Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314255 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Applied Economics [ISSN:] 1667-6726 [Volume:] 27 [Issue:] 1 [Article No.:] 2308951 [Year:] 2024 [Pages:] 1-31
Publisher: 
Taylor & Francis, Abingdon
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.
Subjects: 
biased green innovation
digital transformation
social network analysis
spatial and evolutionary analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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