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How does artificial intelligence affect the transformation of China's green economic growth? An analysis from internal-structure perspective.

Authors :
Feng C
Ye X
Li J
Yang J
Source :
Journal of environmental management [J Environ Manage] 2024 Feb; Vol. 351, pp. 119923. Date of Electronic Publication: 2024 Jan 03.
Publication Year :
2024

Abstract

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1095-8630
Volume :
351
Database :
MEDLINE
Journal :
Journal of environmental management
Publication Type :
Academic Journal
Accession number :
38176382
Full Text :
https://doi.org/10.1016/j.jenvman.2023.119923