School of Economics and Management, Anhui Open University, Hefei, China.
School of Business, Jiangsu Open University, Nanjing, China.
PLoS One. 2024 Apr 10;19(4):e0299119. doi: 10.1371/journal.pone.0299119. eCollection 2024.
The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.
长三角地区（YRD）承担着推动中国装备制造业（EMI）智能化增长的重要任务，是一个先进的地区。培育长三角地区 EMI 的转型升级，构建现代生产模式，对于发展和改革中国制造业至关重要。本文利用工业机器人数据，评估了 2016 年至 2019 年 EMI 的智能化水平（LoI）。采用 OLS（普通最小二乘法）模型进行测量，并采用 MQ（修正贡献指数）来估计众多变量的贡献程度。结果表明，LoI 呈上升趋势。然而，不包括铁路、航空航天、船舶等交通运输设备制造业，LoI 明显高于其他子行业。研究还发现，技术创新能力、人力资本密度和企业成本压力决定了该行业的 LoI。此外，虽然不同行业的 LoI 主要影响因素存在差异，但研发投入、技术创新能力和企业成本压力对大多数装备制造业子行业的影响最为显著。
