中国城市产业智能化空间关联网络及其驱动机制

Spatial Association Network of Urban Industrial Intelligence in China and Its Driving Mechanisms

《财经研究》2024年50卷第1期 页码：109-123 online：2024年1月3日

作者

中：罗良文 , 张郑秋

英：Luo Liangwen, Zhang Zhengqiu

作者单位：中南财经政法大学 经济学院，湖北 武汉 430073

摘要及关键词

摘要：把握新技术革命发展机遇，推动产业智能化升级，对于新形势下实现经济高质量发展具有重要作用。文章基于2003—2019年中国283个地级市数据和网络爬虫获得的企业微观数据构建城市产业智能化指数，采用社会网络分析方法考察产业智能化的空间关联网络特征；此外，运用机器学习中的极限梯度提升树算法（XGBoost）识别出产业智能化的核心驱动因素，在此基础上借助加权指数随机图模型（ERGM）探析产业智能化空间关联网络驱动机制。研究发现：（1）样本期间产业智能化空间关联强度不断提高，但整体处于较低水平，存在较大的提升空间；大部分城市位于网络边缘位置，主要依靠城市群或中心城市对周边城市产业智能化产生辐射带动作用。（2）XGBoost算法测算结果表明，技术创新、产业结构升级和对外开放是产业智能化的核心影响因素，累积贡献率高达92.72%。（3）技术创新、产业结构升级和对外开放有利于加强城市间的产业智能化合作。异质性分析发现，产业结构升级主要推动外围城市、高产业智能化水平城市形成紧密的产业智能化空间关联，而技术创新和对外开放对其驱动作用有限。据此，文章提出了推动产业智能化空间协调发展的相应政策建议。

关键词：产业智能化；空间关联网络；机器学习；ERGM

Summary: Industrial intelligence is an intelligent economic development mode driven by AI, which is of great significance for overcoming the regional economic “polarization trap” and achieving the high-quality development of China’s economy. The existing research mainly focuses on the impact of industrial intelligence on various aspects of economic and social development, while neglecting the driving mechanisms of the spatial associate network of industrial intelligence, and the differences in the contribution of different factors to industrial intelligence. This paper constructs an urban industrial intelligence index based on data from 283 prefecture-level cities in China from 2003 to 2019 and enterprise microdata obtained by web crawlers, and explores the driving mechanisms of the spatial association network of industrial intelligence through the ERGM model. The results are as follows: First, the spatial association intensity of industrial intelligence continues to increase during the sample period, but the overall level is low along with a large room for improvement; most of the cities are located at the edge of the network and mainly rely on the urban agglomeration to exert a radiation-driven effect on the industrial intelligence of peripheral cities. Second, the XGBoost algorithm demonstrates that technological innovation, industrial structure upgrading, and opening up are the core factors affecting industrial intelligence, with a cumulative contribution rate of 92.72%. Third, technological innovation, industrial structure upgrading, and opening up contribute to enhancing intercity cooperation in industrial intelligence. Heterogeneity analysis reveals that industrial structure upgrading mainly drives the formation of a close spatial association network of industrial intelligence among peripheral cities and highly industrial intelligent cities, while technological innovation and opening up have a limited driving effect on it. The marginal contributions of this paper are as follows: First, from the perspective of spatial association, it analyzes the characteristics of the spatial association network of industrial intelligence and discusses the driving mechanisms of industrial intelligence, making up for the shortage of the existing research. Second, in terms of research content, it combines city data with enterprise microdata to build a multidimensional index system of urban industrial intelligence, more accurately portraying the development level of industrial intelligence; and it uses the machine learning method to identify the importance of various influencing factors in industrial intelligence, and employs the ERGM model to examine the impact of core driving factors on the spatial association network of industrial intelligence. Third, in terms of research methodology, it introduces the XGBoost algorithm into the empirical research on industrial intelligence, and provides a research approach for accurately identifying the core driving factors of industrial intelligence with the powerful computing and solving capabilities of machine learning.

Key words: industrial intelligence; spatial association network; machine learning; ERGM

其他信息

DOI：10.16538/j.cnki.jfe.20230919.202

收稿日期：2023-05-20

基金项目：国家社会科学基金重大项目（21&ZD072）；国家社会科学基金一般项目（21BJY111）

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