Construction of Financial Risk Evaluation Index System for Biomass Graphene Fibre Industrialisation Project

Liwei Zhang

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Detalles de la revista

Formato: Revista
eISSN: 2444-8656
Primera edición: 01 Jan 2016
Calendario de la edición: 2 veces al año
Idiomas: Inglés

With the rapid development of science and technology, more and more novel and functional new materials are gradually entering our real life. Graphene, which is the most popular material in recent years, exists in nature; but it is difficult to peel out the monolayer structure of graphene, layer by layer, which is graphite.A quantity of 1 mm thick graphite contains about 3 million layers of graphene. The main functions of graphene are mainly reflected in the following aspects, as shown in Table 1: Firstly, graphene has good mechanical properties [1–3], which is one of the materials with the highest known strength. At the same time, though graphene is tough, it is also flexible and can be bent. In addition, graphene has a good electronic effect [4–6], and the carrier mobility at room temperature is about 10 times that of commonly used silicon materials,which is not affected by ambient temperature. Graphene material can also be used for various purposes of heat dissipation [7–9], which can increase the heat dissipation efficiency of objects. The optical properties of graphene [10–12] show that it looks almost transparent in a wide wavelength range. Finally, graphene shows good solubility in non-polar solvents, superhydrophobicity and superlipophilicity, and can adsorb and desorb various molecules and atoms [13–15]. It claims the distinction of being the first material to be applied in commercially, such as in mobile devices, aerospace, new energy batteries [16] and other relevant fields. For industrialisation of graphene project, however, the influences of external factors are still very strong. As the most critical part of project research, finance is the basis to guarantee the stability of industrialisation. Therefore, for the emergence of graphene industry projects, it is imperative to comply with its system of risk evaluation which is crucial to help graphene industry projects to solve some of the common risks, and to provide financial support for the industrialisation of graphene to become bigger and stronger.

Table 1

Main functional characteristics of graphene

Specific function	Detailed description
Mechanical properties	High strength, but also has good toughness
Electronic effect	The carrier mobility is high at room temperature and is little affected by temperature
Thermal performance	It has very good heat conduction performance
Optical properties	Graphene films have excellent optical properties, and their optical properties change with the thickness of graphene
Solubility	It is super hydrophobic and super lipophilic, and can adsorb and desorbed various molecules and atoms

Biomass in industrialisation of grapheme; graphene material and differences and similarities with the biomass [17–19]: structurally, there is essentially no difference between graphene which is the basic element of carbon and biomass and the biomass materials which contain carbon, and if they are added with other elements and the impurities removed, graphene can be prepared. In terms of properties, graphene prepared from biomass materials and non-biomass materials has the same physical and chemical properties. However, the sources of the two materials are different. Biomass graphene is prepared from leaves and other biomass materials, while non-biomass materials are graphite ore and other biomass materials. In the preparation of grapheme, it mainly uses biomass materials such as carbon tubesfollowing the mechanical stripping method, epitaxial growth method, chemical vapour deposition method, oxidation-reduction method, and cutting method. With the use of biomass and cellulose catalysts, complex ion formation occurs, and with the deoxidisation process under high temperature, graphene precursor can be obtained. It goes through heat treatment, acid treatment, drying and so on in the process of graphene formation. In contrast, biomass graphene is cost-effective, and the materials come from a wide range of sources with high yield, suitable for industrial large-scale production. In the financial risk system of mass production, Cincinelli et al. [20] studied the granger causality between firm-level factors and systemic risk.

Bellavite Pellegrini [21] studied how the characteristics of different financial institutions are related to systemic risk. They compared traditional banks with shadow entities such as money market funds and financial services. It was found that the size of large financial institutions, especially money market funds, significantly increased systemic risk. Gao [22] studied the final boundary region of the financial risk system through numerical analysis. Based on the optimisation method and Lagrange multiplier method, the analytical expression of the limit boundary region is derived, the economic variables can be detected and observed and the financial risk is reduced. Financial risk handling is crucial for emerging enterprises. Gu [23] studied the different influences of major leaders of companies on financial risk handling, and their research shows that the overseas experience of CEOS reduces the risk of financial misconduct in Chinese enterprises. Cheng et al. [24] conducted a comprehensive evaluation and analysis of various financial risks brought by new technologies. They classified these risks in a reasonable framework. Financial risk can be effectively reduced through the use of multi-channel information, charts and long-term reliance on networks to effectively identify financial risks. The financial sector now faces new challenges, namely deep forgery, counterparties, causal and interpretable reasoning, privacy protection, and microsimulation.

According to the above analysis, financial risk is crucial for the development of the biomass graphene industry. In the emerging field of biomass, the evaluation of the financial risk system is more important. In this paper, we compare several models commonly used in financial risk research [25], choose the TOPSIS method for industrial project analysis, and use a mathematical model to analyse the financial risk of biomass graphene fibre industrialisation project. By comparing the advantages and disadvantages of each method, the method suitable for financial risk evaluation of biomass graphene fibre industrialisation project is selected. In the process of model and method evaluation, this paper will comprehensively consider the external environment of the market and the factors of the industrial project to analyse the applicability of each model to the industrial project.

The necessity of constructing the financial risk evaluation of biomass graphene fibre industrialisation project

The key to the success of the project is to evaluate the financial risk correctly and prevent it beforehand. The financial condition of the project can reflect the good condition of the project, so the risk evaluation not only evaluates the financial problems but also analyses the comprehensive condition of the project. Through the establishment of a financial risk evaluation model, financial personnel can evaluate the financial risk of the project based on historical data and the market environment. By analysing the historical financial data of industrial projects, we can get the development trend of industrial projects and the risks they face. Therefore, through the construction of a financial evaluation model, we can predict the operation and management risks of an industrial project, find the shortcomings in the operation process and implement effective measures.

2.1

Comparison of financial risk assessment methods

The author selected several models commonly used in financial risk research for comparison and selected the method suitable for financial risk evaluation of biomass graphene fibre industrialisation project by comparing the advantages and disadvantages of each method. In the process of model and method evaluation, this paper will comprehensively consider the external environment of the market and the factors of the industrial project to analyse the applicability of each model to the industrial project.

2.1.1

Z-Score model

Financial indicators are screened by mathematical statistics. The specific equation is as follows: 1 $Z = 1.2 x_{1} + 1.4 x_{2} + 3.3 x_{3} + 0.6 x_{4} + 0.999 x_{5}$ x₁ is working capital/total assets; x₂ is retained earnings/total assets; x₃ is EBIT/total assets; x₄ represents the market value of ownership/total liabilities, and finally, x₅ refers to total sales/total assets. When, Z < 1.8, it means that the company is facing the risk of bankruptcy; $1.8 \leq Z \leq 2.99$ indicates that the enterprise is facing certain financial risks; $Z > 2.99$ indicating that the financial status of the enterprise is better. This model only focuses on the measurement of profit factors and does not consider other risk dimensions. It can only figure out whether the project has the risk of failure, but cannot deeply explore the position of the project in the market and the specific aspects of the risk.

2.1.2

Logistic discriminant function method

This method evaluates the financial risks of enterprises by establishing a linear regression model and determines whether the evaluation objects have risks through the probability of event occurrence. The equation is as follows: 2 $\ln (\frac{p}{1 - p}) = a + b_{1} x_{1} + \dots b_{i} x_{i}$ $$\ln \left( {{p \over {1 - p}}} \right) = a + {b_1}{x_1} + \cdots {b_i}{x_i}$$ where p is the probability of occurrence of a certain situation; a, b_i and x_i are the constant term, slope and independent variable respectively. In this model, financial indicators have mutual influence, and the influence related to indicators cannot be excluded at present, which may result in inaccurate evaluation results.

2.1.3

Artificial neural network analysis method

This method simulates the human brain and builds an artificial network model that can ‘learn’, and uses knowledge accumulation and utilisation, thus reducing the difference between the obtained solution and the actual value. This method is introduced into the study of financial risk early warning. A neural network structure has an input layer, an intermediate layer and an output layer. This three-layer neural network model has a super powerful learning ability. However, this method requires a large amount of input data.

2.1.4

Analytic hierarchy process

This method analyses the nature of the problem and the target to be completed, decomposes the problem layer by layer, stratifies it according to the correlation and subordination of the influencing factors of the problem, and constructs a multi-level risk warning model. However, when determining the index weight, this method makes a pairwise comparison between the influencing factors, which requires a high level of professional judgement and strong subjectivity.

2.1.5

Efficiency coefficient method

By setting two values for each evaluation index, one is a satisfactory value, and one is not an allowed value. The satisfactory value is the upper limit of the evaluation index, and the unpermitted value is the lower limit. The two values are used to calculate the score of each index, and then the weighted average is used to calculate the financial risk of the evaluation object. But this method is difficult to determine the satisfactory value and not allowed value, so the operation is difficult. In addition, the influence degree of the index is different, so it is necessary to combine the objective weighting method to ensure the validity of the calculation results.

2.1.6

Principal component analysis

This method transforms multiple variables into a few unrelated comprehensive variables to reflect the whole data set comprehensively. At present, the model needs to ensure that the principal components contain a high level of information and can give meaningful and realistic explanations. In addition, the principal component may be fuzzy, not clear or precise.

2.1.7

TOPSIS method

In this method, the evaluation objects are ranked by their proximity to the set of optimal and worst solutions. By constructing the evaluation matrix, the financial risk of the evaluation object is ranked by a mathematical model, which is more objective. It can be compared with both historical data and industry data.

Through the comparison of various models, this paper chooses the TOPSIS method to analyse industrial projects. The financial risk of the biomass graphene fibre industrialisation project was analysed by a mathematical model.

2.2

Application steps of the TOPSIS method

The application of the model can be divided into six steps: data standardisation processing, using an evaluation matrix to determine the weight, constructing weighted normalisation matrix, determining positive and negative ideal solutions, calculating Euclidean distance, and calculating relative closeness degree. See Figure 1 below:

Schematic diagram of entropy weight TOPSIS model

2.3

Introduction of TOPSIS algorithm

2.3.1

Construct an evaluation matrix

3 $R_{q} = [\begin{matrix} Q_{11} & \dots & Q_{m 1} \\ \dots & Q_{i j} & \dots \\ Q_{1 n} & \dots & Q_{m n} \end{matrix}]$ $${R_q} = \left[ {\matrix{ {{Q_{11}}} & \cdots & {{Q_{m1}}} \cr \cdots & {{Q_{ij}}} & \cdots \cr {{Q_{1n}}} & \cdots & {{Q_{mn}}} \cr } } \right]$$

Where n is the number of evaluation objects; m is the number of evaluation indicators; Q_ij is the data corresponding to the i evaluation indicator of the j evaluated object.

2.3.2

Standardise the indicators

The standardisation of the decision matrix is mainly to eliminate the dimension inconsistency of the evaluation index. For indicators of different properties, the treatment method will be different.

Positive indicators: 4 $S_{i j} = \frac{Q_{i j} - min (Q_{1 j}, \dots Q_{m j})}{max (Q_{1 j}, \dots Q_{m j}) - min (Q_{1 j}, \dots Q_{m j})}$ $${S_{ij}} = {{{Q_{ij}} - \min ({Q_{1j}}, \cdots {Q_{mj}})} \over {\max ({Q_{1j}}, \cdots {Q_{mj}}) - \min ({Q_{1j}}, \cdots {Q_{mj}})}}$$

Negative indicators: 5 $S_{i j} = \frac{max (Q_{1 j}, \dots Q_{m j}) - Q_{i j}}{max (Q_{1 j}, \dots Q_{m j}) - min (Q_{1 j}, \dots Q_{m j})}$ $${S_{ij}} = {{\max ({Q_{1j}}, \cdots {Q_{mj}}) - {Q_{ij}}} \over {\max ({Q_{1j}}, \cdots {Q_{mj}}) - \min ({Q_{1j}}, \cdots {Q_{mj}})}}$$ 6 $R_{s} = [\begin{matrix} S_{11} & \dots & S_{m 1} \\ \dots & S_{i j} & \dots \\ S_{1 n} & \dots & S_{m n} \end{matrix}]$ $${R_s} = \left[ {\matrix{ {{S_{11}}} & \cdots & {{S_{m1}}} \cr \cdots & {{S_{ij}}} & \cdots \cr {{S_{1n}}} & \cdots & {{S_{mn}}} \cr } } \right]$$ where R_s is to form a new matrix after processing the positive and negative indicators, respectively.

2.3.3

Calculate the proportion of items i in the j object to be evaluated

7 $P_{i j} = \frac{S_{i j}}{\sum_{j = 1}^{n} S_{i j}}$ $${P_{ij}} = {{{S_{ij}}} \over {\mathop \sum \limits_{j = 1}^n {S_{ij}}}}$$ where $i \in (1, m)$ ; $j \in (1, n)$ .

2.3.4

Calculate the entropy value of the evaluation index

8 $H_{i} = - \frac{1}{\ln n} \sum_{j = 1}^{n} P_{i j} \ln P_{i j}$ where H_i is the entropy value of the evaluation index.

2.3.5

Determine the weight

This step determines the weight of each evaluation index to prepare for the subsequent construction of the weighted evaluation matrix.

9 $ω_{i} = \frac{1 - H_{i}}{m - \sum_{i = 1}^{m} H_{i}}$ $${\omega _i} = {{1 - {H_i}} \over {m - \mathop \sum \limits_{i = 1}^m {H_i}}}$$

2.3.6

Cochemotactic treatment

Because the evaluation criteria of indicators are not consistent, the sixth step is used to cochemize negative indicators, which is convenient for the calculation of positive and negative ideal solutions in the following paper.

10 $Q_{i j}^{'} = 1 / Q_{i j} (i \in I^{'})$ where, set I is a positive indicator. 11 $Q_{i j}^{'} = Q_{i j} (i \in I)$

Where $I^{'}$ is a negative indicator.

12 $R_{q}^{'} = [\begin{matrix} {Q^{'}}_{11} & \dots & {Q^{'}}_{m 1} \\ \dots & {Q^{'}}_{i j} & \dots \\ {Q^{'}}_{1 n} & \dots & {Q^{'}}_{m n} \end{matrix}]$ $$R_q^\prime = \left[ {\matrix{ {{Q^\prime }_{11}} & \cdots & {{Q^\prime }_{m1}} \cr \cdots & {{Q^\prime }_{ij}} & \cdots \cr {{Q^\prime }_{1n}} & \cdots & {{Q^\prime }_{mn}} \cr } } \right]$$ where, $R_{q}^{'}$ is the new matrix formed after the negative indicator is processed.

2.3.7

Normalisation

In order to improve the comparability of evaluation indexes, the author normalised the matrix on the basis of cochemotaxis.

13 $Q_{i j}^{''} = Q_{i j}^{'} / \sqrt{\sum_{j = 1}^{n} {Q^{'}}_{i j}^{2}}$ where, $Q_{i j}^{''}$ is the matrix normalised $Q_{i j}^{'}$ . 14 $R_{q}^{''} = [\begin{matrix} {Q^{''}}_{11} & \dots & {Q^{''}}_{m 1} \\ \dots & {Q^{''}}_{i j} & \dots \\ {Q^{''}}_{1 n} & \dots & {Q^{''}}_{m n} \end{matrix}]$ $$R_q^{\prime \prime } = \left[ {\matrix{ {{Q^{\prime \prime }}_{11}} & \cdots & {{Q^{\prime \prime }}_{m1}} \cr \cdots & {{Q^{\prime \prime }}_{ij}} & \cdots \cr {{Q^{\prime \prime }}_{1n}} & \cdots & {{Q^{\prime \prime }}_{mn}} \cr } } \right]$$ where $R_{q}^{''}$ is the normalised decision matrix.

2.3.8

Construct a weighted normalisation matrix

This step is mainly for the objective weight assignment of each evaluation index in the subsequent ranking of evaluation objects by USING the TOPSIS method: 15 $U = [\begin{matrix} u_{11} & \dots & u_{m 1} \\ \dots & u_{i j} & \dots \\ u_{1 n} & \dots & u_{m n} \end{matrix}]$ $$U = \left[ {\matrix{ {{u_{11}}} & \cdots & {{u_{m1}}} \cr \cdots & {{u_{ij}}} & \cdots \cr {{u_{1n}}} & \cdots & {{u_{mn}}} \cr } } \right]$$ where U is the normalisation matrix constructed by multiplying the weight by the normalised $Q_{i j}^{''}$ .

2.3.9

Determine the positive ideal solution and negative the ideal solution and calculate Euclidean distance

The positive ideal solution of the positive index and the negative index is the set composed of the maximum value of the positive index and the maximum value of the negative index, respectively.

16 $U^{+} = {u_{1}^{+}, \dots u_{m}^{+}}$ 17 $U^{-} = {u_{1}^{-}, \dots u_{m}^{-}}$ where $U^{+}$ is the set of positive ideal solutions and $U^{-}$ is the set of negative ideal solutions. After the positive and negative ideal solutions are determined, the Euclidean distance can be calculated.

18 $d_{j}^{+} = \sqrt{\sum_{i = 1}^{m} (u_{i j} - u_{1}^{+})}$ 19 $d_{j}^{-} = \sqrt{\sum_{i = 1}^{m} (u_{i j} - u_{1}^{-})}$ where, $d_{j}^{+}$ and $d_{j}^{-}$ are the Euclidean distances of positive and negative ideal solutions respectively.

2.3.10

Calculate the relative proximity

By calculating Euclidean distance, the relative closeness degree of the evaluation object can be further obtained 20 $\begin{aligned} C_{j} = \frac{d_{j}^{-}}{d_{j}^{+} + d_{j}^{-}} . \end{aligned}$

Among them, the greater the closeness degree C_j is, the better the evaluation object is, and the lower the risk is.

Analysis and discussion

The graphene preparation process is short, highly efficient, cost-effective and pollution-free. At present, the single-layer rate of finished products in China can reach more than 99%, achieving a higher level in the global industry. High-quality and low-cost graphene raw materials have laid a solid foundation for the wide application of downstream composite fibrosis materials. Finally, this paper puts forward corresponding countermeasures according to the identified risks and analysed reasons. The research conclusions are as follows. The financial risks of the biomass graphene fibre industrialisation project from 2017 to 2021 were calculated and ranked according to the relevant risk assessment parameters of the project and related material utilisation projects in each year. The details are shown in Table 2. The risk index for each year is as follows: 2017>2021>2019>2020>2018. In order to specifically present the trend of the average financial risk of the project in 2018-2021, Figure 2 shows the rule of the average security index changing with years, which is also the financial security trend chart. As can be seen from the figure, in early 2017, the average financial situation of all companies in the development of the whole project industry was at the bottom point, but there was a huge improvement in 2015, and the financial situation tended to be stable after the decline of the average safety index in 2019. In terms of financial ratios for 2018, cash flow capacity, operational capacity and growth capacity are optimal for the 2018-2021 period. However, in 2019 and 2020, with the continuous financing expansion of companies in the industry project, the overall risk remains at a high level, which to some extent indicates that most enterprises sacrifice certain cash flow in order to make their business revenue follow the pace of expansion. However, based on the development trend in recent years, the financial status of each company in the project is still relatively stable.

Table 2

Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Year	2017	2018	2019	2020	2021
Risk index (5 is the highest risk index)	5	1	3	2	4

Average safety index varies with the years

Graphene nanoribbon is a new type of carbon nanomaterial, because it has an ideal planar two-dimensional structure, unique thermal properties, mechanical properties, electrical properties, optical properties, etc., and has a good application prospect in the fields of electronics, machinery and medicine. We must seek benefits from both production and management. Management is also a productive force. Financial management is closely related to economic efficiency. The central goal of the enterprise is around how to obtain as much economic benefit as possible with less consumption, to strengthen financial management which can promote enterprises to save potential, and to control costs and reduce consumption. Through raising and dispatching of funds and its joint use, it helps improve the effective use of funds and prevents wasteful expenditure. Therefore, giving full play to the leading role of financial management can more effectively improve economic benefits. Therefore, this paper makes a detailed analysis of the above financial risk performance and index. The specific quantitative data are shown in Table 3. During 2017-2021, the average total assets of companies in the biomass graphene fibre industrialisation project show an increasing trend, with the most significant increase in 2019, which is 514,135,000 yuan. This is an increase of 119.14% compared to 2018. Secondly, the average value of debt also reached the highest in 2019, with the value of debt reaching 383.248 million yuan, an increase of 427.54% compared to 2018. The average financial position of each company is shown in Figure 3. It can be observed that 2019 is indeed a year of great transformation in the engineering application of graphene fibre industrialisation, but the value appreciation of assets and liabilities have also greatly increased this year, along with the degree of risk which has also risen sky high.

Table 3

Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Year	2017	2018	2019	2020	2021
Average gross assets	14,561.2	23,461.5	51,413.5	61,748.8	68,523.2
Average total liabilities	4187.7	7264.8	38,324.8	11,574.97	14,123.85
Average net profit	1345.1	2343.2	2124.4	3251.7	6123.5

Average economic parameters vary with the year

Using the mathematical model of entropy weight TOPSIS method can realise the optimisation of an inventory structure, reduce inventory overstocking, and achieve economic inventory; through the price pull, can increase the enterprise income; Through the management of assets, enterprises can make reasonable and effective use of assets, and achieve the value preservation and increase of assets. Finally, the effective reduction of the economic risk in the biomass graphene fibre industrialisation project of each company in the industry will be realised. Figure 4 shows the correlation between calculation accuracy and calculation times of entropy weight TOPSIS method. It is observed that the calculation accuracy fluctuates slightly with the increase in the number of words, but the maximum oscillation is less than 3%, which indicates that the model is reliable for the economic risk assessment of projects.

Accuracy of a mathematical model of entropy weight TOPSIS method

Conclusion

The graphene preparation process is short, highly efficiencient, cost-effective and pollution-free. At present, the single-layer rate of finished products in China can reach more than 99%, achieving a higher level in the global industry. High-quality and low-cost graphene raw materials have laid a solid foundation for the wide application of downstream composite fibrosis materials. In this paper, the biomass graphene fibre industrialisation project is taken as the research object, the development characteristics of the project in the field of industrial application and the financial status of the current company are taken into comprehensive consideration, and the financial data from 2014 to 2018 are selected for quantitative analysis. Finally, this paper puts forward corresponding countermeasures according to the identified risks and analysed reasons. The research conclusions are as follows:

The financial risk status of the biomass graphene fibre industrialisation project was analysed, and the preliminary identification of the risk was realised. The risk identification and analysis of this project focuses on financing, investment, operation and other risks. In terms of investment risks, the main risks faced by the biomass graphene fibre industrialisation project are the poor sales performance of some subsidiaries and the difficulty in fund recovery. At the same time, the lack of investment in research and development of the project will easily lead to difficulties in the follow-up business development of enterprises. The results show that the risk index for each year is: 2017>2021>2019>2020>2018. In early 2017, the average financial status of all companies in the development of the whole project industry was at the bottom point, but in 2015, there was a huge improvement, and in 2019, the financial status stabilised after the decline of the average safety index.

Use the entropy weight TOPSIS method to build a financial risk evaluation model and make a horizontal and a vertical comparison. In this paper, a risk evaluation model is constructed, and the entropy value and entropy weight of evaluation indicators are realised by MATLAB software. In addition, the financial risks of enterprises in the industry are ranked, and the industry status of the graphene fibre industrialisation project is analysed. According to the study, companies in the biomass graphene fibre industrialisation project should focus on cash flow risk and growth risk. The calculation results show that the average total assets of each company in the biomass graphene fibre industrialisation project showed an increasing trend from 2017 to 2021, with the most significant increase in 2019 (514.135 million yuan), an increase of 119.14% compared to 2018. Secondly, the average value of debt also reached the highest in 2019, with the value of debt reaching 383.248 million yuan, an increase of 427.54% compared to 2018.

Figuras y tablas

Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Year	2017	2018	2019	2020	2021
Average gross assets	14,561.2	23,461.5	51,413.5	61,748.8	68,523.2
Average total liabilities	4187.7	7264.8	38,324.8	11,574.97	14,123.85
Average net profit	1345.1	2343.2	2124.4	3251.7	6123.5

Main functional characteristics of graphene

Specific function	Detailed description
Mechanical properties	High strength, but also has good toughness
Electronic effect	The carrier mobility is high at room temperature and is little affected by temperature
Thermal performance	It has very good heat conduction performance
Optical properties	Graphene films have excellent optical properties, and their optical properties change with the thickness of graphene
Solubility	It is super hydrophobic and super lipophilic, and can adsorb and desorbed various molecules and atoms

Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Year	2017	2018	2019	2020	2021
Risk index (5 is the highest risk index)	5	1	3	2	4

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Applied Mathematics and Nonlinear Sciences

Construction of Financial Risk Evaluation Index System for Biomass Graphene Fibre Industrialisation Project

Liwei Zhang
Liwei Zhang

Publicado en línea: 23 Dec 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 15 Jun 2022

Aceptado: 09 Jul 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Figuras y tablas

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Main functional characteristics of graphene

Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Applied Mathematics and Nonlinear Sciences

Construction of Financial Risk Evaluation Index System for Biomass Graphene Fibre Industrialisation Project

Liwei ZhangLiwei Zhang

Publicado en línea: 23 Dec 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 15 Jun 2022

Aceptado: 09 Jul 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Figuras y tablas

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Main functional characteristics of graphene

Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Liwei Zhang
Liwei Zhang