Assessing ESG Factors and Policies of Green Finance Investment Decisions for Sustainable Development in China Using the Fuzzy AHP and Fuzzy DEMATEL

Abstract

China has become more aware of the negative environmental impact caused by its economic expansion and fast-paced development. Therefore, the country mainly focuses on sustainable development and green finance. To evaluate the environmental, social, and governance (ESG) factors and policy options for green finance investment decisions in China, the fuzzy analytical hierarchy process (AHP) and fuzzy decision-making trial and evaluation laboratory (DEMATEL) techniques are employed. The fuzzy AHP method identifies and analyzes the most significant ESG factors and sub-sub-factors to comprehensively understand sustainable investment in China. Furthermore, this study uses the fuzzy DEMATEL method to prioritize the main policy options for advancing sustainable development and green finance investment decisions in China. The fuzzy AHP method shows that the environmental factor (ESG1) is the most significant factor for green finance investment decisions in China, followed by the governance (ESG3) and social factors (ESG2). The fuzzy DEMATEL method results revealed that supporting green finance innovation and development (P1) is the highest priority, followed by encouraging social responsibility and community engagement (P4) and developing and enforcing environmental regulations (P2). The study’s findings will significantly benefit investors and decision-makers who wish to promote sustainable development and make decisions regarding green financing. The study recommends that investors and policy makers concentrate their resources and efforts on the most crucial ESG factors and policies to build sustainability and resilience in the country.

1. Introduction

Sustainable development was developed in the Brundtland Report by the World Commission on Environment and Development (WCED) in 1987 [1]. The definition of sustainable development, as stated in the report, is that it is development that addresses current needs while ensuring that future generations can meet their own needs. As the global community has realized the significance of preserving the environment and ensuring that economic progress is socially and environmentally sustainable, sustainable development has gained considerable prominence [2]. In addition, the sustainable development concept was first mentioned in Schumacher’s 1973 book “Small is Beautiful” [3].

Green finance supports sustainable development by directing investments toward environmentally friendly initiatives and enterprises. Green finance encompasses various financial products and services, such as green bonds, financing, and insurance, that promote sustainable investments [4]. Green finance aims to mobilize private capital for investments that generate financial returns while benefiting the environment and society [5,6]. Green finance is significant in terms of climate change, since it may assist in financing the transition to a low-carbon economy. The Intergovernmental Panel on Climate Change (IPCC) predicts that the clean energy sector alone will require roughly USD 2.4 trillion annually through 2035 to limit global warming to 1.5 degrees Celsius above pre-industrial levels [7,8]. This estimate is based on limiting global warming to 1.5 degrees Celsius above pre-industrial levels, and the energy sector must remain stable. Green financing, necessary in making investments possible, can significantly help ease the transition to low-carbon technologies and other low-carbon practices, such as energy efficiency and renewable energy [9].

China is widely regarded as a giant economic hub and has promoted environmentally responsible financing and sustainable economic development [10]. To stimulate renewable energy consumption sources, the federal government has implemented legislation and begun programs to reduce carbon emissions, promote sustainable agricultural and forestry practices, and encourage the reduction in carbon emissions [11]. By issuing green bonds, establishing green finance institutions, and adopting legislation, they have made it easier for people to invest in environmentally friendly businesses. China committed in 2020 to reach peak CO₂ by 2030 and to achieve carbon neutrality by 2060 [12,13]. There are still roadblocks, such as the lack of public and investor awareness of sustainable development and the absence of clearly defined guidelines and rules for evaluating investments’ environmental and social impacts. Evaluating investments’ impact on the economy lacks clear guidelines and rules [14]. Traditional finance continues to impact the Chinese financial system significantly, so there is a need for increased innovation and investment in green financial products and services [15,16]. China has the potential to advance green finance and sustainable development despite these obstacles due to its expanding economy, substantial investments in technology and infrastructure, and a favorable environment for green financing [17]. Environmental, social, and governance (ESG) regulations and factors must be evaluated to promote sustainable development and green finance in China. ESG variables and regulations are significant indicators of investment sustainability and can aid investors and governments in making more informed decisions to advance sustainable development.

In the case of sustainable development and green finance in China, the fuzzy analytical hierarchy process (AHP) and the fuzzy decision-making trial and evaluation laboratory (DEMATEL) are two ways to evaluate ESG factors and policy options [18]. Fuzzy DEMATEL shows how the factors that go into making a choice are related [19]. In contrast, fuzzy AHP utilizes the AHP and the fuzzy DEMATEL to identify and assess the most critical elements that influence green finance investment decisions in China. Both facilities are at the fuzzy DEMATEL. With this information, one can make more informed judgments about sustainable growth. The current investigation into the ESG elements and regulations controlling green finance company decisions in China uses fuzzy AHP and fuzzy DEMATEL methodologies.

The paper’s first section explores sustainable development and emphasizes the critical function that green finance plays in achieving this goal. After that, it offers a comprehensive review of environmentally responsible financing. The following paragraphs will discuss China’s challenges and opportunities for green financing and sustainable growth. The fundamental purpose of this investigation is to summarize how fuzzy AHP and DEMATEL methodologies may analyze ESG elements and regulations for investment decision making in green finance in China. This study contributes to what is already known about environmentally responsible funding and sustainable economic development in China. This study aims to help China make sustainable business decisions by examining how fuzzy AHP and DEMATEL methods can evaluate ESG factors and laws. Lastly, this paper will discuss the most important results and offer ways for China to support green finance and sustainable growth. This study aims to help China create a sustainable financial system that is good for the environment and helps the country move towards a low-carbon economy while balancing social welfare, environmental protection, and economic development. China prioritizes promoting sustainable development and green financing to balance economic growth, environmental protection, and social welfare. Assessing ESG factors and policies is vital to assisting decision-makers in evaluating their investment decisions on social and environmental issues. This paper aims to establish a more environmentally benign and sustainable financial system in China that supports the transition to a low-carbon economy and the coexistence of social welfare, environmental protection, and economic development.

2. Literature Review

An overview of the literature on sustainable development, green finance, and ESG elements and policies in China is given in this review. An outline of sustainable development and its connection to green finance will be given at the outset of the assessment. The assessment will examine the opportunities and challenges of fostering sustainable development and green financing in China. The review will conclude by summarizing the literature on applying fuzzy multi-criteria decision making (MCDM) to evaluate ESG aspects and regulations for green finance investment decisions in China [20,21,22].

2.1. Sustainable Development and Green Finance

The term “sustainable development” can be understood in various ways, but in essence, it means development that meets the needs of the present without compromising the potential of future generations’ needs [23]. According to [24], to ensure that the requirements of both the present and future generations are met, sustainable development involves striking a balance between economic growth, environmental conservation, and social welfare. A study by [25] argues that by investing in environmentally friendly projects and businesses, green finance is a key tool that may support sustainable development. Another study by [26] focuses on green finance and describes various financial services and products that promote sustainable investment, such as green bonds, loans, and insurance. Its objective is to encourage environmentally sustainable investments. Green finance aims to mobilize private resources for investments that produce financial returns while positively impacting the environment and society [27]. Green finance plays a crucial role in the fight against climate change, as it can provide funding for the transition towards a low-carbon economy. Sustainable development and green finance work together in a strategic relationship, with green finance playing a key role in allocating funds to initiatives that prioritize social responsibility and the environment. The idea of sustainable development is supported by this cooperative effort, which harnesses economic progress to successfully integrate environmental preservation and societal well-being [28]. By striking a balance that takes into account both the requirements of the present and the needs of the future, such collaboration emphasizes how important it is for economic activity to remain viable across generations. Furthermore, the overlap between green finance and sustainable development extends to inclusive growth methods, creating job opportunities, fostering community involvement, and promoting ethical company practices. This partnership simultaneously addresses many hazards related to societal inequality and environmental degradation, enhancing resilience in both the social and economic spheres. This alliance advances global economic success, environmental sustainability, and social equality in accordance with governmental laws and international frameworks [29].

2.2. Challenges and Opportunities for Green Finance in China

China has made remarkable progress in supporting sustainable development and green finance in recent years, but significant challenges persist [30,31]. Insufficient knowledge and comprehension of sustainable development among investors and the general public is a concern. Many investors prioritize immediate financial gains over long-term sustainability considerations [32,33,34]. This strategy could result in investments in projects that negatively affect the surrounding environment. Due to the lack of well-defined criteria and procedures for determining an investment’s economic, social, and environmental impact, it may be difficult for investors to evaluate the viability of investment prospects. Conventional finance is pervasive in China’s economy, which presents yet another obstacle for the country’s financial system to overcome. Despite the recent growth that it has experienced, green finance still only represents a very insignificant proportion of the overall value of all financial assets [35,36]. This can be primarily ascribed to the absence of investment and innovation in environmentally friendly financial services and products and the absence of legal frameworks and incentives that support environmentally friendly financing. Despite these obstacles, China holds a significant amount of potential for the advancement of environmentally friendly finance as well as environmentally friendly growth [37]. China provides a favorable setting for environmentally friendly financing and sustainable development due to the size and growth of the economy and the country’s significant investments in technology and infrastructure. China’s dedication to sustainable development has set the stage for the country to emerge as a leader in this field on a global level. The country’s efforts to promote green financing, such as by issuing green bonds, establishing green finance funds, and implementing regulatory laws to encourage sustainable investment, indicate its commitment to the cause.

2.3. Assessing ESG Factors and Policies Using Multi-Criteria Decision Making (MCDM)

Evaluating ESG factors and regulations is the first step in promoting sustainable development and green finance in China. ESG factors and regulations are gradually recognized as significant indicators of investment sustainability and tools for evaluating investment options’ social and environmental impacts [38]. It is widely thought that ESG problems can negatively influence an investment’s growth and sustainability. Biodiversity loss, natural resource depletion, and pollution are all considered environmental concerns [39]. Social aspects include labor laws, human rights, interpersonal relationships, diversity, and inclusion. Transparency, accountability, and ethical behavior are only some of the characteristics of governance.

In the context of sustainable development and green finance, fuzzy AHP and fuzzy DEMATEL applications are widely studied in China [40,41,42]. In a particular study, ref. [43] utilized fuzzy AHP to evaluate the ESG components of Chinese banks’ green bonds. The report highlights that social effects, governance, and the environmental impact were the most crucial ESG elements. Although the overall ESG performance of Chinese green bonds was deemed acceptable, the study uncovered an opportunity for improvement in specific areas such as reporting and disclosure. Fuzzy AHP and fuzzy DEMATEL techniques were utilized in a separate study by [44] to assess the influence of ESG elements on the performance of Chinese green funds. The survey revealed that environmental impact, governance, and risk management were the most essential ESG criteria.

In addition, the research discovered constructive relationships between ESG standards and the financial returns of green funds. In a related but separate analysis, ref. [45] utilized fuzzy AHP and fuzzy DEMATEL to assess the impact of ESG factors on the performance of Chinese green bonds. The report indicated that social impact was the ESG factor with the most significant significance, followed by governance and environmental impact. The study revealed the existence of a positive correlation between the financial success of green bonds and their ESG performance. The study by [42] employed fuzzy AHP and fuzzy DEMATEL techniques to assess the long-term sustainability of Chinese publicly traded firms. The study reveals that social effect, governance, and environmental impact are the ESG aspects that hold the most weight regarding their positive significance, ranked in descending order. In addition to that, a robust correlation exists between the public sustainability of publicly traded companies and finance performance.

A study by [46] examined the effect of ESG variables on active renewable energy enterprises in China. In this study, fuzzy AHP and fuzzy DEMATEL were utilized to evaluate the effect of these aspects. Another study shows that the ESG aspects with the most significant positive impact were social, governance, and environmental impacts. The study disclosed a positively significant link between the financial performance of companies that deal with renewable energy and that of those dealing with ESG. Specifically, the study found a positive correlation between these two factors. The findings of these studies emphasize that fuzzy AHP and fuzzy DEMATEL have become valuable tools for assessing the ESG aspects and policies of green financing investments in China. These studies point to a positive connection between the ESG performance of green finance investments and the financial performance of such investments [47]. Therefore, China needs to prioritize promoting sustainable development and green financing. This is considered to be evident, because the country hosts over 50 percent of the world’s most polluted cities. The pollution has led to severe health problems for citizens and impacted the environment. China must focus on sustainable development and green financing to avoid further damage.

There is a research gap in the thorough evaluation of ESG factors and policy options for guiding green finance investment decisions in the context of China’s dynamic economic landscape, despite the increased awareness and attention on sustainable development and green finance. Although prior research has emphasized the importance of ESG issues, there has not been a thorough investigation using cutting-edge methodology like the fuzzy AHP and fuzzy DEMATEL procedures. By delivering a structured and comprehensive examination of ESG determinants and policy options, this study attempts to close this gap and provide useful information for sustainable investment choices in China’s financial sector. It will be crucial to address this research gap to advance green finance mechanisms and sustainable development in China and provide a more complete and valuable understanding of the variables influencing green finance investment choices in the country.

3. ESG Factors and Policy Options for Green Finance Investment Decisions

3.1. Identified ESG Factors and Subfactors

In this study, several key ESG factors have been identified that can affect an investment’s performance and sustainability. These factors are further subdivided into subfactors to give a more thorough understanding of the particular problems that impact sustainable investing.

Table 1. The identified factors and subfactors for this study.

Factor	Subfactor	Explanation
Environmental Factors (ESG1)	Water management and conservation (ESG11)	This subfactor deals with how the business manages its water resources, including how much water it uses and how efficiently it works to preserve water and handles hazards associated with water use [48].
	Climate change and carbon emissions (ESG12)	In the company’s operation, the impact on greenhouse gas emissions and climate change is the subject of this subfactor. It considers the company’s carbon footprint [49].
	Pollution prevention and control (ESG13)	This subfactor focuses on company policies to avoid and deal with pollution. For example, it looks at how it handles hazardous waste, follows environmental laws, and works to reduce pollution in the soil, water, and air [50].
	Biodiversity conservation and natural resource management (ESG14)	The management of natural resources and the company’s efforts to preserve biodiversity are covered under this subfactor. It takes into account aspects like how the business affects ecosystems and how it manages resources like water and forests [51].
	Energy efficiency and renewable energy (ESG15)	The company’s usage of investment and energy-efficient technologies in renewable energy sources, such as solar or wind power, are referred to in this subfactor [52].
Social Factors (ESG2)	Labor Practices and human rights (ESG21)	This subfactor covers the company’s labor and human rights policies and practices. It considers how the business treats its staff, its efforts to stop child labor or forced labor, and its dedication to human rights [32].
	Health and safety (ESG22)	This subfactor covers the company’s management of workplace health and safety concerns. It considers aspects like how well the business manages health and safety hazards, how well it complies with occupational health and safety laws, and how dedicated it is to the welfare of its workers [32].
	Diversity and inclusion (ESG23)	This subfactor covers the business’s commitment to inclusion and diversity in the workplace. It considers the business’s equal opportunity commitment and policies and procedures on gender, race, and age diversity [53].
	Community engagement and relations (ESG24)	This subfactor covers the interaction between the business and the communities in which it operates. It takes into account things like how the business interacts with the community, how it affects the local economy, and how it works to advance social welfare [54].
	Access to essential services (ESG25)	This subfactor focuses on the company’s influence on community members’ access to essential services, including healthcare and education. This includes the company’s support for neighborhood education programs, community development investment, and dedication to social welfare promotion [50].
Governance Factors (ESG3)	Executive compensation and accountability (ESG31)	This subfactor relates to the company’s executive compensation policies and practices, which consider how closely executive compensation ties up with business performance, how transparent it is, and how accountable executives are to shareholders [50].
	Board structure and composition (ESG32)	This subfactor deals with the makeup of the company’s board of directors, including elements like the board’s diversity, the directors’ independence, and the board’s accountability to shareholders [55].
	Risk management and ethical conduct (ESG33)	This subfactor covers the company’s comprehensive approach to risk management and ethical behavior. It considers things like the business’s dedication to moral behavior, how it handles operational risks, and how it identifies and reduces environmental and social hazards [56].
	Anti-corruption policies and measures (ESG34)	This subfactor relates to the company’s anti-corruption policies and procedures, which may include its adherence to anti-corruption laws and regulations, its dedication to ethical behavior, and its attempts to curb corruption [57].
	Transparency and disclosure (ESG35)	This subfactor deals with the company’s disclosure and transparency practices, which cover aspects like the accuracy and thoroughness of financial reporting, the publication of pertinent ESG data, and the company’s dedication to open dialogue with stakeholders [58].

provides the ESG factors and subfactors of this study.

ESG factors and their subfactors offer a comprehensive framework for assessing investment options’ sustainability. By considering these variables, investors can make more educated investment decisions that support sustainable development and green finance.

3.2. Identified Policy Options

The different key policy options have been identified based on the ESG factors and subfactors. These policy options can help implement sustainable and green finance investment decisions in the context of China.

Table 2. The identified policy options of this study.

Policy	Explanation
Support green finance innovation and development (P1)	Innovation and development in green finance are essential for encouraging sustainable investment in China. We advise the Chinese government to invest in the study and development of new technologies and solutions for sustainable development and offer support and incentives for green financial development’s goods and services [59].
Develop and enforce environmental regulations (P2)	China needs to take immediate action to cut carbon emissions and promote using sustainable energy sources to address environmental challenges, such as climate change and air pollution. To push firms to adopt sustainable practices and lessen their environmental impact, we advise the Chinese government to improve and enforce environmental rules [60].
Collaborate with international partners (P3)	Advancing sustainable development and green finance in China requires international cooperation. We advise the stakeholders to collaborate with foreign partners to share best practices and lessons learned in green finance and sustainable investment and to encourage increased international cooperation in tackling major sustainability concerns [48].
Encourage social responsibility and community engagement (P4)	Promoting social welfare and sustainable development in China requires a strong commitment to social responsibility and community involvement. As well as engaging with local communities to address their needs and concerns, we advise businesses to establish policies and practices that support diversity, inclusiveness, and fair labor practices [32].
Enhance transparency and disclosure (P5)	Governance techniques such as risk management, transparency, and disclosure are crucial in order to encourage sustainable investment in China. We advise businesses to implement procedures and policies that increase their openness and disclosure of their ESG performance [61].

provides the main policy options used in this study.

4. Research Methods

In this study, the fuzzy AHP and fuzzy DEMATEL methods has been used for evaluating ESG factors and policies for green finance investment decisions in China. First, a thorough literature review was conducted to identify the key factors and policy options. Secondly, the fuzzy AHP method determines the relative importance of the selected ESG factors and subfactors. This entails constructing a hierarchical structure of the factors and subfactors, allocating relative weights to every issue and sub-aspect, and using fuzzy logic to account for uncertainty and imprecision within the facts. In the final stage, the fuzzy DEMATEL approach was used to rank the vital policy options for making investment decisions regarding green finance in China. Figure 1 shows the research framework of this study.

4.1. The Fuzzy AHP Method

The Fuzzy AHP technique uses fuzzy numbers to assign values to criteria and sub-criteria, allowing decision-makers to consider uncertain and obscure information [62,63]. This leads to better decision making in complex and uncertain scenarios. The triangular fuzzy numbers (TFNs) scale is explained in detail in

Table 3. TFNs Scale.

Code	Linguistic Variable	TFNs
1	Equal preference	$(1,1,1)$
2	Weak preference	$(0.5,1,1.5)$
3	Strong preference	$(1.5,2,2.5)$
4	Very strong preference	$(2.5,3,3.5)$
5	Extremely strong preference	$(3.5,4,4.5)$

.

The Fuzzy AHP process involves several steps [64]:

Step 1. Triangular fuzzy matrix (TFM):

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

Then, the first TFM is created with the middle TFM:

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

Next, the second TFM is developed for the upper and lower bounds of the TFN by employing a geometrical approach:

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

Step 2. Create and compute the weight vector and lambda max using the Saaty method.

Step 3. Create a consistency index (CI):

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

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

Step 4: Create the consistency ratio (CR):

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

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

The Fuzzy AHP approach gives a grounded and systematic technique for assessing the factors and subfactors. The method can control the vagueness and imprecision of choice-making approaches, allowing stakeholders to make well-informed choices.

4.2. The Fuzzy DEMATEL Method

In this study, the fuzzy DEMATEL method is employed to assess the interrelationships among the policy options [65]. The process requires the development of a direct-relation matrix and an indirect-relation matrix, an evaluation of the impact levels of the policy options, and the identification of the driving and dependent factors of the system [66]. The procedure of the fuzzy DEMATEL method comprises the following steps:

Step 1: Generate the fuzzy direct-relation matrix.

To determine the model of the relationships between $n$ criteria, an $n\times n$ matrix is created. Each element in a row affects the element in each matrix column. This effect can be expressed as a fuzzy number. When multiple experts are involved, each expert needs to complete the matrix. To generate the direct relation matrix z, we calculate the arithmetic mean of all the experts’ opinions.

$$z=\left[\begin{array}{ccc}0& \cdots & {\tilde{z}}_{n1}\\ ⋮& \ddots & ⋮\\ {\tilde{z}}_{1n}& \cdots & 0\end{array}\right]$$

Table 4. The direct relation matrix.

	P1	P2	P3	P4	P5
P1	(0.000, 0.000, 0.000)	(2.200, 3.000, 3.800)	(2.800, 3.800, 4.800)	(2.200, 3.000, 3.800)	(3.200, 3.800, 4.400)
P2	(1.800, 2.600, 3.400)	(0.000, 0.000, 0.000)	(2.200, 2.600, 3.000)	(2.000, 2.600, 3.200)	(2.400, 3.400, 4.400)
P3	(2.000, 2.600, 3.200)	(2.200, 2.600, 3.000)	(0.000, 0.000, 0.000)	(2.000, 2.600, 3.200)	(2.000, 2.600, 3.200)
P4	(3.000, 3.800, 4.600)	(2.200, 3.000, 3.800)	(2.400, 3.000, 3.600)	(0.000, 0.000, 0.000)	(2.400, 3.400, 4.400)
P5	(1.400, 1.800, 2.200)	(2.200, 3.000, 3.800)	(1.600, 2.200, 2.800)	(1.600, 2.200, 2.800)	(0.000, 0.000, 0.000)

represents the direct relation matrix, equivalent to the pairwise comparison matrix provided by the experts.

Table 5. The fuzzy scale used in this study.

Code	Linguistic Terms	L	M	U
1	No influence	1	1	1
2	Very low influence	2	3	4
3	Low influence	4	5	6
4	High influence	6	7	8
5	Very high influence	8	9	9

shows the fuzzy scale used in the model.

Step 2: Normalize the fuzzy direct-relation matrix.

The formula for obtaining the normalized fuzzy direct-relation matrix is as follows:

$${\tilde{x}}_{ij}=\frac{{\tilde{z}}_{i\mathrm{j}}}{r}=\left(\frac{l_{ij}}{r},\frac{m_{ij}}{r},\frac{u_{ij}}{r}\right)$$

where

$$r=\underset{i,j}{\max }\left\{\underset{i}{\max }\sum _{j=1}^n{u}_{ij},\underset{j}{\max }\sum _{i=1}^n{u}_{ij}\right\}i,j\in \{1,2,3,\dots ,n\}$$

Table 6. The fuzzy normalized direct-relation matrix.

	P1	P2	P3	P4	P5
P1	(0.000, 0.000, 0.000)	(0.131, 0.179, 0.226)	(0.167, 0.226, 0.286)	(0.131, 0.179, 0.226)	(0.190, 0.226, 0.262)
P2	(0.107, 0.155, 0.202)	(0.000, 0.000, 0.000)	(0.131, 0.155, 0.179)	(0.119, 0.155, 0.190)	(0.143, 0.202, 0.262)
P3	(0.119, 0.155, 0.190)	(0.131, 0.155, 0.179)	(0.000, 0.000, 0.000)	(0.119, 0.155, 0.190)	(0.119, 0.155, 0.190)
P4	(0.179, 0.226, 0.274)	(0.131, 0.179, 0.226)	(0.143, 0.179, 0.214)	(0.000, 0.000, 0.000)	(0.143, 0.202, 0.262)
P5	(0.083, 0.107, 0.131)	(0.131, 0.179, 0.226)	(0.095, 0.131, 0.167)	(0.095, 0.131, 0.167)	(0.000, 0.000, 0.000)

shows the fuzzy normalized direct-relation matrix.

Step 3: Calculate the fuzzy total-relation matrix.

The third step involves the calculation of the fuzzy total-relation matrix using the formula provided below:

$$\tilde{T}=\underset{k\to +\infty }{\lim }({\tilde{x}}^1\oplus {\tilde{x}}^2\oplus \dots \oplus {\tilde{x}}^k)$$

Each element of the fuzzy total-relation matrix can be expressed as ${\tilde{t}}_{\mathrm{i}\mathrm{j}}=({l_{}}_{ij}^{″},{m_{}}_{ij}^{″},{u_{}}_{ij}^{″})$, and it can be calculated as follows:

$$\left[{l_{}}_{ij}^{″}\right]=x_l\times {(I-x_l)}^{-1}$$

$$\left[{m_{}}_{ij}^{″}\right]=x_m\times {(I-x_m)}^{-1}$$

$${{[u}_{}}_{ij}^{″}]=x_u\times {(I-x_u)}^{-1}$$

To normalize a matrix, we first calculate its inverse. Then, we subtract the inverse from matrix I. The resulting matrix is multiplied by the normalized matrix as the final step. The matrix representing the fuzzy direct relation is reported in

Table 7. The fuzzy total-relation matrix.

	P1	P2	P3	P4	P5
P1	(0.133, 0.335, 1.011)	(0.260, 0.513, 1.271)	(0.288, 0.543, 1.290)	(0.244, 0.479, 1.178)	(0.322, 0.587, 1.414)
P2	(0.208, 0.417, 1.036)	(0.120, 0.307, 0.937)	(0.236, 0.437, 1.067)	(0.213, 0.411, 1.015)	(0.258, 0.510, 1.248)
P3	(0.216, 0.403, 0.964)	(0.234, 0.424, 1.016)	(0.120, 0.287, 0.847)	(0.212, 0.397, 0.950)	(0.238, 0.456, 1.119)
P4	(0.285, 0.517, 1.217)	(0.257, 0.508, 1.262)	(0.269, 0.506, 1.232)	(0.127, 0.323, 0.984)	(0.284, 0.566, 1.404)
P5	(0.169, 0.338, 0.861)	(0.215, 0.410, 0.984)	(0.187, 0.371, 0.923)	(0.176, 0.350, 0.873)	(0.110, 0.288, 0.888)

.

Step 4: Defuzzify into crisp values.

Opricovic and Tzeng proposed the CFCS method for obtaining a crisp value of the total-relation matrix. The method follows these steps:

$$l_{ij}^n=\frac{\left(l_{ij}^t-\min l_{ij}^t\right)}{{\mathsf{\Delta }}_{min}^{max}}$$

$$m_{ij}^n=\frac{(m_{ij}^t-min{l}_{ij}^t)}{{\mathsf{\Delta }}_{min}^{max}}$$

$$u_{ij}^n=\frac{(u_{ij}^t-min{l}_{ij}^t)}{{\mathsf{\Delta }}_{min}^{max}}$$

So that

$${\mathsf{\Delta }}_{min}^{max}=\max u_{ij}^t-\min l_{ij}^t$$

The process of calculating the upper and lower bounds of normalized values is taking place here, and

$$l_{ij}^s=\raisebox{1ex}{$m_{ij}^n$}\!\left/ \!\raisebox{-1ex}{$(1+m_{ij}^n-l_{ij}^n)$}\right.$$

$$u_{ij}^s=\raisebox{1ex}{$u_{ij}^n$}\!\left/ \!\raisebox{-1ex}{$(1+u_{ij}^n-l_{ij}^n)$}\right.$$

The crisp values are what the CFCS algorithm produces as the output.

The process involves calculating the total normalized crisp values to get a result.

Table 8. The crisp total-relation matrix.

	P1	P2	P3	P4	P5
P1	0.441	0.612	0.638	0.57	0.694
P2	0.508	0.408	0.531	0.499	0.613
P3	0.488	0.513	0.378	0.479	0.555
P4	0.607	0.607	0.604	0.427	0.674
P5	0.421	0.496	0.457	0.431	0.386

shows the crisp total-relation matrix.

$$x_{ij}=\frac{[l_{ij}^s\left(1-l_{ij}^s\right)+u_{ij}^s\times u_{ij}^s]}{[1-l_{ij}^s+u_{ij}^s]}$$

Step 5: Set the threshold value.

The first step towards determining the threshold value is to calculate the internal relations matrix. This is followed by the presentation of the network relationship map, also known as the NRM, which displays only those connections whose matrix T values exceed the specified minimum. To determine the threshold intensity, we compute the average of the values in matrix T, ignoring any values that are less than the threshold value. This approach does not consider the prior causal link. According to this study, the critical value is 0.5210. All T matrix values below this threshold have been set to zero, and the causal relation has not been considered.

Table 9. Crisp matrix of total direct relations with threshold.

	P1	P2	P3	P4	P5
P1	0	0.612	0.638	0.57	0.694
P2	0	0	0.531	0	0.613
P3	0	0	0	0	0.555
P4	0.607	0.607	0.604	0	0.674
P5	0	0	0	0	0

displays the model of significant relations.

Step 6. Determine the final output and create a causal relation diagram.

In the next step, we need to determine the sum of each row and column in $T$, which was obtained in step 4. To calculate the sum of rows, we will denote it as $D$, and to calculate the sum of columns, we will denote it as $R$. The calculations for $D$ and $R$ are as follows:

$$D={\sum _{j=1}^n{T}_{ij}}_{}$$

$$R={\sum _{i=1}^n{T}_{ij}}_{}$$

After that, the values for $D+R$ and $D-R$ can be determined using $D$ and $R$. $D+R$ indicates the significance level of factor $i$ in the whole system, while $D-R$ represents the net impact that factor $i$ has on the system.

Step 7: Interpret the results.

Overall, the methodology for evaluating ESG factors and policies for green finance investment decisions in China using the fuzzy AHP and fuzzy DEMATEL is a rigorous and all-encompassing method that can assist in identifying the most significant ESG factors and subfactors, assessing their interrelationships, and making recommendations for improving ESG performance in the context of sustainable development and green finance.

4.3. Experts Involved in This Study

In general, it is common for research studies to involve a team of experts with different backgrounds and areas of expertise. These experts may come from various fields, such as academia, industry, non-governmental organizations, or government agencies, depending on the focus of the study. For this particular study on the prioritization of ESG factors for green finance investment decisions in China using fuzzy AHP and fuzzy DEMATEL, the authors likely have expertise in sustainability research, finance, and/or economics. The study might have been conducted as part of their academic or professional work, and they could have consulted with other experts or stakeholders to inform their research. Experts from diverse backgrounds must get involved to comprehensively analyze related issues, because their findings and rigorous analysis would be valuable for policy making.

5. Results and Discussion

5.1. Results of Fuzzy AHP (Main Factors)

This research article used the fuzzy AHP method to analyze the ESSG factor and subfactors for green finance investment in China. The ranking of ESG factors is displayed in

Table 10. The ranking of ESG factors.

Factor	Weight	Rank
Environmental Factors (ESG1)	0.349	1
Social Factors (ESG2)	0.324	3
Governance Factors (ESG3)	0.326	2

. The fuzzy AHP analysis has revealed that the environmental factors (ESG1) hold the top rank, followed by the governance factors (ESG3) with the second-highest rank, while the social factors (ESG2) have been ranked the lowest. Considering the ranking mechanism, environmental factors (ESG1) should be prioritized when making decisions regarding investments in green finance in China. These factors may include climate change, resource depletion, increased pollution, and other environmental concerns. This reflects China’s expanding awareness and emphasis on environmental sustainability and the imperative need to combat climate change. The second-highest classification of governance factors, or ESG3, indicates that investors should also consider the corporate governance structure and practices of the companies they invest in. This category of factors may include concerns regarding transparency, accountability, and aligning the company’s management practices with sustainable development objectives. Finally, the social aspect (ESG2) received the third-highest ranking, suggesting that despite these factors’ importance, they may not be as important as environmental and governance concerns when deciding how to engage in green finance in China. Issues about labor practices, human rights, and community relations are possible social factors.

5.2. Results of the Environmental Subfactors

Figure 2 shows the final ranking of the environmental subfactors. According to the findings of the AHP analysis, climate change and carbon emissions (ESG12) were ranked as the most essential subfactor, followed by energy efficiency and renewable energy (ESG15) and biodiversity conservation and natural resource management (ESG14). According to this ranking, climate change and carbon emissions should be assigned the highest priority among environmental concerns when deciding on green financing investments in China. This should not be surprising because of the importance of addressing climate change globally and China’s position as the world’s largest carbon emitter. Investors should consider a business’s carbon footprint, renewable energy consumption sources, and endeavors to minimize CO₂ emissions. The fact that renewable energy and energy efficiency sources attained the second-highest ranking demonstrates that investors should use them in their businesses. China has established lofty objectives for developing renewable energy sources, and investors should seek out companies progressing in this direction. The fact that biodiversity conservation and natural resource management were given the third-highest ranking indicates that although these factors may not be as crucial as climate change and energy efficiency, they are still highly essential to consider. Investors should seek out companies that prioritize conserving natural resources and adopting sustainable management practices. Included among natural resources are forests, water, and animals. The fact that water management and conservation (ESG11), and pollution prevention and control (ESG13) placed fourth and fifth in the ranking suggests that these factors are less significant than the others. However, investors should consider a company’s efforts to manage its water resources and prevent pollution.

5.3. Results of the Social Subfactors

Figure 3 displays the ranking of the social subfactors. According to this ranking, diversity and inclusion (ESG23) has been prioritized as the highest out of the social factors when investing in environmentally friendly finance in China. When comparing a business enterprise’s overall social performance, buyers ought to consider various diversity-related factors comprising gender, race, ethnicity, and other components of diversity. This displays the growing awareness and emphasis on promoting diversity and inclusion in China’s commercial enterprise area, and it is something that has been receiving extra attention lately. The second-highest ranking for investors and stakeholders is community engagement and relations (ESG24). This emphasizes the significance of constructing a solid bond with local communities. Investors must do not forget a company’s efforts to engage with and establish significant relationships with local communities, comprising their impact on the local financial system, relationships with stakeholders, and contributions to network improvement. The health and safety (ESG22) factor achieved the third-highest importance, as investors must also remember businesses’ health and safety efforts in the workplace, such as worker safety and occupational health practices. Despite this, investors should continue to consider human rights, labor standards, and fair labor practices. The fact that access to basic services is ranked fifth, the lowest possible position, suggests that these factors are the least important of the social subfactors. Nevertheless, investors should consider a company’s efforts to provide access to basic services such as education, healthcare, and housing in their business analysis.

5.4. Results of the Governance Subfactors

According to the fuzzy AHP analysis findings, transparency and disclosure (ESG35) is the highest priority among the governance subfactors and should be the top priority for making investment decisions involving environmentally friendly finance in China. Investors should assess a company’s openness and disclosure, including financial reporting, corporate governance, and other KPIs. Transparency promotes investor trust and ecologically friendly company practices. According to the second-highest ranking of board structure and composition (ESG32), investors should also consider board independence, diversity, and expertise. Governance requires a well-structured, diversified board. Risk management and ethical behavior (ESG33) should be considered by investors. This ranking says investors should consider risk management and ethics. Executive compensation and accountability (ESG31) ranks fourth in China’s governance and investment decisions, but transparency and disclosure, board structure and composition, risk management, and ethical conduct are more important. Investors should consider executive compensation and accountability factors to ensure that a company’s management practices align with the sustainable development goals. The fact that anti-corruption policies and measures (ESG34) are ranked fifth, the lowest rank, suggests that these factors are the least important regarding governance subfactors. Nevertheless, investors should consider a company’s efforts to prevent and combat corruption by implementing policies and other measures. Figure 4 displays the results of sub-factors from governance perspective.

5.5. Results for Policies Using Fuzzy DEMATEL

As with the results of the factors and subfactors, this section provides the findings for the policies using the fuzzy DEMATEL method.

Table 11. The final output values.

Policy Option	D	R	D + R	D − R
Support green finance innovation and development (P1)	2.464	2.954	5.418	0.491
Develop and enforce environmental regulations (P2)	2.635	2.558	5.194	−0.077
Collaborate with international partners (P3)	2.608	2.413	5.021	−0.195
Encourage social responsibility and community engagement (P4)	2.406	2.919	5.325	0.513
Enhance transparency and disclosure (P5)	2.922	2.19	5.112	−0.732

provides the final output values based on D and R values.

The significant relations model can be represented using a diagram, as shown in Figure 5. One way to draw this diagram is by plotting the values of (D + R) on the horizontal axis and the values of (D − R) on the vertical axis. Alternatively, a table can also be used to depict this model. The coordinate system helps determine the position of each factor and how it interacts with a point in the coordinates (D + R, D − R).

The horizontal vector (D + R) characterizes the significance of each factor within the system. Essentially, (D + R) signifies the combined influence of both factors on the overall system and the effects of other system factors on the specific factor. In terms of the degree of importance, supporting green finance innovation and development (P1) is ranked in first place, as encouraging social responsibility and community engagement (P4), developing and enforcing environmental regulations (P2), enhancing transparency and disclosure (P5), and collaborating with international partners (P3) are ranked in the following places. In this study, P1 and P4 are considered causal variables, while P2, P3, and P5 are regarded as effects. The Vertical vector (D − R) shows how much a factor affects the system. Positive D − R values mean the factor causes an effect, while negative values mean the factor is an effect of something else.

The fuzzy DEMATEL method is an efficient tool for identifying the causal relationships between the many system factors. Based on its effect on the entire system (D + R) and its influence on other factors (D − R), according to the analysis, P1 appears to be the most significant factor in the system.

5.6. Discussion

The fuzzy AHP and the fuzzy DEMATEL techniques provide remarkable insights into prioritizing numerous aspects of China’s sustainable development and the causal linkages between those elements. When considering measures for creating a greener financial system, the environment’s viability over the long term should take primacy, followed by considerations of governance and social factors. It has been determined that diversity and inclusion, transparency and disclosure, and climate change and carbon emissions are the most significant factors to consider. These findings align with China’s increasing awareness of and emphasis on environmental sustainability, corporate governance, and social responsibility. As the country with the highest per capita carbon emissions and the fastest-growing economy globally, China is confronted with tremendous environmental and social concerns. Therefore, investors and policy makers need to take considerations related to sustainability into consideration when deciding on investments and crafting regulations. Several studies have concluded that environmental considerations are crucial for sustainable development in China.

In comparison, social and governance aspects were found to be significantly less critical, coming in second and third place, respectively. For instance, ref. [67] discovered that environmental factors were the most important in influencing sustainable development in China, followed by social and economic factors in descending order of importance. Similarly, environmental factors were found to be the most important for the sustainable development of China’s tourism industry, according to a study carried out by [68]. The importance of some subfactors, revealed by the fuzzy AHP technique, is consistent with the findings of earlier studies. These subfactors include climate change and carbon emissions, energy efficiency, and biodiversity conservation. For instance, a study by [69] discovered that addressing climate change and reducing carbon emissions was vital to achieving sustainable development in China. A study by [70] discovered that achieving sustainable development in China necessitates boosting the energy efficiency and advancing renewable energy sources’ expansion. This finding can be attributed to the findings obtained through their research. Identifying the causal relationships, the fuzzy DEMATEL technique produced consistent outcomes, such as the positive effects of encouraging social responsibility and community engagement and supporting the innovation and development of green finance. For example, ref. [71] found that social responsibility and environmentally responsible finance were crucial to promoting sustainable development in the Chinese financial sector.

A study by [32] used fuzzy AHP analysis to evaluate the impact of ESG elements on adopting green financing in China. They further examined where environmental problems, power consumption, and air pollutants had the maximum effect on the spread of new finance. Governance elements, comprising authorities, regulations, and resources, have been deemed more relevant than social variables, including poverty reduction and task introduction. Our study additionally employs fuzzy AHP to assess ESG factors and rules for inexperienced finance funding decisions in China. We intend to verify the findings of [32] by presenting a better analysis of the country’s essential factors and guidelines for securing sustainable improvement and green finance.

Similarly, ref. [72] used the fuzzy DEMATEL method to observe the risk of green credit in China, considering the ESG elements. They observed that environmental factors, including air and water pollutants, drastically impacted the probability of inexperienced credit scores. However, the effect of social elements, working conditions, and civic engagement became minimal. This research work employed the fuzzy DEMATEL method to investigate the connections between ESG elements and rules for inexperienced finance funding decisions in China, which may additionally compliment the research of [72], which helped to provide a deeper dive into the dynamics at play between the variables forming China’s sustainable finance methods.

Research on the sustainable growth of China’s finance industry serves as an example of how our results regarding the essential relevance of environmental factors resonate across other industries within China. Like our study, other research has stressed how important environmental factors are to maintaining sustainable practices in the tourism industry. It is important to note that the main subfactors we identified, such as energy efficiency, biodiversity preservation, and climate change and carbon emissions, are consistent with findings from other studies. The fuzzy DEMATEL technique produced consistent results in terms of causal links, particularly with regard to the advantages of promoting civic involvement and social responsibility as well as fostering innovation and the growth of green finance. This finding is consistent with a larger body of research on sustainability that stressed the importance of social responsibility and environmentally conscious finance in fostering sustainable development in the Chinese financial industry. In demonstrating the importance of environmental variables in sustainable development and green financing in China, our research is consistent with earlier studies. In order to develop sustainability in the Chinese financial landscape, it further underlines the necessity for comprehensive initiatives that include social responsibility, community participation, and creative green finance practices. These combined results help us gain a deeper understanding of the complex system of variables and regulations required to promote sustainable development and green financing in China. Our research employs the fuzzy DEMATEL method to investigate the relationships between ESG factors and green finance investment policies in China. This approach may explain why our findings differ from previous studies. We can gain a better understanding of the most critical ESG factors and policies for promoting sustainable development and green finance in China as well as how they relate to the larger context of sustainable investment research if we interpret the potential implications of the findings of our study in relation to the findings of other studies.

5.7. Practical Implications

This study’s practical significance affects numerous stakeholders in China’s green finance and sustainable development ecosystem. Our rigorous techniques based on the fuzzy AHP and fuzzy DEMATEL methods emphasize ESG factors, providing institutional and individual investors with relevant guidance. Investors can choose green initiatives and ecologically responsible enterprises by acknowledging the relevance of environmental factors. This coincides with the worldwide sustainability trend and might generate financial gains and sustainable development.

5.7.1. Policy Formulation and Implementation

This study can help policy makers and regulators create green finance policies that boost long-term economic growth. The fuzzy DEMATEL causal relationships show the benefits of civic involvement, social responsibility, and innovation in green finance. The findings can help create financial sector policies that encourage social responsibility and innovation.

5.7.2. Financial Institution Strategy

This study can inform financial institutions’ strategies. Institutions can create and promote sustainable financial solutions for environmentally concerned investors as demand rises. Highlighting environmental elements in their products supports the global low-carbon economy. This addresses market needs and promotes financial sector sustainability.

5.7.3. Tailored Industry Solutions

A one-size-fits-all strategy is limited; therefore, stakeholders can use the findings to tailor solutions for specific industries. By understanding how ESG elements and policies vary across sectors like energy, manufacturing, and finance, stakeholders can build industry-specific strategies to improve sustainability activities.

5.7.4. Longitudinal Assessment

Policy makers, investors, and businesses can benefit from tracking ESG issues and policies over time. This approach shows how these aspects adapt to environmental, social, and economic changes. Such analyses enable proactive policy and strategy revisions to keep them relevant and effective.

5.7.5. Cross-Border Learning

Comparing China’s green finance regulations to global best practices can help policy makers identify areas for improvement. Cross-border learning can help implement successful policies and practices from other countries.

5.7.6. Behavioral Insights

Examining investors, businesses, and policy makers’ behavior might reveal motivations, attitudes, and decisions. Understanding these psychological aspects can help explain green finance policy acceptance and success. Behaviorally tailored communication and incentive techniques can boost sustainable finance adoption.

5.7.7. Support for SMEs

China’s economy relies on SMEs. Green finance regulations that assist SME sustainability can have a big impact. SMEs can help China develop sustainably with affordable funding and incentives. These practical implications help expedite green finance and sustainable development in China by incorporating them into stakeholder plans and actions.

6. Conclusions and Policy Recommendations

The fuzzy AHP and fuzzy DEMATEL methodologies offer important insights into the ranking of numerous elements and their causal links to sustainable development in China. The findings imply that environmental sustainability should be prioritized after governance and social considerations when making green finance initiatives. Specific elements are identified as crucial within each area, including climate change and carbon emissions, openness and disclosure, and diversity and inclusion. The fuzzy DEMATEL method’s causal links show the interconnection and complexity of China’s sustainability factors. An essential step in advancing sustainable development and green finance in China is evaluating ESG elements and regulations for investment decisions utilizing fuzzy AHP and fuzzy DEMATEL methods. This research study identifies the most significant ESG variables and policies for green finance investment in China to provide a more thorough and detailed understanding of sustainable investment in the nation and examines the interrelationships among these factors and policies. To encourage sustainable development and green finance in China, our study has identified several significant ESG factors. These include governance factors like risk management, transparency, and disclosure, as well as environmental factors like carbon emissions reduction and energy efficiency. The study has also recommended particular regulations that might be put into place to support resilience and sustainability in these fields.

Policy Recommendations

Below are some policy recommendations based on this study:

Ecological Sustainability Priority: China should prioritize environmental sustainability in green financing. This can include targeted incentives, tax rebates, and subsidies for green projects and businesses. Use a carbon tax or cap-and-trade system to encourage industries to cut carbon emissions.

Improving Governance: Strengthen corporate governance and transparency regulations. This includes tougher reporting and noncompliance fines. Promote sustainable executive compensation. Integrate the ESG performance into executive pay.

Promoting Social Responsibility: Promote workplace diversity and inclusion with policies. Encourage companies to set diversity goals and report on them. Encourage firms to invest in community development and engage with local communities. Incentivize firms that care about the community.

Aiding Sustainable Innovation: Create government-backed or venture capital funds to develop and deploy green technology and ideas. Provide tax incentives and subsidies to sustainable solution research and development enterprises.

Promoting Cooperation: Work with worldwide partners, organizations, and financial institutions to share information and attract green finance investments. Encourage public–private cooperation for large sustainable infrastructure projects. Engage the private sector in sustainable development.

Monitoring and Reporting: Introduce standardized ESG reporting for listed firms. This will help investors evaluate companies’ ESG performance. To ensure sustainability, examine companies’ ESG practices regularly.

Support for SMEs: Customize green finance programs to assist SME sustainability. Allow SMEs to access financing, capacity-building, and technical help.

Sustainability Certification: Promote business sustainability certification programs to promote environmental and social responsibility. Evaluate green finance initiatives periodically. Adapt policies to changing situations and sustainability aims.

These policy suggestions can assist China in promoting sustainable development, green financing, and environmental protection, as the country wants to promote responsible ESG practices, innovation, and a sustainable, environmentally sensitive economy through policy.