人工智能赋能制造业转型升级的实证研究
Artificial Intelligence Driving Manufacturing Transformation and Upgrading: An Empirical Study
摘要: 本文基于2014~2023年省级面板数据，构建人工智能与制造业转型升级评价指标体系，运用熵值法测度发展水平，结合通用目的技术(GPT)理论，通过固定效应模型、Bootstrap中介效应模型探究人工智能对制造业转型升级的直接影响与间接传导路径。研究发现：人工智能作为典型的通用目的技术，不仅直接促进制造业转型升级，还通过提升技术创新水平和优化人力资本结构产生间接推动作用。据此，提出差异化区域战略、全链条融合、构建“技术–创新–人才”生态及跨区域协同机制等建议。
Abstract: Based on provincial panel data from 2014 to 2023, this study constructs evaluation index systems for artificial intelligence and manufacturing transformation and upgrading. Using the entropy method to measure development levels and drawing on the General-Purpose Technology (GPT) theory, it employs fixed-effects models and Bootstrap mediation effect models to investigate the direct impact and indirect transmission paths of artificial intelligence on manufacturing transformation and upgrading. The findings reveal that artificial intelligence, as a typical GPT, not only directly promotes manufacturing transformation and upgrading but also exerts an indirect driving effect by enhancing the level of technological innovation and optimizing the structure of human capital. Accordingly, this paper proposes recommendations such as differentiated regional strategies, full-chain integration, building a “technology-innovation-talent” ecosystem, and establishing cross-regional coordination mechanisms.
文章引用：刘蝶, 范乔希. 人工智能赋能制造业转型升级的实证研究[J]. 可持续发展, 2025, 15(12): 288-298. https://doi.org/10.12677/sd.2025.1512358

参考文献

[1] Acemoglu, D. and Restrepo, P. (2020) Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128, 2188-2244. [Google Scholar] [CrossRef] 
[2] 钟义信. 人工智能范式的革命与通用智能理论的创生[J]. 智能系统学报, 2021, 16(4): 792-800.
[3] 朱巧玲, 李敏. 人工智能、技术进步与劳动力结构优化对策研究[J]. 科技进步与对策, 2018, 35(6): 36-41.
[4] Shoham, Y. (2017) Toward the AI Index. AI Magazine, 38, 71-77. [Google Scholar] [CrossRef] 
[5] Humphrey, J. and Schmitz, H. (2004) Chain Governance and Upgrading: Taking Stock. In: Local Enterprises in the Global Economy, Edward Elgar Publishing, 349. [Google Scholar] [CrossRef] 
[6] 赵玉林, 裴承晨. 技术创新、产业融合与制造业转型升级[J]. 科技进步与对策, 2019, 36(11): 70-76.
[7] Banister, J. and Cook, G. (2011) China’s Employment and Compensation Costs in Manufacturing through 2008. Monthly Labor Review, 2011, 39-52.
[8] Nahm, J. and Steinfeld, E.S. (2014) Scale-Up Nation: China’s Specialization in Innovative Manufacturing. World Development, 54, 288-300. [Google Scholar] [CrossRef] 
[9] Restrepo, P. (2018) Artificial Intelligence Automation and Work. National Bureau of Economic Research.
[10] 邓洲. 促进人工智能与制造业深度融合发展的难点及政策建议[J]. 经济纵横, 2018(8): 41-49.
[11] 耿子恒, 汪文祥, 郭万福. 人工智能与中国产业高质量发展——基于对产业升级与产业结构优化的实证分析[J]. 宏观经济研究, 2021(12): 38-52+82.
[12] Baron, R.M. and Kenny, D.A. (1986) The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51, 1173-1182. [Google Scholar] [CrossRef] [PubMed]
[13] 潘为华, 潘红玉, 陈亮, 等. 中国制造业转型升级发展的评价指标体系及综合指数[J]. 科学决策, 2019(9): 28-48.
[14] 顾国达, 马文景. 人工智能综合发展指数的构建及应用[J]. 数量经济技术经济研究, 2021, 38(1): 117-134.
[15] 黄辉. 人工智能发展水平测度研究——基于区域对比分析的视角[J]. 高科技与产业化, 2024, 30(8): 74-78.

为你推荐