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Article

Static and Dynamic Evaluation of Financing Efficiency in Enterprises’ Low-Carbon Supply Chain: PCA–DEA–Malmquist Model Method

by
Fayu Chen
1,
Jinhao Liu
2,
Xiaoyu Liu
3 and
Hua Zhang
4,*
1
Department of Electronic Business, South China University of Technology, Guangzhou 510006, China
2
School of Business, Central South University, Changsha 410083, China
3
School of Shi Cheng, Hunan Normal University, Changsha 410081, China
4
Institute of Innovation and Entrepreneurship, Loughborough University London, London E20 3BS, UK
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2510; https://doi.org/10.3390/su15032510
Submission received: 10 January 2023 / Revised: 23 January 2023 / Accepted: 25 January 2023 / Published: 31 January 2023
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
The dual-carbon target brings severe challenges to enterprise financing. Low-carbon supply chain financing has become a key measure by which enterprises break through the financing bottleneck. How to accurately evaluate and optimize the financial efficiency of a low-carbon supply chain is of great significance. We developed a financial behavior scale of the low-carbon supply chain for enterprises and constructed the evaluation index system for financing efficiency of enterprises’ low-carbon supply chain. Based on the qualitative and quantitative data of 205 listed companies, we combine the PCA model with the DEA–Malmquist model to conduct static and dynamic analysis of the financing efficiency of enterprises’ low-carbon supply chain. The results show that the financing efficiency of the culture, sports, and entertainment industries needs to be improved. The pure technical efficiency of information transmission, software, and information technology services is low. The total factor productivity index of water conservancy, environment and public facilities management, and the real estate industry fluctuated significantly. In this regard, enterprises must combine the characteristics of the industry and improve the financing efficiency of the supply chain using low-carbon processes of improvement, selection of low-carbon environmental protection materials, and collaborative supply chain emission reduction to break the financing constraints of enterprises and promote the realization of the national dual-carbon target.

1. Introduction

Achieving the dual-carbon target (“carbon peaking and carbon neutralization”) is the common consensus of all countries in the world for high-quality development and the inevitable choice for China’s ecological civilization construction [1,2,3]. However, China’s total CO2 emissions rank first in the world, accounting for 28% of the global CO2 emissions in 2019 [4]. This means that it is extremely difficult for China to complete the task of carbon emission reduction with the largest total amount and the highest intensity in the world [2,4]. The dual-carbon target in the new era poses new challenges to the low-carbon development of China’s industries.
Under the background of the dual-carbon target, enterprises need to accelerate the transformation of the energy economy, and sufficient funds are the basis of the transformation. It is difficult for enterprises subject to capital constraints to obtain loans directly from banks and other financial institutions. Financing constraints are an inevitable development problem for enterprises [5]. Global financing uncertainty has increased with the epidemic of COVID-19. Enterprises’ capacity for financing has decreased significantly, and the risk of capital chain disruption has continued to increase [6,7,8]. In this environment, the dual-carbon target makes it urgent for enterprises to achieve green transformation through a large amount of financing [4,9]. However, enterprises with financing constraints are finding it difficult to achieve financing through traditional means. Enterprises need to seek a new way to solve the problem of financing constraints under the dual-carbon target.
Unlike traditional bank lending, supply chain financing focuses on transactions and capital flows between supply chain network structures. Supply chain financing improves financing performance by optimizing enterprise financing structure and cash flow. It also plays an important role in solving the financing difficulties of upstream and downstream enterprises [10,11,12]. To solve the capital problem, enterprises need to combine the concept of green environmental protection with supply chain financing and improve green manufacturing, resource regeneration, and other links to finally achieve environment-friendly, civilized, and green sustainable development. Therefore, low-carbon supply chain finance has become the focus of attention of scholars and enterprises [10,13,14,15,16,17]. However, when enterprises conduct low-carbon supply chain financing, there are still problems such as large industry gaps, low-efficiency levels, and unclear improvement direction. Therefore, in the face of the strict constraints of the current dual-carbon target, it is necessary to evaluate the financing efficiency of enterprises’ low-carbon supply chains [18]. By selecting evaluation indicators, this paper offers a systematic and standardized comprehensive evaluation of the financing efficiency of a low-carbon supply chain, which is helpful in improving enterprises’ self-cognition, finding out the shortcomings of their own operation and management, and provide an important reference for the management to make low-carbon decisions.
Using the PCA–DEA–Malmquist index model, we constructed an index system for evaluating the financing efficiency of enterprises’ low-carbon supply chains and used the data of 205 listed enterprises to systematically evaluate the financing efficiency of enterprises’ low-carbon supply chains. This paper offers the following innovations and contributions:
(1)
Previous studies on supply chain financing rarely involve low-carbon levels, some of them are mostly related to corporate decision-making models, and there is still no research on its efficiency [14,15]. This paper takes the lead in designing an enterprise low-carbon supply chain financing efficiency evaluation scale, which enriches the existing literature on supply chain financing;
(2)
The research on enterprise low-carbon behavior is a hot topic at present, but there are few studies on low-carbon supply chain financial behavior [19,20]. The reason is that the measurement of low-carbon supply chain finance behavior is a major challenge. This paper develops the enterprise low-carbon supply chain financial behavior scale and uses the text information of the corporate social responsibility report and NVivo software to quantify the low-carbon supply chain financial behavior, expanding on the existing research on low-carbon supply chain management;
(3)
It is the first time PCA, DEA, and other models have been used to calculate the financing efficiency of a low-carbon supply chain; the evaluation process has strong logic and maneuverability. According to the evaluation indicators and evaluation results, this paper put forward targeted optimization suggestions, which have important guidance and reference significance for some enterprises and industries aiming to improve the financing efficiency of the low-carbon supply chain.

2. Literature Review

Traditional supply chains tend to take cost, service, and quality into consideration, while closed-loop supply chains and green supply chains emphasize resource utilization [21,22,23]. A low-carbon supply chain is developed in the context of a low-carbon economy, considering carbon tax, carbon tariff, carbon trading, carbon footprint, and other factors. A low-carbon supply chain improves the entire production mode, improves production efficiency, and reduces resource consumption [15,22]. In addition, a low-carbon supply chain also prevents the financial bubble that may be generated by China’s “double carbon goal” and the strengthening of carbon policy [8,24].
Low-carbon supply chains integrate the concept of environmental protection into product design, production, transportation, and sales. They pay attention to environmental protection and consider resource utilization to enhance enterprises’ low-carbon awareness [25]. In addition to emphasizing the compatibility between actors and the environment in the supply chain, the low-carbon supply chain also attaches importance to the overall carbon emission index of the supply chain [26,27].
The development of supply chain financing theory and technology has promoted the improvement of financing efficiency and a reduction to the financing cost of supply chain participating enterprises under a low-carbon background. This has become a hot issue in enterprise financing analysis in recent years [28,29]. Existing studies mainly elaborate on the financing strategy, influencing factors, and consequences of a low-carbon supply chain from the perspective of financing strategy [26,29,30,31]. For example, in terms of the influencing factors of low-carbon supply chain financing, the research on internal influencing factors mainly focuses on the factors of the participating members of the supply chain and the factors determined by the characteristics of the supply chain itself [13,32]. Research on external influencing factors mainly focuses on government policies, low-carbon preferences of consumers, and carbon emission reduction investment of carbon emission enterprises [15,28,32].
The general government’s emission reduction policy has two modes: direct control and economic incentive. Direct regulation is usually implemented by the government through legislation and direct intervention, while economic incentive measures regulate the carbon emissions of carbon-emitting enterprises through economic means [15,32,33]. In addition, the existence of consumers’ low-carbon preference affects consumers’ demand for low-carbon products to a certain extent, and thus affects the operation of the whole supply chain [28,32]. In terms of the carbon emission reduction investment of carbon-emitting enterprises, on the one hand, the government’s mandatory emission reduction policy is understood to promote enterprises to control carbon emissions to achieve mandatory carbon emission reduction investment [34]; on the other hand, for carbon emission reduction investment, the influence of consumers’ low-carbon preference will promote their higher demand for low-carbon products. However, the existence of carbon emission reduction investment will increase the cost of a low-carbon supply chain, thus bringing capital pressure to the operation of supply chains to a certain extent [35].
In addition, scholars have been actively exploring a variety of comprehensive evaluation methods to evaluate enterprise supply chain financing, including back propagation (BP), grey relational analysis (GRA), factor analysis (FA), data envelope analysis (DEA), etc. [36,37,38,39]. Back propagation (BP) was proposed by Rumelhart et al. [40] as a multi-level feedforward network trained by an error inverse propagation algorithm. It mainly simulates human thinking and can store distributed information and process information in parallel. A key characteristic of grey relational analysis (GRA) is that it applies to small samples of uncertainty and explores the existing laws of reality with the least information while allowing an arbitrary distribution [41,42,43]. The basic purpose of factor analysis (FA) is to describe the relationship between many indicators with a few factors and reflect most of the information from the original data with a few factors [44]. Data envelope analysis (DEA) was originally proposed by Charnes et al. [45] to improve the efficiency measurement of public institutions. So far, the DEA method has quickly become a popular method for performance measurement. As a globally recognized management research tool, it has been gradually applied to the supply chain efficiency evaluation [41,46,47].

3. Research Design

3.1. Model Selection

To comprehensively evaluate the financing efficiency of enterprises’ low-carbon supply chains, it is necessary to measure the financial behavior of enterprises’ low-carbon supply chains as an original indicator. This financial behavior contains a lot of non-financial information, which is of different importance to enterprises. Therefore, it is necessary to measure the financial behavior index of the low-carbon supply chain. Due to the characteristic of transforming multiple indicators into several representative comprehensive indicators, the PCA model can simplify complex problems without losing too much useful information, thus greatly improving the efficiency of analysis. In addition, the PCA model can also assign weights to each index according to the variance contribution rate. Therefore, the influence of human subjective factors on the research results is avoided. Because of these two advantages, the PCA model is widely used in management [48,49]. For example, Florackis and Ozkan [50] used the PCA model and a large sample of listed non-financial UK firms over the period 1999–2005 to calculate managerial entrenchment. Therefore, this paper first applies the PCA model to assign weight to the scale of enterprise low-carbon supply chain financial behavior, then uses panel data to calculate the low-carbon supply chain financial score of each sample enterprise, and finally applies the score to the efficiency measurement of DEA.
In the selection of input–output indicators, it is necessary to involve various aspects, and the measurement units of each indicator may be inconsistent. When applying the DEA–Malmquist index model, there is no need to consider the units of measurement of each index, which brings great convenience to the calculation [51,52]. In addition, as the supply chain system is relatively complex, it is relatively difficult to design a consistent production function, and there may be factors such as artificial settings. According to linear programming, the DEA–Malmquist index model does not need to set the production function in advance, thus avoiding certain subjectivity [52]. When using the DEA–Malmquist index model to measure efficiency, we can not only attain the final result of efficiency level but also find out the cause of the low efficiency of the unit according to the analysis results. Finally, suggestions for optimization are put forward according to specific influencing factors [53,54,55]. Based on the above analysis, we adopted the PCA–DEA–Malmquist index model to comprehensively measure the financing efficiency of enterprises’ low-carbon supply chain.
Specifically, in the process of using the PCA–DEA–Malmquist index model, we first constructed an index system for evaluating the financing efficiency of enterprises’ low-carbon supply chain, including the total assets of enterprises, asset–liability ratio, commercial credit, and other traditional indicators for measuring financing efficiency. We innovatively designed the financial behavior scale of the low-carbon supply chain for enterprises and integrated it into a measurement index system. Secondly, to overcome the difficulty in measuring qualitative indicators, we collected corporate social responsibility reports of 205 listed companies and used principal component analysis (PCA) to calculate the weights of enterprises’ financial behavior scale on the low-carbon supply chain. Finally, we used the DEA-BBC model for static analysis of efficiency value, and the DEA–Malmquist index model for dynamic analysis of efficiency value. Due to space limitation, the detailed formulas of PCA and DEA models will not be listed in this article.

3.2. Evaluation Index Selection

To achieve an objective and comprehensive evaluation of the financing efficiency of listed enterprises’ low-carbon supply chain, we preliminarily determined the input and output indicators by fully considering various factors. Input and output indicators include both quantitative indicators at the financial level and qualitative indicators at the corporate information disclosure level. The values are extracted by the qualitative analysis software NVivo 11. Next, we will introduce the input and output indicators.
  • Input Indicators
Since low-carbon supply chain financing is an emerging concept, its efficiency measurement is a blank area in the current research [14,15]. Therefore, we tried to refer to literature on sustainable supply chain finance and financing efficiency [36,56,57,58,59] when designing the input index of financing efficiency of enterprises’ low-carbon supply chain. What we discuss is the calculation of financing efficiency of a low-carbon supply chain, focusing on low carbon, which is somewhat different from the indicators selected in the previous literature. Based on the studies of domestic and foreign scholars [3,58,60], we selected the traditional indicators for measuring financing efficiencies, such as total enterprise assets, asset–liability ratio, and commercial credit. The financial behavior scale of the low-carbon supply chain is innovatively designed and integrated into the measurement index system.
①
Total assets. Total assets mainly measure the financing scale of the company. Total assets refer to everything owned by the enterprise as a result of previous transactions or events. This index can reflect the size of a company. Since the assets contain capital from different financing channels, this index can represent a certain financing scale of an enterprise;
②
Asset–liability ratio. The asset–liability ratio is the ratio of total liabilities to total assets of an enterprise, and mainly measures the capital structure of a company. This index shows the proportion of debt in the assets of an enterprise. It is also the degree of risk that creditors provide loans to the enterprise. It is generally believed that the level of debt to asset ratio is 40–60%;
③
Commercial credit. Commercial credit is a common financing method and operation means in the purchase and sale activities of enterprises. It has an important impact on the financing of enterprises and the financing activities of the whole supply chain. As an alternative financing method, commercial value can help enterprises relieve financing constraints, so companies meet their capital needs by incorporating commercial credit from upstream and downstream companies into the supply chain. At the same time, the company will actively provide business credit to these companies to increase the stickiness of the supply chain [61]. We measure commercial credit by the total amount of accounts payable, notes payable, and accounts received in advance;
④
Low carbon supply chain finance behavior. Based on the research results of Tseng et al. [57], Zhou et al. [62], and The CDP2017 Climate Change Questionnaire [63], we designed eight items as shown in Table 1. If the sample company has disclosed the project, the value is 1; otherwise, the value is 0. The disclosure of financial behavior data of a low-carbon supply chain mainly appears in the environment and sustainability section of corporate social responsibility reports. Therefore, qualitative analysis software NVivo 11 was used to search for keywords in annual reports, social responsibility reports, or sustainable development reports, and these keywords and statements were collected and sorted. Because NVivo software can only search files with extractable fields, social responsibility reports from some companies are presented in the form of pictures, and corporate reports with missing information were collected manually. We mainly investigated the following chapters: supplier, customer, and consumer rights protection (e.g., low-carbon procurement, low-carbon industrial chain), environmental protection, and sustainable development. In the calculation of low carbon supply chain finance, the different effects of enterprises’ different financial behaviors are considered comprehensively. The PCA model was first used to measure the low carbon supply chain financial behaviors, and then the data were applied to evaluate the financing efficiency of enterprises’ low carbon supply chain.
2.
Output indicators
The output index mainly measures the financing effect of enterprises’ low-carbon supply chain and reflects the final operating results of enterprises influenced by the input index. To determine the output indicators for evaluating the financing efficiency, we reflected on the three traditional financing efficiency indicators of enterprises, including total asset turnover, operating income, and return on equity [3,64]. To reflect the effectiveness of enterprises’ low-carbon supply chain financing from the perspective of the supply chain, we added the index of supply chain integration [65].
①
Total asset turnover. Total asset turnover rate refers to the turnover times of all the assets of the company in an accounting period; that is, the total efficiency of the effective operation of all the assets of the company. Generally speaking, the larger the value of total asset turnover is, the better. On the one hand, it reflects the strong turnover capacity of the company’s total assets; on the other hand, it indicates that the company’s assets have been effectively utilized, which can create more production benefits and operating profits for the company;
②
Operating income. Operating income is the most direct index to evaluate the development ability of an enterprise. The growth or decline of income directly reflects an enterprise’s operating efficiency and the use of funds;
③
Return on equity. Return on equity indicates the profitability of an enterprise’s net assets. Generally speaking, highly profitable assets can attract capital inflows. Therefore, highly profitable enterprises usually have a strong financing capacity;
④
Supply chain integration. Supply chain integration refers to the strategic cooperation between enterprises and the node companies in the supply chain. These enterprises have aligned goals and jointly manage their internal and external business processes to maximize value. Supply chain integration can reflect the real situation of the company’s supply chain relationship. We adopted the ratio of mean and variance of the proportion of the trading volume of five suppliers and customers in the annual total trading volume in three consecutive years to measure supply chain integration. This was to better reflect the transaction scale and stability between the company and the upstream/downstream companies in the supply chain.
The indicators selected and detailed introduction in this paper are shown in Table 2.

3.3. Data Collection

We chose to research Chinese enterprises. As China is highly dependent on heavy industry for development, environmental challenges must therefore be addressed in parallel with economic development. According to the outline of China’s 12th Five-Year Plan in 2011, addressing climate change and low-carbon development was taken as one of the policy directions. The National Standards Committee also announced the formulation of national standards for social responsibility and the launching of carbon trading pilot projects in seven provinces and cities [62,66]. In 2012, China issued a total of 1705 social responsibility reports, of which 1496 were issued by enterprises and 209 by non-enterprise organizations. The overall number of reports increased by 70% over the previous year. Therefore, we set the time window from 2012 to 2019 and took A-share listed companies in Shanghai and Shenzhen stock markets as the research focus.
We screened the data as follows. Samples that had not issued corporate social responsibility reports for at least one year in the eight years were excluded. We retained the sample of A-share issuance only. The samples with missing data were deleted. Considering the possible influence of extreme values, the upper and lower 1% of all continuous variables were winsorized. Finally, the research samples in line with the DEA model were 205 listed enterprises that issued social responsibility reports in China, involving 11 subsectors and a wide range. The original data of input and output indices in this paper are from the Ruisi database: http://www.resset.cn/ (accessed on 9 January 2023). The data analysis software involved includes SPSS19.0 and DEAP2.1.

4. Empirical Results

4.1. Financial Behavior Measurement of Enterprise Low-Carbon Supply Chain

4.1.1. Reliability Test

Reliability refers to the reliability or stability of the scale. With the improvement of the reliability of the scale, the validity and reliability of the empirical analysis and investigation results based on the scale are also improved. Since the low carbon supply chain financial behavior is proposed tentatively for the first time in this paper, the reliability of the qualitative analysis results of the scale was tested. We used the coefficient method to test the reliability of scale indicators and the overall reliability of the scale. SPSS19.0 software was used to test the reliability of the scale with Cronbach’s alpha coefficient [67]. In general, reliability above 0.7 passes the reliability test. The standardized Cronbach’s alpha coefficient was 0.879, indicating good reliability of the scale used in the study.

4.1.2. Applicability Test

Before principal component analysis, an adaptability test is needed to test whether the PCA is feasible and whether statistical results are accurate and reliable. We used SPSS22.0 analysis software to conduct practical analysis on the data of financial behavior and the scale of enterprises’ low-carbon supply chain, mainly through the Bartlett sphericity test and KMO (Kaiser–Meyer–Olkin) test. The KMO statistic was 0.853 > 0.5, indicating that the PCA could be carried out.

4.1.3. Principal Component Analysis

Principal component analysis was conducted on the ten indicators, and component characteristic values and cumulative contribution rates were obtained (see Table 3). Three principal components F1, F2, and F3 were extracted, with characteristic root values greater than 1 and variance contribution rates of 53.528%, 16.669%, and 10.131%, respectively. The results show that F1, F2, and F3 can be used to explain the financial behavior level of enterprises’ low-carbon supply chain.

4.1.4. Low Carbon Supply Chain Financial Behavior Measurement

Three principal components were extracted in this analysis, and the initial characteristic values of variance of each component were 5.353, 1.667, and 1.013, respectively, as shown in Table 3. The principal component coefficient matrix is obtained by combining the initial characteristic value of variance and factor load value, as shown in Table 4. According to Table 3, the variance of each component is 53.528, 16.669, and 10.131, respectively, and the comprehensive score coefficient and weight coefficient of each item are obtained accordingly; the results are shown in Table 4. Finally, we calculated the score of each enterprise’s low-carbon supply chain financial behavior according to the weight coefficient.

4.2. Static Measurement of Financing Efficiency of Low-Carbon Supply Chain

The DEA model requires the input and output index values to be non-negative; however, in the actual data collection, there were a few cases of negative data. To ensure the stability of data, we needed to normalize the sample data so that the original data could be adjusted to a positive range.
After data normalization, input and output data of 205 sample companies in 2019 were substituted into the DEA–BCC model. DEAP2.1 software was used to calculate the efficiency of the low-carbon supply chain financing of these companies. The average value obtained by industry categories is shown in Table 5. Static low carbon supply chain financing efficiency calculation mainly includes two categories, namely overall performance (total efficiency) and specific performance (pure technical efficiency and scale efficiency evaluation).

4.2.1. Measurement of the Total Efficiency

Total efficiency refers to the comprehensive evaluation of enterprises’ low-carbon supply chain resource allocation ability and capital use efficiency of low-carbon supply chain financing under the current resource input and technology level. This reflects the overall financing efficiency. According to Figure 1 and Table 5, we found that the culture, sports, and entertainment industries are all in the worst position for their comprehensive technical efficiency. The total efficiency value was 0.662, differing from the DEA valid state of 0.338. This indicates that the culture, sports, and entertainment industries did not achieve the maximum revenue and profit output in terms of operating costs, operational management level, and interest-bearing liabilities in 2019, and the low-carbon supply chain financing capacity of the average enterprise in the industry did not meet the expectations.

4.2.2. Measurement of the Pure Technical Efficiency

Pure technical efficiency reflects the degree of technology utilization and management ability of enterprises under the condition of variable scale—which is closely related to the size of enterprises’ financing ability—and financing direction is reasonable. Combined with Table 5 and Figure 2, the average pure technical efficiency of the 12 industries was 0.861, indicating that all of these industries attach importance to the improvement of financing efficiency of low-carbon supply chain enterprises. However, the pure technical efficiency of the information transmission, software, and information technology service industries was only 0.712, which is significantly different from other industries. These industries need to make use of scientific and reasonable technical models to ensure precise financing direction.

4.2.3. Measurement of the Scale Efficiency and Scale Return

Scale efficiency is used to evaluate whether the investment scale and output income scale of current financing are in the optimal state under the existing management system and technological level of the company. Scale efficiency of 1 means that the enterprise is already in the best state of input and output and no adjustment is required. In this case, the return to scale remains unchanged (“—”). If the scale efficiency is less than 1, it indicates that the current input and output scale of the enterprise has not reached the optimal state and needs to be adjusted. Returns to scale show that IRS (increasing returns to scale) means that an equal proportion of financing resources input will produce a larger proportion of output, and investment should be increased. Returns to scale show that DRS (decreasing returns to scale) indicates that increasing resource input at the same ratio will generate a lower proportion of income. The existing financing capital input cannot obtain the expected income, so the enterprise should reduce the investment of financing resources, reduce the scale, and stop expansion.
Combined with Table 5 and Figure 3, the scale efficiency of the culture, sports, and entertainment industries is 0.995, which is higher than the industry average of 0.944, indicating that the scale efficiency of the culture, sports, and entertainment industries is acceptable. Returns to scale in culture, sports, and entertainment industries (R) show a gradual increase. The results show that culture, sports, and entertainment enterprises need to increase the investment of financing resources, and the increased investment will bring a larger proportion of output income, thus gaining a competitive advantage and reducing the average fixed cost.

4.3. Dynamic Measurement of Financing Efficiency of Low-Carbon Supply Chain

We put all the input–output indicators of sample companies from 2012 to 2019 into the Malmquist model. We then calculated the Malmquist index (total factor productivity index) of each sample enterprise in each adjacent two years through panel data and obtained the specific changes in low-carbon supply chain financing efficiency of each sample enterprise in each year relative to the previous year. We studied the dynamic changes in the financing efficiency of the low-carbon supply chain of each sample enterprise during the eight years from 2012 to 2019.

4.3.1. Measurement of the Change in Total Factor Productivity Index

Figure 4 shows the change in the total factor productivity index of each sample company’s industry from 2012 to 2019. The total factor productivity index represents the overall change in financing efficiency of the sample firms. If the total factor productivity index is greater than 1, it indicates that the financing efficiency of the current period has improved compared with that of the previous period. If the total factor productivity index is less than 1, it indicates that the financing efficiency of the current period has regressed compared with that of the previous period.
As can be seen from Figure 4, the total factor productivity index of the water, environment and utilities management industry (N) fluctuates significantly and lacks stability, indicating that on average, the financing efficiency of the enterprises in these industries changes greatly every year, and most of them may be in the growth period. In 2012–2013 and 2014–2015, the Malmquist index of water conservancy, environment, and public facilities management was greater than 1, while in 2015–2016 and 2017–2018, the Malmquist index was less than 1. These results indicate that the financing efficiency of enterprises has on average, decreased significantly in recent years.

4.3.2. Measurement of the Change in Pure Technical Efficiency

The change in pure technical efficiency shows the change in the sample companies’ ability to create output with the input of existing financing resources. When the change of pure technical efficiency is greater than 1, it indicates that the pure technical efficiency of the current period is higher than that of the previous period; a result of less than 1 indicates that the pure technical efficiency is lower than that of the previous period. Figure 5 shows the changes in pure technical efficiency across the industries in which the sample companies are located between 2012 to 2019.
As can be seen in Figure 5, the pure technical efficiency of the water, environment, and utilities management industry (N) changed significantly from 2012 to 2016. This may be because it is an emerging industry in the early stage of technical exploration. After 2016, the pure technical efficiency of various industries gradually tends to 1.

4.3.3. Measurement of Changes in Scale Efficiency

When the change of scale efficiency is greater than 1, it indicates that the scale efficiency of the current period has improved compared with the previous period, and the enterprise is appropaching to the optimal scale. Figure 6 shows the changes in scale efficiency of the industries in which the sample companies are located from 2012 to 2019. Only in the mining industry was the change of scale efficiency of the company less than 1 from 2013 to 2016; in other years, it was more than 1.

4.3.4. Measurement of Changes in Technological Progress

The change in technological progress shows the technological update and enterprise innovation capability of the sample companies during the eight years. From the perspective of financing technology, this change can represent the degree of accumulation, renewal, and promotion of financing technology by using financial knowledge. Financing technology usually includes the improvement of organizational structure and the design of cost control. Figure 7 show the change in the technological progress index of the industry in which each sample company is located from 2012 to 2019.

4.3.5. Agriculture, Forestry, Animal Husbandry, and Fishing Industry (A)

From 2012 to 2016, the average progress of financial technology in the agriculture, forestry, animal husbandry, and fishery industries (A) was slow, and the technological capacity of enterprises decreased, as seen in Figure 7. From 2016 to 2019, the technological progress index of companies in these industries was significantly greater than 1 on average. This indicates that the technology level of the company has increased significantly and the financing efficiency of enterprises has been greatly improved.

5. Conclusions

We analyzed the financing dilemma of enterprises under the current dual-carbon target and the importance of financing choice of a low-carbon supply chain. Referring to relevant theories and references [36,56,57,58,59], we have constructed the PCA–DEA–Malmquist model to measure the financing efficiency of enterprises’ low-carbon supply chain. In this process, we developed the enterprise low-carbon supply chain financial behavior scale and quantified this financial behavior by using the text information from the corporate social responsibility reports and NVivo software. Our research is the first attempt to open the black box of enterprise low-carbon supply chain financing systems. Our research provides an effective tool for scientifically evaluating and improving the financing efficiency of enterprises’ low-carbon supply chains.
Through the research, we found the following results. First, through the analysis of industry classification indicators, it is concluded that the financing efficiency of the culture, sports, and entertainment industries needs to be improved. Their comprehensive technical efficiency is in the worst position among all industries, and there is a large mismatch between input and output. Second, from the average level of the 12 industries, the whole industry currently attaches importance to the cultivation of management ability and technological development, while the pure technical efficiency of information transmission, software, and information technology services is much lower than that of other industries. Third, the total factor productivity index of the water conservancy, environment, and public facilities management industries and the real estate industry fluctuates significantly and lacks stability, indicating that on average, the financial performance of the enterprises in the industry changes greatly every year, and most of them may be in the growth period.
This paper has important practical implications: first, it provides experience reference for enterprise low-carbon supply chain financing and sustainable development. This paper focuses on the indicators and evaluation of the financing efficiency of the low-carbon supply chain. The evaluation process and results are helpful for enterprises aiming to clarify their own financing problems of low-carbon supply chains and the direction of improvement. Second, it is helpful to provide the basis for the design of the government’s low-carbon policy. This paper evaluates the financing efficiency of a low-carbon supply chain, which is helpful in analyzing the financing level of the low-carbon supply chain in various industries and exploring the potential of carbon emission reduction. It is helpful for the government in formulating more reasonable carbon policies, strengthening enterprise environmental governance, and promoting the realization of the national dual-carbon target.
We obtain the following insight: the low-carbon supply chain financing efficiency evaluation system based on the DEA–Malmquist index model can fully illuminate the financing risks faced by enterprises and the deficiencies of operation and management. Through the calculation of enterprise financing indicators, we can accurately find the change range of a certain indicator in the current year and adjust the input–output volume of various important financing indicators. This will help us to explore more efficient financing resource allocation, quickly respond to changes in the market environment, and grasp the key points of operation and management in the future [3,56]. On the other hand, improving enterprise management according to the evaluation results of low-carbon supply chain financing efficiency will not only reduce the consumption of raw materials and carbon emissions generated in the process of processing and production, but also significantly improve the level of upstream and downstream management synergy in the enterprise supply chain [39,46]. Small and medium-sized enterprises can start from the aspects of low-carbon process improvement: selection and procurement of low-carbon environmental protection materials; improving the utilization rate of raw materials; using emission reduction technology to deal with enterprise emissions; and adopting efficient organizational structure and management processes. These will help to minimize the environmental pollution caused by production activities in the stages of product design, raw material procurement, and product production, and to obtain higher supply chain financing [25]. Large manufacturing enterprises can rely on their network resource advantages to require upstream and downstream businesses in the supply chain to cooperate in the emissions reduction scheme [68], reducing their carbon emissions in raw material procurement, production, storage, transportation, distribution, and recycling.
Since the financing efficiency of the low-carbon supply chain was first proposed by us, there are still some defects in our paper and avenues for further research. For example, the more cutting-edge functional principal component analysis (FPCA) model can also be used in model selection. In terms of index selection, more indicators of the financing efficiency of low-carbon supply chains can be selected in later studies.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by F.C., J.L., X.L. and H.Z. The first draft of the manuscript was written by F.C., and all authors commented on the previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Hunan Province (2021JJ30890), the Fundamental Research Funds for the Central Universities of Central South University (2021zzts0029), and the Outstanding Youth Scientist Foundation of Hunan Province (21B0016). Any remaining errors are the authors’ own.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Comprehensive technical efficiency of sample companies in different industries.
Figure 1. Comprehensive technical efficiency of sample companies in different industries.
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Figure 2. Pure technical efficiency of sample companies in different industries.
Figure 2. Pure technical efficiency of sample companies in different industries.
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Figure 3. Scale efficiency chart of sample companies in different industries.
Figure 3. Scale efficiency chart of sample companies in different industries.
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Figure 4. Changes in total factor productivity index of various industries from 2012 to 2019.
Figure 4. Changes in total factor productivity index of various industries from 2012 to 2019.
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Figure 5. Changes in pure technical efficiency of various industries from 2012 to 2019.
Figure 5. Changes in pure technical efficiency of various industries from 2012 to 2019.
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Figure 6. Changes in scale efficiency of various industries from 2012 to 2019.
Figure 6. Changes in scale efficiency of various industries from 2012 to 2019.
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Figure 7. Changes in the technological progress of various industries from 2012 to 2019.
Figure 7. Changes in the technological progress of various industries from 2012 to 2019.
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Table
Table 1. Financial behavior scale of enterprise low-carbon supply chain.
Table 1. Financial behavior scale of enterprise low-carbon supply chain.
ItemItem DescriptionKeywordsReference
Q1Enterprises have adopted market mechanisms to save carbon during the reporting periodCarbon, CO2, environment, environmental protection, emission reduction, value chain, industrial chain, green, ecology, climate change, sustainable, clean, target, control, management, strategy, conservation, risk, internal control, awareness, monitoring, monitoring, energy savingTseng et al. [57], Zhou et al. [62] and THE CDP2017 Climate Change Questionnaire [63]
Q2Enterprises actively purchase low-carbon materials in the reporting period
Q3Enterprises and supply chain enterprises work together to solve the low-carbon problem
Q4Enterprises have formulated low-carbon management regulations or documents to guide carbon emission reduction work
Q5Enterprises regularly monitor the carbon reduction process through a comprehensive management assessment system and/or specific carbon management assessment system
Q6Enterprises have established relatively effective emission reduction targets during the reporting year
Q7Enterprises report ongoing mitigation actions during the year
Q8Enterprises have a perfect monitoring and measurement system for carbon emissions
Q9Enterprises disclose specific numbers of carbon emissions, emissions reductions, or emission reduction rates
Q10Enterprises through emission reduction management, emission reduction targets to obtain social or government recognition
Table
Table 2. Evaluation indicators.
Table 2. Evaluation indicators.
Input/Output IndicatorsIndex NameIndex Definition
Input indicatorsTotal assetsLn (total assets)
Asset–liability ratioTotal liabilities/total assets
Commercial creditLn (Accounts payable + Notes payable + Accounts received in advance)
Low carbon supply chain finance behaviorObtained by qualitative analysis
Output indicatorsTotal asset turnoverOperating income/total average assets
Operating incomeLn (Income from main business + income from other business)
Return on equityNet profit/net asset
Supply chain integrationThe ratio of mean and variance of the proportion of the trading volume of five suppliers and customers in annual total trading volume in three consecutive years
Table
Table 3. Principal component characteristic values and cumulative contribution rates.
Table 3. Principal component characteristic values and cumulative contribution rates.
ComponentInitial EigenvalueExtract the Sum of Squares of LoadsSum of Squares of Rotational Loads
EigenvaluePercentage of VarianceCumulative (%)EigenvaluePercentage of VarianceCumulative (%)EigenvaluePercentage of VarianceCumulative (%)
15.35353.52853.5285.35353.52853.5283.76537.65437.654
21.66716.66970.1971.66716.66970.1973.02330.22867.881
31.01310.13180.3281.01310.13180.3281.24512.44680.328
Table
Table 4. Component score and weight coefficient matrix.
Table 4. Component score and weight coefficient matrix.
ItemComponent Composite Score CoefficientWeight Coefficient
F1F2F3
Q1−0.1150.1550.4160.0080.004
Q20.3720.137−0.1460.2580.146
Q3−0.1160.3800.1920.0260.014
Q40.380−0.213−0.0160.2070.117
Q5−0.1090.467−0.0300.0200.012
Q60.308−0.1790.1130.1820.103
Q70.2740.432−0.2970.2350.133
Q80.1540.0970.8750.2330.132
Q90.3210.3560.2340.3170.179
Q100.3080.460−0.1380.2830.160
Table
Table 5. Financing efficiency of low-carbon supply chain of sample companies in 2019.
Table 5. Financing efficiency of low-carbon supply chain of sample companies in 2019.
IndustryIndustry CodeTotal Efficiency (TE)Pure Technical Efficiency (PTE)Scale Efficiency (SE)Return to Scale
Agriculture, Forestry, Animal Husbandry, and Fishing IndustryA0.68210.681IRS
Extractive IndustriesB0.7790.7960.979IRS
Manufacturing IndustryC0.8590.8960.958IRS
Electricity, Heat, Gas, and Water Production and Supply IndustriesD0.8510.8620.986IRS
Construction IndustryE0.7920.8690.910IRS
Wholesale and Retail IndustryF0.9160.9400.974IRS
Transportation, Warehousing, and Postal IndustriesG0.8430.8590.979IRS
Information Transmission, Software, and Information Technology Service IndustryI0.6780.7120.947IRS
Real Estate IndustryK0.7900.8490.933IRS
Water, Environment, and Utilities Management IndustryN111—
Culture, Sports, and Entertainment IndustryR0.6620.6660.995IRS
Comprehensive IndustryS0.8720.8830.985IRS
MeanMean0.8100.8610.944——
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Chen, F.; Liu, J.; Liu, X.; Zhang, H. Static and Dynamic Evaluation of Financing Efficiency in Enterprises’ Low-Carbon Supply Chain: PCA–DEA–Malmquist Model Method. Sustainability 2023, 15, 2510. https://doi.org/10.3390/su15032510

AMA Style

Chen F, Liu J, Liu X, Zhang H. Static and Dynamic Evaluation of Financing Efficiency in Enterprises’ Low-Carbon Supply Chain: PCA–DEA–Malmquist Model Method. Sustainability. 2023; 15(3):2510. https://doi.org/10.3390/su15032510

Chicago/Turabian Style

Chen, Fayu, Jinhao Liu, Xiaoyu Liu, and Hua Zhang. 2023. "Static and Dynamic Evaluation of Financing Efficiency in Enterprises’ Low-Carbon Supply Chain: PCA–DEA–Malmquist Model Method" Sustainability 15, no. 3: 2510. https://doi.org/10.3390/su15032510

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