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Evaluation of China’s High-Advanced Industrial Policy: A PMC Index Model Approach

Author

Listed:
  • Ying Tian
  • Kai Zhang
  • Jiayi Hong
  • Fanglin Meng
  • Wei Liu

Abstract

High-advanced industries upgraded by digital empowerment have gradually become an important support industry. Therefore, various provinces in China have issued relevant policies to support the prosperous of the digital economy and high-advanced industries. The collection and analysis of high-advanced industrial policy help to scientifically evaluate industrial policies and formulate scientific policy optimization paths. Based on a total of 168 high-advanced industrial policy documents from 26 cities in the Yangtze River Delta region from 2009–2021, this study adopts the PMC-Index model to evaluate the high-advanced industry policies in the digitalization context quantitatively. 12 representative high-advanced industry policy texts were selected for specific analysis. In addition, this study visualizes the measurement results of the internal structure and policy effectiveness of policies by PMC-Surface diagrams and then concludes that the design of high-advanced industry policies was relatively reasonable overall, with 11 policies rated as “Good Consistency†and only one “Acceptable Consistency.†The sample policies lack reasonable arrangements for different period plans, lack incentives, or have relatively single incentives. The policy influence among cities in the Yangtze River Delta urban agglomerations is small, and the integration trend is not apparent. There is a particular gap in the scores between Shanghai, Zhejiang, Jiangsu, and Anhui province. This study provides references and suggestions for formulating and revising high-advanced industrial policies.

Suggested Citation

  • Ying Tian & Kai Zhang & Jiayi Hong & Fanglin Meng & Wei Liu, 2022. "Evaluation of China’s High-Advanced Industrial Policy: A PMC Index Model Approach," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, September.
  • Handle: RePEc:hin:jnlmpe:9963611
    DOI: 10.1155/2022/9963611
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