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 |
Innovation Efficiency, Meteorological S&T, Influencing Factors
| [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. |
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
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
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
Share This Article
Twitter
Linked In
Facebook
Copy
Download@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}
}
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 PY - 2019 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 -
Information
Email: