with international climate agreements.	Investments in sectors with low carbon emissions are increasingly favored due to regulatory pressures and the growing importance of sustainability.
Resource management and efficiency [32,33,45,59]	RME	Ensures sustainable use of natural resources, reducing waste and promoting circular economy practices.	Investors seek companies that optimize resource usage, as it indicates operational efficiency and sustainability.
Biodiversity and land use [10,19,26,29,42,67,71]	BLU	Protecting biodiversity and responsible land use are crucial for maintaining ecological balance. Essential for conserving ecosystems, promoting sustainable agriculture, and protecting natural habitats.	Companies with positive impacts on biodiversity are preferred, reflecting a commitment to environmental stewardship.
Pollution and waste management [37,55,74,79]	PWM	Important for minimizing environmental pollution and promoting recycling and waste reduction practices.	Investors are increasingly focused on companies that minimize pollution and manage waste efficiently.
Social Factors (SFs)
Labor practices and working conditions [12,16,20,25,29,66]	LPWC	Vital for ensuring fair wages, safe working conditions, and employee rights, contributing to social stability.	Ethical treatment of employees is a key indicator of a company’s social responsibility and can impact its reputation and performance.
Diversity and inclusion [16,33,34,38,47,73]	DI	Promotes equality and enhances company performance by leveraging diverse perspectives and talents.	Diverse and inclusive companies are seen as more adaptable and better able to attract and retain talent.
Community engagement and social impact [22,33,43,44,74,76]	CESI	Crucial for building strong community relationships and ensuring that business operations benefit local societies.	Investors value companies that contribute to social welfare, as this can improve public perception and reduce social risks.
Customer relations and product safety [75,76,78,79]	CRPS	Ensuring customer satisfaction and product safety is essential for maintaining trust and avoiding legal issues. Ensures consumer protection and trust, critical for business sustainability and reputation.	Strong customer relations and safe products can lead to brand loyalty and long-term success.
Governance Factors (GFs)
Board composition and independence [61,62,64,67,71]	BCI	Ensures robust governance structures, reducing risks of mismanagement and enhancing decision-making.	Good governance practices are crucial for strategic decision-making and accountability, reducing the risk of mismanagement.
Executive compensation and incentives [17,18,21,24,27]	ECI	Aligning executive compensation with company performance and sustainability goals motivates leadership to prioritize long-term value. It promotes leadership goals with long-term company performance and ethical behavior.	Investors prefer companies where executive pay is linked to performance, ensuring management’s interests align with those of shareholders.
Transparency and disclosure [34,38,43,55,69,71]	TD	Promotes accountability and investor confidence by ensuring clear and honest reporting of company practices.	Comprehensive disclosure practices enable investors to make informed decisions and assess company risks accurately.
Anti-corruption and ethical practices [15,16,28,31,48,57]	ACEP	Strong anti-corruption measures and ethical practices are essential for legal compliance and maintaining corporate integrity. Essential for maintaining ethical business operations and preventing financial and reputational damage.	Ethical companies are less likely to face legal issues and reputational damage, making them more attractive to investors.

(Source: Author’s own elaboration.)

and

Table 5. List of European investment sectors and their key characteristics.

European Investment Sectors	Designation	Focus Area	Key Characteristics
Green bonds [13,14,22,24,36]	GB	Finance projects with positive environmental impacts, such as renewable energy and energy efficiency, which are crucial for achieving sustainability goals.	Attracts investors focused on sustainability and offers potential for stable returns.
Sustainable and responsible investment funds [41,45,47,57,64]	SRI	Integrate ESG criteria into investment decisions, promoting long-term sustainability and responsible business.	These funds are popular among investors seeking both financial returns and positive social impact.
ESG-focused exchange-traded funds [72,73,75,78]	ETF	Provide diversified exposure to ESG leaders, making it easier for investors to support sustainable companies.	They are attractive to investors wanting a balanced portfolio with a focus on sustainability.
Impact investing funds [20,21,29,30,32,39]	IIF	Target investments that generate measurable social and environmental impacts, driving positive change.	They appeal to investors with a mission-driven focus, seeking to address global challenges.
Renewable energy investments [40,41,59,60]	REI	Essential for transitioning to a low-carbon economy and reducing dependence on fossil fuels.	This sector is growing rapidly due to regulatory support and increasing demand for clean energy.
Corporate bonds from ESG leaders [50,51,60,69,70]	CBL	Invest in companies with strong ESG practices, ensuring lower risk and stable sustainable returns.	Investors value the reduced risk and potential for higher returns from companies leading in ESG performance.
Social bonds [11,12,18,28]	SB	Finance social projects with positive social outcomes, such as affordable housing and healthcare, contributing to community development and social welfare.	They attract investors interested in addressing social issues while earning financial returns.
Real estate investment trusts with ESG focus [27,33,44,47,58,67]	REIT	Promote sustainable property development and management, enhancing environmental and social outcomes.	These investments are attractive due to the growing demand for sustainable real estate.
Thematic funds [17,21,28,39,46,54,66,77]	TF	Focus on specific ESG themes, allowing targeted investment in areas like clean energy or water sustainability.	They allow investors to target specific areas of interest within the broader ESG landscape.
ESG-compliant stocks [60,71,77,78]	CS	Invest in companies adhering to high ESG standards, ensuring ethical and sustainable business practices.	These stocks are favored by investors seeking to incorporate ESG criteria into their equity portfolios.

(Source: Author’s own elaboration.)

. The list of twelve ESG factors along with their significance and relevancy to the ongoing research are presented in Table 4. The list of ten identified European investment sectors and their focus areas are clearly illustrated in Table 5.

The brainstorming sessions facilitated a collaborative approach to defining the key ESG factors and identifying suitable European investment sectors. The panel of experts, with their diverse expertise, ensured a comprehensive and balanced evaluation of environmental, social, and governance aspects. The resulting criteria and alternatives provide a robust foundation for the subsequent analysis using the Fuzzy-MEREC-AROMAN decision-making model.

The selection process for the ESG factors and investment alternatives involved a rigorous review of the academic literature, industry reports, and expert insights. The chosen factors and alternatives are highly relevant and reflective of current trends and priorities in sustainable investing [2,3,8]. This comprehensive approach ensures that the analysis is grounded in both theoretical rigor and practical applicability, providing valuable insights for investors looking to align their portfolios with ESG principles. The twelve chosen ESG factors and ten alternative European investment sectors were selected based on their significance in promoting sustainable, responsible, and ethical investment practices. The comprehensive review of academic and industry sources ensured that the factors and alternatives are relevant, reflecting current trends and priorities in ESG investing [9,10,11,12]. This selection provides a robust foundation for analyzing and prioritizing European investment sectors using the Fuzzy-MEREC-AROMAN decision-making model.

Table 6. Alignment of different sectors with the specific ESG factors.

ESG Factors	GB	SRI	ETF	IIF	REI	CBL	SB	REIT	TF	CS
Environmental factors
CCCE	Critical for financing projects that reduce emissions.	Supports low-carbon companies.	Includes companies reducing emissions.	Invests in initiatives reducing carbon footprint.	Directly reduces emissions through renewable energy.	Supports companies with strong emission reduction strategies.	-	-	Focuses on low-carbon themes.	Supports companies with low carbon footprints.
RME	Funds projects promoting sustainable resource use.	Focuses on resource-efficient firms.	Targets companies with efficient resource use.	Invests in resource-efficient enterprises.	Promotes efficient energy use in renewables.	Invests in leaders of resource management.	-	Invests in energy-efficient buildings.	Focuses on resource management themes.	Supports firms with efficient resource utilization.
BLU	Funds conservation and sustainable land use projects.	Invests in companies protecting biodiversity.	-	Supports projects promoting biodiversity.	Supports sustainable land use in renewable projects.	-	-	-	-	Supports firms with strong biodiversity practices.
PWM	Invests in pollution control and waste management initiatives.	Focuses on firms with strong waste management practices.	Targets companies with pollution control measures.	Supports waste management initiatives.	Reduces pollution through clean energy projects.	Invests in companies with robust pollution control.	-	Invests in properties with waste management systems.	Focuses on waste management themes.	Supports firms with strong waste management.
Social factors
LPWC	-	Invests in companies with fair labor practices.	Includes firms with strong labor standards.	Invests in improving labor conditions.	-	-	Funds projects improving labor conditions.	-	-	Supports companies with good labor practices.
DI	-	Invests in companies promoting workplace diversity.	Includes firms with strong diversity policies.	Invests in diversity initiatives.	-	Invests in companies promoting diversity.	Funds diversity-promoting projects.	-	-	Supports firms with good diversity practices.
CESI	-	Invests in companies engaging positively with communities.	-	Invests in projects benefiting communities.	-	-	Funds community development projects.	-	-	Supports companies with strong community engagement.
CRPS	-	Invests in firms with strong customer relations.	Includes companies with good product safety records.	Invests in companies prioritizing customer safety.	-	-	-	-	Focuses on sectors with critical product safety.	Supports companies with strong customer relations.
Governance factors
BCI	-	Invests in firms with independent boards.	Includes companies with strong governance.	Supports companies with robust governance.	-	Invests in firms with independent boards.	-	-	-	Supports companies with strong board independence.
ECI	-	Invests in firms aligning executive pay with sustainability.	-	Supports companies with equitable executive compensation.	-	Invests in companies with aligned compensation.	-	-	-	Supports firms with good executive compensation practices.
TD	Requires transparency in fund usage.	Invests in firms with high transparency.	-	-	-	Invests in companies with comprehensive disclosure.	-	Invests in properties with clear reporting.	Focuses on high-transparency themes.	Supports firms with strong disclosure practices.
ACEP	-	Invests in firms adhering to ethical standards.	-	Supports companies emphasizing ethical practices.	-	Invests in companies with strong ethical policies.	-	-	-	Supports firms with strong anti-corruption practices.

(Source: Author’s own elaboration.)

highlights how different sectors align with specific ESG factors, ensuring comprehensive coverage and relevance to the research objectives.

It is evident from Table 6 that the chosen ESG factors contribute significantly to the evaluation and performance of the ten selected European investment sectors. Each factor addresses critical aspects of environmental, social, and governance considerations, ensuring that investments are sustainable, responsible, and ethical. This comprehensive approach aligns with current trends in ESG investing and provides a robust framework for assessing and prioritizing investment opportunities [42,43,44,45,58,61]. Each ESG factor is mapped to the respective investment sectors in

Table 7. Mapping of each ESG factor to the respective European investment sectors.

ESG Factors	GB	SRI	ETF	IIF	REI	CBL	SB	REIT	TF	CS
Environmental factors
CCCE	✔	✔	✔	✔	✔					✔
RME	✔	✔		✔	✔	✔		✔
BLU	✔	✔		✔
PWM	✔	✔	✔		✔	✔		✔
Social factors
LPWC		✔	✔	✔			✔			✔
DI		✔	✔	✔		✔	✔			✔
CESI		✔		✔			✔			✔
CRPS		✔	✔	✔					✔	✔
Governance factors
BCI		✔	✔	✔		✔				✔
ECI		✔		✔		✔
TD	✔	✔				✔		✔	✔	✔
ACEP		✔		✔		✔				✔

(Source: Author’s own elaboration.)

to show their significance and relevance to the ongoing research. Table 7 also highlights how different sectors align with specific ESG factors, ensuring comprehensive coverage and relevance to the research objectives.

3.2. Fuzzy Decision-Making Model

Fuzzy logic is a mathematical approach that deals with uncertainty and ambiguity, making it particularly useful in decision-making processes where expert opinions are subjective and may vary. In this research, fuzzy logic is employed to handle the inherent uncertainty in the experts’ judgments regarding the performance of different European investment sectors based on various ESG factors [62,67,73]. The following are the detailed steps of how fuzzy logic is incorporated in this decision-making model:

• Step 1: A structured questionnaire was designed to capture the experts’ evaluations. The questionnaire included sections for each of the twelve ESG criteria, with sub-sections for detailed aspects of each criterion. Each expert team was asked to rate the performance of each investment sector with respect to each ESG criterion using linguistic variables (e.g., “very high”, “high”, “moderate”, “low”, “very low”). These linguistic variables help capture subjective judgments that are later converted into numeric values followed by fuzzy numbers according to the scale provided in

  Table 8. Conversion of qualitative expert judgments into respective TFNs.

  Qualitative Expressions	Designation	Quantification	TFNs
  Very Low	VL	1	(1,1,2)
  Low	L	3	(2,3,4)
  Moderate	M	5	(4,5,6)
  High	H	7	(6,7,8)
  Very High	VH	9	(8,9,9)

  (Source: Committee of expert members.)

  . These are the variables that experts use to express their opinions in natural language. In this research, the linguistic variables could be the performance levels of the investment sectors with respect to each ESG factor. The questionnaire was distributed to the three expert teams through various channels, including email, online survey platforms, and face-to-face meetings, depending on their preferences and availability. Each team was given a specific timeframe to complete and return the questionnaires. Regular follow-ups were conducted to ensure timely responses and address any queries. To reduce bias and encourage honest feedback, the members of each team remained completely anonymous to other team members and each expert was only allowed to discuss with their own team members regarding the judgments made throughout the whole analysis. This helped in obtaining unbiased and genuine evaluations.
• Step 2: Each expert team provides their ratings by verbally indicating the performance of the investment sectors with respect to each criterion, as shown in

  Table 9. Judgments from three expert teams.

  Expert Team 1 (ET 1)
  	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
  GB	VH	H	H	VH	M	M	H	VH	L	L	H	VH
  SRI	H	VH	M	H	H	VH	VH	H	M	M	VH	H
  ETF	M	H	L	M	M	H	H	M	H	H	H	M
  IIF	VH	VH	VH	H	H	VH	VH	VH	H	H	VH	VH
  REI	VH	VH	VH	VH	M	H	H	VH	M	M	VH	VH
  CBL	H	H	M	M	M	M	M	H	H	H	H	H
  SB	H	H	H	H	H	H	VH	H	M	M	VH	H
  REIT	M	M	M	M	H	H	M	H	VH	VH	H	M
  TF	H	H	H	H	M	H	H	H	M	M	H	H
  CS	M	M	M	M	H	H	M	M	VH	H	H	M
  Expert Team 2 (ET 2)
  	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
  GB	VH	H	H	H	H	M	H	H	M	L	H	VH
  SRI	H	VH	M	H	H	VH	VH	H	H	H	H	H
  ETF	M	H	L	H	M	H	H	M	M	H	H	M
  IIF	VH	VH	VH	VH	H	VH	VH	VH	H	H	VH	VH
  REI	VH	VH	VH	VH	H	H	H	VH	M	M	VH	VH
  CBL	H	H	M	M	M	M	H	H	M	H	H	H
  SB	H	H	H	H	H	H	VH	H	M	M	VH	H
  REIT	M	M	M	M	M	H	M	H	H	VH	H	M
  TF	H	H	H	H	M	H	H	H	H	M	H	H
  CS	M	M	M	M	M	H	M	M	H	H	H	M
  Expert Team 3 (ET 3)
  	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
  GB	VH	H	VH	VH	M	M	VH	VH	M	L	H	VH
  SRI	H	VH	H	H	H	VH	VH	H	M	M	VH	H
  ETF	H	H	L	M	M	H	H	M	H	H	H	M
  IIF	VH	VH	VH	H	H	VH	VH	VH	H	H	VH	VH
  REI	VH	VH	VH	VH	M	H	VH	VH	M	M	VH	VH
  CBL	H	H	H	M	M	M	M	H	H	H	H	H
  SB	H	VH	H	H	H	H	VH	H	M	M	VH	H
  REIT	M	H	M	M	H	H	M	H	VH	VH	H	M
  TF	H	H	VH	H	M	H	H	H	M	M	H	H
  CS	M	H	M	M	H	H	M	M	VH	H	H	M

  (Source: Committee of expert members.)

  . Following the scale provided in Table 8, these ratings are then converted into Triangular Fuzzy Numbers (TFNs) using the predefined membership functions illustrated in Figure 2.
• Step 3: Since multiple expert teams are involved, their individual fuzzy ratings need to be aggregated to form a collective assessment known as a fuzzy decision matrix. This can be achieved using Equation (1) by taking the average of the fuzzy numbers provided by different teams, known as the fuzzy aggregation method [80]. Finally, the three performance matrices from three expert teams are aggregated using Equation (1) to obtain the fuzzy decision matrix shown in

  Table 10. Aggregated fuzzy decision matrix.

  	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
  GB	(8,9,9)	(6,7,8)	(6.667,7.667,8.333)	(7.333,8.333,8.667)	(4.667,5.667,6.667)	(4,5,6)	(6.667,7.667,8.333)	(7.333,8.333,8.667)	(3.333,4.333,5.333)	(2,3,4)	(6,7,8)	(8,9,9)
  SRI	(6,7,8)	(8,9,9)	(4.667,5.667,6.667)	(6,7,8)	(6,7,8)	(8,9,9)	(8,9,9)	(6,7,8)	(4.667,5.667,6.667)	(4.667,5.667,6.667)	(7.333,8.333,8.667)	(6,7,8)
  ETF	(4.667,5.667,6.667)	(6,7,8)	(2,3,4)	(4.667,5.667,6.667)	(4,5,6)	(6,7,8)	(6,7,8)	(4,5,6)	(5.333,6.333,7.333)	(6,7,8)	(6,7,8)	(4,5,6)
  IIF	(8,9,9)	(8,9,9)	(8,9,9)	(6.667,7.667,8.333)	(6,7,8)	(8,9,9)	(8,9,9)	(8,9,9)	(6,7,8)	(6,7,8)	(8,9,9)	(8,9,9)
  REI	(8,9,9)	(8,9,9)	(8,9,9)	(8,9,9)	(4.667,5.667,6.667)	(6,7,8)	(6.667,7.667,8.333)	(8,9,9)	(4,5,6)	(4,5,6)	(8,9,9)	(8,9,9)
  CBL	(6,7,8)	(6,7,8)	(4.667,5.667,6.667)	(4,5,6)	(4,5,6)	(4,5,6)	(4.667,5.667,6.667)	(6,7,8)	(5.333,6.333,7.333)	(6,7,8)	(6,7,8)	(6,7,8)
  SB	(6,7,8)	(6.667,7.667,8.333)	(6,7,8)	(6,7,8)	(6,7,8)	(6,7,8)	(8,9,9)	(6,7,8)	(4,5,6)	(4,5,6)	(8,9,9)	(6,7,8)
  REIT	(4,5,6)	(4.667,5.667,6.667)	(4,5,6)	(4,5,6)	(5.333,6.333,7.333)	(6,7,8)	(4,5,6)	(6,7,8)	(7.333,8.333,8.667)	(8,9,9)	(6,7,8)	(4,5,6)
  TF	(6,7,8)	(6,7,8)	(6.667,7.667,8.333)	(6,7,8)	(4,5,6)	(6,7,8)	(6,7,8)	(6,7,8)	(4.667,5.667,6.667)	(4,5,6)	(6,7,8)	(6,7,8)
  CS	(4,5,6)	(4.667,5.667,6.667)	(4,5,6)	(4,5,6)	(5.333,6.333,7.333)	(6,7,8)	(4,5,6)	(4,5,6)	(7.333,8.333,8.667)	(6,7,8)	(6,7,8)	(4,5,6)

  (Source: Author’s own elaboration.)

  .

Suppose that there are ‘d’ decision-makers providing ‘d’ number of decisions in TFNs—say, {$\widetilde{N_1}$ = (a₁, b₁, c₁), $\widetilde{N_2}$ = (a₂, b₂, c₂), $\widetilde{N_3}$ = (a₃, b₃, c₃)…., $\widetilde{N_d}$ = (a_d, b_d, c_d)}. Then, these ‘d’ fuzzy numbers can be aggregated using Equation (1). $\widetilde{F_1}$ = (f₁, f₂, f₃) represents the aggregated fuzzy number in TFNs.

$$\widetilde{F_1}=(f_1 ,f_2 ,f_3) ({\displaystyle \frac{1}{d}}{\sum }_{i=1}^d{a}_i,{\displaystyle \frac{1}{d}}{\sum }_{i=1}^d{b}_i,{\displaystyle \frac{1}{d}}{\sum }_{i=1}^d{c}_i)\hspace{1em}\hspace{1em}(\mathrm{where}, \mathrm{i} = 1, 2, 3\dots ,\mathrm{d})$$

(1)

• Step 4: To use the fuzzy ratings in the adopted MCDM tools like MEREC and AROMAN, they need to be converted back into crisp (non-fuzzy) scores. This process is called defuzzification. Although, various defuzzification approaches are available, the simple mean method is currently used here due to its simplicity and ease of use. Therefore, all the TFNs in Table 10 are defuzzified using Equation (2) to obtain the final decision matrix Now, the aggregated fuzzy number $\widetilde{F_1}$ = (f₁, f₂, f₃) can be defuzzified using Equation (2) to convert it into crisp numeric values.

$$F_1 = {\displaystyle \frac{f_1+f_2+f_3}{3}}$$

(2)

• Incorporating fuzzy logic into this research allows for a more nuanced and accurate representation of expert opinions, capturing the ambiguity and subjectivity inherent in human judgments. By converting these opinions into fuzzy numbers, aggregating them, and then defuzzifying, it can effectively integrate expert insights into the MCDM process, ensuring that the final prioritization of investment sectors is robust and reliable.

3.3. MEREC Decision-Making Model

The MEREC is a novel approach for determining objective weights in MCDM problems. In the realm of MCDM, the selection of an appropriate method is crucial for accurately evaluating the critical factors. The fundamental idea behind MEREC is to assess the impact of removing each criterion on the overall performance of the alternatives [5]. The MEREC approach stands out due to its unique ability to consider the interdependencies among criteria, offering a more sophisticated and realistic assessment framework. MEREC uniquely accounts for the interdependence among criteria by evaluating the removal effect of each criterion [60]. This approach recognizes that the importance of a criterion can be influenced by the presence or absence of other criteria, offering a more nuanced and realistic assessment compared to methods that treat criteria as independent.

In the context of prioritizing European investment sectors based on various ESG factors, the MEREC MCDM method offers several distinct advantages over other MCDM methods. The unique capabilities of MEREC make it particularly suited for this research, ensuring a comprehensive evaluation of the ESG criteria impacting investment decisions [66]. In evaluating investment sectors, ESG factors often interact and influence each other. MEREC considers these interdependencies by evaluating the impact of removing each criterion. This ensures a more realistic and integrated assessment of how various ESG factors collectively influence investment decisions. The reliability of criteria weights is crucial when assessing investment sectors with conflicting ESG factors. MEREC’s focus on removal effects provides a robust framework for determining the true impact of each criterion, leading to more dependable weight assignments [68,69]. This is particularly important in ensuring that the prioritization reflects the actual significance of each ESG factor. Moreover, investment environments are dynamic, with the importance of ESG factors evolving over time. MEREC allows for the dynamic adjustment of weights based on the contextual significance of each criterion. This adaptability ensures that the weighting remains relevant and accurate, reflecting current trends and emerging priorities in ESG considerations.

ESG assessments must consider the collective impact of environmental, social, and governance factors. MEREC evaluates criteria holistically, considering their combined effects rather than in isolation [70,71]. This holistic evaluation aligns with the complex nature of ESG assessments, ensuring a comprehensive and integrated analysis of investment sectors. Additionally, the ESG factors involved in this research are diverse and often conflicting. MEREC effectively handles such complexity by systematically identifying the most influential criteria based on their overall effects [63,70,78]. This makes it suitable for multifaceted evaluations, ensuring that the prioritization of investment sectors accurately reflects the complex interplay of various ESG factors. Informed decision-making is critical for investors and policymakers. By providing a clear and comprehensive framework for criteria evaluation, MEREC aids in understanding the relative importance of different ESG factors [67,73]. This informed perspective supports better-aligned and more strategic investment decisions, aligning with sustainability objectives and enhancing long-term investment outcomes. Therefore, the MEREC method’s capabilities of providing robust and reliable weight assignments and handling complex and conflicting criteria make it an ideal choice for this research [62,63,64,65,69,76]. Its application ensures a realistic, reliable, and adaptable framework for prioritizing European investment sectors based on ESG factors, ultimately supporting more sustainable and strategic investment decisions.

Table 11. Comparisons of MEREC with other MCDM tools.

Aspect	MEREC Method	Other Weight Estimation MCDM Tools	Explanation
Criteria weight calculation [10,11,13,22]	Utilizes the removal effects of criteria to determine their relative importance, ensuring each criterion’s impact is accurately measured.	Often rely on subjective weighting or arbitrary assignment of weights.	MEREC provides a systematic and objective approach to weight determination, reducing subjectivity and improving accuracy compared to methods relying on subjective pairwise comparisons or predefined weights.
Handling ambiguity [48,49,53,66]	Employs fuzzy logic to manage ambiguity and subjectivity in expert judgments.	Many MCDM tools do not incorporate fuzzy logic, leading to potential biases and inaccuracies.	By converting qualitative assessments to quantitative data, MEREC effectively handles uncertainties and expert opinion variabilities better than methods that lack fuzzy logic capabilities.
Consistency and robustness [5,32,68,71]	It maintains consistency in the weighting process, reducing inconsistencies that can arise from subjective judgments.	Consistency and robustness are often less thoroughly validated across various scenarios.	The validation process for MEREC confirms its reliability and robustness in diverse decision-making scenarios, providing a stronger basis for decision-making than some other tools that may not undergo extensive validation.
Transparency in decision-making [11,22,52,65,70]	Clear and transparent process for determining the impact of each criterion on the overall decision-making process.	Transparency varies among MCDM tools; some may not clearly explain how criteria impact decisions.	MEREC’s methodical approach ensures transparency, making the decision-making process more understandable and credible compared to tools that may lack clarity in demonstrating criterion impacts.
Flexibility and adaptability [64,73,74,76]	Adaptable to different contexts and criteria, making it versatile for various decision-making scenarios.	Some methods may be more rigid and less adaptable to different contexts or sets of criteria.	MEREC’s flexibility allows it to be tailored to specific needs, enhancing its applicability across diverse situations compared to methods that may be more rigid in their approach.
Quantitative assessment [28,40,45]	Converts qualitative assessments into quantitative data, ensuring comprehensive and balanced evaluation of all criteria.	Some tools may rely primarily on qualitative or quantitative data, potentially leading to imbalanced evaluations.	The integration of qualitative and quantitative data in MEREC ensures a more holistic and balanced assessment compared to tools that may prioritize one type of data over the other.
Highlighting critical factors [5,6,13,19,23]	Effectively identifies and highlights the most critical factors, ensuring focus on the most impactful criteria.	Methods vary in their ability to identify critical factors; some may not effectively prioritize critical criteria.	By accurately identifying critical factors, MEREC ensures that decision-makers can focus on what truly matters, enhancing the quality of the decision-making process more effectively than methods that may not prioritize critical criteria as effectively.

(Source: Author’s own elaboration.)

and

Table 12. Comparisons of AROMAN with other MCDM tools.

Aspects	AROMAN Method	Other MCDM Tools	Explanation
Normalization approach [51,57,63,67]	Utilizes a two-step normalization process to standardize criteria across different scales and units, ensuring fair comparison.	Methods like TOPSIS and ELECTRE may not employ a two-step normalization process, potentially leading to biases from diverse measurement scales.	AROMAN’s normalization approach ensures all criteria are treated equally and comparably, enhancing the accuracy of sector prioritization in diverse ESG contexts.
Ranking order determination [32,34,46,52]	Generates an alternative ranking order based on normalized scores, identifying sectors that consistently perform well across criteria.	AHP relies on pairwise comparisons to determine criteria weights; TOPSIS identifies the alternative closest to the ideal solution; ELECTRE focuses on outranking methods.	AROMAN’s ranking order determination provides a systematic way to prioritize sectors based on holistic performance, integrating complex ESG considerations effectively.
Handling complex interdependencies [31,38,40,49,54]	Addresses intricate interdependencies between criteria through normalization and ranking, ensuring comprehensive sector evaluation.	Other methods may overlook or less effectively manage interdependencies, potentially leading to suboptimal prioritization in complex ESG contexts.	AROMAN’s approach considers the holistic impact of interdependencies, offering a nuanced evaluation of sector performance, crucial for sustainable investing decisions.
Validation and reliability [61,62,70]	Enhances validation and reliability through a rigorous normalization process that minimizes the impact of outliers and data variations.	While other methods have validation mechanisms, AROMAN’s normalization process strengthens reliability by standardizing criteria assessments comprehensively.	AROMAN’s validation ensures robust and credible results in ESG investing, supporting informed decision-making aligned with sustainable and responsible investment strategies.
Comprehensive evaluation [6,7,17,21,26]	Integrates and synthesizes sector performance across all criteria, providing a holistic evaluation, crucial for ESG-focused investment decisions.	Other methods vary in their ability to comprehensively evaluate sector performance across diverse ESG factors.	AROMAN’s comprehensive evaluation approach ensures stakeholders gain clarity on how sectors align with ESG goals, supporting strategic and informed investment decisions effectively.

(Source: Author’s own elaboration.)

provides a brief summary highlighting the advantages of the MEREC and AROMAN MCDM method compared to other MCDM tools, along with explanations of how MEREC MCDM outperforms and overcomes the drawbacks of other tools. The following is a detailed step-by-step explanation of the MEREC method:

• Step 1: Construct a decision matrix X =${\left[x_{ij}\right]}_{m\times n}$ according to Equation (3), where ‘x_ij’ represents the performance score of alternative ‘A_i’ with respect to criterion ‘C_j’. The ‘m’ alternatives and ‘n’ criteria may be represented by Equations (4) and (5) that have been already identified in the prior sub-sections, illustrated in Table 4 and Table 5, respectively. The final decision table for this ongoing analysis is shown in Table 13.

$$X (m_i\times n_j)=\begin{array}{c}\\ A_1\\ \begin{array}{c}{A}_2\\ \dots \\ \begin{array}{c}{A}_i\\ \dots \\ A_m\end{array}\end{array}\end{array}\left[\begin{array}{c}{C}_1\\ x_{11}\\ x_{21}\\ \dots \\ x_{i1}\\ \dots \\ x_{m1}\end{array} \begin{array}{c}{C}_2\\ x_{12}\\ x_{22}\\ \dots \\ x_{i2}\\ \dots \\ x_{m2}\end{array} \begin{array}{c}\dots \\ \dots \\ \dots \\ \dots \\ \dots \\ \dots \\ \dots \end{array} \begin{array}{c}{C}_j\\ x_{1j}\\ x_{2j}\\ \dots \\ x_{ij}\\ \dots \\ x_{mj}\end{array} \begin{array}{c}\dots \\ \dots \\ \dots \\ \dots \\ \dots \\ \dots \\ \dots \end{array} \begin{array}{c}{C}_n\\ x_{1n}\\ x_{2n}\\ \dots \\ x_{in}\\ \dots \\ x_{mn}\end{array}\right]$$

(3)

A_i = {A₁, A₂…, A_m} is the set of alternatives

(4)

C_j = {C₁, C₂…, C_n} is the set of criteria

(5)

• Step 2: Simple linear normalization given by Equation (6) is followed to normalize the decision matrix ‘X’. ‘B’ represents the set of beneficial criteria, whereas ‘NB’ represents the set of non-beneficial criteria. Table 14 illustrates the normalized matrix, and the normalized elements of the normalized matrix are denoted by ‘r_ij’. Note that this normalization process differs from other methods, as it transforms all criteria into minimization criteria.

$$r_{\mathrm{ij}}=\left\{\begin{array}{c}{\displaystyle \frac{\begin{array}{c}\mathit{min}{x}_{ij}\\ j\end{array}}{x_{ij}}} j ϵ B\\ {\displaystyle \frac{x_{ij}}{\begin{array}{c}\mathit{max}{x}_{ij}\\ j\end{array}}} j ϵ NB\end{array}\right.$$

(6)

• Step 3: Calculate the overall performances of each alternative ‘S_i’ using a logarithmic measure with equal weights for all criteria as shown in Equation (7). This nonlinear function ensures that smaller values of ‘r_ij’ yield higher performance scores ‘S_i’. The ‘S_i’ values of each alternative are computed and presented in Table 15.

$$S_i=\ln \left(1+\left({\displaystyle \frac{1}{n}}{\sum }_{j=1}^n\left|ln\left(r_{ij}\right)\right|\right)\right)$$

(7)

• Step 4: In this step, the performance scores of each alternative ($S_{ij}^{\prime }$) are calculated using Equation (8) by removing each criterion one at a time. This step is similar to step 3; the only difference is that the performance measures of the i-th alternative are computed on the basis of the removal of the j-th criterion. Hence, this step results in ‘n’ sets of performance scores, associated with ‘n’ criteria removed one at a time. The ‘$S_{ij}^{\prime }$’ values of the i-th alternative concerned with the removal of the j-th criterion are computed and presented in Table 15.

$$S_{ij}^{\prime } \left(1+\left({\displaystyle \frac{1}{n}}{\sum }_{k=1,k\ne j}^{n-1}\left|ln\left(r_{ik}\right)\right|\right)\right)$$

(8)

• Step 5: The removal effect of ‘E_j’ for each criterion is calculated in Table 15 using Equation (9) based on the differences between the performance scores obtained from Step 3 and Step 4.

$$E_j={\sum }_{i=1}^m\left|S_{ij}^{\prime }-S_i\right|$$

(9)

• Step 6: The objective weight ‘w_j’ of each criterion is calculated using Equation (10) and presented in Table 15. This final step normalizes the removal effects to assign a weight to each criterion, reflecting its importance in the decision-making process. The criteria weights are also illustrated graphically in Figure 3.

$$w_j={\displaystyle \frac{E_j}{\sum _{j=1}^n{E}_j}}$$

(10)

3.4. AROMAN Decision-Making Model

AROMAN is an MCDM model designed to provide a systematic and balanced approach to ranking alternatives by considering two distinct normalization steps. It plays a crucial role in this research by enhancing the accuracy and relevance of sector prioritization based on ESG criteria. AROMAN employs a two-step normalization process, which is essential when dealing with diverse criteria and varying measurement scales inherent in ESG factors [6]. This ensures that all criteria are treated equally and comparably, avoiding biases that might arise from different units or scales used in the evaluation. By generating an alternative ranking order, AROMAN facilitates the identification of sectors that perform consistently well across multiple criteria. This is particularly relevant in ESG investing, where the integration of environmental, social, and governance factors requires a balanced approach to prioritize sectors that excel holistically [12,13]. Furthermore, many MCDM methods may overlook the intricate interdependencies between criteria, leading to suboptimal prioritization. AROMAN’s methodical approach considers these interdependencies through normalization and ranking, providing a more nuanced assessment of sector performance. AROMAN’s ability to integrate and synthesize performance across all criteria ensures a comprehensive evaluation of each investment sector. This is crucial in sustainable investing, where stakeholders seek clarity on how well sectors align with their ESG goals and strategies [6,7]. The method’s two-step normalization process enhances the validation and reliability of results by minimizing the impact of outliers and ensuring robustness against variations in data. This validation strengthens the credibility of findings, crucial for stakeholders making informed investment decisions.

In contrast, AHP uses pairwise comparisons to derive criteria weights, which may introduce subjectivity and require extensive data input. AROMAN’s normalization process provides a more objective and systematic way to rank alternatives based on their performance across criteria. Similarly, TOPSIS identifies the alternative closest to the ideal solution and farthest from the negative ideal solution [16,29,30,31,32,42]. However, it may not address the normalization of criteria as comprehensively as AROMAN does, potentially leading to less accurate rankings in the context of diverse ESG factors. Additionally, ELECTRE focuses on outranking methods, emphasizing pairwise comparisons and preference thresholds. While ELECTRE is robust in handling qualitative preferences, AROMAN’s normalization approach ensures a more quantitative and standardized evaluation across criteria, crucial for integrating complex ESG considerations [81]. Table 12 clearly highlights the advantages of the AROMAN MCDM technique compared to other MCDM tools.

In the research context of prioritizing European investment sectors based on ESG factors using the Fuzzy-MEREC-AROMAN decision-making model, the AROMAN method stands out for its rigorous normalization process, systematic ranking order determination, and ability to handle complex interdependencies among criteria. These qualities make AROMAN particularly relevant for ensuring accurate sector prioritization aligned with sustainable and responsible investment strategies, thereby enhancing decision-making processes in ESG-focused investments [33,36,47]. These qualities distinguish the AROMAN method in the MCDM landscape and make it particularly suitable for prioritizing European investment sectors based on diverse ESG factors, ensuring reliable, comprehensive, and standardized evaluations that are crucial for sustainable and responsible investment decision-making. The following is a detailed breakdown of each step in the AROMAN model:

• Step 1: Before initiating the decision-making process, it is essential to define the initial decision matrix with the input data as already depicted in Table 13 according to Equation (3).
• Step 2: Once the decision matrix is defined, the next step is to normalize the input data, structuring them within the range [0, 1]. In the AROMAN MCDM model, two types of normalization procedure are applied. The first variant offers the linear normalization using Equation (11), whereas the second variant offers the vector normalization using Equation (12). However, it should be noted that both the normalization techniques should be used for normalizing both the minimum and maximum type of criteria.

$$n_{ij}={\displaystyle \frac{x_{ij}-x_{ij}^{min}}{x_{ij}^{max}-x_{ij}^{min}}}$$

(11)

$$n_{ij}^{*}={\displaystyle \frac{x_{ij}}{\sqrt{\sum _{i=1}^m{x}_{ij}^2}}}$$

(12)

• Step 3: The results of the linear and vector normalization from step 2 are combined using Equation (13), and the aggregated average normalized matrix is presented in Table 16. Here, ‘$n_{ij}^{norm}$’ represents the aggregated average normalized values, and ‘β’ is a weighting factor ranging from 0 to 1. In this method, the value of ‘β’ is set to 0.5. The arithmetic mean is used for aggregation in this current analysis, since it is most widely used.

$$n_{ij}^{norm}={\displaystyle \frac{\beta n_{ij}-\left(1-\beta \right)n_{ij}^{*}}{2}}$$

(13)

• Step 4: Multiply each element of the aggregated average normalized decision matrix by the corresponding criterion weight ‘w_j’ using Equation (14) as shown in Table 17.

$$\widehat{n_{ij}}=w_j·n_{ij}^{norm}$$

(14)

• Step 5: Add the normalized weighted values separately for criteria that need to be minimized (L_i) and maximized (M_i) according to Equation (15) based on the criteria nature shown in Table 17.

$$\left\{\begin{array}{c}{L}_i=\sum _{j=1}^n{\widehat{n_{ij}}}^{min} for minimum criteria\\ M_i=\sum _{j=1}^n{\widehat{n_{ij}}}^{max} for maximum criteria\end{array}\right.$$

(15)

• Step 6: The ranking scores of the alternatives (R_i) are obtained using Equation (16) and presented in Table 17. The chosen European investment sectors are prioritized accordingly based on these ‘R_i’ scores as shown in Table 17. The investment sector with the highest score is mostly the preferred option in the group. In Equation (16), ‘λ’ represents the coefficient degree of the criterion type and it is set to 0.5 in order to balance both types of beneficial and non-beneficial criteria. However, ‘λ’ can be adjusted based on the number of min and max criteria and the nature of the decision-making problem. The ranking scores of the alternatives are also portrayed graphically in Figure 4, clearly conveying the preference rating order of the ten European investment sectors.

$$R_i=L_i^{\lambda }+M_i^{(1-\lambda )}$$

(16)

4. Results and Validation

The application of the Fuzzy-MEREC-AROMAN decision-making model to prioritize European investment sectors based on different ESG factors has yielded significant insights. The results are summarized in the following section, detailing the ESG criteria weights, sector rankings, and comparative and sensitivity analysis. The following section contains the detailed mathematical analysis for prioritizing the ten European investment sectors based on twelve ESG parameters. The MEREC method is applied initially under a fuzzy environment to evaluate the criteria weights followed by the application of AROMAN to prioritize the ten investment options. One of the primary strategies employed in this study to mitigate bias is the careful selection of a diverse panel of experts. By including experts from various disciplines—environmental science, social governance, and financial analysis—the study ensures that a wide range of perspectives is considered. This diversity helps balance any individual biases that might arise from a single expert’s background or experience. Each expert team provided evaluations based on their domain expertise, which were then synthesized to form a comprehensive assessment of each investment sector. A transparent and iterative evaluation process was adopted to enhance the reliability of the expert assessments. Experts were provided with the opportunity to review and discuss their initial evaluations, allowing a consensus to be reached on contentious points. This iterative approach not only helps in refining the assessments but also fosters greater accountability, as experts are aware that their inputs are subject to scrutiny and discussion. Additionally, documentation of the evaluation process, including how disagreements were resolved, adds another layer of transparency, making it easier to identify and address any residual biases. Fuzzy logic was employed in this study to address the inherent subjectivity and uncertainty in expert judgments. Unlike traditional crisp evaluations, fuzzy logic allows for the representation of uncertainty and ambiguity in expert opinions, enabling more nuanced assessments. By converting qualitative assessments into quantitative data, fuzzy logic reduces the likelihood of extreme or biased judgments disproportionately affecting the overall results. This approach also facilitates the aggregation of opinions from multiple experts, ensuring that the final evaluation reflects a balanced view rather than being overly influenced by any single expert. The following steps are followed to achieve the primary objectives of the current research:

• Step 1: Starting with the MEREC analysis, the final decision matrix is formed in Table 13 according to Equation (3).

Table 13. Final decision matrix.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	8.667	7	7.556	8.111	5.667	5	7.556	8.111	4.333	3	7	8.667
SRI	7	8.667	5.667	7	7	8.667	8.667	7	5.667	5.667	8.111	7
ETF	5.667	7	3	5.667	5	7	7	5	6.333	7	7	5
IIF	8.667	8.667	8.667	7.556	7	8.667	8.667	8.667	7	7	8.667	8.667
REI	8.667	8.667	8.667	8.667	5.667	7	7.556	8.667	5	5	8.667	8.667
CBL	7	7	5.667	5	5	5	5.667	7	6.333	7	7	7
SB	7	7.556	7	7	7	7	8.667	7	5	5	8.667	7
REIT	5	5.667	5	5	6.333	7	5	7	8.111	8.667	7	5
TF	7	7	7.556	7	5	7	7	7	5.667	5	7	7
CS	5	5.667	5	5	6.333	7	5	5	8.111	7	7	5
Max	8.667	8.667	8.667	8.667	7	8.667	8.667	8.667	8.111	8.667	8.667	8.667
Min	5	5.667	3	5	5	5	5	5	4.333	3	7	5
Max-Min	3.667	3	5.667	3.667	2	3.667	3.667	3.667	3.778	5.667	1.667	3.667
Square sum	503.466	542.674	436.650	452.113	366.444	494.234	519.652	511.022	393.795	387.232	585.139	496.351
Square root	22.438	23.295	20.896	21.263	19.143	22.231	22.796	22.606	19.844	19.678	24.190	22.279

(Source: Author’s own elaboration.)

Table 13. Final decision matrix.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	8.667	7	7.556	8.111	5.667	5	7.556	8.111	4.333	3	7	8.667
SRI	7	8.667	5.667	7	7	8.667	8.667	7	5.667	5.667	8.111	7
ETF	5.667	7	3	5.667	5	7	7	5	6.333	7	7	5
IIF	8.667	8.667	8.667	7.556	7	8.667	8.667	8.667	7	7	8.667	8.667
REI	8.667	8.667	8.667	8.667	5.667	7	7.556	8.667	5	5	8.667	8.667
CBL	7	7	5.667	5	5	5	5.667	7	6.333	7	7	7
SB	7	7.556	7	7	7	7	8.667	7	5	5	8.667	7
REIT	5	5.667	5	5	6.333	7	5	7	8.111	8.667	7	5
TF	7	7	7.556	7	5	7	7	7	5.667	5	7	7
CS	5	5.667	5	5	6.333	7	5	5	8.111	7	7	5
Max	8.667	8.667	8.667	8.667	7	8.667	8.667	8.667	8.111	8.667	8.667	8.667
Min	5	5.667	3	5	5	5	5	5	4.333	3	7	5
Max-Min	3.667	3	5.667	3.667	2	3.667	3.667	3.667	3.778	5.667	1.667	3.667
Square sum	503.466	542.674	436.650	452.113	366.444	494.234	519.652	511.022	393.795	387.232	585.139	496.351
Square root	22.438	23.295	20.896	21.263	19.143	22.231	22.796	22.606	19.844	19.678	24.190	22.279

(Source: Author’s own elaboration.)

• Step 2: All the performance values of Table 13 are normalized using Equation (6) and the normalized decision matrix is presented in Table 14.

Table 14. Normalized decision matrix (MEREC).

	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	1	0.810	0.397	0.616	0.882	1	0.662	0.616	1	1	1	0.577
SRI	0.808	0.654	0.529	0.714	0.714	0.577	0.577	0.714	0.765	0.529	0.863	0.714
ETF	0.654	0.810	1	0.882	1	0.714	0.714	1	0.684	0.429	1	1
IIF	1	0.654	0.346	0.662	0.714	0.577	0.577	0.577	0.619	0.429	0.808	0.577
REI	1	0.654	0.346	0.577	0.882	0.714	0.662	0.577	0.867	0.6	0.808	0.577
CBL	0.808	0.810	0.529	1	1	1	0.882	0.714	0.684	0.429	1	0.714
SB	0.808	0.75	0.429	0.714	0.714	0.714	0.577	0.714	0.867	0.6	0.808	0.714
REIT	0.577	1	0.6	1	0.790	0.714	1	0.714	0.534	0.346	1	1
TF	0.808	0.810	0.397	0.714	1	0.714	0.714	0.714	0.765	0.6	1	0.714
CS	0.577	1	0.6	1	0.790	0.714	1	1	0.534	0.429	1	1

(Source: Author’s own elaboration.)

Table 14. Normalized decision matrix (MEREC).

	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	1	0.810	0.397	0.616	0.882	1	0.662	0.616	1	1	1	0.577
SRI	0.808	0.654	0.529	0.714	0.714	0.577	0.577	0.714	0.765	0.529	0.863	0.714
ETF	0.654	0.810	1	0.882	1	0.714	0.714	1	0.684	0.429	1	1
IIF	1	0.654	0.346	0.662	0.714	0.577	0.577	0.577	0.619	0.429	0.808	0.577
REI	1	0.654	0.346	0.577	0.882	0.714	0.662	0.577	0.867	0.6	0.808	0.577
CBL	0.808	0.810	0.529	1	1	1	0.882	0.714	0.684	0.429	1	0.714
SB	0.808	0.75	0.429	0.714	0.714	0.714	0.577	0.714	0.867	0.6	0.808	0.714
REIT	0.577	1	0.6	1	0.790	0.714	1	0.714	0.534	0.346	1	1
TF	0.808	0.810	0.397	0.714	1	0.714	0.714	0.714	0.765	0.6	1	0.714
CS	0.577	1	0.6	1	0.790	0.714	1	1	0.534	0.429	1	1

(Source: Author’s own elaboration.)

• Step 3: The overall performances of each alternative ‘S_i’ are computed using Equation (7) and presented in Table 15.
• Step 4: Similarly, Table 15 also highlights the performance scores of the i-th alternative ($S_{ij}^{\prime }$) on removing the j-th criterion computed using Equation (8).
• Step 5: The removal effect ‘E_j’ for each criterion is calculated using Equation (9) and shown in Table 15.
• Step 6: Moving towards the final step of the MEREC weighting method analysis, the objective weight ‘w_j’ of each criterion is computed using Equation (10) and presented in Table 15. The same has also been illustrated using a bar chart shown in Figure 3.

Table 15. Computation of criteria weights using MEREC.

	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP	Si
GB	0.236	0.222	0.173	0.203	0.228	0.236	0.208	0.203	0.236	0.236	0.236	0.199	0.236
SRI	0.322	0.309	0.296	0.315	0.315	0.301	0.301	0.315	0.319	0.296	0.326	0.315	0.335
ETF	0.171	0.186	0.200	0.192	0.200	0.177	0.177	0.200	0.174	0.141	0.200	0.200	0.200
IIF	0.404	0.380	0.343	0.381	0.385	0.373	0.373	0.373	0.377	0.356	0.392	0.373	0.404
REI	0.341	0.316	0.276	0.308	0.334	0.321	0.316	0.308	0.333	0.310	0.328	0.308	0.341
CBL	0.215	0.215	0.186	0.229	0.229	0.229	0.221	0.206	0.203	0.171	0.229	0.206	0.229
SB	0.302	0.298	0.262	0.295	0.295	0.295	0.281	0.295	0.307	0.284	0.302	0.295	0.315
REIT	0.230	0.266	0.233	0.266	0.251	0.244	0.266	0.244	0.225	0.196	0.266	0.266	0.266
TF	0.262	0.262	0.216	0.254	0.276	0.254	0.254	0.254	0.259	0.243	0.276	0.254	0.276
CS	0.193	0.230	0.196	0.230	0.215	0.208	0.230	0.230	0.188	0.173	0.230	0.230	0.230
Ei	0.156	0.149	0.451	0.160	0.107	0.194	0.204	0.203	0.213	0.427	0.047	0.186	2.496
Weights	0.063	0.060	0.181	0.064	0.043	0.078	0.082	0.081	0.085	0.171	0.019	0.075	1
%	6.3	6.0	18.1	6.4	4.3	7.8	8.2	8.1	8.5	17.1	1.9	7.5	100

(Source: Author’s own elaboration.)

Table 15. Computation of criteria weights using MEREC.

	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP	Si
GB	0.236	0.222	0.173	0.203	0.228	0.236	0.208	0.203	0.236	0.236	0.236	0.199	0.236
SRI	0.322	0.309	0.296	0.315	0.315	0.301	0.301	0.315	0.319	0.296	0.326	0.315	0.335
ETF	0.171	0.186	0.200	0.192	0.200	0.177	0.177	0.200	0.174	0.141	0.200	0.200	0.200
IIF	0.404	0.380	0.343	0.381	0.385	0.373	0.373	0.373	0.377	0.356	0.392	0.373	0.404
REI	0.341	0.316	0.276	0.308	0.334	0.321	0.316	0.308	0.333	0.310	0.328	0.308	0.341
CBL	0.215	0.215	0.186	0.229	0.229	0.229	0.221	0.206	0.203	0.171	0.229	0.206	0.229
SB	0.302	0.298	0.262	0.295	0.295	0.295	0.281	0.295	0.307	0.284	0.302	0.295	0.315
REIT	0.230	0.266	0.233	0.266	0.251	0.244	0.266	0.244	0.225	0.196	0.266	0.266	0.266
TF	0.262	0.262	0.216	0.254	0.276	0.254	0.254	0.254	0.259	0.243	0.276	0.254	0.276
CS	0.193	0.230	0.196	0.230	0.215	0.208	0.230	0.230	0.188	0.173	0.230	0.230	0.230
Ei	0.156	0.149	0.451	0.160	0.107	0.194	0.204	0.203	0.213	0.427	0.047	0.186	2.496
Weights	0.063	0.060	0.181	0.064	0.043	0.078	0.082	0.081	0.085	0.171	0.019	0.075	1
%	6.3	6.0	18.1	6.4	4.3	7.8	8.2	8.1	8.5	17.1	1.9	7.5	100

(Source: Author’s own elaboration.)

• Step 7: Starting with the AROMAN ranking method, it also starts with a decision matrix as already defined in Table 13.
• Step 8: In the AROMAN method, two normalization techniques have been applied using Equations (11) and (12) to stabilize the performance data provided in Table 13.
• Step 9: Both the normalized values from the previous step are aggregated using Equation (13) to form the aggregated average normalized matrix shown in Table 16.

Table 16. Aggregated normalized matrix.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max
Weights	0.063	0.060	0.181	0.064	0.043	0.078	0.082	0.081	0.085	0.171	0.019	0.075
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	0.347	0.186	0.291	0.307	0.157	0.056	0.257	0.302	0.055	0.038	0.072	0.347
SRI	0.214	0.343	0.185	0.219	0.341	0.347	0.345	0.214	0.160	0.190	0.250	0.215
ETF	0.109	0.186	0.036	0.112	0.065	0.215	0.213	0.055	0.212	0.265	0.072	0.056
IIF	0.347	0.343	0.354	0.263	0.341	0.347	0.345	0.346	0.265	0.265	0.340	0.347
REI	0.347	0.343	0.354	0.352	0.157	0.215	0.257	0.346	0.107	0.152	0.340	0.347
CBL	0.214	0.186	0.185	0.059	0.065	0.056	0.108	0.214	0.212	0.265	0.072	0.215
SB	0.214	0.239	0.260	0.219	0.341	0.215	0.345	0.214	0.107	0.152	0.340	0.215
REIT	0.056	0.061	0.148	0.059	0.249	0.215	0.055	0.214	0.352	0.360	0.072	0.056
TF	0.214	0.186	0.291	0.219	0.065	0.215	0.213	0.214	0.160	0.152	0.072	0.215
CS	0.056	0.061	0.148	0.059	0.249	0.215	0.055	0.055	0.352	0.265	0.072	0.056

(Source: Author’s own elaboration.)

Table 16. Aggregated normalized matrix.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max
Weights	0.063	0.060	0.181	0.064	0.043	0.078	0.082	0.081	0.085	0.171	0.019	0.075
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	0.347	0.186	0.291	0.307	0.157	0.056	0.257	0.302	0.055	0.038	0.072	0.347
SRI	0.214	0.343	0.185	0.219	0.341	0.347	0.345	0.214	0.160	0.190	0.250	0.215
ETF	0.109	0.186	0.036	0.112	0.065	0.215	0.213	0.055	0.212	0.265	0.072	0.056
IIF	0.347	0.343	0.354	0.263	0.341	0.347	0.345	0.346	0.265	0.265	0.340	0.347
REI	0.347	0.343	0.354	0.352	0.157	0.215	0.257	0.346	0.107	0.152	0.340	0.347
CBL	0.214	0.186	0.185	0.059	0.065	0.056	0.108	0.214	0.212	0.265	0.072	0.215
SB	0.214	0.239	0.260	0.219	0.341	0.215	0.345	0.214	0.107	0.152	0.340	0.215
REIT	0.056	0.061	0.148	0.059	0.249	0.215	0.055	0.214	0.352	0.360	0.072	0.056
TF	0.214	0.186	0.291	0.219	0.065	0.215	0.213	0.214	0.160	0.152	0.072	0.215
CS	0.056	0.061	0.148	0.059	0.249	0.215	0.055	0.055	0.352	0.265	0.072	0.056

(Source: Author’s own elaboration.)

• Step 10: The weighted values are evaluated using Equation (14) and the aggregated average normalized decision matrix is obtained in Table 17.
• Step 11: The weighted normalized values have been separately summarized in Table 17 using Equation (15) according to the criteria nature.
• Step 12: The ranking score of each alternative computed in Table 17 using Equation (15) represents the final step of AROMAN analysis. The same has also been illustrated using a column chart shown in Figure 4.
• Step 13: Finally, the preference ranking order of the alternatives has been prescribed in Table 17, hereby concluding the final mathematical step of the overall analysis.

Table 17. Ranking of European investment sectors.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Li	Ai	Ri	Rank
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	0.022	0.011	0.053	0.020	0.007	0.004	0.021	0.025	0.005	0.007	0.001	0.026	0.022	0.179	0.570	5
SRI	0.013	0.020	0.034	0.014	0.015	0.027	0.028	0.017	0.014	0.032	0.005	0.016	0.013	0.222	0.587	3
ETF	0.007	0.011	0.006	0.007	0.003	0.017	0.017	0.005	0.018	0.045	0.001	0.004	0.007	0.135	0.450	9
IIF	0.022	0.020	0.064	0.017	0.015	0.027	0.028	0.028	0.023	0.045	0.006	0.026	0.022	0.299	0.694	1
REI	0.022	0.020	0.064	0.023	0.007	0.017	0.021	0.028	0.009	0.026	0.006	0.026	0.022	0.247	0.644	2
CBL	0.013	0.011	0.034	0.004	0.003	0.004	0.009	0.017	0.018	0.045	0.001	0.016	0.013	0.163	0.519	7
SB	0.013	0.014	0.047	0.014	0.015	0.017	0.028	0.017	0.009	0.026	0.006	0.016	0.013	0.210	0.574	4
REIT	0.003	0.004	0.027	0.004	0.011	0.017	0.004	0.017	0.030	0.062	0.001	0.004	0.003	0.181	0.484	8
TF	0.013	0.011	0.053	0.014	0.003	0.017	0.017	0.017	0.014	0.026	0.001	0.016	0.013	0.189	0.551	6
CS	0.003	0.004	0.027	0.004	0.011	0.017	0.004	0.005	0.030	0.045	0.001	0.004	0.003	0.151	0.448	10

(Source: Author’s own elaboration.)

Table 17. Ranking of European investment sectors.

Nature	Min	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Max	Li	Ai	Ri	Rank
	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
GB	0.022	0.011	0.053	0.020	0.007	0.004	0.021	0.025	0.005	0.007	0.001	0.026	0.022	0.179	0.570	5
SRI	0.013	0.020	0.034	0.014	0.015	0.027	0.028	0.017	0.014	0.032	0.005	0.016	0.013	0.222	0.587	3
ETF	0.007	0.011	0.006	0.007	0.003	0.017	0.017	0.005	0.018	0.045	0.001	0.004	0.007	0.135	0.450	9
IIF	0.022	0.020	0.064	0.017	0.015	0.027	0.028	0.028	0.023	0.045	0.006	0.026	0.022	0.299	0.694	1
REI	0.022	0.020	0.064	0.023	0.007	0.017	0.021	0.028	0.009	0.026	0.006	0.026	0.022	0.247	0.644	2
CBL	0.013	0.011	0.034	0.004	0.003	0.004	0.009	0.017	0.018	0.045	0.001	0.016	0.013	0.163	0.519	7
SB	0.013	0.014	0.047	0.014	0.015	0.017	0.028	0.017	0.009	0.026	0.006	0.016	0.013	0.210	0.574	4
REIT	0.003	0.004	0.027	0.004	0.011	0.017	0.004	0.017	0.030	0.062	0.001	0.004	0.003	0.181	0.484	8
TF	0.013	0.011	0.053	0.014	0.003	0.017	0.017	0.017	0.014	0.026	0.001	0.016	0.013	0.189	0.551	6
CS	0.003	0.004	0.027	0.004	0.011	0.017	0.004	0.005	0.030	0.045	0.001	0.004	0.003	0.151	0.448	10

(Source: Author’s own elaboration.)

4.1. Validation

To ensure the robustness and reliability of the prioritization results derived from the Fuzzy-MEREC-AROMAN decision-making model, a comprehensive validation process was conducted. This process involved two primary strategies: comparative analysis with other well-established MCDM tools and detailed sensitivity analysis. The goal of these validation strategies was to confirm the accuracy, consistency, and adaptability of the proposed model in evaluating and prioritizing European investment sectors based on ESG factors. The comparative analysis aimed to benchmark the Fuzzy-MEREC-AROMAN model against other MCDM methods to identify any disparities or consistencies in the results. The sensitivity analysis was designed to test the model’s stability under varying conditions by employing two techniques, varying the trade-off parameter ‘β’ and single-dimensional sensitivity analysis. These validation steps are crucial in demonstrating the efficacy and reliability of the model in providing robust investment sector rankings.

4.1.1. Comparison with Other MCDM Techniques

The comparative analysis aims to evaluate the performance of the AROMAN method relative to seven other established MCDM tools, namely TOPSIS, ARAS, COCOSO, EDAS, WSM, WPM, and WASPAS. This comparison is crucial for understanding the robustness and reliability of the AROMAN method in prioritizing European investment sectors based on various ESG factors. Comparative analysis is an essential aspect of the MCDM field. It involves evaluating and comparing different MCDM methods to understand their performance, strengths, weaknesses, and applicability in various decision-making scenarios. This analysis is crucial for ensuring that the chosen method aligns with the decision context and provides reliable and accurate results. Different MCDM methods have unique characteristics and approaches. Comparative analysis helps to identify the most suitable method for a specific decision problem. It allows for the evaluation of the robustness and reliability of different MCDM methods. By applying multiple methods to the same decision problem and comparing the results, inconsistencies and potential biases can be identified. This process ensures that the chosen method produces stable and dependable results, which is particularly important in complex decision-making scenarios with conflicting criteria. Comparative analysis also highlights the strengths and weaknesses of each technique, aiding in informed method selection. It contributes to this by ensuring that the selected method aligns with the decision-maker’s objectives and preferences and the nature of the decision problem. By comparing the outcomes of various methods, decision-makers can validate their choices and gain confidence in the final decision, knowing that it is backed by a thorough evaluation of alternative approaches. To further mitigate the influence of expert bias, the results obtained from the Fuzzy-MEREC-AROMAN model were cross-validated using other well-established MCDM methods. This comparative analysis serves as a robustness check, ensuring that the rankings produced by the proposed model are consistent with those derived from alternative methodologies. If significant discrepancies were observed, they would prompt a re-examination of the expert inputs, helping to identify and correct potential biases.

Comparative analysis not only helps in selecting the appropriate method but also contributes to the development and improvement of MCDM techniques. By understanding the limitations and performance of existing methods, researchers can innovate and develop new methods that address identified gaps and enhance decision-making processes. This continuous improvement cycle is vital for the evolution of the MCDM field, leading to more effective and sophisticated decision-making tools. In many decision-making scenarios, especially in public policy and business, transparency and justification of the chosen method are crucial. Comparative analysis provides a systematic way to justify the selection of a specific MCDM method, enhancing the credibility and acceptability of the decision process. Decision-makers can demonstrate that their choice is based on a comprehensive evaluation of alternatives, thereby increasing stakeholder trust and support.

Table 18. Ranking comparisons of investment sectors among different MCDM models.

Rank	AROMAN	TOPSIS	ARAS	COCOSO	EDAS	WSM	WPM	WASPAS
1	IIF	IIF	IIF	IIF	IIF	IIF	IIF	IIF
2	REI	REI	REI	SRI	REI	REI	REI	REI
3	SRI	REIT	SRI	REI	SRI	SRI	SRI	SRI
4	SB	TF	REIT	SB	SB	SB	SB	SB
5	GB	CBL	SB	TF	TF	REIT	TF	REIT
6	TF	SB	TF	REIT	REIT	TF	REIT	TF
7	CBL	SRI	CBL	CBL	CBL	GB	CBL	CBL
8	REIT	CS	CS	GB	CS	CBL	CS	CS
9	ETF	GB	GB	ETF	GB	CS	GB	GB
10	CS	ETF	ETF	CS	ETF	ETF	ETF	ETF

(Source: Author’s own elaboration.)

clearly highlights the ranking comparisons among seven applied tools, and the spearman correlation coefficient (SCC) shown in

Table 19. Spearman correlation coefficient.

	AROMAN	TOPSIS	ARAS	COCOSO	EDAS	WSM	WPM	WASPAS
AROMAN	-	0.552	0.770	0.903	0.842	0.903	0.842	0.818
TOPSIS		-	0.842	0.697	0.794	0.745	0.794	0.806
ARAS			-	0.915	0.964	0.952	0.964	0.988
COCOSO				-	0.952	0.952	0.952	0.939
EDAS					-	0.952	1.000	0.988
WSM						-	0.952	0.964
WPM							-	0.988
WASPAS								-

(Source: Author’s own elaboration.)

highlights the reliability and alignment of the AROMAN method with other established MCDM tools. The rankings obtained from different MCDM tools are also compared with the help of a clustered column chart shown in Figure 5.

The SCC is a non-parametric measure of rank correlation. It assesses how well the relationship between two variables can be described using a monotonic function. In MCDM analysis, the SCC plays a crucial role in comparing and validating the rankings produced by different MCDM methods. The SCC helps to determine the consistency between rankings generated by different MCDM methods. High correlation indicates that the methods produce similar rankings, enhancing confidence in the results. This is crucial for validating the robustness of the decision-making process and ensuring that different methods lead to comparable conclusions. By comparing the rankings of alternatives from various MCDM methods, the SCC provides a statistical measure of reliability. A high correlation suggests that the methods are reliable and consistent, even if they use different approaches or criteria weighting mechanisms. Low SCC highlights the divergences between methods, indicating potential methodological issues or differences in how criteria are handled. On the other hand, a high SCC among different MCDM methods increases confidence in the decision-making process by demonstrating that the chosen method aligns well with others. This is particularly important in stakeholder-driven decision processes where transparency and justification are crucial. Therefore, the SCC is significant in MCDM analysis for ensuring ranking consistency and enhancing decision confidence. By providing a statistical measure of the agreement between different MCDM methods, it plays a vital role in the robustness and reliability of multi-criteria decision-making processes.

4.1.2. Sensitivity Analysis

In the context of MCDM, sensitivity analysis is an indispensable tool for assessing the stability and robustness of the obtained results. The primary objective of this study necessitates a comprehensive understanding of how variations in input parameters can influence the final rankings of investment sectors. Sensitivity analysis provides insights into the reliability of the decision-making model and highlights the critical factors that significantly impact the prioritization process. Sensitivity analysis serves as a robustness check for the MCDM model. By examining how sensitive the results are to variations in input parameters, we can validate the reliability of the final rankings. This validation is particularly important for decision-makers who rely on these rankings to make informed investment choices. Sensitivity analysis also helps to identify which criteria have the most significant impact on the final decision. Understanding the influence of individual criteria allows for a more nuanced approach to decision-making, where critical factors are given appropriate attention and less influential factors are considered accordingly. By demonstrating that the model’s results are stable under various scenarios, sensitivity analysis enhances confidence in the recommendations. Decision-makers can be assured that the prioritized sectors are robustly chosen, even when there are uncertainties in the input data. Moreover, sensitivity analysis promotes transparency in the decision-making process. By openly examining how changes in criteria weights affect the outcomes, the process becomes more transparent, allowing stakeholders to understand and trust the results. Sensitivity analysis plays a critical role in controlling for expert bias by testing the stability of the results under varying assumptions and input weights. In this study, sensitivity analysis was conducted to assess how changes in the weight of different ESG criteria influenced the final sector rankings. By systematically varying these weights and observing the impact on the outcomes, the study was able to determine whether any particular expert’s judgment had an outsized influence on the results. If the rankings remained stable across different scenarios, it would indicate that the model is robust to potential biases.

In MCDM analysis, sensitivity analysis is used to provide a deeper understanding of the model’s behavior to the decision-makers. This informed perspective allows for better judgment and more strategic decision-making. MCDM models often involve complex calculations and the integration of diverse criteria. Sensitivity analysis tests whether small changes in inputs lead to significant changes in outputs, thus evaluating the model’s stability. Real-world data are often subject to uncertainties and variations. Sensitivity analysis helps address these uncertainties by exploring a range of possible scenarios and their impacts on the decision-making outcomes. By understanding how different factors influence the results, MCDM models can be refined and improved to ensure robustness. This iterative process of model improvement is essential for developing reliable decision-support tools. Hence, sensitivity analysis is a fundamental component of the MCDM process. It validates the model’s reliability, identifies critical criteria, enhances decision-making confidence, and ensures transparency. By systematically examining the impact of varying parameters, sensitivity analysis fortifies the robustness of the Fuzzy-MEREC-AROMAN decision-making model, thereby providing a solid foundation for prioritizing European investment sectors based on ESG factors. In this research, two distinct sensitivity analysis methods were employed:, varying the trade-off parameter (β) and single-dimensional sensitivity analysis, which are discussed thoroughly in the upcoming sub-sections.

Varying the Trade-Off Parameter

In the Fuzzy-MEREC-AROMAN decision-making model, the trade-off parameter, beta (β), plays a crucial role in determining the relative importance of different alternatives. ‘β’ essentially acts as a balancing factor that adjusts the aggregated normalized values, thereby influencing the final rankings of the investment sectors. ‘β’ is a parameter that adjusts the level of compromise or trade-off between two normalization process in an MCDM context. In the Fuzzy-MEREC-AROMAN model, ‘β’ determines how the importance of each normalization procedure is factored into the overall evaluation of each investment sector. To understand the impact of ‘β’ on the final rankings, it is varied systematically within a predefined range. Typically, ‘β’ is adjusted from 0 to 1 with an incremental step of 0.1. At β = 0, the model might prioritize the linear normalization heavily over the other technique, while at β = 1, the model might treat the vector normalization that is mostly preferred over the linear process, depending on the model’s specific formulation. This involves applying the developed model with new ‘β’ values to determine the influence of each normalization technique on the overall score of each investment sector. The rankings of the ten European investment sectors are then recalculated based on these updated values as presented in

Table 20. Ranking variations at different trade-off parameter values.

	β = 0	β = 0.1	β = 0.2	β = 0.3	β = 0.4	β = 0.5	β = 0.6	β = 0.7	β = 0.8	β = 0.9	β = 1
GB	5	5	5	5	5	5	5	5	5	5	5
SRI	3	3	3	3	3	3	3	3	3	3	3
ETF	10	10	10	10	10	9	9	9	9	9	9
IIF	1	1	1	1	1	1	1	1	1	1	1
REI	2	2	2	2	2	2	2	2	2	2	2
CBL	7	7	7	7	7	7	7	7	7	7	7
SB	4	4	4	4	4	4	4	4	4	4	4
REIT	8	8	8	8	8	8	8	8	8	8	8
TF	6	6	6	6	6	6	6	6	6	6	6
CS	9	9	9	9	9	10	10	10	10	10	10

(Source: Author’s own elaboration.)

. The ranking variations obtained at different ‘β’ values have been compared graphically in Figure 6.

Single-Dimensional Sensitivity Analysis

Single-dimensional sensitivity analysis (SDSA) is a technique used to evaluate the robustness and stability of a decision-making model by varying the weight of the most important criterion while adjusting the weights of other criteria accordingly. This method helps in understanding how sensitive the final rankings are to changes in the weight of the most critical criterion. SDSA focuses on the most important criterion, which is typically identified based on its initial weight in the decision-making model. By varying the weight of this criterion, the analysis aims to observe the effect on the final ranking of alternatives. This helps in assessing the influence of the most critical criterion on the overall decision. The weight of the most important criterion is varied within a feasible range. This range is determined by the initial weight and the constraints that all criteria weights must sum up to 1. The remaining weights of the other criteria are adjusted proportionally to maintain the total weight sum constraint, i.e., $\sum _{j=1}^n{w}_j=1$. In SDSA, the variation in the criterion weights is non-proportional, meaning that the ratio of weights among criteria changes with each adjustment. This results in a new combination of weights for every variation of the most important criterion. The maximum possible weight ($w_j^{*}$) for the chosen criterion is determined using Equation (17) to define the feasible range of weight variation. The weight can be reduced to 0 or increased to ‘$w_j^{*}$’ without violating the total weight constraint. The value of ‘$w_j^{*}$’ is calculated using an equation that ensures the additivity of weights and non-negativity constraint. This ensures that the weights of the remaining criteria are adjusted properly.

$$w_j^{*}=\left[w_j^{max}+(1-n)\times w_j^{min}\right]$$

(17)

• SDSA helps in understanding the influence of the most important criterion on the overall decision. This is crucial for verifying the robustness of the decision-making model. By analyzing the stability of rankings with varying weights, SDSA ensures that the model is robust and reliable. If the rankings remain consistent, the model can be considered stable and dependable. This analysis helps in identifying which criteria have the most significant impact on the final decision. It provides insights into which criteria are critical and need careful consideration. Conducting SDSA enhances confidence in the decision-making model by demonstrating that the model has been tested for various scenarios. This ensures that the prioritization of alternatives is based on a thorough and rigorous analysis.

  Table 21. New sets of criteria weights.

  	CCCE	RME	BLU	PWM	LPWC	DI	CESI	CRPS	BCI	ECI	TD	ACEP
  General	0.063	0.060	0.181	0.064	0.043	0.078	0.082	0.081	0.085	0.171	0.019	0.075
  Set 1	0.0789	0.0760	0.0000	0.0804	0.0591	0.0942	0.0981	0.0979	0.1016	0.1876	0.0351	0.0910
  Set 2	0.0744	0.0715	0.0500	0.0759	0.0546	0.0897	0.0935	0.0933	0.0970	0.1831	0.0305	0.0864
  Set 3	0.0698	0.0669	0.1000	0.0713	0.0500	0.0851	0.0890	0.0888	0.0925	0.1785	0.0260	0.0819
  Set 4	0.0653	0.0624	0.1500	0.0668	0.0455	0.0806	0.0845	0.0842	0.0879	0.1740	0.0215	0.0774
  Set 5	0.0625	0.0596	0.1807	0.0640	0.0427	0.0778	0.0817	0.0815	0.0851	0.1712	0.0187	0.0746
  Set 6	0.0608	0.0579	0.2000	0.0623	0.0409	0.0761	0.0799	0.0797	0.0834	0.1694	0.0169	0.0728
  Set 7	0.0562	0.0533	0.2500	0.0577	0.0364	0.0715	0.0754	0.0752	0.0788	0.1649	0.0124	0.0683
  Set 8	0.0517	0.0488	0.3000	0.0532	0.0318	0.0670	0.0708	0.0706	0.0743	0.1604	0.0078	0.0637
  Set 9	0.0471	0.0442	0.3500	0.0486	0.0273	0.0624	0.0663	0.0661	0.0698	0.1558	0.0033	0.0592
  Set 10	0.0438	0.0410	0.3860	0.0453	0.0240	0.0591	0.0630	0.0628	0.0665	0.1525	0.0000	0.0559

  (Source: Author’s own elaboration.)

  shows the 10 sets of criteria weights obtained by varying the weightages of the maximum criteria within a range with an incremental value of 0.05. The alternative rankings obtained from different criteria sets are plotted graphically in Figure 7 to observe the ranking variations.

4.2. Discussions

This section discusses the results obtained from applying the Fuzzy-MEREC-AROMAN decision-making model to prioritize European investment sectors based on various ESG factors. The criteria weights and sector rankings were derived from the model, providing insights into the relative importance of ESG factors and the preferred investment sectors. The criteria weights computed in Table 15 indicate that biodiversity and land use (BLU) and executive compensation and incentives (ECI) have the highest significance, with weights of 18.1% and 17.1%, respectively. Transparency and disclosure (TD) is considered to be the least significant criterion with a weight of 1.9%. Similarly, the ranking presented in Table 17 reveals that impact investing funds (IIFs), renewable energy investments (REIs), and sustainable and responsible investment (SRI) funds are the top three preferred sectors for investment. These sectors ranked highest due to their strong performance across various ESG factors, particularly in areas like biodiversity and land use, community engagement, and anti-corruption practices. In contrast, ESG-compliant stocks (CSs) and ESG-focused exchange-traded funds (ETFs) are ranked the lowest, indicating they are less favorable for investment based on the evaluated ESG criteria.

The high weight assigned to biodiversity and land use and executive compensation and incentives highlights the critical importance of environmental sustainability and governance practices in investment decisions. This aligns with growing global awareness and regulatory pressures for companies to address biodiversity loss and ensure fair executive compensation practices. Impact investing funds ranking highest among the alternatives suggests that investments specifically targeting positive social and environmental impacts are highly valued when considering ESG factors. This reflects the increasing investor preference for impact-driven investment strategies that contribute to societal and environmental well-being. The lower ranking of ESG-focused ETFs and ESG-compliant stocks may indicate that while these investment vehicles are popular, they may not address ESG factors as comprehensively as direct impact investments or sector-specific funds. The computed weights and rankings also underscore the relative importance of governance factors, with board composition and independence and anti-corruption and ethical practices having substantial influence on sector prioritization. This highlights the role of robust governance structures in driving sustainable investment decisions. The results demonstrate the efficacy of the Fuzzy-MEREC-AROMAN model in integrating diverse ESG factors into a cohesive framework for investment prioritization. This approach provides a clear understanding of how different ESG factors influence investment decisions and offers a robust tool for investors seeking to align their portfolios with sustainable and responsible investment principles.

Now let us pay attention on the rankings obtained from different MCDM methods and compared in Table 18. Table 18 clearly reveals that all models consistently rank impact investing funds as the top performer. This unanimous agreement underscores the strong ESG performance of IIFs, making them a highly reliable and attractive investment option across different MCDM methodologies. Renewable energy investment and sustainable responsible investment sectors also show high consistency in their rankings, being placed in the top positions by most of the methods. This indicates their robustness as favorable investment options when evaluated through different decision-making perspectives. Green bonds and real estate investment trust sectors exhibit significant variability in their rankings. For instance, GB is ranked fifth by AROMAN but ninth by TOPSIS, and REIT is ranked eighth by AROMAN but third by TOPSIS. This variability suggests that the evaluation criteria and weighting used by different methods significantly impact the rankings of these sectors. ESG-focused exchange-traded funds and ESG-compliant stock sectors are consistently ranked lower across all methods. The consistent lower rankings indicate a general consensus on their relatively weaker performance in terms of ESG criteria, making them less favorable investment options. Similarly, thematic funds and social bond sectors tend to occupy mid-tier positions across different models, indicating a moderate level of agreement on their performance. This consistency provides some level of confidence in their evaluation, though not as strong as the top or bottom performers.

Moving towards Table 19, the Spearman correlation coefficients provide valuable insights into the degree of agreement among the different MCDM methods used to rank European investment sectors based on ESG factors. Table 19 clearly suggests that AROMAN exhibits a very high correlation with COCOSO and WSM (both at 0.903). This indicates that AROMAN shares significant similarities in ranking patterns with these methods, suggesting a comparable weighting and evaluation process. It is also noticeable that most MCDM methods show high correlation coefficients (above 0.8), indicating a strong agreement in their rankings of the investment sectors. This high level of agreement suggests that despite methodological differences, these methods generally produce similar outcomes. The moderate correlation of AROMAN with TOPSIS (0.552) indicates some level of agreement, but also some notable differences in its ranking approach compared to others. This divergence may be due to the employment of different normalization or aggregation techniques that lead to different prioritization compared to AROMAN. The high correlation values of AROMAN with most of the other methods validate the robustness of this MCDM approach. It suggests that the differences in methodological details do not significantly affect the overall ranking outcomes, providing confidence in their use for ESG-based investment prioritization. While AROMAN shows strong correlations with some methods, its moderate correlation with TOPSIS indicates that AROMAN maintains some level of independence. This independence can be valuable in offering a unique perspective that might capture different aspects of ESG performance not emphasized by other methods. The general high correlations across all methods support the reliability of the entire MCDM analysis. The use of multiple methods provides a comprehensive analysis, ensuring that the final investment sector prioritization is well rounded and robust.

From the first round of sensitivity analysis, the ranking outcomes provided in Table 20 at different ‘β’ values suggest that IIFs consistently ranked first across all values of ‘β’ from 0 to 1, demonstrating their exceptional stability and robustness. This indicates that IIFs are the top choice regardless of the trade-off parameter, reflecting strong overall performance across all ESG criteria. REI also highlights its consistent performance and robustness, maintained a stable ranking at second position across all ‘β’ values. Investment options SRI, SB, GB, TF, CBL and REIT maintained their positions from third to eighth consistently across all the ‘β’ variations, showing highly stable performance and reliability as an investment option. European sector options that rank first to eighth indicate that the performance is not significantly affected by changes in the trade-off parameter. However, some ranking alterations are observed in the last two positions between the ETF and CS sectors. ETF initially ranked 10th for ‘β’ values from 0 to 0.4, then improved to 9th position for ‘β’ values from 0.5 to 1. This slight improvement suggests some sensitivity to the trade-off parameter but generally remains in the lower ranks. Similarly, CS ranked ninth for ‘β’ values from 0 to 0.4 and then shifted to tenth for ‘β’ values from 0.5 to 1, indicating some sensitivity but overall consistent low performance.

The model’s top (IIF, REI) and bottom (ETF, CS) performers exhibit significant stability in their rankings across varying trade-off parameters. This suggests that these sectors’ performance is highly robust and not significantly influenced by changes in the trade-off parameter, indicating strong reliability for decision-makers. Sectors such as SRI, SB, and TF also show consistent rankings, reflecting the model’s robustness in maintaining stable performance across the middle tier. Some sectors, like ETF and CS, exhibit minor shifts in their rankings, indicating that while the model is generally robust, there is some sensitivity to the trade-off parameter for certain sectors. However, these shifts are not drastic, underscoring the model’s overall stability. Therefore, the consistent rankings across varying ‘β’ values imply that investors can rely on the Fuzzy-MEREC-AROMAN model to provide stable and robust recommendations for investment sectors based on ESG criteria. The stability of the model enhances its credibility and reliability in decision-making processes.

We will now focus on the ranking variations obtained from single-dimensional sensitivity analysis presented in Table 21. The same scenario is observed in that impact investing funds consistently ranked first across all sets followed by renewable energy investments in the second position, demonstrating their outstanding stability and robustness. This indicates that IIFs followed by REIs are the top choices regardless of variations in the weight of the most important criterion. The model’s two top performers exhibit exceptional stability across all sets, underscoring their robustness and reliability as prime investment choices based on ESG criteria. The mid-tier performer, SRI, ranked 3rd across most sets, with slight variations (4th in set 8, 5th and 6th in sets 9 and 10, respectively), indicating overall stability with minor sensitivity to weight changes. SB generally ranked fourth with minor ranking deviations in sets 7 and 8, showing robust mid-tier performance with slight sensitivity. Although the lower-tier performers exhibit some slight ranking deviations, no significant changes in positions have been found, indicating stable and strong low performance. This stability is crucial for identifying sectors that may need improvement in their ESG performance. The minor variations observed in some sectors suggest that while the model is generally robust, it is sensitive to changes in the weight of the most important criterion. However, these variations do not significantly impact the overall stability and reliability of the model. The consistent rankings across varying sets of criteria weights imply that investors can rely on the Fuzzy-MEREC-AROMAN model to provide stable and robust recommendations for investment sectors. This consistency enhances the model’s credibility and reliability in decision-making processes.

In conclusion, the sensitivity analysis indicates that the Fuzzy-MEREC-AROMAN MCDM model is highly stable and robust, particularly in identifying top and bottom performers among the European investment sectors based on ESG factors. This stability ensures that the model can be trusted for making consistent and reliable investment decisions, even when the trade-off parameter and the weight of the most important criterion are varied.

4.2.1. Broader Implications for International Investors and Linking to Global Trends

The prioritization of impact investing funds aligns with findings from several global studies, which emphasize the effectiveness of impact investing in achieving both financial returns and positive social outcomes. For instance, Yan et al. [82] discuss the dual benefits of impact investing in generating financial returns while also addressing critical social and environmental issues. Similarly, recent research by Wang et al. [83] demonstrates that impact investing has gained significant traction worldwide, particularly in Europe and North America, where investors are increasingly prioritizing sectors that contribute to sustainable development goals (SDGs). Renewable energy investments have also been identified as a top sector in various studies due to their role in mitigating climate change. According to a report by the International Renewable Energy Agency (IRENA, 2021) [84], investments in renewable energy have surged globally, driven by the urgent need to transition to low-carbon economies. This aligns with the present study’s findings, where REI ranked highly due to its positive impact on climate change and carbon emissions, a factor that has been weighted significantly in this study.

Sustainable and responsible investment funds have been highlighted in previous studies as an evolving trend, particularly in Europe and North America. Studies by Eurosif (2018) [85] and the Global Sustainable Investment Alliance (GSIA, 2020) [86] show that SRI funds have grown substantially, driven by investor demand for ethical investment options. The present study confirms these findings, showing that SRI funds are among the top-ranked sectors, particularly excelling in diversity and inclusion and community engagement. On the other hand, the lower ranking of ESG-compliant stocks and ESG-focused exchange-traded funds reflects findings from previous studies which indicate that while these options provide a broad-based approach to ESG investing, they may lack the targeted impact that more specialized funds like IIFs or REIs can offer. For instance, Yang et al. [87] found that ETFs, while popular, often dilute the impact of ESG considerations by spreading investments across a wide array of companies, some of which may not fully adhere to high ESG standards.

The findings of this study are not only relevant within the European context but also have significant implications for global investors. The emphasis on biodiversity and land use as the most critical factor resonates with global environmental concerns, particularly in regions where deforestation, habitat loss, and land degradation are major issues, such as in the Amazon basin [88] and Southeast Asia [89]. This suggests that investment strategies prioritizing biodiversity could be universally applicable, offering both ecological and financial benefits. Moreover, the high ranking of renewable energy investments and impact investing funds aligns with the global shift towards sustainable energy and social equity, as seen in the Paris Agreement [90] and the United Nation’s SDGs. Investors in regions like North America, Asia, and emerging markets can use these findings to better align their portfolios with global sustainability trends, potentially enhancing long-term returns while contributing to positive societal outcomes. The consistency of the study’s results with global trends reinforces the importance of ESG factors in investment decision-making worldwide. As sustainability becomes a core consideration for investors globally, the relevance of these findings extends beyond Europe. For instance, in a study by Aldowaish et al. [91], a meta-analysis of over 2000 empirical studies demonstrated a positive correlation between ESG criteria and corporate financial performance, highlighting that the integration of ESG factors is beneficial across different markets. Similarly, the application of the Fuzzy-MEREC-AROMAN model in this study offers a robust decision-making framework that can be adapted and applied in other regions. The model’s ability to handle the complexities of ESG criteria and its validation through multiple MCDM methods underscores its utility in various contexts, making it a valuable tool for investors globally.

In conclusion, this study contributes significantly to the literature on ESG-focused investment by providing a comprehensive framework for evaluating investment sectors based on multiple ESG criteria. The alignment of these findings with those from other countries and regions underscores the global relevance of the identified investment priorities. This study not only adds to the growing body of knowledge on sustainable investing but also provides practical insights for international investors looking to integrate ESG factors into their decision-making processes [92,93]. By linking the findings with global trends and studies, this research enhances its appeal to a broader audience, offering a valuable resource for investors worldwide.

4.2.2. Theoretical Contributions

The following research makes several significant theoretical contributions to the field of MCDM and sustainable investment evaluation. Some of the contributions are outlined below:

• The integration of fuzzy logic with the MEREC-AROMAN decision-making model addresses the inherent ambiguity and uncertainty in expert judgment, providing a more accurate and reliable evaluation framework. By incorporating fuzzy logic, the model captures the complexities of ESG factors, leading to a detailed and precise prioritization of investment sectors.
• The research develops a comprehensive framework that assesses investment sectors across 12 diverse and conflicting ESG criteria, categorized into environmental, social, and governance factors. The use of the MEREC method for evaluating criteria weights ensures a systematic and unbiased determination of the relative importance of each ESG factor.
• The application of the AROMAN method provides a robust mechanism for ranking investment sectors based on their aggregated ESG performance scores, enhancing the reliability of the prioritization process. The AROMAN method’s ability to be compared with other MCDM tools validates its robustness and reliability, contributing to the broader MCDM literature.
• The sensitivity analysis, including varying the trade-off parameter, demonstrates the model’s flexibility and adaptability to different weighting scenarios, ensuring the robustness of the results. This approach highlights the impact of individual criteria weights on the final rankings, providing deeper insights into the significance of each ESG factor in the decision-making process.
• This research offers a practical tool for investors and policymakers to make informed decisions regarding sustainable investments, aligning financial goals with ESG considerations. By providing a detailed and validated model for ESG investment prioritization, the study contributes to the growing body of literature on sustainable finance and responsible investing.

This research makes substantial theoretical contributions by integrating fuzzy logic with the MEREC-AROMAN model, developing a comprehensive ESG evaluation framework, applying robust prioritization techniques, and validating the model through sensitivity analysis. These contributions advance the field of MCDM and sustainable investment evaluation, offering a sophisticated approach to prioritizing European investment sectors based on ESG factors.

4.2.3. Managerial Implications

This research also has several significant managerial implications that can guide stakeholders and managers in the ESG sectors. Some potential implications are discussed as follows:

• Managers and investors can use the results of this research to make more informed investment choices by understanding which sectors align best with ESG criteria. By prioritizing sectors with strong ESG performance, managers can mitigate risks associated with poor environmental, social, and governance practices.
• The model helps in identifying sectors that are not only financially viable but also socially and environmentally responsible, enabling optimal allocation of resources towards sustainable investments. Investing in sectors with high ESG ratings can lead to long-term value creation for stakeholders by fostering sustainable business practices.
• The findings encourage companies to align their corporate governance practices with ESG goals, improving transparency, accountability, and ethical standards. Emphasizing ESG factors in decision-making can enhance trust and credibility among stakeholders, including customers, employees, and investors.
• Policymakers can use the insights from this research to formulate regulations and policies that promote sustainable investments and corporate responsibility. The model provides a framework for companies to ensure compliance with evolving ESG standards and reporting requirements.
• Companies and investment firms that prioritize ESG factors can differentiate themselves in the market, attracting socially conscious investors and customers. Focusing on ESG criteria helps in managing and enhancing the company’s reputation, which is increasingly important in today’s business environment.
• The model provides a structured approach for companies to communicate their ESG efforts and performance to stakeholders, fostering better engagement and support; this encourages collaboration between businesses, investors, and other stakeholders to achieve common ESG goals, promoting a more sustainable economic ecosystem.

The managerial implications of this research are profound, offering a comprehensive framework for integrating ESG factors into investment decision-making, resource allocation, corporate governance, and policy formulation. By adopting the Fuzzy-MEREC-AROMAN decision-making model, managers and policymakers can enhance their strategies to foster sustainable development and long-term value creation, ultimately contributing to a more responsible and ethical business environment.

5. Conclusions

This research aimed to identify the optimal investment sectors among ten alternatives based on twelve conflicting ESG factors using advanced MCDM methods. By integrating expert evaluations with fuzzy logic and employing the MEREC and AROMAN methods, the study navigates the complexities and uncertainties inherent in ESG data to provide a comprehensive prioritization of European investment sectors. Fuzzy logic was effectively used to handle the ambiguity and subjectivity in expert judgments, converting qualitative assessments into quantitative data. This ensured the management of uncertainties and variability, leading to more accurate evaluations of ESG criteria.

Firstly, the MEREC method has proven effective in calculating criteria weights, as evidenced by the detailed distribution of weights across various ESG factors. This foundational step ensures that the model incorporates a comprehensive range of criteria, essential for evaluating investment sectors. The MEREC method identified biodiversity and land use as the most critical ESG factor followed by executive compensation and incentives, with a weight of 18.1% and 17.1%, respectively, while transparency and disclosure received the lowest weight of 1.9%, thus making it the least important parameter in the list. This highlights the varying significance of different ESG factors in investment decision-making.

Secondly, the AROMAN method’s rankings provide a clear hierarchy of the European investment sectors. The AROMAN method was used to rank the investment sectors based on their overall ESG performance. Impact investing funds emerged as the most preferred sector, followed by the renewable energy sector and sustainable responsible investment funds. These sectors demonstrated strong performance across most ESG criteria, making them attractive options for sustainable and responsible investment. This ranking underscores the potential of these sectors to lead sustainable investment initiatives in Europe.

Thirdly, the comparative analysis with other MCDM tools and the high Spearman correlation coefficients validate the robustness and reliability of the AROMAN method. The strong correlations with other established MCDM tools affirm that the AROMAN model produces consistent and credible results, further establishing its validity as a decision-making tool.

Moreover, the sensitivity analysis by varying the trade-off parameter indicates the stability of the AROMAN model. The rankings remain largely consistent across different values of ‘β’, demonstrating that the model’s outcomes are not overly sensitive to the changes in trade-off parameter values. This stability is crucial for investors seeking reliable and predictable decision-making frameworks.

Lastly, the single-dimensional sensitivity analysis also supports the model’s robustness. While minor variations are observed, the overall ranking structure remains stable, reaffirming the model’s resilience to changes in individual criteria weights.

In conclusion, the Fuzzy-MEREC-AROMAN decision-making model is a robust and reliable tool for prioritizing European investment sectors based on ESG factors. Its stability across various sensitivity analyses and strong correlations with other MCDM tools provide confidence in its application for sustainable investment decision-making. The integration of fuzzy logic with MCDM methods ensures a reliable and comprehensive evaluation process, offering valuable insights for both theoretical advancements and practical applications in sustainable investment. These findings support its use as a strategic framework for investors aiming to make informed and sustainable investment choices in the European market.

Limitations and Future Scope

Despite of the numerous significant contributions to the field, there are several limitations that may be associated with the following research that must be acknowledged:

• The reliance on expert opinions for evaluating the performance of investment sectors can introduce bias, as experts may have subjective views influenced by their experiences and backgrounds. This study uses a specific group of experts whose opinions may not comprehensively represent the broader spectrum of views within the field.
• The quality and availability of ESG data can vary significantly across different sectors and regions, potentially affecting the accuracy and reliability of the analysis. ESG factors are dynamic and can change rapidly due to new regulations, market conditions, or social movements. This study’s findings might become outdated if significant changes occur.
• Integrating fuzzy logic with the MEREC-AROMAN model adds complexity to the decision-making process, which may require advanced understanding and expertise to implement and interpret correctly. The combined use of fuzzy logic and multiple MCDM tools can be computationally intensive, potentially limiting its practical application for organizations with limited resources.
• This study focuses on European investment sectors, which may limit the generalizability of the findings to other regions with different regulatory and market environments. Some ESG factors may have different levels of importance across various sectors. This study’s generalized approach might overlook sector-specific nuances and impacts.
• The model’s results are sensitive to the assigned weights of the ESG criteria. Minor changes in these weights can significantly alter the prioritization of investment sectors, raising questions about the robustness of the outcomes. While sensitivity analysis is conducted, it primarily focuses on single-dimensional adjustments. Multi-dimensional sensitivity analysis could provide a more comprehensive understanding of weight variations.
• The validation through comparative analysis with other MCDM tools, while useful, is still limited by the inherent differences in the methodologies and assumptions of these tools. The scope of sensitivity analysis might be limited, and further extensive testing could provide deeper insights into the model’s robustness and reliability.

Acknowledging these limitations is crucial for understanding the scope and applicability of the research findings. Future studies could address these limitations by incorporating a broader range of expert opinions, improving the quality and granularity of ESG data, simplifying the model for practical use, and expanding the scope to include other regions and more comprehensive sensitivity analyses.

Future studies could incorporate additional ESG factors to capture a more comprehensive range of sustainability issues. This would provide a more detailed and nuanced evaluation of investment sectors. The model could be applied to other geographical regions beyond Europe to test its applicability and robustness in different economic and regulatory environments. Additionally, expanding the scope to include more diverse investment sectors could provide broader insights. Conducting a longitudinal study to observe how the prioritization of investment sectors evolves over time in response to changes in ESG performance and market dynamics would be valuable. This would help in understanding the long-term impact of ESG factors on investment decisions. Combining the Fuzzy-MEREC-AROMAN model with other decision-making frameworks or techniques, such as machine learning or artificial intelligence, could enhance the model’s predictive power and decision-making accuracy. Future research could also tailor the model to specific stakeholders, such as institutional investors, individual investors, or policymakers. This would allow for the development of customized investment strategies that align with the unique objectives and constraints of different stakeholders.

By addressing these areas, future research can build upon the foundation laid by the Fuzzy-MEREC-AROMAN model, providing deeper insights and more comprehensive tools for ESG-focused investment decision-making. These advancements will contribute to the ongoing development of sustainable and responsible investment practices, aligning financial goals with broader societal and environmental objectives.