American Journal of Management Science and Engineering

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

Received: 12 April 2019    Accepted: 05 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.

DOI 10.11648/j.ajmse.20190402.13
Published in American Journal of Management Science and Engineering (Volume 4, Issue 2, March 2019)
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), 2024. Published by Science Publishing Group

Keywords

Innovation Efficiency, Meteorological S&T, Influencing Factors

References
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[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.
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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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  • 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://download.sciencepg.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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    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
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    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
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