人工智能技术应用背景下的城市制造业空间演变* ——基于东莞的研究
Spatial Evolution of Urban Manufacturing Industry in the Context of Artificial Intelligence Technology Application: An Study of Dongguan
杨石琳
广州市城市规划勘测设计研究院有限公司 广州市资源规划和海洋科技协同创新中心 广东省城市感知与监测预警企业重点实验室 研究员，硕士
黄经南（通信作者）
武汉大学城市设计学院 湖北省人居环境工程技术研究中心 教授，博士生导师，huangjn73@qq.com
摘要: 聚焦受人工智能影响较大的制造业空间，以我国人工智能应用水平、制造业水平领先的城市——东莞市为例，基于2009— 2019年制造业企业数据，采用空间分析方法，探究人工智能应用背景下城市制造业空间的演变特征，并采用双重差分和线性 回归模型对其进行验证。研究发现：（1） 应用人工智能的制造业逐渐呈现以研发高地为中心的分布模式，尤其是中、高机器 替代率的企业；（2） 人工智能技术的应用使制造业分布更加集聚，且中机器替代率的制造业企业空间集聚程度最强；（3） 人 工智能的应用提高了制造业对其他企业的空间吸引力，吸引了其他企业在应用人工智能的制造业企业周边集聚，尤其是往 高机器替代率的制造业企业周边集聚；（4） 需从产业布局、土地利用和产城关系3个方面对现行规划进行重新审视。通过将 人工智能与城市发展的关系研究从社会经济层面拓展至空间层面，以期为新技术革命下的城市健康发展提供研究支撑。
Abstract: Focusing on the manufacturing sector significantly impacted by artificial intelligence, this study takes Dongguan City as an example to investigate the evolution of urban manufacturing space under the influence of artificial intelligence. Dongguan is renowned for having the highest level of artificial intelligence application and the most advanced manufacturing capabilities in China. Utilizing data from manufacturing enterprises between 2009 and 2019, this paper uses spatial analysis methods and differential and linear regression models to validate the findings. The results show that: (1) The distribution of the manufacturing industry applying artificial intelligence is gradually centered in the research and development hubs, especially the enterprises with medium and high machine replacement rates. (2) The application of artificial intelligence technology makes the distribution of the manufacturing industry more concentrated, and the spatial agglomeration degree of manufacturing enterprises with medium machine replacement rate is the strongest. (3) The application of artificial intelligence has improved the spatial attractiveness of the manufacturing industry to other enterprises, attracting other enterprises to gather around manufacturing enterprises that apply artificial intelligence, especially around manufacturing enterprises with high machine replacement rates. (4) The current planning can be re-examined from three aspects: industrial layout, land use, and industry-city relationship. This paper extends the research on the relationship between artificial intelligence and urban development from the socio-economic level to the spatial level, providing theoretical support for the healthy development of cities under the new technological revolution.
关键词：人工智能；机器代人；产业空间；制造业空间；东莞
Keyword: artificial intelligence; machine substitutes; industrial space; manufacturing space; Dongguan
中图分类号：TU984
文献标识码: A
张鑫，王明辉. 中国人工智能发展态势及其促进
策略[J]. 改革，2019（9）：31-44.
ZHANG Xin, WANG Minghui. Development status
and promotion strategy of artificial intelligence in
China[J]. Reform, 2019(9): 31-44.
姜红德. 机器代人：新工业革命[J]. 中国信息化，
2015（12）：30-31.
JIANG Hongde. The robot generation: the new
industrial revolution[J]. iChina, 2015(12): 30-31.
林昌华. 中国实施“机器换人”战略促进产业
转型研究[J]. 中国发展，2015，15（3）：71-73.
LIN Changhua. Implementing the strategy of
"machine substitution" to promote industrial transformation in China[J]. China Development, 2015,
15(3): 71-73.
杨石琳. 人工智能应用背景下城市制造业空间
演变及规划对策——以东莞市为例[D]. 武汉：
武汉大学，2022.
YANG Shilin. Spatial evolution and planning
of urban manufacturing industries under the
background of artificial intelligence application: a
case study of Dongguan City[D]. Wuhan: Wuhan
University, 2022.
DADASHPOOR H, YOUSEFI Z. Centralization
or decentralization? A review on the effects of
information and communication technology on
urban spatial structure[J]. Cities, 2018, 78: 194-205.
毛琦梁. 技术进步、空间竞争与产业区位变
化——基于产业区位一般均衡模型的实证与模
拟[J]. 科技管理研究，2018，38（15）：127-138.
MAO Qiliang. Technological progress, spatial
competition and industrial location variation: an
empirical analysis and simulation based on the
general equilibrium model of industrial location[J].
Science and Technology Management Research,
2018, 38(15): 127-138.
韩民春，冯乐兰. 工业机器人应用对我国劳动力
就业影响的研究[J]. 工业技术经济，2020，39
（6）：117-122.
HAN Minchun, FENG Lelan. Research on the
impact of industrial robot application on labor
employment in China[J]. Journal of Industrial
Technological Economics, 2020, 39(6): 117-122.
谢璐，韩文龙，陈翥. 人工智能对就业的多重效
应及影响[J]. 当代经济研究，2019（9）：33-41.
XIE Lu, HAN Wenlong, CHEN Zhu. The multiple
effects and impacts of artificial intelligence on
employment[J]. Contemporary Economic Research,
2019(9): 33-41.
AUTOR D H, DORN D. The growth of lowskill servive jobs and polarization of the US labor
market[J]. American Economic Review, 2013,
103(5): 1553-1597.
GORDEN R J. The demise of US economic growth:
restatement, rebuttal and reflections[J]. National
Bureau of Economic Research, 2014(11): 95-98.
COWEN T. The great stagnation: how America
ate all the low-hanging fruit of modern history, got
sick, and will (eventually) feel better[J]. Survival,
2012, 53(3): 190-191.
PANAYIDES A, KERN C R. Information technology
and the future of cities: an alternative analysis[J].
Urban Studies, 2005, 42: 163-167.
黄经南，杨石琳. 机器代人驱动下城市空间演变
及规划对策——一些初步的设想[J]. 城市发展
研究，2021，28（3）：58-64.
HUANG Jingnan, YANG Shilin. Urban spatial
evolution and planning strategy driven by "machines replacing people": some preliminary ideas[J].
Urban Development Studies, 2021, 28(3): 58-64.
黄经南，马灿，周俊. 人工智能引领的新一轮技术
革命冲击下城市空间变革趋势、对策及对我国的
启示[J]. 城市发展研究，2023，30（6）：16-23.
HUANG Jingnan, MA Can, ZHOU Jun. The trend
and countermeasures of urban space change under
the impact of new technological revolution led
by artificial intelligence and its enlightenment
to China[J]. Urban Development Studies, 2023,
30(6): 16-23.
黄玫瑜，秦小珍，周金苗. “机器代人”与城镇新
增长模式——以广东省佛山市顺德区为例[J].
热带地理，2019，39（1）：11-19.
HUANG Meiyu, QIN Xiaozhen, ZHOU Jinmiao.
"Replacing human labor with robots" and the new
growth model of towns: a case study of Shunde
District in Foshan City, Guangdong Province[J].
Tropical Geography, 2019, 39(1): 11-19.
国际机器人联合会. 2025年世界机器人报告[R].
2025.
IFR. World robotics 2025[R]. 2025.
Oxford Economics. How robots change the world[R].
2019.
LEJPRAS A, STEPHAN A. Locational conditions,
cooperation, and innovativeness: evidence from
research and company spin-offs[J]. The Annals of
Regional Science, 2011, 46: 543-575.
王璐，张东生. 产业集群规模及影响因素的实
证研究[J]. 河北大学学报（哲学社会科学版），
2016，41（2）：91-97.
WANG Lu, ZHANG Dongsheng. Empirical research
on the scale and influence factors of industrial
clusters[J]. Journal of Hebei University (Philosophy
and Social Science), 2016, 41(2): 91-97.
冯子钰，施润和. 中国近地面PM2.5浓度与排放的
时空分布及其关联分析[J]. 地球信息科学学报，
2021（7）：1221-1230.
FENG Ziyu, SHI Runhe. Spatio-temporal features
and the association of ground-level PM2.5 concentration and its emission in China[J]. Journal of Geoinformation Science, 2021, 23(7): 1221-1230.
吴春涛，李隆杰，何小禾，等. 长江经济带旅游景
区空间格局及演变[J]. 资源科学，2018，40（6）：
1196-1208.
WU Chuntao, LI Longjie, HE Xiaohe, et al. Spatial
analysis of scenic spots in the Yangtze River Economic Belt[J]. Resources Science, 2018, 40(6):
1196-1208.
李悦，杨玖伦，谢子涵，等. 海绵城市建设能否缓
解城市蔓延?[J]. 城市规划，2024，48（6）：25-
35.
LI Yue, YANG Jiulun, XIE Zihan, et al. Can
sponge city construction mitigate urban sprawl?[J].
City Planning Review, 2024, 48(6): 25-35.
