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Research On Green Credit Risk Under The Construction Of Liaocheng Water Ecological Civilized City

Posted on:2021-05-05Degree:MasterType:Thesis
Country:ChinaCandidate:X H FanFull Text:PDF
GTID:2491306038954629Subject:Hydraulic engineering
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Water ecological civilization city construction is an important development direction of urban construction.Commercial Banks,as the market subject of non-governmental investment,have played a great role in the construction of water ecological civilization city.However,in practice,large demand for water conservancy construction funds,long construction cycle,low operation efficiency,lack of effective risk tracking and measurement,low enthusiasm of commercial Banks in lending,and insufficient investment in water conservancy construction funds restrict the development of water ecological civilization city construction.This paper studies the credit risk control and credit strategy of water conservancy projects under the construction of water ecological civilization city by combining the characteristics of water conservancy projects and the particularity of the industry.In this paper,the construction of water ecological civilization is analyzed in detail,combined with the geography,climate and hydrology of Liaocheng city,the water environment of Liaocheng is analyzed,and the situation of key polluting enterprises in Liaocheng counties and cities is studied.It is concluded that Liaocheng has a serious shortage of water resources,the water supply is mainly surface water,the pollution sources are mainly industrial pollution and the water conservancy construction fund is insufficient.Therefore,the strategy of developing green credit for water conservancy projects is put forward to solve the problem of insufficient funds.A credit risk analysis and evaluation system for water conservancy projects was established based on the PSR model,and the operational risk,financial risk,government performance risk,technical risk and PPP normative risk existed in the construction of water conservancy projects were analyzed based on the industry characteristics.The analysis is conducted from three aspects of pressure,status and response,covering operational risk,financial risk,government performance risk,technical risk,PPP normative factors,involving 20 indicators such as water price,water infrastructure demand,project investment scale and so on.Under the framework of index system of credit risk assessment of water conservancy projects,23 similar water conservancy projects were selected for empirical study,and each index in each water conservancy project was scored by AHP method and expert scoring method.Using the BP neural network model,the first 20 groups of data were trained and simulated,and the remaining 3 groups of data were verified.The results show that the error rates of the three test samples are 6.5%,-6.2%and 2.6%,respectively.The prediction accuracy is relatively high,indicating that the BP neural network model has a good applicability to the credit risk assessment of commercial bank water conservancy projects.Taking Qingyuan water company of Linqing city as an example,this paper analyzes the borrower’s financial status,non-financial factors,compliance and guarantee conditions,USES BP neural network to calculate its credit risk,comprehensively evaluates the risk status of Chengnan reservoir construction project,and proposes targeted credit risk management measures.Taking the construction of Zhangguantun reservoir in Linqing city as an example to analyze the investment benefit of water conservancy project,the investment amount is estimated,and the national economy and financial evaluation are carried out in combination with the project fund raising plan.According to the type,region,investment amount,operation mode and other factors,the expected return on investment of water PPP projects is analyzed to determine the reasonable return on investment of water PPP projects.In terms of credit risk assessment and credit strategy,this paper puts forward credit risk management countermeasures related to water and water environment management industry.
Keywords/Search Tags:water ecological civilization city, water conservancy construction, green credit risk assessment, BP neural network
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