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Biased innovation and network evolution: digital driver for green innovation of manufacturing in China

Author

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  • Yang Liu
  • Jing Cheng
  • Jingjing Dai

Abstract

The study aims to explore the spatial association network characteristics of biased green innovation in the manufacturing sector and its core drivers. This study constructs a Malmquist-Luenberger decomposition index model to identify the input and output biases of green technological innovation (GIIM and GIOM) in the manufacturing industry. This study uses a modified gravity model and social network analysis method to conduct a robust assessment of GIIM spatial association network of 30 provinces in China from 2012 to 2021. The results show: (1) The GIIM association network structure is stable and has good accessibility, with close connections between provinces and blocks, and significant spillover effects between provinces. (2) The regional network shows a “core-periphery” spatial variation, with the core area expanding and the peripheral area shrinking. (3) The digital transformation characteristics of the network components and the intensity of environmental regulation have a significant impact on GIIM.

Suggested Citation

  • Yang Liu & Jing Cheng & Jingjing Dai, 2024. "Biased innovation and network evolution: digital driver for green innovation of manufacturing in China," Journal of Applied Economics, Taylor & Francis Journals, vol. 27(1), pages 2308951-230, December.
    • Handle: RePEc:taf:recsxx:v:27:y:2024:i:1:p:2308951
    DOI: 10.1080/15140326.2024.2308951
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