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**Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry.**
*(English)*
Zbl 1442.62762

The paper studies the green technology innovation (GTI) efficiency shown by high-tech industry in China between the years 2008 and 2016, as well as, the factors which are preponderant to explain such efficiency. The study is done in 4 steps. At Step 1, taking into account that development level on high-tech industry is highly correlated to the GTI efficiency and China’s development is very heterogeneous in its different regions, 30 of its provinces are divided in 4 clusters, according to their development levels, using K-means technique. At Step 2, a measure of GTI efficiency is computed for each province, at each year. Slacks based measure (SBM) joint with data envelopment analysis (DEA) techniques are used in such computation. At Step 3, 13 factors are considered in each of the 4 clusters to identify which ones are important (significant) for explaining the GTI efficiency, using Lasso regression. \(C_p\) criterium is used to indicate that 4 is a reasonable number of non-zero parameters to consider in the regression at each cluster, if it is true the assumption that variable selection in each cluster is similar to the one for the whole country, as assumed. Two comments are to be done at this point. One, the foreign direct investment (FDI) factor, being a proportion, exhibits an odd 4.466 maximum value in Table 1. Two, the observations are done during 9 consecutive years. In that sense, the independence assumption for the errors in the regression model seems to be inappropriate. At Step 4, the analyses of factor influences are deepened in two ways. First, by using quantile regression model, it is studied how the factors coefficients in the regression change at three different quantile levels of the GTI efficiency, in each one of the 4 different clusters. Second, by using DEA-Tobit model, more robust estimations of the regression parameters are obtained for the 4 different clusters. The estimation results obtained by the 3 regression models look to be coherent.

Reviewer: Nelson I. Tanaka (São Paulo)

### MSC:

62P30 | Applications of statistics in engineering and industry; control charts |

62J07 | Ridge regression; shrinkage estimators (Lasso) |

62G08 | Nonparametric regression and quantile regression |

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |

62J20 | Diagnostics, and linear inference and regression |

### Keywords:

innovation efficiency; regional differences; influencing factors; Lasso regression; K-means; quantile regression
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\textit{Y. Lyu} et al., J. Comput. Appl. Math. 369, Article ID 112597, 12 p. (2020; Zbl 1442.62762)

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### References:

[1] | Meng, W. Z.; Li, C. Y.; Shi, X. D., Analysis of innovation efficiency of high-tech industry in China in stages – based on three-stage DEA model, Macroecon. Res., 02, 78-91 (2019) |

[2] | Carayannis, E. G.; Grigoroudis, E.; Goletsis, Y., A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach, Expert Syst. Appl., 62, 63-80 (2016) |

[3] | Kaya Samut, P.; Cafrı, R., Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel tobit, Soc. Indic. Res., 129, 1, 113-132 (2016) |

[4] | Lafarga, C. V.; Balderrama, J. I.L., Efficiency of Mexico’s regional innovation systems: an evaluation applying data envelopment analysis (DEA), Afr. J. Sci. Technol. Innov. Dev. (2015) |

[5] | Yeung, L. L.; Azevedo, P. F., Measuring efficiency of Brazilian courts with data envelopment analysis (DEA), IMA J. Manag. Math., 22, 4, 343-356 (2011) |

[6] | Ji, Y.; Dou, J., Study on stage impacts of factor price distortion on chinese technology innovation based on data mining, J. Comput. Theor. Nanosci., 13, 12, 10504-10513 (2016) |

[7] | Li, X.; Ran, G.; Wei, Z., How does financial factor distortion affect enterprise innovation investment?——Analysis from the perspective of financing constraints, Stud. Int. Finance, 12, 25-35 (2017) |

[8] | Li, X.; Ran, G.; Wei, Z., The innovative effect of financial factor distortion and its regional differences, Stud. Sci. Sci., 36, 03, 558-568 (2018) |

[9] | Gao, X. Y.; Lyu, Y. W.; Shi, F.; Zeng, J. T.; Liu, C. Y., The impact of financial factor market distortion on green innovation efficiency of high-tech industry, Ekoloji, 28, 107, 3449-3461 (2019) |

[10] | Shen, Chien-wen; Chen*, Min; Wang, Chiao-chen, Analyzing the trend of O2O commerce by bilingual text mining on social media, Comput. Hum. Behav. (2018) |

[11] | Fang, J. W.; Chiu, Y. H., Research on innovation efficiency and technology gap in China economic development, Asia Pac. J. Oper. Res., 34, 2, Article 1750005 pp. (2017) |

[12] | Kalapouti, K.; Petridis, K.; Malesios, C., Measuring efficiency of innovation using combined data envelopment analysis and structural equation modeling: empirical study in EU regions, Ann. Oper. Res. (2017) |

[13] | Hong, J.; Feng, B.; Wu, Y., Do government grants promote innovation efficiency in China’s high-tech industries?, Technovation, Article S0166497216301018 pp. (2016) |

[14] | Castro, M. D.; Gregorio, B., Knowledge management and innovation in knowledge-based and high-tech industrial markets: The role of openness and absorptive capacity, Ind. Mark. Manage., 47, 143-146 (2015) |

[15] | Liu, R. Z.; Du, W., Portfolio construction using variable selection: Based on LASSO method, Econ. Probl., 9, 103-107 (2012) |

[16] | Fang, K. N.; Zhang, G. J.; Zhang, H. Y., Individual credit risk prediction method: Application of a lasso-logistic model, J. Quant. Tech. Econ., 31, 02, 125-136 (2014) |

[17] | Mansiaux, Y.; Carrat, F., Detection of independent associations in a large epidemiologic dataset: a comparison of random forests, boosted regression trees, conventional and penalized logistic regression for identifying independent factors associated with H1N1pdm influenza infections, BMC Med. Res. Methodol., 14, 1 (2014) |

[18] | Pereira, J. M.; Basto, M.; Silva, A. F.D., The logistic lasso and ridge regression in predicting corporate failure, Procedia Econ. Finance, 39, 634-641 (2016) |

[19] | Tibshirani, R., Regression shrinkage and selection via the lasso: a retrospective, J. R. Stat. Soc., 58, 1, 267-288 (1996) · Zbl 0850.62538 |

[20] | Matsui, R.; Hidetoshi, M., Sparse group Lasso for multiclass functional logistic regression models, Comm. Statist. Simulation Comput., 1-14 (2018) |

[21] | Alhamzawi, R.; Yu, K., Variable selection in quantile regression via Gibbs sampling, J. Appl. Stat., 39, 4, 799-813 (2012) |

[22] | Jiang, L.; Bondell, H. D.; Wang, H. J., Interquantile shrinkage and variable selection in quantile regression, Comput. Statist. Data Anal., 69, 208-219 (2014) · Zbl 1471.62098 |

[23] | Fan, Y. L., Two-step variable selection in quantile regression models, J. Shanghai Normal Univ. (Nat. Sci.), 44, 03, 270-283 (2015) · Zbl 1340.62050 |

[24] | Hashem, H.; Vinciotti, V.; Alhamzawi, R., Quantile regression with group lasso for classification, Adv. Data Anal. Classif., 10, 3, 375-390 (2016) · Zbl 1414.62318 |

[25] | Xie, Z.; Xu, Y., Sparse group Lasso based uncertain feature selection, Int. J. Mach. Learn. Cybern., 5, 2, 201-210 (2014) |

[26] | Benoit, D. F.; Alhamzawi, R.; Yu, K., BayesIan lasso binary quantile regression, Comput. Stat., 28, 6, 2861-2873 (2013) · Zbl 1306.65029 |

[27] | K., Tone, A slacks-based measure of efficiency in data envelopment analysis, Eur. J. Oper. Res., 130, 3, 498-509 (2001) · Zbl 0990.90523 |

[28] | Likas, A.; Vlassis, N.; J. J., Verbeek., The global \(k\)-means clustering algorithm, Pattern Recognit., 36, 2, 451-461 (2003) |

[29] | Lyu, Yanwei; Li, Ping, Science and technology system reform and innovation driven impact: 1998-2013, Reform., 01, 76-87 (2016) |

[30] | Li, S. Z.; Fu, X. H., Research on the incentive mechanism of domestic research cooperation and scientific research innovation to promote the complementary effect of knowledge, Sci. Manag. Res., 03, 54-57 (2014) |

[31] | Furman, J. L.; Porter, M. E.; S., Stern, The determinants of national innovative capacity, Res. Policy, 31, 6, 899-933 (2000) |

[32] | B.A. Lundvall, E. Lorenz, Innovation and Competence Building in the Learning Economy: Implications for Innovation Policy, Post-Print, 2012. |

[33] | Y., Zou; W., Zhao; R., Zhong, The spatial distribution of green buildings in china: regional imbalance, economic fundamentals, and policy incentives, Appl. Geography, 88, 38-47 (2017) |

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