Research on the Impact of Artificial Intelligence on Green Total Factor Productivity in Manufacturing
- DOI
- 10.2991/aebmr.k.220402.010How to use a DOI?
- Keywords
- Artificial intelligence; Green total factor productivity in manufacturing; Technological progress; Technological efficiency
- Abstract
This paper measures the green total factor productivity of manufacturing in China’s provinces from 2003 to 2017 and its decomposition indicators, empirically analyzes the impact of artificial intelligence on the green total factor productivity of manufacturing, and draws the following conclusion: Artificial intelligence helps promote the improvement of green total factor productivity in China’s manufacturing industry. This improvement mainly comes from technological progress, and the impact of technological efficiency is not significant. Further inspection found that artificial intelligence has improved the pure technical efficiency of China’s manufacturing industry. This paper puts forward the following policy suggestions: In order to further improve the green total factor productivity of the manufacturing industry, efforts can be made in the direction of improving technical efficiency, and the focus of improvement is on how to improve scale efficiency. Manufacturing enterprises should recognize the role and role of artificial intelligence as a “general purpose technology”, and the government needs to play a leading role, provide necessary public goods, and guide complementary innovation and investment.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license.
Cite this article
TY - CONF AU - Mengfan Zhou AU - Yunping Chen PY - 2022 DA - 2022/04/12 TI - Research on the Impact of Artificial Intelligence on Green Total Factor Productivity in Manufacturing BT - Proceedings of the 2022 International Conference on County Economic Development, Rural Revitalization and Social Sciences (ICCRS 2022) PB - Atlantis Press SP - 47 EP - 50 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220402.010 DO - 10.2991/aebmr.k.220402.010 ID - Zhou2022 ER -