Research on regional differences and influencing factors of green technology innovation efficiency of China's high-tech industry

被引：137
|
作者：
Liu, Chunyang [1 ]
Gao, Xingyu [1 ]
Ma, Wanli [1 ]
Chen, Xiangtuo [2 ]
机构：
[1] Shandong Univ, Coll Business, Weihai 264209, Peoples R China
[2] Paris Saclay Univ, Lab MICS, CentraleSupelec, F-91190 Gif Sur Yvette, France
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2020年 / 369卷
关键词：
Innovation efficiency; Regional differences; Influencing factors; QUANTILE REGRESSION; VARIABLE SELECTION; GROUP LASSO; DETERMINANTS; SHRINKAGE; SYSTEMS; DEA;
D O I：
10.1016/j.cam.2019.112597
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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. (C) 2019 Elsevier B.V. All rights reserved.
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页数：12
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