| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 02054 | |
| Number of page(s) | 6 | |
| Section | Machine Learning and Energy Industry Structure Forecast Analysis | |
| DOI | https://doi.org/10.1051/e3sconf/202021402054 | |
| Published online | 07 December 2020 | |
Big Data Industrial Agglomeration Promoting Regional Innovation: Comparison between Guangzhou and Zhaoqing in China
1 School of Finance, Guangdong University of Finance & Economics, Guangzhou, China
2 School of Liberal Arts, Zhaoqing University, Zhaoqing, China
a hefly_2007@126.com
* Corresponding author: blijinfeng_1989@163.com
This paper selects the data of big data industry in China’s “Guangzhou Development Zone Big Data Industrial Park” and “Zhaoqing Big Data Cloud Service Industrial Park” from 2014 to 2018, uses the improved knowledge production function to establish an OLS model, and compares the impact of MAR and Jacobs external aggregation on the R&D input and patent output in Guangzhou and Zhaoqing. It is found that: (1) MAR externality is not conducive to the technological innovation of the two cities, and has a stronger negative effect on innovation in Zhaoqing; Jacobs externality can actively promote the innovation of the two cities, and has a stronger positive effect on innovation in Guangzhou. (2) In the impact of Jacobs externality on innovation output of the two cities, R&D plays a part of intermediary effect, and the effect on Guangzhou is stronger; in the impact of MAR externality on innovation output of the two cities, R&D only plays a part of negative intermediary effect in Zhaoqing. The conclusions show that the MAR and Jacobs agglomeration in big data industry all play more effective roles in promoting technological innovation in economically developed cities.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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