Evaluating and Prioritizing the Green Infrastructure Finance Risks for Sustainable Development in China

Abstract

China has become a global leader in green infrastructure finance, investing heavily in renewable energy, sustainable transportation, and green buildings. However, there are multiple risks and challenges that impede the development of green infrastructure finance. Thus, this study analyzes and prioritizes the risks associated with green infrastructure finance in China and proposes policy plans to mitigate these risks. A Fuzzy analytical hierarchy process (AHP) is used to identify the main risks associated with green infrastructure finance. The main risks are further decomposed into sub-risks. After, the Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is used to prioritize the key policy plans to mitigate risks and sub-risks. The results of Fuzzy AHP show that policy and regulations are the most significant risk associated with green infrastructure finance in China, followed by financial risks, and technical risks. The results of Fuzzy VIKOR reveal that increasing the availability of financing options is the most crucial policy plan to mitigate the risks and sub-risks for green infrastructure finance. The developed standardized technical guidelines and procedures and a legal and regulatory framework are ranked second and third are the most effective and feasible policy plans.

1. Introduction

In 2015, China launched the “Green Belt and Road” initiative which aims to promote sustainable infrastructure development in countries participating in the Belt and Road Initiative [1,2]. China has established a number of regulations to encourage green investment, including carbon trading, green bonds, renewable energy subsidies, and green investment funds [3]. However, there are uncertainties and obstacles in the country’s green infrastructure financing sector. The cost of financing green infrastructure projects is high, and there is a chance that some of them will not accomplish their environmental goals or the promised returns. China, the top producer of greenhouse gases in the world, has acknowledged the urgent need to move towards a low-carbon economy and deal with environmental issues [4,5]. In order to direct investment choices and guarantee that green infrastructure projects are successfully realized, there is also a need for clear policies and regulations. As a result, the country has established challenging goals for lowering carbon emissions, boosting the production of renewable energy, and encouraging sustainable development [6].

The need to solve environmental issues is one of the main forces for green infrastructure financing in China. Numerous Chinese cities have suffered from severe air pollution which has an adverse effect on health and has a financial impact [7]. With rising temperatures and an increase in extreme weather occurrences, climate change poses a serious threat to China [8,9]. China has established challenging goals for cutting carbon emissions and expanding the use of renewable energy sources in order to address these issues. The country wants to increase the share of non-fossil fuels in primary energy consumption to about 20% and reduce carbon emissions per unit of GDP by 60–65% from 2005 levels by 2030 [10]. In addition, China has established goals for increasing forest cover, enhancing water quality, and lowering the use of single-use plastics. Finance for green infrastructure is a crucial element for accomplishing these goals. China has made significant investments in renewable energy, especially solar and wind power, and has emerged as a world leader in these fields [11,12]. Additionally, the country has supported environmentally friendly transportation, such as public transportation and electric vehicles, and has also created green building guidelines and rewards to encourage energy efficiency and lower waste [13].

There is a chance that some green projects may not generate the anticipated returns because they sometimes demand a sizable initial investment [14]. Additionally, green projects could be hampered by legislative ambiguity or other issues, such as land-use constraints or technology difficulties [15]. A further factor that can frighten away investors and delay the development of green infrastructure projects is the absence of defined laws and regulations to direct investment choices. Prioritizing risks and policy plans are a must for policymakers and investors in the green infrastructure finance industry to successfully handle these issues. This calls for a thorough comprehension of both the risks associated with financing green infrastructure projects and the many policy alternatives available to encourage sustainable development and economic growth [16].

To overcome these challenges, decisionmakers and investors in green infrastructure financing must give priority to risks and strategic objectives. This necessitates a thorough understanding of both the risks associated with financing green infrastructure projects and the many policy options available to encourage sustainable investment. In order to analyze and rank risks and policy plans in China’s financing of green infrastructure, we used the Fuzzy multi-criteria decisionmaking (MCDM) technique based on the Fuzzy Analytical Hierarchy Process (AHP) and Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating and ranking risks and strategic goals in green infrastructure financing. To address uncertainties and imprecise information in decisionmaking, this strategy uses fuzzy logic [17]. Policymakers and investors can rate each risk or policy plan based on how well it satisfies each criterion by utilizing a set of criteria to evaluate each risk or policy plan. Each criterion can be given a weight based on its significance.

2. Literature Review

Due to the urgent need to address environmental issues and advance sustainable development, the topic of green infrastructure finance is one that is rapidly gaining attention [15,18]. There is a growing body of research on financing green infrastructure projects that focuses on a variety of factors, such as risks, regulatory frameworks, and funding methods [19,20]. Several studies investigated risk factors related to financing green infrastructure and suggested strategies to control and reduce these risks. In order to identify and assess the main risks associated with investments in renewable energy in Greece, the authors found that capital, high investment, and financing costs are key risks for the development of renewable energy projects [21]. Policy frameworks play a crucial role in shaping the environment for green infrastructure finance by providing incentives and regulations that encourage sustainable investments. Several studies have examined the policy frameworks for green infrastructure finance in different countries and regions, including China, Japan, Europe, and the United States [22,23,24,25,26]. In China, the government has implemented various policy frameworks to support green infrastructure finance, including renewable energy subsidies, green bonds, and carbon trading. In a recent study [27], authors applied the Fuzzy MCDM approach to evaluate the environmental performance of green infrastructure projects in China and found that this approach can effectively prioritize sustainable investments based on multiple criteria. Similarly, another study [28] used the Fuzzy MCDM methodology to analyze the risks in renewable energy investments in China and found that this approach can effectively identify and manage the risks involved in green infrastructure finance.

Table 1. Some previous studies on green infrastructure finance.

Study Objective	Findings	Ref.
Green infrastructure planning principles: Literature review	The findings demonstrated that there is substantial increase in the studies on green infrastructure with American and European countries leading green infrastructure.	[29]
Financing barriers and strategies for urban nature-based solutions	The study identified two main barriers, first, coordination between private and public financiers and, second, integration of urban nature-based solutions benefits into valuation and accounting methods.	[19]
Green finance impact on the upgrading of China’s regional industrial structure	The findings revealed that the correlation between green finance and output value is top in the tertiary industry followed by the primary and secondary industries.	[15]
Public awareness towards India’s innovation lab for green finance	The results of the study show that green growth is the top priority factor with respect to awareness level of respondents. Whereas, the idea generation and submission is considered as least importance.	[30]
Selecting green aviation fleet program management strategies	The main findings indicated that a mixed strategy portfolio for green aviation fleet management is best for using limited resources.	[31]
Ranking of green materials based on sustainable development goals	The results revealed that natural renewable and affordability from cradle to gate, and affordability during operation are the highest important selection criteria for green materials.	[32]
Factors influencing the development of green bond market	The results demonstrated that legal infrastructure is the most important factor, following official interest rate of green bonds and economic stability which directly affecting green bond market expansion.	[33]

presents several recent studies on green infrastructure finance.

The literature review presented in this study highlights the key issues and challenges in green infrastructure finance and provides a foundation for further research and analysis. By examining the risks, policy frameworks, and financing mechanisms in different contexts, we can identify best practices and lessons learned that can inform the development of effective strategies and policies for promoting sustainable investments. Ultimately, by prioritizing green infrastructure finance and investing in sustainable development, we can build a more resilient and sustainable future for all. Thus, green infrastructure finance is a complex and rapidly evolving field that requires a comprehensive understanding of the risks, policy frameworks, and financing mechanisms involved. A useful method for evaluating and ranking these criteria and spotting opportunities for sustainable investment is the fuzzy MCDM approach. As a result, in this study, we evaluated and ranked China’s green infrastructure financing risks and policy plans for sustainable development using fuzzy AHP and fuzzy VIKOR methodologies. Policymakers and investors may support sustainable development and solve numerous environmental and social issues at once by combining green infrastructure finance with other policy sectors.

3. Green Infrastructure Finance Risks and Policy Plans for Sustainable Development

3.1. Identified Green Infrastructure Finance Risks

There are certain potential risks and sub-risks associated with financing green infrastructure projects that have been highlighted in the literature. Risks associated with green infrastructure finance range from financial to policy and regulatory, to technical to environmental to social, and community to legal and contractual. The major risks and minor risks associated with financing green infrastructure are shown in

Table 2. Identified green infrastructure finance risks and sub-risks.

Main Risk	Sub-Risk	Ref.
Policy and regulatory risks (PR)	Changes in government policies and regulations (PR1)	[19,20]
	Changes in tax incentives and subsidy programs (PR2)	[20,34]
	Uncertainty in policy implementation and enforcement (PR3)	[35,36]
	Political instability and changes in government leadership (PR4)	[33,37]
Environmental risks (ER)	Environmental impacts and damage caused by green infrastructure projects (ER1)	[22,36]
	Climate change and natural disasters affecting the viability of green infrastructure projects (ER2)	[22,31]
	Uncertainty in the long-term environmental benefits and performance of green infrastructure projects (ER3)	[38,39]
	Inadequate environmental assessments and monitoring of green infrastructure projects (ER4)	[40,41]
Technical risks (TR)	Technical failures and defects in green infrastructure projects (TR1)	[40,42]
	Inadequate project planning and design (TR2)	[36,43]
	Lack of skilled labor and expertise for green infrastructure projects (TR3)	[44,45]
	Complexity and scale of green infrastructure projects (TR4)	[29]
Financial risks (FR)	Uncertainty in the financial returns of green infrastructure investments (FR1)	[46,47]
	Market volatility and fluctuations in demand for green infrastructure projects (FR2)	[48,49]
	Lack of financing options and investment capital for green infrastructure projects (FR3)	[15,18]
	Difficulty in estimating the long-term financial benefits of green infrastructure projects (FR4)	[19]
Legal and contractual risks (LR)	Contractual breaches and legal challenges involving investments in green infrastructure (LR1)	[33,50]
	Inadequate legal structures and rules guiding investments in green infrastructure (LR2)	[50,51]
	Insufficient legal and contractual risk assessment and due diligence (LR3)	[52,53]
	Projects involving green infrastructure are notoriously complex and large in size (LR4)	[33]
Social and community risks (SR)	Local stakeholder’s and community’s opposition and resistance (SR1)	[54,55]
	Negative social effects and community displacement (SR2)	[33,44]
	Insufficient community participation and engagement in green infrastructure projects (SR3)	[56,57]
	Monitoring and social effect evaluations of green infrastructure initiatives are inadequate (SR4)	[33,56]

.

To assess their relative importance and prioritize them for efficient risk management and decisionmaking in green infrastructure finance, each of these risks and sub-risks will be examined using the Fuzzy AHP technique. Therefore, encouraging sustainable investments and achieving the potential advantages of green infrastructure finance depend on an understanding of and skillful management of these risks.

3.2. Policy Plans to Mitigate Green Infrastructure Risks

To mitigate the risks associated with green infrastructure finance in China, several policy plans can be implemented. These policy plans are based on the risks identified through the literature review and are aimed at managing the risks and promoting sustainable green infrastructure investments [58]. Therefore, this research identified six important policy plans to mitigate green infrastructure risks in China as follows.

3.2.1. Develop a Stable and Consistent Policy Framework (PP1)

It is important to develop a stable and consistent policy framework for green infrastructure investments that provides clear guidelines and incentives for investors [15]. Moreover, establish a regulatory framework that ensures compliance with environmental standards and regulations. Moreover, it increases transparency and communication between the government and investors to promote predictability and stability in green infrastructure investments.

3.2.2. Increase the Availability of Financing Options (PP2)

This is one of the important policy plans for enhancing the availability of financing options for green infrastructure projects, including government-backed loans and private investments and provides guarantees and risk-sharing mechanisms for investors to mitigate the risk of financial losses [59]. Additionally, to develop financial models that consider the long-term nature of green infrastructure investments to promote their sustainable economic viability [60,61].

3.2.3. Develop Standardized Technical Guidelines and Procedures (PP3)

To mitigate the risks, it is crucial to develop standardized technical guidelines and procedures for green infrastructure projects to ensure that they are designed and implemented effectively. Likewise, increase the availability of skilled labor and technical expertise to support the planning and implementation of green infrastructure projects [31]. Further, to conduct regular technical assessments and audits of green infrastructure projects to identify and address any technical issues.

3.2.4. Conduct Comprehensive Environmental Assessments and Monitoring (PP4)

It is vital to conduct comprehensive environmental assessments and monitoring of green infrastructure projects to identify and mitigate any potential environmental risks and develop guidelines and standards for environmental management and restoration of green infrastructure projects [62]. Moreover, it is crucial to develop emergency response plans to address potential environmental risks and ensure that they are effectively communicated to all stakeholders.

3.2.5. Establish a Participatory Approach (PP5)

This policy plan establishes a community- and stakeholder-involved participatory planning and implementation process for green infrastructure. In order to identify and mitigate any potential negative social impacts and to adequately compensate and support any communities that are adversely impacted by green infrastructure projects, comprehensive social impact assessments, and monitoring of green infrastructure projects are also necessary [63].

3.2.6. Develop a Legal and Regulatory Framework (PP6)

It is essential to create a legal and regulatory framework that offers investors and stakeholders in green infrastructure initiatives with clear rules and protection. So, it is necessary to conduct due diligence and risk assessments of potential legal and contractual risks before investing in green infrastructure projects [53]. Moreover, establishing dispute resolution mechanisms can address any legal and contractual issues that may arise during green infrastructure investments.

Implementing these policy plans can help to mitigate the risks associated with green infrastructure investments in China and promote sustainable green infrastructure development. However, it is essential to continue monitoring and evaluating these policy plans to ensure their effectiveness and adapt them to any changing circumstances.

4. Methodology

The methodology used in this study is based on the Fuzzy AHP and Fuzzy VIKOR techniques. The Fuzzy AHP method is used to assess and prioritize the main risks associated with green infrastructure finance. The Fuzzy VIKOR approach is utilized to evaluate and rank the policy plans to mitigate the risks associated with green infrastructure finance in China. Figure 1 provides the decision methodology of the study.

4.1. The Fuzzy AHP Method

The Fuzzy AHP approach allows for the consideration of uncertain and imprecise information by assigning fuzzy numbers to the criteria used in the decisionmaking process [64]. This allows for a more realistic representation of the decisionmaking problem, particularly when dealing with complex and uncertain situations [11].

Table 3. TFNs Scale [65].

Code	Linguistic Variable	TFNs
1	Equally 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 Triangular Fuzzy Numbers scale.

The Fuzzy AHP process involves several steps [66], including:

Step 1. Triangular fuzzy matrix (TFM):

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

(1)

After, 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 TFN using a geometric mean approach:

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

(3)

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

Step 3. Create consistency index (CI):

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

(4)

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

(5)

Step 4: Create the consistency ratio (CR):

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

(6)

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

(7)

If the consistency ratio is below a certain threshold, i.e., less than 0.10, the pairwise comparisons are considered consistent.

Table 4. 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

shows the RI scale [66].

4.2. The Fuzzy VIKOR Method

The Fuzzy VIKOR approach helps to identify the best compromise solution among a set of alternatives. This approach is particularly useful when dealing with conflicting criteria and objectives and reaching a final decision [67]. The several steps of Fuzzy VIKOR are as follows [2]:

Step 1. Establish the fuzzy performance matrix and the weight vector.

$$\tilde{D}=\begin{array}{c}{O}_1\\ ⋮\\ O_n\end{array}\left[\begin{array}{cccc}{C}_1& C_2& \cdots & C_n\\ {\tilde{p}}_{11}& {\tilde{p}}_{12}& \cdots & {\tilde{p}}_{1n}\\ {\tilde{p}}_{21}& {\tilde{p}}_{22}& \cdots & {\tilde{p}}_{2n}\\ ⋮& ⋮& ⋮& ⋮\\ {\tilde{p}}_{m1}& {\tilde{p}}_{m2}& \cdots & {\tilde{p}}_{mn}\end{array}\right]$$

(8)

$$\tilde{W}=\left(w_1,w_2,w_3\right),{\sum }_{j=1}^n{w}_j=1$$

Step 2. Determine the benefit and the cost criteria values.

$$\begin{array}{cc}{\tilde{p}}_i^{+}={max}_j{\tilde{p}}_{ij}, {\tilde{p}}_i^{-}={min}_j{\tilde{p}}_{ij}& \mathrm{for} i\in l^b (\text{benefit} \text{criteria}),\\ {\tilde{p}}_i^{+}={min}_j{\tilde{p}}_{ij}, {\tilde{p}}_i^{-}={max}_j{\tilde{p}}_{ij}& \mathrm{for} i\in l^c (\text{cost} \text{criteria}).\end{array}$$

(9)

Step 3. Calculate the normalized fuzzy decision matrix ${\tilde{D}}_{ij}$:

$$\begin{array}{cc}{\tilde{D}}_{ij}=\frac{{\tilde{p}}_i^{+}(-){\tilde{p}}_{ij}}{z_i^{+}-l_i^{-}}& \mathrm{for} i\in l^b\\ {\tilde{D}}_{ij}=\frac{{\tilde{p}}_{ij}(-){\tilde{p}}_i^{+}}{z_i^{-}-l_i^{+}}& \mathrm{for} i\in l^c\end{array}$$

(10)

Step 4. Calculate the values ${\tilde{S}}_j=({\tilde{S}}_j^x,{\tilde{S}}_j^y,{\tilde{S}}_j^z)$ and ${\tilde{R}}_j=({\tilde{R}}_j^x,{\tilde{R}}_j^y,{\tilde{R}}_j^z)$:

$${\tilde{S}}_j={\sum }_{i=1}^n{\tilde{W}}_i\left(\times \right){\tilde{D}}_{ij}$$

(11)

$${\tilde{R}}_j={max}_i{\tilde{W}}_i\left(\times \right){\tilde{D}}_{ij}$$

(12)

Step 5. Calculate the values ${\tilde{Q}}_j=({\tilde{Q}}_j^x,{\tilde{Q}}_j^y,{\tilde{Q}}_j^z)$:

$${\tilde{Q}}_j=v\frac{{\tilde{S}}_j\left(-\right){\tilde{S}}^{+}}{S^{-z}-S^{+x}}(+)(1-v)\frac{{\tilde{R}}_j\left(-\right){\tilde{R}}^{+}}{R^{-z}-R^{+x}}$$

(13)

here ${\tilde{S}}^{+}={min}_j{\tilde{S}}_j$; $S^{-z}={max}_j{S}_j^z$; ${\tilde{R}}^{+}={min}_j{\tilde{R}}_j$; $R^{-\mathit{z}}={max}_j{R}_j^x$

Step 6 and 7. Defuzzify ${\tilde{S}}_j$, ${\tilde{R}}_j$ and ${\tilde{Q}}_j$ values.

Step 8. Propose a solution to the alternative which is the optimal solution of the measure ${\tilde{Q}}_j$.

Overall, the Fuzzy AHP and Fuzzy VIKOR approaches provide a systematic and comprehensive framework for identifying and prioritizing the risks associated with green infrastructure finance in China and identifying the best policy plans to mitigate these risks. In the study, we consulted with five experts to assign the weights to risks, sub-risks, and policy plans based on their importance to one and each other. All the experts were experienced and professional from the field of academia, government, and green industry.

5. Results

5.1. Results of Main Risks Using Fuzzy AHP

In the study, we analyzed the main risk weights using a pairwise comparison matrix of the Fuzzy AHP method. The pairwise comparison matrix is presented in

Table 5. Pairwise comparison matrix of main-risks using Fuzzy AHP.

Main-Risk	Policy and Regulatory Risks (PR)	Environmental Risks (ER)	Technical Risks (TR)	Financial Risks (FR)	Legal and Contractual Risks (LR)	Social and Community Risks (SR)
Policy and regulatory risks (PR)	(1,1,1)	(1,1.542,2.5)	(1,1.375,2.5)	(1,1.225,2)	(1,1.311,2)	(1,2.422,3.5)
Environmental risks (ER)	(0.400,0.649,1)	(1,1,1)	(0.500,0.874,1)	(0.400,0.728,1)	(0.400,0.794,1)	(1,1.697,2.5)
Technical risks (TR)	(0.400,0.727,1)	(1,1.144,2)	(1,1,1)	(0.500,0.874,2)	(1,1.285,2.5)	(1,1.815,2.5)
Financial risks (FR)	(0.500,0.816,1)	(1,1.374,2.5)	(0.500,1.144,2)	(1,1,1)	(1,1.225,2)	(1,1.848,3)
Legal and contractual risks (LR)	(0.500,0.763,1)	(1,1.259,2.5)	(0.400,0.778,1)	(0.500,0.816,1)	(1,1,1)	(1,1.602,3)
Social and community risks (SR)	(0.286,0.413,1)	(0.400,0.589,1)	(0.400,0.551,1)	(0.333,0.541,1)	(0.333,0.624,1)	(1,1,1)

. Moreover, the weights and ranking of the main risks associated with green infrastructure finance in China are presented in

Table 6. The ranking of main risks associated with green infrastructure finance.

Main Risk	Weight	Rank
Policy and regulatory risks (PR)	0.212	1
Environmental risks (ER)	0.146	5
Technical risks (TR)	0.182	3
Financial risks (FR)	0.190	2
Legal and contractual risks (LR)	0.167	4
Social and community risks (SR)	0.102	6

.

The above results suggest that policy and regulatory risks (PR) are the most significant risks associated with green infrastructure finance in China with a weight of 0.212. This shows that the success of green infrastructure projects in China may be significantly impacted by uncertainties and prospective changes in rules and regulations regulating green infrastructure investment and development. With a weight of 0.190, financial risks (FR) were ranked as the second-most important risk. This shows that concerns about the sustainability of green infrastructure projects financially and economically could have a limited effect on their success. With a weight of 0.182, technical risks (TR) were ranked as the third most important risk. This suggests that implementing cutting-edge technologies into green infrastructure projects could come with possible risks and uncertainties, such as technical setbacks, delays, and cost overruns.

5.2. Results of Sub Risks Using Fuzzy AHP

The results of the Fuzzy AHP analysis for the sub-risks associated with the PR are presented in Figure 2, along with their corresponding weights. The results suggest that the most significant sub-risks associated with PR are changes in government policies and regulations (PR1) with a weight of 0.277, followed by changes in tax incentives and subsidy programs (PR2), political instability, and changes in government leadership (PR4), and uncertainty in policy implementation and enforcement (PR3). These sub-risks of PR can hinder and delay the development of green infrastructure finance projects.

For environmental risks (ER), the environmental impacts and damage caused by green infrastructure projects (ER1) were identified as the most significant sub-risk with a weight of 0.285, followed by inadequate environmental assessments and monitoring of green infrastructure projects (ER4), uncertainty in the long-term environmental benefits and performance of green infrastructure projects (ER3), and climate change and natural disasters affecting the viability of green infrastructure projects (ER2). Figure 3 displays the weights and rankings of sub-risks from ER perspectives.

For technical risks (TR), the technical failures and defects in green infrastructure projects (TR1) were determined as the most important sub-risk with a weight of 0.324, followed by inadequate project planning and design (TR2), complexity and scale of green infrastructure projects (TR4), and lack of skilled labor and expertise for green infrastructure projects (TR3). Figure 4 presents the weights and rankings of sub-risks from TR perspectives.

For financial risks (FR), the uncertainty in the financial returns of green infrastructure investments (FR1) was recognized as the most crucial sub-risk with a weight of 0.295, followed by market volatility and fluctuations in demand for green infrastructure projects (FR2), lack of financing options and investment capital for green infrastructure projects (FR3), and lack of financing options and investment capital for green infrastructure projects (FR3). Figure 5 illustrates the ranking of sub-risks relating to FR.

For legal and contractual risks (LR), the contractual breaches and legal challenges involving investments in green infrastructure (LR1) were ranked first as major risks with a weight of 0.289. The insufficient legal and contractual risk assessment and due diligence (LR3) was identified as the second important sub-risks, following inadequate legal structures and rules guiding investments in green infrastructure (LR2) as projects involving green infrastructure are notoriously complex and large in size (LR4) and are considered as lowest ranked sub-risks for the development of green infrastructure projects in China. Figure 6 shows the ranking of sub-risks from the LR perspective.

For social and community risks (SR), the local stakeholder’s and community’s opposition and resistance (SR1) obtained the highest weight of 0.288, so it is considered as the first ranked sub-risk which impedes the projects’ development related to green infrastructure finance. The insufficient community participation and engagement in green infrastructure projects (SR3) is considered as the second-ranked sub-risks, following monitoring and social effect evaluations of green infrastructure initiatives are inadequate (SR4) and negative social effects and community displacement (SR2). Figure 7 displays the ranking of sub-risks relating to SR.

5.3. Overall Sub Risks Results Using Fuzzy AHP

The results of the Fuzzy AHP analysis for the overall sub-risks associated with green infrastructure finance in China indicate that the most significant sub-risk across all sub-risks is technical failures and defects in green infrastructure projects (TR1) with a weight of 0.590. The second most significant sub-risk is changes in government policies and regulations (PR1) with a weight of 0.587. The third most significant sub-risk is changes in tax incentives and subsidy programs (PR2) with a weight of 0.566. Overall, the results indicate that these sub-risks pose the greatest challenge to the success of green infrastructure projects in China. Figure 8 presents the ranking of overall sub-risks in the context of the decision goal of the study.

5.4. Results of Policy Plans Using Fuzzy VIKOR

The results of the Fuzzy VIKOR analysis for the policy plans aimed at mitigating the risks associated with green infrastructure finance in China are presented in this section, along with their corresponding scores. In this regard, the fuzzy decision matrix, normalized matrix, and weighted normalized decision matrix. In the study, all the policy plans are taken as benefit criteria. In the Fuzzy VIKOR, we obtained the values of S, R, and Q. The best-suited policy plans ranked on the basis of the lowest value of $Q_i$. So,

Table 7. The final ranking of policy plans based on lowest Q value.

Policy Plan	${\mathit{Q}}_{\mathit{i}}$	Rank
Increase the availability of financing options (PP2)	0.023	1
Develop standardized technical guidelines and procedures (PP3)	0.047	2
Develop a legal and regulatory framework (PP6)	0.073	3
Develop a stable and consistent policy framework (PP1)	0.084	4
Conduct comprehensive environmental assessments and monitoring (PP4)	0.097	5
Establish a participatory approach (PP5)	0.099	6

shows the final ranking of policy plans. The findings indicate that increase the availability of financing options (PP2) is the most suitable policy plan to mitigate the risks for green infrastructure finance development in China. The developed standardized technical guidelines and procedures (PP3) has been recognized as the second important policy plan to overcome the risks. Moreover, the developed legal and regulatory framework (PP6) has achieved the third rank. The other policy plans are ranked as follows: develop a stable and consistent policy framework (PP1), conduct comprehensive environmental assessments and monitoring (PP4), and establish a participatory approach (PP5).

5.5. Discussion

The results of this study highlight the significant risks associated with green infrastructure finance in China and the policy plans that can be implemented to mitigate these risks. According to the Fuzzy AHP study of the major risks, legislative and regulatory, financial, and technical risks are the key ones impeding the growth of green infrastructure financing in China. This emphasizes how crucial it is to have an effective policy, a functioning financing system, and a program that encourages and supports knowledge transfer and innovation in order to guarantee the success of green infrastructure projects’ technical aspects. This is in line with the conclusions of earlier research, which also emphasized the significance of innovation and technology transfer in fostering sustainable development in China [68]. The conclusions of this study are in accordance with those of earlier studies that also demonstrated the challenges involved in financing green infrastructure in China.

Previous studies [69,70] have also emphasized the significance of technology transfers and innovation in fostering sustainable development. These studies stressed the significance of China’s indigenous green technology development, promotion, and incorporation into infrastructure projects. Additionally, a number of studies [71,72,73] have emphasized how government policies and regulations can encourage investment in green infrastructure. These studies stress the significance of formulating rules and policies that are precise and consistent in order to provide a secure and encouraging environment for green infrastructure investment. The findings provide support for government efforts to promote sustainable development and achieve its environmental objectives. The government has established guidelines and procedures to encourage green financing, such as the Green Bond Guidelines, which provide a framework for issuing green bonds. The policy measures outlined in this report, which include enhancing the policy and regulatory environment and creating green finance instruments, can support these initiatives and further China’s commitment to green development.

This study offers insight into future government efforts and contributes to the understanding of the risks associated with financing green infrastructure. The attainment of sustainable development goals can be aided by investors and policymakers using the study’s findings to develop and implement green infrastructure initiatives.

6. Conclusions

China has made significant progress toward promoting green growth and achieving its environmental objectives. To achieve sustainable development, considerable investments in green infrastructure projects are required. As a result, it is crucial to assess and reduce the risks related to financing green infrastructure to draw more investments and advance sustainable development in China. This study used a Fuzzy AHP and Fuzzy VIKOR approach, in order to identify and prioritize the major risks, sub-risks, and policy plans related to financing green infrastructure in the country. The findings of the Fuzzy AHP method revealed that policy and regulatory risks, financial risks, and technological risks are the main risks associated with green infrastructure finance in China. These risks are consistent with previous research which has highlighted the challenges of promoting green finance and achieving sustainable development in China. The sub-risks identified in this study included technical failures and defects in green infrastructure projects, changes in government policies and regulations, and changes in tax incentives and subsidy programs.

To mitigate these risks, the study developed policy plans that can complement the efforts of the Chinese government to promote green development. Therefore, the Fuzzy VIKOR method is used to rank these policy plans, so increasing the availability of financing options is considered as the most vital policy plan following standardized technical guidelines and procedures and developing legal regulations. One of the strengths of this study is the use of a Fuzzy AHP and Fuzzy VIKOR approach which enabled the researchers to incorporate subjective and ambiguous factors into the analysis. This approach is particularly useful in complex decisionmaking situations where uncertainty and imprecision are prevalent. This study contributes to the literature on green finance and sustainable development by providing insights into the risks associated with green infrastructure finance in China and developing policy plans to mitigate these risks. The findings can provide valuable insights to policymakers and investors in promoting green development and achieving sustainable development in China.

6.1. Policy Recommendations

The following policy recommendations can be made to promote green infrastructure finance and mitigate the associated risks in China:

• The government should develop a comprehensive and transparent policy and regulatory framework that provides clear guidelines and incentives for promoting green finance and sustainable development. This framework should address issues, such as information disclosure, risk assessment, and green bond standards.
• The government must develop a range of green financial products, such as green bonds, green funds, and green loans. To meet the needs of several stakeholders and encourage the development of green infrastructure projects, these tools should be developed.
• The government should boost the capacity of financial institutions by promoting the development of green financial products, aiding in the establishment of green finance institutions, and providing staff incentives and training.
• The government should promote innovation and knowledge transfer by providing funding and incentives for research and development, promoting the establishment of demonstration projects, and supporting international cooperation.
• By encouraging communication and participation among stakeholders, creating forums for knowledge and experience sharing, and supporting collaborative projects and initiatives, the government should promote collaboration and partnership.

These policy recommendations are consistent with the efforts of the government to promote green finance and achieve sustainable development and can contribute to the country’s long-term development goals.

6.2. Limitations and Future Research

Despite the valuable insights generated from this study, there are some limitations that should be addressed in future research. This study only focused on the risks and policy plans associated with green infrastructure finance in China, and the results may not be generalizable to other countries. Future research could expand the scope of the study to include other countries and regions to compare the risks and policy plans associated with green infrastructure finance. Moreover, this study used a Fuzzy AHP and Fuzzy VIKOR approach to analyze the risks and policy plans associated with green infrastructure finance. In future research, some other MCDM methods can be used to analyze and compare the results with the current study.