Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry
Through the K-means clustering analysis, it divides the regions of China into four clusters according to the differences in high-tech industry development level between 2008 and 2016. Considering ”environmental pollution” and ”innovation failure”, an improved SBM-DEA efficiency measurement model was...
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| Published in | Journal of computational and applied mathematics Vol. 369; p. 112597 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2020
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Through the K-means clustering analysis, it divides the regions of China into four clusters according to the differences in high-tech industry development level between 2008 and 2016. Considering ”environmental pollution” and ”innovation failure”, an improved SBM-DEA efficiency measurement model was constructed to measure the green technology innovation efficiency of China’s high-tech industry clusters. Lasso regression was used to screen out the factors affecting the green technology innovation efficiency of high-tech industry in each cluster area. On this basis, quantile regression method is used to study the influence degree and regional differences of various influencing factors on green innovation efficiency of high-tech industry at different quantile. Meanwhile, DEA-tobit model is used for robustness test. The research shows that in each cluster area, the factors that significantly affect the green innovation efficiency of high-tech industry are different, and the degree of influence of each factor on the innovation efficiency at different quantile is also different. Combining the empirical results with the reality of high-tech industries in various regions, the corresponding policy recommendations are put forward. |
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| AbstractList | Through the K-means clustering analysis, it divides the regions of China into four clusters according to the differences in high-tech industry development level between 2008 and 2016. Considering ”environmental pollution” and ”innovation failure”, an improved SBM-DEA efficiency measurement model was constructed to measure the green technology innovation efficiency of China’s high-tech industry clusters. Lasso regression was used to screen out the factors affecting the green technology innovation efficiency of high-tech industry in each cluster area. On this basis, quantile regression method is used to study the influence degree and regional differences of various influencing factors on green innovation efficiency of high-tech industry at different quantile. Meanwhile, DEA-tobit model is used for robustness test. The research shows that in each cluster area, the factors that significantly affect the green innovation efficiency of high-tech industry are different, and the degree of influence of each factor on the innovation efficiency at different quantile is also different. Combining the empirical results with the reality of high-tech industries in various regions, the corresponding policy recommendations are put forward. |
| ArticleNumber | 112597 |
| Author | Liu, Chunyang Gao, Xingyu Ma, Wanli Chen, Xiangtuo |
| Author_xml | – sequence: 1 givenname: Chunyang surname: Liu fullname: Liu, Chunyang organization: College of Business, Shandong University, Weihai 264209, China – sequence: 2 givenname: Xingyu surname: Gao fullname: Gao, Xingyu organization: College of Business, Shandong University, Weihai 264209, China – sequence: 3 givenname: Wanli surname: Ma fullname: Ma, Wanli email: mawanli@sdu.edu.cn organization: College of Business, Shandong University, Weihai 264209, China – sequence: 4 givenname: Xiangtuo surname: Chen fullname: Chen, Xiangtuo organization: Laboratory MICS, CentraleSupélec, Paris-Saclay University, Gif sur Yvette 91190, France |
| BackLink | https://centralesupelec.hal.science/hal-03197864$$DView record in HAL |
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| Title | Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry |
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