Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model—A Case Study of Anhui Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Model Construction
2.1.1. Phase I: Analysis of the Traditional DEA Model
2.1.2. Phase II: SFA Model Analysis
2.1.3. Phase III: Adjusted DEA Model
2.2. Variable Selection
2.3. Data Source
3. Results
3.1. Phase I: DEA Estimation Result Analysis
3.2. Phase II: SFA Estimation Result Analysis
3.3. Phase III: DEA Estimation Result Analysis
4. Discussion and Conclusions
4.1. Conclusions
4.2. Policy Implications
4.2.1. Optimize the Relationship between the Government and the Market
4.2.2. Accelerate the Construction of Platform and Carrier
4.2.3. Attach Importance to the Construction of an Agricultural Science and Technology Personnel Training System
4.2.4. Improve the Open Sharing Mechanism
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type | Variable | Variable Definition |
---|---|---|
Input index | Agricultural R&D personnel input | Full-time equivalent of agricultural R&D personnel |
Agricultural R&D funds input | Internal expenditure of agricultural R&D funds | |
Output index | Patent output | Number of patents granted |
Paper output | Number of published scientific papers | |
Economic output | Gross output value of agriculture, forestry, animal husbandry and fishery |
Type | Variable | Variable Definition |
---|---|---|
Dependent variable | Input relaxation variable | Relaxation value of input variables of each decision unit |
Independent variable | Trade openness | Total import and export/regional GDP |
Level of economic development | Per capita GDP | |
Higher education level | Number of full-time teachers in colleges and universities | |
Financial support | Financial expenditure on science and technology/financial expenditure | |
Internet penetration | Broadband access number |
Time | Comprehensive Efficiency | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|
2010 | 0.766 | 0.853 | 0.899 |
2011 | 0.770 | 0.906 | 0.856 |
2012 | 0.704 | 0.899 | 0.793 |
2013 | 0.785 | 0.907 | 0.840 |
2014 | 0.743 | 0.920 | 0.813 |
2015 | 0.746 | 0.840 | 0.894 |
2016 | 0.696 | 0.789 | 0.871 |
2017 | 0.759 | 0.819 | 0.922 |
2018 | 0.764 | 0.830 | 0.912 |
2019 | 0.784 | 0.821 | 0.954 |
2020 | 0.698 | 0.818 | 0.854 |
Serial Number | Region | Comprehensive Efficiency | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|---|
1 | Hefei | 0.837 | 1.000 | 0.837 |
2 | Huaibei | 0.666 | 0.821 | 0.809 |
3 | Bozhou | 0.995 | 0.998 | 0.996 |
4 | Suzhou | 1.000 | 1.000 | 1.000 |
5 | Bengbu | 0.755 | 0.847 | 0.894 |
6 | Fuyang | 0.958 | 1.000 | 0.958 |
7 | Huainan | 0.903 | 0.922 | 0.980 |
8 | Chuzhou | 0.567 | 0.731 | 0.823 |
9 | Lu’an | 0.978 | 0.988 | 0.991 |
10 | Maanshan | 0.489 | 0.535 | 0.914 |
11 | Wuhu | 0.568 | 0.722 | 0.834 |
12 | Xuancheng | 0.636 | 0.653 | 0.966 |
13 | Tongling | 0.325 | 0.793 | 0.430 |
14 | Chizhou | 0.919 | 0.999 | 0.921 |
15 | Anqing | 0.703 | 0.720 | 0.975 |
16 | Huangshan | 0.611 | 0.961 | 0.647 |
Variable | Relaxation Variables of Agricultural R&D Personnel Input | The Slack Variable of Agricultural R&D Investment | ||
---|---|---|---|---|
Coefficient of Regression | Standard Error | Coefficient of Regression | Standard Error | |
constant term | 424.120 *** | 118.599 | 8023.316 *** | 1.480 |
Import and export degree | 127.995 | 432.481 | −4526.784 *** | 1.113 |
Level of economic development | −2.643 | 49.705 | −157.773 *** | 1.823 |
Higher education level | −489.459 *** | 94.158 | −14,266.387 *** | 1.207 |
Financial support | 8851.221 *** | 17.652 | 30.830 × 104 *** | 1.000 |
Internet penetration | 1.5609 * | 0.801 | 85.598 *** | 1.553 |
sigma-squared | 224,451.3 *** | 1.683 | 212.656 × 106 *** | 1.000 |
gamma | 0.458 *** | 0.061 | 0.482 *** | 0.059 |
Logarithmic likelihood value | −1294.028 | −1892.063 | ||
Unilateral test value | 18.995 *** | 21.082 *** |
Time | Comprehensive Efficiency | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|
2010 | 0.623 | 0.998 | 0.623 |
2011 | 0.676 | 0.998 | 0.677 |
2012 | 0.697 | 0.998 | 0.698 |
2013 | 0.692 | 0.998 | 0.693 |
2014 | 0.699 | 0.997 | 0.700 |
2015 | 0.678 | 0.995 | 0.682 |
2016 | 0.679 | 0.988 | 0.688 |
2017 | 0.691 | 0.992 | 0.697 |
2018 | 0.662 | 0.997 | 0.664 |
2019 | 0.663 | 0.991 | 0.670 |
2020 | 0.707 | 0.987 | 0.718 |
Serial Number | Region | Comprehensive Efficiency | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|---|
1 | Hefei | 1.000 | 1.000 | 1.000 |
2 | Huaibei | 0.308 | 0.990 | 0.311 |
3 | Bozhou | 0.802 | 1.000 | 0.802 |
4 | Suzhou | 0.988 | 1.000 | 0.988 |
5 | Bengbu | 0.881 | 0.991 | 0.888 |
6 | Fuyang | 1.000 | 1.000 | 1.000 |
7 | Huainan | 0.505 | 0.999 | 0.505 |
8 | Chuzhou | 0.889 | 0.973 | 0.914 |
9 | Lu’an | 0.911 | 1.000 | 0.911 |
10 | Maanshan | 0.445 | 0.990 | 0.449 |
11 | Wuhu | 0.891 | 0.988 | 0.901 |
12 | Xuancheng | 0.671 | 0.990 | 0.677 |
13 | Tongling | 0.152 | 1.000 | 0.152 |
14 | Chizhou | 0.360 | 1.000 | 0.360 |
15 | Anqing | 0.807 | 0.992 | 0.813 |
16 | Huangshan | 0.250 | 1.000 | 0.250 |
Type | Classification Standard | Region |
---|---|---|
Excellent configuration type | Pure technical efficiency = 1 Scale efficiency = 1 | Hefei, Fuyang |
Good configuration type | 0.9 ≤ pure technical efficiency < 1 0.75 ≤ Scale efficiency < 1 | Bozhou, Suzhou, Bengbu, Chuzhou, Lu’an, Wuhu and Anqing |
Scale efficiency improvement | 0.9 ≤ pure technical efficiency < 1 0 ≤ Scale efficiency < 0.75 0 ≤ Scale efficiency < 0.75 | Huaibei, Huainan, Maanshan, Xuancheng, Tongling, Chizhou and Huangshan |
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Yao, S.; Wu, G. Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model—A Case Study of Anhui Province, China. Int. J. Environ. Res. Public Health 2022, 19, 13683. https://doi.org/10.3390/ijerph192013683
Yao S, Wu G. Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model—A Case Study of Anhui Province, China. International Journal of Environmental Research and Public Health. 2022; 19(20):13683. https://doi.org/10.3390/ijerph192013683
Chicago/Turabian StyleYao, Sheng, and Guosong Wu. 2022. "Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model—A Case Study of Anhui Province, China" International Journal of Environmental Research and Public Health 19, no. 20: 13683. https://doi.org/10.3390/ijerph192013683
APA StyleYao, S., & Wu, G. (2022). Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model—A Case Study of Anhui Province, China. International Journal of Environmental Research and Public Health, 19(20), 13683. https://doi.org/10.3390/ijerph192013683