# Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

Authors: Yuqi Huang
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

This paper adopts the information entropy fuzzy hierarchical analysis model to conduct an in-depth study and analysis of the green investment risk evaluation system and to process and analyze its indicators. The entropy method-fuzzy hierarchical analysis is used to evaluate the green financial capital operation risk. Firstly, I point out the research trends and shortcomings, define the concepts of fund operation, operation risk, risk evaluation, and green finance, and elaborate the relevant basic theories to lay the theoretical foundation for the later research; secondly, I identify the external and internal fund operation risks by describing the development plan, overall operation, and specific operation plan of green finance; then, according to the existing fund operation risks, I integrate commercial banks’ risk. Then, based on the existing fund operation risks, I integrate the risk types of commercial banks, combine the characteristics of green finance business, design the green finance fund operation risk evaluation index system, initially determine the index weights through hierarchical analysis method, introduce the entropy value method to improve the index weights, and use the fuzzy comprehensive evaluation method to obtain the final evaluation results: in the process of green finance fund operation, internal risk is the most important risk, among which credit risk and financial innovation risk have the most significant impact; external risk is the second most important risk, among which economic risk and political risk are the most important. External risks are the second most important risks, among which economic risks and political risks are more obvious. The risks are judged from single-factor aspects, and the scoring values are used to determine the higher possibility of schedule risks caused by human factors, and finally, the schedule risk level of the reservoir project is determined to be medium through a comprehensive evaluation. The reasonableness of the calculation results shows that the entropy theory and the fuzzy comprehensive evaluation method are still applicable to the assessment of green investment risks.


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## Body

## 1. Introduction

As market competition continues to become more complex, many world-renowned companies have been acquired or gone bankrupt due to failures in risk management. To effectively assist enterprises in risk management, the continuous establishment and improvement of the risk management system have played a certain role in guiding the risk evaluation of enterprises, but due to the different risks faced by each industry, it is difficult to adopt a unified framework or guidelines to the specific requirements of different enterprises, and the establishment of a risk evaluation model suitable for industry characteristics can solve this problem to a certain extent and break through the quantitative evaluation of risk [1]. The technical bottleneck of quantitative risk evaluation can be broken. The importance of risk evaluation in academic and practical circles contributes to its importance in case studies and is the fundamental reason for choosing risk evaluation as the research topic in this paper. This paper introduces risk indicators reflecting the characteristics of green finance into the risk evaluation index system of commercial banks’ capital operation, establishes a scientific risk evaluation index system of green finance capital operation, and provides a theoretical basis for improving the risk management system of commercial banks and green finance risk research. As an emerging investment and financing tool, equity crowdfunding has the dual attributes of Internet and finance, and the sources of risk are relatively extensive, and the risk factors are more complex, and there are relatively few academic results that quantitatively study the risk of equity crowdfunding [2]. This paper identified and evaluated the investment risks in equity crowdfunding, classified and controlled the sensitivity of various potential risk indicators, and provided new analysis perspectives, ideas, and methods for the risk management theory of Internet equity crowdfunding. In addition, investors, as important participants in the crowdfunding market, face the greatest risks due to information asymmetry, but there is a lack of research in the literature to evaluate and control the risks of crowdfunding from investors’ perspective [3]. Therefore, this paper analyzes what types of risks investors encounter in the process of equity crowdfunding investment, how to quantitatively assess the risk level of equity crowdfunding projects with scientific models, how to rank the risk level of several crowdfunding projects with scientific methods, how to identify which risk indicators have a greater impact on investment returns with scientific models, and how this paper identifies and evaluates the risk level of equity crowdfunding projects. This paper identifies and evaluates the investment risks in equity crowdfunding, classifies and controls the sensitivity of various potential risk indicators, and provides a new analytical perspective, ideas, and methods for the risk management theory of Internet equity crowdfunding.By combining green financial policies with the development of the financial industry, this paper systematizes the formation process of China’s green financial policy system and analyzes the performance and shortcomings of green financial policies in the context of the development of the green financial industry while better understanding the green financial policy system. Commercial banks’ business object is capital, and all business activities revolve around the capital. The rational flow and optimal allocation of capital elements are used to improve the efficiency of capital use and achieve maximum economic benefits with the smallest capital cost, thereby ensuring the stable, sustained, and efficient development of commercial banks. This paper considers that commercial banks’ capital operation is the general term for commercial banks’ activities such as financing, investment, and capital operation. Commercial banks obtain funds through financing, gain income through investing, and ensure the orderly implementation of financing and investment activities through the operation of funds. The purpose of the capital operation of commercial banks is to make effective use of funds to achieve the business objectives of commercial banks, to improve the efficiency of capital use through reasonable flow and optimal allocation of capital elements [4], to achieve maximum economic benefits with minimum capital costs, and to ensure stable, sustainable, and efficient development of commercial banks [5]. The management activities of commercial banks will ultimately affect capital management, and the risks faced by commercial banks will ultimately be reflected in the operational risks of capital. Risk evaluation refers to the process of considering the probability of occurrence of the risk and the degree of loss based on risk identification and risk estimation, combining the principles of comprehensiveness, objectivity, and importance, compared with the recognized safety indicators, assessing the possibility of occurrence of the risk and the degree of harm, and determining whether to take risk reduction measures. At present, the mainstream risk evaluation method is divided into the subjective assignment evaluation method, which determines the weight through subjective judgment scoring; the objective assignment evaluation method which determines the weight according to the relationship between indicators or variation coefficient. The mainstream risk evaluation method has a wide range of applications, such as avionics systems [6], wireless sensor networks [7], and UAV systems [8].While traditional finance focuses on economic benefits, green finance emphasizes the harmonious development of economic and social benefits. Green finance starts from protecting resources and the environment, effectively managing pollution problems and promoting sustainable economic development, analyzes and judges whether the requirements of balanced economic and social benefits are met, and takes the degree of protecting the environment and saving resources as an important basis for measuring the effectiveness of operation, to achieve a win-win situation for financial development, environmental improvement, and resource-saving. The elements in the risk evaluation system are interconnected and interact with each other and jointly influence the enterprise risk. In this paper, when designing the risk index system, I consider both internal and external risks. External risks include four aspects of policy risk, legal risk, economic risk, and market risk, which represent the main external risks that may be faced, and internal risks include strategic risk, credit risk, liquidity risk, operational risk, financial innovation risk, and reputation risk, which represent the main internal risks that may be faced, not only focusing on risk evaluation as an organic whole but also considering the interaction between risk evaluation and other activities of the company, which better explains the idea of system theory.


## 2. Current Status of Research

In terms of investment risk research, Shaktawat and Vadhera argue that equity crowdfunding provides investors with more options, but micro and small enterprises are at the beginning stage of their business and have a higher percentage of failed crowdfunding investment projects [9]. Liu et al. argue that equity crowdfunding investment is risky, but the actual participation of the investing public has limited investment ability [10]. Bhowmik et al. argue that nonprofessional investors like to blindly follow hot projects and need to pay attention to the potential herding effect [11]. Peng et al. point out that the traditional equity-based high-risk investment nonprofessional investors can use the services of professional investment institutions, while the public involved in crowdfunding cannot scientifically assess the actual value of the project, and is at a disadvantage in the current valuation game, facing the risk of overvaluation of the underlying [12]. For example, there are no clear and fixed boundaries for such rubrics as investment risky, moderate risk, and small risk. Internal risks include strategic risk, credit risk, liquidity risk, operational risk, financial innovation risk, and reputation risk, which represent the main internal risks that may be faced, and not only focus on considering risk evaluation as an organic whole.Therefore, the investment risk evaluation of equity crowdfunding is a multiattribute decision problem with a high degree of fuzziness. Therefore, this paper adopts the fuzzy comprehensive evaluation method to evaluate the risk level of equity crowdfunding projects, based on interval intuitionistic fuzzy set theory to rank several projects’ crowdfunding investment risks [13]. At present, the research on fuzzy comprehensive evaluation method is relatively mature and widely used in many fields. Considering the different categories of risks in Internet finance in practical situations, Cao Lingyan combined hierarchical analysis with fuzzy mathematics to assess the overall risk level of current Internet finance in China. Wang et al. proposed an index system for evaluating the innovation performance of scientific research institutions in Guizhou province using a fuzzy comprehensive evaluation method and designed an evaluation model based on this index system [14]. He et al. completed the study of beer taste harmonization by selecting several comprehensive evaluation indexes that can describe the taste of beer and introducing the fuzzy comprehensive evaluation method [15]. One of the current research hotspots in the field of fuzzy evaluation is the interval intuitionistic fuzzy multiattribute decision method [16]. The classical fuzzy set theory suffers from numerous limitations in practice due to the small amount of embedded information expressed. In this regard, the intuitionistic fuzzy set theory is proposed, and three concepts of affiliation, nonaffiliation, and hesitation are defined [17]. To solve the problem that information such as affiliation degree and nonaffiliation degree sometimes cannot be portrayed by quasireal values, the intuitionistic fuzzy set is further extended, and the theoretical system of interval fuzzy set is proposed. The advantage of this set is that both affiliation and nonaffiliation degrees are interval values, which can portray fuzziness more flexibly, so the related theory is also often used to solve multiattribute evaluation problems such as investment evaluation [18].The theoretical and applied research on risk evaluation for the three major activities of financing, investment, and operation of enterprises is mature, and the evaluation of different risks in different industries from various perspectives is realized by combining various mathematical models. The evaluation of risk is comprehensive and focused, while the evaluation of specific risks is targeted and practical. Foreign scholars are more mature in both qualitative and quantitative evaluation of risks, and the research in recent years has focused on introducing new methods to improve the original risk evaluation models to enhance quantitative accuracy and thus adapt to the characteristics of industries and risks, but the improved models are too complex and reduce the practicality. Based on foreign risk management systems, Chinese scholars are keen to establish evaluation index systems for risks in various industries from different perspectives and to verify the reliability of the systems through empirical analyses using different methods, which has realized the transformation from qualitative analysis to a combination of qualitative and quantitative analysis, but the evaluation results are highly subjective due to the limitations of research methods. Then, construct a weighted standardized evaluation matrix according to TOPSIS. The model determines the maximum value and minimum value of each index of the ideal solution and negative ideal solution according to the maximum value, calculates the Euclidean distance, and then, uses the calculated distance to observe the relative sticking progress, and is based on the relative closeness. Due to the special characteristics of commercial banks’ capital operations, it is difficult to accurately divide financing activities, investment activities, and operation activities, so this paper divides risks for collation, determines the main structure of this paper’s risk evaluation index system, introduces entropy value method-fuzzy hierarchical analysis method to process relevant data, reduces the subjectivity of fuzzy hierarchical analysis method in determining index weights, and improves the research results. The reliability of the research results is improved.


## 3. Information Entropy Fuzzy Hierarchical Analysis Model for Green Investment Risk Evaluation Index System Construction

### 3.1. Information Entropy Fuzzy Hierarchical Analysis Model Design

Mathematicians were the first to publish the concept of information entropy [19, 20], laying a solid foundation for quantitative information analysis, providing a detailed explanation of entropy theory, constructing the basic model, and writing it into its mathematical theory of propagation [21]. The concept of thermal entropy in physics is an index used to measure the disorder of molecular states. Shannon adopted the concept of information entropy to perfectly explain the uncertain information source, and the amount of information entropy is used to determine the amount of information. The specific definition of entropy is as follows: let a discrete random variable X=x1,x2,⋯,xn. The information source included given appears with pi=pxi probability and ∑i−1npi=2, then the information entropy of X can be expressed by the formula.
(1)Hx=∑i−1npiln1pi.By calculating the information entropy, the size of the information can be known, and the original information can also be reflected objectively. There is an inverse proportional relationship between information entropy and the amount of information; the greater the information entropy, the smaller the information of the index, and the smaller the role produced by the comprehensive evaluation, and vice versa, which shows the important significance of information entropy in the evaluation. Similarly, the smaller the information entropy, the greater the amount of information provided by the index, and the greater its contribution to the comprehensive evaluation results. Therefore, the weight of indexes in the comprehensive evaluation results is larger.In this paper, the risk evaluation matrix of green building developers is constructed by selecting the secondary indicators of each evaluation object from four aspects: political risk, economic risk, social risk, and technical risk according to the actual cases, conducting a comprehensive scientific analysis of the detected raw data, further processing the model criteria, carrying out entropy values and entropy weights for different indicators, and determining the degree of influence of each indicator on the risk evaluation. Then, a weighted standardized evaluation matrix is constructed according to TOPSIS [22]. Everyone’s boundary definition of fuzzy things is not the same, which shows that it is necessary for us to maintain a certain degree of subjectivity when solving fuzzy problems. Even for the treatment of objective things, ambiguity is applicable. The model determines the maximum and minimum values of each indicator of the ideal solution and negative ideal solution evaluation object based on the maximum value, by the calculation of the Euclidean distance, and then uses the calculated distance to observe the relative fit progress and based on the relative fit. The higher the ranking, the lower the risk, and vice versa, the higher the risk.The study object is analyzed for its relevant indicators, and data information is derived for the original matrix dataM with the following formula.
(2)Mijm×n=p11p21⋯pm1p12p22⋯pm2⋯⋯⋯⋯p1np2n⋯pmn.The authorized matrixPijm×n is obtained after doing uniform standard processing on the matrix of the study subject, and the formula is as follows.
(3)Pijm×n=p11p21⋯pm1p12p22⋯pm2⋯⋯⋯⋯p1np2n⋯pmn.The entropy value of each index has been determined by applying the entropy weighting formula to calculate the respective entropy value.(4)ej=−k∑i−1npiln1pi.The weights calculated by entropy weighting are combined with TOPSIS to form a weighted normalization matrixV, which is calculated for each indicator with different levels of importance in the risk assessment.
(5)V=XiPijmn,(6)Vijmn=X1p11X1p21⋯X1pm1X2p12X2p22⋯X2pm2⋯⋯⋯⋯Xmp1nXmp2n⋯Xmpmn.When the factor setU contains a lot of data, the relative membership degree weight coefficient will be smaller when the weight vector sum is 1. The incomplete consideration of the index information of each evaluation object in the fuzzy comprehensive evaluation process will also affect the evaluation result distinction. The risk evaluation index system should involve the internal and external risks that may be faced in the operation of green finance funds, but there are many risks faced, and it is difficult to highlight the focus of prevention and control if all the risks are included, so the risk evaluation index system should be designed by selecting representative indicators according to the current situation of risks, while avoiding the mutual influence of each indicator, taking into account the focus and comprehensiveness, so that the risk evaluation index system will have practicality and guidance. The system is shown in Figure 1.Figure 1
Information entropy fuzzy hierarchical analysis model.Intelligent technology [23–25] is one of the main tools to achieve risk evaluation and risk management. Quantitative analysis can visually measure the risk level, but the quality of data required for quantitative analysis directly affects the credibility of risk evaluation, and it is difficult to achieve quantitative evaluation if there is insufficient data [26]. Qualitative analysis, on the other hand, is an empirical judgment of indicators, which can be used as a reference for quantitative analysis, but care should be taken to avoid the influence of its subjectivity as much as possible. Corporate loans have grown significantly. The balance of green project loans accounted for a slight decline in company’s loan balance, but remained at about 10%. The environmental impact assessment rate of loan projects was 100%. Therefore, the process of green financial capital operation risk evaluation should combine the advantages of quantitative and qualitative evaluation to reduce the influence brought by insufficient data and strong subjectivity.A fuzzy concept means that the boundary of this concept has uncertainty, and it can also be said that its boundary is unclear and vague. Ambiguity is an objective property of things, and the uncertainty of the state of the event itself, i.e., it is difficult to determine whether the object of study meets the requirements of a certain concept. It should be noted that when people perceive vagueness, it is subjective, which means that everyone does not have the same boundary limits for vague things, which means that it is also necessary for us to maintain some subjectivity when solving vague problems. Even for the treatment of objective things, vagueness is applicable.Fuzzy mathematics is a theoretical approach to deal with fuzzy phenomena, and its research is about the uncertainty of things. It replaces classical sets with fuzzy sets and thus extends the concepts in classical mathematics so that mathematical methods can be used not only to solve exact problems but also to play an important role in fuzzy areas. Since fuzzy concepts can be described utilizing fuzzy sets, fuzzy mathematical methods can also be used to solve problems when applying these concepts for judgment, evaluation, reasoning, decision-making, and control, such as fuzzy cluster analysis, fuzzy pattern recognition, fuzzy comprehensive judgment, fuzzy decision-making, fuzzy prediction, fuzzy control, and fuzzy information processing. The fuzzy comprehensive evaluation method used in this paper is based on fuzzy mathematics, and through the quantitative analysis of fuzzy risk factors, the risk level of the evaluated object is comprehensively evaluated from the perspective of multiple indicators.The basic principle of the fuzzy comprehensive evaluation method is to first determine the set of factors and evaluation set of the evaluated object, and then use a suitable method to calculate the weights of each factor separately, and establish a fuzzy relationship matrix by the determined affiliation degree, and then, synthesize and normalize the fuzzy relationship matrix and the weight vector by the fuzzy operator to get the comprehensive evaluation results. The overall framework of the fuzzy comprehensive evaluation method is shown in Figure2.Figure 2
The overall framework of the fuzzy integrated judging method.For multiple evaluated objects, the weighted average method can sort them based on their rank positions. If the elements in the rubric set V are not quantified, eachvj needs to be quantified first when the weighted average method is used. After obtaining the fuzzy comprehensive evaluation results, to make full use of the information contained in B, the evaluation results and the rubric set can be considered together to make the evaluation results more realistic, i.e., the corresponding evaluation grade can be determined by calculating the comprehensive score of each evaluation object.
(7)F=B2VT.When there are more influencing factors involved in the judging object, it is difficult to determine the allocation of weights reasonably, which requires the use of multilevel fuzzy comprehensive judging. The results of the first-level fuzzy evaluation are composed into a new evaluation set for the second-level or multilevel fuzzy comprehensive evaluation, and the final evaluation results are obtained by superimposing the evaluation calculations at all levels. The fuzzy comprehensive evaluation method is used for all the evaluation objects, and the evaluation objects are ranked by the comprehensive score through the data processing of the corresponding evaluation indexes. The fuzzy comprehensive evaluation is based on the status of the affiliation of multiple indicators to the evaluated thing, divides the change interval of the evaluated object, and reflects the fuzziness of the evaluation criteria and influencing factors. The fuzzy comprehensive evaluation result is ultimately a vector, which contains rich information and not only can reflect the fuzzy condition of the thing itself more accurately but also can be further processed to get reference information. In dealing with fuzzy evaluation objects, the fuzzy comprehensive evaluation method can make more scientific and reasonable, close to the actual quantitative evaluation, especially in the subjective evaluation, the evaluation process of fuzzy comprehensive evaluation method can make full use of the index data and has wide applicability. First, the relevant background, ideas, methods, indicator design, goals, and other content of this research are explained to the experts, and the experts are required to evaluate the relevant green finance policies based on the evaluation indicators and calculate them in accordance with the corresponding preset indicators. Evaluation results of various policies.There are shortcomings in the application of the fuzzy comprehensive evaluation method; first, it does not solve the problem of duplication of information caused by the relevant evaluation indexes, and it is more subjective in the determination of the index weight vector. In addition, when the factor setU contains more data, the relative affiliation coefficient will be small when the weight vector sum is 1. The incomplete consideration of the information of each evaluation object in the fuzzy comprehensive evaluation process will also affect the differentiation of the evaluation results. The fuzzy comprehensive evaluation method provides a new mathematical tool to solve the problem of multi-indicator evaluation and decision-making in a fuzzy environment, and the reasonable determination of the weights of each indicator is an important step to get reliable evaluation results. The weight is a quantitative value that compares and weighs the relative importance of each factor in the overall evaluation. It can be done through expert questionnaires and other simple and easy ways. In addition, the meaning of the evaluation index must be accurate, the data sorting must be standardized, and there must be corresponding quantitative methods available. Indicator weights can be calculated by dividing multiple levels, and to reflect the importance of each indicator in the established evaluation index system, different weight coefficients must be assigned to each indicator. For the same set of indicator values in the comprehensive evaluation process, different weights can lead to completely different or even opposite evaluation conclusions.


### 3.2. Green Investment Risk Evaluation Index System Construction

In the green finance business, I implement all green finance requirements in all aspects of due diligence, review, and approval, and postaward inspection, and establish a long-term mechanism for green finance [27]. I focus on supporting the development of industries such as the environmental protection industry and clean energy generation and include the environmental protection industry in the active support category, insisting on a preferential selection of regions, active entry, control of maturity, and locking of loan repayment. Large-scale lending in projects such as green transportation projects, building energy-saving, and green building and industrial energy-saving and water-saving, and environmental protection with good development prospect at present and the loan balance of green finance projects from 2015 to 2017 is outlined as shown in Figure 3.Figure 3
Green project balance.Against the backdrop of intensifying competition in the banking industry, the balance of green project loans grew year by year with positive YoY growth rates. Due to rapid business development, company’s loans grew significantly, and the balance of green project loans accounted for a slight decrease in company’s loan balance, but remained at around 10%, with a loan project EIA rate of 100%. While formulating credit policies to support the development of the environmental protection industry, I continue to innovate the green financial service system and take the lead in developing green financial services such as CDM financial advisory business, international carbon finance collateral business, contract energy management factoring financing, future income rights pledge financing, building energy efficiency financing products, offshore escrow of certified emissions, carbon bonds, and domestic certified voluntary emission reduction pledge financing loans, and other green financial services for specific. To carry out effective risk response strategies to the risks existing in the project development process, formulate practical and reasonable risk response strategies. I adopt different financial services and mortgages for specific projects to help environmentally friendly enterprises obtain financial support through various financing channels, reduce financing costs, and at the same time diversify operational risks and reduce the probability of losses.Loans in high energy-consuming and high-polluting industries are mainly concentrated in chemical raw materials and chemical products manufacturing and ferrous metal smelting and rolling processing industries, which together account for a three-year average of 70.68% of loans in high energy-consuming and high-polluting industries, and the overall loan stock in high energy-consuming and high-polluting industries has not decreased significantly, but with the significant increase in company’s loan balance, the loan stock in high energy-consuming and high-polluting industries account for a significant declined. Loans in overcapacity industries were mainly concentrated in the iron and steel industry and the cement industry, which together accounted for 78.99% of the loans in overcapacity industries on average over three years. Due to the significant increase in total loans, the proportion of loan stock in the “two high and one surplus” industries decreased by 1.13 percentage points [28]. It is necessary to strengthen the training of itself and management personnel. At the same time, it is necessary to train and assess the staff engaged in green building development and management at each stage. At the same time, some managers with relevant green building experience can be hired from outside to manage the project. The environmental risk control of loans to chemical raw materials and chemical products manufacturing, ferrous metal smelting and rolling processing industry, iron and steel industry, and cement industry is crucial to reduce the loan balance of “two high and one surplus” industries and promote industrial transformation.Government’s financial investment in energy conservation and pollution prevention and control scale and tax incentives continue to rise, but due to the lack of integration of state financial investment, mostly for short-term financial support rather than long-term investment planning, the supervision of financial investment is difficult to ensure. Many environmental projects are not profitable enough on their own, the project construction early to meet environmental standards to obtain many state subsidies and tax incentives, but high maintenance costs later, the sustainability and stability of state financial support, and tax incentives directly affect the risk of capital investment. Tolerance of industries destroys the environment seriously but contributes to tax revenue. The supervision and management of green finance still exist imperfect systems, unclear division of authority and responsibility, policy enforceability that cannot be guaranteed, and insufficient motivation of personnel to implement policies, which makes it difficult for many favorable policies of the central government to be put into practice for environmental protection. The development of polluting industries not strictly limited will further weaken the environment. The profitability of the environmental protection industry, which in turn affects the risk of capital recovery, is shown in Figure4.Figure 4
Number of green investments.The trend of the global real economy slippage is obvious, whether to achieve a turnaround is still difficult to determine, the U.S. economic growth is under great pressure, the U.S.-China trade war is still ongoing, and the intensification of the debt of many countries is the hidden risks in the international economic development. The economy is currently in a critical period of “major adjustment,” economic growth too fast decline. As a member of the financial industry, which is affected by the macroeconomic situation, the international and domestic macroeconomic situation is not optimistic, which directly leads to the reduction of business scale and project capital recovery difficulties.The design of evaluation indicators is a key part of the evaluation process, and a scientific indicator system is developed to examine whether the indicators can adequately reflect the quality of green finance policies so that the basic characteristics of the evaluation objects can be accurately reflected. Firstly, the background, idea, method, indicator design, and objective of this study are explained to the experts, and they are asked to evaluate the relevant green financial policies based on the evaluation indicators and calculate them according to the corresponding preset indicators, to obtain the evaluation results of various policies.The more indicators are not the better, but the selected influencing factors should be representative, play an important role in the evaluation process, and reflect the characteristics of the evaluation object [29]. The establishment of the indicator system is also simplified as much as possible, either by qualitative analysis of the interrelationships among the indicators or by using quantitative methods to select representative indicators to achieve the optimization of the indicator system. This can reduce the time and workload of evaluation, and the distribution of the weights of each factor is visible. The factors at the same level of the indicator system should be independent of each other, not duplicated as much as possible, and there is no causal relationship between them, and the system is hierarchical. The evaluation index system needs to be established according to the hierarchical development of the comprehensive evaluation process, and satisfactory evaluation results can be obtained. The established index system should be able to be applied to the evaluation of water conservancy projects, so the selection of indicators should be in line with objective reality and have a reliable source of data, through expert questionnaires and other easy-to-use ways. In addition, the meaning of evaluation indicators should be accurate, the data should be standardized, and in addition, the corresponding quantification methods can be used.


## 3.1. Information Entropy Fuzzy Hierarchical Analysis Model Design

Mathematicians were the first to publish the concept of information entropy [19, 20], laying a solid foundation for quantitative information analysis, providing a detailed explanation of entropy theory, constructing the basic model, and writing it into its mathematical theory of propagation [21]. The concept of thermal entropy in physics is an index used to measure the disorder of molecular states. Shannon adopted the concept of information entropy to perfectly explain the uncertain information source, and the amount of information entropy is used to determine the amount of information. The specific definition of entropy is as follows: let a discrete random variable X=x1,x2,⋯,xn. The information source included given appears with pi=pxi probability and ∑i−1npi=2, then the information entropy of X can be expressed by the formula.
(1)Hx=∑i−1npiln1pi.By calculating the information entropy, the size of the information can be known, and the original information can also be reflected objectively. There is an inverse proportional relationship between information entropy and the amount of information; the greater the information entropy, the smaller the information of the index, and the smaller the role produced by the comprehensive evaluation, and vice versa, which shows the important significance of information entropy in the evaluation. Similarly, the smaller the information entropy, the greater the amount of information provided by the index, and the greater its contribution to the comprehensive evaluation results. Therefore, the weight of indexes in the comprehensive evaluation results is larger.In this paper, the risk evaluation matrix of green building developers is constructed by selecting the secondary indicators of each evaluation object from four aspects: political risk, economic risk, social risk, and technical risk according to the actual cases, conducting a comprehensive scientific analysis of the detected raw data, further processing the model criteria, carrying out entropy values and entropy weights for different indicators, and determining the degree of influence of each indicator on the risk evaluation. Then, a weighted standardized evaluation matrix is constructed according to TOPSIS [22]. Everyone’s boundary definition of fuzzy things is not the same, which shows that it is necessary for us to maintain a certain degree of subjectivity when solving fuzzy problems. Even for the treatment of objective things, ambiguity is applicable. The model determines the maximum and minimum values of each indicator of the ideal solution and negative ideal solution evaluation object based on the maximum value, by the calculation of the Euclidean distance, and then uses the calculated distance to observe the relative fit progress and based on the relative fit. The higher the ranking, the lower the risk, and vice versa, the higher the risk.The study object is analyzed for its relevant indicators, and data information is derived for the original matrix dataM with the following formula.
(2)Mijm×n=p11p21⋯pm1p12p22⋯pm2⋯⋯⋯⋯p1np2n⋯pmn.The authorized matrixPijm×n is obtained after doing uniform standard processing on the matrix of the study subject, and the formula is as follows.
(3)Pijm×n=p11p21⋯pm1p12p22⋯pm2⋯⋯⋯⋯p1np2n⋯pmn.The entropy value of each index has been determined by applying the entropy weighting formula to calculate the respective entropy value.(4)ej=−k∑i−1npiln1pi.The weights calculated by entropy weighting are combined with TOPSIS to form a weighted normalization matrixV, which is calculated for each indicator with different levels of importance in the risk assessment.
(5)V=XiPijmn,(6)Vijmn=X1p11X1p21⋯X1pm1X2p12X2p22⋯X2pm2⋯⋯⋯⋯Xmp1nXmp2n⋯Xmpmn.When the factor setU contains a lot of data, the relative membership degree weight coefficient will be smaller when the weight vector sum is 1. The incomplete consideration of the index information of each evaluation object in the fuzzy comprehensive evaluation process will also affect the evaluation result distinction. The risk evaluation index system should involve the internal and external risks that may be faced in the operation of green finance funds, but there are many risks faced, and it is difficult to highlight the focus of prevention and control if all the risks are included, so the risk evaluation index system should be designed by selecting representative indicators according to the current situation of risks, while avoiding the mutual influence of each indicator, taking into account the focus and comprehensiveness, so that the risk evaluation index system will have practicality and guidance. The system is shown in Figure 1.Figure 1
Information entropy fuzzy hierarchical analysis model.Intelligent technology [23–25] is one of the main tools to achieve risk evaluation and risk management. Quantitative analysis can visually measure the risk level, but the quality of data required for quantitative analysis directly affects the credibility of risk evaluation, and it is difficult to achieve quantitative evaluation if there is insufficient data [26]. Qualitative analysis, on the other hand, is an empirical judgment of indicators, which can be used as a reference for quantitative analysis, but care should be taken to avoid the influence of its subjectivity as much as possible. Corporate loans have grown significantly. The balance of green project loans accounted for a slight decline in company’s loan balance, but remained at about 10%. The environmental impact assessment rate of loan projects was 100%. Therefore, the process of green financial capital operation risk evaluation should combine the advantages of quantitative and qualitative evaluation to reduce the influence brought by insufficient data and strong subjectivity.A fuzzy concept means that the boundary of this concept has uncertainty, and it can also be said that its boundary is unclear and vague. Ambiguity is an objective property of things, and the uncertainty of the state of the event itself, i.e., it is difficult to determine whether the object of study meets the requirements of a certain concept. It should be noted that when people perceive vagueness, it is subjective, which means that everyone does not have the same boundary limits for vague things, which means that it is also necessary for us to maintain some subjectivity when solving vague problems. Even for the treatment of objective things, vagueness is applicable.Fuzzy mathematics is a theoretical approach to deal with fuzzy phenomena, and its research is about the uncertainty of things. It replaces classical sets with fuzzy sets and thus extends the concepts in classical mathematics so that mathematical methods can be used not only to solve exact problems but also to play an important role in fuzzy areas. Since fuzzy concepts can be described utilizing fuzzy sets, fuzzy mathematical methods can also be used to solve problems when applying these concepts for judgment, evaluation, reasoning, decision-making, and control, such as fuzzy cluster analysis, fuzzy pattern recognition, fuzzy comprehensive judgment, fuzzy decision-making, fuzzy prediction, fuzzy control, and fuzzy information processing. The fuzzy comprehensive evaluation method used in this paper is based on fuzzy mathematics, and through the quantitative analysis of fuzzy risk factors, the risk level of the evaluated object is comprehensively evaluated from the perspective of multiple indicators.The basic principle of the fuzzy comprehensive evaluation method is to first determine the set of factors and evaluation set of the evaluated object, and then use a suitable method to calculate the weights of each factor separately, and establish a fuzzy relationship matrix by the determined affiliation degree, and then, synthesize and normalize the fuzzy relationship matrix and the weight vector by the fuzzy operator to get the comprehensive evaluation results. The overall framework of the fuzzy comprehensive evaluation method is shown in Figure2.Figure 2
The overall framework of the fuzzy integrated judging method.For multiple evaluated objects, the weighted average method can sort them based on their rank positions. If the elements in the rubric set V are not quantified, eachvj needs to be quantified first when the weighted average method is used. After obtaining the fuzzy comprehensive evaluation results, to make full use of the information contained in B, the evaluation results and the rubric set can be considered together to make the evaluation results more realistic, i.e., the corresponding evaluation grade can be determined by calculating the comprehensive score of each evaluation object.
(7)F=B2VT.When there are more influencing factors involved in the judging object, it is difficult to determine the allocation of weights reasonably, which requires the use of multilevel fuzzy comprehensive judging. The results of the first-level fuzzy evaluation are composed into a new evaluation set for the second-level or multilevel fuzzy comprehensive evaluation, and the final evaluation results are obtained by superimposing the evaluation calculations at all levels. The fuzzy comprehensive evaluation method is used for all the evaluation objects, and the evaluation objects are ranked by the comprehensive score through the data processing of the corresponding evaluation indexes. The fuzzy comprehensive evaluation is based on the status of the affiliation of multiple indicators to the evaluated thing, divides the change interval of the evaluated object, and reflects the fuzziness of the evaluation criteria and influencing factors. The fuzzy comprehensive evaluation result is ultimately a vector, which contains rich information and not only can reflect the fuzzy condition of the thing itself more accurately but also can be further processed to get reference information. In dealing with fuzzy evaluation objects, the fuzzy comprehensive evaluation method can make more scientific and reasonable, close to the actual quantitative evaluation, especially in the subjective evaluation, the evaluation process of fuzzy comprehensive evaluation method can make full use of the index data and has wide applicability. First, the relevant background, ideas, methods, indicator design, goals, and other content of this research are explained to the experts, and the experts are required to evaluate the relevant green finance policies based on the evaluation indicators and calculate them in accordance with the corresponding preset indicators. Evaluation results of various policies.There are shortcomings in the application of the fuzzy comprehensive evaluation method; first, it does not solve the problem of duplication of information caused by the relevant evaluation indexes, and it is more subjective in the determination of the index weight vector. In addition, when the factor setU contains more data, the relative affiliation coefficient will be small when the weight vector sum is 1. The incomplete consideration of the information of each evaluation object in the fuzzy comprehensive evaluation process will also affect the differentiation of the evaluation results. The fuzzy comprehensive evaluation method provides a new mathematical tool to solve the problem of multi-indicator evaluation and decision-making in a fuzzy environment, and the reasonable determination of the weights of each indicator is an important step to get reliable evaluation results. The weight is a quantitative value that compares and weighs the relative importance of each factor in the overall evaluation. It can be done through expert questionnaires and other simple and easy ways. In addition, the meaning of the evaluation index must be accurate, the data sorting must be standardized, and there must be corresponding quantitative methods available. Indicator weights can be calculated by dividing multiple levels, and to reflect the importance of each indicator in the established evaluation index system, different weight coefficients must be assigned to each indicator. For the same set of indicator values in the comprehensive evaluation process, different weights can lead to completely different or even opposite evaluation conclusions.


## 3.2. Green Investment Risk Evaluation Index System Construction

In the green finance business, I implement all green finance requirements in all aspects of due diligence, review, and approval, and postaward inspection, and establish a long-term mechanism for green finance [27]. I focus on supporting the development of industries such as the environmental protection industry and clean energy generation and include the environmental protection industry in the active support category, insisting on a preferential selection of regions, active entry, control of maturity, and locking of loan repayment. Large-scale lending in projects such as green transportation projects, building energy-saving, and green building and industrial energy-saving and water-saving, and environmental protection with good development prospect at present and the loan balance of green finance projects from 2015 to 2017 is outlined as shown in Figure 3.Figure 3
Green project balance.Against the backdrop of intensifying competition in the banking industry, the balance of green project loans grew year by year with positive YoY growth rates. Due to rapid business development, company’s loans grew significantly, and the balance of green project loans accounted for a slight decrease in company’s loan balance, but remained at around 10%, with a loan project EIA rate of 100%. While formulating credit policies to support the development of the environmental protection industry, I continue to innovate the green financial service system and take the lead in developing green financial services such as CDM financial advisory business, international carbon finance collateral business, contract energy management factoring financing, future income rights pledge financing, building energy efficiency financing products, offshore escrow of certified emissions, carbon bonds, and domestic certified voluntary emission reduction pledge financing loans, and other green financial services for specific. To carry out effective risk response strategies to the risks existing in the project development process, formulate practical and reasonable risk response strategies. I adopt different financial services and mortgages for specific projects to help environmentally friendly enterprises obtain financial support through various financing channels, reduce financing costs, and at the same time diversify operational risks and reduce the probability of losses.Loans in high energy-consuming and high-polluting industries are mainly concentrated in chemical raw materials and chemical products manufacturing and ferrous metal smelting and rolling processing industries, which together account for a three-year average of 70.68% of loans in high energy-consuming and high-polluting industries, and the overall loan stock in high energy-consuming and high-polluting industries has not decreased significantly, but with the significant increase in company’s loan balance, the loan stock in high energy-consuming and high-polluting industries account for a significant declined. Loans in overcapacity industries were mainly concentrated in the iron and steel industry and the cement industry, which together accounted for 78.99% of the loans in overcapacity industries on average over three years. Due to the significant increase in total loans, the proportion of loan stock in the “two high and one surplus” industries decreased by 1.13 percentage points [28]. It is necessary to strengthen the training of itself and management personnel. At the same time, it is necessary to train and assess the staff engaged in green building development and management at each stage. At the same time, some managers with relevant green building experience can be hired from outside to manage the project. The environmental risk control of loans to chemical raw materials and chemical products manufacturing, ferrous metal smelting and rolling processing industry, iron and steel industry, and cement industry is crucial to reduce the loan balance of “two high and one surplus” industries and promote industrial transformation.Government’s financial investment in energy conservation and pollution prevention and control scale and tax incentives continue to rise, but due to the lack of integration of state financial investment, mostly for short-term financial support rather than long-term investment planning, the supervision of financial investment is difficult to ensure. Many environmental projects are not profitable enough on their own, the project construction early to meet environmental standards to obtain many state subsidies and tax incentives, but high maintenance costs later, the sustainability and stability of state financial support, and tax incentives directly affect the risk of capital investment. Tolerance of industries destroys the environment seriously but contributes to tax revenue. The supervision and management of green finance still exist imperfect systems, unclear division of authority and responsibility, policy enforceability that cannot be guaranteed, and insufficient motivation of personnel to implement policies, which makes it difficult for many favorable policies of the central government to be put into practice for environmental protection. The development of polluting industries not strictly limited will further weaken the environment. The profitability of the environmental protection industry, which in turn affects the risk of capital recovery, is shown in Figure4.Figure 4
Number of green investments.The trend of the global real economy slippage is obvious, whether to achieve a turnaround is still difficult to determine, the U.S. economic growth is under great pressure, the U.S.-China trade war is still ongoing, and the intensification of the debt of many countries is the hidden risks in the international economic development. The economy is currently in a critical period of “major adjustment,” economic growth too fast decline. As a member of the financial industry, which is affected by the macroeconomic situation, the international and domestic macroeconomic situation is not optimistic, which directly leads to the reduction of business scale and project capital recovery difficulties.The design of evaluation indicators is a key part of the evaluation process, and a scientific indicator system is developed to examine whether the indicators can adequately reflect the quality of green finance policies so that the basic characteristics of the evaluation objects can be accurately reflected. Firstly, the background, idea, method, indicator design, and objective of this study are explained to the experts, and they are asked to evaluate the relevant green financial policies based on the evaluation indicators and calculate them according to the corresponding preset indicators, to obtain the evaluation results of various policies.The more indicators are not the better, but the selected influencing factors should be representative, play an important role in the evaluation process, and reflect the characteristics of the evaluation object [29]. The establishment of the indicator system is also simplified as much as possible, either by qualitative analysis of the interrelationships among the indicators or by using quantitative methods to select representative indicators to achieve the optimization of the indicator system. This can reduce the time and workload of evaluation, and the distribution of the weights of each factor is visible. The factors at the same level of the indicator system should be independent of each other, not duplicated as much as possible, and there is no causal relationship between them, and the system is hierarchical. The evaluation index system needs to be established according to the hierarchical development of the comprehensive evaluation process, and satisfactory evaluation results can be obtained. The established index system should be able to be applied to the evaluation of water conservancy projects, so the selection of indicators should be in line with objective reality and have a reliable source of data, through expert questionnaires and other easy-to-use ways. In addition, the meaning of evaluation indicators should be accurate, the data should be standardized, and in addition, the corresponding quantification methods can be used.


## 4. Analysis of Results

### 4.1. Model Performance Results

The raw data were obtained by interviewing experts and scoring risk factors, and the data were first normalized. A total of 30 questionnaires were distributed through electronic questionnaires to project managers and interviews with experts in this area of green building, and all responses were received, of which 28 were valid. To complete the development of green building projects, the risks of the projects need to be controlled effectively. To make an effective risk response strategy for the risks existing in the project development process, a practical and reasonable risk response strategy is formulated. However, for the developer of the green building project, the resources, time, and human resources needed in the project process are limited; to the effect of limited resources, the problems that will be encountered in the whole development process, and the risks faced, these factors that appear should be analyzed specifically, analyze the size of the impact of the risk on the development process, and at the same time, combined with the entropy right and TOPSIS in the project. At the same time, the top three risk factors in the project are selected as the key assessment objects, and effective prevention and management methods are given to the risk factors to better deal with the risks. The top three risk factors were selected from four aspects and summarized in a table (as shown in Figure5).Figure 5
Risk weighting.At present, the visibility of green buildings has also achieved a certain status in the construction market, but for the public, there is also no better understanding of the green building. One of the key issues in the green building field is how to get the public to properly recognize and accept it. At present, as the green building industry in China is still in its initial stage, the development is not mature enough, and the market is not perfect. The public subconsciously believes that green building is high-grade building, and there are some misunderstandings about the cost of green building. To improve public acceptance, the public needs to have a correct understanding of green building; first, the W project development department of company B can obtain public’s concerns and questions about green building through the social questionnaire. According to the ranking of political risk factors, the relative closeness of imperfect laws and regulations of green building is 0.8152.Then, I can use the huge advantage of the Internet to design an APP to release the data related to development and operation on the Internet so that the public can have a better understanding of the whole process of green building and at the same time cooperate with relevant media to promote the connotation of green building and green building-related products, and establish our brand to gain public recognition. In the green building industry, since developers do not have enough management experience, if they want to engage in the development of green building projects and succeed, they need to strengthen the training of themselves and their managers, and at the same time, they need to train and assess the staff engaged in the development and management of green building at each stage, and at the same time, they can hire some managers from outside who have relevant green building experience to manage the projects (as shown in Figure6).Figure 6
Mutation detection results.The actual green building project as a case, according to the risk list, is in line with the actual situation; using the expert interview method, the risk index system is in line with the risk list of the project, according to the indicators of the risk list issued questionnaires to evaluate the 18 risk indicators, political risk as an example, and according to the entropy weight method and TOPSIS combined with the evaluation, the ranking of political risk factors. The relative proximity of imperfect laws and regulations of green building is 0.8152, the proximity of approval procedure and efficiency of administrative departments to the project implementation is 0.7798, and the proximity of corrupt behavior and bureaucracy of the government is 0.5326. The top three risk coping strategies are given, considering the policies of city A and the actual operation mode of city B. On the premise of obtaining the evaluation result vector Bui of each level index, the preliminary judgment on the risk level of the construction period is made from different perspectives according to the principle of maximum subordination.


### 4.2. Indicator Evaluation Results

The results of the experts’ scoring of the secondary indicators in the external risk were aggregated, and the weights were calculated as shown in Figure7.Figure 7
Indicator weights.As a business operating in money, the macroeconomic growth rate has a clear impact on the number of customers and the scale of business of a single customer, while the growth prospects and profit margins of customers affect the most critical repayment factor—the level of cash flow. When the macroeconomic trend is good and stable, they usually face lower external risks; thus, they will lower the customer entry threshold and increase the scale of financial support to earn spreads on a large scale, benefiting from both benefits and risks; once the economic growth rate drops steeply, the large-scale financial support originally provided with lowered standards in a good economic situation will face serious credit losses. Thus, economic growth rate and stability have a huge impact on the risk of green financial fund operation.The proportion of “two high and one leftover” industries in the industrial structure affects the exit speed. Due to the profit-oriented, the huge market scale of “two high and one leftover” industries in China makes it difficult to withdraw from the market quickly. Although the head office will issue a certain stock exit target and incremental scale limit every year, subordinate branches will usually meet the assessment requirements by posting lines to meet the target or manipulating statements, etc. Compared with the huge base, the exit speed is slow. A large proportion of the environmental industry in the industrial structure has a positive effect on green finance business, but the development within the environmental industry is seriously unbalanced, and the immaturity of some green projects leads to small market size and poor project profitability, which in turn affects the enthusiasm for financial support.However, with huge risk exposure and insignificant profitability, it is difficult to guarantee the initiative to develop green finance. Therefore, the establishment of a sound risk mechanism for green finance has a significant impact on improving returns, reducing risks, and thus improving the initiative to develop green finance; as the risk characteristics of green finance projects are significantly different from those of traditional business, the government is gradually building a regulatory system adapted to the characteristics of green finance. It is bound to face serious regulatory requirements.The final scores of each level of indicators can also be represented more visually by creating bar charts (as shown in Figure8). Due to the strong policy relevance and high technical content of the green finance business, it is difficult for nonspecialized departments and personnel to control the risk and nodes of green projects in the whole process of operation. Although the separation of incompatible positions and authorization and approval system is set in the business process, branches at all levels may lower the entry and risk review standards of the green finance business and neglect the implementation of the exit policy of the “two high and one leftover” industries out of the consideration of customer relationship and performance, making the green finance process a mere formality. Although the whole process of business informatization has been realized, key information and approval still require manual intervention, and business and management personnel at all levels may help customers “greenwash” or weaken pollution data to obtain financial services out of their interests or under pressure from superiors.Figure 8
Risk indicator scoring chart.Any problems and risks faced in the operation of green finance funds will affect reputation risk to varying degrees, and the public stakeholders are numerous, so reputation risk in the operation of green finance funds cannot be ignored. Green financial brand influence, social responsibility, and green financial publicity have a positive impact on reputation risk. The comprehensive service strength in the field of green finance determines brand influence, while the development of green finance is one aspect of its social responsibility, and green financial publicity affects the ability to promote green financial services. Strengthening all other aspects of risk management is also a part of reputation risk management. It is difficult for nonprofessional departments and personnel to control the risks and nodes of the whole process of green projects in the operation process. Although the separation of incompatible duties and authorization and approval systems are set up in the business process, branches at all levels may be out of consideration for maintaining customer relationships and performance.The internal risk of green financial operation risk is 4.26, which is between very likely and very likely, which is consistent with the late start of green financial business, insufficient development enthusiasm, and imperfect national policy and regulatory system in China. The overall evaluation of external risk in green financial fund operation risk is 4.05, which is between very likely and very likely, which is consistent with the current situation that green financial development is strongly policy-oriented, and commercial banks are influenced by economic and market risks. To cope with liquidity risk, it is necessary to enrich the financing channels of the green finance business and increase the proportion of specialized green finance financing channels in the investment. To cope with strategic risks, it is necessary to formulate strategic plans in line with the objective environment, supervise the implementation of the strategies, and ensure the consistency of the strategies of the head office and branches. To deal with operational risks, it is necessary to set up a dedicated institution for green finance, allocate professional staff, and improve employees’ risk awareness. To deal with reputation risk, we should actively assume social responsibility and promote the concept of green finance through various media channels, thereby improving social influence.


## 4.1. Model Performance Results

The raw data were obtained by interviewing experts and scoring risk factors, and the data were first normalized. A total of 30 questionnaires were distributed through electronic questionnaires to project managers and interviews with experts in this area of green building, and all responses were received, of which 28 were valid. To complete the development of green building projects, the risks of the projects need to be controlled effectively. To make an effective risk response strategy for the risks existing in the project development process, a practical and reasonable risk response strategy is formulated. However, for the developer of the green building project, the resources, time, and human resources needed in the project process are limited; to the effect of limited resources, the problems that will be encountered in the whole development process, and the risks faced, these factors that appear should be analyzed specifically, analyze the size of the impact of the risk on the development process, and at the same time, combined with the entropy right and TOPSIS in the project. At the same time, the top three risk factors in the project are selected as the key assessment objects, and effective prevention and management methods are given to the risk factors to better deal with the risks. The top three risk factors were selected from four aspects and summarized in a table (as shown in Figure5).Figure 5
Risk weighting.At present, the visibility of green buildings has also achieved a certain status in the construction market, but for the public, there is also no better understanding of the green building. One of the key issues in the green building field is how to get the public to properly recognize and accept it. At present, as the green building industry in China is still in its initial stage, the development is not mature enough, and the market is not perfect. The public subconsciously believes that green building is high-grade building, and there are some misunderstandings about the cost of green building. To improve public acceptance, the public needs to have a correct understanding of green building; first, the W project development department of company B can obtain public’s concerns and questions about green building through the social questionnaire. According to the ranking of political risk factors, the relative closeness of imperfect laws and regulations of green building is 0.8152.Then, I can use the huge advantage of the Internet to design an APP to release the data related to development and operation on the Internet so that the public can have a better understanding of the whole process of green building and at the same time cooperate with relevant media to promote the connotation of green building and green building-related products, and establish our brand to gain public recognition. In the green building industry, since developers do not have enough management experience, if they want to engage in the development of green building projects and succeed, they need to strengthen the training of themselves and their managers, and at the same time, they need to train and assess the staff engaged in the development and management of green building at each stage, and at the same time, they can hire some managers from outside who have relevant green building experience to manage the projects (as shown in Figure6).Figure 6
Mutation detection results.The actual green building project as a case, according to the risk list, is in line with the actual situation; using the expert interview method, the risk index system is in line with the risk list of the project, according to the indicators of the risk list issued questionnaires to evaluate the 18 risk indicators, political risk as an example, and according to the entropy weight method and TOPSIS combined with the evaluation, the ranking of political risk factors. The relative proximity of imperfect laws and regulations of green building is 0.8152, the proximity of approval procedure and efficiency of administrative departments to the project implementation is 0.7798, and the proximity of corrupt behavior and bureaucracy of the government is 0.5326. The top three risk coping strategies are given, considering the policies of city A and the actual operation mode of city B. On the premise of obtaining the evaluation result vector Bui of each level index, the preliminary judgment on the risk level of the construction period is made from different perspectives according to the principle of maximum subordination.


## 4.2. Indicator Evaluation Results

The results of the experts’ scoring of the secondary indicators in the external risk were aggregated, and the weights were calculated as shown in Figure7.Figure 7
Indicator weights.As a business operating in money, the macroeconomic growth rate has a clear impact on the number of customers and the scale of business of a single customer, while the growth prospects and profit margins of customers affect the most critical repayment factor—the level of cash flow. When the macroeconomic trend is good and stable, they usually face lower external risks; thus, they will lower the customer entry threshold and increase the scale of financial support to earn spreads on a large scale, benefiting from both benefits and risks; once the economic growth rate drops steeply, the large-scale financial support originally provided with lowered standards in a good economic situation will face serious credit losses. Thus, economic growth rate and stability have a huge impact on the risk of green financial fund operation.The proportion of “two high and one leftover” industries in the industrial structure affects the exit speed. Due to the profit-oriented, the huge market scale of “two high and one leftover” industries in China makes it difficult to withdraw from the market quickly. Although the head office will issue a certain stock exit target and incremental scale limit every year, subordinate branches will usually meet the assessment requirements by posting lines to meet the target or manipulating statements, etc. Compared with the huge base, the exit speed is slow. A large proportion of the environmental industry in the industrial structure has a positive effect on green finance business, but the development within the environmental industry is seriously unbalanced, and the immaturity of some green projects leads to small market size and poor project profitability, which in turn affects the enthusiasm for financial support.However, with huge risk exposure and insignificant profitability, it is difficult to guarantee the initiative to develop green finance. Therefore, the establishment of a sound risk mechanism for green finance has a significant impact on improving returns, reducing risks, and thus improving the initiative to develop green finance; as the risk characteristics of green finance projects are significantly different from those of traditional business, the government is gradually building a regulatory system adapted to the characteristics of green finance. It is bound to face serious regulatory requirements.The final scores of each level of indicators can also be represented more visually by creating bar charts (as shown in Figure8). Due to the strong policy relevance and high technical content of the green finance business, it is difficult for nonspecialized departments and personnel to control the risk and nodes of green projects in the whole process of operation. Although the separation of incompatible positions and authorization and approval system is set in the business process, branches at all levels may lower the entry and risk review standards of the green finance business and neglect the implementation of the exit policy of the “two high and one leftover” industries out of the consideration of customer relationship and performance, making the green finance process a mere formality. Although the whole process of business informatization has been realized, key information and approval still require manual intervention, and business and management personnel at all levels may help customers “greenwash” or weaken pollution data to obtain financial services out of their interests or under pressure from superiors.Figure 8
Risk indicator scoring chart.Any problems and risks faced in the operation of green finance funds will affect reputation risk to varying degrees, and the public stakeholders are numerous, so reputation risk in the operation of green finance funds cannot be ignored. Green financial brand influence, social responsibility, and green financial publicity have a positive impact on reputation risk. The comprehensive service strength in the field of green finance determines brand influence, while the development of green finance is one aspect of its social responsibility, and green financial publicity affects the ability to promote green financial services. Strengthening all other aspects of risk management is also a part of reputation risk management. It is difficult for nonprofessional departments and personnel to control the risks and nodes of the whole process of green projects in the operation process. Although the separation of incompatible duties and authorization and approval systems are set up in the business process, branches at all levels may be out of consideration for maintaining customer relationships and performance.The internal risk of green financial operation risk is 4.26, which is between very likely and very likely, which is consistent with the late start of green financial business, insufficient development enthusiasm, and imperfect national policy and regulatory system in China. The overall evaluation of external risk in green financial fund operation risk is 4.05, which is between very likely and very likely, which is consistent with the current situation that green financial development is strongly policy-oriented, and commercial banks are influenced by economic and market risks. To cope with liquidity risk, it is necessary to enrich the financing channels of the green finance business and increase the proportion of specialized green finance financing channels in the investment. To cope with strategic risks, it is necessary to formulate strategic plans in line with the objective environment, supervise the implementation of the strategies, and ensure the consistency of the strategies of the head office and branches. To deal with operational risks, it is necessary to set up a dedicated institution for green finance, allocate professional staff, and improve employees’ risk awareness. To deal with reputation risk, we should actively assume social responsibility and promote the concept of green finance through various media channels, thereby improving social influence.


## 5. Conclusion

Based on the current situation of green finance business development, I comprehensively analyzed the main risks it may face in the process of fund operation, firstly introduced AHP-fuzzy comprehensive assessment method to rate the risks of crowdfunding investment projects, then focused on improving the traditional intuitionistic fuzzy ranking model, and proposed an interval intuitionistic fuzzy ranking method considering experts’ hesitation preference to realize the risk ranking of several crowdfunding projects. Specifically, it is divided into risk factors such as funding method, project access criteria, development speed, industry configuration, and dedicated institutional staff. A green finance fund operation risk evaluation index system is constructed. This paper conducts a comprehensive analysis through four external risk perspectives and six internal risk perspectives and finally comes up with the possibility of each risk occurring in the process of green financial fund operation and gives corresponding suggestions. The weights of secondary and primary indicators are improved by the entropy value method, which reduces the subjectivity of the fuzzy hierarchical analysis model in determining the weights of indicators and improves the reliability of the research results. The validation of the constructed evaluation system by only one case is inevitably insufficient. In the future, the evaluation of green financial capital operation risk of domestic commercial banks should take the commercial banks developing green financial business as the research object to ensure the universality of the research results.



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*Source: 4850321-2021-08-30.xml*