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Calculation of the contribution rate of China’s hydraulic science and technology based on a feedforward neural network

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  • Rongrong Xu
  • Yongxiang Wu
  • Ming Chen
  • Xuan Zhang
  • Wei Wu
  • Long Tan
  • Gaoxu Wang
  • Yi Xu
  • Bing Yan
  • Yuedong Xia

Abstract

Quantitative analysis of the contribution rate of China’s hydraulic science and technology and analysis of the underlying reasons behind changes provide an important foundation upon which the government can formulate water policies. This paper abandons the assumption of a scale economy and separates the changes of benefits brought about by the scale from scientific and technological progress, thus changing the C-D production function from linear to nonlinear. Based on a feedforward neural network, it calculates the coefficient of the output elasticity, the economic contribution rate of China’s hydraulic science and technology and the scale economies for each year using relevant data from 1981 to 2016. The results show that (1) the average contribution rate of capital investment from 1981 to 2016 was 47.3%, and the average contribution rate of labor from 1981 to 2016 was 9.1%. It is not obvious that the significant increase in the labor force has contributed to the growth of China’s water conservancy industry. (2) The average contribution rate of scale economies in 1981–2016 was 26.7%, and the contribution rate of scale economies is negatively correlated with the capital contribution rate. (3) The average contribution rate of China’s hydraulic science and technology was 43.6% from 1981 to 2016, and the average contribution rate of the total factor productivity after removing scale economies from 1981 to 2016 was 16.9%. During the period of the 6th Five-Year Plan(1981~1985), the contribution rate of water conservancy science and technology was relatively high. Since that time, it has remained at 40%. In recent years, as water conservancy reforms in key areas have made positive progress, scientific and technological progress has increased the growth of water conservancy benefits annually.

Suggested Citation

  • Rongrong Xu & Yongxiang Wu & Ming Chen & Xuan Zhang & Wei Wu & Long Tan & Gaoxu Wang & Yi Xu & Bing Yan & Yuedong Xia, 2019. "Calculation of the contribution rate of China’s hydraulic science and technology based on a feedforward neural network," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-22, September.
    • Handle: RePEc:plo:pone00:0222091
    DOI: 10.1371/journal.pone.0222091
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