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Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors

Received: 12 April 2019     Accepted: 5 June 2019     Published: 24 June 2019
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Abstract

Based on the meteorological statistics from 2014 to 2017, this paper adopts the DEA-Tobit Two Step method to estimate the innovation efficiency of China meteorological science and technology and then analyses its influencing factors. It is found that during 2014-2017, Beijing has been at the forefront in innovation efficiency of meteorological S&T, followed by Tianjin. Some other provinces and cities have a decline in technology efficiency. Therefore, pure technology inefficiency still remains a major problem faced by most provinces and cities. Meanwhile, it also reveals that innovation efficiency of meteorological S&T is significantly and positively impacted by scientific research input and academic structure, but without any significant linear interrelationship with economic development and government influence.

Published in American Journal of Management Science and Engineering (Volume 4, Issue 2)
DOI 10.11648/j.ajmse.20190402.13
Page(s) 32-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Innovation Efficiency, Meteorological S&T, Influencing Factors

References
[1] Zhang Yuting, Yang Hualing. (2018). An Overview of Evaluation Methods of Technological Innovation Efficiency. China Management Informatization, (4), 82-84.
[2] Chen Xingxing. (2016). Measurement and Analysis of China’s Energy Consumption and Output Efficiency. Statistics & Decision, (23), 114-119.
[3] Chen Yongjun, Zhang Feilian, Liu Shang. (2015). Research on Technological Innovation Efficiency of Industry-University-Research Institute Based on Stochastic Frontier Analysis. Science & Technology Progress and Policy, (24), 21-24.
[4] Kohl S, Schoenfelder J, Fügener A, et al. (2018). Correction to: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, (15), 1-1.
[5] Ouenniche J, Carrales S. (2018). Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback. Annals of Operations Research, (1), 1-37.
[6] Wolszczak-Derlacz J, Parteka A. (2011). Efficiency of European public higher education institutions: a two-stage multicountry approach. Scientometrics, (89), 887-917.
[7] Guan J, Zuo K. (2014). A cross-country comparison of innovation efficiency. Scientometrics, 100 (2): 541-575.
[8] Fan Hua, Zhou Dequn. (2012). Regional Science and Technology Innovation Efficiency Evolution and Its Affect Factors in Chinese Provinces. Science Research Management, 33 (1): 10-18.
[9] Zhao Shukuan, Yu Haiqing, Gong Shunlong. (2013). The Innovation Efficiency of Hi-tech Enterprises in Jilin Province Based on DEA Method. Science Research Management, 34 (2), 36-43.
[10] Yang Guoliang, Liu Wenbin, Zheng Haijun. (2013). Review of Data Envelopment Analysis. Journal of Systems Engineering, 28 (6), 840-860.
[11] Wang Tingting. (2013). Efficiency Measurement of Interprovincial Energy Based on DEA and FDA Methods in China. Tsinghua University Press.
[12] Huang Funing. (2013). Evaluation of ChiNext Innovation Efficiency. Economy & Management Publishing House.
[13] Chen Bing, Ji Shengbao. (2013). The Performance Evaluation of Listed Chinese Pharmaceutical Companies and the Influencing Factors: An Empirical DEA-Tobit Evidence Based on the Panel Data. Journal of Central University of Finance & Economics, 1 (8).
[14] Chen Xiaowei. (2011). Study on Efficiency Evaluation and Its Influencing Factors of Chinese Commercial Banks. Southwest Jiaotong University Press.
[15] Shen Jiangjian, Long We. (2015). Treatment of Negative Output in DEA Model-Based on the Application of Software DEAP. Hefei: Chinese Academy of Management.
[16] Guo Danbo, Lei Jiaxiao, Zhang Junfang, etc. (2012). Research on the Efficiency and Influencing Factors of National Innovation System-Based on DEA-Tobit Two-Step Analysis. Journal of Tsinghua University (Philosophy and Social Sciences), (2), 142-150.
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  • APA Style

    Shen Danna, Li Yan. (2019). Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors. American Journal of Management Science and Engineering, 4(2), 32-38. https://doi.org/10.11648/j.ajmse.20190402.13

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    ACS Style

    Shen Danna; Li Yan. Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors. Am. J. Manag. Sci. Eng. 2019, 4(2), 32-38. doi: 10.11648/j.ajmse.20190402.13

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    AMA Style

    Shen Danna, Li Yan. Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors. Am J Manag Sci Eng. 2019;4(2):32-38. doi: 10.11648/j.ajmse.20190402.13

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  • @article{10.11648/j.ajmse.20190402.13,
      author = {Shen Danna and Li Yan},
      title = {Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors},
      journal = {American Journal of Management Science and Engineering},
      volume = {4},
      number = {2},
      pages = {32-38},
      doi = {10.11648/j.ajmse.20190402.13},
      url = {https://doi.org/10.11648/j.ajmse.20190402.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20190402.13},
      abstract = {Based on the meteorological statistics from 2014 to 2017, this paper adopts the DEA-Tobit Two Step method to estimate the innovation efficiency of China meteorological science and technology and then analyses its influencing factors. It is found that during 2014-2017, Beijing has been at the forefront in innovation efficiency of meteorological S&T, followed by Tianjin. Some other provinces and cities have a decline in technology efficiency. Therefore, pure technology inefficiency still remains a major problem faced by most provinces and cities. Meanwhile, it also reveals that innovation efficiency of meteorological S&T is significantly and positively impacted by scientific research input and academic structure, but without any significant linear interrelationship with economic development and government influence.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Analysis on Innovation Efficiency of China Meteorological Science and Technology and Its Influencing Factors
    AU  - Shen Danna
    AU  - Li Yan
    Y1  - 2019/06/24
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    N1  - https://doi.org/10.11648/j.ajmse.20190402.13
    DO  - 10.11648/j.ajmse.20190402.13
    T2  - American Journal of Management Science and Engineering
    JF  - American Journal of Management Science and Engineering
    JO  - American Journal of Management Science and Engineering
    SP  - 32
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2575-1379
    UR  - https://doi.org/10.11648/j.ajmse.20190402.13
    AB  - Based on the meteorological statistics from 2014 to 2017, this paper adopts the DEA-Tobit Two Step method to estimate the innovation efficiency of China meteorological science and technology and then analyses its influencing factors. It is found that during 2014-2017, Beijing has been at the forefront in innovation efficiency of meteorological S&T, followed by Tianjin. Some other provinces and cities have a decline in technology efficiency. Therefore, pure technology inefficiency still remains a major problem faced by most provinces and cities. Meanwhile, it also reveals that innovation efficiency of meteorological S&T is significantly and positively impacted by scientific research input and academic structure, but without any significant linear interrelationship with economic development and government influence.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • Development and Research Center, China Meteorological Administration, Beijing, China

  • School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China

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