Assessing the Environmental Sustainability Performance of the Banking Sector: A Novel Integrated Grey Multi-Criteria Decision-Making (MCDM) Approach

The financial system plays a pivotal role in the economic landscape, facilitating the efficient allocation of resources among economic agents and contributing to the growth and development of the economy. Key institutions within the financial system include investment trusts, insurance companies, asset management companies, and banks, which play a crucial role in ensuring the effective operation of the financial system. In comparison to alternative financial intermediaries, banks are the paramount financial intermediaries supporting sustainable economic growth worldwide and ensuring the efficient allocation of financial resources among economic agents [1]. Banks are one of the most crucial financial intermediary institutions that meet the financing needs of firms and individuals by making the deposits they collect from savers available as loans to those who demand funds. This intermediation function enables banks to stimulate economic activities by encouraging investment and consumption activities [2]. Especially in developing countries, banks support economic development and contribute to poverty reduction by facilitating the financial access of economic agents [3]. In addition, the banking sector also plays an important role in ensuring economic stability. With the financing support provided to small and medium-sized firms, banks can help both the growth of firms and the decrease in unemployment rates by providing diversity in the economy [4]. In addition, banks have a critical role in economic development activities by providing a balance between investments and loans [5].

The concept of sustainability can be most simply defined as the practice of not destroying the natural resources required for use by future generations, while simultaneously meeting the needs and expectations of those involved in the firm, including employees, shareholders, customers and investors [6]. Sustainability represents long-term prosperity by balancing the environmental, social and institutional impacts of economic growth activities [7], [8]. The concept of sustainability has three main dimensions: environmental, social and institutional. However, the environmental sustainability dimension is of greater importance than the other dimensions. This is because this dimension is more prominent than the other dimensions in issues that require a global struggle, such as global warming or climate change [9]. Global issues such as the accelerated depletion of natural resources, the precipitous decline in biodiversity, the deterioration of ecological balance and the surge in environmental pollution have a profound negative impact on both human activities and the planet. Consequently, the concept of environmental encompasses the endeavors to minimize the detrimental effects of these activities on natural resources while pursuing robust activities [10]. Considering the problems occurring in the ecosystem, it is necessary for banks, like all other firms, to take environmental issues into account, especially when realizing their long-term goals [11]. On the other hand, banks that integrate environmental activities will gain a significant competitive advantage by becoming more resistant to regulatory pressures [12]. In light of the aforementioned considerations, it can be stated with a priori clarity that the environmental dimension stands out more than other dimensions [13].

In the 21st century, which is called the century of the environment, the concept of the environment has become one of the most important issues in the banking sector, as in all other sectors. The increasing concerns about changing climates and increasing environmental problems have led firms and banks to implement policies to reduce their harmful effects on the environment [14]. The banking sector has many opportunities to lead society towards sustainability, especially through the so-called green banking initiatives [15]. Consequently, banking activities conducted in accordance with sustainability principles not only minimize environmental risks but also contribute to the enhancement of corporate reputation and performance [16]. On the other hand, by adopting sustainability principles, banks can encourage innovation in the economy and support global efforts in sustainable development [17]. To this end, the present work develops an integrated Grey MCDM model to gauge the environmental sustainability performance of banks. In the developed model, LOPCOW and PIV approaches were integrated with interval grey numbers. The fundamental rationale for employing grey numbers is their capacity to facilitate flexible decision-making in complex scenarios. In comparison to fuzzy sets, grey numbers offer a more effective utilization of grey theory, particularly in circumstances where data is limited, restricted, or partial [18]. In accordance with the suggested approach, Grey LOPCOW has the responsibility of computing the weights of the criteria, while Grey PIV serves to rank the performance of the banking institutions. A real case study was performed, consisting of 7 experts, 13 environmental performance criteria and 6 deposit banks. The objective of the case study was to demonstrate the applicability and suitability of the newly introduced grey-based hybrid decision-making framework.

In general, with the help of the presented MCDM framework, this research aims to answer the following research questions:

• RQ1. Why is it important to analyze the environmental performance of banks?

• RQ2. Which assessment criteria should be used to analyze the environmental performance of the banking sector?

• RQ3. What is the most important indicator of environmental performance in the banking sector?

• RQ4. Which of the commercial banks listed on the BIST is more successful than its competitors in terms of environmental performance?

Through research questions aimed at filling gaps in previous studies, bank managers and other decision makers in the banking sector can identify a practical and reliable methodological approach to analyze the environmental performance of banks in detail. The contributions of the proffered decision support tool to the past literature are as follows:

$\sqrt{ }$ The existing work presents a methodological framework for solving environmental performance measurement problems for decision-makers in the banking industry.

$\sqrt{ }$ The Grey LOPCOW approach is employed to compute the weight coefficients of the environmental performance criteria.

$\sqrt{ }$ The Grey PIV procedure, which is a relatively new ranking technique, is implemented for the first time in the MCDM literature to rank banks’ environmental performance.

$\sqrt{ }$ To investigate the sustainable environmental performance of banks, a case study was conducted employing 13 environmental performance measures. This is the first study to examine the environmental performance of banks via an integrated decision methodology.

$\sqrt{ }$ Managerial implications are provided for baking decision-makers to improve and sustain the environmental performance of the banking sector.

$\sqrt{ }$ A comprehensive sensitivity and benchmarking analysis is conducted to test the validity of the proposed decision-making process.

The following section of the study is organized as follows: The second section contains a literature review and explains how the study will fill the gaps in the literature. In the third section, the proposed MCDM methods are discussed from a theoretical perspective. Then, in the fourth section, the case analysis conducted within the scope of the study is presented, and in the fifth section, the results of the proposed model for the evaluation of the environmental performance of selected banks are shared. In the sixth section, sensitivity analyses and related validation analyses are presented. In the seventh section, practical and managerial implications are discussed. Finally, the eighth section summarizes the results obtained and provides recommendations for future work.

This section is divided into two sub-sections. The first section presents a summary of the sustainability studies conducted in the banking sector employing MCDM methodologies. The second subsection addresses research gaps regarding previous studies in the banking industry.

Since banks are the most vital institutions of the financial system, there are many studies in the earlier literature focusing on gauging their performance from diverse perspectives. This subsection presents a comprehensive overview of extant studies within the banking industry that concentrate on measuring and assessing the bank's performance. The studies outlined in Table 1 concentrate on multidimensional performance or sustainability performance, as opposed to those that focus on one-dimensional performance, specifically financial performance analysis.

The general outputs of the previous study pointed to two critical gaps in the literature regarding the research topic. There exist no generally accepted criteria in the current industry for assessing the environmental performance of banks. Some prior papers have presented some assessment criteria, but it is unclear how these criteria are identified. Hence, the first critical research gap can be related to the lack of a set of criteria that evaluates comprehensive environmental performance in preceding studies. To fill this gap, unlike previous studies, this research proffers a comprehensive and up-to-date set of criteria that includes 13 criteria in 5 main dimensions to assess the bank's environmental performance. The second critical gap pertains to the methodological framework that can be applied in assessing bank environmental performance.

As seen in Table 1, past studies mostly prefer traditional methods in evaluating bank performance. However, these approaches, such as AHP, Entropy, TOPSIS, VIKOR, GIA, ARAS, and DEA etc., have many drawbacks and structural problems. Consequently, owing to their inherent restrictions, they are unable to satisfy the requirements of decision-makers in the banking industry with regard to environmental performance analysis and evaluation. To fill the second research gap, this research proposes a dependable, applicable and robust mathematical tool as a methodological framework for assessing banks' environmental performance by integrating MCDM techniques with grey systems theory. In this context, the developed methodology utilizes extended versions of two very recent techniques such as the LOPCOW and PIV based on the utilization of interval grey numbers.

In comparison to fuzzy numbers, grey interval numbers possess several important advantages. First, it allows DMs to reduce the possible inconsistencies resulting from sophisticated circumstances and ambiguities associated with the decision problem by employing interval numbers. Second, the lower computational complexity allows DMs to make more effective decisions. Third, using this approach facilitates achieving more robust, durable and reliable results when dealing with limited, uncertain, and small data [41], [42], [43], [44], [45], [46], [47], [48].

The developed decision-making framework combines the advantages of the LOPCOW and PIV algorithms for gauging the alternative banks' environmental performance. The following are the primary advantages of the LOPCOW method. Firstly, it has the capacity to decrease the discrepancies between the weight values of the most and least important criteria. Secondly, it possesses significant computational capability and requires a comparatively brief computational time. Thirdly, the system's unique algorithm enables the elimination of any discrepancy resulting from variations in data size. Besides, criteria with negative values can be incorporated directly into the analysis, without the need for any transformation [49]. As for PIV methodology, this methodology is notable for its simple algorithm, which is easily comprehensible and can be readily implemented by decision-makers. Thus, this algorithm offers DMs a practical, powerful and systematic approach to problem-solving. Moreover, the algorithm's notable resistance to the rank reversal issue is a significant advantage over its counterparts [44].

This section explains the integrated model consisting of Grey LOPCOW and Grey PIV techniques proposed to solve the environmental sustainable performance decision-making problem for the banking sector, as shown in Figure 1.

The theoretical framework of grey system theory, along with the concept of interval grey numbers, was structured by Deng [50]. This theoretical paradigm posits that systems that contain incomplete information or are not clearly understood are designated as grey systems. A significant aspect of the grey system logic is its role as an effective methodological framework for the resolution of uncertain problems characterized by incomplete information. In the context of grey theory, grey numbers serve as instruments for addressing the management of incomplete information within the problem structure. These numbers are characterized by their ability to represent unknown values, while simultaneously operating within a defined and known bound. Furthermore, numerical data may also present variations of grey numbers as black and white numbers. The presence of a black number indicates that the analyzed data contains no meaningful information, whereas a white number signifies that the data is fully understood [45]. Due to this reason, grey numbers are defined as numbers that are expressed within a certain range but whose exact numerical values are unknown [46]. A grey number can be represented as $\otimes Z$ and $Z^1$ and $Z^u$ represent the lower and upper bounds of a grey number, respectively [47], [48]. Thus, a grey number is defined as; $\otimes \mathrm{Z} \in\left[\mathrm{Z}^1, \mathrm{Z}^{\mathrm{u}}\right], \mathrm{Z}^1 \leq \mathrm{Z}^{\mathrm{u}}$. According to Li et al. [51], the arithmetic of grey numbers is typically similar to that of interval values. Furthermore, the mathematical operation rules of grey numbers can be expressed as the operation rules of real numbers [47], [48]. In consequence, the mathematical operations for two grey numbers $\left(\otimes \mathrm{Z}_1\right.$ and $\otimes \mathrm{Z}_2$ ) are performed in accordance with the following Eqs. (1)-(4).

The distance between two grey numbers is calculated according to Eq. (5).

The grey probability degree, which was developed by Li et al. [51] and introduced to the literature, is used to compare grey number values with each other. The grey degree of likelihood shows the magnitude or smallness of two grey numbers relative to each other [52]. Therefore, the degree of probability of two grey numbers is determined in Eq. (6).

Based on Eq. (6), the comparison of two grey numbers can yield four distinct results.

$\text { If } \otimes Z_1=\otimes Z_2 \text { then } P\left\{\otimes Z_1 \leq \otimes Z_2\right\}=0,5 \text { if } P\left\{\otimes Z_1>\otimes Z_2\right\} \text { then } P\left\{\otimes Z_1 \leq \otimes Z_2\right\}=1$

$If \otimes \mathrm{Z}_1<\otimes \mathrm{Z}_2 then \left\{\otimes \mathrm{Z}_1 \leq \otimes \mathrm{Z}_2\right\}=0$

$\text { If } \mathrm{P}\left\{\otimes \mathrm{Z}_1 \leq \otimes \mathrm{Z}_2\right\}>0,5 \text { then } \otimes \mathrm{Z}_2>\otimes \mathrm{Z}_1$

$\text { Otherwise if } P\left\{\otimes \mathrm{Z}_1 \leq \otimes \mathrm{Z}_2\right\}<0,5 \text { then } \otimes \mathrm{Z}_2<\otimes \mathrm{Z}_1$

The LOPCOW method, introduced to the literature by Badi and Pamucar [46], provides an objective weighting methodology for decision-makers. The LOPCOW method differs from the other MCDM methods in that it also takes into account the correlation coefficients and standard deviation values between the criteria when calculating the weight scores. Furthermore, the method enables the difference between the most and least important criteria to be reduced to a reasonable level by calculating the criteria weights with a logarithmic function [53], [54]. Furthermore, the fact that the method is not influenced by negative data provides researchers with a significant advantage over other methods. The application procedure of the method consists of four steps, as described below [55].

Step 1: In the implementation stages, the initial step is to create the grey decision matrix (⊗Z), which contains the evaluation criteria and decision alternatives, in accordance with Eq. (7). Subsequently, the experts consider the language values provided in Table 2 when making their decisions.

In the equation, $\mathrm{z}_{\mathrm{ij}}=\left[\mathrm{z}_{\mathrm{ij}}^\mathrm{l}, \mathrm{z}_{\mathrm{ij}}^{\mathrm{u}}\right]$, as shown in the grey matrix, it represents the value of the i-th alternative as regards the j-th criterion.

Step 2: The initial grey matrix, which is initially created within the scope of DMs evaluations, is normalized at this stage by taking into account the non-beneficial and beneficial characteristics. Accordingly, the normalization process is carried out using the following Eq. (8) for non-beneficial criteria and Eq. (9) for beneficial criteria.

Step 3: The $\otimes P V_{i j}$ values, expressed as grey percentage values for the assessment criteria, are computing employing Eq. (10).

Step 4: In the final stage, the grey weight scores ($\otimes \mathrm{w}_{\mathrm{jLOP}}$) for each chosen assessment criterion are determined by employing Eq. (11).

In the final stage of this methodology, crisp weights are identified by averaging the weights generated for each assessment criterion.

The PIV technique, introduced in the literature by Mufazzal and Muzakkir [44], is a mathematical tool often preferred by researchers. The implementation procedure of this technique consists of 5 steps [57].

Step 1: As in all other MCDM algorithms, the first step of the Grey PIV technique starts with the grey decision matrix formed in Eq. (7).

Step 2: Grey decision matrix values are normalized with the aid of Eq. (12).

Step 3: The weight scores computing employing the Grey LOPCOW method are incorporated into the Grey PIV approach in this step, and a weighted normalized matrix is then computed in accordance with Eq. (13).

Step 4: In this phase, the $\otimes g_{i j}$ values, which are expressed as a grey weighted proximity index, are computed by considering the non-beneficial and beneficial characteristics of the chosen assessment criteria. In this context, Eq. (14) is used for non-beneficial criteria and Eq. (15) is used for beneficial criteria.

Step 5: In the final stage of the method, grey $\otimes {d}_{{i}}$ and crisp ${d}_{{i}}$ values, which are expressed as overall proximity index values for ranking decision alternatives, are determined according to Eq. (16) and Eq. (17).

When ranking the decision alternatives, the alternative with the smallest $\mathrm{d}_{\mathrm{i}}$ value is considered the most successful, while the alternative with the largest $\mathrm{d}_{\mathrm{i}}$ value is considered the most unsuccessful.

The banking industry plays a pivotal role in the financial system, providing a variety of financial services and intermediation functions to its stakeholders. It is of great importance to analyze the financial and sustainability performance of the banking sector and the banks operating within it. This is necessary for the system to continue its activities in a stable manner and to gain competitive power. To this end, the existing work aims to propound a novel hybrid MCDM framework for the evaluation of environmental performance in the banking industry. The present work focuses on a case study involving 6 deposit banks whose shares are listed on BIST and which regularly publish sustainability reports. The names and market shares of the alternative banks included in the existing work are provided in Table 3. Additionally, Table 4 presents the assessment criteria chosen to analyze the environmentally sustainable performance of the banks.

This section of the study presents the findings of the application of the decision framework for measuring the environmental performance of the selected banks.

The Grey LOPCOW procedure was preferred in determining the importance weights of the selected environmental performance indicators. A committee was established to assess the environmental performance indicators. A face-to-face interview was conducted with the members of this committee. This committee consists of experts who have been selected for their experience and knowledge in the field. The committee members include four board members, two branch managers, and one regional manager. Additionally, these individuals have at least 15 years of experience in evaluating banking activities and their environmental impacts. Table 5 shows the details of seven sector professionals who have been identified as evaluators.

Each DM opinion was obtained in accordance with the grey linguistic values given in Table 2. The linguistic data obtained on the basis of the DM opinions are given in Table 6.

The linguistic data obtained within the framework of the DM opinions have been converted into quantitative values by means of the grey numbers shown in Table 2. The quantitative data for each of the DM opinions are presented in Table 7.

The data based on the DM opinions shown in Table 7 are integrated within the framework of Eq. (7) and the grey initial decision matrix is created as shown in Table 8.

Each value in the grey decision matrix is normalized by taking into account the characteristics of the non-beneficial and beneficial. The normalized values are calculated using Eq. (8) for the non-beneficial environmental performance indicators and Eq. (9) for the beneficial environmental performance indicators. The resulting normalized values are shown in Table 9.

In the final stage of the proposed approach, the grey percentage values ($\otimes P V_{i j}$) of the evaluation criteria were initially calculated by utilizing Eq. (10). Subsequently, the grey objective importance weights of each evaluation criterion were determined using Eq. (11). The findings and crisp weights obtained from these calculations are presented in Table 10.

The findings of the Grey LOPCOW method, as presented in Table 10, indicate that the three evaluation criteria with the most significant impact on the environmental performance of the selected banks are EI9 (amount of disposed waste), EI7 (non-hazardous waste) and EI8 (amount of recycled waste), respectively. On the other hand, the three evaluation criteria that have the least impact on the environmental performance of banks are EI4 (direct emissions), EI10 (water consumption) and EI1 (fuel consumption), respectively.

In the subsequent stage of the analytical process, the environmental performance of the bank was appraised through the implementation of the Grey PIV methodology. The initial step within the Grey PIV method involves the formulation of the Grey initial decision matrix. This matrix is obtained by means of Eq. (7) and is presented in Table 8. In the second step of the proposed method, all values for the criteria in the grey initial matrix are normalized using Eq. (12). The normalized grey values are reported in Table 11.

The weighted normalized matrix created using Eq. (13) is given in Table 12.

The grey weighted proximity index values ($g_{i j}$) were calculated using Eq. (14) and Eq. (15) with consideration for the non-beneficial/beneficial characteristics of the criteria. The findings obtained by using Eq. (14) for nonbeneficial criteria and Eq. (15) for beneficial criteria are shown in Table 13.

In the final step of the Grey PIV procedure, firstly, the general proximity index values ($d_i^l, d_i^u$) utilized for the purpose of ranking the decision alternatives were calculated by Eq. (16). Subsequently, the success scores ($d_i$) of the banks were determined according to Eq. (17). The findings of these calculations and the results of the success rankings of the banks are presented in Table 14.

The findings presented in Table 14 demonstrate that, within the context of the banks whose shares are listed on BIST, the Yapı ve Kredi Bank stands out as a leader in environmentally sustainable performance. The subsequent banks in order of environmental sustainable performance are Akbank, Garanti Bank, Şekerbank, Halkbank and Vakıfbank, respectively.

The validity and applicability of the Grey LOPCOW-PIV approach were tested by performing a robustness test consisting of three stages. Firstly, the impact of fluctuations in the weight values of environmental assessment criteria on the ultimate banking ranking was examined through 130 distinct scenarios. Secondly, the resilience of the ranking outcomes to the rank reversal issue was assessed via six scenarios. Finally, the ranking results of the proposed Grey MCDM model were compared with other Grey MCDM methodologies.

In the initial phase of the sensitivity analysis, the impact of each environmental criterion on the ranking position of alternative banks was examined through 130 distinct scenarios. According to the first 10 scenarios, the importance weight of the first evaluation criterion was reduced by 10%, 20%, ..., 100% in each scenario. The weights of the remaining criteria were modified proportionally so that their total weight values would be 1. The same weight calculation process was repeated for the remaining 12 criteria, resulting in a total of 130 scenarios. As a result, thanks to these sensitivity scenarios, all possible effects of changes in criteria weights on the final ranking results can be taken into account [70]. The outcomes concerning the new rankings obtained from 130 scenarios are illustrated in Figure 2. As demonstrated in Figure 2, it is evident that the ranking positions of YKBNK, AKBNK and VAKBN remain constant when the criteria weights are altered. Minor alterations in the ranking positions are observed for the remaining three bank alternatives (i.e., GARAN, SKBNK, and HALKB). However, these minor changes in the rankings are not substantial enough to impact the overall ranking outcome. Consequently, the findings obtained from this analysis indicate that the proposed model produces robust and dependable outcomes.

Whether the ranking results are resistant to the order reversal problem was examined with a total of 6 scenarios in which the worst alternatives in the ranking were deleted in order [71], [72]. The ranking results derived from six distinct scenarios are presented in Figure 3. According to Figure 3, the obtained results confirm the initial ranking results and reveal that the bank ranking positions are robust to the rank reversal problem.

In the last stage of robustness analysis, the rankings obtained with Grey PIV were compared with the results of other classical and new Grey MCDM approaches. In this context, the used methods for comparative analysis are Grey SAW [73], Grey EDAS [74], Grey WASPAS [75], Grey MARCOS [76], Grey MACONT [77], and Grey WEDBA [78], respectively. The results of the comparative analysis are demonstrated in Figure 4. Based on Figure 4, no change was observed in the ranking position of the alternative banks, indicating that the ranking results obtained from the proposed grey hybrid approach produce stable and dependable outputs.

Banks have a high potential to create environmental impacts through both their lending and investment activities and other financial service activities. Assessing the environmental sustainability performance of the banking sector is critical for effective risk management, strategic planning and ecosystem sustainability. Increasing climate and environmental problems have led banking institutions, like other non-financial firms, to develop policies aimed at reducing their harmful effects on the environment. As a result, banking activities that take environmental issues into account not only serve to minimize environmental risks, but also contribute to improving corporate reputation and long-term performance.

The current study has some practical implications for bank decision-makers as follows:

•A novel and comprehensive decision-making framework for assessing and analysing banks' environmental sustainability performance is proposed in this paper.

•The presented decision-making approach is designed to be simple and straightforward to implement for bank decision-makers who do not possess advanced mathematical skills.

•Integrating LOPCOW approach with interval grey numbers provides significant flexibility to decision-makers in the relevant sector by facilitating the calculation of weights of predetermined criteria.

•Combining the PIV method with interval grey numbers can aid DMs and practitioners in the effective management of processes related to environmental issues, enabling the making of reliable and rational decisions.

The managerial implications of the existing work are as follows:

•The application of Grey LOPCOW and Grey PIV methods in evaluating bank environmental performance can contribute to the existing literature by providing new insights and methodologies. This can pave the way for further research and development in the field of environmental sustainability in the banking industry.

•The usage of MCDM tools assists banks in aligning their operations with broader sustainability goals, such as the United Nations Sustainable Development Goals.

•The utilization of MCDM algorithms in the assessment of environmental performance has the potential to assist banking executives in adhering to prevailing environmental regulations and standards. A structured methodology for evaluating environmental performance can enable comprehensive and transparent sustainability reporting, a prerequisite in the contemporary context where regulators and stakeholders are demanding such information with increased frequency.

•The introduced framework provides a detailed assessment of environmental risks associated with banking activities. By identifying and prioritizing these risks, banks can develop more effective mitigation strategies, thereby reducing potential liabilities and enhancing their resilience to environmental challenges.

•The comparative analysis allows banks to benchmark their performance against peers. This can highlight best practices and areas needing improvement, fostering a culture of continuous improvement in environmental sustainability.

•The empirical findings from the suggested decision-making approach have the potential to guide the board of directors of banks in enhancing their environmental sustainability performance, thereby establishing a sustainable competitive advantage within the industry.

Analysing the performance of the banking sector from different perspectives is important for all those involved in banking. To manage risk, comply with regulations, attract investors, enhance reputation, improve operational efficiency, ensure long-term sustainability and build stakeholder confidence, it is essential that banks measure and assess their performance.

Recently, the sustainability performance of financial and non-financial institutions has begun to attract attention from a variety of stakeholders, including regulators, academics, practitioners and policymakers. In this context, sustainability performance is generally analyzed by researchers based on environmental, social and governance indicators. Therefore, the current research aimed to make a comparison between banks by focusing on the environmental performance dimension, which is one of the three dimensions of ESG.

This work puts forward a combined Grey MCDM approach with the aim of addressing the problem of measuring banks' environmental performance. The suggested framework is tested through a case study to analyze the environmental sustainability indicators of six deposit banks whose shares are traded on BIST. In this context, the environmental indicators are weighted employing the Grey LOPCOW approach, while the Grey PIV model is implemented to rank the banks' environmental performance.

Grey LOPCOW results show that the quantity of disposed waste, non-hazardous waste, and amount of recycled waste are the most significant factors influencing the environmental performance of banks. In addition, the three evaluation criteria, such as direct emissions, water consumption, and fuel consumption were found to be the least influential criteria on environmental performance. The findings from the existing work are similar to those obtained in previous studies [24], [31], [34], [35], [39]. However, the results of this paper differ from the findings of other researches [23], [29], [33], [40].

The outcomes obtained from the application of the Grey PIV method show that the Yapı ve Kredi bank is a deposit bank with the highest environmental performance compared to other banks. In the environmental performance ranking, Yapı ve Kredi Bank was followed by Akbank, Garanti Bank, Şekerbank, Halkbank and Vakıfbank, respectively. This finding aligns with the conclusions presented in previous studies [19], [24], [28], [34], while it differs from the findings of other researches [31], [35], [37].

In accordance with the objective of the study, the accuracy and validity of the presented conceptual framework were tested in three stages through sensitivity analyses. In the initial phase of the study, the impact of fluctuations in the weight values of the criteria on the initial ranking of the alternative banks was examined. In the second stage, the robustness of the proposed MCDM model to the problem of ranking reversal was analyzed. In the third and final stage, the outputs of the suggested decision support model are compared with the results of a variety of MCDM techniques. The findings of the sensitivity analysis reveal that the developed MCDM tool in the existing work is a stable, reliable, and robust decision support tool for practitioners and decision makers in the banking industry. Similar to other studies, this research has some limitations. First of all, the evaluation of only 6 deposit banks can be considered a limitation. Secondly, the use of only grey linguistic variables and their corresponding interval grey numbers in this work can be seen as another limitation. In addition, only the LOPCOW method was employed in the assessment of environmental indicators in this research. There are many weighting approaches in the MCDM literature. Hence, considering the critical role of criterion weight values in determining the positions of alternatives, the use of only the LOPCOW technique can be considered as another limitation. Weighting methods such as LBWA, LMAW, SIWEC, RANCOM can be utilized in future studies. Besides, more consistent criterion weights can be obtained by integrating these methods. Moreover, 7 expert opinions were consulted in terms of criteria evaluations in the existing research. Therefore, more experts can be included in the analysis in future studies to generalize the results. Finally, in future studies, researchers can add depth to the analysis by using fuzzy linguistic variables instead of grey linguistic variables. In this context, it may be suggested to use methodologies such as intuitionistic fuzzy numbers, Pythagorean fuzzy numbers, picture fuzzy numbers or spherical fuzzy numbers, which provide significant flexibility to DMs and are frequently employed in the literature for modeling uncertainty.