算力部署与未来产业关键核心技术突破:基于上市公司专利授权数据的实证研究
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F273.1

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国家社会科学基金一般项目“东西部协作的数智共富机制与实现路径研究”(25BGL264)。


Computing power deployment and breakthroughs in key core technologies for future industries: An empirical study based on the patent authorization data of listed companies
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    摘要:

    在识别出符合未来产业特征的A股上市公司的基础上,基于互联网数据中心经营许可证所构建的准自然实验,考察算力部署对未来产业关键核心技术突破的影响。研究发现:算力部署显著促进未来产业关键核心技术突破。机制分析表明,算力资源通过提升企业的知识整合能力、创新效率以及优化创新资源配置影响未来产业关键核心技术突破。异质性分析表明,位于国家大数据综合试验区、数字化转型程度较高的企业,算力部署对未来产业关键核心技术创新的影响更为显著。进一步研究发现,算力部署的创新赋能效应并没有在部署主体之间形成有效溢出。研究结果为构建创新政策体系、培育协同创新生态,以及企业完善内部算力资源配置策略等提供了经验证据与实践启示。

    Abstract:

    This paper identifies A-share listed companies that conform to the characteristics of future industries and, based on the quasi-natural experiment constructed with the Internet data center operation license, examines the impact of computing power deployment on the breakthroughs of key core technologies in future industries. Research findings show that the deployment of computing power significantly promotes breakthroughs in key core technologies for future industries. Mechanism analysis indicates that computing power resources influence the breakthroughs of key core technologies in future industries by enhancing enterprises’ knowledge integration capabilities, innovation efficiency, and optimizing the allocation of innovation resources. Heterogeneity analysis indicates that for enterprises located in national big data comprehensive pilot zones and with a higher degree of digital transformation, the impact of computing power deployment on the innovation of key core technologies in future industries is more significant. Further research has found that the innovative enabling effect of computing power has not overflowed among the deployment entities. The research results provide empirical evidence and practical inspiration for building an innovation policy system, fostering a collaborative innovation ecosystem, and enterprises improving their internal computing power resource allocation strategies.

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郭子宇,王小林.算力部署与未来产业关键核心技术突破:基于上市公司专利授权数据的实证研究[J].中国软科学,2026,(7):122-135

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  • 在线发布日期: 2026-08-20
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