Abstract
Based on the Entropy Method, ESDA and spatial panel data model methods using urban patents database of China’s 285 cities during 2014-2015, this article explored the spatial pattern and determinants of innovative output. The results show that: Innovative output shows obvious characteristics of geographical agglomeration and spatial agglomeration. Most cities had strong path dependence and lock-in characteristics, and transition didn’t occur. When the inter-city correlation is considered, human capital and industrial base are the major driving forces for boosting innovation output. The input of innovation elements will not only promote the improvement of local innovation ability, but also promote the development of innovation capability in the neighboring cities.
Highlights
Since Schumpeter [1], a representative of neoclassical economists, put forward that innovation is the core of economic growth, and technological progress as well as technological innovation has received more and more attention and research
It is of great practical significance to comprehensively grasp the spatial evolution of innovation output, explore the spatial distribution of innovation ability and implement innovation development strategy based on differentiation
This paper uses invention patents, utility models and design patents to represent innovation output, and comprehensively grasps the pattern of innovation output based on regional differences, spatial evolution of regional differences and spatial autocorrelation of innovation output
Summary
Since Schumpeter [1], a representative of neoclassical economists, put forward that innovation is the core of economic growth, and technological progress as well as technological innovation has received more and more attention and research. Wang Chunyang [7] uses ESDA analysis tools to study the spatial and temporal patterns of innovation output based on the patent application authorization of 341 Prefectural Administrative Regions in China He Shunhui [8] based on the number of patents and papers in 287 prefecture-level cities in China from 2001 to 2014, using the mutation series to get the comprehensive score of innovation to represent the innovation output, and reveals the spatial differentiation characteristics and influencing factors of China’s innovation capacity through the spatial cross-section model. These studies provide a new perspective for the pattern and evolution of China’s innovation output. Using the data from 2004 to 2015, this paper explores the dynamic evolution pattern of innovation output in 285 prefecture-level cities in China, explores the influencing factors of innovation output with the help of spatial dynamic panel model, and seeks to optimize the allocation of innovation resources
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