工业智能化何以提升绿色全要素生产率？

王钰　周歆雨　刘山峰

（首都经济贸易大学）

摘要：工业智能化是人工智能对工业的深度再造，赋能工业实现质量效率与绿色低碳协同发展。本文采用超效率基于松弛变量测度的全局马姆奎斯特-卢恩伯格（SBM-GML）模型，测算2008—2023年全国235个地级及以上城市工业绿色全要素生产率，并利用工业机器人渗透率衡量工业智能化水平，探究工业智能化对绿色全要素生产率的影响及其作用机制。研究结果显示，工业智能化提升了绿色全要素生产率。机制分析表明，工业智能化通过推动绿色技术创新、降低能源消耗强度促进绿色全要素生产率。交互效应分析表明，环境规制增强了工业智能化对绿色全要素生产率的提升作用。异质性分析表明，工业智能化的提升作用在东部地区、沿海城市和教育投入高的地区更明显。基于上述研究结论，本文提出应积极推动城市工业智能化发展、加大绿色技术创新扶持、深化智能节能全域改造、注重因城施策等政策建议。

关键词：工业智能化；新型工业化；绿色全要素生产率；环境规制；工业机器人

作者简介：王钰，首都经济贸易大学经济学院教授、博士生导师，北京，100070；周歆雨，首都经济贸易大学经济学院博士研究生，通信作者；刘山峰，首都经济贸易大学经济学院博士研究生。

基金项目：国家社会科学基金重点项目“降碳、减污、扩绿、增长协同推进的理论与实践研究”（24AZD074）；首都经济贸易大学研究生科技创新项目“工业智能化对工业绿色发展效率的影响研究”（2025KJCX074）

引用格式：王钰，周歆雨，刘山峰. 工业智能化何以提升绿色全要素生产率？［J］. 经济与管理研究，2026，47（9）：49-62.

How Can Industrial Intelligence Enhance Green Total Factor Productivity?

WANG Yu, ZHOU Xinyu, LIU Shanfeng

(Capital University of Economics and Business, Beijing 100070)

Abstract: Industrial intelligence represents the deep transformation of industry driven by artificial intelligence and serves as a core driver for industrial transformation and upgrading, empowering industry to achieve coordinated advancement of quality and efficiency as well as green and low-carbon development. However, the path of China's industrialization is shaped by the domestic development stage and changes in the global landscape. On the one hand, new technologies are undergoing rapid iterations and becoming deeply integrated with traditional industries, promoting global industries towards greater sophistication and intelligence. On the other hand, following a period of rapid economic growth, China's economy has been increasingly constrained by resources and the environment. Coupled with intensifying global climate change, the extensive development mode is no longer sustainable, making the transition towards high-quality, more efficient, green, and low-carbon development imperative. Facing the demands of industrial transformation and upgrading, China still faces problems such as uncoordinated progress, insufficient independent innovation, and lagging informatization and digitalization. Therefore, this paper aims to assess the impact of industrial intelligence on green total factor productivity (GTFP), to provide implications for the development of industrial intelligence and the green transformation of industry at the city level.

This paper adopts the super-efficiency slack-based measure-global Malmquist-Luenberger (SBM-GML) model to calculate industrial GTFP of 235 prefecture-level and above cities in China from 2008 to 2023, and uses the penetration rate of industrial robots to measure the level of industrial intelligence. It explores the impact of industrial intelligence on GTFP and its underlying mechanisms. Empirical results show that industrial intelligence enhances GTFP. The findings remain valid after endogeneity analysis and a series of robustness tests. Mechanism analyses reveal two major channels, including driving green technology innovation and reducing energy consumption intensity. Interaction effect analysis demonstrates that environmental regulation strengthens the positive effect of industrial intelligence on GTFP. Heterogeneity analysis indicates that this positive effect is more pronounced in the eastern region, coastal cities, and areas with high levels of investment in education.

The possible marginal contributions are as follows. First, by conducting the analysis at the city level, this paper examines the impact of industrial intelligence on GTFP within a more detailed spatial scope. Second, it expands the research boundaries regarding the underlying mechanisms and the interaction effect of environmental regulations. Third, by incorporating the heterogeneity in geographical locations, regional endowments, and educational investment, it clarifies the boundary scenarios where industrial intelligence affects GTFP, thereby providing empirical support for formulating differentiated policies.

Keywords: industrial intelligence; new industrialization; green total factor productivity; environmental regulation; industrial robot

下载全文