Local knowledge bases, related diversification, and the development of artificial intelligence technologies: The case of Yangtze River Delta

doi:10.3969/j.issn.1004-9479.2025.12.20231011

Abstract

Abstract:

The fourth industrial revolution, with Artificial Intelligence(AI) as its core technologies, is profoundly impacting the high-quality economic development in the Yangtze River Delta (YRD) in the new era. The local knowledge bases and related diversification have an influence on the development of AI technologies. Exploring the impact of local knowledge bases and related diversification on the development of AI technologies is of great significance for realizing the national strategy of integrated development of YRD. Based on patent application data from 2007 to 2021 in YRD, this paper utilizes GIS spatial analysis and a linear probability model to measure the impact of knowledge bases and related diversification on the development of AI technologies in the cities of YRD. The results indicate: ① There are differences in the required knowledge bases among different cities and different technologies, leading to significant spatial heterogeneity in the knowledge bases for AI technology development in YRD; ② The spatial distributions of the knowledge bases for AI technologies in YRD are uneven, with high-value areas mainly concentrated in a "Z"-shaped axis area formed by cities including Hefei, Nanjing, Suzhou, Shanghai, Hangzhou, Jinhua, and Ningbo, while the cities around Huainan, Hefei, and Wuhu belong to low-value areas. Regional disparities are showing a trend of narrowing in the development process. The spatial disparities in the knowledge bases of different divisions of AI technologies are also very pronounced, with the spatial agglomeration in application-layer technologies being more significant than that in non-application-layer technologies; ③ Local knowledge bases promote the process of related diversification into AI technologies in YRD, but this impact is not entirely the same in the different divisions of AI technologies. Taking full advantage of the strategic opportunities brought by the integrated development of YRD, fully leveraging the comparative advantages of each region, and promoting healthy development of AI technologies in YRD according to local conditions, is an issue that relevant government departments should pay close attention to.

Key words: local knowledge bases, related diversification, artificial intelligence technologies, Yangtze River Delta

摘要：

以人工智能技术为核心的第四次产业革命深刻地影响着新时期长三角地区经济高质量发展，本地知识基础、相关多样化又影响着人工智能技术的发展，因此探讨本地知识基础、相关多样化对人工智能技术发展的影响对于服务长三角区域一体化发展的国家战略具有十分重要的意义。基于2007—2021年长三角申请的发明专利数据，借助GIS空间分析和线性概率模型，分析了长三角城市知识基础、相关多样化对人工智能技术发展的影响。结果表明：①长三角人工智能技术发展中，不同城市和不同技术在发展所需知识基础上存在区别，导致该区域技术发展知识基础具有明显的空间异质性；②长三角发展人工智能技术的知识基础水平空间分布不均衡，高值区主要集中于由合肥、南京、苏州、上海、杭州、金华、宁波等城市构成的“Z”字形轴线区，而环淮南、合肥、芜湖地带则属于低值区。从发展演化过程看，地区差异呈现缩小的趋势。同时，不同环节技术的相关知识基础空间差异显著，应用层技术知识基础布局较非应用层技术具有更强的空间集聚性；③本地知识基础支持了长三角人工智能技术的相关多样化发展，但这种影响在不同环节技术上不完全相同。充分利用长三角区域一体化发展国家战略带来的机遇，充分发挥各地比较优势，因地制宜，促进长三角人工智能技术健康发展，是值得政府相关部门重视的问题。

关键词: 本地知识基础, 相关多样化, 人工智能技术, 长三角地区

Sitong LIN, Gang ZENG. Local knowledge bases, related diversification, and the development of artificial intelligence technologies: The case of Yangtze River Delta[J]. World Regional Studies, 2025, 34(12): 103-117.

林思彤, 曾刚. 本地知识基础、相关多样化与人工智能技术发展[J]. 世界地理研究, 2025, 34(12): 103-117.

Figures/Tables 9

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环节	技术类别代码
基础层	G01N、H04L
技术层	G06F、G06K、G06N、G06T、G10L、H04W
应用层	A61B、B23Q、B25J、B64C、G01C、G05B、G05D、G06Q、G07C、G08B、G08G、H04N

环节	技术类别代码
基础层	G01N、H04L
技术层	G06F、G06K、G06N、G06T、G10L、H04W
应用层	A61B、B23Q、B25J、B64C、G01C、G05B、G05D、G06Q、G07C、G08B、G08G、H04N

变量	样本量	最小值	最大值	均值	标准差
Entry	1 354	0	1	0.077	0.266
RD	1 640	0.030	87.800	21.310	16.580
GDPP	1 640	0.733	11.260	4.106	2.333
PD	1 640	0.123	3.803	0.743	0.553
FR	1 640	1.219	5.069	2.375	0.873
SR	1 640	1.700	96.400	17.460	16.520

变量	样本量	最小值	最大值	均值	标准差
Entry	1 354	0	1	0.077	0.266
RD	1 640	0.030	87.800	21.310	16.580
GDPP	1 640	0.733	11.260	4.106	2.333
PD	1 640	0.123	3.803	0.743	0.553
FR	1 640	1.219	5.069	2.375	0.873
SR	1 640	1.700	96.400	17.460	16.520

时期	2007—2011年	2012—2016年	2017—2021年
总体技术	0.478^***	0.343^***	0.159
基础层技术	0.197^*	0.073	-0.002
技术层技术	0.357^***	0.170^*	0.013
应用层技术	0.580^***	0.447^***	0.282^**