智能制造如何提升企业碳全要素生产率? ———基于要素投入成本与结构的视角

How Can Intelligent Manufacturing Improve the Carbon Total Factor Productivity of Enterprises: From the Perspective of Factor Input Cost and Structure

摘要:

党的二十大报告强调要推动制造业高端化、智能化、绿色化发展。本文基于GML-SBM模型和 SBM-DDF 模型测算了2012—2022年制造业 A 股上市公司的碳全要素生产率,从投入和应用两个方面探究智能制造提升企业碳全要素生产率的效应和渠道。结果发现,智能制造能显著提高企业碳全要素生产率,且技术效率变动是主要驱动力;智能制造主要通过要素成本和要素结构渠道,即降低资本、劳动使用成本和促进资本深化、提高资本产出效率四条路径提升企业碳全要素生产率;对年轻企业和非国有企业、处在竞争程度较高行业和技术密集型行业的企业、处在知识产权保护程度较高地区及工业基地的企业,智能制造的作用效果更强。研究结果为智能制造战略的实施和“ 双碳” 目标的实现提供了政策启示,对制造业企业的智能化转型具有重要参考意义。

Abstract:

The report of the 20th National Congress of the Communist Party of China emphasizes the promotion of high-end, intelligent, and green development in the manufacturing industry. Based on the GML-SBM and SBM-DDF models, the carbon total factor productivity(CTFP) of manufacturing A-share listed companies from 2012 to 2022 was calculated. The impact and channels of intelligent manufacturing on enhancing enterprise CTFP were explored from both input and application perspectives. It is found that intelligent manufacturing significantly improves enterprise CTFP, with technological efficiency change being the main driving force. Intelligent manufacturing primarily enhances enterprise CTFP through four pathways: reducing capital and labor usage costs, promoting capital deepening, and improving capital output efficiency. The impact and channels are stronger for young enterprises and non-state-owned enterprises, enterprises in highly competitive industries and technology-intensive industries, enterprises in regions with high levels of intellectual property protection, and enterprises in industrial bases. The findings provide policy insights for the implementation of intelligent manufacturing strategies and the achievement of “dual carbon” targets, and have significant reference value for the intelligent transformation of manufacturing enterprises.