Evaluating Environmental, Social, and Governance Criteria and Green Finance Investment Strategies Using Fuzzy AHP and Fuzzy WASPAS

Abstract

The evaluation and prioritization of environmental, social, and governance (ESG) factors are critical for green finance investment strategies. However, ESG criteria are complex and varied concepts that call for a systematic and reliable ranking system to handle ambiguity and uncertainty in decision-makers’ preferences and assessments. The objective of this study was to examine and prioritize environmental, social, and governance (ESG) factors and investment strategies for the development of green finance. Although ESG criteria have gained importance recently, some research gaps still need to be filled. For this purpose, evaluating ESG criteria and integrating them with green finance investment strategies is imperative. This study employed the fuzzy analytical hierarchy process (AHP) method to assess and rank ESG criteria and sub-criteria and the fuzzy weighted aggregated sum product assessment (WASPAS) method to assess and prioritize the key investment strategies for the development of green finance. According to the fuzzy AHP findings, governance and social factors are secondary to environmental considerations in the creation of green finance. Green bonds, ESG integration, and renewable energy funds are essential to green finance methods, according to the fuzzy WASPAS data. This research provides information on creating sustainable and ethical investment strategies for green finance and successfully including ESG factors in investment decision-making processes.

1. Introduction

Green finance is a fast-emerging topic increasingly acknowledged as critical for achieving sustainable development and mitigating climate change [1]. Green finance, defined as financial goods and services promoting environmentally sustainable operations, has become a priority for governments, investors, and financial institutions. Green money is strongly related to sustainable development principles and the United Nations Sustainable Development Goals (SDGs) [2]. In other words, it refers to various financial goods and services that promote long-term development by incorporating environmental, social, and governance (ESG) issues into investment choices. The goal is to provide investors with financial returns while financing the transition to a low-carbon, resource-efficient, and sustainable economy [3]. The global financial system is critical in promoting long-term growth. The need for long-term investments has skyrocketed with growing worries about climate change, environmental degradation, and societal challenges [4].

Investors are increasingly using ESG criteria to assess the sustainability and effect of their investments. Carbon emissions, water consumption, labor standards, diversity, and corporate governance are all examples of ESG criteria [5]. Incorporating ESG factors into investment choices has become a critical component of green finance. However, as investment decisions get more complicated and there are more ESG aspects to consider, investment managers must work on adequately incorporating ESG criteria into their investment strategies [6]. Despite the rising interest in and acknowledgment of its significance, there are still substantial obstacles to adopting and implementing green finance [7]. The need for precise rules and criteria for identifying and assessing green investment is one of the main problems. Due to this, it may be challenging for investors to discern genuinely sustainable projects from those that only make environmental claims. Additionally, it is sometimes difficult for investors to appropriately analyze the ESG implications of investments due to a lack of clarity and transparency, which negatively influences sustainability [8,9].

Some studies have focused on investigating how ESG transparency and disclosure impact firms’ value and investments. For instance, Yu et al. [10] examined whether the ESG disclosure volume affects business value. Better ESG openness may influence business value by reducing investors’ information symmetry and agency costs. The empirical findings of [10] indicated that for the typically listed corporation, the advantages of ESG disclosure exceed their disadvantages. The authors discovered evidence of the increased disclosure of ESG concerns enhancing business value metrics such as Tobin’s Q. Moreover, the findings implied that companies with larger asset sizes, stronger liquidity, higher R&D intensity, fewer insider holdings, and historically solid financial success would be more open about ESG problems. Calvin et al. [11] investigated how the report of the Dow 30 corporations in the United States on the Global Core Indicators (GCIs) for the UN’s SDGs 2030 Agenda revealed that the Dow 30 disclosed more GCIs that matched US market expectations, current events, and financial significance, and they prioritized institutional and economic concerns above environmental and social issues. The study also discovered that GCI disclosure levels corresponded with various MSCI sustainability ratings but not CDP climate change ratings. MSCI ratings for particular industries have grown over time. To route loans to “sustainable” borrowers and eventually promote sustainable growth, regulators and investors increasingly require banks to incorporate ESG elements into credit risk assessment [12]. According to Brogi et al. [12] improved creditworthiness is closely correlated with increased ESG awareness. The authors found it appropriate to introduce ESG awareness parameters in the creditworthiness assessment of borrowers. The findings of [13] implied that stock exchange membership and ownership status increase the frequency of ESG disclosure. As a result, ESG reporting impacts both environmental and financial performance.

Since finance has been considered a pivotal driver of sustainability, developing and adapting a financial system according to the essentials of sustainable development has become indispensable. This is where ESG comes into play. The incorporation of ESG dimensions has been considered incredibly challenging in the financial decision making of financial institutions. In this regard, Ziolo et al. [14] hypothesized that the incorporation of ESG factors into the decision making of financial institutions would drive financial system sustainability, using MCDA methods for the analysis. The study identified the factors and examined the dependencies between ESG factors to incorporate them into financial institutions’ financial decision making. Schumacher et al. [15] reviewed the importance of sustainable finance and investment in Japan and how the Japanese financial industry may alleviate rising climate risks and aid the transition to a zero-carbon and sustainable economy in Japan.

ESG criteria and green financing are crucial for investors, decision-makers, and society because they assess an investment’s sustainability and social effect and provide funding for initiatives or activities that will benefit the environment [16]. ESG factors and green financing are essential for a company’s performance and value creation since they may help with growth, cut expenses, decrease risks, boost productivity, and maximize capital. Nevertheless, ESG criteria and green finance are complicated and varied concepts, and they need a systematic and rigorous assessment and ranking process that can handle uncertainty and ambiguity in decision-makers’ preferences and judgments [16]. Given the importance of ESG criteria from the perspective of green finance investment strategy promulgation and implementation, the authors found it imperative to identify and evaluate ESG criteria and sub-criteria and alternative green investment strategies under ESG criteria to ensure environmental sustainability.

The objective of this study was to analyze environmental, social, and governance (ESG) criteria and rank green finance investment strategies using the fuzzy analytic hierarchy process (AHP) and fuzzy weighted aggregated sum product evaluation (WASPS) method. ESG criteria are used to assess an investment’s sustainability and social effect. Fuzzy AHP and fuzzy WASPAS are multi-criteria decision-making techniques that deal with ambiguity and uncertainty in decision-makers’ preferences and assessments [17]. With the help of stakeholders’ and experts’ input, this study sought to create a comprehensive and reliable framework for evaluating and choosing the best ESG criteria and green finance investment methods. This research aimed to create an innovative and effective tool for investors, politicians, and academics interested in ESG and green finance issues. This study has important implications for the development of green finance and the promotion of sustainable development. By defining and prioritizing critical ESG criteria and green finance investment techniques, this study can guide investment decisions for investment managers, governments, and stakeholders to promote the transition to a more sustainable and low-carbon economy.

This paper is structured as follows: Section 2 presents an overview of the literature on essential topics and theories connected to green finance. Section 3 represents the ESG criteria and investment strategies for green finance development. The study design and analytical methodologies are described in Section 4. The study results were based on the fuzzy AHP and fuzzy WASPAS methodologies, and a discussion of these results is provided in the results section (Section 5). Finally, the conclusion summarizes the essential findings and recommends further research directions (Section 6).

2. Literature Review

Investors increasingly use ESG criteria to evaluate the sustainability and long-term viability of companies and projects [18]. This literature review will examine the research and literature related to ESG criteria and investment strategies for green finance. Numerous studies have highlighted the importance of environmental criteria in investment decisions for green finance. For example, a study found that investors prioritizing environmental criteria are more likely to invest in companies with strong environmental policies and practices [19]. Another study revealed that companies with strong environmental performance outperform their peers over time, implying that environmental criteria may be an important factor in generating financial returns [20]. In green finance, several investment strategies have been identified as effective ways to support environmental sustainability. Research has also revealed the significance of social criteria in green finance investment decisions. According to one study, social criteria such as labor practices and human rights are critical for socially responsible investors [21]. Another study, conducted by Eccles and Serafeim [22], discovered that companies with strong social performance have better long-term financial performance. Several investment strategies for social sustainability in green finance have been identified as effective. Governance criteria are also important in green finance because they promote transparent and responsible business practices. Several studies have highlighted the importance of governance criteria in green finance investment decisions. For example, one study found that companies with strong governance practices have better long-term financial performance [23].

Green finance is paying more and more attention to MCDM techniques to support decision-making processes that take ESG factors into account. The interest in creating investment strategies that adhere to sustainable and responsible investment principles has increased as a result. Because they offer a structured framework for analyzing multiple criteria and alternatives concurrently, MCDM methods are useful tools for decision making [24]. The use of MCDM techniques in the context of ESG standards and investment strategies for green finance was examined in this literature review. The fuzzy MCDM approach was used by the authors to assess the effectiveness of ESG investment strategies [25]. In a different study, the authors used the AHP method to create a decision-making framework for green finance investments [17]. The framework aimed to identify the most essential ESG criteria for green finance investment and to rank alternative investment strategies based on their performance and influence on the development of green bond markets. The authors suggested that investors could use the framework to develop sustainable investment strategies that align with their ESG priorities. In the previous study, the authors identified the performance of Chinese-listed companies and the key ESG factors that impact their financial performance [26]. The study found that environmental and social factors substantially impacted financial performance more than governance factors. The authors suggested that investors consider these factors when evaluating investment opportunities in Chinese-listed companies. In another study, the authors assessed the ESG performance of Chinese-listed companies and developed a sustainable investment strategy [8]. The study found that companies with better ESG performance had higher financial performance and that environmental criteria had the most substantial impact on financial performance. The authors suggested that investors prioritize environmental standards when developing sustainable investment strategies.

Several recent studies have used various MCDM methods [27,28,29,30,31] to evaluate the performance and strategies for green finance development in multiple sectors. The authors identified that green finance is essential to determine ESG criteria and investment strategies for sustainable development. These studies also suggested that MCDM methods can help evaluate ESG criteria and develop sustainable investment strategies. Since investors increasingly use ESG criteria to assess the sustainability and long-term viability of companies and projects, ESG criteria are critical components of green finance, and various investment strategies have been identified as effective ways to support sustainable and responsible investing. Investing in companies and projects promoting environmental sustainability, social justice, and transparent and responsible business practices can support the transition toward a more sustainable economy.

Research Gap

Despite the growing interest in the application of MCDM methods in the context of ESG criteria and investment strategies for green finance [27], several research gaps still need to be addressed. One significant research gap is the need for more focus on integrating social and environmental criteria in investment decision-making processes. While environmental criteria are often prioritized, social factors are also crucial in ensuring sustainable and responsible investment practices. Another research gap is the need for more consensus on the most appropriate MCDM method to use in the context of ESG criteria and green finance investment strategies. While several studies have used AHP, TOPSIS, and simple additive weighting (SAW), other MCDM methods have also been used.

Further research is needed to use hybrid fuzzy MCDM methods to evaluate the effectiveness and robustness of ESG criteria and green finance investment strategies. Finally, there is a need for more research on the application of MCDM methods in the context of green bonds and other green finance instruments [32]. Therefore, in the present study, we will address these research gaps, providing insights into the most effective approaches to integrating ESG criteria in investment decision-making processes and developing sustainable and responsible investment strategies.

3. ESG Criteria and Investment Strategies for Green Finance Development

There are three main criteria for green finance investment decisions, including ESG criteria. Environmental criteria focus on a company’s impact on the environment. Social criteria examine a company’s impact on its stakeholders. Finally, the governance criteria examine a company’s management and oversight practices. Overall, incorporating ESG criteria into investment decisions can play an important role in promoting sustainable and responsible investment practices and advancing the goals of green finance [33].

3.1. Proposed ESG Criteria and Sub-Criteria

In this study, after a vast literature review and the analysis of relevant standards and guidelines, we identified fifteen critical ESG criteria and sub-criteria. In this regard, the fuzzy AHP method was used to evaluate and rank ESG criteria and sub-criteria for green finance investment decisions.

Table 1. ESG criteria and sub-criteria with brief descriptions.

ESG Criteria	Sub-Criteria	Description	Reference
Environmental (E)	Natural resource conservation (E1)	This sub-criterion refers to investments that aim to preserve natural resources and ecosystems. It includes investments in sustainable forestry, the conservation of biodiversity, and sustainable agriculture.	[34,35]
	Climate change mitigation (E2)	This sub-criterion includes investments in projects and technologies that aim to reduce greenhouse gas emissions and mitigate the effects of climate change. Such investments include renewable energy projects, energy-efficient technologies, and carbon capture and storage.	[36,37]
	Circular economy (E3)	This sub-criterion refers to investments in projects and technologies to create a closed-loop system where waste is reduced, reused, and recycled. It includes investments in circular business models, sustainable product design, and waste reduction initiatives.	[38,39]
	Pollution prevention (E4)	This sub-criterion includes investments in projects and technologies that prevent or reduce pollution. Such assets include waste management systems, pollution prevention technologies, and clean transportation.	[35,40]
	Environmental impact assessment (E5)	This sub-criterion refers to the evaluation of the environmental impact of a project or investment. Air and water quality, soil quality, and ecosystem health are all factors that must be considered when evaluating a project’s environmental impact.	[35,41]
Social (S)	Community engagement (S1)	This sub-criterion evaluates the amount of community involvement in the creation and implementation of green finance investments, as well as the ability of communities to have an impact on policy.	[42,43]
	Human rights (S2)	This sub-criterion evaluates the level of community participation in the creation and implementation of green finance investments, as well as the power of communities to shape policy.	[44,45]
	Social responsibility (S3)	This sub-criterion involves the degree to which a business accepts accountability for its deeds and takes into account the needs of all its stakeholders. Transparency, good corporate governance, and ethical business practises are part of this criterion.	[42,46]
	Income distribution (S4)	This sub-criterion assesses how investments in green finance affect how income is distributed, including whether they have the potential to lessen income inequality and encourage the equitable distribution of resources.	[47]
	Employee satisfaction (S5)	This is a crucial social criterion for determining how well a business treats its employees. Businesses that put a high priority on employee satisfaction will probably have employees who are more motivated and productive.	[48]
Governance (G)	Corporate governance (G1)	This sub-criterion assesses the rules and practises that guarantee accountability and openness in corporate management. This covers the make-up of the board of directors, the level of member independence, and the oversight procedures for the board.	[8,26]
	Regulatory compliance (G2)	This sub-criterion assesses how well businesses adhere to applicable laws, rules, and standards regarding social and environmental issues. Companies with a proven track record of regulatory compliance are less likely to experience legal issues that could harm their financial performance.	[39,49]
	Risk management (G3)	This sub-criterion assesses a company’s capacity to manage risks associated with social and environmental concerns. Businesses with effective risk management procedures can anticipate and reduce risks better, which lowers the likelihood of unfavourable financial effects.	[31,50]
	Stakeholder engagement (G4)	Companies’ environmental and social risk management is assessed by this sub-criterion. Strong risk management processes help companies anticipate and mitigate risks, reducing financial risks.	[39,49]
	Ethics and values (G5)	The moral principles that direct a company’s behavior, including its effects on the environment and society, are covered by this sub-criterion. Even when it may not be in the company’s immediate financial interest, a company with strong ethics and values is more likely to act in the interests of the environment and society.	[8,51]

presents the ESG criteria and sub-criteria of the study.

3.2. Proposed Investment Strategies

The transition to a low-carbon, more sustainable economy is supported by a number of investment strategies for green finance. Investors can identify and steer clear of businesses with significant negative environmental and societal impacts by using ESG criteria when making investment decisions. Additionally, these criteria can assist them in locating businesses that exhibit strong environmental and social performance and may be more durable in the long run [8]. Investors can support green finance by employing a variety of strategies in addition to taking ESG factors into account. Based on their performance across various criteria, investment strategies for green finance can be evaluated and ranked using the fuzzy WASPAS method. Below are presented the various suggested investment plans for green finance.

3.2.1. Impact Investing (S1)

Impact investing is a strategy that involves making financial gains while investing in businesses, initiatives, or projects that have a positive social or environmental impact [7]. Impact over financial returns is typically a priority for impact investors as they work to support solutions to social and ecological issues such as climate change, poverty, and inequality.

3.2.2. ESG Integration (S2)

ESG integration is a strategy that incorporates ESG aspects into the analysis and decision-making process for investments. This strategy seeks to locate and invest in businesses that are sustainably run and socially accountable [52]. Investors can assess a company’s sustainability and long-term viability using ESG integration, and they can also spot risks and opportunities that conventional financial analysis might not have picked up on.

3.2.3. Green Bonds (S3)

Green bonds are fixed-income securities used to finance environmentally sustainable projects or initiatives [53]. Green bonds can be issued by governments, corporations, or other entities, and the proceeds are earmarked for specific green projects, such as renewable energy, energy efficiency, or sustainable infrastructure. Green bonds offer investors a way to support sustainability goals while generating fixed-income returns.

3.2.4. Sustainable Agriculture Funds (S4)

These investment funds focus on sustainable agriculture practices, such as organic farming, regenerative agriculture, and sustainable forestry [54]. Sustainable agriculture funds can support the transition to a more sustainable food system while potentially generating attractive returns for investors [55]. The performance of this investment strategy was evaluated based on its potential to support sustainable agriculture practices, its level of diversification, and its ability to generate returns for investors.

3.2.5. Shareholder Engagement (S5)

Shareholder engagement is a strategy that involves active engagement with companies to encourage sustainable and responsible business practices [56]. This strategy can include dialogue with companies on ESG issues, filing shareholder proposals to address sustainability concerns, and voting on shareholder resolutions at annual meetings.

3.2.6. Renewable Energy Funds (S6)

These investment funds concentrate on hydroelectric, solar, and wind energy projects. Investors can help the transition to a low-carbon economy by contributing to renewable energy funds and possibly earning attractive returns [57]. This investment strategy’s performance was assessed based on its capacity to fund renewable energy projects, degree of diversification, and capacity to produce returns for investors.

3.2.7. Thematic Investing (S7)

Thematic investing is a strategy that involves making investments in particular themes or industries that are in line with responsible and sustainable investing objectives [58]. This strategy aims to seize growth opportunities in sectors that are anticipated to profit from the shift to a more sustainable economy, such as sustainable agriculture, clean technology, renewable energy, and circular economy.

These investment strategies were assessed and ranked using the fuzzy WASPAS method based on how well they performed against a variety of criteria, such as their capacity to produce returns for investors, their capacity to fund environmentally friendly projects, and their capacity to produce favorable social and environmental outcomes.

4. Methodology

The fuzzy AHP and fuzzy WASPAS MCDM methods were used to evaluate and rank ESG-based investment strategies. ESG criteria and sub-criteria for investment decisions had to be identified first. The fuzzy AHP was used to determine ESG criteria and sub-criteria weights. Pairwise comparisons of criteria and sub-criteria were used to determine their importance. To account for uncertainty and imprecision in real-world decision making, linguistic variables were used for pairwise comparisons and converted into fuzzy numbers [59]. Fuzzy WASPAS was used to evaluate sustainable investment opportunities using ESG criteria and sub-criteria. This involved determining the degree of conformity of each investment strategy to the criteria and sub-criteria. The degree of conformity was calculated using fuzzy numbers and the fuzzy WASPAS method. The investment strategies were ranked based on their overall performance, which was calculated using the fuzzy WASPAS method. The best strategy was the investment option with the highest overall performance score. This methodology can be used by investors, financial institutions, and other stakeholders to develop sustainable and responsible investment strategies that align with their ESG goals and objectives. Figure 1 shows the decision methodology of this study.

4.1. The Fuzzy AHP Method

The AHP method was developed by Thomas L. Saaty [60]. In this study, the fuzzy AHP approach was used to evaluate and rank a set of criteria and sub-criteria. Fuzzy AHP extended the traditional AHP method by incorporating fuzzy set theory to account for ambiguity and fuzziness during decision making [61,62].

Table 2. Triangular fuzzy numbers (TFNs) scale [63].

Code	Linguistic Variable	TFNs
1	Equal preference	(1,1,3)
2	Weak preference	(1,3,5)
3	Strong preference	(3,5,7)
4	Very strong preference	(5,7,9)
5	Extremely strong preference	(7,9,11)

presents the TFN scale.

The following key steps of the fuzzy AHP method were utilized and developed by Gogus and Boucher [64].

Step I. Triangular fuzzy matrix (TFM):

$$X_i=(l_i,m_i,u_i)$$

(1)

After this, the first TFM is created with the middle TFM:

$$X_m=\left[x_{ijm}\right]$$

(2)

Next, the second TFM is established for the upper and lower bounds of the TFN using a geometric mean approach:

$$X_g=[\sqrt{x_{iju}{x}_{ijl}}]$$

(3)

Step II. The weight vector and lambda max are created and computed using the Saaty method.

Step III. The consistency index (CI) is created:

$${CI}_m=\frac{{\lambda }_{max}^m-n}{n-1}$$

(4)

$${CI}_g=\frac{{\lambda }_{max}^g-n}{n-1}$$

(5)

Step IV: The consistency ratio (CR) is created:

$${CR}_m=\frac{{CI}_m}{{RI}_m}$$

(6)

$${CR}_g=\frac{{CI}_g}{{RI}_g}$$

(7)

If the values of ${CR}_m$ and ${CR}_g$ are less than 0.10, then the fuzzy pairwise matrices are considered consistent.

Table 3. RI scale.

$\mathit{n}$	${\mathit{R}\mathit{I}}_{\mathit{m}}$	${\mathit{R}\mathit{I}}_{\mathit{g}}$
1	0	1
2	0	2
3	0.48	0.17
4	0.79	0.26
5	1.07	0.35
6	1.19	0.38
7	1.28	0.40
8	1.34	0.41
9	1.37	0.43
10	1.40	0.44

presents the RI scale used in the study proposed by Gogus and Boucher [64].

The fuzzy AHP method helped obtain significant findings regarding ESG criteria and sub-criteria for green finance development.

4.2. The Fuzzy WASPAS Method

Zavadskas proposed the WASPAS method [65]. This is another MCDM method that is used to evaluate alternatives based on a set of criteria. Fuzzy WASPAS is an extension of the traditional WASPAS method that incorporates fuzzy logic to account for uncertainty and imprecision in decision making [66]. The linguistic variables matching the TFNs are presented in

Table 4. Linguistic variables for the importance weight of each attribute.

Linguistic Scale	TFNs
Very poor	(0,0,1)
Poor	(0,1,3)
Medium poor	(1,3,5)
Fair	(3,5,7)
Good	(5,7,9)
Very good	(7,9,10)
Extremely good	(9,10,10)

.

The main steps in the fuzzy WASPAS method are as follows [67]:

Step I. A fuzzy decision matrix is constructed as follows:

$$\tilde{A}=\left[\begin{array}{ccccc}{\tilde{a}}_{11}& \cdots & {\tilde{a}}_{1j}& \cdots & {\tilde{a}}_{1n}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{a}}_{i1}& \cdots & {\tilde{a}}_{ij}& \cdots & {\tilde{a}}_{in}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{a}}_{m1}& \cdots & {\tilde{a}}_{mj}& \cdots & {\tilde{a}}_{mn}\end{array}\right] \mathrm{Here} i=1,\dots ,m\mathrm{and} j=1,\dots ,n$$

(8)

Afterward, the priorities of the alternatives are determined through several steps, presented below.

Step II. The normalized decision-making matrix is constructed as follows:

$$\left\{\begin{array}{c}\frac{{\tilde{a}}_{ij}}{\underset{i}{\max }{\tilde{a}}_{ij}}if \underset{i}{\max }{\tilde{a}}_{ij} is prefereable,\\ \frac{\underset{i}{\min }{\tilde{a}}_{ij}}{{\tilde{a}}_{ij}}if \underset{i}{\min }{\tilde{a}}_{ij} is preferable,\end{array}\right. \mathrm{where}, i=1,\dots ,m\mathrm{and} j=1,\dots ,n$$

(9)

The initial values of all the attributes ${\tilde{a}}_{ij}$ are normalized, as well as the defining values ${\widetilde{\stackrel{-}{a}}}_{ij}$; then, the normalized decision matrix is ${\widetilde{\stackrel{-}{A}}}_{ij}={[{\widetilde{\stackrel{-}{a}}}_{ij}]}_{m\times n}$.

Step III (a). The weighted normalized decision-making matrix ${\tilde{\widehat{A}}}_q$ is constructed for WSM.

$${\tilde{\widehat{A}}}_x=\left[\begin{array}{ccccc}{\tilde{\widehat{a}}}_{11}& \cdots & {\tilde{\widehat{a}}}_{1j}& \cdots & {\tilde{\widehat{a}}}_{1n}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{\widehat{a}}}_{i1}& \cdots & {\tilde{\widehat{a}}}_{ij}& \cdots & {\tilde{\widehat{a}}}_{in}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{\widehat{a}}}_{m1}& \cdots & {\tilde{\widehat{a}}}_{mj}& \cdots & {\tilde{\widehat{a}}}_{mn}\end{array}\right]{\tilde{\widehat{a}}}_{ij}={\widetilde{\stackrel{-}{a}}}_{ij}{\tilde{w}}_j,i=1,\dots ,m \mathrm{and} j=1,\dots ,n$$

(10)

Step III (b). The weighted normalized decision-making matrix ${\tilde{\widehat{A}}}_p$ is constructed for WPM.

$${\tilde{\widehat{A}}}_y=\left[\begin{array}{ccccc}{\stackrel{\cong }{a}}_{11}& \cdots & {\stackrel{\cong }{a}}_{1j}& \cdots & {\stackrel{\cong }{a}}_{1n}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\stackrel{\cong }{a}}_{i1}& \cdots & {\stackrel{\cong }{a}}_{ij}& \cdots & {\stackrel{\cong }{a}}_{in}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\stackrel{\cong }{a}}_{m1}& \cdots & {\stackrel{\cong }{a}}_{mj}& \cdots & {\stackrel{\cong }{a}}_{mn}\end{array}\right];{\tilde{\widehat{a}}}_{ij}={\widetilde{\stackrel{-}{a}}}_{ij}{\tilde{w}}_j,i=1,\dots ,m \mathrm{a}\mathrm{n}\mathrm{d} j=1,\dots ,n$$

(11)

Step IV. The values of the optimality function are computed.

$${\tilde{X}}_i=\frac{1}{3}({\tilde{X}}_{i\alpha }+{\tilde{X}}_{i\beta }+{\tilde{X}}_{i\gamma })$$

(12)

$${\tilde{Y}}_i=\frac{1}{3}({\tilde{Y}}_{i\alpha }+{\tilde{Y}}_{i\beta }+{\tilde{Y}}_{i\gamma })$$

(13)

Step V. For the F-WASPAS method, the integrated utility function value for an alternative is determined as follows:

$$K_i=\lambda \sum _{j=1}^m{X}_i+(1-\lambda )\sum _{j=1}^m{Y}_i,\lambda =0,\dots ,\mathrm{1,0}\le K_i\le 1.$$

(14)

Here, $\lambda$ is based on several assumptions, e.g., that the total of all alternative WSM scores/weights should be equal to the total of the WPM scores/weights:

$$\lambda =\frac{\sum _{i=1}^m{Y}_i}{\sum _{i=1}^m{X}_i+\sum _{i=1}^m{Y}_i}$$

(15)

Step VI. The preference order of the alternatives is determined, and an alternative with the maximal $K_i$ value is selected.

4.3. Experts for the Study

In this study, five experts contributed to analyzing the importance of ESG criteria, sub-criteria, and investment strategies for green finance. All the experts were consulted through a webmail service. The various experienced and professional experts, such as financial analysts, environmental experts, social scientists, academics, and policymakers, were asked to analyze and rank based on fuzzy AHP and fuzzy WASPAS methods. The financial analysts specialized in sustainable investing and had experience with evaluating ESG criteria. The environmental experts knew the impacts of different industries and activities on the environment, and the social scientists and experts understood social issues, such as labor practices, human rights, and community impacts. The academics specialized in sustainable finance and had conducted research on MCDM methods and their application to green finance. Moreover, the policymakers, responsible for developing and implementing policies related to sustainable finance, could provide insight into the regulatory environment and policy priorities. These experts could offer a range of perspectives and knowledge to inform the study and ensure that it was comprehensive and well-informed.

5. Results and Discussion

In this study, the fuzzy AHP and fuzzy WASPAS methodologies were adopted to analyze ESG criteria and sustainable investment strategies for green finance development. The fuzzy AHP method was used to assess and rank ESG criteria and sub-criteria; the findings are provided in the following sub-sections.

5.1. Fuzzy AHP Results (ESG Criteria)

These results indicate the relative importance of each main criterion in the investment decision-making process for green finance development. The environmental criteria were considered the most important, followed by governance and social criteria.

Table 5. Ranking of ESG criteria with respect to the goal.

Code	ESG Factor	Weight	Rank
E	Environmental	0.372	1
S	Social	0.270	3
G	Governance	0.358	2

shows the ranking of ESG criteria based on the fuzzy AHP method.

Among the criteria, environmental (E) criteria came in first, since there have been increasingly severe weather incidents around the world, which have been associated with climate change. Investors now more than ever understand the significance of environmental factors in choosing investments for green finance. As a result, renewable energy initiatives such as wind and solar power are regarded as eco-friendly investments that can aid in lowering greenhouse gas emissions and reducing the effects of climate change [68,69]. The second most significant aspect was governance (G). Transparency and accountability in investment decisions for green finance are dependent on good governance. Investors may, for instance, take into account the governance practices of the businesses they invest in, such as their CSR policies and adherence to legal and regulatory requirements [32]. Investors may also take into account the track record of investment managers’ ethical and sustainable business practices. The social (S) criterion was regarded as the least important one. However, it remains a key factor in developing investment strategies for the growth of green finance, because investors understand the significance of social factors in choosing investments for the development of green finance.

These findings were consistent with sustainable development principles, which place a strong emphasis on the need to balance environmental, social, and economic factors when making decisions [70]. By prioritizing environmental criteria, investors can ensure that their investments contribute to long-term sustainability and help mitigate the negative impacts of climate change. Similarly, by considering social criteria, investors can ensure that their investments support positive social outcomes, such as community development and labor rights. Governance criteria are also important for ensuring transparency and accountability, which can help prevent unethical or unsustainable practices.

5.2. Fuzzy AHP Results (Sub-Criteria)

Figure 2 shows the ranking of sub-criteria with respect to environmental criteria (E). These results indicate the relative importance of each sub-criterion within each main set of criteria. Within the environmental criteria (E), climate change mitigation (E2) was considered an essential sub-criterion, while environmental impact assessment (E5) was regarded as the least important.

Similarly, within the social criteria (S), community engagement (S1) was considered the most critical sub-criterion, while employee satisfaction (S5) was regarded as the least important. These results can guide investment decisions by prioritizing investments that perform well in the most critical sub-criteria for each main criterion. Figure 3 presents the ranking of sub-criteria with respect to social criteria.

Figure 4 shows the ranking of sub-criteria with respect to governance (G) criteria. The findings indicated that risk management (G3) was seen as an essential sub-criterion for investment in green finance. Stakeholder engagement (G4) was recognized as the second key sub-criterion from a governance perspective. Ethics and values (G5), regulatory compliance (G2), and corporate governance (G1) were the next most important sub-criteria.

5.3. Fuzzy AHP Results (Overall Sub-Criteria)

In this stage, we examined all 15 ESG sub-criteria using the fuzzy AHP method. The weight was determined by multiplying the main criteria and their sub-criteria to obtain the overall sub-criteria results. Figure 5 shows the overall ESG sub-criteria ranking with respect to the goal of the study. The results indicated that climate change mitigation (E2) was the most significant ESG sub-criterion for green finance development. Pollution prevention (E4) and natural resource conservation (E1) were prioritized as the second and third key ESG sub-criteria for green finance development. The least essential ESG sub-criteria were environmental impact assessment (E5), human rights (S2), and employee satisfaction (S5).

In this regard, this study shall help governments and investors to evaluate RE investment risk factors to reduce the uncertainty of future project losses.

5.4. Fuzzy WASPAS Results (Investment Strategy)

The ranking of seven investment strategies based on $X_i$, $Y_i$, and $K_i$ values is presented in

Table 6. Investment strategy (alternative) values based on $X_i$, $Y_i$, and $K_i$.

Code	Investment Strategy	$${\mathit{X}}_{\mathit{i}}$$	$${\mathit{Y}}_{\mathit{i}}$$	$${\mathit{K}}_{\mathit{i}}$$
S1	Impact investing	0.2176	0.4646	0.2978
S2	ESG integration	0.2514	0.5054	0.3339
S3	Green bonds	0.2623	0.5185	0.3455
S4	Sustainable agriculture funds	0.2160	0.4534	0.2931
S5	Shareholder engagement	0.2134	0.4464	0.2890
S6	Renewable energy funds	0.2287	0.4582	0.3033
S7	Thematic investing	0.1270	0.3072	0.1855

. Moreover, the final ranking was based on the highest value of $K_i$, so Figure 6 shows the ranking of investment strategies for green finance development.

The fuzzy WASPAS analysis conducted to rank the investment strategies for green finance development indicated that green bonds (S3) had the highest score among all the strategies, as they are specifically designed to fund environmentally friendly projects such as renewable energy, sustainable transportation, and energy-efficient buildings. The bonds are backed by the issuer’s commitment to environmental performance, and the proceeds are used exclusively for green projects [71]. The findings showed that ESG integration (S2) was the second most crucial strategy among the seven evaluated options, since ESG integration has gained significant popularity in recent years as investors have become more conscious of the impact of their investments on society and the environment. The results showed that renewable energy funds (S6) was the third most favorable option for green finance investment. This was because renewable energy funds invest in various sustainable energy projects, such as solar, wind, and hydropower, which are considered environmentally friendly and have a positive social impact. The other strategies were ranked as follows: impact investing (S1), sustainable agriculture funds (S4), shareholder engagement (S5), and thematic investing (S7).

The main objective of this research was to analyze and rank ESG criteria, sub-criteria, and investment strategies for green finance development. Thus, the fuzzy AHP and fuzzy WASPAS approaches were used effectively to address this decision-making process.

5.5. Discussion

This study analyzed ESG criteria and investment strategies for green finance development based on MCDM methods, namely the fuzzy AHP and fuzzy WASPAS methods. The study involved identifying the main criteria and sub-criteria for evaluating green finance investment strategies and applying the fuzzy WASPAS method to rank the investment strategies based on their performance against these criteria. The results obtained from the Fuzzy AHP method showed that the most important criteria for evaluating green finance investment strategies were environmental, governance, and social impact criteria. The sub-criteria that were found to be most important within each of these main criteria were climate change mitigation (E2), community engagement (S1), and risk management (G3). The results obtained from the fuzzy WASPAS method showed that green bonds (S3) were the most preferred investment strategy, followed by ESG integration (S2) and renewable energy funds (S6). These findings have important implications for investors and financial institutions investing in green finance. The results suggest that environmental impact should play a central role in evaluating investment strategies. Based on the findings of this research, financial institutions and investors should prioritize green bond strategies [32]. This research highlighted the importance of using fuzzy-based MCDM techniques to evaluate green finance investment strategies. By using these methods, we can objectively compare the performance of different investment strategies across a wide range of criteria. Using MCDM techniques can help financial institutions, investors, and other stakeholders build a sustainable economy [72].

This research contributed to the expanding literature on green finance by discussing the importance of ESG standards and investment strategies for the industry’s development [73]. The results suggest that banks and other financial institutions should prioritize investing in green bonds because of the positive effects they can have on the environment and society. Investment decisions can benefit from MCDM approaches because they allow for the evaluation of multiple investment strategies against multiple criteria and sub-criteria.

6. Conclusions and Policy Recommendations

The goal of this study was to use MCDM techniques to rank and analyze ESG investment strategies and criteria. Using the fuzzy AHP approach, the relative weights of the various criteria and sub-criteria were assessed. Using the fuzzy WASPAS method, the ideal green finance investment strategy was determined. According to the findings, out of the three categories, the environment was the most important, followed by the social and governance categories. The most critical sub-criterion for each ESG criterion were risk management (S1), community participation (S1), and climate change mitigation (E2) (G3).

Moreover, green bonds were the most favored investment strategy (S3), followed by ESG integration (S2) and renewable energy funds (S6). This study provides insights for investors and policymakers so that they can make educated judgments when adopting green finance investment methods. It underlined the significance of incorporating ESG variables into investment choices and the potential advantages of green finance for both the environment and society.

6.1. Policy Recommendations

Based on this study’s findings, several policy recommendations could encourage the adoption of green finance and support sustainable development.

• Governments could offer tax incentives or subsidies for investments in green projects, create green bonds, or establish green investment funds to encourage private investors to support sustainable development.
• To promote accountability and better inform investors, companies should be required to disclose their ESG performance and provide regular updates on progress toward sustainability goals.
• Clear standards and certification schemes for green investments could help investors identify credible and trustworthy investment opportunities, reduce information asymmetry, and increase transparency in the market.
• Engaging diverse stakeholders, including local communities and civil society organizations, in the decision-making process could help ensure that green investments’ social and environmental impacts are fully considered and that assets are more responsive to local needs and concerns.
• Governments could invest in research and development to support the development of new technologies and innovative solutions that support sustainability goals and create innovation hubs to encourage collaboration and knowledge-sharing between researchers, industry, and policymakers.

These policy recommendations could create an enabling environment for green finance, support sustainable development, and promote the transition to a more sustainable and resilient economy.

6.2. Study Limitations and Future Research Directions

This work had various areas for improvement that point to future research options. Initially, the study looked at a small set of ESG criteria and investing techniques. Other factors, such as biodiversity; water consumption; supply chain management; and different investing techniques, including green mutual funds, should be included in a future study. Second, expert views were employed in the study to determine the criteria’s priority and the weights of the sub-criteria. While expert judgments are valuable, they might be impacted by prejudices and personal preferences. Other approaches, such as surveys or stakeholder interviews, might be used in a future study to acquire varied ideas and validate the results. Finally, the study was conducted in a specific environment and may not be relevant in other situations. The study might be replicated in different nations or areas in the future to evaluate variances in priorities and uncover particular problems and possibilities for green financing. Overall, this study laid the groundwork for future research to broaden and improve the evaluation of ESG criteria and investment methods for green finance development.