The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model
Abstract
:1. Introduction
2. Literature Review
3. Research Methodology
3.1. Three-Stage DEA Model
3.2. Tobit Model
4. Data and Indicators
4.1. Data Source
4.2. Indicators Selection
4.2.1. Output Variables
4.2.2. Input Variables
4.2.3. External Environmental Variables
4.2.4. Influencing Factors of Technological Innovation Efficiency
5. Efficiency Analysis of Technological Innovation Efficiency of China’s Renewable Energy Enterprises
5.1. Renewable Energy Enterprises’ Overall Technological Innovation Efficiency
5.1.1. Initial DEA Efficiency Evaluation
5.1.2. The Effects of External Environmental Variables on TIE
- (1)
- The variable of human resource relaxation is significantly negatively impacted by the degree of dependency on foreign trade (Tra), demonstrating that the degree of dependence on foreign trade is favorable to the effective level of human resources. The variable of financial resource relaxation is significantly negatively impacted by the degree of reliance on foreign trade, demonstrating that the amount of reliance on international trade is favorable to the actual level of financial resources.
- (2)
- The relationship between industrial structure (Ind) and human resource relaxation variables shows that an industrial structure is advantageous to the effective level of human resources. This shows that the effective level of financial resources, which is advantageous to the relaxation of industrial structure, has a significant negative impact on industrial structure.
- (3)
- Local science and technology expenditures (Tec) have a significant positive impact on human resource relaxation variables, demonstrating the waste of human resources caused by these expenditures; they also have a significant positive impact on financial resource relaxation variables, illustrating the waste of financial resources caused by these expenditures.
5.1.3. The DEA Efficiency Evaluation after Using Adjustment Variables
5.2. Renewable Energy Enterprises’ Technological Innovation Efficiency in Each Province
5.3. Study of the Elements That Affect the Innovation Efficiency of Renewable Energy Firms
6. Robustness Test
7. Conclusions and Policy Implications
8. Limitations and Directions for Future Research
8.1. Limitations
8.2. Directions for Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Models | Variables | Abbreviation | Indicators | Definitions |
---|---|---|---|---|
Three-stage DEA model | Output variables | Rdo | Scientific research output | Number of patents granted to the company each year |
Mon | Economic output | Increase in intangible assets | ||
Input variables | Peo | Technical personnel input | The total number of technical professionals employed annually | |
Rdi | R&D capital input | The total R&D investment | ||
Tobit model | External environmental variables | Tra | Foreign trade dependence | Percentage of regional GDP attributable to total import and export volume |
Ind | Industrial structure | Proportion of secondary Industry in GDP | ||
Tec | Local science and technology expenditure | Local science and technology expenditure | ||
influencing factors | Gov | Government subsidy | Government subsidies/operating income | |
Sale | Net profit margin | Net profit/operating income | ||
Debt | Debt asset ratio | Total liabilities/total assets | ||
Rev | Prime operating revenue | ln(Prime operating revenue) | ||
Hum | Education level of employees | Number of people with at least a bachelor’s degree/total population | ||
Size | Enterprise scale | Final asset natural logarithm for the business | ||
Age | Enterprise age | Current year - the year the company was founded |
Variable | Obs | Mean | Std. dev. | Min | Max |
---|---|---|---|---|---|
PEO | 1575 | 1033.811 | 2423.422 | 28 | 17,917 |
RDI | 1575 | 37,059.17 | 95,201.89 | 532.24 | 734,700.5 |
RDO | 1575 | 130.386 | 277.659 | 1 | 2056 |
MON | 1575 | 13,177.46 | 36,234.22 | 0 | 248,527.3 |
TRA | 1575 | 45.327 | 25.862 | 5.771 | 100.408 |
IND | 1575 | 39.564 | 7.357 | 15.967 | 48.718 |
TEC | 1575 | 423.747 | 305.092 | 32.07 | 1168.79 |
CRSTE | 1575 | 0.149 | 0.193 | 0.001 | 1 |
GOV | 1575 | 0.013 | 0.015 | 0 | 0.083 |
SALE | 1575 | 0.051 | 0.124 | −0.657 | 0.283 |
DEBT | 1575 | 0.465 | 0.169 | 0.094 | 0.848 |
REVE | 1575 | 22.084 | 1.306 | 19.275 | 25.884 |
HUM | 1575 | 0.395 | 0.237 | 0 | 0.971 |
ACAD | 1575 | 0.086 | 0.124 | 0 | 0.5 |
SIZE | 1575 | 22.72 | 1.181 | 20.412 | 26.438 |
AGE | 1575 | 20.998 | 5.137 | 11 | 37 |
Variable | Human Resource Slack Variable | Financial Resource Relaxation Variable |
---|---|---|
Constant term | 138.064 *** | 26,434.912 *** |
Tra | −7.054 *** | −241.033 *** |
Ind | −12.333 *** | −878.861 *** |
Tec | 0.542 *** | 13.181 *** |
Sigma Squared | 5,540,178.7 | 4,799,282,500 |
Gamma | 0.868 | 0.736 |
Likelihood | −13,186.222 | −18,963.107 |
LR | 1326.188 | 705.589 |
F.crste | Model (1) | Model (2) |
---|---|---|
Gov | 1.923 *** | 1.499 *** |
(5.983) | (4.697) | |
Sale | −0.025 | −0.039 |
(−0.813) | (−1.265) | |
Debt | −0.078 ** | −0.140 *** |
(−2.090) | (−3.741) | |
Rev | 0.100 *** | 0.040 *** |
(18.278) | (3.884) | |
Hum | 0.002 | |
(0.110) | ||
Size | 0.081 *** | |
(6.949) | ||
Age | 0 | |
(0.389) | ||
cons | −2.041 *** | −2.509 *** |
(−17.687) | (−19.673) | |
N | 1260 | 1260 |
chi2 | 383.011 | 479.382 |
p | 0.000 | 0.000 |
F.crste | Model (1) | Model (2) |
---|---|---|
Gov | 2.338 *** | 1.888 *** |
(6.557) | (5.327) | |
Sale | −0.028 | −0.049 |
(−0.698) | (−1.216) | |
Debt | −0.071 * | −0.135 *** |
(−1.710) | (−3.243) | |
Rev | 0.107 *** | 0.043 *** |
(17.684) | (3.785) | |
Hum | −0.013 | |
(−0.544) | ||
Size | 0.083 *** | |
(6.375) | ||
Age | 0.001 | |
(1.077) | ||
cons | −2.187 *** | −2.646 *** |
(−17.391) | (−18.870) | |
N | 945 | 945 |
chi2 | 382.177 | 457.978 |
p | 0.000 | 0.000 |
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Chen, Y.; Song, J. The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model. Sustainability 2023, 15, 6342. https://doi.org/10.3390/su15086342
Chen Y, Song J. The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model. Sustainability. 2023; 15(8):6342. https://doi.org/10.3390/su15086342
Chicago/Turabian StyleChen, Yuanyuan, and JungHyun Song. 2023. "The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model" Sustainability 15, no. 8: 6342. https://doi.org/10.3390/su15086342
APA StyleChen, Y., & Song, J. (2023). The Technological Innovation Efficiency of China’s Renewable Energy Enterprises: An Estimation Based on a Three-Stage DEA Model. Sustainability, 15(8), 6342. https://doi.org/10.3390/su15086342