Sustainable Financing Efficiency and Environmental Value in China’s Energy Conservation and Environmental Protection Industry under the Double Carbon Target
Abstract
:1. Introduction
2. Literature
2.1. Finance Theory
2.2. Financing Efficiency Measures
2.3. Factors Influencing the Efficiency of Financing
3. Methods and Materials
3.1. Methods
3.1.1. DEA Model
3.1.2. Malmquist Index Model
3.1.3. Tobit Model
3.2. Materials
3.2.1. Selection of Evaluation Indicators
3.2.2. Dimensionless Data Processing
3.2.3. Selection of Factors Influencing the Efficiency of Financing
- (1)
- Digital transformation
- (2)
- Green technology innovation
- (3)
- Size of the business
- (4)
- Gearing ratio
- (5)
- Profitability
- (6)
- Operating capacity
3.2.4. Model Construction
3.3. Data Sources
4. Results and Discussion
4.1. Results
4.1.1. Criteria for Grading the Efficiency of Financing
4.1.2. Analysis of Financing Methods
4.1.3. Comprehensive Analysis of Static Financing Efficiency
4.1.4. Comprehensive Analysis of Dynamical Financing Efficiency
4.2. Discussion
4.2.1. Different Ways of Analyzing Static Financing Efficiency
- (1)
- An analysis of Table 5 and Figure 2 shows that the average total financing efficiency of the four financing methods varied widely, with the combined efficiency of endogenous financing and equity financing being greater at 0.81 and 0.733, respectively, and maintaining a combined efficiency value above 0.7 in most years. The combined technical effect of debt financing and fiscal financing was relatively low, at 0.358 and 0.366, respectively. The reason for this may be that debt financing and fiscal financing have more demanding and stringent conditions, making it difficult to meet the demand for the financing scale for the digital transformation and green technology innovation of energy-saving and environmental protection enterprises. Reliance on debt and government financial support makes it difficult to adequately cover the funds needed for the high-quality development of energy-saving and environmental protection enterprises.
- (2)
- An analysis of Table 5 and Figure 3 shows that endogenous financing had the highest efficiency of pure technical financing, with an average of 0.929 and a value greater than 0.9 for each year. Equity financing had the second highest efficiency of pure technical financing, with a value of 0.908, but there is still room for improvement.
- (3)
- An analysis of Table 5 and Figure 4 shows that endogenous financing and equity financing were more efficient in scale, both being greater than 0.8, while debt financing and financial financing were lower. This is related to the characteristics of energy-saving and environmental protection enterprises, which have large initial investments in digital transformation and green technology innovation, uncertainty, and long lead times in generating returns, and are overlaid with information asymmetry. For this reason, energy-saving and environmental protection enterprises need to seek the optimal scale of production in order to improve the efficiency of financing.
4.2.2. Different Ways of Analyzing Dynamical Financing Efficiency
- The four types of financing, i.e., equity financing, endogenous financing, financial financing, and debt financing, have annual average Malmquist indices of 0.916, 0.923, 1.020 and 1.025, respectively.
- The Malmquist index of equity financing declined by an average of 8.4%, and in further analysis, the comprehensive technical efficiency change index declined by 0.9% and the technological change advances index, on average, declined by 7.5% in further analysis. Technological regression has led to a decline in equity financing.
- 3.
- The main reasons for the fluctuations from 2015 to 2020, in terms of changes in the Malmquist index are follows. Firstly, the energy conservation and environmental protection industry needs to build a long-term core model for competitiveness. The advancing level of technology, the accumulation of management experience, and the strength of policy support and enforcement are key issues that constrain the high-quality development of energy-saving and environmental protection enterprises. Secondly, under the guidance of policies, capital is pouring into the energy-saving and environmental protection industry, but the industry’s economic efficiency is low and profitability levels continue to decline, leading to under-performance in financing efficiency. This is also where the difficulty of financing and the low efficiency of financing comes in.
- 4.
- The Malmquist index fell by 34.5% in 2016–2017, while it rose by 52.6% in 2017–2018. This may be because the energy conservation and environmental protection industry is relatively more subjected to the influence of environmental protection policies. A series of supportive policies were released in 2016 to promote the great development of environmental protection; since 2016–2017, debt financing and equity financing have risen sharply, bringing about the expansion of enterprise scales, due to the large scale of initial investment in the environmental protection industry and the relatively long investment cycle. As a result, the expansion of industry scale has in turn brought about a reduction in financing efficiency and operational efficiency. In 2017–2018, the change in financing scale levelled off, with technology and scale factors coming into play.
4.2.3. Analysis of the Influencing Factors of Financing Efficiency
5. Conclusions and Enlightenment
5.1. Conclusions
5.2. Suggestions
5.3. Deficiencies and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type of Indicator | Tier 1 Indicators | Secondary Indicators | Indicator Description |
---|---|---|---|
Input indicators | Endogenous financing | Surplus reserves | Reflects the accumulated capital of a business drawn from its profit after tax. |
Unallocated profit | To provide security for the future capital needs of the business. | ||
Debt financing | Non-current liabilities | All debts of one year or more that are used in return for payment and are required to be repaid on a regular basis. | |
Equity financing | Paid-in capital | The capital contributed by an investor to an energy-saving and environmental protection enterprise after it has been effectively controlled by the enterprise. | |
Capital surplus | The amount of capital invested by investors in energy-saving and environmental protection enterprises exceeds the legal registered capital. | ||
Financial financing | Government grants | Reflects the level of financial support provided by the government for energy-saving and environmental protection enterprises. | |
Output indicators | Operating income | Revenue from main business | Reflects the stage of growth of energy-saving and environmental protection companies and determines their growth capability. |
Operating profit | Net profit | It reflects the profitability, solvency, and management level of energy-saving and environmental protection enterprises. | |
Corporate assets | Intangible assets | It reflects the technological innovation capability of energy-saving and environmental protection enterprises and can measure the potential of enterprises to gain future economic benefits and financing. |
Variable Type | Variable Name | Variable Symbols | Variable Definitions |
---|---|---|---|
Explained variables | Financing efficiency | FE | Pure technical efficiency index [67,68] |
Explanatory variables | Business size | SIZE | Logarithm of total assets |
Digital transformation | DLTN | The text content of the annual reports of enterprises was extracted by IntelliJ IDEA, matched with word frequencies related to digital transformation, and the word frequencies were summed to obtain logarithms as proxy variables for digital transformation. The selection of keywords was based on Wu Fei’s study [61] | |
Green technology innovation | INNO | (Closing intangible assets-Opening intangible assets)/Total assets at the end of the period [69] | |
Gearing ratio | DA | Total liabilities/total assets | |
Profitability | ROA | Total net asset margin | |
Operating capacity | CAT | Current asset turnover ratio |
Distribution of Efficiency Intervals | 0 < H < 0.5 | 0.5 ≤ H < 0.8 | 0.8 ≤ H < 1 | H = 1 |
---|---|---|---|---|
Efficiency levels | Inefficient | Less efficient | Higher efficiency | Optimum efficiency |
2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|
Comprehensive Technical Efficiency | ||||||
Best efficiency as a percentage (%) | 23.41 | 19.02 | 5.85 | 17.56 | 17.07 | 12.2 |
Higher efficiency as a percentage (%) | 72.68 | 76.59 | 12.68 | 74.63 | 78.05 | 80.98 |
Percentage of lower efficiency (%) | 3.41 | 3.09 | 80.49 | 7.8 | 4.88 | 6.34 |
Percentage of inefficiencies (%) | 0.49 | 0.49 | 0.98 | 0 | 0 | 0.49 |
Average value | 0.95 | 0.941 | 0.75 | 0.928 | 0.936 | 0.917 |
Pure Technical Efficiency | ||||||
Best efficiency as a percentage (%) | 40 | 39.51 | 37.56 | 40.98 | 43.41 | 38.54 |
Higher efficiency as a percentage (%) | 57.07 | 58.05 | 59.51 | 55.61 | 55.61 | 59.51 |
Percentage of lower efficiency (%) | 2.44 | 1.95 | 2.93 | 3.41 | 0.98 | 1.95 |
Percentage of inefficiencies (%) | 0.49 | 0.49 | 0 | 0 | 0 | 0 |
Average value | 0.968 | 0.969 | 0.969 | 0.966 | 0.971 | 0.965 |
Scale Efficiency | ||||||
Best efficiency as a percentage (%) | 5.47 | 7.46 | 6.47 | 9.45 | 5.97 | 8.46 |
Higher efficiency as a percentage (%) | 70.73 | 77.56 | 21.95 | 76.1 | 80 | 81.95 |
Percentage of lower efficiency (%) | 0 | 0.98 | 72.2 | 2.44 | 2.44 | 3.9 |
Percentage of inefficiencies (%) | 0 | 0 | 0 | 0 | 0 | 0 |
Average value | 0.98 | 0.97 | 0.775 | 0.96 | 0.963 | 0.949 |
Periods | Comprehensive Technical Efficiency Change Index | Technological Advances Index | Pure Technical Efficiency Change Index | Scale Efficiency Index | Malmquist Index |
---|---|---|---|---|---|
2015–2016 | 0.991 | 0.827 | 1.002 | 0.989 | 0.820 |
2016–2017 | 0.795 | 0.812 | 1.000 | 0.795 | 0.645 |
2017–2018 | 1.241 | 1.230 | 0.997 | 1.244 | 1.526 |
2018–2019 | 1.010 | 0.813 | 1.006 | 1.004 | 0.821 |
2019–2020 | 0.978 | 1.048 | 0.993 | 0.985 | 1.025 |
Mean | 0.993 | 0.932 | 1.000 | 0.993 | 0.926 |
Year | Endogenous Financing | Debt Financing | Equity Financing | Fiscal Financing | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Crste | Vrste | Scale | Crste | Vrste | Scale | Crste | Vrste | Scale | Crste | Vrste | Scale | |
2015 | 0.897 | 0.935 | 0.958 | 0.31 | 0.928 | 0.341 | 0.789 | 0.904 | 0.872 | 0.363 | 0.918 | 0.401 |
2016 | 0.833 | 0.92 | 0.904 | 0.397 | 0.93 | 0.433 | 0.75 | 0.897 | 0.834 | 0.399 | 0.928 | 0.432 |
2017 | 0.641 | 0.925 | 0.699 | 0.366 | 0.909 | 0.413 | 0.542 | 0.9 | 0.606 | 0.359 | 0.928 | 0.393 |
2018 | 0.849 | 0.93 | 0.913 | 0.379 | 0.911 | 0.426 | 0.783 | 0.908 | 0.861 | 0.343 | 0.918 | 0.378 |
2019 | 0.86 | 0.934 | 0.921 | 0.351 | 0.91 | 0.399 | 0.779 | 0.917 | 0.85 | 0.359 | 0.907 | 0.404 |
2020 | 0.782 | 0.929 | 0.843 | 0.345 | 0.905 | 0.396 | 0.754 | 0.924 | 0.817 | 0.375 | 0.916 | 0.414 |
Average | 0.81 | 0.929 | 0.873 | 0.358 | 0.916 | 0.401 | 0.733 | 0.908 | 0.807 | 0.366 | 0.919 | 0.404 |
Periods | Comprehensive Technical Efficiency Change Index | Technological Advances Index | Pure Technical Efficiency Change Index | Scale Efficiency Index | Malmquist Index |
---|---|---|---|---|---|
2015–2016 | 0.926 | 0.834 | 0.982 | 0.942 | 0.772 |
2016–2017 | 0.771 | 0.75 | 1.005 | 0.767 | 0.578 |
2017–2018 | 1.323 | 1.352 | 1.007 | 1.314 | 1.789 |
2018–2019 | 1.017 | 0.784 | 1.006 | 1.011 | 0.797 |
2019–2020 | 0.904 | 1.164 | 0.993 | 0.911 | 1.053 |
mean | 0.972 | 0.949 | 0.999 | 0.973 | 0.923 |
Periods | Comprehensive Technical Efficiency Change Index | Technological Advances Index | Pure Technical Efficiency Change Index | Scale Efficiency Index | Malmquist Index |
---|---|---|---|---|---|
2015–2016 | 0.948 | 0.789 | 0.991 | 0.957 | 0.748 |
2016–2017 | 0.722 | 0.834 | 1.006 | 0.718 | 0.603 |
2017–2018 | 1.442 | 1.152 | 1.008 | 1.430 | 1.660 |
2018–2019 | 1.005 | 0.847 | 1.013 | 0.992 | 0.851 |
2019–2020 | 0.961 | 1.056 | 1.007 | 0.954 | 1.015 |
mean | 0.990 | 0.925 | 1.005 | 0.986 | 0.916 |
Periods | Comprehensive Technical Efficiency Change Index | Technological Advances Index | Pure Technical Efficiency Change Index | Scale Efficiency Index | Malmquist Index |
---|---|---|---|---|---|
2015–2016 | 1.291 | 0.843 | 1.004 | 1.285 | 1.088 |
2016–2017 | 0.931 | 1.395 | 0.973 | 0.957 | 1.299 |
2017–2018 | 1.021 | 0.758 | 1.000 | 1.020 | 0.774 |
2018–2019 | 0.955 | 1.081 | 1.000 | 0.955 | 1.032 |
2019-2020 | 0.950 | 1.055 | 0.989 | 0.960 | 1.002 |
mean | 1.022 | 1.003 | 0.993 | 1.028 | 1.025 |
Periods | Comprehensive Technical Efficiency Change Index | Technological Advances Index | Pure Technical Efficiency Change Index | Scale efficiency Index | Malmquist Index |
---|---|---|---|---|---|
2015–2016 | 1.106 | 0.960 | 1.016 | 1.088 | 1.061 |
2016–2017 | 0.910 | 1.646 | 0.999 | 0.911 | 1.498 |
2017–2018 | 0.938 | 0.694 | 0.989 | 0.948 | 0.651 |
2018–2019 | 1.059 | 1.024 | 0.985 | 1.075 | 1.084 |
2019–2020 | 1.039 | 0.948 | 1.012 | 1.027 | 0.985 |
mean | 1.008 | 1.012 | 1.000 | 1.007 | 1.020 |
Explanatory Variables | Coefficient | Std. Err. | t | p > |t| |
---|---|---|---|---|
SIZE | −0.0408998 *** | 0.0031479 | −12.99 | 0.000 |
DLTN | 0.004056 * | 0.0021897 | 1.85 | 0.064 |
INNO | 0.3867846 *** | 0.1032167 | 3.75 | 0.000 |
DA | 0.0387421 * | 0.0222210 | 1.74 | 0.082 |
ROA | 0.1032485 * | 0.0566296 | 1.82 | 0.069 |
CAT | 0.035325 *** | 0.0054728 | 6.45 | 0.000 |
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Li, B.; Huo, Y.; Yin, S. Sustainable Financing Efficiency and Environmental Value in China’s Energy Conservation and Environmental Protection Industry under the Double Carbon Target. Sustainability 2022, 14, 9604. https://doi.org/10.3390/su14159604
Li B, Huo Y, Yin S. Sustainable Financing Efficiency and Environmental Value in China’s Energy Conservation and Environmental Protection Industry under the Double Carbon Target. Sustainability. 2022; 14(15):9604. https://doi.org/10.3390/su14159604
Chicago/Turabian StyleLi, Baohong, Yingdong Huo, and Shi Yin. 2022. "Sustainable Financing Efficiency and Environmental Value in China’s Energy Conservation and Environmental Protection Industry under the Double Carbon Target" Sustainability 14, no. 15: 9604. https://doi.org/10.3390/su14159604