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A Random Forest Method for Identifying the Effectiveness of Innovation Factor Allocation.

Mo Xu1,2, Yawei Qi3, Changqi Tao4

  • 1School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.

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This summary is machine-generated.

Innovation factor allocation in China is often ineffective, with over half of provinces needing improvement. This study uses a novel random forest method to identify key factors and optimize allocation for better innovation output.

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Area of Science:

  • Economics
  • Innovation Management
  • Regional Science

Background:

  • Assessing innovation factor allocation effectiveness is crucial for economic growth.
  • Traditional methods like Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) have limitations in bias and constraints.
  • A novel approach is needed to reliably evaluate innovation efficiency.

Purpose of the Study:

  • To evaluate the effectiveness of innovation factor allocation across Chinese provinces.
  • To identify key innovation factors and their nonlinear relationships with output.
  • To provide data-driven recommendations for improving innovation efficiency.

Main Methods:

  • Utilized a random forest method for evaluating innovation factor allocation effectiveness.
  • Employed resampling techniques to ensure measurement reliability.
  • Analyzed data from 30 Chinese provinces spanning 2009-2018.

Main Results:

  • Innovation factor allocation was found to be ineffective in over 50% of Chinese provinces.
  • Identified critical innovation factors influencing allocation effectiveness.
  • Revealed nonlinear characteristics and optimal combinations for key innovation inputs.

Conclusions:

  • Significant room for improvement exists in China's innovation factor allocation.
  • Understanding and optimizing key innovation inputs is essential for enhancing efficiency.
  • Provincial-level strategies are needed to adjust current inputs for greater innovation output.