# Financing Efficiency Calculation of Energy Enterprises Based on Internet of Things

Authors: JingJing Wu; YaJuan Zhang
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to conduct a special study on the financing efficiency of a certain industry, the authors propose a method for calculating the financing efficiency of energy enterprises based on the Internet of Things. Combining the DEA method with the Bootstrap method, taking the IoT data of 30 SME boards and 30 energy companies listed on the ChiNext listed in 2010 as a research sample, and using R language and Deap2.1 software, the financing efficiency from 2011 to 2015 is calculated. Experimental results show that from 2011 to 2015, only 28.3% of the enterprises reached the effective state of technical efficiency on average, and the financing efficiency of energy enterprises was generally inefficient. The pure technical efficiency value of the whole enterprise decreases year by year, and its technical efficiency value lower than its scale efficiency is the main reason that its technical efficiency is generally not high.


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## Body

## 1. Introduction

Affected by the financial crisis, the world situation is complex and volatile, and economic growth lacks impetus [1]. Although the financial crisis occurred in the United States, with the intensification of the European sovereign debt crisis, the global financial crisis also shifted to Europe. In the past few decades, Asian economies, especially East Asian economies, have been the most dynamic in global economic growth. Looking at the overall situation, although the international situation is turbulent and my country is also hit by the financial crisis, the development environment facing my country is generally good. The first 20 years of the 21st century or even longer is a period of strategic opportunities that my country needs to seize and make full use of, and it is also a period of overcoming difficulties in the adjustment of my country’s economic structure.After more than 30 years of development, China has grown into a big manufacturing country. The manufacturing industry has played an important role in supporting economic and social development and meeting people’s living needs; the prosperity and development of the manufacturing industry have driven the rise of China’s GDP [2]. However, with the development of economy, some enterprises in our country have the problem of overcapacity. In terms of quantitative standards, the capacity utilization rate of traditional industries such as steel, cement, electrolytic aluminum, flat glass, and coke is between 70% and 75%. Internationally, the capacity utilization rate of a normal competitive market should exceed 80%-85%.In order to solve the problem of overcapacity of enterprises, we should first start with improving the quality of enterprises. Secondly, the excess production capacity of a group of enterprises can be digested through mergers and reorganizations, and a group of backward production capacity can be eliminated through the survival of the fittest. Finally, companies can also go overseas to develop and transfer production capacity. In the market economy environment, the product has a moderate surplus, which can stimulate market competition and can also promote the improvement and progress of enterprise management level. However, overcapacity will not only cause waste of resources and labor but also be detrimental to the long-term development of enterprises and even affect the healthy operation of the economy. Enterprises in the industrial industry have difficulties in their own development and low profits, and a considerable number of enterprises are in a state of loss. Some companies still need to produce even knowing that they are losing money; in order to sell their products, they fight a “price war”; this leads to vicious competition among enterprises [3]. Figure 1 shows the financing processing system and method. Vicious competition among enterprises will lead to protectionism in some places, resulting in market segmentation. If his happens, it is very unfavorable for our country to change the mode of economic development and adjust the economic structure. Therefore, resolving the problem of overcapacity in enterprises has become one of the priorities for adjusting the economic structure and transforming the mode of economic development at present and in the future.Figure 1
Financing processing system and method.


## 2. Literature Review

Chau and others believe that, without taking corporate tax into account, first of all, the size of an enterprise’s debt will not affect the value of the enterprise; that is, the financing structure of the enterprise has nothing to do with the value of the enterprise. Secondly, the cost of equity of a debt-burdened company is the sum of the cost of equity of a non-debt-burdened company with the same risk and the risk premium, and the risk premium is also determined by the cost of equity, debt financing cost, and risk premium of a non-debt-burdened company; the proportion of property rights of the enterprise is determined [4]. Jwo et al. believe that if personal income tax is taken into account, the interest expenses incurred by enterprises due to debt financing will be deducted from the total tax payment, and the effect of the increase in enterprise value will be reduced, and the tax shield of debt will be reduced; the effect is not so obvious [5]. Zhang et al. proposed that enterprises should not only consider the tax saving effect brought by interest deduction when choosing debt financing methods but should also consider the increase in financial costs caused by interest expenses and the agency cost pressure on company managers because of growing conflicts between creditors and the company’s original shareholders. Bankruptcy is inevitable if the financial burden and agency costs faced by the business are large enough that the business itself cannot bear it. Therefore, it is concluded that the optimal debt financing structure of an enterprise should be when its debt burden is equal to the marginal cost of agency problems and the marginal benefit brought by its tax savings [6]. Sidloski and Diab considered the contradiction between corporate shareholders and creditors caused by the increase in the proportion of debt financing and also took into account the issue of entrustment and agency within the company; that is, because the principal is the shareholder of the company, the agency cost problem is caused by the unequal information held by the managers of the agency companies and the agency companies [7]. Modisane and Jokonya believe that information asymmetry exists not only within shareholders and managers but also between companies and external investors. When a company chooses equity or debt financing, investors will have a positive or negative impression of the value of the company, so that the actual value of the company is overvalued or undervalued to varying degrees. Specifically, investors believe that stocks will only be issued when the operating conditions of the company are not ideal; of course, investors will not buy shares at this time, and the value of the company is undervalued [8]. Gao and others believe that the financing efficiency of an enterprise should be the ability of the enterprise to obtain funds, and the size of the financing ability determines the financing efficiency of the enterprise. Before China entered the market economy, the financing efficiency of enterprises could not be reflected independently, and it was integrated with the overall economic efficiency of the country; in modern economic society, the level of corporate financing ability is an important manifestation of the efficiency of economic development [9].From the above review, it can be seen that the existing research literature rarely conducts special research on the financing efficiency of a certain industry, and there is not much improvement in research methods. Based on this, the authors select the energy industry that has received high attention in recent years and add the Bootstrap method to evaluate the financing efficiency of enterprises on the basis of using the DEA method.


## 3. Research Methods

### 3.1. Research Method Design of Financing Efficiency of Energy Enterprises

(1)
Model establishmentThe DEA (Data Envelope Analysis) method, namely, the data envelopment analysis method, is based on the concept of relative efficiency and is a method of evaluating the relative effectiveness or benefit of similar decision-making units (DMU) according to multi-indicator input and multi-indicator output; this method can divide the efficiency of the evaluation unit into technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE), and the relationship between the three efficiencies isPTE=TE/SE; therefore, it has an absolute advantage in processing multi-input-multioutput effectiveness evaluation [10]. The basic evaluation principle of the DEA method is as follows: take each enterprise as an efficiency evaluation unit (DMU), evaluate the efficiency of each DMU according to the input and output indicators, and determine an efficiency after comprehensively considering the efficiency of all DMUs, the frontier surface, and then according to the distance between each DMU and the efficiency frontier surface; it is determined whether the efficiency of the evaluation unit is DEA effective.Although the DEA method has many advantages in efficiency evaluation, because what it measures is only a kind of “relative efficiency,” an upper limit of “absolute efficiency,” and a biased and inconsistent estimator, the true value of efficiency should be below this “relative efficiency” [11]. The Bootstrap-DEA method solves the defects of the DEA method; the main steps of the method are as follows:
(1)
Measure the efficiency valuesθ^j and j=1,2,⋯,n of each decision-making unit under the DEA method(2)
Using the Bootstrap method, repeated sampling with replacement in the original efficiency valueθ^j produces a sample θ∗1b,⋯,θ∗nb of size n, where b represents the number of iterations of Bootstrap and b=1,⋯,B(3)
Calculate the simulated sampleX∗jb,Xj, where X∗jb=θ^j/θ∗jb·Xj,j=1,⋯,n(4)
Using such a simulated sample, calculate the efficiency valuesθ∗j and j=1,⋯,n using the DEA method(5)
Repeat steps (2) to (4) times for each decision-making unit (take=2000) to generate a series of efficiency values θ∗jb and b=1,⋯,B(6)
Correct the estimated deviation of DEA efficiency value:biasθ^j=Eθ^j−θ^j(7)
Bias^θ^j=B−1∑b=1Bθ^∗jb−θ^j, the corrected efficiency value is θ¯j=θ^j−Bias^θ^j=2θ^j−B−1∑b=1Bθ^∗jb [12, 13](2)
Construction of evaluation index systemThe DEA model belongs to the multi-input-multioutput relative efficiency evaluation model; whether the selection of model input and output indicators is reasonable will directly affect the evaluation effect of the model. According to the characteristics of the energy industry and the experience of previous index selection, the authors constructed the following input and output index system, as shown in Table1.
(3)
Data source and processingTable 1
Input and output index system of financing efficiency of energy enterprises.
Indicator categoryIndicator nameIndicator meaningInput indicatorTotal assetsX1It reflects the size of the overall asset size of the enterprise and reflects the level of the enterprise’s financing abilityAssets and liabilitiesX2Equal to the total assets divided by the total liabilities, is a comprehensive evaluation index to measure the structure of an enterprise’s assets and liabilitiesFinancial expensesX3It reflects the interest expense caused by financing in the business process of the enterprise, that is, the cost of using fundsReturn on net assetsY1Net profit divided by total net assets reflects the company’s ability to use assets to obtain income and also measures the income of shareholders’ equityOutput indicatorTotal asset turnoverY2It reflects the utilization efficiency of all the assets owned by the enterprise and measures the capital operation ability and management level of the enterpriseMain business income growth rateY3It reflects the growth and development ability of the enterprise; the larger the index, the stronger the profitability of the enterpriseGrowth rate of intangible assetsY4The growth rate of intangible assets of energy enterprises, which shows the transformation efficiency of their research and development investmentThe authors take energy companies as research objects and, on the basis of ensuring the integrity and continuity of corporate financial data, selected 30 SME board and 30 ChiNext energy companies listed in 2010 as research samples [14, 15]. The study interval span was selected from 2011 to 2015. The financial index data of the sample companies involved in the research all come from wind information financial terminal. Since the values of the input and output indicators cannot be negative values when using the DEA method, the authors perform dimensionless processing on all the indicator data; the specific processing method is as follows (1):
(1)x∗ij=xij−minijmaxij−minij×0.9+0.1.Among them,xij represents the i input or output index of the jth decision-making unit; maxij and minij, respectively, represent the maximum or minimum value of the i input or output indicators of the jth decision-making unit [16].


## 3.1. Research Method Design of Financing Efficiency of Energy Enterprises

(1)
Model establishmentThe DEA (Data Envelope Analysis) method, namely, the data envelopment analysis method, is based on the concept of relative efficiency and is a method of evaluating the relative effectiveness or benefit of similar decision-making units (DMU) according to multi-indicator input and multi-indicator output; this method can divide the efficiency of the evaluation unit into technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE), and the relationship between the three efficiencies isPTE=TE/SE; therefore, it has an absolute advantage in processing multi-input-multioutput effectiveness evaluation [10]. The basic evaluation principle of the DEA method is as follows: take each enterprise as an efficiency evaluation unit (DMU), evaluate the efficiency of each DMU according to the input and output indicators, and determine an efficiency after comprehensively considering the efficiency of all DMUs, the frontier surface, and then according to the distance between each DMU and the efficiency frontier surface; it is determined whether the efficiency of the evaluation unit is DEA effective.Although the DEA method has many advantages in efficiency evaluation, because what it measures is only a kind of “relative efficiency,” an upper limit of “absolute efficiency,” and a biased and inconsistent estimator, the true value of efficiency should be below this “relative efficiency” [11]. The Bootstrap-DEA method solves the defects of the DEA method; the main steps of the method are as follows:
(1)
Measure the efficiency valuesθ^j and j=1,2,⋯,n of each decision-making unit under the DEA method(2)
Using the Bootstrap method, repeated sampling with replacement in the original efficiency valueθ^j produces a sample θ∗1b,⋯,θ∗nb of size n, where b represents the number of iterations of Bootstrap and b=1,⋯,B(3)
Calculate the simulated sampleX∗jb,Xj, where X∗jb=θ^j/θ∗jb·Xj,j=1,⋯,n(4)
Using such a simulated sample, calculate the efficiency valuesθ∗j and j=1,⋯,n using the DEA method(5)
Repeat steps (2) to (4) times for each decision-making unit (take=2000) to generate a series of efficiency values θ∗jb and b=1,⋯,B(6)
Correct the estimated deviation of DEA efficiency value:biasθ^j=Eθ^j−θ^j(7)
Bias^θ^j=B−1∑b=1Bθ^∗jb−θ^j, the corrected efficiency value is θ¯j=θ^j−Bias^θ^j=2θ^j−B−1∑b=1Bθ^∗jb [12, 13](2)
Construction of evaluation index systemThe DEA model belongs to the multi-input-multioutput relative efficiency evaluation model; whether the selection of model input and output indicators is reasonable will directly affect the evaluation effect of the model. According to the characteristics of the energy industry and the experience of previous index selection, the authors constructed the following input and output index system, as shown in Table1.
(3)
Data source and processingTable 1
Input and output index system of financing efficiency of energy enterprises.
Indicator categoryIndicator nameIndicator meaningInput indicatorTotal assetsX1It reflects the size of the overall asset size of the enterprise and reflects the level of the enterprise’s financing abilityAssets and liabilitiesX2Equal to the total assets divided by the total liabilities, is a comprehensive evaluation index to measure the structure of an enterprise’s assets and liabilitiesFinancial expensesX3It reflects the interest expense caused by financing in the business process of the enterprise, that is, the cost of using fundsReturn on net assetsY1Net profit divided by total net assets reflects the company’s ability to use assets to obtain income and also measures the income of shareholders’ equityOutput indicatorTotal asset turnoverY2It reflects the utilization efficiency of all the assets owned by the enterprise and measures the capital operation ability and management level of the enterpriseMain business income growth rateY3It reflects the growth and development ability of the enterprise; the larger the index, the stronger the profitability of the enterpriseGrowth rate of intangible assetsY4The growth rate of intangible assets of energy enterprises, which shows the transformation efficiency of their research and development investmentThe authors take energy companies as research objects and, on the basis of ensuring the integrity and continuity of corporate financial data, selected 30 SME board and 30 ChiNext energy companies listed in 2010 as research samples [14, 15]. The study interval span was selected from 2011 to 2015. The financial index data of the sample companies involved in the research all come from wind information financial terminal. Since the values of the input and output indicators cannot be negative values when using the DEA method, the authors perform dimensionless processing on all the indicator data; the specific processing method is as follows (1):
(1)x∗ij=xij−minijmaxij−minij×0.9+0.1.Among them,xij represents the i input or output index of the jth decision-making unit; maxij and minij, respectively, represent the maximum or minimum value of the i input or output indicators of the jth decision-making unit [16].


## 4. Result Analysis

### 4.1. Bootstrap-DEA of Overall Financing Efficiency

(1)
Analysis of the overall financing efficiency under the DEA modelFirst, through the rDEA package and Deap2.1 software in R language, the input and output data of 30 SME boards and 30 GEM energy companies from 2011 to 2015 were analyzed and processed by the DEA method; finally, the calculation results of technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of 60 enterprises are obtained, as shown in Tables2 and 3 below.Table 2
Calculation results of DEA financing efficiency of small and medium-sized energy enterprises.
Number of SMEs with DEA valid (percentage)YearsTEPTESE20119 (15.0%)12 (20.0%)9 (15.0%)20128 (13.3%)14 (23.3%)8 (13.3%)20136 (10.0%)10 (16.7%)6 (10.0%)20149 (15.0%)10 (16.7%)11 (18.3%)20159 (15.0%)12 (20.0%)11 (18.3%)Average number of homes8 (13.7%)12 (19.3%)9 (15.0%)Efficiency mean0.7870.8410.935Table 3
Calculation results of DEA financing efficiency of GEM energy companies.
Number of effective DEA companies on the Growth Enterprise Market (proportion)YearsTEPTESE20118 (13.3%)12 (20.0%)8 (13.3%)20129 (15.0%)11 (18.3%)9 (15.0%)20139 (15.0%)13 (21.7%)10 (16.7%)20141 (18.3%)17 (28.3%)12 (20.0%)20157 (11.7%)14 (23.3%)7 (11.7%)Average number of homes9 (14.6%)13 (22.3%)9 (15.3%)Efficiency mean0.8370.9040.926According to the calculation results of the DEA model in Tables2 and 3, it can be seen from the average from 2011 to 2015 that the financing technical efficiency of the sample energy enterprises on the small and medium-sized board and the ChiNext board reached 1; that is to say, the number of companies that achieved DEA is 8 and 9, respectively, and the two together account for 28.3% of the total number of samples; this shows that less than 1/3 of the sample companies’ financing behavior has reached a “relatively efficient” state with no redundancy in input and maximizing output; for most companies, the financing efficiency is not ideal, and its input and output still have room for further improvement. In addition, we can see from the average efficiency that whether it is a small and medium-sized board or a ChiNext board, the main reason for the low technical efficiency is that the pure technical efficiency is lower than the scale efficiency [17].
(2)
Analysis of overall financing efficiency after modification by the Bootstrap methodAs mentioned above, in order to reduce the negative impact of the DEA model due to its defects in the efficiency measurement and make the results more reliable, the authors use the Benchmarking package in the R language, set the number of Bootstrap iterations to 2000, set the confidence interval to 95%, and then correct the original DEA efficiency value. According to the revised efficiency value, as shown in Table4 (due to limited space, only the average efficiency of all sample companies in each year is listed here), we found that the financing efficiency originally reached a “relatively efficient” state under the DEA method enterprise [18]. At this time, each efficiency value did not reach 1, and the efficiency values of all enterprises after Bootstrap correction were lower than the efficiency values under the DEA method, which indicated that the overall financing efficiency of the sample enterprises after the correction was better than the calculation results of the DEA method.Table 4
Bootstrap-DEA method revised financing efficiency calculation results.
Annual average technical efficiency of SMEsAnnual average technical efficiency of GEM companiesYearsOriginal TE valueCorrection valueDifferenceLower boundaryUpper boundaryOriginal TE valueCorrection valueDifferenceLower boundaryUpper boundary20110.7970.7070.090.6460.7810.8020.710.0920.6430.78620120.8410.7690.0710.7130.8310.8390.7680.0710.7120.82720130.7690.6950.0740.6410.7570.8620.7730.0890.7060.84820140.7760.7020.0750.6460.7650.8810.7890.0930.7200.86720150.7530.6540.0980.5930.7360.8000.7030.0970.6400.783Mean0.7870.7050.0820.6470.7720.8370.7490.0880.6950.831


### 4.2. Analysis of Pure Technical Efficiency and Scale Efficiency

By decomposing technical efficiency, we can specifically analyze the pure technical efficiency and scale efficiency of enterprises [19]. As shown in Figure 2 (GEM is the ChiNext board, SME is the small and medium-sized board, and the black and white dots are the values before and after correction), from the distribution of the average pure technical efficiency value and the average scale efficiency value of each sample enterprise from 2011 to 2015, the following features can be found:
(1)
Scale efficiency (SE) is significantly better than pure technical efficiency (PTE)Figure 2
The mean scatter plot of pure technical efficiency and scale efficiency from 2011 to 2015.It can be clearly seen from the distribution shape of the scatter points that almost all sample points are concentrated in the upper half of the graph, whether before correction (solid black points) or after correction (open white points), that is, the scale efficiency value (0 .75, 1.0) range, and there is almost no sample point distribution below 0.75; although the sample points in the (0.75, 1.0) interval are more densely distributed than the (0.5, 0.75) interval for pure technical efficiency, it is still not as good as the overall distribution of scale efficiency [20]. This further illustrates that the financing efficiency of most energy companies is mainly limited by their lower pure technical efficiency. Therefore, when the scale of financing reaches a relatively ideal state, how to improve their pure technical efficiency and effectively manage and make good use of funds is the key point that energy companies should pay attention to in the future.
(2)
The Bootstrap correction efficiency value is lower than the original efficiency valueAs shown in Figure2, before the original financing efficiency value is revised, most of the black solid sample points are concentrated in the upper right area, that is, closer to the two “effective frontiers” with an efficiency value of 1, and some sample points just fall on the “effective frontier.” After correcting the efficiency value, it can be found that there is no white hollow sample point distribution on the “effective frontier” and its overall left shift, but the downward shift is not obvious [21]. The corrected efficiency value provides a more accurate measurement result and the change in the distribution shape before and after the scatter; it also confirms the previous finding that the contribution of scale efficiency to technical efficiency is greater than that of pure technical efficiency [22].


### 4.3. Trend Analysis of Overall Financing Efficiency

The financing efficiency trend of all 60 SME and ChiNext energy sample companies before and after the revision is shown in Figure3 [23]. As can be seen from the figure, the revised technical efficiency (bcTE) values from 2011 to 2015 were 0.708, 0.768, 0.732, 0.740, and 0.675, showing a slight and slow decreasing trend in the fluctuating state as a whole [24]. As far as the revised pure technical efficiency (bcPTE) is concerned, from 0.856 in 2011 to 0.726 in 2015, it shows a significant decline in technical efficiency. Comparatively speaking, the scale efficiency showed a different increasing trend year by year from 2011 to 2014, but it showed a downward trend in 2015 [25].Figure 3
Trends in the overall financing efficiency of energy companies from 2011 to 2015.In addition, we can notice that compared with the original efficiency values, the three efficiency values after the Bootstrap method correction have not changed in trend, and compared to the technical efficiency and pure technical efficiency, the correction of scale efficiency is the slightest. Therefore, for energy companies, reversing the declining trend of pure technical efficiency and maintaining the growth trend of scale efficiency are the key to improving the overall financing efficiency, and the former is more important.


## 4.1. Bootstrap-DEA of Overall Financing Efficiency

(1)
Analysis of the overall financing efficiency under the DEA modelFirst, through the rDEA package and Deap2.1 software in R language, the input and output data of 30 SME boards and 30 GEM energy companies from 2011 to 2015 were analyzed and processed by the DEA method; finally, the calculation results of technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of 60 enterprises are obtained, as shown in Tables2 and 3 below.Table 2
Calculation results of DEA financing efficiency of small and medium-sized energy enterprises.
Number of SMEs with DEA valid (percentage)YearsTEPTESE20119 (15.0%)12 (20.0%)9 (15.0%)20128 (13.3%)14 (23.3%)8 (13.3%)20136 (10.0%)10 (16.7%)6 (10.0%)20149 (15.0%)10 (16.7%)11 (18.3%)20159 (15.0%)12 (20.0%)11 (18.3%)Average number of homes8 (13.7%)12 (19.3%)9 (15.0%)Efficiency mean0.7870.8410.935Table 3
Calculation results of DEA financing efficiency of GEM energy companies.
Number of effective DEA companies on the Growth Enterprise Market (proportion)YearsTEPTESE20118 (13.3%)12 (20.0%)8 (13.3%)20129 (15.0%)11 (18.3%)9 (15.0%)20139 (15.0%)13 (21.7%)10 (16.7%)20141 (18.3%)17 (28.3%)12 (20.0%)20157 (11.7%)14 (23.3%)7 (11.7%)Average number of homes9 (14.6%)13 (22.3%)9 (15.3%)Efficiency mean0.8370.9040.926According to the calculation results of the DEA model in Tables2 and 3, it can be seen from the average from 2011 to 2015 that the financing technical efficiency of the sample energy enterprises on the small and medium-sized board and the ChiNext board reached 1; that is to say, the number of companies that achieved DEA is 8 and 9, respectively, and the two together account for 28.3% of the total number of samples; this shows that less than 1/3 of the sample companies’ financing behavior has reached a “relatively efficient” state with no redundancy in input and maximizing output; for most companies, the financing efficiency is not ideal, and its input and output still have room for further improvement. In addition, we can see from the average efficiency that whether it is a small and medium-sized board or a ChiNext board, the main reason for the low technical efficiency is that the pure technical efficiency is lower than the scale efficiency [17].
(2)
Analysis of overall financing efficiency after modification by the Bootstrap methodAs mentioned above, in order to reduce the negative impact of the DEA model due to its defects in the efficiency measurement and make the results more reliable, the authors use the Benchmarking package in the R language, set the number of Bootstrap iterations to 2000, set the confidence interval to 95%, and then correct the original DEA efficiency value. According to the revised efficiency value, as shown in Table4 (due to limited space, only the average efficiency of all sample companies in each year is listed here), we found that the financing efficiency originally reached a “relatively efficient” state under the DEA method enterprise [18]. At this time, each efficiency value did not reach 1, and the efficiency values of all enterprises after Bootstrap correction were lower than the efficiency values under the DEA method, which indicated that the overall financing efficiency of the sample enterprises after the correction was better than the calculation results of the DEA method.Table 4
Bootstrap-DEA method revised financing efficiency calculation results.
Annual average technical efficiency of SMEsAnnual average technical efficiency of GEM companiesYearsOriginal TE valueCorrection valueDifferenceLower boundaryUpper boundaryOriginal TE valueCorrection valueDifferenceLower boundaryUpper boundary20110.7970.7070.090.6460.7810.8020.710.0920.6430.78620120.8410.7690.0710.7130.8310.8390.7680.0710.7120.82720130.7690.6950.0740.6410.7570.8620.7730.0890.7060.84820140.7760.7020.0750.6460.7650.8810.7890.0930.7200.86720150.7530.6540.0980.5930.7360.8000.7030.0970.6400.783Mean0.7870.7050.0820.6470.7720.8370.7490.0880.6950.831


## 4.2. Analysis of Pure Technical Efficiency and Scale Efficiency

By decomposing technical efficiency, we can specifically analyze the pure technical efficiency and scale efficiency of enterprises [19]. As shown in Figure 2 (GEM is the ChiNext board, SME is the small and medium-sized board, and the black and white dots are the values before and after correction), from the distribution of the average pure technical efficiency value and the average scale efficiency value of each sample enterprise from 2011 to 2015, the following features can be found:
(1)
Scale efficiency (SE) is significantly better than pure technical efficiency (PTE)Figure 2
The mean scatter plot of pure technical efficiency and scale efficiency from 2011 to 2015.It can be clearly seen from the distribution shape of the scatter points that almost all sample points are concentrated in the upper half of the graph, whether before correction (solid black points) or after correction (open white points), that is, the scale efficiency value (0 .75, 1.0) range, and there is almost no sample point distribution below 0.75; although the sample points in the (0.75, 1.0) interval are more densely distributed than the (0.5, 0.75) interval for pure technical efficiency, it is still not as good as the overall distribution of scale efficiency [20]. This further illustrates that the financing efficiency of most energy companies is mainly limited by their lower pure technical efficiency. Therefore, when the scale of financing reaches a relatively ideal state, how to improve their pure technical efficiency and effectively manage and make good use of funds is the key point that energy companies should pay attention to in the future.
(2)
The Bootstrap correction efficiency value is lower than the original efficiency valueAs shown in Figure2, before the original financing efficiency value is revised, most of the black solid sample points are concentrated in the upper right area, that is, closer to the two “effective frontiers” with an efficiency value of 1, and some sample points just fall on the “effective frontier.” After correcting the efficiency value, it can be found that there is no white hollow sample point distribution on the “effective frontier” and its overall left shift, but the downward shift is not obvious [21]. The corrected efficiency value provides a more accurate measurement result and the change in the distribution shape before and after the scatter; it also confirms the previous finding that the contribution of scale efficiency to technical efficiency is greater than that of pure technical efficiency [22].


## 4.3. Trend Analysis of Overall Financing Efficiency

The financing efficiency trend of all 60 SME and ChiNext energy sample companies before and after the revision is shown in Figure3 [23]. As can be seen from the figure, the revised technical efficiency (bcTE) values from 2011 to 2015 were 0.708, 0.768, 0.732, 0.740, and 0.675, showing a slight and slow decreasing trend in the fluctuating state as a whole [24]. As far as the revised pure technical efficiency (bcPTE) is concerned, from 0.856 in 2011 to 0.726 in 2015, it shows a significant decline in technical efficiency. Comparatively speaking, the scale efficiency showed a different increasing trend year by year from 2011 to 2014, but it showed a downward trend in 2015 [25].Figure 3
Trends in the overall financing efficiency of energy companies from 2011 to 2015.In addition, we can notice that compared with the original efficiency values, the three efficiency values after the Bootstrap method correction have not changed in trend, and compared to the technical efficiency and pure technical efficiency, the correction of scale efficiency is the slightest. Therefore, for energy companies, reversing the declining trend of pure technical efficiency and maintaining the growth trend of scale efficiency are the key to improving the overall financing efficiency, and the former is more important.


## 5. Conclusion

By using the DEA method, the authors measured the financing efficiency of 30 SME board and 30 ChiNext energy companies listed in 2010 from 2011 to 2015 and introduced the Bootstrap method to improve the technical efficiency and pure technology of enterprises, the measurement accuracy of efficiency, and scale efficiency; on this basis, the following research conclusions are drawn: first, the overall financing efficiency of my country’s energy enterprises is in a state of inefficiency, more than 70% of the enterprises’ financing behavior cannot reach the effective level of DEA, and the financing efficiency after the Bootstrap method is revised. Second, the financing inefficiency of energy companies is mostly caused by their pure technical inefficiency; therefore, the capital management and application technology of enterprises need to be improved urgently. Third, from the perspective of the vertical time trend, although the scale efficiency shows an increasing trend as a whole, the pure technical efficiency is decreasing year by year.Energy is a key industry that the world pays attention to at present; under the background of “mass entrepreneurship and innovation,” China has also given policy support in many aspects of the energy industry; however, based on the above research, it can be seen that the financing efficiency of energy companies is generally low, which will undoubtedly bring difficulties to their future development, which is a small hindrance. In view of this, the authors provide the following policy suggestions: first, enterprises should use funds reasonably and effectively, increase their own R&D investment, and use the raised funds more for technological innovation. Secondly, clarify the purpose of financing, correct the motivation of financing, and avoid blind expansion of enterprise scale in the capital market. Finally, while improving the issuance mechanism and strengthening postevent supervision, the state still strives to improve the construction of a multilevel capital market system; a capital market with a complete structure and rich levels is the demand for corporate financing and development; at the same time, it is also a powerful guarantee for enterprises to have a good financing environment.



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*Source: 7262788-2022-08-27.xml*